Integrating Genetic and Transcriptomic Data to Identify Genes Underlying Obesity Risk Loci.

Genome-wide association studies (GWAS) have identified numerous body mass index (BMI) loci. However, most underlying mechanisms from risk locus to BMI remain unknown. Leveraging omics data through integrative analyses could provide more comprehensive views of biological pathways on BMI. We analyzed genotype and blood gene expression data in up to 5,619 samples from the Framingham Heart Study (FHS). Using 3,992 single nucleotide polymorphisms (SNPs) at 97 BMI loci and 20,692 transcripts within 1 Mb, we performed separate association analyses of transcript with BMI and SNP with transcript (PBMI and PSNP, respectively) and then a correlated meta-analysis between the full summary data sets (PMETA). We identified transcripts that met Bonferroni-corrected significance for each omic, were more significant in the correlated meta-analysis than each omic, and were at least nominally associated with BMI in FHS data. Among 308 significant SNP-transcript-BMI associations, we identified seven genes (NT5C2, GSTM3, SNAPC3, SPNS1, TMEM245, YPEL3, and ZNF646) in five association regions. Using an independent sample of blood gene expression data, we validated results for SNAPC3 and YPEL3. We tested for generalization of these associations in hypothalamus, nucleus accumbens, and liver and observed significant (PMETA<0.05 & PMETA

Identifying People at High Risk for Severe Aortic Stenosis: Aortic Valve Calcium Versus Lipoprotein(a) and Low-Density Lipoprotein Cholesterol.

BACKGROUND: Aortic valve calcification (AVC), Lp(a) [lipoprotein(a)], and low-density lipoprotein cholesterol (LDL-C) are associated with severe aortic stenosis (AS). We aimed to determine which of these risk factors were most strongly associated with the risk of incident severe AS. METHODS: A total of 6792 participants from the MESA study (Multi-Ethnic Study of Atherosclerosis) had computed tomography-quantified AVC, Lp(a), and LDL-C values at MESA visit 1 (2000-2002). We calculated the absolute event rate of incident adjudicated severe AS per 1000 person-years and performed multivariable adjusted Cox proportional hazards regression. RESULTS: The mean age was 62 years old, and 47% were women. Over a median 16.7-year follow-up, the rate of incident severe AS increased exponentially with higher AVC, regardless of Lp(a) or LDL-C values. Participants with AVC=0 had a very low rate of severe AS even with elevated Lp(a) ≥50 mg/dL (<0.1/1000 person-years) or LDL-C ≥130 mg/dL (0.1/1000 person-years). AVC >0 was strongly associated with severe AS when Lp(a) <50 mg/dL hazard ratio (HR) of 33.8 (95% CI, 16.4-70.0) or ≥50 mg/dL HR of 61.5 (95% CI, 7.7-494.2) and when LDL-C <130 mg/dL HR of 31.1 (95% CI, 14.4-67.1) or ≥130 mg/dL HR of 50.2 (95% CI, 13.2-191.9). CONCLUSIONS: AVC better identifies people at high risk for severe AS compared with Lp(a) or LDL-C, and people with AVC=0 have a very low long-term rate of severe AS regardless of Lp(a) or LDL-C level. These results suggest AVC should be the preferred prognostic risk marker to identify patients at high risk for severe AS, which may help inform participant selection for future trials testing novel strategies to prevent severe AS.

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Plasma Proteomics of Exercise Blood Pressure and Incident Hypertension.

Importance: Blood pressure response during acute exercise (exercise blood pressure [EBP]) is associated with the future risk of hypertension and cardiovascular disease (CVD). Biochemical characterization of EBP could inform disease biology and identify novel biomarkers of future hypertension. Objective: To identify protein markers associated with EBP and test their association with incident hypertension. Design, Setting, and Participants: This study assayed 4977 plasma proteins in 681 healthy participants (from 763 assessed) of the Health, Risk Factors, Exercise Training and Genetics (HERITAGE; data collection from January 1993 to December 1997 and plasma proteomics from January 2019 to January 2020) Family Study at rest who underwent 2 cardiopulmonary exercise tests. Individuals were free of CVD at the time of recruitment. Individuals with resting SBP ≥160 mm Hg or DBP ≥100 mm Hg or taking antihypertensive drug therapy were excluded from the study. The association between resting plasma protein levels to both resting BP and EBP was evaluated. Proteins associated with EBP were analyzed for their association with incident hypertension in the Framingham Heart Study (FHS; n = 1177) and validated in the Jackson Heart Study (JHS; n = 772) and Multi-Ethnic Study of Atherosclerosis (MESA; n = 1367). Proteins associated with incident hypertension were tested for putative causal links in approximately 700 000 individuals using cis-protein quantitative loci mendelian randomization (cis-MR). Data were analyzed from January 2023 to January 2024. Exposures: Plasma proteins. Main Outcomes and Measures: EBP was defined as the BP response during a fixed workload (50 W) on a cycle ergometer. Hypertension was defined as BP ≥140/90 mm Hg or taking antihypertensive medication. Results: Among the 681 participants in the HERITAGE Family Study, the mean (SD) age was 34 (13) years; 366 participants (54%) were female; 238 (35%) were self-reported Black and 443 (65%) were self-reported White. Proteomic profiling of EBP revealed 34 proteins that would not have otherwise been identified through profiling of resting BP alone. Transforming growth factor ß receptor 3 (TGFBR3) and prostaglandin D2 synthase (PTGDS) had the strongest association with exercise systolic BP (SBP) and diastolic BP (DBP), respectively (TGFBR3: exercise SBP, ß estimate, -3.39; 95% CI, -4.79 to -2.00; P = 2.33 × 10-6; PTGDS: exercise DBP ß estimate, -2.50; 95% CI, -3.29 to -1.70; P = 1.18 × 10-9). In fully adjusted models, TGFBR3 was inversely associated with incident hypertension in FHS, JHS, and MESA (hazard ratio [HR]: FHS, 0.86; 95% CI, 0.75-0.97; P = .01; JHS, 0.87; 95% CI, 0.77-0.97; P = .02; MESA, 0.84; 95% CI, 0.71-0.98; P = .03; pooled cohort, 0.86; 95% CI, 0.79-0.92; P = 6 × 10-5). Using cis-MR, genetically predicted levels of TGFBR3 were associated with SBP, hypertension, and CVD events (SBP: ß, -0.38; 95% CI, -0.64 to -0.11; P = .006; hypertension: odds ratio [OR], 0.99; 95% CI, 0.98-0.99; P < .001; heart failure with hypertension: OR, 0.86; 95% CI, 0.77-0.97; P = .01; CVD: OR, 0.84; 95% CI, 0.77-0.92; P = 8 × 10-5; cerebrovascular events: OR, 0.77; 95% CI, 0.70-0.85; P = 5 × 10-7). Conclusions and Relevance: Plasma proteomic profiling of EBP identified a novel protein, TGFBR3, which may protect against elevated BP and long-term CVD outcomes.

Association of aortic valve calcium with dementia and stroke: The Multi-Ethnic Study of Atherosclerosis.

BACKGROUND AND AIMS: Calcific aortic valve disease is associated with increased thrombin formation, platelet activation, decreased fibrinolysis, and subclinical brain infarcts. We examined the long-term association of aortic valve calcification (AVC) with newly diagnosed dementia and incident stroke in the Multi-Ethnic Study of Atherosclerosis (MESA). METHODS: AVC was measured using non-contrast cardiac CT at Visit 1. We examined AVC as a continuous (log-transformed) and categorical variable (0, 1-99, 100-299, ≥300). Newly diagnosed dementia was adjudicated using International Classification of Disease codes. Stroke was adjudicated from medical records. We calculated absolute event rates (per 1000 person-years) and multivariable adjusted Cox proportional hazards ratios (HR). RESULTS: Overall, 6812 participants had AVC quantified with a mean age of 62.1 years old, 52.9 % were women, and the median 10-year estimated atherosclerotic cardiovascular disease (ASCVD) risk was 13.5 %. Participants with AVC >0 were older and less likely to be women compared to those with AVC=0. Over a median 16-year follow-up, there were 535 cases of dementia and 376 cases of stroke. The absolute risk of newly diagnosed dementia increased in a stepwise pattern with higher AVC scores, and stroke increased in a logarithmic pattern. In multivariable analyses, AVC was significantly associated with newly diagnosed dementia as a log-transformed continuous variable (HR 1.09; 95 % CI 1.04-1.14) and persons with AVC ≥300 had nearly a two-fold higher risk (HR 1.77; 95 % CI 1.14-2.76) compared to those with AVC=0. AVC was associated with an increased risk of stroke after adjustment for age, sex, and race/ethnicity, but not after adjustment for ASCVD risk factors. CONCLUSIONS: After multivariable adjustment, AVC >0 was significantly associated with an increased risk of newly diagnosed dementia, but not incident stroke. This suggests that AVC may be an important risk factor for the long-term risk of dementia beyond traditional ASCVD risk factors.

LXR signaling pathways link cholesterol metabolism with risk for prediabetes and diabetes.

BACKGROUNDPreclinical studies suggest that cholesterol accumulation leads to insulin resistance. We previously reported that alterations in a monocyte cholesterol metabolism transcriptional network (CMTN) - suggestive of cellular cholesterol accumulation - were cross-sectionally associated with obesity and type 2 diabetes (T2D). Here, we sought to determine whether the CMTN alterations independently predict incident prediabetes/T2D risk, and correlate with cellular cholesterol accumulation.METHODSMonocyte mRNA expression of 11 CMTN genes was quantified among 934 Multi-Ethnic Study of Atherosclerosis (MESA) participants free of prediabetes/T2D; cellular cholesterol was measured in a subset of 24 monocyte samples.RESULTSDuring a median 6-year follow-up, lower expression of 3 highly correlated LXR target genes - ABCG1 and ABCA1 (cholesterol efflux) and MYLIP (cholesterol uptake suppression) - and not other CMTN genes, was significantly associated with higher risk of incident prediabetes/T2D. Lower expression of the LXR target genes correlated with higher cellular cholesterol levels (e.g., 47% of variance in cellular total cholesterol explained by ABCG1 expression). Further, adding the LXR target genes to overweight/obesity and other known predictors significantly improved prediction of incident prediabetes/T2D.CONCLUSIONThese data suggest that the aberrant LXR/ABCG1-ABCA1-MYLIP pathway (LAAMP) is a major T2D risk factor and support a potential role for aberrant LAAMP and cellular cholesterol accumulation in diabetogenesis.FUNDINGThe MESA Epigenomics and Transcriptomics Studies were funded by NIH grants 1R01HL101250, 1RF1AG054474, R01HL126477, R01DK101921, and R01HL135009. This work was supported by funding from NIDDK R01DK103531 and NHLBI R01HL119962.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Association analysis between an epigenetic risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

Low MBOAT7 expression, a genetic risk for MASH, promotes a pro-fibrotic pathway involving hepatocyte TAZ upregulation.

BACKGROUND AND AIMS: The common genetic variant rs641738 C>T is a risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH), including liver fibrosis, and is associated with decreased expression of the phospholipid-remodeling enzyme MBOAT7 (LPIAT1). However, whether restoring MBOAT7 expression in established MASLD dampens the progression to liver fibrosis and, importantly, the mechanism through which decreased MBOAT7 expression exacerbates MASH fibrosis remain unclear. APPROACH AND RESULTS: We first showed that hepatocyte MBOAT7 restoration in mice with diet-induced steatohepatitis slows the progression to liver fibrosis. Conversely, when hepatocyte-MBOAT7 was silenced in mice with established hepatosteatosis, liver fibrosis but not hepatosteatosis was exacerbated. Mechanistic studies revealed that hepatocyte-MBOAT7 restoration in MASH mice lowered hepatocyte-TAZ (WWTR1), which is known to promote MASH fibrosis. Conversely, hepatocyte-MBOAT7 silencing enhanced TAZ upregulation in MASH. Finally, we discovered that changes in hepatocyte phospholipids due to MBOAT7 loss-of-function promote a cholesterol trafficking pathway that upregulates TAZ and the TAZ-induced profibrotic factor Indian hedgehog (IHH). As evidence for relevance in humans, we found that the livers of individuals with MASH carrying the rs641738-T allele had higher hepatocyte nuclear TAZ, indicating higher TAZ activity, and increased IHH mRNA. CONCLUSIONS: This study provides evidence for a novel mechanism linking MBOAT7-LoF to MASH fibrosis; adds new insight into an established genetic locus for MASH; and, given the druggability of hepatocyte TAZ for MASH fibrosis, suggests a personalized medicine approach for subjects at increased risk for MASH fibrosis due to inheritance of variants that lower MBOAT7.

Determinants of mosaic chromosomal alteration fitness.

Clonal hematopoiesis (CH) is characterized by the acquisition of a somatic mutation in a hematopoietic stem cell that results in a clonal expansion. These driver mutations can be single nucleotide variants in cancer driver genes or larger structural rearrangements called mosaic chromosomal alterations (mCAs). The factors that influence the variations in mCA fitness and ultimately result in different clonal expansion rates are not well understood. We used the Passenger-Approximated Clonal Expansion Rate (PACER) method to estimate clonal expansion rate as PACER scores for 6,381 individuals in the NHLBI TOPMed cohort with gain, loss, and copy-neutral loss of heterozygosity mCAs. Our mCA fitness estimates, derived by aggregating per-individual PACER scores, were correlated (R2 = 0.49) with an alternative approach that estimated fitness of mCAs in the UK Biobank using population-level distributions of clonal fraction. Among individuals with JAK2 V617F clonal hematopoiesis of indeterminate potential or mCAs affecting the JAK2 gene on chromosome 9, PACER score was strongly correlated with erythrocyte count. In a cross-sectional analysis, genome-wide association study of estimates of mCA expansion rate identified a TCL1A locus variant associated with mCA clonal expansion rate, with suggestive variants in NRIP1 and TERT.

Genetic Subtypes of Prediabetes, Healthy Lifestyle, and Risk of Type 2 Diabetes.

Prediabetes is a heterogenous metabolic state with various risks for development of type 2 diabetes (T2D). In this study, we used genetic data on 7,227 US Hispanic/Latino participants without diabetes from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and 400,149 non-Hispanic White participants without diabetes from the UK Biobank (UKBB) to calculate five partitioned polygenetic risk scores (pPRSs) representing various pathways related to T2D. Consensus clustering was performed in participants with prediabetes in HCHS/SOL (n = 3,677) and UKBB (n = 16,284) separately based on these pPRSs. Six clusters of individuals with prediabetes with distinctive patterns of pPRSs and corresponding metabolic traits were identified in the HCHS/SOL, five of which were confirmed in the UKBB. Although baseline glycemic traits were similar across clusters, individuals in cluster 5 and cluster 6 showed an elevated risk of T2D during follow-up compared with cluster 1 (risk ratios [RRs] 1.29 [95% CI 1.08, 1.53] and 1.34 [1.13, 1.60], respectively). Inverse associations between a healthy lifestyle score and risk of T2D were observed across different clusters, with a suggestively stronger association observed in cluster 5 compared with cluster 1. Among individuals with a healthy lifestyle, those in cluster 5 had a similar risk of T2D compared with those in cluster 1 (RR 1.03 [0.91, 1.18]). This study identified genetic subtypes of prediabetes that differed in risk of progression to T2D and in benefits from a healthy lifestyle.

Methylation patterns associated with C-reactive protein in racially and ethnically diverse populations.

Systemic low-grade inflammation is a feature of chronic disease. C-reactive protein (CRP) is a common biomarker of inflammation and used as an indicator of disease risk; however, the role of inflammation in disease is not completely understood. Methylation is an epigenetic modification in the DNA which plays a pivotal role in gene expression. In this study we evaluated differential DNA methylation patterns associated with blood CRP level to elucidate biological pathways and genetic regulatory mechanisms to improve the understanding of chronic inflammation. The racially and ethnically diverse participants in this study were included as 50% White, 41% Black or African American, 7% Hispanic or Latino/a, and 2% Native Hawaiian, Asian American, American Indian, or Alaska Native (total n = 13,433) individuals. We replicated 113 CpG sites from 87 unique loci, of which five were novel (CADM3, NALCN, NLRC5, ZNF792, and cg03282312), across a discovery set of 1,150 CpG sites associated with CRP level (p < 1.2E-7). The downstream pathways affected by DNA methylation included the identification of IFI16 and IRF7 CpG-gene transcript pairs which contributed to the innate immune response gene enrichment pathway along with NLRC5, NOD2, and AIM2. Gene enrichment analysis also identified the nuclear factor-kappaB transcription pathway. Using two-sample Mendelian randomization (MR) we inferred methylation at three CpG sites as causal for CRP levels using both White and Black or African American MR instrument variables. Overall, we identified novel CpG sites and gene transcripts that could be valuable in understanding the specific cellular processes and pathogenic mechanisms involved in inflammation.

MagicalRsq-X: A cross-cohort transferable genotype imputation quality metric.

Since genotype imputation was introduced, researchers have been relying on the estimated imputation quality from imputation software to perform post-imputation quality control (QC). However, this quality estimate (denoted as Rsq) performs less well for lower-frequency variants. We recently published MagicalRsq, a machine-learning-based imputation quality calibration, which leverages additional typed markers from the same cohort and outperforms Rsq as a QC metric. In this work, we extended the original MagicalRsq to allow cross-cohort model training and named the new model MagicalRsq-X. We removed the cohort-specific estimated minor allele frequency and included linkage disequilibrium scores and recombination rates as additional features. Leveraging whole-genome sequencing data from TOPMed, specifically participants in the BioMe, JHS, WHI, and MESA studies, we performed comprehensive cross-cohort evaluations for predominantly European and African ancestral individuals based on their inferred global ancestry with the 1000 Genomes and Human Genome Diversity Project data as reference. Our results suggest MagicalRsq-X outperforms Rsq in almost every setting, with 7.3%-14.4% improvement in squared Pearson correlation with true R2, corresponding to 85-218 K variant gains. We further developed a metric to quantify the genetic distances of a target cohort relative to a reference cohort and showed that such metric largely explained the performance of MagicalRsq-X models. Finally, we found MagicalRsq-X saved up to 53 known genome-wide significant variants in one of the largest blood cell trait GWASs that would be missed using the original Rsq for QC. In conclusion, MagicalRsq-X shows superiority for post-imputation QC and benefits genetic studies by distinguishing well and poorly imputed lower-frequency variants.

Genome-wide analysis in over 1 million individuals of European ancestry yields improved polygenic risk scores for blood pressure traits.

Hypertension affects more than one billion people worldwide. Here we identify 113 novel loci, reporting a total of 2,103 independent genetic signals (P < 5 × 10-8) from the largest single-stage blood pressure (BP) genome-wide association study to date (n = 1,028,980 European individuals). These associations explain more than 60% of single nucleotide polymorphism-based BP heritability. Comparing top versus bottom deciles of polygenic risk scores (PRSs) reveals clinically meaningful differences in BP (16.9 mmHg systolic BP, 95% CI, 15.5-18.2 mmHg, P = 2.22 × 10-126) and more than a sevenfold higher odds of hypertension risk (odds ratio, 7.33; 95% CI, 5.54-9.70; P = 4.13 × 10-44) in an independent dataset. Adding PRS into hypertension-prediction models increased the area under the receiver operating characteristic curve (AUROC) from 0.791 (95% CI, 0.781-0.801) to 0.826 (95% CI, 0.817-0.836, ∆AUROC, 0.035, P = 1.98 × 10-34). We compare the 2,103 loci results in non-European ancestries and show significant PRS associations in a large African-American sample. Secondary analyses implicate 500 genes previously unreported for BP. Our study highlights the role of increasingly large genomic studies for precision health research.

Longitudinal change in mitochondrial heteroplasmy exhibits positive selection for deleterious variants.

A common feature of human aging is the acquisition of somatic mutations, and mitochondria are particularly prone to mutation due to their inefficient DNA repair and close proximity to reactive oxygen species, leading to a state of mitochondrial DNA heteroplasmy1,2. Cross-sectional studies have demonstrated that detection of heteroplasmy increases with participant age3, a phenomenon that has been attributed to genetic drift4-7. In this first large-scale longitudinal study, we measured heteroplasmy in two prospective cohorts (combined n=1405) at two timepoints (mean time between visits, 8.6 years), demonstrating that deleterious heteroplasmies were more likely to increase in variant allele fraction (VAF). We further demonstrated that increase in VAF was associated with increased risk of overall mortality. These results challenge the claim that somatic mtDNA mutations arise mainly due to genetic drift, instead demonstrating positive selection for predicted deleterious mutations at the cellular level, despite an negative impact on overall mortality.

Association analysis between an epigenetic alcohol risk score and blood pressure.

Background: Epigenome-wide association studies have revealed multiple DNA methylation sites (CpGs) associated with alcohol consumption, an important lifestyle risk factor for cardiovascular diseases. Results: We generated an alcohol consumption epigenetic risk score (ERS) based on previously reported 144 alcohol-associated CpGs and examined the association of the ERS with systolic blood pressure (SBP), diastolic blood pressure (DBP), and hypertension (HTN) in 3,898 Framingham Heart Study (FHS) participants. We found an association of alcohol intake with the ERS in the meta-analysis with 0.09 units higher ERS per drink consumed per day (p < 0.0001). Cross-sectional analyses in FHS revealed that a one-unit increment of the ERS was associated with 1.93 mm Hg higher SBP (p = 4.64E-07), 0.68 mm Hg higher DBP (p = 0.006), and an odds ratio of 1.78 for HTN (p < 2E-16). Meta-analysis of the cross-sectional association of the ERS with BP traits in eight independent external cohorts (n = 11,544) showed similar relationships with blood pressure levels, i.e., a one-unit increase in ERS was associated with 0.74 (p = 0.002) and 0.50 (p = 0.0006) mm Hg higher SBP and DBP, but could not confirm the association with hypertension. Longitudinal analyses in FHS (n = 3,260) and five independent external cohorts (n = 4,021) showed that the baseline ERS was not associated with a change in blood pressure over time or with incident HTN. Conclusions: Our findings provide proof-of-concept that utilizing an ERS is a useful approach to capture the recent health consequences of lifestyle behaviors such as alcohol consumption.

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.

Peripheral Blood Mononuclear Cell Gene Expression Associated with Pulmonary Microvascular Perfusion: The Multi-Ethnic Study of Atherosclerosis Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) and emphysema are associated with endothelial damage and altered pulmonary microvascular perfusion. The molecular mechanisms underlying these changes are poorly understood in patients, in part because of the inaccessibility of the pulmonary vasculature. Peripheral blood mononuclear cells (PBMCs) interact with the pulmonary endothelium. Objectives: To test the association between gene expression in PBMCs and pulmonary microvascular perfusion in COPD. Methods: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study recruited two independent samples of COPD cases and controls with ⩾10 pack-years of smoking history. In both samples, pulmonary microvascular blood flow, pulmonary microvascular blood volume, and mean transit time were assessed on contrast-enhanced magnetic resonance imaging, and PBMC gene expression was assessed by microarray. Additional replication was performed in a third sample with pulmonary microvascular blood volume measures on contrast-enhanced dual-energy computed tomography. Differential expression analyses were adjusted for age, gender, race/ethnicity, educational attainment, height, weight, smoking status, and pack-years of smoking. Results: The 79 participants in the discovery sample had a mean age of 69 ± 6 years, 44% were female, 25% were non-White, 34% were current smokers, and 66% had COPD. There were large PBMC gene expression signatures associated with pulmonary microvascular perfusion traits, with several replicated in the replication sets with magnetic resonance imaging (n = 47) or dual-energy contrast-enhanced computed tomography (n = 157) measures. Many of the identified genes are involved in inflammatory processes, including nuclear factor-κB and chemokine signaling pathways. Conclusions: PBMC gene expression in nuclear factor-κB, inflammatory, and chemokine signaling pathways was associated with pulmonary microvascular perfusion in COPD, potentially offering new targetable candidates for novel therapies.

Genome-wide association analysis of composite sleep health scores in 413,904 individuals.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, and together may provide a more complete picture of sleep health, while also illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p<8.3e-9), along with 341 previously reported loci (p<5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2=0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections to behavioral, psychological, and cardiometabolic traits.