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Machine learning reveals key markers for healthy aging, separate from chronic disease risks

In a study published within the journal Nature Aging, researchers applied machine learning to investigate the health trajectories of healthy individuals over time and distinguish inherent aging aspects from chronic disease risks. They found that the model could consistently discover early indicators of healthy aging, equivalent to neutrophil counts and alkaline phosphatase levels across individuals from Israel, the UK (UK), and america of America (USA).

Study: Longitudinal machine learning uncouples healthy aging aspects from chronic disease risks


The “geroscience hypothesis” suggests that targeting universal aging processes may promote healthy aging, improve lifespan, and reduce the prevalence of age-related diseases, including type 2 diabetes mellitus (T2D), heart problems (CVD), chronic kidney disease (CKD), liver disease (LD), and chronic obstructive pulmonary disease (COPD). The co-occurrence and correlation of age-related diseases with aging pose challenges in modeling causality. This calls for unbiased approaches to analyze the interplay between healthy aging and age-related diseases.

Although electronic health records (EHRs) offer significant potential in capturing the health trajectories of patients, the prevailing data is restricted (as much as 20 years), hindering our understanding of the connection between aging, disease, and disease risk. Moreover, previous studies conducted to model mortality and age using clinical markers lack the usage of a longitudinal model. To handle this gap, researchers in the current study developed a machine learning-based model to discover predictive clinical markers for disease-free healthy aging. They revisited the heritability and genetic associations of phenotypes linked to longevity.

In regards to the study

Medical history data of 4.57 million individuals aged 30 to 85 years was obtained from the Clalit Healthcare Services database, tracking them for a median of 16.6 years. First, a machine learning model was developed using the three-year history of patients aged above 80 years. Laboratory tests correlating with longevity were analyzed. Next, longevity potential was assessed across ages by implementing a machine-learning model that would infer longitudinal trajectories using partial patient histories. A long life potential rating was determined for every age, predicting five-year mortality or a change in longevity potential.

Further, to know how lifelong disease predisposition potentially affected the longevity rating, the researchers implemented an prolonged disease risk Markov model using disease-onset data for T2D, CVD, LD, CKD, and COPD. The physiological processes underlying longevity potential were investigated in very healthy individuals using clinical markers over a >10-year follow-up.

The model was then tested on the UKBB (short for UK Biobank) and NHANES (short for National Health and Nutrition Examination Survey) population databases. Patients aged 50 were classified into 15 groups, and their disease predisposition, allele frequencies, and parental mortality were analyzed.

Results and discussion

The three-year history model could discern an in depth spectrum of risk levels, highlighting significant prognostic differences even throughout the top 4% of healthy patients. Laboratory tests could discover red blood cell distribution width (RDW), C-reactive protein, and albumin as markers continually related to prognosis. The model provided a generalizable metric for health that would classify patients as healthy and unhealthy, encouraging the usage of models that quantitatively track the changes in health potential. The model accurately distinguished individuals’ survival probabilities beyond 85 years, even at age 30.

Clinical markers contributing to the longevity rating were found to differ across ages. While alkaline phosphatase was found to affect younger adults, glucose and cholesterol appeared to affect mid-adulthood and albumin and RDW were found to affect older ages. Key features like chubby, blood sugar, and cholesterol were observed to play a big role in predicting lifelong disease risk. Markers of chronic disease risk were found to be consistently low in very healthy individuals. A high longevity rating was indicated by low levels of neutrophils, alkaline phosphatase, and the ratio of microcytic and hypochromatic red blood cells, in addition to medium levels of body mass index, creatinine, and liver enzymes.

The models’ predictive power was shown to extend with age, particularly in identifying high-risk individuals for diseases like T2D at ages 50–60 as a consequence of improved sensitivity from routine tracking. The estimated lifelong disease predispositions were found to be strongly related to one another and correlated with the longevity rating. Nonetheless, a subset of people exhibited variation in longevity potential despite low disease risk.

The longevity scores were found to be robust across Israeli, US, and UK populations, demonstrating significant predictive power for longevity in individuals without known predisposition to diseases. Moreover, the degree of disease predisposition was found to differ between populations at age 50. Parents of highest longevity scoring-individuals were found to have a one-year increase in lifespan. As per the study, genetic variation may additionally contribute to longevity. The researchers recommend using a multivariate disease risk model to interpret genome-wide association studies.


In conclusion, the current study improves our understanding of the interplay between aging and major chronic diseases, paving the best way for comprehensive, longitudinal models to interchange static representations of healthy aging and customary diseases. Further research is required to quantify a “healthy state” and investigate the physiological processes underlying the disease-related findings highlighted within the study.

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