Abstract
Background
Obesity is an emerging public health issue and a significant risk factor for type 2 diabetes mellitus (T2DM). The growing prevalence of obesity in T2DM patients worsens disease outcomes, elevates medical expenses, and increases the risk of cardiovascular complications. Recognizing the trends and related factors of obesity in T2DM patients is essential for guiding specific interventions and public health strategies. This study aims to analyze the trends in obesity prevalence among T2DM patients in Thailand from 2014 to 2018 and to identify significant demographic, clinical, and lifestyle factors linked to obesity.
Method
This sequential cross-sectional study utilized secondary data from the Thailand DM/HT database in 2014 and 2018. It did not include individuals under 18 or those with incomplete data. Descriptive statistics were used to analyze demographic characteristics and obesity prevalence, while multivariable logistic regression was employed to identify factors independently associated with obesity. Additionally, linear regression analysis assessed the relationships between BMI and continuous variables, including age, systolic blood pressure (SBP), and HbA1c levels.
Results
A total of 32,196 patients in 2014 and 38,119 in 2018 were included. The prevalence of obesity among T2DM patients rose from 49.9% in 2014 to 51.1% in 2018, with obesity prevalence in males increasing from 48.5 to 51.0% (P < 0.001), whereas females exhibited a higher prevalence. Obesity was associated with younger adults (AOR 4.91, 95% CI 4.06–5.94) and those with hypertension (AOR 1.83, 95% CI 1.72–1.94). The risk of obesity showed an inverse correlation with a longer duration of diabetes (AOR 0.67, 95% CI 0.57–0.78). An inverse association was observed between smoking and obesity, with never-smokers having a greater risk of obesity compared to current smokers (AOR 1.69, 95% CI 1.48–1.92). Moreover, indoor occupations were associated with an increased rate of obesity (AOR 1.36, 95% CI 1.24–1.48).
Conclusions
The prevalence of obesity among T2DM patients in Thailand has continued to rise, particularly among men and young people. These findings suggest that T2DM obesity requires targeted lifestyle changes, weight management programs, and policy initiatives. Strengthening public health strategies that promote physical activity, dietary improvements, and access to effective diabetes management resources is critical for reducing obesity-related complications.
Keywords: Prevalence, Obesity, Type 2 diabetes mellitus, Thailand, Trends, Risk factors, Public health, Lifestyle factors
Introduction
Obesity is a critical global health issue, with its prevalence nearly tripling between 1975 and 2016. Currently, over 650 million adults are considered obese, significantly increasing the risk for chronic diseases such as cardiovascular disease (CVD), hypertension, stroke, and various cancers [1–6]. The World Obesity Federation projects that over one billion adults may be affected by obesity by 2030, further escalating healthcare costs and burdens [5, 7].
The connection between obesity and type 2 diabetes mellitus (T2DM) is well-established, as obesity is a key driver of insulin resistance and metabolic dysfunction [8]. Excess adiposity, particularly visceral fat, worsens insulin resistance and beta-cell dysfunction, elevating the risk of T2DM onset and progression [8]. Moreover, obesity complicates diabetes management by diminishing treatment effectiveness, resulting in poor glycemic control, increased medication needs, and a greater chance of developing diabetes-related complications such as nephropathy, retinopathy, and CVD [9]. The simultaneous presence of obesity and T2DM further amplifies the burden on healthcare systems, highlighting the necessity for targeted prevention and intervention strategies.
While global obesity trends differ across regions, low- and middle-income countries, including those in Asia, continue to experience rising prevalence [10–12]. Contributing factors include urbanization, dietary changes, sedentary behavior, and genetic predispositions [13–15]. Among individuals with T2DM, this trend is particularly concerning due to its impact on disease progression and management [16].
In Thailand, as in many Asian countries undergoing rapid urbanization, the dual burden of obesity and T2DM has emerged as a pressing national concern. According to the 5th National Health Examination Survey (NHES V) in 2014, the prevalence of diabetes among adults aged ≥ 20 years was 9.9%, with high rates of undiagnosed or untreated cases [17]. Despite national efforts, the increasing obesity prevalence among Thai patients with T2DM remains underexplored at the population level. Previous studies in Thailand have focused primarily on obesity trends in the general population, with limited attention to individuals living with T2DM—a group particularly vulnerable to adverse outcomes. Furthermore, there is a lack of nationwide analyses assessing changes in the prevalence of obesity over time and the sociodemographic and clinical factors contributing to these changes. This study aimed to compare the prevalence of obesity among Thai patients with T2DM between 2014 and 2018 using hospital-based data, and identify sociodemographic, clinical, and lifestyle factors associated with obesity. Understanding these patterns can support the development of targeted prevention strategies and more effective public health policies.
Methods
Study design and subjects
This study utilized data from the database titled Assessment of Quality of Care among Patients Diagnosed with T2DM and Hypertension Visiting the Ministry of Public Health (MoPH) and Bangkok Metropolitan Administration Hospital in Thailand (Thailand DM/HT). Data were collected from hospitals participating in the Universal Coverage Scheme across all provinces in Thailand, including hospitals of all levels [18].
The investigation employed a cross-sectional, stratified two-stage cluster sampling design in both 2014 and 2018. In the first stage, hospitals were stratified by administrative region and hospital level (regional, general, and community), then selected using probability proportional to size sampling. In the second stage, patient records were abstracted using a standardized case report form (CRF) developed by the Medical Research Network of the Consortium of Thai Medical Schools (MedResNet) [18].
Of the total participants with a confirmed diagnosis of T2DM, 32,716 individuals were identified in 2014 and 38,568 in 2018. After excluding those with missing BMI data (520 in 2014 and 449 in 2018), the final analytical sample comprised 32,196 participants in 2014 and 38,119 in 2018 (Fig. 1). These years were selected because they represent the most recent survey waves available from the Thailand DM/HT database with standardized nationwide data collection. Only patients aged 18 years or older with complete data on BMI, age, sex, and relevant covariates were included in the analysis. Obesity was defined as BMI ≥ 25 kg/m² based on the World Health Organization (WHO) criteria for Asian populations [19].
Fig. 1.
Flow of enrolled with Type 2 diabetes receiving care in Thailand in 2014, and 2018
Hospitals were selected based on their affiliation with the MoPH outside the Bangkok Metropolitan Region (BMR) and their enrollment in the Thai National Health Security Office (NHSO) program within the region. The study encompassed public and private clinics in the BMR under the NHSO program and a subset of MoPH hospitals nationwide. Hospitals were categorized by province and size, resulting in 77 province-level groupings and five categories based on hospital bed capacity. University hospitals were excluded due to inconsistent participation and incomplete data reporting.
Data collection
All patients with pre-existing T2DM were sequentially invited to participate by healthcare personnel at each clinic. Written informed consent was obtained to allow review and abstraction of their medical records, including baseline characteristics, diabetes-related complications, laboratory results, and prescribed medications. Trained registered nurses conducted medical record abstraction using standardized paper-based CRFs, following a uniform protocol. Completed forms were forwarded to the MedResNet central data management unit in Nonthaburi, Thailand. The data were then digitized using a scan-to-database system with optical character recognition (OCR). To ensure data accuracy, the digitized records underwent dual verification and routine quality control checks at the central processing center.
Variable definitions
Obesity was defined as a BMI of ≥ 25 kg/m² according to the WHO criteria for Asian populations. Age was recorded in years and categorized into six groups: <40, 40–49, 50–59, 60–69, 70–79, and ≥ 80. Sex was classified as male or female. Smoking status was categorized as current smoker, former smoker, or never smoker. Occupation was grouped into indoor, outdoor, or mixed types based on patient-reported job classification. Duration of diabetes was calculated in years from the recorded date of T2DM diagnosis. Hypertension, dyslipidemia, and gout were identified through documented physician diagnoses. Clinical and laboratory variables included SBP, hemoglobin A1c (HbA1c), and low-density lipoprotein cholesterol, each recorded as continuous variables. These values were based on the most recent measurements available in the medical records during the respective survey year.
Statistical analysis
Data analysis was conducted using IBM SPSS Statistics for Windows, Version 23.0. Descriptive statistics were used to analyze the demographic characteristics of the participants. Chi-square tests were applied for categorical variables, and independent t-tests were used to compare continuous variables across study years. The prevalence of obesity was calculated and reported as a percentage. Categorical variables were summarized using frequencies and percentages, while continuous data, such as BMI, were presented using means and standard deviations.
We conducted a complete-case analysis and excluded individuals with missing key variables (e.g., BMI). The extent of missingness for each variable was assessed, and variables with more than 10% missing were not included in multivariable models. To assess the relationship between independent variables and obesity, a univariate analysis was first conducted to examine the association between each independent variable and obesity. Variables showing meaningful differences were considered potential predictors for further study. Before conducting multivariable logistic regression, we assessed multicollinearity using the variance inflation factor (VIF). All included variables had VIF values < 2, indicating no significant multicollinearity. A multivariate analysis using logistic regression was then performed to determine the adjusted odds ratios (AORs) for obesity while accounting for confounding factors. Only variables that remained statistically significant after adjusting for other factors were retained in the final model. This approach allowed for a robust evaluation of the factors associated with obesity among individuals with T2DM. Additionally, linear regression analysis was conducted to evaluate the relationship between BMI and selected metabolic and cardiovascular parameters.
Result
Demographic characteristics
The study included 32,196 patients in 2014 and 38,119 in 2018 (Table 1), with a higher proportion of female patients in both years (69.0% in 2014 and 66.9% in 2018). The mean age of participants increased from 60.96 ± 10.94 years in 2014 to 62.29 ± 10.96 years in 2018. The proportion of older adults (> 70 years) also increased from 22.3 to 25.5%, while the percentage of individuals under 50 years declined. The mean BMI slightly increased from 25.50 ± 4.60 kg/m² in 2014 to 25.68 ± 4.76 kg/m² in 2018. A significant increase was observed in both males (25.26 ± 4.31 to 25.52 ± 4.45 kg/m²) and females (25.61 ± 4.72 to 25.75 ± 4.91 kg/m²). Regarding healthcare access, most patients received treatment at primary healthcare facilities, accounting for 75.9% in 2014 and 73.9% in 2018. Most participants were covered under Universal Healthcare Coverage (79.2% in 2014 and 78.5% in 2018), with an increasing proportion under the Civil Servant Medical Benefit scheme (15.5% in 2014 and 16.2% in 2018). Occupational data revealed that the proportion of unemployed patients increased from 29.3 to 33.4%, while those engaged in outdoor occupations decreased from 56.6 to 52.0%. For lifestyle factors, the proportion of current smokers decreased from 4.5% in 2014 to 3.6% in 2018, while the proportion of never-smokers increased from 80.3 to 84.2%. Among comorbidities, hypertension remained the most prevalent (76.0% in 2014 and 78.0% in 2018), followed by dyslipidemia (69.2% in 2014 and 70.5% in 2018). However, diabetic retinopathy declined from 6.9 to 5.0%, and macroalbuminuria decreased from 7.0 to 3.2%.
Table 1.
Demographic characteristics of diabetes mellitus patients in 2014 and 2018
| Characteristics | 2014 (n = 32,196) |
2018 (n = 38,119) |
|---|---|---|
| Gender, n(%) | ||
| Male | 9,989 (31.0) | 12,605 (33.1) |
| Female | 22,207 (69.0) | 25,514 (66.9) |
| Age (years), mean ± SD | 60.96 ± 10.94 | 62.29 ± 10.96 |
| < 40 | 845 (2.6) | 773 (2.0) |
| 40–49 | 4,025 (12.5) | 3,846 (10.1) |
| 50–59 | 9,437 (29.3) | 10,553 (27.7) |
| 60–69 | 10, 695 (33.2) | 13,212 (34.7) |
| ≥ 70 | 7,194 (22.3) | 9,735 (25.5) |
| Body mass index (kg/m2), mean ± SD | 25.50 ± 4.60 | 25.68 ± 4.76 |
| < 18.5 | 1,249 (3.9) | 1,534 (4.0) |
| 18.5–22.9 | 8,375 (26.0) | 9,610 (25.2) |
| 23.0–24.9 | 6,499 (20.2) | 7,501 (19.7) |
| 25.0–29.9 | 11,386 (35.4) | 13,360 (35.0) |
| ≥ 30 | 4,687 (14.6) | 6,114 (16.0) |
| Obesity (BMI ≥ 25 kg/m2), n(%) | 16,073 (49.9) | 19,474 (51.1) |
| Hospital level, n(%) | ||
| Tertiary care | 2,492 (7.7) | 2,974 (7.8) |
| Secondary care | 5,266 (16.4) | 6,983 (18.3) |
| Primary care | 24,438 (75.9) | 28,162 (73.9) |
| Scheme, n(%) | ||
| Universal healthcare coverage | 25,488 (79.2) | 29,933 (78.5) |
| Civil servant medical benefit | 4,979 (15.5) | 6,184 (16.2) |
| Social security | 1,271 (3.9) | 1,509 (4.0) |
| Others | 407 (1.3) | 493 (1.3) |
| Occupation, n(%) | ||
| Unemployed | 9,433 (29.3) | 12,720 (33.4) |
| Indoor occupation | 3,144 (9.8) | 4,302 (11.3) |
| Outdoor occupation | 18,208 (56.6) | 19,837 (52.0) |
| Others | 1,119 (3.5) | 122 (0.3) |
| No data | 292 (0.9) | 1,138 (3.0) |
| Smoking status | ||
| Current smoker | 1,446 (4.5) | 1,358 (3.6) |
| Ex-smoker | 3,040 (9.4) | 3,984 (10.5) |
| Never | 25,846 (80.3) | 32, 096 (84.2) |
| No data | 1,864 (5.8) | 681 (1.8) |
| Comorbidities, n(%) | ||
| Hypertension | 24,482 (76.0) | 29,748 (78.0) |
| Dyslipidemia | 22,278 (69.2) | 26,873 (70.5) |
| Gout | 1,242 (3.9) | 2,133 (5.6) |
| Diabetic kidney disease | 2,473 (7.7) | 2,728 (7.2) |
| Diabetic retinopathy | 2,229 (6.9) | 1,911 (5.0) |
| Microalbuminuria | 5,841 (18.1) | 3,084 (8.1) |
| Macroalbuminuria | 2,264 (7.0) | 1,232 (3.2) |
| CVA | 936 (2.9) | 1,171 (3.1) |
| CAD | 2,037 (6.3) | 1,570 (4.1) |
| PAD | 181 (0.6) | 133 (0.3) |
| CKD stage, n(%) | ||
| Stage 1 | 5,443 (22.6) | 14,241 (37.7) |
| Stage 2 | 9,006 (37.3) | 13,050 (34.5) |
| Stage 3a | 4,821 (20.0) | 5,105 (13.5) |
| Stage 3b | 3,200 (13.3) | 3,466 (9.2) |
| Stage 4 | 1,305 (5.4) | 1,451 (3.8) |
| Stage 5 | 349 (1.4) | 496 (1.3) |
| Waist circumference (inch) | ||
| Male, mean ± SD | 35.33 ± 4.15 (n = 7,019) | 34.61 ± 6.14 (n = 11,495) |
| < 40 | 6,129 (87.3) | 9,956 (86.6) |
| ≥ 40 | 890 (12.7) | 1,536 (13.4) |
| Female, mean ± SD | 34.58 ± 4.16 (n = 15,110) | 33.77 ± 5.94 (n = 23,584) |
| < 35 | 7,791 (51.6) | 13,439 (57.0) |
| ≥ 35 | 7,319 (48.4) | 10,145 (43.0) |
| Oral hypoglycemic drug use, n(%) | ||
| Biguanides | 23,020 (71.5) | 28,875 (75.7) |
| Sulfonylurea | 20,821 (64.7) | 22,556 (59.2) |
| Non-sulfonylurea | 45 (0.1) | 207 (0.5) |
| Thiazolidinedione | 2,642 (8.2) | 4649 (12.2) |
| Alpha-glucosidase Inhibitor | 172 (0.5) | 211 (0.6) |
| DPP-4 Inhibitor | 205 (0.6) | 567 (1.5) |
| SGLT2 inhibitor | - | 89 (0.2) |
| GLP-1 Analog | 16 (0.1) | 71 (0.2) |
| Insulin | 7,160 (22.2) | 8,363 (21.9) |
| Number of oral hypoglycemic drug, n(%) | ||
| None | 1,090 (3.4) | 1,538 (4.0) |
| 1 | 12,103 (37.6) | 13,910 (36.5) |
| 2 | 15,310 (47.6) | 16,929 (44.4) |
| ≥ 3 | 3,693 (11.5) | 5742 (15.1) |
| HbA1c level (%), mean ± SD | 7.99 ± 2.07 (n = 25,835) | 7.93 ± 2.02 (n = 35,636) |
| <7.0 | 8,856 (35.3) | 12,902 (36.2) |
| 7.0-7.9 | 5,709 (22.8) | 8,390 (23.5) |
| 8.0-8.9 | 3,929 (15.7) | 5,665 (15.9) |
| 9.0-9.9 | 2,563 (8.0) | 3,391 (9.5) |
| ≥ 10 | 4,007 (16.0) | 5,288 (14.8) |
| SBP (mmHg), mean ± SD | 129.85 ± 15.96 (n = 32,139) | 132.98 ± 15.23 (n = 38,044) |
| < 120 | 7,185 (22.4) | 5,989 (15.7) |
| 120–129 | 120 (23.8) | 8,194 (21.5) |
| 130–139 | 8,831 (27.5) | 12,794 (33.6) |
| 140–159 | 7,035 (21.9) | 9,020 (23.7) |
| 160–179 | 1,236 (3.8) | 1,787 (4.7) |
| ≥ 180 | 218 (0.7) | 260 (0.7) |
BMI Body mass index, SD Standard deviation, HbA1c Hemoglobin A1c, SBP Systolic blood pressure, CKD Chronic kidney disease, CVA Cerebrovascular accident, CAD Coronary artery disease, PAD Peripheral arterial disease, DPP-4 Dipeptidyl peptidase-4, SGLT2 Sodium-glucose cotransporter-2, GLP-1 Glucagon-like peptide-1
Prevalence and trend of obesity in diabetic patients
The prevalence of obesity increased significantly from 49.9% (95% CI 49.4–50.5) in 2014 to 51.1% (95% CI 50.6–51.6) in 2018 (P < 0.001). A greater increase was observed among males (Tables 2 and 3), with prevalence rising from 48.5% in 2014 to 51.0% in 2018 (P < 0.001). In contrast, the prevalence among females showed only a slight increase from 50.5% in 2014 to 51.1% in 2018, which was not statistically significant (P = 0.200).
Table 2.
Body mass index classification stratified by age group and gender in 2014
| BMI (kg/m2) | Age group | |||||
|---|---|---|---|---|---|---|
| ≤ 40 | 40–49 | 50–59 | 60–69 | ≥ 70 | ||
| Total (n = 32,196) | ||||||
| < 18.5 | 31 (3.7) | 109 (2.7) | 226 (2.4) | 323 (3.0) | 560 (7.8) | |
| 18.5–22.9 | 136 (16.1) | 752 (18.7) | 2,063 (21.9) | 2,816 (26.3) | 2,608 (36.3) | |
| 23.0–24.9 | 113 (13.4) | 741 (18.4) | 1,890 (20.0) | 2,289 (21.4) | 1,466 (20.4) | |
| 25.0–29.9 | 303 (35.9) | 1,558 (38.7) | 3,632 (38.5) | 3,869 (36.2) | 2,024 (28.1) | |
| ≥ 30 | 262 (31.0) | 865 (21.5) | 1,626 (17.2) | 1,398 (13.1) | 536 (7.5) | |
| mean ± SD | 27.89 ± 7.55 | 26.69 ± 4.88 | 26.11 ± 4.53 | 25.40 ± 4.37 | 23.91 ± 4.20 | |
| Male (n = 9,989) | ||||||
| < 18.5 | 17 (6.7) | 53 (4.5) | 79 (2.7) | 97 (2.9) | 138 (6.0) | |
| 18.5–22.9 | 47 (18.6) | 236 (20.1) | 636 (21.9) | 859 (25.7) | 805 (34.8) | |
| 23.0–24.9 | 34 (13.4) | 208 (17.7) | 634 (21.8) | 777 (23.3) | 521 (22.5) | |
| 25.0–29.9 | 76 (30.0) | 445 (37.9) | 1,111 (38.2) | 1,236 (37.0) | 706 (30.5) | |
| ≥ 30 | 79 (31.2) | 232 (19.8) | 448 (15.4) | 372 (11.1) | 143 (6.2) | |
| mean ± SD | 28.99 ± 5.93 | 26.19 ± 4.83 | 25.78 ± 4.27 | 25.19 ± 4.02 | 24.03 ± 3.95 | |
| Female (n = 22,207) | ||||||
| < 18.5 | 14 (2.4) | 56 (2.0) | 147 (2.3) | 226 (3.1) | 422 (8.6) | |
| 18.5–22.9 | 89 (15.0) | 516 (18.1) | 1,427 (21.9) | 1,957 (26.6) | 1,803 (36.9) | |
| 23.0–24.9 | 79 (13.3) | 533 (18.7) | 1,256 (19.2) | 1,512 (20.6) | 945 (19.4) | |
| 25.0–29.9 | 227 (38.3) | 1,113 (39.0) | 2,521 (38.6) | 2,633 (35.8) | 1,318 (27.0) | |
| ≥ 30 | 183 (30.9) | 633 (22.2) | 1,178 (18.0) | 1,026 (14.0) | 393 (8.1) | |
| mean ± SD | 28.03 ± 5.71 | 26.90 ± 4.89 | 26.25 ± 4.63 | 25.49 ± 4.52 | 23.86 ± 4.31 | |
Table 3.
Body mass index classification stratified by age group and gender in 2018
| BMI (kg/m2) | Age group | |||||
|---|---|---|---|---|---|---|
| ≤ 40 | 40–49 | 50–59 | 60–69 | ≥ 70 | ||
| Total (n = 38,119) | ||||||
| < 18.5 | 19 (2.5) | 88 (2.3) | 268 (2.5) | 420 (3.2) | 739 (7.6) | |
| 18.5–22.9 | 107 (13.8) | 714 (18.6) | 2,276 (21.6) | 3,200 (24.2) | 3,313 (34.0) | |
| 23.0–24.9 | 89 (11.5) | 619 (16.1) | 2,041 (19.3) | 2,712 (20.5) | 2,040 (21.0) | |
| 25.0–29.9 | 269 (34.8) | 1,432 (37.2) | 3,971 (37.6) | 4,918 (37.2) | 2,770 (28.5) | |
| ≥ 30 | 289 (37.4) | 993 (25.8) | 1,997 (18.9) | 1,962 (14.9) | 873 (9.0) | |
| mean ± SD | 28.95 ± 6.31 | 27.19 ± 5.26 | 26.29 ± 4.75 | 25.68 ± 4.46 | 24.14 ± 4.32 | |
| Male (n = 12,605) | ||||||
| < 18.5 | 17 (6.5) | 41 (3.2) | 109 (3.1) | 110 (2.5) | 171 (5.5) | |
| 18.5–22.9 | 48 (18.5) | 246 (19.2) | 758 (21.4) | 1,079 (24.4) | 983 (31.7) | |
| 23.0–24.9 | 24 (9.2) | 209 (16.3) | 718 (20.3) | 954 (21.6) | 709 (22.8) | |
| 25.0–29.9 | 81 (31.2) | 479 (37.4) | 1,386 (39.1) | 1,731 (39.2) | 993 (32.0) | |
| ≥ 30 | 90 (34.6) | 306 (23.9) | 573 (16.2) | 541 (12.3) | 249 (8.0) | |
| mean ± SD | 28.32 ± 7.24 | 26.87 ± 5.34 | 25.95 ± 4.40 | 25.47 ± 4.08 | 24.34 ± 3.93 | |
| Female (n = 25,514) | ||||||
| < 18.5 | 2 (0.4) | 47 (1.8) | 159 (2.3) | 310 (3.5) | 568 (8.6) | |
| 18.5–22.9 | 59 (11.5) | 468 (18.2) | 1,518 (21.7) | 2,121 (24.1) | 2,330 (35.1) | |
| 23.0–24.9 | 65 (12.7) | 410 (16.0) | 1,323 (18.9) | 1,758 (20.0) | 1,331 (20.1) | |
| 25.0–29.9 | 188 (36.6) | 953 (37.2) | 2,585 (36.9) | 3,187 (36.2) | 1,777 (26.8) | |
| ≥ 30 | 199 (38.8) | 687 (26.8) | 1,424 (20.3) | 1,421 (16.2) | 624 (9.4) | |
| mean ± SD | 29.27 ± 5.77 | 27.36 ± 5.21 | 26.47 ± 4.90 | 25.78 ± 4.64 | 24.05 ± 4.49 | |
Age-stratified analysis revealed that obesity was most prevalent in younger age groups, with significant increases observed in individuals aged < 40 years (67.8–73.5%, P = 0.016), 40–49 years (60.6–63.4%, P = 0.014), 60–69 years (49.5–52.2%, P < 0.001), and those over 70 years (35.6–37.4%, P = 0.044). In both years, obesity was more prevalent among females than males across all age groups, except for patients over 70 years, where males had a slightly higher prevalence (Table 4).
Table 4.
Prevalence of obesity among diabetes patients, stratified by age and gender between 2014 and 2018
| Age group | 2014 | 2018 | Odds ratio** (95%CI) P-valuea |
Odds ratio** (95%CI) P-valueb |
Odds ratio** (95%CI) P-valuec |
||||
|---|---|---|---|---|---|---|---|---|---|
| Total (n = 32,196) |
Male (n = 9,989) |
Female (n = 22,207) |
Total (n = 38,119) |
Male (n = 12,605) |
Female (n = 25,514) |
||||
| < 40 |
565 (67.8 [64.5–71.0]) |
155 (61.3 [55.1–67.1]) |
410 (69.3 [65.4–72.8]) |
558 (73.5 [70.2–76.6]) |
171 (65.8 [59.8–71.3]) |
387 (75.4 [71.5–79.1]) |
1.31 (1.05–1.64) 0.016* |
1.22 (0.85–1.74) 0.290 |
1.36 (1.05–1.78) 0.023* |
| 40–49 |
2,423 (60.2 [58.7–61.7]) |
677 (57.7 [54.8–60.5]) |
1,746 (61.2 [59.4–63.0]) |
2,425 (63.1 [61.5–64.6]) |
785 (61.3 [58.6–64.0]) |
1,640 (63.9 [62.0-65.8]) |
1.13 (1.03–1.24) 0.008* |
1.16 (0.99–1.37) 0.070 |
1.12 (1.01–1.25) 0.041* |
| 50–59 |
5,258 (55.7 [54.7–56.7]) |
1,559 (53.6 [51.8–55.4]) |
3,699 (56.7 [55.4–57.9]) |
5,968 (56.6 [55.6–57.5]) |
1,959 (55.3 [53.6–56.9]) |
4,009 (57.2 [56.0-58.4]) |
1.03 (0.98–1.09) 0.201 |
1.07 (0.97–1.18) 0.172 |
1.02 (0.96–1.09) 0.557 |
| 60–69 |
5,267 (49.2 [48.3–50.2]) |
1,608 (48.1 [46.4–49.8]) |
3,659 (49.8 [48.6–50.9]) |
6,880 (52.1 [51.2–52.9]) |
2,272 (51.5 [50.0-52.9]) |
4,608 (52.4 [51.3–53.4]) |
1.12 (1.06–1.18) < 0.001* |
1.14 (1.04–1.25) 0.003* |
1.11 (1.04–1.18) 0.001* |
| ≥ 70 |
2,560 (35.6 [34.5–36.7]) |
849 (36.7 [34.7–38.7]) |
1,711 (35.1 [33.7–36.4]) |
3,643 (37.4 [36.5–38.4]) |
1,242 (40.0 [38.3–41.7]) |
2.401 (36.2 [35.1–37.4]) |
1.08 (1.02–1.15) 0.016* |
1.15 (1.03–1.28) 0.005* |
1.05 (0.97–1.14) 0.224 |
| Total |
16,073 (49.9 [49.4–50.5]) |
4,848 (48.5 [47.5–49.5]) |
11,225 (50.5 [49.9–51.2]) |
19,474 (51.1 [50.6–51.6]) |
6,429 (51.0 [50.1–51.9]) |
13,045 (51.1 [50.5–51.7]) |
1.06 (1.03–1.09) < 0.001* |
1.10 (1.05–1.16) < 0.001* |
1.02 (0.99–1.06) 0.191 |
Data were presented by n (% [95%CI])
Data were analyzed with Rao-Scott adjusted chi-square in complex survey design: compare between overall a, male b, and female c
*Statistically significant at the 0.05 level (α = 0.05)
**Unadjusted odds ratios
Factors associated with obesity among diabetic patients in 2018
Multivariable analysis identified several factors significantly associated with obesity among diabetic patients. Younger individuals had higher odds of obesity, with adjusted odds ratios (AORs) of 4.91 (95% CI 4.06–5.94) for those aged < 40 years and 3.29 (95% CI 2.97–3.63) for those aged 40–49 years compared to individuals aged over 70 years. Patients receiving treatment at tertiary hospitals (AOR 1.20, 95% CI 1.09–1.33) and secondary hospitals (AOR 1.15, 95% CI 1.08–1.23) were more likely to be obese compared to those attending primary care facilities. Occupation also played a role, as patients in indoor occupations had a higher likelihood of obesity (AOR 1.36, 95% CI 1.24–1.48) compared to unemployed individuals.
Smoking status was strongly associated with obesity, with ex-smokers (AOR 1.47, 95% CI 1.28–1.69) and never-smokers (AOR 1.69, 95% CI 1.48–1.92) having higher odds of obesity than current smokers. Several comorbidities were linked to an increased risk of obesity, including hypertension (AOR 1.83, 95% CI 1.72–1.94), dyslipidemia (AOR 1.24, 95% CI 1.17–1.30), gout (AOR 1.27, 95% CI 1.15–1.41), and coronary artery disease (AOR 1.18, 95% CI 1.04–1.33).
A longer duration of diabetes was inversely associated with obesity, with AORs of 0.84 (95% CI 0.79–0.89) for 5–10 years, 0.70 (95% CI 0.65–0.75) for 11–15 years, 0.66 (95% CI 0.59–0.74) for 16–20 years, and 0.67 (95% CI 0.57–0.78) for more than 20 years compared to those with less than five years of diabetes. The number of oral hypoglycemic drugs taken also contributed to obesity risk, with those taking one drug (AOR 1.34, 95% CI 1.08–1.65), two drugs (AOR 1.87, 95% CI 1.30–2.69), or three or more drugs (AOR 2.29, 95% CI 1.34–3.90) having increased odds of obesity compared to those not taking any medications.
Higher HbA1c levels were also associated with increased obesity risk, with AORs of 1.09 (95% CI 1.03–1.16) for HbA1c levels of 7.0-7.9% and 1.11 (95% CI 1.08–1.98) for HbA1c levels above 10%. Higher SBP was significantly associated with obesity, with AORs of 1.55 (95% CI 1.44–1.66) for SBP of 130–139 mmHg and 1.69 (95% CI 1.27–2.25) for SBP above 180 mmHg (Table 5).
Table 5.
Univariable analysis and multivariable analysis of factor associated patients with diabetes in 2018 (n = 38,119)
| Factors | BMI < 25 kg/m2, n(%) | BMI ≥ 25 kg/m2, n(%) | Crude odds ratio (95%CI) | P-value | Adjusted odds ratio (95%CI)# | P-value |
|---|---|---|---|---|---|---|
| Gender | ||||||
| Male | 6,176 (33.1) | 6,429 (33.0) | Reference | Reference | ||
| Female | 12,469 (66.9) | 13,045 (67.0) | 1.01 (0.96, 1.05) | 0.818 | 0.98 (0.92–1.04) | 0.429 |
| Age (years) | ||||||
| <40 | 215 (1.2) | 558 (2.9) | 4.34 (3.69, 5.11) | < 0.001* | 4.91 (4.06, 5.94) | < 0.001* |
| 40–49 | 1,421 (7.6) | 2,425 (12.5) | 2.85 (2.64–3.08) | < 0.001* | 3.29 (2.97–3.63) | < 0.001* |
| 50–59 | 4,585 (24.6) | 5,968 (30.6) | 2.18 (2.06–2.30) | < 0.001* | 2.25 (2.09–2.42) | < 0.001* |
| 60–69 | 6,332 (34.0) | 6,880 (35.3) | 1.82 (1.72–1.92) | < 0.001* | 1.81 (1.70–1.94) | < 0.001* |
| ≥ 70 | 6,092 (32.7) | 3,643 (18.7) | Reference | Reference | ||
| Hospital level | ||||||
| Tertiary care | 1,256 (6.7) | 1,718 (8.8) | 1.42 (1.32–1.53) | < 0.001* | 1.20 (1.09–1.33) | < 0.001* |
| Secondary care | 3,046 (16.3) | 3,937 (20.2) | 1.34 (1.27–1.41) | < 0.001* | 1.15 (1.08–1.23) | < 0.001* |
| Primary care | 14,343 (76.9) | 13,819 (71.0) | Reference | Reference | ||
| Scheme | ||||||
| Universal healthcare coverage | 14,889 (79.9) | 15,044 (77.3) | Reference | Reference | ||
| Civil servant medical benefit | 2,968 (15.9) | 3,216 (16.5) | 1.07 (1.02–1.13) | 0.012* | 1.04 (0.97–1.12) | 0.221 |
| Social security | 560 (3.0) | 949 (4.9) | 1.68 (1.51–1.87) | < 0.001* | 1.10 (0.97–1.25) | 0.155 |
| Others | 228 (1.2) | 265 (1.4) | 1.15 (0.96–1.38) | 0.124 | 0.91 (0.73–1.13) | 0.650 |
| Occupation | ||||||
| Unemployed | 6,942 (38.3) | 5,778 (30.7) | Reference | Reference | ||
| Indoor occupation | 1,582 (8.7) | 2,720 (14.4) | 2.07 (1.92–2.22) | < 0.001* | 1.36 (1.24–1.48) | < 0.001* |
| Outdoor occupation | 9,555 (52.7) | 10,282 (54.6) | 1.29 (1.24–1.35) | < 0.001* | 0.98 (0.93–1.04) | 0.530 |
| Others | 54 (0.3) | 68 (0.4) | 1.51 (1.06–2.17) | 0.024* | 1.32 (0.88–1.98) | 0.180 |
| Smoking status | ||||||
| Current smoker | 775 (4.2) | 583 (3.1) | Reference | Reference | ||
| Ex-smoker | 2,046 (11.2) | 1,938 (10.1) | 1.26 (1.11–1.43) | < 0.001* | 1.47 (1.28–1.69) | < 0.001* |
| Never | 15,512 (84.6) | 16,584 (86.8) | 1.42 (1.27–1.59) | < 0.001* | 1.69 (1.48–1.92) | < 0.001* |
| Comorbidities, n(%) | ||||||
| Hypertension | 13,825 (74.1) | 15,923 (81.8) | 1.56 (1.49–1.64) | < 0.001* | 1.83 (1.72–1.94) | < 0.001* |
| Dyslipidemia | 12,545 (67.3) | 14,328 (73.6) | 1.35 (1.30–1.42) | < 0.001* | 1.24 (1.17–1.30) | < 0.001* |
| Gout | 956 (5.1) | 1,177 (6.0) | 1.19 (1.09–1.30) | < 0.001* | 1.27 (1.15–1.41) | < 0.001* |
| Diabetic kidney disease | 1,473 (7.9) | 1,255 (6.4) | 0.80 (0.74–0.87) | < 0.001* | 0.93 (0.84–1.02) | 0.136 |
| Diabetic retinopathy | 934 (5.0) | 977 (5.0) | 1.00 (0.91–1.10) | 0.973 | 0.83 (0.74–1.92) | 0.056 |
| Microalbuminuria | 1,572 (8.4) | 1,512 (7.8) | 0.91 (0.85–0.98) | 0.017* | 0.95 (0.85–1.02) | 0.136 |
| Macroalbuminuria | 594 (3.2) | 638 (3.3) | 1.03 (0.92–1.15) | 0.618 | 1.15 (1.00-1.32) | 0.046* |
| CVA | 620 (3.3) | 551 (2.8) | 0.85 (0.75–0.95) | 0.005* | 0.89 (0.78–1.02) | 0.105 |
| CAD | 764 (4.1) | 806 (4.1) | 1.01 (0.91–1.12) | 0.840 | 1.18 (1.04–1.33) | 0.009* |
| PAD | 66 (0.4) | 67 (0.3) | 0.97 (0.69–1.37) | 0.869 | 1.09 (0.73–1.62) | 0.688 |
| Duration of diabetes diagnosis (year) | ||||||
| < 5 | 5,107 (30.2) | 6,218 (35.0) | Reference | Reference | ||
| 5–10 | 7,015 (41.5) | 7,472 (42.1) | 0.88 (0.83–0.92) | < 0.001* | 0.84 (0.79–0.89) | < 0.001* |
| 11–15 | 3,373 (20.0) | 2,939 (16.5) | 0.72 (0.67–0.76) | < 0.001* | 0.70 (0.65–0.75) | < 0.001* |
| 16–20 | 935 (5.5) | 762 (4.3) | 0.67 (0.60–0.74) | < 0.001* | 0.66 (0.59–0.74) | < 0.001* |
| > 20 | 467 (2.8) | 368 (2.1) | 0.65 (0.56–0.75) | < 0.001* | 0.67 (0.57–0.78) | < 0.001* |
| Oral hypoglycemic drug use | ||||||
| Biguanides | 13,611 (73.0) | 15,264 (78.4) | 1.34 (1.28–1.41) | < 0.001* | 0.89 (0.75–1.06) | 0.194 |
| Sulfonylurea | 10,625 (57.0) | 11,931 (61.3) | 1.19 (1.15–1.24) | < 0.001* | 0.87 (0.73–1.04) | 0.135 |
| Thiazolidinedione | 1,741 (9.3) | 2,908 (14.9) | 1.70 (1.60–1.82) | < 0.001* | 1.20 (0.99–1.43) | 0.052 |
| Alpha-glucosidase Inhibitor | 88 (0.5) | 123 (0.6) | 1.34 (1.02–1.76) | 0.036* | 0.86 (0.61–1.21) | 0.379 |
| DPP-4 Inhibitor | 239 (1.3) | 328 (1.7) | 1.32 (1.12–1.56) | 0.001* | 0.86 (0.66, 1.11) | 0.251 |
| SGLT2 inhibitor | 33 (0.2) | 56 (0.3) | 1.63 (1.06–2.50) | 0.027* | 0.96 (0.55–1.68) | 0.896 |
| GLP-1 Analog | 31 (0.2) | 40 (0.2) | 1.24 (0.77–1.98) | 0.377 | 1.10 (0.60–2.03) | 0.759 |
| Insulin | 3,826 (20.6) | 4,527 (23.2) | 1.17 (1.11–1.23) | < 0.001* | 1.05 (0.88–1.25) | 0.607 |
| Number of oral hypoglycemic drug | ||||||
| None | 936 (5.0) | 602 (3.1) | Reference | Reference | ||
| 1 | 7,515 (40.3) | 6,395 (32.8) | 1.32 (1.19–1.47) | < 0.001* | 1.34 (1.08–1.65) | 0.007* |
| 2 | 8,007 (42.9) | 8,922 (45.8) | 1.73 (1.56–1.93) | < 0.001* | 1.87 (1.30–2.69) | < 0.001* |
| ≥ 3 | 2,187 (11.7) | 3,555 (18.3) | 2.53 (2.25–2.84) | < 0.001* | 2.29 (1.34–3.90) | 0.002* |
| HbA1c level (%) | ||||||
| <7.0 | 6,675 (38.6) | 6,227 (33.9) | Reference | Reference | ||
| 7.0-7.9 | 3,944 (22.8) | 4,446 (24.2) | 1.21 (1.14–1.28) | < 0.001* | 1.09 (1.03–1.16) | 0.006* |
| 8.0-8.9 | 2,592 (15.0) | 3,073 (16.8) | 1.27 (1.19–1.35) | < 0.001* | 1.07 (1.01–1.15) | 0.041* |
| 9.0-9.9 | 1,525 (8.8) | 1,866 (10.2) | 1.31 (1.22–1.42) | < 0.001* | 1.10 (1.01–1.21) | 0.027* |
| ≥ 10 | 2,557 (14.8) | 2,731 (14.9) | 1.15 (1.07–1.22) | < 0.001* | 1.11 (1.08–1.98) | 0.017* |
| SBP (mmHg) | ||||||
| < 120 | 3,511 (18.9) | 2,478 (12.7) | Reference | Reference | ||
| 120–129 | 4,021 (21.6) | 4,173 (21.5) | 1.47 (1.38–1.57) | < 0.001* | 1.41 (1.31–1.52) | < 0.001* |
| 130–139 | 6,061 (32.3) | 6,784 (34.9) | 1.60 (1.50–1.70) | < 0.001* | 1.55 (1.44–1.66) | < 0.001* |
| 140–159 | 4,096 (22.0) | 4,924 (25.3) | 1.70 (1.59–1.82) | < 0.001* | 1.65 (1.53–1.79) | < 0.001* |
| 160–179 | 841 (4.5) | 946 (4.9) | 1.59 (1.43–1.77) | < 0.001* | 1.60 (1.41–1.82) | < 0.001* |
| ≥ 180 | 123 (0.7) | 137 (0.7) | 1.58 (1.23–2.02) | < 0.001* | 1.69 (1.27–2.25) | < 0.001* |
Data were analyzed with binary logistic regression
*Statistically significant at the 0.05 level (α = 0.05)
# Multivaraible analysis use sample size as n = 31,115
BMI Body mass index, SBP Systolic blood pressure, HbA1c Hemoglobin A1c, CI Confidence interval, SD Standard deviation, CVA Cerebrovascular accident, CAD Coronary artery disease, PAD Peripheral arterial disease, CKD Chronic kidney disease, DPP-4 Dipeptidyl peptidase-4, SGLT2 Sodium-glucose cotransporter-2, GLP-1 Glucagon-like peptide-1
Linear regression analysis demonstrated significant associations between age, SBP, and HbA1c with BMI. Age was negatively associated with BMI in both males and females (P < 0.001), indicating that as age increased, BMI decreased, with β coefficients of -0.204 kg/m² for males and − 0.256 kg/m² for females (Fig. 2). In contrast, HbA1c levels were positively associated with BMI in females (P < 0.001), with a β coefficient of 0.031 kg/m², while no significant association was observed in males (Fig. 3). A significant positive correlation was also observed between SBP and BMI in both males and females (P < 0.001), with β coefficients of 0.113 kg/m² for males and 0.087 kg/m² for females (Fig. 4). These findings suggest that younger individuals, those with higher HbA1c levels, and those with elevated SBP tend to have higher BMI.
Fig. 2.
Scatter plot with a linear fit line illustrating the correlation between BMI and age
Fig. 3.
Scatter plot with a linear fit line illustrating the correlation between BMI and HbA1c level
Fig. 4.
Scatter plot with a linear fit line illustrating the correlation between BMI and systolic blood pressure
Discussion
The results of this study reveal an increasing trend in obesity among patients with T2DM in Thailand from 2014 to 2018, establishing it as the most recent nationwide cohort in the country. The prevalence of obesity rose from 49.9% in 2014 to 51.1% in 2018. Although the increase in obesity prevalence among individuals with T2DM over the four-year period was relatively small, we believe this change remains important from a public health perspective. In a large national population, even a small percentage increase can represent a significant number of additional individuals at risk for complications. Additionally, the significant rise among male patients may indicate emerging disparities that require early intervention. Therefore, while the clinical implications of a 1.2% change should be interpreted with caution, the findings support continued monitoring and the need for preventive strategies, particularly among high-risk subgroups. Notably, the prevalence of obesity has remained consistently higher among females than males, except in the oldest age group (> 70 years), where males exhibited a slightly higher prevalence. Obesity was most prevalent among younger patients and individuals with hypertension. Furthermore, patients who had never smoked or were former smokers showed higher odds of obesity, while patients who had diabetes for a longer period were less likely to be obese. These results provide valuable insights into the shifting epidemiology of obesity among T2DM patients in Thailand.
The rising trend in the prevalence of obesity among T2DM patients in Thailand corresponds with studies conducted in various regions, including Europe, Asia, and North America [20–23]. Nonetheless, there are variations in prevalence across different populations. In the US, the percentage of obese T2DM patients grew from 46.9 to 58.1% between 1999 and 2020 [20]. In Spain, obesity prevalence surged from 18.2 to 39.8% between 1987 and 2012 [21]. A study conducted in South Korea observed that the proportion of obese individuals increased from 29.7 to 36.3% between 2009 and 2019 [22]. In Japan, research indicated that while the percentage of obese individuals with T2DM was the lowest, it still climbed from 5.1 to 10.0% between 2000 and 2012 [23]. In comparison to these studies, the prevalence of obesity among T2DM patients in Thailand appears to be relatively high.
Regionally, studies from Southeast Asia echo the growing concern over obesity among individuals with T2DM. A hospital-based study in Vietnam reported a 35.4% prevalence of overweight and obesity in diabetic patients, with higher rates in males (51.2%) than females (22.2%). Limited family support and lack of professional counseling were associated with increased obesity risk, highlighting the role of behavioral and educational interventions [24]. In Malaysia, a national review on T2DM found that nearly half of adults with diabetes were overweight or obese, with contributing factors including poor diet, physical inactivity, and ethnic disparities [25]. These findings align with our study and underscore the need for culturally tailored prevention strategies across the region.
Our findings revealed that females had a higher overall prevalence of obesity, a pattern noted in prior studies conducted in most countries [26–30]. This disparity may be attributed to biological differences in fat distribution and higher body fat percentages among females [31]. Hormonal changes, particularly postmenopausal shifts, may also contribute to weight gain [26, 32]. However, the increase in obesity prevalence was more pronounced in males, rising from 48.5 to 51.0% compared to from 50.5 to 51.1% in females. A similar trend was observed in South Korea, where obesity prevalence among males has continuously increased over the past two decades, while female obesity has slowed and may have plateaued [33]. Lifestyle differences could account for this shift. Males may become more sedentary and consume a larger quantity of high-calorie foods, whereas females, despite their higher likelihood of being overweight, may be more health-conscious and more inclined to seek medical assistance [27, 34, 35]. These findings suggest that future public health programs should prioritize targeted interventions for men with T2DM to reduce obesity rates.
Alarmingly, obesity was highest among younger patients, particularly those aged under 40 (< 40 years: 73.5% obese in 2018; 67.8% in 2014). This high prevalence aligns with findings from Indonesia, South Korea, and Japan, where younger individuals with T2DM displayed higher obesity rates [22, 23, 26]. This trend reflects the global increase in obesity among younger adults [36, 37]. Obese young individuals are at an elevated risk of developing cardiovascular issues, including heart failure and atrial fibrillation, while the potential for cardiovascular-related mortality rises in those with severe chronic obesity. Early intervention and treatment are crucial to mitigate long-term consequences [38, 39].
A strong link was observed between indoor occupations and obesity, consistent with research from the US [40]. Individuals engaged in sedentary jobs, such as public administration and healthcare, showed higher rates of obesity, likely due to reduced physical activity, work-related stress, and limited access to healthy lifestyle choices [41]. Desk-based roles, in particular, were significantly associated with obesity [40]. Additionally, exposure to indoor air pollution, including volatile organic compounds, has been linked to increased body weight [42]. These findings indicate that workplace interventions promoting physical activity and reducing sedentary behavior could help lower obesity risk.
According to our study, individuals who had never smoked or had quit were also more likely to be obese than those who were currently smoking. This aligns with earlier research conducted in Thailand, which found that current smokers showed a significantly lower BMI compared to non-smokers [43]. Studies in Japan and the UK revealed similar results, indicating that current smokers had a lower obesity risk than former smokers or non-smokers [44, 45]. This phenomenon is likely due to nicotine-induced metabolic effects, appetite suppression, and increased energy utilization [46]. It is widely recognized that individuals who quit smoking tend to gain weight. A study in the UK indicated that former smokers had a higher likelihood of obesity compared to current smokers or those who had never smoked. Additionally, a further study in Australia found that individuals who stopped smoking experienced an average weight gain of 3.14 kg and an increase in BMI of 0.82 compared to those who continued smoking [44, 47]. However, the risk of cardiovascular disease from smoking far outweighs any potential weight-control benefits, and smoking cessation should not be discouraged due to concerns about weight gain [47]. Our findings suggest that former smokers may require additional support to manage their weight after quitting. These results highlight the need for integrated smoking cessation programs that incorporate weight management strategies to address post-cessation weight gain.
An inverse association between diabetes duration and obesity was observed, particularly among patients with long-standing disease. Patients with a longer disease duration, particularly those diagnosed for over 10 years, exhibited a lower prevalence of obesity. These findings may result from lifestyle changes and weight reduction prompted by both physicians and patients, which are commonly observed after the diagnosis [48]. Additionally, aging-related factors, such as muscle mass loss, may contribute to declining body weight in long-term diabetic patients [48]. Existing studies have mainly focused on the duration of obesity as a predictor for developing T2DM rather than examining how the duration of T2DM impacts obesity risk [49, 50]. However, some studies suggest that longstanding diabetes is not associated with obesity. A study in the Netherlands found that longer diabetes duration was not linked to an increase in BMI, despite the progressive nature of T2DM and the intensification of therapy over time [51]. A meta-analysis also revealed no significant association between diabetes duration and changes in body weight for patients treated with liraglutide or placebo [52]. On the other hand, a study found that significant weight reduction and body weight fluctuations are potential markers of increased mortality and cardiovascular events in T2DM patients [49]. These findings underscore the complex relationship between diabetes progression and body weight.
The findings revealed a significant association between obesity and hypertension, showing a clear trend where elevated SBP correlates with an increase in the prevalence of obesity. This result aligns with previous research indicating that obesity considerably contributes to the risk of hypertension [53]. The underlying mechanisms include increased sympathetic nervous system activity, hormonal dysregulation, renal compression, and leptin resistance, all of which contribute to elevated blood pressure [54]. Visceral adiposity worsens obesity-induced hypertension by exerting pressure on abdominal organs and promoting systemic inflammation [55]. The renin-angiotensin-aldosterone system (RAAS) is also involved, as excessive adiposity leads to the overactivation of RAAS, further raising blood pressure [56]. Given these physiological connections, our findings reinforce the need for weight management strategies in hypertension prevention and treatment in diabetic populations.
The findings of this study highlight the importance of developing individualized interventions directed toward younger individuals, particularly those with sedentary occupations and metabolic risk factors. To prevent post-cessation weight gain, integrated smoking cessation and weight management support, national weight management programs specifically designed for T2DM patients, occupational health initiatives that promote workplace physical activity, and increased public awareness of hypertension-obesity interactions, given the substantial correlation between SBP and BMI, are all policy recommendations.
This study has several strengths, including a large, nationally representative dataset that enhances the generalizability of the findings to the broader Thai population with T2DM. A standardized data collection process across multiple healthcare facilities ensures data consistency and reliability. Additionally, the study offers valuable insights into obesity trends over time, identifying key demographic, clinical, and lifestyle factors associated with obesity in T2DM patients, which can inform targeted public health interventions. However, several limitations should be acknowledged. The cross-sectional design prevents causal inferences between obesity and related factors. Moreover, the study was limited to hospital-attending patients, potentially excluding T2DM individuals managed in primary care or those with limited healthcare access. The reliance on self-reported data for lifestyle factors, such as smoking and alcohol consumption, introduces the potential for recall bias. Furthermore, using BMI as the sole measure of obesity does not account for variations in body composition, which may lead to misclassification in certain populations. While our models adjusted for a wide range of sociodemographic and clinical variables, including age, sex, smoking status, occupation, hospital level, duration of diabetes, hypertension, dyslipidemia, gout, coronary artery disease, HbA1c, and systolic blood pressure, some important confounders were not captured in the dataset. Specifically, information on dietary intake, physical activity, and socioeconomic status was unavailable. The absence of these variables may have contributed to residual confounding and should be considered when interpreting the results. Although the data used in this study were collected in 2014 and 2018, and more recent data were not available at the time of analysis, the findings remain valuable for understanding population-level trends and risk profiles. We acknowledge that rapid shifts in health behaviors, dietary patterns, and healthcare delivery may affect the current applicability of the findings. Nevertheless, the results provide important historical benchmarks and inform future monitoring and interventions.
Conclusion
This study highlights the increasing prevalence of obesity among T2DM patients in Thailand, particularly among younger adults and males. The findings emphasize the need for early lifestyle interventions and public health policies targeting at-risk populations. Future research should investigate longitudinal trends and assess targeted interventions to mitigate obesity-related complications in T2DM patients.
Acknowledgements
The authors would like to extend their appreciation to all those who contributed to the successful completion of the study and for their invaluable support throughout the duration of the research. We would like to express our gratitude to the entire staff of the Research Unit for Military Medicine at Phramongkutklao College of Medicine in Bangkok, Thailand, for their exceptional support and facilities.
Abbreviations
- AOR
Adjusted Odds Ratio
- BMI
Body Mass Index
- CI
Confidence Interval
- CVD
Cardiovascular Disease
- CRF
Case Report Form
- HbA1c
Hemoglobin A1c
- MoPH
Ministry of Public Health
- MedResNet
Medical Research Network of the Consortium of Thai Medical Schools
- NHSO
National Health Security Office
- NHES
National Health Examination Survey
- P
P-value (statistical significance)
- RAAS
Renin Angiotensin Aldosterone System
- SBP
Systolic Blood Pressure
- T2DM
Type 2 Diabetes Mellitus
Author contributions
The study concept was developed by MP, CK, RR, WK, NS, AB, and PS. Data collection was carried out by RR, while MP and CK performed the analysis and wrote the initial draft. PS supervised all the research protocols. The final version was reviewed and approved by all authors.
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
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The Royal Thai Army (RTA) Medical Department Institutional Review Board provides oversight and evaluation of the research, ensuring that it adheres to the ethical conduct guidelines (Approved No Q023h/66_Exp). The study follows international guidelines such as 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). Prior to analysis, data underwent anonymization, ensuring that all patient information was eliminated in accordance with national data protection regulations. The Institutional Review Board, RTA Medical Department, waived the requirement for documentation of informed consent because the study utilized secondary data.
Consent for publication
Not applicable.
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.
Methavee Poochanasri and Chutawat Kookanok contributed equally to this work.
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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
No datasets were generated or analysed during the current study.