Health consequences of early-onset compared with late-onset type 2 diabetes mellitus

Abstract

Background: Although cumulating evidence has suggested that early-onset type 2 diabetes mellitus (T2DM) conferred on patients a broader tendency for complications beyond vascular ones, a comprehensive analysis of patterns of complications across all relevant systems is currently lacking.

Method: We prospectively studied 1 777 early-onset (age at diagnosis ≤ 45 years) and 35 889 late-onset (>45 years) T2DM patients with matched unexposed individuals from the UK Biobank. Diabetes-specific and -related complications were examined using phenome-wide association analysis, with patterns identified by comorbidity network analysis. We also evaluated the effect of lifestyle modifications and glycemic control on complication development.

Results: The median follow-up times for early-onset and late-onset T2DM patients were 17.83 and 9.39 years, respectively. Compared to late-onset T2DM patients, patients with early-onset T2DM faced a significantly higher relative risk of developing subsequent complications that primarily affected sense organs [hazard ratio (HR) 3.46 vs. 1.72], the endocrine/metabolic system (HR 3.08 vs. 2.01), and the neurological system (HR 2.70 vs. 1.81). Despite large similarities in comorbidity patterns, a more complex and well-connected network was observed for early-onset T2DM. Furthermore, while patients with early-onset T2DM got fewer benefits (12.67% reduction in pooled HR for all studied complications) through fair glycemic control (median HbA_1c ≤ 53 mmol/mol) compared to late-onset T2DM patients (18.01% reduction), they seemed to benefit more from favorable lifestyles, including weight control, healthy diet, and adequate physical activity.

Conclusions: Our analyses reveal that early-onset T2DM is an aggressive disease resulting in more complex complication networks than late-onset T2DM. Aggressive glucose-lowering intervention, complemented by lifestyle modifications, are feasible strategies for controlling early-onset T2DM-related complications.

Keywords: comorbidity network analysis; early-onset type 2 diabetes mellitus; late-onset type 2 diabetes mellitus; phenome-wide association analysis.

Figure 1.

Flowchart for population selection. *The algorithm for identifying probable T1DM patients is shown in Supplementary Fig. 1. ^#For each T2DM patient, we randomly selected up to 5 (for late-onset T2DM patients) or 10 (for early-onset T2DM patients) individuals without T2DM diagnosis at the diagnosis date of the index patient (i.e. the index date) from the study population, individually matched by date of birth (±1 year), sex, and decile of Townsend deprivation index.

Figure 2.

Risks of complications after T2DM diagnosis according to category. Estimates of HRs with confidence intervals were calculated from different conditional Cox models. Cox models were stratified by matching identifier (i.e. date of birth, sex, and Townsend deprivation index) (model A) and further adjusted for household income (model B) or household income, BMI, smoking and drinking status (model C). Detailed results are shown in Supplementary Table 4.

Figure 3.

Risks of complications after T2DM diagnosis. The outer and inner rings show the point estimates of HRs of the corresponding complications for early-onset and late-onset T2DM respectively. Cox models were stratified by matching identifier (i.e. date of birth, sex, and Townsend deprivation index) and adjusted for household income, BMI, smoking and drinking status. Complications with P-value < Bonferroni corrected threshold are marked with an asterisk. Detailed results are shown in Supplementary Table 5.

Figure 4.

Comorbidity networks for early-onset and late-onset T2DM. The number of nodes and links in each network and their logical relation are shown in the Venn diagrams in the upper center of the figure. Each node represents a diabetes-associated complication, while the size and color of the node indicate the prevalence and the category of the corresponding complication (see legend). Reference nodes with percentages representing the prevalence scales are shown in the lower center of the figure. The width of the link represents the strength of comorbidity association, measured by RR. Reference links with RR are shown in the legend. Complications with prevalence ≥5% are labeled with its phenotype name along with the ‘phecode’, while others are labeled only with the ‘phecode’ (see Supplementary Table 2 for a description of each ‘phecode’). The two networks were partitioned into several modules using the Louvain algorithm, and nodes belonging to the same module are grouped together and separated from other nodes using dashed lines.