Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension

Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension | 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 Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension Bruno Basil, Jamila Aminu Mohammed, Izuchukwu Nnachi Mba, Isiaku Mary Nkemakolam, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6626378/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Aug, 2025 Read the published version in BMC Endocrine Disorders → Version 1 posted 15 You are reading this latest preprint version Abstract Background Cardiovascular disease (CVD) remains a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), particularly when complicated by hypertension. This study evaluated markers of glycaemic control, systemic inflammation, and lipid-related atherogenicity, and their relationship with CVD risk among a population of Nigerian patients with T2DM and co-morbid hypertension. Method This hospital-based cross-sectional analytical study was conducted over a period of 13 months among T2DM patients, including those with co-morbid hypertension. The 10-year estimated CVD risk was determined using the WHO CVD risk assessment chart validated for Western sub-Saharan Africa, while glycated haemoglobin (HbA1c), atherogenic index of plasma (AIP), and high-sensitivity C-reactive protein (hsCRP) were assessed as markers of glycaemic control, atherogenicity, and inflammation, respectively. Statistical analyses, including multivariable linear regression, were conducted using SPSS version 25, with significance set at p < 0.05. Results Hypertensive T2DM patients had significantly higher hsCRP (2.57 mg/L, IQR: 2.63 vs. 0.86 mg/L, IQR: 1.72; p < 0.001) and AIP (0.071, IQR: 0.39 vs. 0.002, IQR: 0.34; p = 0.015). They also had significantly higher mean WHO CVD risk scores (11.3 ± 4.7 vs. 7.2 ± 4.1; p < 0.001), with 60.0% (n = 75) classified as moderate-to-high risk. Following multivariable analysis and adjustment for potential confounders, only age (β = 0.801, p < 0.001) and systolic blood pressure (β = 0.333, p < 0.001) were independently associated with CVD risk scores, while hsCRP (β = 0.078, p = 0.152), AIP (β = 0.023, p = 0.669), and HbA1c (β = 0.026, p = 0.649) were not significant predictors. Conclusion Elevated hsCRP and AIP levels are prevalent among hypertensive T2DM patients but may not serve as reliable predictors of 10-year estimated CVD risk, while age and blood pressure were the primary determinants. This highlights the importance of early risk stratification and optimal blood pressure control in T2DM management, especially in resource-limited settings. Longitudinal, multicentre studies are needed to validate these findings and inform targeted interventions across sub-Saharan African populations. Type 2 diabetes mellitus co-morbid hypertension cardiovascular disease risk high-sensitivity C-reactive protein atherogenic index of plasma Figures Figure 1 Introduction Type 2 diabetes mellitus (T2DM) and hypertension represent two of the most prevalent and interlinked non-communicable diseases globally, with sub-Saharan Africa (SSA) experiencing a rapidly escalating burden due to urbanization, dietary transitions, and sedentary lifestyles( 1 , 2 ) Recent estimates suggest that over 24 million people in SSA are living with diabetes, a figure projected to rise by 129% to 55 million by 2045, while hypertension affects about 48% of women and 34% of men in the region.( 3 , 4 ) The dual burden of T2DM and hypertension substantially contributes to the region’s rising morbidity and mortality from cardiovascular disease (CVD), which remains the leading cause of death among individuals with diabetes.( 5 ) Insulin resistance in T2DM mediates an increased risk of cardiovascular events through a complex interplay of chronic hyperglycaemia, elevated systemic inflammation, and dyslipidaemia.( 6 , 7 ) Prolonged dysglycaemia, a hallmark of poorly controlled T2DM, is known to initiate and propagate vascular injury through multiple mechanisms, including oxidative stress, endothelial dysfunction, and the activation of pro-inflammatory pathways.( 8 ) On the other hand, high-sensitivity C-reactive protein (hsCRP), a well-established biomarker of systemic inflammation, is frequently elevated in T2DM and independently predicts CVD events. Chronic low-grade inflammation enhanced by insulin resistance accelerates atherosclerotic plaque formation, further compounding cardiovascular risk( 6 , 9 ) Notably, the superimposition of hypertension on T2DM further magnifies this risk, placing individuals with co-morbid hypertension at an even higher risk for adverse cardiovascular outcomes.( 10 ) In parallel, lipid-driven atherogenicity, particularly reflected by the atherogenic index of plasma (AIP), a logarithmic ratio of triglycerides to high-density lipoprotein cholesterol, has emerged as a potent surrogate marker of small, dense low-density lipoprotein particles.( 11 , 12 ) Elevated AIP is strongly associated with atherosclerosis and heightened CVD risk, especially in insulin-resistant states.( 13 ) When compounded by co-morbid hypertension, which induces hemodynamic stress and vascular remodelling, the synergistic effect on cardiovascular pathology becomes even more pronounced.( 14 ) Hypertension in T2DM patients not only accelerates the progression of micro- and macrovascular complications but also alters the trajectory of lipid metabolism and inflammatory responses, creating a complex interplay that necessitates stratified risk assessment.( 15 ) These observations inform the hypothesis that markers of systemic inflammation and atherogenicity may serve as useful predictors of cardiovascular risk in this population. There is a paucity of studies in Africa assessing the combined influence of chronic hyperglycaemia, systemic inflammation, and lipid-driven atherogenicity on cardiovascular risk stratification among T2DM patients with co-morbid hypertension, a high-risk group, despite the known role of these factors in CVD pathogenesis. This emphasizes the need for further investigation, particularly in most SSA settings where CVD risk stratification tools are underutilized or poorly validated.( 16 )( 17 , 18 ) Moreover, existing risk assessment tools often neglect the additive effects of these biomarkers particularly in this category of patients, limiting both their predictive performance and clinical relevance. Tailored approaches that incorporate biochemical markers alongside locally validated tools such as the WHO 10-year CVD risk chart are required, as this may foster the development of region-specific CVD risk assessment tools that address limitations of current models, including age restrictions. This study evaluated markers of glycaemic control, systemic inflammation, and lipid-driven atherogenicity among Nigerian T2DM patients with co-morbid hypertension, and examined how these markers are associated with CVD risk stratification. By focusing on this high-risk population, the study sought to provide evidence that may inform targeted strategies for early CVD risk identification and proactive intervention, especially given that risk reduction becomes increasingly challenging with prolonged diabetes duration and advancing age.( 19 ) Materials and Methods Study Design and Setting This hospital-based study was conducted over 13 months (October 2019 to October 2020) at the endocrinology clinic of the medical outpatient department of Benue State University Teaching Hospital (BSUTH) in Makurdi, North-central Nigeria. It is recognized as a leading referral centre for Benue and the neighbouring states, offering specialized medical services through diverse clinical specialities. The study employed a cross-sectional analytical design to investigate HbA1c, hsCRP and AIP levels and their associations with CVD risk among known T2DM patients with co-morbid hypertension. Ethical Considerations This study was conducted as part of a larger research project approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi, under protocol number BSUTH/CMAC/HREC/101/V.I/47, dated 21st January 2019. Ethical conduct was ensured per the principles outlined in the Declaration of Helsinki.( 20 ) Written informed consent was obtained from all participants before enrolment and data collection. To maintain confidentiality, each participant was assigned a unique identification code, and all clinical data and laboratory results were securely stored in restricted areas throughout the study. Sample Size Determination and Participant Selection The sample size was determined using a reported hypertension prevalence of 32.1% among individuals with T2DM in Nigeria,( 21 ) with a 5% margin of error and 95% confidence level. Using Fisher’s formula for cross-sectional studies with adjustments for a finite population and 10% non-response rate,( 22 ) a minimum of 186 participants was required to ensure sufficient power to detect associations between HbA1c, hsCRP, AIP, and cardiovascular risk categories in hypertensive T2DM patients. However, 190 participants were recruited using a convenience sampling method, with eligible individuals consecutively enrolled during routine clinic visits at BSUTH. Potential participants were identified via clinic records and approached by trained researchers. Following an initial eligibility screening with available medical records, the study included adult patients aged ≥ 40 years with a documented diagnosis of T2DM with and without hypertension who were receiving specialized care at the BSUTH medical outpatient clinic for at least 6 months before enrolment during the period of the study. Exclusion criteria included type 1 diabetes mellitus, gestational diabetes, and other identifiable secondary causes of hypertension (e.g., chronic kidney disease, adrenal pathology, or medication-induced hypertension). Additionally, participants with incomplete clinical or laboratory data necessary for analysis were also excluded. Data Collection and Interpretation Data on participants’ socio-demographic characteristics, medical history, medication use, and clinical parameters were collected using a structured research proforma. Blood pressure was measured in mmHg using an AccuSure mercury sphygmomanometer, with participants seated upright and the cuff positioned at chest level. Two readings of systolic and diastolic blood pressure (BP) were taken at 5-minute intervals, and the average was recorded. Hypertension was defined as Systolic BP ≥ 140 mmHg, DBP ≥ 90 mmHg, or current use of antihypertensive medications.( 23 ) Anthropometric measurements were obtained using a SECA scale with height attachment. Body mass index (BMI) was then calculated as weight in kg divided by the square of the height in m² (kg/m²) and values ≥ 30kg/m 2 .( 24 ) Venous blood samples were collected for biochemical analyses, including fasting plasma glucose (FPG), total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), HbA1c, hsCRP, and calculation of low-density lipoprotein cholesterol (LDL-C), non-HDL cholesterol and atherogenic index of plasma (AIP). The AIP was calculated as Log 10 (triglycerides/HDL-C), with values 0.21 indicating low, moderate, and high atherogenic risk, respectively.( 25 ) Glycaemic control was defined as well-controlled if HbA1c 3 mg/L.( 27 , 28 ) Cardiovascular risk was assessed using the WHO 10-year laboratory-based CVD risk prediction chart validated for Western sub-Saharan African populations. Risk categories included very low (< 5%), low (5%–<10%), moderate (10%–<20%), and high (≥ 20%), based on age, sex, Systolic BP, smoking status, diabetes status, and total cholesterol.( 29 ) This was re-classified as “low risk” (< 10%) and moderate-high risk (≥ 10%), a framework that enabled the structured evaluation of CVD risk among T2DM patients with co-morbid hypertension in the study population. Sample Collection and Assays Blood samples were drawn from participants using aseptic techniques after an overnight fast of at least 8 hours into five-millilitre vacutainers. Samples for FPG were collected into a fluoride oxalate container and HbA1c into an EDTA container, while samples for lipid profile and hs-CRP were collected into lithium heparin containers to ensure accurate and reliable results. Samples for FPG, lipid profile and hsCRP were centrifuged at 5000 revolutions per minute (rpm) for 5 minutes after collection and the plasma/serum samples were aliquoted in plain cryovial tubes. Glucose, HbA 1c , and lipid profile (Total cholesterol, HDL-cholesterol, and Triglyceride) were assayed immediately, thereafter LDL-cholesterol and non-HDL-cholesterol levels were calculated. Heparinised plasma for hsCRP was aliquoted and stored at -25°C for up to three months before batch analysis. All assays were conducted using the Cobas c311® automated analyzer (Roche Diagnostics, Mannheim, Germany). Statistical Analysis Data were analyzed using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA) and Microsoft Excel. Derived variables, such as the atherogenic index of plasma (AIP) were computed in Microsoft Excel using logical functions and subsequently exported to SPSS for further analysis. Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. The normality of continuous variables was assessed using the Shapiro-Wilk test. Based on the distribution, continuous variables were presented as mean ± standard deviation (SD) or median (inter-quartile range), while categorical variables were reported as frequencies and percentages. Comparisons between T2DM patients with and without hypertension were conducted using the independent samples t-test or Mann-Witney test for continuous variables, depending on the distribution, and the Chi-square test or Fisher’s exact test for categorical variables. Multiple linear regression analysis was performed to assess the independent associations between potential predictive biomarkers (HbA1c, hsCRP, and AIP) and CVD risk scores derived from the 10-year WHO CVD risk assessment tool validated for the Western SSA region. The model adjusted for potential confounders, including age, duration of DM, and systolic BP. Standardized beta coefficients with corresponding p-values were used to determine the strength and direction of associations. Model performance was assessed using R-squared and ANOVA statistics. All statistical tests were two-tailed, with a p-value < 0.05 considered statistically significant. Results A total of 190 T2DM participants were included in this study out of which 125 had co-morbid hypertension. Table 1 summarizes participants' baseline socio-demographic, clinical, and biochemical characteristics by hypertension status. Compared to non-hypertensive T2DM patients, those with hypertension were significantly older (59.0 IQR: 13.5 vs. 52.0 IQR: 13.5 years, p < 0.001) and had a lower proportion of females (50.4% vs. 64.6%, p = 0.043). They also had a longer duration of diabetes (10.0 IQR: 10.0 vs. 3.0 IQR: 8.0 years, p < 0.001), higher systolic (130.0 IQR: 20.0 vs. 120.0 IQR: 10.0 mmHg, p < 0.001) and diastolic blood pressure (80.0 IQR: 10.0 vs. 70.0 IQR: 10.0 mmHg, p < 0.001), and elevated hsCRP levels (2.57 IQR: 2.63 vs. 0.86 IQR: 1.72 mg/L, p < 0.001). Male hypertensive patients had lower HDL-C (29.0 IQR: 16.0 vs. 44.0 IQR: 9.0 mg/dL, p < 0.001), while AIP was significantly higher in the hypertensive group (0.071 IQR: 0.39 vs. 0.002 IQR: 0.34, p = 0.015). CVD risk scores were also markedly higher among hypertensive patients (11.3 ± 4.7 vs. 7.2 ± 4.1, p < 0.001). Additionally, non-hypertensive individuals were more likely to be managed with diet and lifestyle modifications alone (16.9% vs. 2.4%, p = 0.001). There were no significant differences between groups in ethnicity, occupational status, smoking, alcohol use, BMI, glycaemic control, or most lipid profile parameters. Table 1 Baseline characteristics of the study participants with type 2 diabetes mellitus stratified by hypertension Status. Participants Characteristics Non-hypertensive T2DM (N = 65) Mean (IQR), Mean ± SD or n(%) Hypertensive T2DM (N = 125) Mean (IQR), Mean ± SD or n(%) p-value Age (years) 52.0 (13.5) 59.0 (13.5) < 0.001* Sex Female 42 (64.6%) 63 (50.4%) 0.043* Male 23 (35.4%) 62 (49.6%) Ethnicity/Tribe Idoma 17 (26.2%) 21 (16.8%) 0.333 Igbo 13 (20.0%) 18 (14.4%) Igede 2 (3.1%) 3 (2.4%) Tiv 26 (40.0%) 63 (50.4%) Others 7 (10.8%) 20 (16.0%) Occupational Status Employed 16 (24.6%) 50 (40.0%) 0.132 Self-employed 25 (38.5%) 32 (25.6%) Unemployed 11 (16.9%) 17 (13.6%) Retired 13 (20.0%) 26 (20.8%) Smoking Yes 3 (4.6%) 11 (8.8%) 0.388 No 62 (95.4%) 114 (91.2%) Alcohol Consumption: Yes 8 (12.3%) 10 (8.0%) 0.501 No 57 (87.7%) 115 (92.0%) BMI (kg/m²) 27.51 (6.8) 26.89 (7.7) 0.268 Duration of Diabetes (years) 3.0 (8.0) 10.0 (10.0) < 0.001* Blood Pressure: Systolic BP (mmHg) 120.0 (10.0) 130.0 (20.0) < 0.001* Diastolic BP (mmHg) 70.0 (10.0) 80.0 (10.0) < 0.000* Glycaemic control: FBG (mmol/L) 7.0 (3.3) 7.0 (3.8) 0.987 HbA1c (%) 7.0 (3.0) 7.3 (3.0) 0.413 Lipid profile: Triglycerides 99.0 (76.0) 102.0 (60.0) 0.905 Total Cholesterol (mg/dL) 189.0 (60) 189.0 (63) 0.315 HDL-C (mg/dL) - Female 43.0 (13.0) 42.0 (12.0) 0.067 HDL-C (mg/dL) - Male 44.0 (9.0) 29 ( 16 ) < 0.001* LDL-C (mg/dL) 115.00 (64) 127.80 (62) 0.983 Non-HDL-C (mg/dL) - Female 148.0 (62.0) 164.0 (49.0) 0.117 Non-HDL-C (mg/dL) - Male 127.0 (60.0) 131.0 (68.0) 0.360 Atherogenic Index of Plasma (AIP) 0.002 (0.34) 0.071 (0.39) 0.015* hsCRP (mg/L) 0.86 (1.72) 2.57 (2.63) < 0.001* Diabetes management approach Diet/lifestyle modifications 11 (16.9%) 3 (2.4%) 0.001* Anti-diabetic drugs 54 (83.1%) 122 (97.6%) CVD Risk Scores 7.2 ± 4.1 11.3 ± 4.7 < 0.001* *p-value < 0.05 statistically significant; SD – Standard deviation, IQR – Interquartile range, n – Number within group. Among participants with co-morbid hypertension, 6.4% (n = 8) were classified as having very low CVD risk (< 5%), 33.6% (n = 42) as low risk (5–<10%), 51.2% (n = 64) as moderate risk (10–<20%), and 8.8% (n = 11) as high risk (≥ 20%) (Fig. 1 ). For analytical purposes, participants were grouped into low-risk (very low and low risk; 40.0%, n = 50) and moderate-to-high-risk (moderate and high risk; 60.0%, n = 75) categories based on WHO CVD risk classification. Table 2 presents the distribution of CVD risk stratified by socio-demographic and clinical characteristics. Moderate-to-high-risk patients were significantly older, with 78.7% aged ≥ 60 years compared to only 4.0% in the low-risk group (p < 0.001). Conversely, a greater proportion of low-risk individuals were aged 40–49 years (40.0% vs. 2.7%) or 50–59 years (56.0% vs. 18.7%). Longer diabetes duration was associated with increased CVD risk, as 84.0% of moderate-to-high-risk patients had diabetes for ≥ 5 years compared to 66.0% in the low-risk group (p = 0.030). No significant differences were observed between the CVD risk groups in terms of sex, obesity status, smoking, glycaemic control, hsCRP, AIP categories, or use of anti-hypertensive and lipid-lowering therapies. Table 2 Pattern of CVD risk among T2DM patients with hypertension, stratified by socio-demographic and clinical characteristics. Participants Characteristics Low CVD Risk (N = 50) Mean ± SD or n(%) Moderate-high CVD risk (N = 75) Mean ± SD or n(%) p-value Age Category: 40–49 years 20 (40.0%) 2 (2.7%) < 0.001* 50–59 years 28 (56.0%) 14 (18.7%) 60 years and above 2 (4.0%) 59 (78.7%) Sex Female 29 (58.0%) 34 (45.3%) 0.114 Male 21 (42.0%) 41 (54.7%) Body Weight Status Non-obese 33 (66.0%) 52 (69.3%) 0.701 Obese 17 (34.0%) 23 (30.7%) Duration of T2DM Short-term (< 5 years) 17 (34.0%) 12 (16.0%) 0.030* Long-term (≥ 5 years) 33 (66.0%) 63 (84.0%) Smoking Status No 48 (96.0%) 66 (88.0%) 0.197 Yes 2 (4.0%) 9 (12.0%) Glycaemic Control Well-controlled 14 (28.0%) 30 (40.0%) 0.186 Poorly controlled 36 (72.0%) 45 (60.0%) Systemic Inflammation (hsCRP) Low to moderate risk 26 (52.0%) 39 (52.0%) 0.901 High risk 24 (48.0%) 36 (48.0%) Atherogenic Index of Plasma (AIP) Low risk 25 (50.0%) 42 (56.0%) 0.797 Intermediate risk 8 (16.0%) 10 (13.3%) High risk 17 (34.0%) 23 (30.7%) Anti-hypertensive Drug Therapy None 5 (10.0%) 5 (6.7%) 0.519 ≥1 drug 45 (90.0%) 70 (93.3%) Anti-lipidemic Drug Therapy No 5 (10.0%) 2 (2.7%) 0.115 Yes 45 (90.0%) 73 (97.3%) *p-value < 0.05 statistically significant; SD – Standard deviation, n – Number within group. A multiple linear regression analysis was conducted to examine the independent associations of hsCRP, HbA1c, and AIP with CVD risk scores after adjusting for age, systolic BP, and duration of diabetes mellitus (DM). The model was statistically significant, F(6, 118) = 38.80, p < 0.001, and accounted for 66.4% of the variance in CVD risk scores (Adjusted R² = 0.647). In the model, age (β = 0.801, p < 0.001) and Systolic BP (β = 0.333, p < 0.001) were significantly and positively associated with CVD risk scores. However, no statistically significant association was found between CVD risk scores and hsCRP (β = 0.078, p = 0.152), HbA1c (β = 0.026, p = 0.649), and AIP (β = 0.023, p = 0.669). Discussion This study highlights a substantial burden of systemic inflammation, atherogenic risk, and elevated CVD risk among Nigerian patients with T2DM and co-morbid hypertension. Hypertensive T2DM patients demonstrated significantly higher levels of inflammatory and atherogenic markers, along with markedly increased CVD risk compared to their non-hypertensive counterparts. These findings reflect the additive pathophysiological burden that hypertension imposes in T2DM, likely through synergistic mechanisms such as endothelial dysfunction, oxidative stress, and chronic low-grade inflammation, processes that accelerate atherosclerosis and cardiovascular events.( 30 – 32 ) Similar trends have been reported in other populations, where systemic inflammation, atherogenic dyslipidaemia, and persistent hyperglycaemia have been independently linked to subclinical atherosclerosis and major adverse cardiovascular outcomes, particularly in individuals with overlapping metabolic conditions.( 33 – 35 ) A previous study reported that hypertension comorbidity in T2DM was linked to heightened inflammation and atherogenicity, as indicated by elevated markers of systemic inflammation and lipid-related risk among Africans.( 31 ) In the current study, although similar associations were observed, these biomarkers did not independently predict CVD risk in hypertensive T2DM patients beyond their predictive value in the overall T2DM population. Instead, traditional clinical factors, particularly age and systolic blood pressure, emerged as the most consistent predictors. This highlights the multifactorial nature of CVD in T2DM, where non-modifiable factors and chronic hemodynamic stress may outweigh the added prognostic value of individual biochemical markers. The predictive role of traditional clinical factors in this study aligns with both regional and global evidence. A Nigerian study similarly found that T2DM patients with hypertension had a higher CVD risk than those without, with blood pressure emerging as the primary driver rather than glycaemic control or lipid levels.( 10 ) On a global scale, findings from the ADVANCE and UKPDS trials further demonstrate that inadequate blood pressure control significantly contributes to cardiovascular morbidity in diabetes, often amplifying the impact of hyperglycaemia and dyslipidaemia.( 36 , 37 ) This may reflect the direct vascular burden imposed by elevated blood pressure, which exerts a more consistent and causal influence on CVD risk than systemic markers like hsCRP, whose predictive value may be limited by biological variability and non-specificity. Furthermore, this study evaluated the pattern of CVD risk within the cohort, revealing that over 60% of hypertensive participants had moderate-to-high CVD risk. This finding echoes global concerns, particularly those raised by the World Health Organization regarding the escalating cardiovascular burden in sub-Saharan Africa, a region facing the dual challenge of communicable and non-communicable diseases, compounded by limited healthcare resources.( 38 , 39 ) Notably, Africa accounts for only 1.3% of global diabetes-related health expenditure, yet it bears approximately 4.5% of the global diabetes burden. Alarmingly, projections indicate a 129% increase in the number of adults with diabetes in Africa by 2045, rising from 24 million in 2021 to 55 million, the highest percentage increase among all International Diabetes Federation regions.( 40 ) The clustering of advanced age and longer diabetes duration among individuals with moderate-to-high CVD risk emphasizes the importance of early and proactive cardiovascular risk stratification in T2DM. As patients age or develop complications, risk reduction becomes increasingly challenging,( 41 ) making timely intervention essential. Notably, a significant proportion of non-hypertensive patients in this study were managed with lifestyle modification alone, highlighting the need to prioritise normotension through dietary counselling, physical activity, and regular monitoring. These findings support integrating comprehensive cardiovascular screening into primary diabetes care and emphasize blood pressure control as a cornerstone of risk mitigation in sub-Saharan Africa, while also exploring other potential markers. This study has some limitations that should be considered when interpreting the findings. Firstly, the cross-sectional design of the study prevents establishing causal relationships between hypertension, T2DM, and cardiovascular disease (CVD) risk. Additionally, the potential influence of other confounding factors, such as specific medications, diet, and physical activity levels, which could have affected both the biochemical markers and CVD risk, was not addressed. The single-center nature of the study also limits the generalizability of the results to broader populations or different geographical regions. Finally, the exclusion of patients under 40 years of age due to the lack of validation of the WHO CVD risk tool for this age group, also means that younger individuals were not represented, limiting the applicability of the findings to this demographic. Conclusion This study evaluated markers of glycaemic control, systemic inflammation, and lipid-driven atherogenicity in the study population, and their association with CVD risk stratification. Although elevated hsCRP and AIP levels were observed in higher CVD risk categories, they did not independently predict CVD risk; instead, age and systolic blood pressure were the primary determinants. These findings underline the clinical importance of early cardiovascular risk assessment and strict blood pressure control in the management of T2DM. Future longitudinal, multicentre studies involving a broader age range and accounting for lifestyle and pharmacologic confounders are recommended to validate these results and guide targeted risk reduction strategies in in resource-limited settings. Abbreviations T2DM; type 2 diabetes mellitus, SSA; sub-Saharan Africa, CVD; cardiovascular disease, HbA1c; glycated haemoglobin, hsCRP; high-sensitivity C-reactive protein, AIP; atherogenic index of plasma, LDL-C; low-density lipoprotein cholesterol, HDL-C; high-density lipoprotein cholesterol, BMI; body mass index, FPG; fasting plasma glucose, WHO; World Health Organization, BSUTH: Benue State University Teaching Hospital, BP; blood pressure, SD: Standard Deviation, IQR; inter-quartile range. Declarations Consent for publication Not Applicable in this study. Availability of Data and Materials 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. Clinical trial number: Not applicable Ethics Approval Declaration: This study was conducted as part of a larger research project approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi, under protocol number BSUTH/CMAC/HREC/101/V.I/47, dated 21st January 2019. Ethical conduct was ensured per the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants before enrolment and data collection. To maintain confidentiality, each participant was assigned a unique identification code, and all clinical data and laboratory results were securely stored in restricted areas throughout the study. Human Ethics and Consent to Participate declarations: Not applicable Competing Interests The authors declare that they have no conflict of interest. FundingTop of Form This research did not receive any dedicated funding from a public, commercial, or not-for-profit agency. Authors’ contributions All authors collaborated on this research project. BB and JAM contributed to the study design, data analysis, and interpretation of results. INM, BKM and IMN were involved in data collection, interpretation, and manuscript drafting. All authors participated in drafting and critically revising the manuscript. They collectively approved the final version for publication and accepted responsibility for all aspects of the work. Acknowledgements 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. References World Health Organization. Noncommunicable diseases [Internet], Geneva WHO. 2024 [cited 2025 Apr 27]. 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Hypertension among persons with type 2 diabetes and its related demographic, socioeconomic and lifestyle factors in the Fasa cohort study. Sci Rep. 2024;14(1):18892. 10.1038/s41598-024-69062-7 . Mohammed JA, Basil B, Mba IN, Abubakar ND, Lawal AO, Momoh JA, et al. Elevated high-sensitivity C-reactive protein and dyslipidaemia in type 2 diabetes mellitus: implications for cardiovascular risk prediction in Nigerian patients. BMC Endocr Disord. 2025;25(1):100. 10.1186/s12902-025-01930-3 . Lu X, Xie Q, Pan X, Zhang R, Zhang X, Peng G, et al. Type 2 diabetes mellitus in adults: pathogenesis, prevention and therapy. Signal Transduct Target Ther. 2024;9(1):262. 10.1038/s41392-024-01951-9 . Caturano A, Rocco M, Tagliaferri G, Piacevole A, Nilo D, Di Lorenzo G, et al. Oxidative Stress and Cardiovascular Complications in Type 2 Diabetes: From Pathophysiology to Lifestyle Modifications. Antioxidants. 2025;14(1):72. 10.3390/antiox14010072 . Pellegrini V, La Grotta R, Carreras F, Giuliani A, Sabbatinelli J, Olivieri F, et al. Inflammatory Trajectory of Type 2 Diabetes: Novel Opportunities for Early and Late Treatment. Cells. 2024;13(19):1662. 10.3390/cells13191662 . Basil B, Mohammed JA, Mba IN. Hypertension in Type 2 Diabetes Mellitus: Prevalence, Patterns, Determinants and Implications for cardiovascular risk prediction in Nigerian patients. 2024, PREPRINT (Version 1) available at Research Square [ https://doi.org/10.21203/rs.3.rs-5476606/v1] Zhao M, Xiao M, Zhang H, Tan Q, Ji J, Cheng Y, et al. Relationship between plasma atherogenic index and incidence of cardiovascular diseases in Chinese middle-aged and elderly people. Sci Rep. 2025;15(1):8775. 10.1038/s41598-025-86213-6 . Zeng Q, Zhong Q, Zhao L, An Z, Li S. Combined effect of triglyceride-glucose index and atherogenic index of plasma on cardiovascular disease: a national cohort study. Sci Rep. 2024;14(1):31092. 10.1038/s41598-024-82305-x . Cao J, Su Z, Yang J, Zhang B, Jiang R, Lu W, Huang Z, Xie Z. The atherogenic index of plasma is associated with an increased risk of diabetes in non-obese adults: a cohort study. Front Endocrinol. 2025;15:1477419. 10.3389/fendo.2024.1477419 . Dąbrowska E, Narkiewicz K. Hypertension and Dyslipidemia: the Two Partners in Endothelium-Related Crime. Curr Atheroscler Rep. 2023;25(9):605–12. 10.1007/s11883-023-01132-z . Zhang X, Zhao S, Huang Y, Ma M, Li B, Li C, et al. Diabetes-Related Macrovascular Complications Are Associated with an Increased Risk of Diabetic Microvascular Complications: A Prospective Study of 1518 Patients with Type 1 Diabetes and 20 802 Patients with Type 2 Diabetes in the UK Biobank. J Am Heart Assoc. 2024;13(11):e032626. 10.1161/JAHA.123.032626 . Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations - Comparative analysis of risk algorithms in the RODAM study. Int J Cardiol. 2018;254:310–5. 10.1016/j.ijcard.2017.11.082 . Ofori S, Wachukwu C. Awareness and attitudes towards total cardiovascular disease risk assessment in clinical practice among physicians in Southern Nigeria. Nigerian Health J. 2016;16(1):1–12. 10.60787/tnhj.v16i1.191 . Bendera A, Nakamura K, Seino K, Alemi S. Performance of the non-laboratory based 2019 WHO cardiovascular disease risk prediction chart in Eastern Sub-Saharan Africa. Nutr Metab Cardiovasc Dis. 2024;34(6):1448–55. 10.1016/j.numecd.2024.01.026 . Li Y, Luo J, Bao K, Wei Q, Wang X, Chen J, et al. Association of age at diagnosis of type 2 diabetes mellitus with the risks of the morbidity of cardiovascular disease, cancer and all-cause mortality: Evidence from a real-world study with a large population-based cohort study. Diabetes Res Clin Pract. 2024;217:111870. 10.1016/j.diabres.2024.111870 . Parums DV, Editorial. The 2024 Revision of the Declaration of Helsinki and its Continued Role as a Code of Ethics to Guide Medical Research. Med Sci Monit. 2024;30:e947428. 10.12659/MSM.947428 . 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. Bhardwaj R, Agrawal U, Vashist P, Manna S. Determination of sample size for various study designs in medical research: A practical primer. J Family Med Prim Care. 2024;13(7):2555–61. 10.4103/jfmpc.jfmpc_1675_23 . 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. Front Cardiovasc Med. 2023;10:1044638. 10.3389/fcvm.2023.1044638 . Khanna D, Peltzer C, Kahar P, Parmar MS. Body Mass Index (BMI): A Screening Tool Analysis. Cureus. 2022;14(2):e22119. 10.7759/cureus.22119 . Askin L, Tanriverdi O. Is the Atherogenic Index of Plasma (AIP) a Cardiovascular Disease Marker? Cor et Vasa. 2023;65:100–3. Wicaksana A, Rachman T, editors. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics. 6th ed. St. Louis (MO): Elsevier; 2018. Huang H, Yu Y, Chen L, Liling C, Shiqun C, Ronghui T, Qiang L, et al. Independent and joint effects of high-sensitivity c-reactive protein and hypoalbuminemia on long-term all-cause mortality among coronary artery disease: a prospective and multi-center cohort study. BMC Cardiovasc Disord. 2021;21:613. 10.1186/s12872-021-02431-6 . Mba IN, Basil B, Olayanju OA, Okpara IC, Adebisi SA. Serum High Sensitivity C-Reactive Protein and Uric Acid as Biomarkers of Left Ventricular Hypertrophy in Hypertensive Patients in Makurdi, Nigeria. Afr J Biomed Res. 2023;26:25–9. 10.4314/ajbr.v26i1.3 . WHO CVD Risk Chart Working Group. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7(10):e1332–45. 10.1016/S2214-109X(19)30318-3 . Chakraborty S, Verma A, Garg R, Singh J, Verma H. Cardiometabolic Risk Factors Associated with Type 2 Diabetes Mellitus: A Mechanistic Insight. Clin Med Insights Endocrinol Diabetes. 2023;16:11795514231220780. 10.1177/11795514231220780 . Groenewald EJ, Nkambule BB, Nyambuya TM. Aggravated Systemic Inflammation and Atherogenicity in African Patients Living with Type 2 Diabetes and Hypertension Comorbidity. Clin Med Insights Endocrinol Diabetes. 2024;17:1–7. 10.1177/11795514241263298 . Yousef H, Khandoker AH, Feng SF, Helf C, Jelinek HF. Inflammation, oxidative stress and mitochondrial dysfunction in the progression of type II diabetes mellitus with coexisting hypertension. Front Endocrinol. 2023;14:1173402. 10.3389/fendo.2023.1173402 . Koziarska-Rościszewska M, Gluba-Brzózka A, Franczyk B, Rysz J, High-Sensitivity C-R. Protein Relationship with Metabolic Disorders and Cardiovascular Diseases Risk Factors. Life (Basel). 2021;11(8). 10.3390/life11080742 . Qin Z, Zhou K, Li Y, Cheng W, Wang Z, Wang J, et al. The atherogenic index of plasma plays an important role in predicting the prognosis of type 2 diabetic subjects undergoing percutaneous coronary intervention: results from an observational cohort study in China. Cardiovasc Diabetol. 2020;19(1):23. 10.1186/s12933-020-0989-8 . Ménégaut L, Laubriet A, Crespy V, Leleu D, Pilot T, Van Dongen K, et al. Inflammation and oxidative stress markers in type 2 diabetes patients with Advanced Carotid atherosclerosis. Cardiovasc Diabetol. 2023;22(1):248. 10.1186/s12933-023-01979-1 . Wang N, Chalmers J, Harris K, Poulter N, Mancia G, Harrap S, et al. Combination blood pressure lowering therapy in patients with type 2 diabetes: messages from the ADVANCE trial. J Hypertens. 2024;42(12):2055–64. 10.1097/HJH.0000000000003855 . Vargas-Uricoechea H, Cáceres-Acosta MF. Control of Blood Pressure and Cardiovascular Outcomes in Type 2 Diabetes. Open Med (Warsaw). 2018;13:304–23. 10.1515/med-2018-0048 . World Health Organization. Noncommunicable Diseases [Internet]. 2025 [cited 2025 Apr 30]. Available from: https://www.afro.who.int/health-topics/noncommunicable-diseases Alhuneafat L, Ta’ani O, Al, Tarawneh T, ElHamdani A, Al-Adayleh R, Al-Ajlouni Y, et al. Burden of cardiovascular disease in Sub-Saharan Africa, 1990–2019: An analysis of the Global Burden of Disease Study. Curr Probl Cardiol. 2024;49(6):102557. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183:109119. 10.1016/j.diabres.2021.109119 . Goyal P, Kwak MJ, Al Malouf C, Kumar M, Rohant N, Damluji AA, et al. Geriatric Cardiology: Coming of Age. JACC Adv. 2022;1(3):100070. 10.1016/j.jacadv.2022.100070 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6626378","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459413449,"identity":"a2dd1bc0-c78e-4299-9902-a656c0a65394","order_by":0,"name":"Bruno Basil","email":"","orcid":"","institution":"David Umahi Federal University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bruno","middleName":"","lastName":"Basil","suffix":""},{"id":459413450,"identity":"0101804f-73d1-4ec1-ad43-a02c1b561e5d","order_by":1,"name":"Jamila Aminu Mohammed","email":"","orcid":"","institution":"Nile University of Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Jamila","middleName":"Aminu","lastName":"Mohammed","suffix":""},{"id":459413451,"identity":"1903b5c8-35ce-41ac-9e45-bc0d68c2f621","order_by":2,"name":"Izuchukwu Nnachi Mba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDACCQaGA0AqgYEHSH4AYjZ2UrQwzgBpYSZCCwNMCzPIJgZCWnRnNz88+LPNLs/gzBkzaZtf2+T5mBkYP3zMwa3F7M4xgwOSbcnFBmd7zKRz+24btjEzMEvO3IZHy40EgwOGbQcSN5zn3Sad23ObEaiFjZkXr5b0DwcSYVose27bE6Elx+DAQZCWs73bpBl+3E4krOXOmYKDDeeSE2eeOf/ZsrfhdnIbM2Mzfr/cbt/88UeZXWLfmbTEGz/+3Lad39588MNHPFpQAWMbmGwgVj0I/CFF8SgYBaNgFIwUAABiolmwf5mf+QAAAABJRU5ErkJggg==","orcid":"","institution":"Nile University of Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Izuchukwu","middleName":"Nnachi","lastName":"Mba","suffix":""},{"id":459413452,"identity":"290bb915-cdd1-422e-ab98-52894576dd63","order_by":3,"name":"Isiaku Mary Nkemakolam","email":"","orcid":"","institution":"Alex Ekwueme Federal University","correspondingAuthor":false,"prefix":"","firstName":"Isiaku","middleName":"Mary","lastName":"Nkemakolam","suffix":""},{"id":459413453,"identity":"37965c45-6d0f-41f6-8fa1-4b65a19e0570","order_by":4,"name":"Blessing Kenechi Myke-Mbata","email":"","orcid":"","institution":"Benue State University","correspondingAuthor":false,"prefix":"","firstName":"Blessing","middleName":"Kenechi","lastName":"Myke-Mbata","suffix":""}],"badges":[],"createdAt":"2025-05-09 07:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6626378/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6626378/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12902-025-02020-0","type":"published","date":"2025-08-27T15:56:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83280576,"identity":"974eb4e1-030f-44dd-821e-a4ad6a18de8b","added_by":"auto","created_at":"2025-05-22 10:11:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15710,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of 10-year WHO cardiovascular disease (CVD) risk categories among participants with co-morbid hypertension, showing the proportions classified as very low, low, moderate, and high risk.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6626378/v1/a2d926c6414b99ab5de451a4.png"},{"id":90344831,"identity":"99d8d085-b15e-44bc-b484-91091fea145e","added_by":"auto","created_at":"2025-09-01 16:04:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924887,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6626378/v1/ecc521d7-3a25-4c8f-bafc-d185d3a2c2f5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension","fulltext":[{"header":"Introduction","content":"\u003cp\u003eType 2 diabetes mellitus (T2DM) and hypertension represent two of the most prevalent and interlinked non-communicable diseases globally, with sub-Saharan Africa (SSA) experiencing a rapidly escalating burden due to urbanization, dietary transitions, and sedentary lifestyles(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Recent estimates suggest that over 24\u0026nbsp;million people in SSA are living with diabetes, a figure projected to rise by 129% to 55\u0026nbsp;million by 2045, while hypertension affects about 48% of women and 34% of men in the region.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The dual burden of T2DM and hypertension substantially contributes to the region\u0026rsquo;s rising morbidity and mortality from cardiovascular disease (CVD), which remains the leading cause of death among individuals with diabetes.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eInsulin resistance in T2DM mediates an increased risk of cardiovascular events through a complex interplay of chronic hyperglycaemia, elevated systemic inflammation, and dyslipidaemia.(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) Prolonged dysglycaemia, a hallmark of poorly controlled T2DM, is known to initiate and propagate vascular injury through multiple mechanisms, including oxidative stress, endothelial dysfunction, and the activation of pro-inflammatory pathways.(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) On the other hand, high-sensitivity C-reactive protein (hsCRP), a well-established biomarker of systemic inflammation, is frequently elevated in T2DM and independently predicts CVD events. Chronic low-grade inflammation enhanced by insulin resistance accelerates atherosclerotic plaque formation, further compounding cardiovascular risk(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Notably, the superimposition of hypertension on T2DM further magnifies this risk, placing individuals with co-morbid hypertension at an even higher risk for adverse cardiovascular outcomes.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn parallel, lipid-driven atherogenicity, particularly reflected by the atherogenic index of plasma (AIP), a logarithmic ratio of triglycerides to high-density lipoprotein cholesterol, has emerged as a potent surrogate marker of small, dense low-density lipoprotein particles.(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) Elevated AIP is strongly associated with atherosclerosis and heightened CVD risk, especially in insulin-resistant states.(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) When compounded by co-morbid hypertension, which induces hemodynamic stress and vascular remodelling, the synergistic effect on cardiovascular pathology becomes even more pronounced.(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Hypertension in T2DM patients not only accelerates the progression of micro- and macrovascular complications but also alters the trajectory of lipid metabolism and inflammatory responses, creating a complex interplay that necessitates stratified risk assessment.(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) These observations inform the hypothesis that markers of systemic inflammation and atherogenicity may serve as useful predictors of cardiovascular risk in this population.\u003c/p\u003e \u003cp\u003eThere is a paucity of studies in Africa assessing the combined influence of chronic hyperglycaemia, systemic inflammation, and lipid-driven atherogenicity on cardiovascular risk stratification among T2DM patients with co-morbid hypertension, a high-risk group, despite the known role of these factors in CVD pathogenesis. This emphasizes the need for further investigation, particularly in most SSA settings where CVD risk stratification tools are underutilized or poorly validated.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) Moreover, existing risk assessment tools often neglect the additive effects of these biomarkers particularly in this category of patients, limiting both their predictive performance and clinical relevance. Tailored approaches that incorporate biochemical markers alongside locally validated tools such as the WHO 10-year CVD risk chart are required, as this may foster the development of region-specific CVD risk assessment tools that address limitations of current models, including age restrictions.\u003c/p\u003e \u003cp\u003eThis study evaluated markers of glycaemic control, systemic inflammation, and lipid-driven atherogenicity among Nigerian T2DM patients with co-morbid hypertension, and examined how these markers are associated with CVD risk stratification. By focusing on this high-risk population, the study sought to provide evidence that may inform targeted strategies for early CVD risk identification and proactive intervention, especially given that risk reduction becomes increasingly challenging with prolonged diabetes duration and advancing age.(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis hospital-based study was conducted over 13 months (October 2019 to October 2020) at the endocrinology clinic of the medical outpatient department of Benue State University Teaching Hospital (BSUTH) in Makurdi, North-central Nigeria. It is recognized as a leading referral centre for Benue and the neighbouring states, offering specialized medical services through diverse clinical specialities. The study employed a cross-sectional analytical design to investigate HbA1c, hsCRP and AIP levels and their associations with CVD risk among known T2DM patients with co-morbid hypertension.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e This study was conducted as part of a larger research project approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi, under protocol number BSUTH/CMAC/HREC/101/V.I/47, dated 21st January 2019. Ethical conduct was ensured per the principles outlined in the Declaration of Helsinki.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Written informed consent was obtained from all participants before enrolment and data collection. To maintain confidentiality, each participant was assigned a unique identification code, and all clinical data and laboratory results were securely stored in restricted areas throughout the study.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination and Participant Selection\u003c/h3\u003e\n\u003cp\u003eThe sample size was determined using a reported hypertension prevalence of 32.1% among individuals with T2DM in Nigeria,(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) with a 5% margin of error and 95% confidence level. Using Fisher\u0026rsquo;s formula for cross-sectional studies with adjustments for a finite population and 10% non-response rate,(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) a minimum of 186 participants was required to ensure sufficient power to detect associations between HbA1c, hsCRP, AIP, and cardiovascular risk categories in hypertensive T2DM patients. However, 190 participants were recruited using a convenience sampling method, with eligible individuals consecutively enrolled during routine clinic visits at BSUTH.\u003c/p\u003e \u003cp\u003ePotential participants were identified via clinic records and approached by trained researchers. Following an initial eligibility screening with available medical records, the study included adult patients aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years with a documented diagnosis of T2DM with and without hypertension who were receiving specialized care at the BSUTH medical outpatient clinic for at least 6 months before enrolment during the period of the study. Exclusion criteria included type 1 diabetes mellitus, gestational diabetes, and other identifiable secondary causes of hypertension (e.g., chronic kidney disease, adrenal pathology, or medication-induced hypertension). Additionally, participants with incomplete clinical or laboratory data necessary for analysis were also excluded.\u003c/p\u003e\n\u003ch3\u003eData Collection and Interpretation\u003c/h3\u003e\n\u003cp\u003eData on participants\u0026rsquo; socio-demographic characteristics, medical history, medication use, and clinical parameters were collected using a structured research proforma. Blood pressure was measured in mmHg using an AccuSure mercury sphygmomanometer, with participants seated upright and the cuff positioned at chest level. Two readings of systolic and diastolic blood pressure (BP) were taken at 5-minute intervals, and the average was recorded. Hypertension was defined as Systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or current use of antihypertensive medications.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) Anthropometric measurements were obtained using a SECA scale with height attachment. Body mass index (BMI) was then calculated as weight in kg divided by the square of the height in m\u0026sup2; (kg/m\u0026sup2;) and values\u0026thinsp;\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eVenous blood samples were collected for biochemical analyses, including fasting plasma glucose (FPG), total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), HbA1c, hsCRP, and calculation of low-density lipoprotein cholesterol (LDL-C), non-HDL cholesterol and atherogenic index of plasma (AIP). The AIP was calculated as Log\u003csub\u003e10\u003c/sub\u003e(triglycerides/HDL-C), with values\u0026thinsp;\u0026lt;\u0026thinsp;0.11, 0.11\u0026ndash;0.21, and \u0026gt;\u0026thinsp;0.21 indicating low, moderate, and high atherogenic risk, respectively.(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) Glycaemic control was defined as well-controlled if HbA1c\u0026thinsp;\u0026lt;\u0026thinsp;7% and poorly controlled if HbA1c\u0026thinsp;\u0026ge;\u0026thinsp;7%.(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) Also, systemic inflammation was considered elevated if hsCRP\u0026thinsp;\u0026gt;\u0026thinsp;3 mg/L.(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eCardiovascular risk was assessed using the WHO 10-year laboratory-based CVD risk prediction chart validated for Western sub-Saharan African populations. Risk categories included very low (\u0026lt;\u0026thinsp;5%), low (5%\u0026ndash;\u0026lt;10%), moderate (10%\u0026ndash;\u0026lt;20%), and high (\u0026ge;\u0026thinsp;20%), based on age, sex, Systolic BP, smoking status, diabetes status, and total cholesterol.(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) This was re-classified as \u0026ldquo;low risk\u0026rdquo; (\u0026lt;\u0026thinsp;10%) and moderate-high risk (\u0026ge;\u0026thinsp;10%), a framework that enabled the structured evaluation of CVD risk among T2DM patients with co-morbid hypertension in the study population.\u003c/p\u003e\n\u003ch3\u003eSample Collection and Assays\u003c/h3\u003e\n\u003cp\u003e Blood samples were drawn from participants using aseptic techniques after an overnight fast of at least 8 hours into five-millilitre vacutainers. Samples for FPG were collected into a fluoride oxalate container and HbA1c into an EDTA container, while samples for lipid profile and hs-CRP were collected into lithium heparin containers to ensure accurate and reliable results. Samples for FPG, lipid profile and hsCRP were centrifuged at 5000 revolutions per minute (rpm) for 5 minutes after collection and the plasma/serum samples were aliquoted in plain cryovial tubes. Glucose, HbA\u003csub\u003e1c\u003c/sub\u003e, and lipid profile (Total cholesterol, HDL-cholesterol, and Triglyceride) were assayed immediately, thereafter LDL-cholesterol and non-HDL-cholesterol levels were calculated. Heparinised plasma for hsCRP was aliquoted and stored at -25\u0026deg;C for up to three months before batch analysis. All assays were conducted using the Cobas c311\u0026reg; automated analyzer (Roche Diagnostics, Mannheim, Germany).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using IBM SPSS Statistics version 25 (IBM Corp., Armonk, NY, USA) and Microsoft Excel. Derived variables, such as the atherogenic index of plasma (AIP) were computed in Microsoft Excel using logical functions and subsequently exported to SPSS for further analysis. Descriptive statistics were used to summarize the demographic and clinical characteristics of the study population. The normality of continuous variables was assessed using the Shapiro-Wilk test. Based on the distribution, continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (inter-quartile range), while categorical variables were reported as frequencies and percentages. Comparisons between T2DM patients with and without hypertension were conducted using the independent samples t-test or Mann-Witney test for continuous variables, depending on the distribution, and the Chi-square test or Fisher\u0026rsquo;s exact test for categorical variables.\u003c/p\u003e \u003cp\u003eMultiple linear regression analysis was performed to assess the independent associations between potential predictive biomarkers (HbA1c, hsCRP, and AIP) and CVD risk scores derived from the 10-year WHO CVD risk assessment tool validated for the Western SSA region. The model adjusted for potential confounders, including age, duration of DM, and systolic BP. Standardized beta coefficients with corresponding p-values were used to determine the strength and direction of associations. Model performance was assessed using R-squared and ANOVA statistics. All statistical tests were two-tailed, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 190 T2DM participants were included in this study out of which 125 had co-morbid hypertension. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes participants' baseline socio-demographic, clinical, and biochemical characteristics by hypertension status. Compared to non-hypertensive T2DM patients, those with hypertension were significantly older (59.0 IQR: 13.5 vs. 52.0 IQR: 13.5 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had a lower proportion of females (50.4% vs. 64.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043). They also had a longer duration of diabetes (10.0 IQR: 10.0 vs. 3.0 IQR: 8.0 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), higher systolic (130.0 IQR: 20.0 vs. 120.0 IQR: 10.0 mmHg, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and diastolic blood pressure (80.0 IQR: 10.0 vs. 70.0 IQR: 10.0 mmHg, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and elevated hsCRP levels (2.57 IQR: 2.63 vs. 0.86 IQR: 1.72 mg/L, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Male hypertensive patients had lower HDL-C (29.0 IQR: 16.0 vs. 44.0 IQR: 9.0 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while AIP was significantly higher in the hypertensive group (0.071 IQR: 0.39 vs. 0.002 IQR: 0.34, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). CVD risk scores were also markedly higher among hypertensive patients (11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 vs. 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, non-hypertensive individuals were more likely to be managed with diet and lifestyle modifications alone (16.9% vs. 2.4%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). There were no significant differences between groups in ethnicity, occupational status, smoking, alcohol use, BMI, glycaemic control, or most lipid profile parameters.\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\u003eBaseline characteristics of the study participants with type 2 diabetes mellitus stratified 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=\"left\" 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\u003eParticipants Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-hypertensive T2DM (N\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003cp\u003eMean (IQR), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertensive T2DM\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003cp\u003eMean (IQR), 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\u003e52.0 (13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0 (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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e42 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043*\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\u003e23 (35.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62 (49.6%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e17 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.333\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\u003e13 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (14.4%)\u003c/p\u003e \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\u003eIgede\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.4%)\u003c/p\u003e \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\u003eTiv\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (50.4%)\u003c/p\u003e \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\u003eOthers\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (16.0%)\u003c/p\u003e \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\u003eOccupational Status\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\u003eEmployed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.132\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\u003e25 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (25.6%)\u003c/p\u003e \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\u003eUnemployed\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (13.6%)\u003c/p\u003e \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\u003eRetired\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (20.8%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e3 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.388\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\u003e62 (95.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (91.2%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e8 (12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.501\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 (87.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (92.0%)\u003c/p\u003e \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\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.51 (6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.89 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\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 (8.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.0 (10.0)\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\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 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.0 (20.0)\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\u003e70.0 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000*\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\u003eFBG (mmol/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.0 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.0 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.987\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 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.3 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.413\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 (76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102.0 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.905\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\u003e189.0 (60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e189.0 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.315\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHDL-C (mg/dL) - Female\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.0 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.0 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHDL-C (mg/dL) - Male\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.0 (9.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\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\u003eLDL-C (mg/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e115.00 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.80 (62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.983\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) - Female\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148.0 (62.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e164.0 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\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) - Male\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e127.0 (60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.0 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAtherogenic Index of Plasma (AIP)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002 (0.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.071 (0.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehsCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86 (1.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.57 (2.63)\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e11 (16.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\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\u003e54 (83.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (97.6%)\u003c/p\u003e \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\u003eCVD Risk Scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant; SD \u0026ndash; Standard deviation, IQR \u0026ndash; Interquartile range, n \u0026ndash; Number within group.\u003c/p\u003e \u003cp\u003eAmong participants with co-morbid hypertension, 6.4% (n\u0026thinsp;=\u0026thinsp;8) were classified as having very low CVD risk (\u0026lt;\u0026thinsp;5%), 33.6% (n\u0026thinsp;=\u0026thinsp;42) as low risk (5\u0026ndash;\u0026lt;10%), 51.2% (n\u0026thinsp;=\u0026thinsp;64) as moderate risk (10\u0026ndash;\u0026lt;20%), and 8.8% (n\u0026thinsp;=\u0026thinsp;11) as high risk (\u0026ge;\u0026thinsp;20%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For analytical purposes, participants were grouped into low-risk (very low and low risk; 40.0%, n\u0026thinsp;=\u0026thinsp;50) and moderate-to-high-risk (moderate and high risk; 60.0%, n\u0026thinsp;=\u0026thinsp;75) categories based on WHO CVD risk classification. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of CVD risk stratified by socio-demographic and clinical characteristics. Moderate-to-high-risk patients were significantly older, with 78.7% aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years compared to only 4.0% in the low-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, a greater proportion of low-risk individuals were aged 40\u0026ndash;49 years (40.0% vs. 2.7%) or 50\u0026ndash;59 years (56.0% vs. 18.7%). Longer diabetes duration was associated with increased CVD risk, as 84.0% of moderate-to-high-risk patients had diabetes for \u0026ge;\u0026thinsp;5 years compared to 66.0% in the low-risk group (p\u0026thinsp;=\u0026thinsp;0.030). No significant differences were observed between the CVD risk groups in terms of sex, obesity status, smoking, glycaemic control, hsCRP, AIP categories, or use of anti-hypertensive and lipid-lowering therapies.\u003c/p\u003e \u003cp\u003e \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\u003ePattern of CVD risk among T2DM patients with hypertension, stratified by socio-demographic and clinical characteristics.\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\u003eParticipants Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow CVD Risk\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate-high CVD risk (N\u0026thinsp;=\u0026thinsp;75)\u003c/p\u003e \u003cp\u003eMean\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 Category:\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\u003e40\u0026ndash;49 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\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\u003e50\u0026ndash;59 years\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14 (18.7%)\u003c/p\u003e \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\u003e60 years and above\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59 (78.7%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e29 (58.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34 (45.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.114\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\u003e21 (42.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (54.7%)\u003c/p\u003e \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\u003eBody Weight Status\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\u003eNon-obese\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (69.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eObese\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (30.7%)\u003c/p\u003e \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\u003eDuration of T2DM\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\u003eShort-term (\u0026lt;\u0026thinsp;5 years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLong-term (\u0026ge;\u0026thinsp;5 years)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33 (66.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63 (84.0%)\u003c/p\u003e \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\u003eSmoking Status\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\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66 (88.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\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\u003e2 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (12.0%)\u003c/p\u003e \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\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\u003eWell-controlled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (28.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoorly controlled\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36 (72.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (60.0%)\u003c/p\u003e \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\u003eSystemic Inflammation (hsCRP)\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\u003eLow to moderate risk\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39 (52.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigh risk\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36 (48.0%)\u003c/p\u003e \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\u003eAtherogenic Index of Plasma (AIP)\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\u003eLow risk\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42 (56.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIntermediate risk\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10 (13.3%)\u003c/p\u003e \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\u003eHigh risk\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (30.7%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5 (6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026ge;1 drug\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70 (93.3%)\u003c/p\u003e \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\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=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003e5 (10.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.115\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\u003e45 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (97.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 statistically significant; SD \u0026ndash; Standard deviation, n \u0026ndash; Number within group.\u003c/p\u003e \u003cp\u003eA multiple linear regression analysis was conducted to examine the independent associations of hsCRP, HbA1c, and AIP with CVD risk scores after adjusting for age, systolic BP, and duration of diabetes mellitus (DM). The model was statistically significant, F(6, 118)\u0026thinsp;=\u0026thinsp;38.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and accounted for 66.4% of the variance in CVD risk scores (Adjusted R\u0026sup2; = 0.647). In the model, age (β\u0026thinsp;=\u0026thinsp;0.801, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Systolic BP (β\u0026thinsp;=\u0026thinsp;0.333, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly and positively associated with CVD risk scores. However, no statistically significant association was found between CVD risk scores and hsCRP (β\u0026thinsp;=\u0026thinsp;0.078, p\u0026thinsp;=\u0026thinsp;0.152), HbA1c (β\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.649), and AIP (β\u0026thinsp;=\u0026thinsp;0.023, p\u0026thinsp;=\u0026thinsp;0.669).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study highlights a substantial burden of systemic inflammation, atherogenic risk, and elevated CVD risk among Nigerian patients with T2DM and co-morbid hypertension. Hypertensive T2DM patients demonstrated significantly higher levels of inflammatory and atherogenic markers, along with markedly increased CVD risk compared to their non-hypertensive counterparts. These findings reflect the additive pathophysiological burden that hypertension imposes in T2DM, likely through synergistic mechanisms such as endothelial dysfunction, oxidative stress, and chronic low-grade inflammation, processes that accelerate atherosclerosis and cardiovascular events.(\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) Similar trends have been reported in other populations, where systemic inflammation, atherogenic dyslipidaemia, and persistent hyperglycaemia have been independently linked to subclinical atherosclerosis and major adverse cardiovascular outcomes, particularly in individuals with overlapping metabolic conditions.(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eA previous study reported that hypertension comorbidity in T2DM was linked to heightened inflammation and atherogenicity, as indicated by elevated markers of systemic inflammation and lipid-related risk among Africans.(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) In the current study, although similar associations were observed, these biomarkers did not independently predict CVD risk in hypertensive T2DM patients beyond their predictive value in the overall T2DM population. Instead, traditional clinical factors, particularly age and systolic blood pressure, emerged as the most consistent predictors. This highlights the multifactorial nature of CVD in T2DM, where non-modifiable factors and chronic hemodynamic stress may outweigh the added prognostic value of individual biochemical markers.\u003c/p\u003e \u003cp\u003eThe predictive role of traditional clinical factors in this study aligns with both regional and global evidence. A Nigerian study similarly found that T2DM patients with hypertension had a higher CVD risk than those without, with blood pressure emerging as the primary driver rather than glycaemic control or lipid levels.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) On a global scale, findings from the ADVANCE and UKPDS trials further demonstrate that inadequate blood pressure control significantly contributes to cardiovascular morbidity in diabetes, often amplifying the impact of hyperglycaemia and dyslipidaemia.(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) This may reflect the direct vascular burden imposed by elevated blood pressure, which exerts a more consistent and causal influence on CVD risk than systemic markers like hsCRP, whose predictive value may be limited by biological variability and non-specificity.\u003c/p\u003e \u003cp\u003eFurthermore, this study evaluated the pattern of CVD risk within the cohort, revealing that over 60% of hypertensive participants had moderate-to-high CVD risk. This finding echoes global concerns, particularly those raised by the World Health Organization regarding the escalating cardiovascular burden in sub-Saharan Africa, a region facing the dual challenge of communicable and non-communicable diseases, compounded by limited healthcare resources.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) Notably, Africa accounts for only 1.3% of global diabetes-related health expenditure, yet it bears approximately 4.5% of the global diabetes burden. Alarmingly, projections indicate a 129% increase in the number of adults with diabetes in Africa by 2045, rising from 24\u0026nbsp;million in 2021 to 55\u0026nbsp;million, the highest percentage increase among all International Diabetes Federation regions.(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe clustering of advanced age and longer diabetes duration among individuals with moderate-to-high CVD risk emphasizes the importance of early and proactive cardiovascular risk stratification in T2DM. As patients age or develop complications, risk reduction becomes increasingly challenging,(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) making timely intervention essential. Notably, a significant proportion of non-hypertensive patients in this study were managed with lifestyle modification alone, highlighting the need to prioritise normotension through dietary counselling, physical activity, and regular monitoring. These findings support integrating comprehensive cardiovascular screening into primary diabetes care and emphasize blood pressure control as a cornerstone of risk mitigation in sub-Saharan Africa, while also exploring other potential markers.\u003c/p\u003e \u003cp\u003eThis study has some limitations that should be considered when interpreting the findings. Firstly, the cross-sectional design of the study prevents establishing causal relationships between hypertension, T2DM, and cardiovascular disease (CVD) risk. Additionally, the potential influence of other confounding factors, such as specific medications, diet, and physical activity levels, which could have affected both the biochemical markers and CVD risk, was not addressed. The single-center nature of the study also limits the generalizability of the results to broader populations or different geographical regions. Finally, the exclusion of patients under 40 years of age due to the lack of validation of the WHO CVD risk tool for this age group, also means that younger individuals were not represented, limiting the applicability of the findings to this demographic.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study evaluated markers of glycaemic control, systemic inflammation, and lipid-driven atherogenicity in the study population, and their association with CVD risk stratification. Although elevated hsCRP and AIP levels were observed in higher CVD risk categories, they did not independently predict CVD risk; instead, age and systolic blood pressure were the primary determinants. These findings underline the clinical importance of early cardiovascular risk assessment and strict blood pressure control in the management of T2DM. Future longitudinal, multicentre studies involving a broader age range and accounting for lifestyle and pharmacologic confounders are recommended to validate these results and guide targeted risk reduction strategies in in resource-limited settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eT2DM; type 2 diabetes mellitus, SSA; sub-Saharan Africa, CVD; cardiovascular disease, HbA1c; glycated haemoglobin, hsCRP; high-sensitivity C-reactive protein, AIP; atherogenic index of plasma, LDL-C; low-density lipoprotein cholesterol, HDL-C; high-density lipoprotein cholesterol, BMI; body mass index, FPG; fasting plasma glucose, WHO; World Health Organization, BSUTH: Benue State University Teaching Hospital, BP; blood pressure, SD: Standard Deviation, IQR; inter-quartile range.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable in this study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\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\n\u003cp\u003e\u003cem\u003eClinical trial number:\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics Approval Declaration:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted as part of a larger research project approved by the Health Research Ethics Committee of Benue State University Teaching Hospital, Makurdi, under protocol number BSUTH/CMAC/HREC/101/V.I/47, dated 21st January 2019. Ethical conduct was ensured per the principles outlined in the Declaration of Helsinki. Written informed consent was obtained from all participants before enrolment and data collection. To maintain confidentiality, each participant was assigned a unique identification code, and all clinical data and laboratory results were securely stored in restricted areas throughout the study.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHuman Ethics and Consent to Participate declarations:\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFundingTop of Form\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any dedicated funding from a public, commercial, or not-for-profit agency.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll authors collaborated on this research project.\u0026nbsp;BB and JAM contributed to the study design, data analysis, and interpretation of results. INM, BKM and IMN were involved in data collection, interpretation, and manuscript drafting.\u0026nbsp;All authors participated in drafting and critically revising the manuscript. They collectively approved the final version for publication and accepted responsibility for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Noncommunicable diseases [Internet], Geneva WHO. 2024 [cited 2025 Apr 27]. 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JACC Adv. 2022;1(3):100070. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacadv.2022.100070\u003c/span\u003e\u003cspan address=\"10.1016/j.jacadv.2022.100070\" 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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, co-morbid hypertension, cardiovascular disease risk, high-sensitivity C-reactive protein, atherogenic index of plasma","lastPublishedDoi":"10.21203/rs.3.rs-6626378/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6626378/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) remains a major cause of morbidity and mortality in patients with type 2 diabetes mellitus (T2DM), particularly when complicated by hypertension. This study evaluated markers of glycaemic control, systemic inflammation, and lipid-related atherogenicity, and their relationship with CVD risk among a population of Nigerian patients with T2DM and co-morbid hypertension.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis hospital-based cross-sectional analytical study was conducted over a period of 13 months among T2DM patients, including those with co-morbid hypertension. The 10-year estimated CVD risk was determined using the WHO CVD risk assessment chart validated for Western sub-Saharan Africa, while glycated haemoglobin (HbA1c), atherogenic index of plasma (AIP), and high-sensitivity C-reactive protein (hsCRP) were assessed as markers of glycaemic control, atherogenicity, and inflammation, respectively. Statistical analyses, including multivariable linear regression, 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\u003eHypertensive T2DM patients had significantly higher hsCRP (2.57 mg/L, IQR: 2.63 vs. 0.86 mg/L, IQR: 1.72; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AIP (0.071, IQR: 0.39 vs. 0.002, IQR: 0.34; p\u0026thinsp;=\u0026thinsp;0.015). They also had significantly higher mean WHO CVD risk scores (11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 vs. 7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with 60.0% (n\u0026thinsp;=\u0026thinsp;75) classified as moderate-to-high risk. Following multivariable analysis and adjustment for potential confounders, only age (β\u0026thinsp;=\u0026thinsp;0.801, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and systolic blood pressure (β\u0026thinsp;=\u0026thinsp;0.333, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were independently associated with CVD risk scores, while hsCRP (β\u0026thinsp;=\u0026thinsp;0.078, p\u0026thinsp;=\u0026thinsp;0.152), AIP (β\u0026thinsp;=\u0026thinsp;0.023, p\u0026thinsp;=\u0026thinsp;0.669), and HbA1c (β\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;=\u0026thinsp;0.649) were not significant predictors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated hsCRP and AIP levels are prevalent among hypertensive T2DM patients but may not serve as reliable predictors of 10-year estimated CVD risk, while age and blood pressure were the primary determinants. This highlights the importance of early risk stratification and optimal blood pressure control in T2DM management, especially in resource-limited settings. Longitudinal, multicentre studies are needed to validate these findings and inform targeted interventions across sub-Saharan African populations.\u003c/p\u003e","manuscriptTitle":"Beyond blood pressure and glucose: exploring potential biochemical predictors of cardiovascular disease risk in type 2 diabetes mellitus patients with co-morbid hypertension","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-22 10:11:25","doi":"10.21203/rs.3.rs-6626378/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T04:59:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-10T01:24:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-03T21:06:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-28T16:51:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-27T06:59:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"25527009282618438239120835021844881858","date":"2025-05-26T13:28:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315796077442140416143767477624219339126","date":"2025-05-21T19:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224991889022894521453421917362454157580","date":"2025-05-21T16:56:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199191210363981651235823328761954223459","date":"2025-05-20T14:59:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150187355225388289266023202199535143522","date":"2025-05-19T16:14:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-19T14:47:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-15T08:21:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T06:23:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T06:22:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-05-09T07:51:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine 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":"May 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-01T15:59:18+00:00","versionOfRecord":{"articleIdentity":"rs-6626378","link":"https://doi.org/10.1186/s12902-025-02020-0","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2025-08-27 15:56:52","publishedOnDateReadable":"August 27th, 2025"},"versionCreatedAt":"2025-05-22 10:11:25","video":"","vorDoi":"10.1186/s12902-025-02020-0","vorDoiUrl":"https://doi.org/10.1186/s12902-025-02020-0","workflowStages":[]},"version":"v1","identity":"rs-6626378","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6626378","identity":"rs-6626378","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}