About this item:

90 Views | 46 Downloads

Author Notes:

Ram Jagannathan, Department of Medicine, Division of Hospital Medicine, Emory University School of Medicine, Atlanta, GA, 30322, United States.. Email: ram.jagannathan@emory.edu

Shuchi Anand, Assistant Professor of Medicine, Division of Nephrology, Stanford University School of Medicine, 3180 Porter Drive, Palo Alto, California 94304, United States. Email: sanand2@stanford.edu

Conceptualization: RJ and SA; Formal analysis: JR; Data analysis supervision: SA; Data curation: DK; Project administration: DK, RG, MD; Funding acquisition: DP, NT, VM, KMVN, and MKA; Writing – original draft: RJ and SA; Writing-critical review and editing: JH, SM, DK, RG, SAP, RMA, MD, MKA, VM, NT, KMV, and DP.

We are grateful to the study participants. We are also grateful to the dedicated research personnel and field assistants who undertook outreach and follow-up despite challenging study conditions, and cared for the health and wellbeing of our study participants.

Nothing to disclose.

Subject:

Keywords:

  • Albuminuria
  • Latent class trajectory modeling
  • Personalized medicine
  • South Asians
  • eGFR trajectories

Estimated glomerular filtration rate trajectories in south Asians: Findings from the cardiometabolic risk reduction in south Asia study

Show all authors Show less authors

Tools:

Journal Title:

The Lancet Regional Health - Southeast Asia

Volume:

Volume 6

Publisher:

, Pages 100062-100062

Type of Work:

Article | Final Publisher PDF

Abstract:

Background: Few longitudinal data characterize kidney function decline among South Asians, one of the world's largest population groups. We aimed to identify estimated glomerular filtration rate (eGFR) trajectories in a population-based cohort from India and assess predictors of rapid kidney function decline. Methods: We used 6-year longitudinal data from participants of a population-representative study from Delhi and Chennai, India who had at least two serum creatinine measures and baseline CKD-EPI eGFR> 60 ml/min/1.73m2 (n=7779). We used latent class trajectory modeling to identify patterns of kidney function trajectory (CKD-EPI eGFR) over time. In models accounting for age, sex, education, and city, we tested the association between 15 hypothesized risk factors and rapid kidney function decline. Findings: Baseline mean eGFR was 108 (SD 16); median eGFR was 110 [IQR: 99-119] ml/min/1.73m2. Latent class trajectory modeling and functional characterization identified three distinct patterns of eGFR: class-1 (no decline; 58%) annual eGFR change 0.2 [0.1, 0.3]; class-2 (slow decline; 40%) annual eGFR change −0.2 [−0.4, −0.1], and class-3 (rapid decline; 2%) annual eGFR change −2.7 [−3.4, −2.0] ml/min/1.73m2. Albuminuria (>30 mg/g) was associated with rapid eGFR decline (OR for class-3 vs class-1: 5.1 [95% CI: 3.2; 7.9]; class-3 vs. class-2: 4.3 [95% CI:2.7; 6.6]). Other risk factors including self-reported diabetes, cardiovascular disease, peripheral arterial disease, and metabolic biomarkers such as HbA1c and systolic blood pressure were associated with rapid eGFR decline phenotype but potential ‘non-traditional’ risk factors such as manual labor or household water sources were not. Interpretation: Although mean and median eGFRs in our population-based cohort were higher than those reported in European cohorts, we found that a sizeable number of adults residing in urban India are experiencing rapid kidney function decline. Early and aggressive risk modification among persons with albuminuria could improve kidney health among South Asians. Funding: The CARRS study has been funded with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under Contract No. HHSN2682009900026C and P01HL154996. Dr. Anand was supported by NIDDK K23DK101826 and R01DK127138.

Copyright information:

© 2022 The Author(s)

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/).
Export to EndNote