Biomedical subjects

Girish N Nadkarni

Publications and source records attributed to Girish N Nadkarni.

4 recordsLinked to original sources

PubMed2026

Genomic analyses implicate hormonal and metabolic dysregulation in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) and its underlying features remain poorly understood. In this genetic study (n = 544,513), we expand the number of genetic loci from 16 to 29, and additionally identify 31 associated plasma proteins. Many risk-increasing loci were associated with later age at menopause, underscoring the reproductive longevity related to an increased oocyte number and/or availability across the lifespan. Hormonal regulation in the etiology of this condition, through metabolic and reproductive features, was emphasized. The proteomic analysis highlighted metabolic biology known to be related to PCOS. A polygenic risk score (PRS) was associated with adverse cardiometabolic outcomes, with differing relevance of testosterone and body mass index in women and men. Finally, while oligo-anovulation and anovulatory infertility are features of PCOS, we observed no impact of PCOS susceptibility on childlessness. We suggest that PCOS susceptibility confers balanced pleiotropic influences on fertility in women, and life-long adverse metabolic consequences in both sexes.

Humans↗

PubMed2025

Liquid Biopsy-Multiomics Link Adhesion Pathway Dysregulation to Kidney Injury Severity.

INTRODUCTION: Severe acute kidney injury (AKI) is strongly associated with the risk of developing chronic kidney disease; however, little is known about the cell type-specific mechanisms driving kidney injury severity. METHODS: In this multicenter observational study, we used clinically obtained liquid biopsy proteomics and machine learning (ML) to predict severe outcomes in patients with COVID-associated and non-COVID AKI. Further, we orthogonally combined 169 urine proteomics with 437 plasma proteomics samples and 40 urine sediment single-cell transcriptomics samples to identify complementary dysregulated mechanisms. RESULTS: Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrate predictive power for both discovery and validation set with AUC of 87% and 76%, respectively. These predictive proteomics features obtained demonstrate that cell adhesion and autophagy-associated pathways are uniquely impacted in severe AKI. Differentially abundant proteins (DAPSs) associated with these pathways are highly expressed in cells of the juxtamedullary nephron, endothelial cells (ECs), and podocytes, indicating that these kidney cell types could be potential targets. Single-cell transcriptomic analysis in the in vitro model of kidney organoids infected with SARS-CoV-2 reveal dysregulation of extracellular matrix (ECM) organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters shows significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. CONCLUSION: Collectively, these data suggest that ECM degradation and adhesion-associated mechanisms could be the main driver of severe kidney injury.

AKI↗

PubMed2025

Large-scale multi-omics analyses in Hispanic/Latino populations identify genes for cardiometabolic traits.

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

Female↗

PubMed2025

The impact of common and rare genetic variants on bradyarrhythmia development.

To broaden our understanding of bradyarrhythmias and conduction disease, we performed common variant genome-wide association analyses in up to 1.3 million individuals and rare variant burden testing in 460,000 individuals for sinus node dysfunction (SND), distal conduction disease (DCD) and pacemaker (PM) implantation. We identified 13, 31 and 21 common variant loci for SND, DCD and PM, respectively. Four well-known loci (SCN5A/SCN10A, CCDC141, TBX20 and CAMK2D) were shared for SND and DCD, while others were more specific for SND or DCD. SND and DCD showed a moderate genetic correlation (rg = 0.63). Cardiomyocyte-expressed genes were enriched for contributions to DCD heritability. Rare-variant analyses implicated LMNA for all bradyarrhythmia phenotypes, SMAD6 and SCN5A for DCD and TTN, MYBPC3 and SCN5A for PM. These results show that variation in multiple genetic pathways (for example, ion channel function, cardiac developmental programs, sarcomeric structure and cellular homeostasis) appear critical to the development of bradyarrhythmias.

Humans↗