kidney disease research paper

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• HHS Author Manuscripts

Chronic Kidney Disease Diagnosis and Management

Author Contributions: Dr Grams had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Acquisition, analysis, or interpretation of data: Chen, Grams.

Drafting of the manuscript: Chen.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Grams.

Administrative, technical, or material support: Chen, Knicely.

Supervision: Grams.

Additional Contributions: We thank Andrew S. Levey, MD, Tufts Medical Center, and Natalie Daya, MS, Johns Hopkins University, for helpful input on the manuscript (uncompensated).

Chronic kidney disease (CKD) is the 16th leading cause of years of life lost worldwide. Appropriate screening, diagnosis, and management by primary care clinicians are necessary to prevent adverse CKD-associated outcomes, including cardiovascular disease, end-stage kidney disease, and death.

OBSERVATIONS

Defined as a persistent abnormality in kidney structure or function (eg, glomerular filtration rate [GFR] <60 mL/min/1.73 m 2 or albuminuria ≥30 mg per 24 hours) for more than 3 months, CKD affects 8% to 16% of the population worldwide. In developed countries, CKD is most commonly attributed to diabetes and hypertension. However, less than 5% of patients with early CKD report awareness of their disease. Among individuals diagnosed as having CKD, staging and new risk assessment tools that incorporate GFR and albuminuria can help guide treatment, monitoring, and referral strategies. Optimal management of CKD includes cardiovascular risk reduction (eg, statins and blood pressure management), treatment of albuminuria (eg, angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers), avoidance of potential nephrotoxins (eg, nonsteroidal anti-inflammatory drugs), and adjustments to drug dosing (eg, many antibiotics and oral hypoglycemic agents). Patients also require monitoring for complications of CKD, such as hyperkalemia, metabolic acidosis, hyperphosphatemia, vitamin D deficiency, secondary hyperparathyroidism, and anemia. Those at high risk of CKD progression (eg, estimated GFR <30 mL/min/1.73 m 2 , albuminuria ≥300 mg per 24 hours, or rapid decline in estimated GFR) should be promptly referred to a nephrologist.

CONCLUSIONS AND RELEVANCE

Diagnosis, staging, and appropriate referral of CKD by primary care clinicians are important in reducing the burden of CKD worldwide.

Chronic kidney disease (CKD) affects between 8% and 16% of the population worldwide and is often underrecognized by patients and clinicians. 1 – 4 Defined by a glomerular filtration rate (GFR) of less than 60 mL/min/1.73 m 2 , albuminuria of at least 30 mg per 24 hours, or markers of kidney damage (eg, hematuria or structural abnormalities such as polycystic or dysplastic kidneys) persisting for more than 3 months, 5 CKD is more prevalent in low- and middle-income than in high-income countries. 6 Globally, CKD is most commonly attributed to diabetes and/or hypertension, but other causes such as glomerulonephritis, infection, and environmental exposures (such as air pollution, herbal remedies, and pesticides) are common in Asia, sub-Saharan Africa, and many developing countries. 4 Genetic risk factors may also contribute to CKD risk. For example, sickle cell trait and the presence of 2 APOL1 risk alleles, both common in people of African ancestry but not European ancestry, may double the risk of CKD. 4 , 7 – 10

In the United States, the average rate of GFR decline is approximately 1 mL/min/1.73 m 2 per year in the general population, 11 , 12 and the lifetime risk of developing a GFR of less than 60 mL/min/1.73 m 2 is more than 50%. 13 Early detection and treatment by primary care clinicians is important because progressive CKD is associated with adverse clinical outcomes, including end-stage kidney disease (ESKD), cardiovascular disease, and increased mortality. 14 – 17 Recent professional guidelines suggest a risk-based approach to the evaluation and management of CKD. 5 , 18 – 20 This review includes discussion of new calculators for determining risk of CKD progression that may be useful in clinical practice (eg, https://kidneyfailurerisk.com/ ) and focuses on the diagnosis, evaluation, and management of CKD for primary care clinicians. Considerations for referral to a nephrologist and dialysis initiation are also covered.

A literature search to April 2019 was conducted using Medline and PubMed with search terms including CKD , chronic renal failure , chronic renal insufficiency , epidemiology , incidence , prevalence , occurrence , diagnosis , assessment , identification , screening , workup , etiology , causes , management , treatment , intervention , therapy , and prevention . Results were restricted to English-language, human studies, and academic journals and guidelines. The initial search resulted in 998 articles, including clinical trials, meta-analyses, practice guidelines, and systematic reviews, and was later expanded to include review articles and observational studies, including cross-sectional studies, and more recent publications contained in reference lists of identified articles. All clinical trials for treatment or prevention of CKD were included without regard to study size or age of patient population.

Clinical Presentation

Chronic kidney disease is typically identified through routine screening with serum chemistry profile and urine studies or as an incidental finding. Less commonly, patients may present with symptoms such as gross hematuria, “foamy urine” (a sign of albuminuria), nocturia, flank pain, or decreased urine output. If CKD is advanced, patients may report fatigue, poor appetite, nausea, vomiting, metallic taste, unintentional weight loss, pruritus, changes in mental status, dyspnea, or peripheral edema. 21

In evaluating a patient with known or suspected CKD, clinicians should inquire about additional symptoms that might suggest a systemic cause (eg, hemoptysis, rash, lymphadenopathy, hearing loss, neuropathy) or urinary obstruction (eg, urinary hesitancy, urgency, or frequency or incomplete bladder emptying). 21 Moreover, patients should be assessed for risk factors of kidney disease, including prior exposure to potential nephrotoxins (eg, nonsteroidal anti-inflammatory drugs [NSAIDs], phosphate-based bowel preparations, herbal remedies such as those containing aristolochic acid, antibiotic therapies such as gentamicin, and chemotherapies), history of nephrolithiasis or recurrent urinary tract infections, presence of comorbidities (eg, hypertension, diabetes, autoimmune disease, chronic infections), family history of kidney disease, and, if available, other known genetic risk factors such as sickle cell trait. 9 , 18 , 21 – 24

A detailed physical examination may provide additional clues regarding the underlying cause of CKD and should include careful evaluation of a patient’s volume status. Signs of volume depletion may reflect poor oral intake, vomiting, diarrhea, or overdiuresis, whereas signs of volume overload may be due to decompensated heart failure, liver failure, or nephrotic syndrome. The presence of arterial-venous nicking or retinopathy on retinal examination suggests long-standing hypertension or diabetes. Patients with carotid or abdominal bruits may have renovascular disease. Flank pain or enlarged kidneys should prompt consideration of obstructive uropathy, nephrolithiasis, pyelonephritis, or polycystic kidney disease. Neuropathy may be due to diabetes or less commonly vasculitis, or amyloidosis. Skin findings may include rash (systemic lupus erythematosus, acute interstitial nephritis), palpable purpura (Henoch-Schonlein purpura, cryoglobulinemia, vasculitis), telangiectasias (scleroderma, Fabry disease), or extensive sclerosis (scleroderma). Patients with advanced CKD may exhibit pallor, skin excoriations, muscle wasting, asterixis, myoclonic jerks, altered mental status, and pericardial rub. 21

CKD Definition and Staging

Chronic kidney disease is defined as the presence of an abnormality in kidney structure or function persisting for more than 3 months. 5 , 25 This includes 1 or more of the following: (1) GFR less than 60 mL/min/1.73 m 2 ; (2) albuminuria (ie, urine albumin ≥30 mg per 24 hours or urine albumin-to-creatinine ratio [ACR] ≥30 mg/g); (3) abnormalities in urine sediment, histology, or imaging suggestive of kidney damage; (4) renal tubular disorders; or (5) history of kidney transplantation. 5 If the duration of kidney disease is unclear, repeat assessments should be performed to distinguish CKD from acute kidney injury (change in kidney function occurring within 2–7 days) and acute kidney disease (kidney damage or decreased kidney function present for ≤3 months). 25 Evaluation for the etiology of CKD should be guided by a patient’s clinical history, physical examination, and urinary findings ( Figure 1 ). 5 , 18 , 21

a Other imaging modalities or urine studies may also be considered.

b A variety of scores are available, eg, https://kidneyfailurerisk.com/ .

Once a diagnosis of CKD has been made, the next step is to determine staging, which is based on GFR, albuminuria, and cause of CKD ( Figure 2 ). 5 Staging of GFR is classified as G1 (GFR ≥90 mL/min/1.73 m 2 ), G2 (GFR 60–89 mL/min/1.73 m 2 ), G3a (45–59 mL/min/1.73 m 2 ), G3b (30–44 mL/min/1.73 m 2 ), G4 (15–29 mL/min/1.73 m 2 ), and G5 (<15 mL/min/1.73 m 2 ). 5 Although GFR can be directly measured by clearance of agents such as iohexol or iothalamate, 26 – 28 the development of estimating equations (eg, the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] and Modification of Diet in Renal Disease Study [MDRD] equations) has largely replaced the need for direct measurement in clinical practice. 29 – 31 Clinical laboratories now routinely report estimated GFR (eGFR) based on filtration markers. The most common filtration marker used is creatinine, a 113 dalton byproduct of creatine metabolism 25 and one for which laboratory assays have been standardized since 2003. 32 The preferred estimating equation in the United States and much of the world is the CKD-EPI 2009 creatinine equation, which is more accurate than the earlier MDRD equation, particularly for eGFR values greater than 60 mL/min/1.73 m 2 ( https://www.kidney.org/professionals/kdoqi/gfr_calculator). 29 , 30 In situations requiring additional accuracy and precision, cystatin C can be used with creatinine in the CKD-EPI 2012 creatinine-cystatin C equation. 31 Adding cystatin C may be particularly useful for individuals with altered creatinine production and/or metabolism (eg, extremely high or low body size or muscle mass, limb amputation, high-protein diet, use of creatinine supplements, or use of drugs affecting tubular secretion of creatinine). 5 , 25

GFR indicates glomerular filtration rate; KDIGO, Kidney Disease Improving Global Outcomes. Categories are grouped by risk of progression, which includes chronic kidney disease progression, defined by a decline in GFR category (accompanied by a ≥25% decrease in estimated GFR from baseline) or sustained decline in estimated GFR greater than 5 mL/min/1.73 m 2 per year. Green indicates low risk (if no other markers of kidney disease and no CKD); yellow, moderately increased risk; orange: high risk; and red, very high risk. Reproduced with permission from Kidney International Supplements . 5

Albuminuria should ideally be quantified by a urine ACR. Albuminuria staging is classified as A1 (urine ACR <30 mg/g), A2 (30–300 mg/g), and A3 (>300 mg/g). 5 Guidelines recommend the use of urine ACR to stage CKD rather than urine protein-to-creatinine ratio because assays for the former are more likely to be standardized and have better precision at lower values of albuminuria. 5 , 33 The most precise measurements come from a first morning sample or 24-hour collection, as there is high biological variability in urine albumin excretion over the course of the day. 5 , 34 , 35 Random samples, however, are also acceptable in initial screening. 5 Compared with urine protein-to-creatinine ratio, urine ACR is believed to be a more sensitive and specific marker of glomerular pathology 5 since some urine proteins such as uromodulin are present (and may even be protective) in normal physiology. 36 – 38 If tubular or overflow proteinuria is suspected, then urine protein electrophoresis or testing for the specific protein can be pursued (eg, immunoglobulin heavy and light chains, α 1 -microglobulin, and β 2 -microglobulin). 5 Imaging by kidney ultrasound to assess morphology and to rule out urinary obstruction should be considered in all patients diagnosed as having CKD. 5

Cause of CKD can be difficult to discern but is generally classified by the presence or absence of systemic disease and the location of anatomic abnormality. Examples of systemic disease include diabetes, autoimmune disorders, chronic infection, malignancy, and genetic disorders in which the kidney is not the only organ affected. Anatomic locations are divided into glomerular, tubulointerstitial, vascular, and cystic/congenital diseases. 5 Determining the cause of CKD may have important implications on prognosis and treatment. For example, polycystic kidney disease may progress to ESKD faster than other causes and often requires evaluation for extrarenal manifestations and consideration of specific therapies such as tolvaptan, a vasopressin V2 receptor antagonist that slows decline in GFR. 39 , 40 Patients with unexplained causes of CKD should be referred to a nephrologist.

Screening for CKD

Given that most patients with CKD are asymptomatic, screening may be important to early detection of disease. 18 The National Kidney Foundation has developed a kidney profile test that includes measuring both serum creatinine for estimating GFR and urine ACR. 41 A risk-based approach to screening is suggested by many clinical practice guidelines, with screening recommended in those older than 60 years or with a history of diabetes or hypertension. 18 – 20 Screening should also be considered in those with clinical risk factors, including autoimmune disease, obesity, kidney stones, recurrent urinary tract infections, reduced kidney mass, exposure to certain medications such as NSAIDs or lithium, and prior episodes of acute kidney injury, among others ( Box ). 9 , 18 , 42 – 45 However, no randomized clinical trials have demonstrated that screening asymptomatic patients for CKD improves outcomes.

Clinical, Sociodemographic, and Genetic Risk Factors for Chronic Kidney Disease

Hypertension

Autoimmune diseases

Systemic infections (eg, HIV, hepatitis B virus, hepatitis C virus)

Nephrotoxic medications (eg, nonsteroidal anti-inflammatory drugs, herbal remedies, lithium)

Recurrent urinary tract infections

Kidney stones

Urinary tract obstruction

Reduced kidney mass (eg, nephrectomy, low birth weight)

History of acute kidney injury

Intravenous drug use (eg, heroin, cocaine)

Family history of kidney disease

Sociodemographic

Age >60 years

Nonwhite race

Low education

APOL1 risk alleles

Sickle cell trait and disease

Polycystic kidney disease

Alport syndrome

Congenital anomalies of the kidney and urinary tract

Other familial causes

Other Risk Factors for CKD

There are several sociodemographic factors that contribute to increased risk of CKD, including nonwhite race, low education, low income, and food insecurity. 18 , 43 , 46 Compared with whites, African Americans and Pacific Islanders have a substantially greater risk of ESKD. 47 This is in part due to an increased prevalence of hypertension, diabetes, and obesity. 11 However, genetic factors likely also contribute. More specifically, risk alleles in the gene encoding apolipoprotein L1 ( APOL1 ) may increase risk of kidney disease in a recessive genetic manner 7 , 8 : individuals with 2 APOL1 risk alleles (present in approximately 13% of African Americans) have a 2-fold risk of CKD progression and up to a 29-fold risk of specific CKD etiologies (eg, focal-segmental glomerulosclerosis and HIV-associated nephropathy) compared with those with 0 or 1 risk allele. 11 , 44 , 45 , 48 , 49 Sickle cell trait (present in approximately 8% of African Americans) has also been associated with an increased risk of kidney disease. Compared with noncarriers, individuals with sickle cell trait have a 1.8-fold odds of incident CKD, 1.3-fold odds of eGFR decline greater than 3 mL/min/1.73 m 2 , and 1.9-fold odds of albuminuria. 9

Management of Patients With CKD

Reducing risk of cardiovascular disease.

The prevalence of cardiovascular disease is markedly higher among individuals with CKD compared with those without CKD. For example, in a Medicare 5% sample, 65% of the 175 840 adults aged 66 years or older with CKD had cardiovascular disease compared with 32% of the 1 086 232 without CKD. 47 Moreover, presence of CKD is associated with worse cardiovascular outcomes. For example, in the same population, the presence of CKD was associated with lower 2-year survival in people with coronary artery disease (77% vs 87%), acute myocardial infarction (69% vs 82%), heart failure (65% vs 76%), atrial fibrillation (70% vs 83%), and cerebrovascular accident/transient ischemic attack (73% vs 83%). 47

Therefore, a major component of CKD management is reduction of cardiovascular risk. It is recommended that patients aged 50 years or older with CKD be treated with a low- to moderate-dose statin regardless of low-density lipoprotein cholesterol level. 50 – 52 Smoking cessation should also be encouraged. 5 , 53 Both the Eighth Joint National Committee (JNC 8) and Kidney Disease: Improving Global Outcomes (KDIGO) guidelines have recommended goal systolic and diastolic blood pressures of less than 140 mm Hg and less than 90 mm Hg, respectively, among adults with CKD based on expert opinion. 5 , 54 The KDIGO guidelines further recommend that adults with urine ACR of at least 30 mg per 24 hours (or equivalent) have systolic and diastolic blood pressures maintained below 130 mm Hg and 80 mm Hg, respectively. 5 More recently, the Systolic Blood Pressure Intervention Trial (SPRINT) demonstrated that among individuals with increased risk of cardiovascular disease but without diabetes, more intensive blood pressure control (goal systolic blood pressure <120 mm Hg) was associated with a 25% lower (1.65% vs 2.19% per year) risk of a major cardiovascular event and a 27% lower risk of all-cause mortality compared with standard blood pressure control (goal systolic blood pressure <140 mm Hg). 55 The intensive treatment group had a greater risk of at least a 30% decline in eGFR to a level below 60 mL/min/1.73 m 2 ; however, this may have been due to hemodynamic changes rather than true kidney function loss. 55 , 56 Importantly, the benefits of intensive blood pressure control on cardiovascular events were similar in participants with and without baseline CKD. 57

Management of Hypertension

Many guidelines provide algorithms detailing which agents should be used to treat hypertension in people with CKD. 54 , 58 Presence and severity of albuminuria should be evaluated. Blockade of the renin-angiotensin-aldosterone system with either an angiotensin-converting enzyme inhibitor (ACE-I) or an angiotensin II receptor blocker (ARB) is recommended for adults with diabetes and a urine ACR of at least 30 mg per 24 hours or any adult with a urine ACR of at least 300 mg per 24 hours. 5 , 18 , 58 Dual therapy with an ACE-I and an ARB is generally avoided, given associated risks of hyperkalemia and acute kidney injury. 5 , 18 , 59 Aldosterone receptor antagonists may also be considered in patients with albuminuria, resistant hypertension, or heart failure with reduced ejection fraction. 58 , 60 – 64

Management of Diabetes Mellitus

Optimal management of diabetes is also important. First, glycemic control may delay progression of CKD, with most guidelines recommending a goal hemoglobin A1c of ~ 7.0%. 5 , 18 , 19 , 65 – 67 Second, dose adjustments in oral hypoglycemic agents may be necessary. In general, drugs that are largely cleared by the kidneys (eg, glyburide) should be avoided, whereas drugs metabolized by the liver and/or partially excreted by the kidneys (eg, metformin and some dipeptidyl peptidase 4 [DPP-4] and sodium-glucose cotransporter-2 [SGLT-2] inhibitors) may require dose reduction or discontinuation, particularly when eGFR falls below 30 mL/min/1.73 m 2 . 18 , 19 Third, use of specific medication classes such as SGLT-2 inhibitors in those with severely increased albuminuria should be considered. The Canagliflozin and Renal Events in Diabetes with Established Nephropathy Clinical Evaluation (CREDENCE) trial demonstrated that, among 4401 patients with type 2 diabetes and CKD stage G2-G3/A3 (baseline eGFR 30 to <90 mL/min/1.73 m 2 and urine ACR>300 to 5000 mg/24 hours) taking ACE-I or ARB therapy, those randomized to canagliflozin had a 30% lower risk (43.2 vs 61.2 events per 1000 patient-years) of developing the primary composite renal outcome (doubling of serum creatinine, ESKD, or death from a renal or cardiovascular cause) compared with those randomized to placebo. 68 Prior trials have also suggested cardiovascular benefit with this class of medications, which may extend to patients with CKD who have lower levels of albuminuria. 69 , 70

Nephrotoxins

All patients with CKD should be counseled to avoid nephrotoxins. Although a complete list is beyond the scope of this review, a few warrant mentioning. Routine administration of NSAIDs in CKD is not recommended, especially among individuals who are taking ACE-I or ARB therapy. 5 , 18 Herbal remedies are not regulated by the US Food and Drug Administration, and some (such as those containing aristolochic acid or anthraquinones) have been reported to cause a myriad of kidney abnormalities, including acute tubular necrosis, acute or chronic interstitial nephritis, nephrolithiasis, rhabdomyolysis, hypokalemia, and Fanconi syndrome. 22 Phosphate-based bowel preparations (both oral and enema formulations) are readily available over the counter and can lead to acute phosphate nephropathy. 23 , 24 Proton pump inhibitors are widely used and have been associated with acute interstitial nephritis in case reports and incident CKD in population-based studies. 71 – 73 In the population-based Atherosclerosis Risk in Communities cohort, the incidence of CKD was 14.2 events in those taking proton pump inhibitors and 10.7 per 1000 events in people who did not take them. 71 Uniform discontinuation of proton pump inhibitors in CKD is not necessary. However, indications for use should be addressed at each primary care visit.

Drug Dosing

Adjustments in drug dosing are frequently required in patients with CKD. Of note, the traditional Cockcroft-Gault equation often poorly reflects measured GFR, whereas estimation of GFR using the CKD-EPI equation likely correlates better with drug clearance by the kidneys. 74 , 75 Common medications that require dose reductions include most antibiotics, direct oral anticoagulants, gabapentin and pregabalin, oral hypoglycemic agents, insulin, chemotherapeutic agents, and opiates, among others. 5 , 18 In general, use of medications with low likelihood of benefit should be minimized because patients with CKD are at high risk of adverse drug events. 76 – 79 Gadolinium-based contrast agents are contraindicated in individuals with acute kidney injury, eGFR less than 30 mL/min/1.73 m 2 , or ESKD given the risk of nephrogenic systemic fibrosis, a painful and debilitating disorder characterized by marked fibrosis of the skin and occasionally other organs. 5 , 18 , 80 , 81 Newer macrocyclic chelate formulations (eg, gadoteridol, gadobutrol, or gadoterate) are much less likely to cause nephrogenic systemic fibrosis, but the best prevention may still be to avoid gadolinium altogether. If administration of gadolinium is deemed essential, the patient must be counseled on the potential risk of nephrogenic systemic fibrosis and a nephrologist may be consulted for consideration of postexposure hemodialysis. 5 , 18 , 80 – 82

Dietary Management

Dietary management to prevent CKD progression is controversial since large trials have had equivocal results. 83 – 85 For example, the MDRD study evaluated 2 levels of protein restriction in 840 patients, finding that a low-protein diet compared with usual protein intake resulted in slower GFR decline only after the initial 4 months, and that a very low-protein diet compared with a low-protein diet was not significantly associated with slower GFR decline. Both levels of protein restriction appeared to have benefit in the subgroup with proteinuria greater than 3 g per day, although this group was small. 83 Other, smaller trials have suggested a benefit of protein restriction in the prevention of CKD progression or ESKD. 86 – 88 The KDIGO guidelines recommend that protein intake be reduced to less than 0.8 g/kg per day (with proper education) in adults with CKD stages G4-G5 and to less than 1.3 g/kg per day in other adult patients with CKD at risk of progression. 5 The possible benefits of dietary protein restriction must be balanced with the concern of precipitating malnutrition and/or protein wasting syndrome. 5 , 83 , 84 , 89 Lower dietary acid loads (eg, more fruits and vegetables and less meats, eggs, and cheeses) may also help protect against kidney injury. 90 , 91 Low-sodium diets (generally <2 g per day) are recommended for patients with hypertension, proteinuria, or fluid overload. 5

Monitoring of Established CKD and Treatment of Complications

Once CKD is established, the KDIGO guidelines recommend monitoring eGFR and albuminuria at least once annually. For patients at high risk, these measures should be monitored at least twice per year; patients at very high risk should be monitored at least 3 times per year ( Figure 2 ). 5 Patients with moderate to severe CKD are at increased risk of developing electrolyte abnormalities, mineral and bone disorders, and anemia. 92 Screening and frequency of assessment for laboratory abnormalities is dictated by stage of CKD and includes measurement of complete blood count, basic metabolic panel, serum albumin, phosphate, parathyroid hormone, 25-hydroxyvitamin D, and lipid panel ( Table ). 5 , 50 , 93 , 94

Screening, Monitoring, and Management of the Complications of Chronic Kidney Disease (CKD)

Anemia and the Role of Erythropoietin in CKD

Anemia is among the most common complications of CKD. In a study that included 19 CKD cohorts from across the world, 41% of the 209 311 individuals had low levels of hemoglobin (defined as <13 g/dL in men and <12 g/dL in women). 92 The initial workup of anemia should include assessment of iron stores: those who are iron deficient may benefit from oral or intravenous iron repletion. Patients with hemoglobin levels persistently below 10 g/dL despite addressing reversible causes can be referred to a nephrologist for consideration of additional medical therapy, including erythropoietin-stimulating agents; however, erythropoietin-stimulating agents have been associated with increased risk of death, stroke, and venous thromboembolism, and these risks must be weighed against any potential benefits. 93

Electrolyte, Mineral, and Bone Abnormalities in CKD

Electrolyte abnormalities are present in 3% to 11% of patients with CKD. 92 Initial treatment strategies usually involve dietary restrictions and prescription of supplements. For example, primary care clinicians should recommend low-potassium diets for patients with hyperkalemia and low-phosphorus diets for patients with hyperphosphatemia. 5 , 18 , 94 , 95 For patients with a serum bicarbonate level persistently below 22 mmol/L, oral bicarbonate supplementation should be considered, as studies have suggested that chronic metabolic acidosis is associated with faster CKD progression. 5 , 18 , 96 – 99

Mineral and bone disorders are also common. In a study that included 42 985 patients with CKD, 58% had intact parathyroid hormone levels greater than 65 pg/mL. 92 Although the optimal intact parathyroid hormone level for CKD remains unclear, most nephrologists agree that concomitant hyperphosphatemia, hypocalcemia, and vitamin D deficiency should be addressed, such as with a low-phosphate diet, phosphate binders, adequate elemental calcium intake, and vitamin D supplementation ( Table ). 94 , 95

Prognosis of CKD

The incidence of ESKD varies by the presence of risk factors and geographical location. For example, in North America, the incidence among individuals with eGFR less than 60 mL/min/1.73 m 2 ranged from 4.9 to 168.3 ESKD events per 1000 patient-years in 16 cohorts; in 15 non–North American cohorts, the incidence ranged from 1.2 to 131.3 ESKD events per 1000 patient-years. 100 Most patients with CKD do not require kidney replacement therapy during their lifetime. 101 Simple online tools are available to help with risk stratification. For example, the Kidney Failure Risk Equation (KFRE; https://kidneyfailurerisk.com/ ) predicts the 2-year and 5-year probabilities of requiring dialysis or transplant among individuals with eGFR less than 60 mL/min/1.73 m 2 . 100 , 102 The KFRE, which has been validated in more than 700 000 individuals from more than 30 countries, uses readily available clinical and laboratory variables. The 4-variable equation includes age, sex, eGFR, and urine ACR, whereas the 8-variable equation further incorporates serum albumin, phosphate, calcium, and bicarbonate levels. 100 , 102 Some health systems have tested the implementation of KFRE in clinical practice: nephrology referrals based on a 5-year KFRE greater than 3% led to shorter wait times, 103 and a 2-year KFRE greater than 10% was used to guide referrals to multidisciplinary CKD clinics. 104 An ongoing trial is evaluating whether a KFRE risk-based approach improves CKD management. 105 For patients with eGFR less than 30mL/min/1.73m 2 , the CKD G4+ risk calculator ( https://www.kdigo.org/equation/ ) may provide additional information on the risks of cardiovascular disease and death. 106 , 107 Importantly, risk prognostication may be helpful in not only identifying individuals at high risk of disease progression but also providing reassurance to those with mild CKD such as stage G3a A1.

Referral to a Nephrologist and Timing of Kidney Replacement Therapy

The KDIGO guidelines recommend that patients with CKD be referred to a nephrologist when eGFR falls below 30 mL/min/1.73 m 2 (stage G4) and/or urine ACR increases above 300 mg per 24 hours (stage A3). 5 The presence of albuminuria greater than 2200 mg per 24 hours should prompt expedited evaluation by a nephrologist and consideration of nephrotic syndrome. Additional indications for referral include the following: presence of greater than 20 red blood cells per high-power field of unclear etiology, red blood cell casts on urine microscopy or other indication of glomerulonephritis, CKD with uncontrolled hypertension despite 4 or more antihypertensive medications, persistent hypokalemia or hyperkalemia, anemia requiring erythropoietin replacement, recurrent or extensive kidney stones, hereditary kidney disease, acute kidney injury, and rapid CKD progression (a decrease in eGFR ≥25% from baseline or a sustained decline in eGFR >5 mL/min/1.73 m 2 ). 5 In persons without CKD, even small changes in serum creatinine (eg, from 0.7 mg/dL to 1.2 mg/dL) reflect large declines in eGFR, and primary care clinicians should attempt to identify reversible causes. Indications for kidney biopsy may include but are not limited to unexplained persistent or increasing albuminuria, presence of cellular casts or dysmorphic red blood cells on urine sediment, and unexplained or rapid decline in GFR. 5 Specific thresholds vary depending on patient characteristics and by institution. Patients with polycystic kidney disease, certain types of glomerulonephritis, and nephrotic-range albuminuria are at particularly high risk of progressing to ESKD. 5 , 39 , 102

Referral to nephrology is important for planning kidney replacement therapy and transplant evaluation. The decision to begin kidney replacement therapy is based on the presence of symptoms and not solely on level of GFR. 108 Urgent indications include encephalopathy, pericarditis, and pleuritis due to severe uremia. 109 Otherwise, initiation of dialysis should be individualized and considered when patients have uremic signs or symptoms (eg, nausea, vomiting, poor appetite, metallic taste, pericardial rub or effusion, asterixis, or altered mental status), electrolyte abnormalities (eg, hyperkalemia or metabolic acidosis), or volume overload (eg, pulmonary or lower extremity edema) refractory to medical management. 5 , 18 , 109 A shared decision-making approach is best. Patients should be educated about treatment options and actively contribute to decision-making. Early education should include information on the potential complications of CKD as well as the different modalities of kidney replacement therapy. Kidney transplantation is considered the optimal therapy for ESKD, with living donor kidney transplantations performed before or shortly after dialysis initiation having the best outcomes. 110 , 111 As such, early referral (eg, eGFR <30 mL/min/1.73 m 2 and an elevated 2-year risk of ESKD) for transplant evaluation is important. 112 , 113 Alternative therapies for ESKD may include in-center hemodialysis, home hemodialysis, peritoneal dialysis, or conservative care without dialysis. 107 Patient preference should be taken into consideration when selecting dialysis modality; however, patients with multiple abdominal surgeries with resultant peritoneal scarring or unstable housing are likely poor candidates for peritoneal dialysis. 107 , 109 Patients planning for hemodialysis who exhibit rapid decline in eGFR should be referred to an experienced vascular surgeon for arteriovenous fistula placement. The KDOQI guidelines recommend that access creation should occur when eGFR is between 15 and 20 mL/min/1.73 m 2 . 114 Of note, dialysis initiation has been associated with accelerated functional decline and high short-term mortality among older patients with poor functional status. 115 , 116 Patient preferences for conservative approaches to medical management should be discussed and honored.

Conclusions

Chronic kidney disease affects 8% to 16% of the population worldwide and is a leading cause of death. Optimal management of CKD includes cardiovascular risk reduction, treatment of albuminuria, avoidance of potential nephrotoxins, and adjustments to drug dosing. Patients also require monitoring for complications of CKD, such as hyperkalemia, metabolic acidosis, anemia, and other metabolic abnormalities. Diagnosis, staging, and appropriate referral of CKD by primary care clinicians are important in reducing the burden of CKD worldwide.

Funding/Support:

Dr Chen was supported by a Clinician Scientist Career Development Award from Johns Hopkins University and is supported by a George M. O’Brien Center for Kidney Research Pilot and Feasibility Grant from Yale University and award K08DK117068 from the National Institutes of Health/NIDDK. Dr Grams is supported by NIDDK grants DK1008803, DK100446, and DK115534.

Role of the Funder/Sponsor: The supporting institutions had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Conflict of Interest Disclosures: Dr Chen reported receipt of grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) and Yale University. Dr Grams reported receipt of grants from the NIDDK and the National Kidney Foundation and travel support from Dialysis Clinics Inc for an invited speakership at a directors’ meeting in May 2019. No other disclosures were reported.

Submissions: We encourage authors to submit papers for consideration as a Review. Please contact Edward Livingston, MD, at Edward. gro.krowtenamaj@notsgnivil or Mary McGrae McDermott, MD, at ude.nretsewhtron@806mdm .

• Open access
• Published: 10 January 2022

Chronic kidney disease and its health-related factors: a case-control study

• Mousa Ghelichi-Ghojogh 1 ,
• Mohammad Fararouei 2 ,
• Mozhgan Seif 3 &
• Maryam Pakfetrat 4  

BMC Nephrology volume  23 , Article number:  24 ( 2022 ) Cite this article

18k Accesses

10 Citations

8 Altmetric

Metrics details

Chronic kidney disease (CKD) is a non-communicable disease that includes a range of different physiological disorders that are associated with abnormal renal function and progressive decline in glomerular filtration rate (GFR). This study aimed to investigate the associations of several behavioral and health-related factors with CKD in Iranian patients.

A hospital-based case-control study was conducted on 700 participants (350 cases and 350 controls). Logistic regression was applied to measure the association between the selected factors and CKD.

The mean age of cases and controls were 59.6 ± 12.4 and 58.9 ± 12.2 respectively ( p  = 0.827). The results of multiple logistic regression suggested that many factors including low birth weight (OR yes/no  = 4.07, 95%CI: 1.76–9.37, P  = 0.001), history of diabetes (OR yes/no  = 3.57, 95%CI: 2.36–5.40, P  = 0.001), history of kidney diseases (OR yes/no  = 3.35, 95%CI: 2.21–5.00, P  = 0.001) and history of chemotherapy (OR yes/no  = 2.18, 95%CI: 1.12–4.23, P  = 0.02) are associated with the risk of CKD.

Conclusions

The present study covered a large number of potential risk/ preventive factors altogether. The results highlighted the importance of collaborative monitoring of kidney function among patients with the above conditions.

Peer Review reports

Chronic kidney disease (CKD) is a non-communicable disease that includes a range of different physiological disorders that are associated with an abnormal renal function and progressive decline in glomerular filtration rate (GFR) [ 1 , 2 , 3 ]. Chronic kidney disease includes five stages of kidney damage, from mild kidney dysfunction to complete failure [ 4 ]. Generally, a person with stage 3 or 4 of CKD is considered as having moderate to severe kidney damage. Stage 3 is broken up into two levels of kidney damage: 3A) a level of GFR between 45 to 59 ml/min/1.73 m 2 , and 3B) a level of GFR between 30 and 44 ml/min/1.73 m 2 . In addition, GFR for stage 4 is 15–29 ml/min/1.73 m 2 [ 4 , 5 ]. It is reported that both the prevalence and burden of CKD are increasing worldwide, especially in developing countries [ 6 ]. The worldwide prevalence of CKD (all stages) is estimated to be between 8 to 16%, a figure that may indicate millions of deaths annually [ 7 ]. According to a meta-analysis, the prevalence of stage 3 to 5 CKD in South Africa, Senegal, and Congo is about 7.6%. In China, Taiwan, and Mongolia the rate of CKD is about 10.06% and in Japan, South Korea, and Oceania the rate is about 11.73%. In Europe the prevalence of CKD is about 11.86% [ 8 ], and finally, about 14.44% in the United States and Canada. The prevalence of CKD is estimated to be about 11.68% among the Iranian adult population and about 2.9% of Iranian women and 1.3% of Iranian men are expected to develop CKD annually [ 9 ]. Patients with stages 3 or 4 CKD are at much higher risk of progressing to either end-stage renal disease (ESRD) or death even prior to the development of ESRD [ 10 , 11 ].

In general, a large number of risk factors including age, sex, family history of kidney disease, primary kidney disease, urinary tract infections, cardiovascular disease, diabetes mellitus, and nephrotoxins (non-steroidal anti-inflammatory drugs, antibiotics) are known as predisposing and initiating factors of CKD [ 12 , 13 , 14 ]. However, the existing studies are suffering from a small sample size of individuals with kidney disease, particularly those with ESRD [ 15 ].

Despite the fact that the prevalence of CKD in the world, including Iran, is increasing, the factors associated with CKD are explored very little. The present case-control study aimed to investigate the association of several behavioral and health-related factors with CKD in the Iranian population.

Materials and methods

In this study, participants were selected among individuals who were registered or were visiting Faghihi and Motahari hospitals (two largest referral centers in the South of Iran located in Shiraz (the capital of Fars province). Cases and controls were frequency-matched by sex and age. The GFR values were calculated using the CKD-EPI formula [ 16 , 17 ].

Data collection

An interview-administered questionnaire and the participant’s medical records were used to obtain the required data. The questionnaire and interview procedure were designed, evaluated, and revised by three experts via conducting a pilot study including 50 cases and 50 controls. The reliability of the questionnaire was measured using the test-retest method (Cronbach’s alpha was 0.75). The interview was conducted by a trained public health‌ nurse at the time of visiting the clinics.

Avoiding concurrent conditions that their association may interpreted as reverse causation; the questionnaire was designed to define factors preceding at least a year before experiencing CKD first symptoms. Accordingly participants reported their social and demographic characteristics (age, sex, marital status, educational level, place of residency), history of chronic diseases (diabetes, cardiovascular diseases, hypertension, kidney diseases, family history of kidney diseases, autoimmune diseases and thyroid diseases [ 18 ]). Also history of other conditions namely (smoking, urinary tract infection (UTI), surgery due to illness or accident, low birth weight, burns, kidney pain (flank pain), chemotherapy, taking drugs for weight loss or obesity, taking non-steroidal anti-inflammatory drugs, and taking antibiotic) before their current condition was started. Many researchers reported recalling birth weight to be reliable for research purposes [ 19 ]. Moreover, we asked the participants to report their birth weight as a categorical variable (< 2500 g or low, 2500- < 3500 g or normal, and > 3500 g or overweight). Medical records of the participants were used to confirm/complete the reported data. In the case of contradiction between the self-reported and recorded data, we used the recorded information for our study.

Verbal informed consent was obtained from patients because the majority of the participants were illiterate. The study protocol was reviewed and approved by the ethical committee of Shiraz University of Medical Sciences (approval number: 1399.865).

Sample size

The sample size was calculated to detect an association‌ between the history of using antibiotics (one of our main study variables) and CKD as small as OR = 1.5 [ 20 ]. With an alpha value of 0.05 (2-sided) and a power of 80%, the required sample size was estimated as large as n  = 312 participants for each group.

Selection of cases

The selected clinics deliver medical care to patients from the southern part of the country. In this study, patients with CKD who were registered with the above centers from June to December 2020 were studied. A case was a patient with a GFR < 60 (ml/min/1.73 m 2 ) at least twice in 3 months. According to the latest version of the International Classification of Diseases (2010), Codes N18.3 and N18.4 are assigned to patients who have (GFR = 30–59 (ml/min/1.73 m 2 ) and GFR = 15–29 (ml/min/1.73 m 2 ) respectively [ 21 ]. In total, 350 patients who were diagnosed with CKD by a nephrologist during the study period.

Selection of the controls

We used hospital controls to avoid recall-bias. The control participants were selected from patients who were admitted to the general surgery (due to hernia, appendicitis, intestinal obstruction, hemorrhoids, and varicose veins), and orthopedic wards‌ from June to December 2020. Using the level of creatinine in the participants’ serum samples, GFR was calculated and the individuals with normal GFR (ml/min/1.73 m 2 ) GFR > 60) and those who reported no history of CKD were included ( n  = 350).

Inclusion criteria

Patients were included if they were ≥ 20 years old and had a definitive diagnosis of CKD by a nephrologist.

Exclusion criteria

Participants were excluded if they were critically ill, had acute kidney injury, those undergone renal transplantation, and those with cognitive impairment.

Statistical analysis

The Chi-square test was used to measure the unadjusted associations between categorical variables and CKD. Multiple logistic regression was applied to measure the adjusted associations for the study variables and CKD. The backward variable selection strategy was used to include variables in the regression model. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. All p -values were two-sided and the results were considered statistically significant at p  < 0.05. All analyses were conducted using Stata version 14.0 (Stata Corporation, College Station, TX, USA).

In total, 350 cases and 350 age and sex-matched controls were included in the analysis. The mean age of cases and controls were 59.6 ± 12.4 and 58.9 ± 12.2 respectively ( p  = 0.83). Overall, 208 patients (59.4%) and 200 controls (57.1%) were male ( p  = 0.54). Also, 149 patients (42.6%) and 133 controls (38.0%) were illiterate or had elementary education ( p  = 0.001). Most cases (96.9%) and controls (95.7%) were married ( p  = 0.42). The mean GFR for CKD and control groups were 38.6 ± 11.4 and 78.3 ± 10.2 (ml/min/1.73 m2) respectively.

Result of univariate analysis

Table  1 illustrates the unadjusted associations of demographic and health-related variables with CKD. Accordingly, significant (unadjusted) associations were found between the risk of CKD and several study variables including education, history of chronic diseases (diabetes, cardiovascular, hypertension, kidney diseases, autoimmune diseases, and hypothyroidism), family history of kidney diseases, smoking, UTI, surgery due to illness or accident, low birth weight, burns, kidney pain, chemotherapy, taking non-steroidal anti-inflammatory drugs, and taking antibiotics) ( P  < 0.05 for all).

Results of multivariable analysis

Table  2 illustrates the adjusted associations between the study variables and the risk of CKD. Most noticeably, low birth weight (OR yes/no  = 4.07, 95%CI: 1.76–9.37, P  = 0.001), history of surgery (OR yes/no  = 1.74, 95%CI: 1.18–2.54, P  = 0.004), family history of kidney diseases (OR yes/no  = 1.97, 95%CI: 1.20–3.23, P  = 0.007), and history of chemotherapy (OR yes/no  = 2.18, 95%CI: 1.12–4.23, P  = 0.02) were significantly associated with a higher risk of CKD. On the other hand, education (OR college/illiterate or primary  = 0.54, 95%CI: 0.31–0.92, P  = 0.025) was found to be inversely associated with CKD.

The results of the present study suggested that several variables including, education, history of diabetes, history of hypertension, history of kidney diseases or a family history of kidney diseases, history of surgery due to illness or accident, low birth weight, history of chemotherapy, history of taking non-steroidal anti-inflammatory drugs, and history of taking antibiotics may affect the risk of CKD.

In our study, the level of education was inversely associated with the risk of CKD. This finding is in accordance with the results of a study conducted by K Lambert et.al, who suggested that illiteracy or elementary education may raise the risk of CKD [ 22 ]. The fact that education level is associated with health literacy, may partly explain our results that lower education and inadequate health literacy in individuals with CKD is associated with worse health outcomes including poorer control of biochemical parameters, higher risk of cardiovascular diseases (CVDs); a higher rate of hospitalization, and a higher rate of infections [ 23 ].

In the current study, the history of diabetes was associated with a higher risk of CKD. This finding is consistent with the results of other studies on the same subject [ 20 , 21 , 24 , 25 , 26 , 27 ]. It is not surprising that people with diabetes have an increased risk of CKD as diabetes is an important detrimental factor for kidney functioning as approximately, 40% of patients with diabetes develop CKD [ 27 ].

The other variable that was associated with an increased risk of CKD was a history of hypertension. Our result is consistent with the results of several other studies [ 20 , 24 , 25 , 28 ]. It is reported that hypertension is both a cause and effect of CKD and accelerates the progression of the CKD to ESRD [ 29 ].

After controlling for other variables, a significant association was observed between family history of kidney diseases and risk of CKD. Published studies suggested the same pattern [ 24 ]. Inherited kidney diseases (IKDs) are considered as the foremost reasons for the initiation of CKD and are accounted for about 10–15% of kidney replacement therapies (KRT) in adults [ 30 ].

The importance of the history of surgery due to illness or accident in this study is rarely investigated by other researchers who reported the effect of surgery in patients with acute kidney injury (AKI), and major abdominal and cardiac surgeries [ 31 , 32 ] on the risk of CKD. Also, AKI is associated with an increased risk of CKD with progression in various clinical settings [ 33 , 34 , 35 ]. In a study by Mizota et.al, although most AKI cases recovered completely within 7 days after major abdominal surgery, they were at higher risk of 1-year mortality and chronic kidney disease compared to those without AKI [ 31 ].

The present study also showed that low birth weight is a significant risk factor for CKD. This finding is consistent with the results of some other studies. However, the results of very few studies on the association between birth weight and risk of CKD are controversial as some suggested a significant association [ 19 , 36 , 37 ] whereas others suggested otherwise [ 36 ]. This may be explained by the relatively smaller size and volume of kidneys in LBW infants compared to infants that are normally grown [ 38 ]. This can lead to long-term complications in adolescence and adulthood including hypertension, decreased glomerular filtration, albuminuria, and cardiovascular diseases. Eventually, these long-term complications can also cause CKD [ 39 ].

Another important result of the current study is the association between chemotherapy for treating cancers and the risk of CKD. According to a study on chemotherapy for testicular cancer by Inai et al., 1 year after chemotherapy 23% of the patients showed CKD [ 40 ]. Another study suggested that the prevalence of stage 3 CKD among patients with cancer was 12, and < 1% of patients had stage 4 CKD [ 41 , 42 ]. Other studies have shown an even higher prevalence of CKD among cancer patients. For instance, only 38.6% of patients with breast cancer, 38.9% of patients with lung cancer, 38.3% of patients with prostate cancer, 27.5% of patients with gynecologic cancer, and 27.2% of patients with colorectal cancer had a GFR ≥90 (ml/min/1.73 m 2 ) at the time of therapy initiation [ 43 , 44 ]. The overall prevalence of CKD ranges from 12 to 25% across many cancer patients [ 45 , 46 , 47 ]. These results clearly demonstrate that, when patients with cancer develop acute or chronic kidney disease, outcomes are inferior, and the promise of curative therapeutic regimens is lessened.

In our study, the history of taking nephrotoxic agents (antibiotics or NSAIDs drugs) was associated with a higher risk of CKD. Our result is following the results reported by other studies [ 48 , 49 ]. Common agents that are associated with AKI include NSAIDs are different drugs including antibiotics, iodinated contrast media, and chemotherapeutic drugs [ 50 ].

Strengths and limitations of our study

Our study used a reasonably large sample size. In addition, a considerably large number of study variables was included in the study. With a very high participation rate, trained nurses conducted the interviews with the case and control participants in the same setting. However, histories of exposures are prone to recall error (bias), a common issue in the case-control studies. It is to be mentioned that the method of selecting controls (hospital controls) should have reduced the risk of recall bias when reporting the required information. In addition, we used the participants’ medical records to complete/ confirm the reported data. Although the design of the present study was not able to confirm a causal association between the associated variables and CKD, the potential importance and modifiable nature of the associated factors makes the results potentially valuable and easily applicable in the prevention of CKD.

Given that, chemotherapy is an important risk factor for CKD, we suggest the imperative for collaborative care between oncologists and nephrologists in the early diagnosis and treatment of kidney diseases in patients with cancer. Training clinicians and patients are important to reduce the risk of nephrotoxicity. Electronic medical records can simultaneously be used to monitor prescription practices, responsiveness to alerts and prompts, the incidence of CKD, and detecting barriers to the effective implementation of preventive measures [ 51 ]. Routine follow-up and management of diabetic patients is also important for the prevention of CKD. We suggest a tight collaboration between endocrinologists and nephrologists to take care of diabetic patients with kidney problems. In addition, surgeons in major operations should refer patients, especially patients with AKI, to a nephrologist for proper care related to their kidney function. Treatment of hypertension is among the most important interventions to slow down the progression of CKD [ 12 ]. Moreover, all patients with newly diagnosed hypertension should be screened for CKD. We suggest all patients with diabetes have their GFR and urine albumin-to-creatinine ratio (UACR) checked annually. Finally, the aging population and obesity cause the absolute numbers of people with diabetes and kidney diseases to raise significantly. This will require a more integrated approach between dialectologists/nephrologists and the primary care teams (55).

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to their being the intellectual property of Shiraz University of Medical Sciences but are available from the corresponding author on reasonable request.

Abbreviations

• Chronic kidney disease

End-stage renal disease

Glomerular filtration rate

Renal replacement treatment

Urinary tract infection

Odds ratios

Confidence intervals

Hypertension

Acute kidney injury

Ghelichi Ghojogh M, Salarilak S, Taghezadeh Afshari A, Khalkhali HR, Mohammadi-Fallah MR, Mkhdoomi K. The effect of body mass index on patient and graft survival rate in kidney transplanted patients in Iran. Nephrourol Monthly. 2017;9(4):e14386.

Zeba Z, Fatema K, Sumit AF, Zinnat R, Ali L. Early screening of chronic kidney disease patients among the asymptomatic adult population in Bangladesh. J Prev Epidemiol. 2020;5(1):e10–e.

Article   Google Scholar  

Mahajan C, Tiwari V, Divyaveer SS, Patil MR, Banerjee A, Bagur V, et al. Spectrum of renal biopsies; a three-year data from a tertiary care Centre of eastern India. J Nephropharmacol. 2020;9(2):e20–e.

Article   CAS   Google Scholar  

Kimura K, Hosoya T, Uchida S, Inaba M, Makino H, Maruyama S, et al. Febuxostat therapy for patients with stage 3 CKD and asymptomatic Hyperuricemia: a randomized trial. Am J Kidney Dis. 2018;72(6):798–810.

Article   CAS   PubMed   Google Scholar  

Foster MC, Hwang S-J, Larson MG, Lichtman JH, Parikh NI, Vasan RS, et al. Overweight, obesity, and the development of stage 3 CKD: the Framingham heart study. Am J Kidney Dis. 2008;52(1):39–48.

Article   PubMed   PubMed Central   Google Scholar  

Rachmi CN, Agho KE, Li M, Baur LA. Stunting, underweight and overweight in children aged 2.0–4.9 years in Indonesia: prevalence trends and associated risk factors. PLoS One. 2016;11(5):e0154756.

Asghari G, Momenan M, Yuzbashian E, Mirmiran P, Azizi F. Dietary pattern and incidence of chronic kidney disease among adults: a population-based study. Nutr Metab. 2018;15(1):1–11.

Ruggles DR, Freyman RL, Oxenham AJ. Influence of musical training on understanding voiced and whispered speech in noise. PLoS One. 2014;9(1):e86980.

Moazzeni SS, Arani RH, Hasheminia M, Tohidi M, Azizi F, Hadaegh F. High incidence of chronic kidney disease among Iranian diabetic adults: using CKD-EPI and MDRD equations for estimated glomerular filtration rate. Korean Diabetes J. 2021;45(5):684-97.

Salam SN, Eastell R, Khwaja A. Fragility fractures and osteoporosis in CKD: pathophysiology and diagnostic methods. Am J Kidney Dis. 2014;63(6):1049–59.

Zahmatkesh M, Tamadon MR. World kidney day 2018; chronic kidney disease in women. J Nephropathol. 2017;7(1):4–6.

Noble R, Taal MW. Epidemiology and causes of chronic kidney disease. Medicine. 2019;47(9):562–6.

Lopez-Garcia E, Schulze MB, Fung TT, Meigs JB, Rifai N, Manson JE, et al. Major dietary patterns are related to plasma concentrations of markers of inflammation and endothelial dysfunction. Am J Clin Nutr. 2004;80(4):1029–35.

Sepahi MA, Niknafs B. Multifaceted role of apolipoprotein L1 risk variants and nephropathy. J Nephropathol. 2020;9(1):1-3.

Cohen JB, Tewksbury CM, Landa ST, Williams NN, Dumon KR. National postoperative bariatric surgery outcomes in patients with chronic kidney disease and end-stage kidney disease. Obes Surg. 2019;29(3):975–82.

Levey AS, Andreoli SP, DuBose T, Provenzano R, Collins AJ. Chronic kidney disease: common, harmful and treatable–world kidney day 2007. Am J Nephrol. 2007;27(1):108–12.

Article   PubMed   Google Scholar  

Argulian E, Sherrid MV, Messerli FH. Misconceptions and facts about hypertrophic cardiomyopathy. Am J Med. 2016;129(2):148–52.

Rhee CM. The interaction between thyroid and kidney disease: an overview of the evidence. Curr Opin Endocrinol Diabetes Obes. 2016;23(5):407.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Al Salmi I, Hoy WE, Kondalsamy-Chennakes S, Wang Z, Healy H, Shaw JE. Birth weight and stages of CKD: a case-control study in an Australian population. Am J Kidney Dis. 2008;52(6):1070–8.

Yacoub R, Habib H, Lahdo A, Al Ali R, Varjabedian L, Atalla G, et al. Association between smoking and chronic kidney disease: a case control study. BMC Public Health. 2010;10(1):1–6.

Saucier NA, Sinha MK, Liang KV, Krambeck AE, Weaver AL, Bergstralh EJ, et al. Risk factors for CKD in persons with kidney stones: a case-control study in Olmsted County, Minnesota. Am J Kidney Dis. 2010;55(1):61–8.

Lambert K, Mullan J, Mansfield K, Lonergan M. A cross-sectional comparison of health literacy deficits among patients with chronic kidney disease. J Health Commun. 2015;20(sup2):16–23.

Fraser SD, Roderick PJ, Casey M, Taal MW, Yuen HM, Nutbeam D. Prevalence and associations of limited health literacy in chronic kidney disease: a systematic review. Nephrol Dial Transplant. 2013;28(1):129–37.

Ji MY, Park YS, Yi SE. A case-control study to identify the risk factors of school accidents. Korean J Epidemiol. 2005;27(2):80–94.

Google Scholar  

Khajehdehi P, Malekmakan L, Pakfetrat M, Roozbeh J, Sayadi M. Prevalence of chronic kidney disease and its contributing risk factors in southern Iran a cross-sectional adult population-based study; 2014.

Li H, Lu W, Wang A, Jiang H, Lyu J. Changing epidemiology of chronic kidney disease as a result of type 2 diabetes mellitus from 1990 to 2017: estimates from global burden of disease 2017. J Diabetes Investig. 2021;12(3):346.

Xu Y, Surapaneni A, Alkas J, Evans M, Shin J-I, Selvin E, et al. Glycemic control and the risk of acute kidney injury in patients with type 2 diabetes and chronic kidney disease: parallel population-based cohort studies in US and Swedish routine care. Diabetes Care. 2020;43(12):2975–82.

Sepanlou SG, Barahimi H, Najafi I, Kamangar F, Poustchi H, Shakeri R, et al. Prevalence and determinants of chronic kidney disease in northeast of Iran: results of the Golestan cohort study. PLoS One. 2017;12(5):e0176540.

Pugh D, Gallacher PJ, Dhaun N. Management of hypertension in chronic kidney disease. Drugs. 2019;79(4):365–79.

Torra R, Furlano M, Ortiz A, Ars E. Genetic kidney diseases as an underecognized cause of chronic kidney disease: the key role of international registry reports. Clin Kidney J. 2021;14(8):1879-85.

Mizota T, Dong L, Takeda C, Shiraki A, Matsukawa S, Shimizu S, et al. Transient acute kidney injury after major abdominal surgery increases chronic kidney disease risk and 1-year mortality. J Crit Care. 2019;50:17–22.

Madsen NL, Goldstein SL, Frøslev T, Christiansen CF, Olsen M. Cardiac surgery in patients with congenital heart disease is associated with acute kidney injury and the risk of chronic kidney disease. Kidney Int. 2017;92(3):751–6.

Newsome BB, Warnock DG, McClellan WM, Herzog CA, Kiefe CI, Eggers PW, et al. Long-term risk of mortality and end-stage renal disease among the elderly after small increases in serum creatinine level during hospitalization for acute myocardial infarction. Arch Intern Med. 2008;168(6):609–16.

Ishani A, Nelson D, Clothier B, Schult T, Nugent S, Greer N, et al. The magnitude of acute serum creatinine increase after cardiac surgery and the risk of chronic kidney disease, progression of kidney disease, and death. Arch Intern Med. 2011;171(3):226–33.

James MT, Ghali WA, Knudtson ML, Ravani P, Tonelli M, Faris P, et al. Associations between acute kidney injury and cardiovascular and renal outcomes after coronary angiography. Circulation. 2011;123(4):409–16.

Esmeijer K, de Vries AP, Mook-Kanamori DO, de Fijter JW, Rosendaal FR, Rabelink TJ, et al. Low birth weight and kidney function in middle-aged men and women: the Netherlands epidemiology of obesity study. Am J Kidney Dis. 2019;74(6):751–60.

White SL, Perkovic V, Cass A, Chang CL, Poulter NR, Spector T, et al. Is low birth weight an antecedent of CKD in later life? A systematic review of observational studies. Am J Kidney Dis. 2009;54(2):248–61.

Harer MW, Charlton JR, Tipple TE, Reidy KJ. Preterm birth and neonatal acute kidney injury: implications on adolescent and adult outcomes. J Perinatol. 2020;40(9):1286–95.

Al Salmi I, Hannawi S. Birth weight and susceptibility to chronic kidney disease. Saudi J Kidney Dis Transplant. 2020;31(4):717.

Inai H, Kawai K, Ikeda A, Ando S, Kimura T, Oikawa T, et al. Risk factors for chronic kidney disease after chemotherapy for testicular cancer. Int J Urol. 2013;20(7):716–22.

Launay-Vacher V, Oudard S, Janus N, Gligorov J, Pourrat X, Rixe O, et al. Prevalence of renal insufficiency in cancer patients and implications for anticancer drug management: the renal insufficiency and anticancer medications (IRMA) study. Cancer. 2007;110(6):1376–84.

Launay-Vacher V. Epidemiology of chronic kidney disease in cancer patients: lessons from the IRMA study group. Semin Nephrol. 2010;30(6):548–56.

Launay-Vacher V, Janus N, Deray G. Renal insufficiency and cancer treatments. ESMO Open. 2016;1(4):e000091.

Janus N, Launay-Vacher V, Byloos E, Machiels JP, Duck L, Kerger J, et al. Cancer and renal insufficiency results of the BIRMA study. Br J Cancer. 2010;103(12):1815–21.

Kitchlu A, McArthur E, Amir E, Booth CM, Sutradhar R, Majeed H, et al. Acute kidney injury in patients receiving systemic treatment for Cancer: a population-based cohort study. J Natl Cancer Inst. 2019;111(7):727–36.

Kidney Disease Improving Global Outcomes (KDIGO). KDIGO clinical practice guideline for acute kidney injury. kdigo.org/wpcontent/uploads/2016/10/KDIGO-2012-AKI-Guide line-Engli sh.pdf . Accessed 23 Mar 2020.

Königsbrügge O, Lötsch F, Zielinski C, Pabinger I, Ay C. Chronic kidney disease in patients with cancer and its association with occurrence of venous thromboembolism and mortality. Thromb Res. 2014;134(1):44–9.

Goldstein SL, Mottes T, Simpson K, Barclay C, Muething S, Haslam DB, et al. A sustained quality improvement program reduces nephrotoxic medication-associated acute kidney injury. Kidney Int. 2016;90(1):212–21.

Menon S, Kirkendall ES, Nguyen H, Goldstein SL. Acute kidney injury associated with high nephrotoxic medication exposure leads to chronic kidney disease after 6 months. J Pediatr. 2014;165(3):522–7.e2.

Perazella MA, Izzedine H. New drug toxicities in the onco-nephrology world. Kidney Int. 2015;87(5):909–17.

Luyckx VA, Tuttle KR, Garcia-Garcia G, Gharbi MB, Heerspink HJL, Johnson DW, et al. Reducing major risk factors for chronic kidney disease. Kidney Int Suppl. 2017;7(2):71–87.

Download references

Acknowledgments

This paper is part of a thesis conducted by Mousa Ghelichi-Ghojogh, Ph.D. student of epidemiology, and a research project conducted at the Shiraz University of Medical sciences (99-01-04-22719). We would like to thank Dr. Bahram Shahryari and all nephrologists of Shiraz‌ University of medical sciences, interviewers, and CKD patients in Shiraz for their voluntary participation in the study and for providing data for the study.

Shiraz University of Medical Sciences financially supported this study. (Grant number: 99–01–04-22719).

Author information

Authors and affiliations.

Candidate in Epidemiology, Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

Mousa Ghelichi-Ghojogh

HIV/AIDS research center, School of Health, Shiraz University of Medical Sciences, P.O.Box: 71645-111, Shiraz, Iran

Mohammad Fararouei

Department of Epidemiology, School of Health, Shiraz University of Medical Sciences, Shiraz, Iran

Mozhgan Seif

Nephrologist, Shiraz Nephro-Urology Research Center, Department of Internal Medicine, Emergency Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

Maryam Pakfetrat

You can also search for this author in PubMed   Google Scholar

Contributions

MGG: Conceptualization, Methodology, Statistical analysis, Investigation, and writing the draft of the manuscript. MP: were involved in methodology, writing the draft of the manuscript, and clinical consultation. MS: was involved in the methodology and statistical analysis. MF: was involved in conceptualization, methodology, supervision, writing, and reviewing the manuscript. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohammad Fararouei .

Ethics declarations

Ethics approval and consent to participate.

The study protocol was reviewed and approved by the ethical committee of Shiraz University of Medical Sciences (approval number: 1399.865). All methods were performed in accordance with the relevant guidelines and regulations of the Declaration of Helsinki. The participants were assured that their information is used for research purposes only. Because of the illiteracy of a considerable number of the patients, verbal informed consent was obtained from the participants. Using verbal informed consent was also granted by the ethical committee of Shiraz University of Medical Sciences.

Consent for publication

Not applicable.

Competing interests

None of the authors declare disclosures of direct relevance to the submitted work.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Ghelichi-Ghojogh, M., Fararouei, M., Seif, M. et al. Chronic kidney disease and its health-related factors: a case-control study. BMC Nephrol 23 , 24 (2022). https://doi.org/10.1186/s12882-021-02655-w

Download citation

Received : 14 August 2021

Accepted : 24 December 2021

Published : 10 January 2022

DOI : https://doi.org/10.1186/s12882-021-02655-w

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Related factors
• Case-control

BMC Nephrology

ISSN: 1471-2369

• Submission enquiries: [email protected]
• General enquiries: [email protected]

• Search Menu
• Advance Articles
• Editor's Choice
• Cover Archive
• Author videos
• Supplements
• Cover Images
• Author Guidelines
• Submission Site
• Open Access Options
• Why publish with NDT?
• About the ERA
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Terms and Conditions
• Editorial Fellowship
• The ERA Journals
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

The global burden of non-communicable diseases, the case of chronic kidney disease, causes of ckd vary in developed and developing nations, ckd is a major risk factor for cardiovascular disease, the need to raise awareness about early ckd and implement prevention programs, conclusions.

• < Previous

Chronic kidney disease: a research and public health priority

• Article contents
• Figures & tables
• Supplementary Data

Norberto Perico, Giuseppe Remuzzi, Chronic kidney disease: a research and public health priority, Nephrology Dialysis Transplantation , Volume 27, Issue suppl_3, October 2012, Pages iii19–iii26, https://doi.org/10.1093/ndt/gfs284

• Permissions Icon Permissions

The growing global burden of non-communicable diseases (NCDs) worldwide has been disregarded until recently by policy makers, major aid donors and academics. However, NCDs are the leading cause of death in the world [ 1–3 ]. In 2008, there were 57 million deaths globally, of which 63% were due to NCDs. These chronic diseases are the largest cause of death, led by cardiovascular disease (17 million deaths, mainly from ischaemic heart disease and stroke) followed by cancer (7.6 million), chronic lung disease (4.2 million, including asthma and chronic obstructive pulmonary disease) and diabetes mellitus (1.3 million deaths) [ 4 ]. They share key risk factors: tobacco use, unhealthy diets, lack of physical activity and alcohol abuse [ 4 ]. The current burden of chronic diseases reflects past exposure to these risk factors, and the future burden will be largely determined by the current exposure. Actually, worldwide the prevalence of these chronic diseases is projected to increase substantially over the next decades [ 5 ]. According to WHO, the global number of individuals with diabetes in 2000 was estimated to be 171 million (2.8% of the world's population), a figure anticipated to increase in 2030 to 366 million (6.5%), 298 million of whom will live in developing countries [ 6 ].

As a consequence, predictions for the next two decades show a near 3-fold increase in the ischaemic heart disease and stroke mortality rate in Latin America, Sub-Saharan Africa and the Middle East [ 4 ]. Countries in transition in the South-East and East Asia have also witnessed a rapid deterioration of their chronic disease risk and mortality profile [ 7 ]. India, the second most populous country, has the highest number of diabetics in the world, and in 2008, the estimates for age-standardized deaths per 100 000 population due to diabetes and cardiovascular disease were 386.3 and 283.0 in males and females, respectively [ 7 ]. In China, age-specific death rates from cardiovascular disease increased between 200 and 300% in those aged 35 through 44 years between 1986 and 1999, and by more than 100% in those aged 45–54 years [ 8 ]. Of note, the 2011 WHO report on CKD Country Profiles [ 7 ] shows that globally low- and lower-middle income countries have the highest proportion of deaths under 60 years of age from NCDs. In 2008, the proportion of these premature NCD deaths was 41% in low-income and 28% in lower-middle income countries, respectively, threefold and more than twofold as compared with the proportion in the high-income countries (13%).

Risk factors for chronic diseases are also escalating. Smoking prevalence and obesity levels among adolescents have risen considerably worldwide over the past decade and portend a rapid increase in chronic diseases [ 9 , 10 ].

In all countries, the increased burden of NCDs is also leading to growing economic costs. For example, it has been anticipated that in the United States, cardiovascular diseases and diabetes together cost $750 billion annually [ 11 ]. In the next 10 years the United Kingdom will lose $33 billion in national income as a result of largely preventable heart disease, stroke and diabetes [ 12 , 13 ]. Over the same period, the national income loss for NCDs in India and China will account for $237 and $558 billion, respectively [ 12 , 13 ].

Thus, NCDs are among the most severe threats to global economic development, probably more detrimental than fiscal crisis, as underlined by the World Economic Forum's 2009 report.

Chronic kidney disease (CKD) is a key determinant of the poor health outcomes for major NCDs [ 14 ]. CKD is a worldwide threat to public health, but the size of the problem is probably not fully appreciated. Estimates of the global burden of the diseases report that diseases of the kidney and urinary tract contribute with ∼830 000 deaths annually and 18 867 000 disability-adjusted life years (DALY), making them the 12th highest cause of death (1.4% of all deaths) and the 17th cause of disability (1% of all DALY). This ranking is similar across World Bank regions, but, among developing areas, East Asia and Pacific regions have the highest annual rate of death due to diseases of the genitourinary system [ 15 ].

National and international renal registries offer an important source of information on several aspects of CKD. In particular, they are useful in characterizing the population on renal replacement therapy (RRT) due to end-stage renal disease (ESRD), describing the prevalence and incidence of ESRD and trends in mortality and disease rates. One of the most comprehensive sources of information about the prevalence of ESRD worldwide is the United States Renal Data System (USRDS). We have implemented the USRDS dataset with ESRD data from renal registries identified after searches of web resources for registry databases, annual reports and published literature. According to this analysis, the most recent available data indicate that the prevalence of ESRD ranges from 2447 pmp in Taiwan to 10 pmp in Nigeria (Figure  1 ). However, there is paucity of renal registries globally with an international standard for registry data collection, especially in low- and middle-income countries, where, in addition, the use of RRT is scarce or non-existent, eventually making it difficult to compare ESRD results [ 16 ]. For these reasons, the reported prevalence rate of ESRD varies widely among countries, especially in the emerging world, which may be related more to the capacity of the health system to provide the costly RRT treatment than true difference in epidemiology of renal disease. Thus, in Latin America, the ESRD prevalence ranges from 1019 pmp in Uruguay to 34 pmp in Honduras, a difference that may also reflect the relationship with the gross national product [ 17 ]. Much less is known in Africa, with the highest ESRD prevalence in Tunisia (713 pmp) and Egypt (669 pmp) [ 18 ]. In relatively developed regions of China, especially in major cities, the prevalence of ESRD has been reported to be 102 pmp [ 19 ], whereas in Japan, it is more than 2200 pmp, one of the highest rates worldwide.

Prevalence of ESRD (dialysis and transplantation) worldwide. Data are from the 2011 USRDS Annual Report and from national registry database and published literature. All rates are unadjusted and presented as prevalence rate per million population.

Therefore, overall there are ∼1.8 million people in the world who are alive simply because they have access to one form or another of RRT [ 20 ]. Ninety per cent of those live in industrialized countries, where the average gross income is in excess of US $10 000 per capita [ 21 ]. The size of this population has been expanding at a rate of 7% per year. As an example, over the last decade, the number of those requiring dialysis has increased annually by 6.1% in Canada [ 22 ], 11% in Japan [ 23 ] and 9% in Australia [ 24 ]. However, <10% of all patients with ESRD receive any form of RRT in countries such as India and Pakistan. In India, ∼100 000 patients develop ESRD each year [ 25 ]. Of these, 90% never see a nephrologist. Of the 10 000 patients who do consult a nephrologist, RRT is initiated in 90%; the remaining 10% are unable to afford any form of RRT. Of the 8900 patients who start haemodialysis, 60% are lost to follow-up within 3 months. These patients drop out of therapy, because they realize that dialysis is not a cure and has to be performed over the long-term, ultimately causing impoverishment of their families.

Patients on RRT can be regarded as the tip of the iceberg, whereas the number of those with CKD not yet in need of RRT is much greater. However, the exact prevalence of pre-dialysis CKD is not known and only rough estimates exist. In industrialized countries such as the USA, the Third National Health and Nutrition Evaluation Survey (NHANES III, 1999–2006) has shown a prevalence of CKD in the adult population of 11.5% (∼23.2 million people) [ 26 ]. A sizeable proportion of these people will experience the progression of their disease to ESRD. In Europe, the Prevention of End-Stage Renal and Vascular End-points (PREVEND) study undertaken in the city of Groningen (the Netherlands) evaluated almost 40 000 individuals in a cross-sectional cohort study [ 27 ]. It was found that no less than 16.6% had high normal albuminuria and ∼7% of those screened had microalbuminuria. If these data were to be extrapolated to the world population, the number of people with CKD could be estimated as hundreds of millions.

Although data concerning the prevalence of pre-dialysis CKD in developing countries are scarce, we would expect that there are comparable numbers of patients with CKD in poor countries as in industrialized nations. To this, the International Society of Nephrology (ISN) Global Outreach (GO) funded the Kidney Disease Data Center database to house data from sponsored programmes aimed at preventing CKD and its complications in developing nations. Some examples indicate that the overall prevalence of CKD, diagnosed based on a urinary albumin/creatinine ratio ≥30 or glomerular filtration rate (GFR) ≤60 L/min/1.73 m 2 (as Modification of Diet in Renal Disease, four variables), is 11 and 10.6% in urban areas, respectively, of Moldova [ 28 ] and Nepal [ 29 ]. Moreover, in the attempt to compare the burden of illness among centres in Nepal, China and Mongolia, in 11 394 adult subjects, it has been found that decreased estimated GFR (<60 L/min/1.73 m 2 ) was present in 7.3–14% of participants across centres; proteinuria (≥1+) on dipstick (2.4–10%) was also common [ 30 ]. By a recent cross-sectional survey of a nationally representative sample of Chinese adults, the overall prevalence of CKD was 10.8% [ 31 ].

Data from India also suggest that in a developing country, the prevalence rate of CKD could vary almost 5-fold between the rural and city population [ 32 , 33 ]. These observations imply that CKD would affect not only very many people in the developing world, but preferentially the poor within these countries who usually have no information about disease and risk factors, and cannot have access to healthcare. Interestingly, low socioeconomic status is associated with CKD also in developed nations, as shown in Unites States by the NHANES survey, which reported people with lower income being disproportionately afflicted with a higher burden of CKD risk factors [ 34 ]. Similarly, in Sweden [ 35 ] and the UK [ 36 ], lower income and social deprivation are associated with micro- or macro-albuminuria, reduced GFR and progressive kidney function loss.

Diabetes and hypertension

Diabetes and hypertension are the major causes of CKD leading to kidney failure in the USA, accounting for 153 and 99 pmp, respectively [ 37 ], of incident causes of ESRD. Definitely lower is the contribution of glomerulonephritis (23.7 pmp) [ 37 ]. The proportion of people with CKD not explained by diabetes and hypertension is substantially lower in the USA (28% of stage 3–4 CKD) than in developing countries [ 37 , 38 ]. Indeed, in a recent study analysing screening programs in Nepal, China and Mongolia, 43% of people with CKD did not have diabetes or hypertension [ 30 ].

Infectious diseases

There is also increasing evidence that infectious diseases, still a major health problem in low-income countries, may substantially contribute to the burden of chronic nephropathies. This mainly relates to poor environmental conditions, unsafe life habit and malnutrition. Urinary tract infections, occurring in the entire population, but with particular impact on females of all ages, especially during pregnancy, may have long-term consequences over and above the direct infectious disease morbidity and mortality these infections cause. They include chronic injury of the kidney which eventually may lead to loss of renal function, development of secondary hypertension and, for pregnant women, increased risk of maternal toxaemia, neonatal prematurity and low birth weight which is usually associated with lower-than-normal nephron number anticipating the high risk for hypertension and chronic renal injury during the life time [ 39 ]. Moreover, in several regions worldwide, tuberculosis is still an endemic infection with many cases of renal tuberculosis remaining clinically silent for years while irreversible renal destruction takes place [ 40 ]. Glomerular involvement with parasitic diseases, including malaria [ 41 ], schistosomiasis [ 42 ] and leishmaniasis [ 43 ], may also pave the way to progressive renal disease. A variety of glomerular lesions, and in particular a unique form of glomerular damage, HIV-associated nephropathy, have emerged as significant forms of renal disease in HIV-infected patients [ 44 ]. With the increasing rate of this viral infection, kidney failure in HIV-infected patients will progressively become a major public health problem, particularly in Sub-Saharan Africa. Therefore, in developing countries, infectious diseases add substantial burden to non-communicable risk factors, in enhancing the global prevalence of CKDs.

Malnutrition

There are also factors that link early malnutrition with being overweight in adulthood, ultimately developing into diabetes and diabetic nephropathy [ 45 ]. A number of observational epidemiological studies have postulated that early (intrauterine or early postnatal) malnutrition causes an irreversible differentiation of the metabolic system, which may, in turn, increase the risk of certain chronic diseases in adulthood. For example, a fetus of an undernourished mother will respond to a reduced energy supply by switching on genes that optimize energy conservation. This survival strategy means a permanent differentiation of regulatory systems that result in an excess accumulation of energy (and consequently body fat) when the adult is exposed to an unrestricted dietary energy supply [ 45 ]. Because intrauterine growth retardation and low birth weight are common in developing countries or within minority groups, this mechanism may result in the establishment of a population in which many adults are particularly susceptible to developing obesity and CKD. These observations further imply that CKD would affect preferentially the poor within these countries.

Acute kidney injury

CKD is also linked to acute kidney injury (AKI). Thus, both the rate of progression to ESRD and all-cause mortality are increased in patients with CKD after transient increases in serum creatinine when compared with patients without CKD [ 46 ]. Moreover, up to 28% of the patients with no pre-existing kidney disease who recover from AKI develop de novo CKD [ 47 ]. Non-steroidal anti-inflammatory medications, several cardiovascular and diabetes drugs, as well as traditional medicines used in the primary-care setting in developing countries, may lead to the development of transient episodes of AKI. These findings emphasize the relevance of CKD detection and appropriate adjustments in management to optimal outcome in major NCDs.

It is increasingly recognized that the burden of CKD is not limited to its implication on demands for RRT but has a major impact on the health of the overall population. Indeed, patients with reduced kidney function represent a population not only at risk for the progression of kidney disease and development of ESRD, but also at even greater risk for cardiovascular diseases. CKD is a major risk factor for cardiovascular mortality, and kidney disease is a major complication of diabetes. In ∼400 000 Medicare patients with diabetes and CKD, in USA over 2 years of follow-up, the risk of death for cardiovascular diseases (32.3%) far exceeded that of the development of ESRD (6:1) [ 48 ]. Moreover, CKD has been documented as an independent risk factor for angina, myocardial infarction, heart failure, stroke, peripheral vascular disease and arrhythmias [ 49 , 50 ]. The increased risk of cardiovascular disease associated with CKD has been shown in both general [ 37 , 51 , 52 ] and high-risk [ 52 ] populations, in young and elderly people [ 53 ], as well as in Caucasians [ 49 ], African blacks [ 54 ] and in Asian people [ 55 ].

There is also evidence that the increased cardiovascular risk in CKD patients does not just coexist with diabetes or hypertension. Indeed, an independent and progressive association between GFR and risk of cardiovascular events and death has been found in a community-based study in more than 1 million adult subjects in the USA [ 56 ]. Similarly, a recent study in more than 6000 people followed on average 7 years has shown that the risk of cardiovascular death was increased 46% in subjects with a mild-to-moderate reduction in GFR (30–60 L/min), independent of conventional risk factors such as diabetes and hypertension [ 57 ].

The reason why CKD is a risk factor for cardiovascular outcomes is not entirely clear, but it seems largely related to the excess prevalence of traditional cardiovascular risk factors, including hypertension, diabetes and dyslipidaemia associated with the renal disease. In addition, other factors such as hyperhomocystinaemia, abnormalities of mineral metabolism and parathyroid function may become more prevalent and have pathogenetic relevance as CKD progresses [ 58 , 59 ]. Even patients with microalbuminuria and proteinuria, but still normal renal function, are at increased risk of cardiovascular morbidity and mortality [ 60 ]. Large studies in the general population showed that the presence of microalbuminuria or proteinuria is associated with enhanced risk of all-cause mortality at all levels of baseline kidney function [ 27 , 49 , 61–63 ].

Thus, through its impact on cardiovascular morbidity, CKD may directly contribute to the increasing global burden of death caused by cardiovascular disease. Therefore, these are the patients in whom efforts should be focused.

The major societal effect of CKD is the enormous financial cost and loss of productivity with associated advanced or ESRD. In many developed countries, treatment for ESRD accounts for more than 2–3% of their annual health-care budget, while the population with ESRD represents ∼0.02–0.03% of the total population [ 64 ]. This situation is even worse in most developing countries, where RRT is often unavailable or unaffordable, and ∼1 million people die with ESRD each year [ 65 ]. On the other hand, awareness of early and advanced CKD is low, even in developed nations, being <20% [ 38 ]. For example, in a recent survey in almost 500 000 people in Taiwan, as a part of medical screening programme, <4% of those with CKD (12%) were aware of their condition [ 66 ]. Moreover, it should be considered that CKD, even at more advanced stages, is treatable. Ample evidence from clinical trials has shown that control of hypertension and of proteinuria, especially with inhibitors of the renin–angiotensin system, are highly effective interventions for slowing the progression of diabetic and non-diabetic CKD [ 67 , 68 ]. Studies have also documented that even sustained remission or regression of proteinuric CKD is achievable especially in a large proportion of non-diabetic patients [ 69 ].

Together, these observations underline the urgent need for strategies to enhance awareness about CKD, especially in developing countries, where the low awareness may serve as a barrier to accessing appropriate care even when available [ 70 ] (Table  1 ). To this purpose, recently, the International Society of Nephrology and the International Federation of Kidney Foundation joined efforts to raise awareness regarding CKD by promoting the annual World Kidney Day (WKD). On this particular day, public activities such as free screening for CKD and its risk factors and meeting with the community population and leaders are planned and performed in numerous centres worldwide [ 71 ]. Nevertheless, the resources to implement effective early awareness, detection and prevention programmes for CKD should ultimately come from government health programmes as part of global strategy to improve public health. Some examples are the National Health Programme in Uruguay that has already incorporated CKD into their NCD prevention programmes, and the Strategic Network of Health Services against Chronic Kidney Disease in Mexico.

Public health initiatives targeting CKD

These programmes will help to decrease the costs of managing ESRD and cardiovascular disease and respond to public health demand. However, before these surveillance and intervention efforts are expanded, information on their sustainability and affordability to the public sector, especially in low-income countries, should be collected.

Medicine is developing evidence for the importance of CKD to public health and its contribution to the global burden of major NCDs, but has no equity plan [ 14 , 72 ]. A more concerted, strategic and multisectorial approach, underpinned by solid research, is essential to help reverse the negative trends in the incidence of CKD and its risk factors, not just for a few beneficiaries but on a global health equity programme. Thus, a pragmatic approach to reduce the global burden of renal and cardiovascular diseases has to be adopted. For that, well-defined screening of community or high-risk populations followed by intervention programmes have to be initiated, especially in developing countries.

In recognition of the increasing burden and importance of chronic diseases, a high-level United Nations meeting with heads of governments of member states was organized last September in New York to discuss a global NCD Action Plan prepared by WHO. Although this document did provide the unique opportunity to bring attention to the pandemic of NCDs, it prioritized four chronic diseases, namely cardiovascular disease, cancer, diabetes and chronic respiratory disease [ 73 ]. Nevertheless, through intensive lobbying also by ISN, CKD has gained recognition in the final Political Declaration [ 73 ]. Indeed, a paragraph of the NCD Action Plan stated that the members of States of the UN General Assembly ‘recognize that renal, oral and eye disease pose a major health burden for many countries and that diseases share common risk factors and can benefit from common responses to non-communicable diseases’ [ 73 ]. However, NCD advocacy groups, such as ISN [ 74 ], as well as the editors of The Lancet and The British Medical Journal have underlined their disappointment over the insufficient emphasis on action to be taken by governments [ 75 , 76 ]. In addition, they pointed out that a major opportunity to advance global health was in danger of being lost since the Political Declaration did not set substantive targets or timelines in the need for member states to activate policies in their public health programmes to address NCD issues [ 74–77 ].

In developing nations, there must also be a commitment to create in-country capacity, notably a human capacity that can determine for itself locally specific problems dealing with kidney diseases to be addressed through clinical research programmes. However, this implies greater efforts by the developed nations to limit the brain drain of scientists and health personnel from low- and middle-income countries [ 78 ]. The North-South capacity gap in health science, including nephrology, continues to narrow, but it has by no means disappeared. At the same time, a new gap in capacity has emerged between scientifically proficient and scientifically lagging developing countries, the so-called South–South gap. This divide has surfaced because the number of developing countries making significant strides in building scientific capacity remains small (Brazil, Argentina, Mexico, Chile, South Africa, India, China and Malaysia). There are examples of increasing South–South cooperation that are helping to close this gap. However, even developing countries that have successfully strengthened their scientific capacity have proven more adept at building their knowledge base than applying the know-how, scientists/physicians acquire to address societal concerns. Along these lines, ISN through its Global Outreach programmes, especially the Research and Prevention programme, has developed several initiatives for emerging countries that can be implemented according to the peculiar needs and organization facilities of the given nation [ 79 ]. Overall, the emphasis is on models to promote and foster autonomous programmes in regions where they are most needed.

The hope is that all these efforts will assist to make a major advance in addressing the neglected aspect of the renal health of people worldwide.

Conflict of interest statement . None declared.

Google Scholar

Google Preview

• kidney failure, chronic
• public health medicine

Email alerts

Citing articles via.

• ndt Twitter
• ERA Twitter
• ERA Facebook
• ERA Instagram
• ERA LinkedIn
• Recommend to Your Librarian

Affiliations

• Online ISSN 1460-2385
• Print ISSN 0931-0509
• Copyright © 2024 European Renal Association
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Advertisement

Chronic Kidney Disease Prediction Using Machine Learning Techniques

• Original Paper
• Published: 31 August 2022
• Volume 1 , pages 534–540, ( 2023 )

Cite this article

• Saurabh Pal   ORCID: orcid.org/0000-0001-9545-7481 1  

6359 Accesses

11 Citations

Explore all metrics

Chronic kidney disease (CKD) is a life-threatening condition that can be difficult to diagnose early because there are no symptoms. The purpose of the proposed study is to develop and validate a predictive model for the prediction of chronic kidney disease. Machine learning algorithms are often used in medicine to predict and classify diseases. Medical records are often skewed. We have used chronic kidney disease dataset from UCI Machine learning repository with 25 features and applied three machine learning classifiers Logistic Regression (LR), Decision Tree (DT), and Support Vector Machine (SVM) for analysis and then used bagging ensemble method to improve the results of the developed model. The clusters of the chronic kidney disease dataset were used to train the machine learning classifiers. Finally, the Kidney Disease Collection is summarized by category and non-linear features. We get the best result in the case of decision tree with accuracy of 95.92%. Finally, after applying the bagging ensemble method we get the highest accuracy of 97.23%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Yogesh Kumar, Apeksha Koul, … Muhammad Fazal Ijaz

Heart Disease Prediction using Machine Learning Techniques

Devansh Shah, Samir Patel & Santosh Kumar Bharti

Machine Learning in Healthcare Analytics: A State-of-the-Art Review

Surajit Das, Samaleswari P. Nayak, … Sarat Chandra Nayak

Aljaaf, A.J. 2018 Early Prediction of Chronic Kidney Disease Using Machine Learning Supported by Predictive Analytics. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC). Wellington. New Zealand

A. Nishanth, T. Thiruvaran, Identifying important attributes for early detection of chronic kidney disease. IEEE Rev. Biomed. Eng. 11 , 208–216 (2018)

Article   Google Scholar  

A. Ogunleye, Q.-G. Wang, XGBoost model for chronic kidney disease diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinform. 17 , 2131–2140 (2020)

F. Aqlan, R. Markle, A. Shamsan, "Data mining for chronic kidney disease prediction." in IIE Annual Conference. Proceedings, Institute of Industrial and Systems Engineers , (IISE 2017), pp. 1789–1794

N. Borisagar, D. Barad, P. Raval, Chronic kidney disease prediction using back propagation neural network algorithm. Proce. Int. Confe. Commun. Netw. 19–20 , 295–303 (2017)

Google Scholar  

C. Bemando, E. Miranda, M. Aryuni, "Machine-Learning-Based Prediction Models of Coronary Heart Disease Using Naïve Bayes and Random Forest Algorithms," in 2021 International Conference on Software Engineering & Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM) , (IEEE, 2021), pp. 232–237

R.P. Ram Kumar, SanjeevaPolepaka, Performance comparison of random forest classifier and convolution neural network in predicting heart diseases, in Proceedings of the Third International Conference on Computational Intelligence and Informatics . ed. by K. SrujanRaju, A. Govardhan, B. PadmajaRani, R. Sridevi, M. Ramakrishna Murty (Springer, Singapore, 2020)

H. Singh, N. V. Navaneeth, G. N. Pillai, "Multisurface proximal SVM based decision trees for heart disease classification," in TENCON 2019-2019 IEEE Region 10 Conference (TENCON) , (IEEE 2019), pp. 13–18

S.D. Desai, S. Giraddi, P. Narayankar, N.R. Pudakalakatti, S. Sulegaon, Backpropagation neural network versus logistic regression in heart disease classification in advanced computing and communication technologies (Springer, Singapore, 2019)

D.D. Patil, R.P. Singh, V.M. Thakare, A.K. Gulve, Analysis of ecg arrhythmia for heart disease detection using svm and cuckoo search optimized neural network. Int. J. Eng. Technol. 7 (217), 27–33 (2018)

N. Liu, Z. Lin, J. Cao, Z. Koh, T. Zhang, G.-B. Huang, W. Ser, M.E.H. Ong, An intelligent scoring system and its application to cardiac arrest prediction. IEEE Trans. Inf Technol. Biomed. 16 (6), 1324–1331 (2012)

U. Rajendra Acharya, Oh. Shu Lih, Y. Hagiwara, J.H. Tan, M. Adam, A. Gertych, R.S. Tan, A deep convolutional neural network model to classify heartbeats. Comput. Biol. Med. 89 , 389–396 (2017)

R.S. Walse, G.D. Kurundkar, S.D. Khamitkar, A.A. Muley, P.U. Bhalchandra, S.N. Lokhande, Effective use of naïve bayes, decision tree, and random forest techniques for analysis of chronic kidney disease, in International Conference on Information and Communication Technology for Intelligent Systems . ed. by T. Senjyu, P.N. Mahalle, T. Perumal, A. Joshi (Springer, Singpore, 2020)

A. Nithya, A. Appathurai, N. Venkatadri, D.R. Ramji, C.A. Palagan, Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images. Measurement (2020). https://doi.org/10.1016/j.measurement.2019.106952

Abdullah Al Imran, Md Nur Amin, and Fatema Tuj Johora. Classification of chronic kidney disease using logistic regression, feedforward neural network and wide & deep learning. In 2018 International Conference on Innovation in Engineering and Technology (ICIET), pages 1–6. IEEE, 2018.

B. Navaneeth, M. Suchetha, A dynamic pooling based convolutional neural network approach to detect chronic kidney disease. Biomed. Signal Proce. Control 62 , 102068 (2020)

A. Brunetti, G.D. Cascarano, I. De Feudis, M. Moschetta, L. Gesualdo, V. Bevilacqua, Detection and segmentation of kidneys from magnetic resonance images in patients with autosomal dominant polycystic kidney disease, in International Conference on Intelligent Computing . ed. by D.-S. Huang, K.-H. Jo, Z.-K. Huang (Springer International Publishing, Cham, 2019)

D. Ramos et al., Using decision tree to select forecasting algorithms in distinct electricity consumption context of an office building. Energy Rep. 8 , 417–422 (2022)

H.E. Song et al., Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest. Chemosphere 290 , 133388 (2022)

H.U. Rongyao et al., Multi-task multi-modality SVM for early COVID-19 diagnosis using chest CT data. Inf. Proc. Manag. 59 (1), 102782 (2022)

X.U. Ankun et al., Artificial neural network (ANN) modeling for the prediction of odor emission rates from landfill working surface. Waste Manag. 138 , 158–171 (2022)

D.C. Yadav, S. Pal, An Ensemble Approach on the behalf of Classification and Prediction of Diabetes Mellitus Disease Emerging Trends in Data Driven Computing and Communications (Springer, Singapore, 2021)

D.C. Yadav, S. Pal, Performance based evaluation of algorithms on chronic kidney disease using hybrid ensemble model in machine learning. Biomed. Pharmacol. J. 14 (3), 1633–1646 (2021)

D.C. Yadav, S. Pal, Discovery of Thyroid Disease Using Different Ensemble Methods with Reduced Error Pruning Technique, in Computer-aided Design and Diagnosis Methods on the behalf of Biomedical Applications . ed. by G.R. Varun Bajaj, V.B. Sinha, G.R. Sinha (CRC Press, Boca Raton, 2021)

A. Zoda et al., Inferring genetic characteristics of Japanese Black cattle populations using genome-wide single nucleotide polymorphism markers. J. Animal Genet. 50 (1), 3–9 (2022)

G.M. Ifraz, M.H. Rashid, T. Tazin, S. Bourouis, M.M. Khan, Comparative analysis for prediction of kidney disease using intelligent machine learning methods. Comput. Math. Methods Med. (2021). https://doi.org/10.1155/2021/6141470

S. Krishnamurthy, K.S. Kapeleshh, E. Dovgan, M. Luštrek, B.G. Piletič, K. Srinivasan, Y.C. Li, A. Gradišek, S. Syed-Abdul, "Machine learning prediction models for chronic kidney disease using national health insurance claim data in Taiwan." medRxiv. (2020). https://doi.org/10.1101/2020.06.25.20139147

Z.U. Rehman, M.S. Zia, G.R. Bojja, M. Yaqub, F. Jinchao, K. Arshid, Texture based localization of a brain tumor from MR-images by using a machine learning approach. Med. Hypotheses 141 , 109705 (2020)

X. Han, X. Zheng, Y. Wang, X. Sun, Y. Xiao, Y. Tang, W. Qin, Random forest can accurately predict the development of end-stage renal disease in immunoglobulin a nephropathy patient. Annals Transl. Med. (2019). https://doi.org/10.21037/atm.2018.12.11

E.H.A. Rady, A.S. Anwar, Prediction of kidney disease stages using data mining algorithms. Inform. Med. Unlocked (2019). https://doi.org/10.1016/j.imu.2019.100178

Z. Dong, Q. Wang, Y. Ke, W. Zhang, Q. Hong, C. Liu, X. Chen, Prediction of 3-year risk of diabetic kidney disease using machine learning based on electronic medical records. J. Transl. Med. 20 (1), 1–10 (2022)

D.C. Yadav, S. Pal, Prediction of thyroid disease using decision tree ensemble method. Human-Intell. Syst. Integr. 2 (1), 89–95 (2020)

V. Chaurasia, S. Pal, Applications of machine learning techniques to predict diagnostic breast cancer. SN Compu. Sci. 1 (5), 1–11 (2020)

Chaurasia, V., & Pal, S. (2014). Performance analysis of data mining algorithms for diagnosis and prediction of heart and breast cancer disease. Review of research. 3(8).

Download references

Acknowledgements

Author thanks to Veer Bahadur Singh Purvanchal University, Jaunpur for providing the support for conducting this research work as a part of minor project “Analysis of Hidden Pattern and Discover Real Fact of Medical Diseases using Integrated Machine Learning Techniques.

Author information

Authors and affiliations.

Department of Computer Applications, VBS Purvanchal University, Jaunpur, India

Saurabh Pal

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Saurabh Pal .

Ethics declarations

Conflict of interest.

Author declares no conflict of interest.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Pal, S. Chronic Kidney Disease Prediction Using Machine Learning Techniques. Biomedical Materials & Devices 1 , 534–540 (2023). https://doi.org/10.1007/s44174-022-00027-y

Download citation

Received : 20 April 2022

Accepted : 16 August 2022

Published : 31 August 2022

Issue Date : March 2023

DOI : https://doi.org/10.1007/s44174-022-00027-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Chronic kidney disease
• Decision Tree
• Support Vector Machine
• Logistic Regression and Bagging Ensemble Method
• Find a journal
• Publish with us
• Track your research

ORIGINAL RESEARCH article

Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990-2019.

• 1 Department of Clinical Pharmacy, Beilun District People’s Hospital, Ningbo, China
• 2 Department of Clinical Laboratory, The Second Hospital of Anhui University of Traditional Chinese Medicine, Hefei, Anhui, China
• 3 Department of Clinical Pharmacy, Yuyao People’s Hospital, Ningbo, Zhejiang, China

Background: This study investigates the burden of chronic kidney disease attributed to type 2 diabetes (CKD-T2D) across different geographical locations and time periods from 1990 to 2019. A total of 204 countries and regions are included in the analysis, with consideration given to their socio-demographic indexes (SDI). The aim is to examine both spatial and temporal variations in CKD-T2D burden.

Methods: This research utilized data from the 2019 Global Burden of Diseases Study to evaluate the age-standardized incidence rates (ASIR), Disability-Adjusted Life Years (DALYs), and Estimated Annual Percentage Change (EAPC) associated with CKD-T2D.

Results: Since 1990, there has been a noticeable increase of CKD age-standardized rates due to T2D, with an EAPCs of 0.65 (95% confidence interval [CI]: 0.63 to 0.66) for ASIR and an EAPC of 0.92 (95% CI: 0.8 to 1.05) for age-standardized DALYs rate. Among these regions, Andean Latin America showed a significant increase in CKD-T2D incidence [EAPC: 2.23 (95% CI: 2.11 to 2.34) and North America showed a significant increase in CKD-T2D DALYs [EAPC: 2.73 (95% CI: 2.39 to 3.07)]. The burden was higher in male and increased across all age groups, peaking at 60-79 years. Furthermore, there was a clear correlation between SDI and age-standardized rates, with regions categorized as middle SDI and High SDI experiencing a significant rise in burden.

Conclusion: The global burden of CKD-T2D has significantly risen since 1990, especially among males aged 60-79 years and in regions with middle SDI. It is imperative to implement strategic interventions to effectively address this escalating health challenge.

1 Introduction

Chronic kidney disease (CKD)-Type 2 Diabetes (T2D) is a common chronic complication resulting from diabetes mellitus, characterized by alternating or sustained albuminuria and/or a progressive decline in glomerular filtration rate. In the absence of comprehensive treatment approaches, CKD-T2D frequently progresses to End-Stage Renal Disease (ESRD) ( 1 ). Importantly, older adults with T2D, especially those who have had the disease for ten years or more, have a higher likelihood of developing CKD compared to non-diabetic individuals ( 2 ). With an aging population, increased life expectancy, and changing lifestyle patterns, the CKD-T2D is rising, contributing significantly to the global increase in ESRD ( 3 ).

Research has indicated that obesity, hypertension, and being male are key risk factors for the onset and advancement of CKD-T2D ( 4 , 5 ). Furthermore, the occurrence and fatality rates of CKD resulting from diabetes are closely linked to socio-economic, cultural, and healthcare management factors at the national level, as well as the age of the patient ( 3 , 6 ). Several studies have examined the global and regional patterns of CKD, including its incidence, incidence, and mortality, and have noted variations based on sex and age groups ( 7 ). For instance, Pan and Liu et al. conducted a study on the incidence of diabetes and CKD in China ( 8 ), while another study focused on mortality and trends in diabetes cases among individuals under the age of 25 ( 9 ). However, there is still a lack of research on the epidemiological aspects of CKD-T2D on a global and regional scale, across all age groups.

Given the lengthy evolution of diabetes mellitus into CKD-T2D and the need for long-term interventions to effectively manage CKD-T2D, it is imperative to prioritize early prevention, timely detection, and prompt intervention. In light of the consequences of CKD associated with T2D, our study utilizes age-standardized rates (ASRs) to quantify its incidence and disability trends. These include the age-standardized incidence rate (ASIR) and the age-standardized disability-adjusted life year (DALY) rate. Understanding the global impact of CKD-T2D across all age groups is crucial for devising strategies to prevent and ultimately reduce its incidence.

This study aims to analyze the global burden of CKD-T2D in individuals across all age groups. The data from the Global Burden of Disease (GBD2019) study is examined to accomplish this objective. The analysis encompasses the observation of disease trends over the period from 1990 to 2019, the identification of disparities between different countries and regions, and the evaluation of variations by age.

2.1 Data source and measures of burden

The Global Burden of Disease (GBD) 2019 is a comprehensive international initiative that provides estimates on the impact of 369 diseases and injuries across 204 countries and territories from 1990 to 2019 ( 10 ). To gather data for each specific disease or injury, the GBD utilizes a wide range of sources including 7,333 national and 24,657 sub-national vital registration systems, 16,984 scholarly publications, and 1,654 household surveys, as well as other relevant sources such as population censuses, healthcare usage records, and satellite imagery ( 10 ). This study is updated annually to incorporate refinements to the range of diseases, data sources, and methodologies. These updates aim to accurately capture yearly variations in the same diseases and injuries, stratified by age, sex, country, and region, using standard epidemiological and health metrics such as incidence, prevalence, mortality rates, and DALYs ( 10 ).

DALYs, a crucial metric in epidemiological research, quantify the overall burden of disease by measuring the total years of healthy life lost from disease onset to death. This measurement encompasses both years lost due to premature mortality and years lost due to disability, and can be expressed as either a numerical count or a rate ( 11 ). This study is based on a publicly available database and does not require ethical approval.

2.2 Clinical criteria

This study focuses on CKD-T2D, which is classified under ICD-10 codes E11.2 to E11.29 and under ICD-9 codes 250.40 and 250.42. According to the 2019 Global Burden of Disease Study guidelines, diabetes is defined as a fasting blood glucose level equal to or greater than 126 mg/dL (7 mmol/L) or through reported diabetes treatment. CKD-T2D, a subtype of chronic kidney disease caused by Type 2 Diabetes, is characterized by a duration exceeding three months. It is primarily identified through a urinary albumin/creatinine ratio exceeding 30 mg/g and/or an estimated glomerular filtration rate below 60 mL/min per 1.73 m² ( 12 ).

2.3 Estimation of attributable risk factors for CKD-T2D

The GBD 2019 study estimated the disease burden attributable to 87 risk (or risk cluster) factors at the global, regional, and national levels. Population Attributable Fractions (PAF) are employed to assess the contribution of specific risk factors to disease or mortality within an entire population, as well as the proportion by which disease incidence or mortality can be reduced if the population were at the theoretical minimum risk exposure level. The product of PAF with the disease’s DALYs and deaths represents the DALYs and deaths attributed to that risk factor. The attributed standardized rates were used to measure the attributable disease burden of global CKD risk factors.

where a is the age group, s is the sex, l is the location, and y is the year; PAFasly is the PAF for the burden of diseases due to T2D; RR is the relative risks between exposure level x (from 1 to k) of T2D and the burden of CKD; and P is the proportion of the population exposed to T2D.

2.4 Statistical analysis

The Global Burden of Disease studies examine the impact of 329 diseases across 204 countries and territories. These countries are divided into 21 clusters based on epidemiological and geographical factors ( 13 ). For our research, we used the Socio-Demographic Index (SDI) to categorize countries. The SDI combines various factors such as per capita income, educational attainment, and fertility rates among women under 25 years old. The index ranges from 0, representing lower income, education, and higher fertility rates, to 1, indicating higher income, education, and lower fertility rates ( 14 ). Based on the SDI, countries were classified into five tiers: low (below 0.46), low-middle (0.46–0.60), middle (0.61–0.69), high-middle (0.70–0.81), and high (above 0.81).

Using global standard population data from the GBD 2019 study, Age-Standardized Rates (ASRs) for incidence and DALY (per 100,000 population), along with their 95% Uncertainty Intervals (UIs), were calculated using the direct standardization method. To examine the temporal trends in CKD-T2D incidence and DALY rates from 1990 to 2019 globally, Estimated Annual Percent Changes (EAPCs) and their 95% Confidence Intervals (CIs) were computed. EAPC is a commonly used measure to assess rate trends over specified time periods. It was determined by fitting a regression line to the natural logarithm of the rates (y = α + βx + ϵ), where y represents the natural logarithm of the rate and x represents the calendar year. The EAPC calculation involved multiplying 100 by (exp[β]−1), and its 95% UI was obtained using linear regression modeling ( 15 ). All statistical analyses were performed using R software (version 4.2.1), and a P-value less than 0.05 was considered statistically significant.

3.1 Global trends and burden of CKD-T2D

The incidence of CKD-T2D was 2,501,248 thousand cases, corresponding to an age-standardized incidence rates (ASIR) of 30.29 per 100,000 population, representing a 21.8% increase since 1990. The number of DALYs attributed to CKD-T2D was 9,870.4 thousand, with an age-standardized rate of 120.2 per 100,000 population, demonstrating an 18.2% increase since 1990 ( Figure 1 , Tables 1 , 2 ). The mortality rate of CKD-T2D showed an upward trend with age, reaching its peak in the 60-79 age group and declining thereafter ( Figure 2B ). The highest incidence rate was observed in the same age group ( Figure 2B ). From 1990 to 2019, both the global age-standardized DALY rate and ASIR of CKD-T2D experienced a slight increase, with EAPCs of 0.65 (95%CI: 0.63 to 0.66) for the ASIR and 0.75 (95%CI: 0.63 to 0.87) for the age-standardized DALY rate, respectively ( Tables 1 , 2 , Figures 1 , 3 ).

Figure 1 The ASRs of Global Burden of Chronic kidney disease attributed to type 2 diabetes mellitus by region: age-standardized DALY rate and ASIR in 2019. (A) ASIR, (B) Age-Standardized DALY Rate; ASRs, age-standardized rates; ASIR, age-standardized Incidence rate; DALY, disability adjusted life-year.

Table 1 Incidence of chronic kidney disease attributed to type 2 diabetes mellitus in 1990 and 2019 for both sexes and all regions.

Table 2 DALYs of chronic kidney disease attributed to type 2 diabetes mellitus in 1990 and 2019 for both sexes and all regions.

Figure 2 Global Heatmap of Chronic kidney disease attributed to type 2 diabetes mellitus 2019 by Country and Age Group: (A) Age-Standardized DALY Rate, (B) ASIR. DALY, Disability-Adjusted Life-Year; ASIR, Age-Standardized Incidence Rate.

Figure 3 Trends in EAPCs of Chronic kidney disease attributed to type 2 diabetes mellitus by region: age-standardized DALY Rate and ASIR, 1990-2019. (A) ASIR, (B) Age-Standardized DALY Rate. EAPCs, Estimated Annual Percentage Changes; DALY, disability-adjusted life-year.

3.2 Regional and national levels of CKD-T2D

In 2019, the regions with the highest ASIR of CKD-T2D were North Africa and the Middle East, reporting a rate of 61.33 per 100,000 people (95% UI: 55.98 to 67.44). This was followed by Central Latin America with a rate of 53.46 per 100,000 (95% UI: 49.17 to 58.15), and Southern Latin America with a rate of 38.54 per 100,000 (95% UI: 34.99 to 42.37). Notably, Andean Latin America experienced a substantial increase in ASIR of CKD-T2D, with an estimated annual percentage change (EAPC) of 2.23 (95% CI: 2.11 to 2.34). Similar increases were observed in Central Asia (EAPC = 1.89, 95% CI: 1.72 to 2.06) and North Africa and the Middle East (EAPC = 2.03, 95% CI: 1.92 to 2.14). Conversely, no region showed a noteworthy reduction in ASIR of CKD-T2D, as Table 1 , Figure 1 , Supplementary Figures 1, 2 demonstrate.

The age-standardized DALY rate of CKD-T2D in 2019 varied across different regions. Central Latin America had the highest rate at 320.2 per 100,000 (95% UI 255.6 to 388.56). Southeast Asia followed at 235.7 per 100,000 (95% UI: 194.17 to 281.24), and Oceania at 206.91 per 100,000 (95% UI: 164.32 to 256.32). The North America region showed a noticeable upward trend in rates (EAPC = 2.73, 95% CI: 2.39 to 3.07), while the High-income Asia Pacific region exhibited a significant decrease (EAPC = -0.87, 95% CI: -1.05 to -0.68); these trends can be found in Table 2 , Figure 3 , and Supplementary Figure 1 .

In 2019, the ASIR of CKD-T2D ranged from 13.91 to 78.37 per 100,000, with the highest rates observed in Saudi Arabia and the lowest in the Republic of Moldova ( Supplementary Figure 1 , Supplementary Table 1 ). The DALYs rates for CKD-T2D varied from 18.13 to 733.03 per 100,000. Mauritius had the highest DALY rate, while Iceland had the lowest ( Supplementary Tables 1, 2 , Supplementary Figure 1 ).

From 1990 to 2019, Morocco experienced the largest increase in ASIR for CKD-T2D, with an EAPC of 2.72 (95% CI 2.65 to 2.8). Conversely, Greece showed the largest decrease in ASIR for CKD-T2D, with an EAPC of -0.14 (95% CI -0.27 to -0.01). These findings are depicted in Supplementary Figure 1 and further supported by the data presented in Supplementary Table 1, 2 . Furthermore, Armenia exhibited the greatest increase in DALYs rates, with an EAPC of 4.26 (95% CI 3.95 to 4.58). In contrast, Mongolia demonstrated the largest decrease in DALY rates, with an EAPC of -3.18 (95% CI -3.62 to -2.74). These results are illustrated in Supplementary Figure 1 and corroborated by the information provided in Supplementary Table 2 and Table 2 .

3.3 Age and sex specific CKD-T2D

The burden of CKD-T2D is particularly prominent among the elderly, especially in the age group of 60-79 years and older. This trend is more noticeable in males, indicating that the health risks associated with CKD-T2D may escalate with increasing age, particularly for older men. These findings suggest that older populations are affected more severely by CKD-T2D, and this impact tends to accelerate after the age of 60-79. The rate of DALYs for CKD-T2D exhibited a significant increase starting at the age of 55 in 2019, accompanied by a noteworthy rise in ASIR for those aged 60-79. It is undeniable that the global burden of CKD-T2D is at its lowest among individuals under 20 years old and reaches its highest level in individuals over 80 years old ( Supplementary Table 3, 4 , Figure 2 , Supplementary Figure 3 ).

3.4 Correlation between socio-demographic index and CKD-T2D burden

Supplementary Figure 4 demonstrates a significant negative correlation between the SDI and both the ASIR and the age-standardized DALYs rate for CKD-T2D in 2019 at the national level. The correlation coefficients (ρ) are 0.38 (P< 0.001) for the SDI and ASIR, and -0.264 (P< 0.001) for the SDI and DALY rate. This suggests that as socioeconomic status improves, the disease burden is expected to decrease over time, while the ASIR may increase. Generally, countries with higher SDI levels have a lower burden of CKD-T2D measured by the age-standardized DALY rate, with the highest ASIR of 37.74 observed among the five SDI regions (refer to Supplementary Figure 4 , Tables 1 , 2 ).

The association between SDI and CKD-T2D is intricate and displays notable regional variations. As depicted in Supplementary Figure 4 , the ASIR and age-standardized DALY rate for CKD-T2D demonstrate an initial increase in countries with an SDI below 0.7. However, as the SDI increases, these rates begin to decline, with the largest burden observed in countries with a moderate SDI range. Notably, over the span of the past three decades, only countries with a low SDI have witnessed a decrease in age-standardized DALY rate for CKD-T2D, whereas countries with a high SDI have experienced the most substantial increases, as evidenced in Table 2 and Figure 3 .

Regions with a high SDI demonstrated the highest ASIR for CKD-T2D at 37.74 per 100,000 (95% UI: 34.43 to 41.16), while the lowest ASIR in 2019 was observed in regions with a low SDI, at 20.48 per 100,000 (95% UI: 18.48 to 22.69). Regarding the Age-Standardized DALYs rate for CKD-T2D, there were contrasting patterns compared to ASIR. The middle SDI regions exhibited the highest rate at 159.63 per 100,000 (95% UI: 132.14 to 188.19). Conversely, regions with a high SDI had the lowest age-standardized DALY rate at 80.85 per 100,000 (95% UI: 66.99 to 95.28).

4 Discussion

Between 1990 and 2019, there was a clear global increase in the burden of CKD-T2D, as shown by a notable rise in both incidence and DALYs. Our findings demonstrate that CKD is a common complication of T2DM, with the risk being particularly significant among older individuals who are more susceptible to comorbidities. As a result, there is a higher DALY rate among individuals aged 65 and above. We also observed a rising trend in ASRs of CKD-T2D, with a marked increase in incidence among males over 50 years old. This indicates the compounded risks associated with age and gender-specific factors. Previous studies have shown notable racial disparities in diabetes between women and men, with a distinct life course relationship observed in women. Although men had a greater disease incidence in 2019, the increasing rates in women suggest an imminent rise in CKD-T2D incidence among female populations. Contributing factors to this trend include the potential overestimation of disease in women through glomerular filtration rate-estimating equations, age-related decline in protective hormones, and societal factors such as a higher likelihood of women seeking screening or diagnosis ( 16 , 17 ).

The burden of CKD-T2D varies significantly among countries and regions, and is greatly influenced by levels of social development. Regions with high SDI are particularly affected, showing the highest ASIR. In contrast, countries with lower levels of SDI often face challenges in social progress and healthcare effectiveness, resulting in limited burden of CKD-T2D. These disparities have likely contributed to the increased ASIR of CKD-T2D in high-middle SDI regions ( 3 ). The screening for CKD, through regular measurement of the albumin-to-creatinine ratio and glomerular filtration rate, is widely recommended for individuals with type 2 diabetes as part of the annual cycle of care starting from the time of diagnosis ( 4 ). Therefore, implementing screening and intervention measures for CKD-T2D in high SDI regions may lead to the highest ASIR and lowest age-standardized DALYs rate. In nations with higher SDI, aging plays a more noteworthy role, whereas in countries with lower SDI, population growth characterized by high fertility and lower life expectancy is a major contributing factor ( 18 ).

The main focus of this study is to evaluate the spatial and temporal changes in the burden of CKD-T2D. In previous studies, it was found that the incidence rate, and mortality of CKD-T2D were different in age group and gender. Therefore, in order to control the impact of confounding factors, age and gender specific CKD-T2D burdens were calculated to understand their impact. In addition, age standardization was carried out to reduce the impact of age on the results.

Despite the overall increase in the burden of CKD-T2D, certain regions have made notable progress in preventing and managing this condition, particularly in low- and high-middle SDI regions, where a decrease in age-standardized DALYs rates has been observed. However, the effectiveness of interventions varies significantly between countries. For example, Mauritius and Nauru have demonstrated less favorable outcomes, revealing missed opportunities and emphasizing the need for targeted healthcare strategies. From 1990 to 2019, some countries have made significant advancements that align with rapid socioeconomic development, while others, particularly Mauritius and Nauru, have fallen behind. In 2019, there were evident disparities in DALY rates among countries, highlighting the potential for narrowing these gaps. The impressive performances in some countries across the development spectrum should inspire others with similar SDI to optimize their resources for improved health outcomes. The challenges posed by low socioeconomic development, though substantial, are not insurmountable ( 19 ). Health progress, driven by sociodemographic advancement, can be influenced by additional factors. This variability is partially explained by shifts in patterns of risk factors such as hyperglycemia, hypertension, and obesity ( 20 ), providing an opportunity to move beyond the notion that managing the burden of kidney disease is solely about addressing diabetes and hypertension. Emerging evidence on the interplay of smoking and ambient pollution with diabetic nephropathy in different regions of the world further underscores this point ( 21 , 22 ).

Interpretation of these findings requires consideration of certain limitations inherent in the GBD 2019 study. The estimation of CKD burden by GBD relies on statistical methods and predicted covariate values from various sources, including census data, disease registration records, household surveys, health service utilization statistics, air pollution monitoring, and vital statistics. While countries like China, USA, India, Australia, UK, and Russia provide high-quality results through established medical registration systems, there is a lack of large, high-quality, population-based CKD studies in certain countries or territories such as the Cook Islands, Niue, Vatican City, Liechtenstein, Order of Malta, and Palestine ( 23 ). This introduces bias to the primary data from these areas. Therefore, caution is needed when extrapolating specific data to WHO non-member countries and regions with underdeveloped medical systems. The limited data also hinders further investigation into the burden of CKD-T2D at different stages, underscoring the need for increased investment in improving vital registration and data collection efforts in developing countries. Despite these limitations, this analysis offers valuable new insights into the global burden of CKD-T2D.

The study period spanning from 1990 to 2019 witnessed a significant rise in the global burden of CKD-T2D. This increase was accurately measured through ASIR and age-standardized DALYs rates. Additionally, we found substantial variations in the demographic and epidemiological trends across different levels of SDI regions. Although we observed a negative correlation between socioeconomic status and disease burden, countries demonstrated a wide range of performance. While some countries surpassed expectations in managing the disease burden, others experienced a rise in DALY rates despite sociodemographic advancements. With limited resources available, it is crucial to prioritize early-stage interventions and focus on establishing causal pathways to alleviate the death burden in low-to-middle SDI countries. Furthermore, the increasing public health concerns related to overweight and obesity, coupled with CKD resulting from type 2 diabetes mellitus, call for immediate implementation of tailored prevention programs. Such programs may include interventions like diet modification or physical activity initiatives, particularly in high-income countries.

5 Conclusion

From 1990 to 2019, there were notable regional and national variations in the increasing burden of CKD-T2D. All age-standarized rates of CKD-T2D showed a consistent upward trend, with higher rates among males compared to females, starting at the age of 60. The majority of the global burden was concentrated in countries with a middle SDI. Central Latin America, followed closely by Southeast Asia and Andean Latin America, experienced the most significant impact of CKD-T2D. Among countries, Mauritius, Palau, and Nicaragua had the highest disease burdens. These findings are crucial for directing epidemiological surveillance efforts and designing targeted health interventions. Therefore, it is important to enhance disease detection strategies and develop tailored early intervention approaches that reflect the varying levels of socioeconomic development. This approach could effectively reduce the burden of CKD-T2D. In conclusion, these findings highlight the need for strategic planning in healthcare policies to address the changing landscape of CKD-T2D.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.

Ethics statement

The studies involving humans were approved by Beilun District People’s Hospital. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

XD: Writing – original draft, Writing – review & editing. XL: Writing – original draft, Writing – review & editing. YY: Writing – original draft, Writing – review & editing. JJ: Writing – original draft, Writing – review & editing. ML: Writing – original draft, Writing – review & editing. LS: Writing – original draft, Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by 2022 Zhejiang Provincial Medical Association Hospital Pharmacy Special Research Grant Project (2022ZYY36).

Acknowledgments

We thank all authors for their contributions to the article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fendo.2024.1383777/full#supplementary-material

1. Ruiz-Ortega M, Rodrigues-Diez RR, Lavoz C, Rayego-Mateos S. Special issue diabetic nephropathy: diagnosis, prevention and treatment. J Clin Med . (2020) 9(3):813. doi: 10.3390/jcm9030813

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Kim KS, Park SW, Cho YW, Kim SK. Higher prevalence and progression rate of chronic kidney disease in elderly patients with type 2 diabetes mellitus. Diabetes Metab J . (2018) 42:224–32. doi: 10.4093/dmj.2017.0065

3. Xie D, Ma T, Cui H, Li J, Zhang A, Sheng Z, et al. Global burden and influencing factors of chronic kidney disease due to type 2 diabetes in adults aged 20-59 years, 1990-2019. Sci Rep . (2023) 13(1):20234. doi: 10.1038/s41598-023-47091-y

4. Hill CJ, Cardwell CR, Maxwell AP, Young RJ, Matthews B, O'Donoghue DJ, et al. Obesity and kidney disease in type 1 and 2 diabetes: an analysis of the National Diabetes Audit. QJM: Int J Med . (2013) 106:933–42. doi: 10.1093/qjmed/hct123

CrossRef Full Text | Google Scholar

5. Möllsten A, Svensson M, Waernbaum I, Berhan Y, Schön S, Nyström L, et al. Cumulative risk, age at onset, and sex-specific differences for developing end-stage renal disease in young patients with type 1 diabetes: a nationwide population-based cohort study. Diabetes . (2010) 59(7):1803–8. doi: 10.2337/db09-1744

6. Teng TH, Katzenellenbogen JM, Thompson SC, Sanfilippo FM, Knuiman M, Geelhoed E, et al. Incidence of first heart failure hospitalisation and mortality in Aboriginal and non-Aboriginal patients in Western Australia, 2000–2009. Int J Cardiol . (2014) 173:110–7. doi: 10.1016/j.ijcard.2014.02.020

7. Thomas B. The global burden of diabetic kidney disease: time trends and gender gaps. Curr Diabetes Rep . (2019) 19(4):1–7. doi: 10.1007/s11892-019-1133-6

8. Pan X, Lin X, Huang X, Xu J, Ye L, Zhang T, et al. The Burden of diabetes-related chronic kidney disease in China from 1990 to 2019. Front Endocrinol . (2022) 13:892860. doi: 10.3389/fendo.2022.892860

9. GBD 2019 Diabetes Mortality Collaborators. Diabetes mortality and trends before 25 years of age: an analysis of the Global Burden of Disease Study 2019. Lancet Diabetes Endocrinol . (2022) 10:177–92. doi: 10.1016/s2213-8587(21)00349-1

10. Collaborators, G, Ärnlöv J. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet . (2020) 396(10258):1223–49. doi: 10.1016/s0140-6736(20)30752-2

11. Murray CJ. Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bull World Health Organ . (1994) 72:429–45.

PubMed Abstract | Google Scholar

12. Kidney Disease: Improving Global Outcomes (KDIGO) Diabetes Work Group. KDIGO 2020 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int . (2020) 98(4s):S1–S115. doi: 10.1016/j.kint.2020.06.019

13. GBD 2016 Alcohol and Drug Use Collaborators. The global burden of disease attributable to alcohol and drug use in 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Psychiatry . (2018) 5(12):987–1012. doi: 10.1016/S2215-0366(18)30337-7

14. Hu K, Ding P, Wu Y, Tian W, Pan T, Zhang S. Global patterns and trends in the breast cancer incidence and mortality according to sociodemographic indices: an observational study based on the global burden of diseases. BMJ Open . (2019) 9(10):e028461. doi: 10.1136/bmjopen-2018-028461

15. Zhao G, Zhu S, Zhang F, Zhang X, Zhang X, Li T, et al. Global Burden of osteoarthritis associated with high body mass index in 204 countries and territories, 1990–2019: findings from the Global Burden of Disease Study 2019. Endocrine . (2023) 79(1):60–71. doi: 10.1007/s12020-022-03201-w

16. Roderick PJ, Atkins RJ, Smeeth L, Mylne A, Nitsch DD, Hubbard RB, et al. CKD and mortality risk in older people: a community-based population study in the United Kingdom. Am J Kidney Dis . (2009) 53(6):950–60. doi: 10.1053/j.ajkd.2008.12.036

17. Insaf TZ, Strogatz DS, Yucel RM, Chasan-Taber L, Shaw BA. Associations between race, lifecourse socioeconomic position and prevalence of diabetes among US women and men: results from a population-based panel study. J Epidemiol Community Health . (2014) 68(4):318–25. doi: 10.1136/jech-2013-202585

18. Liu W, Zhang D, Wang R, Chen J, Zhang J, Tao D, et al. Global trends in the burden of chronic kidney disease attributable to type 2 diabetes: An age-period-cohort analysis. Diab Obes Metab . (2023) 26(2):602–10. doi: 10.1111/dom.15349

19. Ameh OI, Ekrikpo UE, Kengne A-P. Preventing CKD in low-and middle-income countries: a call for urgent action. Kidney Int Rep . (2020) 5(3):255–62. doi: 10.1016/j.ekir.2019.12.013

20. Horlyck-Romanovsky MF, Wyka K, Echeverria SE, Leung MM, Fuster M, Huang TT. Foreign-born blacks experience lower odds of obesity but higher odds of diabetes than US-born blacks in New York City. J Immigr Minor Health . (2019) 21(1):47–55. doi: 10.1007/s10903-018-0708-7

21. Clements JM, West BT, Yaker Z, Lauinger B, McCullers D, Haubert J, et al. Disparities in diabetes-related multiple chronic conditions and mortality: The influence of race. Diabetes Res Clin Pract . (2020) 159:107984. doi: 10.1016/j.diabres.2019.107984

22. Golden SH, Brown A, Cauley JA, Chin MH, Gary-Webb TL, Kim C, et al. Health disparities in endocrine disorders: biological, clinical, and nonclinical factors—an Endocrine Society scientific statement. J Clin Endocrinol Metab . (2012) 97(9):E1579–639. doi: 10.1210/jc.2012-2043

23. Vashisth S, Singh G, Nanda A. A comparative study of regulatory trends of pharmaceuticals in Brazil, Russia, India and China (BRIC) countries. J Gene Medicines . (2012) 9(3):128–43. doi: 10.1177/1741134312459187

Keywords: type 2 diabetes, chronic kidney disease, EAPC, socio-demographic index, epidemiology

Citation: Ding X, Li X, Ye Y, Jiang J, Lu M and Shao L (2024) Epidemiological patterns of chronic kidney disease attributed to type 2 diabetes from 1990-2019. Front. Endocrinol. 15:1383777. doi: 10.3389/fendo.2024.1383777

Received: 08 February 2024; Accepted: 28 March 2024; Published: 17 April 2024.

Reviewed by:

Copyright © 2024 Ding, Li, Ye, Jiang, Lu and Shao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Lv Shao, [email protected]

† These authors have contributed equally to this work

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 19 May 2022

Machine learning to predict end stage kidney disease in chronic kidney disease

• Qiong Bai 1 ,
• Chunyan Su 1 ,
• Wen Tang 1 &
• Yike Li 2  

Scientific Reports volume  12 , Article number:  8377 ( 2022 ) Cite this article

11k Accesses

31 Citations

22 Altmetric

Metrics details

• Chronic kidney disease
• End-stage renal disease
• Kidney diseases

The purpose of this study was to assess the feasibility of machine learning (ML) in predicting the risk of end-stage kidney disease (ESKD) from patients with chronic kidney disease (CKD). Data were obtained from a longitudinal CKD cohort. Predictor variables included patients’ baseline characteristics and routine blood test results. The outcome of interest was the presence or absence of ESKD by the end of 5 years. Missing data were imputed using multiple imputation. Five ML algorithms, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors were trained and tested using fivefold cross-validation. The performance of each model was compared to that of the Kidney Failure Risk Equation (KFRE). The dataset contained 748 CKD patients recruited between April 2006 and March 2008, with the follow-up time of 6.3 ± 2.3 years. ESKD was observed in 70 patients (9.4%). Three ML models, including the logistic regression, naïve Bayes and random forest, showed equivalent predictability and greater sensitivity compared to the KFRE. The KFRE had the highest accuracy, specificity, and precision. This study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Three ML models with adequate performance and sensitivity scores suggest a potential use for patient screenings. Future studies include external validation and improving the models with additional predictor variables.

Similar content being viewed by others

A machine learning driven nomogram for predicting chronic kidney disease stages 3–5

Samit Kumar Ghosh & Ahsan H. Khandoker

A simplified prediction model for end-stage kidney disease in patients with diabetes

Toyoshi Inoguchi, Tasuku Okui, … Naoki Nakashima

An independent validation of the kidney failure risk equation in an Asian population

Min Woo Kang, Navdeep Tangri, … Jung Pyo Lee

Introduction

Chronic kidney disease (CKD) is a significant healthcare burden that affects billions of individuals worldwide 1 , 2 and makes a profound impact on global morbidity and mortality 3 , 4 , 5 . In the United States, approximately 11% of the population or 37 million people suffer from CKD that results in an annual Medicare cost of $84 billion 6 . The prevalence of this disease is estimated at 10.8% in China, affecting about 119.5 million people 7 .

Gradual loss of the kidney function can lead to end stage kidney disease (ESKD) in CKD patients, precipitating the need for kidney replacement therapy (KRT). Timely intervention in those CKD patients who have a high risk of ESKD may not only improve these patients’ quality of life by delaying the disease progression, but also reduce the morbidity, mortality and healthcare costs resulting from KRT 8 , 9 . Because the disease progression is typically silent 10 , a reliable prediction model for risk of ESKD at the early stage of CKD can be clinically essential. Such a model is expected to facilitate physicians in making personalized treatment decisions for high-risk patients, thereby improving the overall prognosis and reducing the economic burden of this disease.

A few statistical models were developed to predict the likelihood of ESKD based on certain variables, including age, gender, lab results, and most commonly, the estimated glomerular filtration rate (eGFR) and albuminuria 11 , 12 . Although some of these models demonstrated adequate predictability in patients of a specific race, typically Caucasians 13 , 14 , 15 , literature on their generalizability in other ethnic groups, such as Chinese, remains scarce 13 , 16 . In addition, models based on non-urine variables, such as patients’ baseline characteristics and routine blood tests, have reportedly yield sufficient performance 17 , 18 . Therefore, it may be feasible to predict ESKD without urine tests, leading to a simplified model with equivalent reliability.

With the advent of the big data era, new methods became available in developing a predictive model that used to rely on traditional statistics. Machine learning (ML) is a subset of artificial intelligence (AI) that allows the computer to perform a specific task without explicit instructions. When used in predictive modeling, ML algorithm can be trained to capture the underlying patterns of the sample data and make predictions about the new data based on the acquired information 19 . Compared to traditional statistics, ML represents more sophisticated math functions and usually results in better performance in predicting an outcome that is determined by a large set of variables with non-linear, complex interactions 20 . ML has recently been applied in numerous studies and demonstrated high level of performance that surpassed traditional statistics and even humans 20 , 21 , 22 , 23 .

This article presents a proof-of-concept study with the major goal to establish ML models for predicting the risk of ESKD on a Chinese CKD dataset. The ML models were trained and tested based on easily obtainable variables, including the baseline characteristics and routine blood tests. Results obtained from this study suggest not only the feasibility of ML models in performing this clinically critical task, but also the potential in facilitating personalized medicine.

Materials and methods

Study population.

The data used for this retrospective work were obtained from a longitudinal cohort previously enrolled in an observational study 24 , 25 . The major inclusion criteria for the cohort were adult CKD patients (≥ 18 years old) with stable kidney functions for at least three months prior to recruitment. Patients were excluded if they had one or more of the following situations: (1) history of KRT in any form, including hemodialysis, peritoneal dialysis or kidney transplantation; (2) any other existing condition deemed physically unstable, including life expectancy < 6 months, acute heart failure, and advanced liver disease; (3) any pre-existing malignancy. All patients were recruited from the CKD management clinic of Peking University Third Hospital between April 2006 and March 2008. Written informed consent was obtained from all patients. They were treated according to routine clinical practice determined by the experienced nephrologists and observed until December 31 st , 2015. Detailed information regarding patient recruitment and management protocol has been described in a previous publication 24 .

Data acquisition

Patient characteristics included age, gender, education level, marriage status, and insurance status. Medical history comprised history of smoking, history of alcohol consumption, presence of each comorbid condition—diabetes, cardiovascular disease and hypertension. Clinical parameters contained body mass index (BMI), systolic pressure and diastolic pressure. Blood tests consisted of serum creatinine, uric acid, blood urea nitrogen, white blood cell count, hemoglobin, platelets count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total protein, albumin, alkaline phosphatase (ALP), high-density lipoprotein, low-density lipoprotein, triglycerides, total cholesterol, calcium, phosphorus, potassium, sodium, chloride, and bicarbonate. The estimated glomerular filtration rate and type of primary kidney disease were also used as predictors.

All baseline variables were obtained at the time of subject enrollment. The primary study end point was kidney failure which necessitated the use of any KRT. Subjects with the outcome of kidney failure were labeled as ESKD+, and the rest ESKD−. Patients who died before reaching the study end point or lost to follow up were discarded. Patients who developed ESKD after five years were labeled as ESKD−.

Data preprocessing

All categorical variables, such as insurance status, education, and primary disease, were encoded using the one-hot approach. Any variable was removed from model development if the missing values were greater than 50%. Missing data were handled using multiple imputation with five times of repetition, leading to five slightly different imputed datasets where each of the missing values was randomly sampled from their predictive distribution based on the observed data. On each imputed set, all models were trained and tested using a fivefold cross validation method. To minimize selection bias, subject assignment to train/test folds was kept consistent across all imputed sets. Data were split in a stratified fashion to ensure the same distribution of the outcome classes (ESKD+ vs. ESKD−) in each subset as the entire set.

Model development

The model was trained to perform a binary classification task with the goal of generating the probability of ESKD+ based on the given features. Five ML algorithms were employed in this study, including logistic regression, naïve Bayes, random forest, decision tree, and K-nearest neighbors. Grid search was performed to obtain the best hyperparameter combination for each algorithm.

Assessment of model performance

The performance of a classifiers was measured using accuracy, precision, recall, specificity, F1 score and area under the curve (AUC), as recommended by guidelines for results reporting of clinical prediction models 26 . All classifiers developed in this study were further compared with the Kidney Failure Risk Equation (KFRE), which estimates the 5-year risk of ESKD based on patient’s age, gender, and eGFR 12 . The KFRE is currently the most widely used model in predicting CKD progression to ESKD. The reported outcome of a model represented the average performance of 5 test folds over all imputed sets.

Statistical analysis

Basic descriptive statistics were applied as deemed appropriate. Results are expressed as frequencies and percentages for categorical variables; the mean ± standard deviation for continuous, normally distributed variables; and the median (interquartile range) for continuous variables that were not normally distributed. Patient characteristics were compared between the original dataset and the imputed sets using one-way analysis of variance (ANOVA). The AUC of each model was measured using the predicted probability. The optimal threshold of a classifier was determined based on the receiver operating characteristic (ROC) curve at the point with minimal distance to the upper left corner. For each ML model, this threshold was obtained during the training process and applied unchangeably to the test set. For the KFRE, the threshold was set at a default value of 0.5. Model development, performance evaluation and data analyses were all performed using Python 27 . The alpha level was set at 0.05.

Ethical approval

This research was conducted ethically in accordance with the World Medical Association Declaration of Helsinki. The study protocol has been approved by the Peking University Third Hospital Medical Science Research Ethics Committee on human research (No. M2020132).

Cohort characteristics

The dataset contained a total of 748 subjects with the follow-up duration of 6.3 ± 2.3 years. The baseline characteristics are summarized in Table 1 . Most patients were in stage 2 (24.5%) or 3 (47.1%) CKD at baseline. ESKD was observed in 70 patients (9.4%), all of whom subsequently received KRT, including hemodialysis in 49 patients, peritoneal dialysis in 17 and kidney transplantation in 4.

Model performance

Details of the five imputed sets are provided in the supplemental materials . There was no significant difference between the imputed sets and the original dataset in each variable where missing data were replaced by imputed values. The hyperparameter settings for each classifier are displayed in Table 2 . The best overall performance, as measured by the AUC score, was achieved by the random forest algorithm (0.81, see Table 3 ). Nonetheless, this score and its 95% confidence interval had overlap with those of the other three models, including the logistic regression, naïve Bayes, and the KFRE (Fig.  1 ). Interestingly, the KFRE model that was based on 3 simple variables, demonstrated not only a comparable AUC score but also the highest accuracy, specificity, and precision. At the default threshold, however, the KFRE was one of the least sensitive models (47%).

ROC curves of the random forest algorithm and the KFRE model.

With extensive utilization of electronic health record and recent progress in ML research, AI is expanding its impact on healthcare and has gradually changed the way clinicians pursue for problem-solving 28 . Instead of adopting a theory-driven strategy that requires a preformed hypothesis from prior knowledge, training an ML model typically follows a data-driven approach that allows the model to learn from experience alone. Specifically, the model improves its performance iteratively on a training set by comparing the predictions to the ground truths and adjusting model parameters so as to minimize the distance between the predictions and the truths. In nephrology, ML has demonstrated promising performances in predicting acute kidney injury or time to allograft loss from clinical features 29 , 30 , recognizing specific patterns in pathology slides 31 , 32 , choosing an optimal dialysis prescription 33 , or mining text in the electronic health record to find specific cases 34 , 35 . Additionally, a few recent studies were performed to predict the progression of CKD using ML methods. These models were developed to estimate the risk of short-term mortality following dialysis 36 , calculate the future eGFR values 37 , or assess the 24-h urinary protein levels 18 . To our best knowledge, there hasn’t been any attempt to apply ML methods to predict the occurrence of ESKD in CKD patients.

In the present study, a prediction model for ESKD in CKD patients was explored using ML techniques. Most classifiers demonstrated adequate performance based on easily accessible patient information that is convenient for clinical translation. In general, three ML models, including the logistic regression, naïve Bayes and random forest, showed non-inferior performance to the KFRE in this study. These findings imply ML as a feasible approach for predicting disease progression in CKD, which could potentially guide physicians in establishing personalized treatment plans for this condition at an early stage. These ML models with higher sensitivity scores may also be practically favored in patient screening over the KFRE.

To our best understanding, this study was also the first to validate the KFRE in CKD patients of Mainland China. The KFRE was initially developed and validated using North American patients with CKD stage 3–5 12 . There were seven KFRE models that consisted of different combinations of predictor variables. The most commonly used KFRE included a 4-variable model (age, gender, eGFR and urine ACR) or an 8-variable model (age, gender, eGFR, urine ACR, serum calcium, phosphorous, bicarbonate, and albumin). Besides, there was a 3-variable model (age, gender, and eGFR) that required no urine ACR and still showed comparable performance to the other models in the original article. Despite its favorable performance in prediction for ESKD in patients of Western countries 14 , 15 , 38 , 39 , the generalizability of KFRE in Asian population remained arguable following the suboptimal results revealed by some recent papers 13 , 40 , 41 . In the current study, the KFRE was validated in a Chinese cohort with CKD stage 1–5 and showed an AUC of 0.80. This result indicated the KFRE was adequately applicable to the Chinese CKD patients and even earlier disease stages. In particular, the high specificity score (0.95) may favor the use of this equation in ruling in patients who require close monitoring of disease progression. On the other hand, a low sensitivity (0.47) at the default threshold may suggest it may be less desirable than the other models for ruling out patients.

Urine test is a critical diagnostic approach for CKD. The level of albuminuria (i.e. ACR) has also been regarded as a major predictor for disease progression and therefore used by most prognostic models. However, quantitative testing for albuminuria is not always available in China especially in rural areas, which precludes clinicians from using most urine-based models for screening patients. In this regard, several simplified models were developed to predict CKD progression without the need of albuminuria. These models were based on patient characteristics (e.g. age, gender, BMI, comorbidity) and/or blood work (e.g. creatinine/eGFR, BUN), and still able to achieve an AUC of 0.87–0.89 12 , 18 or a sensitivity of 0.88 37 . Such performance was largely consistent with the findings of this study and comparable or even superior to some models incorporating urine tests 16 , 42 . Altogether, it suggested a reliable prediction for CKD progression may be obtained from routine clinical variables without urine measures. These models are expected to provide a more convenient screening tool for CKD patients in developing regions.

Missing data are such a common problem in ML research that they can potentially lead to a biased model and undermine the validity of study outcomes. Traditional methods to handle missing data include complete case analysis, missing indicator, single value imputation, sensitivity analyses, and model-based methods (e.g. mixed models or generalized estimating equations) 43 , 44 , 45 . In most scenarios, complete case analysis and single value imputation are favored by researchers primarily due to the ease of implementation 45 , 46 , 47 . However, these methods may be associated with significant drawbacks. For example, by excluding samples with missing data from analyses, complete case analysis can result in reduction of model power, overestimation of benefit and underestimation of harm 43 , 46 ; Single value imputation replaces the missing data by a single value—typically the mean or mode of the complete cases, thereby increasing the homogeneity of data and overestimating the precision 43 , 48 . In this regard, multiple imputation solves these problems by generating several different plausible imputed datasets, which account for the uncertainty about the missing data and provide unbiased estimates of the true effect 49 , 50 . It is deemed effective regardless of the pattern of missingness 43 , 51 . Multiple imputation is now widely recognized as the standard method to deal with missing data in many areas of research 43 , 45 . In the current study, a 5-set multiple imputation method was employed to obtain reasonable variability of the imputed data. The performance of each model was analyzed on each imputed set and pooled for the final result. These procedures ensured that the model bias resulting from missing data was minimized. In the future, multiple imputation is expected to become a routine method for missing data handling in ML research, as the extra amount of computation associated with multiple imputation over those traditional methods can simply be fulfilled by the high level of computational power required by ML.

Although ML has been shown to outperform traditional statistics in a variety of tasks by virtue of the model complexity, some studies demonstrated no gain or even declination of performance compared to traditional regression methods 52 , 53 . In this study, the simple logistic regression model also yielded a comparable or even superior predictability for ESKD to other ML algorithms. The most likely explanation is that the current dataset only had a small sample size and limited numbers of predictor variables, and the ESKD+ cases were relatively rare. The lack of big data and imbalanced class distribution may have negative impact on the performance of complex ML algorithms, as they are typically data hungry 54 . On the other hand, this finding could imply simple interactions among the predictor variables. In other words, the risk of ESKD may be largely influenced by only a limited number of factors in an uncomplicated fashion, which is consistent with some previous findings 12 , 18 , 55 . The fact that the 3-variable KFRE, which is also a regression model, yielded equivalent outcomes to the best ML models in this study may further support this implication. It is therefore indicated that traditional regression models may continue to play a key role in disease risk prediction, especially when a small sample size, limited predictor variables, or an imbalanced dataset is encountered. The fact that some of the complex ML models are subject to the risk of overfitting and the lack of interpretability further favors the use of simple regression models, which can be translated to explainable equations.

Several limitations should be noted. First, this cohort consisted of less than 1000 subjects and ESKD only occurred in a small portion of them, both of which might have affected model performance as discussed earlier. Second, although this study aimed to assess the feasibility of a prediction model for ESKD without any urine variables, this was partially due to the lack of quantitative urine tests at our institute when this cohort was established. As spot urine tests become increasingly popular, urine features such as ACR will be as accessible and convenient as other lab tests. They are expected to play a critical role in more predictive models. Third, the KFRE was previously established on stages 3–5 CKD patients while the current cohort contained stages 1–5. This discrepancy may have affected the KFRE performance. Forth, the generalizability of this model has not been tested on any external data due to the lack of such resource in this early feasibility study. Therefore, additional efforts are required to improve and validate this model before any clinical translation. Finally, although a simple model without urine variables is feasible and convenient, model predictability may benefit from a greater variety of clinical features, such as urine tests, imaging, or biopsy. Future works should include training ML models with additional features using a large dataset, and validating them on external patients.

In conclusion, this study showed the feasibility of ML in evaluating the prognosis of CKD based on easily accessible features. Logistic regression, naïve Bayes and random forest demonstrated comparable predictability to the KFRE in this study. These ML models also had greater sensitivity scores that were potentially advantageous for patient screenings. Future studies include performing external validation and improving the model with additional predictor variables.

Zhang, L. et al. Trends in chronic kidney disease in China. N. Engl. J. Med. 375 , 905–906. https://doi.org/10.1056/NEJMc1602469 (2016).

Article   PubMed   Google Scholar  

Bello, A. K. et al. Effective CKD care in European countries: Challenges and opportunities for health policy. Am. J. Kidney Dis. 65 , 15–25. https://doi.org/10.1053/j.ajkd.2014.07.033 (2015).

Subbiah, A. K., Chhabra, Y. K. & Mahajan, S. Cardiovascular disease in patients with chronic kidney disease: A neglected subgroup. Heart Asia 8 , 56–61. https://doi.org/10.1136/heartasia-2016-010809 (2016).

Article   PubMed   PubMed Central   Google Scholar  

Pecoits-Filho, R. et al. Interactions between kidney disease and diabetes: Dangerous liaisons. Diabetol. Metab. Syndr. 8 , 50. https://doi.org/10.1186/s13098-016-0159-z (2016).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Weiner, D. E. et al. Chronic kidney disease as a risk factor for cardiovascular disease and all-cause mortality: A pooled analysis of community-based studies. J. Am. Soc. Nephrol. 15 , 1307–1315. https://doi.org/10.1097/01.asn.0000123691.46138.e2 (2004).

Saran, R., Robinson, B., Abbott, K. C. et al. US Renal Data System 2019 Annual Data Report: Epidemiology of kidney disease in the United States. Am. J. Kidney Dis. 75 , A6–A7. https://doi.org/10.1053/j.ajkd.2019.09.003 (2020).

Zhang, L. et al. Prevalence of chronic kidney disease in China: A cross-sectional survey. Lancet (London, England) 379 , 815–822. https://doi.org/10.1016/S0140-6736(12)60033-6 (2012).

Article   Google Scholar  

Johns, T. S., Yee, J., Smith-Jules, T., Campbell, R. C. & Bauer, C. Interdisciplinary care clinics in chronic kidney disease. BMC Nephrol. 16 , 161. https://doi.org/10.1186/s12882-015-0158-6 (2015).

Lin, E., Chertow, G. M., Yan, B., Malcolm, E. & Goldhaber-Fiebert, J. D. Cost-effectiveness of multidisciplinary care in mild to moderate chronic kidney disease in the United States: A modeling study. PLoS Med. 15 , e1002532. https://doi.org/10.1371/journal.pmed.1002532 (2018).

Zhong, J., Yang, H. C. & Fogo, A. B. A perspective on chronic kidney disease progression. Am. J. Physiol. Ren. Physiol. 312 , F375–F384. https://doi.org/10.1152/ajprenal.00266.2016 (2017).

Article   CAS   Google Scholar  

Tangri, N. et al. A dynamic predictive model for progression of CKD. Am. J. Kidney Dis. 69 , 514–520. https://doi.org/10.1053/j.ajkd.2016.07.030 (2017).

Tangri, N. et al. A predictive model for progression of chronic kidney disease to kidney failure. JAMA 305 , 1553–1559. https://doi.org/10.1001/jama.2011.451 (2011).

Article   CAS   PubMed   Google Scholar  

Tangri, N. et al. Multinational assessment of accuracy of equations for predicting risk of kidney failure: A meta-analysis. JAMA 315 , 164–174. https://doi.org/10.1001/jama.2015.18202 (2016).

Major, R. W. et al. The Kidney Failure Risk Equation for prediction of end stage renal disease in UK primary care: An external validation and clinical impact projection cohort study. PLoS Med. 16 , e1002955. https://doi.org/10.1371/journal.pmed.1002955 (2019).

Peeters, M. J. et al. Validation of the kidney failure risk equation in European CKD patients. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 28 , 1773–1779. https://doi.org/10.1093/ndt/gft063 (2013).

Echouffo-Tcheugui, J. B. & Kengne, A. P. Risk models to predict chronic kidney disease and its progression: A systematic review. PLoS Med. 9 , e1001344. https://doi.org/10.1371/journal.pmed.1001344 (2012).

Chang, H. L. et al. A predictive model for progression of CKD. Medicine (Baltimore) 98 , e16186. https://doi.org/10.1097/MD.0000000000016186 (2019).

Xiao, J. et al. Comparison and development of machine learning tools in the prediction of chronic kidney disease progression. J. Transl. Med. 17 , 119. https://doi.org/10.1186/s12967-019-1860-0 (2019).

Song, H., Triguero, I. & Özcan, E. A review on the self and dual interactions between machine learning and optimisation. Prog. Artif. Intell. 8 , 143–165. https://doi.org/10.1007/s13748-019-00185-z (2019).

Mortazavi, B. J. et al. Analysis of machine learning techniques for heart failure readmissions. Circ. Cardiovasc. Qual. Outcomes 9 , 629–640. https://doi.org/10.1161/CIRCOUTCOMES.116.003039 (2016).

Deo, R. C. Machine learning in medicine. Circulation 132 , 1920–1930. https://doi.org/10.1161/CIRCULATIONAHA.115.001593 (2015).

Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M. & Qureshi, N. Can machine-learning improve cardiovascular risk prediction using routine clinical data?. PLoS ONE 12 , e0174944. https://doi.org/10.1371/journal.pone.0174944 (2017).

Meiring, C. et al. Optimal intensive care outcome prediction over time using machine learning. PLoS ONE 13 , e0206862. https://doi.org/10.1371/journal.pone.0206862 (2018).

Lai, X. et al. Outcomes of stage 1–5 chronic kidney disease in Mainland China. Ren. Fail. 36 , 520–525. https://doi.org/10.3109/0886022X.2013.875859 (2014).

Bai, Q., Su, C. Y., Zhang, A. H., Wang, T. & Tang, W. Loss of the normal gradient in arterial compliance and outcomes of chronic kidney disease patients. Cardiorenal Med. 9 , 297–307. https://doi.org/10.1159/000500479 (2019).

Steyerberg, E. W. & Vergouwe, Y. Towards better clinical prediction models: Seven steps for development and an ABCD for validation. Eur. Heart J. 35 , 1925–1931. https://doi.org/10.1093/eurheartj/ehu207 (2014).

Python: A dynamic, open source programming language. https://www.python.org/ (2019).

Yu, K., Beam, A. L. & Kohane, I. S. Artificial intelligence in healthcare. Nat. Biomed. Eng. 2 , 719–731. https://doi.org/10.1038/s41551-018-0305-z (2018).

Mohamadlou, H. et al. Prediction of acute kidney injury with a machine learning algorithm using electronic health record data. Can. J. Kidney Health Dis. 5 , 2054358118776326. https://doi.org/10.1177/2054358118776326 (2018).

Lee, H. C. et al. Derivation and validation of machine learning approaches to predict acute kidney injury after cardiac surgery. J. Clin. Med. https://doi.org/10.3390/jcm7100322 (2018).

Hermsen, M. et al. Deep learning-based histopathologic assessment of kidney tissue. J. Am. Soc. Nephrol. 30 , 1968–1979. https://doi.org/10.1681/ASN.2019020144 (2019).

Boor, P. Artificial intelligence in nephropathology. Nat. Rev. Nephrol. 16 , 4–6. https://doi.org/10.1038/s41581-019-0220-x (2020).

Barbieri, C. et al. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: Proof of concept and first clinical assessment. Kidney Dis. (Basel) 5 , 28–33. https://doi.org/10.1159/000493479 (2019).

Garcelon, N., Burgun, A., Salomon, R. & Neuraz, A. Electronic health records for the diagnosis of rare diseases. Kidney Int. 97 , 676–686. https://doi.org/10.1016/j.kint.2019.11.037 (2020).

Rajkomar, A. et al. Scalable and accurate deep learning with electronic health records. NPJ Digit. Med. 1 , 18. https://doi.org/10.1038/s41746-018-0029-1 (2018).

Akbilgic, O. et al. Machine learning to identify dialysis patients at high death risk. Kidney Int. Rep. 4 , 1219–1229. https://doi.org/10.1016/j.ekir.2019.06.009 (2019).

Zhao, J., Gu, S. & McDermaid, A. Predicting outcomes of chronic kidney disease from EMR data based on Random Forest Regression. Math. Biosci. 310 , 24–30. https://doi.org/10.1016/j.mbs.2019.02.001 (2019).

Article   MathSciNet   PubMed   PubMed Central   MATH   Google Scholar  

Tangri, N., Ferguson, T. & Komenda, P. Pro: Risk scores for chronic kidney disease progression are robust, powerful and ready for implementation. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 32 , 748–751. https://doi.org/10.1093/ndt/gfx067 (2017).

Marks, A. et al. Looking to the future: Predicting renal replacement outcomes in a large community cohort with chronic kidney disease. Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc. Eur. Ren. Assoc. 30 , 1507–1517. https://doi.org/10.1093/ndt/gfv089 (2015).

Wang, Y. et al. Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia. BMC Nephrol. 20 , 451. https://doi.org/10.1186/s12882-019-1643-0 (2019).

Yamanouchi, M. et al. Value of adding the renal pathological score to the kidney failure risk equation in advanced diabetic nephropathy. PLoS ONE 13 , e0190930. https://doi.org/10.1371/journal.pone.0190930 (2018).

Lin, C. C. et al. Development and validation of a risk prediction model for end-stage renal disease in patients with type 2 diabetes. Sci. Rep. 7 , 10177. https://doi.org/10.1038/s41598-017-09243-9 (2017).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Pedersen, A. B. et al. Missing data and multiple imputation in clinical epidemiological research. Clin. Epidemiol. 9 , 157–166. https://doi.org/10.2147/CLEP.S129785 (2017).

Sterne, J. A. et al. Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. BMJ (Clin. Res. Ed.) 338 , b2393. https://doi.org/10.1136/bmj.b2393 (2009).

Jakobsen, J. C., Gluud, C., Wetterslev, J. & Winkel, P. When and how should multiple imputation be used for handling missing data in randomised clinical trials: A practical guide with flowcharts. BMC Med. Res. Methodol. 17 , 162. https://doi.org/10.1186/s12874-017-0442-1 (2017).

Karadaghy, O. A., Shew, M., New, J. & Bur, A. M. Development and assessment of a machine learning model to help predict survival among patients with oral squamous cell carcinoma. JAMA Otolaryngol. Head Neck Surg. 145 , 1115–1120. https://doi.org/10.1001/jamaoto.2019.0981 (2019).

Bur, A. M. et al. Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma. Oral Oncol. 92 , 20–25. https://doi.org/10.1016/j.oraloncology.2019.03.011 (2019).

Zhang, Z. Missing data imputation: Focusing on single imputation. Ann. Transl. Med. 4 , 9. https://doi.org/10.3978/j.issn.2305-5839.2015.12.38 (2016).

Graham, J. W. Missing data analysis: Making it work in the real world. Annu. Rev. Psychol. 60 , 549–576. https://doi.org/10.1146/annurev.psych.58.110405.085530 (2009).

Klebanoff, M. A. & Cole, S. R. Use of multiple imputation in the epidemiologic literature. Am. J. Epidemiol. 168 , 355–357. https://doi.org/10.1093/aje/kwn071%JAmericanJournalofEpidemiology (2008).

Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. Multiple imputation by chained equations: What is it and how does it work?. Int. J. Methods Psychiatr. Res. 20 , 40–49. https://doi.org/10.1002/mpr.329 (2011).

Desai, R. J., Wang, S. V., Vaduganathan, M., Evers, T. & Schneeweiss, S. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw. Open 3 , e1918962. https://doi.org/10.1001/jamanetworkopen.2019.18962 (2020).

Christodoulou, E. et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J. Clin. Epidemiol. 110 , 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004 (2019).

Nusinovici, S. et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J. Clin. Epidemiol. 122 , 56–69. https://doi.org/10.1016/j.jclinepi.2020.03.002 (2020).

Nusinovici, S. et al. Logistic regression was as good as machine learning for predicting major chronic diseases. J. Clin. Epidemiol. https://doi.org/10.1016/j.jclinepi.2020.03.002 (2020).

Download references

This work was supported by PKU-Baidu Fund (2020BD030 to Wen Tang), and by fund from China International Medical Foundation (Z-2017-24-2037 to Wen Tang). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and affiliations.

Department of Nephrology, Peking University Third Hospital, 49 North Garden Rd, Haidian District, Beijing, 100191, People’s Republic of China

Qiong Bai, Chunyan Su & Wen Tang

Department of Otolaryngology-Head and Neck Surgery, Bill Wilkerson Center, Vanderbilt University Medical Center, Nashville, TN, USA

You can also search for this author in PubMed   Google Scholar

Contributions

Q.B. was involved in the data collection, data analysis, and drafting the manuscript. C.S. was involved in data collection. W.T. conceptualized the idea, interpreted the results and wrote part of the draft. Y.L. conceptualized the idea, analyzed the data, performed all coding, evaluated all machine learning models, drafted and edited the manuscript.

Corresponding authors

Correspondence to Wen Tang or Yike Li .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Supplementary information., rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Bai, Q., Su, C., Tang, W. et al. Machine learning to predict end stage kidney disease in chronic kidney disease. Sci Rep 12 , 8377 (2022). https://doi.org/10.1038/s41598-022-12316-z

Download citation

Received : 19 July 2021

Accepted : 09 May 2022

Published : 19 May 2022

DOI : https://doi.org/10.1038/s41598-022-12316-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Development and validation of a machine learning model to predict time to renal replacement therapy in patients with chronic kidney disease.

• Takeshi Nakata
• Hirotaka Shibata

BMC Nephrology (2024)

Predicting CKD progression using time-series clustering and light gradient boosting machines

• Hirotaka Saito
• Hiroki Yoshimura
• Junichiro J. Kazama

Scientific Reports (2024)

Statistical Analysis of Renal Risk Factors and Prediction of Chronic Kidney Disease

• Soham Bandyopadhyay
• Monalisa Sarma
• Debasis Samanta

SN Computer Science (2024)

Machine learning to predict occult metastatic lymph nodes along the recurrent laryngeal nerves in thoracic esophageal squamous cell carcinoma

• Yiliang Zhang
• Longfu Zhang
• Haiquan Chen

BMC Cancer (2023)

Machine learning models to predict end-stage kidney disease in chronic kidney disease stage 4

• Kullaya Takkavatakarn

BMC Nephrology (2023)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.