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.

Triglyceride synthesis by hepatocytes is currently thought to be the rate-limiting step for lipoprotein formation. In order to determine whether triglyceride and cholesteryl ester syntheses in hepatocytes are sensitive to physiological fluctuations of serum fatty acids, the fatty acid dependence of these pathways was examined. Uptake of various fatty acids and incorporation into triglyceride and cholesteryl esters were studied in isolated rat hepatocytes. Rates were determined under conditions of linear incorporation and related to the concentrations of total and unbound fatty acid in a mixture of fatty acids and albumin. The findings were: pathways for triglyceride and cholesteryl ester synthesis saturated at unbound fatty acid concentrations (or fatty acid albumin ratios) within the range of serum values and thus would be acutely modulated by fluctuations in serum fatty acids. Addition of cholesterol to the medium increased cellular cholesterol, but did not alter rates of cholesterol esterification, suggesting that endogenous cholesterol synthesis provided the needed substrate. Oleate, palmitate, and linoleate were comparable in their saturation kinetics and ability to support triglyceride and cholesteryl ester synthesis. Consequently, their binding affinity for serum albumin would determine their lipid-incorporation rates. On this basis, in humans, oleate would yield the lowest rates as it has the lowest unbound fatty acid at each fatty acid-albumin ratio. Stearate, in contrast to the other fatty acids, was poorly esterified into neutral lipids by hepatocytes. Poor hepatic metabolism of stearate most likely explains previous findings of a hypocholesteremic effect of diets high in stearate as compared to other saturated fatty acids.(ABSTRACT TRUNCATED AT 250 WORDS)

We consider identification of linear dynamical systems comprising of high-dimensional signals, where the output noise components exhibit strong serial and cross-sectional correlations. Although such settings occur in many modern applications, such dependency structure has not been fully incorporated in existing approaches in the literature. In this paper, we explicitly incorporate the dependency structure present in the output noise through lagged values of the observed multivariate signals. We formulate a constrained optimization problem to identify the space spanned by the latent states and the transition matrices of the lagged values simultaneously, wherein the constraints reflect the low rank nature of the state information and the sparsity of the transition matrices. We establish theoretical properties of the estimators, and introduce an easy-to-implement computational procedure for empirical applications. The performance of the proposed approach and the implementation procedure is evaluated on synthetic data and compared with competing approaches, and further illustrated on a data set involving weekly log-returns of 75 US large financial institutions for the 2001--2016 period.

Analyzing multi-layered graphical models provides insight into understanding the conditional relationships among nodes within layers after adjusting for and quantifying the effects of nodes from other layers. We obtain the penalized maximum likelihood estimator for Gaussian multi-layered graphical models, based on a computational approach involving screening of variables, iterative estimation of the directed edges between layers and undirected edges within layers and a final refitting and stability selection step that provides improved performance in finite sample settings. We establish the consistency of the estimator in a high-dimensional setting. To obtain this result, we develop a strategy that leverages the biconvexity of the likelihood function to ensure convergence of the developed iterative algorithm to a stationary point, as well as careful uniform error control of the estimates over iterations. The performance of the maximum likelihood estimator is illustrated on synthetic data.