BACKGROUND In this study, we identified the lipidomic predictors of early type 2 diabetic kidney disease (DKD) progression, which are currently undefined.METHODS This longitudinal study included 92 American Indians with type 2 diabetes. Serum lipids (406 from 18 classes) were quantified using mass spectrometry from baseline samples when iothalamate-based glomerular filtration rate (GFR) was at least 90 mL/min. Affymetrix GeneChip Array was used to measure renal transcript expression. DKD progression was defined as at least 40% decline in GFR during follow-up.RESULTS Participants had a mean age of 45 ± 9 years and median urine albumin/creatinine ratio of 43 (interquartile range 11–144). The 32 progressors had significantly higher relative abundance of polyunsaturated triacylglycerols (TAGs) and a lower abundance of C16–C20 acylcarnitines (ACs) (P < 0.001). In a Cox regression model, the main effect terms of unsaturated free fatty acids and phosphatidylethanolamines and the interaction terms of C16–C20 ACs and short-low-double-bond TAGs by categories of albuminuria independently predicted DKD progression. Renal expression of acetyl-CoA carboxylase–encoding gene (ACACA) correlated with serum diacylglycerols in the glomerular compartment (r = 0.36, and P = 0.006) and with low-double-bond TAGs in the tubulointerstitial compartment (r = 0.52, and P < 0.001).CONCLUSION Collectively, the findings reveal a previously unrecognized link between lipid markers of impaired mitochondrial β-oxidation and enhanced lipogenesis and DKD progression in individuals with preserved GFR. Renal acetyl-CoA carboxylase activation accompanies these lipidomic changes and suggests that it may be the underlying mechanism linking lipid abnormalities to DKD progression.TRIAL REGISTRATION ClinicalTrials.gov, NCT00340678.FUNDING NIH R24DK082841, K08DK106523, R03DK121941, P30DK089503, P30DK081943, and P30DK020572.
Farsad Afshinnia, Viji Nair, Jiahe Lin, Thekkelnaycke M. Rajendiran, Tanu Soni, Jaeman Byun, Kumar Sharma, Patrice E. Fort, Thomas W. Gardner, Helen C. Looker, Robert G. Nelson, Frank C. Brosius, Eva L. Feldman, George Michailidis, Matthias Kretzler, Subramaniam Pennathur
Usage data is cumulative from March 2023 through March 2024.
Usage | JCI | PMC |
---|---|---|
Text version | 642 | 391 |
78 | 91 | |
Figure | 143 | 2 |
Table | 27 | 0 |
Supplemental data | 26 | 11 |
Citation downloads | 17 | 0 |
Totals | 933 | 495 |
Total Views | 1,428 |
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.