Summary: Researchers have designed a new model of life expectancy that’s based less on disease diagnosis, and more on other factors including cholesterol levels and lifestyle.
Source: Duke University
A new model to predict the life expectancy of older people relies less on their specific disease diagnoses and more on factors such as the ability to grocery shop, the amount of certain small cholesterol particles circulating in their blood, and whether they never or only occasionally smoked.
The findings from a study led by Duke Health researchers provide a way to predict whether a person over the age of 70 is likely to live two, five or 10 years. The markers may be obtained during a doctor visit, so they could be a useful guide for clinical care.
“This study was designed to determine the proximal causes of longevity—the factors that portend whether someone is likely to live two more years or 10 more years,” said Virginia Byers Kraus, M.D., Ph.D., professor in the departments of Medicine, Pathology and Orthopedic Surgery at Duke University School of Medicine and lead author of the study appearing online in the journal eBioMedicine.
“Properly applied, these measures could help determine the benefits and burdens of screening tests and treatment for older people,” Kraus said.
Kraus and colleagues launched their inquiry at an opportune time, having been directed to a cache of 1,500 blood samples from a 1980s longitudinal study that enrolled older people.
The banked samples had been drawn in 1992 when participants were at least 71 years old and then stored at the NIH. They were scheduled for destruction, but the researchers arrived in time to transfer 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 medications such as statins, which could have skewed the results. More good luck: study participants had been followed for several years and had filled out questionnaires about their health histories and habits.
Capitalizing on all the features of the older study, the researchers were able to apply current sophisticated analytical tools. Led by Constantin Aliferis and Sisi Ma at the University of Minnesota, the researchers were able to delve into health factors to identify a core set of 17 predictive variables that have a causal impact on longevity.
The analysis found that a leading factor associated with longevity across each of the study’s benchmarks—two-, five- and 10-years after participants had their blood drawn—was physical function, which was defined as an ability to go grocery shopping or perform housecleaning chores. Surprisingly, having cancer or heart disease was not among the main predictors.
For older people living two years beyond the time their blood had been drawn, the leading factor associated with longevity was having an abundance of high-density lipoprotein (HDL) cholesterol—and not just any HDL lipids, but high volumes of very small HDL particles.
“This was especially surprising,” Kraus said. “We hypothesize that these very small HDL particles are the size that is best at scavenging and clearing endotoxin, a potent inflammation-causing molecule from gut microbes, from the circulation [VBKMP1] .
“The small particle may also be best able to get into the nooks and crannies of cells to remove the bad cholesterol, so having more of them could provide this protective benefit.”
At five years beyond the original blood draw, just being of a younger age was predictive of longevity, along with cognitive function. And among the longest survivors—those living 10 years—the best predictor was a person’s smoking history, with non-smokers faring best.
“These measures clarify and enrich our understanding of mechanisms underlying longevity and could point to appropriate tests and potential interventions,” Kraus said.
She said the next stage of research is to use additional analytical tools to improve the predictivity and identify potential targets for therapies.
About this aging and mortality research news
Author: Alexis Porter
Source: Duke University
Contact: Alexis Porter – Duke University
Image: The image is in the public domain
Original Research: Open access.
“Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults” by Virginia Byers Kraus et al. eBioMedicine
Abstract
Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of 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 direct causes of longevity from medically accessible data.
Methods
Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the “Markov boundaries” of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study.
Findings
As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers 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 prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity.
Interpretation
The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan.
Funding
NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.
