Abstract
Type 2 diabetes mellitus (T2DM) is strongly associated with cardiovascular disease (CVD). This study aimed to examine the association between diabetes duration and major cardiovascular outcomes, including coronary artery disease (CAD), congestive heart failure (CHF), cerebrovascular accident (CVA), and peripheral arterial disease (PAD), among Thai adults with T2DM using nationwide data from 2018. This cross-sectional study utilized data from the “Assessment in Quality of Care among Patients Receiving a Diagnosis of Type 2 Diabetes and Hypertension Visiting the Ministry of Public Health and Bangkok Metropolitan Administration Hospital” database in Thailand. Adult patients aged ≥ 18 years with T2DM were included, and those without documented diabetes duration were excluded. Diabetes duration was stratified into four groups: <5 years, 5–9 years, 10–14 years, and ≥ 15 years. Multivariable logistic regression analyses were performed to evaluate the association between diabetes duration and cardiovascular outcomes after adjusting for age, sex, body mass index, hypertension, dyslipidemia, smoking status, alcohol consumption, and glycemic control. Among 34,131 participants, the overall prevalence of CAD, CHF, CVA, and PAD was 1.9%, 0.8%, 1.5%, and 0.3%, respectively. Compared with a diabetes duration of < 5 years, a duration of ≥ 15 years was associated with higher odds of CAD (adjusted odds ratio [AOR] 1.80, 95% confidence interval [CI] 1.38–2.34), CHF (AOR 2.34, 95% CI 1.60–3.44), and PAD (AOR 6.47, 95% CI 3.09–13.58), whereas no significant association was observed for CVA. These findings suggest a cross-sectional association between longer diabetes duration and a higher prevalence of certain cardiovascular diseases, particularly CAD, CHF, and PAD, among adults with T2DM. Longitudinal studies are needed to confirm causality and to guide clinical interventions targeting cardiovascular risk in this population.
Keywords: Duration of diabetes, Cardiovascular disease, CAD, Heart failure, Cerebrovascular accident, PAD
Subject terms: Type 2 diabetes, Cardiovascular diseases
Introduction
Diabetes mellitus (DM) remains a significant global health issue and a leading cause of mortality and reduced life expectancy worldwide1. The prevalence of DM is steadily increasing, affecting 9.3% of the global population as of 2019, with projections indicating a rise to 10.9% (approximately 578 million people) by 20302. Type 2 Diabetes mellitus (T2DM) accounts for approximately 90% of all diabetes cases and presents a considerable public health burden due to its association with various complications, particularly cardiovascular disease (CVD). 3,4
CVD is the primary cause of morbidity and mortality among individuals with T2DM, affecting approximately one-third of this population5–7. The prevalence of CVD is notably higher in patients with T2DM compared to the general population, with a positive correlation between the burden of diabetes and cardiovascular complications8–10. Existing studies also show that mortality rates are significantly higher among patients with diabetes than among individuals without diabetes7,8,10. Several risk factors, including hypertension, dyslipidemia, obesity, and genetic predisposition, contribute to cardiovascular disease in diabetes11–13.
Among these risk factors, the duration of diabetes is a critical determinant of CVD risk. For instance, a prospective study in South Korea demonstrated that diabetes duration influences the risk of acute myocardial infarction, with varying effects depending on LDL levels14. As a result, diabetes duration has been incorporated into guidelines for determining LDL targets in CAD prevention15. Similarly, studies from both Western and Asian populations have shown that longer diabetes duration is significantly associated with increased risks of coronary artery disease (CAD), congestive heart failure (CHF), cerebrovascular accident (CVA), peripheral arterial disease (PAD), and all-cause mortality16–19. A large prospective cohort study using UK Biobank data involving nearly 19,000 patients with T2DM reported that every five-year increase in diabetes duration was associated with approximately a 20% higher risk of CVD, myocardial infarction, and stroke in both men and women, with no significant sex-based differences in these associations20. Similarly, a population-based study in Singapore that categorized over 71,000 patients with T2DM into five clinically distinct phenotypes found that individuals with longer disease duration and a higher burden of comorbidities experienced significantly elevated risks of cardiovascular events, end-stage renal disease, lower-extremity amputation, and mortality. Patients with psychiatric comorbidities, such as depression, also demonstrated disproportionately high healthcare utilization21. These findings underscore the importance of considering diabetes duration in the clinical stratification of cardiovascular risk, especially for high-risk subgroups.
However, while some studies have explored stratified diabetes duration or examined individual CVD outcomes, few have comprehensively addressed both stratification of diabetes duration and the full spectrum of major CVD outcomes collectively19,20. This limits the broader applicability of their findings to clinical decision-making and risk stratification. This study aims to investigate the association between stratified diabetes duration and cardiovascular outcomes in Asian individuals with T2DM. By utilizing nationwide data from Thai adults with T2DM in 2018, we seek to provide a more complete understanding of how diabetes duration impacts cardiovascular complications. Our findings aim to inform clinical screening strategies, patient education, and healthcare policy development, ultimately improving outcomes for individuals with diabetes.
Methods
Study design and data source
This study utilized a cross-sectional design, analyzing data from the Assessment in Quality of Care among Patients Receiving a Diagnosis of Type 2 Diabetes and Hypertension Visiting the Ministry of Public Health (MoPH) and Bangkok Metropolitan Administration Hospital in Thailand (Thailand DM/HT) database22. The database was developed by the Medical Research Network of the Consortium of Thai Medical Schools (MedResNet) and was designed to represent patients nationwide using a two-stage stratified cluster sampling method proportional to hospital size. Eligible participants were adults aged 18 years or older with T2DM who had received continuous care for at least 12 months at selected public hospitals or clinics. Data were extracted by trained nurses using standardized case report forms and submitted to the central data management unit at MedResNet, where verification procedures, including consistency checks, data cleaning, and quality control audits, were conducted to ensure accuracy and completeness. The study protocol was approved by the Institutional Review Board of the Royal Thai Army Medical Department (Approval No: Q023h/66_Exp, Date: February 20, 2023). Variables recorded included demographic information, diabetes duration, comorbid conditions, medication use, and complications, as well as laboratory values such as HbA1c, fasting plasma glucose, lipid profiles, creatinine, and blood pressure. The dataset thus offers a comprehensive overview of diabetes care quality across Thailand’s public healthcare facilities.
Study population
The study included adult patients aged 18 years or older diagnosed with T2DM. Patients were excluded if their duration of diabetes was not recorded. The final sample consisted of 34,131 individuals (Fig. 1).
Fig. 1.
Flowchart of patients with type 2 diabetes enrolled in the study receiving care in Thailand in 2018
Definition and stratification of diabetes duration
Diabetes duration was defined as the time elapsed from the initial diagnosis of T2DM to the date of data collection in 2018. The duration was stratified into four groups for analysis: <5 years, 5–9 years, 10–14 years, and ≥ 15 years. This stratification was informed by previous studies and chosen to reflect clinically relevant thresholds of disease progression17,19. The classification also supported meaningful comparisons across categories while preserving statistical power.
Study outcomes
The primary outcomes were the prevalence of major cardiovascular diseases, including CAD, CHF, CVA, and PAD. Diagnoses of these conditions were identified using medical records based on the International Classification of Diseases, Tenth Revision Codes (ICD-10) I25, I50, I64, and I73, respectively23.
Statistical analysis
All analyses were performed using IBM SPSS version 29.0.2.0 (IBM Corp. Released 2023, Armonk, NY: IBM Corp); Categorical data were presented as numbers and percentages, while continuous data were presented as mean and standard deviation (SD) for normally distributed data or median and interquartile range (IQR) for non-normal distributed data. Normality of continuous variables was assessed using the Shapiro–Wilk test. For variables with multiple recorded measurements, the most recent value available in 2018 was used for analysis. Binary logistic regression analysis was used to identify risk factors and potential confounders for cardiovascular diseases. The final model was performed using multivariable logistic regression analysis between the duration of diabetes and cardiovascular diseases adjusting for potential confounders, including age, sex, body mass index (BMI), hypertension, dyslipidemia, smoking status (never smokers vs. current or former smokers), alcohol consumption (never drinkers vs. current or former drinkers based on self-reported history), and glycemic control (HbA1c levels). BMI categories were defined according to the World Health Organization (WHO) recommendations for Asian populations, with a cut-off value of 25 kg/m² used to classify obesity24. The magnitude of association was demonstrated as an adjusted odds ratio (AOR) with a 95% confidence interval (CI). All statistical tests were two-sided; a p-value less than 0.05 was considered statistically significant. Prior to constructing the multivariable models, pairwise correlations among metabolic covariates (BMI, HbA1c, and dyslipidemia) were examined to assess the potential for multicollinearity. No substantial correlations were identified that would preclude simultaneous inclusion of these variables. Formal variance inflation factor (VIF) diagnostics were not systematically performed; therefore, residual multicollinearity among metabolic variables cannot be entirely excluded as a potential source of instability in individual coefficient estimates.
Ethics consideration
The Institutional Review Board approved the Royal Thai Army’s study (Approved No Q023h/66_Exp) in accordance with international guidelines, including the Declaration of Helsinki, the Belmont Report, CIOMS Guidelines, and the International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use—Good Clinical Practice (ICH-GCP). Documentation of informed consent was waived because the study involved secondary data.
Results
Characteristics of study participants
Table 1 demonstrates the baseline characteristics of the 34,131 study participants, stratified by diabetes duration. Overall, 67.1% of participants were female, with the proportion of females remaining relatively consistent across all diabetes duration groups. The proportion of individuals aged ≥ 75 years increased with longer diabetes duration, from 8.8% in the < 5 years group to 17% in the ≥ 15 years group. Most participants (95.9%) were treated in secondary- and tertiary-level hospitals, with a smaller percentage receiving care in primary care hospitals, and this distribution differed significantly across diabetes duration categories (p < 0.001). In terms of lifestyle factors, 85.7% of participants were never smokers, while 14.3% were categorized as current or former smokers. Similarly, 86.3% of participants reported no alcohol consumption, with 13.7% categorized as current or former drinkers. The proportion of smokers and drinkers decreased as diabetes duration increased (p < 0.001). The distribution of BMI categories differed across diabetes duration groups (p < 0.001). Participants with obesity (BMI ≥ 25 kg/m²) were more frequently observed in the shorter duration groups, particularly in the < 5 years (37.2%) and 5–9 years (37.2%) categories. In contrast, those with a BMI < 25 kg/m² were more commonly seen among individuals with a diabetes duration of ≥ 15 years (11.3%) and 10–14 years (25.8%) (p < 0.001). Regarding comorbidities, hypertension and dyslipidemia were present in 77.9% and 70.7% of participants, respectively, with prevalences increasing significantly with longer diabetes duration (p < 0.001). Glycemic control differed significantly across groups, with the proportion of participants with HbA1c ≥ 7% increasing from 42.4% in the < 5 years group to 67.8% in the ≥ 15 years group (p < 0.001). The overall prevalence of cardiovascular disease was 4.5%, composed of 1.9% for CAD, 0.8% for CHF, 1.5% for CVA, and 0.3% for PAD.
Table 1.
Baseline characteristics of enrolled patients with type 2 diabetes.
| Variable | Overall (N = 34131) | Duration of diabetes (year) | p-value | |||
|---|---|---|---|---|---|---|
| n (%) | < 5 | 5–9 | 10–14 | ≥ 15 | ||
| Gender | < 0.001 | |||||
| Male | 11224 (32.9) | 3779 (33.7) | 4207 (37.5) | 2406 (21.4) | 832 (7.4) | |
| Female | 22907 (67.1) | 6318 (27.6) | 8224 (35.9) | 5778 (25.2) | 2587 (11.3) | |
| Age (year) | < 0.001 | |||||
| < 55 | 8276 (24.2) | 3537 (42.7) | 3112 (37.6) | 1311 (15.8) | 316 (3.8) | |
| 55–64 | 11706 (34.3) | 3577 (30.6) | 4342 (37.1) | 2777 (23.7) | 1010 (8.6) | |
| 65–74 | 9430 (27.6) | 2096 (22.2) | 3393 (36) | 2652 (28.1) | 1289 (13.7) | |
| ≥ 75 | 4719 (13.8) | 887 (18.8) | 1584 (33.6) | 1444 (30.6) | 804 (17) | |
| Hospital level | < 0.001 | |||||
| Standard/Advanced hospital | 32723 (95.9) | 9651 (29.5) | 11843 (36.2) | 7867 (24) | 3362 (10.3) | |
| Community hospital | 1408 (4.1) | 446 (31.7) | 588 (41.8) | 317 (22.5) | 57 (4.0) | |
| Smoking status | < 0.001 | |||||
| Never | 29240 (85.7) | 8461 (28.9) | 10558 (36.1) | 7131 (24.4) | 3090 (10.6) | |
| Current/Ex-smoker | 4891 (14.3) | 1636 (33.4) | 1873 (38.3) | 1053 (21.5) | 329 (6.7) | |
| Alcohol drinking | < 0.001 | |||||
| Never | 29445 (86.3) | 8463 (28.7) | 10671 (36.2) | 7197 (24.4) | 3114 (10.6) | |
| Current/Ex-drinkers | 4686 (13.7) | 1634 (34.9) | 1760 (37.6) | 987 (21.1) | 305 (6.5) | |
| BMI (kg/m2) | < 0.001 | |||||
| < 25 | 16559 (48.5) | 4530 (27.4) | 5891 (35.6) | 4266 (25.8) | 1872 (11.3) | |
| ≥ 25 | 17572 (51.5) | 5567 (31.7) | 6540 (37.2) | 3918 (22.3) | 1547 (8.8) | |
| HbA1C level (%) | < 0.001 | |||||
| ≤ 7 | 12440 (36.4) | 4276 (34.4) | 4591 (36.9) | 2628 (21.1) | 945 (7.6) | |
| > 7 | 21691 (63.6) | 5821 (26.8) | 7840 (36.1) | 5556 (25.6) | 2474 (11.4) | |
| Hypertension | 26598 (77.9) | 7033 (26.4) | 9802 (36.9) | 6796 (25.6) | 2967 (11.2) | < 0.001 |
| DLP | 24126 (70.7) | 6765 (28) | 8844 (36.7) | 5951 (24.7) | 2566 (10.6) | < 0.001 |
| Gout | 1912 (5.6) | 498 (26) | 718 (37.6) | 477 (24.9) | 219 (11.5) | 0.003 |
| Coronary artery disease | 649 (1.9) | 133 (20.5) | 227 (35) | 174 (26.8) | 115 (17.7) | < 0.001 |
| Congestive heart failure | 289 (0.8) | 56 (19.4) | 96 (33.2) | 86 (29.8) | 51 (17.6) | < 0.001 |
| Cerebrovascular accident | 517 (1.5) | 146 (28.2) | 180 (34.8) | 129 (25.0) | 62 (12.0) | 0.400 |
| Peripheral arterial disease | 112 (0.3) | 11 (9.8) | 43 (38.4) | 36 (32.1) | 22 (19.6) | < 0.001 |
BMI Body mass index, DM Diabetes mellitus, DLP dyslipidemia, IQR interquartile range, SD standard deviation.
Figure 2 illustrates a box plot of diabetes duration categorized by CAD, CHF, CVA, and PAD. The median diabetes duration among cardiovascular disease in the study population was 9.1, 9.6, 7.7, and 10.6 years for CAD, CVA, CHF, and PAD, respectively. Figure 3 shows the prevalence of CAD, CHF, CVA, and PAD categorized by the duration of diabetes mellitus. The prevalence of CAD increased with the duration of DM, starting at 1.3% for those with less than five years and peaking at 3.4% for those with more than 15 years. CHF displayed a slight increase in prevalence with a longer duration of DM, starting at 0.6% in individuals with less than five years of DM and increasing to 1.5% in those with more than 15 years of DM. Prevalence of CVA appeared relatively stable (1.4%) from the duration of DM less than five years to 5–9 years, and the prevalence slightly increased from 1.6% in the duration of DM 10–14 years to 1.8% in ≥ 15 years. PAD had a low prevalence overall but demonstrated an increase with longer DM duration, reaching 0.6% in individuals with a DM duration of more than 15 years.
Fig. 2.
Boxplot of the duration of diabetes categorized by cardiovascular diseases.
Fig. 3.
The prevalence of cardiovascular diseases categorized by duration of diabetes.
Logistic regression analysis of diabetes duration and cardiovascular disease
Univariable logistic regression was initially performed to explore the crude associations between diabetes duration and CVD outcomes (Tables 2, 3). Stepwise increases in odds were observed with longer diabetes duration categories, particularly for CAD and PAD. Other clinical variables, such as age, hypertension, and gout, were also significantly associated with various cardiovascular outcomes.
Table 2.
Univariable logistic regression analysis of duration of diabetes with CAD and CHF.
| Variables | Coronary artery disease | Congestive heart failure | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P-value | OR | 95% CI | P-value | |
| Duration of DM (year) | 1.05 | 1.04–1.07 | < 0.001 | 1.06 | 1.05–1.08 | < 0.001 |
| < 5 | Ref | Ref | ||||
| 5–9 | 1.39 | 1.12–1.73 | 0.003 | 1.40 | 1.01–1.94 | 0.048 |
| 10–14 | 1.63 | 1.30–2.04 | < 0.001 | 1.90 | 1.36–2.67 | < 0.001 |
| ≥ 15 | 2.61 | 2.03–3.36 | < 0.001 | 2.72 | 1.85–3.98 | < 0.001 |
| Gender | ||||||
| Male | Ref | Ref | ||||
| Female | 0.68 | 0.58–0.79 | < 0.001 | 1.001 | 0.78–1.28 | 0.996 |
| Age (year) | ||||||
| < 55 | Ref | Ref | ||||
| 55–64 | 2.22 | 1.66–2.99 | < 0.001 | 2.23 | 1.46–3.42 | < 0.001 |
| 65–74 | 3.62 | 2.72–4.82 | < 0.001 | 2.58 | 1.68–3.97 | < 0.001 |
| ≥ 75 | 5.11 | 3.79–6.89 | < 0.001 | 5.79 | 3.78–8.86 | < 0.001 |
| Hospital level | ||||||
| Secondary-/Tertiary-level hospital | Ref | Ref | ||||
| Primary care Hospital | 0.07 | 0.02–0.28 | < 0.001 | 0.16 | 0.04–0.65 | 0.010 |
| Smoking status | ||||||
| Never | Ref | Ref | ||||
| Current/Former-smoker | 1.27 | 1.03–1.56 | 0.024 | 0.96 | 0.69–1.34 | 0.812 |
| Alcohol drinking | ||||||
| Never | Ref | Ref | ||||
| Current/Former-drinker | 0.97 | 0.77–1.22 | 0.809 | 0.78 | 0.54–1.13 | 0.189 |
| BMI (kg/m2) | ||||||
| < 25 | Ref | Ref | ||||
| ≥ 25 | 1.04 | 0.93–1.16 | 0.496 | 1.27 | 1.01–1.61 | 0.042 |
| HbA1C level (%) | ||||||
| < 7.0 | Ref | Ref | ||||
| ≥ 7.0 | 0.87 | 0.74–1.02 | 0.078 | 0.95 | 0.74–1.21 | 0.652 |
| Hypertension | ||||||
| No | Ref | Ref | ||||
| Yes | 4.06 | 2.97–5.54 | < 0.001 | 4.3 | 2.67–6.93 | < 0.001 |
| DLP | ||||||
| No | Ref | Ref | ||||
| Yes | 1.61 | 1.33–1.95 | < 0.001 | 1.49 | 1.13–1.98 | 0.005 |
| Gout | ||||||
| No | Ref | Ref | ||||
| Yes | 2.11 | 1.64–2.71 | < 0.001 | 1.97 | 1.34–2.88 | < 0.001 |
OR odds ratio, DM diabetes mellitus, DLP dyslipidemia.
Table 3.
Univariable logistic regression analysis of diabetes duration with CVA and PAD.
| Variables | Cerebrovascular accident | Peripheral arterial disease | ||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P-value | OR | 95% CI | P-value | |
| Duration of DM (year) | 1.02 | 1.01–1.03 | 0.040 | 1.09 | 1.06–1.12 | < 0.001 |
| < 5 | Ref | Ref | ||||
| 5–9 | 1.00 | 0.80–1.25 | 0.990 | 3.18 | 1.64–6.18 | < 0.001 |
| 10–14 | 1.09 | 0.86–1.39 | 0.472 | 4.05 | 2.06–7.96 | < 0.001 |
| ≥ 15 | 1.26 | 0.93–1.70 | 0.132 | 5.94 | 2.88–12.26 | < 0.001 |
| Gender | ||||||
| Male | Ref | – | – | |||
| Female | 0.60 | 0.51–0.72 | < 0.001 | 0.70 | 0.48–1.02 | 0.066 |
| Age (year) | ||||||
| < 55 | Ref | – | – | |||
| 55–64 | 1.56 | 1.19–2.05 | < 0.001 | 1.19 | 0.72–1.95 | 0.495 |
| 65–74 | 1.96 | 1.49–2.57 | < 0.001 | 0.70 | 0.39–1.26 | 0.238 |
| ≥ 75 | 2.48 | 1.84–3.34 | < 0.001 | 1.76 | 1.01–3.06 | 0.047 |
| Hospital level | ||||||
| Standard/Advanced hospital | Ref | – | – | |||
| Community Hospital | 0.13 | 0.04–0.42 | < 0.001 | 0.21 | 0.03–1.50 | 0.119 |
| Smoking status | ||||||
| Never | Ref | – | – | |||
| Current/Ex-smoker | 1.63 | 1.32–2.02 | < 0.001 | 1.81 | 1.17–2.81 | 0.008 |
| Alcohol drinking | ||||||
| Never | Ref | – | – | |||
| Current/Ex-drinker | 1.26 | 1.00-1.59 | 0.054 | 1.81 | 1.16–2.83 | 0.009 |
| BMI (kg/m2) | ||||||
| < 25 | Ref | – | – | |||
| ≥ 25 | 0.91 | 0.80–1.03 | 0.122 | 1.01 | 0.70–1.47 | 0.946 |
| HbA1C level (%) | ||||||
| < 7.0 | Ref | – | Ref | – | ||
| ≥ 7.0 | 0.98 | 0.94–1.03 | 0.456 | 1.09 | 0.73–1.62 | 0.671 |
| Hypertension | ||||||
| No | Ref | – | – | |||
| Yes | 2.62 | 1.96–3.50 | < 0.001 | 1.10 | 0.69–1.74 | 0.695 |
| DLP | ||||||
| No | Ref | – | – | |||
| Yes | 1.14 | 0.94–1.39 | 0.187 | 0.99 | 0.66–1.49 | 0.972 |
| Gout | ||||||
| No | Ref | – | – | |||
| Yes | 1.34 | 0.96–1.88 | 0.083 | 1.84 | 0.99–3.43 | 0.055 |
OR odds ratio, DM diabetes mellitus, DLP dyslipidemia.
In the multivariable models (Table 4), the association between diabetes duration and each cardiovascular outcome was assessed after adjusting for relevant covariates to account for potential confounding. For CAD, after adjusting for several covariates, including sex, age, hospital level, smoking status, HbA1c level, HT, DLP, and gout comorbidity, a one-year increase in diabetes duration was independently associated with higher odds of disease (AOR = 1.03, 95% CI 1.02–1.05). Participants with a duration of DM of ≥ 15 years displayed higher odds of having CAD than those with less than five years (AOR 1.80, 95% CI 1.38–2.34), while no significant associations were observed in the 5-9- or 10-14-year categories. For CHF, after adjusting for sex, age, BMI, HT, DLP, and gout, a one-year increase in diabetes duration was also significantly associated with CHF (AOR = 1.06, 95% CI 1.04–1.08). Compared to those with less than five years of diabetes, individuals with 10–14 years (AOR = 1.70, 95% CI 1.21–2.38) and ≥ 15 years (AOR = 2.34, 95% CI 1.60–3.44) showed significantly increased odds. Regarding CVA, no significant associations were found between diabetes duration (as a continuous or categorical variable) and outcome after adjusting for sex, age, hospital level, smoking status, BMI, and HT. For PAD, after adjusting for age, smoking status, and alcohol consumption, diabetes duration remained strongly associated with PAD. A one-year increase in duration was associated with an AOR of 1.09 (95% CI 1.07–1.12), and all categorical duration groups showed elevated odds compared to the reference group. Notably, those with a diabetes duration ≥ 15 years had more than a sixfold increased risk of PAD (AOR = 6.47, 95% CI 3.09–13.58) (Fig. 4A and B).
Table 4.
Multivariable logistic regression analysis of duration of diabetes and cardiovascular diseases.
| Variables | AOR | 95% CI | P-value |
|---|---|---|---|
| CADa | |||
| Duration of DM (year) | 1.03 | 1.02–1.05 | < 0.001 |
| < 5 | Ref | ||
| 5–9 | 1.19 | 0.95–1.47 | 0.127 |
| 10–14 | 1.24 | 0.98–1.57 | 0.070 |
| ≥ 15 | 1.80 | 1.38–2.34 | < 0.001 |
| CHFb | |||
| Duration of DM (year) | 1.06 | 1.04–1.08 | < 0.001 |
| <5 | Ref | ||
| 5–9 | 1.28 | 0.92–1.78 | 0.151 |
| 10–14 | 1.69 | 1.21–2.38 | 0.002 |
| ≥ 15 | 2.34 | 1.60–3.44 | < 0.001 |
| CVAc | |||
| Duration of DM (year) | 1.01 | 0.99–1.02 | 0.731 |
| <5 | Ref | ||
| 5–9 | 0.89 | 0.72–1.11 | 0.316 |
| 10–14 | 0.90 | 0.70–1.15 | 0.386 |
| ≥ 15 | 0.96 | 0.71–1.31 | 0.793 |
| PADd | |||
| Duration of DM (year) | 1.09 | 1.07–1.12 | < 0.001 |
| <5 | Ref | ||
| 5–9 | 3.25 | 1.67–6.32 | < 0.001 |
| 10–14 | 4.25 | 2.15–8.42 | < 0.001 |
| ≥ 15 | 6.47 | 3.09–13.58 | < 0.001 |
AOR adjusted odds ratio, DM diabetes mellitus CAD coronary artery disease, CHF congestive heart failure, CVA cerebrovascular accident, PAD peripheral arterial disease.
a Adjusted for sex, age, hospital level, smoking status, HT, DLP, and gout comorbidity, HbA1c level.
b Adjusted for sex, age, BMI, HT, DLP, and gout comorbidity.
c Adjusted for sex, age, hospital level, smoking status, BMI, HT.
d Adjusted for age, smoking status, alcohol drinking status.
Fig. 4.
Forest plot of the multivariable logistic regression analysis of CVDs and (A) Duration of DM categories (B) a year increase of the duration of DM. a Adjusted for sex, age, hospital level, smoking status, HT, DLP, and gout comorbidity, HbA1c level. b Adjusted for sex, age, BMI, HT, DLP, and gout comorbidity. c Adjusted for sex, age, hospital level, smoking status, BMI, HT. d Adjusted for age, smoking status, alcohol drinking status
Discussion
This study demonstrated a significant association between the duration of T2DM and the prevalence of major cardiovascular diseases, including CAD, CHF, CVA, and PAD. Our findings highlight the importance of considering diabetes duration as a critical factor in cardiovascular risk assessment and management.
The baseline characteristics of our study population showed a predominance of female participants (67.1%). This gender disparity could be attributed to differences in healthcare-seeking behavior between males and females, with women generally utilizing medical care services more than men, rather than a true difference in the underlying prevalence of diabetes25. A mean age of 62.2 years reflects a demographic particularly vulnerable to the long-term effects of DM26,27. The high prevalence of hypertension (77.9%) and dyslipidemia (70.7%) among participants further underscores the multifaceted risk profile for cardiovascular complications in individuals with diabetes28,29.
Patients with a diabetes duration ≥ 15 years had markedly higher odds of CAD, CHF, and PAD compared to those with a duration < 5 years. This aligns with previous studies indicating that prolonged exposure to chronic hyperglycemia, insulin resistance, and systemic inflammation exacerbates vascular damage over time30. Notably, the risk of CAD becomes more pronounced when diabetes duration exceeds 15 years, consistent with findings in Egyptian patients with diabetes, where longer durations were associated with more extensive coronary involvement and stenosis31. This suggests chronic hyperglycemia contributes to endothelial dysfunction, atherosclerosis, factors leading to the development of CAD and CHF32,33. Variations in CAD risk may also reflect differences in diabetes management, healthcare accessibility, and environmental factors across populations34,35.
Conversely, our findings showed an inconsistent association between diabetes duration and CVA, which has been similarly reported in prior studies16. This inconsistency can be attributed to several factors. First, diabetes causes vascular changes, including large artery atherosclerosis and small vessel disease, both of which increase stroke risk; however, the progression of these changes can vary significantly among individuals36. Second, the presence of other comorbidities may modify the association. For example, in patients with atrial fibrillation, longer diabetes duration was associated with higher stroke rates, but this association was weaker than for other cardiovascular outcomes, suggesting that atrial fibrillation itself may be a more dominant risk factor for stroke in these individuals37. Finally, long-term glycemic control might play a more critical role than diabetes duration alone in predicting stroke risk. Increased long-term glycemic variability has been identified as an independent risk factor for CVA, beyond the effects of persistent hyperglycemia38.
The strong correlation between DM duration and PAD is noteworthy, with patients having diabetes for ≥ 15 years showing markedly higher odds of PAD compared to those with < 5 years of diabetes. Previous studies similarly demonstrated the cumulative impact of diabetes duration on PAD risk39–41. These findings underscore the need for early screening and interventions, as PAD is often underdiagnosed due to overlapping symptoms with diabetic neuropathy and its asymptomatic nature39,42. Current guidelines recommend PAD screening in those who have DM duration > 10 years or with additional risk factors, such as smoking, hypertension, and dyslipidemia43,44.
An intriguing finding is the absence of a significant association between obesity and the prevalence of PAD. Existing evidence regarding the relationship between BMI and PAD remains controversial. Vogt et al. demonstrated that upper body obesity, as assessed by the waist-to-hip ratio rather than BMI, was associated with an increased risk of PAD45. Conversely, a large cohort from Israel reported that men with new-onset intermittent claudication (IC) had a higher BMI (0.5 kg/m2) compared to men without IC 46. Some studies suggest that a higher BMI might act as a protective factor against PAD47,48. Overall, the association between BMI and PAD appears to be complex and influenced by population characteristics, age, gender, and the methods used to define PAD49–51. These findings suggest that the impact of T2DM on PAD may be partially independent of generalized obesity as measured by BMI and that alternative measures of adiposity or unmeasured factors may better explain PAD risk in this population.
This study offers critical insights into how the duration of DM influences the prevalence of various CVDs. Early diagnosis and sustained management of T2DM are essential to mitigate these risks. Targeted interventions, such as routine PAD screening and management of cardiovascular risk factors, should be prioritized in patients with long-standing diabetes. The strengths of this study lie in its large sample size and the use of a nationally representative dataset which enhances the generalizability of our findings to the Thai population. Additionally, the comprehensive stratification of diabetes duration provides novel insights into the relationship between duration and cardiovascular outcomes. Although the data were collected in 2018, this dataset represents the most recent nationally representative Thai DM/HT database with comprehensive cardiovascular outcome data available for analysis. Regarding generalizability, the study cohort was drawn predominantly from secondary- and tertiary-level public hospitals within the Thailand DM/HT database framework. Patients attending higher-level facilities tend to have greater disease complexity, more comorbidities, and more systematic access to diagnostic services compared with those managed in primary care settings. This may result in a higher detected prevalence of cardiovascular outcomes in this cohort than would be observed in a community- or primary-care-based population. Accordingly, our findings may not be fully generalizable to patients with T2DM who are managed exclusively in primary care or who have less frequent healthcare contact. Future nationally representative studies incorporating primary care data would provide a more comprehensive population-level estimate of cardiovascular burden in patients with T2DM. However, this study has several limitations. First, the cross-sectional design precludes causal inference between diabetes duration and cardiovascular outcomes. Specifically, reverse causation cannot be excluded: individuals with prevalent cardiovascular disease may undergo more intensive metabolic surveillance, potentially increasing the likelihood of a diabetes diagnosis and subsequent follow-up, thereby artificially inflating longer duration categories. Temporal ambiguity further limits our ability to determine whether diabetes duration preceded or followed cardiovascular events. These findings should therefore be interpreted as cross-sectional associations rather than as evidence of causal relationships. Second, an important consideration related to the cross-sectional design is the potential for survivor bias. Patients with aggressive early-onset cardiovascular disease may experience premature mortality or dropout before accumulating longer diabetes duration, resulting in underrepresentation of severe cardiovascular phenotypes in the ≥ 15-year duration group; the true cardiovascular burden associated with prolonged diabetes may therefore be underestimated in this analysis. Conversely, patients with longer diabetes duration are more likely to have established follow-up with secondary- or tertiary-care providers who perform systematic cardiovascular screening. This increased diagnostic intensity may lead to higher rates of detected — but not necessarily higher rates of incident — cardiovascular disease in the longer-duration strata, potentially overestimating the association between duration and prevalent CVD. The odds ratios reported herein reflect associations among survivors at the time of the survey and should be interpreted with these competing biases in mind. Third, the recorded diagnosis date in the database may not accurately reflect the true onset of T2DM, as patients may have had undiagnosed disease for a period preceding formal clinical recognition. This may result in underestimation of true diabetes duration, leading to non-differential exposure misclassification that would likely attenuate the observed associations toward the null. This limitation is inherent to registry-based studies and has been similarly acknowledged in prior epidemiological research using administrative data52. Fourth, medication data — including cardioprotective agents such as statins, SGLT2 inhibitors, and GLP-1 receptor agonists, as well as insulin — were not available in the current dataset and could not be included in the regression models. Longer diabetes duration is typically associated with more complex pharmacological regimens; SGLT2 inhibitors and GLP-1 receptor agonists in particular have demonstrated independent cardiovascular benefits, and their omission may contribute to residual confounding. Furthermore, longer diabetes duration may serve as a proxy for cumulative disease burden and treatment complexity rather than duration per se. The absence of markers for complication severity and treatment intensity may result in additional residual confounding that could affect the magnitude of the observed associations. Fifth, cardiovascular outcomes were ascertained using ICD-10 diagnostic codes recorded in the electronic health system without independent clinical validation. Diagnostic coding errors or inconsistencies across facilities may have introduced outcome misclassification. If such misclassification was non-differential with respect to diabetes duration, this would likely attenuate rather than inflate the observed associations. Sixth, approximately 11.5% of eligible participants were excluded due to missing data on diabetes duration. A formal comparison of characteristics between included and excluded participants was not feasible given the structure of the available dataset. If excluded individuals differed systematically from those included — for example, having more severe disease or greater cardiovascular morbidity — our estimates may not fully represent the intended population, and selection bias cannot be entirely excluded. Future research should focus on longitudinal studies to establish causality and explore the temporal relationship between diabetes duration and cardiovascular outcomes. Additionally, further investigations are needed to identify specific interventions that effectively reduce cardiovascular risk in patients with prolonged diabetes duration, particularly in resource-limited settings.
Conclusion
The duration of T2DM is significantly associated with cardiovascular outcomes, particularly for CAD, CHF, and PAD, in this cross-sectional analysis of Thai adults with T2DM. These findings underscore the importance of early identification and sustained management of T2DM as potential strategies to reduce long-term cardiovascular burden. Given the cross-sectional nature of the study, these results reflect associations rather than causal relationships, and longitudinal research is needed to clarify the temporal relationship between diabetes duration and cardiovascular outcomes and to evaluate interventions that may mitigate cardiovascular risk in patients with long-standing diabetes.
Acknowledgements
The authors wish to express their gratitude to all those involved in conducting the study for their dedication and invaluable assistance during this study, especially all staff of the Research Unit for Military Medicine at Phramongkutklao College of Medicine, Bangkok.
Abbreviations
- AOR
Adjusted odds ratio
- BMI
Body mass index
- CAD
Coronary artery disease
- CHF
Congestive heart failure
- CI
Confidence interval
- CVA
Cerebrovascular accident
- CVD
Cardiovascular disease
- DLP
Dyslipidemia
- IQR
Interquartile range
- OR
Odds ratio
- SD
Standard deviation
- T2DM
Type 2 diabetes mellitus
Author contributions
MP, CK, RR, WK, MM, and PS developed the study concept. RR collected the data, MP analyzed it and wrote the first draft. PS oversaw all the study protocols. All authors contributed to and approved the final version.
Funding
This research was supported by (1) the FETP-NCD, Division of Epidemiology, Department of Disease Control, Ministry of Public Health, Thailand and (2) the Research Unit for Military Medicine, Phramongkutklao College of Medicine, Bangkok, Thailand.
Data availability
Data cannot be shared publicly because it contains identifying information; additionally, it belongs to the Thailand DM/HT study of MedResNet. Thus, ethics restrictions exist on the data set. Data are available from the Thai NHSO, Bangkok, Thailand (contact Sirikorn Khunsri via sirikorn@nhso.go.th) for researchers meeting the criteria to access confidential data.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data cannot be shared publicly because it contains identifying information; additionally, it belongs to the Thailand DM/HT study of MedResNet. Thus, ethics restrictions exist on the data set. Data are available from the Thai NHSO, Bangkok, Thailand (contact Sirikorn Khunsri via sirikorn@nhso.go.th) for researchers meeting the criteria to access confidential data.