Determinants of accelerated metabolomic and epigenetic aging in a UK cohort

Markers of biological aging have potential utility in primary care and public health. We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality. The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.     

Global metabolic profiling to model biological processes of aging in twins

Aging is intimately linked to system‐wide metabolic changes that can be captured in blood. Understanding biological processes of aging in humans could help maintain a healthy aging trajectory and promote longevity. We performed untargeted plasma metabolomics quantifying 770 metabolites on a cross‐sectional cohort of 268 healthy individuals including 125 twin pairs covering human lifespan (from 6 months to 82 years). Unsupervised clustering of metabolic profiles revealed 6 main aging trajectories throughout life that were associated with key metabolic pathways such as progestin steroids, xanthine metabolism, and long‐chain fatty acids. A random forest (RF) model was successful to predict age in adult subjects (≥16 years) using 52 metabolites (R² = .97). Another RF model selected 54 metabolites to classify pediatric and adult participants (out‐of‐bag error = 8.58%). These RF models in combination with correlation network analysis were used to explore biological processes of healthy aging. The models highlighted established metabolites, like steroids, amino acids, and free fatty acids as well as novel metabolites and pathways. Finally, we show that metabolic profiles of twins become more dissimilar with age which provides insights into nongenetic age‐related variability in metabolic profiles in response to environmental exposure.     

Biological age and tempos of aging in women over 60 in connection with their morphofunctional characteristics

Background

The study of aging processes and the changes in morphological, physiological, and functional characteristics that are associated with aging is of great interest not only for researchers, but also for the general public. The aim of the present paper is to study the biological age and tempos of aging in women older than 60 years, including long-lived females (over 90-years-old), and their associations with morphofunctional characteristics.

Results

Somatic traits, body mass components, and functional characteristics were investigated in 119 elderly (between 60 and 74-years-old) and long-lived (over 90-years-old) women in Tiraspol. With the special PC software ‘Diagnostics of Aging: BioAge’ (National Gerontological Center, Moscow, Russia) the biological age and tempos of aging were evaluated in the study participants. The results show close connections between morphofunctional changes, particularly in body mass components, and biological age. The software demonstrated its validity in the estimation of biological age in the group of elderly women. In the homogenous (according to their chronological age) group of women, three subgroups were separated with different tempos of aging: those with lower rates of aging (biological age less than chronological age by two years or more); those consistent with their chronological age, and those with accelerated tempos of aging (biological age higher than chronological age by two years or more).

Conclusions

Morphofunctional characteristics in the studied groups of women demonstrate the trends of age-involutive changes which can be traced through all groups, from those with slow rates of aging, to those with average rates, to those with accelerated tempos of aging, and finally in long-lived women. The results of comparative analysis show that women with accelerated aging are characterized with such traits as lower skeletal muscle mass, lower hand grip strength, and higher metabolic rate. Canonical discriminant analysis revealed a number of morphofunctional characteristics which differentiate the early-aging women from women with average rates of aging: higher BMI values, excessive fat mass, lower skeletal muscle mass and low values of hand grip strength. Thus the presence of such characteristics in elderly women can be considered as additional risk factor towards the early onset of the aging process.     

Circulating levels of monocyte chemoattractant protein‐1 as a potential measure of biological age in mice and frailty in humans

A serum biomarker of biological versus chronological age would have significant impact on clinical care. It could be used to identify individuals at risk of early‐onset frailty or the multimorbidities associated with old age. It may also serve as a surrogate endpoint in clinical trials targeting mechanisms of aging. Here, we identified MCP‐1/CCL2, a chemokine responsible for recruiting monocytes, as a potential biomarker of biological age. Circulating monocyte chemoattractant protein‐1 (MCP‐1) levels increased in an age‐dependent manner in wild‐type (WT) mice. That age‐dependent increase was accelerated in Ercc1^?/Δ and Bubr1^H/H mouse models of progeria. Genetic and pharmacologic interventions that slow aging of Ercc1^?/Δ and WT mice lowered serum MCP‐1 levels significantly. Finally, in elderly humans with aortic stenosis, MCP‐1 levels were significantly higher in frail individuals compared to nonfrail. These data support the conclusion that MCP‐1 can be used as a measure of mammalian biological age that is responsive to interventions that extend healthy aging.     

Effects of age,sex, and genotype on high‐sensitivity metabolomic profiles in the fruit fly,Drosophila melanogaster

Researchers have used whole‐genome sequencing and gene expression profiling to identify genes associated with age, in the hope of understanding the underlying mechanisms of senescence. But there is a substantial gap from variation in gene sequences and expression levels to variation in age or life expectancy. In an attempt to bridge this gap, here we describe the effects of age, sex, genotype, and their interactions on high‐sensitivity metabolomic profiles in the fruit fly, Drosophila melanogaster. Among the 6800 features analyzed, we found that over one‐quarter of all metabolites were significantly associated with age, sex, genotype, or their interactions, and multivariate analysis shows that individual metabolomic profiles are highly predictive of these traits. Using a metabolomic equivalent of gene set enrichment analysis, we identified numerous metabolic pathways that were enriched among metabolites associated with age, sex, and genotype, including pathways involving sugar and glycerophospholipid metabolism, neurotransmitters, amino acids, and the carnitine shuttle. Our results suggest that high‐sensitivity metabolomic studies have excellent potential not only to reveal mechanisms that lead to senescence, but also to help us understand differences in patterns of aging among genotypes and between males and females.     

Proteome-wide profiling reveals dysregulated molecular features and accelerated aging in osteoporosis: A 9.8-year prospective study

The role of circulatory proteomics in osteoporosis is unclear. Proteome-wide profiling holds the potential to offer mechanistic insights into osteoporosis. Serum proteome with 413 proteins was profiled by liquid chromatography–tandem mass spectrometry (LC–MS/MS) at baseline, and the 2nd, and 3rd follow-ups (7704 person-tests) in the prospective Chinese cohorts with 9.8 follow-up years: discovery cohort (n = 1785) and internal validation cohort (n = 1630). Bone mineral density (BMD) was measured using dual-energy X-ray absorptiometry (DXA) at follow-ups 1 through 3 at lumbar spine (LS) and femoral neck (FN). We used the Light Gradient Boosting Machine (LightGBM) to identify the osteoporosis (OP)-related proteomic features. The relationships between serum proteins and BMD in the two cohorts were estimated by linear mixed-effects model (LMM). Meta-analysis was then performed to explore the combined associations. We identified 53 proteins associated with osteoporosis using LightGBM, and a meta-analysis showed that 22 of these proteins illuminated a significant correlation with BMD (p < 0.05). The most common proteins among them were PHLD, SAMP, PEDF, HPTR, APOA1, SHBG, CO6, A2MG, CBPN, RAIN APOD, and THBG. The identified proteins were used to generate the biological age (BA) of bone. Each 1 SD-year increase in KDM-Proage was associated with higher risk of LS-OP (hazard ratio [HR], 1.25; 95% CI, 1.14–1.36, p = 4.96 × 10⁻⁰⁶), and FN-OP (HR, 1.13; 95% CI, 1.02–1.23, p = 9.71 × 10⁻⁰³). The findings uncovered that the apolipoproteins, zymoproteins, complements, and binding proteins presented new mechanistic insights into osteoporosis. Serum proteomics could be a crucial indicator for evaluating bone aging.     

Large-Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC–MS measurements

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age—a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra-high pressure liquid chromatography-quadruple time of flight mass spectrometry (UHPLC- QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small-scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r² = 0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole-3-aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu-pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large-s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC-MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.     

A large-scale candidate gene association study of age at menarche and age at natural menopause

Recent genome-wide association (GWA) studies have identified several novel genetic loci associated with age at menarche and age at natural menopause. However, the stringent significance threshold used in GWA studies potentially led to false negatives and true associations may have been overlooked. Incorporating biologically relevant information, we examined whether common genetic polymorphisms in candidate genes of nine groups of biologically plausible pathways and related phenotypes are associated with age at menarche and age at natural menopause. A total of 18,862 genotyped and imputed single nucleotide polymorphisms (SNPs) in 278 genes were assessed for their associations with these two traits among a total of 24,341 women from the Nurses’ Health Study (NHS, N = 2,287) and the Women’s Genome Health Study (WGHS, N = 22,054). Linear regression was used to assess the marginal association of each SNP with each phenotype. We adjusted for multiple testing within each gene to identify statistically significant SNP associations at the gene level. To evaluate the overall evidence for an excess of statistically significant gene associations over the proportion expected by chance, we applied a one-sample test of proportion to each group of candidate genes. The steroid-hormone metabolism and biosynthesis pathway was found significantly associated with both age at menarche and age at natural menopause (P = 0.040 and 0.011, respectively). In addition, the group of genes associated with precocious or delayed puberty was found significantly associated with age at menarche (P = 0.013), and the group of genes involved in premature ovarian failure with age at menopause (P = 0.025).     

Associations of Aging and Birth Cohort with Body Mass Index in a Biethnic Cohort

Objective: To examine associations of aging and birth cohort with body mass index (BMI) in a biethnic cohort. Research Methods and Procedures: This was a longitudinal closed cohort study of 14, 500 white and African‐American men and women, 45 to 64 years of age, followed for 9 years. Aging was defined as the length of the interval in years between baseline and following visits. Birth cohort was defined by the year in which participants were born. Mixed model analyses were used to examine associations of aging, birth cohort, and BMI in four ethnicity‐gender groups. Results: We found that aging was associated with an increase in BMI in white and African‐American men and women. The associations between aging and BMI were stronger in the younger birth cohorts. Except for white women, younger birth cohort was associated with a higher BMI. After adjusting for aging, birth cohort was associated with an increase in BMI of 0.1 kg/m² [95% confidence interval (95% CI): ?0.1, 0.3] among white women. The corresponding values for African‐American women, white men, and African‐American men are 0.5 kg/m² (95% CI: 0.1, 0.9), 0.6 kg/m² (95% CI: 0.4, 0.8), and 0.6 kg/m² (95% CI: 0.2, 1.0), respectively. Discussion: Our analyses show that, in all except white women, people in this age range who were born later have a higher BMI at the same attained age. In all groups, people who are born later gained more weight as they aged. In general, subjects ages 45 to 64 years gained weight as they aged 9 years.     

A Genome-Wide Association Study of the Metabolic Syndrome in Indian Asian Men

We conducted a two-stage genome-wide association study to identify common genetic variation altering risk of the metabolic syndrome and related phenotypes in Indian Asian men, who have a high prevalence of these conditions. In Stage 1, approximately 317,000 single nucleotide polymorphisms were genotyped in 2700 individuals, from which 1500 SNPs were selected to be genotyped in a further 2300 individuals. Selection for inclusion in Stage 1 was based on four metabolic syndrome component traits: HDL-cholesterol, plasma glucose and Type 2 diabetes, abdominal obesity measured by waist to hip ratio, and diastolic blood pressure. Association was tested with these four traits and a composite metabolic syndrome phenotype. Four SNPs reaching significance level p<5×10⁻⁷ and with posterior probability of association >0.8 were found in genes CETP and LPL, associated with HDL-cholesterol. These associations have already been reported in Indian Asians and in Europeans. Five additional loci harboured SNPs significant at p<10⁻⁶ and posterior probability >0.5 for HDL-cholesterol, type 2 diabetes or diastolic blood pressure. Our results suggest that the primary genetic determinants of metabolic syndrome are the same in Indian Asians as in other populations, despite the higher prevalence. Further, we found little evidence of a common genetic basis for metabolic syndrome traits in our sample of Indian Asian men.     

Genetic associations for two biological age measures point to distinct aging phenotypes

Biological age measures outperform chronological age in predicting various aging outcomes, yet little is known regarding genetic predisposition. We performed genome‐wide association scans of two age‐adjusted biological age measures (PhenoAgeAcceleration and BioAgeAcceleration), estimated from clinical biochemistry markers (Levine et al., 2018; Levine, 2013) in European‐descent participants from UK Biobank. The strongest signals were found in the APOE gene, tagged by the two major protein‐coding SNPs, PhenoAgeAccel—rs429358 (APOE e4 determinant) (p = 1.50 × 10⁻⁷²); BioAgeAccel—rs7412 (APOE e2 determinant) (p = 3.16 × 10⁻⁶⁰). Interestingly, we observed inverse APOE e2 and e4 associations and unique pathway enrichments when comparing the two biological age measures. Genes associated with BioAgeAccel were enriched in lipid related pathways, while genes associated with PhenoAgeAccel showed enrichment for immune system, cell function, and carbohydrate homeostasis pathways, suggesting the two measures capture different aging domains. Our study reaffirms that aging patterns are heterogeneous across individuals, and the manner in which a person ages may be partly attributed to genetic predisposition.     

A genome‐wide association study of the frailty index highlights brain pathways in ageing

Frailty is a common geriatric syndrome and strongly associated with disability, mortality and hospitalization. Frailty is commonly measured using the frailty index (FI), based on the accumulation of a number of health deficits during the life course. The mechanisms underlying FI are multifactorial and not well understood, but a genetic basis has been suggested with heritability estimates between 30 and 45%. Understanding the genetic determinants and biological mechanisms underpinning FI may help to delay or even prevent frailty. We performed a genome‐wide association study (GWAS) meta‐analysis of a frailty index in European descent UK Biobank participants (n = 164,610, 60–70 years) and Swedish TwinGene participants (n = 10,616, 41–87 years). FI calculation was based on 49 or 44 self‐reported items on symptoms, disabilities and diagnosed diseases for UK Biobank and TwinGene, respectively. 14 loci were associated with the FI (p < 5*10⁻⁸). Many FI‐associated loci have established associations with traits such as body mass index, cardiovascular disease, smoking, HLA proteins, depression and neuroticism; however, one appears to be novel. The estimated single nucleotide polymorphism (SNP) heritability of the FI was 11% (0.11, SE 0.005). In enrichment analysis, genes expressed in the frontal cortex and hippocampus were significantly downregulated (adjusted p < 0.05). We also used Mendelian randomization to identify modifiable traits and exposures that may affect frailty risk, with a higher educational attainment genetic risk score being associated with a lower degree of frailty. Risk of frailty is influenced by many genetic factors, including well‐known disease risk factors and mental health, with particular emphasis on pathways in the brain.     

TRP64ARG β3-adrenergic receptor and obesity in Mexican Americans

The β3-adrenergic receptor (β3AR) is expressed in visceral fat and is a regulator of resting metabolic rate, thermogenesis, and lipolysis. We genotyped 61 unrelated Mexican Americans for a variant in the β3AR gene (codon 64 TGG^Trp→CGG^Arg; TRP64ARG). The allele frequency was 0.13. The TRP64ARG variant was significantly associated with an earlier age of onset of non-insulin-dependent diabetes mellitus (41.3 ± 4.6 years vs 55.6 ± 2.6 years; P < 0.02) and in non-diabetics, with elevated 2-h insulin levels during an oral glucose tolerance test (810 ± 120 pmol/l vs 384 ± 6 pmol/l; P < 0.005). Non-diabetic subjects with the variant allele tended to have higher body mass indices (BMI), waist-to-hip ratios, and diastolic blood pressures. The study group was expanded to include 421 related subjects from 31 families in the San Antonio Family Diabetes Study. Using a measured genotype analysis approach to estimate genotype-specific means for each trait, those who were homozygous for the TRP64ARG variant had significantly higher 2-h insulin levels (P = 0.036) and trends towards higher BMI compared to the other two genotypes. We detected no associations of these traits in the TRP64ARG heterozygotes in the larger group. We conclude that the TRP64ARG β3AR variant is a susceptibility gene for several features of the insulin resistance syndrome in Mexican Americans. Since its effects are modest, study design (e.g., subject selection, genetic background, and statistical analyses) may influence which traits are associated with this variant and whether or not the effect is detectable in heterozygotes. Received: 7 April 1997 / Accepted: 22 July 1997     

Age‐associated microRNA expression in human peripheral blood is associated with all‐cause mortality and age‐related traits

Recent studies provide evidence of correlations of DNA methylation and expression of protein‐coding genes with human aging. The relations of microRNA expression with age and age‐related clinical outcomes have not been characterized thoroughly. We explored associations of age with whole‐blood microRNA expression in 5221 adults and identified 127 microRNAs that were differentially expressed by age at P < 3.3 × 10^?4 (Bonferroni‐corrected). Most microRNAs were underexpressed in older individuals. Integrative analysis of microRNA and mRNA expression revealed changes in age‐associated mRNA expression possibly driven by age‐associated microRNAs in pathways that involve RNA processing, translation, and immune function. We fitted a linear model to predict ‘microRNA age’ that incorporated expression levels of 80 microRNAs. MicroRNA age correlated modestly with predicted age from DNA methylation (r = 0.3) and mRNA expression (r = 0.2), suggesting that microRNA age may complement mRNA and epigenetic age prediction models. We used the difference between microRNA age and chronological age as a biomarker of accelerated aging (Δage) and found that Δage was associated with all‐cause mortality (hazards ratio 1.1 per year difference, P = 4.2 × 10^?5 adjusted for sex and chronological age). Additionally, Δage was associated with coronary heart disease, hypertension, blood pressure, and glucose levels. In conclusion, we constructed a microRNA age prediction model based on whole‐blood microRNA expression profiling. Age‐associated microRNAs and their targets have potential utility to detect accelerated aging and to predict risks for age‐related diseases.     

Lifestyle impacts on the aging‐associated expression of biomarkers of DNA damage and telomere dysfunction in human blood

Cellular aging is characterized by telomere shortening, which can lead to uncapping of chromosome ends (telomere dysfunction) and activation of DNA damage responses. There is some evidence that DNA damage accumulates during human aging and that lifestyle factors contribute to the accumulation of DNA damage. Recent studies have identified a set of serum markers that are induced by telomere dysfunction and DNA damage, and these markers showed an increased expression in blood during human aging. Here, we investigated the influence of lifestyle factors (such as exercise, smoking, body mass) on the aging‐associated expression of serum markers of DNA damage (CRAMP, EF‐1α, stathmin, n‐acetyl‐glucosaminidase and chitinase) in comparison with other described markers of cellular aging (p16^INK4a upregulation and telomere shortening) in human peripheral blood. The study shows that lifestyle factors have an age‐independent impact on the expression level of biomarkers of DNA damage. Smoking and increased body mass indices were associated with elevated levels of biomarkers of DNA damage independent of the age of the individuals. In contrast, exercise was associated with an age‐independent reduction in the expression of biomarkers of DNA damage in human blood. The expression of biomarkers of DNA damage correlated positively with p16^INK4a expression and negatively with telomere length in peripheral blood T‐lymphocytes. Together, these data provide experimental evidence that both aging and lifestyle impact on the accumulation of DNA damage during human aging.     

Associations of Variants in FTO and Near MC4R With Obesity Traits in South Asian Indians

Recent genome‐wide association studies show that loci in FTO and melanocortin 4 receptor (MC4R) associate with obesity‐related traits. Outside Western populations the associations between these variants have not always been consistent and in Indians it has been suggested that FTO relates to diabetes without an obvious intermediary obesity phenotype. We investigated the association between genetic variants in FTO (rs9939609) and near MC4R (rs17782313) with obesity‐ and type 2 diabetes (T2DM)‐related traits in a longitudinal birth cohort of 2,151 healthy individuals from the Vellore birth cohort in South India. The FTO locus displayed significant associations with several conventional obesity‐related anthropometric traits. The per allele increase is about 1% for BMI, waist circumference (WC), hip circumference (HC), and waist—hip ratio. Consistent associations were observed for adipose tissue‐specific measurements such as skinfold thickness reinforcing the association with obesity‐related traits. Obesity associations for the MC4R locus were weak or nonsignificant but a signal for height (P < 0.001) was observed. The effect on obesity‐related traits for FTO was seen in adulthood, but not at younger ages. The loci also showed nominal associations with increased blood glucose but these associations were lost on BMI adjustment. The effect of FTO on obesity‐related traits was driven by an urban environmental influence. We conclude that rs9939609 variant in the FTO locus is associated with measures of adiposity and metabolic consequences in South Indians with an enhanced effect associated with urban living. The detection of these associations in Indians is challenging because conventional anthropometric obesity measures work poorly in the Indian “thin‐fat” phenotype.