Prevalence and risk factors of type 2 diabetes among HIV-infected patients at Asella Teaching and Referral Hospital, Southeast Ethiopia, 2023

Prevalence and risk factors of type 2 diabetes among HIV-infected patients at Asella Teaching and Referral Hospital, Southeast Ethiopia, 2023 | 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 Article Prevalence and risk factors of type 2 diabetes among HIV-infected patients at Asella Teaching and Referral Hospital, Southeast Ethiopia, 2023 Biniyam Lakew Tilahun, Gizaw Hailiye Teferi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9055114/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background : The utilization of highly active antiretroviral therapy (HAART) significantly extends the lifespan of individuals living with Human Immunodeficiency Virus (PLWHIV) and markedly reduces HIV-related morbidity and mortality. On a global scale, non-communicable diseases associated with HIV, such as diabetes, are emerging as significant public health concerns. The prolonged use of HAART and the increase in chronic comorbidities as life expectancy increase are becoming substantial causes of morbidity and mortality among HIV patients. Objective : This study aimed to assess the prevalence of diabetes mellitus and associated risk factors among adult HIV patients at Asella Teaching and Referral Hospitals. Methods : A hospital-based cross-sectional study was conducted from November 1,023, to December 30,023, among selected HIV patients at Asella Teaching and Referral Hospital. The data were collected using a structured interviewer administered questionnaire adapted from the STEP wise approach of the World Health Organization. The collected data were analysed using SPSS 26. Descriptive analysis such as frequency and mean was used to represent the characteristic of the study population and the prevalence of diabetes among the study participants. Bi-variable logistic regression analysis was used to identify candidate variables at p<0.2, and multivariable logistic regression analysis was employed to identify significant factors associated with prevalence of diabetes among HIV patients at a p value <0.05 and 95% confidence interval. Results : A total of 159 Responses were received with a response rate of 100%, and the prevalence of diabetes among PLWHIV exposed to HAART was 10.7% (95% CI=2.70-24.73) and 5.0%, of the study participants were pre-diabetes. Multivariable logistic regression result revealed that being physically inactive (AOR: 3.82, 95% C.I=1.0-14.0), having hypertension (AOR: 6.2, 95% C. I =2.5- 23.1) and LDL-C ≥130 mg/dl (AOR: 4.5, 95% CI=1.13-18.30) were found to be a risk factors for diabetes mellitus in PLWHIV. Conclusion and recommendation : Individuals living with HIV (PLWHIV) are at increased risk of developing diabetes, and it is recommended that they participate in regular physical activity and undergo regular screenings for blood pressure and lipid profiles to effectively manage and monitor potential health risks. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Diabetes HIV Prevalence Risk factors ART clinic and Ethiopia Figures Figure 1 Plain text summary Because of advances in ART, people living with HIV are now living longer; however, they are developing long-term medical conditions like type 2 diabetes. Currently, the information regarding the incidence of diabetes in people with HIV living in Ethiopia, particularly at the local hospital level, is limited. Knowledge of this information will assist providers in managing patient care as well as reducing complications from diabetes. The study was conducted at Asella Teaching and Referral Hospital in Ethiopia. A total of 159 adults living with HIV who were receiving routine follow-up care were participated in this study. The participants were interviewed and their BMI and waist circumference were measured and blood tests (blood glucose, blood pressure, and cholesterol) performed on each to assess the level of glucose in their blood and the pressure on the arteries. Statistical analyses were used to identify associations between certain variables that contributed to the development of diabetes. 1 out of every 10 people living with HIV has diabetes and 5% of all participants are pre-diabetes. Most participants did not know they had diabetes prior to this research study. Individuals who were physically inactive, had elevated blood pressure, or had elevated Levels of Bad (LDL) Cholesterol had an increased rate of diabetes. The research indicates that individuals with HIV have a greater chance of experiencing health issues related to diabetes than people who do not have HIV. Routine testing, stimulating exercise, and tracking blood pressure and cholesterol levels assist in minimizing the chances of developing complications associated with diabetes and will promote healthier, longer-term effects. Background Diabetes is a chronic condition caused by insufficient insulin produced by the pancreas or how the body uses that insulin, affecting roughly 540 million people worldwide ( 1 , 2 ). People with human immune virus(HIV) are at increased risk for developing type 2 diabetes mellitus (T2DM) due to HIV-related metabolic changes and the effects of antiretroviral therapy (ART), ultimately leading to increased morbidity and mortality( 3 , 4 ). Diabetes and pre-diabetes in HIV patients are diagnosed using the same criteria as in the general population, following ADA 2023 guidelines ( 5 , 6 ). However, HbA1C may underestimate glycemia in those with low CD4 counts or on ART, so it is not recommended for diagnosis, Instead, fasting plasma glucose ≥ 126 mg/dL, 2-h OGTT ≥ 200 mg/dL, or random plasma glucose ≥ 200 mg/dL with symptoms are used( 7 ) The incidence of diabetes worldwide was 9.3% (463 million people) in 2019. The prevalence of associated comorbidities such as non-alcoholic fatty liver disease (NAFLD) varied by geography and socioeconomic status, with higher rates in urban areas (10.8%) than in rural areas (7.2%), and high-income (10.4%) versus low-income regions (4.0%) ( 8 ). In Africa, a systematic review of estimated prevalence of T2DM among adults aged 20–79 years indicated 4.9%, with most cases in individuals younger than 60 years old. The authors estimated cases were especially prevalent in the 40–59 age group (43.2%)( 9 ) In sub-Saharan Africa, and especially Ethiopia, the dual burden of HIV and T2DM has created an urgent public health problem. ART (antiretroviral therapy) has improved survival outcomes for PLHIV (people living with HIV), but with this increased survival is a growing incidence of non-communicable diseases (NCDs), including diabetes, with pooled national estimates of 6.5% ( 10 , 11 ). The authors noted while HIV-related there have declined, deaths from NCDs (especially from cardiovascular disease and diabetes) are increasing at approximately 4% yearly ( 12 ). Diabetes risk factors in people living with HIV (PLHIV) are multifactorial. In addition to the presence of HIV infection and exposure to antiretroviral treatment (ART), traditional risk factors such as age, obesity, lack of physical inactivity, prior gestational diabetes, and polycystic ovary syndrome are significant ( 7 , 12 , 13 ). Other risk factors include hypertension, dyslipidemia, and genetics, and the prevalence rate varies by race/ethnic group, including African, Asian, Native American, and Hispanic/Latino groups ( 7 , 10 , 11 ). Socioeconomic risk factors such as higher education and government employment have also been associated with additional risk ( 10 , 12 ). Additionally, HIV-specific ART regimens, especially protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NRTIs), have influenced susceptibility to diabetes as the risk of diabetes increases with longer duration of HIV infection and ART exposure ( 5 , 10 , 11 , 14 , 15 ). Research gap While there is established evidence of global and regional diabetes burden among people living with HIV (PLHIV), much of the evidence is limited to high-income countries or systematic reviews including studies with heterogeneous samples. These studies have described how HIV infection and exposure to antiretroviral therapy (ART) increase the risk for type 2 diabetes mellitus (T2DM) but there is little evidence for contextualized understandings relevant to sub-Saharan Africa. Furthermore, using global or national estimates does not reflect local context and levels of prevalence and risk factors, which vary based on socioeconomic status, access to healthcare, and lifestyle. In Ethiopia, a growing body of literature indicates a significant and increasing comorbidity burden of HIV and diabetes, with national prevalence estimates indicating considerable variability among and across regions. However, many of these studies remain geographically circumscribe, and their implications may not be generalizable to all healthcare settings. Additionally, there remains limited evidence of how ART regimens and exposure duration, along with individual demographics, play a role in diabetes risk for PLHIV in Ethiopia. The absence of disaggregated data remains problematic in the design of preventive and therapeutic interventions specific for the context. There has been no systematic study previously done at Asella Teaching and Referral Hospital to assess the rate and risk factors of T2DM among HIV-infected patients. As a huge teaching and referral hospital in Southeast Ethiopia, exploring the local epidemiology of this comorbidity is ideal. Addressing this provide evidence-based information to support the management of patients, evaluate ART regimens, and structure public health planning for the needs of this population. METHODOLOGY Study area and period The study was conducted from November 1 to December 30, 2023, at the Asella Referral and Teaching Hospital, which is situated in Asella, the administrative centre of the East Arsi zone in the Oromia region. There were 4236 total patients at the Asella referral and Teaching Hospital ART clinic, 3647 of whom were older than 18 years. Among the patients older than 18 years, 1721 were male and 1926 were female. Study Design An institution-based cross-sectional study was employed. Source and study population All patients were followed up at the ART clinic of the Arsi University Referral Hospital was the source population while the study population were older than 18 years and were admitted to the follow-up clinic at the art clinic of the AURTH. Inclusion criteria All patients who were aged above 18 years and were enrolled for follow-up care at the AURTH ART clinic Exclusion criteria Patients with acute illnesses or opportunistic infections, as well as those with a history of diabetes treated before learning about RVI, were excluded. Furthermore, patients with type 1 diabetes and those under the age of 18 were not included. Moreover, individuals receiving systemic steroid medication and pregnant women were excluded. Sample size determination and sampling technique Sample size determination The finite single population Cochran formula was used to calculate the sample size. where no= sample size before correction for the population n =corrected sample size N=Adult population size 3647, currently available at the follow-up clinic Z = standard normal distribution corresponding to a significance level of α = 0.05, P = proportion of HIV patients who will be diagnosed with diabetes. e = is the margin of error of 5%. According to a previous study, the prevalence of diabetes in HIV patients was 11.5% of 271 adult HIV patients in the HAART Jima Zone Public Hospitals from May to July 30, 2018 ( 7 ). Using the above formula, we obtain an initial sample size of n0 = 150; when this value is corrected for the population, we obtain N = 144, and after adding 10% ( 15 ) non-respondents, the final sample size will be 159. When we compare the sample size from the dependent variable, it is the largest sample, and we took this sample for the investigation. Sampling procedure All RVI patients who visited medical follow-up at the ART clinic between November 1, 2023, and December 30, 2023, who fulfilled the inclusion criteria were selected using a systematic probability sampling technique, and every 8th patient was included. Variables of the study Dependent Diabetes among HIV patients (Yes/ No) Independent variables The independent variables: In this study included age, gender, family history of diabetes, and educational level. Clinical and lifestyle factors such as duration of illness, physical inactivity, history of hypertension, dyslipidemia, and body mass index (BMI) were also assessed. In relation to HIV-specific factors, the duration of HAART, types of HAART regimens, RVI stage, CD4 T-cell count, and stage of HIV were examined Operational definition Diabetes: A fasting plasma glucose level of 126 mg/dl or above at two consecutive study visits, a previous diagnosis of diabetes supported by recorded evidence, or the current use of diabetes medication were the criteria used in this investigation to define diabetes mellitus( 16 ). The quantity of time spent sitting, reclining, or lying down was used to characterize a sedentary lifestyle. People were classified as sedentary if they spent more than four hours a day in this position; those who spent at least four hours were classified as non-sedentary( 17 ). Where as physical Activities is defined as the ratio of work metabolic rate to the standard resting metabolic rate (RMR) of 1 kcal/(kg/h). One MET is the RMR or energy cost for a person at rest ( 18 ). Data collection procedure and quality control The data were collected using a structured, interviewer-administered questionnaire adapted from a the STEP wise approach of the World Health Organization( 19 ) and adjusted to fit to our context. The tool comprises of questions related to socio-demographic characteristics, clinical characteristics, and laboratory work-up. The blood pressure was measured after the patient had rested for 3 to 5 min, and height, hip circumference and waist circumference were measured using a nonexpanding plastic tap meter. Blood samples were collected at the hospital main laboratory and analysed using a Canvas C311 fully automated machine. Prior to the main data collection, a pilot study was conducted with 15 patients from Bekoji hospital. The tool's validity was assessed by panel of expert and factor analysis. Internal consistency was assessed using Cronbach’s alpha (0.78). Based on the pre-test findings, adjustments were made to the questionnaire. Data collectors received two days of training on the study’s objectives, participants’ rights, and data collection procedures. Supervisors closely monitored adherence to participants’ rights and ensured the quality of the data collected. This cross-sectional study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines( 20 ). The STROBE framework was utilized to ensure accurate reporting of the study design, methodology, and findings. Data analysis After the collection the data was pre-processed to remove missing values and invalid entries and then analyzed with SPSS version 26. Descriptive analyses were performed to summarize the socio-demographic characteristics of patients on ARTs and prevalence of DM was presented using frequency and cross tabulation. Binary logistic regression was performed to examine the effect of each independent variable on DM among HIV patients at a 95% confidence level. Variables with a p-value of 0.2 were considered for inclusion in the multivariable logistic regression. Multivariable analysis was then conducted to identify key factors influencing the prevalence of DM among HIV patients. Adjusted odds ratios, along with 95% confidence intervals and p-values, were calculated to determine the strength and significance of associations between dependent and independent variables. Ethical consideration Ethical clearance was obtained from the institutional review board of AURTH, and permission was secured from the ART focal person at Arsi Teaching and Referral Hospital to access information from records for research purposes. The acquired data will be kept anonymized to maintain confidentiality. Result Socio-demographic characteristics of the respondents Among the 159 study participants, 88 were female, representing 55.3% of the sample, with a female-to-male ratio of 1.24:1. The average age was 42.13 years (SD = 14.97). The largest age group was in between 36–50 years, comprising 42.1% of participants, indicating a broad age distribution overall. In terms of demographics, 57.2% of the participants were married. Most of the respondents (79.2%) lived in urban areas. Regarding education, 40.3% of the study participants had obtained a diploma or higher qualification, as illustrated in Table 1 . Table 1 Socio-demographic characteristics among HIV-infected patients at Asella Teaching and Referral Hospital (N = 159) Variable Category Frequency Percentage Age 18–35 47 29.6 36–50 67 42.1 > 50 45 28.3 Gender Male 71 44.7 Female 88 55.3 Marriage Unmarried 39 24.5 Married 91 57.2 Divorced 23 14.5 Widowed 6 3.8 Residence Urban 126 79.2 Rural 33 20.8 Occupation Employed 53 33.3 unemployed 31 19.5 housewife 49 30.8 merchant 13 8.2 Other 14 8.8 Education Illiterate 29 18.2 1st and 2nd School 66 41.5 Diploma and above 64 40.3 Physical activity Sedentary 61 38.4 Non sedentary 98 61.6 Alcohol Yes 35 22.0 No 124 78.0 Smoking Yes 23 14.5 No 136 85.5 Laboratory and clinical characteristics In this study, about 6.3% of patients reported having a personal history of diabetes, whereas only 2.5% reported a family history of the condition. Among participants diagnosed with diabetes, the most common ART regimen was TDF+3TC + EFV (35.8%), and 82.3% of participants demonstrated good adherence. Metabolic and physical screening revealed significant cardiovascular risk factors. High blood pressure was observed in 25.2% of participants. Although most (71.7%) had a normal BMI, central obesity was noted in 17.0% via waist circumference and 25.8% via waist-to-hip ratio. Lipid abnormalities were also prevalent, specifically high total cholesterol (18.86%) and high LDL-C (18.23%) Table 2 . Table 2 Laboratory and clinical characteristics among HIV-infected patients at Asella Teaching and Referral Hospital (N = 159) Variable Category Glucose value (%) Total (%) FBS ≥ 126 FBS < 126 Known diabetes Yes 10 7 10 (6.3) No 149 149 (93.7) Family history of diabetes Yes 3 1 4 (2.5) No 14 141 155 (97.5) Duration of HIV 5years 14 81 95 (59.7) Baseline CD4 500 4 28 32 (20.2) First ART regimen AZT+3TC + EFV 2 18 20 (12.6) TDF+3TC + NVP 0 16 16 (10.1) AZT+3TC + NVP 5 25 30 (18.9) TDF+3TC + EVF 7 50 57 (35.8) TDF+3TC + DTG 1 16 17 (10.7) D4T+3TC + NVP 2 10 12 (7.5) Others 0 7 7 (4.4) Adherence to ART Good 15 116 131(82.3) Fair 0 2 2 (1.3) Poor 2 24 26 (16.4) BP Normal 5 108 119 (74.8) High 12 34 40 (25.2) BMI < 18.5 1 30 31 (19.5) 18.5–24.9 16 98 114 (71.7) ≥ 25 0 14 14 (8.8) Waist circumference Normal 13 119 132 (83.0) High 4 23 27 (17.0) Waist-to-hip ratio Normal 13 105 118 (74.2) High 4 37 41 (25.8) TC Normal 7 122 126 (79.24) High 10 20 33(20.76) LDL-c Normal 7 123 130(81.76) High 10 19 29 (18.23) HDL-C Normal 4 122 126(79.2) Low 13 20 33(20.8) TG Normal 8 128 137 (85.54) High 9 14 23(14.46) Clinical Stage I 17 127 144 (90.6) II 0 8 8 (5.0) III 0 7 7 (4.4) Prevalence of diabetes This study revealed that a total of 17 participants were identified as having diabetes, representing 10.7% of the study population (95% CI: 6.35–16.57%). Within this group, 5 individuals (3.1%) exhibited fasting plasma glucose levels between 111 and 125 mg/dl, placing them within the range considered indicative of impaired fasting glucose or pre-diabetes. Notably, only 10 participants (6.3% of the total sample) were aware of their diabetic status before the study. This indicates that 41.2% of the diabetic cases identified in this study were previously undiagnosed. This finding suggests a significant gap in metabolic screening within HIV clinical settings.(Fig. 1 ). Risk factors associated with diabetes Variables associated with diabetes in the bivariate analysis (p < 0.25) were entered into a multivariate logistic regression model to adjust for confounding. In the final multivariable logistic regression model, four variables remained independent predictors of diabetes. Sedentary participants were 3.82 times more likely to be diabetic than those who were non-sedentary (AOR = 3.82, 95% CI: 1.0–14.0, p = 0.04). High blood pressure was also significantly associated with diabetes (AOR = 6.2, 95% CI: 2.5–23.1, p = 0.02). In addition to these high total cholesterol (AOR = 7.0, 95% CI: 1.6–30.89, p = 0.001) and high LDL-C (AOR = 4.5, 95% CI: 1.1–18.3, $ p = 0.03 $ ) were both strongly associated with the dependent variable. While duration of ART showed a high point estimate for risk (AOR = 5.19), it did not reach statistical significance ( $ p = 0.12 $ ), suggesting that in this population, traditional metabolic risk factors and lifestyle choices were more influential than the duration of antiretroviral therapy (Table 3 ). Table 3 Bivariant and multivariate logistic regression analysis among HIV-infected patients at Asella Teaching and Referral Hospital (N = 159) Variables category Glucose value (%) COR(95% CI) P- value AOR (95% CI) P value FBS ≥ 126 FBS < 126 Exercise Sedentary 12 49 6.3(1.5–13.6) 0.007 3.82(1.0–14.0) 0.04* Non sedentary 5 93 1 1 1 1 Duration on ART 5 14 81 3.51(0.89–54.7) 0.012 5.19(0.65-41.0) 0.12 Baseline CD4 500 4 28 2.98(0.5-17.09) 0.23 0.97(0.11–8.1) 0.17 BP Normal 5 108 1 1 1 1 High 12 34 7.6 (2.2–39.1) 0.00 6.2 (2.5–23.1) 0.02* Total cholesterol Normal 7 122 1 1 1 1 High 10 20 8.7(1.4–11.0) .00 7.0(1.6-30.89) 0.001* LDL-C Normal 7 123 1 1 1 1 High 10 19 9.24(3.1–27.3) .000 4.5(1.1–18.3) 0.03* TG Normal 8 128 1 1 1 1 High 9 14 10.28(2.7–24.4) .00 3.5(0.6–18.7) 0.013 DISCUSSION A study conducted to determine the prevalence of and associated risk factors associated with diabetes mellitus type 2 among HIV positive patients receiving treatment at Asella Teaching and Referral Hospital. The overall prevalence of diabetes in that population was 10.7%. This is significantly higher than previous studies conducted in the Harar( 21 ) and Gondar( 22 ), Ethiopia. However, it is less than the prevalence as reported by a prior study in Jimma Ethiopia(11.4%)( 23 ). The differing diabetes prevalence rates in these studies can be attributed to differences in the population characteristics of each study population, screening methods, and regional lifestyle practices, not sample size alone. Additionally, some of the patients in their study had coexisting conditions, especially those classified as WHO clinical stage II/III. This could have increased the incidence and resulted in false-positive results because of stress hyperglycemia ( 24 , 25 ). The result of this research is consistent with the most recent countrywide meta-analysis of people living with HIV in Ethiopia that found the overall prevalence of diabetes is 16.04%, but has significant regional variation from 8.53%(Amhara) to 24.37% (SNNPR). The results of this review demonstrate the impact of geographic context, availability of ART, dietary practices, urban growth, and changing demographics on the presence of diabetes among people living with HIV.( 26 ). Because our study was conducted several years after many prior regional assessments, rising diabetes rates in the general population and among HIV-positive individuals may also partly account for our higher prevalence estimate. This study examined different factors which could affect the prevalence of diabetes type 2 among HIV. Physical inactivity emerged as a significant predictor of diabetes in this, although the association exhibited a wide confidence interval (AOR = 3.82, 95% CI: 1.0–14.0), indicating limited precision patients. This result is consistent with case‒control study, which identified low levels of physical activity (AOR = 3.83, 95% CI 1.46–10.05) as a significant factor associated with metabolic syndrome (MS), where diabetes is one of its components( 27 ). Additionally, this is supported by a cohort study from Gode Ethiopia that examined comorbidity and HIV status and revealed that individuals who did not participate in regular physical activity were twice as likely as their counterparts to develop chronic comorbidity (AOR = 2.16, 95% CI: 1.09–4.29; P = 0.027( 27 ). Contrary to our findings, the rate of physical inactivity was greater in this group of patients (40%, 68%) in the studies performed in Yirgalem and Bahir Dar Ethiopia, respectively. The variance in outcomes could be ascribed to their use of the WHO International Physical Activity Questionnaire (IPAQ-TM). Conversely, another reason for physical inactivity in our investigation was linked to spending more than four hours during work hours involved in low physical activity, aligning with the criteria proposed by the WHO ( 28 , 29 ). In this study, the prevalence of high blood pressure (BP) was 28.8%, which aligns closely with the findings of a study from North Ethiopia (29.7%)( 9 ) and Southwest Ethiopia (34.4%)( 8 ). Additionally, high BP is implicated in causing diabetes, as evidenced by adjusted odds ratios (AORs) of 6.2 (95% CI 2.5–23.1, P = 0.02) and 4.779 (95% CI: 1.646–13.874, P = 0.004) in the abovementioned studies. However, our study is inconsistent with these findings, considering the wider confidence interval due to the lower sample size (AOR = 7.6, 95% CI = 2.2–39.1, P = 0.02). In contrast to this findings, the reported incidence of high blood pressure (BP) in western Ethiopia is 12.7%, and it is linked to diabetes, with an odds of AOR = 3.3 (95% CI = 1.1–9.5). Additionally, in eastern Ethiopia, where the prevalence of high BP is 7.7%, no connection was identified between hypertension (HTN) and HIV-related diabetes in this particular study ( 23 , 25 ). The observed difference in the abovementioned studies may be explained by variations in the definition of hypertension, which could also contribute to the observed differences the above studies defined, hypertension based on elevated blood pressure measurements ranging between 170–180 mmHg (systolic) and 100–110 mmHg (diastolic), a definition markedly distinct from the criteria employed in our study . The prevalence of dyslipidaemia, specifically characterized by elevated LDL, was 18.2% in this study, and it was identified as a contributing factor to diabetes among the group of patients (AOR = 4.5, 95% CI = 1.1–18.3, P = 0.03). This observation aligns with findings from a study in eastern Ethiopia, where a similar prevalence of 18.5% was associated with diabetes (AOR: 4.04, 95% CI = 1.33–12.30; P = 0.014). Similarly, in the same region, low-density lipoprotein cholesterol was significantly linked to diabetes (AOR = 5.669, 95% CI = 1.849–17.382, P = 0.004)( 30 ). In contrast, the incidence of high LDL-c was lower than that reported in an Eritrean study, which documented a rate of 55%. This difference may be attributed to the greater proportion of female and elderly participants in their study than in our study, as we had a smaller sample size ( 31 ). Conclusion, recommendation and limitations This study identified low physical activity, hypertension, and high levels of low-density lipoprotein cholesterol (LDL-C) as significant risk factors for causing diabetes among HIV patients. Therefore, it is recommended to conduct regular screenings for hypertension and lipid profiles and provide advice on engaging in regular physical activity to mitigate or avoid diabetes risk factors among PLWHIV. We also recommend future cohort or control studies with a general population. A limitation of this study is its cross-sectional design with a relatively small sample size and single site. Declarations Ethics approval and consent to participate As stated in the methodology section ethical clearance was secured from Arsi University ethical review board and written consent to participate were received from the participants. Contribution BLT conceived research idea, performed statistical analysis, wrote the main manuscript. 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M. et al. Diabetes mellitus among HIV-infected individuals in follow-up care at University of Gondar Hospital, Northwest Ethiopia. BMJ Open. 6 (8), e011175 (2016). Ataro, Z., Ashenafi, W., Fayera, J., Abdosh, T. & Auckland Magnitude and associated factors of diabetes mellitus and hypertension among adult HIV-positive individuals receiving highly active antiretroviral therapy at Jugal Hospital, Harar, Ethiopia. HIV/AIDS (NZ). ;10:181 – 92. (2018). Mesfin Belay, D. et al. Diabetes mellitus among adults on highly active anti-retroviral therapy and its associated factors in Ethiopia: Systematic review and meta-analysis. Diabetes Res. Clin. Pract. 182 , 109125 (2021). Bune, G. T., Yalew, A. W. & Kumie, A. Predictors of Metabolic Syndrome Among People Living with HIV in Gedeo-Zone, Southern-Ethiopia: A Case-Control Study. HIV/AIDS (Auckland, NZ). ;12:535 – 49. (2020). Tegene, Y. et al. Development of Hypertension and Diabetes Mellitus, and Associated Factors, Among Adult HIV Patients in Ethiopia. HIV/AIDS (NZ). ;15:41–51. (2023). Getahun, Z., Azage, M., Abuhay, T. & Abebe, F. Comorbidity of HIV, hypertension, and diabetes and associated factors among people receiving antiretroviral therapy in Bahir Dar city. Ethiopia J. comorbidity . 10 , 2235042x19899319 (2020). Mohammed, A. E., Shenkute, T. Y. & Gebisa, W. C. Diabetes mellitus and risk factors in human immunodeficiency virus-infected individuals at Jimma University Specialized Hospital, Southwest Ethiopia. Diabetes metabolic syndrome obesity: targets therapy . 8 , 197–206 (2015). Achila, O. O. et al. Dyslipidemia and associated risk factors among HIV/AIDS patients on HAART in Asmara, Eritrea. PLoS One . 17 (7), e0270838 (2022). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9055114","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633181674,"identity":"908ca8c6-81df-44ae-96b6-a53f78584460","order_by":0,"name":"Biniyam Lakew Tilahun","email":"","orcid":"","institution":"Arsi University","correspondingAuthor":false,"prefix":"","firstName":"Biniyam","middleName":"Lakew","lastName":"Tilahun","suffix":""},{"id":633181675,"identity":"93bf2a5e-45f4-4b4d-9f31-93bab649b5a6","order_by":1,"name":"Gizaw Hailiye Teferi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYBAC/mYwdYCBjZ2xgYGhghnMlcCnReIwTAszSMsZhBac2gwYoFoYQIoZ24jRws7+8DMPwx15PmbmNomP86yjDQ4wH7zNw2BTh1MLM0OyNA/DM8M2ZsY2yZnb0nM3HGBLtuZhSMNpi+FhhgPSOQyHGUFapHm3HQZq4TEDGnIYt8MOMzb/BmqxB2v5Owekhf8bUMt/PFqY2UC2JIK1MDaAbWEDajmAU4vEYTY26z8Mh5OBWpote46l5848zGZsOccgWbIBhxb+/uOPb85gOGw7v7394Y0fNda5fcebH954U2HHj8sWMGD8B6ZYIG4BR40BXg1wwPyBOHWjYBSMglEw0gAAaphQqOy+TY0AAAAASUVORK5CYII=","orcid":"","institution":"Debre Markos University","correspondingAuthor":true,"prefix":"","firstName":"Gizaw","middleName":"Hailiye","lastName":"Teferi","suffix":""}],"badges":[],"createdAt":"2026-03-07 03:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9055114/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9055114/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108839674,"identity":"0c2cd017-dd5e-4e05-b56c-fd66c8609ed2","added_by":"auto","created_at":"2026-05-09 00:49:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":43510,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of impaired glycemia and diabetes among HIV-infected patients at Asella Teaching and Referral Hospital (N=159)\u003c/p\u003e","description":"","filename":"drawingimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9055114/v1/127a25f1517d0e15d87eec8e.png"},{"id":108839678,"identity":"a89bfc68-c1e5-4921-83e0-915aa89bca55","added_by":"auto","created_at":"2026-05-09 00:49:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":519986,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9055114/v1/0844bb61-1995-4d84-8e3d-71ccc8b945e8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and risk factors of type 2 diabetes among HIV-infected patients at Asella Teaching and Referral Hospital, Southeast Ethiopia, 2023","fulltext":[{"header":"Plain text summary","content":"\u003cp\u003eBecause of advances in ART, people living with HIV are now living longer; however, they are developing long-term medical conditions like type 2 diabetes. Currently, the information regarding the incidence of diabetes in people with HIV living in Ethiopia, particularly at the local hospital level, is limited. Knowledge of this information will assist providers in managing patient care as well as reducing complications from diabetes.\u003c/p\u003e\n\u003cp\u003eThe study was conducted at Asella Teaching and Referral Hospital in Ethiopia. A total of 159 adults living with HIV who were receiving routine follow-up care were participated in this study. The participants were interviewed and their BMI and waist circumference were measured and blood tests (blood glucose, blood pressure, and cholesterol) performed on each to assess the level of glucose in their blood and the pressure on the arteries. Statistical analyses were used to identify associations between certain variables that contributed to the development of diabetes.\u003c/p\u003e\n\u003cp\u003e1 out of every 10 people living with HIV has diabetes and 5% of all participants are pre-diabetes. Most participants did not know they had diabetes prior to this research study. Individuals who were physically inactive, had elevated blood pressure, or had elevated Levels of Bad (LDL) Cholesterol had an increased rate of diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe research indicates that individuals with HIV have a greater chance of experiencing health issues related to diabetes than people who do not have HIV. Routine testing, stimulating exercise, and tracking blood pressure and cholesterol levels assist in minimizing the chances of developing complications associated with diabetes and will promote healthier, longer-term effects.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eDiabetes is a chronic condition caused by insufficient insulin produced by the pancreas or how the body uses that insulin, affecting roughly 540\u0026nbsp;million people worldwide (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e). People with human immune virus(HIV) are at increased risk for developing type 2 diabetes mellitus (T2DM) due to HIV-related metabolic changes and the effects of antiretroviral therapy (ART), ultimately leading to increased morbidity and mortality(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes and pre-diabetes in HIV patients are diagnosed using the same criteria as in the general population, following ADA 2023 guidelines (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e). However, HbA1C may underestimate glycemia in those with low CD4 counts or on ART, so it is not recommended for diagnosis, Instead, fasting plasma glucose ≥ 126 mg/dL, 2-h OGTT ≥ 200 mg/dL, or random plasma glucose ≥ 200 mg/dL with symptoms are used(\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe incidence of diabetes worldwide was 9.3% (463\u0026nbsp;million people) in 2019. The prevalence of associated comorbidities such as non-alcoholic fatty liver disease (NAFLD) varied by geography and socioeconomic status, with higher rates in urban areas (10.8%) than in rural areas (7.2%), and high-income (10.4%) versus low-income regions (4.0%) (\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). In Africa, a systematic review of estimated prevalence of T2DM among adults aged 20–79 years indicated 4.9%, with most cases in individuals younger than 60 years old. The authors estimated cases were especially prevalent in the 40–59 age group (43.2%)(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eIn sub-Saharan Africa, and especially Ethiopia, the dual burden of HIV and T2DM has created an urgent public health problem. ART (antiretroviral therapy) has improved survival outcomes for PLHIV (people living with HIV), but with this increased survival is a growing incidence of non-communicable diseases (NCDs), including diabetes, with pooled national estimates of 6.5% (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe authors noted while HIV-related there have declined, deaths from NCDs (especially from cardiovascular disease and diabetes) are increasing at approximately 4% yearly (\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetes risk factors in people living with HIV (PLHIV) are multifactorial. In addition to the presence of HIV infection and exposure to antiretroviral treatment (ART), traditional risk factors such as age, obesity, lack of physical inactivity, prior gestational diabetes, and polycystic ovary syndrome are significant (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e). Other risk factors include hypertension, dyslipidemia, and genetics, and the prevalence rate varies by race/ethnic group, including African, Asian, Native American, and Hispanic/Latino groups (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e). Socioeconomic risk factors such as higher education and government employment have also been associated with additional risk (\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, HIV-specific ART regimens, especially protease inhibitors (PIs) and nucleoside reverse transcriptase inhibitors (NRTIs), have influenced susceptibility to diabetes as the risk of diabetes increases with longer duration of HIV infection and ART exposure (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eResearch gap\u003c/h3\u003e\n\u003cp\u003eWhile there is established evidence of global and regional diabetes burden among people living with HIV (PLHIV), much of the evidence is limited to high-income countries or systematic reviews including studies with heterogeneous samples. These studies have described how HIV infection and exposure to antiretroviral therapy (ART) increase the risk for type 2 diabetes mellitus (T2DM) but there is little evidence for contextualized understandings relevant to sub-Saharan Africa. Furthermore, using global or national estimates does not reflect local context and levels of prevalence and risk factors, which vary based on socioeconomic status, access to healthcare, and lifestyle.\u003c/p\u003e \u003cp\u003eIn Ethiopia, a growing body of literature indicates a significant and increasing comorbidity burden of HIV and diabetes, with national prevalence estimates indicating considerable variability among and across regions. However, many of these studies remain geographically circumscribe, and their implications may not be generalizable to all healthcare settings. Additionally, there remains limited evidence of how ART regimens and exposure duration, along with individual demographics, play a role in diabetes risk for PLHIV in Ethiopia. The absence of disaggregated data remains problematic in the design of preventive and therapeutic interventions specific for the context.\u003c/p\u003e \u003cp\u003eThere has been no systematic study previously done at Asella Teaching and Referral Hospital to assess the rate and risk factors of T2DM among HIV-infected patients. As a huge teaching and referral hospital in Southeast Ethiopia, exploring the local epidemiology of this comorbidity is ideal. Addressing this provide evidence-based information to support the management of patients, evaluate ART regimens, and structure public health planning for the needs of this population.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003ch2\u003eStudy area and period\u003c/h2\u003e\u003cp\u003eThe study was conducted from November 1 to December 30, 2023, at the Asella Referral and Teaching Hospital, which is situated in Asella, the administrative centre of the East Arsi zone in the Oromia region. There were 4236 total patients at the Asella referral and Teaching Hospital ART clinic, 3647 of whom were older than 18 years. Among the patients older than 18 years, 1721 were male and 1926 were female.\u003c/p\u003e\u003ch3\u003eStudy Design\u003c/h3\u003e\u003cp\u003eAn institution-based cross-sectional study was employed.\u003c/p\u003e\u003ch3\u003eSource and study population\u003c/h3\u003e\u003cp\u003eAll patients were followed up at the ART clinic of the Arsi University Referral Hospital was the source population while the study population were older than 18 years and were admitted to the follow-up clinic at the art clinic of the AURTH.\u003c/p\u003e\u003ch3\u003eInclusion criteria\u003c/h3\u003e\u003cp\u003eAll patients who were aged above 18 years and were enrolled for follow-up care at the AURTH ART clinic\u003c/p\u003e\u003ch2\u003eExclusion criteria\u003c/h2\u003e\u003cp\u003ePatients with acute illnesses or opportunistic infections, as well as those with a history of diabetes treated before learning about RVI, were excluded. Furthermore, patients with type 1 diabetes and those under the age of 18 were not included. Moreover, individuals receiving systemic steroid medication and pregnant women were excluded.\u003c/p\u003e\u003ch3\u003eSample size determination and sampling technique\u003c/h3\u003e\u003ch2\u003eSample size determination\u003c/h2\u003e\u003cp\u003eThe finite single population Cochran formula was used to calculate the sample size.\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\u003cp\u003ewhere\u003c/p\u003e\u003cp\u003eno= sample size before correction for the population n =corrected sample size\u003c/p\u003e\u003cp\u003eN=Adult population size 3647, currently available at the follow-up clinic\u003c/p\u003e\u003cp\u003eZ = standard normal distribution corresponding to a significance level of α = 0.05,\u003c/p\u003e\u003cp\u003eP = proportion of HIV patients who will be diagnosed with diabetes. e = is the margin of error of 5%.\u003c/p\u003e\u003cp\u003eAccording to a previous study, the prevalence of diabetes in HIV patients was 11.5% of 271 adult HIV patients in the HAART Jima Zone Public Hospitals from May to July 30, 2018 (\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUsing the above formula, we obtain an initial sample size of n0 = 150; when this value is corrected for the population, we obtain N = 144, and after adding 10% (\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e) non-respondents, the final sample size will be 159. When we compare the sample size from the dependent variable, it is the largest sample, and we took this sample for the investigation.\u003c/p\u003e\u003ch2\u003eSampling procedure\u003c/h2\u003e\u003cp\u003eAll RVI patients who visited medical follow-up at the ART clinic between November 1, 2023, and December 30, 2023, who fulfilled the inclusion criteria were selected using a systematic probability sampling technique, and every 8th patient was included.\u003c/p\u003e\u003ch2\u003eVariables of the study\u003c/h2\u003e\u003ch2\u003eDependent\u003c/h2\u003e\u003ch2\u003eDiabetes among HIV patients (Yes/ No)\u003c/h2\u003e\u003cp\u003e \u003cb\u003eIndependent variables\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe independent variables: In this study included age, gender, family history of diabetes, and educational level. Clinical and lifestyle factors such as duration of illness, physical inactivity, history of hypertension, dyslipidemia, and body mass index (BMI) were also assessed. In relation to HIV-specific factors, the duration of HAART, types of HAART regimens, RVI stage, CD4 T-cell count, and stage of HIV were examined\u003c/p\u003e\u003ch2\u003eOperational definition\u003c/h2\u003e\u003cp\u003eDiabetes: A fasting plasma glucose level of 126 mg/dl or above at two consecutive study visits, a previous diagnosis of diabetes supported by recorded evidence, or the current use of diabetes medication were the criteria used in this investigation to define diabetes mellitus(\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe quantity of time spent sitting, reclining, or lying down was used to characterize a sedentary lifestyle. People were classified as sedentary if they spent more than four hours a day in this position; those who spent at least four hours were classified as non-sedentary(\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e). Where as physical Activities is defined as the ratio of work metabolic rate to the standard resting metabolic rate (RMR) of 1 kcal/(kg/h). One MET is the RMR or energy cost for a person at rest (\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eData collection procedure and quality control\u003c/h2\u003e\u003cp\u003eThe data were collected using a structured, interviewer-administered questionnaire adapted from a the STEP wise approach of the World Health Organization(\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e) and adjusted to fit to our context. The tool comprises of questions related to socio-demographic characteristics, clinical characteristics, and laboratory work-up. The blood pressure was measured after the patient had rested for 3 to 5 min, and height, hip circumference and waist circumference were measured using a nonexpanding plastic tap meter. Blood samples were collected at the hospital main laboratory and analysed using a Canvas C311 fully automated machine. Prior to the main data collection, a pilot study was conducted with 15 patients from Bekoji hospital. The tool's validity was assessed by panel of expert and factor analysis. Internal consistency was assessed using Cronbach’s alpha (0.78). Based on the pre-test findings, adjustments were made to the questionnaire. Data collectors received two days of training on the study’s objectives, participants’ rights, and data collection procedures. Supervisors closely monitored adherence to participants’ rights and ensured the quality of the data collected.\u003c/p\u003e\u003cp\u003eThis cross-sectional study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology guidelines(\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e). The STROBE framework was utilized to ensure accurate reporting of the study design, methodology, and findings.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eAfter the collection the data was pre-processed to remove missing values and invalid entries and then analyzed with SPSS version 26. Descriptive analyses were performed to summarize the socio-demographic characteristics of patients on ARTs and prevalence of DM was presented using frequency and cross tabulation. Binary logistic regression was performed to examine the effect of each independent variable on DM among HIV patients at a 95% confidence level. Variables with a p-value of 0.2 were considered for inclusion in the multivariable logistic regression. Multivariable analysis was then conducted to identify key factors influencing the prevalence of DM among HIV patients. Adjusted odds ratios, along with 95% confidence intervals and p-values, were calculated to determine the strength and significance of associations between dependent and independent variables.\u003c/p\u003e\u003ch2\u003eEthical consideration\u003c/h2\u003e\u003cp\u003eEthical clearance was obtained from the institutional review board of AURTH, and permission was secured from the ART focal person at Arsi Teaching and Referral Hospital to access information from records for research purposes. The acquired data will be kept anonymized to maintain confidentiality.\u003c/p\u003e"},{"header":"Result","content":"\u003ch2\u003eSocio-demographic characteristics of the respondents\u003c/h2\u003e\u003cp\u003eAmong the 159 study participants, 88 were female, representing 55.3% of the sample, with a female-to-male ratio of 1.24:1. The average age was 42.13 years (SD = 14.97). The largest age group was in between 36–50 years, comprising 42.1% of participants, indicating a broad age distribution overall.\u003c/p\u003e\u003cp\u003eIn terms of demographics, 57.2% of the participants were married. Most of the respondents (79.2%) lived in urban areas. Regarding education, 40.3% of the study participants had obtained a diploma or higher qualification, as illustrated in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab1\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics among HIV-infected patients at Asella Teaching and Referral Hospital (N = 159)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003c/colgroup\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18–35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e36–50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e42.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e28.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e55.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eMarriage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e79.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e20.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eunemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e19.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ehousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003emerchant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e18.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1st and 2nd School\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e41.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eDiploma and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e38.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNon sedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e61.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eAlcohol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e22.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSmoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003eLaboratory and clinical characteristics\u003c/h2\u003e\u003cp\u003eIn this study, about 6.3% of patients reported having a personal history of diabetes, whereas only 2.5% reported a family history of the condition. Among participants diagnosed with diabetes, the most common ART regimen was TDF+3TC + EFV (35.8%), and 82.3% of participants demonstrated good adherence.\u003c/p\u003e\u003cp\u003eMetabolic and physical screening revealed significant cardiovascular risk factors. High blood pressure was observed in 25.2% of participants. Although most (71.7%) had a normal BMI, central obesity was noted in 17.0% via waist circumference and 25.8% via waist-to-hip ratio. Lipid abnormalities were also prevalent, specifically high total cholesterol (18.86%) and high LDL-C (18.23%) Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab2\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLaboratory and clinical characteristics among HIV-infected patients at Asella Teaching and Referral Hospital (N = 159)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003c/colgroup\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eGlucose value (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTotal (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFBS ≥ 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFBS \u0026lt; 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eKnown diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10 (6.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e149 (93.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFamily history of diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4 (2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e155 (97.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDuration of HIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e66(41.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e93 (58.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDuration of HAART\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≤ 5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e65 (40.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 5years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e95 (59.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBaseline CD4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e43 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e201–499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e84 (52.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e32 (20.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eFirst ART regimen\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAZT+3TC + EFV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20 (12.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTDF+3TC + NVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16 (10.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAZT+3TC + NVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e30 (18.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTDF+3TC + EVF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e57 (35.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eTDF+3TC + DTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eD4T+3TC + NVP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12 (7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eAdherence to ART\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e131(82.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2 (1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e26 (16.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e119 (74.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e40 (25.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e31 (19.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e18.5–24.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e114 (71.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e≥ 25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14 (8.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eWaist\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003ecircumference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e132 (83.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e27 (17.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eWaist-to-hip ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e118 (74.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41 (25.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e126 (79.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e33(20.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLDL-c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e130(81.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e29 (18.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e126(79.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e33(20.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e137 (85.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e23(14.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eClinical Stage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e144 (90.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8 (5.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e\u003ch2\u003ePrevalence of diabetes\u003c/h2\u003e\u003cp\u003eThis study revealed that a total of 17 participants were identified as having diabetes, representing 10.7% of the study population (95% CI: 6.35–16.57%). Within this group, 5 individuals (3.1%) exhibited fasting plasma glucose levels between 111 and 125 mg/dl, placing them within the range considered indicative of impaired fasting glucose or pre-diabetes.\u003c/p\u003e\u003cp\u003eNotably, only 10 participants (6.3% of the total sample) were aware of their diabetic status before the study. This indicates that 41.2% of the diabetic cases identified in this study were previously undiagnosed. This finding suggests a significant gap in metabolic screening within HIV clinical settings.(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eRisk factors associated with diabetes\u003c/h2\u003e\u003cp\u003eVariables associated with diabetes in the bivariate analysis (p \u0026lt; 0.25) were entered into a multivariate logistic regression model to adjust for confounding. In the final multivariable logistic regression model, four variables remained independent predictors of diabetes. Sedentary participants were 3.82 times more likely to be diabetic than those who were non-sedentary (AOR = 3.82, 95% CI: 1.0–14.0, p = 0.04). High blood pressure was also significantly associated with diabetes (AOR = 6.2, 95% CI: 2.5–23.1, p = 0.02). In addition to these high total cholesterol (AOR = 7.0, 95% CI: 1.6–30.89, p = 0.001) and high LDL-C (AOR = 4.5, 95% CI: 1.1–18.3, \u003cspan\u003e$\u003c/span\u003ep = 0.03\u003cspan\u003e$\u003c/span\u003e) were both strongly associated with the dependent variable.\u003c/p\u003e\u003cp\u003eWhile duration of ART showed a high point estimate for risk (AOR = 5.19), it did not reach statistical significance (\u003cspan\u003e$\u003c/span\u003ep = 0.12\u003cspan\u003e$\u003c/span\u003e), suggesting that in this population, traditional metabolic risk factors and lifestyle choices were more influential than the duration of antiretroviral therapy (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\"\u003e\u003c/div\u003e\u003ctable id=\"Tab3\" border=\"1\"\u003e \u003ccaption\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBivariant and multivariate logistic regression analysis\u003c/b\u003e among \u003cb\u003eHIV-infected patients at Asella Teaching and Referral Hospital (N = 159)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003c/colgroup\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003ecategory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\"\u003e \u003cp\u003eGlucose value (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eCOR(95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eP-\u003c/p\u003e \u003cp\u003evalue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFBS ≥ 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eFBS\u003c/p\u003e \u003cp\u003e\u0026lt; 126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eExercise\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eSedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.3(1.5–13.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.82(1.0–14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.04*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNon sedentary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eDuration on ART\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.51(0.89–54.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5.19(0.65-41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eBaseline CD4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026lt; 200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e201–499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.08(0.65–14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1.35(0.2–8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e\u0026gt; 500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e2.98(0.5-17.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.97(0.11–8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eBP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.6 (2.2–39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e6.2 (2.5–23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTotal cholesterol\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8.7(1.4–11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7.0(1.6-30.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eLDL-C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9.24(3.1–27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e4.5(1.1–18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.03*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e10.28(2.7–24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e3.5(0.6–18.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/table\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eA study conducted to determine the prevalence of and associated risk factors associated with diabetes mellitus type 2 among HIV positive patients receiving treatment at Asella Teaching and Referral Hospital. The overall prevalence of diabetes in that population was 10.7%. This is significantly higher than previous studies conducted in the Harar(\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) and Gondar(\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e), Ethiopia. However, it is less than the prevalence as reported by a prior study in Jimma Ethiopia(11.4%)(\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe differing diabetes prevalence rates in these studies can be attributed to differences in the population characteristics of each study population, screening methods, and regional lifestyle practices, not sample size alone. Additionally, some of the patients in their study had coexisting conditions, especially those classified as WHO clinical stage II/III. This could have increased the incidence and resulted in false-positive results because of stress hyperglycemia (\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe result of this research is consistent with the most recent countrywide meta-analysis of people living with HIV in Ethiopia that found the overall prevalence of diabetes is 16.04%, but has significant regional variation from 8.53%(Amhara) to 24.37% (SNNPR). The results of this review demonstrate the impact of geographic context, availability of ART, dietary practices, urban growth, and changing demographics on the presence of diabetes among people living with HIV.(\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e). Because our study was conducted several years after many prior regional assessments, rising diabetes rates in the general population and among HIV-positive individuals may also partly account for our higher prevalence estimate. This study examined different factors which could affect the prevalence of diabetes type 2 among HIV.\u003c/p\u003e \u003cp\u003ePhysical inactivity emerged as a significant predictor of diabetes in this, although the association exhibited a wide confidence interval (AOR = 3.82, 95% CI: 1.0–14.0), indicating limited precision\u003c/p\u003e \u003cp\u003epatients. This result is consistent with case‒control study, which identified low levels of physical activity (AOR = 3.83, 95% CI 1.46–10.05) as a significant factor associated with metabolic syndrome (MS), where diabetes is one of its components(\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e). Additionally, this is supported by a cohort study from Gode Ethiopia that examined comorbidity and HIV status and revealed that individuals who did not participate in regular physical activity were twice as likely as their counterparts to develop chronic comorbidity (AOR = 2.16, 95% CI: 1.09–4.29; P = 0.027(\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eContrary to our findings, the rate of physical inactivity was greater in this group of patients (40%, 68%) in the studies performed in Yirgalem and Bahir Dar Ethiopia, respectively. The variance in outcomes could be ascribed to their use of the WHO International Physical Activity Questionnaire (IPAQ-TM). Conversely, another reason for physical inactivity in our investigation was linked to spending more than four hours during work hours involved in low physical activity, aligning with the criteria proposed by the WHO (\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the prevalence of high blood pressure (BP) was 28.8%, which aligns closely with the findings of a study from North Ethiopia (29.7%)(\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e) and Southwest Ethiopia (34.4%)(\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e). Additionally, high BP is implicated in causing diabetes, as evidenced by adjusted odds ratios (AORs) of 6.2 (95% CI 2.5–23.1, P = 0.02) and 4.779 (95% CI: 1.646–13.874, P = 0.004) in the abovementioned studies. However, our study is inconsistent with these findings, considering the wider confidence interval due to the lower sample size (AOR = 7.6, 95% CI = 2.2–39.1, P = 0.02).\u003c/p\u003e \u003cp\u003eIn contrast to this findings, the reported incidence of high blood pressure (BP) in western Ethiopia is 12.7%, and it is linked to diabetes, with an odds of AOR = 3.3 (95% CI = 1.1–9.5). Additionally, in eastern Ethiopia, where the prevalence of high BP is 7.7%, no connection was identified between hypertension (HTN) and HIV-related diabetes in this particular study (\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed difference in the abovementioned studies may be explained by variations in the definition of hypertension, which could also contribute to the observed differences the above studies defined, hypertension based on elevated blood pressure measurements ranging between 170–180 mmHg (systolic) and 100–110 mmHg (diastolic), a definition markedly distinct from the criteria employed in our study .\u003c/p\u003e \u003cp\u003eThe prevalence of dyslipidaemia, specifically characterized by elevated LDL, was 18.2% in this study, and it was identified as a contributing factor to diabetes among the group of patients (AOR = 4.5, 95% CI = 1.1–18.3, P = 0.03). This observation aligns with findings from a study in eastern Ethiopia, where a similar prevalence of 18.5% was associated with diabetes (AOR: 4.04, 95% CI = 1.33–12.30; P = 0.014). Similarly, in the same region, low-density lipoprotein cholesterol was significantly linked to diabetes (AOR = 5.669, 95% CI = 1.849–17.382, P = 0.004)(\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the incidence of high LDL-c was lower than that reported in an Eritrean study, which\u003c/p\u003e \u003cp\u003edocumented a rate of 55%. This difference may be attributed to the greater proportion of female and elderly participants in their study than in our study, as we had a smaller sample size (\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e "},{"header":"Conclusion, recommendation and limitations","content":"\u003cp\u003eThis study identified low physical activity, hypertension, and high levels of low-density lipoprotein cholesterol (LDL-C) as significant risk factors for causing diabetes among HIV patients. Therefore, it is recommended to conduct regular screenings for hypertension and lipid profiles and provide advice on engaging in regular physical activity to mitigate or avoid diabetes risk factors among PLWHIV. We also recommend future cohort or control studies with a general population. A limitation of this study is its cross-sectional design with a relatively small sample size and single site.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eEthics approval and consent to participate\u003c/h3\u003e\n\u003cp\u003eAs stated in the methodology section ethical clearance was secured from Arsi University ethical review board and written consent to participate were received from the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eBLT conceived research idea, performed statistical analysis, wrote the main manuscript. GHT Participated in data cleaning and statistical analysis, prepared figures. Both authors reviewed and approved the manuscript.\u003c/p\u003e\n\u003ch3\u003eConsent for publication\u003c/h3\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch3\u003eAvailability of data and materials\u003c/h3\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch3\u003eCompeting interests\u003c/h3\u003e\n\u003cp\u003eAuthors declare conflict of interest.\u003c/p\u003e\n\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eNo funding received to conduct this study.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBhavya, E. \u0026amp; Sanjay, G. Diabetes and the Importance of Insulin. \u003cem\u003eInt. J. Health Sci.\u003c/em\u003e (I):8479\u0026ndash;8487. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun, H. et al. 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Diabetes\u003c/em\u003e. \u003cb\u003e127\u003c/b\u003e (S 01), S1\u0026ndash;S7 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJochem, C., Wallmann-Sperlich, B. \u0026amp; Leitzmann, M. F. The Influence of Sedentary Behavior on Cancer Risk: Epidemiologic Evidence and Potential Molecular Mechanisms. \u003cem\u003eCurr. Nutr. Rep.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e (3), 167\u0026ndash;174 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAinsworth, B. E. et al. 2011 Compendium of Physical Activities: a second update of codes and MET values. \u003cem\u003eMed. Sci. Sports. Exerc.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (8), 1575\u0026ndash;1581 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrganization, W. H. \u003cem\u003eWHO STEPS instrument (core and expanded). The WHO STEPwise approach to noncommunicable disease risk factor surveillance\u003c/em\u003e (World Health Organization, 2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCuschieri, S. The STROBE guidelines. \u003cem\u003eSaudi J. Anaesth.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (Suppl 1), S31\u0026ndash;S4 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtaro, Z., Ashenafi, W., Fayera, J. \u0026amp; Abdosh, T. Magnitude and associated factors of diabetes mellitus and hypertension among adult HIV-positive individuals receiving highly active antiretroviral therapy at Jugal Hospital, Harar, Ethiopia. HIV/AIDS -. \u003cem\u003eRes. Palliat. Care\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (null), 181\u0026ndash;192 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbebe, S. M. et al. 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Comorbidity of HIV, hypertension, and diabetes and associated factors among people receiving antiretroviral therapy in Bahir Dar city. \u003cem\u003eEthiopia J. comorbidity\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 2235042x19899319 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohammed, A. E., Shenkute, T. Y. \u0026amp; Gebisa, W. C. Diabetes mellitus and risk factors in human immunodeficiency virus-infected individuals at Jimma University Specialized Hospital, Southwest Ethiopia. \u003cem\u003eDiabetes metabolic syndrome obesity: targets therapy\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 197\u0026ndash;206 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchila, O. O. et al. Dyslipidemia and associated risk factors among HIV/AIDS patients on HAART in Asmara, Eritrea. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (7), e0270838 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diabetes, HIV, Prevalence, Risk factors, ART clinic and Ethiopia","lastPublishedDoi":"10.21203/rs.3.rs-9055114/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9055114/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The utilization of highly active antiretroviral therapy (HAART) significantly extends the lifespan of individuals living with Human Immunodeficiency Virus (PLWHIV) and markedly reduces HIV-related morbidity and mortality. On a global scale, non-communicable diseases associated with HIV, such as diabetes, are emerging as significant public health concerns. The prolonged use of HAART and the increase in chronic comorbidities as life expectancy increase are becoming substantial causes of morbidity and mortality among HIV patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study aimed to assess the prevalence of diabetes mellitus and associated risk factors among adult HIV patients at Asella Teaching and Referral Hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A hospital-based cross-sectional study was conducted from November 1,023, to December 30,023, among selected HIV patients at Asella Teaching and Referral Hospital.\u003c/p\u003e\n\u003cp\u003eThe data were collected using a structured interviewer administered questionnaire adapted from the STEP wise approach of the World Health Organization. The collected data were analysed using SPSS 26. Descriptive analysis such as frequency and mean was used to represent the characteristic of the study population and the prevalence of diabetes among the study participants. Bi-variable logistic regression analysis was used to identify candidate variables at p\u0026lt;0.2, and multivariable logistic regression analysis was employed to identify significant factors associated with prevalence of diabetes among HIV patients at a p value \u0026lt;0.05 and 95% confidence interval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 159 Responses were received with a response rate of 100%, and the prevalence of diabetes among PLWHIV exposed to HAART was 10.7% (95% CI=2.70-24.73) and 5.0%, of the study participants were pre-diabetes.\u003c/p\u003e\n\u003cp\u003eMultivariable logistic regression result revealed that being physically inactive (AOR: 3.82, 95% C.I=1.0-14.0), having hypertension (AOR: 6.2, 95% C. I =2.5- 23.1) and LDL-C ≥130 mg/dl (AOR: 4.5, 95% CI=1.13-18.30) were found to be a risk factors for diabetes mellitus in PLWHIV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion and recommendation\u003c/strong\u003e: Individuals living with HIV (PLWHIV) are at increased risk of developing diabetes, and it is recommended that they participate in regular physical activity and undergo regular screenings for blood pressure and lipid profiles to effectively manage and monitor potential health risks.\u003c/p\u003e","manuscriptTitle":"Prevalence and risk factors of type 2 diabetes among HIV-infected patients at Asella Teaching and Referral Hospital, Southeast Ethiopia, 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-09 00:49:23","doi":"10.21203/rs.3.rs-9055114/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T09:46:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143433194466338912612474195063871346865","date":"2026-05-11T03:48:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110546768141970306307721594391149209940","date":"2026-05-09T06:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234187607967541100690051450674645365263","date":"2026-05-02T03:30:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61698172712235601233536097202578737829","date":"2026-04-27T20:20:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-27T02:44:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-27T02:43:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-31T21:38:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-15T18:30:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-15T18:27:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2c45579-f11c-4f8c-9e49-0bcbf052acf6","owner":[],"postedDate":"May 9th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T09:46:34+00:00","index":144,"fulltext":""},{"type":"reviewerAgreed","content":"143433194466338912612474195063871346865","date":"2026-05-11T03:48:40+00:00","index":143,"fulltext":""},{"type":"reviewerAgreed","content":"110546768141970306307721594391149209940","date":"2026-05-09T06:29:03+00:00","index":141,"fulltext":""},{"type":"reviewerAgreed","content":"234187607967541100690051450674645365263","date":"2026-05-02T03:30:47+00:00","index":98,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67395654,"name":"Health sciences/Diseases"},{"id":67395655,"name":"Health sciences/Endocrinology"},{"id":67395656,"name":"Health sciences/Health care"},{"id":67395657,"name":"Health sciences/Medical research"},{"id":67395658,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-09T00:49:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-09 00:49:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9055114","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9055114","identity":"rs-9055114","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}