Birth weight and maternal energy status during pregnancy as predictors of epigenetic age acceleration in young adults from metropolitan Cebu, Philippines

Maximizing Insights from Longitudinal Epigenetic Age Data: Simulations, Applications, and Practical Guidance

Background Epigenetic Age (EA) is an age estimate, developed using DNA methylation (DNAm) states of selected CpG sites across the genome. Although EA and chronological age are highly correlated, EA may not increase uniformly with time. Departures, known as epigenetic age acceleration (EAA), are common and have been linked to various traits and future disease risk. Limited by available data, most studies investigating these relationships have been cross-sectional - using a single EA measurement. However, the recent growth in longitudinal DNAm studies has led to analyses of associations with EA over time. These studies differ in (i) their choice of model; (ii) the primary outcome (EA vs. EAA); and (iii) in their use of chronological age or age-independent time variables to account for the temporal dynamic. We evaluated the robustness of each approach using simulations and tested our results in two real-world examples, using biological sex and birthweight as predictors of longitudinal EA. Results Our simulations showed most accurate effect sizes in a linear mixed model or generalized estimating equation, using chronological age as the time variable. The use of EA versus EAA as an outcome did not strongly impact estimates. Applying the optimal model in real-world data uncovered an accelerated EA rate in males and an advanced EA that decelerates over time in children with higher birthweight. Conclusion Our results can serve as a guide for forthcoming longitudinal EA studies, aiding in methodological decisions that may determine whether an association is accurately estimated, overestimated, or potentially overlooked.

Better Life's Essential 8 contributes to slowing the biological aging process: a cross-sectional study based on NHANES 2007-2010 data

Objective

To investigate the relationship between Life's Essential 8 (LE8) and Phenotypic Age Acceleration (PhenoAgeAccel) in United States adults and to explore the impact of LE8 on phenotypic biological aging, thereby providing references for public health policies and health education.

Methods

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2007 and 2010, this cross-sectional study analyzed 7,339 adults aged 20 and above. Comprehensive assessments of LE8, PhenoAgeAccel, and research covariates were achieved through the integration of Demographics Data, Dietary Data, Laboratory Data, and Questionnaire Data derived from NHANES. Weighted generalized linear regression models and restricted cubic spline plots were employed to analyze the linear and non-linear associations between LE8 and PhenoAgeAccel, along with gender subgroup analysis and interaction effect testing.

Results

(1) Dividing the 2007-2010 NHANES cohort into quartiles based on LE8 unveiled significant disparities in age, gender, race, body mass index, education level, marital status, poverty-income ratio, smoking and drinking statuses, diabetes, hypertension, hyperlipidemia, phenotypic age, PhenoAgeAccel, and various biological markers (p < 0.05). Mean cell volume demonstrated no intergroup differences (p > 0.05). (2) The generalized linear regression weighted models revealed a more pronounced negative correlation between higher quartiles of LE8 (Q2, Q3, and Q4) and PhenoAgeAccel compared to the lowest LE8 quartile in both crude and fully adjusted models (p < 0.05). This trend was statistically significant (p < 0.001) in the full adjustment model. Gender subgroup analysis within the fully adjusted models exhibited a significant negative relationship between LE8 and PhenoAgeAccel in both male and female participants, with trend tests demonstrating significant results (p < 0.001 for males and p = 0.001 for females). (3) Restricted cubic spline (RCS) plots elucidated no significant non-linear trends between LE8 and PhenoAgeAccel overall and in gender subgroups (p for non-linear > 0.05). (4) Interaction effect tests denoted no interaction effects between the studied stratified variables such as age, gender, race, education level, and marital status on the relationship between LE8 and PhenoAgeAccel (p for interaction > 0.05). However, body mass index and diabetes manifested interaction effects (p for interaction < 0.05), suggesting that the influence of LE8 on PhenoAgeAccel might vary depending on an individual's BMI and diabetes status.

Conclusion

This study, based on NHANES data from 2007-2010, has revealed a significant negative correlation between LE8 and PhenoAgeAccel, emphasizing the importance of maintaining a healthy lifestyle in slowing down the biological aging process. Despite the limitations posed by the study's design and geographical constraints, these findings provide a scientific basis for the development of public health policies focused on healthy lifestyle practices. Future research should further investigate the causal mechanisms underlying the relationship between LE8 and PhenoAgeAccel and consider cross-cultural comparisons to enhance our understanding of healthy aging.

Socioeconomic status is negatively associated with immunosenescence but positively associated with inflammation among middle-aged women in Cebu, Philippines

BACKGROUND

Socioeconomic status (SES) gradients in health are well-documented, and while biological pathways are incompletely understood, chronic inflammation and accelerated immune aging (immunosenescence) among lower SES individuals have been implicated. However, previous findings have come from samples in higher income countries, and it is unclear how generalizable they are to lower- and middle-income countries (LMIC) with different infectious exposures and where adiposity-an important contributor to chronic inflammation-might show different SES patterning. To address this gap, we explored associations between SES and inflammation and immunosenescence in a sample of women in Cebu, Philippines.

METHODS

Data came from the mothers of the Cebu Longitudinal Health and Nutrition Survey birth cohort (mean age: 47.7, range: 35-69 years). SES was measured as a combination of annual household income, education level, and assets. Chronic inflammation was measured using C-reactive protein (CRP) in plasma samples from 1,834 women. Immunosenescence was measured by the abundance of exhausted CD8T (CD8 + CD28-CD45RA-) and naïve CD8T and CD4T cells, estimated from DNA methylation in whole blood in a random subsample of 1,028. Possible mediators included waist circumference and a collection of proxy measures of pathogen exposure.

RESULTS

SES was negatively associated with the measures of immunosenescence, with slight evidence for mediation by a proxy measure for pathogen exposure from the household's drinking water source. In contrast, SES was positively associated with CRP, which was explained by the positive association with waist circumference.

CONCLUSIONS

Similar to higher income populations, in Cebu there is an SES-gradient in pathogen exposures and immunosenescence. However, lifestyle changes occurring more rapidly among higher SES individuals is contributing to a positive association between SES and adiposity and inflammation. Our results suggest more studies are needed to clarify the relationship between SES and inflammation and immunosenescence across LMIC.

Association between infectious exposures in infancy and epigenetic age acceleration in young adulthood in metropolitan Cebu, Philippines

OBJECTIVES

The drivers of human life expectancy gains over the past 200 years are not well-established, with a potential role for historical reductions in infectious disease. We investigate whether infectious exposures in infancy predict biological aging using DNA methylation-based markers that forecast patterns of morbidity and mortality later in life.

METHODS

N = 1450 participants from the Cebu Longitudinal Health and Nutrition Survey-a prospective birth cohort initiated in 1983-provided complete data for the analyses. Mean chronological age was 20.9 years when venous whole blood samples were drawn for DNA extraction and methylation analysis, with subsequent calculation of three epigenetic age markers: Horvath, GrimAge, and DunedinPACE. Unadjusted and adjusted least squares regression models were evaluated to test the hypothesis that infectious exposures in infancy are associated with epigenetic age.

RESULTS

Birth in the dry season, a proxy measure for increased infectious exposure in the first year of life, as well as the number of symptomatic infections in the first year of infancy, predicted lower epigenetic age. Infectious exposures were associated with the distribution of white blood cells in adulthood, which were also associated with measures of epigenetic age.

CONCLUSIONS

We document negative associations between measures of infectious exposure in infancy and DNA methylation-based measures of aging. Additional research, across a wider range of epidemiological settings, is needed to clarify the role of infectious disease in shaping immunophenotypes and trajectories of biological aging and human life expectancy.

Birth size and the serum level of biological age markers in men

Previous studies showed that intrauterine growth restrictions, resulting in smaller body size at birth, are associated with altered development and the risk of age-related diseases in adult life. Thus, prenatal development may predict aging trajectories in humans. The study aimed to verify if body size at birth is related to biological age in adult men. The study sample consisted of 159 healthy, non-smoking men with a mean age of 35.24 (SD 3.44) years. Birth weight and length were taken from medical records. The ponderal index at birth was calculated. Biological age was evaluated based on serum levels of s-Klotho, hsCRP, DHEA/S, and oxidative stress markers. Pregnancy age at birth, lifestyle, weight, cortisol, and testosterone levels were controlled. The results showed no relationship between birth size and s-Klotho, DHEA/S level, inflammation, or oxidative stress. Also, men born as small-for-gestational-age (N = 49) and men born as appropriate-for-gestational-age (N = 110) did not differ in terms of biological age markers levels. The results were similar when controlled for pregnancy week at birth, chronological age, BMI, testosterone, or cortisol level. The results suggest that there is no relationship between intrauterine growth and biomarkers of aging in men aged 30-45 years from the affluent population.

Low birthweight is associated with epigenetic age acceleration in the first 3 years of life

Background and objectives

The Developmental Origins of Health and Disease hypothesis posits that early life adversity is associated with poor adult health outcomes. Epidemiological evidence has supported this framework by linking low birthweight with adult health and mortality, but the mechanisms remain unclear. Accelerated epigenetic aging may be a pathway to connect early life experiences with adult health outcomes, based on associations of accelerated epigenetic aging with increased morbidity and mortality.

Methodology

Sixty-seven mother-infant dyads were recruited in the eastern Democratic Republic of Congo. Birthweight data were collected at birth, and blood samples were collected at birth and follow-up visits up to age 3. DNA methylation data were generated with the Illumina MethylationEPIC array and used to estimate epigenetic age. A multilevel model was used to test for associations between birthweight and epigenetic age acceleration.

Results

Chronological age was highly correlated with epigenetic age from birth to age 3 (r = 0.95, p < 2.2 × 10^-16). Variation in epigenetic age acceleration increased over time. Birthweight, dichotomized around 2500 g, predicted epigenetic age acceleration over the first 3 years of life (b = -0.39, p = 0.005).

Conclusions and implications

Our longitudinal analysis provides the first evidence for accelerated epigenetic aging that emerges between birth and age 3 and associates with low birthweight. These results suggest that early life experiences, such as low birthweight, may shape the trajectory of epigenetic aging in early childhood. Furthermore, accelerated epigenetic aging may be a pathway that links low birthweight and poor adult health outcomes.

Biomarkers of aging through the life course: A Recent Literature Update

Purpose of review

The development of biomarkers of aging has greatly advanced epidemiological studies of aging processes. However, much debate remains on the timing of aging onset and the causal relevance of these biomarkers. In this review, we discuss the most recent biomarkers of aging that have been applied across the life course.

Recent findings

The most recently developed aging biomarkers that have been applied across the life course can be designated into three categories: epigenetic clocks, epigenetic markers of chronic inflammation, and mitochondrial DNA copy number. While these have been applied at different life stages, the development, validation, and application of these markers has been largely centered on populations of older adults. Few studies have examined trajectories of aging biomarkers across the life course. As the wealth of molecular and biochemical data increases, emerging biomarkers may be able to capture complex and system-specific aging processes. Recently developed biomarkers include novel epigenetic clocks; clocks based on ribosomal DNA, transcriptomic profiles, proteomics, metabolomics, and inflammatory markers; clonal hematopoiesis of indeterminate potential gene mutations; and multi-omics approaches.

Summary

Attention should be placed on aging at early and middle life stages to better understand trajectories of aging biomarkers across the life course. Additionally, novel biomarkers will provide greater insight into aging processes. The specific mechanisms of aging reflected by these biomarkers should be considered when interpreting results.