Elevated serum urate levels cause gout, and correlate with cardio-metabolic diseases via poorly understood mechanisms. We performed a trans-ethnic genome-wide association study of serum urate among 457,690 individuals, identifying 183 loci (147 novel) that improve prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardio-metabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urateassociated loci and co-localization with gene expression in 47 tissues implicated kidney and liver as main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A trans-activated the promoter of the major urate transporter ABCG2 in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardio-metabolic traits.

Increased levels of the urinary albumin-to-creatinine ratio (UACR) are associated with higher risk of kidney disease progression and cardiovascular events, but underlying mechanisms are incompletely understood. Here, we conduct trans-ethnic (n = 564,257) and European-ancestry specific meta-analyses of genome-wide association studies of UACR, including ancestry- and diabetes-specific analyses, and identify 68 UACR-associated loci. Genetic correlation analyses and risk score associations in an independent electronic medical records database (n = 192,868) reveal connections with proteinuria, hyperlipidemia, gout, and hypertension. Fine-mapping and trans-Omics analyses with gene expression in 47 tissues and plasma protein levels implicate genes potentially operating through differential expression in kidney (including TGFB1, MUC1, PRKCI, and OAF), and allow coupling of UACR associations to altered plasma OAF concentrations. Knockdown of OAF and PRKCI orthologs in Drosophila nephrocytes reduces albumin endocytosis. Silencing fly PRKCI further impairs slit diaphragm formation. These results generate a priority list of genes and pathways for translational research to reduce albuminuria.

Background: There is strong biologic plausibility to support change in albuminuria as a surrogate endpoint for progression of chronic kidney disease (CKD), but empirical evidence to supports its validity in epidemiologic studies is lacking. Methods: We analyzed 28 cohorts including 693,816 individuals (80% with diabetes) and 7,461 end-stage kidney disease (ESKD) events, defined as initiation of kidney replacement therapy. Percent change in albuminuria was quantified during a baseline period of 1, 2 and 3 years using linear regression. Associations with subsequent ESKD were quantified using Cox regression in Coresh et al.

Patients with chronic kidney disease and severely decreased glomerular filtration rate (GFR) are at high risk for kidney failure, cardiovascular disease (CVD) and death. Accurate estimates of risk and timing of these clinical outcomes could guide patient counseling and therapy. Therefore, we developed models using data of 264,296 individuals in 30 countries participating in the international Chronic Kidney Disease Prognosis Consortium with estimated GFR (eGFR)s under 30 ml/min/1.73m. Median participant eGFR and urine albumin-to-creatinine ratio were 24 ml/min/1.73m and 168 mg/g, respectively. Using competing-risk regression, random-effect meta-analysis, and Markov processes with Monte Carlo simulations, we developed two- and four-year models of the probability and timing of kidney failure requiring kidney replacement therapy (KRT), a non-fatal CVD event, and death according to age, sex, race, eGFR, albumin-to-creatinine ratio, systolic blood pressure, smoking status, diabetes mellitus, and history of CVD. Hypothetically applied to a 60-year-old white male with a history of CVD, a systolic blood pressure of 140 mmHg, an eGFR of 25 ml/min/1.73m and a urine albumin-to-creatinine ratio of 1000 mg/g, the four-year model predicted a 17% chance of survival after KRT, a 17% chance of survival after a CVD event, a 4% chance of survival after both, and a 28% chance of death (9% as a first event, and 19% after another CVD event or KRT). Risk predictions for KRT showed good overall agreement with the published kidney failure risk equation, and both models were well calibrated with observed risk. Thus, commonly-measured clinical characteristics can predict the timing and occurrence of clinical outcomes in patients with severely decreased GFR.

M Gorski et al.: Rapid kidney function decline c l i n i c a l i n v e s t i g a t i o n

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Context As many as 75% of patients with polycystic ovary syndrome (PCOS) are estimated to be unidentified in clinical practice. Objective Utilizing polygenic risk prediction, we aim to identify the phenome-wide comorbidity patterns characteristic of PCOS to improve accurate diagnosis and preventive treatment. Design, Patients, and Methods Leveraging the electronic health records (EHRs) of 124 852 individuals, we developed a PCOS risk prediction algorithm by combining polygenic risk scores (PRS) with PCOS component phenotypes into a polygenic and phenotypic risk score (PPRS). We evaluated its predictive capability across different ancestries and perform a PRS-based phenome-wide association study (PheWAS) to assess the phenomic expression of the heightened risk of PCOS. Results The integrated polygenic prediction improved the average performance (pseudo-R2) for PCOS detection by 0.228 (61.5-fold), 0.224 (58.8-fold), 0.211 (57.0-fold) over the null model across European, African, and multi-ancestry participants respectively. The subsequent PRS-powered PheWAS identified a high level of shared biology between PCOS and a range of metabolic and endocrine outcomes, especially with obesity and diabetes: “morbid obesity”, “type 2 diabetes”, “hypercholesterolemia”, “disorders of lipid metabolism”, “hypertension”, and “sleep apnea” reaching phenome-wide significance. Conclusions Our study has expanded the methodological utility of PRS in patient stratification and risk prediction, especially in a multifactorial condition like PCOS, across different genetic origins. By utilizing the individual genome–phenome data available from the EHR, our approach also demonstrates that polygenic prediction by PRS can provide valuable opportunities to discover the pleiotropic phenomic network associated with PCOS pathogenesis.