Usage Information
Abstract
BACKGROUND Youth with type 2 diabetes (T2D) and severe obesity face high risk of diabetic kidney disease, which metabolic bariatric surgery (MBS) can mitigate. This study explores structural and molecular changes in kidneys after vertical sleeve gastrectomy (VSG), a form of MBS.METHODS We performed paired analyses, including metabolic profiling, kidney volume assessment, histological evaluation, and single-cell RNA sequencing (scRNA-seq), on kidney biopsies from 5 youth with T2D and obesity pre- and 12 months post-VSG in the IMPROVE-T2D (Impact of Metabolic surgery on Pancreatic, Renal and cardiOVascular hEalth in youth with T2D) cohort. Circulating proteomics with kidney transcriptomics were linked using data from an independent cohort of youth with obesity, with or without T2D, undergoing MBS in Teen-Longitudinal Assessment of Bariatric Surgery (Teen-LABS, n = 64).RESULTS After VSG, participants lost weight and had improvements in insulin sensitivity and metabolic parameters. Kidney changes included reduced renal hyperfiltration, total kidney volume, mesangial matrix area, and microalbuminuria. scRNA-seq in proximal tubule (PT) and thick ascending limb cells indicated repression of glycolysis, gluconeogenesis, and tricarboxylic acid (TCA) cycle genes, with upregulation of AMP-activated protein kinase (AMPK) and forkhead box O3 (FOXO3). Decreased metabolic signaling aligned with reduced ribosomal phosphorylated S6K (pS6K), suggesting attenuated mTORC1 activity. Janus kinase/signal transducer and activator of transcription (JAK/STAT) pathway activation in PT was diminished, correlating with lower circulating ligands from Teen-LABS proteomic data.CONCLUSION MBS/VSG prompts kidney molecular adaptations, providing potential targets for nonsurgical interventions against obesity- and diabetes-associated kidney disease.FUNDING University of Washington with the American Diabetes Association, University of Michigan with Chan Zuckerberg Initiative, and Breakthrough T1D.
Authors
Abhijit S. Naik, Fadhl M. Alakwaa, Viji Nair, Phillip J. McCown, Jennifer A. Schaub, Edgar A. Otto, Rajasree Menon, Francesca Annese, Ye Ji Choi, Hailey E. Hampson, Thomas H. Inge, John Hartman, Sean Eddy, Cathy Smith, Jeffrey B. Hodgin, Ken Inoki, Swayam Prakash Srivastava, Kareem Al-Fagih, Shota Yoshida, Jesse A. Goodrich, Melanie G. Cree, Phoom Narongkiatikhun, Long Yuan, Kalie L. Tommerdahl, Pottumarthi Prasad, Daniël H. van Raalte, Megan M. Kelsey, Justin R. Ryder, Tyler J. Dobbs, Patricia Ladd, Subramaniam Pennathur, Robert G. Nelson, Yusuke Okabayashi, Victor G. Puelles, Jenna Ferrence-Salo, Jeffrey A. Beamish, Frank C. Brosius, Kristen J. Nadeau, Laura Pyle, Matthias Kretzler, Petter Bjornstad
Usage data is cumulative from February 2026 through October 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 4,284 | 116 |
| 1,177 | 33 | |
| Figure | 1,794 | 0 |
| Table | 354 | 0 |
| Supplemental data | 976 | 14 |
| Citation downloads | 481 | 0 |
| Totals | 9,066 | 163 |
| Total Views | 9,229 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.
Copyright © 2026 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)