Associations of Epigenetic Age Acceleration With CVD Risks Across the Lifespan: The Bogalusa Heart Study.

Although epigenetic age acceleration (EAA) might serve as a molecular signature of childhood cardiovascular disease (CVD) risk factors and further promote midlife subclinical CVD, few studies have comprehensively examined these life course associations. This study sought to test whether childhood CVD risk factors predict EAA in adulthood and whether EAA mediates the association between childhood CVD risks and midlife subclinical disease. Among 1,580 Bogalusa Heart Study participants, we estimated extrinsic EAA, intrinsic EAA, PhenoAge acceleration (PhenoAgeAccel), and GrimAge acceleration (GrimAgeAccel) during adulthood. We tested prospective associations of longitudinal childhood body mass index (BMI), blood pressure, lipids, and glucose with EAAs using linear mixed effects models. After confirming EAAs with midlife carotid intima-media thickness and carotid plaque, structural equation models examined mediating effects of EAAs on associations of childhood CVD risk factors with subclinical CVD measures. After stringent multiple testing corrections, each SD increase in childhood BMI was significantly associated with 0.6-, 0.9-, and 0.5-year increases in extrinsic EAA, PhenoAgeAccel, and GrimAgeAccel, respectively (P < 0.001 for all 3 associations). Likewise, each SD increase in childhood log-triglycerides was associated with 0.5- and 0.4-year increases in PhenoAgeAccel and GrimAgeAccel (P < 0.001 for both), respectively, whereas each SD increase in childhood high-density lipoprotein cholesterol was associated with a 0.3-year decrease in GrimAgeAccel (P = 0.002). Our findings indicate that PhenoAgeAccel mediates an estimated 27.4% of the association between childhood log-triglycerides and midlife carotid intima-media thickness (P = 0.022). Our data demonstrate that early life CVD risk factors may accelerate biological aging and promote subclinical atherosclerosis.

Clonal hematopoiesis of indeterminate potential contributes to accelerated chronic kidney disease progression.

Background: Clonal hematopoiesis of indeterminate potential (CHIP) is a common inflammatory condition of aging that causes myriad end-organ damage. We have recently shown associations for CHIP with acute kidney injury and with kidney function decline in the general population, with stronger associations for CHIP driven by mutations in genes other than DNMT3A (non- DNMT3A CHIP). Longitudinal kidney function endpoints in individuals with pre-existing chronic kidney disease (CKD) and CHIP have been examined in two previous studies, which reported conflicting findings and were limited by small sample sizes. Methods: In this study, we examined the prospective associations between CHIP and CKD progression events in four cohorts of CKD patients (total N = 5,772). The primary outcome was a composite of 50% kidney function decline or kidney failure. The slope of eGFR decline was examined as a secondary outcome. Mendelian randomization techniques were then used to investigate potential causal effects of CHIP on eGFR decline. Finally, kidney function was assessed in adenine-fed CKD model mice having received a bone marrow transplant recapitulating Tet2 -CHIP compared to controls transplanted wild-type bone marrow. Results: Across all cohorts, the average age was 66.4 years, the average baseline eGFR was 42.6 ml/min/1.73m 2 , and 24% had CHIP. Upon meta-analysis, non- DNMT3A CHIP was associated with a 59% higher relative risk of incident CKD progression (HR 1.59, 95% CI: 1.02-2.47). This association was more pronounced among individuals with diabetes (HR 1.29, 95% CI: 1.03-1.62) and with baseline eGFR ≥ 30 ml/min/1.73m (HR 1.80, 95% CI: 1.11-2.90). Additionally, the annualized slope of eGFR decline was steeper among non- DNMT3A CHIP carriers, relative to non-carriers (ß -0.61 ± 0.31 ml/min/1.73m 2 , p = 0.04). Mendelian randomization analyses suggested a causal role for CHIP in eGFR decline among individuals with diabetes. In a dietary adenine mouse model of CKD, Tet2 -CHIP was associated with lower GFR as well as greater kidney inflammation, tubular injury, and tubulointerstitial fibrosis. Conclusion: Non- DNMT3A CHIP is a potentially targetable novel risk factor for CKD progression.

Novel Metabolites Associated With Blood Pressure After Dietary Interventions.

BACKGROUND: The blood pressure (BP) etiologic study is complex due to multifactorial influences, including genetic, environmental, lifestyle, and their intricate interplays. We used a metabolomics approach to capture internal pathways and external exposures and to study BP regulation mechanisms after well-controlled dietary interventions. METHODS: In the ProBP trail (Protein and Blood Pressure), a double-blinded crossover randomized controlled trial, participants underwent dietary interventions of carbohydrate, soy protein, and milk protein, receiving 40 g daily for 8 weeks, with 3-week washout periods. We measured plasma samples collected at baseline and at the end of each dietary intervention. Multivariate linear models were used to evaluate the association between metabolites and systolic/diastolic BP. Nominally significant metabolites were examined for enriching biological pathways. Significant ProBP findings were evaluated for replication among 1311 participants of the BHS (Bogalusa Heart Study), a population-based study conducted in the same area as ProBP. RESULTS: After Bonferroni correction for 77 independent metabolite clusters (α=6.49×10-4), 18 metabolites were significantly associated with BP at baseline or the end of a dietary intervention, of which 11 were replicated in BHS. Seven emerged as novel discoveries, which are as follows: 1-linoleoyl-GPE (18:2), 1-oleoyl-GPE (18:1), 1-stearoyl-2-linoleoyl-GPC (18:0/18:2), 1-palmitoyl-2-oleoyl-GPE (16:0/18:1), maltose, N-stearoyl-sphinganine (d18:0/18:0), and N6-carbamoylthreonyladenosine. Pathway enrichment analyses suggested dietary protein intervention might reduce BP through pathways related to G protein-coupled receptors, incretin function, selenium micronutrient network, and mitochondrial biogenesis. CONCLUSIONS: Seven novel metabolites were identified to be associated with BP at the end of different dietary interventions. The beneficial effects of protein interventions might be mediated through specific metabolic pathways.

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.

Metabolites Associated With Uremic Symptoms in Patients With CKD: Findings From the Chronic Renal Insufficiency Cohort (CRIC) Study.

RATIONALE & OBJECTIVE: The toxins that contribute to uremic symptoms in patients with chronic kidney disease (CKD) are unknown. We sought to apply complementary statistical modeling approaches to data from untargeted plasma metabolomic profiling to identify solutes associated with uremic symptoms in patients with CKD. STUDY DESIGN: Cross-sectional. SETTING & PARTICIPANTS: 1,761 Chronic Renal Insufficiency Cohort (CRIC) participants with CKD not treated with dialysis. PREDICTORS: Measurement of 448 known plasma metabolites. OUTCOMES: The uremic symptoms of fatigue, anorexia, pruritus, nausea, paresthesia, and pain were assessed by single items on the Kidney Disease Quality of Life-36 instrument. ANALYTICAL APPROACH: Multivariable adjusted linear regression, least absolute shrinkage and selection operator linear regression, and random forest models were used to identify metabolites associated with symptom severity. After adjustment for multiple comparisons, metabolites selected in at least 2 of the 3 modeling approaches were deemed "overall significant." RESULTS: Participant mean estimated glomerular filtration rate was 43mL/min/1.73m2, with 44% self-identifying as female and 41% as non-Hispanic Black. The prevalence of uremic symptoms ranged from 22% to 55%. We identified 17 metabolites for which a higher level was associated with greater severity of at least one uremic symptom and 9 metabolites inversely associated with uremic symptom severity. Many of these metabolites exhibited at least a moderate correlation with estimated glomerular filtration rate (Pearson's r≥0.5), and some were also associated with the risk of developing kidney failure or death in multivariable adjusted Cox regression models. LIMITATIONS: Lack of a second independent cohort for external validation of our findings. CONCLUSIONS: Metabolomic profiling was used to identify multiple solutes associated with uremic symptoms in adults with CKD, but future validation and mechanistic studies are needed. PLAIN-LANGUAGE SUMMARY: Individuals living with chronic kidney disease (CKD) often experience symptoms related to CKD, traditionally called uremic symptoms. It is likely that CKD results in alterations in the levels of numerous circulating substances that, in turn, cause uremic symptoms; however, the identity of these solutes is not known. In this study, we used metabolomic profiling in patients with CKD to gain insights into the pathophysiology of uremic symptoms. We identified 26 metabolites whose levels were significantly associated with at least one of the symptoms of fatigue, anorexia, itchiness, nausea, paresthesia, and pain. The results of this study lay the groundwork for future research into the biological causes of symptoms in patients with CKD.

Clonal haematopoiesis, ageing and kidney disease.

Clonal haematopoiesis of indeterminate potential (CHIP) is a preclinical condition wherein a sizeable proportion of an individual's circulating blood cells are derived from a single mutated haematopoietic stem cell. CHIP occurs frequently with ageing - more than 10% of individuals over 65 years of age are affected - and is associated with an increased risk of disease across several organ systems and premature death. Emerging evidence suggests that CHIP has a role in kidney health, including associations with predisposition to acute kidney injury, impaired recovery from acute kidney injury and kidney function decline, both in the general population and among those with chronic kidney disease. Beyond its direct effect on the kidney, CHIP elevates the susceptibility of individuals to various conditions that can detrimentally affect the kidneys, including cardiovascular disease, obesity and insulin resistance, liver disease, gout, osteoporosis and certain autoimmune diseases. Aberrant pro-inflammatory signalling, telomere attrition and epigenetic ageing are potential causal pathophysiological pathways and mediators that underlie CHIP-related disease risk. Experimental animal models have shown that inhibition of inflammatory cytokine signalling can ameliorate many of the pathological effects of CHIP, and assessment of the efficacy and safety of this class of medications for human CHIP-associated pathology is ongoing.

A statistical framework for powerful multi-trait rare variant analysis in large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.

A longitudinal study of polygenic score and cognitive function decline considering baseline cognitive function, lifestyle behaviors, and diabetes among middle-aged and older US adults.

BACKGROUND: Genomic study of cognition decline while considering baseline cognition and lifestyle behaviors is scarce. We aimed to evaluate the impact of a polygenic score for general cognition on cognition decline rate, while considering baseline cognition and lifestyle behaviors, among the general population and people with diabetes, a patient group commonly affected by cognition impairment. METHODS: We tested associations of the polygenic score for general cognition with annual changing rates of cognition measures in 8 years of follow-up among 12,090 White and 3100 Black participants of the Health and Retirement Study (HRS), a nationally representative sample of adults aged 50 years and older in the USA. Cognition measures including word recall, mental status, and total cognitive score were measured biannually. To maximize sample size and length of follow-up, we treated the 2010 wave of survey as baseline, and follow-up data until 2018 were analyzed. Baseline lifestyle behaviors, APOE status, and measured cognition were sequentially adjusted. Given racial differences in polygenic score, all analyses were conducted by race. RESULTS: The polygenic score was significantly associated with annual changing rates of all cognition measures independent of lifestyle behaviors and APOE status. Together with age and sex, the polygenic score explained 29.9%, 15.9%, and 26.5% variances of annual changing rates of word recall, mental status, and total cognitive scores among Whites and explained 17.2%, 13.9%, and 18.7% variance of the three traits among Blacks. Among both White and Black participants, those in the top quartile of polygenic score had the three cognition measures increased annually, while those in the bottom quartile had the three cognition measures decreased annually. After further adjusting for the average cognition assessed in 3 visits around baseline, the polygenic score was still positively associated with annual changing rates of all cognition measures for White (P ≤ 2.89E - 19) but not for Black (P ≥ 0.07) participants. In addition, among participants with diabetes, physical activity offset the genetic susceptibility to decline of mental status (interaction P ≤ 0.01) and total cognitive scores (interaction P = 0.03). CONCLUSIONS: Polygenic score predicted cognition changes in addition to measured cognition. Physical activity offset genetic risk for cognition decline among diabetes patients.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

WHOLE GENOME SEQUENCING ANALYSIS OF BODY MASS INDEX IDENTIFIES NOVEL AFRICAN ANCESTRY-SPECIFIC RISK ALLELE.

Obesity is a major public health crisis associated with high mortality rates. Previous genome-wide association studies (GWAS) investigating body mass index (BMI) have largely relied on imputed data from European individuals. This study leveraged whole-genome sequencing (WGS) data from 88,873 participants from the Trans-Omics for Precision Medicine (TOPMed) Program, of which 51% were of non-European population groups. We discovered 18 BMI-associated signals (P < 5 × 10-9). Notably, we identified and replicated a novel low frequency single nucleotide polymorphism (SNP) in MTMR3 that was common in individuals of African descent. Using a diverse study population, we further identified two novel secondary signals in known BMI loci and pinpointed two likely causal variants in the POC5 and DMD loci. Our work demonstrates the benefits of combining WGS and diverse cohorts in expanding current catalog of variants and genes confer risk for obesity, bringing us one step closer to personalized medicine.

Serum Metabolomic Markers of Dairy Consumption: Results from the Atherosclerosis Risk in Communities Study and the Bogalusa Heart Study.

BACKGROUND: Dairy consumption is related to chronic disease risk; however, the measurement of dairy consumption has largely relied upon self-report. Untargeted metabolomics allows for the identification of objective markers of dietary intake. OBJECTIVES: We aimed to identify associations between dietary dairy intake (total dairy, low-fat dairy, and high-fat dairy) and serum metabolites in 2 independent study populations of United States adults. METHODS: Dietary intake was assessed with food frequency questionnaires. Multivariable linear regression models were used to estimate cross-sectional associations between dietary intake of dairy and 360 serum metabolites analyzed in 2 subgroups of the Atherosclerosis Risk in Communities study (ARIC; n = 3776). Results from the 2 subgroups were meta-analyzed using fixed effects meta-analysis. Significant meta-analyzed associations in the ARIC study were then tested in the Bogalusa Heart Study (BHS; n = 785). RESULTS: In the ARIC study and BHS, the mean age was 54 and 48 years, 61% and 29% were Black, and the mean dairy intake was 1.7 and 1.3 servings/day, respectively. Twenty-nine significant associations between dietary intake of dairy and serum metabolites were identified in the ARIC study (total dairy, n = 14; low-fat dairy, n = 10; high-fat dairy, n = 5). Three associations were also significant in BHS: myristate (14:0) was associated with high-fat dairy, and pantothenate was associated with total dairy and low-fat dairy, but 23 of the 27 associations significant in the ARIC study and tested in BHS were not associated with dairy in BHS. CONCLUSIONS: We identified metabolomic associations with dietary intake of dairy, including 3 associations found in 2 independent cohort studies. These results suggest that myristate (14:0) and pantothenate (vitamin B5) are candidate biomarkers of dairy consumption.

Examination of Serum Metabolome Altered by Dietary Carbohydrate, Milk Protein, and Soy Protein Interventions Identified Novel Metabolites Associated with Blood Pressure: The ProBP Trial.

SCOPE: This study aims to discover metabolites of dietary carbohydrate, soy and milk protein supplements and evaluate their roles in blood pressure (BP) regulation in the protein and blood pressure (ProBP), a cross-over trial. METHODS AND RESULTS: Plasma metabolites are profiled at pre-trial baseline and after 8 weeks of supplementation with carbohydrate, soy protein, and milk protein, respectively, among 80 ProBP participants. After Bonferroni correction (α = 6.49 × 10-4 ), dietary interventions significantly changed 40 metabolites. Changes of erucate (22:1n9), an omega-9 fatty acid, are positively associated with systolic BP changes (Beta = 1.90, p = 6·27 × 10-4 ). This metabolite is also associated with higher odds of hypertension among 1261 participants of an independent cohort (odds ratio per unit increase = 1.34; 95% confidence interval: 1.07-1.68). High levels of acylcholines dihomo-linolenoyl-choline (p = 4.71E-04) and oleoylcholine (p = 3.48E-04) at baseline predicted larger BP lowering effects of soy protein. Increasing cheese intake during the trial, as reflected by isobutyrylglycine and isovalerylglycine, reduces the BP lowering effect of soy protein. CONCLUSIONS: The study identifies molecular signatures of dietary interventions. Erucate (22:1n9) increases systolic BP. Acylcholine enhances and cheese intake reduces the BP lowering effect of soy protein supplement.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed Whole Genome Sequencing Study.

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions. Large-scale whole genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess the associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with blood lipid levels (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare variant aggregate association tests using the STAAR (variant-Set Test for Association using Annotation infoRmation) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare coding variants in nearby protein coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a ±500 kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variations and rare protein coding variations at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNA, implicating new therapeutic opportunities.

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.

Genomic Innovation in Early Life Cardiovascular Disease Prevention and Treatment.

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality globally. Although CVD events do not typically manifest until older adulthood, CVD develops gradually across the life-course, beginning with the elevation of risk factors observed as early as childhood or adolescence and the emergence of subclinical disease that can occur in young adulthood or midlife. Genomic background, which is determined at zygote formation, is among the earliest risk factors for CVD. With major advances in molecular technology, including the emergence of gene-editing techniques, along with deep whole-genome sequencing and high-throughput array-based genotyping, scientists now have the opportunity to not only discover genomic mechanisms underlying CVD but use this knowledge for the life-course prevention and treatment of these conditions. The current review focuses on innovations in the field of genomics and their applications to monogenic and polygenic CVD prevention and treatment. With respect to monogenic CVD, we discuss how the emergence of whole-genome sequencing technology has accelerated the discovery of disease-causing variants, allowing comprehensive screening and early, aggressive CVD mitigation strategies in patients and their families. We further describe advances in gene editing technology, which might soon make possible cures for CVD conditions once thought untreatable. In relation to polygenic CVD, we focus on recent innovations that leverage findings of genome-wide association studies to identify druggable gene targets and develop predictive genomic models of disease, which are already facilitating breakthroughs in the life-course treatment and prevention of CVD. Gaps in current research and future directions of genomics studies are also discussed. In aggregate, we hope to underline the value of leveraging genomics and broader multiomics information for characterizing CVD conditions, work which promises to expand precision approaches for the life-course prevention and treatment of CVD.

Effects of epigenetic age acceleration on kidney function: a Mendelian randomization study.

BACKGROUND: Previous studies have reported cross-sectional associations between measures of epigenetic age acceleration (EAA) and kidney function phenotypes. However, the temporal and potentially causal relationships between these variables remain unclear. We conducted a bidirectional two-sample Mendelian randomization study of EAA and kidney function. Genetic instruments for EAA and estimate glomerular filtration rate (eGFR) were identified from previous genome-wide association study (GWAS) meta-analyses of European-ancestry participants. Causal effects of EAA on kidney function and kidney function on EAA were assessed through summary-based Mendelian randomization utilizing data from the CKDGen GWAS meta-analysis of log-transformed estimated glomerular filtration rate (log-eGFR; n = 5,67,460) and GWAS meta-analyses of EAA (n = 34,710). An allele score-based Mendelian randomization leveraging individual-level data from UK Biobank participants (n = 4,33,462) further examined the effects of EAA on kidney function. RESULTS: Using summary-based Mendelian randomization, we found that each 5 year increase in intrinsic EAA (IEAA) and GrimAge acceleration (GrimAA) was associated with - 0.01 and - 0.02 unit decreases in log-eGFR, respectively (P = 0.02 and P = 0.09, respectively), findings which were strongly supported by allele-based Mendelian randomization study (both P < 0.001). Summary-based Mendelian randomization identified 24% increased odds of CKD with each 5-unit increase in IEAA (P = 0.05), with consistent findings observed in allele score-based analysis (P = 0.07). Reverse-direction Mendelian randomization identified potentially causal effects of decreased kidney function on HannumAge acceleration (HannumAA), GrimAA, and PhenoAge acceleration (PhenoAA), conferring 3.14, 1.99, and 2.88 year decreases in HanumAA, GrimAA, and PhenoAA, respectively (P = 0.003, 0.05, and 0.002, respectively) with each 1-unit increase in log-eGFR. CONCLUSION: This study supports bidirectional causal relationships between EAA and kidney function, pointing to potential prevention and therapeutic strategies.

Analytical and Biological Variability of a Commercial Modified Aptamer Assay in Plasma Samples of Patients with Chronic Kidney Disease.

BACKGROUND: We carried out a study of the aptamer proteomic assay, SomaScan V4, to evaluate the analytical and biological variability of the assay in plasma samples of patients with moderate to severe chronic kidney disease (CKD). METHODS: Plasma samples were selected from 2 sources: (a) 24 participants from the Chronic Renal Insufficiency Cohort (CRIC) and (b) 49 patients from the Brigham and Women's Hospital-Kidney/Renal Clinic. We calculated intra-assay variability from both sources and examined short-term biological variability in samples from the Brigham clinic. We also measured correlations of aptamer measurements with traditional biomarker assays. RESULTS: A total of 4656 unique proteins (4849 total aptamer measures) were analyzed in all samples. Median (interquartile range [IQR] intra-assay CV) was 3.7% (2.8-5.3) in CRIC and 5.0% (3.8-7.0) in Brigham samples. Median (IQR) biological CV among Brigham samples drawn from one individual on 2 occasions separated by median (IQR) 7 (4-14) days was 8.7% (6.2-14). CVs were independent of CKD stage, diabetes, or albuminuria but were higher in patients with systemic lupus erythematosus. Rho correlations between aptamer and traditional assays for biomarkers of interest were cystatin C = 0.942, kidney injury model-1 = 0.905, fibroblast growth factor-23 = 0.541, tumor necrosis factor receptors 1 = 0.781 and 2 = 0.843, P < 10-100 for all. CONCLUSIONS: Intra-assay and within-subject variability for SomaScan in the CKD setting was low and similar to assay variability reported from individuals without CKD. Intra-assay precision was excellent whether samples were collected in an optimal research protocol, as were CRIC samples, or in the clinical setting, as were the Brigham samples.

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.

Novel Genetic Variants Associated with Chronic Kidney Disease Progression.

SIGNIFICANCE STATEMENT: eGFR slope has been used as a surrogate outcome for progression of CKD. However, genetic markers associated with eGFR slope among patients with CKD were unknown. We aimed to identify genetic susceptibility loci associated with eGFR slope. A two-phase genome-wide association study identified single nucleotide polymorphisms (SNPs) in TPPP and FAT1-LINC02374 , and 22 of them were used to derive polygenic risk scores that mark the decline of eGFR by disrupting binding of nearby transcription factors. This work is the first to identify the impact of TPPP and FAT1-LINC02374 on CKD progression, providing predictive markers for the decline of eGFR in patients with CKD. BACKGROUND: The incidence of CKD is associated with genetic factors. However, genetic markers associated with the progression of CKD have not been fully elucidated. METHODS: We conducted a genome-wide association study among 1738 patients with CKD, mainly from the KoreaN cohort study for Outcomes in patients With CKD. The outcome was eGFR slope. We performed a replication study for discovered single nucleotide polymorphisms (SNPs) with P <10 -6 in 2498 patients with CKD from the Chronic Renal Insufficiency Cohort study. Several expression quantitative trait loci (eQTL) studies, pathway enrichment analyses, exploration of epigenetic architecture, and predicting disruption of transcription factor (TF) binding sites explored potential biological implications of the loci. We developed and evaluated the effect of polygenic risk scores (PRS) on incident CKD outcomes. RESULTS: SNPs in two novel loci, TPPP and FAT1-LINC02374 , were replicated (rs59402340 in TPPP , Pdiscovery =7.11×10 -7 , PCRIC =8.13×10 -4 , Pmeta =7.23×10 -8 ; rs28629773 in FAT1-LINC02374 , Pdiscovery =6.08×10 -7 , PCRIC =4.33×10 -2 , Pmeta =1.87×10 -7 ). The eQTL studies revealed that the replicated SNPs regulated the expression level of nearby genes associated with kidney function. Furthermore, these SNPs were near gene enhancer regions and predicted to disrupt the binding of TFs. PRS based on the independently significant top 22 SNPs were significantly associated with CKD outcomes. CONCLUSIONS: This study demonstrates that SNP markers in the TPPP and FAT1-LINC02374 loci could be predictive markers for the decline of eGFR in patients with CKD.

Emerging evidence on the role of clonal hematopoiesis of indeterminate potential in chronic kidney disease.

Chronic kidney disease (CKD) was responsible for 1.2 million deaths globally in 2016. Despite the large and growing burden of CKD, treatment options are limited and generally only preserve kidney function. Characterizing molecular precursors to incident and progressive CKD could point to critically needed prevention and treatment strategies. Clonal hematopoiesis of indeterminate potential (CHIP) is typically characterized by the clonal expansion of blood cells carrying somatic mutations in specific driver genes. An age-related disorder, CHIP is rare in the young but common in older adults. Recent studies have identified causal associations between CHIP and atherosclerotic cardiovascular disease which are most likely mediated by inflammation, a hallmark of CKD. Animal evidence has supported causal effects of CHIP on kidney injury, inflammation, and fibrosis, providing impetus for human research. Although prospective epidemiologic studies investigating associations of CHIP with development and progression of CKD are few, intriguing findings have been reported. CHIP was significantly associated with kidney function decline and end stage kidney disease in the general population, although effect sizes were modest. Recent work suggests larger associations of CHIP with kidney disease progression in CKD patients, but further investigations in this area are needed. In addition, the accumulating literature has identified some heterogeneity in associations between CHIP and kidney endpoints across study populations, but reasons for these differences remain unclear. The current review provides an in-depth exploration into this nascent area of research, develops a conceptual framework linking CHIP to CKD, and discusses the clinical and public health implications of this work.