Abstract
Lifespan is a complex trait, and longitudinal data for humans are naturally scarce. We report the results of Cox regression and Pearson correlation analyses using data of the Study of Health in Pomerania (SHIP), with mortality data of 1518 participants (113 of which died), over a time span of more than 10 years. We found that in the Cox regression model based on the Bayesian information criterion, apart from chronological age of the participant, six baseline variables were considerably associated with higher mortality rates: smoking, mean attachment loss (i.e. loss of tooth supporting tissue), fibrinogen concentration, albumin/creatinine ratio, treated gastritis, and medication during the last 7 days. Except for smoking, the causative contribution of these variables to mortality was deemed inconclusive. In turn, four variables were found to be associated with decreased mortality rates: treatment of benign prostatic hypertrophy, treatment of dyslipidemia, IGF-1 and being female. Here, being female was an undisputed causative variable, the causal role of IFG-1 was deemed inconclusive, and the treatment effects were deemed protective to the degree that treated subjects feature better survival than respective controls. Using Cox modeling based on the Akaike information criterion, diabetes, mean corpuscular hemoglobin concentration, red blood cell count and serum calcium were also associated with mortality. The latter two, together with albumin and fibrinogen, aligned with an”integrated albunemia” model of aging proposed recently.
Highlights
Despite its importance in a world of rapid demographic change towards an increasing proportion of elderly citizens, we do not understand in detail what aging is, nor do we understand what is cause and what is consequence of aging; i.e. which marker changes are causal to aging and which ones are just the consequences of the aging process
The Bayesian information criterion (BIC) model was smaller, as expected, due to the heavier penalization of model complexity in case of BIC. Both models were nested; all variables of the BICmodel were present in the Akaike information criterion (AIC) model
We found that specific treatments were protective such that treated subjects featured better survival than non-treated subjects; these were treatment of benign prostatic hypertrophy and treatment of dyslipidemia
Summary
Despite its importance in a world of rapid demographic change towards an increasing proportion of elderly citizens, we do not understand in detail what aging is, nor do we understand what is cause and what is consequence of aging; i.e. which marker changes are causal to aging and which ones are just the consequences of the aging process. Genetic data afford genome-wide association studies of any traits that are measured in the population sample, while gene (or protein, or metabolite) expression data may allow deep molecular mechanistic insights into mortality determinants such as hypotheses about pathway activations or inhibitions related to mortality. Laboratory data allow such mechanistic insights on a more aggregative level; e.g. anemia, inflammation, immunity or growth can be estimated by specific markers such as blood cell counts. On the most aggregative level, very general traits and socio-demographic attributes such as chronological age, gender, education, income or life style risk factors (smoking, alcohol consumption, physical activity) were shown in the past to have a strong influence on mortality [3]
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