Health determinants for major noncommunicable diseases among people living with HIV in Rwanda (NCOHIRWA) cohort study: rationale, protocol and baseline characteristics of participants

Health determinants for major noncommunicable diseases among people living with HIV in Rwanda (NCOHIRWA) cohort study: rationale, protocol and baseline characteristics of participants | 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 Health determinants for major noncommunicable diseases among people living with HIV in Rwanda (NCOHIRWA) cohort study: rationale, protocol and baseline characteristics of participants Valentine Dushimiyimana, Marc Twagirumukiza, Madeleine Durand, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5897717/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 8 You are reading this latest preprint version Abstract Background Noncommunicable diseases (NCDs) are the leading cause of death worldwide. Cardiovascular diseases (CVDs) are the top NCD killers globally as well as in Rwanda and other African countries where NCDs and infectious diseases, such as HIV, coexist. To date, the intersection of CVDs and HIV in Rwanda has not been sufficiently studied. We aimed to conduct a comprehensive analysis of CVD-related risk factors and quality of life in people living with HIV (PLHIV) and to develop a country profile on the basis of the prevalence and incidence of CVD, hypertension, and diabetes to inform future interventions. Methods We assessed the risk factors for major NCDs, including CVDs, hypertension, and diabetes among PLHIV in Rwanda in a prospective, controlled cohort study. Men and women aged 18 years and above were recruited from 12 health facilities that offer services for HIV and NCDs. Baseline characteristics and demographic data were collected at baseline via an electronic questionnaire. The follow-up physical, cardiovascular, and laboratory metrics are assessed at each visit. Results A total of 1,546 participants were recruited, comprising 1,234 (79.81%) PLHIV and 312 (20.18%) people without HIV (PWoH). The median age was 44 years (interquartile range, IQR: 17) for PLHIV and 42 years (IQR: 17) for PWoH. A total of 785 (63.6%) PLHIV and 197 (63.1%) PWoH were women. The prevalence of cardiovascular risk factors for PLHIV and PWoH were as follows: 256 (21.0%) versus 75 (24.4%) were overweight; 118 (9.7%) versus 39 (12.6%) were obese; 260 (21.1%) versus 55 (17.7%) reported having ever smoked; 219 (17.8%) versus 84 (26.9%) had a family history of hypertension; and 37 (3.0%) versus 28 (9.1%) had very high fasting blood glucose levels. Conclusion This manuscript presents the protocol and rationale of the NCOHIRWA cohort and baseline data revealing a high prevalence of modifiable NCD risk factors among PLHIV in Rwanda. Findings underscore the need to integrate NCD care into HIV services. The study supports person-centered models addressing dual disease burdens and provides a foundation for longitudinal research and policy to improve chronic disease care for PLHIV in low-resource settings. HIV noncommunicable diseases Rwanda hypertension diabetes cardiovascular diseases Figures Figure 1 Figure 2 Figure 3 Background Noncommunicable diseases (NCDs) are the foremost cause of death worldwide, accounting for 41 million fatalities each year (1). Additionally, every two seconds, an individual succumbs to an NCD before turning 70, resulting in premature death. A staggering 77% of these deaths transpire in low- and middle-income countries (LMICs)(1). Cardiovascular diseases (CVDs) are the predominant cause of mortality within the spectrum of NCDs. They are largely driven by modifiable risk factors, including hypertension, diabetes mellitus, tobacco use, dyslipidemia, excess weight, poor nutrition, and physical inactivity (2). In Rwanda, CVD accounted for 17% of all deaths in 2019, representing 51% of deaths attributed to NCDs (3). At the end of 2023, approximately 39.9 million people were living with HIV, 65% of whom lived in Africa, with the sub-Saharan Africa (SSA) region being home to approximately 20.8 million people living with HIV (PLHIV) (4,5) The widespread availability of antiretroviral therapy (ART) has transformed HIV/AIDS into a manageable chronic condition. Recent figures indicate that 218,314 (92.3%) PLHIV in Rwanda receive ART (6), while globally, 29.8 million people have access to ART (5). Despite improved access to care, PLHIV, particularly those in SSA, with societies undergoing rapid epidemiologic and sociodemographic transitions, are at increased risk of developing NCDs because of accelerated aging and urbanisation (7). In addition to traditional risk factors, PLHIV face chronic, subclinical immune activation and inflammation due to residual antigenic stimulation from HIV and immune dysfunction, as well as side effects from lifelong ART(8). The assessment of CVDs, hypertension, diabetes, and other NCDs is becoming an important element of care in SSA (9). The importance of researching the epidemiology, pathophysiology, prevention, and treatment of complications related to CVDs, hypertension, and diabetes in the context of HIV and subsequently utilising the derived evidence to influence policy and clinical practices is widely recognised (10). Since June 2016, Rwanda has implemented immediate ART initiation upon a person being diagnosed with HIV (11). However, a deficit in capacity persists for providing comprehensive, long-term chronic care services, encompassing the integrated screening and management of prevalent NCDs. Further measures are imperative, as the progress attained through established HIV care and treatment initiatives is at risk of being eroded by the increasing mortality burden attributed to NCDs among PLHIV. The current cohort study was designed with the overarching goal of conducting an extensive analysis of risk factors and determinants associated with CVD, hypertension , and diabetes in PLHIV while also assessing their quality of life. Additionally, it seeks to compile a comprehensive country profile regarding the prevalence and incidence of CVD, hypertension, and diabetes to inform future healthcare services. We hypothesised that the adoption of affordable and validated screening methods, such as lipid profile analysis, fasting blood glucose testing, coagulation and inflammation marker assessments, coupled with precise blood pressure monitoring, will be instrumental in developing a risk stratification framework for CVD occurrence in PLHIV in resource-constrained environments. Here, we present initial quantitative findings from the NCOHIRWA cohort. Methods Design This study utilised a mixed-methods approach within a prospective cohort that comprises PLHIV, newly diagnosed with HIV and people without HIV (PWoH). The qualitative component features perspectives from PLHIV serving as peer educators, healthcare providers, and stakeholders involved in the rollout of HIV and NCD initiatives. Setting The participants were selected from health facilities in five provinces across Rwanda. The preselection process identified health facilities with HIV and NCD services that have suitable infrastructure, trained healthcare professionals capable of conducting exams, a functional electronic medical record (EMR) system, and the capacity to follow up with patients who develop NCDs. Each of Rwanda’s five provinces is represented; in each province, a district or referral hospital or the surrounding health center was included, as shown in the map below in Figure 1. Participants The enrolled participants were 18 years of age or older, engaged in clinical care in an HIV care and treatment program, were capable of providing informed consent, and were willing to commit to long-term follow-up. The exclusion criteria included pregnancy or being less than 3 months postpartum and having a preexisting diagnosis of CVD, hypertension, diabetes, or cancer or the presence of evident mental health disorders. Eligible PLHIV participants already on ART were identified from the program databases of the selected health facilities. Proportional and random selection was used to obtain the needed participants per site. Those who agreed to participate received an appointment for further screening. To confirm participation in the study, on the day of the appointment, all participants were screened for hypertension, those with record of equal to 140/90 mmHg and above were excluded and advised to seek care in the NCD clinic for confirmation and further management. Newly diagnosed HIV-positive participants were defined as individuals first diagnosed with HIV either during recruitment or within six months of starting ART after diagnosis. Upon confirmation of their HIV status, the voluntary testing and counselling (VCT) services team facilitated the connection of these patients with trained mobilisers, comprising nurses and social workers from HIV clinics located at selected sites in proximity to the university teaching hospital of Kigali (CHUK), who assessed the participants' eligibility criteria. If participants agreed to take part in the study, the mobiliser contacted the research personnel at CHUK to arrange enrollment and examinations within a one-week timeframe. PWoH were identified and mobilised from VCT services and the neighboring community, which opted to test for HIV, thus ensuring that our control group came from the same population as the PLHIV enrolled in our study. The inclusion criterion for PWoH was identical to that for PLHIV, with the additional criterion that they must be PWoH and live in the catchment area of the participating health facility. At each visit, all participants were screened for HIV to confirm their status. Outcomes The primary outcomes of the study are as follows: Incidence of CVD risk factors, including high blood pressure, metabolic syndrome features, lipid disorders, elevated blood glucose, and type 2 diabetes The occurrence of CVD events such as ischaemic heart disease (myocardial infarction, hospitalisation for unstable angina, coronary revascularization by percutaneous intervention or surgery, hospitalisation for heart failure, stroke, and cardiovascular mortality). The secondary outcomes include the occurrence of any other NCDs during follow-up and all-cause mortality. The key covariates included age, sex, marital status, education level, employment status, socioeconomic status, smoking status, alcohol use, physical activity, diet, and body mass index (BMI) for PLHIV; type of ARV medication; and known CD4 and viral load. Study procedures Upon enrollment, participants undergo scheduled study visits every 12 months for follow up data collection with a ±12-month window to accommodate missed appointments over a 10 years period . At each visit, participants characteristics are collected to track the specified outcomes, and HIV screening coupled with a counselling session is necessary for PWoH to ascertain their HIV status. Participants diagnosed with HIV at any point during the study are promptly referred to an HIV care and treatment program for confirmation and subsequent management. When an NCD is identified at any visit, the observation is recorded, and the patient is directed to the NCD service for continued care in line with established management protocols. Data collection Data collection and measurement tools included a questionnaire, physical examination, cardiovascular (CV) examination assessement (carotid intima–media thickness measurement (CIMT), electrocardiogram (ECG), and echocardiogram (ECHO)), and laboratory testing. More details are provided in Appendix Table 1. The questionnaire used in this study was adapted from the WHO STEPwise approach to noncommunicable disease risk factor surveillance (STEPS) and customized for this study (12). Weight was measured in kilograms (kg), and height was measured in centimeters (cm). Body mass index (BMI) was calculated by dividing weight (kg) by height (m) squared and expressed as kg/m². The following BMI classifications were used: underweight (<18.5 kg/m²), normal (18.5–24.9 kg/m 2 ), overweight (25.0–29.9 kg/m²), and obese (≥30 kg/m²) (13). Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone). The hip circumference was measured around the widest portion of the buttocks, with the tape parallel to the floor (14). The waist-to-hip ratio (WHR) is determined by dividing the waist circumference (cm) by the hip circumference (cm). A WHR greater than 0.90 in men and 0.85 in women is considered indicative of an elevated metabolic disease risk. For both men and women, a waist-to-height ratio (WHtR) greater than 0.49 is considered indicative of abdominal obesity. WHtR is calculated by dividing the waist measurement (cm) by the height (cm) (15). Blood pressure (BP) readings were taken three times following a 10-minute rest period, spaced 3–5 minutes apart, via a digital automatic blood pressure monitor (Omron M2 Eco (HEM-7120-AF)). For analytical purposes, the mean of these three readings was used. BP classifications were as follows: normal-optimal (less than 130/85 mmHg), high-normal (130–139/85–89 mmHg), grade 1 hypertension (140–159/90–99 mmHg), and grade 2 hypertension (greater than 160/100 mmHg) (16) Glycemia was assessed after 8 to 12 hours of fasting. The results were categorised as follows: low fasting blood glucose (<3.9 mmol/l); normal fasting blood glucose ( < 5.6 mmol/l); high fasting blood glucose ( 5.6 - 6.9 mmol/l ); and ≥7 mmol/l ) on two separate occasions is indicative of diabetes (17). The lipid profile measurements included total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and triglycerides. An abnormal lipid profile was classified as follows: total cholesterol ≥ 200 mg/dL, HDL-c < 40 mg/dL, LDL-c ≥ 130 mg/dL, non-HDL cholesterol ≥ 160 mg/dL, TG ≥ 150 mg/dL, and total cholesterol/HDL-c ratio ≥ 5 (18). LDL-c was calculated via the Martin–Hopkins formula (19), chosen because it provides a comparable estimate for participants with low and normal triglyceride concentrations (400 mg/dL) (18,19). Abnormal levels of any of the aforementioned lipid parameters, one or more, are considered dyslipidemic. Metabolic syndrome was defined as the presence of any three or more of the following conditions: high blood glucose levels, low HDL-c levels, high triglyceride levels, a large waist circumference (>102 cm in men and >88 cm in women), and hypertension. Blood samples for laboratory tests were drawn at healthcare facilities by certified laboratory technicians via a uniform protocol. After centrifugation, the samples were allocated into two cryotubes, one for immediate analysis and the other for biobanking, and stored at -20°C. These samples were later conveyed to the Rwanda National Reference Laboratory, a facility with ISO15189 accreditation. The samples were analysed using the roche Cobas c311, c411, and c4800 systems. Sample size It is assumed that the PLHIV in Rwanda are three times more likely to experience CVD events than the PWoH. Data on the occurrence of CVD events are collected at each study visit and analysed via a generalised linear model (20), considering the occurrence of CVD events as the outcome in the model. A significance level of 5% or 80% probability of detecting a true difference between the occurrence of CVD events in PLHIV and PWoH (power) were assumed. The significance level is set at 5%, with an 80% probability of detecting true differences between CVD events in PLHIV- and PWoH. The total sample size of 1,516 participants was estimated. Accounting for a 10% dropout rate or loss to follow-up, we estimated the enrollment number needed at 1,668 participants. Participants are allocated at a ratio of 4:1 because adults with HIV on treatment are recommended to undergo NCD testing every 3 months, whereas PWoH are advised to test once a year. This allocation corresponds to 1,334 PLHIV and 334 PWoH participants. Additionally, we planned to enrol a convenience sample of 120 newly diagnosed HIV-positive participants. Statistical considerations The categorical variables were summarised using frequencies and percentages. Continuous variables were summarised using means and standard deviations for normally distributed data and medians with interquartile ranges (IQRs) for non-normally distributed data. The incidence of CVD events among PLHIV and PWoH will be calculated as the ratio of the number of CVD events divided by the total number of study participants during the study period. A modified Poisson regression model with robust error variance will be used to evaluate the risk of any CVD event among PLHIV and PWoH. The model is preferred for binary data to estimate adjusted relative risk (21). Given that for any CVD event, the error term is binomially distributed, the misspecification of the variance is corrected by using robust error variance. Initially, an unadjusted analysis will be performed to estimate the risk of any CVD event among the PLHIV and PWoH. HIV status is the main exposure of interest. All variables deemed to be potential confounders (based on prior knowledge and literature) will be considered in the multivariable modified Poisson regression model, including age, sex, family history of CVD, systolic blood pressure, smoking status, total cholesterol, HDL cholesterol, HIV status, and diabetes. The adjusted risk and its corresponding 95% confidence intervals (95% CI) estimated from the model will be reported. The occurrence of CVD risk factors for each subject will be assessed from the baseline visit to the time at which each major CVD risk factor is diagnosed, the time of death, or the time of the last visit to the study site, whichever may occur first. The incidence rates and 95% confidence intervals (CIs) for the occurrence of major CVD risk factors will be calculated as the number of new CVD risk factor events divided by the respective patient years at risk. Kaplan‒Meier time‒to-major CVD risk factor analyses will be used to visualise the major CVD risk factors, and the results will be compared via log-rank tests. Multivariate Cox proportional hazards regression analyses adjusted for participant characteristics will be used to determine the incidence of major CVD risk factors among PLHIV and PWoH. The proportional hazards assumption will be assessed via log-log plots. The results will be reported as hazard ratios (HRs) and 95% CIs. Our data will be benchmarked against the established cardiovascular disease risk models, including the Data Collection on Adverse Events of Anti-HIV drugs (D: A:D) 2010 and 2016, the Framingham Heart Study (FHS-CVD), the coronary heart disease model from the same study (FHS-CHD), Predicting Risk of CVD Events (PREVENT), the Atherosclerotic Cardiovascular Disease (ASCVD) models, and WHO cardiovascular disease risk charts (22–28). These models will be evaluated for their applicability in local contexts, with the objective of developing a suitable model for Rwanda and other countries with similar settings (26, 29). Baseline and participant characteristics as well as physical measurement data were collected via the Research Electronic Data Capture (REDCap) application preloaded on tablets. The laboratory results were extracted from the server of the laboratory information system. Microsoft Excel (MS Excel 16.89.1) and the Statistical Package for the Social Sciences (SPSS , version 28.0.1) were used for data processing and data cleaning, and R (version 4.2.1) was used for data analysis. Ethical considerations The study was approved by the Rwanda National Ethics (RNEC) under approvals No. 210/RNEC/2020 and No.84/RNEC/2021 and aligns with good clinical practice guidelines. To comply with ethical principles, all participants provided written informed consent before taking part in the study. The participants’ information is kept confidential through the use of coded identifiers and a password-protected database. Each participant received a unique research identification number. Results The enrollment of PLHIV and PWoH participants was performed from March 2021 onwards. To date, 80 of the targeted 120 newly diagnosed HIV-positive participants have been recruited. The baseline characteristics of 1546 participants (1234 PLHIV and 312 PWoH) are presented below. The subject selection flowchart is presented in Figure 2, and an overview of the baseline characteristics is presented in Table 2 and Figure 3 (Appendix). The median age was 44 years ( interquartile rage [IQR]: 17) for PLHIV and 42 years (IQR: 17) for PWoH. In total, 785 (63.6%) PLHIV participants and 197 (63.1) PWoH participants were women, 683 (55.3%) and 209 (67.0%) participants were married or in stable relationships, and 603 (48.9%) and 152 (48.7%) participants had completed primary or partial secondary school, respectively. The prevalence of CVD risk factors in PLHIV compared with PWoH at baseline was as follows: The mean body mass index (23.48 ± (kg/m 2 ) ) vs (24.28 ± 4.9 (kg/m 2 )) overweight, 256 (21.0%) vs 75 (24.4%) PWoH; high-normal blood pressure, 262 (21.2%) vs 63 (20.2) participants. A total of 260 (21.1%) of the PLHIV and 55 (17.7%) of the PWoH had a history of smoking, 210 (80.6%) and 36 (71.9%) of whom were current smokers, respectively. A total of 710 (57.6%) PLHIV and 163 (52.2%) PWoH drank alcohol. 37 (3.0%) in PLHIV than 28 (9.1%) PWoH . Liver enzymes (ALAT and ASAT) and creatinine level were higher in PLHIV (ALAT: 17.6 U/L vs 14.1 U/L; ASAT 29.1 U/L vs 24.0 U/L and [77.0 µmol/L (IQR:64.0-91.0)] vs 69.0 µmol/L(IQR:60.0 – 80.0)] in PLHIV compared to PWoH respectively.Total protein levels were similar in PLHIV and PHoW. Discussion To the best of our knowledge, this is the first ongoing cohort study to perform a follow-up of CVD, hypertension, and diabetes epidemiology among PLHIV in Rwanda. HIV in itself, ART, and interactions with underlying (and often unrecognized) risk factors create an important risk of premature NCDs, requiring early detection and preventive measures. This paper presents the design, rationale, and baseline characteristics of participants from a prospective study (NCOHIRWA cohort study) aimed at evaluating the health determinants of major NCDs among PLHIV in Rwanda and supporting the tailoring or development of a suitable model for CVD prediction in Rwanda and other similar settings. This study is expected to provide evidence regarding CVD, hypertension, and diabetes risk in PLHIV in Rwanda. The study results will contribute to improving existing local policies, guide preventive measures, enhance the pathophysiological understanding of the interplay between HIV and NCDs, and ultimately contribute to the adoption of evidence-based guidelines for PLHIV. Compared with PWoH participants, PLHIV are notably more likely to be widowed and have lower educational levels and are also more inclined to present with high blood pressure and be current smokers ( 30 , 31 ). The age and sex distributions of the PLHIV included in our cohort study are in line with the age and sex distributions of all the PLHIV in Rwanda receiving antiretroviral therapy, with more than 20% above 50 years of age and predominantly being women ( 32 ). This women predominance is consistent with other studies ( 33 – 40 ) and global statistics, where 53% of PLHIV were women and girls ( 41 , 42 ). Previous research has shown that the prevalence of overweight among PLHIV ranges from 22.7–28.8% ( 43 – 45 ). In our cohort, 21.0% of PLHIV were overweight and 9.7% were obese at baseline. These preliminary findings from the baseline data align closely with the NCDzz cohort in Zambia and Zimbabwe, which reported similar proportions of adults meeting ideal cardiovascular health (ICVH) metrics, with 60% achieving ideal LS7 scores ( 50 ). The NCDzz study also found that 59% of adults in Uganda, 61% in rural Ghana, and 53% in rural South Africa met ideal cardiovascular health criteria, suggesting that our Rwandan cohort is representative of urban African populations in terms of NCD risk factor distribution. In addition, a recent study conducted in Kenya has similar prevalence of overweight among PLHIV ( 46 ), whereas in the general population, similar high prevalence rates ranging from 18.6–26.8% have been reported ( 47 – 49 ). Differences in prevalence across cohorts may be partly attributable to variations in age structure, ART status at enrollment, and lifestyle factors such as diet and physical activity. Prehypertension rates vary, with our findings falling between those reported in Tanzania (30.3%) and Uganda (16.8%) ( 42 , 51 ). The smoking prevalence among PLHIV in our study was lower than that reported in many other studies, which is attributed to Rwanda's generally low smoking rates. However, it remains higher in PLHIV than in PWoH ( 33 , 34 , 52 – 62 ). Alcohol consumption was greater among PLHIV than among PWoH participants, a trend consistent with findings from other studies indicating a greater prevalence of alcohol use among PLHIV ( 63 , 64 ). Furthermore, in the context of alcohol screening and brief interventions for PLHIV, over half of the participants reported alcohol consumption ( 65 ). Conversely, in separate cohorts, one study reported that 20% of participants had used alcohol in the past six months, whereas another study reported a current alcohol use rate of 43% among the participants, which was lower than that reported in our cohort ( 66 , 67 ). The elevated prevalence of alcohol consumption within the study population is a cause for concern, as it amplifies the risk of CVD, is associated with reduced efficacy of ART, and increases the likelihood of nonadherence to ART ( 68 ). The preliminary findings from the baseline data of the investigated lipid profile among PLHIV compared to PWoH observed modest but noteworthy differences between the participant groups.TC levels were significantly lower (157.3 ± 39.7 mg/dL) vs (165.8 ± 47.6 mg/dL), whereas TG level were comparable in both groups (94.8 mg/dL vs 96.1 mg/dL) and HDLc level were closely similar across groups of participants (47.1 mg/dL vs 46.4 mg/dL). There is a consistency of recent findings with our results ( 69 – 72 ), but the result of lipid profiles varies between studies( 70 ). For triglycerides, the results are consistent with Click or tap here to enter text.( 71 ), and HDLc show similar findings with( 72 ). In contrast to our study, in a cross-sectional study conducted in central Kenya, a substantial prevalence of prediabetes (14.2%) was identified among PLHIV ( 73 ). Most studies consistently revealed elevated fasting blood glucose levels in PLHIV compared with those in PWoH ( 74 ). Additionally, a systematic review reported a high prevalence of prediabetes (15%) among PLHIV in Africa ( 75 ), and another study from a high-income country in the general population reported 8.1% prediabetes in the studied population ( 76 ). Studies have shown that the liver enzyme (ALAT and ASAT) and creatinine are often elevated in PLHIV than PWoH, as documented in PLHIV, due to the hepatotoxicity linked to the taken ART for creatinine level may indicate the early renal impairment ( 77 , 78 ) Total protein level of our participants was similar to the results of Iranian participants, which was similar in PLHIV and PWoH ( 79 ). Our preliminary findings from baseline data are in line with those from other African studies that find a high burden of NCDs and risk factors such as ( 46 , 80 , 81 ); this emphasise the need for reinforcing the health care system to prevent the high burden of NCDs. Across the region, integration of NCD and HIV care remains a challenge. Studies from South Africa and other SSA countries highlight barriers such as under-resourced facilities, fragmented care pathways, and limited access to NCD medications, which may contribute to suboptimal management of comorbid conditions ( 82 ). Our findings reinforce the need for health system strengthening and the adoption of integrated, patient-centered models of care. A notable strength of the methodology of the NCOHIRWA cohort study, as shown by already available preliminary data from baseline findings, lies in its substantial sample size, which exceeds that of comparable studies. Moreover, in contrast to these studies, which adopted a cross-sectional design and were limited to a few specific sites and small sample sizes, the NCOHIRWA cohort study employs a prospective cohort approach. This means that it not only includes a large number of participants but also follows them over an extended period, providing valuable longitudinal data. Rwanda, amidst its socioeconomic transformation similar to other African countries and resulting chaotic lifestyle changes, has experienced an increase in the burden of NCDs among its more vulnerable population ( 46 ). Longitudinal studies in the region, such as those from Zimbabwe and Uganda, have documented a rising cumulative incidence of NCDs among PLHIV, particularly with increasing duration of ART exposure ( 83 , 84 ). In Zimbabwe, NCDs accounted for 56% of morbidity and 40% of mortality among PLHIV in a decade-long cohort, underscoring the importance of integrated NCD and HIV care ( 83 ). This evolution necessitates greater efforts to support the existing healthcare system, especially in addressing the shifting pattern of morbidity from HIV as an acute disease to NCDs. Another aspect of previous studies in LMICs compared with well-established cohorts in high-income countries is that in those cohorts, participants have readily available medical history data. In our LMIC, accessing comprehensive medical histories can be challenging, which hinders the ability to identify risk factors and their influence on health status. A prospective study design offers a more robust approach to establishing risk factors and causation. This cohort will facilitate long-term follow-up and provide a valuable foundation for analysis, moving beyond prediction and allowing us to construct profiles based on actual data. The absence of adequate research facilities in sub-Saharan Africa poses significant challenges for conducting CV examinations and implementing measures across healthcare facilities. Furthermore, the scarcity of experts inclined toward research impedes the development of evidence-based interventions and policies to address the CVD burden in the region. To address these issues, a substudy focused on CV examinations is underway at Kigali (CHUK), where the necessary facilities and expertise are readily accessible. By harnessing the existing infrastructure and the expertise available at CHUK, this substudy holds promise for generating valuable insights into cardiovascular health and disease management within the SSA context. The lack of research funding for public health research on NCDs in SSA poses a significant threat to the region's health. Despite the disproportionate burden of NCDs in LMICs, including SSA, research funding continues to focus primarily on communicable diseases, neglecting the needs of populations experiencing excess morbidity and mortality from NCDs, as well as low research output compared with other regions ( 85 – 89 ). Additionally, the absence of quality health information due to the funding gap is a major obstacle in developing national strategies for an effective response to NCDs, perpetuating the dependence of African research on Western nations for support and perpetuating global health disparities in research output and funding allocation ( 90 , 91 ). This study employs a rigorous approach, utilising standard and validated questionnaires along with precise measurement protocols, to gather data on socioeconomic factors and lifestyle. Notably, our study stands out for its inclusion of both rural and urban participants, whereas many other studies tend to concentrate solely on either urban or rural populations. Challenges and limitations Due to the COVID-19 pandemic, studies have faced numerous logistic challenges, mainly in the supply chain of the needed laboratory reagents and consumables procured abroad. The procurement process is delayed on the basis of transportation facility closure and stock out of the key materials from the manufacturers, and it is representative of Rwanda. The protocol was adjusted, and the ethics committee was notified during the annual renewal of the approval. Another challenge encountered in practice was the participants' lack of awareness regarding their blood pressure status. Approximately 10% of the participants had blood pressure measurements exceeding 140/90, and they were subsequently excluded on their appointment day. This necessitated an extension of the enrollment period to achieve the desired sample size. While our cohort is broadly representative of PLHIV attending health facilities in Rwanda, differences in health care access, urbarnisation, and demographic structure may limit direct comparability with non-facility based population in other countries. This cohort included participants who were still young, as PLHIV have a two- to three-fold greater relative risk of cardiovascular diseases than the general population does, as those who are born with HIV at 18 years of age are at high risk. It is easy to know when a participant is enrolled in HIV treatment, but one can know the duration of the infection from the existing record or from the participants themselves. This important information was not considered for this study. Furthermore our cohort baseline data reflect a relatively young cohort,which may partially explain the higher proportion of participants meeting ideal CVD health criteria compared to older cohort such as in the REPRIEVE trial ( 92 ). As our study progresses, longitudinal follow up will allow for more nuanced comparison of incident NCD outcomes and the evolving risk profile of PLHIV in Rwanda. Conclusion and recommendations Although the primary aim of this manuscript is to describe the study protocol, methodology, and rationale, we have also reported already available baseline data. These findings reveal a significant prevalence of modifiable risk factors for NCDs among PLHIV in Rwanda. This has important implications for long-term HIV care, highlighting the urgent need to integrate NCD screening, prevention, and management into routine HIV services. As Rwanda and other low-resource settings continue to make strides in HIV treatment, person-centered models that address both infectious and chronic disease burdens are essential to sustaining health gains and improving quality of life. The NCOHIRWA cohort methodology and rationale work provides a vital foundation for future longitudinal analyses and policy development to support integrated chronic disease care for PLHIV. Abbreviations ALAT: alanine aminotransferase ART: Antiretroviral therapy ASAT: Aspartate aminotransferase BMI: Body mass index BP: Blood pressure CI: Confidence interval CHUK: University Teaching Hospital of Kigali CMIT: Carotid Intima Media Thickness CV: cardiovascular CVDs: Cardiovascular diseases D:A:D: Data collection on the adverse effects of anti-HIV drugs ECHO: Echocardiogram ECG: Electrocardiogram EMR: Electronic medical record HDL-c: high-density lipoprotein cholesterol FHS-CHD: Framingham Heart Study-coronary heart disease FHS-CVD: Framingham Heart Study Cardiovascular Disease hsCRP: Highly sensitive C-reactive protein; IQR: interquartile range LDL-c: Low-density lipoprotein cholesterol LMICs: Low- and middle-income countries Max: Maximum Min: Minimum NCDs: Non-Communicable Diseases PLHIV: People living with HIV PWoH: People without HIV REDCap: Research Electronic Data Capture RNEC: Rwanda National Ethics Committee SSA: Sub-Saharan Africa SPPS: Statistical Package for the Social Sciences STEPS: STEPwise approach to surveillance VCT: Voluntary Counselling and Testing WHR: waist-to-hip ratio WHtR: waist-to-height ratio WHO: World Health Organisation Declarations Ethics approval and consent to participate The study was conducted in accordance with the Helsinki Declaration and approved by the Rwanda National Ethics Committee, reference number 210/RNEC/2020 and No. 84/RNEC/2021 and aligns with good clinical practice. All participants gave their informed consent for inclusion before they participated in the study. Consent for publication Not applicable Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study was implemented under funds from the Rwanda National Council for Science and Technology (NCST) under excellent research funding. The funder of the study had no role in the design, data collection, data analysis, data interpretation or writing of the manuscript. Authors’ contributions VD,MT,MD,NABN,BAK,NB,LN,PN,VNU,PK,VN,JM,AD,CI,YDNU,KMI,CH,SN,ER,SC, CM,JCSN,ER,JPM,CMM,AT,GNR,GM,ST,FU,EN,SPN,NG,KE,FU,TK,AM,MS,LB,JDK,AU, MM,FB,VK,FD, AK,PM,AM,SK,KG,JBT,DM : The study inception, implementation, input to the drafted manuscript, read and approved manuscript VD,MT,MD,NABN,BAK,NB,LN,PK,VNU,PK,VN,JM,AD,CIYDNU,KMI,CH,SN,ER,SC : Data collection tools design VD,MT,MD, ER, VNU, AD, VN, and LN : Data cleaning , analysis and interpretation MT,MD,NABN, ER,SC: Supervision Acknowledgement We acknowledge all the technical assistance provided to the authors by the NCOHIRWA study group and anyone who had helpful discussions or contributed fewer tangible concepts. The Rwanda Biomedical Centre, a research implementation coordinating division including the Research, Innovation, and Data Science, HIV, Noncommunicable Diseases, National Reference Laboratory, and Finance Department for facilitating the study, is in strong collaboration with the Rwanda Network for the People Living with HIV and the Rwanda NCD Alliance. The research assistants from the research sites, research sites leadership, and the study participants, for their time and effort, conducted this study. The collaborators contributed in this study with their affialtion: NCOHIRWA cohort study group members contributed in this manuscripts: Clarisse Musanabaganwa 1 , Jean Claude Semuto Ngabonziza 1 , Eric Remera 1 , Jean Pierre Musabyimana 1 , Claude Mambo Muvunyi 1 , Albert Tuyishime 1 , Gallican Nshogoza Rwibasira 1 , Gregorie Muhorakeye 1 , Simeon Tuyishime 1,7 , Francois Uwinkindi 1 , Evarist Ntaganda 1 , Simon Pierre Niyonsenga 1 , Noel Gahamanyi 1 , Kayigi Etienne 1 , Fidel Umwanankabandi 1 , Theogene Kubahoniyesu 1, Alphonse Mbarushiman 14 , Muhammed Semakula 12 , Leopold Bitunguhari 5,18 , Jean Damascene Kabakambira 18,19 , Annette Uwineza 5 , Marcellin Musabende 18 , Felix Babane 18 , Vincent Karamuka 18 , Fidens Dusabeyezu 5,18 , Alyson Kelvin 20 , Pacifique Mukashema 1,21 , Augustin Murindabigwi 22 , Steven Karera 1,23 , Katia Giguère 6 , Katende Godfrey 5,24 , Jean Berchmans Tugirimana 21 , Deo Mutambuka 21 References Noncommunicable diseases [Internet]. [cited 2024 Oct 7]. 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Incidence of non-communicable diseases (NCDs) in HIV patients on ART in a developing country: Case of Zimbabwe’s Chitungwiza Central Hospital—A retrospective cohort study (2010–2019). PLoS One. 2021 May 27;16(5):e0252180. Kintu A, Sando D, Guwatudde D, Bahendeka S, Kawungezi PC, Mutungi G, et al. Quantifying the burden of cardiovascular diseases among people living with HIV in sub-Saharan Africa: findings from a modeling study for Uganda. J Glob Health Rep. 2020 Sep 1;4. Pastakia SD, Pekny CR, Manyara SM, Fischer L. Diabetes in sub-Saharan Africa – from policy to practice to progress: targeting the existing gaps for future care for diabetes. Diabetes Metab Syndr Obes [Internet]. 2017 Jun 22 [cited 2024 Jan 9];10:247. Available from: /pmc/articles/PMC5489055/ Jones AC, Geneau R. Assessing research activity on priority interventions for non-communicable disease prevention in low- and middle-income countries: a bibliometric analysis. Glob Health Action [Internet]. 2012 [cited 2024 Jan 8];5(1):1–13. Available from: https://doi.org/10.3402/gha.v5i0.18847 Kwanjo Banda C, Hosseinipour MC, Kumwenda J, Peter N, Banda K, Makwero M, et al. Systems Capacity To Conduct Non-Communicable Disease Focused Implementation Research In The Malawian Health Sector: A National Needs Assessment. 2021 May 26 [cited 2024 Jan 8]; Available from: https://www.researchsquare.com Chu KM, Jayaraman S, Kyamanywa P, Ntakiyiruta G. Building Research Capacity in Africa: Equity and Global Health Collaborations. PLoS Med [Internet]. 2014 [cited 2024 Jan 8];11(3):e1001612. Available from: https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001612 Tindana P, Ramsay M, Klipstein-Grobusch K, Amoakoh-Coleman M. Advancing non-communicable diseases research in Ghana: key stakeholders’ recommendations from a symposium. Ghana Med J [Internet]. 2020 Jun 30 [cited 2024 Jan 8];54(2):121–5. Available from: https://www.ajol.info/index.php/gmj/article/view/199209 Maher D, Harries AD, Zachariah R, Enarson D. A global framework for action to improve the primary care response to chronic non-communicable diseases: A solution to a neglected problem. BMC Public Health [Internet]. 2009 Sep 22 [cited 2024 Jan 8];9(1):1–7. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-9-355 Laabes EP, Desai R, Zawedde SM, Glew RH. How much longer will Africa have to depend on western nations for support of its capacity‐building efforts for biomedical research? Tropical Medicine & International Health [Internet]. 2011 Mar 5 [cited 2024 Jan 8];16(3):258–62. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/j.1365-3156.2010.02709.x Grinspoon SK, Fitch K V., Overton ET, Fichtenbaum CJ, Zanni M V., Aberg JA, et al. Rationale and design of the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE). Am Heart J [Internet]. 2019 Jun;212:23–35. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0002870319300456 WHO. Cardiovascular diseases (CVDs). KEY FACTS [Internet]. 2021; Available from: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) WHO. WHO. [cited 2023 May 15]. Diabetes. Available from: https://www.who.int/health-topics/diabetes#tab=tab_1 Brown JS, Amend SR, Austin RH, Gatenby RA, Hammarlund EU, Pienta KJ. Updating the Definition of Cancer. Mol Cancer Res [Internet]. 2023 Nov 1 [cited 2024 Jan 4];21(11):1142–7. Available from: https://pubmed.ncbi.nlm.nih.gov/37409952/ Mental disorders [Internet]. [cited 2025 Jan 4]. Available from: https://www.who.int/news-room/fact-sheets/detail/mental-disorders Tables Table 1. Description of the study tools and measurement Tools/items Description Which participants Questionnaires STEPwise approach to NCDS risk factor surveillance (STEPS): demographic data (age, sex, marital status, education level and employment status, social-economic status, health insurance) Past individual and family diseases history (hypertension, diabetes, and CVD), lifestyle information. All Physical examinations Height, weight, BMI, waist and hip circumference, resting BP and heart rate All CV examinations 1 Carotid intima-media thickness (CIMT): in 12 segments including the bilateral far and near walls of the common carotid, bifurcation and internal carotid regions). Electrocardiogram (ECG) and Echocardiogram (ECHO). Newly diagnosed HIV positive 2 Laboratory testing 3 Lipid profile (total cholesterol, high-density lipoprotein cholesterol (HDL-c), Low-density lipoprotein cholesterol (LDL-c), triglycerides). Fasting blood glucose Aspartate Aminotransferase (ASAT), Alanine Aminotransferase (ALAT), creatinine and total protein All Viral Load (VL) PLHIV HIV recency test CD4 count Newly diagnosed HIV positive Potential biomarkers of CVD: high sensitive C-Reactive Protein (hsCRP), interleukin-6 (IL-6) and associated cytokines, D-dimer, fibrinogen and early markers of cardiovascular diseases. Newly diagnosed HIV positive 4 Key definitions: Definitions of the eligibility criteria Hypertension is characterised as the presence of two or three consecutive measurements of systolic (SBP) and diastolic blood pressure (DBP), where the mean SBP is equal to or exceeds 140 mmHg and/or the mean DBP is equal to or exceeds 90 mmHg, and includes individuals who are using antihypertensive medication (16). CVDs are defined as a group of disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease, rheumatic heart disease, peripheral arterial disease, congenital heart diseases, deep vein thrombosis and pulmonary embolism (93). Diabetes is a chronic, metabolic disease characterised by elevated levels of blood glucose (or blood sugar) (94). Cancer is defined as a disease of uncontrolled proliferation by transformed cells subjected to evolution by natural selection (95). Mental disorders are characterised by a clinically significant disturbance in an individual’s cognition, emotional regulation or behavior(96). Table 2. Baseline characteristics of the study participants Variable PLHIV PWoH Sociodemographics N= 1234 N=312 Mean Age (years) 44.0 ±11.62 42 ± 12.03 Age (Min, Max) 18-81 18-75 Sex (women; %) 785 (63.6) 197(63.1) Marital status (%) Single 191(15.5) 60 (19.2) Married or cohabitant 684 (55.5) 209 (67.0) Divorced or separated 121 (9.8) 18 (5.8) Widowed 238 (19.3) 25 (8.0) Educational level (%) No formal education 168 (13.6) 29 (9.3) Less than primary 324 (26.3) 66 (21.2) Primary or partial secondary 603 (48.9) 152 (48.7) Secondary 90 (7.3) 33 (10.6) Beyond secondary 49 (4.0) 32 (10.3) Occupation (%) Employed (government or nongovernment) 71 (5.8) 71 (22.9) Self-employed 266 (21.7) 61 (19.7) Farmer 396 (32.1) 87 (28.1) Other 316 (25.7) 67 (21.6) Unemployed 177 (14.9) 24 (7.8) Physical measurement Height(cm) 162.07 ± 8.3 161.4 ± 8.3 5 Weight (kg) 61.64 ± 12.4 63.19 ± 12.8 BMI 6 (kg/m 2 ) 23.48 ± 4.51 24.28 ± 4.93 Underweight/normal/overweight/obese (%) 7 118 (9.7)/725 (59.6)/256 (21.0)/118 (9.7) 21(6.8)/174 (56.3)/75 (24.3)/39 (12.6) Systolic BP 8 116.5 ± 11.5 116.3 ± 11.8 Diastolic BP 74.3 ± 8.15 74.15 ± 7.72 High-normal BP (at exam; %) 9 262 (21.2) 63 (20.2) Heart rate 72.9 ± 11.42 71.7 ± 12.13 CV risk factors Smoking (have smoked %) 260 (21.1) 55 (17.7) Smoking (current %), based on ever smoke 209.6 (80.6) 39.5 (71.9) Alcohol drinking (%) 710.8 (57.6) 162.9 (52.2) Eating fruit (%) 1025.5 (83.1) 264.9 (84.9) Salt intake (%) 1194.5 (96.8) 298.9 (95.8) Family History of: - Hypertension (%) 219 (17.8) 84 (26.9) - CVD (%) 209.6(5.6) 18.0 (5.8) - Diabetes (%) 132.0 (10.7) 34.9 (11.2) - Stroke (%) 7.4 (0.6) 4.9 (1.6) Laboratory data Total cholesterol (mg/dl) 157.3 ± 39.7 165.8 ± 47.6 Triglycerides (mg/dl) 94.8 (70.3 – 129.3) 96.1 (72.3 – 134.0) 10 HDL Cholesterol (mg/dl) 47.1 (39.4-58.3) 46.4 (38.6-56.8) Glucose (mmol/l) 4.95 ± 1.10 5.00 ± 1.82 High fasting blood glucose (from lab; %) 37 (3.0) 28 (9.1) ALAT (U/L) 17.6 (12.7-26.3) 14.1 (10.3-20.1) ASAT (U/L) 29.1 (23.7-37.8) 24.0 (19.7-30.8) Total Protein(g/l) 79.30 ± 10.91 79.64 ± 14.31 Creatinine(µmol/L) 77.0 (64.0-91.0) 69.0 (60.0-80.0) Taken at CHUK by the trained cardiologist and radiology resident, analyses will be performed by cardiologist from Cape Town and Ghent university. Exam for Newly diagnosed HIV positive only Laboratory testing for all participants (PLHIV, PWoH and Newly diagnosed HIV positive) Laboratory exams for Newly diagnosed HIV positive with possible extension to other groups if funds allow. Data are either mean± SD for normally distributed data or median (interquartile range) for nonnormally distributed data, or percentages for categorical data. Body Mass Index BMI category: Underweight= 30 Blood Pressure (Systolic and diastolic) BP range used to summarise data with skewed distribution includes normal-optimal (160/100 mmHg). Data are either the mean ± SD used to summarise without skewed distribu Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 07 Nov, 2025 Reviewers agreed at journal 01 Oct, 2025 Reviewers invited by journal 30 Sep, 2025 Submission checks completed at journal 06 May, 2025 Editor assigned by journal 06 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5897717","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452440444,"identity":"6439a80d-a9a9-4baf-b0de-2e8a663af8a8","order_by":0,"name":"Valentine 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research sites composed of health centers and hospitals (districts, provinces and referrals)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5897717/v1/89f1525f5d7feb818dfe2018.png"},{"id":87572433,"identity":"c2b5ffe8-4b9c-4c35-a795-b824c3fc9a89","added_by":"auto","created_at":"2025-07-25 11:00:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17293,"visible":true,"origin":"","legend":"\u003cp\u003eNCOHIRWA cohort study participant selection flow chart\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5897717/v1/bc4aa5d733852d8c948eba64.png"},{"id":87572744,"identity":"938adbc7-4c13-4f9a-b014-adfe56064d85","added_by":"auto","created_at":"2025-07-25 11:08:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19305,"visible":true,"origin":"","legend":"\u003cp\u003eBaseline characteristics of the study participants\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5897717/v1/7b2288286c365e425f87e288.png"},{"id":87574259,"identity":"243807d9-01ee-4b89-bfa4-d03ab26f19da","added_by":"auto","created_at":"2025-07-25 11:24:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1013763,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5897717/v1/37dc71ec-d693-4abe-9c3d-4202d7432b0d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Health determinants for major noncommunicable diseases among people living with HIV in Rwanda (NCOHIRWA) cohort study: rationale, protocol and baseline characteristics of participants","fulltext":[{"header":"Background","content":"\u003cp\u003eNoncommunicable diseases (NCDs) are the foremost cause of death worldwide, accounting for 41 million fatalities each year (1). Additionally, every two seconds, an individual succumbs to an NCD before turning 70, resulting in premature death. A staggering 77% of these deaths transpire in low- and middle-income countries (LMICs)(1). Cardiovascular diseases (CVDs) are the predominant cause of mortality within the spectrum of NCDs. They are largely driven by modifiable risk factors, including hypertension, diabetes mellitus, tobacco use, dyslipidemia, excess weight, poor nutrition, and physical inactivity (2).\u0026nbsp;In Rwanda, CVD accounted for 17% of all deaths in 2019, representing 51% of deaths attributed to NCDs (3).\u003c/p\u003e\n\u003cp\u003eAt the end of 2023, approximately 39.9 million people were living with HIV, 65% of whom lived in Africa, with the sub-Saharan Africa (SSA) region being home to approximately 20.8 million people living with HIV (PLHIV) (4,5)\u003c/p\u003e\n\u003cp\u003eThe widespread availability of antiretroviral therapy (ART) has transformed HIV/AIDS into a manageable chronic condition. Recent figures indicate that 218,314 (92.3%) PLHIV in Rwanda receive ART\u0026nbsp;(6), while globally, 29.8 million people have access to ART (5). Despite improved access to care, PLHIV, particularly those in SSA, with societies undergoing rapid epidemiologic and sociodemographic transitions, are at increased risk of developing NCDs because of accelerated aging and urbanisation (7). In addition to traditional risk factors, PLHIV face chronic, subclinical immune activation and inflammation due to residual antigenic stimulation from HIV and immune dysfunction, as well as side effects from lifelong ART(8).\u003c/p\u003e\n\u003cp\u003eThe assessment of CVDs, hypertension, diabetes, and other NCDs is becoming an important element of care in SSA\u0026nbsp;(9). The importance of \u0026nbsp;researching the epidemiology, pathophysiology, prevention, and treatment of complications related to CVDs, hypertension, and diabetes in the context of HIV and subsequently utilising the derived evidence to influence policy and clinical practices is widely recognised (10).\u003c/p\u003e\n\u003cp\u003eSince June 2016, Rwanda has implemented immediate ART initiation upon a person being diagnosed with HIV (11). However, a deficit in capacity persists for \u0026nbsp;providing comprehensive, long-term chronic care services, encompassing the integrated screening and management of prevalent NCDs.\u0026nbsp;Further measures are imperative, as the progress attained through established HIV care and treatment initiatives is at risk of being eroded by the increasing mortality burden attributed to NCDs among PLHIV.\u003c/p\u003e\n\u003cp\u003eThe current cohort study was designed with the overarching goal of conducting an extensive analysis of risk factors and determinants associated with CVD, hypertension , and diabetes in PLHIV while also assessing their quality of life. Additionally, it seeks to compile a comprehensive country profile regarding the prevalence and incidence of CVD, hypertension, and diabetes to inform future healthcare services. We hypothesised that the adoption of affordable and validated screening methods, such as lipid profile analysis, fasting blood glucose testing, coagulation and inflammation marker assessments, coupled with precise blood pressure monitoring, will be instrumental in developing a risk stratification framework for CVD occurrence in PLHIV in resource-constrained environments. Here, we present initial quantitative findings from the NCOHIRWA cohort.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch3\u003eDesign\u003c/h3\u003e\n\u003cp\u003eThis study utilised a mixed-methods approach within a prospective cohort that comprises PLHIV, newly diagnosed with HIV and people without HIV (PWoH). The qualitative component features perspectives from PLHIV serving as peer educators, healthcare providers, and stakeholders involved in the rollout of HIV and NCD initiatives.\u003c/p\u003e\n\u003ch3\u003eSetting\u003c/h3\u003e\n\u003cp\u003eThe participants were selected from health facilities in five provinces across Rwanda. The preselection process identified health facilities with HIV and NCD services that have suitable infrastructure, trained healthcare professionals capable of conducting exams, a functional electronic medical record (EMR) system, and the capacity to follow up with patients who develop NCDs. Each of Rwanda\u0026rsquo;s \u0026nbsp;five provinces \u0026nbsp;is represented; in each province, a district or referral hospital or the surrounding health center was included, as shown in the map below in Figure 1.\u003c/p\u003e\n\u003ch3\u003eParticipants\u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe enrolled participants were 18 years of age or older, engaged in clinical care in an HIV care and treatment program, were capable of providing informed consent, and were willing to commit to long-term follow-up. The exclusion criteria included pregnancy or being less than 3 months postpartum and having a preexisting diagnosis of CVD, hypertension, diabetes, or cancer or the presence of evident mental health disorders.\u003c/p\u003e\n\u003cp\u003eEligible PLHIV participants already on ART were identified from the program databases of the selected health facilities. Proportional and random selection was used to obtain the needed participants per site. Those who agreed to participate received an appointment for further screening. To confirm participation in the study, on the day of the appointment, all participants were screened for hypertension, those with record of equal to 140/90\u0026nbsp;mmHg and above were excluded and advised to seek care in the NCD clinic for confirmation and further management.\u003c/p\u003e\n\u003cp\u003eNewly diagnosed HIV-positive participants were defined as individuals first diagnosed with HIV either during recruitment or within six months of starting ART after diagnosis.\u003c/p\u003e\n\u003cp\u003eUpon confirmation of their HIV status, the voluntary testing and counselling (VCT) services team facilitated the connection of these patients with trained mobilisers, comprising nurses and social workers from HIV clinics located at selected sites in proximity to the university teaching hospital of Kigali (CHUK), who assessed the participants\u0026apos; eligibility criteria. If participants agreed to take part in the study, the mobiliser contacted the research personnel at CHUK to arrange enrollment and examinations within a one-week timeframe.\u003c/p\u003e\n\u003cp\u003ePWoH were identified and mobilised from VCT services and the neighboring community, which opted to test for HIV, thus ensuring that our control group came from the same population as the PLHIV enrolled in our study. The inclusion criterion for PWoH was identical to that for PLHIV, with the additional criterion that they must be PWoH and live in the catchment area of the participating health facility. At each visit, all participants were screened for HIV to confirm their status.\u003c/p\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcomes of the study are as follows:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIncidence of CVD risk factors, including high blood pressure, metabolic syndrome features, lipid disorders, elevated blood glucose, and type 2 diabetes\u003c/li\u003e\n \u003cli\u003eThe occurrence of CVD events such as ischaemic heart disease (myocardial infarction, hospitalisation for unstable angina, coronary revascularization by percutaneous intervention or surgery, hospitalisation for heart failure, stroke, and cardiovascular mortality).\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe secondary outcomes include the occurrence of any other NCDs during follow-up and all-cause mortality.\u003c/p\u003e\n\u003cp\u003eThe key covariates included age, sex, marital status, education level, employment status, socioeconomic status, smoking status, alcohol use, physical activity, diet, and body mass index (BMI) for PLHIV; type of ARV medication; and known CD4 and viral load.\u003c/p\u003e\n\u003ch3\u003eStudy procedures\u003c/h3\u003e\n\u003cp\u003eUpon enrollment, participants undergo scheduled study visits every 12 months for follow up data collection \u0026nbsp;with a \u0026plusmn;12-month window to accommodate missed appointments over a 10 years period . At each visit, participants characteristics are collected to track the specified outcomes, and HIV screening coupled with a counselling session is necessary for PWoH\u0026nbsp;to ascertain their HIV status. Participants diagnosed with HIV at any point during the study are promptly referred to an HIV care and treatment program for confirmation and subsequent management. When an NCD is identified at any visit, the observation is recorded, and the patient is directed to the NCD service for continued care in line with established management protocols.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eData collection and measurement\u0026nbsp;tools included a questionnaire, physical examination, cardiovascular (CV) examination assessement (carotid intima\u0026ndash;media thickness measurement (CIMT), electrocardiogram (ECG), and echocardiogram (ECHO)), and laboratory testing. More details are provided in Appendix Table 1. The questionnaire used in this study was adapted from the WHO STEPwise approach to noncommunicable disease risk factor surveillance (STEPS) and customized for this study (12).\u003c/p\u003e\n\u003cp\u003eWeight was measured in kilograms (kg), and height was measured in centimeters (cm). Body mass index (BMI) was calculated by dividing weight (kg) by height (m) squared and expressed as kg/m\u0026sup2;. The following BMI classifications were used: underweight (\u0026lt;18.5 kg/m\u0026sup2;), normal (18.5\u0026ndash;24.9 kg/m\u003csup\u003e2\u003c/sup\u003e), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese\u0026nbsp;(\u0026ge;30\u0026nbsp;kg/m\u0026sup2;) (13). Waist circumference was measured at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone). The hip circumference was measured around the widest portion of the buttocks, with the tape parallel to the floor (14). The waist-to-hip ratio (WHR) is determined by dividing the waist circumference (cm) by the hip circumference (cm). A WHR greater than 0.90 in men and 0.85 in women is considered indicative of an elevated metabolic disease risk. For both men and women, a waist-to-height ratio (WHtR) greater than 0.49 is considered indicative of abdominal obesity. WHtR is calculated by dividing the waist measurement (cm) by the height (cm) (15).\u003c/p\u003e\n\u003cp\u003eBlood pressure (BP) readings were taken three times following a 10-minute rest period, spaced 3\u0026ndash;5 minutes apart, via a digital automatic blood pressure monitor (Omron M2 Eco (HEM-7120-AF)). For analytical purposes, the mean of these three readings was used. BP classifications were as follows: normal-optimal (less than 130/85 mmHg), high-normal (130\u0026ndash;139/85\u0026ndash;89 mmHg), grade 1 hypertension (140\u0026ndash;159/90\u0026ndash;99 mmHg), and grade 2 hypertension (greater than 160/100 mmHg) (16)\u003c/p\u003e\n\u003cp\u003eGlycemia was assessed after 8 to 12 hours of fasting. The results were categorised as follows: low fasting blood glucose (\u0026lt;3.9 mmol/l); normal fasting blood glucose ( \u0026lt; 5.6 mmol/l); high fasting blood glucose ( 5.6 - 6.9 mmol/l ); and \u0026ge;7 mmol/l ) on two separate occasions is indicative of diabetes (17).\u003c/p\u003e\n\u003cp\u003eThe lipid profile measurements included total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), and triglycerides. An abnormal lipid profile was classified as follows: total cholesterol \u0026ge; 200 mg/dL, HDL-c \u0026lt; 40 mg/dL, LDL-c \u0026ge; 130 mg/dL, non-HDL cholesterol \u0026ge; 160 mg/dL, TG \u0026ge; 150 mg/dL, and total cholesterol/HDL-c ratio \u0026ge; 5 (18).\u003c/p\u003e\n\u003cp\u003eLDL-c was calculated via the Martin\u0026ndash;Hopkins formula (19), chosen because it provides a comparable estimate for participants with low and normal triglyceride concentrations (\u0026lt;70 mg/dL and 70 - 400 mg/dL, respectively) and a more accurate estimate at high triglyceride concentrations (\u0026gt;400 mg/dL) (18,19). Abnormal levels of any of the aforementioned lipid parameters, one or more, are considered dyslipidemic.\u003c/p\u003e\n\u003cp\u003eMetabolic syndrome was defined as the presence of any three or more of the following conditions: high blood glucose levels, low HDL-c levels, high triglyceride levels, a large waist circumference (\u0026gt;102 cm in men and \u0026gt;88 cm in women), and hypertension.\u003c/p\u003e\n\u003cp\u003eBlood samples for laboratory tests were drawn at healthcare facilities by certified laboratory technicians via a uniform protocol. After centrifugation, the samples were allocated into two cryotubes, one for immediate analysis and the other for biobanking, and stored at -20\u0026deg;C. These samples were later conveyed to the Rwanda National Reference Laboratory, a facility with ISO15189 accreditation. The samples were analysed using the roche Cobas c311, c411, and c4800 systems.\u003c/p\u003e\n\u003ch3\u003eSample size\u003c/h3\u003e\n\u003cp\u003eIt is assumed that the PLHIV in Rwanda are three times more likely to experience CVD events than the PWoH. Data on the occurrence of CVD events are collected at each study visit and analysed via a generalised linear model (20), considering the occurrence of CVD events as the outcome in the model. A significance level of 5% or 80% probability of detecting a true difference between the occurrence of CVD events in PLHIV and PWoH (power) were assumed. The significance level is set at 5%, with an 80% probability of detecting true differences between CVD events in PLHIV- and PWoH. The total sample size of 1,516 participants was estimated. Accounting for a 10% dropout rate or loss to follow-up, we estimated the enrollment number needed at 1,668 participants. Participants are allocated at a ratio of 4:1 because adults with HIV on treatment are recommended to undergo NCD testing every 3 months, whereas PWoH are advised to test once a year. This allocation corresponds to 1,334 PLHIV and 334 PWoH participants. Additionally, we planned to enrol a convenience sample of 120 newly diagnosed HIV-positive participants.\u003c/p\u003e\n\u003ch3\u003eStatistical considerations\u003c/h3\u003e\n\u003cp\u003eThe categorical variables were summarised using frequencies and percentages. Continuous variables were summarised using means and standard deviations for normally distributed data and medians with interquartile ranges (IQRs) for non-normally distributed data.\u003c/p\u003e\n\u003cp\u003eThe incidence of CVD events among PLHIV and PWoH will be calculated as the ratio of the number of CVD events divided by the total number of study participants during the study period.\u003c/p\u003e\n\u003cp\u003eA modified Poisson regression model with robust error variance will be used to evaluate the risk of any CVD event among PLHIV and PWoH. The model is preferred for binary data to estimate adjusted relative risk (21).\u003c/p\u003e\n\u003cp\u003eGiven that for any CVD event, the error term is binomially distributed, the misspecification of the variance is corrected by using robust error variance. Initially, an unadjusted analysis will be performed to estimate the risk of any CVD event among the PLHIV and PWoH.\u003c/p\u003e\n\u003cp\u003eHIV status is the main exposure of interest. All variables deemed\u0026nbsp;to be potential confounders (based on prior knowledge and literature) will be considered in the multivariable modified Poisson regression model, including age, sex, family history of CVD, systolic blood pressure, smoking status, total cholesterol, HDL cholesterol, HIV status, and diabetes. The adjusted risk and its corresponding 95% confidence intervals (95% CI) estimated from the model will be reported.\u003c/p\u003e\n\u003cp\u003eThe occurrence of CVD risk factors for each subject will be assessed from the baseline visit to the time at which each major CVD risk factor is diagnosed, the time of death, or the time of the last visit to the study site, whichever may occur first. The incidence rates and 95% confidence intervals (CIs) for the occurrence of major CVD risk factors will be calculated as the number of new CVD risk factor events divided by the respective patient years at risk. Kaplan‒Meier time‒to-major CVD risk factor analyses will be used to visualise the major CVD risk factors, and the results will be compared via log-rank tests.\u003c/p\u003e\n\u003cp\u003eMultivariate Cox proportional hazards regression analyses adjusted for participant characteristics will be used to determine the incidence of major CVD risk factors among PLHIV and PWoH. The proportional hazards assumption will be assessed via log-log plots. The results will be reported as hazard ratios (HRs) and 95% CIs. Our data will be benchmarked against the established cardiovascular disease risk models, including the Data Collection on Adverse Events of Anti-HIV drugs (D: A:D) 2010 and 2016, the Framingham Heart Study (FHS-CVD), the coronary heart disease model from the same study (FHS-CHD), Predicting Risk of CVD Events (PREVENT), the Atherosclerotic Cardiovascular Disease (ASCVD) models, and WHO cardiovascular disease risk charts (22\u0026ndash;28).\u003c/p\u003e\n\u003cp\u003eThese models will be evaluated for their applicability in local contexts, with the objective of developing a suitable model for Rwanda and other countries with similar settings (26, 29).\u003c/p\u003e\n\u003cp\u003eBaseline and participant characteristics as well as physical measurement data were collected via the Research Electronic Data Capture (REDCap) application preloaded on tablets. The laboratory results were extracted from the server of the laboratory information system. Microsoft Excel (MS Excel 16.89.1) and the Statistical Package for the Social Sciences (SPSS , version 28.0.1) were used for data processing and data cleaning, and R (version 4.2.1) was used for data analysis.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThe study was approved by the Rwanda National Ethics (RNEC) under approvals No. 210/RNEC/2020 and No.84/RNEC/2021 and aligns with good clinical practice guidelines. To comply with ethical principles, all participants provided written informed consent before taking part in the study. The participants\u0026rsquo; information is kept confidential through the use of coded identifiers and a password-protected database. Each participant received a unique research identification number.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe enrollment of PLHIV and PWoH\u0026nbsp;participants was performed from March 2021 onwards. To date, 80 of the targeted 120 newly diagnosed HIV-positive participants have been recruited. The baseline characteristics of 1546 participants (1234 PLHIV and 312 PWoH) are presented below. The subject selection flowchart is presented in Figure 2, and an overview of the baseline characteristics is presented in Table 2 and Figure 3 (Appendix).\u003c/p\u003e\n\u003cp\u003eThe median age was 44 years ( interquartile\u0026nbsp;rage [IQR]: 17) for\u0026nbsp;PLHIV and 42 years (IQR: 17) for PWoH. In total, 785 (63.6%) PLHIV participants and 197 (63.1) PWoH participants were women, 683 (55.3%) and 209 (67.0%) participants were married or in stable relationships, and 603 (48.9%) and 152 (48.7%) participants had completed primary or partial secondary school, respectively. The prevalence of CVD risk factors in PLHIV compared with PWoH at baseline was as follows: The mean body mass index (23.48\u0026nbsp;\u0026plusmn; (kg/m\u003csup\u003e2\u003c/sup\u003e) ) vs (24.28\u0026nbsp;\u0026plusmn;\u0026nbsp;4.9 (kg/m\u003csup\u003e2\u003c/sup\u003e)) overweight, 256 (21.0%) vs 75 (24.4%) PWoH; high-normal blood pressure, 262 (21.2%) vs 63 (20.2) participants. A total of 260 (21.1%) of the PLHIV and 55 (17.7%) of the PWoH had a history of smoking, 210 (80.6%) and 36 (71.9%) of whom were current smokers, respectively. A total of 710 (57.6%) PLHIV and 163 (52.2%) PWoH drank alcohol. 37 (3.0%) in PLHIV \u0026nbsp;than 28 (9.1%) PWoH . Liver enzymes (ALAT and ASAT) and creatinine level were higher in PLHIV (ALAT: 17.6 U/L vs 14.1 U/L; ASAT 29.1 U/L vs 24.0 U/L and [77.0 \u0026micro;mol/L (IQR:64.0-91.0)] vs \u0026nbsp;69.0 \u0026micro;mol/L(IQR:60.0 \u0026ndash; 80.0)] in PLHIV compared to PWoH respectively.Total protein levels were similar in PLHIV and PHoW.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first ongoing cohort study to perform a follow-up of CVD, hypertension, and diabetes epidemiology among PLHIV in Rwanda. HIV in itself, ART, and interactions with underlying (and often unrecognized) risk factors create an important risk of premature NCDs, requiring early detection and preventive measures. This paper presents the design, rationale, and baseline characteristics of participants from a prospective study (NCOHIRWA cohort study) aimed at evaluating the health determinants of major NCDs among PLHIV in Rwanda and supporting the tailoring or development of a suitable model for CVD prediction in Rwanda and other similar settings.\u003c/p\u003e\n\u003cp\u003eThis study is expected to provide evidence regarding CVD, hypertension, and diabetes risk in PLHIV in Rwanda. The study results will contribute to improving existing local policies, guide preventive measures, enhance the pathophysiological understanding of the interplay between HIV and NCDs, and ultimately contribute to the adoption of evidence-based guidelines for PLHIV.\u003c/p\u003e\n\u003cp\u003eCompared with PWoH participants, PLHIV are notably more likely to be widowed and have lower educational levels and are also more inclined to present with high blood pressure and be current smokers (\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe age and sex distributions of the PLHIV included in our cohort study are in line with the age and sex distributions of all the PLHIV in Rwanda receiving antiretroviral therapy, with more than 20% above 50 years of age and predominantly being women (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e). This women predominance is consistent with other studies (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e) and global statistics, where 53% of PLHIV were women and girls (\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003ePrevious research has shown that the prevalence of overweight among PLHIV ranges from 22.7\u0026ndash;28.8% (\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn our cohort, 21.0% of PLHIV were overweight and 9.7% were obese at baseline. These preliminary findings from the baseline data align closely with the NCDzz cohort in Zambia and Zimbabwe, which reported similar proportions of adults meeting ideal cardiovascular health (ICVH) metrics, with 60% achieving ideal LS7 scores (\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e). The NCDzz study also found that 59% of adults in Uganda, 61% in rural Ghana, and 53% in rural South Africa met ideal cardiovascular health criteria, suggesting that our Rwandan cohort is representative of urban African populations in terms of NCD risk factor distribution. In addition, a recent study conducted in Kenya has similar prevalence of overweight among PLHIV (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e), whereas in the general population, similar high prevalence rates ranging from 18.6\u0026ndash;26.8% have been reported (\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e). Differences in prevalence across cohorts may be partly attributable to variations in age structure, ART status at enrollment, and lifestyle factors such as diet and physical activity.\u003c/p\u003e\n\u003cp\u003ePrehypertension rates vary, with our findings falling between those reported in Tanzania (30.3%) and Uganda (16.8%) (\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe smoking prevalence among PLHIV in our study was lower than that reported in many other studies, which is attributed to Rwanda\u0026apos;s generally low smoking rates. However, it remains higher in PLHIV than in PWoH (\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAlcohol consumption was greater among PLHIV than among PWoH participants, a trend consistent with findings from other studies indicating a greater prevalence of alcohol use among PLHIV (\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e). Furthermore, in the context of alcohol screening and brief interventions for PLHIV, over half of the participants reported alcohol consumption (\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e). Conversely, in separate cohorts, one study reported that 20% of participants had used alcohol in the past six months, whereas another study reported a current alcohol use rate of 43% among the participants, which was lower than that reported in our cohort (\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e). The elevated prevalence of alcohol consumption within the study population is a cause for concern, as it amplifies the risk of CVD, is associated with reduced efficacy of ART, and increases the likelihood of nonadherence to ART (\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe preliminary findings from the baseline data of the investigated lipid profile among PLHIV compared to PWoH observed modest but noteworthy differences between the participant groups.TC levels were significantly lower (157.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39.7 mg/dL) vs (165.8\u0026thinsp;\u0026plusmn;\u0026thinsp;47.6 mg/dL), whereas TG level were comparable in both groups (94.8 mg/dL vs 96.1 mg/dL) and HDLc level were closely similar across groups of participants (47.1 mg/dL vs 46.4 mg/dL). There is a consistency of recent findings with our results (\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e), but the result of lipid profiles varies between studies(\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e). For triglycerides, the results are consistent with Click or tap here to enter text.(\u003cspan class=\"CitationRef\"\u003e71\u003c/span\u003e), and HDLc show similar findings with(\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e). In contrast to our study, in a cross-sectional study conducted in central Kenya, a substantial prevalence of prediabetes (14.2%) was identified among PLHIV (\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e). Most studies consistently revealed elevated fasting blood glucose levels in PLHIV compared with those in PWoH (\u003cspan class=\"CitationRef\"\u003e74\u003c/span\u003e). Additionally, a systematic review reported a high prevalence of prediabetes (15%) among PLHIV in Africa (\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e), and another study from a high-income country in the general population reported 8.1% prediabetes in the studied population (\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eStudies have shown that the liver enzyme (ALAT and ASAT) and creatinine are often elevated in PLHIV than PWoH, as documented in PLHIV, due to the hepatotoxicity linked to the taken ART for creatinine level may indicate the early renal impairment (\u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e) Total protein level of our participants was similar to the results of Iranian participants, which was similar in PLHIV and PWoH (\u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eOur preliminary findings from baseline data are in line with those from other African studies that find a high burden of NCDs and risk factors such as (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e); this emphasise the need for reinforcing the health care system to prevent the high burden of NCDs.\u003c/p\u003e\n\u003cp\u003eAcross the region, integration of NCD and HIV care remains a challenge. Studies from South Africa and other SSA countries highlight barriers such as under-resourced facilities, fragmented care pathways, and limited access to NCD medications, which may contribute to suboptimal management of comorbid conditions (\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e). Our findings reinforce the need for health system strengthening and the adoption of integrated, patient-centered models of care.\u003c/p\u003e\n\u003cp\u003eA notable strength of the methodology of the NCOHIRWA cohort study, as shown by already available preliminary data from baseline findings, lies in its substantial sample size, which exceeds that of comparable studies. Moreover, in contrast to these studies, which adopted a cross-sectional design and were limited to a few specific sites and small sample sizes, the NCOHIRWA cohort study employs a prospective cohort approach. This means that it not only includes a large number of participants but also follows them over an extended period, providing valuable longitudinal data. Rwanda, amidst its socioeconomic transformation similar to other African countries and resulting chaotic lifestyle changes, has experienced an increase in the burden of NCDs among its more vulnerable population (\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e). Longitudinal studies in the region, such as those from Zimbabwe and Uganda, have documented a rising cumulative incidence of NCDs among PLHIV, particularly with increasing duration of ART exposure (\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e84\u003c/span\u003e). In Zimbabwe, NCDs accounted for 56% of morbidity and 40% of mortality among PLHIV in a decade-long cohort, underscoring the importance of integrated NCD and HIV care (\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e). This evolution necessitates greater efforts to support the existing healthcare system, especially in addressing the shifting pattern of morbidity from HIV as an acute disease to NCDs. Another aspect of previous studies in LMICs compared with well-established cohorts in high-income countries is that in those cohorts, participants have readily available medical history data. In our LMIC, accessing comprehensive medical histories can be challenging, which hinders the ability to identify risk factors and their influence on health status. A prospective study design offers a more robust approach to establishing risk factors and causation. This cohort will facilitate long-term follow-up and provide a valuable foundation for analysis, moving beyond prediction and allowing us to construct profiles based on actual data.\u003c/p\u003e\n\u003cp\u003eThe absence of adequate research facilities in sub-Saharan Africa poses significant challenges for conducting CV examinations and implementing measures across healthcare facilities. Furthermore, the scarcity of experts inclined toward research impedes the development of evidence-based interventions and policies to address the CVD burden in the region. To address these issues, a substudy focused on CV examinations is underway at Kigali (CHUK), where the necessary facilities and expertise are readily accessible. By harnessing the existing infrastructure and the expertise available at CHUK, this substudy holds promise for generating valuable insights into cardiovascular health and disease management within the SSA context.\u003c/p\u003e\n\u003cp\u003eThe lack of research funding for public health research on NCDs in SSA poses a significant threat to the region\u0026apos;s health. Despite the disproportionate burden of NCDs in LMICs, including SSA, research funding continues to focus primarily on communicable diseases, neglecting the needs of populations experiencing excess morbidity and mortality from NCDs, as well as low research output compared with other regions (\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e). Additionally, the absence of quality health information due to the funding gap is a major obstacle in developing national strategies for an effective response to NCDs, perpetuating the dependence of African research on Western nations for support and perpetuating global health disparities in research output and funding allocation (\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e91\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThis study employs a rigorous approach, utilising standard and validated questionnaires along with precise measurement protocols, to gather data on socioeconomic factors and lifestyle. Notably, our study stands out for its inclusion of both rural and urban participants, whereas many other studies tend to concentrate solely on either urban or rural populations.\u003c/p\u003e\n\u003ch3\u003eChallenges and limitations\u003c/h3\u003e\n\u003cp\u003eDue to the COVID-19 pandemic, studies have faced numerous logistic challenges, mainly in the supply chain of the needed laboratory reagents and consumables procured abroad. The procurement process is delayed on the basis of transportation facility closure and stock out of the key materials from the manufacturers, and it is representative of Rwanda. The protocol was adjusted, and the ethics committee was notified during the annual renewal of the approval.\u003c/p\u003e\n\u003cp\u003eAnother challenge encountered in practice was the participants\u0026apos; lack of awareness regarding their blood pressure status. Approximately 10% of the participants had blood pressure measurements exceeding 140/90, and they were subsequently excluded on their appointment day. This necessitated an extension of the enrollment period to achieve the desired sample size. While our cohort is broadly representative of PLHIV attending health facilities in Rwanda, differences in health care access, urbarnisation, and demographic structure may limit direct comparability with non-facility based population in other countries.\u003c/p\u003e\n\u003cp\u003eThis cohort included participants who were still young, as PLHIV have a two- to three-fold greater relative risk of cardiovascular diseases than the general population does, as those who are born with HIV at 18 years of age are at high risk. It is easy to know when a participant is enrolled in HIV treatment, but one can know the duration of the infection from the existing record or from the participants themselves. This important information was not considered for this study.\u003c/p\u003e\n\u003cp\u003eFurthermore our cohort baseline data reflect a relatively young cohort,which may partially explain the higher proportion of participants meeting ideal CVD health criteria compared to older cohort such as in the REPRIEVE trial (\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e). As our study progresses, longitudinal follow up will allow for more nuanced comparison of incident NCD outcomes and the evolving risk profile of PLHIV in Rwanda.\u003c/p\u003e"},{"header":"Conclusion and recommendations","content":"\u003cp\u003eAlthough the primary aim of this manuscript is to describe the study protocol, methodology, and rationale, we have also reported already available baseline data. These findings reveal a significant prevalence of modifiable risk factors for NCDs among PLHIV in Rwanda. This has important implications for long-term HIV care, highlighting the urgent need to integrate NCD screening, prevention, and management into routine HIV services. As Rwanda and other low-resource settings continue to make strides in HIV treatment, person-centered models that address both infectious and chronic disease burdens are essential to sustaining health gains and improving quality of life. The NCOHIRWA cohort methodology and rationale work provides a vital foundation for future longitudinal analyses and policy development to support integrated chronic disease care for PLHIV.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALAT: alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eART: Antiretroviral therapy\u003c/p\u003e\n\u003cp\u003eASAT: Aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eBMI: Body mass index\u003c/p\u003e\n\u003cp\u003eBP: Blood pressure\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003eCHUK: University Teaching Hospital of Kigali\u003c/p\u003e\n\u003cp\u003eCMIT: Carotid Intima Media Thickness\u003c/p\u003e\n\u003cp\u003eCV: cardiovascular\u003c/p\u003e\n\u003cp\u003eCVDs: Cardiovascular diseases\u003c/p\u003e\n\u003cp\u003eD:A:D: Data collection on the adverse effects of anti-HIV drugs\u003c/p\u003e\n\u003cp\u003eECHO: Echocardiogram\u003c/p\u003e\n\u003cp\u003eECG: Electrocardiogram\u003c/p\u003e\n\u003cp\u003eEMR: Electronic medical record\u003c/p\u003e\n\u003cp\u003eHDL-c: high-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eFHS-CHD:\u0026nbsp;Framingham Heart Study-coronary heart disease\u003c/p\u003e\n\u003cp\u003eFHS-CVD:\u0026nbsp;Framingham Heart Study Cardiovascular Disease\u003c/p\u003e\n\u003cp\u003ehsCRP: Highly sensitive C-reactive protein;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR: interquartile range\u003c/p\u003e\n\u003cp\u003eLDL-c: Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eLMICs: Low- and middle-income countries\u003c/p\u003e\n\u003cp\u003eMax: Maximum\u003c/p\u003e\n\u003cp\u003eMin: Minimum\u003c/p\u003e\n\u003cp\u003eNCDs: Non-Communicable Diseases\u003c/p\u003e\n\u003cp\u003ePLHIV: People living with HIV\u003c/p\u003e\n\u003cp\u003ePWoH: People without HIV\u003c/p\u003e\n\u003cp\u003eREDCap: Research Electronic Data Capture\u003c/p\u003e\n\u003cp\u003eRNEC: Rwanda National Ethics Committee\u003c/p\u003e\n\u003cp\u003eSSA: Sub-Saharan Africa\u003c/p\u003e\n\u003cp\u003eSPPS: Statistical Package for the Social Sciences\u003c/p\u003e\n\u003cp\u003eSTEPS: STEPwise approach to surveillance\u003c/p\u003e\n\u003cp\u003eVCT: Voluntary Counselling and Testing\u003c/p\u003e\n\u003cp\u003eWHR: waist-to-hip ratio\u003c/p\u003e\n\u003cp\u003eWHtR: waist-to-height ratio\u003c/p\u003e\n\u003cp\u003eWHO: World Health Organisation\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe study was conducted in accordance with the Helsinki Declaration and approved by the Rwanda National Ethics Committee, reference number 210/RNEC/2020 and No. 84/RNEC/2021 and aligns with good clinical practice. All participants gave their informed consent for inclusion before they participated in the study.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe study was implemented under funds from the Rwanda National Council for Science and Technology (NCST) under excellent research funding. The funder of the study had no role in the design, data collection, data analysis, data interpretation or writing of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eVD,MT,MD,NABN,BAK,NB,LN,PN,VNU,PK,VN,JM,AD,CI,YDNU,KMI,CH,SN,ER,SC, CM,JCSN,ER,JPM,CMM,AT,GNR,GM,ST,FU,EN,SPN,NG,KE,FU,TK,AM,MS,LB,JDK,AU, MM,FB,VK,FD, AK,PM,AM,SK,KG,JBT,DM \u0026nbsp;: The study inception, implementation, input to the drafted manuscript, read and approved manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVD,MT,MD,NABN,BAK,NB,LN,PK,VNU,PK,VN,JM,AD,CIYDNU,KMI,CH,SN,ER,SC : Data collection tools design\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVD,MT,MD, ER, VNU, AD, VN, and LN : Data cleaning , analysis and interpretation\u003c/p\u003e\n\u003cp\u003eMT,MD,NABN, ER,SC: Supervision\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe acknowledge all the technical assistance provided to the authors by the NCOHIRWA study group and anyone who had helpful discussions or contributed fewer tangible concepts. The Rwanda Biomedical Centre, a research implementation coordinating division including the Research, Innovation, and Data Science, HIV, Noncommunicable Diseases, National Reference Laboratory, and Finance Department for facilitating the study, is in strong collaboration with the Rwanda Network for the People Living with HIV and the Rwanda NCD Alliance. The research assistants from the research sites, research sites leadership, and the study participants, for their time and effort, conducted this study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The collaborators contributed in this study with their affialtion:\u003c/p\u003e\n\u003cp\u003eNCOHIRWA cohort study group members contributed in this manuscripts: Clarisse Musanabaganwa\u003csup\u003e1\u003c/sup\u003e, Jean Claude Semuto Ngabonziza\u003csup\u003e1\u003c/sup\u003e, Eric Remera\u003csup\u003e1\u003c/sup\u003e, Jean Pierre Musabyimana\u003csup\u003e1\u003c/sup\u003e, Claude Mambo Muvunyi\u003csup\u003e1\u003c/sup\u003e, Albert Tuyishime\u003csup\u003e1\u003c/sup\u003e, Gallican Nshogoza Rwibasira\u003csup\u003e1\u003c/sup\u003e, Gregorie Muhorakeye\u003csup\u003e1\u003c/sup\u003e, Simeon Tuyishime\u003csup\u003e1,7\u003c/sup\u003e, Francois Uwinkindi\u003csup\u003e1\u003c/sup\u003e, Evarist Ntaganda\u003csup\u003e1\u003c/sup\u003e, Simon Pierre Niyonsenga\u003csup\u003e1\u003c/sup\u003e, Noel Gahamanyi\u003csup\u003e1\u003c/sup\u003e, Kayigi Etienne\u003csup\u003e1\u003c/sup\u003e, Fidel Umwanankabandi\u003csup\u003e1\u003c/sup\u003e, Theogene Kubahoniyesu\u003csup\u003e1,\u0026nbsp;\u003c/sup\u003eAlphonse Mbarushiman\u003csup\u003e14\u003c/sup\u003e, Muhammed Semakula\u003csup\u003e12\u003c/sup\u003e, Leopold Bitunguhari\u003csup\u003e5,18\u0026nbsp;\u003c/sup\u003e, Jean Damascene Kabakambira\u003csup\u003e18,19\u003c/sup\u003e, Annette Uwineza\u003csup\u003e5\u003c/sup\u003e, Marcellin Musabende\u003csup\u003e18\u003c/sup\u003e, Felix Babane\u003csup\u003e18\u003c/sup\u003e, Vincent Karamuka\u003csup\u003e18\u003c/sup\u003e, Fidens Dusabeyezu\u003csup\u003e5,18\u003c/sup\u003e, Alyson Kelvin\u003csup\u003e20\u003c/sup\u003e, Pacifique Mukashema\u003csup\u003e1,21\u003c/sup\u003e, Augustin Murindabigwi\u003csup\u003e22\u003c/sup\u003e, Steven Karera\u003csup\u003e1,23\u003c/sup\u003e, Katia Gigu\u0026egrave;re\u003csup\u003e6\u003c/sup\u003e, Katende Godfrey\u003csup\u003e5,24\u0026nbsp;\u003c/sup\u003e, Jean Berchmans Tugirimana \u003csup\u003e21\u003c/sup\u003e, Deo Mutambuka\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNoncommunicable diseases [Internet]. 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Available from: https://www.who.int/health-topics/diabetes#tab=tab_1\u003c/li\u003e\n\u003cli\u003eBrown JS, Amend SR, Austin RH, Gatenby RA, Hammarlund EU, Pienta KJ. Updating the Definition of Cancer. Mol Cancer Res [Internet]. 2023 Nov 1 [cited 2024 Jan 4];21(11):1142\u0026ndash;7. Available from: https://pubmed.ncbi.nlm.nih.gov/37409952/\u003c/li\u003e\n\u003cli\u003eMental disorders [Internet]. [cited 2025 Jan 4]. Available from: https://www.who.int/news-room/fact-sheets/detail/mental-disorders\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Description of the study tools and measurement\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"691\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003eTools/items\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003eDescription\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eWhich participants\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003eQuestionnaires\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003eSTEPwise approach to NCDS risk factor surveillance (STEPS): demographic data (age, sex, marital status, education level and employment status, social-economic status, health insurance)\u003cbr\u003ePast individual and family diseases history (hypertension, diabetes, and CVD), lifestyle information.\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eAll\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003ePhysical examinations\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003eHeight, weight, BMI, waist and hip circumference, resting BP and heart rate\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eAll\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003eCV examinations\u003csup\u003e1\u003c/sup\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003eCarotid intima-media thickness (CIMT): in 12 segments including the bilateral far and near walls of the common carotid, bifurcation and internal carotid regions). Electrocardiogram (ECG) and Echocardiogram (ECHO).\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eNewly diagnosed HIV positive\u003csup\u003e2\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 122px;\"\u003eLaboratory testing\u003csup\u003e3\u003c/sup\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eLipid profile (total cholesterol, high-density lipoprotein cholesterol (HDL-c), Low-density lipoprotein cholesterol (LDL-c), triglycerides).\u003c/li\u003e\n \u003cli\u003eFasting blood glucose\u003c/li\u003e\n \u003cli\u003eAspartate Aminotransferase (ASAT), Alanine Aminotransferase (ALAT), creatinine and total protein\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eAll\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eViral Load (VL)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003ePLHIV\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eHIV recency test\u003c/li\u003e\n \u003cli\u003eCD4 count\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eNewly diagnosed HIV positive\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 437px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePotential biomarkers of CVD: high sensitive C-Reactive Protein (hsCRP), interleukin-6 (IL-6) and associated cytokines, D-dimer, fibrinogen and early markers of cardiovascular diseases.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003eNewly diagnosed HIV positive\u003csup\u003e4\u003c/sup\u003e\u003cbr\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eKey definitions:\u003c/p\u003e\n\u003cp\u003eDefinitions of the eligibility criteria\u003c/p\u003e\n\u003cp\u003eHypertension is characterised as the presence of two or three consecutive measurements of systolic (SBP) and diastolic blood pressure (DBP), where the mean SBP is equal to or exceeds 140 mmHg and/or the mean DBP is equal to or exceeds 90 mmHg, and includes individuals who are using antihypertensive medication\u0026nbsp;(16).\u003c/p\u003e\n\u003cp\u003eCVDs are defined as a group of disorders of the heart and blood vessels and include coronary heart disease, cerebrovascular disease, rheumatic heart disease, peripheral arterial disease, congenital heart diseases, deep vein thrombosis and pulmonary embolism\u0026nbsp;(93).\u003c/p\u003e\n\u003cp\u003eDiabetes is a chronic, metabolic disease characterised by elevated levels of blood glucose (or blood sugar)\u0026nbsp;(94).\u003c/p\u003e\n\u003cp\u003eCancer is defined as a disease of uncontrolled proliferation by transformed cells subjected to evolution by natural selection\u0026nbsp;(95).\u003c/p\u003e\n\u003cp\u003eMental disorders are characterised by a clinically significant disturbance in an individual\u0026rsquo;s cognition, emotional regulation or behavior(96).\u003c/p\u003e\n\u003cp\u003eTable 2. Baseline characteristics of the study participants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eVariable\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003ePLHIV\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003ePWoH\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSociodemographics\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003eN= 1234\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003eN=312\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eMean Age (years)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e44.0 \u0026plusmn;11.62\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e42 \u0026plusmn; 12.03\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eAge (Min, Max)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e18-81\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e18-75\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSex (women; %)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e785 (63.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e197(63.1)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eMarital status (%)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eSingle\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e191(15.5)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e60 (19.2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eMarried or cohabitant\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e684 (55.5)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e209 (67.0)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eDivorced or separated\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e121 (9.8)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e18 (5.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eWidowed\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e238 (19.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e25 (8.0)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eEducational level (%)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eNo formal education\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e168 (13.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e29 (9.3)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eLess than primary\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e324 (26.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e66 (21.2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003ePrimary or partial secondary\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e603 (48.9)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e152 (48.7)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eSecondary\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e90 (7.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e33 (10.6)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eBeyond secondary\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e49 (4.0)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e32 (10.3)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eOccupation (%)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eEmployed (government or nongovernment)\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e71 (5.8)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e71 (22.9)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eSelf-employed\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e266 (21.7)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e61 (19.7)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eFarmer\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e396 (32.1)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e87 (28.1)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eOther\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e316 (25.7)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e67 (21.6)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e\n \u003cul\u003e\n \u003cli\u003eUnemployed\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e177 (14.9)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e24 (7.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"694\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003ePhysical measurement\u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 336px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eHeight(cm)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e162.07 \u0026plusmn; 8.3\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e161.4 \u0026plusmn; 8.3\u003csup\u003e5\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eWeight (kg)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e61.64 \u0026plusmn; 12.4\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e63.19 \u0026plusmn; 12.8\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eBMI\u003ca href=\"#_ftn6\" name=\"_ftnref6\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e6\u003c/sup\u003e (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e23.48 \u0026plusmn; 4.51\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e24.28 \u0026plusmn; 4.93\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eUnderweight/normal/overweight/obese (%)\u003csup\u003e7\u003c/sup\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e118 (9.7)/725 (59.6)/256 (21.0)/118 (9.7)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e21(6.8)/174 (56.3)/75 (24.3)/39 (12.6)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSystolic BP\u003csup\u003e8\u003c/sup\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e116.5 \u0026plusmn; 11.5\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e116.3 \u0026plusmn; 11.8\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eDiastolic BP\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e74.3 \u0026plusmn; 8.15\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e74.15 \u0026plusmn; 7.72\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eHigh-normal BP (at exam; %)\u003csup\u003e9\u003c/sup\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e262 (21.2)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e63 (20.2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eHeart rate\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e72.9 \u0026plusmn; 11.42\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e71.7 \u0026plusmn; 12.13\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eCV risk factors\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSmoking (have smoked %)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e260 (21.1)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e55 (17.7)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSmoking (current %), based on ever smoke\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e209.6 (80.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e39.5 (71.9)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eAlcohol drinking (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e710.8 (57.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e162.9 (52.2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eEating fruit (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e1025.5 (83.1)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e264.9 (84.9)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eSalt intake (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e1194.5 (96.8)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e298.9 (95.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eFamily History of:\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e- Hypertension (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e219 (17.8)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e84 (26.9)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e- CVD (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e209.6(5.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e18.0 (5.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e- Diabetes (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e132.0 (10.7)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e34.9 (11.2)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003e- Stroke (%)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e7.4 (0.6)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e4.9 (1.6)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eLaboratory data\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eTotal cholesterol (mg/dl)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e157.3 \u0026plusmn; 39.7\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e165.8 \u0026plusmn; 47.6\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eTriglycerides (mg/dl)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e94.8 (70.3 \u0026ndash; 129.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e96.1 (72.3 \u0026ndash; 134.0)\u003csup\u003e10\u003c/sup\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eHDL Cholesterol (mg/dl)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e47.1 (39.4-58.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e46.4 (38.6-56.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eGlucose (mmol/l)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e4.95 \u0026plusmn; 1.10\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e5.00 \u0026plusmn; 1.82\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eHigh fasting blood glucose (from lab; %)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e37 (3.0)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e28 (9.1)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eALAT (U/L)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e17.6 (12.7-26.3)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e14.1 (10.3-20.1)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eASAT (U/L)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e29.1 (23.7-37.8)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e24.0 (19.7-30.8)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eTotal Protein(g/l)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e79.30 \u0026plusmn; 10.91\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e79.64 \u0026plusmn; 14.31\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 358px;\"\u003eCreatinine(\u0026micro;mol/L)\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 145px;\"\u003e77.0 (64.0-91.0)\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 191px;\"\u003e69.0 (60.0-80.0)\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003col\u003e\n \u003cli\u003eTaken at CHUK by the trained cardiologist and radiology resident, analyses will be performed by cardiologist from Cape Town and Ghent university.\u003c/li\u003e\n \u003cli\u003eExam for Newly diagnosed HIV positive only\u003c/li\u003e\n \u003cli\u003eLaboratory testing for all participants (PLHIV, PWoH and Newly diagnosed HIV positive)\u003c/li\u003e\n \u003cli\u003eLaboratory exams for Newly diagnosed HIV positive with possible extension to other groups if funds allow.\u003c/li\u003e\n \u003cli\u003eData are either mean\u0026plusmn; SD for normally distributed data or median (interquartile range) for nonnormally distributed data, or percentages for categorical data.\u003c/li\u003e\n \u003cli\u003eBody Mass Index\u003c/li\u003e\n \u003cli\u003eBMI category: Underweight= \u0026lt;18.5, normal=.18.5-24.9, overweight=25.0-29.9, obese = \u0026gt;30\u003c/li\u003e\n \u003cli\u003eBlood Pressure (Systolic and diastolic)\u003c/li\u003e\n \u003cli\u003eBP range used to summarise data with skewed distribution includes normal-optimal (\u0026lt;130/85 mmHg) and High normal (130-139/85-89 mmHg), Grade 1 Hypertension (140-159/90-99 mmHg), grade 2 Hypertension (\u0026gt;160/100 mmHg).\u003c/li\u003e\n \u003cli\u003eData are either the mean \u0026plusmn; SD used to summarise without skewed distribu\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HIV, noncommunicable diseases, Rwanda, hypertension, diabetes, cardiovascular diseases","lastPublishedDoi":"10.21203/rs.3.rs-5897717/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5897717/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNoncommunicable diseases (NCDs) are the leading cause of death worldwide. Cardiovascular diseases (CVDs) are the top NCD killers globally as well as in Rwanda and other African countries where NCDs and infectious diseases, such as HIV, coexist. To date, the intersection of CVDs and HIV in Rwanda has not been sufficiently studied. We aimed to conduct a comprehensive analysis of CVD-related risk factors and quality of life in people living with HIV (PLHIV) and to develop a country profile on the basis of the prevalence and incidence of CVD, hypertension, and diabetes to inform future interventions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe assessed the risk factors for major NCDs, including CVDs, hypertension, and diabetes among PLHIV in Rwanda in a prospective, controlled cohort study. Men and women aged 18 years and above were recruited from 12 health facilities that offer services for HIV and NCDs. Baseline characteristics and demographic data were collected at baseline via an electronic questionnaire. The follow-up physical, cardiovascular, and laboratory metrics are assessed at each visit.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 1,546 participants were recruited, comprising 1,234 (79.81%) PLHIV and 312 (20.18%) people without HIV (PWoH). The median age was 44 years (interquartile range, IQR: 17) for PLHIV and 42 years (IQR: 17) for PWoH. A total of 785 (63.6%) PLHIV and 197 (63.1%) PWoH were women. The prevalence of cardiovascular risk factors for PLHIV and PWoH were as follows: 256 (21.0%) versus 75 (24.4%) were overweight; 118 (9.7%) versus 39 (12.6%) were obese; 260 (21.1%) versus 55 (17.7%) reported having ever smoked; 219 (17.8%) versus 84 (26.9%) had a family history of hypertension; and 37 (3.0%) versus 28 (9.1%) had very high fasting blood glucose levels.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis manuscript presents the protocol and rationale of the NCOHIRWA cohort and baseline data revealing a high prevalence of modifiable NCD risk factors among PLHIV in Rwanda. Findings underscore the need to integrate NCD care into HIV services. The study supports person-centered models addressing dual disease burdens and provides a foundation for longitudinal research and policy to improve chronic disease care for PLHIV in low-resource settings.\u003c/p\u003e","manuscriptTitle":"Health determinants for major noncommunicable diseases among people living with HIV in Rwanda (NCOHIRWA) cohort study: rationale, protocol and baseline characteristics of participants","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 11:00:10","doi":"10.21203/rs.3.rs-5897717/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T11:49:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T05:57:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2025-11-07T14:23:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184790269254508716710317912437895205663","date":"2025-10-01T10:48:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T10:42:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T06:19:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T04:35:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-05-03T22:03:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9e75911c-d477-442e-a519-c57ac4899538","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T11:53:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 11:00:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5897717","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5897717","identity":"rs-5897717","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}