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What Is the Life-Cycle Hypothesis (LCH)?

Understanding the life-cycle hypothesis, life-cycle hypothesis vs. keynesian theory, special considerations for the life-cycle hypothesis.

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What Is the Life-Cycle Hypothesis in Economics?

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The life-cycle hypothesis (LCH) is an economic theory that describes the spending and saving habits of people over the course of a lifetime. The theory states that individuals seek to smooth consumption throughout their lifetime by borrowing when their income is low and saving when their income is high.

The concept was developed by economists Franco Modigliani and his student Richard Brumberg in the early 1950s.

Key Takeaways

The Life-Cycle Hypothesis (LCH) is an economic theory developed in the early 1950s that posits that people plan their spending throughout their lifetimes, factoring in their future income.
A graph of the LCH shows a hump-shaped pattern of wealth accumulation that is low during youth and old age and high in middle age.
One implication is that younger people have a greater capacity to take investment risks than older individuals who need to draw down accumulated savings.

The LCH assumes that individuals plan their spending over their lifetimes, taking into account their future income. Accordingly, they take on debt when they are young, assuming future income will enable them to pay it off. They then save during middle age in order to maintain their level of consumption when they retire.

A graph of an individual's spending over time thus shows a hump-shaped pattern in which wealth accumulation is low during youth and old age and high during middle age.

The LCH replaced an earlier hypothesis developed by economist John Maynard Keynes in 1937. Keynes believed that savings were just another good and that the percentage that individuals allocated to their savings would grow as their incomes rose. This presented a potential problem in that it implied that as a nation’s incomes grew, a savings glut would result, and aggregate demand and economic output would stagnate.

Another problem with Keynes' theory is that he did not address people's consumption patterns over time. For example, an individual in middle age who is the head of a family will consume more than a retiree. Although subsequent research has generally supported the LCH, it also has its problems.

The LCH has largely supplanted Keynesian economic thinking about spending and savings patterns.

The LCH makes several assumptions. For example, the theory assumes that people deplete their wealth during old age. Often, however, the wealth is passed on to children, or older people may be unwilling to spend their wealth. The theory also assumes that people plan ahead when it comes to building wealth, but many procrastinate or lack the discipline to save.

Another assumption is that people earn the most when they are of working age. However, some people choose to work less when they are relatively young and continue working part-time when they reach retirement age.

As a result, one implication is that younger people are more able to take on investment risks than older individuals, which remains a widely accepted tenet of personal finance.

Other assumptions of note are that those with high incomes are more able to save and have greater financial savvy than those on low incomes. People with low incomes may have credit card debt and less disposable income. Lastly, safety nets or means-tested benefits for aging adults may discourage people from saving as they anticipate receiving a higher social security payment when they retire.

Franco Modigliani. "Life cycle, individual thrift, and the wealth of nations." American Economic Review, 1986, Vol. 76, Issue 3, Pages 297-313.

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Financial Planning

What Is the Life-Cycle Hypothesis?

The Life-Cycle Hypothesis Explained

Definition and Examples of the Life-Cycle Hypothesis

How the life-cycle hypothesis works, criticisms of the life-cycle hypothesis, life-cycle hypothesis theory vs. permanent income hypothesis theory.

Ridofranz / Getty Images

The life-cycle hypothesis (LCH) is an economic theory that suggests that individuals have a tendency to maintain the same level of spending over time. They achieve this goal by borrowing in when they're younger and their income is low, saving during their middle years when income is high, and living off their assets in their older years when income is low again.

Here’s a closer look at how the LCH works and why it’s important.

The LCH states that households save and spend their wealth in an effort to keep their consumption level steady over time. Even though wealth and income may vary over your lifetime, the theory states, your spending habits stay relatively the same.

Acronym: LCH
Alternate name: Life-cycle model

Saving for retirement is a good example of the LCH in action. You know your income may disappear when you’re older, so you save money during your working years to afford the same lifestyle later on.

The LCH predicts that, in general, you maintain the same level of consumption throughout your lifetime by:

Borrowing money when you’re young (either by borrowing money or liquidating assets you already own)
Saving more money when you’re middle aged and at the peak of your career
Living off the wealth you’ve accumulated when you’re old and retired

Franco Modigliani published the life-cycle hypothesis theory in 1954 with Richard Brumberg and later won a Nobel Prize for his economic analyses.

The LCH predicts that your savings habits follow a hump-shaped pattern as in the diagram below where your savings rate is low during your younger and older years and peaks during your middle years:

For example, suppose you make $20,000 this year, but you expect your income will increase to $80,000 next year because you’ve got a job lined up after you graduate from college.

According to the LCH, you may spend money today with your future income in mind, which may lead you to borrow money. As you reach the peak of your career, you’ll pay off any debt you accumulated and ramp up your savings. Then, you’ll draw down that savings in retirement so you can continue your same level of spending.

The LCH has withstood the test of time but it’s not without its flaws:

LCH Doesn’t Account for Financial Windfalls

Traditional LCH models don’t apply to individuals who run into financial windfalls or have sporadic income throughout their lives.

Take NFL players, for example. The LCH would imply that NFL players save considerable amounts of money while they’re at the peak of their careers so they can sustain the same level of consumption when they retire.

But the reality is that some NFL athletes go from enormously wealthy to near poverty shortly after the end of their careers. A 2015 National Bureau of Economic Research study that focused on LCH and the NFL predicted that an NFL player has a 15% to 40% chance of going bankrupt 25 years after they retire.

The study said the high bankruptcy rates may be due to the fact that players:

Think their career will last longer than it typically does
Make poor financial decisions with the money they receive
Have social pressures to spend more than they should

LCH Assumes Your Consumption Level Will Stay the Same

The LCH predicts that you’ll maintain roughly your same level of spending by borrowing money when income is low and saving when income is high. But this isn’t always realistic.

For example, younger workers may not have access to the credit needed to fund their ideal level of spending now. So, naturally, their consumption habits would change as their income increased and those options became available to them.

Likewise, a family with parents in their 30s with three young kids, student loan debt, and a mortgage may consume more now than they will in their 70s when they’re retired, possibly debt-free, and no longer have dependents to care for.

Both the LCH theory and the permanent income hypothesis (PIH) theory seek to understand how individuals spend and save money. The main difference is that the LCH is based on a finite timeline where a person saves only enough to sustain their spending habits during their lifetime. The PIH, on the other hand, is based on an infinite timeline where a person saves enough for both themselves and their heirs.

Key Takeaways

The life-cycle hypothesis (LCH) is an economic theory that describes how an individual maintains roughly the same level of consumption over time by saving when their income is high and borrowing when income is low.
The LCH predicts that wealth accumulation follows a hump-shaped curve where you have a low savings rate when you’re young, a high rate when you’re middle-aged, and a low rate again when you’re old.
Some experts criticize the LCH because consumption doesn’t always stay consistent over time. For example, a middle-aged worker with three kids and a mortgage probably consumes more than they will when they’re retired with no debt or dependents.

Massachusetts Institute of Technology. " The Collected Papers of Franco Modigliani, Volume 6 ."

Federal Reserve Board. " A Primer on the Economics and Time Series Econometrics of Wealth Effects ," Page 8.

Carnegie Mellon University. " The Life Cycle Theory of Consumption ," Page 340.

National Bureau of Economic Research. " Bankruptcy Rates Among NFL Players With Short-Lived Income Spikes ," Page 8.

Centre for Economic Studies and Finance. " Working Paper No. 140: The Life-Cycle Hypothesis, Fiscal Policy,and Social Security ," Page 7.

Life-Cycle Hypothesis

Definition: The Life-cycle hypothesis was developed by Franco Modigliani in 1957. The theory states that individuals seek to smooth consumption over the course of a lifetime – borrowing in times of low-income and saving during periods of high income.

As a student, it is rational to borrow to fund education.
Then during your working life, you pay off student loans and begin saving for your retirement.
This saving during working life enables you to maintain similar levels of income during your retirement.

It suggests wealth will build up in working age, but then fall in retirement.

Wealth in the Life-Cycle Hypothesis

The theory states consumption will be a function of wealth, expected lifetime earnings and the number of years until retirement.

Consumption will depend on

C= consumption
R = Years until retirement. Remaining years of work
T= Remaining years of life

It suggests for the whole economy consumption will be a function of both wealth and income.

Prior to life-cycle theories, it was assumed that consumption was a function of income. For example, the Keynesian consumption function saw a more direct link between income and spending.

However, this failed to account for how consumption may vary depending on the position in life-cycle.

Motivation for life-cycle consumption patterns

Diminishing marginal utility of income. If income is high during working life, there is a diminishing marginal utility of spending extra money at that particular time.
Harder to work and earn money, in old age. Life Cycle enables people to work hard and spend less.

Does the Life-cycle theory happen in reality?

Mervyn King suggests life-cycle consumption patterns can be found in approx 75% of the population. However, 20-25% don’t plan in the long term. (NBER paper on economics of saving )

Reasons for why people don’t smooth consumption over a lifetime.

Present focus bias – People can find it hard to value income a long time in the future
Inertia and status quo bias . Planning for retirement requires effort, forward thinking and knowledge of financial instruments such as pensions. People may prefer to procrastinate – even though they know they should save more – and so saving gets put off.

Criticisms of Life Cycle Theory

It assumes people run down wealth in old age, but often this doesn’t happen as people would like to pass on inherited wealth to children. Also, there can be an attachment to wealth and an unwillingness to run it down. See: Prospect theory and the endowment effect.
It assumes people are rational and forward planning. Behavioural economics suggests many people have motivations to avoid planning.
People may lack the self-control to reduce spending now and save more for future.
Life-cycle is easier for people on high incomes. They are more likely to have financial knowledge, also they have the ‘luxury’ of being able to save. People on low-incomes, with high credit card debts, may feel there is no disposable income to save.
Leisure. Rather than smoothing out consumption, individuals may prefer to smooth out leisure – working fewer hours during working age, and continuing to work part-time in retirement.
Government means-tested benefits for old-age people may provide an incentive not to save because lower savings will lead to more social security payments.

Other theories

Permanent income hypothesis of Milton Friedman – This states people only spend more when they see it as an increase in permanent income.
Ricardian Equivalence – consumers may see tax cuts as only a temporary rise in income so will not alter spending.
Autonomous consumption – In Keynesian consumption function, the level of consumption that is independent of income.
Marginal propensity to consume – how much of extra income is spent.

15 thoughts on “Life-Cycle Hypothesis”

Thanks for the reminder of the theory… Am a moi university Economic student in Nairobi Kenya.

Thanks for the most summarised note ever. it will help me with presentation. Gulu university. JALON

prof premraj pushpakaran writes — 2018 marks the 100th birth year of Franco Modigliani!!!

Thanks for the analysis on the hypothesis

Nice piece of work for economist. Been applicable in my presentation at Kyambogo university Uganda

This piece of paper is very important as far as consumption is concerned…

This piece is the best I have seen so far, this is a great work

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Good job. Thanks for this masterpiece. It reconnects me with the consumption theories.

A good summarised piece of work on life cycle hypothesis, it will help me in my group presentation. Kenyatta University economics student.

Comments are closed.

Quickonomics

Life-Cycle Hypothesis (Lch)

Definition of life-cycle hypothesis (lch).

The Life-Cycle Hypothesis (LCH) is an economic theory that suggests individuals base their consumption and savings decisions on their expected lifetime income rather than their current income. According to the LCH, individuals strive to maintain a stable standard of living throughout their lifetime by adjusting their savings and consumption patterns. This hypothesis takes into account the different stages of life, such as education, working years, and retirement, and assumes individuals plan and save accordingly for these stages.

To illustrate the Life-Cycle Hypothesis, let’s consider two individuals: Alan, a young professional just starting his career, and Sarah, a retiree. Alan expects his income to increase significantly over time as he gains experience and advances in his profession. To maintain a stable standard of living, he saves a portion of his income during his early working years, which allows him to enjoy a more comfortable retirement.

On the other hand, Sarah has already retired and relies on savings and pensions for her income. Since she is no longer earning a salary, her consumption decreases to meet her reduced income. She draws from her accumulated savings to support her lifestyle in retirement.

Throughout their lives, both Alan and Sarah make consumption and savings decisions based on their expected lifetime income, adjusting their behavior accordingly.

Why the Life-Cycle Hypothesis Matters

The Life-Cycle Hypothesis provides a framework for understanding individuals’ consumption and savings patterns over their lifetimes. It emphasizes the importance of long-term financial planning and highlights the trade-off between current consumption and saving for the future.

Understanding the Life-Cycle Hypothesis is useful for policymakers, financial planners, and individuals themselves. Policymakers can design policies and programs that support retirement savings and encourage long-term financial stability. Financial planners can help individuals develop strategies to achieve their desired lifestyles in retirement. Lastly, individuals can benefit from understanding their own consumption patterns and making informed decisions about savings and retirement planning.

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Life Cycle Hypothesis

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First Online: 01 January 2018
pp 7846–7850
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Malcolm R. Fisher 1

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The life cycle hypothesis presents a well-defined linkage between the consumption plans of an individual and his income and expectations as to income as he passes from childhood, through the work participating years, into retirement and eventual decease. Early attempts to establish such a linkage were made by Irving Fisher (1930) and again by Harrod (1948) with his notion of hump saving, but a sharply defined hypothesis which carried the argument forward both theoretically and empirically with its range of well-specified tests for cross-section and time series evidence was first advanced in 1954 by Modigliani and Brumberg. Both their papers and advance copies of the permanent income theory of Milton Friedman (1957) were circulating in 1953 and led to M.R. Fisher carrying out tests of the theories even preceding publication of Friedman’s work (1956). Both the Modigliani–Brumberg and the Friedman theories are referred to as life cycle theories and they certainly have many similar implications, but the one that is more closely related to the life cycle with emphasis on age – Modigliani and Brumberg – is the one to which we confine ourselves here.

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Biology library

Course: biology library > unit 1, the scientific method.

Controlled experiments
The scientific method and experimental design

Introduction

Make an observation.
Ask a question.
Form a hypothesis , or testable explanation.
Make a prediction based on the hypothesis.
Test the prediction.
Iterate: use the results to make new hypotheses or predictions.

Scientific method example: Failure to toast

1. make an observation..

Observation: the toaster won't toast.

2. Ask a question.

Question: Why won't my toaster toast?

3. Propose a hypothesis.

Hypothesis: Maybe the outlet is broken.

4. Make predictions.

Prediction: If I plug the toaster into a different outlet, then it will toast the bread.

5. Test the predictions.

Test of prediction: Plug the toaster into a different outlet and try again.
If the toaster does toast, then the hypothesis is supported—likely correct.
If the toaster doesn't toast, then the hypothesis is not supported—likely wrong.

Logical possibility

Practical possibility, building a body of evidence, 6. iterate..

Iteration time!
If the hypothesis was supported, we might do additional tests to confirm it, or revise it to be more specific. For instance, we might investigate why the outlet is broken.
If the hypothesis was not supported, we would come up with a new hypothesis. For instance, the next hypothesis might be that there's a broken wire in the toaster.

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The Life-Cycle Theory of Consumption (With Diagram)

Let us make an in-depth study of the Life-Cycle Theory of Consumption:- 1. Explanation to the Theory of Consumption 2. The Reconciliation 3. Critics of the Life Cycle Hypothesis.

Explanation to the Theory of Consumption:

The life-cycle theory of the consumption function was developed by Franco Modigliani, Alberto Ando and Brumberg.

According to Modigliani, The point of departure of the life cycle model is the hypothesis that consumption and saving decisions of households at each point of time reflect more or less a conscious attempt at achieving the preferred distribution of consumption over the life cycle, subject to the constraint imposed by the resources accruing to the household over its life time.

An individual’s or household’s level of consumption depends not just on current income but also, and more importantly, on long-term expected earnings.

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Individuals are assumed to plan a pattern of consumer expenditure based on expected earnings over their lifetime.

To see the implications of this theory for the form of the consumption function, we first look at a simplified example.

Consider an individual of a given age who is in the labour force, has a life expectancy of T years, and plans to remain in the labour force for N years. Our representative consumer might, for example, be 30 with a life expectancy of 50 (additional) years, plan to retire after 40 years, and, therefore, have expected years in retirement equal to (T – N), or 10. We make the following assumptions about the individual’s plans.

The individual is assumed to desire a constant consumption flow throughout life. Further, we assume that this person intends to consume the total amount of lifetime earnings plus current assets and plans no bequests. Finally, we assume that the interest paid on assets is zero; current saving results in dollar-for-dollar future consumption. These assumptions are purely to keep the example simple and are relaxed later.

These assumptions imply that consumption in a given period will be a constant proportion, 1/T, of expected lifetime resources. The individual plans to consume lifetime earnings in T equal installments. The consumption function implied by this simple version of the life cycle hypothesis is:

Consumption is shown as rising gradually over the life cycle. Income rises sharply over the early working years, peaks, and then declines, especially with retirement. This pattern of consumption and income results in periods of dissaving in the early working years and the late stage of the life cycle, with positive saving over the high-income middle period of the life cycle.

To use Equation (2) to study actual consumer behaviour, we must make some assumption about the way in which individuals form expectations concerning lifetime labour income. In a study for the United States, Ando and Modigliani make the assumption that expected average future labour income is just a multiple of current labour income:

If the ratios of wealth and labour income to disposable personal income are relatively constant over time, the life cycle consumption function [Equation (5)] is also consistent with the evidence from long-run time series data that the long-run consumption-income relationship (LCF in Figure 6.17) is proportional, with the APC(C/Y d ) relatively stable in the neighborhood of 0.9. To see this relationship, first note that the ratio of labour income to disposable personal income has been approximately 0.88; that is, Y t 1 = 0.88 Y D . The ratio of wealth to disposable income is approximately 4.75; A, = 4.75 Y D . Substitution of these expressions for A t and Y t 1 in the estimated aggregate consumption function (5) yields which is approximately the average value of the APC over the post-World War II period.

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

Operationalization

Hypothesis Types

Hypotheses examples.

Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

Forming a question
Performing background research
Creating a hypothesis
Designing an experiment
Collecting data
Analyzing the results
Drawing conclusions
Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

Is your hypothesis based on your research on a topic?
Can your hypothesis be tested?
Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

Collect as many observations about a topic or problem as you can.
Evaluate these observations and look for possible causes of the problem.
Create a list of possible explanations that you might want to explore.
After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

Does your hypothesis focus on something that you can actually test?
Does your hypothesis include both an independent and dependent variable?
Can you manipulate the variables?
Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

"Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
"Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."
"Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
"Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

"People with high-sugar diets and sedentary activity levels are more likely to develop depression."
"Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

"There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
"There is no difference in scores on a memory recall task between children and adults."
"There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

"People who take St. John's wort supplements will have less anxiety than those who do not."
"Adults will perform better on a memory task than children."
"Children who play first-person shooter games will show higher levels of aggression than children who do not."

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Lifecycle Hypothesis - Explained

What is the Lifecycle Hypothesis?

Written by Jason Gordon

Updated at April 23rd, 2024

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What is the Life-Cycle Hypothesis?

The Life-Cycle Hypothesis (LCH) refers to an economic theory which involves people's spending, as well as, saving habits over the course of a lifetime. Franco Modigliani and Richard Brumberg, his student, developed the concept.

LCH presumes that people plan their spending over their lifetime, considering their future income. Also, they pile up debt at a young age, supposing future income would make it possible for them to pay off the debt.

They then save during middle age so as to maintain their consumption level once they retire. This brings about a "hump-shaped" pattern whereby there is a low accumulation of wealth during youth, as well as, old age, and high during middle age.

How did the Life-Cycle Hypothesis Develop?

The Life Cycle Hypothesis replaced a previous hypothesis which economist John Maynard Keynes developed. His belief was that savings were just another good and that the percentage which people earmarked for savings would rise as their incomes increased.

This brought about a potential challenge in that it meant that as a nation's incomes rose, a savings glut would result, and aggregate demand and also economic output would remain stagnant. There has been a support to the Life Cycle Hypothesis from subsequent research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

It is made using what we already know and have seen, and it’s the basis for scientific research.
A clear guess tells us what we think will happen in an experiment or study.
It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

Studying more can help you do better on tests.
Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

Drinking more sweet drinks is linked to a higher body weight score.
Too much stress makes people less productive at work.

Non-directional Hypothesis Example

Drinking caffeine can affect how well you sleep.
People often like different kinds of music based on their gender.
The average test scores of Group A and Group B are not much different.
There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

Patients on Diet A have much different cholesterol levels than those following Diet B.
Exposure to a certain type of light can change how plants grow compared to normal sunlight.
The average smarts score of kids in a certain school area is 100.
The usual time it takes to finish a job using Method A is the same as with Method B.
Having more kids go to early learning classes helps them do better in school when they get older.
Using specific ways of talking affects how much customers get involved in marketing activities.
Regular exercise helps to lower the chances of heart disease.
Going to school more can help people make more money.
Playing violent video games makes teens more likely to act aggressively.
Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Life-cycle hypothesis: Ando and Modigliani

Post author: Viren Rehal
Post published: August 18, 2022
Post category: Consumption function / Macroeconomics
Post comments: 0 Comments

The life-cycle hypothesis was postulated by Ando and Modigliani in an attempt to explain the behaviour of consumption function in the long and short run. According to this theory, current consumption decisions are based on future expected income over an individual’s lifetime. The major advantage of this theory is that Ando and Modigliani incorporated the role of assets in determining consumption decisions. Other theories don’t incorporate assets into consumption ( absolute income hypothesis ) or the role of assets is only implicit ( permanent income hypothesis ).

MPC < APC in cross-sections

Over the lifetime of any individual, income is low in the early years of life. As people reach middle age, their income rises as they start earning more. In later years of life, however, income starts falling again to reach low levels owing to retirement or the inability to work as people grow older. Therefore, income starts from a low level, keeps increasing in middle age, and declines back to a low level in old age.

In the case of consumption, individuals are expected to maintain a constant or slightly increasing level of consumption as they keep growing older. Since consumption is dependent on future expected income, the present value of consumption is constrained by the present value of income.

APC and MPS at different age

During the early years, income is low as compared to consumption and individuals are borrowers during this period, i.e. APC is high. However, they expect their future income to rise during their middle age. Because income is higher than consumption level, they pay off their borrowings during this period and save for retirement in old age. With high income, APC is low because consumption is much lower as compared to income in this period. In the later years of the life-cycle, income is again below consumption level. Hence, this is a period of dissavings or negative savings by individuals, leading to a low APC.

Therefore, across different sections based on income levels, APC will be high for low-income groups. These low-income groups primarily include people in their early years of life and people in old age, who have low incomes. On average, APC will be high as the low-income group consists of higher than average young and older people. On the contrary, high-income groups will have a higher than average proportion of middle-aged people. This is because they have higher incomes. APC, in this case, will be lower because consumption is low compared to income level during middle age.

Hence, APC will decline as income increases and MPC<APC in cross sections because of different income levels in a life-cycle.

consumption function of Life-Cycle Hypothesis

According to the life-cycle hypothesis, the consumption of any individual is based on expected income in the future. If the expected income rises, the consumption of that consumer will also increase. Ando and Modigliani use the Present Value criterion to represent expected income in the future. Therefore, the consumption of an individual can be expressed as follows:

If the present value of expected income increases, then consumption of that consumer will increase by a given proportion ( theta ) of that increase in income.

If the income distribution and age of the population are stable along with the constant taste and preferences of consumers, then the aggregate consumption function can simply be obtained by summation of all individual consumption functions.

Present Value of expected future income

Future income is unknown and cannot be measured directly. Therefore, the present value of expected income has to be estimated indirectly. Ando and Modigliani divided income into two types- income labour and income from assets, and estimated the present value variable as follows:

In an efficient capital market, we can assume that the present value of income from assets is equal to the value of assets in the current time period. Therefore, we can substitute the second element of the equation as:

In the case of the first element of income from labour, we know the labour income in the current period and can therefore separate current income from the present value of expected income.

Therefore, the above equation simply divides the expected future labour income by the average life remaining of the population in years to estimate an average expected labour income. From this equation, we have:

In this equation, Y e is the only unknown which is an estimate of average expected labour income.

Ando and Modigliani found that assuming this average expected labour income as a function of current labour income worked well and they stated this as:

This implies that if current income increases, people expect average future income to rise as well. The amount of this rise is determined by the proportion coefficient (beta), such that an increase in average expected income is a proportion of current labour income.

Hence, we can modify the PV 0 equation and the consumption function as follows:

Life-cycle hypothesis consumption function

This equation represents the consumption function associated with the life-cycle hypothesis. Every variable in this equation can be measured which allows empirical estimation of the consumption function.

cyclical fluctuation and long-run consumption: empirical results

The consumption function put forward by Ando and Modigliani can be used to carry out empirical analysis to understand the behaviour of consumption in the long run and the effect of business cycles on consumption.

Ando and Modigliani applied this consumption function to annual data of the United States. They obtained the following results:

The marginal propensity to consume from labour income is 0.7. This means that a $1 increase in labour income will lead to a $0.7 increase in consumption. Similarly, the marginal propensity to consume from assets is 0.06 implying that a $1 increase in assets (net worth of assets) will lead to a $0.06 increase in consumption.

Let us apply these results to the life cycle consumption function:

Positive coefficient beta suggests that with an increase in current labour income, the average expected labour income increases. As current labour income increases by $1, the average expected labour income increases by $0.25.

MPC and APC

This estimated consumption function has a slope of 0.7 or MPC, corresponding to the coefficient of Y t L . The intercept of the consumption function is equal to 0.06a t because assets remain the same in the short run. As seen in the figure, APC is falling with a rise in labour income and MPC<APC in the short run consumption function.

In the long run, however, assets will not be constant. With an increase in assets, the short-run consumption functions will keep shifting upwards as the economy grows. Therefore, long-run consumption will be along the trend where APC is constant and APC=MPC along this long-run consumption.

The APC will be constant if the share of labour income in total income ( Y t L / Y t ) and the ratio of total assets to total income (at / Yt) remain constant in the long run as the economy grows along the trend. Ando and Modigliani observed that both these ratios remained fairly constant in the long run in the annual U.S. data. The labour share in income was around 75 per cent and the ratio of assets to income was around 3. Therefore, the estimated APC is:

criticism of Life-cycle Hypothesis

The life cycle hypothesis assumes that everyone is a rational consumer aiming to maximize utility based on expected income. However, this may not be necessarily true because not everyone’s consumption decisions are based on future income. Some individuals may not even consider future outcomes while spending in the current period. Or they may be impulsive and lacking in self-control. And, they do not focus on having a smooth consumption over a long period.
The life-cycle hypothesis assumes that every increase in current income leads to an increase in average future expected income. This may not be true in every case because every income change will not necessarily affect expected future income. For instance, a temporary tax that changes current income, but, consumers are aware that it is temporary and they will not change their expected future income.

Nevertheless, life-cycle theory explains consumer behaviour across cross sections, short run as well as long run. Additionally, it takes into account the role of wealth or assets in determining consumption and can be empirically tested.

Permanent income hypothesis, stagflation: meaning, causes and consequences, tobin’s q theory of investment, leave a reply cancel reply.

Study shows vicious cycle of protein clumping in Alzheimer's disease and normal aging

I t has long been known that a hallmark of Alzheimer's disease, and most other neurodegenerative diseases, is the clumping together of insoluble protein aggregates in the brain. During normal disease-free aging, there is also an accumulation of insoluble proteins.

To date, approaches to treatments for Alzheimer's disease have not addressed the contribution of protein insolubility as a general phenomenon, instead focusing on one or two insoluble proteins.

Buck researchers have recently completed a systematic study in worms that paints an intricate picture of the connections between insoluble proteins in neurodegenerative diseases and aging. Furthermore, the work demonstrated an intervention that could reverse the toxic effects of the aggregates by boosting mitochondrial health.

"Based on our discoveries, targeting insoluble proteins could provide a strategy for the prevention and treatment of a variety of age-related diseases," said Edward Anderton, Ph.D., a postdoctoral fellow in Gordon Lithgow's lab and co-first author of a study that appears in the May 16 issue of the journal GeroScience .

"Our study shows how maintaining healthy mitochondria can combat protein clumping linked to both aging and Alzheimer's," said Manish Chamoli, Ph.D., a research scientist in Gordon Lithgow's and Julie Andersen's lab, and co-first author of the study. "By boosting mitochondrial health, we can potentially slow down or reverse these harmful effects, offering new ways to treat both aging and age-related diseases."

Results support the geroscience hypothesis

The strong link between insoluble proteins promoting normal aging and diseases also builds a case for the bigger picture of how aging and age-related diseases occur. "We would argue that this work really supports the geroscience hypothesis that there is a common pathway to Alzheimer's disease and aging itself," said Buck Professor Gordon Lithgow. Ph.D., Vice President of Academic Affairs and the senior author of the study.

"Aging is driving the disease, but the factors that put you on the track toward the disease actually occur very early."

The fact that the team found a core insoluble proteome enriched with numerous proteins that had not been considered before creates new targets for exploration, said Lithgow. "In some ways it raises the flag about whether we should be thinking about what Alzheimer's looks like in very young people," he said.

Beyond amyloid and tau

The focus of most research on Alzheimer's disease to date has been targeting accumulations of two proteins: amyloid beta and tau. But there are actually thousands of other proteins in these insoluble aggregations, said Anderton, and their role in Alzheimer's disease was unknown.

Additionally, he added, their lab and others' have observed that during the normal disease-free aging process there is also an accumulation of insoluble proteins. These insoluble proteins from aged animals, when mixed with amyloid beta in the test tube, accelerate the aggregation of the amyloid.

What was the connection between the accumulation aggregates Alzheimer's and disease-free aging, the team wondered. Focusing on the amyloid beta protein, they used a strain of the microscopic worm Caenorhabditis elegans, long been used in aging studies, that has been engineered to produce human amyloid protein.

Anderton said the team suspected they might see that amyloid beta is driving some level of insolubility in other proteins. "What we found is that amyloid beta causes a massive amount of insolubility, even in a very young animal," said Anderton.

They found that there is a subset of proteins that seem to be very vulnerable to becoming insoluble, either by adding amyloid beta or during the normal aging process. They called that vulnerable subset the "core insoluble proteome."

The team went on to demonstrate that the core insoluble proteome is full of proteins that have already been linked to different neurodegenerative diseases in addition to Alzheimer's disease, including Parkinson's disease, Huntington's disease and prion disease.

"Our paper shows that amyloid could be acting as a driver of this normal aging aggregation," said Anderton. "Now we've got clear evidence, I think for the first time, that both amyloid and aging are affecting the same proteins in a similar way. It's quite possibly a vicious cycle where aging is driving insolubility and amyloid beta is also driving insolubility, and they're just making each other worse."

The amyloid protein is very toxic to the worms and the team wanted to find a way to reverse that toxicity. "Since hundreds of mitochondrial proteins become insoluble both during aging and after expressing amyloid beta, we thought if we can boost the mitochondrial protein quality using a compound, then maybe we can reverse some of the negative effects of amyloid beta," said Anderton.

That's exactly what they found, using Urolithin A, a natural gut metabolite produced when we eat raspberries, walnuts, and pomegranates which is known to improve mitochondrial function: it significantly delayed the toxic effects of amyloid beta.

"Something that was glaringly obvious from our dataset is that the importance of mitochondria keeps coming up," said Anderton. A takeaway, the authors say, is the reminder that the health of mitochondria is critical to overall health.

"Mitochondria have a strong link with aging. They've got a strong link with amyloid beta," he said. "I think ours is one of the few studies that shows that insolubility and aggregation of those proteins might be the link between the two."

"Because the mitochondria are so central to all of this, one way to break the vicious cycle of decline is to replace damaged mitochondria with new mitochondria," said Lithgow. "And how do you do that? You exercise and follow a healthy diet."

More information: Edward Anderton et al, Amyloid β accelerates age-related proteome-wide protein insolubility, GeroScience (2024). DOI: 10.1007/s11357-024-01169-1

Provided by Buck Institute for Research on Aging

Amyloid β accelerates age-related proteome-wide protein insolubility. Credit: GeroScience (2024). DOI: 10.1007/s11357-024-01169-1

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Title: the platonic representation hypothesis.

Abstract: We argue that representations in AI models, particularly deep networks, are converging. First, we survey many examples of convergence in the literature: over time and across multiple domains, the ways by which different neural networks represent data are becoming more aligned. Next, we demonstrate convergence across data modalities: as vision models and language models get larger, they measure distance between datapoints in a more and more alike way. We hypothesize that this convergence is driving toward a shared statistical model of reality, akin to Plato's concept of an ideal reality. We term such a representation the platonic representation and discuss several possible selective pressures toward it. Finally, we discuss the implications of these trends, their limitations, and counterexamples to our analysis.

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by Buck Institute

May 16, 2024 . News

The Vicious Cycle of Protein Clumping in Alzheimer’s Disease and Normal Aging

Buck researchers uncover connections and suggestions for interventions to improve both.

It has long been known that a hallmark of Alzheimer’s disease, and most other neurodegenerative diseases, is the clumping together of insoluble protein aggregates in the brain. During normal disease-free aging, there is also an accumulation of insoluble proteins.

To date, approaches to treatments for Alzheimer’s disease have not addressed the contribution of protein insolubility as a general phenomenon, instead focusing on one or two insoluble proteins. Buck researchers have recently completed a systematic study in worms that paints an intricate picture of the connections between insoluble proteins in neurodegenerative diseases and aging. Furthermore, the work demonstrated an intervention that could reverse the toxic effects of the aggregates by boosting mitochondrial health.

“Our study shows how maintaining healthy mitochondria can combat protein clumping linked to both aging and Alzheimer’s," said Manish Chamoli, PhD, a research scientist in Gordon Lithgow ’s and Julie Andersen’s lab , and co-first author of the study. "By boosting mitochondrial health, we can potentially slow down or reverse these harmful effects, offering new ways to treat both aging and age-related diseases.”

Results support the geroscience hypothesis

The strong link between insoluble proteins promoting normal aging and diseases also builds a case for the bigger picture of how aging and age-related diseases occur. “We would argue that this work really supports the geroscience hypothesis that there is a common pathway to Alzheimer’s disease and aging itself,” said Buck Professor Gordon Lithgow. PhD, Vice President of Academic Affairs and the senior author of the study. “Aging is driving the disease, but the factors that put you on the track toward the disease actually occur very early.”

The fact that the team found a core insoluble proteome enriched with numerous proteins that had not been considered before creates new targets for exploration, said Lithgow. “In some ways it raises the flag about whether we should be thinking about what Alzheimer’s looks like in very young people,” he said.

Beyond amyloid and tau

The focus of most research on Alzheimer’s disease to date has been targeting accumulations of two proteins: amyloid beta and tau. But there are actually thousands of other proteins in these insoluble aggregations, said Anderton, and their role in Alzheimer’s disease was unknown. Additionally, he added, their lab and others’ have observed that during the normal disease-free aging process there is also an accumulation of insoluble proteins. These insoluble proteins from aged animals, when mixed with amyloid beta in the test tube, accelerate the aggregation of the amyloid.

What was the connection between the accumulation aggregates Alzheimer’s and disease-free aging, the team wondered. Focusing on the amyloid beta protein, they used a strain of the microscopic worm Caenorhabditis elegans, long been used in aging studies, that has been engineered to produce human amyloid protein.

Anderton said the team suspected they might see that amyloid beta is driving some level of insolubility in other proteins. “What we found is that amyloid beta causes a massive amount of insolubility, even in a very young animal,” said Anderton. They found that there is a subset of proteins that seem to be very vulnerable to becoming insoluble, either by adding amyloid beta or during the normal aging process. They called that vulnerable subset the “core insoluble proteome”.

The team went on to demonstrate that the core insoluble proteome is full of proteins that have already been linked to different neurodegenerative diseases in addition to Alzheimer’s disease, including Parkinson’s disease, Huntington’s disease and prion disease.

“Our paper shows that amyloid could be acting as a driver of this normal aging aggregation,” said Anderton. “Now we’ve got clear evidence, I think for the first time, that both amyloid and aging are affecting the same proteins in a similar way. It’s quite possibly a vicious cycle where aging is driving insolubility and amyloid beta is also driving insolubility, and they’re just making each other worse.”

The amyloid protein is very toxic to the worms and the team wanted to find a way to reverse that toxicity. “Since hundreds of mitochondrial proteins become insoluble both during aging and after expressing amyloid beta, we thought if we can boost the mitochondrial protein quality using a compound, then maybe we can reverse some of the negative effects of amyloid beta,” said Anderton. That’s exactly what they found, using Urolithin A, a natural gut metabolite produced when we eat raspberries, walnuts, and pomegranates which is known to improve mitochondrial function: it significantly delayed the toxic effects of amyloid beta.

“Something that was glaringly obvious from our dataset is that the importance of mitochondria keeps coming up,” said Anderton. A takeaway, the authors say, is the reminder that the health of mitochondria is critical to overall health. “Mitochondria have a strong link with aging. They’ve got a strong link with amyloid beta,” he said. “I think ours is one of the few studies that shows that insolubility and aggregation of those proteins might be the link between the two.”

“Because the mitochondria are so central to all of this, one way to break the vicious cycle of decline is to replace damaged mitochondria with new mitochondria,” said Lithgow. “And how do you do that? You exercise and follow a healthy diet.”

CITATION: Amyloid β accelerates age-related proteome-wide protein insolubility .

DOI: https://doi.org/10.1007/s11357-024-01169-1

Other Buck researchers involved in the study include Dipa Bhaumik, Christina D. King, Xueshu Xie, Anna Foulger, Julie K. Andersen, and Birgit Schilling.

Acknowledgements: This work was supported in part through funds from the National Institute on Aging (NIA RF1AG057358 NIA U01AG045844), a National Institutes of Health shared instrumentation grant, and the Larry L. Hillblom Medical Foundation.

COI: The authors declare no conflict of interest.

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The life-cycle hypothesis suggests that population aging will initially lead to an increase in national savings as the proportion of the population in the maximum savings years increases. Cantor and Yuengart (1994) estimate that saving by the baby boom generation may add as much as 1.4 percent to the national savings rate between 1990 and 2010. ...
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What Is the Life-Cycle Hypothesis (LCH)?

What Is the Life-Cycle Hypothesis in Economics?

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What Is the Life-Cycle Hypothesis?

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LCH Doesn’t Account for Financial Windfalls

LCH Assumes Your Consumption Level Will Stay the Same

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Scientific method example: Failure to toast

2. Ask a question.

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4. Make predictions.

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The Life-Cycle Theory of Consumption (With Diagram)

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