Living longer is one of modern medicine’s greatest achievements. Over the past century, average life expectancy has nearly doubled thanks to advances in science, health care and public health. But longer lives also mean more time for chronic illnesses to accumulate.

Many older adults live with several long-term conditions at once, a situation known as multimorbidity. These combinations can increase the risk of hospitalization, emergency care and disability. Yet medical recommendations are still often organized around chronological age.

A new study of more than 238,000 adults suggests that age alone may be too simple a guide. Researchers found that as people get older, their combinations of chronic conditions become increasingly different from those of other people the same age.

Older People Become Less Alike

The study examined whether mathematical patterns could explain how multimorbidity changes across the lifespan. Researchers found that the average burden of chronic disease generally rises with age, which is not surprising. What stood out was the growing variation between people.

Two 40-year-olds may have relatively similar levels of chronic illness, while two 70-year-olds can have dramatically different health profiles. That gap continues to widen with increasing age. In practical terms, this means that two patients born in the same year may require completely different approaches to prevention, screening and treatment.

Why This Matters for Medical Guidelines

Age is commonly used to determine when patients should begin or stop certain preventive procedures.

For example, screening recommendations for cancers often specify broad age ranges. These rules are useful because they provide clear guidance for large populations, but they may not fully reflect the complexity of individual health. The new findings suggest that doctors could gain a more accurate picture by considering a patient’s overall burden of chronic disease alongside age.

A healthy 70-year-old and another 70-year-old living with several serious conditions may face very different risks and benefits from the same intervention. The researchers argue that multimorbidity could therefore become an important additional factor in personalized medicine.

Studying Patients Often Left Out of Research

The work grew out of research focused specifically on people with high levels of multimorbidity. These patients are among those most likely to need medical care, yet they are frequently excluded from clinical trials because having several illnesses at once introduces additional variables that make studies more complicated. Instead of excluding these individuals, researchers made them the focus.

De-identified health records from 238,156 people receiving care through health centers in Chicago and New York were examined to identify patterns in chronic disease.

A related clinical trial enrolled nearly 2,000 participants with multimorbidity. Health coaches worked with them to explore whether specific interventions could help patients manage their conditions before reaching a “tipping point” that might lead to hospitalization or increased disability.

Measuring the Burden of Chronic Disease

For the analysis, each participant was assigned a score using the enhanced Charlson Comorbidity Index, or eCCI.

The index estimates hospitalization risk and health care burden based on the number and severity of chronic conditions across 39 categories. Different illnesses carry different weights. Conditions such as myocardial infarction, congestive heart failure and peripheral vascular disease receive relatively low scores, while serious conditions including metastatic solid tumors, AIDS and orgаn transplants carry much higher values.

Researchers then compared both the average eCCI score and the variation in scores among people of the same age.

A Mathematical Pattern Emerges

The results followed a pattern similar to Taylor’s law, a mathematical relationship previously observed in many different populations, ranging from wildlife and infectious diseases to human demographic data and even weather. In this study, the pattern showed that as the average multimorbidity score increased with age, the variation between individuals increased as well.

In other words, older patients did not simply accumulate more health problems. Their combinations of illnesses also became increasingly unique. That finding provides a mathematical explanation for why age-based medical recommendations may become less precise later in life.

Toward More Personalized Prevention

The implications extend beyond screening schedules. Understanding a patient’s specific combination of conditions could potentially help clinicians identify when the risk of hospitalization is increasing and intervene earlier.

It may also improve decisions about treatment intensity, preventive care and long-term disease management. The researchers emphasize that age remains useful, but it should not necessarily be the only—or even the dominant—factor guiding care for people with several chronic illnesses.

One Age, Many Health Profiles

The study highlights an important consequence of longer lifespans: people become increasingly different from one another as their medical histories accumulate.

Someone’s age may tell a doctor how many years that person has lived, but it cannot fully describe decades of different diseases, treatments, lifestyle factors and health experiences.