Correlates of life course physical activity in participants of the Baltimore longitudinal study of aging

Physical activity is consistently associated with better health and longer life spans. However, the extent to which length and intensity of exercise across the life course impact health outcomes relative to current activity is undefined. Participants of the Baltimore Longitudinal Study of Aging were asked to categorize their level of physical activity in each decade of life from adolescence to the current decade. In linear mixed effects models, self-reported past levels of physical activity were significantly associated with activity assessed at study visits in the corresponding decade of life either by questionnaire or accelerometry. A pattern of life course physical activity (LCPA) derived by ranking participants on reported activity intensity across multiple decades was consistent with the trajectories of activity estimated from standard physical activity questionnaires assessed at prior study visits. In multivariable linear regression models LCPA was associated with clinical characteristics, measures of body composition and indicators of physical performance independent of current physical activity. After adjustment for minutes of high intensity exercise, LCPA remained significantly associated with peak VO2, fasting glucose, thigh muscle area and density, abdominal subcutaneous fat, usual gait speed, lower extremity performance, and multimorbidity (all p < 0.01) at the index visit. The observed associations suggest that an estimate of physical activity across decades provides complementary information to information on current activity and reemphasizes the importance of consistently engaging in physical activity over the life course.

Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization

Purpose

Two-dimensional single-slice abdominal computed tomography (CT) provides a detailed tissue map with high resolution allowing quantitative characterization of relationships between health conditions and aging. However, longitudinal analysis of body composition changes using these scans is difficult due to positional variation between slices acquired in different years, which leads to different organs/tissues being captured.

Approach

To address this issue, we propose C-SliceGen, which takes an arbitrary axial slice in the abdominal region as a condition and generates a pre-defined vertebral level slice by estimating structural changes in the latent space.

Results

Our experiments on 2608 volumetric CT data from two in-house datasets and 50 subjects from the 2015 Multi-Atlas Abdomen Labeling Challenge Beyond the Cranial Vault (BTCV) dataset demonstrate that our model can generate high-quality images that are realistic and similar. We further evaluate our method's capability to harmonize longitudinal positional variation on 1033 subjects from the Baltimore longitudinal study of aging dataset, which contains longitudinal single abdominal slices, and confirmed that our method can harmonize the slice positional variance in terms of visceral fat area.

Conclusion

This approach provides a promising direction for mapping slices from different vertebral levels to a target slice and reducing positional variance for single-slice longitudinal analysis. The source code is available at: https://github.com/MASILab/C-SliceGen.

Cross-sectional analysis of healthy individuals across decades: Aging signatures across multiple physiological compartments

The study of age-related biomarkers from different biofluids and tissues within the same individual might provide a more comprehensive understanding of age-related changes within and between compartments as these changes are likely highly interconnected. Understanding age-related differences by compartments may shed light on the mechanism of their reciprocal interactions, which may contribute to the phenotypic manifestations of aging. To study such possible interactions, we carried out a targeted metabolomic analysis of plasma, skeletal muscle, and urine collected from healthy participants, age 22-92 years, and identified 92, 34, and 35 age-associated metabolites, respectively. The metabolic pathways that were identified across compartments included inflammation and cellular senescence, microbial metabolism, mitochondrial health, sphingolipid metabolism, lysosomal membrane permeabilization, vascular aging, and kidney function.

Plant Protein but Not Animal Protein Consumption Is Associated with Frailty through Plasma Metabolites

There is evidence that the association of protein intake and frailty may depend on the source of dietary protein. The mechanism underlying this association is not clear. In this study, we explore circulating metabolites as mediators of the relationship between dietary protein and of frailty in participants of the Baltimore Longitudinal Study of Aging (BLSA). Cross-sectional analyses in 735 BLSA participants of associations between plant and animal protein intake and frailty. Usual protein intake from plant and animal sources were estimated with a Food Frequency Questionnaire (FFQ) and frailty was assessed with a 44-item Frailty Index (FI). Compared with the lowest quartile, higher quartiles of plant, but not animal, protein were associated with lower FI. Twenty-five plasma metabolites were associated with plant protein intake; of these, fifteen, including phosphatidylcholines, cholesterol esters, sphingomyelins, and indole metabolites, mediated the association between plant protein intake and FI. The protective association between plant protein consumption and FI is mediated by lower abundance of lipid metabolites and higher abundance of tryptophan-related metabolites.

Single slice thigh CT muscle group segmentation with domain adaptation and self-training

Purpose

Thigh muscle group segmentation is important for assessing muscle anatomy, metabolic disease, and aging. Many efforts have been put into quantifying muscle tissues with magnetic resonance (MR) imaging, including manual annotation of individual muscles. However, leveraging publicly available annotations in MR images to achieve muscle group segmentation on single-slice computed tomography (CT) thigh images is challenging.

Approach

We propose an unsupervised domain adaptation pipeline with self-training to transfer labels from three-dimensional MR to single CT slices. First, we transform the image appearance from MR to CT with CycleGAN and feed the synthesized CT images to a segmenter simultaneously. Single CT slices are divided into hard and easy cohorts based on the entropy of pseudo-labels predicted by the segmenter. After refining easy cohort pseudo-labels based on anatomical assumption, self-training with easy and hard splits is applied to fine-tune the segmenter.

Results

On 152 withheld single CT thigh images, the proposed pipeline achieved a mean Dice of 0.888 (0.041) across all muscle groups, including gracilis, hamstrings, quadriceps femoris, and sartorius muscle.

Conclusions

To our best knowledge, this is the first pipeline to achieve domain adaptation from MR to CT for thigh images. The proposed pipeline effectively and robustly extracts muscle groups on two-dimensional single-slice CT thigh images. The container is available for public use in GitHub repository available at: https://github.com/MASILab/DA_CT_muscle_seg.

Longitudinal Variability Analysis on Low-dose Abdominal CT with Deep Learning-based Segmentation

Metabolic health is increasingly implicated as a risk factor across conditions from cardiology to neurology, and efficiency assessment of body composition is critical to quantitatively characterizing these relationships. 2D low dose single slice computed tomography (CT) provides a high resolution, quantitative tissue map, albeit with a limited field of view. Although numerous potential analyses have been proposed in quantifying image context, there has been no comprehensive study for low-dose single slice CT longitudinal variability with automated segmentation. We studied a total of 1816 slices from 1469 subjects of Baltimore Longitudinal Study on Aging (BLSA) abdominal dataset using supervised deep learning-based segmentation and unsupervised clustering method. 300 out of 1469 subjects that have two year gap in their first two scans were pick out to evaluate longitudinal variability with measurements including intraclass correlation coefficient (ICC) and coefficient of variation (CV) in terms of tissues/organs size and mean intensity. We showed that our segmentation methods are stable in longitudinal settings with Dice ranged from 0.821 to 0.962 for thirteen target abdominal tissues structures. We observed high variability in most organ with ICC<0.5, low variability in the area of muscle, abdominal wall, fat and body mask with average ICC≥0.8. We found that the variability in organ is highly related to the cross-sectional position of the 2D slice. Our efforts pave quantitative exploration and quality control to reduce uncertainties in longitudinal analysis.

Label efficient segmentation of single slice thigh CT with two-stage pseudo labels

Purpose: Muscle, bone, and fat segmentation from thigh images is essential for quantifying body composition. Voxelwise image segmentation enables quantification of tissue properties including area, intensity, and texture. Deep learning approaches have had substantial success in medical image segmentation, but they typically require a significant amount of data. Due to the high cost of manual annotation, training deep learning models with limited human label data is desirable, but it is a challenging problem. Approach: Inspired by transfer learning, we proposed a two-stage deep learning pipeline to address the thigh and lower leg segmentation issue. We studied three datasets, 3022 thigh slices and 8939 lower leg slices from the BLSA dataset and 121 thigh slices from the GESTALT study. First, we generated pseudo labels for thigh based on approximate handcrafted approaches using CT intensity and anatomical morphology. Then, those pseudo labels were fed into deep neural networks to train models from scratch. Finally, the first stage model was loaded as the initialization and fine-tuned with a more limited set of expert human labels of the thigh. Results: We evaluated the performance of this framework on 73 thigh CT images and obtained an average Dice similarity coefficient (DSC) of 0.927 across muscle, internal bone, cortical bone, subcutaneous fat, and intermuscular fat. To test the generalizability of the proposed framework, we applied the model on lower leg images and obtained an average DSC of 0.823. Conclusions: Approximated handcrafted pseudo labels can build a good initialization for deep neural networks, which can help to reduce the need for, and make full use of, human expert labeled data.

A computational solution for bolstering reliability of epigenetic clocks: Implications for clinical trials and longitudinal tracking

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.

Accelerating 2D Abdominal Organ Segmentation with Active Learning

Abdominal computed tomography CT imaging enables assessment of body habitus and organ health. Quantification of these health factors necessitates semantic segmentation of key structures. Deep learning efforts have shown remarkable success in automating segmentation of abdominal CT, but these methods largely rely on 3D volumes. Current approaches are not applicable when single slice imaging is used to minimize radiation dose. For 2D abdominal organ segmentation, lack of 3D context and variety in acquired image levels are major challenges. Deep learning approaches for 2D abdominal organ segmentation benefit by adding more images with manual annotation, but annotation is resource intensive to acquire given the large quantity and the requirement of expertise. Herein, we designed a gradient based active learning annotation framework by meta-parameterizing and optimizing the exemplars to dynamically select the 'hard cases' to achieve better results with fewer annotated slices to reduce the annotation effort. With the Baltimore Longitudinal Study on Aging (BLSA) cohort, we evaluated the performance with starting from 286 subjects and added 50 more subjects iteratively to 586 subjects in total. We compared the amount of data required to add to achieve the same Dice score between using our proposed method and the random selection in terms of Dice. When achieving 0.97 of the maximum Dice, the random selection needed 4.4 times more data compared with our active learning framework. The proposed framework maximizes the efficacy of manual efforts and accelerates learning.

Quantification of muscle, bones, and fat on single slice thigh CT

Muscle, bone, and fat segmentation of CT thigh slice is essential for body composition research. Voxel-wise image segmentation enables quantification of tissue properties including area, intensity and texture. Deep learning approaches have had substantial success in medical image segmentation, but they typically require substantial data. Due to high cost of manual annotation, training deep learning models with limited human labelled data is desirable but also a challenging problem. Inspired by transfer learning, we proposed a two-stage deep learning pipeline to address this issue in thigh segmentation. We study 2836 slices from Baltimore Longitudinal Study of Aging (BLSA) and 121 slices from Genetic and Epigenetic Signatures of Translational Aging Laboratory Testing (GESTALT). First, we generated pseudo-labels based on approximate hand-crafted approaches using CT intensity and anatomical morphology. Then, those pseudo labels are fed into deep neural networks to train models from scratch. Finally, the first stage model is loaded as initialization and fine-tuned with a more limited set of expert human labels. We evaluate the performance of this framework on 56 thigh CT scans and obtained average Dice of 0.979,0.969,0.953,0.980 and 0.800 for five tissues: muscle, cortical bone, internal bone, subcutaneous fat and intermuscular fat respectively. We evaluated generalizability by manually reviewing external 3504 BLSA single thighs from 1752 thigh slices. The result is consistent and passed human review with 150 failed thigh images, which demonstrates that the proposed method has strong generalizability.

Epigenetic Age Acceleration and Hearing: Observations From the Baltimore Longitudinal Study of Aging

Objectives: Age-related hearing loss (ARHL) is highly prevalent among older adults, but the potential mechanisms and predictive markers for ARHL are lacking. Epigenetic age acceleration has been shown to be predictive of many age-associated diseases and mortality. However, the association between epigenetic age acceleration and hearing remains unknown. Our study aims to investigate the relationship between epigenetic age acceleration and audiometric hearing in the Baltimore Longitudinal Study of Aging (BLSA).

Methods: Participants with both DNA methylation and audiometric hearing measurements were included. The main independent variables are epigenetic age acceleration measures, including intrinsic epigenetic age acceleration—“IEAA,” Hannum age acceleration—“AgeAccelerationResidualHannum,” PhenoAge acceleration—“AgeAccelPheno,” GrimAge acceleration—“AgeAccelGrim,” and methylation-based pace of aging estimation—“DunedinPoAm.” The main dependent variable is speech-frequency pure tone average. Linear regression was used to assess the association between epigenetic age acceleration and hearing.

Results: Among the 236 participants (52.5% female), after adjusting for age, sex, race, time difference between measurements, cardiovascular factors, and smoking history, the effect sizes were 0.11 995% CI: (–0.00, 0.23), p = 0.054] for Hannum’s clock, 0.08 [95% CI: (–0.03, 0.19), p = 0.143] for Horvath’s clock, 0.10 [95% CI: (–0.01, 0.21), p = 0.089] for PhenoAge, 0.20 [95% CI: (0.06, 0.33), p = 0.004] for GrimAge, and 0.21 [95% CI: (0.09, 0.33), p = 0.001] for DunedinPoAm.

Discussion: The present study suggests that some epigenetic age acceleration measurements are associated with hearing. Future research is needed to study the potential subclinical cardiovascular causes of hearing and to investigate the longitudinal relationship between DNA methylation and hearing.

Personality traits are consistently associated with blood mitochondrial DNA copy number estimated from genome sequences in two genetic cohort studies

Background

Mitochondrial DNA copy number (mtDNAcn) in tissues and blood can be altered in conditions like diabetes and major depression and may play a role in aging and longevity. However, little is known about the association between mtDNAcn and personality traits linked to emotional states, metabolic health, and longevity. This study tests the hypothesis that blood mtDNAcn is related to personality traits and mediates the association between personality and mortality.

Methods

We assessed the big five personality domains and facets using the Revised NEO Personality Inventory (NEO-PI-R), assessed depressive symptoms with the Center for Epidemiologic Studies Depression Scale (CES-D), estimated mtDNAcn levels from whole-genome sequencing, and tracked mortality in participants from the Baltimore Longitudinal Study of Aging. Results were replicated in the SardiNIA Project.

Results

We found that mtDNAcn was negatively associated with the Neuroticism domain and its facets and positively associated with facets from the other four domains. The direction and size of the effects were replicated in the SardiNIA cohort and were robust to adjustment for potential confounders in both samples. Consistent with the Neuroticism finding, higher depressive symptoms were associated with lower mtDNAcn. Finally, mtDNAcn mediated the association between personality and mortality risk.

Conclusions

To our knowledge, this is the first study to show a replicable association between mtDNAcn and personality. Furthermore, the results support our hypothesis that mtDNAcn is a biomarker of the biological process that explains part of the association between personality and mortality.

Funding

Support for this work was provided by the Intramural Research Program of the National Institute on Aging (Z01-AG000693, Z01-AG000970, and Z01-AG000949) and the National Institute of Neurological Disorders and Stroke of the National Institutes of Health. AT was also supported by the National Institute on Aging of the National Institutes of Health Grant R01AG068093.

DNA methylation signatures reveal that distinct combinations of transcription factors specify human immune cell epigenetic identity

Epigenetic reprogramming underlies specification of immune cell lineages, but patterns that uniquely define immune cell types and the mechanisms by which they are established remain unclear. Here, we identified lineage-specific DNA methylation signatures of six immune cell types from human peripheral blood and determined their relationship to other epigenetic and transcriptomic patterns. Sites of lineage-specific hypomethylation were associated with distinct combinations of transcription factors in each cell type. By contrast, sites of lineage-specific hypermethylation were restricted mostly to adaptive immune cells. PU.1 binding sites were associated with lineage-specific hypo- and hypermethylation in different cell types, suggesting that it regulates DNA methylation in a context-dependent manner. These observations indicate that innate and adaptive immune lineages are specified by distinct epigenetic mechanisms via combinatorial and context-dependent use of key transcription factors. The cell-specific epigenomics and transcriptional patterns identified serve as a foundation for future studies on immune dysregulation in diseases and aging.

Mitochondrial DNA copy number and heteroplasmy load correlate with skeletal muscle oxidative capacity by P31 MR spectroscopy

The association between blood‐based estimates of mitochondrial DNA parameters, mitochondrial DNA copy number (mtDNA‐CN) and heteroplasmy load, with skeletal muscle bioenergetic capacity was evaluated in 230 participants of the Baltimore Longitudinal Study of Aging (mean age:74.7 years, 53% women). Participants in the study sample had concurrent data on muscle oxidative capacity (τ_PCr) assessed by ³¹P magnetic resonance spectroscopy, and mitochondrial DNA parameters estimated from whole‐genome sequencing data. In multivariable linear regression models, adjusted for age, sex, extent of phosphocreatine (PCr) depletion, autosomal sequencing coverage, white blood cell total, and differential count, as well as platelet count, mtDNA‐CN and heteroplasmy load were not significantly associated with τ_PCr (both p > 0.05). However, in models evaluating whether the association between mtDNA‐CN and τ_PCr varied by heteroplasmy load, there was a significant interaction between mtDNA‐CN and heteroplasmy load (p = 0.037). In stratified analysis, higher mtDNA‐CN was significantly associated with lower τ_PCr among participants with high heteroplasmy load (n = 84, β (SE) = −0.236 (0.115), p‐value = 0.044), but not in those with low heteroplasmy load (n = 146, β (SE) = 0.046 (0.119), p‐value = 0.702). Taken together, mtDNA‐CN and heteroplasmy load provide information on muscle bioenergetics. Thus, mitochondrial DNA parameters may be considered proxy measures of mitochondrial function that can be used in large epidemiological studies, especially when comparing subgroups.

Mitochondrial genetic variation is enriched in G-quadruplex regions that stall DNA synthesis in vitro

As the powerhouses of the eukaryotic cell, mitochondria must maintain their genomes which encode proteins essential for energy production. Mitochondria are characterized by guanine-rich DNA sequences that spontaneously form unusual three-dimensional structures known as G-quadruplexes (G4). G4 structures can be problematic for the essential processes of DNA replication and transcription because they deter normal progression of the enzymatic-driven processes. In this study, we addressed the hypothesis that mitochondrial G4 is a source of mutagenesis leading to base-pair substitutions. Our computational analysis of 2757 individual genomes from two Italian population cohorts (SardiNIA and InCHIANTI) revealed a statistically significant enrichment of mitochondrial mutations within sequences corresponding to stable G4 DNA structures. Guided by the computational analysis results, we designed biochemical reconstitution experiments and demonstrated that DNA synthesis by two known mitochondrial DNA polymerases (Pol γ, PrimPol) in vitro was strongly blocked by representative stable G4 mitochondrial DNA structures, which could be overcome in a specific manner by the ATP-dependent G4-resolving helicase Pif1. However, error-prone DNA synthesis by PrimPol using the G4 template sequence persisted even in the presence of Pif1. Altogether, our results suggest that genetic variation is enriched in G-quadruplex regions that impede mitochondrial DNA replication.

Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

BACKGROUND

Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field.

RESULTS

Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels.

CONCLUSION

This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

Proteomics and Epidemiological Models of Human Aging

Human aging is associated with a decline of physical and cognitive function and high susceptibility to chronic diseases, which is influenced by genetics, epigenetics, environmental, and socio-economic status. In order to identify the factors that modulate the aging process, established measures of aging mechanisms are required, that are both robust and feasible in humans. It is also necessary to connect these measures to the phenotypes of aging and their functional consequences. In this review, we focus on how this has been addressed from an epidemiologic perspective using proteomics. The key aspects of epidemiological models of aging can be incorporated into proteomics and other omics which can provide critical detailed information on the molecular and biological processes that change with age, thus unveiling underlying mechanisms that drive multiple chronic conditions and frailty, and ideally facilitating the identification of new effective approaches for prevention and treatment.

Chromatin accessibility differences of human monocyte identify age associated inflammatory heterogeneity

Human aging is associated with low grade chronic inflammation termed, inflammaging, which is a significant risk factor for morbidity and mortality in elderly. The innate immune system is master regulator of this process via regulating expression of cytokines and other key mediators. We assayed chromatin landscape of the human monocytes during healthy aging with an aim to predict the inflammation associated contemporary and imminent states of monocytes. Our deep sequencing ATAC-seq data from healthy donors of the GESTALT cohort identified, chromatin landscape signatures defining heterogeneity of monocytes. Differentially open chromatin (DOC) clustered old individual monocytes in two distinct groups, while young donors demonstrated ethnicity-based differences. DOC’s heterogeneity of old donors was characterized by presence of intergenic enhancer-like elements dominated by the DNA binding motif of the transcription factor NF-κB. We identified the relationships of age-associated DOC with DNA methylation, gene expression and proteome of monocytes and serum. Finally, the intracellular cytokine staining analysis using multi-parameter flow cytometry demonstrated the DOC’s influence on the monocyte’s response to LPS stimulation. Our data revels the age associated heterogenous functional states of monocytes which can help us understanding decision making mechanisms during an immune response.

Age-associated difference in circulating ACE2, the gateway for SARS-COV-2, in humans: results from the InCHIANTI study

Levels of angiotensin-converting enzyme 2 (ACE2), the gateway for COVID-19 virus into the cells, have been implicated in worse COVID-19 outcomes associated with aging and cardiovascular disease (CVD). Data on age-associated differences in circulating ACE2 levels in humans and the role of CVD and medications is limited. We analyzed data from 967 participants of the InCHIANTI study, a community-dwelling cohort in the Chianti region, Italy. Relative abundance of ACE2 in plasma was assessed using a proteomics platform. CVD diagnoses, use of renin-angiotensin-aldosterone system (RAAS) antagonists: ACEi, ARBs, and aldosterone antagonists, were ascertained. Multiple linear analyses were performed to examine the independent association of ACE2 with age, CVD, and RAAS antagonist use. Age was independently associated with lower log (ACE2) in persons aged ≥ 55 years (STD β = - 0.12, p = 0.0002). ACEi treatment was also independently associated with significantly lower ACE2 levels, and ACE2 was inversely associated with weight, and positively associated with peripheral artery disease (PAD) status. There was a trend toward higher circulating ACE2 levels in hypertensive individuals, but it did not reach statistical significance. In a stratified analysis, the association between log (ACE2) and log (IL-6) was more evidenced in participants with PAD. Circulating ACE2 levels demonstrate curvilinear association with age, with older individuals beyond the sixth decade age having lower levels. ACEi was associated with greater circulating ACE2 levels. Interestingly, ACE2 was elevated in PAD and positively associated with inflammatory markers, suggesting compensatory upregulation in the setting of chronic inflammation. Further studies are needed to comprehensively characterize RAAS components with aging and disease, and assess its prognostic role in predicting COVID-19 outcomes.

Blood DNA Methylation and Aging: A Cross-Sectional Analysis and Longitudinal Validation in the InCHIANTI Study

Changes in DNA methylation have been found to be highly correlated with aging in humans, but causes or consequences of these changes are not understood. We characterized the DNA methylomes of several hundred people in the Invecchiare in Chianti study to identify DNA sites in which percent methylation was systematically different with age. Then, we tested the hypothesis that changes of percent methylation in the same DNA sites occur longitudinally for the same DNA sites in the same subjects. We identified six differentially methylated regions in which percent methylation showed robust longitudinal changes in the same direction. We then describe functions of the genes near these differentially methylated regions and their potential relationship with aging, noting that the genes appear to regulate metabolism or cell type specificity. The nature of transcription factor binding sites in the vicinity of these differentially methylated regions suggest that these age-associated methylation changes reflect modulation of two biological mechanisms: the polycomb repressive complex 2, a protein complex that trimethylates histone H3 on lysine 27, and the transcriptional repressor CCCTC-binding factor or CTCF, both of which are regulators of chromatin architecture. These findings are consistent with the idea that changes in methylation with aging are of adaptive nature.

Blood DNA methylation sites predict death risk in a longitudinal study of 12, 300 individuals

DNA methylation has fundamental roles in gene programming and aging that may help predict mortality. However, no large-scale study has investigated whether site-specific DNA methylation predicts all-cause mortality. We used the Illumina-HumanMethylation450-BeadChip to identify blood DNA methylation sites associated with all-cause mortality for 12, 300 participants in 12 Cohorts of the Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium. Over an average 10-year follow-up, there were 2,561 deaths across the cohorts. Nine sites mapping to three intergenic and six gene-specific regions were associated with mortality (P < 9.3x10^-7) independently of age and other mortality predictors. Six sites (cg14866069, cg23666362, cg20045320, cg07839457, cg07677157, cg09615688)—mapping respectively to BMPR1B, MIR1973, IFITM3, NLRC5, and two intergenic regions—were associated with reduced mortality risk. The remaining three sites (cg17086398, cg12619262, cg18424841)—mapping respectively to SERINC2, CHST12, and an intergenic region—were associated with increased mortality risk. DNA methylation at each site predicted 5%–15% of all deaths. We also assessed the causal association of those sites to age-related chronic diseases by using Mendelian randomization, identifying weak causal relationship between cg18424841 and cg09615688 with coronary heart disease. Of the nine sites, three (cg20045320, cg07839457, cg07677157) were associated with lower incidence of heart disease risk and two (cg20045320, cg07839457) with smoking and inflammation in prior CHARGE analyses. Methylation of cg20045320, cg07839457, and cg17086398 was associated with decreased expression of nearby genes (IFITM3, IRF, NLRC5, MT1, MT2, MARCKSL1) linked to immune responses and cardiometabolic diseases. These sites may serve as useful clinical tools for mortality risk assessment and preventative care.

A roadmap to build a phenotypic metric of ageing: insights from the Baltimore Longitudinal Study of Aging

Over the past three decades, considerable effort has been dedicated to quantifying the pace of ageing yet identifying the most essential metrics of ageing remains challenging due to lack of comprehensive measurements and heterogeneity of the ageing processes. Most of the previously proposed metrics of ageing have been emerged from cross-sectional associations with chronological age and predictive accuracy of mortality, thus lacking a conceptual model of functional or phenotypic domains. Further, such models may be biased by selective attrition and are unable to address underlying biological constructs contributing to functional markers of age-related decline. Using longitudinal data from the Baltimore Longitudinal Study of Aging (BLSA), we propose a conceptual framework to identify metrics of ageing that may capture the hierarchical and temporal relationships between functional ageing, phenotypic ageing and biological ageing based on four hypothesized domains: body composition, energy regulation, homeostatic mechanisms and neurodegeneration/neuroplasticity. We explored the longitudinal trajectories of key variables within these phenotypes using linear mixed-effects models and more than 10 years of data. Understanding the longitudinal trajectories across these domains in the BLSA provides a reference for researchers, informs future refinement of the phenotypic ageing framework and establishes a solid foundation for future models of biological ageing.

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Plasma proteomic signature of age in healthy humans

To characterize the proteomic signature of chronological age, 1,301 proteins were measured in plasma using the SOMAscan assay (SomaLogic, Boulder, CO, USA) in a population of 240 healthy men and women, 22-93 years old, who were disease- and treatment-free and had no physical and cognitive impairment. Using a p ≤ 3.83 × 10-5 significance threshold, 197 proteins were positively associated, and 20 proteins were negatively associated with age. Growth differentiation factor 15 (GDF15) had the strongest, positive association with age (GDF15; 0.018 ± 0.001, p = 7.49 × 10-56 ). In our sample, GDF15 was not associated with other cardiovascular risk factors such as cholesterol or inflammatory markers. The functional pathways enriched in the 217 age-associated proteins included blood coagulation, chemokine and inflammatory pathways, axon guidance, peptidase activity, and apoptosis. Using elastic net regression models, we created a proteomic signature of age based on relative concentrations of 76 proteins that highly correlated with chronological age (r = 0.94). The generalizability of our findings needs replication in an independent cohort.

Association of Methylation Signals With Incident Coronary Heart Disease in an Epigenome-Wide Assessment of Circulating Tumor Necrosis Factor α

Importance

Tumor necrosis factor α (TNF-α) is a proinflammatory cytokine with manifold consequences for mammalian pathophysiology, including cardiovascular disease. A deeper understanding of TNF-α biology may enhance treatment precision.

Objective

To conduct an epigenome-wide analysis of blood-derived DNA methylation and TNF-α levels and to assess the clinical relevance of findings.

Design, Setting, and Participants

This meta-analysis assessed epigenome-wide associations in circulating TNF-α concentrations from 5 cohort studies and 1 interventional trial, with replication in 3 additional cohort studies. Follow-up analyses investigated associations of identified methylation loci with gene expression and incident coronary heart disease; this meta-analysis included 11 461 participants who experienced 1895 coronary events.

Exposures

Circulating TNF-α concentration.

Main Outcomes and Measures

DNA methylation at approximately 450 000 loci, neighboring DNA sequence variation, gene expression, and incident coronary heart disease.

Results

The discovery cohort included 4794 participants, and the replication study included 816 participants (overall mean [SD] age, 60.7 [8.5] years). In the discovery stage, circulating TNF-α levels were associated with methylation of 7 cytosine-phosphate-guanine (CpG) sites, 3 of which were located in or near DTX3L-PARP9 at cg00959259 (β [SE] = -0.01 [0.003]; P = 7.36 × 10-8), cg08122652 (β [SE] = -0.008 [0.002]; P = 2.24 × 10-7), and cg22930808(β [SE] = -0.01 [0.002]; P = 6.92 × 10-8); NLRC5 at cg16411857 (β [SE] = -0.01 [0.002]; P = 2.14 × 10-13) and cg07839457 (β [SE] = -0.02 [0.003]; P = 6.31 × 10-10); or ABO, at cg13683939 (β [SE] = 0.04 [0.008]; P = 1.42 × 10-7) and cg24267699 (β [SE] = -0.009 [0.002]; P = 1.67 × 10-7), after accounting for multiple testing. Of these, negative associations between TNF-α concentration and methylation of 2 loci in NLRC5 and 1 in DTX3L-14 PARP9 were replicated. Replicated TNF-α-linked CpG sites were associated with 9% to 19% decreased risk of incident coronary heart disease per 10% higher methylation per CpG site (cg16411857: hazard ratio [HR], 0.86; 95% CI, 0.78-1.95; P = .003; cg07839457: HR, 0.89; 95% CI, 0.80-0.94; P = 3.1 × 10-5; cg00959259: HR, 0.91; 95% CI, 0.84-0.97; P = .002; cg08122652: HR, 0.81; 95% CI, 0.74-0.89; P = 2.0 × 10-5).

Conclusions and Relevance

We identified and replicated novel epigenetic correlates of circulating TNF-α concentration in blood samples and linked these loci to coronary heart disease risk, opening opportunities for validation and therapeutic applications.

Influence of cell distribution and diabetes status on the association between mitochondrial DNA copy number and aging phenotypes in the InCHIANTI study

Mitochondrial DNA copy number (mtDNA-CN) estimated in whole blood is a novel marker of mitochondrial mass and function that can be used in large population-based studies. Analyses that attempt to relate mtDNA-CN to specific aging phenotypes may be confounded by differences in the distribution of blood cell types across samples. Also, low or high mtDNA-CN may have a different meaning given the presence of diseases associated with mitochondrial damage. We evaluated the impact of blood cell type distribution and diabetes status on the association between mtDNA-CN and aging phenotypes, namely chronologic age, interleukin-6, hemoglobin, and all-cause mortality, among 672 participants of the InCHIANTI study. After accounting for white blood cell count, platelet count, and white blood cell proportions in multivariate models, associations of mtDNA-CN with age and interleukin-6 were no longer statistically significant. Evaluation of a statistical interaction by diabetes status suggested heterogeneity of effects in the analysis of mortality (P < 0.01). The magnitude and direction of associations between mtDNA-CN estimated from blood samples and aging phenotypes are influenced by the sample cell type distribution and disease status. Therefore, accounting for these factors may aid understanding of the relevance of mitochondrial DNA copy number to health and aging.

Change in Epigenome-Wide DNA Methylation Over 9 Years and Subsequent Mortality: Results From the InCHIANTI Study

Patterns of DNA methylation (DNAm) that track with aging have been identified. However, the relevance of these patterns for aging outcomes remains unclear. Longitudinal epigenome-wide DNAm information was obtained from the InCHIANTI study, a large representative European population. DNAm was evaluated using the Illumina HumanMethylation450 array on blood samples collected at baseline and 9-year follow-up: observations from 499 participants with paired longitudinal blood sample and information on differential blood count were included in analyses. A total of 56,579 markers were significantly associated with age in cross-sectional analysis of DNAm at year 9, 31,252 markers were changed significantly over the 9-year follow-up, and 16,987 markers were both cross-sectionally associated with age and significantly changed over time. Rates of change at 76 markers and year 9 level of DNAm at 88 markers were identified as strongly associated with mortality in Cox proportional hazard models adjusted for age and relevant covariates (mean follow-up time 4.4 years). Less than 0.05% of markers associated with age or that changed over time were also associated with mortality after adjusting for chronological age. Although the influence of DNAm on health and longevity remains unclear, these findings confirm that aging is associated cross-sectionally and longitudinally with robust and consistent patterns of methylation change.

Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization

The identification and characterization of regional body tissues is essential to understand changes that occur with aging and age-related metabolic diseases such as diabetes and obesity and how these diseases affect trajectories of health and functional status. Imaging technologies are frequently used to derive volumetric, area, and density measurements of different tissues. Despite the significance and direct applicability of automated tissue quantification and characterization techniques, these topics have remained relatively underexplored in the medical image analysis literature. We present a method for identification and characterization of muscle and adipose tissue in the midthigh region using MRI. We propose an image-based muscle quality prediction technique that estimates tissue-specific probability density models and their eigenstructures in the joint domain of water- and fat-suppressed voxel signal intensities along with volumetric and intensity-based tissue characteristics computed during the quantification stage. We evaluated the predictive capability of our approach against reference biomechanical muscle quality (MQ) measurements using statistical tests and classification performance experiments. The reference standard for MQ is defined as the ratio of muscle strength to muscle mass. The results show promise for the development of noninvasive image-based MQ descriptors.

Difference in muscle quality over the adult life span and biological correlates in the Baltimore Longitudinal Study of Aging

OBJECTIVES

To examine differences in a proxy measure of muscle quality across the adult life span and explore potential mechanisms of muscle quality change through identification of cross-sectional correlates of muscle quality.

DESIGN

Cross-sectional study.

SETTING

Baltimore Longitudinal Study of Aging.

PARTICIPANTS

Seven hundred eighty-six individuals with a mean age of 66.3 (range 26-96) (N = 786). A sensitivity analysis was conducted in a subset of participants matched according to sex, muscle mass, and body size.

MEASUREMENTS

Muscle quality was operationalized as the ratio of knee-extension strength (isokinetic dynamometry) to thigh muscle cross-sectional area (computed tomography). Differences in muscle strength, muscle area, and muscle quality ratio with age were evaluated, and the association between the muscle quality ratio and measures reflecting domains of cognitive function, motor control, peripheral nerve function, adiposity, glucose homeostasis, and inflammation were assessed through multivariate regression analyses.

RESULTS

A linear relationship between age and muscle quality ratio was observed, suggesting a gradual decline in muscle quality over the adult life course. Associations were observed between muscle quality ratio and measures of adiposity, as well as peroneal nerve motor conduction velocity, finger tapping speed, and memory performance (P < .01). The association between muscle quality ratio and nerve conduction velocity was maintained after adjustment for anthropometric measurements (P < .05).

CONCLUSION

Muscle quality declines progressively with age over the adult life span and is affected by obesity and neurological factors. Studies are needed to clarify the mechanisms of these associations and their implications for functional outcomes.