A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

Blood-based epigenome-wide analyses of chronic low-grade inflammation across diverse population cohorts

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

Effect size and inferential statistical techniques coupled with machine learning for assessing the association between prolactin concentration and metabolic homeostasis

BACKGROUND AND OBJECTIVE

Recent guidelines classify low prolactin levels as low as <7 ng/mL and high levels as >25 ng/mL, while the "Homeostatically Functionally Increased Transient Prolactinemia" (HomeoFIT-PRL) range (25-100 ng/mL) suggests that a temporary increase in prolactin could be metabolically beneficial if no related health issues are present. The aim of this study was to investigate the association between mean prolactin concentrations and disturbances in glycidic and lipidic metabolism and to identify the gray zone associated with prolactin inflection points that correlate with these metabolic changes.

METHODS

This cross-sectional study involved 65,795 adults who underwent HOMA-IR, glucose, insulin, total cholesterol, HDL-c, LDL-c, and triglyceride tests. Data was categorized into 106 partitions based on prolactin results. Employing an approach referred to in this study as "Hierarchical Multicriteria Analysis of Differences Between Groups - Statistical and Effect Size Approach" (HiMADiG-SESA) comparing the mean concentrations of metabolic tests across prolactin ranges. A machine learning model was utilized to determine inflection points and their corresponding confidence intervals (CIs). These CIs helped establish gray zones in mean prolactin results related to metabolic changes.

RESULTS

Statistically and clinically, metabolic test means differed for prolactin <7 ng/mL, except insulin. In the HomeoFIT-PRL range, means were lower except for HDL-c. The gray zones of the mean prolactin results associated with changes in glycidic and lipidic metabolism were 9.58-12.87 ng/mL and 13.81-18.73 ng/mL, respectively.

CONCLUSION

A strong correlation was identified between mean prolactin concentrations and the results of metabolism tests below the gray zones associated with inflection points, indicating the potential role of prolactin in the appearance of metabolic disorders. Mean prolactin results can provide deeper insight into metabolic balance.