Dataset Information

Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors.

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.

SUBMITTER: Leutner M

PROVIDER: S-EPMC8143487 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundMetabolic syndrome (MetS) remains a controversial entity. Specific clusters of MetS components - rather than MetS per se - are associated with accelerated arterial ageing and with cardiovascular (CV) events. To investigate whether the distribution of clusters of MetS components differed cross-culturally, we studied 34,821 subjects from 12 cohorts from 10 European countries and one cohort from the USA in the MARE (Metabolic syndrome and Arteries REsearch) Consortium.MethodsIn accordance with the ATP III criteria, MetS was defined as an alteration three or more of the following five components: elevated glucose (G), fasting glucose ≥110 mg/dl; low HDL cholesterol, < 40mg/dl for men or <50 mg/dl for women; high triglycerides (T), ≥150 mg/dl; elevated blood pressure (B), ≥130/≥85 mmHg; abdominal obesity (W), waist circumference >102 cm for men or >88 cm for women.ResultsMetS had a 24.3% prevalence (8468 subjects: 23.9% in men vs. 24.6% in women, p < 0.001) with an age-associated increase in its prevalence in all the cohorts. The age-adjusted prevalence of the clusters of MetS components previously associated with greater arterial and CV burden differed across countries (p < 0.0001) and in men and women (p < 0.0001). In details, the cluster TBW was observed in 12% of the subjects with MetS, but was far more common in the cohorts from the UK (32.3%), Sardinia in Italy (19.6%), and Germany (18.5%) and less prevalent in the cohorts from Sweden (1.2%), Spain (2.6%), and the USA (2.5%). The cluster GBW accounted for 12.7% of subjects with MetS with higher occurrence in Southern Europe (Italy, Spain, and Portugal: 31.4, 18.4, and 17.1% respectively) and in Belgium (20.4%), than in Northern Europe (Germany, Sweden, and Lithuania: 7.6, 9.4, and 9.6% respectively).ConclusionsThe analysis of the distribution of MetS suggested that what follows under the common definition of MetS is not a unique entity rather a constellation of cluster of MetS components, likely selectively risky for CV disease, whose occurrence differs across countries.