Sanish Sathyan

Abstract Animal studies show aging varies between individuals as well as between organs within an individual 1–4 , but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20–50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer’s disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5 ), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Abstract Aging is a complex trait characterized by a diverse spectrum of endophenotypes. By utilizing the SomaScan ® proteomic platform in 1,025 participants of the LonGenity cohort (age range: 65–95, 55.7% females), we found that 754 of 4,265 proteins were associated with chronological age. Pleiotrophin (PTN; β[SE] = 0.0262 [0.0012]; p = 3.21 × 10 −86 ), WNT1‐inducible‐signaling pathway protein 2 (WISP‐2; β[SE] = 0.0189 [0.0009]; p = 4.60 × 10 −82 ), chordin‐like protein 1 (CRDL1; β[SE] = 0.0203[0.0010]; p = 1.45 × 10 −77 ), transgelin (TAGL; β[SE] = 0.0215 [0.0011]; p = 9.70 × 10 −71 ), and R‐spondin‐1(RSPO1; β[SE] = 0.0208 [0.0011]; p = 1.09 × 10 −70 ), were the proteins most significantly associated with age. Weighted gene co‐expression network analysis identified two of nine modules (clusters of highly correlated proteins) to be significantly associated with chronological age and demonstrated that the biology of aging overlapped with complex age‐associated diseases and other age‐related traits. The correlation between proteomic age prediction based on elastic net regression and chronological age was 0.8 ( p < 2.2E−16). Pathway analysis showed that inflammatory response, organismal injury and abnormalities, cell and organismal survival, and death pathways were associated with aging. The present study made novel associations between a number of proteins and aging, constructed a proteomic age model that predicted mortality, and suggested possible proteomic signatures possessed by a cohort enriched for familial exceptional longevity.

DNA methylation has been implicated in the etiopathology of various complex disorders. DNA methyltransferases are involved in maintaining and establishing new methylation patterns. The aim of the present study was to investigate the inherent genetic variations within DNA methyltransferase genes in predisposing to susceptibility to schizophrenia. We screened for polymorphisms in DNA methyltransferases, DNMT1, DNMT3A, DNMT3B and DNMT3L in 330 schizophrenia patients and 302 healthy controls for association with Schizophrenia in south Indian population. These polymorphisms were also tested for subgroup analysis with patient's gender, age of onset and family history. DNMT1 rs2114724 (genotype P = .004, allele P = 0.022) and rs2228611 (genotype P = 0.004, allele P = 0.022) were found to be significantly associated at genotypic and allelic level with Schizophrenia in South Indian population. DNMT3B rs2424932 genotype (P = 0.023) and allele (P = 0.0063) increased the risk of developing schizophrenia in males but not in females. DNMT3B rs1569686 (genotype P = 0.027, allele P = 0.033) was found to be associated with early onset of schizophrenia and also with family history and early onset (genotype P = 0.009). DNMT3L rs2070565 (genotype P = 0.007, allele P = 0.0026) confers an increased risk of developing schizophrenia at an early age in individuals with family history. In-silico prediction indicated functional relevance of these SNPs in regulating the gene. These observations might be crucial in addressing and understanding the genetic control of methylation level differences from ethnic viewpoint. Functional significance of genotype variations within the DNMTs indeed suggest that the genetic nature of methyltransferases should be considered while addressing epigenetic events mediated by methylation in Schizophrenia.

Abstract Frailty is a state of decreased physiological reserve and increased vulnerability to adverse outcomes in aging, and is characterized by dysregulation across various biological pathways. Frailty may manifest biologically as alteration in protein expression, possibly regulated at genetic, transcriptional and epigenetic levels. In this study, we examined the proteomic profile associated with frailty defined by an established cumulative frailty index (FI). Using the SomaScan ® assay, 4265 proteins were measured in plasma, of which 55 were positively associated and 88 were negatively associated with the FI. The proteins most strongly associated with frailty were fatty acid‐binding proteins, including fatty acid‐binding protein (FABP) ( p = 1.96 × 10 −19 ) and FABPA ( p = 8.10 × 10 −16 ), leptin ( p = 1.43 × 10 −14 ), and ANTR2 ( p = 7.95 × 10 −20 ). Pathway analysis with the top 143 frailty‐associated proteins revealed enrichment for proteins in pathways related to lipid metabolism, musculoskeletal development and function, cell‐to‐cell signaling and interaction, cellular assembly, and organization. Frailty prediction model constructed with elastic net regression utilizing 110 proteins demonstrated a correlation between predicted frailty and observed frailty ( r = 0.57, p < 2.2 × 10 −16 ). Predicted frailty was also more strongly correlated with chronological age ( r = 0.54, p < 2.2 × 10 −16 ) than observed frailty ( r = 0.37, p = 1.2 × 10 −15 ). This study identified novel proteins and pathways related to frailty that may offer improved frailty phenotyping and prediction.