Cross-Sectional Association of Salivary Proteins with Age, Sex, Body Mass Index, Smoking, and Education

Whole saliva is gaining more and more attention as a diagnostic tool to study disease-specific changes in human subjects. Prior to the actual disease-related analyses, it is important to understand the influence of various demographic variables and coupled phenotypes on salivary protein signatures. In a cross-sectional approach, we analyzed the influence of age, sex, body mass index (BMI), smoking, and education on salivary protein signatures in whole saliva samples of 187 individuals. Subjects were randomly selected from the population-based Study of Health in Pomerania (SHIP-Trend). Stimulated whole saliva was collected, and proteins were precipitated and proteolytically digested. Samples were analyzed by label-free tandem mass spectrometry. Of the 602 human proteins identified in at least 40% of the saliva samples, we used 304 proteins, which could be identified with at least two unique peptides, for statistical analyses. Univariate and multivariate linear models were used to reveal associations with the phenotypes. The largest number of proteins was associated with smoking status. Moreover, age had a distinct influence on the salivary protein composition. The study discloses the influence of common phenotypes on the salivary protein pattern of human subjects. These results should be considered when studying disease-related proteome signatures in saliva

Mortality is associated with inflammation, anemia, specific diseases and treatments, and molecular markers

<div><p>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.</p></div

Results for the AIC Model based on 1518 subjects with 113 events.

<p>Results for the AIC Model based on 1518 subjects with 113 events.</p

Flowchart for the building of the AIC and the BIC Model.

<p>We started with 4308 data records, 77 preselected variables and two variables for “total mortality” and “follow-up time”, which are the status indicator and the follow-up time for the right-censored mortality data. Thereafter we obtained 1518 complete data records with these variables. Based on AIC and BIC criteria, we obtained two different models with 25 and 11 variables, respectively, referred to as AIC and BIC models.</p

Results for the BIC Model based on 1518 subjects with 113 events.

<p>Results for the BIC Model based on 1518 subjects with 113 events.</p

Comparison of the AIC and BIC model and correlation analysis (with P-value) of variables against age at baseline (orange: positive correlation, grey: negative correlation; white: correlation not statistically significant).

<p>Comparison of the AIC and BIC model and correlation analysis (with P-value) of variables against age at baseline (orange: positive correlation, grey: negative correlation; white: correlation not statistically significant).</p