Metabolic Diversity of Progressive Kidney Disease in 325 Patients with Type 1 Diabetes (the FinnDiane Study)

Abstract

Type 1 diabetic patients with varying severity of kidney disease were investigated to create multimetabolite models of the disease process. Urinary albumin excretion rate was measured for 3358 patients with type 1 diabetes. Prospective records were available for 1051 patients, of whom 163 showed progression of albuminuria (8.3-year follow-up), and 162 were selected as stable controls. At baseline, serum lipids, lipoprotein subclasses, and low-molecular weight metabolites were quantified by NMR spectroscopy (325 samples). The data were analyzed by the self-organizing map. In cross-sectional analyses, patients with no complications had low serum lipids, less inflammation, and better glycemic control, whereas patients with advanced kidney disease had high serum cystatin-C and sphingomyelin. These phenotype extremes shared low unsaturated fatty acids (UFAs) and phospholipids. Prospectively, progressive albuminuria was associated with high UFAs, phospholipids, and IDL and LDL lipids. Progression at longer duration was associated with high HDL lipids, whereas earlier progression was associated with poor glycemic control, increased saturated fatty acids (SFAs), and inflammation. Diabetic kidney disease consists of diverse metabolic phenotypes: UFAs, phospholipids, IDL, and LDL may be important in the subclinical phase, high SFAs and low HDL suggest accelerated progression, and the sphingolipid pathway in advanced kidney injury deserves further research.

Plasma Metabonomic Profiling of Diabetic Retinopathy

“…The n-3 PUFAs docosahexaenoic acid and eicosapentaenoic acid were detected on our platform, yet we did not find significant differences in these PUFAs between the DR group and control subjects with diabetes in the discovery set (Supplementary Table 8). Similarly, Mäkinen et al (41) found that the baseline serum docosahexaenoic levels did not predict progressive kidney disease among patients with type 1 diabetes. Although n-3 PUFAs have been shown to protect against DR in rodent models (42,43), their efficacy in preventing vascular complications of diabetes can only be proven through randomized controlled trials.…”

Metabolic profiling–multitude of technologies with great research potential, but (when) will translation emerge?

“…All standard statistical approaches can be applied in a straightforward manner, including adjustments for potential confounding factors 6 , 22 , 24 , 25 , 28 . Of course, as with any quantitative molecular data, the analyses are by no means limited to standard approaches but, for example, multivariate non-linear approaches, 76 , 77 network analyses, 77 , 78 pathway approaches 79 , 80 and integration of multi-omic data 26 , 47 , 75 , 81–86 are all feasible. In fact, this is in contrast to spectral-based approaches in which these types of analyses, aiming for detailed biological understanding, would mostly be impossible to perform and interpret at the molecular level.…”

Metabolomics and Diabetes: Analytical and Computational Approaches

“…In addition to predicting risk of developing diabetes, the degree of lipid saturation has been linked to diabetes complications. NMR metabonomics with baseline serum from subjects in the Finnish Diabetic Nephropathy (FinnDiane) Study linked high levels of saturated fatty acids in serum to accelerated progression of kidney disease in patients with type 1 diabetes (79). …”