A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically determined protein-protein networks.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Lac-Phe mediates the effects of metformin on food intake and body weight.

Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. There is ongoing debate about the mechanisms that mediate metformin's effects on energy balance. Here, we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite N-lactoyl-phenylalanine (Lac-Phe) in cells, in mice and two independent human cohorts. Metformin drives Lac-Phe biosynthesis through the inhibition of complex I, increased glycolytic flux and intracellular lactate mass action. Intestinal epithelial CNDP2+ cells, not macrophages, are the principal in vivo source of basal and metformin-inducible Lac-Phe. Genetic ablation of Lac-Phe biosynthesis in male mice renders animals resistant to the effects of metformin on food intake and body weight. Lastly, mediation analyses support a role for Lac-Phe as a downstream effector of metformin's effects on body mass index in participants of a large population-based observational cohort, the Multi-Ethnic Study of Atherosclerosis. Together, these data establish Lac-Phe as a critical mediator of the body weight-lowering effects of metformin.

Lac-Phe mediates the anti-obesity effect of metformin.

Metformin is a widely prescribed anti-diabetic medicine that also reduces body weight. The mechanisms that mediate metformin's effects on energy balance remain incompletely defined. Here we show that metformin is a powerful pharmacological inducer of the anorexigenic metabolite Lac-Phe in mice as well as in two independent human cohorts. In cell culture, metformin drives Lac-Phe biosynthesis via inhibition of complex I, increased glycolytic flux, and intracellular lactate mass action. Other biguanides and structurally distinct inhibitors of oxidative phosphorylation also increase Lac-Phe levels in vitro. Genetic ablation of CNDP2, the principal biosynthetic enzyme for Lac-Phe, in mice renders animals resistant to metformin's anorexigenic and anti-obesity effects. Mediation analyses also support a role for Lac-Phe in metformin's effect on body mass index in humans. These data establish the CNDP2/Lac-Phe pathway as a critical mediator of the effects of metformin on energy balance.

A parametric bootstrap approach for computing confidence intervals for genetic correlations with application to genetically-determined protein-protein networks.

Genetic correlation refers to the correlation between genetic determinants of a pair of traits. When using individual-level data, it is typically estimated based on a bivariate model specification where the correlation between the two variables is identifiable and can be estimated from a covariance model that incorporates the genetic relationship between individuals, e.g., using a pre-specified kinship matrix. Inference relying on asymptotic normality of the genetic correlation parameter estimates may be inaccurate when the sample size is low, when the genetic correlation is close to the boundary of the parameter space, and when the heritability of at least one of the traits is low. We address this problem by developing a parametric bootstrap procedure to construct confidence intervals for genetic correlation estimates. The procedure simulates paired traits under a range of heritability and genetic correlation parameters, and it uses the population structure encapsulated by the kinship matrix. Heritabilities and genetic correlations are estimated using the close-form, method of moment, Haseman-Elston regression estimators. The proposed parametric bootstrap procedure is especially useful when genetic correlations are computed on pairs of thousands of traits measured on the same exact set of individuals. We demonstrate the parametric bootstrap approach on a proteomics dataset from the Jackson Heart Study.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma.

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Surface Electrical Impedance Myography Detects Skeletal Muscle Atrophy in Aged Wildtype Zebrafish and Aged gpr27 Knockout Zebrafish.

Throughout a vertebrate organism's lifespan, skeletal muscle mass and function progressively decline. This age-related condition is termed sarcopenia. In humans, sarcopenia is associated with risk of falling, cardiovascular disease, and all-cause mortality. As the world population ages, projected to reach 2 billion older adults worldwide in 2050, the economic burden on the healthcare system is also projected to increase considerably. Currently, there are no pharmacological treatments for sarcopenia, and given the long-term nature of aging studies, high-throughput chemical screens are impractical in mammalian models. Zebrafish is a promising, up-and-coming vertebrate model in the field of sarcopenia that could fill this gap. Here, we developed a surface electrical impedance myography (sEIM) platform to assess skeletal muscle health, quantitatively and noninvasively, in adult zebrafish (young, aged, and genetic mutant animals). In aged zebrafish (~85% lifespan) as compared to young zebrafish (~20% lifespan), sEIM parameters (2 kHz phase angle, 2 kHz reactance, and 2 kHz resistance) robustly detected muscle atrophy (p < 0.000001, q = 0.000002; p = 0.000004, q = 0.000006; p = 0.000867, q = 0.000683, respectively). Moreover, these same measurements exhibited strong correlations with an established morphometric parameter of muscle atrophy (myofiber cross-sectional area), as determined by histological-based morphometric analysis (r = 0.831, p = 2 × 10-12; r = 0.6959, p = 2 × 10-8; and r = 0.7220; p = 4 × 10-9, respectively). Finally, the genetic deletion of gpr27, an orphan G-protein coupled receptor (GPCR), exacerbated the atrophy of skeletal muscle in aged animals, as evidenced by both sEIM and histology. In conclusion, the data here show that surface EIM techniques can effectively discriminate between healthy young and sarcopenic aged muscle as well as the advanced atrophied muscle in the gpr27 KO animals. Moreover, these studies show how EIM values correlate with cell size across the animals, making it potentially possible to utilize sEIM as a "virtual biopsy" in zebrafish to noninvasively assess myofiber atrophy, a valuable measure for muscle and gerontology research.

CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels.

N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genomic associations of four plasma N-acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2351 individuals from the Jackson Heart Study. We find that plasma levels of specific N-acyl amino acids are associated with cardiometabolic disease endpoints independent of free amino acid plasma levels and in patterns according to the amino acid head group. By integrating whole genome sequencing data with N-acyl amino acid levels, we identify that the genetic determinants of N-acyl amino acid levels also cluster according to the amino acid head group. Furthermore, we identify the CYP4F2 locus as a genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels in human plasma. In experimental studies, we demonstrate that CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids. These studies provide a structural framework for understanding the regulation and disease associations of N-acyl amino acids in humans and identify that the diversity of this lipid signaling family can be significantly expanded through CYP4F-mediated ω-hydroxylation.

CYP4F2 is a human-specific determinant of circulating N-acyl amino acid levels.

N-acyl amino acids are a large family of circulating lipid metabolites that modulate energy expenditure and fat mass in rodents. However, little is known about the regulation and potential cardiometabolic functions of N-acyl amino acids in humans. Here, we analyze the cardiometabolic phenotype associations and genetic regulation of four plasma N-fatty acyl amino acids (N-oleoyl-leucine, N-oleoyl-phenylalanine, N-oleoyl-serine, and N-oleoyl-glycine) in 2,351 individuals from the Jackson Heart Study. N-oleoyl-leucine and N-oleoyl-phenylalanine were positively associated with traits related to energy balance, including body mass index, waist circumference, and subcutaneous adipose tissue. In addition, we identify the CYP4F2 locus as a human-specific genetic determinant of plasma N-oleoyl-leucine and N-oleoyl-phenylalanine levels. In vitro, CYP4F2-mediated hydroxylation of N-oleoyl-leucine and N-oleoyl-phenylalanine results in metabolic diversification and production of many previously unknown lipid metabolites with varying characteristics of the fatty acid tail group, including several that structurally resemble fatty acid hydroxy fatty acids (FAHFAs). By contrast, FAAH-regulated N-oleoyl-glycine and N-oleoyl-serine were inversely associated with traits related to glucose and lipid homeostasis. These data uncover a human-specific enzymatic node for the metabolism of a subset of N-fatty acyl amino acids and establish a framework for understanding the cardiometabolic roles of individual N-fatty acyl amino acids in humans.

Massive Thymic Hyperplasia Masquerading as Cancer: A Case Report and Review of the Literature.

A 34-year-old woman presenting with abdominal pain, chest pressure, weight loss, and tachycardia was found to have an 11.4-cm anterior mediastinal mass associated with intrathoracic lymphadenopathy on chest computed tomography (Fig. 1A). Core needle biopsy was concerning for a type B1 thymoma. During this patient's initial workup, she was found to have both clinical and laboratory evidence of Graves' thyroiditis, raising diagnostic suspicion for thymic hyperplasia rather than thymoma. The case discussed here highlights the unique challenges that arise in the evaluation and management of thymic masses and serves as a prudent reminder that both benign and malignant disorders may present with mass-like changes.

Mixed meal tolerance testing highlights in diabetes altered branched-chain ketoacid metabolism and pathways associated with all-cause mortality.

BACKGROUND: Elevated BCAA levels are strongly associated with diabetes, but how diabetes affects BCAA, branched-chain ketoacids (BCKAs), and the broader metabolome after a meal is not well known. OBJECTIVE: To compare quantitative BCAA and BCKA levels in a multiracial cohort with and without diabetes after a mixed meal tolerance test (MMTT) as well as to explore the kinetics of additional metabolites and their associations with mortality in self-identified African Americans. METHODS: We administered an MMTT to 11 participants without obesity or diabetes and 13 participants with diabetes (treated with metformin only) and measured the levels of BCKAs, BCAAs, and 194 other metabolites at 8 time points across 5 h. We used mixed models for repeated measurements to compare between group metabolite differences at each timepoint with adjustment for baseline. We then evaluated the association of top metabolites with different kinetics with all-cause mortality in the Jackson Heart Study (JHS) (N = 2441). RESULTS: BCAA levels, after adjustment for baseline, were similar at all timepoints between groups, but adjusted BCKA kinetics were different between groups for α-ketoisocaproate (P = 0.022) and α-ketoisovalerate (P = 0.021), most notably diverging at 120 min post-MMTT. An additional 20 metabolites had significantly different kinetics across timepoints between groups, and 9 of these metabolites-including several acylcarnitines-were significantly associated with mortality in JHS, irrespective of diabetes status. The highest quartile of a composite metabolite risk score was associated with higher mortality (HR:1.57; 1.20, 2.05, P = 0.00094) than the lowest quartile. CONCLUSIONS: BCKA levels remained elevated after an MMTT among participants with diabetes, suggesting that BCKA catabolism may be a key dysregulated process in the interaction of BCAA and diabetes. Metabolites with different kinetics after an MMTT may be markers of dysmetabolism and associated with increased mortality in self-identified African Americans.

Protein Markers of Diabetes Discovered in an African American Cohort.

Proteomics has been used to study type 2 diabetes, but the majority of available data are from White participants. Here, we extend prior work by analyzing a large cohort of self-identified African Americans in the Jackson Heart Study (n = 1,313). We found 325 proteins associated with incident diabetes after adjusting for age, sex, and sample batch (false discovery rate q < 0.05) measured using a single-stranded DNA aptamer affinity-based method on fasting plasma samples. A subset was independent of established markers of diabetes development pathways, such as adiposity, glycemia, and/or insulin resistance, suggesting potential novel biological processes associated with disease development. Thirty-six associations remained significant after additional adjustments for BMI, fasting plasma glucose, cholesterol levels, hypertension, statin use, and renal function. Twelve associations, including the top associations of complement factor H, formimidoyltransferase cyclodeaminase, serine/threonine-protein kinase 17B, and high-mobility group protein B1, were replicated in a meta-analysis of two self-identified White cohorts-the Framingham Heart Study and the Malmö Diet and Cancer Study-supporting the generalizability of these biomarkers. A selection of these diabetes-associated proteins also improved risk prediction. Thus, we uncovered both novel and broadly generalizable associations by studying a diverse population, providing a more complete understanding of the diabetes-associated proteome.

Plasma metabolomic signatures of obesity and risk of type 2 diabetes.

OBJECTIVE: The mechanisms linking obesity to type 2 diabetes (T2D) are not fully understood. This study aimed to identify obesity-related metabolomic signatures (MESs) and evaluated their relationships with incident T2D. METHODS: In a nested case-control study of 2076 Chinese adults, 140 plasma metabolites were measured at baseline, linear regression was applied with the least absolute shrinkage and selection operator to identify MESs for BMI and waist circumference (WC), and conditional logistic regression was applied to examine their associations with T2D risk. RESULTS: A total of 32 metabolites associated with BMI or WC were identified and validated, among which 14 showed positive associations and 3 showed inverse associations with T2D; 8 and 18 metabolites were selected to build MESs for BMI and WC, respectively. Both MESs showed strong linear associations with T2D: odds ratio (95% CI) comparing extreme quartiles was 4.26 (2.00-9.06) for BMI MES and 9.60 (4.22-21.88) for WC MES (both p-trend < 0.001). The MES-T2D associations were particularly evident among individuals with normal WC: odds ratio (95% CI) reached 6.41 (4.11-9.98) for BMI MES and 10.38 (6.36-16.94) for WC MES. Adding MESs to traditional risk factors and plasma glucose improved C statistics from 0.79 to 0.83 (p < 0.001). CONCLUSIONS: Multiple obesity-related metabolites and MESs strongly associated with T2D in Chinese adults were identified.

Proteomic profiling platforms head to head: Leveraging genetics and clinical traits to compare aptamer- and antibody-based methods.

High-throughput proteomic profiling using antibody or aptamer-based affinity reagents is used increasingly in human studies. However, direct analyses to address the relative strengths and weaknesses of these platforms are lacking. We assessed findings from the SomaScan1.3K (N = 1301 reagents), the SomaScan5K platform (N = 4979 reagents), and the Olink Explore (N = 1472 reagents) profiling techniques in 568 adults from the Jackson Heart Study and 219 participants in the HERITAGE Family Study across four performance domains: precision, accuracy, analytic breadth, and phenotypic associations leveraging detailed clinical phenotyping and genetic data. Across these studies, we show evidence supporting more reliable protein target specificity and a higher number of phenotypic associations for the Olink platform, while the Soma platforms benefit from greater measurement precision and analytic breadth across the proteome.

Nontargeted and Targeted Metabolomic Profiling Reveals Novel Metabolite Biomarkers of Incident Diabetes in African Americans.

Nontargeted metabolomics methods have increased potential to identify new disease biomarkers, but assessments of the additive information provided in large human cohorts by these less biased techniques are limited. To diversify our knowledge of diabetes-associated metabolites, we leveraged a method that measures 305 targeted or "known" and 2,342 nontargeted or "unknown" compounds in fasting plasma samples from 2,750 participants (315 incident cases) in the Jackson Heart Study (JHS)-a community cohort of self-identified African Americans-who are underrepresented in omics studies. We found 307 unique compounds (82 known) associated with diabetes after adjusting for age and sex at a false discovery rate of <0.05 and 124 compounds (35 known, including 11 not previously associated) after further adjustments for BMI and fasting plasma glucose. Of these, 144 and 68 associations, respectively, replicated in a multiethnic cohort. Among these is an apparently novel isomer of the 1-deoxyceramide Cer(m18:1/24:0) with functional geonomics and high-resolution mass spectrometry. Overall, known and unknown metabolites provided complementary information (median correlation ρ = 0.29), and their inclusion with clinical risk factors improved diabetes prediction modeling. Our findings highlight the importance of including nontargeted metabolomics methods to provide new insights into diabetes development in ethnically diverse cohorts.

Whole Genome Association Study of the Plasma Metabolome Identifies Metabolites Linked to Cardiometabolic Disease in Black Individuals.

Integrating genetic information with metabolomics has provided new insights into genes affecting human metabolism. However, gene-metabolite integration has been primarily studied in individuals of European Ancestry, limiting the opportunity to leverage genomic diversity for discovery. In addition, these analyses have principally involved known metabolites, with the majority of the profiled peaks left unannotated. Here, we perform a whole genome association study of 2,291 metabolite peaks (known and unknown features) in 2,466 Black individuals from the Jackson Heart Study. We identify 519 locus-metabolite associations for 427 metabolite peaks and validate our findings in two multi-ethnic cohorts. A significant proportion of these associations are in ancestry specific alleles including findings in APOE, TTR and CD36. We leverage tandem mass spectrometry to annotate unknown metabolites, providing new insight into hereditary diseases including transthyretin amyloidosis and sickle cell disease. Our integrative omics approach leverages genomic diversity to provide novel insights into diverse cardiometabolic diseases.

Predictors of Mortality in Hypercalcemia of Advanced Chronic Liver Disease.

OBJECTIVE: Hypercalcemia is sometimes observed in patients with cirrhosis, but very little is known about the epidemiology in patients with hypercalcemia of chronic liver disease (HCLD) or how its presence may modulate the overall mortality risk. We assessed the associations between the clinical and laboratory characteristics of patients with HCLD with 90-day mortality. METHODS: A systematic search of the medical records at our institution over a 10-year period was performed to retrospectively identify subjects with HCLD during inpatient admission. Univariate and multivariable regression analyses were performed to detect the risk factors for all-cause 90-day mortality. RESULTS: Thirty-eight subjects with HCLD were identified using stringent inclusion and exclusion criteria to exclude individuals with other secondary causes of hypercalcemia. A total of 35 subjects had 90-day vital status available, which revealed 40% mortality. The model for end-stage liver disease sodium score and duration of inpatient hypercalcemia were positively associated with mortality with respective odds ratios of 1.23 (95% CI, 1.06-3.23) and 1.24 (95% CI, 1.04-1.49) in a univariate regression model and 1.30 (95% CI, 1.04-1.62) and 1.33 (95% CI, 1.04-1.71) in a multivariable regression model. The admission and peak serum calcium levels were not associated with mortality. Only 6 subjects received bisphosphonates or calcitonin during their admission, limiting our ability to assess the impact of treatment on outcomes. CONCLUSION: In patients admitted to the hospital with HCLD, the duration of hypercalcemia was positively associated with 90-day mortality, providing a potential interventional target to reduce mortality in this high-risk population. Studies to validate the utility of treating hypercalcemia are required.

Understanding Myocardial Metabolism in the Context of Cardio-Oncology.

Cardiovascular events, ranging from arrhythmias to decompensated heart failure, are common during and after cancer therapy. Cardiovascular complications can be life-threatening, and from the oncologist's perspective, could limit the use of first-line cancer therapeutics. Moreover, an aging population increases the risk for comorbidities and medical complexity among patients who undergo cancer therapy. Many have established cardiovascular diagnoses or risk factors before starting these therapies. Therefore, it is essential to understand the molecular mechanisms that drive cardiovascular events in patients with cancer and to identify new therapeutic targets that may prevent and treat these 2 diseases. This review will discuss the metabolic interaction between cancer and the heart and will highlight current strategies of targeting metabolic pathways for cancer treatment. Finally, this review highlights opportunities and challenges in advancing our understanding of myocardial metabolism in the context of cancer and cancer treatment.

Stability and reproducibility of proteomic profiles in epidemiological studies: comparing the Olink and SOMAscan platforms.

Limited data exist on the performance of high-throughput proteomics profiling in epidemiological settings, including the impact of specimen collection and within-person variability over time. Thus, the Olink (972 proteins) and SOMAscan7Kv4.1 (7322 proteoforms of 6596 proteins) assays were utilized to measure protein concentrations in archived plasma samples from the Nurses' Health Studies and Health Professionals Follow-Up Study. Spearman's correlation coefficients (r) and intraclass correlation coefficients (ICCs) were used to assess agreement between (1) 42 triplicate samples processed immediately, 24-h or 48-h after blood collection from 14 participants; and (2) 80 plasma samples from 40 participants collected 1-year apart. When comparing samples processed immediately, 24-h, and 48-h later, 55% of assays had an ICC/r ≥ 0.75 and 87% had an ICC/r ≥ 0.40 in Olink compared to 44% with an ICC/r ≥ 0.75 and 72% with an ICC/r ≥ 0.40 in SOMAscan7K. For both platforms, >90% of the assays were stable (ICC/r ≥ 0.40) in samples collected 1-year apart. Among 817 proteins measured with both platforms, Spearman's correlations were high (r > 0.75) for 14.7% and poor (r < 0.40) for 44.8% of proteins. High-throughput proteomics profiling demonstrated reproducibility in archived plasma samples and stability after delayed processing in epidemiological studies, yet correlations between proteins measured with the Olink and SOMAscan7K platforms were highly variable.

Whole Genome Sequence Analysis of the Plasma Proteome in Black Adults Provides Novel Insights Into Cardiovascular Disease.

BACKGROUND: Plasma proteins are critical mediators of cardiovascular processes and are the targets of many drugs. Previous efforts to characterize the genetic architecture of the plasma proteome have been limited by a focus on individuals of European descent and leveraged genotyping arrays and imputation. Here we describe whole genome sequence analysis of the plasma proteome in individuals with greater African ancestry, increasing our power to identify novel genetic determinants. METHODS: Proteomic profiling of 1301 proteins was performed in 1852 Black adults from the Jackson Heart Study using aptamer-based proteomics (SomaScan). Whole genome sequencing association analysis was ascertained for all variants with minor allele count ≥5. Results were validated using an alternative, antibody-based, proteomic platform (Olink) as well as replicated in the Multi-Ethnic Study of Atherosclerosis and the HERITAGE Family Study (Health, Risk Factors, Exercise Training and Genetics). RESULTS: We identify 569 genetic associations between 479 proteins and 438 unique genetic regions at a Bonferroni-adjusted significance level of 3.8×10-11. These associations include 114 novel locus-protein relationships and an additional 217 novel sentinel variant-protein relationships. Novel cardiovascular findings include new protein associations at the APOE gene locus including ZAP70 (sentinel single nucleotide polymorphism [SNP] rs7412-T, ß=0.61±0.05, P=3.27×10-30) and MMP-3 (ß=-0.60±0.05, P=1.67×10-32), as well as a completely novel pleiotropic locus at the HPX gene, associated with 9 proteins. Further, the associations suggest new mechanisms of genetically mediated cardiovascular disease linked to African ancestry; we identify a novel association between variants linked to APOL1-associated chronic kidney and heart disease and the protein CKAP2 (rs73885319-G, ß=0.34±0.04, P=1.34×10-17) as well as an association between ATTR amyloidosis and RBP4 levels in community-dwelling individuals without heart failure. CONCLUSIONS: Taken together, these results provide evidence for the functional importance of variants in non-European populations, and suggest new biological mechanisms for ancestry-specific determinants of lipids, coagulation, and myocardial function.

Metabolomic Analysis of Coronary Heart Disease in an African American Cohort From the Jackson Heart Study.

Importance: African American individuals have disproportionate rates of coronary heart disease (CHD) but lower levels of coronary artery calcium (CAC), a marker of subclinical CHD, than non-Hispanic White individuals. African American individuals may have distinct metabolite profiles associated with incident CHD risk compared with non-Hispanic White individuals, and examination of these differences could highlight important processes that differ between them. Objectives: To identify novel biomarkers of incident CHD and CAC among African American individuals and to replicate incident CHD findings in a multiethnic cohort. Design, Setting, and Participants: This analysis targeted plasma metabolomic profiling of 2346 participants in the Jackson Heart Study (JHS), a prospective population-based cohort study that included 5306 African American participants who were examined at baseline (2000-2004) and 2 follow-up visits. Replication of CHD-associated metabolites was sought among 1588 multiethnic participants from the Women's Health Initiative (WHI), a prospective population-based multiethnic cohort study of 161 808 postmenopausal women who were examined at baseline (1991-1995) and ongoing follow-up visits. Regression analyses were performed for each metabolite to examine the associations with incident CHD and CAC scores. Data were collected from the WHI between 1994 and 2009 and from the JHS between 2000 and 2015. All data were analyzed from November 2020 to August 2021. Exposures: Plasma metabolites. Main Outcomes and Measures: Incident CHD was defined as definite or probable myocardial infarction or definite fatal CHD in both the JHS and WHI cohorts. In the JHS cohort, silent myocardial infarction between examinations (as determined by electrocardiography) and coronary revascularization were included in the incident CHD analysis. Coronary artery calcium was measured using a 16-channel computed tomographic system and reported as an Agatston score. Results: Among 2346 African American individuals in the JHS cohort, the mean (SD) age was 56 (13) years, and 1468 individuals (62.6%) were female. Among 1588 postmenopausal women in the WHI cohort, the mean (SD) age was 67 (7) years; 217 individuals (13.7%) self-identified as African American, 1219 (76.8%) as non-Hispanic White, and 152 (9.6%) as other races or ethnicities. In the fully adjusted model including 1876 individuals, 46 of 303 targeted metabolites were associated with incident CHD (false discovery rate q <0.100). Data for 32 of the 46 metabolites were available in the WHI cohort, and 13 incident CHD-associated metabolites from the JHS cohort were replicated in the WHI cohort. A total of 1439 participants from the JHS cohort with available CAC scores received metabolomic profiling. Nine metabolites were associated with CAC scores. Minimal overlap was found between the results from the incident CHD and CAC analyses, with only 3 metabolites shared between the 2 analyses. Conclusions and Relevance: This cohort study identified metabolites that were associated with incident CHD among African American individuals, including 13 incident CHD-associated metabolites that were replicated in a multiethnic population and 9 novel metabolites that included N-acylamides, leucine, and lipid species. These findings may help to elucidate common and distinct metabolic processes that may be associated with CHD among individuals with different self-identified race.