How long older people will live comes down to 17 often surprising factors

Summary: Researchers have designed a new life expectancy model that relies less on disease diagnosis and more on other factors, such as cholesterol levels and lifestyle.

Source: Duke University

A new model for predicting the life expectancy of older people relies less on their specific disease diagnoses and more on factors such as the ability to buy groceries, the amount of certain small cholesterol particles circulating in the blood and if they never or only smoked occasionally. .

Findings from a study led by Duke Health researchers offer a way to predict whether a person over age 70 is likely to live two, five or 10 years. Markers can be obtained during a doctor’s visit, so they could be a useful guide to clinical care.

“This study was designed to determine the proximate causes of longevity—the factors that predict whether someone will live two more years or 10 more years,” said Virginia Byers Kraus, MD, Ph.D., professor in the Departments of Medicine . , Pathology and Orthopedic Surgery at Duke University School of Medicine and lead author of the study, which appears online in the journal eBioMedicine.

“Applied properly, these measures could help determine the benefits and burdens of screening tests and treatment for older adults,” Kraus said.

Kraus and his colleagues launched their research at an opportune time, having been directed to a cache of 1,500 blood samples from a 1980s longitudinal study of older adults.

Pooled samples had been drawn in 1992 when participants were at least 71 years old and then stored at the NIH. They were slated for destruction, but researchers arrived in time to move them to Duke for analysis.

The blood samples had the additional fortuitous feature of being drawn at a time that preceded the widespread use of drugs such as statins, which could have biased the results. More luck: Study participants had been followed for several years and filled out questionnaires about their health histories and habits.

Taking advantage of all the features of the previous study, the researchers were able to apply today’s sophisticated analytical tools. Led by Constantin Aliferis and Sisi Ma at the University of Minnesota, researchers were able to dig deeper into health factors to identify a core set of 17 predictive variables that have a causal impact on longevity.

The analysis found that one of the main factors associated with longevity at each of the study’s endpoints (two, five and 10 years after participants had their blood drawn) was physical function, which define as the ability to go shopping or clean the house. duties. Surprisingly, having cancer or heart disease was not among the top predictors.

For older people who lived two years past the time their blood was drawn, the main factor associated with longevity was having a lot of high-density lipoprotein (HDL) cholesterol, not just any lipid HDL, but large volumes of very small HDL particles. .

Markers can be obtained during a doctor’s visit, so they could be a useful guide to clinical care. The image is in the public domain

“That was particularly surprising,” Kraus said. “We hypothesize that these very small HDL particles are the most appropriate size to remove and clear endotoxin, a powerful molecule that causes inflammation of gut microbes, from the circulation. [VBKMP1] .

“The small particle may also be better able to get into the nooks and crannies of cells to remove bad cholesterol, so having more of it could provide this protective benefit.”

At five years beyond the original blood draw, only younger age was predictive of longevity, along with cognitive function. And among the longest survivors, those who lived 10 years, the best predictor was a person’s smoking history, and nonsmokers fared better.

“These measurements clarify and enrich our understanding of the mechanisms underlying longevity and could point to appropriate tests and potential interventions,” Kraus said.

He said the next stage of research is to use additional analytical tools to improve prediction and identify potential targets for therapies.

About this research news on aging and mortality

Author: Alexis Porter
Source: Duke University
Contact: Alexis Porter – Duke University
Image: The image is in the public domain

See also

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Original Research: Open access
“Causal Analysis Identifies Small HDL Particles and Physical Activity as Key Determinants of Longevity in Older Adults” by Virginia Byers Kraus et al. eBioMedicine


Summary

Causal analysis identifies small HDL particles and physical activity as key determinants of longevity in older adults

background

The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover the direct causes of longevity from medically accessible data.

methods

Using a framework that combines local causal discovery algorithms with the discovery of maximally predictive and compact feature sets (the “Markov limits” of the response) and equivalence classes, we examined 186 variables and their relationships with survival during 27 years in 1507 participants, aged ≥ 71 years, from the longitudinal and community study D-EPESE.

discoveries

Only 8–15 variables predicted longevity at 2, 5, and 10 years with a predictive performance (area under the receiver operator characteristic curve) of 0.76 (95% CI 0.69, 0.83), 0.76 (0· 72, 0·81) and 0·66 (0·61, 0·71), respectively. The number of small high-density lipoprotein particles, younger age, and fewer pack-years of cigarette smoking were the strongest determinants of longevity at 2, 5, and 10 years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year predictor variables (although significant in univariate analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of factors measured at our data that determines longevity.

interpretation

The findings in this study come from causal data science analyzes of in-depth clinical and molecular phenotyping data in a community-based cohort of older adults with a known lifespan.

financing

NIH/NIA R01AG054840, R01AG12765 and P30-AG028716, NIH/NIA contract N01-AG-12102 and NCRR 1UL1TR002494-01.

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