Abstract
Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.
Highlights
The prevalence of diabetes mellitus is constantly increasing, making this disease a global health problem
The gray color in the boxes corresponding to obesity (E66), recurrent depressive disorder (F33), and unspecific liver disease (K76) indicate increased probability of these diagnoses compared to the baseline of cluster 0
Data from the UK Prospective Diabetes Study (UKPDS) highlighted the central role of glycemic control in the prevention of diabetes-associated complications, showing, among other things, a risk reduction of 25% for microvascular disease upon intensified glucose-lowering therapy [23] and a 32% reduction for any diabetes-related endpoint for overweight diabetics receiving metformin [24]. Additional factors such as cholesterol levels or blood pressure have been shown to influence macrovascular outcomes of patients with diabetes mellitus in the Collaborative Atorvastatin Diabetes Study (CARDS) trial, a randomized controlled trial assessing the effect of an add-on low dose of atorvastatin therapy in type 2 diabetics [5], and the ADVANCE trial, which investigated the effect of a fixed angiotensin converting enzyme (ACE) inhibitor–diuretic combination in type 2 diabetics irrespective of baseline blood pressure [4]
Summary
The prevalence of diabetes mellitus is constantly increasing, making this disease a global health problem. Efficient diabetes management is necessary in order to decrease the risk of both micro- and macrovascular complications such as cardiovascular diseases or diabetic nephropathy [1], especially when patients are diagnosed with other comorbidities such as hypertension or depression [2,3]. It has been shown that comorbidities such as dyslipidemia, depression, arterial hypertension, or fatty liver disease predispose diabetic patients to an increased risk of developing serious diabetes-specific complications later in life [7,8,9,10]. Earlier studies that defined clusters of diabetic patients on the basis of parameters related to the etiology of diabetes such as insulin resistance or body mass index (BMI) were able to show that these clusters differentiated the risk of developing various diabetes-specific complications [11,12,13,14,15]. More recently, Nedyalkova et al showed that k-means clustering to detect clinical variables might be useful to stratify type 2 diabetics into distinct subgroups of risk factors [17]
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