Increased lipogenesis and impaired β-oxidation predict type 2 diabetic kidney disease progression in American Indians

Abstract

BACKGROUNDIn this study, we identified the lipidomic predictors of early type 2 diabetic kidney disease (DKD) progression, which are currently undefined.METHODSThis 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.RESULTSParticipants 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).CONCLUSIONCollectively, 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 REGISTRATIONClinicalTrials.gov, NCT00340678.FUNDINGNIH R24DK082841, K08DK106523, R03DK121941, P30DK089503, P30DK081943, and P30DK020572.

Keywords: Chronic kidney disease; Diabetes; Fatty acid oxidation; Metabolism; Nephrology.

Conflict of interest statement

Conflict of interest: KS reports receipt of consulting fees from Boerhinger Ingelheim. TWG reports grants from Zebra Biologics. MK reports grants from JDRF, Astra-Zeneca, NovoNordisc, Eli Lilly, Gilead, Goldfinch Bio, Merck, Janssen, and Boehringer-Ingelheim and has a patent titled “Biomarkers for CKD progression” (encompassing urinary EGF as biomarker of CKD progression) issued.

Figures

Figure 1. Flow diagram of analytical strategy and posttrial follow-up of participants of the Renoprotection in Early Diabetic Nephropathy in Pima Indians trial analyzed in this study.

Figure 2. Baseline and last measured GFRs in 32 progressors and 60 nonprogressors.

The boxes represent median and IQR, and bars represent 1.5-fold × the IQR below the 25th percentile and above the 75th percentile. Outliers outside the mean + 2 SD are shown with dots. Paired 2-tailed t test was used.

Figure 3. Differences in carbon chain length and number of double bonds in complex lipids and ACs between progressors and nonprogressors.

Total N = 92 in all panels. Abundance of TAGs and ACs were measured at the baseline visit of this study and compared in progressors and nonprogressors. (A) In serum from progressors (n = 32), there was a greater relative abundance of longer TAGs with more double bonds. An opposite trend was observed in nonprogressors (n = 60). (B) A similar pattern was found when all participants (progressors + nonprogressors) were grouped based on whether they achieved a sustained GFR <90 (n = 33), <60 (n = 13), and <30 mL/min (n = 6). Bonferroni’s threshold for multiple comparisons was set to 0.0063 (0.05 divided by 8 panels/class). (C) In progressors (n = 32), there was a lower relative abundance of longer ACs with more double bonds. An opposite trend was observed in nonprogressors (n = 60). (D) A similar trend was noted in abundance of ACs by carbon number, when all participants were grouped based on whether they achieved a sustained GFR <90 (n = 33), <60 (n = 13), or <30 mL/min (n = 6). Bonferroni’s threshold for multiple comparisons was set to 0.0063 (0.05 divided by 8 panels/class). P values are products of testing abundance of lipid by “carbon number × double bond” interaction term in progressors versus nonprogressors using mixed-linear models.

Figure 4. Adjusted correlation of long (C16–C20) to intermediate chain (C6–C14) AC ratio with complex lipids of various chain length and double bonds.

Long/intermediate AC ratio is directly correlated with shorter complex lipids with fewer double bonds but is inversely correlated with longer complex lipids with more double bonds in the CE class. P values are products of testing partial correlation coefficients in multiple linear regression models adjusting for sex and ACR.

Figure 5. AC alterations by categories of baseline ACR in progressors and nonprogressors.

ACs of various chain length and double bonds were quantified in serum at the baseline visit of progressors and nonprogressors. Distribution of various ACs by ACR category revealed lower levels of C16–C20 ACs from nonprogressors with normoalbuminuria (upper left) to progressors with ACR more than 300 mg/g (lower right). Within each ACR category, long chain AC abundance (C16–C20) increased in nonprogressors with ACR 30–299 mg/g (P = 0.006) and decreased in progressors with ACR more than 300 mg/g (P < 0.0001). Sample size 43 in ACR less than 30, 33 in ACR 30–299, and 16 in ACR at least 300 mg/g. Bonferroni’s threshold for multiple comparisons was set to 0.0083 (0.05 divided by 6 panels). P values are products of testing abundance of lipids by “carbon number × double bond” interaction term in progressors versus nonprogressors using mixed-linear models.

Figure 6. Predicting DKD progression with probabilistic risk scores.

Probabilistic risk scores derived from odds of progression by 3 models were compared. Model 1 incorporated baseline ACR and GFR (ACR + GFR), model 2 incorporated independent lipid factors predicting progression (lipids), and model 3 consisted of the lipids plus baseline ACR and GFR (ACR + GFR + lipids). Progressors (n = 32) had a higher probabilistic risk scores compared with nonprogressors (n = 60) in all models, and the largest score was noted in model 3, when lipids were included with baseline ACR and GFR. Bar graphs show mean and error bars are 1 SD above and below the mean. A2-tailed t test for independent variables was used.

Figure 7. Differential network analysis.

To identify inter- and intraclass lipid correlates, we obtained the sparse partial correlation networks that captured the interdependencies between lipids. We used lipid grouping structure, obtained the superset of the network skeleton, and finally obtained the final stable network structures, the latter based on a bootstrapping method. Differential network analysis revealed differential loss of edges between various lipid classes in progressors characterized by 547 significant edges versus 1028 in nonprogressors (P < 0.0001) out of 55,460 possible permutations of bivariate correlations. The lines represent significant edges that were exclusively observed in nonprogressors (shown in blue) or progressors (shown in red). Common edges are shown in gray. The node size is proportional to the number of connectivity levels within and across lipid subclasses, and node colors represent number of cross-class connections (white, low; yellow, middle; red, high). Nodes are categorized by chain length (bottom, middle, top) and double bonds (low, high), with details shown in Supplemental Table 3.

Figure 8. Integrative transcriptomic-lipidomic analysis identifies G protein–coupled signaling pathways and nuclear hormone–activating receptors in regulation of fatty acid synthesis and β-oxidation.

Ingenuity Pathway Analysis reveals enrichment of G protein signaling pathways involved in regulation of NF-κB, CREB, and STAT3 in glomerular (A) and tubulointerstitial compartments (B). CREB is a transcriptional regulator of de novo lipogenesis. Genes regulating the intermediaries highlighted in purple in panels A and B are significantly correlated with the corresponding serum lipids. The genes downstream of nuclear hormone–activating receptor PPARG known to regulate fatty acid metabolism and their β-oxidation in both glomerular (C) and tubulointerstitial compartments (D) are significantly correlated with serum lipids identified via the lipidomic analysis. NF-κB, nuclear factor κ-light-chain-enhancer of activated B cells; CREB, cAMP response element-binding protein; PPARG, peroxisome proliferator–activated receptor γ; STAT3, signal transducer and activator of transcription 3.

Figure 9. Proposed mechanisms underlying lipid abnormalities that predict early renal function decline in DKD.

Upregulation of ACC, mediated by insulin resistance, enhances de novo lipogenesis characterized by increased abundance of palmitate, a C16 fatty acid. With elongation and desaturation, palmitate is converted into longer unsaturated fatty acids, which are incorporated into complex lipids (e.g., glycerolipids). In concert, upregulation of ACC also inhibits CPT1, which in turn decreases the conversion of l-carnitine to C16–C20 ACs. C16–C20 ACs are efficient β-oxidation substrates, and, therefore, their diminished mitochondrial transfer downregulates β-oxidation. The net effect of upregulated de novo lipogenesis is characterized by higher abundance of longer chain polyunsaturated glycerolipids and lower abundance of C16–C20 ACs and shorter low-double-bond glycerolipids. ACACA, acetyl-CoA carboxylase alpha; ACLY, ATP citrate lyase; ACS, acetyl-CoA synthetase; CPT, carnitine palmitoyltransferase; DEGS, delta 4-desaturase; ELOVL, elongation of very long chain fatty acids; FASN, fatty acid synthase; FADS, fatty acid desaturase; SCD, stearoyl-CoA desaturase; SLDB TAG, short-low-double-bond triacylglycerol.