Biological aging and Cox hazard analysis of mortality trends in a Mennonite community from south-central Kansas

Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study

Purpose

A comprehensive health index is needed to measure an individual's overall health and aging status and predict the risk of death and age-related disease incidence, and evaluate the effect of a health management program. The purpose of this study is to demonstrate the validity of estimated biological age (BA) in relation to all-cause mortality and age-related disease incidence based on National Sample Cohort database.

Patients and methods

This study was based on National Sample Cohort database of the National Health Insurance Service - Eligibility database and the National Health Insurance Service - Medical and Health Examination database of the year 2002 through 2013. BA model was developed based on the National Health Insurance Service - National Sample Cohort (NHIS - NSC) database and Cox proportional hazard analysis was done for mortality and major age-related disease incidence.

Results

For every 1 year increase of the calculated BA and chronological age difference, the hazard ratio for mortality significantly increased by 1.6% (1.5% in men and 2.0% in women) and also for hypertension, diabetes mellitus, heart disease, stroke, and cancer incidence by 2.5%, 4.2%, 1.3%, 1.6%, and 0.4%, respectively (p<0.001).

Conclusion

Estimated BA by the developed BA model based on NHIS - NSC database is expected to be used not only as an index for assessing health and aging status and predicting mortality and major age-related disease incidence, but can also be applied to various health care fields.

Biological age as a useful index to predict seventeen-year survival and mortality in Koreans

Background

Many studies have been conducted to quantitatively estimate biological age using measurable biomarkers. Biological age should function as a valid proxy for aging, which is closely related with future work ability, frailty, physical fitness, and/or mortality. A validation study using cohort data found biological age to be a superior index for disease-related mortality than chronological age. The purpose of this study is to demonstrate the validity of biological age as a useful index to predict a person’s risk of death in the future.

Methods

The data consists of 13,106 cases of death from 557,940 Koreans at 20–93 years old, surveyed from 1994 to 2011. Biological ages were computed using 15 biomarkers measured in general health check-ups using an algorithm based on principal component analysis. The influence of biological age on future mortality was analyzed using Cox proportional hazards regression considering gender, chronological age, and event type.

Results

In the living subjects, the average biological age was almost the same as the average chronological age. In the deceased, the biological age was larger than the chronological age: largest increment of biological age over chronological age was observed when their baseline chronological age was within 50–59 years. The death rate significantly increased as biological age became larger than chronological age (linear trend test, p value < 0.0001). The largest hazard ratio was observed in subjects whose baseline chronological age was within 50–59 years when the cause was death from non-cancerous diseases (HR = 1.30, 95% confidence intervals = 1.26 - 1.34). The survival probability, over the 17 year term of the study, was significantly decreased in the people whose biological age was larger than chronological age (log rank test, p value < 0.001).

Conclusions

Biological age could be used to predict future risk of death, and its effect size varied according to gender, chronological age, and cause of death.

Electronic supplementary material

The online version of this article (doi:10.1186/s12877-016-0407-y) contains supplementary material, which is available to authorized users.

Health status and 6 years survival of 552 90+ Italian sib-ships recruited within the EU Project GEHA (GEnetics of Healthy Ageing)

In a scenario of increasing life expectancy worldwide, it is mandatory to identify the characteristics of a healthy aging phenotype, including survival predictors, and to disentangle those related to environment/lifestyle versus those related to familiarity/genetics. To this aim we comprehensively characterised a cohort of 1,160 Italian subjects of 90 years and over (90+, mean age 93 years; age range 90-106 years) followed for 6 years survival, belonging to 552 sib-ships (familiar longevity) recruited (2005-2008) within the EU-funded GEHA project in three Italian geographic areas (Northern, Central and Southern Italy) different for urban/rural and socio-economical characteristics. On the whole, the following factors emerged as significant predictors of survival after 90 years of age: absence of cognitive impairment and physical disability, high hand grip strength scores and body mass index (BMI) values, "excellent/good" self-reported health, high haemoglobin and total cholesterol levels and low creatinine levels. These parameters, excluding BMI values, were also significantly associated within sib-ships, suggesting a strong familial/genetic component. Geographical micro-heterogeneity of survival predictors emerged, such as functional and physical status being more important in Southern than in Central and Northern Italy. In conclusion, we identified modifiable survival predictors related to specific domains, whose role and importance vary according to the geographic area considered and which can help in interpreting the genetic results obtained by the GEHA project, whose major aim is the comprehensive evaluation of phenotypic and genetic data.

Worldwide allele frequencies of the human apolipoprotein E gene: climate, local adaptations, and evolutionary history

The epsilon4 allele of the apolipoprotein E (APOE) gene is associated with increased cholesterol levels and heart disease. Population allele frequencies of APOE have previously been shown to vary, with epsilon4 frequencies generally increasing with latitude. We hypothesize that this trend resulted from natural selection protecting against low-cholesterol levels. In high-latitude cold environments and low-latitude hot environments, metabolic rate is elevated, which could require higher cholesterol levels. To explore this hypothesis, we compiled APOE allele frequencies, latitude, temperature, and elevation from populations around the world. epsilon4 allele frequencies show a curvilinear relationship with absolute latitude, with lowest frequencies found in the mid-latitudes where temperatures generally require less expenditure on cooling/thermogenesis. Controlling for population structure in a subset of populations did not appreciably change this pattern of association, consistent with selection pressures that vary by latitude shaping epsilon4 allele frequencies. Temperature records also predict APOE frequency in a curvilinear fashion, with lowest epsilon4 frequencies at moderate temperatures. The model fit between historical temperatures and epsilon4 is less than between latitude and epsilon4, but strengthened after correcting for estimated temperature differences during the Paleolithic. Contrary to our hypothesis, we find that elevation did not improve predictive power, and an integrated measure of the cholesterol effect of multiple APOE alleles was less related to latitude than was epsilon4 alone. Our results lend mixed support for a link between past temperature and human APOE allele distribution and point to the need to develop better models of past climate in future analyses.