Impaired Kidney Function, Cerebral Small Vessel Disease and Cognitive Disorders: The Framingham Heart Study.

BACKGROUND AND HYPOTHESIS: It remains unclear if the relation of chronic kidney disease (CKD) with cognitive dysfunction is independent of blood pressure (BP). We evaluated kidney function in relation to premorbid BP measurements, cerebral small vessel disease (CSVD) and incident mild cognitive impairment (MCI) and dementia in Framingham Offspring Cohort participants. METHODS: We included Framingham Offspring participants free of dementia, attending an examination during midlife (exam cycle 6, baseline) for ascertainment of kidney function status, with brain MRI late in life (exam cycles 7-9), cognitive outcome data and available interim hypertension and blood pressure assessments. We related CKD (estimated glomerular filtration rate < 60 ml/min/1.73m2) and albuminuria (urine albumin-to-creatinine ratio ≥ 30 mg/g) to CSVD markers and cognitive outcomes using multivariable regression analyses. RESULTS: Among 2604 participants (mean age 67.4 ± 9.2, 64% women, 7% had CKD and 9% albuminuria), albuminuria was independently associated with covert infarcts (adjusted OR, 1.55 [1.00-2.38]; P = 0.049) and incident MCI and dementia (adjusted HR, 1.68 [1.18-2.41]; P = 0.005 and 1.71, [1.11-2.64]; P = 0.015, respectively). CKD was not associated with CSVD markers but was associated with higher risk of incident dementia (HR, 1.53 [1.02-2.29]; P = 0.041), While albuminuria was predictive of the Alzheimer's disease subtype (Adjusted HR = 1.68, [1.03-2.74]; P = 0.04), CKD was predictive of vascular dementia (Adjusted HR, 2.78, [1.16-6.68]; P = 0.023). CONCLUSIONS: Kidney disease was associated with CSVD and cognitive disorders in asymptomatic community dwelling participants. The relation was independent of premorbid BP, suggesting that the link between kidney and brain disease may involve additional mechanisms beyond blood pressure related injury.

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.

Targeted Proteomics Reveals Functional Targets for Early Diabetes Susceptibility in Young Adults.

BACKGROUND: The circulating proteome may encode early pathways of diabetes susceptibility in young adults for surveillance and intervention. Here, we define proteomic correlates of tissue phenotypes and diabetes in young adults. METHODS: We used penalized models and principal components analysis to generate parsimonious proteomic signatures of diabetes susceptibility based on phenotypes and on diabetes diagnosis across 184 proteins in >2000 young adults in the CARDIA (Coronary Artery Risk Development in Young Adults study; mean age, 32 years; 44% women; 43% Black; mean body mass index, 25.6±4.9 kg/m2), with validation against diabetes in >1800 individuals in the FHS (Framingham Heart Study) and WHI (Women's Health Initiative). RESULTS: In 184 proteins in >2000 young adults in CARDIA, we identified 2 proteotypes of diabetes susceptibility-a proinflammatory fat proteotype (visceral fat, liver fat, inflammatory biomarkers) and a muscularity proteotype (muscle mass), linked to diabetes in CARDIA and WHI/FHS. These proteotypes specified broad mechanisms of early diabetes pathogenesis, including transorgan communication, hepatic and skeletal muscle stress responses, vascular inflammation and hemostasis, fibrosis, and renal injury. Using human adipose tissue single cell/nuclear RNA-seq, we demonstrate expression at transcriptional level for implicated proteins across adipocytes and nonadipocyte cell types (eg, fibroadipogenic precursors, immune and vascular cells). Using functional assays in human adipose tissue, we demonstrate the association of expression of genes encoding these implicated proteins with adipose tissue metabolism, inflammation, and insulin resistance. CONCLUSIONS: A multifaceted discovery effort uniting proteomics, underlying clinical susceptibility phenotypes, and tissue expression patterns may uncover potentially novel functional biomarkers of early diabetes susceptibility in young adults for future mechanistic evaluation.

Development and Validation of the American Heart Association's PREVENT Equations.

BACKGROUND: Multivariable equations are recommended by primary prevention guidelines to assess absolute risk of cardiovascular disease (CVD). However, current equations have several limitations. Therefore, we developed and validated the American Heart Association Predicting Risk of CVD EVENTs (PREVENT) equations among US adults 30 to 79 years of age without known CVD. METHODS: The derivation sample included individual-level participant data from 25 data sets (N=3 281 919) between 1992 and 2017. The primary outcome was CVD (atherosclerotic CVD and heart failure). Predictors included traditional risk factors (smoking status, systolic blood pressure, cholesterol, antihypertensive or statin use, and diabetes) and estimated glomerular filtration rate. Models were sex-specific, race-free, developed on the age scale, and adjusted for competing risk of non-CVD death. Analyses were conducted in each data set and meta-analyzed. Discrimination was assessed using the Harrell C-statistic. Calibration was calculated as the slope of the observed versus predicted risk by decile. Additional equations to predict each CVD subtype (atherosclerotic CVD and heart failure) and include optional predictors (urine albumin-to-creatinine ratio and hemoglobin A1c), and social deprivation index were also developed. External validation was performed in 3 330 085 participants from 21 additional data sets. RESULTS: Among 6 612 004 adults included, mean±SD age was 53±12 years, and 56% were women. Over a mean±SD follow-up of 4.8±3.1 years, there were 211 515 incident total CVD events. The median C-statistics in external validation for CVD were 0.794 (interquartile interval, 0.763-0.809) in female and 0.757 (0.727-0.778) in male participants. The calibration slopes were 1.03 (interquartile interval, 0.81-1.16) and 0.94 (0.81-1.13) among female and male participants, respectively. Similar estimates for discrimination and calibration were observed for atherosclerotic CVD- and heart failure-specific models. The improvement in discrimination was small but statistically significant when urine albumin-to-creatinine ratio, hemoglobin A1c, and social deprivation index were added together to the base model to total CVD (ΔC-statistic [interquartile interval] 0.004 [0.004-0.005] and 0.005 [0.004-0.007] among female and male participants, respectively). Calibration improved significantly when the urine albumin-to-creatinine ratio was added to the base model among those with marked albuminuria (>300 mg/g; 1.05 [0.84-1.20] versus 1.39 [1.14-1.65]; P=0.01). CONCLUSIONS: PREVENT equations accurately and precisely predicted risk for incident CVD and CVD subtypes in a large, diverse, and contemporary sample of US adults by using routinely available clinical variables.

Estimated Glomerular Filtration Rate, Albuminuria, and Adverse Outcomes: An Individual-Participant Data Meta-Analysis.

Importance: Chronic kidney disease (low estimated glomerular filtration rate [eGFR] or albuminuria) affects approximately 14% of adults in the US. Objective: To evaluate associations of lower eGFR based on creatinine alone, lower eGFR based on creatinine combined with cystatin C, and more severe albuminuria with adverse kidney outcomes, cardiovascular outcomes, and other health outcomes. Design, Setting, and Participants: Individual-participant data meta-analysis of 27 503 140 individuals from 114 global cohorts (eGFR based on creatinine alone) and 720 736 individuals from 20 cohorts (eGFR based on creatinine and cystatin C) and 9 067 753 individuals from 114 cohorts (albuminuria) from 1980 to 2021. Exposures: The Chronic Kidney Disease Epidemiology Collaboration 2021 equations for eGFR based on creatinine alone and eGFR based on creatinine and cystatin C; and albuminuria estimated as urine albumin to creatinine ratio (UACR). Main Outcomes and Measures: The risk of kidney failure requiring replacement therapy, all-cause mortality, cardiovascular mortality, acute kidney injury, any hospitalization, coronary heart disease, stroke, heart failure, atrial fibrillation, and peripheral artery disease. The analyses were performed within each cohort and summarized with random-effects meta-analyses. Results: Within the population using eGFR based on creatinine alone (mean age, 54 years [SD, 17 years]; 51% were women; mean follow-up time, 4.8 years [SD, 3.3 years]), the mean eGFR was 90 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 11 mg/g (IQR, 8-16 mg/g). Within the population using eGFR based on creatinine and cystatin C (mean age, 59 years [SD, 12 years]; 53% were women; mean follow-up time, 10.8 years [SD, 4.1 years]), the mean eGFR was 88 mL/min/1.73 m2 (SD, 22 mL/min/1.73 m2) and the median UACR was 9 mg/g (IQR, 6-18 mg/g). Lower eGFR (whether based on creatinine alone or based on creatinine and cystatin C) and higher UACR were each significantly associated with higher risk for each of the 10 adverse outcomes, including those in the mildest categories of chronic kidney disease. For example, among people with a UACR less than 10 mg/g, an eGFR of 45 to 59 mL/min/1.73 m2 based on creatinine alone was associated with significantly higher hospitalization rates compared with an eGFR of 90 to 104 mL/min/1.73 m2 (adjusted hazard ratio, 1.3 [95% CI, 1.2-1.3]; 161 vs 79 events per 1000 person-years; excess absolute risk, 22 events per 1000 person-years [95% CI, 19-25 events per 1000 person-years]). Conclusions and Relevance: In this retrospective analysis of 114 cohorts, lower eGFR based on creatinine alone, lower eGFR based on creatinine and cystatin C, and more severe UACR were each associated with increased rates of 10 adverse outcomes, including adverse kidney outcomes, cardiovascular diseases, and hospitalizations.

Proteomics and Precise Exercise Phenotypes in Heart Failure With Preserved Ejection Fraction: A Pilot Study.

BACKGROUND: While exercise impairments are central to symptoms and diagnosis of heart failure with preserved ejection fraction (HFpEF), prior studies of HFpEF biomarkers have mostly focused on resting phenotypes. We combined precise exercise phenotypes with cardiovascular proteomics to identify protein signatures of HFpEF exercise responses and new potential therapeutic targets. METHODS AND RESULTS: We analyzed 277 proteins (Olink) in 151 individuals (N=103 HFpEF, 48 controls; 62±11 years; 56% women) with cardiopulmonary exercise testing with invasive monitoring. Using ridge regression adjusted for age/sex, we defined proteomic signatures of 5 physiological variables involved in HFpEF: peak oxygen uptake, peak cardiac output, pulmonary capillary wedge pressure/cardiac output slope, peak pulmonary vascular resistance, and peak peripheral O2 extraction. Multiprotein signatures of each of the exercise phenotypes captured a significant proportion of variance in respective exercise phenotypes. Interrogating the importance (ridge coefficient magnitude) of specific proteins in each signature highlighted proteins with putative links to HFpEF pathophysiology (eg, inflammatory, profibrotic proteins), and novel proteins linked to distinct physiologies (eg, proteins involved in multiorgan [kidney, liver, muscle, adipose] health) were implicated in impaired O2 extraction. In a separate sample (N=522, 261 HF events), proteomic signatures of peak oxygen uptake and pulmonary capillary wedge pressure/cardiac output slope were associated with incident HFpEF (odds ratios, 0.67 [95% CI, 0.50-0.90] and 1.43 [95% CI, 1.11-1.85], respectively) with adjustment for clinical factors and B-type natriuretic peptides. CONCLUSIONS: The cardiovascular proteome is associated with precision exercise phenotypes in HFpEF, suggesting novel mechanistic targets and potential methods for risk stratification to prevent HFpEF early in its pathogenesis.

Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification.

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study.

Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E-7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E-14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women's Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease.

Genetic insights into resting heart rate and its role in cardiovascular disease.

Resting heart rate is associated with cardiovascular diseases and mortality in observational and Mendelian randomization studies. The aims of this study are to extend the number of resting heart rate associated genetic variants and to obtain further insights in resting heart rate biology and its clinical consequences. A genome-wide meta-analysis of 100 studies in up to 835,465 individuals reveals 493 independent genetic variants in 352 loci, including 68 genetic variants outside previously identified resting heart rate associated loci. We prioritize 670 genes and in silico annotations point to their enrichment in cardiomyocytes and provide insights in their ECG signature. Two-sample Mendelian randomization analyses indicate that higher genetically predicted resting heart rate increases risk of dilated cardiomyopathy, but decreases risk of developing atrial fibrillation, ischemic stroke, and cardio-embolic stroke. We do not find evidence for a linear or non-linear genetic association between resting heart rate and all-cause mortality in contrast to our previous Mendelian randomization study. Systematic alteration of key differences between the current and previous Mendelian randomization study indicates that the most likely cause of the discrepancy between these studies arises from false positive findings in previous one-sample MR analyses caused by weak-instrument bias at lower P-value thresholds. The results extend our understanding of resting heart rate biology and give additional insights in its role in cardiovascular disease development.

Epigenome-wide DNA methylation association study of circulating IgE levels identifies novel targets for asthma.

BACKGROUND: Identifying novel epigenetic signatures associated with serum immunoglobulin E (IgE) may improve our understanding of molecular mechanisms underlying asthma and IgE-mediated diseases. METHODS: We performed an epigenome-wide association study using whole blood from Framingham Heart Study (FHS; n = 3,471, 46% females) participants and validated results using the Childhood Asthma Management Program (CAMP; n = 674, 39% females) and the Genetic Epidemiology of Asthma in Costa Rica Study (CRA; n = 787, 41% females). Using the closest gene to each IgE-associated CpG, we highlighted biologically plausible pathways underlying IgE regulation and analyzed the transcription patterns linked to IgE-associated CpGs (expression quantitative trait methylation loci; eQTMs). Using prior UK Biobank summary data from genome-wide association studies of asthma and allergy, we performed Mendelian randomization (MR) for causal inference testing using the IgE-associated CpGs from FHS with methylation quantitative trait loci (mQTLs) as instrumental variables. FINDINGS: We identified 490 statistically significant differentially methylated CpGs associated with IgE in FHS, of which 193 (39.3%) replicated in CAMP and CRA (FDR < 0.05). Gene ontology analysis revealed enrichment in pathways related to transcription factor binding, asthma, and other immunological processes. eQTM analysis identified 124 cis-eQTMs for 106 expressed genes (FDR < 0.05). MR in combination with drug-target analysis revealed CTSB and USP20 as putatively causal regulators of IgE levels (Bonferroni adjusted P < 7.94E-04) that can be explored as potential therapeutic targets. INTERPRETATION: By integrating eQTM and MR analyses in general and clinical asthma populations, our findings provide a deeper understanding of the multidimensional inter-relations of DNA methylation, gene expression, and IgE levels. FUNDING: US NIH/NHLBI grants: P01HL132825, K99HL159234. N01-HC-25195 and HHSN268201500001I.

Evaluating the use of blood pressure polygenic risk scores across race/ethnic background groups.

We assess performance and limitations of polygenic risk scores (PRSs) for multiple blood pressure (BP) phenotypes in diverse population groups. We compare "clumping-and-thresholding" (PRSice2) and LD-based (LDPred2) methods to construct PRSs from each of multiple GWAS, as well as multi-PRS approaches that sum PRSs with and without weights, including PRS-CSx. We use datasets from the MGB Biobank, TOPMed study, UK biobank, and from All of Us to train, assess, and validate PRSs in groups defined by self-reported race/ethnic background (Asian, Black, Hispanic/Latino, and White). For both SBP and DBP, the PRS-CSx based PRS, constructed as a weighted sum of PRSs developed from multiple independent GWAS, perform best across all race/ethnic backgrounds. Stratified analysis in All of Us shows that PRSs are better predictive of BP in females compared to males, individuals without obesity, and middle-aged (40-60 years) compared to older and younger individuals.

Integrative Mendelian randomization reveals the soluble receptor for advanced glycation end products as protective in relation to rheumatoid arthritis.

Rheumatoid arthritis (RA) is a risk factor for atherosclerotic cardiovascular diseases (CVD). Given the critical roles of the immune system and inflammatory signals in the pathogenesis of CVD, we hypothesized that interrogation of CVD-related proteins using integrative genomics might provide new insights into the pathophysiology of RA. We utilized two-sample Mendelian randomization (MR) for causal inference between circulating protein levels and RA by incorporating genetic variants, followed by colocalization to characterize the causal associations. Genetic variants from three sources were obtained: those associated with 71 CVD-related proteins measured in nearly 7000 Framingham Heart Study participants, a published genome-wide association study (GWAS) of RA (19 234 cases, 61 565 controls), and GWAS of rheumatoid factor (RF) levels from the UK Biobank (n = 30 565). We identified the soluble receptor for advanced glycation end products (sRAGE), a critical inflammatory pathway protein, as putatively causal and protective for both RA (odds ratio per 1-standard deviation increment in inverse-rank normalized sRAGE level = 0.364; 95% confidence interval 0.342-0.385; P = 6.40 × 10-241) and RF levels (ß [change in RF level per sRAGE increment] = - 1.318; SE = 0.434; P = 0.002). Using an integrative genomic approach, we highlight the AGER/RAGE axis as a putatively causal and promising therapeutic target for RA.

Protein Biomarkers of New-Onset Heart Failure: Insights From the Heart Omics and Ageing Cohort, the Atherosclerosis Risk in Communities Study, and the Framingham Heart Study.

BACKGROUND: We sought to identify protein biomarkers of new-onset heart failure (HF) in 3 independent cohorts (HOMAGE cohort [Heart Omics and Ageing], ARIC study [Atherosclerosis Risk in Communities], and FHS [Framingham Heart Study]) and assess if and to what extent they improve HF risk prediction compared to clinical risk factors alone. METHODS: A nested case-control design was used with cases (incident HF) and controls (without HF) matched on age and sex within each cohort. Plasma concentrations of 276 proteins were measured at baseline in ARIC (250 cases/250 controls), FHS (191/191), and HOMAGE cohort (562/871). RESULTS: In single protein analysis, after adjusting for matching variables and clinical risk factors (and correcting for multiple testing), 62 proteins were associated with incident HF in ARIC, 16 in FHS, and 116 in HOMAGE cohort. Proteins associated with incident HF in all cohorts were BNP (brain natriuretic peptide), NT-proBNP (N-terminal pro-B-type natriuretic peptide), eukaryotic translation initiation factor 4E-BP1 (4E-binding protein 1), hepatocyte growth factor (HGF), Gal-9 (galectin-9), TGF-alpha (transforming growth factor alpha), THBS2 (thrombospondin-2), and U-PAR (urokinase plasminogen activator surface receptor). The increment in C-index for incident HF based on a multiprotein biomarker approach, in addition to clinical risk factors and NT-proBNP, was 11.1% (7.5%-14.7%) in ARIC, 5.9% (2.6%-9.2%) in FHS, and 7.5% (5.4%-9.5%) in HOMAGE cohort, all P<0.001), each of which was a larger increase than that for NT-proBNP on top of clinical risk factors. Complex network analysis revealed a number of overrepresented pathways related to inflammation (eg, tumor necrosis factor and interleukin) and remodeling (eg, extracellular matrix and apoptosis). CONCLUSIONS: A multiprotein biomarker approach improves prediction of incident HF when added to natriuretic peptides and clinical risk factors.

Transcriptome-wide association study of circulating IgE levels identifies novel targets for asthma and allergic diseases.

Measurement of circulating immunoglobulin E (IgE) concentration is helpful for diagnosing and treating asthma and allergic diseases. Identifying gene expression signatures associated with IgE might elucidate novel pathways for IgE regulation. To this end, we performed a discovery transcriptome-wide association study to identify differentially expressed genes associated with circulating IgE levels in whole-blood derived RNA from 5,345 participants in the Framingham Heart Study across 17,873 mRNA gene-level transcripts. We identified 216 significant transcripts at a false discovery rate <0.05. We conducted replication using the meta-analysis of two independent external studies: the Childhood Asthma Management Program (n=610) and the Genetic Epidemiology of Asthma in Costa Rica Study (n=326); we then reversed the discovery and replication cohorts, which revealed 59 significant genes that replicated in both directions. Gene ontology analysis revealed that many of these genes were implicated in immune function pathways, including defense response, inflammatory response, and cytokine production. Mendelian randomization (MR) analysis revealed four genes (CLC, CCDC21, S100A13, and GCNT1) as putatively causal (p<0.05) regulators of IgE levels. GCNT1 (beta=1.5, p=0.01)-which is a top result in the MR analysis of expression in relation to asthma and allergic diseases-plays a role in regulating T helper type 1 cell homing, lymphocyte trafficking, and B cell differentiation. Our findings build upon prior knowledge of IgE regulation and provide a deeper understanding of underlying molecular mechanisms. The IgE-associated genes that we identified-particularly those implicated in MR analysis-can be explored as promising therapeutic targets for asthma and IgE-related diseases.

Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing.

Most transcriptome-wide association studies (TWASs) so far focus on European ancestry and lack diversity. To overcome this limitation, we aggregated genome-wide association study (GWAS) summary statistics, whole-genome sequences and expression quantitative trait locus (eQTL) data from diverse ancestries. We developed a new approach, TESLA (multi-ancestry integrative study using an optimal linear combination of association statistics), to integrate an eQTL dataset with a multi-ancestry GWAS. By exploiting shared phenotypic effects between ancestries and accommodating potential effect heterogeneities, TESLA improves power over other TWAS methods. When applied to tobacco use phenotypes, TESLA identified 273 new genes, up to 55% more compared with alternative TWAS methods. These hits and subsequent fine mapping using TESLA point to target genes with biological relevance. In silico drug-repurposing analyses highlight several drugs with known efficacy, including dextromethorphan and galantamine, and new drugs such as muscle relaxants that may be repurposed for treating nicotine addiction.

Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

The inflammatory proteome, obesity, and medical weight loss and regain in humans.

OBJECTIVE: Weight regain occurs after medical weight loss via mechanisms of post-weight-loss "metabolic adaptation." The relationship of inflammatory proteins with weight loss/regain was studied to determine a role for inflammation in metabolic adaptation. METHODS: Seventy-four proteins central to inflammation and immune regulation (Olink) were analyzed in plasma from up to 490 participants in a trial of medical weight-loss maintenance. Cross-sectional and longitudinal associations of proteins with weight were measured using linear and mixed effects regression models and t testing, with replication in the Framingham Heart Study. RESULTS: Broad changes in the inflammatory proteome were observed among the study cohort (60% women, 35% African American) with initial weight loss of ≈8 kg from a median 94 kg at study entry (33/74 proteins; 7 increased; 26 decreased), many of which tracked with weight regain of median ≈2 kg over the next 30 months. Ten proteins were associated with different rates of weight regain, some specifying pathways of chemotaxis and innate immune responses. Several of the observed protein associations were also linked to prevalent obesity in the Framingham Heart Study. CONCLUSIONS: Broad changes in the inflammatory proteome track with changes in weight and may identify specific pathways that modify patterns of weight regain.

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Whole genome DNA and RNA sequencing of whole blood elucidates the genetic architecture of gene expression underlying a wide range of diseases.

To create a scientific resource of expression quantitative trail loci (eQTL), we conducted a genome-wide association study (GWAS) using genotypes obtained from whole genome sequencing (WGS) of DNA and gene expression levels from RNA sequencing (RNA-seq) of whole blood in 2622 participants in Framingham Heart Study. We identified 6,778,286 cis-eQTL variant-gene transcript (eGene) pairs at p < 5 × 10-8 (2,855,111 unique cis-eQTL variants and 15,982 unique eGenes) and 1,469,754 trans-eQTL variant-eGene pairs at p < 1e-12 (526,056 unique trans-eQTL variants and 7233 unique eGenes). In addition, 442,379 cis-eQTL variants were associated with expression of 1518 long non-protein coding RNAs (lncRNAs). Gene Ontology (GO) analyses revealed that the top GO terms for cis-eGenes are enriched for immune functions (FDR < 0.05). The cis-eQTL variants are enriched for SNPs reported to be associated with 815 traits in prior GWAS, including cardiovascular disease risk factors. As proof of concept, we used this eQTL resource in conjunction with genetic variants from public GWAS databases in causal inference testing (e.g., COVID-19 severity). After Bonferroni correction, Mendelian randomization analyses identified putative causal associations of 60 eGenes with systolic blood pressure, 13 genes with coronary artery disease, and seven genes with COVID-19 severity. This study created a comprehensive eQTL resource via BioData Catalyst that will be made available to the scientific community. This will advance understanding of the genetic architecture of gene expression underlying a wide range of diseases.

Elucidating the genetic architecture of DNA methylation to identify promising molecular mechanisms of disease.

DNA methylation commonly occurs at cytosine-phosphate-guanine sites (CpGs) that can serve as biomarkers for many diseases. We analyzed whole genome sequencing data to identify DNA methylation quantitative trait loci (mQTLs) in 4126 Framingham Heart Study participants. Our mQTL mapping identified 94,362,817 cis-mQTLvariant-CpG pairs (for 210,156 unique autosomal CpGs) at P < 1e-7 and 33,572,145 trans-mQTL variant-CpG pairs (for 213,606 unique autosomal CpGs) at P < 1e-14. Using cis-mQTL variants for 1258 CpGs associated with seven cardiovascular disease (CVD) risk factors, we found 104 unique CpGs that colocalized with at least one CVD trait. For example, cg11554650 (PPP1R18) colocalized with type 2 diabetes, and was driven by a single nucleotide polymorphism (rs2516396). We performed Mendelian randomization (MR) analysis and demonstrated 58 putatively causal relations of CVD risk factor-associated CpGs to one or more risk factors (e.g., cg05337441 [APOB] with LDL; MR P = 1.2e-99, and 17 causal associations with coronary artery disease (e.g. cg08129017 [SREBF1] with coronary artery disease; MR P = 5e-13). We also showed that three CpGs, e.g., cg14893161 (PM20D1), are putatively causally associated with COVID-19 severity. To assist in future analyses of the role of DNA methylation in disease pathogenesis, we have posted a comprehensive summary data set in the National Heart, Lung, and Blood Institute's BioData Catalyst.