Network of vascular diseases, death and biochemical characteristics in a set of 4,197 patients with type 1 diabetes (the FinnDiane Study)

Abstract

Background: Cardiovascular disease is the main cause of premature death in patients with type 1 diabetes. Patients with diabetic kidney disease have an increased risk of heart attack or stroke. Accurate knowledge of the complex inter-dependencies between the risk factors is critical for pinpointing the best targets for research and treatment. Therefore, the aim of this study was to describe the association patterns between clinical and biochemical features of diabetic complications.

Methods: Medical records and serum and urine samples of 4,197 patients with type 1 diabetes were collected from health care centers in Finland. At baseline, the mean diabetes duration was 22 years, 52% were male, 23% had kidney disease (urine albumin excretion over 300 mg/24 h or end-stage renal disease) and 8% had a history of macrovascular events. All-cause mortality was evaluated after an average of 6.5 years of follow-up (25,714 patient years). The dataset comprised 28 clinical and 25 biochemical variables that were regarded as the nodes of a network to assess their mutual relationships.

Results: The networks contained cliques that were densely inter-connected (r > 0.6), including cliques for high-density lipoprotein (HDL) markers, for triglycerides and cholesterol, for urinary excretion and for indices of body mass. The links between the cliques showed biologically relevant interactions: an inverse relationship between HDL cholesterol and the triglyceride clique (r < -0.3, P < 10(-16)), a connection between triglycerides and body mass via C-reactive protein (r > 0.3, P < 10(-16)) and intermediate-density cholesterol as the connector between lipoprotein metabolism and albuminuria (r > 0.3, P < 10(-16)). Aging and macrovascular disease were linked to death via working ability and retinopathy. Diabetic kidney disease, serum creatinine and potassium, retinopathy and blood pressure were inter-connected. Blood pressure correlations indicated accelerated vascular aging in individuals with kidney disease (P < 0.001).

Conclusion: The complex pattern of links between diverse characteristics and the lack of a single dominant factor suggests a need for multifactorial and multidisciplinary paradigms for the research, treatment and prevention of diabetic complications.

Figure 1

Correlation network of continuous data. A pruned visualization of the correlation structure within a set of patients with type 1 diabetes. Prior to the analysis, the data were adjusted for gender. Each variable is presented with a symbol; those quantities that were measured directly are filled with ink and the open circles denote derived variables. The width and color of the links indicate the correlation magnitude and type, as shown in the legend. The r denotes Spearman correlation and SRAGE is abbreviation for soluble receptor for advanced glycation end-products. Visualized with the Himmeli software [47].

Figure 2

Regression-correlation network of continuous and binary data. A pruned visualization of the correlation network from regression modeling. Unlike in Figure 1, the data were not adjusted for gender prior to the analysis. Each variable was converted to a surrogate linear predictor before computations. The symbols in the figure correspond to the source of information: directly observed variables are filled, whereas derived variables are denoted by open symbols. A circle is used for continuous quantities, and a diamond for binary traits. The width and color of the links indicate the association magnitude and type, as shown in the legend. The r denotes the correlation of the linear predictors and is not comparable with Figure 1. Abbreviations: history of macrovascular disease (MVD), systolic (SBP) and diastolic (DBP) blood pressure, anti-hypertensive treatment (AHT) and soluble receptor for advanced glycation end-products (SRAGE). Visualized with the Himmeli software [47].