Unveiling the rising Burden of Cardiovascular Disease among People Living with HIV/AIDS in a Sub-Saharan Country of Ghana

Unveiling the rising Burden of Cardiovascular Disease among People Living with HIV/AIDS in a Sub-Saharan Country of Ghana | 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 Unveiling the rising Burden of Cardiovascular Disease among People Living with HIV/AIDS in a Sub-Saharan Country of Ghana Mavis Donkor, Isaac Yaw Massey, Samuel Kofi Amponsah, Philip Amo-kodieh, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6693996/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cardiovascular disease (CVD) is emerging as a significant concern among people living with HIV/AIDS (PLWHA), due to both traditional and HIV-specific risk factors. However, there is inadequate research in Ghana to inform practice and policy decisions. This hospital-based cross-sectional study assessed CVD prevalence, risk factors, and mechanisms among 386 PLWHA in Ghana’s Ahafo Region using interviews, clinical assessments, and biomarkers. We found a 16.6% CVD prevalence, with hypertension being the most common condition and there were significant associations between dietary habits, physical activity, and CVD risk ( p < 0.001). Patients who consumed more than three meals at home per day had significantly higher odds of developing CVD (adjusted odds ratio [AOR] = 315.31, 95% confidence interval [CI]: 16.03–6200.62, p < 0.001). Engaging in moderate-intensity physical activity for 24–48 minutes was associated with lower odds of CVD (AOR = 0.10, 95% CI: 0.01–0.82, p < 0.05). This study contributes to understanding the multifaceted relationship between HIV, traditional CVD risk factors, and emerging pathways such as physical inactivity and diet. While Body Mass Index and blood glucose levels did not significantly correlate with CVD, higher High-density lipoprotein levels and lower triglyceride levels were protective against CVD ( p < 0.05). Our findings underscore the need for integrated HIV and CVD care, particularly in addressing modifiable lifestyle factors. Future research should explore longitudinal outcomes and expand to other regions to provide a broader understanding of CVD risk among PLWHA in Ghana. Cardiovascular Disease HIV/AIDS Risk Factors Prevalence Antiretroviral Therapy Sub-Saharan Africa Biomarkers Non-Communicable Diseases Figures Figure 1 Introduction Cardiovascular disease (CVD) is an increasing global health burden among people living with HIV/AIDS. By 2030, 84% of HIV-positive individuals are projected to have at least one non-communicable disease (NCD), up from 29% in 2010. Additionally, 28% of these individuals will likely have three or more NCDs, elevating CVD as a significant concern ( 1 ). Globally, 8.9 million people with HIV have hypertension, with 59.2% residing in sub-Saharan Africa ( 2 ). In Ghana, over 50% of individuals with HIV/AIDS are at moderate-to-high risk for developing CVD ( 3 ). These statistics highlight the pressing need to address the intersection of HIV and cardiovascular health. The link between HIV and increased CVD risk involves complex pathways, including chronic HIV-related inflammation and immune activation, alongside traditional risk factors like hypertension, dyslipidaemia, diabetes, and smoking ( 4 ). This interplay increases the prevalence of CVD in people with HIV, altering the disease's typical onset. To combat this dual burden, interventions by the Global Fund and the Ghana Ministry of Health include integrating NCD management into HIV care, training healthcare workers to manage CVD risk factors, and expanding access to antiretroviral therapy (ART). These initiatives also promote lifestyle changes, such as smoking cessation and healthy diets, among people with HIV. Despite these efforts, gaps persist in systematic CVD risk screening, long-term management of coexisting HIV and CVD, and adapting treatment protocols for this population's specific needs. A meta-analysis estimated a 61% increase in CVD risk for individuals with HIV ( 5 ). Notably, even those on ART face a two-fold increase in CVD risk compared to HIV-negative individuals and a 1.5-fold increase compared to untreated patients. Limitations in previous studies, including inadequate control groups and unmeasured confounding factors, may affect the accuracy of these estimates ( 6 ). Importantly, recent studies show that CVD risk persists in patients on suppressive ART, indicating a long-term impact on their health ( 4 ). Traditional CVD risk factors, such as high cholesterol, diabetes, smoking, and hypertension, have been more prevalent among people with HIV since the epidemic's early days ( 7 ). Smoking, in particular, is a significant preventable risk factor, with HIV-positive smokers losing more years to smoking than to HIV itself ( 8 ). Smoking cessation offers substantial risk reduction, with benefits increasing the longer one remains smoke-free ( 9 , 10 ). Furthermore, genetic predispositions, such as higher genetic risk scores based on 23 single-nucleotide polymorphisms (SNPs) linked to coronary heart disease, present a risk level similar to traditional factors in this population ( 11 ). The advent of ART revolutionized HIV management but raised concerns regarding its cardiovascular risks ( 12 ). Certain ART classes or medications are linked to an elevated risk of cardiovascular events, leading to changes in treatment recommendations ( 13 , 14 ). This evolving understanding underscores the complex interplay between HIV, ART, and cardiovascular health. There is increasing global evidence linking HIV/AIDS to a higher risk of CVD; however, research focused on this relationship in Ghana is inadequate. Most studies concentrate on sub-Saharan Africa but often fail to offer region-specific information about the prevalence of CVD, its risk factors, and the mechanisms that impact PLWHA. In addition, there is a significant lack of studies that include biomarker analysis to assess the physiological basis of CVD risk in this study population. This research addresses the existing gap by presenting empirical data regarding the prevalence of CVD and its risk factors among PLWHA in Ghana’s Ahafo Region. By integrating biomarkers with clinical assessments and lifestyle considerations, it provides a thorough examination of CVD risk. The results support the development of evidence-based policies, enhance screening methods, and facilitate targeted interventions to tackle the comorbidity challenges of HIV and CVD in Ghana. This research provides valuable insights into the connection between HIV and CVD in Ghana, contributing to the existing literature, informing policy, and guiding clinical practices. By investigating the prevalence of CVD, its associated risk factors, and biomarkers in PLWHA, it addresses a crucial research gap in sub-Saharan Africa. The findings enhance the understanding of how lifestyle changes, ART, and emerging biomarkers influence CVD risk, offering essential data for evidence-based policy development. Policymakers can leverage these results to advocate for integrated screening for HIV and CVD within Ghana's healthcare system, ultimately improving national prevention and management strategies. From a clinical perspective, the study encourages the implementation of targeted interventions in ART clinics, enabling healthcare providers to counsel patients on ways to lower their cardiovascular risk. Moreover, public health initiatives can utilize these findings to develop community-focused programs that promote nutrition, physical activity, and lifestyle modifications for PLWHA. Materials and Methods Study Design This hospital-based cross-sectional study used quantitative methods to investigate the prevalence, risk factors, and mechanistic pathways of CVD among PLWHA in the Ahafo Region. This design is appropriate as it allows for the simultaneous assessment of CVD prevalent, risk factors and biomarkers outcomes at a single point in time, offering a comprehensive understanding of their interplay within the HIV population ( 15 , 16 ). Study Setting and Population This study was conducted in the Ahafo Region (Fig. 1 ), located in southwestern Ghana. It has a mix of rural and urban settings with an estimated population of 585,316 (49.8% males and 50.2% females). The region's health facilities range from community-based health planning services (CHPS) compounds to district hospitals. Due to its relatively high HIV/AIDS prevalence, partly attributed to mining activities, Ahafo is an ideal location for this study. The study population consisted of PLWHA who were receiving care at the two largest district hospitals in the Ahafo Region: St. Elizabeth Hospital in Hwidiem (Asutifi South) and St. John of God Hospital in Duayaw Nkwanta (Tano North), selected due to their high HIV/AIDS prevalence. Eligible participants were 18 years or older, had been on ART for at least six months, and provided informed consent were included in the study. However, pregnant women and individuals with a history of congenital heart disease were excluded to avoid confounding factors that could independently influence cardiovascular outcomes, ensuring the study accurately reflected the cardiovascular risks associated with HIV/AIDS in this population. INSERT FIGURE 1 ABOUT HERE Sampling Technique(s) and Sample Size This study employed a combination of stratified, random, and systematic sampling methods to select the target population. Health facilities in the region were stratified into urban and rural settings to capture diverse healthcare environments. Facilities were then randomly selected from each stratum by drawing names, resulting in the selection of St. Elizabeth Hospital, Hwidiem, and St. John of God Hospital, Duayaw Nkwanta as study sites. Within these hospitals, a systematic random sampling technique was used. Using the ART registers, the sampling interval was calculated by dividing the total number of PLWHA in each facility by the required sample size. Participants were then selected at regular intervals, ensuring proportional representation and enhancing the sample's representativeness. The study targeted 1,667 PLWHA receiving ART at St. Elizabeth Hospital and St. John of God Hospital in the Ahafo Region. Using Yamane's formula with a 5% margin of error and a 95% confidence level, the required sample size was calculated as:: n = \(\:\frac{\text{N}}{1+{\text{N}\left({\alpha\:}\right)}^{2}},\:\) Where n = sample size, N = Sample frame and α = margin of error n \(\:=\frac{1667}{1+{1667\left(0.05\right)}^{2}}\) = 322.5 Rounding up, the minimum sample size was 322. To account for nonresponse and enhance the study's robustness, we increased the sample size by 64, resulting in a final total of 386 PLWHA. The sample was distributed proportionately between the two hospitals based on their patient populations (see Table 1 ). Table 1 Sample Size Distribution Facility Name PLWHA Population Population Distribution (%) Required Sample Size St. Elizabeth Hospital, Hwidiem 1,200 72 278 St. John of God Hospital, Duayaw Nkwanta 467 28 108 Total 1,667 100 386 Source: Field survey, 2024 INSERT Table 1 ABOUT HERE Data Collection Tools and Procedure Data were collected using structured questionnaires and clinical assessments through Kobo Collect (an online data collection tool). The questionnaire, developed from validated instruments in similar studies and pretested at various facilities, included sections on socio-demographic characteristics (age, gender, education, occupation, marital status, income), cardiovascular risk factors (smoking, alcohol use, physical activity, dietary habits), and clinical assessments (blood pressure, lipid profile, blood glucose levels, and Body Mass Index [BMI]) ( 17 – 19 ). Data collection spanned six months and involved trained research assistants, including nurses and biomedical scientists. They received specialized training to ensure accurate questionnaire administration and clinical assessments. Interviews were conducted face-to-face in a private hospital setting to maintain participant comfort and confidentiality. Clinical assessments took place in designated laboratories at St. Elizabeth and St. John of God hospitals. Blood pressure was measured using an automated sphygmomanometer after a five-minute rest period. Biomedical scientists collected 4 ml of blood from each participant for lipid profile and blood glucose analysis. Anthropometric measurements, including weight and height, were recorded to calculate BMI. Data Analysis Data from Kobo Collect, initially in Microsoft Excel format, were imported into Stata version 18 for cleaning and analysis. Descriptive statistics, including frequencies and percentages, summarized the participants' characteristics and key variables. Bivariate analyses were conducted using chi-square tests for associations between categorical variables and independent t-tests for associations between continuous variables. To identify independent predictors of cardiovascular disease among PLWHA, unadjusted and adjusted logistic regression models were employed. Variables with a p-value of less than 0.20 in the bivariate analysis were considered for inclusion in the multivariable models ( 20 ). Odds ratios with 95% confidence intervals were reported for each predictor. The final models were adjusted for potential confounders. A p-value of less than 0.05 was considered statistically significant. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test, and multicollinearity was checked using variance inflation factors ( 21 ). Ethical Consideration <Ethical approval was obtained from the Institutional Review Board of the Christian Health Association of Ghana under reference number 123567. Written permissions were also granted by the management of St. Elizabeth Hospital and St. John of God Hospital. Informed consent was obtained from all participants, ensuring they were fully informed about the study's purpose, procedures, potential risks, and their right to withdraw at any time. Confidentiality and privacy were strictly maintained throughout the study. Results Socio-demographic Characteristics of Respondents The majority of respondents were aged 45 years and older (49.2%), with the remainder aged 34–45 years (30.3%) and 19–34 years (20.5%). The sample was predominantly female (74.1%). In terms of education, 45.9% had completed at least senior high school, while 20.5% had primary education. A smaller percentage had no formal schooling (17.1%) or vocational/technical education (7.3%). Most respondents were married (55.7%), with smaller groups being widowed (9.6%) or cohabiting (10.1%). Christianity was the primary religion (79.8%), followed by Islam (20.2%). Nearly all respondents had health insurance (99.7%). The most common occupations were farming (28.5%), civil service (25.1%), and artisanship (23.1%). A minority were unemployed (10.1%) or health professionals (13.2%). In terms of income, 68.9% reported an average monthly income of less than 1000 units, with smaller proportions earning between 1000–2000 units (14.2%) and over 2000 units (16.9%). Table 2 illustration the descriptive statistics of the socio demographic characteristics of respondents. Table 2 Descriptive Statistics of Socio-demographic Characteristics (n = 386) Characteristics Frequency (percent) Age 19–34 79 (20.5) 34–45 117 (30.3) >=45 190 (49.2) Sex Male 100 (25.9) Female 286 (74.1) Educational level No formal schooling 66 (17.1) Primary school 79 (20.5) JHS completed 88 (22.8) SHS completed 89 (23.1) Vocational or Technical 28 (7.3) University completed 36 (9.3) Marital status Single 53 (13.7) Currently Married 215 (55.7) Divorced 22 (5.7) Widowed 37 (9.6) Cohabiting 39 (10.1) Separated 20 (5.2) Religion Christianity 308 (79.8) Muslim 78 (20.2) Health Insurance status Non-Insured 1 (0.3) Insured 385 (99.7) Occupational status Unemployed 39 (10.1) Farming 110 (28.5) Civil Servant 97 (25.1) Health Professionals 51 (13.2) Artisan 89 (23.1) Average monthly income 2000 25 (16.9) Source: Field work, 2024 INSERT Table 2 ABOUT HERE Prevalence of Cardiovascular Disease (CVD) Among the respondents, 16.6% reported a CVD diagnosis, primarily hypertension, while 83.4% reported no CVD. A review of medical records revealed that 21.9% of those with a medical history had both diabetes and hypertension, and 78.1% had diabetes alone. Regarding specific CVDs, 18.8% had cardiac heart disease, while no cases of stroke, coronary artery disease, heart failure, COPD, osteoarthritis, or musculoskeletal disease were reported. In terms of hospital visits for CVD treatment, 45.1% attended daily, 26.4% visited 1–4 days per week, and 11.9% visited 5–6 days per week. A smaller proportion visited less frequently, with 16.1% seeking care for less than once a month (Table 3 ). Table 3 Descriptive Statistics of Prevalence of Cardiovascular Disease (CVD) (n = 386) Frequency (Percent) Disease Prevalence Been diagnosed with any cardiovascular disease (CVD) Yes 64 (16.6) No 322 (83.4) Review the patient folder if he or she has a medical history of CVD Diabetes and Hypertension 14 (21.9) Diabetes 50 (78.1) Cardiac heart disease Yes 12 (18.8) No 52 (81.2) Stroke No 64 (100.0) Coronary disease No 64 (100.0) Artery disease No 64 (100.0) Heart failure No 64 (100.0) Chronic Obstructed Pulmonary Disease No 64 (100.0) Osteoarthritis No 64 (100.0) Musculoskeletal disease No 64 (100.0) Number of times respondents visit the hospital for CVD treatment Daily 174 (45.1) 5–6 days per week 46 (11.9) 1–4 days per week 102 (26.4) 1–3 days per month 1 (0.3) Less than once a month 62 (16.1) More than twice a month 1 (0.3) Source: Field work, 2024 INSERT Table 3 ABOUT HERE Risk Factors of CVDs among PLWHA All participants (N = 386) reported not smoking, indicating a very low prevalence of smoking in this population. Only 1.8% reported alcohol consumption in the past 30 days, with all consuming alcohol 1–3 days per month. A significant portion regularly consumed fruits and vegetables, with 71.0% eating fruits and 66.8% eating vegetables 1–3 days per week. Most participants (67.6%) had fewer than three meals at home daily, while 67.1% ate meals outside the home less than three times per week. The majority (97.9%) engaged in some form of moderate to vigorous physical activity, suggesting a generally active lifestyle, though few reported spending more than 60 minutes on moderate-intensity activities. Regarding medication adherence, 68.4% consistently adhered to HIV/AIDS medications, while only 16.7% adhered to prescribed CVD medications, with some reporting occasional non-adherence (Table 4 ). Table 4 Descriptive Analysis of Risk Factors (n = 386) Risk Factors Frequency (Percent) Currently smoke No 386 (100.0) Consume alcohol Yes 7 (1.8) No 379 (98.2) For the past 30 days patients consuming alcohol Yes 7 (100.0) Frequency of alcoholic alcohol intake 1–3 days per month 7 (100.0) Number of days fruits are taken within the week 1–3 days 274 (71.0) 4–7 days 112 (29.0) Number of days vegetable is taken within the week 1–3 days 258 (66.8) 4–7 days 128 (33.2) Number of meals taken home per day 3 meals day 125 (32.4) Number of times meals are taken per week outside home 3 times 127 (32.9) Moderate involvement to vigorous intense activity Yes 378 (97.9) No 8 (2.1) Number of times working in a week 4 days 198 (51.6) Patients doing moderate to vigorous-intensity sports, fitness or recreational Yes 337 (87.3) No 49 (12.7) How many minutes or hours patients spend doing moderate-intensity sports, fitness 60min 1 (50.0) How often patients adhere to your prescribed medications for CVD Always 1 (16.7) Often 3 (50.0) Sometimes 1 (16.7) Never 1 (16.7) How often patients adhere to your prescribed HIV/AIDS medications Always 264 (68.4) Sometimes 102 (26.4) Never 20 (5.2) Source: Field work, 2024 INSERT Table 4 ABOUT HERE The number of meals taken at home per day was significantly associated with CVD in the adjusted model (p < 0.001). Patients consuming more than three meals at home per day had substantially higher odds of CVD (adjusted OR = 315.31, 95% CI = 16.03–6200.62). Similarly, consuming meals outside the home more than three times per week was linked to higher odds of CVD (adjusted OR = 2.06, 95% CI = 1.04–4.08, p = 0.038). Engaging in moderate-to-vigorous physical activity showed an inverse relationship with CVD, though not statistically significant. Patients not participating in such activities had lower odds of CVD (adjusted OR = 0.32, 95% CI = 0.07–1.38). Notably, engaging in moderate-intensity activities for 24 to 48 minutes or 48 to 96 minutes was significantly associated with lower odds of CVD (adjusted OR = 0.10, 95% CI = 0.01–0.82 and 0.10, 95% CI = 0.01–0.80, respectively) compared to less than 24 minutes. Patients not engaging in moderate- to vigorous-intensity activities had higher odds of CVD, although this was not statistically significant (p = 0.100) (Table 5 ). Table 5 Logistic Regression of Risk Factors Associated with CVD Factors Bivariate model Multivariable model OR 95% CI p-value aOR 95% CI p-value Number of times meals taken at home per day 3 meals/day 1.69 0.91–3.16 0.097 315.31* 16.03–6200.62 0.001 Number of times meals are taken outside the home per week 3 times/week 1.74 0.94–3.25 0.08 2.06* 1.04–4.08 0.038 Moderate to vigorous intense activity participation Yes (Ref.) 1 + + + No 0.32 0.07–1.38 0.126 Number of days worked per week 4 days/week 0.32 0.04–2.53 0.279 0.49 0.06–4.23 0.518 Minutes/hours spent in moderate-intense activities < 24 minutes (Ref.) 1 1 24–48 minutes 0.27* 0.08–0.89 0.031 0.10* 0.01–0.82 0.032 48–96 minutes 0.21* 0.07–0.67 0.008 0.10* 0.01–0.80 0.03 Moderate to vigorous-intensity sports or fitness Yes (Ref.) 1 + + + No 2.44 0.84–7.03 0.1 Exposed to tobacco smoke No (Ref.) 1 1 Yes 1.58 0.74–3.35 0.238 2.2 0.98–4.97 0.057 Exposed to air pollution in the environment No (Ref.) 1 1 Yes 1.06 0.60–1.86 0.838 1.26 0.68–2.33 0.463 OR : Odds Ratio; aOR : Adjusted odds ratio; CI : Confidence Interval; *p-values < 0.05 indicate statistical significance; - : not applicablee; + : not used in adjusted Source: Field work, 2024 INSERT Table 5 ABOUT HERE Biomarkers of Cardiovascular Disease The majority of respondents (71.5%) were classified as underweight (BMI 30.0). Most participants (97.9%) had normal blood sugar levels (< 5.5), while a small proportion was in the prediabetic (1.8%) and diabetic (0.3%) ranges. Regarding other biochemical markers, the majority had levels within normal ranges: 80.3% had haemoglobin levels below 8.0, 90.7% had LDL levels below 2.83, 95.3% had HDL levels below 3.30, and 69.2% had triglyceride levels below 1.5. These findings indicate a high prevalence of underweight status and generally normal blood sugar and lipid profiles within the study population (Table 6 ). Table 6 Descriptive Statistics of Potential Mechanisms of Cardiovascular Disease (n = 386) Potential Mechanisms Frequency (Percent) BMI of Respondents 18.5–24.9 (Normal) 27 (7.0) < 18.5 (Underweight) 276 (71.5) 25.0–29.9 (Overweight) 60 (15.5) ≥ 30.0 (Obese) 23 (6.0) Blood Sugar Level 6.9 mmol/L (Diabetes) 1 (0.3) Haemoglobin Level 8.0–9.9 g/dL (Moderate Anaemia) 76 (19.7) < 8.0 g/dL (Severe Anaemia) 310 (80.3) Low-Density Lipoprotein (LDL) < 2.83 mmol/L (Optimal) 350 (90.7) 2.83–3.37 mmol/L (Moderate Risk) 27 (7.0) ≥ 3.37 mmol/L (High Risk) 9 (2.3) High-Density Lipoprotein (HDL) < 3.30 mmol/L (Low) 368 (95.3) 3.30–3.81 mmol/L (Normal) 8 (2.1) ≥ 3.82 mmol/L (High) 10 (2.6) Triglycerides < 1.5 mmol/L (Normal) 267 (69.2) 1.5–1.99 mmol/L (Moderate) 105 (27.2) 2.0–4.99 mmol/L (High) 14 (3.6) Source: Field work, 2024 INSERT Table 6 ABOUT HERE Table 7 shows the logistic regression analysis results examining the association between BMI, biomarkers, and CVD among the study population. In the unadjusted model, individuals with a BMI less than 18.5 had an odds ratio (OR) of 1.74 (95% CI: 0.66–4.57, p = 0.264) compared to those with a BMI of 18.5–24.9, suggesting 74% higher odds of CVD. However, this result was not statistically significant, as indicated by the wide confidence interval and a p-value greater than 0.05. Similarly, those with a BMI over 30.0 had an OR of 1.36 (95% CI: 0.33–5.55, p = 0.671), indicating 36% higher odds of CVD, though this association was also not statistically significant. For blood sugar levels, individuals with levels between 5.5 and 6.9 had an OR of 0.46 (95% CI: 0.09–2.41, p = 0.357), suggesting lower odds of CVD compared to those with levels below 5.5, but this finding was not significant. No significant association was found for blood sugar levels above 6.9. In the adjusted model, controlling for potential confounders, the lack of significant associations between BMI categories and blood sugar levels with CVD remained. However, HDL and triglyceride levels emerged as significant predictors. Higher HDL levels were associated with lower odds of CVD (OR < 1, p < 0.05), indicating a protective effect. Similarly, lower triglyceride levels were significantly associated with reduced odds of CVD (p < 0.05). These findings suggest that HDL and triglyceride levels are important biomarkers for assessing CVD risk in this population. Table 7 Logistic Regression Potential Mechanisms and CVD Variable Bivariate model Multivariable model OR (95% CI) p-value aOR (95% CI) p-value BMI of Respondents Normal (Ref.) 1 1 Underweight 1.74 (0.66–4.57) 0.264 1.56 (0.55–4.40) 0.404 Overweight 0.86 (0.29–2.52) 0.78 0.94 (0.29–3.05) 0.913 Moderately Obese 1.36 (0.33–5.55) 0.671 1.20 (0.27–5.32) 0.81 Blood Sugar Level Normal (Ref.) 1 Pre-diabetes 0.46 (0.09–2.41) 0.357 + + Diabetes 1 - + + Haemoglobin Level Moderate Anaemia (Ref.) 1 Severe Anaemia 0.82 (0.41–1.66) 0.582 + + Low-density lipoprotein (LDL) Normal ( 3.3) 0.70 (0.14–3.43) 0.655 + + High-density lipoprotein (HDL) < 3.3 (Ref.) 1 1 - 3.3–3.81 0.17* (0.04–0.71) 0.015 0.12* (0.03–0.51) 0.004 ≥ 3.82 0.11* (0.03–0.42) 0.001 0.18* (0.04–0.73) 0.016 Triglycerides < 1.5 (Ref.) 1 1 1.5–1.99 0.50* (0.28–0.91) 0.022 0.48* (0.26–0.88) 0.018 2.0–4.99 0.11* (0.03–0.32) 0 0.12* (0.04–0.37) 0 Intercept 6.12 (2.21–16.98) 0.001 OR : Odds Ratio; aOR : Adjusted odds ratio; CI : Confidence Interval; *p-values < 0.05 indicate statistical significance; - : not applicablee; + : not used in adjusted Source: Field work, 2024 INSERT Table 7 ABOUT HERE Discussion This study assessed the prevalence, risk factors, and biomarkers linked to CVD among PLWHA in Ghana’s Ahafo Region. The results showed a 16.6% prevalence of CVD, with hypertension being the most prevalent condition. Dietary habits and physical activity were significantly related to CVD risk; specifically, a higher frequency of meals was associated with increased odds of CVD, while engaging in moderate-intensity physical activity was found to lower the risk. Biomarker analysis revealed that elevated HDL levels and reduced triglyceride levels offered protection against CVD. Interestingly, no significant links were identified between BMI, blood glucose levels, and CVD risk. These findings underscore the importance of integrated care for HIV and CVD that focuses on modifiable lifestyle factors. The 16.6% CVD prevalence observed in this study aligns with previous research in sub-Saharan Africa, which estimates CVD rates among PLWHA to range from 6–50% ( 22 , 23 ). These high rates, particularly of hypertension, underscore the increasing burden of CVD in this population, highlighting the need for comprehensive management strategies. A study in Tanzania found a 20–30% prevalence of hypertension among PLWHA ( 24 ), supporting the current findings. Additionally, the comorbidity of diabetes and hypertension in 21.9% of participants suggests a complex clinical challenge that necessitates integrated care approaches. Interestingly, while 18.8% of respondents reported cardiac heart disease, no cases of stroke, coronary artery disease, or other conditions like COPD were self-reported. This discrepancy indicates underdiagnosis or limited access to advanced diagnostic tools, a common issue in many sub-Saharan African settings where resource constraints restrict comprehensive cardiovascular care. Chronic HIV-related inflammation, immune activation, and the metabolic side effects of antiretroviral therapy (ART) likely contribute to the increased risk of CVD in this population. The study reinforces concerns about ART's role in dyslipidemia and insulin resistance, which exacerbate the risk of cardiac conditions. Lifestyle factors further increase CVD risk among PLHIV. While the complete absence of smoking in the study cohort is a positive finding, alcohol consumption remains a concern, with 1.8% of participants reporting recent intake. This low level of alcohol use contrasts with other studies, such as Arora et al., which link alcohol consumption to various CVD risks ( 25 ). Despite moderate fruit and vegetable consumption among participants, with 71% consuming fruits and 66.8% vegetables at least once a week ( 26 ), there remains room for improvement in dietary practices to enhance cardiovascular health. The study also finds significant associations between meal frequency and CVD risk. Participants consuming more than three meals per day had substantially higher odds of developing CVD, supporting findings by Chen et al. that excessive meal frequency correlates with increased energy intake and weight gain, thus raising CVD risk ( 27 ). This highlights the importance of promoting balanced eating habits to mitigate cardiovascular risk among PLHIV. Medication adherence is critical in managing both HIV and CVD. In this study, 68.4% of participants adhered to ART, and 16.7% adhered to CVD medications, indicating relatively positive outcomes compared to other studies, such as Legesse et al. ( 28 ). However, occasional non-adherence observed in some patients underscores the need for ongoing support to ensure optimal treatment outcomes. Physical activity was significantly associated with reduced CVD risk, with participants engaging in moderate-intensity physical activity for 24–48 minutes per day having lower odds of CVD (adjusted OR = 0.10, p < 0.05). This finding aligns with studies emphasizing the protective effects of physical activity against CVD ( 29 ). However, despite high levels of reported physical activity in this study, the quality and intensity of exercise remain unclear. Targeted interventions encouraging structured exercise programs may enhance cardiovascular health in PLWHA. Biomarker analysis revealed that higher HDL levels and lower triglyceride levels were protective against CVD, consistent with findings that HDL plays a crucial role in cardiovascular health ( 30 ). However, no significant associations were found between BMI, blood glucose levels, and CVD, contradicting previous research that identified these factors as key predictors of cardiovascular risk ( 31 ). This discrepancy suggests that additional longitudinal studies are needed to better understand the role of metabolic indicators in CVD risk among PLWHA. Limitations of the study Notwithstanding the valuable contributions of this study, it is not without limitations. First, its cross-sectional design prevents causal inferences between identified risk factors and CVD outcomes. Longitudinal studies are needed to establish temporal relationships. Second, self-reported data on lifestyle behaviors, such as diet and physical activity, may be subject to recall bias. Future research should incorporate objective measures of dietary intake and physical activity levels. Lastly, the study focused on two hospitals in the Ahafo Region, which may limit generalizability to other settings in Ghana. Expanding the study to multiple regions would provide a more comprehensive understanding of CVD risk among PLWHA. Conclusion and implication for policy and practice This study identifies a 16.6% prevalence of CVD among PLWHA in Ghana’s Ahafo Region, with hypertension and diabetes as the most common conditions. Key factors associated with CVD include meal frequency, moderate-to-vigorous physical activity, and biomarkers like HDL and triglycerides. These findings highlight the multifactorial nature of CVD risk in PLWHA, influenced by traditional factors and HIV-specific elements such as chronic inflammation and ART. This study highlights the importance of integrating CVD screening into HIV care settings to facilitate early detection and intervention. Routine assessments of CVD risk, including checks for hypertension and lipid levels, should be part of standard procedures in ART clinics. Given the significant link between meal frequency and CVD risk, it is essential to prioritize nutrition education and dietary counseling to encourage balanced diets and portion control among PLWHA. Furthermore, implementing structured physical activity programs can promote safe and sustainable exercise habits. To tackle the issue of poor adherence to CVD medications, it is fundamental to enhance patient education, provide adherence counseling, and establish medication reminder systems to support long-term cardiovascular health. Moreover, the underdiagnosis of cardiac conditions points to the need for better access to diagnostic tools like echocardiography and lipid profiling, along with training programs for healthcare providers to improve CVD risk management. Policymakers should focus on investing in cost-effective and sustainable interventions, ensuring that HIV care programs adopt comprehensive, multidisciplinary, and multisectoral approaches involving the Ministry of Health, Ghana Health Service, health-based civil society organizations, The Global Fund, World Health Organization, and other relevant organizations to alleviate the CVD burden and enhance long-term health outcomes for PLWHA. Declarations Acknowledgements We would like to express our profound gratitude to Dr. Peter K. Yeboah, Dr. James Duah, and the entire faculty members for their invaluable support and review of the final version of this manuscript. Competing Interests The corresponding author confirms on behalf of all authors that there have been no involvements that might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated. Authors' Contributions MD. prepared the initial draft of the manuscript, with equal contributions and revisions from IYM, SKA, PA-K, WD, JA, VFN on conceptualization design, data collection and analysis, drafting and reviewing the manuscript for critical inputs, final approval and to be collectively responsible for the content. Consent to Publish Declaration: All the authors agree with its submission to Discover Public Health. Ethical Considerations We strictly adhered to all ethical guidelines in the preparation of this article, which involved direct engagement with human subjects. Clinical Trial Number: Not applicable. Funding Information This work did not receive funding from any public, commercial, or charitable organisation. Data Availability We will make available the data supporting this study's findings upon reasonable request. Interested parties can contact the corresponding author to obtain access to the data, following the appropriate data sharing protocols. 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Lifson AR, Neuhaus J, Arribas JR, van den Berg-Wolf M, Labriola AM, Read TRH. Smoking-Related Health Risks Among Persons With HIV in the Strategies for Management of Antiretroviral Therapy Clinical Trial. Am J Public Health. 2010;100(10):1896–903. Rotger M, Glass TR, Junier T, Lundgren J, Neaton JD, Poloni ES, et al. Contribution of genetic background, traditional risk factors, and HIV-related factors to coronary artery disease events in HIV-positive persons. Clin Infect Dis Off Publ Infect Dis Soc Am. 2013;57(1):112–21. DAD Study Group, Friis-Møller N, Reiss P, Sabin CA, Weber R, Monforte A, d’Arminio, et al. Class of antiretroviral drugs and the risk of myocardial infarction. N Engl J Med. 2007;356(17):1723–35. Lang S, Mary-Krause M, Cotte L, Gilquin J, Partisani M, Simon A, et al. Impact of individual antiretroviral drugs on the risk of myocardial infarction in human immunodeficiency virus-infected patients: a case-control study nested within the French Hospital Database on HIV ANRS cohort CO4. Arch Intern Med. 2010;170(14):1228–38. Worm SW, Sabin C, Weber R, Reiss P, El-Sadr W, Dabis F, et al. Risk of Myocardial Infarction in Patients with HIV Infection Exposed to Specific Individual Antiretroviral Drugs from the 3 Major Drug Classes: The Data Collection on Adverse Events of Anti-HIV Drugs (D:A:D) Study. J Infect Dis. 2010;201(3):318–30. Gharib AM, Hadigan C. Imaging to End Points. Circ Cardiovasc Imaging. 2017;10(10):e007120. Vos A, Tempelman H, Devillé W, Barth R, Wensing A, Kretzschmar M, et al. HIV and risk of cardiovascular disease in sub-Saharan Africa: Rationale and design of the Ndlovu Cohort Study. Eur J Prev Cardiol. 2017;24(10):1043–50. Mayega RW, Makumbi F, Rutebemberwa E, Peterson S, Östenson CG, Tomson G, et al. Modifiable Socio-Behavioural Factors Associated with Overweight and Hypertension among Persons Aged 35 to 60 Years in Eastern Uganda. PLoS ONE. 2012;7(10):e47632. Alodhialah AM, Almutairi AA, Almutairi M. Physical Inactivity and Cardiovascular Health in Aging Populations: Epidemiological Evidence and Policy Implications from Riyadh, Saudi Arabia. Life. 2025;15(3):347. Taiek N, El Fadili NEH, Belkacem A, Cheikh AA, Kabbadj K, Damoun N et al. The Knowledge Assessment of Cardiovascular Disease Risk Factors: A Cross-Sectional Study. Cureus 16(5):e59774. Machingura PI, Gomo E, Chikwasha V, Okwanga PN. Prevalence of and Factors Associated with Nephropathy in Diabetic Patients Attending an Outpatients Clinic in Harare, Zimbabwe: Methodological Issues. Am J Trop Med Hyg. 2017;97(3):981–2. Bertolini G, D’Amico R, Nardi D, Tinazzi A, Apolone G. One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model. J Epidemiol Biostat. 2000;5(4):251–3. Hoffmann U, Lu MT, Foldyna B, Zanni MV, Karady J, Taron J, et al. Assessment of Coronary Artery Disease With Computed Tomography Angiography and Inflammatory and Immune Activation Biomarkers Among Adults With HIV Eligible for Primary Cardiovascular Prevention. JAMA Netw Open. 2021;4(6):e2114923. Noumegni SR, Bigna JJ, Ama Moor Epse Nkegoum VJ, Nansseu JR, Assah FK, Jingi AM, et al. Relationship between estimated cardiovascular disease risk and insulin resistance in a black African population living with HIV: a cross-sectional study from Cameroon. BMJ Open. 2017;7(8):e016835. Manavalan P, Minja L, Wanda L, Hertz JT, Thielman NM, Okeke NL, et al. It’s because I think too much: Perspectives and experiences of adults with hypertension engaged in HIV care in northern Tanzania. PLoS ONE. 2020;15(12):e0243059. Arora M, ElSayed A, Beger B, Naidoo P, Shilton T, Jain N, et al. The Impact of Alcohol Consumption on Cardiovascular Health: Myths and Measures. Glob Heart. 2022;17(1):45. Aune D, Giovannucci E, Boffetta P, Fadnes LT, Keum N, Norat T, et al. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality-a systematic review and dose-response meta-analysis of prospective studies. Int J Epidemiol. 2017;46(3):1029–56. Chen HJ, Wang Y, Cheskin LJ. Relationship between frequency of eating and cardiovascular disease mortality in U.S. adults: the NHANES III follow-up study. Ann Epidemiol. 2016;26(8):527–33. Legesse Tesemma A, Girma Abate M, Hailemariam Abebo Z, Estifanos Madebo W. Determinants of Poor Quality of Life Among Adults Living with HIV and Enrolled in Highly Active Anti-Retroviral Therapy at Public Health Facilities of Arba Minch Town Administration in Southern Ethiopia. HIVAIDS Auckl NZ. 2019;11:387–94. Sarfo FS, Nichols M, Singh A, Hardy Y, Norman B, Mensah G, et al. Characteristics of hypertension among people living with HIV in Ghana: Impact of new hypertension guideline. J Clin Hypertens. 2019;21(6):838–50. Soares L. Cardiovascular Disease: A Review. Biomed J Sci Tech Res. 2023;51(3):42696–703. Hemkens LG, Bucher HC. HIV infection and cardiovascular disease. Eur Heart J. 2014;35(21):1373–81. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6693996","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484869221,"identity":"acac0d56-7e15-41be-86f9-2e2f6cfd6f09","order_by":0,"name":"Mavis Donkor","email":"","orcid":"","institution":"Catholic University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Mavis","middleName":"","lastName":"Donkor","suffix":""},{"id":484869222,"identity":"7700cdf1-d092-434e-a09a-cc79a2ef2a1b","order_by":1,"name":"Isaac Yaw Massey","email":"","orcid":"","institution":"Catholic University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"Yaw","lastName":"Massey","suffix":""},{"id":484869223,"identity":"43c8f49f-b619-48ff-a063-5a66da34229f","order_by":2,"name":"Samuel Kofi Amponsah","email":"","orcid":"","institution":"Catholic University of Ghana","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"Kofi","lastName":"Amponsah","suffix":""},{"id":484869224,"identity":"08c4e0b3-5fca-4351-9480-9904921f00de","order_by":3,"name":"Philip Amo-kodieh","email":"","orcid":"","institution":"St. John of God Hospital","correspondingAuthor":false,"prefix":"","firstName":"Philip","middleName":"","lastName":"Amo-kodieh","suffix":""},{"id":484869225,"identity":"34393ae7-bb8c-424b-b9bb-fcd73056feb3","order_by":4,"name":"William Dormechele","email":"","orcid":"","institution":"Navrongo Health Research Centre, Upper East Region","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Dormechele","suffix":""},{"id":484869226,"identity":"f258625f-45ba-48e7-b2f4-06447f566e72","order_by":5,"name":"Joana Apenkwa","email":"","orcid":"","institution":"University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Joana","middleName":"","lastName":"Apenkwa","suffix":""},{"id":484869227,"identity":"c96b3670-3edb-4eeb-941c-941ebb8bb8f0","order_by":6,"name":"Victor Fannam Nunfam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACZjiL8QEDQwWEKQHECXi0MDZAWQYMDGcMJAhrYUDWwthGhBaD48zPH3zcw5BncLuZ8dPNeX/qDA4wH7zNw1Cbh1PLYTbDxhnPGIoN7hxmls7dZiBhcIAt2ZqH4XgxLi2SzTyMzTwHGBI33Mg/ANXCYybNw3AssYGwlmTm37lzQFr4v+HVws+M0MImndsAtoUNqKUGjxY2w5kzDkgkzgRqsc45Ziw58zCbseUcgwM4tbDxH37w4cMBm8Q+oMNu59TI8fMdb354401FHU4tUCCBxAanB4PD+DVgA3WkaxkFo2AUjILhCgDOUVPJsNx2VgAAAABJRU5ErkJggg==","orcid":"","institution":"Takoradi Technical University","correspondingAuthor":true,"prefix":"","firstName":"Victor","middleName":"Fannam","lastName":"Nunfam","suffix":""}],"badges":[],"createdAt":"2025-05-19 00:53:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6693996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6693996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87032691,"identity":"10987c11-a566-420d-ae35-363f97e43c07","added_by":"auto","created_at":"2025-07-18 13:01:03","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233454,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Study Site\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6693996/v1/7b2570d6d2007ea26e581161.jpeg"},{"id":91616741,"identity":"8135ba7b-5f99-4ec1-b023-44ad66de64c1","added_by":"auto","created_at":"2025-09-18 10:40:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2426213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6693996/v1/fcf05137-4c36-425c-a702-df9e5d8bd597.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unveiling the rising Burden of Cardiovascular Disease among People Living with HIV/AIDS in a Sub-Saharan Country of Ghana","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular disease (CVD) is an increasing global health burden among people living with HIV/AIDS. By 2030, 84% of HIV-positive individuals are projected to have at least one non-communicable disease (NCD), up from 29% in 2010. Additionally, 28% of these individuals will likely have three or more NCDs, elevating CVD as a significant concern (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Globally, 8.9\u0026nbsp;million people with HIV have hypertension, with 59.2% residing in sub-Saharan Africa (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In Ghana, over 50% of individuals with HIV/AIDS are at moderate-to-high risk for developing CVD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). These statistics highlight the pressing need to address the intersection of HIV and cardiovascular health.\u003c/p\u003e\u003cp\u003eThe link between HIV and increased CVD risk involves complex pathways, including chronic HIV-related inflammation and immune activation, alongside traditional risk factors like hypertension, dyslipidaemia, diabetes, and smoking (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). This interplay increases the prevalence of CVD in people with HIV, altering the disease's typical onset. To combat this dual burden, interventions by the Global Fund and the Ghana Ministry of Health include integrating NCD management into HIV care, training healthcare workers to manage CVD risk factors, and expanding access to antiretroviral therapy (ART). These initiatives also promote lifestyle changes, such as smoking cessation and healthy diets, among people with HIV. Despite these efforts, gaps persist in systematic CVD risk screening, long-term management of coexisting HIV and CVD, and adapting treatment protocols for this population's specific needs.\u003c/p\u003e\u003cp\u003eA meta-analysis estimated a 61% increase in CVD risk for individuals with HIV (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Notably, even those on ART face a two-fold increase in CVD risk compared to HIV-negative individuals and a 1.5-fold increase compared to untreated patients. Limitations in previous studies, including inadequate control groups and unmeasured confounding factors, may affect the accuracy of these estimates (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Importantly, recent studies show that CVD risk persists in patients on suppressive ART, indicating a long-term impact on their health (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Traditional CVD risk factors, such as high cholesterol, diabetes, smoking, and hypertension, have been more prevalent among people with HIV since the epidemic's early days (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Smoking, in particular, is a significant preventable risk factor, with HIV-positive smokers losing more years to smoking than to HIV itself (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Smoking cessation offers substantial risk reduction, with benefits increasing the longer one remains smoke-free (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Furthermore, genetic predispositions, such as higher genetic risk scores based on 23 single-nucleotide polymorphisms (SNPs) linked to coronary heart disease, present a risk level similar to traditional factors in this population (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe advent of ART revolutionized HIV management but raised concerns regarding its cardiovascular risks (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Certain ART classes or medications are linked to an elevated risk of cardiovascular events, leading to changes in treatment recommendations (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This evolving understanding underscores the complex interplay between HIV, ART, and cardiovascular health.\u003c/p\u003e\u003cp\u003eThere is increasing global evidence linking HIV/AIDS to a higher risk of CVD; however, research focused on this relationship in Ghana is inadequate. Most studies concentrate on sub-Saharan Africa but often fail to offer region-specific information about the prevalence of CVD, its risk factors, and the mechanisms that impact PLWHA. In addition, there is a significant lack of studies that include biomarker analysis to assess the physiological basis of CVD risk in this study population. This research addresses the existing gap by presenting empirical data regarding the prevalence of CVD and its risk factors among PLWHA in Ghana\u0026rsquo;s Ahafo Region. By integrating biomarkers with clinical assessments and lifestyle considerations, it provides a thorough examination of CVD risk. The results support the development of evidence-based policies, enhance screening methods, and facilitate targeted interventions to tackle the comorbidity challenges of HIV and CVD in Ghana.\u003c/p\u003e\u003cp\u003eThis research provides valuable insights into the connection between HIV and CVD in Ghana, contributing to the existing literature, informing policy, and guiding clinical practices. By investigating the prevalence of CVD, its associated risk factors, and biomarkers in PLWHA, it addresses a crucial research gap in sub-Saharan Africa. The findings enhance the understanding of how lifestyle changes, ART, and emerging biomarkers influence CVD risk, offering essential data for evidence-based policy development. Policymakers can leverage these results to advocate for integrated screening for HIV and CVD within Ghana's healthcare system, ultimately improving national prevention and management strategies. From a clinical perspective, the study encourages the implementation of targeted interventions in ART clinics, enabling healthcare providers to counsel patients on ways to lower their cardiovascular risk. Moreover, public health initiatives can utilize these findings to develop community-focused programs that promote nutrition, physical activity, and lifestyle modifications for PLWHA.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eThis hospital-based cross-sectional study used quantitative methods to investigate the prevalence, risk factors, and mechanistic pathways of CVD among PLWHA in the Ahafo Region. This design is appropriate as it allows for the simultaneous assessment of CVD prevalent, risk factors and biomarkers outcomes at a single point in time, offering a comprehensive understanding of their interplay within the HIV population (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Setting and Population\u003c/h3\u003e\n\u003cp\u003eThis study was conducted in the Ahafo Region (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), located in southwestern Ghana. It has a mix of rural and urban settings with an estimated population of 585,316 (49.8% males and 50.2% females). The region's health facilities range from community-based health planning services (CHPS) compounds to district hospitals. Due to its relatively high HIV/AIDS prevalence, partly attributed to mining activities, Ahafo is an ideal location for this study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e The study population consisted of PLWHA who were receiving care at the two largest district hospitals in the Ahafo Region: St. Elizabeth Hospital in Hwidiem (Asutifi South) and St. John of God Hospital in Duayaw Nkwanta (Tano North), selected due to their high HIV/AIDS prevalence. Eligible participants were 18 years or older, had been on ART for at least six months, and provided informed consent were included in the study. However, pregnant women and individuals with a history of congenital heart disease were excluded to avoid confounding factors that could independently influence cardiovascular outcomes, ensuring the study accurately reflected the cardiovascular risks associated with HIV/AIDS in this population.\u003c/p\u003e\n\u003ch3\u003eINSERT FIGURE 1 ABOUT HERE\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eSampling Technique(s) and Sample Size\u003c/h2\u003e\u003cp\u003eThis study employed a combination of stratified, random, and systematic sampling methods to select the target population. Health facilities in the region were stratified into urban and rural settings to capture diverse healthcare environments. Facilities were then randomly selected from each stratum by drawing names, resulting in the selection of St. Elizabeth Hospital, Hwidiem, and St. John of God Hospital, Duayaw Nkwanta as study sites. Within these hospitals, a systematic random sampling technique was used. Using the ART registers, the sampling interval was calculated by dividing the total number of PLWHA in each facility by the required sample size. Participants were then selected at regular intervals, ensuring proportional representation and enhancing the sample's representativeness.\u003c/p\u003e\u003cp\u003eThe study targeted 1,667 PLWHA receiving ART at St. Elizabeth Hospital and St. John of God Hospital in the Ahafo Region. Using Yamane's formula with a 5% margin of error and a 95% confidence level, the required sample size was calculated as::\u003c/p\u003e\u003cp\u003en =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{N}}{1+{\\text{N}\\left({\\alpha\\:}\\right)}^{2}},\\:\\)\u003c/span\u003e\u003c/span\u003eWhere n\u0026thinsp;=\u0026thinsp;sample size, N\u0026thinsp;=\u0026thinsp;Sample frame and α\u0026thinsp;=\u0026thinsp;margin of error\u003c/p\u003e\u003cp\u003en\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\frac{1667}{1+{1667\\left(0.05\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e = 322.5\u003c/p\u003e\u003cp\u003eRounding up, the minimum sample size was 322. To account for nonresponse and enhance the study's robustness, we increased the sample size by 64, resulting in a final total of 386 PLWHA. The sample was distributed proportionately between the two hospitals based on their patient populations (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003e\u003cb\u003eSample Size Distribution\u003c/b\u003e\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\u003eFacility Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePLWHA Population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePopulation Distribution (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRequired Sample Size\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSt. Elizabeth Hospital, Hwidiem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e278\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSt. John of God Hospital, Duayaw Nkwanta\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Field survey, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eINSERT Table 1 ABOUT HERE\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eData Collection Tools and Procedure\u003c/h2\u003e\u003cp\u003eData were collected using structured questionnaires and clinical assessments through Kobo Collect (an online data collection tool). The questionnaire, developed from validated instruments in similar studies and pretested at various facilities, included sections on socio-demographic characteristics (age, gender, education, occupation, marital status, income), cardiovascular risk factors (smoking, alcohol use, physical activity, dietary habits), and clinical assessments (blood pressure, lipid profile, blood glucose levels, and Body Mass Index [BMI]) (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eData collection spanned six months and involved trained research assistants, including nurses and biomedical scientists. They received specialized training to ensure accurate questionnaire administration and clinical assessments. Interviews were conducted face-to-face in a private hospital setting to maintain participant comfort and confidentiality. Clinical assessments took place in designated laboratories at St. Elizabeth and St. John of God hospitals. Blood pressure was measured using an automated sphygmomanometer after a five-minute rest period. Biomedical scientists collected 4 ml of blood from each participant for lipid profile and blood glucose analysis. Anthropometric measurements, including weight and height, were recorded to calculate BMI.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eData from Kobo Collect, initially in Microsoft Excel format, were imported into Stata version 18 for cleaning and analysis. Descriptive statistics, including frequencies and percentages, summarized the participants' characteristics and key variables. Bivariate analyses were conducted using chi-square tests for associations between categorical variables and independent t-tests for associations between continuous variables. To identify independent predictors of cardiovascular disease among PLWHA, unadjusted and adjusted logistic regression models were employed. Variables with a p-value of less than 0.20 in the bivariate analysis were considered for inclusion in the multivariable models (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Odds ratios with 95% confidence intervals were reported for each predictor. The final models were adjusted for potential confounders. A p-value of less than 0.05 was considered statistically significant. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test, and multicollinearity was checked using variance inflation factors (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003e\u003cEthical approval\u003c/strong\u003e was obtained from the Institutional Review Board of the Christian Health Association of Ghana under reference number 123567. Written permissions were also granted by the management of St. Elizabeth Hospital and St. John of God Hospital. Informed consent was obtained from all participants, ensuring they were fully informed about the study's purpose, procedures, potential risks, and their right to withdraw at any time. Confidentiality and privacy were strictly maintained throughout the study.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eSocio-demographic Characteristics of Respondents\u003c/h2\u003e\u003cp\u003eThe majority of respondents were aged 45 years and older (49.2%), with the remainder aged 34\u0026ndash;45 years (30.3%) and 19\u0026ndash;34 years (20.5%). The sample was predominantly female (74.1%). In terms of education, 45.9% had completed at least senior high school, while 20.5% had primary education. A smaller percentage had no formal schooling (17.1%) or vocational/technical education (7.3%). Most respondents were married (55.7%), with smaller groups being widowed (9.6%) or cohabiting (10.1%). Christianity was the primary religion (79.8%), followed by Islam (20.2%). Nearly all respondents had health insurance (99.7%). The most common occupations were farming (28.5%), civil service (25.1%), and artisanship (23.1%). A minority were unemployed (10.1%) or health professionals (13.2%). In terms of income, 68.9% reported an average monthly income of less than 1000 units, with smaller proportions earning between 1000\u0026ndash;2000 units (14.2%) and over 2000 units (16.9%). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustration the descriptive statistics of the socio demographic characteristics of respondents.\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\u003eDescriptive Statistics of Socio-demographic Characteristics (n\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e19\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34\u0026ndash;45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117 (30.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;=45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e190 (49.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100 (25.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e286 (74.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo formal schooling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e66 (17.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79 (20.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJHS completed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e88 (22.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSHS completed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89 (23.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVocational or Technical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUniversity completed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36 (9.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53 (13.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently Married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e215 (55.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22 (5.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37 (9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCohabiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (10.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeparated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReligion\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChristianity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e308 (79.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuslim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78 (20.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHealth Insurance status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-Insured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsured\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e385 (99.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupational status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnemployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39 (10.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e110 (28.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCivil Servant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97 (25.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth Professionals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e51 (13.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArtisan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e89 (23.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage monthly income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (68.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1000\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21 (14.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (16.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003ePrevalence of Cardiovascular Disease (CVD)\u003c/h2\u003e\u003cp\u003eAmong the respondents, 16.6% reported a CVD diagnosis, primarily hypertension, while 83.4% reported no CVD. A review of medical records revealed that 21.9% of those with a medical history had both diabetes and hypertension, and 78.1% had diabetes alone. Regarding specific CVDs, 18.8% had cardiac heart disease, while no cases of stroke, coronary artery disease, heart failure, COPD, osteoarthritis, or musculoskeletal disease were reported. In terms of hospital visits for CVD treatment, 45.1% attended daily, 26.4% visited 1\u0026ndash;4 days per week, and 11.9% visited 5\u0026ndash;6 days per week. A smaller proportion visited less frequently, with 16.1% seeking care for less than once a month (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Prevalence of Cardiovascular Disease (CVD) (n\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (Percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease Prevalence\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBeen diagnosed with any cardiovascular disease (CVD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e322 (83.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eReview the patient folder if he or she has a medical history of CVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes and Hypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (21.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50 (78.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCardiac heart disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (18.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (81.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStroke\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCoronary disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArtery disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart failure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChronic Obstructed Pulmonary Disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOsteoarthritis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMusculoskeletal disease\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of times respondents visit the hospital for CVD treatment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174 (45.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u0026ndash;6 days per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46 (11.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;4 days per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (26.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 days per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than once a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62 (16.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMore than twice a month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003eRisk Factors of CVDs among PLWHA\u003c/h2\u003e\u003cp\u003eAll participants (N\u0026thinsp;=\u0026thinsp;386) reported not smoking, indicating a very low prevalence of smoking in this population. Only 1.8% reported alcohol consumption in the past 30 days, with all consuming alcohol 1\u0026ndash;3 days per month. A significant portion regularly consumed fruits and vegetables, with 71.0% eating fruits and 66.8% eating vegetables 1\u0026ndash;3 days per week. Most participants (67.6%) had fewer than three meals at home daily, while 67.1% ate meals outside the home less than three times per week. The majority (97.9%) engaged in some form of moderate to vigorous physical activity, suggesting a generally active lifestyle, though few reported spending more than 60 minutes on moderate-intensity activities. Regarding medication adherence, 68.4% consistently adhered to HIV/AIDS medications, while only 16.7% adhered to prescribed CVD medications, with some reporting occasional non-adherence (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Analysis of Risk Factors (n\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (Percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrently smoke\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e386 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConsume alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e379 (98.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFor the past 30 days patients consuming alcohol\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFrequency of alcoholic alcohol intake\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 days per month\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (100.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of days fruits are taken within the week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e274 (71.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112 (29.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of days vegetable is taken within the week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u0026ndash;3 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e258 (66.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u0026ndash;7 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e128 (33.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of meals taken home per day\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt; 3 meals day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e261 (67.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;3 meals day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125 (32.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of times meals are taken per week outside home\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;3 times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e259 (67.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;3 times\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e127 (32.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate involvement to vigorous intense activity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e378 (97.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of times working in a week\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;2 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12 (3.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;4 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e174 (45.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;4 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e198 (51.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePatients doing moderate to vigorous-intensity sports, fitness or recreational\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e337 (87.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49 (12.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHow many minutes or hours patients spend doing moderate-intensity sports, fitness\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;60min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;60min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHow often patients adhere to your prescribed medications for CVD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOften\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3 (50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (16.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHow often patients adhere to your prescribed HIV/AIDS medications\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlways\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e264 (68.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSometimes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e102 (26.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNever\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20 (5.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cp\u003eThe number of meals taken at home per day was significantly associated with CVD in the adjusted model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients consuming more than three meals at home per day had substantially higher odds of CVD (adjusted OR\u0026thinsp;=\u0026thinsp;315.31, 95% CI\u0026thinsp;=\u0026thinsp;16.03\u0026ndash;6200.62). Similarly, consuming meals outside the home more than three times per week was linked to higher odds of CVD (adjusted OR\u0026thinsp;=\u0026thinsp;2.06, 95% CI\u0026thinsp;=\u0026thinsp;1.04\u0026ndash;4.08, p\u0026thinsp;=\u0026thinsp;0.038). Engaging in moderate-to-vigorous physical activity showed an inverse relationship with CVD, though not statistically significant. Patients not participating in such activities had lower odds of CVD (adjusted OR\u0026thinsp;=\u0026thinsp;0.32, 95% CI\u0026thinsp;=\u0026thinsp;0.07\u0026ndash;1.38). Notably, engaging in moderate-intensity activities for 24 to 48 minutes or 48 to 96 minutes was significantly associated with lower odds of CVD (adjusted OR\u0026thinsp;=\u0026thinsp;0.10, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.82 and 0.10, 95% CI\u0026thinsp;=\u0026thinsp;0.01\u0026ndash;0.80, respectively) compared to less than 24 minutes. Patients not engaging in moderate- to vigorous-intensity activities had higher odds of CVD, although this was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.100) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression of Risk Factors Associated with CVD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eBivariate model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eMultivariable model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eaOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of times meals taken at home per day\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 meals/day (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3 meals/day\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.91\u0026ndash;3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e315.31*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.03\u0026ndash;6200.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of times meals are taken outside the home per week\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3 times/week (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;3 times/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.94\u0026ndash;3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.06*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.04\u0026ndash;4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate to vigorous intense activity participation\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026ndash;1.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNumber of days worked per week\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2 days/week (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u0026ndash;4 days/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u0026ndash;6.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.13\u0026ndash;10.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.874\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4 days/week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u0026ndash;2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.06\u0026ndash;4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.518\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMinutes/hours spent in moderate-intense activities\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;24 minutes (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e24\u0026ndash;48 minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u0026ndash;0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e48\u0026ndash;96 minutes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.21*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07\u0026ndash;0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.10*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u0026ndash;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModerate to vigorous-intensity sports or fitness\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.84\u0026ndash;7.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExposed to tobacco smoke\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.74\u0026ndash;3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.98\u0026ndash;4.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eExposed to air pollution in the environment\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.60\u0026ndash;1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.68\u0026ndash;2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eOR\u003c/b\u003e: \u003cem\u003eOdds Ratio;\u003c/em\u003e \u003cb\u003eaOR\u003c/b\u003e: \u003cem\u003eAdjusted odds ratio;\u003c/em\u003e \u003cb\u003eCI\u003c/b\u003e: \u003cem\u003eConfidence Interval; *p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate statistical significance;\u003c/em\u003e \u003cb\u003e-\u003c/b\u003e: \u003cem\u003enot applicablee;\u003c/em\u003e \u003cb\u003e+\u003c/b\u003e: \u003cem\u003enot used in adjusted\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\u003ch2\u003eBiomarkers of Cardiovascular Disease\u003c/h2\u003e\u003cp\u003eThe majority of respondents (71.5%) were classified as underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), with only 7.0% falling into the healthy weight range (BMI 18.5\u0026ndash;24.9). In addition, 15.5% were categorized as overweight (BMI 25.0-29.9), and 6.0% were obese (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30.0). Most participants (97.9%) had normal blood sugar levels (\u0026lt;\u0026thinsp;5.5), while a small proportion was in the prediabetic (1.8%) and diabetic (0.3%) ranges. Regarding other biochemical markers, the majority had levels within normal ranges: 80.3% had haemoglobin levels below 8.0, 90.7% had LDL levels below 2.83, 95.3% had HDL levels below 3.30, and 69.2% had triglyceride levels below 1.5. These findings indicate a high prevalence of underweight status and generally normal blood sugar and lipid profiles within the study population (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics of Potential Mechanisms of Cardiovascular Disease (n\u0026thinsp;=\u0026thinsp;386)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotential Mechanisms\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFrequency (Percent)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI of Respondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18.5\u0026ndash;24.9 (Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;18.5 (Underweight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276 (71.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25.0\u0026ndash;29.9 (Overweight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60 (15.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30.0 (Obese)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23 (6.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood Sugar Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;5.5 mmol/L (Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e375 (97.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5.5\u0026ndash;6.9 mmol/L (Pre-diabetes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7 (1.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;6.9 mmol/L (Diabetes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHaemoglobin Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8.0\u0026ndash;9.9 g/dL (Moderate Anaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e76 (19.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;8.0 g/dL (Severe Anaemia)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e310 (80.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow-Density Lipoprotein (LDL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2.83 mmol/L (Optimal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e350 (90.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.83\u0026ndash;3.37 mmol/L (Moderate Risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.37 mmol/L (High Risk)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9 (2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh-Density Lipoprotein (HDL)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.30 mmol/L (Low)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e368 (95.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.30\u0026ndash;3.81 mmol/L (Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.82 mmol/L (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (2.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.5 mmol/L (Normal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e267 (69.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ndash;1.99 mmol/L (Moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105 (27.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.0\u0026ndash;4.99 mmol/L (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e14 (3.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the logistic regression analysis results examining the association between BMI, biomarkers, and CVD among the study population. In the unadjusted model, individuals with a BMI less than 18.5 had an odds ratio (OR) of 1.74 (95% CI: 0.66\u0026ndash;4.57, p\u0026thinsp;=\u0026thinsp;0.264) compared to those with a BMI of 18.5\u0026ndash;24.9, suggesting 74% higher odds of CVD. However, this result was not statistically significant, as indicated by the wide confidence interval and a p-value greater than 0.05. Similarly, those with a BMI over 30.0 had an OR of 1.36 (95% CI: 0.33\u0026ndash;5.55, p\u0026thinsp;=\u0026thinsp;0.671), indicating 36% higher odds of CVD, though this association was also not statistically significant. For blood sugar levels, individuals with levels between 5.5 and 6.9 had an OR of 0.46 (95% CI: 0.09\u0026ndash;2.41, p\u0026thinsp;=\u0026thinsp;0.357), suggesting lower odds of CVD compared to those with levels below 5.5, but this finding was not significant. No significant association was found for blood sugar levels above 6.9.\u003c/p\u003e\u003cp\u003eIn the adjusted model, controlling for potential confounders, the lack of significant associations between BMI categories and blood sugar levels with CVD remained. However, HDL and triglyceride levels emerged as significant predictors. Higher HDL levels were associated with lower odds of CVD (OR\u0026thinsp;\u0026lt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a protective effect. Similarly, lower triglyceride levels were significantly associated with reduced odds of CVD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings suggest that HDL and triglyceride levels are important biomarkers for assessing CVD risk in this population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic Regression Potential Mechanisms and CVD\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eBivariate model\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariable model\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI of Respondents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnderweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.74 (0.66\u0026ndash;4.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.56 (0.55\u0026ndash;4.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86 (0.29\u0026ndash;2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.94 (0.29\u0026ndash;3.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.913\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately Obese\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.36 (0.33\u0026ndash;5.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.20 (0.27\u0026ndash;5.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBlood Sugar Level\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-diabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.46 (0.09\u0026ndash;2.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHaemoglobin Level\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Anaemia (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSevere Anaemia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.41\u0026ndash;1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.582\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLow-density lipoprotein (LDL)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal (\u0026lt;\u0026thinsp;2.5) (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate Risk (2.5\u0026ndash;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14 (0.38\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.813\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Risk (\u0026gt;\u0026thinsp;3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70 (0.14\u0026ndash;3.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.655\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHigh-density lipoprotein (HDL)\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;3.3 (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3.3\u0026ndash;3.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.17* (0.04\u0026ndash;0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12* (0.03\u0026ndash;0.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11* (0.03\u0026ndash;0.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.18* (0.04\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTriglycerides\u003c/b\u003e\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\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.5 (Ref.)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1.5\u0026ndash;1.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.50* (0.28\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48* (0.26\u0026ndash;0.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.018\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2.0\u0026ndash;4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.11* (0.03\u0026ndash;0.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.12* (0.04\u0026ndash;0.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\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\u003cp\u003e6.12 (2.21\u0026ndash;16.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eOR\u003c/b\u003e: \u003cem\u003eOdds Ratio;\u003c/em\u003e \u003cb\u003eaOR\u003c/b\u003e: \u003cem\u003eAdjusted odds ratio;\u003c/em\u003e \u003cb\u003eCI\u003c/b\u003e: \u003cem\u003eConfidence Interval; *p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicate statistical significance;\u003c/em\u003e \u003cb\u003e-\u003c/b\u003e: \u003cem\u003enot applicablee;\u003c/em\u003e \u003cb\u003e+\u003c/b\u003e: \u003cem\u003enot used in adjusted\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eSource: Field work, 2024\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eINSERT Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e ABOUT HERE\u003c/h2\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study assessed the prevalence, risk factors, and biomarkers linked to CVD among PLWHA in Ghana’s Ahafo Region. The results showed a 16.6% prevalence of CVD, with hypertension being the most prevalent condition. Dietary habits and physical activity were significantly related to CVD risk; specifically, a higher frequency of meals was associated with increased odds of CVD, while engaging in moderate-intensity physical activity was found to lower the risk. Biomarker analysis revealed that elevated HDL levels and reduced triglyceride levels offered protection against CVD. Interestingly, no significant links were identified between BMI, blood glucose levels, and CVD risk. These findings underscore the importance of integrated care for HIV and CVD that focuses on modifiable lifestyle factors.\u003c/p\u003e\u003cp\u003eThe 16.6% CVD prevalence observed in this study aligns with previous research in sub-Saharan Africa, which estimates CVD rates among PLWHA to range from 6–50% (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). These high rates, particularly of hypertension, underscore the increasing burden of CVD in this population, highlighting the need for comprehensive management strategies. A study in Tanzania found a 20–30% prevalence of hypertension among PLWHA (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), supporting the current findings. Additionally, the comorbidity of diabetes and hypertension in 21.9% of participants suggests a complex clinical challenge that necessitates integrated care approaches.\u003c/p\u003e\u003cp\u003eInterestingly, while 18.8% of respondents reported cardiac heart disease, no cases of stroke, coronary artery disease, or other conditions like COPD were self-reported. This discrepancy indicates underdiagnosis or limited access to advanced diagnostic tools, a common issue in many sub-Saharan African settings where resource constraints restrict comprehensive cardiovascular care. Chronic HIV-related inflammation, immune activation, and the metabolic side effects of antiretroviral therapy (ART) likely contribute to the increased risk of CVD in this population. The study reinforces concerns about ART's role in dyslipidemia and insulin resistance, which exacerbate the risk of cardiac conditions.\u003c/p\u003e\u003cp\u003eLifestyle factors further increase CVD risk among PLHIV. While the complete absence of smoking in the study cohort is a positive finding, alcohol consumption remains a concern, with 1.8% of participants reporting recent intake. This low level of alcohol use contrasts with other studies, such as Arora et al., which link alcohol consumption to various CVD risks (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Despite moderate fruit and vegetable consumption among participants, with 71% consuming fruits and 66.8% vegetables at least once a week (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), there remains room for improvement in dietary practices to enhance cardiovascular health.\u003c/p\u003e\u003cp\u003eThe study also finds significant associations between meal frequency and CVD risk. Participants consuming more than three meals per day had substantially higher odds of developing CVD, supporting findings by Chen et al. that excessive meal frequency correlates with increased energy intake and weight gain, thus raising CVD risk (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This highlights the importance of promoting balanced eating habits to mitigate cardiovascular risk among PLHIV.\u003c/p\u003e\u003cp\u003eMedication adherence is critical in managing both HIV and CVD. In this study, 68.4% of participants adhered to ART, and 16.7% adhered to CVD medications, indicating relatively positive outcomes compared to other studies, such as Legesse et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, occasional non-adherence observed in some patients underscores the need for ongoing support to ensure optimal treatment outcomes.\u003c/p\u003e\u003cp\u003ePhysical activity was significantly associated with reduced CVD risk, with participants engaging in moderate-intensity physical activity for 24–48 minutes per day having lower odds of CVD (adjusted OR = 0.10, p \u0026lt; 0.05). This finding aligns with studies emphasizing the protective effects of physical activity against CVD (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). However, despite high levels of reported physical activity in this study, the quality and intensity of exercise remain unclear. Targeted interventions encouraging structured exercise programs may enhance cardiovascular health in PLWHA.\u003c/p\u003e\u003cp\u003eBiomarker analysis revealed that higher HDL levels and lower triglyceride levels were protective against CVD, consistent with findings that HDL plays a crucial role in cardiovascular health (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, no significant associations were found between BMI, blood glucose levels, and CVD, contradicting previous research that identified these factors as key predictors of cardiovascular risk (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This discrepancy suggests that additional longitudinal studies are needed to better understand the role of metabolic indicators in CVD risk among PLWHA.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eLimitations of the study\u003c/h2\u003e\u003cp\u003eNotwithstanding the valuable contributions of this study, it is not without limitations. First, its cross-sectional design prevents causal inferences between identified risk factors and CVD outcomes. Longitudinal studies are needed to establish temporal relationships. Second, self-reported data on lifestyle behaviors, such as diet and physical activity, may be subject to recall bias. Future research should incorporate objective measures of dietary intake and physical activity levels. Lastly, the study focused on two hospitals in the Ahafo Region, which may limit generalizability to other settings in Ghana. Expanding the study to multiple regions would provide a more comprehensive understanding of CVD risk among PLWHA.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion and implication for policy and practice","content":"\u003cp\u003eThis study identifies a 16.6% prevalence of CVD among PLWHA in Ghana’s Ahafo Region, with hypertension and diabetes as the most common conditions. Key factors associated with CVD include meal frequency, moderate-to-vigorous physical activity, and biomarkers like HDL and triglycerides. These findings highlight the multifactorial nature of CVD risk in PLWHA, influenced by traditional factors and HIV-specific elements such as chronic inflammation and ART.\u003c/p\u003e\u003cp\u003eThis study highlights the importance of integrating CVD screening into HIV care settings to facilitate early detection and intervention. Routine assessments of CVD risk, including checks for hypertension and lipid levels, should be part of standard procedures in ART clinics. Given the significant link between meal frequency and CVD risk, it is essential to prioritize nutrition education and dietary counseling to encourage balanced diets and portion control among PLWHA. Furthermore, implementing structured physical activity programs can promote safe and sustainable exercise habits. To tackle the issue of poor adherence to CVD medications, it is fundamental to enhance patient education, provide adherence counseling, and establish medication reminder systems to support long-term cardiovascular health. Moreover, the underdiagnosis of cardiac conditions points to the need for better access to diagnostic tools like echocardiography and lipid profiling, along with training programs for healthcare providers to improve CVD risk management. Policymakers should focus on investing in cost-effective and sustainable interventions, ensuring that HIV care programs adopt comprehensive, multidisciplinary, and multisectoral approaches involving the Ministry of Health, Ghana Health Service, health-based civil society organizations, The Global Fund, World Health Organization, and other relevant organizations to alleviate the CVD burden and enhance long-term health outcomes for PLWHA.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our profound gratitude to Dr. Peter K. Yeboah, Dr. James Duah, and the entire faculty members for their invaluable support and review of the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe corresponding author confirms on behalf of all authors that there have been no involvements that might raise the question of bias in the work reported or in the conclusions, implications, or opinions stated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMD. prepared the initial draft of the manuscript, with equal contributions and revisions from IYM, SKA, PA-K, WD, JA, VFN on conceptualization design, data collection and analysis, drafting and reviewing the manuscript for critical inputs, final approval and to be collectively responsible for the content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish Declaration:\u0026nbsp;\u003c/strong\u003eAll the authors agree with its submission to Discover Public Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe strictly adhered to all ethical guidelines in the preparation of this article, which involved direct engagement with human subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work did not receive funding from any public, commercial, or charitable organisation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe will make available the data supporting this study\u0026apos;s findings upon reasonable request. Interested parties can contact the corresponding author to obtain access to the data, following the appropriate data sharing protocols.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSmit M, Brinkman K, Geerlings S, Smit C, Thyagarajan K, van Sighem A, et al. Future challenges for clinical care of an ageing population infected with HIV: a modelling study. Lancet Infect Dis. 2015;15(7):810\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBigna JJ, Ndoadoumgue AL, Nansseu JR, Tochie JN, Nyaga UF, Nkeck JR, et al. Global burden of hypertension among people living with HIV in the era of increased life expectancy: a systematic review and meta-analysis. J Hypertens. 2020;38(9):1659\u0026ndash;68.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNartey ET, Tetteh RA, Anto F, Sarfo B, Adanu RM. Open Cardiovascular disease risk assessment among adults Access attending HIV Clinic at Korle-Bu. 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Life. 2025;15(3):347.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTaiek N, El Fadili NEH, Belkacem A, Cheikh AA, Kabbadj K, Damoun N et al. The Knowledge Assessment of Cardiovascular Disease Risk Factors: A Cross-Sectional Study. Cureus 16(5):e59774.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMachingura PI, Gomo E, Chikwasha V, Okwanga PN. Prevalence of and Factors Associated with Nephropathy in Diabetic Patients Attending an Outpatients Clinic in Harare, Zimbabwe: Methodological Issues. Am J Trop Med Hyg. 2017;97(3):981\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBertolini G, D\u0026rsquo;Amico R, Nardi D, Tinazzi A, Apolone G. One model, several results: the paradox of the Hosmer-Lemeshow goodness-of-fit test for the logistic regression model. J Epidemiol Biostat. 2000;5(4):251\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHoffmann U, Lu MT, Foldyna B, Zanni MV, Karady J, Taron J, et al. Assessment of Coronary Artery Disease With Computed Tomography Angiography and Inflammatory and Immune Activation Biomarkers Among Adults With HIV Eligible for Primary Cardiovascular Prevention. JAMA Netw Open. 2021;4(6):e2114923.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNoumegni SR, Bigna JJ, Ama Moor Epse Nkegoum VJ, Nansseu JR, Assah FK, Jingi AM, et al. Relationship between estimated cardiovascular disease risk and insulin resistance in a black African population living with HIV: a cross-sectional study from Cameroon. BMJ Open. 2017;7(8):e016835.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eManavalan P, Minja L, Wanda L, Hertz JT, Thielman NM, Okeke NL, et al. It\u0026rsquo;s because I think too much: Perspectives and experiences of adults with hypertension engaged in HIV care in northern Tanzania. PLoS ONE. 2020;15(12):e0243059.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArora M, ElSayed A, Beger B, Naidoo P, Shilton T, Jain N, et al. The Impact of Alcohol Consumption on Cardiovascular Health: Myths and Measures. Glob Heart. 2022;17(1):45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAune D, Giovannucci E, Boffetta P, Fadnes LT, Keum N, Norat T, et al. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality-a systematic review and dose-response meta-analysis of prospective studies. Int J Epidemiol. 2017;46(3):1029\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen HJ, Wang Y, Cheskin LJ. Relationship between frequency of eating and cardiovascular disease mortality in U.S. adults: the NHANES III follow-up study. 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HIV infection and cardiovascular disease. Eur Heart J. 2014;35(21):1373\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cardiovascular Disease, HIV/AIDS, Risk Factors, Prevalence, Antiretroviral Therapy, Sub-Saharan Africa, Biomarkers, Non-Communicable Diseases","lastPublishedDoi":"10.21203/rs.3.rs-6693996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6693996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular disease (CVD) is emerging as a significant concern among people living with HIV/AIDS (PLWHA), due to both traditional and HIV-specific risk factors. However, there is inadequate research in Ghana to inform practice and policy decisions. This hospital-based cross-sectional study assessed CVD prevalence, risk factors, and mechanisms among 386 PLWHA in Ghana\u0026rsquo;s Ahafo Region using interviews, clinical assessments, and biomarkers. We found a 16.6% CVD prevalence, with hypertension being the most common condition and there were significant associations between dietary habits, physical activity, and CVD risk (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients who consumed more than three meals at home per day had significantly higher odds of developing CVD (adjusted odds ratio [AOR]\u0026thinsp;=\u0026thinsp;315.31, 95% confidence interval [CI]: 16.03\u0026ndash;6200.62, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Engaging in moderate-intensity physical activity for 24\u0026ndash;48 minutes was associated with lower odds of CVD (AOR\u0026thinsp;=\u0026thinsp;0.10, 95% CI: 0.01\u0026ndash;0.82, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This study contributes to understanding the multifaceted relationship between HIV, traditional CVD risk factors, and emerging pathways such as physical inactivity and diet. While Body Mass Index and blood glucose levels did not significantly correlate with CVD, higher High-density lipoprotein levels and lower triglyceride levels were protective against CVD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Our findings underscore the need for integrated HIV and CVD care, particularly in addressing modifiable lifestyle factors. Future research should explore longitudinal outcomes and expand to other regions to provide a broader understanding of CVD risk among PLWHA in Ghana.\u003c/p\u003e","manuscriptTitle":"Unveiling the rising Burden of Cardiovascular Disease among People Living with HIV/AIDS in a Sub-Saharan Country of Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-18 13:00:58","doi":"10.21203/rs.3.rs-6693996/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15061ecb-b383-4dee-895c-b495edc5c7bc","owner":[],"postedDate":"July 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T10:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-18 13:00:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6693996","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6693996","identity":"rs-6693996","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}