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The set of journals have been ranked according to their SJR and divided into four equal groups, four quartiles. Q1 (green) comprises the quarter of the journals with the highest values, Q2 (yellow) the second highest values, Q3 (orange) the third highest values and Q4 (red) the lowest values.

The SJR is a size-independent prestige indicator that ranks journals by their 'average prestige per article'. It is based on the idea that 'all citations are not created equal'. SJR is a measure of scientific influence of journals that accounts for both the number of citations received by a journal and the importance or prestige of the journals where such citations come from It measures the scientific influence of the average article in a journal, it expresses how central to the global scientific discussion an average article of the journal is.

Evolution of the number of published documents. All types of documents are considered, including citable and non citable documents.

This indicator counts the number of citations received by documents from a journal and divides them by the total number of documents published in that journal. The chart shows the evolution of the average number of times documents published in a journal in the past two, three and four years have been cited in the current year. The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric.

Evolution of the total number of citations and journal's self-citations received by a journal's published documents during the three previous years. Journal Self-citation is defined as the number of citation from a journal citing article to articles published by the same journal.

Evolution of the number of total citation per document and external citation per document (i.e. journal self-citations removed) received by a journal's published documents during the three previous years. External citations are calculated by subtracting the number of self-citations from the total number of citations received by the journal’s documents.

International Collaboration accounts for the articles that have been produced by researchers from several countries. The chart shows the ratio of a journal's documents signed by researchers from more than one country; that is including more than one country address.

Not every article in a journal is considered primary research and therefore "citable", this chart shows the ratio of a journal's articles including substantial research (research articles, conference papers and reviews) in three year windows vs. those documents other than research articles, reviews and conference papers.

Ratio of a journal's items, grouped in three years windows, that have been cited at least once vs. those not cited during the following year.

Evolution of the percentage of female authors.

Evolution of the number of documents cited by public policy documents according to Overton database.

Evoution of the number of documents related to Sustainable Development Goals defined by United Nations. Available from 2018 onwards.

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Human Milk: Benefits, Composition and Evolution

Breastfeeding provides all the energy that the child needs in the form of nutrients in the first months of life. The components cover the nutritional needs in all stages, including colostrum and final or mature milk. It must also be taken into account that the composition of milk varies from one woman to another, between both breasts, between feedings and in the different stages in the same mother. It can be said that variation is an active mechanism to perfectly adjust to the nutritional and immunological needs of each child. Components of breast milk can exert beneficial non-nutritional functions. Breast milk also has bioactive factors, which affect biological processes and, therefore, have an impact on health. In the nutrition of premature babies, parenteral nutrition is carried out first, which later becomes enteral through different strategies, such as early minimal enteral nutrition. Despite this, they still present postnatal growth restrictions, which is associated with adverse neurocognitive outcomes. Breast milk achieves multiple benefits in both preterm and term births. Digestion and absorption in the stomach and intestines follow circadian rhythms in mammals, and these rhythms are regulated by rhythmically expressed clock genes in the intestine, as well as by daily food intake.

Biobased Hyperbranched Poly(ester)s of Precise Structure for the Release of Therapeutics

Hyperbrached poly(ester)s derived from naturally-occurring biomonomers may serve as excellent platforms for the sustained-release of therapeutics. Those generated from glycerol are particularly attractive. Traditionally, the difference in reactivity of the hydroxyl groups of glycerol has precluded the formation of well-defined polymers at high monomer conversion without gelation. Using the Martin-Smith model to select appropriate monomer ratios (ratios of functional groups), polymerization may be carried out to high conversion while avoiding gelation and with the assurance of a single type of endgroup. Various agents may be attached via esterification, amide formation or other process. Sustained release of the active agent may be readily achieved by enzyme-catalyzed hydrolysis.

Systemic soluble Programmed Death-Ligand 1 levels in sarcoidosis subjects does not vary with disease progression

Interaction of programmed cell death 1 (PD-1) receptor and its ligand 1 (PD-L1) is well studied in the field of fibrotic lung diseases, supporting its use as a biomarker of progression of interstitial lung disease. Anti PD-L1 therapy has shown effectiveness in improvement of many malignancies and murine models of autoimmune fibrotic lung diseases. Higher PD-1 expression on T cells and PD-L1 expression on human lung fibroblasts are known to contribute towards severity in sarcoidosis and idiopathic pulmonary fibrosis (IPF), respectively. The focus of this investigation was to determine if soluble form of PD-L1 (sPD-L1) serves as predictive biomarker of disease severity in interstitial lung disease (ILD), such as scleroderma, sarcoidosis and IPF. Comparison of local environments, such as bronchoalveolar lavage, revealed significantly higher sPD-L1 levels compared to systemic environments, such as peripheral blood (p=0.001, paired two-tailed Student’s t test). Investigation of serum samples of healthy control, IPF, scleroderma and sarcoidosis patients reveal significantly higher levels in sarcoidosis and IPF patients, compared to patients with scleroderma (p=0.001; p=0.02, one-way ANOVA with Tukey’s respectively). Comparison of serum levels between sarcoidosis patients and healthy controls revealed no significant differences (p=0.09, unpaired two-tailed t test). In addition, comparison of physiologic parameters, such as percent predicated Forced Vital Capacity (FVC) and sPD-L1 levels in sarcoidosis and IPF patients revealed no correlation. These observations suggest that sPD-L1 will not serve as a biomarker of sarcoidosis disease severity. Additional investigation of sPD-L1 in local environments is warranted.

Ethics or Bioethics for the Medical Profession?

The events that have occurred as a result of the Covid-19 pandemic have brought to the fore the figure of the doctor, as a main actor, in this complex and uncertain scenario. Many of the medical actions carried out have required strength, reflection, wisdom and prudence, all of them essential virtues according to the classical philosophical tradition, and that the ETHOS of the medical profession and the doctor translate, with this it is necessary to emphasize that it is the traditional medical ethics, the basis of this undeniable commitment to humanity, and that Bioethics, born 60 years ago, has been invested with an unthinkable condition, by its creator VR Potter, who proposed that the main objective should be scientific development -Technical but with ecological responsibility, beyond its supposed guiding function of current medicine. Which are the motivations for choosing the School of Medicine? What does it mean to be a good professional? How to respond to an increasingly demanding society? In light of the development of new technologies and communication systems, which today are universally accessible. It seems that the answer to these questions lies in a higher education based on ancestral ethical principles, which have been professed by generations of doctors, in traditional clinical practice and in practicing general medicine to achieve the specific medical training process, thus achieving efficiently meet the primary health demands of society. Therefore, Bioethics must be understood as an incipient discipline whose objective is to warn about the care of ecosystems, necessary for the survival of the human being, different from medical ethics.

Coevolution study of tau and a-synuclein suggests a connection between their normal interaction in neurons and the Parkinson's disease-associated mutation A53T

Alpha-synuclein lies at the center of Parkinson’s disease etiology, and polymorphisms in the gene for the microtubule-associated protein tau are risk factors for getting the disease. Tau and a-synuclein interact in vitro, and a-synuclein can also compete with tau binding to microtubules. To test whether these interactions might be part of their natural biological functions, a correlated mutation analysis was performed between tau and a-synuclein, looking for evidence of coevolution. For comparison, analyses were also performed between tau and b- and g-synuclein. In addition, analyses were performed between tau and the synuclein proteins and the neuronal tubulin proteins. Potential correlated mutations were detected between tau and a-synuclein, one involving an a-synuclein residue known to interact with tau in vitro, Asn122, and others involving the Parkinson’s disease-associated mutation A53T. No significant correlated mutations were seen between tau and b- and g-synuclein. Tau showed potential correlated mutations with the neuron-specific bIII-tubulin protein, encoded by the TUBB3 gene. No convincing correlated mutations were seen between the synuclein and tubulin proteins, with the possible exception of b-synuclein with bIVa-tubulin, encoded by the TUBB4A gene. While the correlated mutations between tau and a-synuclein suggest the two proteins have coevolved, additional study will be needed to confirm that their interaction is part of their normal biological function in cells.

The belated implementation of a long-awaited health system in Cyprus and the role of interest groups

It is really a paradox that 60 years were required to establish a modern health system in Cyprus, despite the expressed positive attitude οf all political parties and most governments. This article investigates the planning and implementation of the National Health System (NHS) and its delay determinants, by employing qualitative research of published sources, audio material and 33 interviews with elite key informants. A major anti-reform alliance, consisting of private doctors, private hospitals and health insurance companies was identified, further supported by doctors of the “old” public system, whose benefits were threatened. Delay contributions additionally arose from media and patient groups, whilst the pharmaceutical sector imposed insignificant influence. Τhe prevailing political, economic and social environment, along with aspects of the proposed reform, fueled this anti-reform movement. However, climate in favour of the NHS implementation gradually developed, attributed to the power balance shift supportingthe Minister of Health and the government, mobilization of important actors/stakeholders, including the Federation of Patients' Organizations of Cyprus and the Media, and significant decrease in the influence of reform-resistant groups. The new dynamics created a supportive environment leading to the NHS launch on June 1st, 2019; thus Cyprus has ceased to be the last state of the European Union (EU) without a universal health coverage system. The process of introducing this new system in Cyprus is a prime example of resource and power redistribution amongst different interest groups and of the catalysts required to exit the orbit of an extremely “path-dependent” system, potentially inspiring future reformers.

History of Trypanosomosis in the One-Humped Camel and Development of its Treatment and Cure, with Special Reference to Sudan

Sudan has one of the largest populations of domestic animals in Africa. One-humped camel (Camelus dromedarius) numbers were estimated at 4.5 million in 2009. Once used extensively for military transport they are still used in the transport role by spatially mobile pastoralist households and are a major source of milk and meat for these people. Trypanosomosis, due to Trypanosoma evansi, generally known as ‘surra’ but as ‘gufar’ in Sudan was first identified in camels in the country in 1902 and is the main cause of disease although T. vivax infections have recently been discovered in parts of Sudan. This protozoan disease is the most important health problem in camels, causing high morbidity and huge production losses. The causal organism, unlike most other trypanosomes, is not transmitted cyclically with tsetse (genus Glossina) flies as the vector but mechanically by biting flies mainly family Tabanidae but also by others of the Muscidae. Identification of the parasite in camel blood was initially by simple microscopic techniques but biotechnology and molecular methods now enable infection to be diagnosed at an earlier stage and with more accuracy. Prophylactic and curative treatments of trypanosomosis are notoriously complicated and uncertain with the situation in camels being exacerbated because of its peculiar physiology. Many trypanocides have been developed over time but the parasite often develops resistance to these drugs. Some drugs are successful, for some time, as both prophylactics and cures but are often accompanied by undesirable side effects. Other drugs used on conventional domestic stock are ineffective in camels or have lower efficacy. Research on diagnosis and treatment of trypanosomosis is continuing but the disease continues to cause production losses to the detriment of national and household incomes and food security.

Time-course of adaptations for electroretinography and pupillography

Cones are primarily involved in photopic vision and light adaptation. Rods are responsible for scotopic vision and dark adaptation. The typical time-courses of light and dark adaptations have been known for century. However, information regarding the minimal adaptation time for electroretinography (ERG) and pupillography would be helpful for practical applications and clinical efficiency. Therefore, we investigated the relationship between adaptation time and the parameters of ERG and pupillography. Forty-six eyes of 23 healthy women (mean age, 21.7 years) were enrolled. ERG and pupillography were tested for right and left eyes, respectively. ERG with a skin electrode was used to determine amplitude (µV) and implicit time (msec) by the records of rod-, flash-, cone-, and flicker-responses with white light (0.01–30 cd·s/m2). Infrared pupillography was used to record the pupillary response to 1-sec stimulation of red light (100 cd/m2). Cone- and flicker- (rod-, flash-, and pupil) responses were recorded after light (dark) adaptation at 1, 5, 10, 15, and 20 min. Amplitude was significantly different between 1 min and ≥5 or ≥10 min after adaptation in b-wave of cone- or rod-response, respectively. Implicit time differed significantly between 1 min and ≥5 min after adaptation with b-wave of cone- and rod-response. There were significant differences between 1 min and ≥10 or ≥5 min after dark adaptation in parameter of minimum pupil diameter or constriction rate, respectively. Consequently, light-adapted ERGs can be recorded, even in 5 min of light adaptation time without special light condition, whereas dark-adapted ERGs and pupillary response results can be obtained in 10 min or longer of dark adaptation time in complete darkness.

A Comparison of Neuropathy Quality of Life Tools: Norfolk QOL-DN, PN-QOL-97, and NeuroQOL-28

Aims To explore the effectiveness of the Norfolk QOL-DN (QOL-DN), PN-QOL-97, and NeuroQOL-28 as tools for early detection of diabetic peripheral neuropathy in overweight, obese, and inactive (OOI), prediabetes (PD), and type 2 diabetes (T2D) individuals. Methods Thirty-four adults were divided by A1C [(10 OOI, 13 PD, and 11 T2D] and the sural nerves were tested bilaterally via NC-Stat DPN Check, conducting a sural nerve conduction study (NCS). Participants were individually timed, filling out questionnaires (QOL-DN, NeuroQOL-28, and PN-QOL-97) at a self-selected pace. Data were analyzed and compared to NCS findings to determine the best instrument for early neuropathy detection, usability in screening settings, and application for individuals with OOI, PD, and T2D. Results Abnormal NCS results were obtained from 27 individuals, of which 25 were bilateral and symmetrical. Confirmed DSPN criteria were met for 24, and 1 case met criteria for subclinical neuropathy. Normal NCS findings, reported symptoms, and reduced bilateral sensation were found in 7 cases. The QOL-DN and NeuroQOL-28 significantly predict neuropathy criteria in OOI, PD, and T2D subjects. Analyses revealed the QOL-DN as the quickest for completion (M=5.17; SD=1.83), followed by the NeuroQOL-28 (M=5.58; SD=3.56), and the PN-QOL-97 (M=13.23; SD=3.606). Conclusions The QOL-DN and NeuroQOL-28 are valid early screening measures for DPN detection. Time completion studies revealed that the QOL-DN and NeuroQOL-28 may be used as excellent short screening measures, completed in approximately 6 minutes or less, with reasonable scoring for both. The NeuroQOL-28 is a better fit for immediate feedback, time constraints, or limited staff. Future investigations should evaluate these tools for detection in DPN-prone individuals and in subclinical populations screenings.

A Pilot Study of At-Home Virtual Reality for Chronic Pain Patients

Chronic pain disorders are a common and expensive health problem worldwide. Available treatments for these disorders have been decreasing and new treatments are needed. Virtual reality (VR) has been used for acute and procedural pain for years but systems are only now becoming available for use with chronic pain. In this study patients with a chronic pain disorder were given the option of using either take-home virtual reality equipment for one month or take-home biofeedback equipment for one month. In the VR condition patients were oriented to the “PainCare” app but could access any free content from the internet as well. Qualitative data was gathered on 23 VR patients and 12 biofeedback patients. Pre-post measures of depression, catastrophizing and function were obtained from 17 VR patients and 8 biofeedback patients. Data found that there was a statistically significant decrease in depression and catastrophizing in the VR group but no such decrease was found in the biofeedback group. No significant increase in function was found in either group though the VR group trended in that direction. One hundred percent (100%) of the patients who tried VR reported that they thought it had helped them overall at least a little. Patient ratings of the VR equipment were more favorable than the biofeedback equipment. This non-randomized small sample study suggests that at-home VR use can be used successfully with patients to decrease the important treatment variables of depression and catastrophizing, and perhaps become a significant contribution to the treatment of chronic pain disorders.

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• Published: 20 May 2024

Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats

• Jakob Steinfeldt   ORCID: orcid.org/0000-0003-1387-2054 1 , 2 , 3 , 4 , 5   na1 ,
• Benjamin Wild   ORCID: orcid.org/0000-0002-7492-8448 6   na1 ,
• Thore Buergel 5 , 6   na1 ,
• Maik Pietzner   ORCID: orcid.org/0000-0003-3437-9963 3 , 7 , 8 ,
• Julius Upmeier zu Belzen   ORCID: orcid.org/0000-0002-0966-4458 6 ,
• Andre Vauvelle 9 ,
• Stefan Hegselmann   ORCID: orcid.org/0000-0002-2145-3258 10 , 11 ,
• Spiros Denaxas 9 , 12 , 13 , 14 ,
• Harry Hemingway   ORCID: orcid.org/0000-0003-2279-0624 9 , 13 , 14 ,
• Claudia Langenberg   ORCID: orcid.org/0000-0002-5017-7344 3 , 7 , 8 ,
• Ulf Landmesser   ORCID: orcid.org/0000-0002-0214-3203 1 , 2 , 4 , 15 , 16   na2 ,
• John Deanfield 5   na2 &
• Roland Eils   ORCID: orcid.org/0000-0002-0034-4036 6 , 17   na2  

Nature Communications volume  15 , Article number:  4257 ( 2024 ) Cite this article

Metrics details

• Disease prevention
• Epidemiology

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1883 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,460 UK Biobank. Importantly, we observed discriminative improvements over basic demographic predictors for 1774 (94.3%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1347 (89.8%) of 1500 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

Introduction

The early phase of the COVID-19 pandemic exposed a global deficiency in delivering systematic, data-driven guidance for individual patients and healthcare providers with critical implications for pandemic preparedness. The assessment of an individual’s risk for future disease is central to guiding preventive interventions, early detection of disease, and the initiation of treatments. However, bespoke risk scores are only available for a subset of common diseases 1 , 2 , 3 , 4 , leaving healthcare providers and individuals with little to no guidance on most relevant diseases. Even for diseases with established risk scores, little consensus exists on which score to use and associated physical or laboratory measurements to obtain, leading to highly fragmented practice in routine care 5 . Importantly, in the early phases of emerging pandemics such as COVID-19, it is necessary to allocate sparse resources, but risk scores to identify vulnerable subpopulations are not available due to the lack of available data.

At the same time, most medical decisions on diagnosis, treatment, and prevention of diseases are fundamentally based on an individual’s medical history 6 . With the widespread digitalization, this information is routinely collected by healthcare providers, insurance, and governmental organizations at a population scale in the form of electronic health records 7 , 8 , 9 , 10 , 11 , 12 . These readily accessible records, which include diseases, medications, and procedures, are potentially informative about future risk trajectories, but their potential to improve medical decision-making is limited by the human ability to process and understand vast amounts of data 13 .

To date, routine health records have been used to guide clinical decision-making with etiological 14 , 15 , 16 , 17 , diagnostic 18 , 19 , and prognostic research 15 , 16 , 20 , 21 , 22 . Existing efforts often extract and leverage known clinical predictors with new methodologies 19 , augment them with additionally extracted data modalities such as clinical notes 23 , or aim to identify novel predictors among the recorded concepts 14 , 15 , 16 , 17 . Prior work on the prediction of disease onset has mainly focused on single diseases, including dementia 15 , 24 , cardiovascular conditions 23 , 25 such as heart failure 26 and atrial fibrillation 27 , 28 . In contrast, phenome-wide association studies (PheWAS) quantifying the associations of genetic variants with comprehensive phenotypic traits are emerging in genetic epidemiology 29 , 30 . While approaches have been developed for high-throughput phenotyping 31 , 32 and to extract information from longitudinal health records 33 , 34 , no studies have investigated the predictive potential and potential utility over the entire human phenome. Consequently, the predictive information in routinely collected health records and its potential to systematically guide medical decision-making is largely unexplored.

Here, we examined the predictive potential of an individual’s entire medical history and propose a systematic approach for phenome-wide risk stratification. We developed, trained, and validated a neural network in the UK Biobank cohort 35 to estimate disease risk from routinely collected health records. Unlike alternative methods, such as linear models or survival trees, which require separate models for each disease, our approach employs a multi-layer perceptron that predicts multiple endpoints concurrently, resulting in a significantly simplified model architecture. These endpoints include preventable diseases (e.g., coronary heart disease), diseases that are not currently preventable, but the early diagnosis has been shown to substantially slow down the progression and development of complications (e.g., heart failure), and outcomes, which are currently neither entirely preventable nor treatable (e.g., death). They also include both diseases with risk prediction models recommended in guidelines and used in practice (e.g., cardiovascular diseases or breast cancer) as well as diseases without current risk prediction models (e.g., psoriasis and rheumatoid arthritis).

We evaluated our approach by integrating the endpoint-specific risk states estimated by the neural network in Cox Proportional Hazard models 36 , investigating the phenome-wide predictive potential over basic demographic predictors, selected comorbidities, and established modifiable risk factors, and illustrating how phenome-wide risk stratification could benefit individuals by providing risk estimates, facilitating early disease diagnosis, and guiding preventive interventions. Furthermore, by externally validating in the All Of Us cohort 37 , we show that our models can generalize across healthcare systems and populations, including communities historically underrepresented in biomedical research.

Finally, we assessed the potential of our approach to aid risk stratification for the primary prevention of cardiovascular disease and to respond to emerging health threats at the example of COVID-19. We then show that the risk states of pneumonia, sepsis & all-cause death can be used to calculate a combined severity risk score using primary and secondary care records available before the global spread of the COVID-19 pandemic. Our results demonstrate the currently unused potential of routine health records to guide medical practice by providing comprehensive phenome-wide risk estimates.

Characteristics of the study population and integration of routine health records

This study is based on the UK Biobank cohort 35 , 38 , a longitudinal population cohort of 502,460 relatively healthy individuals of primarily British descent, with a median age of 58 (IQR 50, 63) years, 54.4% biological females, 11% current smokers, and a median BMI of 26.7 (IQR 24.1, 29.9) at recruitment (Table  1 for detailed information). Individuals recruited between 2006 and 2010 were followed for a median of 12.6 years, resulting in ~6.2 M overall person-years on 1883 phenome-wide endpoints 39 with ≥ 100 incident events (>0.02% of individuals having the event in the observation time). We externally validated our findings in individuals from the All of Us cohort, a longitudinal cohort of 229,830 individuals with linked health records recruited from all over the United States. Individuals in the All of Us cohort are of diverse descent, with 46% of reportedly non-white ethnicity and 78% of groups historically underrepresented in biomedical research 37 , 40 , and have a median age of 54 (IQR 38, 65) years with 61.1% biological females (see Table  1 for detailed information). Individuals were recruited from 2019 on and followed for a median of 3.5 years, resulting in ~787,300 person-years on 1568 endpoints.

Central to this study is the prior medical history, defined as the entirety of routine health records before recruitment. Before further analysis, we mapped all health records to the OMOP vocabulary. While most records originate from primary care and, to a lesser extent, secondary care (Suppl. Figure  1a ), the predominant record domains are drugs and observations, followed by conditions, procedures, and devices (Supplementary Fig.  1b ). Interestingly, while rare medical concepts (with a record in <1% of individuals in the study population) are not commonly included in prediction models 21 , they are often associated with high incident event rates (exemplified by the mortality rate in Supplementary Fig.  1c ) compared to common concepts (a record present in >= 1% of the study population). For example, the concept code for “portal hypertension” (OMOP 34742003) is only recorded in 0.04% (203) of individuals at recruitment, but 48.7% (99 individuals) will die over the course of the observation period. Importantly, there are many distinct rare concepts, and thus 91.7% of individuals have at least one rare record before recruitment, compared with 92.5% for common records. In addition, 60.7% of individuals have ≥ 10 rare records compared with 78.4% for common records, and individuals have only slightly fewer rare than common records (Supplementary Fig.  1d ).

After excluding very rare concepts (<0.01%, less than 50 individuals with the record in this study), we integrated the remaining 15,595 unique concepts (Supplementary Data  2 ) with a multi-task multi-layer perceptron (with 88.4 M parameters) to predict the phenome-wide onset of 1883 endpoints (Supplementary Data  1 ) simultaneously (Fig.  1a ). For comparison, we also include additional comparisons with a linear baseline (with 29.4 M parameters, Supplementary Fig.  2 ), demonstrating superior performance at a minimal increase of complexity.

a The medical history captures encounters with primary and secondary care, including diagnoses, medications, and procedures (ideally) from birth. Here we train a multi-layer perceptron on data before recruitment to predict phenome-wide incident disease onset for 1883 endpoints. b Location and size of the 22 assessment centers of the UK Biobank cohort across England, Wales, and Scotland. c To learn risk states from individual medical histories, the UK Biobank population was partitioned by their respective assessment center at recruitment. d For each of the 22 partitions, the Risk Model was trained to predict phenome-wide incident disease onset for 1883 endpoints. Subsequently, for each endpoint, Cox proportional hazard (CPH) models were developed on the risk states in combination with sets of commonly available predictors to model disease risk. Predictions of the CPH model on the test set were aggregated for downstream analysis. e External validation in the All of US cohort. After mapping to the OMOP vocabulary, we transferred the trained risk model to the All of US cohort and calculated the risk state for all endpoints. To validate these risk states, we compared the unchanged CPH models developed in the UK Biobank with refitted CPH models for age and sex. Source data are provided. The Icons are made by Freepik from www.flaticon.com .

To ensure that our findings are generalizable and transferable, we spatially validate our models in 22 recruitment centers (Fig.  1b ) across England, Wales, and Scotland. We developed 22 models, each trained on individuals from 21 recruitment centers at recruitment, randomly split into training and validation sets (Fig.  1c ). We subsequently tested the models on individuals from the additional recruitment center unseen for model development for internal spatial validation. After checkpoint selection on the validation data sets and obtaining the selected models’ final predictions on the individual test sets, the test set predictions were aggregated for downstream analysis (Fig.  1d ). Subsequently, disease-specific exclusions of prior events and sex-specificity were respected in all downstream analyses. After development, the models were externally validated in the All of Us cohort 37 .

Routine health records stratify phenome-wide disease onset

Central to the utility of any predictor is its potential to stratify risk. The better the stratification of low and high-risk individuals, the more effective targeted interventions and disease diagnoses are.

To investigate whether health records can be used to identify high-risk individuals, we assessed the relationship between the risk states estimated by the neural network for each endpoint and the risk of future disease (Fig.  2 ). For illustration, we first aggregated the incident events over the percentiles of the risk states for each endpoint and subsequently calculated ratios between the top and bottom 10% of risk states over the entire phenome (Fig.  2a ). We found that fewer than 10% of the individuals had an incident hypertension diagnosis in the observation window if they were estimated to be in the bottom risk percentile of the medical history, compared to more than 60% if they were estimated to be in the top risk percentile. Subsequently, the incident event ratio between the top and bottom deciles was ~5.23. Importantly, we found differences in the event rates, reflecting a stratification of high and low-risk individuals for almost all endpoints covering a broad range of disease categories and etiologies: For 1341 of 1883 endpoints (71.2%), we observed >10-times as many events for individuals in the top 10% of the predicted risk states compared to the bottom 10%. For instance, these endpoints included rheumatoid arthritis (Ratio ~11.3), ischemic heart disease (Ratio ~23.5), or chronic obstructive pulmonary disease (Ratio ~65.4). For 230 (12.2%) of the 1883 conditions, including abdominal aortic aneurysm (Ratio ~163.4), more than 100 times the number of individuals in the top 10% of predicted risk states had incident events compared to the bottom 10%. For 542 (28.8%) endpoints, the separation between high and low-risk individuals was smaller (Ratio <10), which included hypertension (Ratio ~5.2) and anemia (Ratio ~6.7), often diagnosed earlier in life or precursors for future comorbidities. Notably, the ratios were >1 for all but one of the 1883 investigated endpoints, even though all models were developed in spatially segregated assessment centers. To illustrate how high-risk individuals differ from the moderate cases, we also provide additional ratios comparing the top 10% to individuals in the median 20% of the population. The complete list of all endpoints and corresponding statistics can be found in Supplementary Data  4 .

a Ratio of incident events in the Top 10% compared with the Bottom 10% of the estimated risk states. Event rates in the Top 10% are higher than in the Bottom 10% for all but one of the 1883 investigated endpoints. Red dots indicate 24 selected endpoints detailed in Fig. 2b. To illustrate, 1198 (2.39%) individuals in the top risk decile for cardiac arrest experienced an event compared with only 30 (0.06%) in the bottom decile, with a risk ratio of 39.93. b Incident event rates for each medical history risk percentile (if medical history was available) for a selection of 24 endpoints. c Cumulative event rates with 95% confidence intervals for the Top 1%, median, and Bottom 1% of risk percentiles in b ) over 15ys. Statistical measures were derived from 502.460 individuals. Individuals with prevalent diseases were excluded from the endpoints-specific analysis. Source data are provided.

In addition to the phenome-wide analysis of 1883 endpoints, we also provide detailed associations between the risk percentiles and incident event ratios (Fig.  2b ), as well as cumulative event rates for up to 15 years (Fig.  2c ) of follow-up for the top, median, and bottom percentiles for a subset of 24 selected endpoints. This set was selected to comprise actionable endpoints and common diseases with significant societal burdens, specific cardiovascular conditions with pharmacological and surgical interventions, as well as endpoints without established tools to stratify risk to date. To exemplify the potential of our approach, among individuals in the top risk decile for heart failure, 8018 (16.06%) experienced an event, in contrast to 178 (0.35%) individuals in the bottom decile, resulting in a risk ratio of 46.35 (Fig.  2a, b , Supplementary Data  4 ). Consequently, those at high risk of heart failure could be prioritized for echocardiographic screening and, if necessary, prescribed effective guideline-directed medical therapy. Similarly, individuals with a high risk of developing COPD—where the top 10% face over 65 times the risk compared to the bottom 10%—may be considered for spirometry, an approach already established in the CAPTURE trial 41 . If confirmed, they could benefit from interventions such as long-acting bronchodilators. As a third example, a high-risk estimate for less common diseases, such as multiple sclerosis (risk ratio ~8.3), could further support referring individuals to a specialist and potentially shorten the often extensive patient journey before a final diagnosis is reached.

In summary, the disease-specific states stratify the risk of onset for all 1883 investigated endpoints across clinical specialties. This indicates that routine health records provide a large and widely unused potential for the systematic risk estimation of disease onset in the general population.

Discriminative performance indicates potential utility

While routine health records can stratify incident event rates, this does not prove utility. To test whether the risk state derived from the routine health records could provide utility and information beyond ubiquitously available predictors, we investigated the predictive information over age and biological sex, selected comorbidities from the Charlson Comorbidity Index 42 , and established modifiable risk factors from the AHA ASCVD pooled cohort equation 3 . We modeled the risk of disease onset using Cox Proportional-Hazards (CPH) models for all 1883 endpoints, which allowed us to estimate adjusted hazard ratios (denoted as HR in Supplementary Data  6 ) and 10-year discriminative improvements (indicated as Delta C-index in Fig.  3a ).

a Differences in discriminatory performance quantified by the C-Index between CPH models trained on Age+Sex and Age+Sex+MedicalHistory for all 1883 endpoints. We found significant improvements over the baseline model (Age+Sex, age, and biological sex only) for 1774 (94.2%) of the 1883 investigated endpoints. Red dots indicate selected endpoints in Fig. 3b. b Absolute discriminatory performance in terms of C-Index comparing the baseline (Age+Sex, black point) with the added routine health records risk state (Age+Sex+RiskState, red point) for a selection of 24 endpoints. c The direct C-index differences for the same models. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. d Example of individual predicted phenome-wide risk profile. Predisposition (10-year risk estimated by Age+Sex+RiskState compared to risk estimated by Age+Sex alone) is displayed in the inner circle, and absolute 10-year risk estimated by Age+Sex+RiskState can be found in the outer circle. Labels indicate endpoints with a high individual predisposition (>2 times higher than the Age+Sex-based reference estimate) and absolute 10-year risk > 10%. e Top 5 highest attributed records for selected endpoints. Statistical measures were derived from 502.460 individuals. Source data are provided.

We found significant improvements over the baseline model (age and biological sex only) for 1774 (94.2%) of the 1883 investigated endpoints (Fig.  3 , Supplementary Data  5 ). For many of these endpoints, the discriminative improvements were considerable (Delta C-Index Q25%: 0.094, Q50: 0.116, Q75: 0.141). We found significant improvements for 23 of the highlighted subset of 24 endpoints (indicated in Fig.  2a ), with the largest increases for the prediction of back pain (Delta C-Index: +0.238 (CI 0.236, 0.241)), suicide attempts (Delta C-Index: +0.224 (CI 0.213, 0.235)), psoriasis (Delta C-Index: +0.171 (CI 0.161, 0.178)), all-cause mortality (Delta C-Index: +0.171 (0.169, 0.174)) and chronic obstructive pulmonary disease (Delta C-Index: +0.156 (0.151, 0.159)). In contrast, we did not find significant improvements in the prediction of 86 (4.6%) of the 1883 endpoints, including, e.g., Parkinson’s disease (Delta C-Index: −0.006 (CI −0.013, 0)) or even deteriorations in the prediction of 23 (1.2%) of the endpoints, including neoplasm like cervical cancer (Delta C-Index: −0.025 (−0.059, −0.004)) and gastrointestinal diseases as chronic hepatitis (Delta C-Index: −0.032 (−0.064, −0.007)).

We also present a comparison between our approach and the Charlson Comorbidity Index’s 42 predictive performance, both of which can be automated. Additionally, we compare our method to the well-established ASCVD predictors, which are widely accessible but require an additional blood draw. Notably, incorporating the comorbidities from the Charlson Comorbidity Index enhances the discriminative capacity beyond age and sex; however, adding medical history proves to be significantly more effective in improving performance (Supplementary Fig.  3 , Supplementary Data  5 ). Likewise, while supplementing ASCVD predictors to age and sex augments the performance for most endpoints, it remains inferior to the combination of age, sex, and medical history alone. Incorporating the medical history alongside the comorbidities or ASCVD predictors further improves the predictive performance for the vast majority of endpoints (AgeSex+Comorbidities augmented by the MedicalHistory: +1726/1883 (91.7%), ASCVD+MedicalHistory: +1727/1883 (91.7%), demonstrating complementary nature of these information sources.

For illustration, we also present individual phenome-wide risk profiles (Fig.  3c , Supplementary Fig.  4a +b and 5a+b). The risk profiles varied substantially in the predispositions relative to the age and sex reference (the inner circle, see methods for details) and the absolute 10-year risk estimates (the outer circle). The first individual (Fig.  3c ), a 60-year-old man, is predicted to be at a particularly high 10-year risk of metabolic, cardiovascular, respiratory, and genitourinary conditions, including diabetes mellitus (19.4%), heart failure (22%), COPD (14.9%), and chronic kidney disease (16.8%). Increased risk of neoplastic, dermatological, and musculoskeletal conditions was not predicted by the prior health records of this individual. In contrast, another individual, a 48-year-old woman (Supplementary Fig.  5b ), is not estimated at increased cardiovascular risk but conversely to have almost 10x the risk for suicide ideation and attempt or self-harm compared to the reference group.

Importantly, the model performance is robust to the removal of recent information, indicating that the model effectively incorporates both the individuals’ long-term medical history and recent interactions with the healthcare system in order to predict future disease onset (Supplementary Fig.  6 ). We provide Shapley attributions 43 for the most important records (Fig.  3d , Suppl. Figure  4c , Suppl. Figure  5c ) and all records for the 24 highlighted endpoints (Supplementary Data  9 ) in the study population, enhancing the interpretability of our findings.

These findings indicate that health records contain substantial predictive information over established predictors for the majority of disease endpoints from across clinical specialties.

Predictive models can generalize across healthcare systems and populations

While our findings indicate potential utility in the UK Biobank, health records vary substantially across healthcare systems and over time due to differences in medical and coding practices (“distribution shift”) and underlying differences in the populations. Thus, predictive models can fail to learn robust and generalizable information 44 , 45 , 46 .

To better understand the generalisability across different healthcare systems, we predicted risk states and absolute risk estimates for all individuals in the All of Us cohort with linked medical records ( N  = 229,830; see Table  1 ). Importantly, we found significant improvements over the baseline model (age and biological sex only) for 1347 (85.9%) of the 1568 investigated endpoints with at least 100 incident events (Fig.  4a , Supplementary Data  8 ), replicating 1347/1500 (89.8%) of all significant improvements in the UK Biobank (Fig.  4b , Supplementary Data  8 ). Generally, larger improvements in the UK Biobank were replicated in the All of Us cohort. It’s noteworthy that smaller improvements in the UK Biobank often corresponded to proportionately larger improvements in All of Us, while larger improvements in the UK Biobank were attenuated in All of Us (Fig.  4c ).

a External validation of the differences in discriminatory performance quantified by the C-Index between CPH models trained on age and biological sex and age, biological sex, and the risk state for 1.568 endpoints in the All of Us cohort. We find significant improvements over the baseline model (age and biological sex only) for 1.347 (85.9%) of the 1.568 investigated endpoints. b Direct comparison of the absolute C-Index in the UK Biobank (x-axis) and the All Of Us cohort (y-axis). Significant improvements can be replicated for 1347 (89.8%, green points) of 1500 endpoints in the All Of Us cohort. c Comparison of mean delta C-Index per delta percentile (derived from the UK Biobank from the 1.568 endpoints available in All Of Us). Improvements in the All Of Us cohort are consistent with the UK Biobank cohort: Small improvements in the UK Biobank tend to be larger in All Of Us, while large improvements in the UK Biobank tend to be attenuated in All Of Us. d Distribution of C-Indices for the 1.568 investigated endpoints stratified by communities historically underrepresented in biomedical research (UPD) 73 . Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. e For the same groups, confidence intervals for the additive performance as measured by the C-Index compared to the baseline model. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. f Absolute discriminatory performance in terms of C-Index comparing the baseline (age and biological sex, black point) with the added routine health records risk state (red points) for a selection of 24 endpoints. g The differences in C-index for the same models. Statistical measures for UKB (in b and c ))were derived from 502.460 individuals and for AoU (in a – g ) were derived from 229.830 individuals. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. Source data are provided.

As the risk states were largely derived from white, middle-aged, and generally affluent and healthy individuals from the UK, it was critical to validate the discriminative performance in diverse and historically underserved and underrepresented groups and ethnicities. Generally, we found comparable discriminative performances (Fig.  4d ) and substantial benefits over basic demographic predictors (example of cardiac arrest in Fig.  4e ) across all investigated groups.

To illustrate these improvements further, we replicated significant improvements for all of the 24 a priori selected endpoints, with improvements ranging from modest for hypertension (Delta C-Index: +0.021 (0.016, 0.024)) and Parkinson’s disease (Delta C-Index: +0.035 (0.021, 0.05)) to substantial for, e.g., All-Cause Death (Delta C-Index: +0.116 (0.104, 0.127), Pulmonary embolism (Delta C-Index: +0.125 (0.112, 0.137)), and Cardiac arrest (Delta C-Index: +0.176 (0.146, 0.206)) (Fig.  4f, g and Supplementary Data  8 ). Only for a subset of 54 (3.44%) significantly improved endpoints in the UK Biobank, the discriminative performance in All Of Us deteriorated significantly upon transferring the pre-trained medical history risk model and integrating the information beyond age and biological sex alone, including hepatitis (Delta C-Index: −0.226 (−0.251, −0.2)), substance abuse (Delta C-Index: −0.037 (−0.05, −0.026)) and osteoporosis (Delta C-Index: −0.015 (−0.021, −0.008)).

Taken together, our findings suggest that predictive models based on medical history can generalize across health systems and are robust to diverse populations.

Predictions can support cardiovascular disease prevention and the response to emerging health threats

While comprehensive phenome-wide risk profiles provide opportunities to guide medical decision-making, not all of the predictions are actionable. To illustrate the potential clinical utility, we focused on the primary prevention of cardiovascular disease and the response to newly emerging health threats at the example of COVID-19.

Risk scores are well established in the primary prevention of cardiovascular events and have been recommended to guide preventive lipid-lowering interventions 47 . While cardiovascular predictors are accessible at a low cost, dedicated visits and resources from healthcare providers for physical and laboratory measurements are required. Therefore, we compared our phenome-wide risk score, based only on age, sex, and routine health records, to models based on established cardiovascular risk scores, the SCORE2 48 , the ASCVD 3 , and the British QRISK3 4 score. Interestingly, the discriminative performance of our phenome-wide model is competitive with the established cardiovascular risk scores for all investigated cardiovascular endpoints (Fig.  5a , Supplementary Data  7 ): we found comparable C-Indices with differences +0.001 (CI −0.002, 0.005) for ischemic stroke, +0.002 (CI 0.002, 0.005) for ischemic heart disease and +0.006 (CI 0.003, 0.009) for myocardial infarction compared with the comprehensive QRISK3 score. It is noteworthy that these discriminative improvements are substantially better for later-stage diseases, including heart failure (+0.018 (CI 0.015, 0.021)), cardiac arrest (+0.05 (CI 0.042, 0.059)), and all-cause mortality (+0.13 (CI 0.128, 0.132)) when prior health records are considered.

a Discriminatory performances in terms of absolute C-Indices comparing risk scores (Age+Sex, SCORE2, ASCVD, and QRISK as indicated, black point) with the risk model based on Age+Sex+RiskState (red segment). b Direct differences between risk scores (Age+Sex, SCORE2, ASCVD, and QRISK as indicated) and the risk model based on Age+Sex+RiskState in terms of C-index. Dots indicate medians and whiskers extend to the Bonferroni-corrected 95% confidence interval for a distribution bootstrapped over 100 iterations. c Estimated cumulative event trajectories, including 95% confidence intervals of severe (with hospitalization) and fatal (death registry) COVID-19 outcomes stratified by the Top, Median, and Bottom 5% based on age (left) or risk states of pneumonia, sepsis, and all-cause mortality as estimated by Kaplan-Meier analysis. Statistical measures were derived from 502.460 individuals. Source data are provided.

To further illustrate potential utility, we look at newly emerging pathogenic health threats, where rapid and reliable risk stratification is required to protect high-risk groups and prioritize preventive interventions. We investigated how our phenome-wide risk states could have been used in the context of COVID-19, a respiratory infection with pneumonia and sepsis as common, life-threatening complications of severe cases. We repurposed the risk states for pneumonia, sepsis, and all-cause mortality to calculate a combined COVID-19 severity risk score using information available at the end of 2019 before the global spread of the COVID-19 pandemic (see Methods for details). The COVID-19 severity risk score resembles the risk for developing severe or fatal COVID-19 and illustrates how health records could have helped to identify individuals at high risk and to prioritize individuals in initial vaccination campaigns better. Augmenting age with the COVID-19 severity risk score, we found substantially improved discriminative performance for both severe and fatal COVID-19 outcomes (Severe: C-Index (age) 0.597 (CI 0.591, 0.604) → C-Index (age + COVID-19 severity risk score) 0.647 (CI 0.641, 0.654); Fatal: C-Index (age) 0.720 (CI 0.710, 0.731) → C-Index (age + COVID-19 severity risk score) 0.780 (CI 0.772, 0.789). These discriminative improvements translate into higher cumulative incidence in the Top 5% population compared to age alone (Suppl. Figure  6C , age (left), COVID-19 severity score (right), severe COVID-19 (top), fatal COVID-19 (bottom)): In the top 5% of the age-based risk group (~79 (IQR 77, 81) years old), 0.42% (CI 0.34%, 0.5%, n  = 105) have been hospitalized, and 0.26% (CI 0.2%, 0.33%, n  = 66) had died by the end of the first wave. By the end of the second wave, around 0.96% (CI 0.83%, 1.08%, n = 240) had been hospitalized, and 0.44% (0.36%, 0.52%, n  = 111) had died. In contrast, for individuals in the top 5% of the COVID-19 severity risk score, by the end of the first wave, around 0.64% (CI 0.54%, 0.74%, n  = 160) had been hospitalized, and 0.32% (0.25%, 0.39%, n  = 80) had died, while by the end of the second wave, 1.74% (CI 1.57%, 1.9%, n  = 436) had been hospitalized and 0.68% (0.58%, 0.79%, n  = 172) had died.

In summary, our findings illustrate the clinical utility of medical history for primary prevention of cardiovascular diseases and the rapid response to emerging health threats.

Current clinical practice lacks systematic, data-driven guidance for individuals and care providers. Our study demonstrated that medical history can systematically inform on phenome-wide risk across clinical specialties, as shown in the British UK Biobank cohort. Subsequently, we show that these risk states can be repurposed to identify individuals vulnerable to severe COVID-19 and mortality. Importantly, we found significant improvements in the discriminated performance for the vast majority of disease endpoints, of which almost 90% could be replicated in the US All of US cohort. Our results indicated utility beyond age, sex, selected comorbidities, and established cardiovascular risk factors commonly considered in clinical practice for preventable diseases, treatable diseases, and diseases without existing risk stratification tools. We anticipate that our approach has the potential to facilitate population health at scale.

Designed for outpatient settings and focused on patients without acute complaints, our approach identifies incident disease onset from early (e.g., hypertension) and later (e.g., bypass surgery) health system contacts. We identified three primary scenarios of potential utility: Firstly, medical history can be exploited in diseases that are preventable with effective interventions, such as the prescription of lipid-lowering medication for primary prevention of coronary heart disease 47 . Lowering LDL cholesterol in 10,000 individuals at increased risk by 2 mmol/L with atorvastatin 40 mg daily (~2€ per month) for 5 years is estimated to prevent 500 vascular events, reducing the individual relative risk by more than a third 49 , 50 . Secondly, in conditions that are not preventable anymore individuals can benefit from early detection and treatment, like in type 2 diabetes or systolic heart failure. In individuals with heart failure with reduced ejection fraction, a comprehensive treatment regime (including ARNI, beta-blockers, MRA, and SGLT2 inhibitors) compared to a conventional regime (ACEi or ARB and beta blockers) reduced the hospital admissions for heart failure by more than two thirds, all-cause mortality by almost half  51 . For a 55-year-old male, this translated into an estimated 8.3 additional years free from cardiovascular death or readmission for heart failure. Lastly, in cases where outcomes are neither preventable nor treatable, estimates of prospective individual risk may be of high importance for personal decisions or the planning of advanced care, e.g., a high short-term mortality could identify patients needing to transition from curative to palliative strategies for optimal care 52 , 53 . Multiple studies have shown that palliative care services can improve patients’ symptoms and life quality and may even increase survival 54 . Overall, our approach could facilitate the identification of high-risk populations for specific screening programs, potentially improving the value of national health programs.

Importantly, our approach, based on routine health records, shows large discriminative improvements for the majority of diseases compared with conventionally tested biomarkers 55 , 56 , 57 and can generalize across diverse health systems, populations, and ethnicities. However, we also see that including the medical history over age and sex deteriorated the performance for a subset of 1.2% (UK Biobank) and 4.9% (All Of Us cohort), respectively. Three central challenges remain: First, health records, being products of interactions with the medical system, are subject to biological, procedural, and socio-economic biases 58 , as well as being dependent on the evolving nature of medical knowledge and policies. Furthermore, certain measurements and laboratory values are often inaccessible at the point of care, and harmonization in and across health systems presents a significant barrier to implementation 59 . Integrating these measures into the model holds considerable promise to improve the predictive performance further. While our approach is based on the standardized OMOP vocabulary, implementation requires a robust harmonization infrastructure, and data drift might necessitate model updates. Second, research cohorts often comprise healthier individuals with lower disease prevalence than the general population 60 , potentially leading to underestimating absolute risks. While discriminative improvements provide evidence of the potential clinical utility, they are insufficient to prove it, as it is highly context-dependent on the population, the disease, and the interventions available. This is particularly relevant for very rare diseases, where screening the general population poses the risk of false positive findings. Future randomized implementation studies must investigate how this discriminatory information can translate into improved clinical outcomes in the respective target populations. The third challenge concerns ensuring the interpretability of our approach on such complex data. Our approach provided unique insights into how the model used patients’ medical history to make risk predictions. The Shapley value attributions highlighted features the model found most informative for inference on both individual and population levels. These attributions are reflective of the model’s decision-making process, and while they aligned with our clinical understanding, they should not replace clinician judgment or other forms of evidence. As we refine and deploy this approach, we must remain vigilant in evaluating its performance and understanding the interpretational limitations. Interestingly, the attributions also expose the challenges of implementing predictive models across primary care and clinical specialties. For example, statins and chest pain are among the most highly attributed records for a high future likelihood of developing heart disease, indicating that in some cases, prior healthcare providers have already considered or even acted upon a high suspected risk of the disease, without entering the actual diagnosis into the records. Consequently, employing the model for such patients, when low-density lipoprotein (LDL) cholesterol levels are already managed, may not lead to further preventive actions if the patient’s care aligns with established standards. Importantly, we find that such cases do not drive the model’s predictive performance by assessing the robustness of the model performance to the removal of recent information (Supplementary Fig.  6 ). Ultimately, if routine health records are to be used for risk prediction, robust governance rules to protect individuals, such as opt-out and usage reports, need to be implemented. With many national initiatives emerging to curate routine health records for millions of individuals in the general population, future studies will allow us to better understand how to overcome these challenges.

Our study presents a systematic approach to simultaneous risk stratification for thousands of diseases across clinical specialties based on readily available medical history. These risk states can then be used to rapidly respond to emerging health threats such as COVID-19. Our findings demonstrate the potential to link clinical practice with already collected data to inform and guide preventive interventions, early diagnosis, and treatment of disease.

Data source and definitions of predictors and endpoints

To derive risk states, we analyzed data from the UK Biobank cohort. Participants were enrolled from 2006 to 2010 in 22 recruitment centers across England, Scotland, and Wales; the follow-up is ongoing, and records until the 24th of September 2021 are included in this analysis. The UK Biobank cohort comprises 273.353 women and 229.107 men aged between 37-73 years at the time of their assessment visit. Participants are linked to routinely collected records from primary care (GP), hospital records (HES, PEDW, and SMR), and death registries (ONS), providing longitudinal information on diagnosis, procedures, and prescriptions for the entire cohort from Scotland, Wales, and England. Routine health records were mapped to the OMOP CDM and represented as a 71.036-dimensional binary vector, indicating whether a concept has been recorded at least once in an individual prior to recruitment. A subset of 15.595 unique concepts, all found in at least 50 individuals, was chosen for model development. Endpoints were defined as the set of PheCodes X 39 , 61 , and after the exclusion of very rare endpoints (recorded in <100 individuals), 1883 PheCodes X endpoints were included in the development of the models. Due to the adult population, congenital, developmental, and neonatal endpoints were excluded. For each endpoint, subsequently, time-to-event outcomes were extracted, defined by the first occurrence after recruitment in primary care, hospital, or death records. Detailed information on the predictors and endpoints is provided in Supplementary Data  1 - 2 .

While all individuals in the UK Biobank were used to integrate the routine health records, develop the model, and estimate phenome-wide log partial hazards, individuals were excluded from endpoint-specific downstream analysis if they were already diagnosed with a disease (defined by a prior record of the respective endpoint) or are generally not eligible for the specific endpoint (females were excluded from the risk estimation for prostate cancer).

To externally validate our risk states, we investigate individuals from the All of Us cohort 37 , containing information on 229,830 individuals of diverse descent and from minorities historically underrepresented in biomedical research 40 . Because we only use the All of Us cohort for validation, we evaluate the predictive performance for the subset of 1568 endpoints with at least 100 incident events in the All of Us cohort.

The study adhered to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) statement for reporting 62 . The completed checklist can be found in the Supplementary Information.

Extraction and preparation of the routine health records

To extract the routine health records of each individual, we first aggregated the linked primary care, hospital records, and mortality records and mapped the aggregated records to the OMOP CDM (mostly SNOMED and RxNorm). Specifically, we used mapping tables provided by the UK Biobank, the OHDSI community, and SNOMED International to map concepts from the provider and country-specific non-standard vocabularies to OMOP standard vocabularies.

We restricted the analysis to the domains “Observation”, “Condition”, “Procedure”, “Drug” and “Device”. To reduce the complexity, we did not include any laboratory measures. The PheCode X endpoints 39 , 61 were derived from either mapping directly from ICD-10 (hospital and death records) or mapping from SNOMED to ICD-10 (using the official mapping table) and subsequently to Phecodes X.

To ensure the accuracy and integrity of our data, we implemented multiple validation steps. After each stage in the extraction and mapping process, we conducted plausibility and sanity checks on the distribution of the mapped records, along with spot checks of individual records. This approach was critical in verifying the validity of the data. Additionally, post-model training, the data underwent further verification. This included analyzing the calculated record attributions and removing recent records, as detailed in Supplementary Fig.  6 . These steps were essential to identify and mitigate any potential issues of record leakage. In the accompanying code release, we have provided the exact code used to extract and prepare the health records.

Spatial validation and data preprocessing

For model development and testing, we split the data set into 22 spatially separated partitions based on the location of the assessment center at recruitment. We analyzed the data in 22-fold nested cross-validation, setting aside one of the spatially separated partitions as a test set, aggregating the remaining partitions, and randomly selecting 10% of the aggregated data for the validation set. Within each of the 22 cross-validation loops, the individual test set (i.e., the spatially separated partition) remained untouched throughout model development, and the validation set was used to validate the fitting progress and checkpoint selection. All 22 obtained models were then evaluated on their respective test sets. We assumed missing data occurred randomly and performed multiple imputations using chained equations with gradient boosting machines 63 , 64 . Imputation models were fitted on the training sets and applied to the respective validation and test sets. Continuous variables were standardized; Categorical variables were one-hot encoded.

Development of the phenome-wide risk model

The risk model is a multi-task neural network that uses the binary representations of an individual’s prior health records before recruitment to simultaneously predict log partial hazards 65 for a set of 1883 endpoints. The model consists of three fully connected linear layers with 4096 hidden units, each with layer normalisation 66 , dropout 67 , and leaky ReLU activations. The last latent representation serves as a regulariser as it incentives the extraction of robust features for multiple diseases. For comparison, we also benchmarked the linear version of our model with 29.4 M instead of 88.4 M parameters (see Suppl. Figure  2 ). The model subsequently computes the log partial hazard (the risk state) for each endpoint with an adapted proportional hazard loss 65 , resulting in a 1,883-dimensional output representation. The individual losses are averaged and then summed to derive the final loss of the model. We subsequently tuned hyperparameters (via Bayesian Optimization) on train and validation splits over a constrained parameter space, tuning batch size, learning rate, weight decay, number of nodes in the layers of the endpoint heads, number of hidden layers, dropout rates, and size of the output vector of the shared network. The final models were trained with batch size 512 using the Adam optimiser 68 with a learning rate of 0.0006 and weight decay of 0.3, and early stopping tracking of the performance on the validation set. We implemented the model in Python 3.9 using PyTorch 1.11 69 and PyTorch-lightning 1.5.5 (for code availability, see below). The training of a single model on an NVIDIA A100 GPU node for 18 epochs required approximately 11 hours, equating to the emission of approximately 1.08 kg CO2 eq, 4.36 km driven by an average ICE car or 0.54 kgs of coal burned as calculated by the mlco2 calculator 70 . The external validation of these models, conducted within the All of Us cloud computing environment and including data preprocessing, inference, and evaluation, incurred a total compute cost of approximately 150 USD.

Downstream analysis and performance comparisons

We fitted Cox proportional hazards models 36 (CPH) to derive absolute risk predictions from the endpoint-specific risk states for the individual endpoints. For each endpoint, we developed models with distinct covariate sets: for all endpoints, we investigated age, biological sex, and the risk states from the health records. For cardiovascular endpoints, we additionally investigated predictors from established and guideline-recommended scores for the primary prevention of cardiovascular diseases, the SCORE2, ASCVD, and QRISK3. Model development was repeated independently for each assessment center thus, for each cross-validation split, models were trained on the respective train set, and checkpoints were selected on the respective validation set. For the final evaluation, test set predictions from the spatially separate recruitment centers were aggregated. Event risk rates were calculated over the full observation period. Harrell’s C-Index 71 was calculated with the lifelines package 72 by bootstrapping both the aggregated test set and individual assessment centers within ten years after recruitment to control for right-censoring. The C-Index is a measure of rank correlation that quantifies the agreement between predicted and observed outcomes. It ranges between 0.5 (no better than random prediction) to 1 (perfect prediction). Statistical inferences about model differences were based on the distribution of bootstrapped differences in the C-Index; models were considered different whenever the Bonferroni-corrected 95% CI of the difference did not overlap cross zero, to account for multiple testing. CPH models were fitted with the CoxPHFitter from the Python package lifelines 72 with default parameters and a step size of 0.5, 0.1, or 0.01 to facilitate model convergence. Confidence intervals for all statistical analyses were calculated over 1000 bootstrapping iterations.

Response to emerging health threats

We retrained our models using data limited to records until the end of December 2019, keeping the setting (in particular time zero for training) unchanged. Using these updated models, we then predicted the risk states using all data available at the end of 2019, just as the first cases of COVID-19 were reported. We then manually selected specific risk states associated with pneumonia, sepsis, and all-cause mortality to create an unweighted COVID-19 severity risk score. This risk score was subsequently tested against age for the identification of incident severe and fatal COVID-19 cases.

Independent validation in the All Of Us cohort

After mapping the linked health records from All Of Us to the OMOP vocabulary, we transferred the neural networks developed in the UK Biobank to the All Of Us research environment. We then used the models to predict the disease-specific risk states for all individuals. Subsequently, we predicted absolute risks with the CPH models developed in the UK Biobank. Finally, we calculated the mean of the predictions from the models for each individual and disease. For baseline comparison with Age and Sex, we fitted new CPH models in the All Of Us cohort.

Calculation of record attributions

To determine which records are most important on an individual level, we calculated attributions for the selection of 24 endpoints based on Shapley values. For computational efficiency, we approximated Shapley values via sampling for only 17,236 individuals unseen to the model during development 43 . Please refer to Supplementary Data  9 for the aggregated attributions from individuals without prior events. Shapley values in the table are provided in two forms: averaged (so-called local attributions to quantify importance for affected individuals) and summed (global attributions to quantify importance for population ranking). The average Shapley attributions, presented in the main text and figures, closely reflect our understanding of importance for affected individuals.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

UK Biobank data, including all linked routine health records, are publicly available to bona fide researchers upon application at http://www.ukbiobank.ac.uk/using-the-resource/ . In this study, primary care data was used following the COPI regulations. The All Of Us cohort data were provided by the All Of Us Research Program by permission that can be sought by scientists and the public alike. Currently, however, data access requires affiliation with a US institution. All patient data used throughout this study has been subject to patient consent as covered by the UK Biobank and All Of Us. Detailed information on the predictors and endpoints is presented in Supplementary Data  1 - 3 . Source data are provided with this paper.

Code availability

All code developed and used throughout this study has been made open source and is available on GitHub. The code to train the medical history model can be found here: github.com/nebw/medhist, while the code to run analysis on trained models can be found here: github.com/JakobSteinfeldt/MedicalHistoryPhenomeWide.

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Acknowledgements

We would like to acknowledge the support of the UK Biobank and the All of Us Research Program in providing access to their respective datasets. This research has been conducted using data from the UK Biobank (application number 51157) and the All of Us Research Program (by S.H. UserID 5703). Both studies have received ethical approval from their respective institutional review boards and have obtained informed consent from participants. We are grateful to the participants who generously contributed their time and data to make this research possible. This project has been funded by the Charité - Universitätsmedizin Berlin and the Einstein Foundation Berlin through the Einstein BIH Visiting Fellowship awarded to J.D. The study has been supported by the BMBF-funded Medical Informatics Initiative (HiGHmed, 01ZZ1802A − 01ZZ1802Z) and the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 437531118 – SFB 1470. SD is supported by a) the BHF Data Science Centre led by HDR UK (grant SP/19/3/34678), b) BigData@Heart Consortium, funded by the Innovative Medicines Initiative-2 Joint Undertaking under grant agreement 116074, c) the NIHR Biomedical Research Centre at University College London Hospital NHS Trust (UCLH BRC), d) a BHF Accelerator Award (AA/18/6/24223), e) the CVD-COVID-UK/COVID-IMPACT consortium and f) the Multimorbidity Mechanism and Therapeutic Research Collaborative (MMTRC, grant number MR/V033867/1). HH is supported by Health Data Research UK and the National Institute for Health Research, Biomedical Research Centre at University College London Hospitals.

Open Access funding enabled and organized by Projekt DEAL.

Author information

These authors contributed equally: Jakob Steinfeldt, Benjamin Wild, Thore Buergel.

These authors jointly supervised this work: Ulf Landmesser, John Deanfield, Roland Eils.

Authors and Affiliations

Department of Cardiology, Angiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité (DHZC), Berlin, Germany

Jakob Steinfeldt & Ulf Landmesser

Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Klinik/Centrum, Charitéplatz 1, 10117, Berlin, Germany

Computational Medicine, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Jakob Steinfeldt, Maik Pietzner & Claudia Langenberg

Friede Springer Cardiovascular Prevention Center@Charite, Charite - University Medicine Berlin, Berlin, Germany

Institute of Cardiovascular Sciences, University College London, London, UK

Jakob Steinfeldt, Thore Buergel & John Deanfield

Center for Digital Health, Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Benjamin Wild, Thore Buergel, Julius Upmeier zu Belzen & Roland Eils

MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK

Maik Pietzner & Claudia Langenberg

Precision Health University Research Institute, Queen Mary University of London and Barts NHS Trust, London, UK

Institute of Health Informatics, University College London, London, UK

Andre Vauvelle, Spiros Denaxas & Harry Hemingway

Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Massachusetts, USA

Stefan Hegselmann

Pattern Recognition and Image Analysis Lab, University of Münster, Münster, Germany

British Heart Foundation Data Science Centre, London, UK

Spiros Denaxas

Health Data Research UK, London, UK

Spiros Denaxas & Harry Hemingway

National Institute for Health Research, Biomedical Research Centre at University College London Hospitals National Institute for Health Research, Biomedical Research Centre, London, UK

Berlin Institute of Health (BIH), Charite - University Medicine Berlin, Berlin, Germany

Ulf Landmesser

DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Berlin, Germany

Health Data Science Unit, Heidelberg University Hospital and BioQuant, Heidelberg, Germany

Roland Eils

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Contributions

J.S., B.W., T.B., M.P., H.H., C.L., U.L., J.D., and R.E. conceived and designed the project. J.S., B.W., and T.B. implemented models, conducted experiments, and performed data analysis. J.U. and A.V. supported the analysis. S.H. performed the external validation. M.P., S.D., H.H., and C.L. provided methodological support and contributed to the discussion of the results. J.S., B.W., T.B., U.L., J.D., and R.E. wrote and prepared the manuscript. All authors read, revised, and approved the manuscript.

Corresponding author

Correspondence to Roland Eils .

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Competing interests.

U.L. received research grants to the institution from Abbott, Amgen, Bayer and Novartis. J.D. received honoraria from Amgen, Boehringer Ingelheim, Merck, Pfizer, Aegerion, Novartis, Sanofi, Takeda, Novo Nordisk, Bayer, and is a Trustee of Our Future Health. R.E. received honoraria from Sanofi and consulting fees from Boehringer Ingelheim. All other authors do declare no competing interests.

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Supplementary information

Supplementary information, description of additional supplementary files, supplementary data 1: endpoints in this study, supplementary data 2: medical history predictors in this study, supplementary data 3: reference predictors in this study, supplementary data 4: incident event stratification, supplementary data 5: discriminative performance of the medical history scores, supplementary data 6: hazard ratios of the medical history scores, 41467_2024_48568_moesm9_esm.xlsx.

Supplementary Data 7: Discriminative Performance of the Medical History Scores to compared to established scores for the Primary Prevention of Cardiovascular Disease

Supplementary Data 8: Discriminative performance in the All of Us cohort

Supplementary data 9: feature attributions for 24 selected endpoints, reporting summary, peer review file, source data, rights and permissions.

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Steinfeldt, J., Wild, B., Buergel, T. et al. Medical history predicts phenome-wide disease onset and enables the rapid response to emerging health threats. Nat Commun 15 , 4257 (2024). https://doi.org/10.1038/s41467-024-48568-8

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DOI : https://doi.org/10.1038/s41467-024-48568-8

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VA earns top honor for medical research

The database Nature Index named the VA one of the Top 10 rising research institutions in North America.

• The Department of Veterans Affairs has earned a top honor for medical research. The database Nature Index named the VA as one of the top 10 rising research institutions in North America. Time Magazine recently put two VA researchers on its top 100 list, for their breakthroughs studying Long COVID and health equity. Past VA-backed research led to the develop of CAT scans and the first liver transplant. ( VA named on of the top 10 fastest rising research institutions - VA News)
• Agencies are getting some help to achieve a key piece of a zero trust cyber architecture. With a deadline looming to encrypt all network traffic across their enterprise, civilian agencies now have a new resource guide to help them achieve that goal. In the Cybersecurity and Infrastructure Security Agency's new Encrypted Domain Name System (DNS Implementation Guidance), agencies can use a checklist, implementation recommendations and technical references to meet OMB's deadline by the end of fiscal 2024. CISA said the document outlines possible ways to meet the requirements based on the current agency and vendor landscapes as well as the current functionality available in CISA’s Protective DNS. The Office of Management and Budget detailed the requirement to encrypt network traffic in the January 2022 zero trust memo. ( CISA Publishes New Encrypted DNS Guidance to Agencies - CISA)
• President Joe Biden has nominated Maj. Gen. Thomas Hensley to lead the Air Force’s information warfare command. If confirmed, Maj. Gen. Hensley will take over for Lt. Gen. Kevin Kennedy. Kennedy has been in this role since 2022. Known as Air Forces Cyber, 16th Air Force provides cyber, intelligence, surveillance, reconnaissance, electronic warfare and information operations capabilities to the service. Air Force Secretary Frank Kendall announced sweeping changes to the service’s structure earlier this year. Air Forces Cyber will be elevated as a standalone component as part of Secretary Kendall’s reoptimization plan. ( Biden nominates Hensley to lead 16th Air Force - DoD)
• For the first time in four years, federal employee engagement is on the rise. More than two-thirds of agencies either maintained or increased their satisfaction scores in the 2023 Best Places to Work rankings. By comparison, last year's Best Places to Work results had only a quarter of agencies increasing in their overall scores. The Partnership for Public Service released its latest annual list and analysis early Monday morning. One powerful factor affecting agencies’ engagement scores was telework. The Partnership reported that employees who teleworked full-time were more satisfied in their jobs and with their agencies, compared to those feds working on-site at headquarters or in field offices. ( 2023 Best Places to Work marks a turning point in employee engagement - Federal News Network)

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• The U.S. Digital Service and the Office of Personnel Management are ready to take the first step to changing the way agencies hire technology experts. USDS and OPM awarded the first of what is expected to be several Challenge.gov awards under the “Tech Hiring Professional Training and Development Program." Consulting firm Mediabarn beat out 59 other competitors to win Phase 3 and $500,000. The company will create a four-month training program for about 30 federal hiring professionals to test out their approach to improve the processes around recruiting, hiring and candidate management. USDS and OPM are looking for agencies to participate in the first cohort to pilot Mediabarn's approach this fall. ( Pilot to improve tech hiring to kick off this fall - Challenge.gov)
• The departments of Labor and Treasury are creating a new data-sharing partnership to support fraud prevention in states. The agencies will provide access to the Do Not Pay Working System through the Unemployment Insurance Integrity Data Hub. The Do Not Pay Working System provides eligibility verification at no cost to agencies and federally funded state programs. This data sharing partnership is part of how Labor is trying to address concerns by the agency's inspector general, who estimated the Unemployment Insurance program handed out more than $190 billion in improper payments over the last four years. ( Labor and Treasury Departments want to prevent fraud at the state level - U.S. Department of Labor)
• House lawmakers want the Defense Department to establish a Quantum Computing Center of Excellence. The House Armed Services Committee’s draft 2025 defense policy bill would require the Defense Secretary to create the center at one of DoD’s laboratories that have experience in quantum computing and related technologies. The center would focus on transitioning quantum computing technology from the research phase into operational use. It would also help facilitate quantum workforce development for the Defense Department. ( NDAA directs DoD to establish Quantum Computing Center of Excellence - House Documents)
• The Office of Personnel Management is looking to a hire a senior technology leader. On Friday, OPM posted a new job opening on USAjobs.gov for a chief artificial intelligence officer (CAIO). The person who takes the position will be tasked with leading the secure development and use of AI at OPM. The new CAIO will be in charge of managing the use of AI both internally and externally. OPM is taking applications for the senior-level open role until June 4. ( Chief AI officer job posting - Office of Personnel Management)

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Research Our Records

Flexoline Index (Flex)

The Alien Registration Division of the Immigration and Naturalization Service (INS) created the original Flexoline Index (Flex) in 1940 to index Alien Registration (AR-2) forms created under the Alien Registration Act of 1940. It ultimately served as an index to multiple iterations of Alien Registration Forms: Alien Registration Form (AR-2); Alien Registration Foreign Service Form (AR-102, completed at consular offices); and Alien Registration Seaman Form (AR-102-S). When the Alien Registration Division disbanded near the end of World War II, responsibility for Flex transferred to the INS records program. Thereafter until the early 1950s, Flex entries were created as INS issued Alien Files (A-Files) related to new arrivals, adjustments, or applications by registered aliens. Flex does not contain entries for all A-Files created during this period.

The original Flex records were strips of ticker tape-like paper pasted onto a carrier sheet. These sheets were microfilmed and later manually keyed into a searchable, electronic database. After conversion, the Flex digital images and database were managed by INS, later United States Citizenship and Immigration Services (USCIS), as a component of the Microfilm Digitization Application System (MiDAS).

Example Flexoline Carrier Sheet

Flexoline strips for twelve individuals

The Flexoline Index series consists of digital images and a database with data extracted from the microfilmed images of Flex. It serves as an index to the microfilmed (and later digitized) Alien Registration Forms (AR-2, AR-102, AR-102-S) issued 1940–1944 and includes records related to immigration and naturalization in the United States prior to 1955. The searchable Flexoline database contains about 6.9 million unique Alien Registration Numbers (A-Numbers). 

The Flex entries follow three different formats identified by INS as “Format A,” “Format B,” and “Format C.” 

NARA staff will search the Flex database to assist in retrieving the associated AR-2 form. See the AR-2 web page for more on the search request process.

Select "+" to display more information:

Original Flexoline Images Versus Flexoline Database

NARA cannot provide a copy of the original Flex strip image for an individual because other records on the same carrier sheet may not be releasable, but NARA can provide an extract of the record from the database.

No Flex Records Found

There are several possible reasons why NARA may not locate any Flex records:

• Flex contains inaccurate information. Individuals completed the AR-2 forms to the best of their knowledge, but may have misremembered their birthdate, country of birth, or other names they had gone by. In addition, the INS manually indexed the AR-2 forms so typos may have occurred as staff transcribed the information.  
• There are known gaps in Flex. There are about 1.25 million Alien Registration Numbers (A-Numbers) associated with the AR-2 forms that are not listed in Flex. About a million of those A-Numbers are included in the United States Citizenship and Immigration Services (USCIS) Master Index, which contains names, year of birth, and A-Numbers. However, NARA has not yet taken custody of nor can search the Master Index at this time. 

Cultural Relativity and Acceptance of Embryonic Stem Cell Research

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There is a debate about the ethical implications of using human embryos in stem cell research, which can be influenced by cultural, moral, and social values. This paper argues for an adaptable framework to accommodate diverse cultural and religious perspectives. By using an adaptive ethics model, research protections can reflect various populations and foster growth in stem cell research possibilities.

INTRODUCTION

Stem cell research combines biology, medicine, and technology, promising to alter health care and the understanding of human development. Yet, ethical contention exists because of individuals’ perceptions of using human embryos based on their various cultural, moral, and social values. While these disagreements concerning policy, use, and general acceptance have prompted the development of an international ethics policy, such a uniform approach can overlook the nuanced ethical landscapes between cultures. With diverse viewpoints in public health, a single global policy, especially one reflecting Western ethics or the ethics prevalent in high-income countries, is impractical. This paper argues for a culturally sensitive, adaptable framework for the use of embryonic stem cells. Stem cell policy should accommodate varying ethical viewpoints and promote an effective global dialogue. With an extension of an ethics model that can adapt to various cultures, we recommend localized guidelines that reflect the moral views of the people those guidelines serve.

Stem cells, characterized by their unique ability to differentiate into various cell types, enable the repair or replacement of damaged tissues. Two primary types of stem cells are somatic stem cells (adult stem cells) and embryonic stem cells. Adult stem cells exist in developed tissues and maintain the body’s repair processes. [1] Embryonic stem cells (ESC) are remarkably pluripotent or versatile, making them valuable in research. [2] However, the use of ESCs has sparked ethics debates. Considering the potential of embryonic stem cells, research guidelines are essential. The International Society for Stem Cell Research (ISSCR) provides international stem cell research guidelines. They call for “public conversations touching on the scientific significance as well as the societal and ethical issues raised by ESC research.” [3] The ISSCR also publishes updates about culturing human embryos 14 days post fertilization, suggesting local policies and regulations should continue to evolve as ESC research develops. [4]  Like the ISSCR, which calls for local law and policy to adapt to developing stem cell research given cultural acceptance, this paper highlights the importance of local social factors such as religion and culture.

I.     Global Cultural Perspective of Embryonic Stem Cells

Views on ESCs vary throughout the world. Some countries readily embrace stem cell research and therapies, while others have stricter regulations due to ethical concerns surrounding embryonic stem cells and when an embryo becomes entitled to moral consideration. The philosophical issue of when the “someone” begins to be a human after fertilization, in the morally relevant sense, [5] impacts when an embryo becomes not just worthy of protection but morally entitled to it. The process of creating embryonic stem cell lines involves the destruction of the embryos for research. [6] Consequently, global engagement in ESC research depends on social-cultural acceptability.

a.     US and Rights-Based Cultures

In the United States, attitudes toward stem cell therapies are diverse. The ethics and social approaches, which value individualism, [7] trigger debates regarding the destruction of human embryos, creating a complex regulatory environment. For example, the 1996 Dickey-Wicker Amendment prohibited federal funding for the creation of embryos for research and the destruction of embryos for “more than allowed for research on fetuses in utero.” [8] Following suit, in 2001, the Bush Administration heavily restricted stem cell lines for research. However, the Stem Cell Research Enhancement Act of 2005 was proposed to help develop ESC research but was ultimately vetoed. [9] Under the Obama administration, in 2009, an executive order lifted restrictions allowing for more development in this field. [10] The flux of research capacity and funding parallels the different cultural perceptions of human dignity of the embryo and how it is socially presented within the country’s research culture. [11]

b.     Ubuntu and Collective Cultures

African bioethics differs from Western individualism because of the different traditions and values. African traditions, as described by individuals from South Africa and supported by some studies in other African countries, including Ghana and Kenya, follow the African moral philosophies of Ubuntu or Botho and Ukama , which “advocates for a form of wholeness that comes through one’s relationship and connectedness with other people in the society,” [12] making autonomy a socially collective concept. In this context, for the community to act autonomously, individuals would come together to decide what is best for the collective. Thus, stem cell research would require examining the value of the research to society as a whole and the use of the embryos as a collective societal resource. If society views the source as part of the collective whole, and opposes using stem cells, compromising the cultural values to pursue research may cause social detachment and stunt research growth. [13] Based on local culture and moral philosophy, the permissibility of stem cell research depends on how embryo, stem cell, and cell line therapies relate to the community as a whole . Ubuntu is the expression of humanness, with the person’s identity drawn from the “’I am because we are’” value. [14] The decision in a collectivistic culture becomes one born of cultural context, and individual decisions give deference to others in the society.

Consent differs in cultures where thought and moral philosophy are based on a collective paradigm. So, applying Western bioethical concepts is unrealistic. For one, Africa is a diverse continent with many countries with different belief systems, access to health care, and reliance on traditional or Western medicines. Where traditional medicine is the primary treatment, the “’restrictive focus on biomedically-related bioethics’” [is] problematic in African contexts because it neglects bioethical issues raised by traditional systems.” [15] No single approach applies in all areas or contexts. Rather than evaluating the permissibility of ESC research according to Western concepts such as the four principles approach, different ethics approaches should prevail.

Another consideration is the socio-economic standing of countries. In parts of South Africa, researchers have not focused heavily on contributing to the stem cell discourse, either because it is not considered health care or a health science priority or because resources are unavailable. [16] Each country’s priorities differ given different social, political, and economic factors. In South Africa, for instance, areas such as maternal mortality, non-communicable diseases, telemedicine, and the strength of health systems need improvement and require more focus. [17] Stem cell research could benefit the population, but it also could divert resources from basic medical care. Researchers in South Africa adhere to the National Health Act and Medicines Control Act in South Africa and international guidelines; however, the Act is not strictly enforced, and there is no clear legislation for research conduct or ethical guidelines. [18]

Some parts of Africa condemn stem cell research. For example, 98.2 percent of the Tunisian population is Muslim. [19] Tunisia does not permit stem cell research because of moral conflict with a Fatwa. Religion heavily saturates the regulation and direction of research. [20] Stem cell use became permissible for reproductive purposes only recently, with tight restrictions preventing cells from being used in any research other than procedures concerning ART/IVF.  Their use is conditioned on consent, and available only to married couples. [21] The community's receptiveness to stem cell research depends on including communitarian African ethics.

c.     Asia

Some Asian countries also have a collective model of ethics and decision making. [22] In China, the ethics model promotes a sincere respect for life or human dignity, [23] based on protective medicine. This model, influenced by Traditional Chinese Medicine (TCM), [24] recognizes Qi as the vital energy delivered via the meridians of the body; it connects illness to body systems, the body’s entire constitution, and the universe for a holistic bond of nature, health, and quality of life. [25] Following a protective ethics model, and traditional customs of wholeness, investment in stem cell research is heavily desired for its applications in regenerative therapies, disease modeling, and protective medicines. In a survey of medical students and healthcare practitioners, 30.8 percent considered stem cell research morally unacceptable while 63.5 percent accepted medical research using human embryonic stem cells. Of these individuals, 89.9 percent supported increased funding for stem cell research. [26] The scientific community might not reflect the overall population. From 1997 to 2019, China spent a total of $576 million (USD) on stem cell research at 8,050 stem cell programs, increased published presence from 0.6 percent to 14.01 percent of total global stem cell publications as of 2014, and made significant strides in cell-based therapies for various medical conditions. [27] However, while China has made substantial investments in stem cell research and achieved notable progress in clinical applications, concerns linger regarding ethical oversight and transparency. [28] For example, the China Biosecurity Law, promoted by the National Health Commission and China Hospital Association, attempted to mitigate risks by introducing an institutional review board (IRB) in the regulatory bodies. 5800 IRBs registered with the Chinese Clinical Trial Registry since 2021. [29] However, issues still need to be addressed in implementing effective IRB review and approval procedures.

The substantial government funding and focus on scientific advancement have sometimes overshadowed considerations of regional cultures, ethnic minorities, and individual perspectives, particularly evident during the one-child policy era. As government policy adapts to promote public stability, such as the change from the one-child to the two-child policy, [30] research ethics should also adapt to ensure respect for the values of its represented peoples.

Japan is also relatively supportive of stem cell research and therapies. Japan has a more transparent regulatory framework, allowing for faster approval of regenerative medicine products, which has led to several advanced clinical trials and therapies. [31] South Korea is also actively engaged in stem cell research and has a history of breakthroughs in cloning and embryonic stem cells. [32] However, the field is controversial, and there are issues of scientific integrity. For example, the Korean FDA fast-tracked products for approval, [33] and in another instance, the oocyte source was unclear and possibly violated ethical standards. [34] Trust is important in research, as it builds collaborative foundations between colleagues, trial participant comfort, open-mindedness for complicated and sensitive discussions, and supports regulatory procedures for stakeholders. There is a need to respect the culture’s interest, engagement, and for research and clinical trials to be transparent and have ethical oversight to promote global research discourse and trust.

d.     Middle East

Countries in the Middle East have varying degrees of acceptance of or restrictions to policies related to using embryonic stem cells due to cultural and religious influences. Saudi Arabia has made significant contributions to stem cell research, and conducts research based on international guidelines for ethical conduct and under strict adherence to guidelines in accordance with Islamic principles. Specifically, the Saudi government and people require ESC research to adhere to Sharia law. In addition to umbilical and placental stem cells, [35] Saudi Arabia permits the use of embryonic stem cells as long as they come from miscarriages, therapeutic abortions permissible by Sharia law, or are left over from in vitro fertilization and donated to research. [36] Laws and ethical guidelines for stem cell research allow the development of research institutions such as the King Abdullah International Medical Research Center, which has a cord blood bank and a stem cell registry with nearly 10,000 donors. [37] Such volume and acceptance are due to the ethical ‘permissibility’ of the donor sources, which do not conflict with religious pillars. However, some researchers err on the side of caution, choosing not to use embryos or fetal tissue as they feel it is unethical to do so. [38]

Jordan has a positive research ethics culture. [39] However, there is a significant issue of lack of trust in researchers, with 45.23 percent (38.66 percent agreeing and 6.57 percent strongly agreeing) of Jordanians holding a low level of trust in researchers, compared to 81.34 percent of Jordanians agreeing that they feel safe to participate in a research trial. [40] Safety testifies to the feeling of confidence that adequate measures are in place to protect participants from harm, whereas trust in researchers could represent the confidence in researchers to act in the participants’ best interests, adhere to ethical guidelines, provide accurate information, and respect participants’ rights and dignity. One method to improve trust would be to address communication issues relevant to ESC. Legislation surrounding stem cell research has adopted specific language, especially concerning clarification “between ‘stem cells’ and ‘embryonic stem cells’” in translation. [41] Furthermore, legislation “mandates the creation of a national committee… laying out specific regulations for stem-cell banking in accordance with international standards.” [42] This broad regulation opens the door for future global engagement and maintains transparency. However, these regulations may also constrain the influence of research direction, pace, and accessibility of research outcomes.

e.     Europe

In the European Union (EU), ethics is also principle-based, but the principles of autonomy, dignity, integrity, and vulnerability are interconnected. [43] As such, the opportunity for cohesion and concessions between individuals’ thoughts and ideals allows for a more adaptable ethics model due to the flexible principles that relate to the human experience The EU has put forth a framework in its Convention for the Protection of Human Rights and Dignity of the Human Being allowing member states to take different approaches. Each European state applies these principles to its specific conventions, leading to or reflecting different acceptance levels of stem cell research. [44]

For example, in Germany, Lebenzusammenhang , or the coherence of life, references integrity in the unity of human culture. Namely, the personal sphere “should not be subject to external intervention.” [45]  Stem cell interventions could affect this concept of bodily completeness, leading to heavy restrictions. Under the Grundgesetz, human dignity and the right to life with physical integrity are paramount. [46] The Embryo Protection Act of 1991 made producing cell lines illegal. Cell lines can be imported if approved by the Central Ethics Commission for Stem Cell Research only if they were derived before May 2007. [47] Stem cell research respects the integrity of life for the embryo with heavy specifications and intense oversight. This is vastly different in Finland, where the regulatory bodies find research more permissible in IVF excess, but only up to 14 days after fertilization. [48] Spain’s approach differs still, with a comprehensive regulatory framework. [49] Thus, research regulation can be culture-specific due to variations in applied principles. Diverse cultures call for various approaches to ethical permissibility. [50] Only an adaptive-deliberative model can address the cultural constructions of self and achieve positive, culturally sensitive stem cell research practices. [51]

II.     Religious Perspectives on ESC

Embryonic stem cell sources are the main consideration within religious contexts. While individuals may not regard their own religious texts as authoritative or factual, religion can shape their foundations or perspectives.

The Qur'an states:

“And indeed We created man from a quintessence of clay. Then We placed within him a small quantity of nutfa (sperm to fertilize) in a safe place. Then We have fashioned the nutfa into an ‘alaqa (clinging clot or cell cluster), then We developed the ‘alaqa into mudgha (a lump of flesh), and We made mudgha into bones, and clothed the bones with flesh, then We brought it into being as a new creation. So Blessed is Allah, the Best of Creators.” [52]

Many scholars of Islam estimate the time of soul installment, marked by the angel breathing in the soul to bring the individual into creation, as 120 days from conception. [53] Personhood begins at this point, and the value of life would prohibit research or experimentation that could harm the individual. If the fetus is more than 120 days old, the time ensoulment is interpreted to occur according to Islamic law, abortion is no longer permissible. [54] There are a few opposing opinions about early embryos in Islamic traditions. According to some Islamic theologians, there is no ensoulment of the early embryo, which is the source of stem cells for ESC research. [55]

In Buddhism, the stance on stem cell research is not settled. The main tenets, the prohibition against harming or destroying others (ahimsa) and the pursuit of knowledge (prajña) and compassion (karuna), leave Buddhist scholars and communities divided. [56] Some scholars argue stem cell research is in accordance with the Buddhist tenet of seeking knowledge and ending human suffering. Others feel it violates the principle of not harming others. Finding the balance between these two points relies on the karmic burden of Buddhist morality. In trying to prevent ahimsa towards the embryo, Buddhist scholars suggest that to comply with Buddhist tenets, research cannot be done as the embryo has personhood at the moment of conception and would reincarnate immediately, harming the individual's ability to build their karmic burden. [57] On the other hand, the Bodhisattvas, those considered to be on the path to enlightenment or Nirvana, have given organs and flesh to others to help alleviate grieving and to benefit all. [58] Acceptance varies on applied beliefs and interpretations.

Catholicism does not support embryonic stem cell research, as it entails creation or destruction of human embryos. This destruction conflicts with the belief in the sanctity of life. For example, in the Old Testament, Genesis describes humanity as being created in God’s image and multiplying on the Earth, referencing the sacred rights to human conception and the purpose of development and life. In the Ten Commandments, the tenet that one should not kill has numerous interpretations where killing could mean murder or shedding of the sanctity of life, demonstrating the high value of human personhood. In other books, the theological conception of when life begins is interpreted as in utero, [59] highlighting the inviolability of life and its formation in vivo to make a religious point for accepting such research as relatively limited, if at all. [60] The Vatican has released ethical directives to help apply a theological basis to modern-day conflicts. The Magisterium of the Church states that “unless there is a moral certainty of not causing harm,” experimentation on fetuses, fertilized cells, stem cells, or embryos constitutes a crime. [61] Such procedures would not respect the human person who exists at these stages, according to Catholicism. Damages to the embryo are considered gravely immoral and illicit. [62] Although the Catholic Church officially opposes abortion, surveys demonstrate that many Catholic people hold pro-choice views, whether due to the context of conception, stage of pregnancy, threat to the mother’s life, or for other reasons, demonstrating that practicing members can also accept some but not all tenets. [63]

Some major Jewish denominations, such as the Reform, Conservative, and Reconstructionist movements, are open to supporting ESC use or research as long as it is for saving a life. [64] Within Judaism, the Talmud, or study, gives personhood to the child at birth and emphasizes that life does not begin at conception: [65]

“If she is found pregnant, until the fortieth day it is mere fluid,” [66]

Whereas most religions prioritize the status of human embryos, the Halakah (Jewish religious law) states that to save one life, most other religious laws can be ignored because it is in pursuit of preservation. [67] Stem cell research is accepted due to application of these religious laws.

We recognize that all religions contain subsets and sects. The variety of environmental and cultural differences within religious groups requires further analysis to respect the flexibility of religious thoughts and practices. We make no presumptions that all cultures require notions of autonomy or morality as under the common morality theory , which asserts a set of universal moral norms that all individuals share provides moral reasoning and guides ethical decisions. [68] We only wish to show that the interaction with morality varies between cultures and countries.

III.     A Flexible Ethical Approach

The plurality of different moral approaches described above demonstrates that there can be no universally acceptable uniform law for ESC on a global scale. Instead of developing one standard, flexible ethical applications must be continued. We recommend local guidelines that incorporate important cultural and ethical priorities.

While the Declaration of Helsinki is more relevant to people in clinical trials receiving ESC products, in keeping with the tradition of protections for research subjects, consent of the donor is an ethical requirement for ESC donation in many jurisdictions including the US, Canada, and Europe. [69] The Declaration of Helsinki provides a reference point for regulatory standards and could potentially be used as a universal baseline for obtaining consent prior to gamete or embryo donation.

For instance, in Columbia University’s egg donor program for stem cell research, donors followed standard screening protocols and “underwent counseling sessions that included information as to the purpose of oocyte donation for research, what the oocytes would be used for, the risks and benefits of donation, and process of oocyte stimulation” to ensure transparency for consent. [70] The program helped advance stem cell research and provided clear and safe research methods with paid participants. Though paid participation or covering costs of incidental expenses may not be socially acceptable in every culture or context, [71] and creating embryos for ESC research is illegal in many jurisdictions, Columbia’s program was effective because of the clear and honest communications with donors, IRBs, and related stakeholders.  This example demonstrates that cultural acceptance of scientific research and of the idea that an egg or embryo does not have personhood is likely behind societal acceptance of donating eggs for ESC research. As noted, many countries do not permit the creation of embryos for research.

Proper communication and education regarding the process and purpose of stem cell research may bolster comprehension and garner more acceptance. “Given the sensitive subject material, a complete consent process can support voluntary participation through trust, understanding, and ethical norms from the cultures and morals participants value. This can be hard for researchers entering countries of different socioeconomic stability, with different languages and different societal values. [72]

An adequate moral foundation in medical ethics is derived from the cultural and religious basis that informs knowledge and actions. [73] Understanding local cultural and religious values and their impact on research could help researchers develop humility and promote inclusion.

IV.     Concerns

Some may argue that if researchers all adhere to one ethics standard, protection will be satisfied across all borders, and the global public will trust researchers. However, defining what needs to be protected and how to define such research standards is very specific to the people to which standards are applied. We suggest that applying one uniform guide cannot accurately protect each individual because we all possess our own perceptions and interpretations of social values. [74] Therefore, the issue of not adjusting to the moral pluralism between peoples in applying one standard of ethics can be resolved by building out ethics models that can be adapted to different cultures and religions.

Other concerns include medical tourism, which may promote health inequities. [75] Some countries may develop and approve products derived from ESC research before others, compromising research ethics or drug approval processes. There are also concerns about the sale of unauthorized stem cell treatments, for example, those without FDA approval in the United States. Countries with robust research infrastructures may be tempted to attract medical tourists, and some customers will have false hopes based on aggressive publicity of unproven treatments. [76]

For example, in China, stem cell clinics can market to foreign clients who are not protected under the regulatory regimes. Companies employ a marketing strategy of “ethically friendly” therapies. Specifically, in the case of Beike, China’s leading stem cell tourism company and sprouting network, ethical oversight of administrators or health bureaus at one site has “the unintended consequence of shifting questionable activities to another node in Beike's diffuse network.” [77] In contrast, Jordan is aware of stem cell research’s potential abuse and its own status as a “health-care hub.” Jordan’s expanded regulations include preserving the interests of individuals in clinical trials and banning private companies from ESC research to preserve transparency and the integrity of research practices. [78]

The social priorities of the community are also a concern. The ISSCR explicitly states that guidelines “should be periodically revised to accommodate scientific advances, new challenges, and evolving social priorities.” [79] The adaptable ethics model extends this consideration further by addressing whether research is warranted given the varying degrees of socioeconomic conditions, political stability, and healthcare accessibilities and limitations. An ethical approach would require discussion about resource allocation and appropriate distribution of funds. [80]

While some religions emphasize the sanctity of life from conception, which may lead to public opposition to ESC research, others encourage ESC research due to its potential for healing and alleviating human pain. Many countries have special regulations that balance local views on embryonic personhood, the benefits of research as individual or societal goods, and the protection of human research subjects. To foster understanding and constructive dialogue, global policy frameworks should prioritize the protection of universal human rights, transparency, and informed consent. In addition to these foundational global policies, we recommend tailoring local guidelines to reflect the diverse cultural and religious perspectives of the populations they govern. Ethics models should be adapted to local populations to effectively establish research protections, growth, and possibilities of stem cell research.

For example, in countries with strong beliefs in the moral sanctity of embryos or heavy religious restrictions, an adaptive model can allow for discussion instead of immediate rejection. In countries with limited individual rights and voice in science policy, an adaptive model ensures cultural, moral, and religious views are taken into consideration, thereby building social inclusion. While this ethical consideration by the government may not give a complete voice to every individual, it will help balance policies and maintain the diverse perspectives of those it affects. Embracing an adaptive ethics model of ESC research promotes open-minded dialogue and respect for the importance of human belief and tradition. By actively engaging with cultural and religious values, researchers can better handle disagreements and promote ethical research practices that benefit each society.

This brief exploration of the religious and cultural differences that impact ESC research reveals the nuances of relative ethics and highlights a need for local policymakers to apply a more intense adaptive model.

[1] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[2] Poliwoda, S., Noor, N., Downs, E., Schaaf, A., Cantwell, A., Ganti, L., Kaye, A. D., Mosel, L. I., Carroll, C. B., Viswanath, O., & Urits, I. (2022). Stem cells: a comprehensive review of origins and emerging clinical roles in medical practice.  Orthopedic reviews ,  14 (3), 37498. https://doi.org/10.52965/001c.37498

[3] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk ; Kimmelman, J., Hyun, I., Benvenisty, N.  et al.  Policy: Global standards for stem-cell research.  Nature   533 , 311–313 (2016). https://doi.org/10.1038/533311a

[4] International Society for Stem Cell Research. (2023). Laboratory-based human embryonic stem cell research, embryo research, and related research activities . International Society for Stem Cell Research. https://www.isscr.org/guidelines/blog-post-title-one-ed2td-6fcdk

[5] Concerning the moral philosophies of stem cell research, our paper does not posit a personal moral stance nor delve into the “when” of human life begins. To read further about the philosophical debate, consider the following sources:

Sandel M. J. (2004). Embryo ethics--the moral logic of stem-cell research.  The New England journal of medicine ,  351 (3), 207–209. https://doi.org/10.1056/NEJMp048145 ; George, R. P., & Lee, P. (2020, September 26). Acorns and Embryos . The New Atlantis. https://www.thenewatlantis.com/publications/acorns-and-embryos ; Sagan, A., & Singer, P. (2007). The moral status of stem cells. Metaphilosophy , 38 (2/3), 264–284. http://www.jstor.org/stable/24439776 ; McHugh P. R. (2004). Zygote and "clonote"--the ethical use of embryonic stem cells.  The New England journal of medicine ,  351 (3), 209–211. https://doi.org/10.1056/NEJMp048147 ; Kurjak, A., & Tripalo, A. (2004). The facts and doubts about beginning of the human life and personality.  Bosnian journal of basic medical sciences ,  4 (1), 5–14. https://doi.org/10.17305/bjbms.2004.3453

[6] Vazin, T., & Freed, W. J. (2010). Human embryonic stem cells: derivation, culture, and differentiation: a review.  Restorative neurology and neuroscience ,  28 (4), 589–603. https://doi.org/10.3233/RNN-2010-0543

[7] Socially, at its core, the Western approach to ethics is widely principle-based, autonomy being one of the key factors to ensure a fundamental respect for persons within research. For information regarding autonomy in research, see: Department of Health, Education, and Welfare, & National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research (1978). The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research.; For a more in-depth review of autonomy within the US, see: Beauchamp, T. L., & Childress, J. F. (1994). Principles of Biomedical Ethics . Oxford University Press.

[8] Sherley v. Sebelius , 644 F.3d 388 (D.C. Cir. 2011), citing 45 C.F.R. 46.204(b) and [42 U.S.C. § 289g(b)]. https://www.cadc.uscourts.gov/internet/opinions.nsf/6c690438a9b43dd685257a64004ebf99/$file/11-5241-1391178.pdf

[9] Stem Cell Research Enhancement Act of 2005, H. R. 810, 109 th Cong. (2001). https://www.govtrack.us/congress/bills/109/hr810/text ; Bush, G. W. (2006, July 19). Message to the House of Representatives . National Archives and Records Administration. https://georgewbush-whitehouse.archives.gov/news/releases/2006/07/20060719-5.html

[10] National Archives and Records Administration. (2009, March 9). Executive order 13505 -- removing barriers to responsible scientific research involving human stem cells . National Archives and Records Administration. https://obamawhitehouse.archives.gov/the-press-office/removing-barriers-responsible-scientific-research-involving-human-stem-cells

[11] Hurlbut, W. B. (2006). Science, Religion, and the Politics of Stem Cells.  Social Research ,  73 (3), 819–834. http://www.jstor.org/stable/40971854

[12] Akpa-Inyang, Francis & Chima, Sylvester. (2021). South African traditional values and beliefs regarding informed consent and limitations of the principle of respect for autonomy in African communities: a cross-cultural qualitative study. BMC Medical Ethics . 22. 10.1186/s12910-021-00678-4.

[13] Source for further reading: Tangwa G. B. (2007). Moral status of embryonic stem cells: perspective of an African villager. Bioethics , 21(8), 449–457. https://doi.org/10.1111/j.1467-8519.2007.00582.x , see also Mnisi, F. M. (2020). An African analysis based on ethics of Ubuntu - are human embryonic stem cell patents morally justifiable? African Insight , 49 (4).

[14] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics , 22 (2), 112–122. https://doi.org/10.1111/dewb.12324

[15] Jecker, N. S., & Atuire, C. (2021). Bioethics in Africa: A contextually enlightened analysis of three cases. Developing World Bioethics, 22(2), 112–122. https://doi.org/10.1111/dewb.12324

[16] Jackson, C.S., Pepper, M.S. Opportunities and barriers to establishing a cell therapy programme in South Africa.  Stem Cell Res Ther   4 , 54 (2013). https://doi.org/10.1186/scrt204 ; Pew Research Center. (2014, May 1). Public health a major priority in African nations . Pew Research Center’s Global Attitudes Project. https://www.pewresearch.org/global/2014/05/01/public-health-a-major-priority-in-african-nations/

[17] Department of Health Republic of South Africa. (2021). Health Research Priorities (revised) for South Africa 2021-2024 . National Health Research Strategy. https://www.health.gov.za/wp-content/uploads/2022/05/National-Health-Research-Priorities-2021-2024.pdf

[18] Oosthuizen, H. (2013). Legal and Ethical Issues in Stem Cell Research in South Africa. In: Beran, R. (eds) Legal and Forensic Medicine. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32338-6_80 , see also: Gaobotse G (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[19] United States Bureau of Citizenship and Immigration Services. (1998). Tunisia: Information on the status of Christian conversions in Tunisia . UNHCR Web Archive. https://webarchive.archive.unhcr.org/20230522142618/https://www.refworld.org/docid/3df0be9a2.html

[20] Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[21] Kooli, C. Review of assisted reproduction techniques, laws, and regulations in Muslim countries.  Middle East Fertil Soc J   24 , 8 (2020). https://doi.org/10.1186/s43043-019-0011-0 ; Gaobotse, G. (2018) Stem Cell Research in Africa: Legislation and Challenges. J Regen Med 7:1. doi: 10.4172/2325-9620.1000142

[22] Pang M. C. (1999). Protective truthfulness: the Chinese way of safeguarding patients in informed treatment decisions. Journal of medical ethics , 25(3), 247–253. https://doi.org/10.1136/jme.25.3.247

[23] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[24] Wang, Y., Xue, Y., & Guo, H. D. (2022). Intervention effects of traditional Chinese medicine on stem cell therapy of myocardial infarction.  Frontiers in pharmacology ,  13 , 1013740. https://doi.org/10.3389/fphar.2022.1013740

[25] Li, X.-T., & Zhao, J. (2012). Chapter 4: An Approach to the Nature of Qi in TCM- Qi and Bioenergy. In Recent Advances in Theories and Practice of Chinese Medicine (p. 79). InTech.

[26] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[27] Luo, D., Xu, Z., Wang, Z., & Ran, W. (2021). China's Stem Cell Research and Knowledge Levels of Medical Practitioners and Students.  Stem cells international ,  2021 , 6667743. https://doi.org/10.1155/2021/6667743

[28] Zhang, J. Y. (2017). Lost in translation? accountability and governance of Clinical Stem Cell Research in China. Regenerative Medicine , 12 (6), 647–656. https://doi.org/10.2217/rme-2017-0035

[29] Wang, L., Wang, F., & Zhang, W. (2021). Bioethics in China’s biosecurity law: Forms, effects, and unsettled issues. Journal of law and the biosciences , 8(1).  https://doi.org/10.1093/jlb/lsab019 https://academic.oup.com/jlb/article/8/1/lsab019/6299199

[30] Chen, H., Wei, T., Wang, H.  et al.  Association of China’s two-child policy with changes in number of births and birth defects rate, 2008–2017.  BMC Public Health   22 , 434 (2022). https://doi.org/10.1186/s12889-022-12839-0

[31] Azuma, K. Regulatory Landscape of Regenerative Medicine in Japan.  Curr Stem Cell Rep   1 , 118–128 (2015). https://doi.org/10.1007/s40778-015-0012-6

[32] Harris, R. (2005, May 19). Researchers Report Advance in Stem Cell Production . NPR. https://www.npr.org/2005/05/19/4658967/researchers-report-advance-in-stem-cell-production

[33] Park, S. (2012). South Korea steps up stem-cell work.  Nature . https://doi.org/10.1038/nature.2012.10565

[34] Resnik, D. B., Shamoo, A. E., & Krimsky, S. (2006). Fraudulent human embryonic stem cell research in South Korea: lessons learned.  Accountability in research ,  13 (1), 101–109. https://doi.org/10.1080/08989620600634193 .

[35] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

[36] Association for the Advancement of Blood and Biotherapies.  https://www.aabb.org/regulatory-and-advocacy/regulatory-affairs/regulatory-for-cellular-therapies/international-competent-authorities/saudi-arabia

[37] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia.  BMC medical ethics ,  21 (1), 35. https://doi.org/10.1186/s12910-020-00482-6

[38] Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: Interviews with researchers from Saudi Arabia. BMC medical ethics , 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6

Culturally, autonomy practices follow a relational autonomy approach based on a paternalistic deontological health care model. The adherence to strict international research policies and religious pillars within the regulatory environment is a great foundation for research ethics. However, there is a need to develop locally targeted ethics approaches for research (as called for in Alahmad, G., Aljohani, S., & Najjar, M. F. (2020). Ethical challenges regarding the use of stem cells: interviews with researchers from Saudi Arabia. BMC medical ethics, 21(1), 35. https://doi.org/10.1186/s12910-020-00482-6), this decision-making approach may help advise a research decision model. For more on the clinical cultural autonomy approaches, see: Alabdullah, Y. Y., Alzaid, E., Alsaad, S., Alamri, T., Alolayan, S. W., Bah, S., & Aljoudi, A. S. (2022). Autonomy and paternalism in Shared decision‐making in a Saudi Arabian tertiary hospital: A cross‐sectional study. Developing World Bioethics , 23 (3), 260–268. https://doi.org/10.1111/dewb.12355 ; Bukhari, A. A. (2017). Universal Principles of Bioethics and Patient Rights in Saudi Arabia (Doctoral dissertation, Duquesne University). https://dsc.duq.edu/etd/124; Ladha, S., Nakshawani, S. A., Alzaidy, A., & Tarab, B. (2023, October 26). Islam and Bioethics: What We All Need to Know . Columbia University School of Professional Studies. https://sps.columbia.edu/events/islam-and-bioethics-what-we-all-need-know

[39] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[40] Ababneh, M. A., Al-Azzam, S. I., Alzoubi, K., Rababa’h, A., & Al Demour, S. (2021). Understanding and attitudes of the Jordanian public about clinical research ethics.  Research Ethics ,  17 (2), 228-241.  https://doi.org/10.1177/1747016120966779

[41] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[42] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[43] The EU’s definition of autonomy relates to the capacity for creating ideas, moral insight, decisions, and actions without constraint, personal responsibility, and informed consent. However, the EU views autonomy as not completely able to protect individuals and depends on other principles, such as dignity, which “expresses the intrinsic worth and fundamental equality of all human beings.” Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[44] Council of Europe. Convention for the protection of Human Rights and Dignity of the Human Being with regard to the Application of Biology and Medicine: Convention on Human Rights and Biomedicine (ETS No. 164) https://www.coe.int/en/web/conventions/full-list?module=treaty-detail&treatynum=164 (forbidding the creation of embryos for research purposes only, and suggests embryos in vitro have protections.); Also see Drabiak-Syed B. K. (2013). New President, New Human Embryonic Stem Cell Research Policy: Comparative International Perspectives and Embryonic Stem Cell Research Laws in France.  Biotechnology Law Report ,  32 (6), 349–356. https://doi.org/10.1089/blr.2013.9865

[45] Rendtorff, J.D., Kemp, P. (2019). Four Ethical Principles in European Bioethics and Biolaw: Autonomy, Dignity, Integrity and Vulnerability. In: Valdés, E., Lecaros, J. (eds) Biolaw and Policy in the Twenty-First Century. International Library of Ethics, Law, and the New Medicine, vol 78. Springer, Cham. https://doi.org/10.1007/978-3-030-05903-3_3

[46] Tomuschat, C., Currie, D. P., Kommers, D. P., & Kerr, R. (Trans.). (1949, May 23). Basic law for the Federal Republic of Germany. https://www.btg-bestellservice.de/pdf/80201000.pdf

[47] Regulation of Stem Cell Research in Germany . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-germany

[48] Regulation of Stem Cell Research in Finland . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-finland

[49] Regulation of Stem Cell Research in Spain . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-spain

[50] Some sources to consider regarding ethics models or regulatory oversights of other cultures not covered:

Kara MA. Applicability of the principle of respect for autonomy: the perspective of Turkey. J Med Ethics. 2007 Nov;33(11):627-30. doi: 10.1136/jme.2006.017400. PMID: 17971462; PMCID: PMC2598110.

Ugarte, O. N., & Acioly, M. A. (2014). The principle of autonomy in Brazil: one needs to discuss it ...  Revista do Colegio Brasileiro de Cirurgioes ,  41 (5), 374–377. https://doi.org/10.1590/0100-69912014005013

Bharadwaj, A., & Glasner, P. E. (2012). Local cells, global science: The rise of embryonic stem cell research in India . Routledge.

For further research on specific European countries regarding ethical and regulatory framework, we recommend this database: Regulation of Stem Cell Research in Europe . Eurostemcell. (2017, April 26). https://www.eurostemcell.org/regulation-stem-cell-research-europe   

[51] Klitzman, R. (2006). Complications of culture in obtaining informed consent. The American Journal of Bioethics, 6(1), 20–21. https://doi.org/10.1080/15265160500394671 see also: Ekmekci, P. E., & Arda, B. (2017). Interculturalism and Informed Consent: Respecting Cultural Differences without Breaching Human Rights.  Cultura (Iasi, Romania) ,  14 (2), 159–172.; For why trust is important in research, see also: Gray, B., Hilder, J., Macdonald, L., Tester, R., Dowell, A., & Stubbe, M. (2017). Are research ethics guidelines culturally competent?  Research Ethics ,  13 (1), 23-41.  https://doi.org/10.1177/1747016116650235

[52] The Qur'an  (M. Khattab, Trans.). (1965). Al-Mu’minun, 23: 12-14. https://quran.com/23

[53] Lenfest, Y. (2017, December 8). Islam and the beginning of human life . Bill of Health. https://blog.petrieflom.law.harvard.edu/2017/12/08/islam-and-the-beginning-of-human-life/

[54] Aksoy, S. (2005). Making regulations and drawing up legislation in Islamic countries under conditions of uncertainty, with special reference to embryonic stem cell research. Journal of Medical Ethics , 31: 399-403.; see also: Mahmoud, Azza. "Islamic Bioethics: National Regulations and Guidelines of Human Stem Cell Research in the Muslim World." Master's thesis, Chapman University, 2022. https://doi.org/10.36837/ chapman.000386

[55] Rashid, R. (2022). When does Ensoulment occur in the Human Foetus. Journal of the British Islamic Medical Association , 12 (4). ISSN 2634 8071. https://www.jbima.com/wp-content/uploads/2023/01/2-Ethics-3_-Ensoulment_Rafaqat.pdf.

[56] Sivaraman, M. & Noor, S. (2017). Ethics of embryonic stem cell research according to Buddhist, Hindu, Catholic, and Islamic religions: perspective from Malaysia. Asian Biomedicine,8(1) 43-52.  https://doi.org/10.5372/1905-7415.0801.260

[57] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[58] Lecso, P. A. (1991). The Bodhisattva Ideal and Organ Transplantation.  Journal of Religion and Health ,  30 (1), 35–41. http://www.jstor.org/stable/27510629 ; Bodhisattva, S. (n.d.). The Key of Becoming a Bodhisattva . A Guide to the Bodhisattva Way of Life. http://www.buddhism.org/Sutras/2/BodhisattvaWay.htm

[59] There is no explicit religious reference to when life begins or how to conduct research that interacts with the concept of life. However, these are relevant verses pertaining to how the fetus is viewed. (( King James Bible . (1999). Oxford University Press. (original work published 1769))

Jerimiah 1: 5 “Before I formed thee in the belly I knew thee; and before thou camest forth out of the womb I sanctified thee…”

In prophet Jerimiah’s insight, God set him apart as a person known before childbirth, a theme carried within the Psalm of David.

Psalm 139: 13-14 “…Thou hast covered me in my mother's womb. I will praise thee; for I am fearfully and wonderfully made…”

These verses demonstrate David’s respect for God as an entity that would know of all man’s thoughts and doings even before birth.

[60] It should be noted that abortion is not supported as well.

[61] The Vatican. (1987, February 22). Instruction on Respect for Human Life in Its Origin and on the Dignity of Procreation Replies to Certain Questions of the Day . Congregation For the Doctrine of the Faith. https://www.vatican.va/roman_curia/congregations/cfaith/documents/rc_con_cfaith_doc_19870222_respect-for-human-life_en.html

[62] The Vatican. (2000, August 25). Declaration On the Production and the Scientific and Therapeutic Use of Human Embryonic Stem Cells . Pontifical Academy for Life. https://www.vatican.va/roman_curia/pontifical_academies/acdlife/documents/rc_pa_acdlife_doc_20000824_cellule-staminali_en.html ; Ohara, N. (2003). Ethical Consideration of Experimentation Using Living Human Embryos: The Catholic Church’s Position on Human Embryonic Stem Cell Research and Human Cloning. Department of Obstetrics and Gynecology . Retrieved from https://article.imrpress.com/journal/CEOG/30/2-3/pii/2003018/77-81.pdf.

[63] Smith, G. A. (2022, May 23). Like Americans overall, Catholics vary in their abortion views, with regular mass attenders most opposed . Pew Research Center. https://www.pewresearch.org/short-reads/2022/05/23/like-americans-overall-catholics-vary-in-their-abortion-views-with-regular-mass-attenders-most-opposed/

[64] Rosner, F., & Reichman, E. (2002). Embryonic stem cell research in Jewish law. Journal of halacha and contemporary society , (43), 49–68.; Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[65] Schenker J. G. (2008). The beginning of human life: status of embryo. Perspectives in Halakha (Jewish Religious Law).  Journal of assisted reproduction and genetics ,  25 (6), 271–276. https://doi.org/10.1007/s10815-008-9221-6

[66] Ruttenberg, D. (2020, May 5). The Torah of Abortion Justice (annotated source sheet) . Sefaria. https://www.sefaria.org/sheets/234926.7?lang=bi&with=all&lang2=en

[67] Jafari, M., Elahi, F., Ozyurt, S. & Wrigley, T. (2007). 4. Religious Perspectives on Embryonic Stem Cell Research. In K. Monroe, R. Miller & J. Tobis (Ed.),  Fundamentals of the Stem Cell Debate: The Scientific, Religious, Ethical, and Political Issues  (pp. 79-94). Berkeley: University of California Press.  https://escholarship.org/content/qt9rj0k7s3/qt9rj0k7s3_noSplash_f9aca2e02c3777c7fb76ea768ba458f0.pdf https://doi.org/10.1525/9780520940994-005

[68] Gert, B. (2007). Common morality: Deciding what to do . Oxford Univ. Press.

[69] World Medical Association (2013). World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA , 310(20), 2191–2194. https://doi.org/10.1001/jama.2013.281053 Declaration of Helsinki – WMA – The World Medical Association .; see also: National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979).  The Belmont report: Ethical principles and guidelines for the protection of human subjects of research . U.S. Department of Health and Human Services.  https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/read-the-belmont-report/index.html

[70] Zakarin Safier, L., Gumer, A., Kline, M., Egli, D., & Sauer, M. V. (2018). Compensating human subjects providing oocytes for stem cell research: 9-year experience and outcomes.  Journal of assisted reproduction and genetics ,  35 (7), 1219–1225. https://doi.org/10.1007/s10815-018-1171-z https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6063839/ see also: Riordan, N. H., & Paz Rodríguez, J. (2021). Addressing concerns regarding associated costs, transparency, and integrity of research in recent stem cell trial. Stem Cells Translational Medicine , 10 (12), 1715–1716. https://doi.org/10.1002/sctm.21-0234

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[75] Pirzada, N. (2022). The Expansion of Turkey’s Medical Tourism Industry.  Voices in Bioethics ,  8 . https://doi.org/10.52214/vib.v8i.9894

[76] Stem Cell Tourism: False Hope for Real Money . Harvard Stem Cell Institute (HSCI). (2023). https://hsci.harvard.edu/stem-cell-tourism , See also: Bissassar, M. (2017). Transnational Stem Cell Tourism: An ethical analysis.  Voices in Bioethics ,  3 . https://doi.org/10.7916/vib.v3i.6027

[77] Song, P. (2011) The proliferation of stem cell therapies in post-Mao China: problematizing ethical regulation,  New Genetics and Society , 30:2, 141-153, DOI:  10.1080/14636778.2011.574375

[78] Dajani, R. (2014). Jordan’s stem-cell law can guide the Middle East.  Nature  510, 189. https://doi.org/10.1038/510189a

[79] International Society for Stem Cell Research. (2024). Standards in stem cell research . International Society for Stem Cell Research. https://www.isscr.org/guidelines/5-standards-in-stem-cell-research

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Mifrah Hayath

SM Candidate Harvard Medical School, MS Biotechnology Johns Hopkins University

Olivia Bowers

MS Bioethics Columbia University (Disclosure: affiliated with Voices in Bioethics)

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Mortality in Patients Hospitalized for COVID-19 vs Influenza in Fall-Winter 2023-2024

• 1 Clinical Epidemiology Center, VA St Louis Health Care System, St Louis, Missouri

In the first year of the COVID-19 pandemic, risk of death in people hospitalized for COVID-19 was substantially higher than in people hospitalized for seasonal influenza. 1 , 2 The risk of death due to COVID-19 has since declined. In fall-winter 2022-2023, people hospitalized for COVID-19 had a 60% higher risk of death compared with those hospitalized for seasonal influenza. 3 New variants of SARS-CoV-2 have continued to appear, including the emergence of JN.1, the predominant variant in the US since December 24, 2023. 4 This study evaluated the risk of death in a cohort of people hospitalized for COVID-19 or seasonal influenza in fall-winter 2023-2024.

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Xie Y , Choi T , Al-Aly Z. Mortality in Patients Hospitalized for COVID-19 vs Influenza in Fall-Winter 2023-2024. JAMA. Published online May 15, 2024. doi:10.1001/jama.2024.7395

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U-shaped link detected between adolescent BMI and mental health

by Elana Gotkine

There is a U-shaped association between adolescent body mass index (BMI) and mental health, according to a study published online May 15 in JAMA Psychiatry .

Shanquan Chen, Ph.D., from the London School of Hygiene & Tropical Medicine, and colleagues estimated the association between BMI and mental health and examined changes from 2002 to 2018 in a repeated multicountry cross-sectional study. Data were obtained from the Health Behavior in School-aged Children survey in Europe and North America, including a study population of 1,036,869 adolescents (527,585 girls) aged 11 to 15 years.

The researchers identified a U-shaped association between BMI and mental health . Compared with those with healthy weight, adolescents with low body mass, overweight, or obesity had increased psychosomatic symptoms (unstandardized β, 0.14, 0.27, and 0.62, respectively), while fewer symptoms were seen for adolescents with underweight (β, −0.18).

Across different years, sex, and grade, the association was observed. Psychosomatic concerns increased significantly in 2006, 2010, 2014, and 2018 compared with 2002 (unstandardized β, 0.19, 0.14, 0.48, and 0.82, respectively).

Significantly higher psychosomatic concerns were seen for girls than boys (unstandardized β, 2.27). Psychosomatic concerns increased significantly in middle and high school versus primary school (unstandardized β, 1.15 and 2.12, respectively).

"These insights can inform public health and school programs, emphasizing correcting body image misconceptions, encouraging healthy weight, and creating supportive peer environments," the authors write.

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