Biomedical subjects

J Brent Richards

Publications and source records attributed to J Brent Richards.

4 recordsLinked to original sources

PubMed2026

Protein mediators of chronic kidney disease in Type 2 diabetes: A mendelian randomization study.

BACKGROUND: Chronic kidney disease (CKD) occurs in 20-50% of the people living with Type 2 diabetes (T2D) and is the leading cause of kidney failure worldwide. The cause of CKD is not fully understood, and few interventions prevent CKD in individuals living with diabetes. Here, we use large-scale proteomics data to identify circulating proteins that mediate the relationship between T2D and kidney disorders. METHODS AND FINDINGS: First, we used two-sample mendelian randomization (MR) and identified 71 circulating proteins whose levels were altered by genetic predisposition to T2D based on circulating proteomic GWAS from deCODE with 35,559 individuals and T2D GWAS with 80,154 cases. Then, we used cis-genetic variants to proxy the causal effect of some of these T2D-influenced circulating proteins and found that, collectively, five proteins (INHBC, GNPTG, LPO, AGRN, and CTSD) affected three kidney traits (blood urea nitrogen [BUN], estimated glomerular filtration rate [eGFR] and CKD risk) based on GWAS with up to 1,004,040 participants. Notably, we found that higher levels of circulating INHBC protein were estimated to lead to a lower eGFR and higher BUN based on MR analyses. We then replicated this MR analysis with proteomic GWAS from four additional cohorts, namely, UKB-PPP, Fenland, ARIC, and EPIC-Norfolk. We observed a consistent direction of effect across all four proteomic GWAS datasets, supporting the robustness of our results against platform and cohort variation. In observational analyses, increased circulating INHBC levels were associated with increased hazard for kidney disease diagnosis in 37,854 UK Biobank participants. We estimated that circulating INHBC levels mediate 1.3% (95% confidence interval [0.85%, 1.9%]) of the association between T2D and kidney disease diagnosis. There are important limitations in this study. Firstly, although we observed limited evidence for violations to the MR assumptions, some are untestable. Secondly, our study was not based on individuals with diabetic kidney diseases, but rather independent population-based studies assessing diabetes and kidney function separately. Therefore, additional functional analyses in disease specific cohort are needed. CONCLUSIONS: Collectively, these findings suggest that T2D influences the risk of CKD, in part, through increased circulating INHBC levels.

Humans↗

PubMed2026

Pulmonary fibrosis after COVID-19 is characterized by airway abnormalities and elevated club cell secretory protein-16.

BACKGROUNDThere are no known serum biomarkers that provide mechanistic insight or prognostic enrichment for post-COVID-19 pulmonary fibrosis.METHODSWe tested associations of serum biomarkers with radiographic fibrosis-like abnormalities (reticulation, traction bronchiectasis, or honeycombing) on thoracic computed tomography (CT) scans 4 months, 15 months, and 3 years after hospitalization in an American discovery cohort of severe-to-critical COVID-19 survivors, and externally validated findings in 2 Canadian cohorts of moderate-to-critical COVID-19 survivors. In the discovery cohort, we investigated the dose-response relationship of the biomarker with CT-derived airway-to-lung ratio. We performed single-cell RNA sequencing (scRNA-seq) of transbronchial lung biopsies from COVID-19 survivors obtained 3 years after COVID-19 hospitalization and conducted immunofluorescence analysis of COVID-19 lung explants.RESULTSAmong 150 discovery cohort participants, only higher levels of circulating club cell secretory protein-16 (CC16, encoded by the SCGB1A1 gene) at hospital discharge, 4 months, 15 months, and 3 years were associated with thoracic CT fibrosis-like abnormalities in cross-sectional and longitudinal analyses. Higher CC16 levels were associated with thoracic CT fibrosis-like abnormalities in 2 validation cohorts (n = 56 and n = 37). CC16 levels were linearly associated with increased airway-to-lung ratio. scRNA-seq revealed increased proportions of epithelial cells expressing SCGB1A1 and SCGB1A1/MUC5B in COVID-19 survivors with fibrosis. Immunofluorescence analysis of COVID-19 lung explants demonstrated increased numbers of SCGB1A1-expressing epithelial cells only in small (<100 μm) airways, with 3-fold more CC16/MUC5B-coexpressing cells in respiratory bronchioles..CONCLUSION. Higher CC16 levels are associated with CT fibrosis-like abnormalities for up to 3 years following moderate-to-critical COVID-19. Increased CC16 reflects dysregulated small airway epithelial progenitor cell remodeling and increased expansion of CC16+MUC5B+ epithelial cells in respiratory bronchioles after COVID-19.TRIAL REGISTRATIONNot applicable.FUNDINGDepartment of Defense, NIH, and Japan Society for the Promotion of Science for Young Scientists.

Humans↗

PubMed2025

No More Free Lunch: Challenges to Mendelian Randomization Due to Sample Selection and Complex Methods.

Mendelian randomization (MR) is increasingly used in epidemiological studies to investigate causal relationships. MR depends on 3 fundamental instrumental variable assumptions: relevance, independence, and exclusion restriction. Studies often assume that MR mitigates bias from confounding due to the random allocation of genetic variants at conception. In this perspective, using causal directed acyclic graphs, we discuss several scenarios where biases in MR analyses may arise due to the nature of the data or methods being used. These include (1) collider bias due to the nonrandom selection of participants into study populations used for conducting genome-wide association studies (GWAS), (2) indirect genetic effects arising from population-based GWAS rather than within-family studies, and (3) collider bias due to gene-environment interaction effects on the exposure in nonlinear MR analyses. We provide practical considerations for examining and reducing these biases in MR analyses.

Humans↗

PubMed2025

Genome-wide association study of long COVID.

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4's role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID.

Humans↗