Publications

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.

BACKGROUNDMost GWAS of plasma proteomics have focused on White individuals of European ancestry, limiting biological insight from other ancestry-enriched protein quantitative loci (pQTL).METHODSWe conducted a discovery GWAS of approximately 3,000 plasma proteins measured by the antibody-based Olink platform in 1,054 Black adults from the Jackson Heart Study (JHS) and validated our findings in the Multi-Ethnic Study of Atherosclerosis (MESA). The genetic architecture of identified pQTLs was further explored through fine mapping and admixture association analysis. Finally, using our pQTL findings, we performed a phenome-wide association study (PheWAS) across 2 large multiethnic electronic health record (EHR) systems in All of Us and BioMe.RESULTSWe identified 1,002 pQTLs for 925 protein assays. Fine mapping and admixture analyses suggested allelic heterogeneity of the plasma proteome across diverse populations. We identified associations for variants enriched in African ancestry, many in diseases that lack precise biomarkers, including cis-pQTLs for cathepsin L (CTSL) and Siglec-9, which were linked with sarcoidosis and non-Hodgkin's lymphoma, respectively. We found concordant associations across clinical diagnoses and laboratory measurements, elucidating disease pathways, including a cis-pQTL associated with circulating CD58, WBC count, and multiple sclerosis.CONCLUSIONSOur findings emphasize the value of leveraging diverse populations to enhance biological insights from proteomics GWAS, and we have made this resource readily available as an interactive web portal.FUNDINGNIH K08 HL161445-01A1; 5T32HL160522-03; HHSN268201600034I; HL133870.

BACKGROUND: Brugada syndrome (BrS) is an inherited arrhythmia syndrome caused by loss-of-function variants in the cardiac sodium channel gene SCN5A (sodium voltage-gated channel alpha subunit 5) in ≈20% of subjects. We identified a family with 4 individuals diagnosed with BrS harboring the rare G145R missense variant in the cardiac transcription factor TBX5 (T-box transcription factor 5) and no SCN5A variant.

METHODS: We generated induced pluripotent stem cells (iPSCs) from 2 members of a family carrying TBX5-G145R and diagnosed with Brugada syndrome. After differentiation to iPSC-derived cardiomyocytes (iPSC-CMs), electrophysiologic characteristics were assessed by voltage- and current-clamp experiments (n=9 to 21 cells per group) and transcriptional differences by RNA sequencing (n=3 samples per group), and compared with iPSC-CMs in which G145R was corrected by CRISPR/Cas9 approaches. The role of platelet-derived growth factor (PDGF)/phosphoinositide 3-kinase (PI3K) pathway was elucidated by small molecule perturbation. The rate-corrected QT (QTc) interval association with serum PDGF was tested in the Framingham Heart Study cohort (n=1893 individuals).

RESULTS: TBX5-G145R reduced transcriptional activity and caused multiple electrophysiologic abnormalities, including decreased peak and enhanced "late" cardiac sodium current (INa), which were entirely corrected by editing G145R to wild-type. Transcriptional profiling and functional assays in genome-unedited and -edited iPSC-CMs showed direct SCN5A down-regulation caused decreased peak INa, and that reduced PDGF receptor (PDGFRA [platelet-derived growth factor receptor α]) expression and blunted signal transduction to PI3K was implicated in enhanced late INa. Tbx5 regulation of the PDGF axis increased arrhythmia risk due to disruption of PDGF signaling and was conserved in murine model systems. PDGF receptor blockade markedly prolonged normal iPSC-CM action potentials and plasma levels of PDGF in the Framingham Heart Study were inversely correlated with the QTc interval (P<0.001).

CONCLUSIONS: These results not only establish decreased SCN5A transcription by the TBX5 variant as a cause of BrS, but also reveal a new general transcriptional mechanism of arrhythmogenesis of enhanced late sodium current caused by reduced PDGF receptor-mediated PI3K signaling.

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.

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.

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.

Cardiovascular disease (CVD) is the leading cause of death in the United States. Hypercholesterolemia is a principal modifiable risk factor for the primary prevention of CVD. In addition to lifestyle modification, statins are an important tool to reduce risk for CVD in selected patients. A useful strategy to identify candidates for statins is to estimate the 10-year risk for CVD through the use of a validated risk calculator. Commonly used calculators include the Framingham risk score and the pooled cohort equation. Multiple randomized controlled trials have shown that statins reduce the risk for CVD in patients without known CVD. Two recent guidelines have proposed an approach to the use of statins in primary prevention of CVD. The American College of Cardiology/American Heart Association and the U.S. Department of Veterans Affairs guidelines form the basis for this discussion. The guidelines differ on the use of advanced testing to modify the 10-year CVD risk estimate and on the need for low-density lipoprotein cholesterol targets to establish the efficacy of statins. Advanced testing with coronary artery calcium measurement may be helpful for patients who are potentially eligible for statin therapy but who are uncertain if they wish to take a statin. In this paper, 2 experts, a preventive cardiologist and a general internist, discuss their approach to the use of statins for primary prevention of CVD and how they would apply the guidelines to an individual patient.

Genetic testing for cardiovascular (CV) disease has had a profound impact on the diagnosis and evaluation of monogenic causes of CV disease, such as hypertrophic and familial cardiomyopathies, long QT syndrome, and familial hypercholesterolemia. The success in genetic testing for monogenic diseases has prompted special interest in utilizing genetic information in the risk assessment of more common diseases such as atherosclerotic cardiovascular disease (ASCVD). Polygenic risk scores (PRS) have been developed to assess the risk of coronary artery disease, which now include millions of single-nucleotide polymorphisms that have been identified through genomewide association studies. Although these PRS have demonstrated a strong association with coronary artery disease in large cross-sectional population studies, there remains intense debate regarding the added value that PRS contributes to existing clinical risk prediction models such as the pooled cohort equations. In this review, we provide a brief background of genetic testing for monogenic drivers of CV disease and then focus on the recent developments in genetic risk assessment of ASCVD, including the use of PRS. We outline the genetic testing that is currently available to all cardiologists in the clinic and discuss the evolving sphere of specialized cardiovascular genetics programs that integrate the expertise of cardiologists, geneticists, and genetic counselors. Finally, we review the possible implications that PRS and pharmacogenomic data may soon have on clinical practice in the care for patients with or at risk of developing ASCVD.

BACKGROUND: Implementation of guideline-directed cholesterol management remains low despite definitive evidence establishing such measures reduce cardiovascular (CV) events, especially in high atherosclerotic CV disease (ASCVD) risk patients. Modern electronic resources now exist that may help improve health care delivery. While electronic medical records (EMR) allow for population health screening, the potential for coupling EMR screening to remotely delivered algorithmic population-based management has been less studied as a way of overcoming barriers to optimal cholesterol management.

METHODS: In an academically affiliated healthcare system, using EMR screening, we sought to identify 1,000 high ASCVD risk patients not meeting guideline-directed low-density lipoprotein-cholesterol (LDL-C) goals within specific system-affiliated primary care practices. Contacted patients received cholesterol education and were offered a remote, guideline-directed, algorithmic cholesterol management program executed by trained but non-licensed "navigators" under professional supervision. Navigators used telephone, proprietary software and internet resources to facilitate algorithm-driven, guideline-based medication initiation/titration, and laboratory testing until patients achieved LDL-C goals or exited the program. As a clinical effectiveness program for cholesterol guideline implementation, comparison was made to those contacted patients who declined program-based medication management, and received education only, along with their usual care.

RESULTS: 1021 patients falling into guideline-defined high ASCVD risk groups warranting statin therapy (ASCVD, type 2 diabetes, LDL ≥ 190 mg/dL, calculated 10-year ASCVD risk ≥7.5%) and not achieving guideline-defined target LDL-C levels and/or therapy were identified and contacted. Among the 698 such patients who opted for program medication management, significant LDL-C reductions occurred in the total cohort (mean -65.4 mg/dL, 45% decrease), and each high ASCVD risk subgroup: ASCVD (-57.2 mg/dL, -48.0%); diabetes mellitus (-53.1 mg/dL, -40.0%); severe hypercholesterolemia (-76.3 mg/dL, -45.7%); elevated ASCVD 10-year risk (-62.8 mg/dL, -41.1%) (P<0.001 for all), without any significant complications. Among 20% of participants with reported statin intolerance, average LDL-C decreased from baseline 143 mg/dL to 85 mg/dL using mainly statins and ezetimibe, with limited PCSK9 inhibitor use. In comparison, eligible high ASCVD risk patients who were contacted but opted for education only, a 17% LDL-C decrease occurred over a similar timeframe, with 80% remaining with an LDL-C over 100 mg/dL.

CONCLUSIONS: A remote, algorithm-driven, navigator-executed cholesterol management program successfully identified high ASCVD risk undertreated patients using EMR screening and was associated with significantly improved guideline-directed LDL-C control, supporting this approach as a novel strategy for improving health care access and delivery.