Exploring the relationship between triglyceride-glucose-body mass index and hypertension in relation to cardiovascular disease risk in an African population of type 2 diabetes patients

Exploring the relationship between triglyceride-glucose-body mass index and hypertension in relation to cardiovascular disease risk in an African population of type 2 diabetes patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the relationship between triglyceride-glucose-body mass index and hypertension in relation to cardiovascular disease risk in an African population of type 2 diabetes patients Bruno Basil, Jamila Aminu Mohammed, Izuchukwu Nnachi Mba, Ngozi Ijeoma Okoro, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7806539/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), with hypertension as a key contributor. The triglyceride–glucose–body mass index (TyG-BMI) has been proposed as a surrogate marker for insulin resistance and cardiometabolic risk. This study aimed to examine whether TyG-BMI is independently associated with hypertension and estimated 10-year CVD risk in Nigerian patients with T2DM. Methods This hospital-based cross-sectional study was conducted over a period of 13 months among patients with T2DM. Hypertension was defined by prior diagnosis or use of antihypertensive therapy, and 10-year CVD risk was estimated using the laboratory-based WHO risk chart validated for Western sub-Saharan Africa. Statistical analyses including correlation and multivariable logistic regression for evaluating associations between TyG-BMI, hypertension, and CVD risk were conducted using SPSS version 25, with significance set at p < 0.05. Results Among participants, 65.5% were hypertensive, and 44% had elevated 10-year CVD risk. TyG-BMI showed no independent association with hypertension (adjusted OR: 1.001, 95% CI: 0.995–1.007, p = 0.768) or elevated CVD risk (adjusted OR: 1.001, 95% CI: 0.994–1.008, p = 0.818). Rather, longer diabetes duration significantly increased the odds of hypertension (aOR per year: 1.170, 95% CI: 1.091–1.255, p < 0.001) and elevated CVD risk (aOR per year: 1.311, 95% CI: 1.211–1.420, p < 0.001). In extended models, hypertension (aOR: 3.923, 95% CI: 1.718–8.960, p = 0.001) and higher HbA1c (aOR: 1.276, 95% CI: 1.032–1.579, p = 0.025) were independently associated with elevated CVD risk, while TyG-BMI remained insignificant. Conclusion These findings suggest that traditional clinical indicators remain more informative than composite metabolic indices like TyG-BMI for cardiovascular risk stratification in sub-Saharan African patients with T2DM. Longitudinal multicentre studies incorporating direct measures of visceral adiposity and accounting for therapy effects are warranted to refine CVD risk prediction in this population. Type 2 diabetes mellitus Triglyceride-glucose-body mass index Hypertension Cardiovascular risk Sub-Saharan Africa Insulin resistance Introduction Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, with a disproportionately high burden among individuals with type 2 diabetes mellitus (T2DM) ( 1 ). T2DM affects approximately 589 million adults globally, exacerbating cardiovascular risk through hyperglycaemia, insulin resistance, hypertension, dyslipidaemia, and other metabolic disturbances ( 2 , 3 ). Patients with T2DM experience a 2- to 4-fold higher CVD mortality rate compared to non-diabetic individuals ( 4 ). Among the established risk factors, hypertension is one of the most prevalent and potent contributors to cardiovascular events in this population, substantially accelerating the progression to heart failure, stroke, and coronary artery disease ( 5 , 6 )( 7 ). Early identification of diabetic patients at high risk of developing hypertension is therefore critical for timely intervention and prevention of downstream CVD complications. Traditional cardiovascular risk assessment tools, while useful, often lack precision when applied across diverse populations due to variations in genetic, metabolic, and environmental factors ( 8 ). In recent years, the triglyceride-glucose body mass index (TyG-BMI) has emerged as a promising, easily obtainable surrogate marker that integrates lipid and glycaemic parameters with body composition to predict insulin resistance and metabolic dysfunction ( 9 ). Calculated as the product of body mass index (BMI) and the triglyceride-glucose (TyG) index, TyG-BMI has been associated with both hypertension and cardiovascular outcomes in several populations ( 10 , 11 ). Its strong correlation with the hyperinsulinemic-euglycemic clamp, the gold standard for insulin sensitivity assessment ( 12 ), underlines its potential clinical utility and forms the basis of the hypothesis that a higher TyG-BMI is positively associated with the presence of hypertension, and that this association, in turn, corresponds to an elevated estimated 10-year CVD risk in the study population. However, most existing studies exploring the relationship between TyG-BMI and hypertension have been conducted in non-African populations, with limited investigation among African patients with T2DM ( 13 ). Africans usually display unique physiological and metabolic characteristics that may influence the performance of TyG-BMI in predicting hypertension and subsequent CVD risk. For instance, Africans generally have a distinct body composition profile characterized by lower visceral adiposity and higher skeletal muscle mass compared to Caucasians, as well as differences in insulin sensitivity and secretion patterns ( 14 ), which may alter the relationship between metabolic indices and cardiovascular outcomes ( 15 ). These differences highlight the need for regionally-relevant research to improve accuracy in risk assessment. This study seeks to determine whether TyG-BMI is independently associated with hypertension in patients with T2DM, and to examine how this relationship contributes to overall cardiovascular disease risk in this population. By establishing this link, the study aims to provide evidence that TyG-BMI could serve as a valuable, low-cost screening tool for identifying high-risk individuals, enabling earlier interventions and potentially reducing the burden of hypertension-related CVD among African patients with type 2 diabetes mellitus. Materials and Methods Study Design and Setting This analytical cross-sectional study was conducted to examine the association between the triglyceride–glucose–body mass index (TyG-BMI) and hypertension, and to evaluate how this association relates to estimated cardiovascular disease (CVD) risk in patients with type 2 diabetes mellitus (T2DM). Participants were consecutively recruited from the endocrinology outpatient clinic of Benue State University Teaching Hospital (BSUTH), Makurdi, North-Central Nigeria, between October 2019 and October 2020. BSUTH is a tertiary referral centre providing specialized diabetes care to the surrounding region. Ethical Considerations The study protocol was reviewed and approved by the Health Research Ethics Committee of BSUTH (protocol number BSUTH/CMAC/HREC/101/V.I/47) on 21 January 2019. The study was conducted in compliance with the Declaration of Helsinki. All participants provided written informed consent prior to enrolment. Confidentiality was ensured by assigning unique identification codes and securely storing participant data with restricted access. Sample Size Determination and Participant Recruitment The minimum required sample size was calculated using the formula for single-proportion studies, assuming a 65.5% prevalence of hypertension among Nigerian adults with T2DM based on a previous study ( 6 ), a 95% confidence level, and a 5% margin of error. This yielded a minimum sample size of 348 participants, and adjusting for 10 percent non-response increased the target to 387. However, due to practical considerations and the exploratory nature of the study, 200 participants were ultimately enrolled via convenience sampling during routine clinic visits. While this is lower than the ideal sample size based on recent prevalence reports, it remains acceptable for an exploratory analysis aimed at investigating associations between TyG-BMI, hypertension, and estimated cardiovascular risk within this clinic population ( 16 ). Eligible participants were adults aged 40 to 74 years with a diagnosis of T2DM of at least 6 months. Exclusion criteria included: diagnosis of type 1 diabetes, gestational diabetes, history of established CVD (such as myocardial infarction, stroke, or coronary revascularization), chronic kidney disease (estimated glomerular filtration rate < 60 mL/min/1.73 m²), liver disease, acute or chronic inflammatory conditions, pregnancy or lactation, recent infections within 4 weeks, and incomplete clinical or laboratory data. Participants on anti-inflammatory medications that could affect study biomarkers were excluded, while those on long-term standard therapies such as antiplatelet drugs were included. Data Collection and Clinical Measurements Data were collected using a structured research proforma designed to capture participants’ socio-demographic information (age, sex, ethnicity, occupation, education), medical history including duration of diabetes, presence of hypertension, dyslipidaemia, smoking status, alcohol use, and medication history (antidiabetic, antihypertensive, lipid-lowering, and antiplatelet therapies). Clinical measurements including blood pressure were recorded using a calibrated mercury sphygmomanometer with two readings taken five minutes apart and averaged. Hypertension was defined as Systolic BP ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, or current use of antihypertensive medications ( 17 ). For the purposes of this study, hypertension status was based on a previous diagnosis or ongoing treatment, irrespective of blood pressure measurements at the time of assessment, to account for individuals with well-controlled disease. Anthropometric measurements were obtained following standardized protocols; height and weight were measured with participants wearing light clothing and no shoes, using a SECA weighing scale with height attachment. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Laboratory data included fasting plasma glucose (FPG), glycated haemoglobin (HbA1c), and lipid profile parameters (total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol). LDL cholesterol was calculated using the Friedewald formula. The triglyceride-glucose (TyG) index was calculated as the natural logarithm of the product of fasting triglycerides and glucose divided by two ( 18 ). Subsequently, the TyG-BMI index was derived by multiplying the TyG index by the participant’s BMI to reflect combined effects of insulin resistance and adiposity ( 19 ). Biochemical Assays After an overnight fast of at least 8 hours, venous blood samples were collected using aseptic technique into appropriate vacutainer tubes: fluoride oxalate for fasting plasma glucose (FPG), EDTA for glycated Haemoglobin (HbA1c), and lithium heparin for lipid profile including triglycerides (TG), total cholesterol, and HDL cholesterol. Samples were centrifuged at 5000 rpm for 5 minutes and plasma or serum aliquoted for immediate or batch analysis. Glucose, HbA1c, and lipid parameters were measured using the Cobas c311® automated analyzer (Roche Diagnostics). Cardiovascular Risk Assessment Cardiovascular risk was estimated using the laboratory-based WHO CVD risk chart tailored for Western sub-Saharan Africa, which incorporates age, sex, systolic blood pressure, smoking status, diabetes status, and total cholesterol to predict 10-year risk of fatal or non-fatal CVD events ( 20 ). Participants were stratified into four risk categories: very low (< 5%), low (5%–<10%), moderate (10%–<20%), and high (≥ 20%). In this study, participants risk categories were further dichotomized into “low risk” (< 10%) and “elevated risk” (≥ 10%). Statistical Analysis Data were analyzed using SPSS version 25 (IBM Corp., Armonk, NY, USA). Continuous variables were assessed for normality using the Shapiro-Wilk test and summarized as mean ± standard deviation for normally distributed data or median with interquartile range for skewed data. Categorical variables were presented as frequencies and percentages. Differences across hypertension status and WHO-defined cardiovascular risk categories (very low, low, moderate, and high) were evaluated using independent-samples t-tests for normally distributed continuous variables, and Mann–Whitney U or Kruskal–Wallis tests for non-normally distributed variables, with appropriate post hoc procedures applied for multiple comparisons. Chi-square or Fisher’s exact tests were applied for categorical variables. Correlation analyses were performed using Pearson’s or Spearman’s correlation coefficients, depending on the distribution, to explore relationships between TyG-BMI and individual cardiovascular risk factors. Binary logistic regression was used to determine whether TyG-BMI was independently associated with hypertension and CVD risk, adjusting for potential confounders including sex, duration of diabetes, lipid-lowering medication use, and smoking status. In a subsequent model, hypertension was also entered as an explanatory variable alongside TyG-BMI to explore their combined influence on elevated (moderate-to-high) CVD risk (≥ 10%). Variables already incorporated in the WHO risk score, such as age, systolic blood pressure, and total cholesterol, were excluded from regression models to avoid multicollinearity. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test, and Nagelkerke’s R² was reported to estimate explained variance. All statistical analyses were two-tailed and p-values of less than 0.05 were considered statistically significant. Results Among the 200 study participants, 69 (34.5%) were non-hypertensive while 131 (65.5%) were hypertensive (Table 1 ). In comparison to non-hypertensive participants, those with hypertension were significantly older (59.0 [51.0–64.0] vs. 51.0 [44.0–58.0] years, p < 0.001), had a longer duration of diabetes (9.0 [3.5–15.0] vs. 3.0 [1.0–6.0] years, p < 0.001), and higher cardiovascular disease risk scores (10.0 [7.0–13.5] vs. 6.0 [4.0–9.0] %, p < 0.001). Their diabetes management approach also differed, with fewer managed by diet/lifestyle modifications alone (4.6% vs. 20.3%) and more receiving antidiabetic drug therapy (95.4% vs. 79.7%, p = 0.001). Conversely, no significant differences were observed between the two groups with respect to sex distribution, ethnicity, occupation, BMI, glycaemic control (FPG, HbA1c), lipid profile parameters (triglycerides, total cholesterol, HDL-C, LDL-C, non-HDL-C), TyG index, TyG-BMI, anti-lipidemic therapy, smoking, or alcohol consumption (all p > 0.05). Table 1 Comparison of the sociodemographic, clinical and laboratory characteristics of the study participants by hypertension status Characteristics Non-hypertensive (N = 69) Median (IQR) or Mean ± SD or n(%) Hypertensive (N = 131) Median (IQR) or Mean ± SD or n(%) p-value Age (years) 51.0 (44.0–58.0) 59.0 (51.0–64.0) < 0.001* Sex 0.073 Female 44 (63.8%) 65 (49.6%) Male 25 (36.2%) 66 (50.4%) Ethnicity/Tribe 0.363 Idoma 18 (26.1%) 21 (16.0%) Igbo 14 (20.3%) 21 (16.0%) Igede 2 (2.9%) 4 (3.1%) Tiv 27 (39.1%) 65 (49.6%) Others 8 (11.6%) 20 (15.3%) Occupation 0.195 Unemployed 11 (39.3) 17 (60.7) Self-employed 26 (43.3) 34 (56.7) Employed 19 (26.0) 54 (74.0) Retired 13 (33.3) 26 (66.7) BMI (kg/m²) 27.3 (25.1–31.8) 26.9 (24.5–32.1) 0.555 Glycaemic control: FPG (mmol/L) 6.9 (5.9–9.2) 7.0 (5.5–9.1) 0.834 HbA1c (%) 7.0 (6.0–9.0) 7.2 (6.0–9.0) 0.398 Lipid profile: Triglycerides 99.0 (82.0–159.0) 102.0 (75.5–134.5) 0.903 Total Cholesterol (mg/dL) 189.8 ± 43.6 180.9 ± 53.2 0.233 HDL-C (mg/dL) 51.0 ± 12.2 48.6 ± 13.5 0.207 LDL-C (mg/dL) 105.0 (88.6–143.4) 108.4 (78.0–133.6) 0.223 Non-HDL-C (mg/dL) 132.0 (107.0–168.0) 134.0 (96.0–156.0) 0.224 Duration of Diabetes (years) 3.0 (1.0–6.0) 9.0 (3.5–15.0) < 0.001 TyG Index 8.9 ± 0.6 8.8 ± 0.6 0.982 TyG-BMI 248.3 (219.5–277.7) 240.9 (221.8–278.1) 0.634 CVD Risk Scores (%) 6.0 (4.0–9.0) 10.0 (7.0–13.5) < 0.001* Diabetes management approach: 0.001* Diet/lifestyle modifications 14 (20.3%) 6 (4.6%) Anti-diabetic drugs 55 (79.7%) 125 (95.4%) Anti-lipidemic drug therapy: 0.167 No 11 (15.9%) 12 (9.2%) Yes 58 (84.1%) 119 (90.8%) Smoking: 0.081 Yes 4 (5.8%) 9 (6.9%) No 65 (94.2%) 122 (93.1%) Alcohol Consumption: 0.169 Yes 10 (14.5%) 18 (13.7%) No 59 (85.5%) 113 (86.3%) Table 2 shows the distribution of the participants based on their 10-year WHO estimated risk of cardiovascular disease. Participants classified as having low CVD risk were 112 (56.0%), while 88 (44.0%) had elevated CVD risk. Those with elevated CVD risk were significantly older (62.5 [60.5–67.0] vs. 49.5 [43.0–54.0] years, p < 0.001), had a longer duration of diabetes (10.0 [9.0–15.0] vs. 3.0 [1.0–6.0] years, p < 0.001), and presented with higher blood pressure values (systolic: 130.0 [120.0–140.0] vs. 120.0 [120.0–130.0] mmHg, p < 0.001; diastolic: 80.0 [80.0–80.0] vs. 80.0 [70.0–80.0] mmHg, p < 0.001). A higher proportion of participants in the elevated risk group had co-morbid hypertension (86.4% vs. 49.1%, p < 0.001). Employment status differed significantly across groups, with a greater proportion of retirees among those at elevated risk (35.2% vs. 7.1%) and more employed participants in the low-risk group (48.2% vs. 21.6%, p < 0.001). In terms of treatment, those with elevated CVD risk were more likely to be on anti-diabetic drugs (98.9% vs. 83.0%, p < 0.001), antihypertensive drugs (79.5% vs. 42.0%, p < 0.001), and anti-lipidemic drugs (96.6% vs. 82.1%, p = 0.001). Conversely, lifestyle modification alone was more common among those with low CVD risk (17.0% vs. 1.1%, p < 0.001). There were no significant differences between groups with respect to sex, ethnicity, BMI, glycaemic control indices (FPG, HbA1c), lipid profile measures, TyG index, TyG-BMI, smoking, or alcohol consumption (all p > 0.05). Table 2 Characteristics of study participants with low versus elevated cardiovascular disease risk Characteristics Low CVD risk (N = 112) Median (IQR) or Mean ± SD or n(%) Elevated CVD risk (N = 88) Median (IQR) or Mean ± SD or n(%) p-value Age (years) 49.5 (43.0–54.0) 62.5 (60.5–67.0) < 0.001 Sex 0.088 Female 67 (59.8%) 42 (47.7%) Male 45 (40.2%) 46 (52.3%) Ethnicity/Tribe 0.062 Idoma 25 (22.3%) 14 (15.9%) Igbo 27 (24.1%) 8 (9.1%) Igede 0 (0.0%) 6 (6.8%) Tiv 45 (40.2%) 47 (53.4%) Others 15 (13.4%) 13 (14.8%) Occupation < 0.001 Unemployed 16 (14.3%) 12 (13.6%) Self-employed 34 (30.4%) 26 (29.5%) Employed 54 (48.2%) 19 (21.6%) Retired 8 (7.1%) 31 (35.2%) BMI (kg/m²) 27.3 (24.5–32.8) 27.8 (24.7–32.0) 0.469 Glycaemic control: FPG (mmol/L) 7.3 (5.8–9.3) 6.8 (5.5–8.9) 0.313 HbA1c (%) 7.2 (6.0–9.0) 7.2 (6.0–9.0) 0.710 Lipid profile: Triglycerides 99.0 (81.0–137.5) 101.0 (75.0–142.0) 0.493 Total Cholesterol (mg/dL) 186.9 ± 41.1 180.3 ± 59.8 0.357 HDL-C (mg/dL) 50.3 ± 12.3 48.3 ± 14.0 0.273 LDL-C (mg/dL) 108.4 (90.6–140.7) 102.1 (75.1–136.5) 0.104 Non-HDL-C (mg/dL) 133.0 (110.0–164.0) 124.0 (90.5–168.0) 0.185 Blood Pressure Systolic BP (mmHg) 120.0 (120.0–130.0) 130.0 (120.0–140.0) < 0.001 Diastolic BP (mmHg) 80.0 (70.0–80.0) 80.0 (80.0–80.0) < 0.001 Duration of Diabetes (years) 3.0 (1.0–6.0) 10.0 (9.0–15.0) < 0.001 TyG Index 8.8 ± 0.5 8.8 ± 0.6 0.989 TyG-BMI 240.3 (213.3–279.7) 246.9 (223.1–278.1) 0.777 Co-morbid Hypertension < 0.001 No 57 (50.9%) 12 (13.6%) Yes 55 (49.1%) 76 (86.4%) Diabetes management approach: < 0.001 Diet/lifestyle modifications 19 (17.0%) 1 (1.1%) Anti-diabetic drugs 93 (83.0%) 87 (98.9%) Anti-hypertensive drug therapy: < 0.001 No 65 (58.0%) 18 (20.5%) Yes 47 (42.0%) 70 (79.5%) Anti-lipidemic drug therapy: 0.001 No 20 (17.9%) 3 (3.4%) Yes 92 (82.1%) 85 (96.6%) Smoking: 0.094 Yes 6 (7.1%) 7 (6.1%) No 79 (92.9%) 108 (93.9%) Alcohol Consumption: 0.182 Yes 11 (12.9%) 17 (14.8%) No 74 (87.1%) 98 (85.2%) As shown in Table 3 , TyG-BMI correlated very strongly with BMI (ρ = 0.923, p < 0.01) and moderately with TyG (ρ = 0.315, p < 0.01). TyG showed significant associations with HbA1c (r = 0.471, p < 0.01) and LDL-C (r = 0.246, p < 0.01), and an inverse relationship with HDL-C (r = − 0.372, p < 0.01). LDL-C was also positively correlated with HDL-C (ρ = 0.284, p < 0.01) but showed no association with BMI or blood pressure. Systolic and diastolic blood pressures were strongly interrelated (ρ = 0.533, p < 0.01) but unrelated to TyG-BMI, TyG, or BMI. Table 3 Correlation matrix of TyG-BMI, TyG, BMI, and selected cardiometabolic parameters (Pearson’s r and Spearman’s rho (ρ), N = 200) Variables TyG-BMI TyG BMI HbA1c LDL-C HDL-C SBP DBP ρ TyG-BMI 1 r TyG 0.315** 1 ρ BMI (kg/m²) 0.923** –0.021 1 ρ HbA1c (%) 0.208** 0.471** 0.024 1 ρ LDL-C (mg/dL) 0.104 0.246** –0.002 0.093 1 r HDL-C (mg/dL) –0.102 –0.372** 0.024 –0.130 0.284** 1 ρ SBP (mmHg) 0.099 0.055 0.069 0.091 0.011 –0.034 1 ρ DBP (mmHg) –0.032 0.139 –0.083 0.076 0.000 –0.070 0.533** 1 Pearson’s correlation used for TyG, TC, and HDL-C; Spearman’s rho used otherwise. * p < 0.05, **p < 0.01 In the initial multivariable analyses, TyG-BMI was not independently associated with hypertension (aOR: 1.001, 95% CI: 0.995–1.007, p = 0.768) or with elevated WHO 10-year CVD risk (aOR: 1.001, 95% CI: 0.994–1.008, p = 0.818). In contrast, duration of diabetes emerged as a strong predictor of both outcomes, with each additional year of disease increasing the odds of hypertension by 17% (aOR: 1.170, 95% CI: 1.091–1.255, p < 0.001) and the odds of elevated CVD risk by 31% (aOR: 1.311, 95% CI: 1.211–1.420, p < 0.001). Other covariates, including sex, use of lipid-lowering therapy, and smoking status, were not significantly associated with either hypertension or CVD risk. Model performance was acceptable, with good calibration (Hosmer–Lemeshow p = 0.483 for hypertension and p = 0.146 for CVD risk) and moderate explanatory power (Nagelkerke R²=0.187 and 0.442, respectively), yielding overall classification accuracies of 70.5% for hypertension and 81.0% for elevated CVD risk. Table 4 Multivariable logistic regression of factors associated with (a) hypertension and (b) elevated WHO 10-year CVD risk among patients with T2DM. Variable Hypertension (Yes/No) aOR (95% CI) p-value Elevated WHO CVD Risk aOR (95% CI) p-value TyG-BMI (per unit) 1.001 (0.995–1.007) 0.768 1.001 (0.994–1.008) 0.818 Gender (Male vs Female) 0.665 (0.350–1.264) 0.213 0.742 (0.370–1.491) 0.402 Duration of Diabetes (years) 1.170 (1.091–1.255) < 0.001* 1.311 (1.211–1.420) < 0.001* On Lipid-lowering Therapy 1.461 (0.535–3.987) 0.460 0.749 (0.175–3.215) 0.698 Smoking status (Yes vs No) 1.208 (0.552–2.642) 0.635 1.341 (0.599–3.002) 0.473 Model statistics Hypertension model : Cox & Snell R² = 0.136; Nagelkerke R² = 0.187; Overall correct classification = 70.5%; Hosmer–Lemeshow p = 0.483 Elevated WHO CVD Risk model : Cox & Snell R² = 0.330; Nagelkerke R² = 0.442; Overall correct classification = 81.0%; Hosmer–Lemeshow p = 0.146 *Significant at p < 0.05 When hypertension was entered alongside TyG-BMI in the extended CVD risk model, TyG-BMI remained non-significant (aOR: 1.001, 95% CI: 0.994–1.009, p = 0.733). Hypertension itself was strongly associated with elevated CVD risk, with hypertensive participants having almost four times higher odds compared to non-hypertensives (aOR: 3.923, 95% CI: 1.718–8.960, p = 0.001). Duration of diabetes remained significant, increasing the odds of elevated CVD risk by 30% per year (aOR: 1.298, 95% CI: 1.187–1.418, p < 0.001), while higher HbA1c levels independently raised the odds by 28% per unit increase (aOR: 1.276, 95% CI: 1.032–1.579, p = 0.025). All other variables, including sex, lipid-lowering therapy, and antidiabetic therapy type, were not significantly associated with CVD risk (p > 0.05). Model performance was robust, explaining 51.3% of the variance (Nagelkerke R² = 0.513) with good calibration (Hosmer–Lemeshow χ²=5.11, p = 0.746), achieving an overall classification accuracy of 76.5%, with specificity (83.0%) exceeding sensitivity (68.2%). Table 5 Multivariable logistic regression of factors associated with elevated WHO 10-year CVD risk among patients with T2DM (including hypertension as an explanatory variable alongside TyG-BMI). Variable B SE Wald χ² p-value aOR (Exp(B)) 95% CI for aOR TyG-BMI (per unit) 0.001 0.004 0.116 0.733 1.001 0.994–1.009 Gender (Male vs Female) -0.251 0.376 0.444 0.505 0.778 0.372–1.627 Duration of Diabetes (years) 0.260 0.045 32.943 < 0.001* 1.298 1.187–1.418 Lipid-lowering therapy -0.132 0.795 0.028 0.868 0.876 0.184–4.163 Hypertension (Yes vs No) 1.367 0.421 10.520 0.001* 3.923 1.718–8.960 Antidiabetic therapy -0.879 1.187 0.549 0.459 0.415 0.041–4.248 HbA1c (%) 0.244 0.109 5.041 0.025* 1.276 1.032–1.579 Constant -1.379 1.304 1.119 0.290 0.252 — Model fit statistics : -2 Log likelihood = 177.8; Nagelkerke R² = 0.513; Cox & Snell R² = 0.383 Hosmer–Lemeshow χ² (df = 8) = 5.11, p = 0.746 (good fit) Overall classification accuracy = 76.5% (specificity 83.0%, sensitivity 68.2%) Discussion In this hospital-based cohort of patients with T2DM attending specialist diabetes clinic in North-Central Nigeria, there was no significant difference in TyG-BMI across hypertension and CVD risk groups and it was not independently associated with either outcome after multivariable logistic regression and adjustment for potential confounders. By contrast, TyG index showed significant correlations with markers of metabolic control, including HbA1c, LDL-C, and HDL-C. However, longer duration of diabetes, higher HbA1c, and the presence of hypertension itself emerged as the dominant predictors of higher CVD risk in this population. Overall, these findings suggest that while the TyG index reflects underlying glycaemic and lipid abnormalities, TyG-BMI primarily mirrors body mass with limited capacity to capture metabolic or cardiovascular risk in African patients with T2DM. These findings differ from recent large population studies and meta-analyses that consistently link the TyG index and TyG-derived measures such as TyG-BMI with hypertension and cardiovascular outcomes. Multiple large observational cohorts and pooled analyses have shown positive associations between TyG-based indices and incident hypertension, atherosclerotic cardiovascular disease, and coronary events, particularly in community and Asian populations ( 12 , 21 ). For instance, a recent cross-sectional study of more than 60,000 Chinese adults reported a clear dose–response relationship between TyG-BMI and prevalent hypertension ( 12 ). In contrast, our study of older Nigerian patients with established type 2 diabetes under treatment did not demonstrate an independent association. The lack of association in this study likely reflects both disease-stage and population differences compared with prior community-based studies. Participants in this study were more of older individuals with long-standing type 2 diabetes, high treatment exposure, and cumulative vascular injury, conditions in which diabetes duration and chronic glycaemic burden may overshadow the incremental contribution of a metabolic index like TyG-BMI measured cross-sectionally ( 22 ). In addition, ethnic variation in body-fat distribution and insulin resistance phenotypes may limit the transferability of TyG-BMI across populations, especially considering that people of African ancestry typically exhibit relatively greater subcutaneous and less visceral adiposity for a given BMI compared with Asians, reducing the ability of BMI-based composites to discriminate visceral-fat–driven cardiovascular risks ( 23 ). Furthermore, previous studies suggest that TyG-BMI associations are often stronger in younger or general populations but attenuate with age. The cohort in the present study was older with longer diabetes duration, where survival bias, competing risks, and cumulative treatment exposure may blunt observed associations ( 12 ). Also, the index marker was highly collinear with BMI (ρ = 0.92), indicating that the composite was largely driven by body mass, and when a composite is dominated by one element, its independent effect can be statistically indistinguishable after adjustment, an artefact noted in other clinical samples ( 24 ). Additionally, the widespread use of glucose-, lipid-, and blood pressure–lowering therapies in this population likely reduced between-individual variability in triglycerides and glucose, thereby attenuating the discriminative power of TyG-based indices risk ( 25 , 26 ). These findings have important implications for clinical practice and research. In patients with established, treated type 2 diabetes in tertiary settings, simple measures such as diabetes duration, blood pressure control, and glycaemic status (HbA1c) may remain the most practical short-term predictors of cardiovascular risk, with TyG-BMI adding limited incremental value in this context; thus, its routine use for hypertension screening in treated clinic populations should be approached cautiously until prospective data in African cohorts are available. From a research perspective, these findings underscore the need to validate TyG-derived indices across diverse contexts (community samples vs clinic populations) and ancestries through longitudinal studies that serially assess TyG, TyG-BMI, and other alternative indices (e.g., TyG-WC, TyG-WHtR), incorporate direct measures of abdominal adiposity (waist circumference, CT/MRI-derived visceral fat) where feasible, and explicitly account for medication effects. Notably, several studies suggest that combining TyG with waist-based measures rather than BMI may improve risk prediction, consistent with the physiological role of visceral fat in cardiometabolic disease ( 27 , 28 ). This study has several strengths, including a well-characterized clinical cohort, use of laboratory-based WHO 10-year estimated CVD risk charts validated for Western sub-Saharan Africa, and deliberate inclusion of treated hypertensive patients to reflect real-world clinical practice. Nonetheless, the exploratory nature of this study warrants certain limitations that should be considered when interpreting these findings. Firstly, the cross-sectional design means that temporal or causal relationships cannot be established, and the reliance on a single-centre convenience sample reduces the generalizability of the findings, while the modest sample size limits statistical power compared with large community cohorts. Additionally, some residual confounding is likely, especially from medication effects and unmeasured visceral adiposity. The strong correlation between TyG-BMI and BMI limited the ability to differentiate metabolic from adiposity components, and widespread use of glucose- and lipid-modifying therapies means that single-timepoint fasting values may not reflect pre-treatment metabolic status. These limitations emphasize the need for larger, prospective studies to confirm and extend the observations reported here. Conclusion This study examined the relationship between TyG-BMI and hypertension in relation to cardiovascular disease (CVD) risk among Nigerian patients with type 2 diabetes mellitus (T2DM). TyG-BMI was not independently associated with prevalent hypertension or elevated CVD risk after multivariable adjustment. Instead, longer diabetes duration, higher HbA1c, and hypertension itself were the dominant predictors of CVD risk. These findings indicate that, in treated T2DM populations, traditional clinical indicators and patient history remain more informative than composite metabolic indices such as TyG-BMI. Future longitudinal, multicentre studies across African populations are warranted to validate TyG-derived indices, consider the influence of pharmacologic therapy, and incorporate direct measures of visceral adiposity, with the aim of improving CVD risk prediction and guiding stratified care for individuals with T2DM in sub-Saharan Africa. Abbreviations T2DM type 2 diabetes mellitus TyG-BMI triglyceride–glucose–body mass index TyG triglyceride–glucose index BMI body mass index WHO World Health Organization CVD cardiovascular disease FPG fasting plasma glucose HbA1c glycated haemoglobin TG triglycerides HDL-C high-density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol non-HDL-C non-high-density lipoprotein cholesterol SBP systolic blood pressure DBP diastolic blood pressure SPSS Statistical Package for the Social Sciences aOR adjusted odds ratio OR odds ratio CI confidence interval SD standard deviation IQR interquartile range TyG-WC TyG combined with waist circumference TyG-WHtR TyG combined with waist-to-height ratio eGFR estimated glomerular filtration rate EDTA ethylenediaminetetraacetic acid. Declarations Clinical trial number Not applicable. Ethics approval and consent to participate The study protocol was reviewed and approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi (protocol number BSUTH/CMAC/HREC/101/V.I/47) on 21 January 2019. The study was conducted in compliance with the Declaration of Helsinki. All participants provided written informed consent prior to enrolment. Confidentiality was ensured by assigning unique identification codes and securely storing participant data with restricted access. Consent for publication Not Applicable in this study. Funding This research did not receive any dedicated funding from a public, commercial, or not-for-profit agency. Author Contribution BB and JAM conceived and designed the study, performed data analysis, and contributed to interpretation of findings. INM and BKM participated in data collection and manuscript drafting. NIO and CJA contributed to interpretation of results, drafting, and critical revision of the manuscript. All authors reviewed the manuscript for important intellectual content, approved the final version for publication, and agreed to be accountable for all aspects of the work. Acknowledgement We acknowledge the management of Benue State University Teaching Hospital (BSUTH), the Department of Medicine for granting access to patients attending the Medical Outpatient Department, and Department of Chemical Pathology for granting use access to laboratory equipment and consumables for sample analyses. Data Availability The datasets from this study will be available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion. References World Health Organization. Diabetes [Internet]. Geneva: World Health Organization. 2024 [cited 2025 Aug 12]. Available from: https://www.who.int/news-room/fact-sheets/detail/diabetes International Diabetes Federation. IDF Diabetes Atlas, 11th edn [Internet]. Brussels, Belgium: International Diabetes Federation. 2025 [cited 2025 Aug 15]. Available from: https://diabetesatlas.org/media/uploads/sites/3/2025/04/IDF_Atlas_11th_Edition_2025.pdf Kalyani RR, Neumiller JJ, Maruthur NM, Wexler DJ. Diagnosis and Treatment of Type 2 Diabetes in Adults: A Review. JAMA. 2025 June;23. 10.1001/jama.2025.5956 . Raghavan S, Vassy JL, Ho YL, Song RJ, Gagnon DR, Cho K, et al. Diabetes Mellitus–Related All-Cause and Cardiovascular Mortality in a National Cohort of Adults. J Am Heart Assoc. 2019;8(4):e011295. 10.1161/JAHA.118.011295 . Adeniyi OA, Eniade OD, Olarinmoye AT, Abiodun BA, Okedare OO, Eniade AA, et al. Prevalence and associated factors of hypertension among type 2 diabetes mellitus patients in Lautech teaching hospital, Osogbo, Nigeria. Afr Health Sci. 2023;23(4):324–32. 10.4314/ahs.v23i4.34 . Basil B, Mohammed J, Mba IN et al. Hypertension in type 2 diabetes mellitus: prevalence, patterns, determinants and implications for cardiovascular risk prediction in Nigerian patients [Internet]. Preprint. Version 1. Research Square; 2024 Dec 26 [cited 2025 Aug 25]. Available from: https://doi.org/10.21203/rs.3.rs-5476606/v1 Basil B, Mohammed JA, Mba IN et al. Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension [Internet]. Preprint. Version 1. Research Square; 2025 May 22 [cited 2025 Aug 25]. Available from: https://doi.org/10.21203/rs.3.rs-6626378/v1 Zhang XR, Zhong WF, Liu RY, Huang JL, Fu JX, Gao J, et al. Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors. Cardiovasc Diabetol [Internet]. 2025;24(1):153. 10.1186/s12933-025-02711-x . Boushehri YG, Meymanatabadi Z, Tanha AE, Azami P, Alaei M, Alamdari AA, et al. Association of triglyceride glucose-body mass index (TyG-BMI) with metabolic dysfunction-associated steatotic liver disease: A systematic review and meta-analysis. PLoS ONE. 2025;20(8):e0324483. 10.1371/journal.pone.0324483 . Li W, Ge C, Zhou J. Association between TyG-BMI and early-onset hypertension: evidence from NHANES. Sci Rep [Internet]. 2025;15(1):8595. https://doi.org/10.1038/s41598-025-92159-6 . Song K, Xu Y, Wu S, Zhang X, Wang Y, Pan S. Research status of triglyceride glucose-body mass index (TyG-BMI index) [Internet]. Front Cardiovasc Med. 2025;12:1597112. 10.3389/fcvm.2025.1597112 . Chen Y, Du J, Zhou N, Song Y, Wang W, Hong X. Correlation between triglyceride glucose-body mass index and hypertension risk: evidence from a cross-sectional study with 60,283 adults in eastern China. BMC Cardiovasc Disord [Internet]. 2024;24(1):270. 10.1186/s12872-024-03934-8 . Rao X, Xin Z, Yu Q, Feng L, Shi Y, Tang T, et al. Triglyceride-glucose-body mass index and the incidence of cardiovascular diseases: a meta-analysis of cohort studies. Cardiovasc Diabetol [Internet]. 2025;24(1):34. 10.1186/s12933-025-02584-0 . Bello O, Mohandas C, Shojee-Moradie F, Jackson N, Hakim O, Alberti KGMM, et al. Black African men with early type 2 diabetes have similar muscle, liver and adipose tissue insulin sensitivity to White European men despite lower visceral fat [Internet]. Diabetologia. 2019;62(5):835–44. 10.1007/s00125-019-4820-6 . do Vale Moreira NC, Ceriello A, Basit A, Balde N, Mohan V, Gupta R, et al. Race/ethnicity and challenges for optimal insulin therapy [Internet]. Diabetes Res Clin Pract. 2021;175:108823. 10.1016/j.diabres.2021.108823 . Althubaiti A. Sample size determination: A practical guide for health researchers. J Gen Fam Med. 2022;24(2):72–8. 10.1002/jgf2.600 . Kabootari M, Tamehri Zadeh SS, Hasheminia M, Azizi F, Hadaegh F. Change in blood pressure status defined by 2017 ACC/AHA hypertension guideline and risk of cardiovascular disease: results of over a decade of follow-up of the Iranian population [Internet]. Front Cardiovasc Med. 2023;10:1044638. 10.3389/fcvm.2023.1044638 . Araújo SP, Juvanhol LL, Bressan J, Hermsdorff HHM. Triglyceride glucose index: a new biomarker in predicting cardiovascular risk [Internet]. Prev Med Rep. 2022;29:101941. 10.1016/j.pmedr.2022.101941 . Er LK, Wu S, Chou HH, Hsu LA, Teng MS, Sun YC, et al. Triglyceride glucose-body mass index is a simple and clinically useful surrogate marker for insulin resistance in nondiabetic individuals [Internet]. PLoS ONE. 2016;11(3):e0149731. 10.1371/journal.pone.0149731 . Kaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, et al. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Heal. 2019;7(10):e1332–45. 10.1016/S2214-109X(19)30318-3 . Liu X, Tan Z, Huang Y, Zhao H, Liu M, Yu P, et al. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis [Internet]. Cardiovasc Diabetol. 2022;21(1):124. 10.1186/s12933-022-01546-0 . de Jong M, Woodward M, Peters SAE. Duration of diabetes and the risk of major cardiovascular events in women and men: A prospective cohort study of UK Biobank participants. Diabetes Res Clin Pract. 2022;188:109899. 10.1016/j.diabres.2022.109899 . Sun C, Kovacs P, Guiu-Jurado E. Genetics of body fat distribution: comparative analyses in populations with European, Asian and African ancestries [Internet]. Genes. 2021;12(6):841. 10.3390/genes12060841 . Wang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study [Internet]. Lipids Health Dis. 2021;20(1):99. 10.1186/s12944-021-01532-7 . Morofuji Y, Nakagawa S, Ujifuku K, Fujimoto T, Otsuka K, Niwa M, et al. Beyond lipid-lowering: Effects of statins on cardiovascular and cerebrovascular diseases and cancer [Internet]. Pharmaceuticals (Basel). 2022;15(2):151. 10.3390/ph15020151 . Rezaei S, Tabrizi R, Nowrouzi-Sohrabi P, Jalali M, Atkin SL, Al-Rasadi K et al. GLP-1 receptor agonist effects on lipid and liver profiles in patients with nonalcoholic fatty liver disease: systematic review and meta-analysis [Internet]. Can J Gastroenterol Hepatol . 2021; 2021:8936865. 10.1155/2021/8936865 Raimi TH, Dele-Ojo BF, Dada SA, Fadare JO, Ajayi DD, Ajayi EA, et al. Triglyceride-Glucose Index and Related Parameters Predicted Metabolic Syndrome in Nigerians [Internet]. Metab Syndr Relat Disord. 2021;19(2):76–82. 10.1089/met.2020.0092 . Wang M, Chang M, Shen P, Wei W, Li H, Shen G. Application value of triglyceride-glucose index and triglyceride-glucose body mass index in evaluating the degree of hepatic steatosis in non-alcoholic fatty liver disease [Internet]. Lipids Health Dis. 2023;22(1):186. 10.1186/s12944-023-01954-5 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 22 Nov, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers invited by journal 13 Nov, 2025 Editor invited by journal 16 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 08 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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T2DM affects approximately 589\u0026nbsp;million adults globally, exacerbating cardiovascular risk through hyperglycaemia, insulin resistance, hypertension, dyslipidaemia, and other metabolic disturbances (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Patients with T2DM experience a 2- to 4-fold higher CVD mortality rate compared to non-diabetic individuals (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Among the established risk factors, hypertension is one of the most prevalent and potent contributors to cardiovascular events in this population, substantially accelerating the progression to heart failure, stroke, and coronary artery disease (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Early identification of diabetic patients at high risk of developing hypertension is therefore critical for timely intervention and prevention of downstream CVD complications.\u003c/p\u003e\u003cp\u003eTraditional cardiovascular risk assessment tools, while useful, often lack precision when applied across diverse populations due to variations in genetic, metabolic, and environmental factors (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). In recent years, the triglyceride-glucose body mass index (TyG-BMI) has emerged as a promising, easily obtainable surrogate marker that integrates lipid and glycaemic parameters with body composition to predict insulin resistance and metabolic dysfunction (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Calculated as the product of body mass index (BMI) and the triglyceride-glucose (TyG) index, TyG-BMI has been associated with both hypertension and cardiovascular outcomes in several populations (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Its strong correlation with the hyperinsulinemic-euglycemic clamp, the gold standard for insulin sensitivity assessment (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), underlines its potential clinical utility and forms the basis of the hypothesis that a higher TyG-BMI is positively associated with the presence of hypertension, and that this association, in turn, corresponds to an elevated estimated 10-year CVD risk in the study population.\u003c/p\u003e\u003cp\u003eHowever, most existing studies exploring the relationship between TyG-BMI and hypertension have been conducted in non-African populations, with limited investigation among African patients with T2DM (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Africans usually display unique physiological and metabolic characteristics that may influence the performance of TyG-BMI in predicting hypertension and subsequent CVD risk. For instance, Africans generally have a distinct body composition profile characterized by lower visceral adiposity and higher skeletal muscle mass compared to Caucasians, as well as differences in insulin sensitivity and secretion patterns (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), which may alter the relationship between metabolic indices and cardiovascular outcomes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). These differences highlight the need for regionally-relevant research to improve accuracy in risk assessment.\u003c/p\u003e\u003cp\u003eThis study seeks to determine whether TyG-BMI is independently associated with hypertension in patients with T2DM, and to examine how this relationship contributes to overall cardiovascular disease risk in this population. By establishing this link, the study aims to provide evidence that TyG-BMI could serve as a valuable, low-cost screening tool for identifying high-risk individuals, enabling earlier interventions and potentially reducing the burden of hypertension-related CVD among African patients with type 2 diabetes mellitus.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eThis analytical cross-sectional study was conducted to examine the association between the triglyceride\u0026ndash;glucose\u0026ndash;body mass index (TyG-BMI) and hypertension, and to evaluate how this association relates to estimated cardiovascular disease (CVD) risk in patients with type 2 diabetes mellitus (T2DM). Participants were consecutively recruited from the endocrinology outpatient clinic of Benue State University Teaching Hospital (BSUTH), Makurdi, North-Central Nigeria, between October 2019 and October 2020. BSUTH is a tertiary referral centre providing specialized diabetes care to the surrounding region.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was reviewed and approved by the Health Research Ethics Committee of BSUTH (protocol number BSUTH/CMAC/HREC/101/V.I/47) on 21 January 2019. The study was conducted in compliance with the Declaration of Helsinki. All participants provided written informed consent prior to enrolment. Confidentiality was ensured by assigning unique identification codes and securely storing participant data with restricted access.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination and Participant Recruitment\u003c/h3\u003e\n\u003cp\u003eThe minimum required sample size was calculated using the formula for single-proportion studies, assuming a 65.5% prevalence of hypertension among Nigerian adults with T2DM based on a previous study (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), a 95% confidence level, and a 5% margin of error. This yielded a minimum sample size of 348 participants, and adjusting for 10 percent non-response increased the target to 387. However, due to practical considerations and the exploratory nature of the study, 200 participants were ultimately enrolled via convenience sampling during routine clinic visits. While this is lower than the ideal sample size based on recent prevalence reports, it remains acceptable for an exploratory analysis aimed at investigating associations between TyG-BMI, hypertension, and estimated cardiovascular risk within this clinic population (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eEligible participants were adults aged 40 to 74 years with a diagnosis of T2DM of at least 6 months. Exclusion criteria included: diagnosis of type 1 diabetes, gestational diabetes, history of established CVD (such as myocardial infarction, stroke, or coronary revascularization), chronic kidney disease (estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u0026sup2;), liver disease, acute or chronic inflammatory conditions, pregnancy or lactation, recent infections within 4 weeks, and incomplete clinical or laboratory data. Participants on anti-inflammatory medications that could affect study biomarkers were excluded, while those on long-term standard therapies such as antiplatelet drugs were included.\u003c/p\u003e\n\u003ch3\u003eData Collection and Clinical Measurements\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured research proforma designed to capture participants\u0026rsquo; socio-demographic information (age, sex, ethnicity, occupation, education), medical history including duration of diabetes, presence of hypertension, dyslipidaemia, smoking status, alcohol use, and medication history (antidiabetic, antihypertensive, lipid-lowering, and antiplatelet therapies). Clinical measurements including blood pressure were recorded using a calibrated mercury sphygmomanometer with two readings taken five minutes apart and averaged. Hypertension was defined as Systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medications (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For the purposes of this study, hypertension status was based on a previous diagnosis or ongoing treatment, irrespective of blood pressure measurements at the time of assessment, to account for individuals with well-controlled disease. Anthropometric measurements were obtained following standardized protocols; height and weight were measured with participants wearing light clothing and no shoes, using a SECA weighing scale with height attachment. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m\u0026sup2;).\u003c/p\u003e\u003cp\u003eLaboratory data included fasting plasma glucose (FPG), glycated haemoglobin (HbA1c), and lipid profile parameters (total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol). LDL cholesterol was calculated using the Friedewald formula. The triglyceride-glucose (TyG) index was calculated as the natural logarithm of the product of fasting triglycerides and glucose divided by two (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Subsequently, the TyG-BMI index was derived by multiplying the TyG index by the participant\u0026rsquo;s BMI to reflect combined effects of insulin resistance and adiposity (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eBiochemical Assays\u003c/h3\u003e\n\u003cp\u003eAfter an overnight fast of at least 8 hours, venous blood samples were collected using aseptic technique into appropriate vacutainer tubes: fluoride oxalate for fasting plasma glucose (FPG), EDTA for glycated Haemoglobin (HbA1c), and lithium heparin for lipid profile including triglycerides (TG), total cholesterol, and HDL cholesterol. Samples were centrifuged at 5000 rpm for 5 minutes and plasma or serum aliquoted for immediate or batch analysis. Glucose, HbA1c, and lipid parameters were measured using the Cobas c311\u0026reg; automated analyzer (Roche Diagnostics).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCardiovascular Risk Assessment\u003c/h2\u003e\u003cp\u003eCardiovascular risk was estimated using the laboratory-based WHO CVD risk chart tailored for Western sub-Saharan Africa, which incorporates age, sex, systolic blood pressure, smoking status, diabetes status, and total cholesterol to predict 10-year risk of fatal or non-fatal CVD events (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Participants were stratified into four risk categories: very low (\u0026lt;\u0026thinsp;5%), low (5%\u0026ndash;\u0026lt;10%), moderate (10%\u0026ndash;\u0026lt;20%), and high (\u0026ge;\u0026thinsp;20%). In this study, participants risk categories were further dichotomized into \u0026ldquo;low risk\u0026rdquo; (\u0026lt;\u0026thinsp;10%) and \u0026ldquo;elevated risk\u0026rdquo; (\u0026ge;\u0026thinsp;10%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eData were analyzed using SPSS version 25 (IBM Corp., Armonk, NY, USA). Continuous variables were assessed for normality using the Shapiro-Wilk test and summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for normally distributed data or median with interquartile range for skewed data. Categorical variables were presented as frequencies and percentages. Differences across hypertension status and WHO-defined cardiovascular risk categories (very low, low, moderate, and high) were evaluated using independent-samples t-tests for normally distributed continuous variables, and Mann\u0026ndash;Whitney U or Kruskal\u0026ndash;Wallis tests for non-normally distributed variables, with appropriate post hoc procedures applied for multiple comparisons. Chi-square or Fisher\u0026rsquo;s exact tests were applied for categorical variables.\u003c/p\u003e\u003cp\u003eCorrelation analyses were performed using Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation coefficients, depending on the distribution, to explore relationships between TyG-BMI and individual cardiovascular risk factors. Binary logistic regression was used to determine whether TyG-BMI was independently associated with hypertension and CVD risk, adjusting for potential confounders including sex, duration of diabetes, lipid-lowering medication use, and smoking status. In a subsequent model, hypertension was also entered as an explanatory variable alongside TyG-BMI to explore their combined influence on elevated (moderate-to-high) CVD risk (\u0026ge;\u0026thinsp;10%). Variables already incorporated in the WHO risk score, such as age, systolic blood pressure, and total cholesterol, were excluded from regression models to avoid multicollinearity. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test, and Nagelkerke\u0026rsquo;s R\u0026sup2; was reported to estimate explained variance. All statistical analyses were two-tailed and p-values of less than 0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 200 study participants, 69 (34.5%) were non-hypertensive while 131 (65.5%) were hypertensive (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In comparison to non-hypertensive participants, those with hypertension were significantly older (59.0 [51.0\u0026ndash;64.0] vs. 51.0 [44.0\u0026ndash;58.0] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had a longer duration of diabetes (9.0 [3.5\u0026ndash;15.0] vs. 3.0 [1.0\u0026ndash;6.0] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher cardiovascular disease risk scores (10.0 [7.0\u0026ndash;13.5] vs. 6.0 [4.0\u0026ndash;9.0] %, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Their diabetes management approach also differed, with fewer managed by diet/lifestyle modifications alone (4.6% vs. 20.3%) and more receiving antidiabetic drug therapy (95.4% vs. 79.7%, p\u0026thinsp;=\u0026thinsp;0.001). Conversely, no significant differences were observed between the two groups with respect to sex distribution, ethnicity, occupation, BMI, glycaemic control (FPG, HbA1c), lipid profile parameters (triglycerides, total cholesterol, HDL-C, LDL-C, non-HDL-C), TyG index, TyG-BMI, anti-lipidemic therapy, smoking, or alcohol consumption (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the sociodemographic, clinical and laboratory characteristics of the study participants by hypertension status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-hypertensive\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;69)\u003c/p\u003e\u003cp\u003eMedian (IQR) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHypertensive\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;131)\u003c/p\u003e\u003cp\u003eMedian (IQR) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.0 (44.0\u0026ndash;58.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e59.0 (51.0\u0026ndash;64.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.073\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e44 (63.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (49.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (36.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e66 (50.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity/Tribe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.363\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIdoma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIgbo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21 (16.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIgede\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4 (3.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTiv\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (39.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (49.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (11.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e0.195\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnemployed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (39.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (60.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSelf-employed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26 (43.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (56.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmployed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (26.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54 (74.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRetired\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13 (33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (66.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.3 (25.1\u0026ndash;31.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.9 (24.5\u0026ndash;32.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.555\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlycaemic control:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFPG (mmol/L)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.9 (5.9\u0026ndash;9.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.0 (5.5\u0026ndash;9.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.834\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHbA1c (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.0 (6.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.2 (6.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.398\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid profile:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTriglycerides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99.0 (82.0\u0026ndash;159.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102.0 (75.5\u0026ndash;134.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTotal Cholesterol (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e189.8\u0026thinsp;\u0026plusmn;\u0026thinsp;43.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180.9\u0026thinsp;\u0026plusmn;\u0026thinsp;53.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.233\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.207\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105.0 (88.6\u0026ndash;143.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108.4 (78.0\u0026ndash;133.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNon-HDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132.0 (107.0\u0026ndash;168.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134.0 (96.0\u0026ndash;156.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Diabetes (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0 (1.0\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.0 (3.5\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.982\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e248.3 (219.5\u0026ndash;277.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e240.9 (221.8\u0026ndash;278.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCVD Risk Scores (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6.0 (4.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.0 (7.0\u0026ndash;13.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes management approach:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiet/lifestyle modifications\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (20.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (4.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnti-diabetic drugs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55 (79.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e125 (95.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-lipidemic drug therapy:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (9.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58 (84.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e119 (90.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9 (6.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65 (94.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e122 (93.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (13.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e113 (86.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the distribution of the participants based on their 10-year WHO estimated risk of cardiovascular disease. Participants classified as having low CVD risk were 112 (56.0%), while 88 (44.0%) had elevated CVD risk. Those with elevated CVD risk were significantly older (62.5 [60.5\u0026ndash;67.0] vs. 49.5 [43.0\u0026ndash;54.0] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), had a longer duration of diabetes (10.0 [9.0\u0026ndash;15.0] vs. 3.0 [1.0\u0026ndash;6.0] years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and presented with higher blood pressure values (systolic: 130.0 [120.0\u0026ndash;140.0] vs. 120.0 [120.0\u0026ndash;130.0] mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; diastolic: 80.0 [80.0\u0026ndash;80.0] vs. 80.0 [70.0\u0026ndash;80.0] mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A higher proportion of participants in the elevated risk group had co-morbid hypertension (86.4% vs. 49.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eEmployment status differed significantly across groups, with a greater proportion of retirees among those at elevated risk (35.2% vs. 7.1%) and more employed participants in the low-risk group (48.2% vs. 21.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of treatment, those with elevated CVD risk were more likely to be on anti-diabetic drugs (98.9% vs. 83.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), antihypertensive drugs (79.5% vs. 42.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and anti-lipidemic drugs (96.6% vs. 82.1%, p\u0026thinsp;=\u0026thinsp;0.001). Conversely, lifestyle modification alone was more common among those with low CVD risk (17.0% vs. 1.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no significant differences between groups with respect to sex, ethnicity, BMI, glycaemic control indices (FPG, HbA1c), lipid profile measures, TyG index, TyG-BMI, smoking, or alcohol consumption (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of study participants with low versus elevated cardiovascular disease risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow CVD risk\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;112)\u003c/p\u003e\u003cp\u003eMedian (IQR) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElevated CVD risk\u003c/p\u003e\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;88)\u003c/p\u003e\u003cp\u003eMedian (IQR) or Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.5 (43.0\u0026ndash;54.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e62.5 (60.5\u0026ndash;67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67 (59.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e42 (47.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45 (40.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46 (52.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity/Tribe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIdoma\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (22.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (15.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIgbo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (24.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eIgede\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0 (0.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6 (6.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTiv\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45 (40.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47 (53.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOthers\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eUnemployed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16 (14.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSelf-employed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (30.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26 (29.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEmployed\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54 (48.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19 (21.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eRetired\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31 (35.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27.3 (24.5\u0026ndash;32.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.8 (24.7\u0026ndash;32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlycaemic control:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFPG (mmol/L)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.3 (5.8\u0026ndash;9.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.8 (5.5\u0026ndash;8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.313\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHbA1c (%)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.2 (6.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.2 (6.0\u0026ndash;9.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.710\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid profile:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTriglycerides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e99.0 (81.0\u0026ndash;137.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e101.0 (75.0\u0026ndash;142.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.493\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eTotal Cholesterol (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e186.9\u0026thinsp;\u0026plusmn;\u0026thinsp;41.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e180.3\u0026thinsp;\u0026plusmn;\u0026thinsp;59.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e108.4 (90.6\u0026ndash;140.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e102.1 (75.1\u0026ndash;136.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNon-HDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e133.0 (110.0\u0026ndash;164.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e124.0 (90.5\u0026ndash;168.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood Pressure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSystolic BP (mmHg)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e120.0 (120.0\u0026ndash;130.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130.0 (120.0\u0026ndash;140.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiastolic BP (mmHg)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.0 (70.0\u0026ndash;80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.0 (80.0\u0026ndash;80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Diabetes (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0 (1.0\u0026ndash;6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.0 (9.0\u0026ndash;15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e240.3 (213.3\u0026ndash;279.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246.9 (223.1\u0026ndash;278.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.777\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCo-morbid Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57 (50.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12 (13.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55 (49.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76 (86.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes management approach:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDiet/lifestyle modifications\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (17.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAnti-diabetic drugs\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e93 (83.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87 (98.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-hypertensive drug therapy:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65 (58.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47 (42.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70 (79.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnti-lipidemic drug therapy:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (17.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3 (3.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92 (82.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85 (96.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (92.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108 (93.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol Consumption:\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.182\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (12.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17 (14.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74 (87.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98 (85.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, TyG-BMI correlated very strongly with BMI (ρ\u0026thinsp;=\u0026thinsp;0.923, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and moderately with TyG (ρ\u0026thinsp;=\u0026thinsp;0.315, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). TyG showed significant associations with HbA1c (r\u0026thinsp;=\u0026thinsp;0.471, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and LDL-C (r\u0026thinsp;=\u0026thinsp;0.246, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and an inverse relationship with HDL-C (r = \u0026minus;\u0026thinsp;0.372, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). LDL-C was also positively correlated with HDL-C (ρ\u0026thinsp;=\u0026thinsp;0.284, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but showed no association with BMI or blood pressure. Systolic and diastolic blood pressures were strongly interrelated (ρ\u0026thinsp;=\u0026thinsp;0.533, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but unrelated to TyG-BMI, TyG, or BMI.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation matrix of TyG-BMI, TyG, BMI, and selected cardiometabolic parameters (Pearson\u0026rsquo;s r and Spearman\u0026rsquo;s rho (ρ), \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;200)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHbA1c\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003er\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.315**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.923**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.208**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.471**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eLDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.246**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003er\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eHDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ndash;0.372**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026ndash;0.130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.284**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.055\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eρ\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eDBP (mmHg)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;0.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026ndash;0.070\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.533**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003ePearson\u0026rsquo;s correlation used for TyG, TC, and HDL-C; Spearman\u0026rsquo;s rho used otherwise. *\u003c/em\u003ep\u0026thinsp;\u003cem\u003e\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/p\u003e\u003cp\u003eIn the initial multivariable analyses, TyG-BMI was not independently associated with hypertension (aOR: 1.001, 95% CI: 0.995\u0026ndash;1.007, p\u0026thinsp;=\u0026thinsp;0.768) or with elevated WHO 10-year CVD risk (aOR: 1.001, 95% CI: 0.994\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.818). In contrast, duration of diabetes emerged as a strong predictor of both outcomes, with each additional year of disease increasing the odds of hypertension by 17% (aOR: 1.170, 95% CI: 1.091\u0026ndash;1.255, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the odds of elevated CVD risk by 31% (aOR: 1.311, 95% CI: 1.211\u0026ndash;1.420, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Other covariates, including sex, use of lipid-lowering therapy, and smoking status, were not significantly associated with either hypertension or CVD risk. Model performance was acceptable, with good calibration (Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.483 for hypertension and p\u0026thinsp;=\u0026thinsp;0.146 for CVD risk) and moderate explanatory power (Nagelkerke R\u0026sup2;=0.187 and 0.442, respectively), yielding overall classification accuracies of 70.5% for hypertension and 81.0% for elevated CVD risk.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of factors associated with (a) hypertension and (b) elevated WHO 10-year CVD risk among patients with T2DM.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension (Yes/No) aOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElevated WHO CVD Risk aOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI (per unit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.001 (0.995\u0026ndash;1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.768\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.001 (0.994\u0026ndash;1.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.818\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male vs Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.665 (0.350\u0026ndash;1.264)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.742 (0.370\u0026ndash;1.491)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Diabetes (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.170 (1.091\u0026ndash;1.255)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.311 (1.211\u0026ndash;1.420)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOn Lipid-lowering Therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.461 (0.535\u0026ndash;3.987)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.749 (0.175\u0026ndash;3.215)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.698\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.208 (0.552\u0026ndash;2.642)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.341 (0.599\u0026ndash;3.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.473\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel statistics\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eHypertension model\u003c/em\u003e: Cox \u0026amp; Snell R\u0026sup2; = 0.136; Nagelkerke R\u0026sup2; = 0.187; Overall correct classification\u0026thinsp;=\u0026thinsp;70.5%; Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.483\u003c/p\u003e\u003cp\u003e\u003cem\u003eElevated WHO CVD Risk model\u003c/em\u003e: Cox \u0026amp; Snell R\u0026sup2; = 0.330; Nagelkerke R\u0026sup2; = 0.442; Overall correct classification\u0026thinsp;=\u0026thinsp;81.0%; Hosmer\u0026ndash;Lemeshow p\u0026thinsp;=\u0026thinsp;0.146\u003c/p\u003e\u003cp\u003e*Significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhen hypertension was entered alongside TyG-BMI in the extended CVD risk model, TyG-BMI remained non-significant (aOR: 1.001, 95% CI: 0.994\u0026ndash;1.009, p\u0026thinsp;=\u0026thinsp;0.733). Hypertension itself was strongly associated with elevated CVD risk, with hypertensive participants having almost four times higher odds compared to non-hypertensives (aOR: 3.923, 95% CI: 1.718\u0026ndash;8.960, p\u0026thinsp;=\u0026thinsp;0.001). Duration of diabetes remained significant, increasing the odds of elevated CVD risk by 30% per year (aOR: 1.298, 95% CI: 1.187\u0026ndash;1.418, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while higher HbA1c levels independently raised the odds by 28% per unit increase (aOR: 1.276, 95% CI: 1.032\u0026ndash;1.579, p\u0026thinsp;=\u0026thinsp;0.025). All other variables, including sex, lipid-lowering therapy, and antidiabetic therapy type, were not significantly associated with CVD risk (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Model performance was robust, explaining 51.3% of the variance (Nagelkerke R\u0026sup2; = 0.513) with good calibration (Hosmer\u0026ndash;Lemeshow χ\u0026sup2;=5.11, p\u0026thinsp;=\u0026thinsp;0.746), achieving an overall classification accuracy of 76.5%, with specificity (83.0%) exceeding sensitivity (68.2%).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression of factors associated with elevated WHO 10-year CVD risk among patients with T2DM (including hypertension as an explanatory variable alongside TyG-BMI).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald χ\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eaOR (Exp(B))\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e95% CI for aOR\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG-BMI (per unit)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.994\u0026ndash;1.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male vs Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.444\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.372\u0026ndash;1.627\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDuration of Diabetes (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e32.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.298\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.187\u0026ndash;1.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid-lowering therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.795\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.868\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.876\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.184\u0026ndash;4.163\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension (Yes vs No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.421\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.001*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.718\u0026ndash;8.960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntidiabetic therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.459\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u0026ndash;4.248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.025*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.032\u0026ndash;1.579\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.304\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModel fit statistics\u003c/b\u003e:\u003c/p\u003e\u003cp\u003e-2 Log likelihood\u0026thinsp;=\u0026thinsp;177.8; Nagelkerke R\u0026sup2; = 0.513; Cox \u0026amp; Snell R\u0026sup2; = 0.383\u003c/p\u003e\u003cp\u003eHosmer\u0026ndash;Lemeshow χ\u0026sup2; (df\u0026thinsp;=\u0026thinsp;8)\u0026thinsp;=\u0026thinsp;5.11, p\u0026thinsp;=\u0026thinsp;0.746 (good fit)\u003c/p\u003e\u003cp\u003eOverall classification accuracy\u0026thinsp;=\u0026thinsp;76.5% (specificity 83.0%, sensitivity 68.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this hospital-based cohort of patients with T2DM attending specialist diabetes clinic in North-Central Nigeria, there was no significant difference in TyG-BMI across hypertension and CVD risk groups and it was not independently associated with either outcome after multivariable logistic regression and adjustment for potential confounders. By contrast, TyG index showed significant correlations with markers of metabolic control, including HbA1c, LDL-C, and HDL-C. However, longer duration of diabetes, higher HbA1c, and the presence of hypertension itself emerged as the dominant predictors of higher CVD risk in this population. Overall, these findings suggest that while the TyG index reflects underlying glycaemic and lipid abnormalities, TyG-BMI primarily mirrors body mass with limited capacity to capture metabolic or cardiovascular risk in African patients with T2DM.\u003c/p\u003e\u003cp\u003eThese findings differ from recent large population studies and meta-analyses that consistently link the TyG index and TyG-derived measures such as TyG-BMI with hypertension and cardiovascular outcomes. Multiple large observational cohorts and pooled analyses have shown positive associations between TyG-based indices and incident hypertension, atherosclerotic cardiovascular disease, and coronary events, particularly in community and Asian populations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For instance, a recent cross-sectional study of more than 60,000 Chinese adults reported a clear dose\u0026ndash;response relationship between TyG-BMI and prevalent hypertension (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In contrast, our study of older Nigerian patients with established type 2 diabetes under treatment did not demonstrate an independent association.\u003c/p\u003e\u003cp\u003eThe lack of association in this study likely reflects both disease-stage and population differences compared with prior community-based studies. Participants in this study were more of older individuals with long-standing type 2 diabetes, high treatment exposure, and cumulative vascular injury, conditions in which diabetes duration and chronic glycaemic burden may overshadow the incremental contribution of a metabolic index like TyG-BMI measured cross-sectionally (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In addition, ethnic variation in body-fat distribution and insulin resistance phenotypes may limit the transferability of TyG-BMI across populations, especially considering that people of African ancestry typically exhibit relatively greater subcutaneous and less visceral adiposity for a given BMI compared with Asians, reducing the ability of BMI-based composites to discriminate visceral-fat\u0026ndash;driven cardiovascular risks (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFurthermore, previous studies suggest that TyG-BMI associations are often stronger in younger or general populations but attenuate with age. The cohort in the present study was older with longer diabetes duration, where survival bias, competing risks, and cumulative treatment exposure may blunt observed associations (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Also, the index marker was highly collinear with BMI (ρ\u0026thinsp;=\u0026thinsp;0.92), indicating that the composite was largely driven by body mass, and when a composite is dominated by one element, its independent effect can be statistically indistinguishable after adjustment, an artefact noted in other clinical samples (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Additionally, the widespread use of glucose-, lipid-, and blood pressure\u0026ndash;lowering therapies in this population likely reduced between-individual variability in triglycerides and glucose, thereby attenuating the discriminative power of TyG-based indices risk (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThese findings have important implications for clinical practice and research. In patients with established, treated type 2 diabetes in tertiary settings, simple measures such as diabetes duration, blood pressure control, and glycaemic status (HbA1c) may remain the most practical short-term predictors of cardiovascular risk, with TyG-BMI adding limited incremental value in this context; thus, its routine use for hypertension screening in treated clinic populations should be approached cautiously until prospective data in African cohorts are available. From a research perspective, these findings underscore the need to validate TyG-derived indices across diverse contexts (community samples vs clinic populations) and ancestries through longitudinal studies that serially assess TyG, TyG-BMI, and other alternative indices (e.g., TyG-WC, TyG-WHtR), incorporate direct measures of abdominal adiposity (waist circumference, CT/MRI-derived visceral fat) where feasible, and explicitly account for medication effects. Notably, several studies suggest that combining TyG with waist-based measures rather than BMI may improve risk prediction, consistent with the physiological role of visceral fat in cardiometabolic disease (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study has several strengths, including a well-characterized clinical cohort, use of laboratory-based WHO 10-year estimated CVD risk charts validated for Western sub-Saharan Africa, and deliberate inclusion of treated hypertensive patients to reflect real-world clinical practice. Nonetheless, the exploratory nature of this study warrants certain limitations that should be considered when interpreting these findings. Firstly, the cross-sectional design means that temporal or causal relationships cannot be established, and the reliance on a single-centre convenience sample reduces the generalizability of the findings, while the modest sample size limits statistical power compared with large community cohorts. Additionally, some residual confounding is likely, especially from medication effects and unmeasured visceral adiposity. The strong correlation between TyG-BMI and BMI limited the ability to differentiate metabolic from adiposity components, and widespread use of glucose- and lipid-modifying therapies means that single-timepoint fasting values may not reflect pre-treatment metabolic status. These limitations emphasize the need for larger, prospective studies to confirm and extend the observations reported here.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the relationship between TyG-BMI and hypertension in relation to cardiovascular disease (CVD) risk among Nigerian patients with type 2 diabetes mellitus (T2DM). TyG-BMI was not independently associated with prevalent hypertension or elevated CVD risk after multivariable adjustment. Instead, longer diabetes duration, higher HbA1c, and hypertension itself were the dominant predictors of CVD risk. These findings indicate that, in treated T2DM populations, traditional clinical indicators and patient history remain more informative than composite metabolic indices such as TyG-BMI. Future longitudinal, multicentre studies across African populations are warranted to validate TyG-derived indices, consider the influence of pharmacologic therapy, and incorporate direct measures of visceral adiposity, with the aim of improving CVD risk prediction and guiding stratified care for individuals with T2DM in sub-Saharan Africa.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eT2DM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etype 2 diabetes mellitus\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTyG-BMI\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etriglyceride\u0026ndash;glucose\u0026ndash;body mass index\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTyG\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etriglyceride\u0026ndash;glucose index\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ebody mass index\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWHO\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWorld Health Organization\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCVD\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ecardiovascular disease\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFPG\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003efasting plasma glucose\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHbA1c\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eglycated haemoglobin\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTG\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003etriglycerides\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHDL-C\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ehigh-density lipoprotein cholesterol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eLDL-C\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003elow-density lipoprotein cholesterol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-HDL-C\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003enon-high-density lipoprotein cholesterol\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSBP\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003esystolic blood pressure\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDBP\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ediastolic blood pressure\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSPSS\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eStatistical Package for the Social Sciences\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eadjusted odds ratio\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eodds ratio\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCI\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003econfidence interval\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003estandard deviation\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIQR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003einterquartile range\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTyG-WC\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTyG combined with waist circumference\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTyG-WHtR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTyG combined with waist-to-height ratio\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eeGFR\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eestimated glomerular filtration rate\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEDTA\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eethylenediaminetetraacetic acid.\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eClinical trial number\u003c/em\u003e\u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e The study protocol was reviewed and approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi (protocol number BSUTH/CMAC/HREC/101/V.I/47) on 21 January 2019. The study was conducted in compliance with the Declaration of Helsinki. All participants provided written informed consent prior to enrolment. Confidentiality was ensured by assigning unique identification codes and securely storing participant data with restricted access.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot Applicable in this study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research did not receive any dedicated funding from a public, commercial, or not-for-profit agency.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBB and JAM conceived and designed the study, performed data analysis, and contributed to interpretation of findings. INM and BKM participated in data collection and manuscript drafting. NIO and CJA contributed to interpretation of results, drafting, and critical revision of the manuscript. All authors reviewed the manuscript for important intellectual content, approved the final version for publication, and agreed to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the management of Benue State University Teaching Hospital (BSUTH), the Department of Medicine for granting access to patients attending the Medical Outpatient Department, and Department of Chemical Pathology for granting use access to laboratory equipment and consumables for sample analyses.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets from this study will be available upon reasonable request to the corresponding author. This is because the dataset includes additional data that are not relevant to this study and may require exclusion.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Diabetes [Internet]. 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Metab Syndr Relat Disord. 2021;19(2):76\u0026ndash;82. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1089/met.2020.0092\u003c/span\u003e\u003cspan address=\"10.1089/met.2020.0092\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang M, Chang M, Shen P, Wei W, Li H, Shen G. Application value of triglyceride-glucose index and triglyceride-glucose body mass index in evaluating the degree of hepatic steatosis in non-alcoholic fatty liver disease [Internet]. Lipids Health Dis. 2023;22(1):186. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-023-01954-5\u003c/span\u003e\u003cspan address=\"10.1186/s12944-023-01954-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, Triglyceride-glucose-body mass index, Hypertension, Cardiovascular risk, Sub-Saharan Africa, Insulin resistance","lastPublishedDoi":"10.21203/rs.3.rs-7806539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7806539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eCardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), with hypertension as a key contributor. The triglyceride\u0026ndash;glucose\u0026ndash;body mass index (TyG-BMI) has been proposed as a surrogate marker for insulin resistance and cardiometabolic risk. This study aimed to examine whether TyG-BMI is independently associated with hypertension and estimated 10-year CVD risk in Nigerian patients with T2DM.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis hospital-based cross-sectional study was conducted over a period of 13 months among patients with T2DM. Hypertension was defined by prior diagnosis or use of antihypertensive therapy, and 10-year CVD risk was estimated using the laboratory-based WHO risk chart validated for Western sub-Saharan Africa. Statistical analyses including correlation and multivariable logistic regression for evaluating associations between TyG-BMI, hypertension, and CVD risk were conducted using SPSS version 25, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong participants, 65.5% were hypertensive, and 44% had elevated 10-year CVD risk. TyG-BMI showed no independent association with hypertension (adjusted OR: 1.001, 95% CI: 0.995\u0026ndash;1.007, p\u0026thinsp;=\u0026thinsp;0.768) or elevated CVD risk (adjusted OR: 1.001, 95% CI: 0.994\u0026ndash;1.008, p\u0026thinsp;=\u0026thinsp;0.818). Rather, longer diabetes duration significantly increased the odds of hypertension (aOR per year: 1.170, 95% CI: 1.091\u0026ndash;1.255, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and elevated CVD risk (aOR per year: 1.311, 95% CI: 1.211\u0026ndash;1.420, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In extended models, hypertension (aOR: 3.923, 95% CI: 1.718\u0026ndash;8.960, p\u0026thinsp;=\u0026thinsp;0.001) and higher HbA1c (aOR: 1.276, 95% CI: 1.032\u0026ndash;1.579, p\u0026thinsp;=\u0026thinsp;0.025) were independently associated with elevated CVD risk, while TyG-BMI remained insignificant.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThese findings suggest that traditional clinical indicators remain more informative than composite metabolic indices like TyG-BMI for cardiovascular risk stratification in sub-Saharan African patients with T2DM. Longitudinal multicentre studies incorporating direct measures of visceral adiposity and accounting for therapy effects are warranted to refine CVD risk prediction in this population.\u003c/p\u003e","manuscriptTitle":"Exploring the relationship between triglyceride-glucose-body mass index and hypertension in relation to cardiovascular disease risk in an African population of type 2 diabetes patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-25 18:39:20","doi":"10.21203/rs.3.rs-7806539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-11-22T07:27:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228594392189796456510270429943749604591","date":"2025-11-22T07:14:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-13T10:25:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-16T13:01:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-16T11:17:40+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-16T11:15:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2025-10-08T09:47:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f3e10b2b-7605-494a-84e0-650472404572","owner":[],"postedDate":"November 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T18:39:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-25 18:39:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7806539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7806539","identity":"rs-7806539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}