Background and Purpose: Clonal hematopoiesis of indeterminate potential (CHIP) is a novel age-related risk factor for cardiovascular disease–related morbidity and mortality. The association of CHIP with risk of incident ischemic stroke was reported previously in an exploratory analysis including a small number of incident stroke cases without replication and lack of stroke subphenotyping. The purpose of this study was to discover whether CHIP is a risk factor for ischemic or hemorrhagic stroke. Methods: We utilized plasma genome sequence data of blood DNA to identify CHIP in 78 752 individuals from 8 prospective cohorts and biobanks. We then assessed the association of CHIP and commonly mutated individual CHIP driver genes ( DNMT3A , TET2 , and ASXL1 ) with any stroke, ischemic stroke, and hemorrhagic stroke. Results: CHIP was associated with an increased risk of total stroke (hazard ratio, 1.14 [95% CI, 1.03–1.27]; P =0.01) after adjustment for age, sex, and race. We observed associations with CHIP with risk of hemorrhagic stroke (hazard ratio, 1.24 [95% CI, 1.01–1.51]; P =0.04) and with small vessel ischemic stroke subtypes. In gene-specific association results, TET2 showed the strongest association with total stroke and ischemic stroke, whereas DMNT3A and TET2 were each associated with increased risk of hemorrhagic stroke. Conclusions: CHIP is associated with an increased risk of stroke, particularly with hemorrhagic and small vessel ischemic stroke. Future studies clarifying the relationship between CHIP and subtypes of stroke are needed.

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Type 2 diabetes (T2D) is associated with reduced gut microbiome diversity, although the cause is unclear. Metabolites generated by gut microbes also appear to be causative factors in T2D. We therefore searched for serum metabolites predictive of gut microbiome diversity in 1018 females from TwinsUK with concurrent metabolomic profiling and microbiome composition. We generated a Microbial Metabolites Diversity (MMD) score of six circulating metabolites that explained over 18% of the variance in microbiome alpha diversity. Moreover, the MMD score was associated with a significantly lower odds of prevalent (OR[95%CI] = 0.22[0.07;0.70], P = .01) and incident T2D (HR[95%CI] = 0.31[0.11,0.90], P = .03). We replicated our results in 1522 individuals from the ARIC study (prevalent T2D: OR[95%CI] = 0.79[0.64,0.96], P = .02, incident T2D: HR[95%CI] = 0.87[0.79,0.95], P = .003). The MMD score mediated 28%[15%,94%] of the total effect of gut microbiome on T2D after adjusting for confounders. Metabolites predicting higher microbiome diversity included 3-phenylpropionate(hydrocinnamate), indolepropionate, cinnamoylglycine and 5-alpha-pregnan-3beta,20 alpha-diol monosulfate(2) of which indolepropionate and phenylpropionate have already been linked to lower incidence of T2D. Metabolites correlating with lower microbial diversity included glutarate and imidazole propionate, of which the latter has been implicated in insulin resistance. Our results suggest that the effect of gut microbiome diversity on T2D is largely mediated by microbial metabolites, which might be modifiable by diet.

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite–disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

Objective—: Alterations in the serum metabolome may be detectable in at-risk individuals before the onset of coronary heart disease (CHD). Identifying metabolomic signatures associated with CHD may provide insight into disease pathophysiology and prevention. Approach and Results—: Metabolomic profiling (chromatography-mass spectrometry) was performed in 2232 African Americans and 1366 European Americans from the ARIC study (Atherosclerosis Risk in Communities). We applied Cox regression with least absolute shrinkage and selection operator to select metabolites associated with incident CHD. A metabolite risk score was constructed to evaluate whether the metabolite risk score predicted CHD risk beyond traditional risk factors. After 30 years of follow-up, we observed 633 incident CHD cases. Thirty-two metabolites were selected by least absolute shrinkage and selection operator to be associated with CHD, and 19 of the 32 showed significant individual associations with CHD, including a sugar substitute, erythritol. Theophylline (hazard ratio [95% CI] =1.16 [1.09–1.25]) and gamma-linolenic acid (hazard ratio [95% CI] =0.89 [0.81–0.97]) showed the greatest positive and negative associations with CHD, respectively. A 1 SD greater standardized metabolite risk score was associated with a 1.37-fold higher risk of CHD (hazard ratio [95% CI] =1.37 [1.27–1.47]). Adding the metabolite risk score to the traditional risk factors significantly improved model predictive performance (30-year risk prediction: Δ C statistics [95% CI] =0.016 [0.008–0.024], continuous net reclassification index [95% CI] =0.522 [0.480–0.556], integrated discrimination index [95% CI] =0.038 [0.019–0.065]). Conclusions—: We identified 19 metabolites from known and novel metabolic pathways that collectively improved CHD risk prediction. Visual Overview—: An online visual overview is available for this article.