Introduction
We comprehensively investigated whether serum acylcarnitine levels are associated with and predict the decline of glomerular filtration rate (GFR) in type 2 diabetes.
Research design and methods
Two cohorts of patients with type 2 diabetes were investigated: a subset of the aggregate Gargano Mortality Study (aGMS, n=575; 9 years of median follow-up; mean age=60.9±9.8; mean diabetes duration=11.6±9.3) as a discovery set from Italy. A sample from the Joslin Kidney Study (JKS, n=252; 10 years of median follow-up; mean age=57.8±5.6; mean diabetes duration=14.2±7.6) was used as an independent validation set with different environmental and ethnic background for some associated metabolites in the aGMS.
Main outcome
estimated GFR (eGFR) change over time (mL/min/1.73 m2/year).
Results
Eleven out of the 40 acylcarnitines (by the AbsoluteIDQTM p180 Kit, BIOCRATES) were significantly associated with the rate of eGFR decline after Bonferroni correction. All 11 molecules were internally validated (p<0.05). Most of these associations survived the adjustment for several confounders, including age, sex, smoking habit, body mass index, glycated hemoglobin, disease duration, albumin excretion rate, triglycerides, low-density lipoprotein and statins treatment (p<0.05). Tiglylcarnitine and methylglutarylcarnitine, but not tetradecenoylcarnitine and hexadecenoylcarnitine, were also associated with eGFR decline in the JKS (p<0.05). Using multivariable least absolute shrinkage and selection operator regression analysis, methylglutarylcarnitine, hydroxyvalerylcarnitine, hexenoylcarnitine, decadienylcarnitine, dodecanedioylcarnitine, tetradecadienylcarnitine were independently associated with kidney function decline. The pairwise correlation among these ranged from –0.02 to 0.55. An acylcarnitine score comprising these six molecules improved discrimination (p<0.01) and reclassification (p<0.001) of two clinical prediction models of GFR decline in diabetes.
Conclusions
In patients with type 2 diabetes, four short, three medium and four long-chain acylcarnitines are associated with the rate of kidney function decline. Adding the acylcarnitine score to clinical prediction models improves the identification of individuals who are at greater risk of progression to kidney failure.
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