Computational analysis of the metabolic phenotypes in type 1 diabetes and their associations with mortality and diabetic complications

Type 1 diabetes is an autoimmune disease that destroys the secretion of insulin (in the pancreas); insulin is a vital hormone for maintaining normal glucose metabolism. Insulin replacement therapy can prevent the acute symptoms, but is not able to fully match the natural regulation, which puts a metabolic stress on tissues. For some patients, the stress manifests as gradual damage to blood vessels and the nervous system over the next few decades after diabetes diagnosis. The aim of the thesis was to describe the metabolic profiles and to investigate their connections with the spectrum of clinical symptoms. Simultaneously, new techniques were applied to measure the profiles (1H NMR spectroscopy) and to visualize the multivariate statistical associations (the self-organizing map). A total of 4,197 patients with type 1 diabetes were recruited for the thesis by the Finnish Diabetic Nephropathy Study. A quarter of the patients exhibited an obesity-related phenotype (high triglycerides, cholesterol, apolipoprotein B-100, low high-density lipoprotein cholesterol, high C-reactive protein). A third of the individuals had a diabetic kidney disease phenotype (high urinary albumin and serum creatinine). The combination of the two was associated with a 10-fold population-adjusted mortality. Nevertheless, there was no discernible metabolic threshold between the phenotype models, nor were there any single variable that could predict the outcomes accurately. These results suggest a need for multifactorial and multidisciplinary paradigms for the research, treatment and prevention of diabetic complications.