Cardiovascular disease risk profile among adolescents at a family medicine clinic in a Nigerian teaching hospital, Benin City: a cross-sectional study

¹Department of Family Medicine, University of Benin Teaching Hospital, Benin City, Nigeria

^&Corresponding author
Roseline Egbe Adah, Department of Family Medicine, University of Benin Teaching Hospital, Benin City, Nigeria

Introduction    

Cardiovascular disease (CVD) is a leading cause of morbidity and premature mortality globally, accounting for 17.9 million deaths in 2019, with projections exceeding 20 million annually by 2030 [1,2]. The burden is pronounced in low- and middle-income countries such as Nigeria, where cardiometabolic syndrome (hypertension, diabetes, obesity, dyslipidaemia) is a major risk factor [3,4]. These conditions converge through atherosclerosis, leading to coronary artery disease and stroke [5].

Cardiovascular disease risk factors often emerge during adolescence (ages 10-19 years), when behaviours such as physical activity, alcohol and tobacco use, and unhealthy diet are established and persist into adulthood [6,7]. Early identification is critical, as clustering of two to four CVD risk factors has been reported in 68.9-72% of adolescents, with 10% having more than four [8,9].

In Nigeria, adolescents constitute over 43 million [6]. Studies in Southwest Nigeria report clustering of modifiable CVD risk factors, with 72% having two to four, and a high-fat, salty diet most common [9]. In Ibadan North, Odunaiya et al. identified smoking as the leading risk factor, while over 82% of participants had low to average knowledge of CVD risks [10]. This reflects a broader gap; most Nigerian studies have not clearly distinguished between lifestyle risk behaviours such as diet and physical inactivity, and lifestyle diseases such as hypertension, diabetes, and obesity. This distinction is important: behaviours may be addressed through health education, whereas established disease requires clinical management.

Data on CVD risk profiles of adolescents in primary care in South-South Nigeria remain limited. Despite WHO recommending routine screening for blood pressure, body mass index, and blood glucose in adolescent health services [11], such practices are rarely implemented in low-resource settings, including Nigeria. Assessing the burden and determinants of CVD among adolescents is therefore essential for targeted interventions.

While genetic factors increase susceptibility to CVD, modifying environmental and lifestyle factors can substantially reduce the risk of familial disease expression [11-13]. While lifestyle behaviour risk factors are high among adolescents, lifestyle diseases may be on the increase. This study aimed to determine the cardiovascular disease risk profile and associated factors among adolescents receiving care at the Family Medicine Clinic in Benin City, South-South Nigeria.

Methods     

Study design and setting: this was a hospital-based cross-sectional study conducted from June 2023 to August 2023 at the Family Medicine Clinic of the University of Benin Teaching Hospital (UBTH), Benin City, Nigeria. UBTH is a federal tertiary hospital that provides primary to tertiary care and receives referrals from neighbouring Southern Nigerian states. The Family Medicine Clinic provides primary care to ~300 adolescents monthly. This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.

Study population: the study population consists of adolescents aged 10-19 years attending the Family Medicine Clinic for care consultations/follow-up over three months, who gave informed consent or assent. Exclusion criteria were acute illness requiring admission, pregnancy, known congenital heart disease, or use of medications affecting blood pressure or glucose. All 180 adolescents who were recruited met eligibility criteria and completed the study. There were no withdrawals, missing data, or exclusions after enrolment. The minimum sample size was calculated using the formula for a single population proportion [14]:

Where Z=1.96 for 95% confidence, p=0.72 based on the 72% prevalence of CVD risk reported by Odunaiya et al. [9], and d=0.05. This gave n=310. Finite population correction [15] was applied for N=300 adolescents attending the clinic in 2019:

After a 15% adjustment for non-response, the final sample size was 180. Systematic random sampling was used: a sampling interval of k=5 was calculated from the estimated population size of 900 by dividing the sample size of 180 adolescents. The first participant each day was selected by balloting, then every 5^th adolescent was approached. All 180 eligible participants who were approached and recruited participated, yielding a 100% response rate.

Operational definitions of terms: dysglycaemia: fasting plasma glucose ≥100 mg/dl, categorized as pre-diabetes (100-125 mg/dL) and diabetes mellitus (≥126 mg/dL), based on ADA 2024 criteria; high-fat/salt diet: self-reported consumption of fried foods, processed snacks, or added salt ≥3 days per week; CVD risk profile: for descriptive purposes in this study, defined by clusters of CVD risk factors and lifestyle behaviours; severe risk (established CVD risk): in this study, it is defined as the presence of any clinical condition (overweight/obesity, hypertension, and dysglycaemia-diabetes mellitus + pre-diabetes), irrespective of lifestyle behaviours; at-risk: in this study, it is defined as the presence of a positive family history of cardiovascular disease in a first-degree relative.

Data collection: a pretested, interviewer-administered questionnaire was used to capture data on sociodemographic characteristics (age, sex, education level, and religion) and for self-reported family history of angina, myocardial infarction, stroke, hypertension, or sudden cardiac death in a first-degree relative. Premature CVD was defined as the occurrence at <55 years in males or <65 years in females. Independent verification of family history was performed for participants whose parents/guardians were present during data collection.

Standardised measurements were done by trained research assistants to assess CVD risk profile, including anthropometry, by measuring weight to 0.1 kg using a calibrated digital scale and height to 0.1 cm using a stadiometer, with participants in light clothing without shoes. Body mass index (BMI)-for-age z-scores were calculated using WHO 2007 growth reference data [16]. Overweight was defined as >+1SD to ≤+2SD, and obesity as >+2SD.

Lifestyle behaviours, which include physical activity, were assessed using the International Physical Activity Questionnaire (IPAQ) and categorised by MET-minutes/week as inactive (<600) or active (≥600) [17]. Smoking and alcohol use were defined as any use in the past 30 days [18]. Sleep was categorised as poor if <8 hours/night [18]. Participants were asked a single screening question on dietary habits: 'Do you consume foods high in fat and/or salt/ eat fast foods more than three times per week?' with yes/no response options. This variable was used for descriptive purposes only and was excluded in bivariate or multivariable analyses due to measurement limitations.

Blood pressure was measured using a calibrated mercury sphygmomanometer with an appropriate cuff size covering 80 - 100% of arm circumference. After 5 minutes of seated rest, with no caffeine, exercise, or smoking 30 minutes before, two readings were taken 2 minutes apart on the right arm. The average was recorded [19]. Elevated blood pressure was defined per 2017 American Academy of Paediatrics guidelines [20]: for adolescents ≥13 years, SBP 120-129 and DBP <80 mmHg = elevated BP; SBP 130-139 or DBP 80-89 mmHg = stage 1 hypertension; SBP ≥140 or DBP ≥90 mmHg = stage 2 hypertension. For children <13 years, ≥90^th to <95^th percentile = elevated BP; ≥95^th percentile = hypertension.

Fasting blood glucose was done using an Accu-Chek Active glucometer after a confirmed 8-10 hours of not eating the night before presentation. Participants who were eligible but did not meet this condition were asked to come the following morning before breakfast. Pre-diabetes was defined as FBG 100-125 mg/dL and diabetes mellitus as FBG ≥126 mg/dL, per American Diabetes Association 2014 criteria [21]. Participants with FBG ≥126 mg/dL were referred and had a repeat test on a separate day for confirmation.

For descriptive purposes, the CVD risk profile was defined by clusters of CVD risk factors and associated lifestyle behaviours. Participants were classified according to the presence or absence of specific clinical conditions and behaviours. One point each was assigned for: physical inactivity, smoking, alcohol use, poor sleep, and high-fat/salt diet. Clinical conditions were: overweight/obesity, stage 1/2 hypertension, or diabetes mellitus. Categories: healthy [0 behaviours, no condition]; mild unhealthy (1-2 behaviours, no condition); moderate unhealthy (3-4 behaviours, no condition); high unhealthy (5 behaviours, no condition); severe/established CVD risk (any clinical condition). Family history was analysed separately as at-risk and not included in the score. The composite risk profile is not a validated CVD prediction model; risk factors were equally weighted for simplicity in a screening context.

Bias: to minimize measurement bias, research assistants underwent a 2-day training on standard operating procedures. The sphygmomanometer was calibrated weekly against a reference device, and the glucometer was checked daily with control solutions. Questionnaires were pretested among 18 adolescents, and the results were not included in the final report; ambiguous items were revised. Interviewer bias was reduced by using standardised scripts.

Data analysis: data were collated and analysed using IBM SPSS Statistics version 21.0. Categorical variables were summarized as frequencies and percentages; continuous variables were expressed as mean ± standard deviation. The Shapiro-Wilk test was used to assess normality of continuous data. For bivariate analyses, associations between categorical variables were tested using Pearson's Chi-square test or Fisher's exact test when expected cell counts were <5. Due to the small number of participants with diabetes mellitus (n=6), multivariable logistic regression for diabetes alone was not performed because of the risk of unstable estimates. Instead, a composite outcome variable, dysglycaemia, defined as the presence of either pre-diabetes or diabetes mellitus, was created given the high prevalence of pre-diabetes in this cohort. To build the multivariable model, univariable logistic regression was first performed for each variable. Variables with p < 0.25 were selected for inclusion in the multivariable binary logistic regression model, using a purposeful selection strategy to avoid omitting potential confounders [22]. Unadjusted and adjusted odds ratios with 95% confidence intervals were reported. Model fit was assessed using the omnibus test of model coefficients and the Hosmer-Lemeshow goodness-of-fit test. Statistical significance was set at p < 0.05 for all final models. Results are represented in tables for clarity.

Ethical approval: ethical approval was obtained from the University of Benin Teaching Hospital Ethics and Research Committee on the 10^th June 2023, with protocol number: ADM/E22/A/VOL.VII/148301131. Written informed consent was obtained from participants aged ≥18 years and from parents/guardians of those <18 years, with assent from the adolescents. All participant information was kept confidential. Participants incurred no financial obligations nor faced negative consequences for refusing to participate. They were allowed to withdraw from the study at any time without impact on future care.

Results     

Sociodemographic characteristics of study participants: a total of 180 adolescents aged 10-19 years participated in this study (100% response rate) (Table 1). The mean age was 15.0 ± 2.8 years, with participants aged 13-16 years constituting the largest age group at 41.1% (n=74). Females accounted for 62.2% (n=112) of participants. Most participants were students (94.4%, n=170), and 63.7% (n=115) had secondary education.

Anthropometric characteristics and lifestyle behaviours among study participants: the mean height and weight of participants were 1.62 ± 0.10 m and 55.04 ± 13.80 kg, respectively, with a mean BMI of 20.71 ± 4.67 kg/m² (Table 2). Based on BMI-for-age categories, 36.7% were underweight, 48.3% had normal weight, and 15.0% had high BMI (overweight or obesity). A high-fat diet was reported by one-third of participants, while poor or irregular sleep was reported by 21.1%. Alcohol use and physical inactivity were reported by 11.7% and 10.6% of adolescents, respectively, and smoking was uncommon (2.8%).

Clinical parameters and prevalence of cardiovascular disease risk factors among study participants: the mean systolic and diastolic blood pressures were 109.0 ± 15.6 mmHg and 64.0 ± 10.0 mmHg, respectively; 11.1% (n=20) of participants had hypertension (Table 3). Mean fasting blood glucose was 98.8 ± 18.4 mg/dL, with 42.8% (n=77) classified as pre-diabetic and 3.3% (n=6) as diabetic. Mean body mass index was 20.7 ± 4.7 kg/m²: 36.7% (n=66) were underweight, 48.3% (n=87) normal weight, and 15.0% (n=27) overweight/obese. Overall, 85.6% (n=154) of participants had ≥1 cardiovascular disease risk factor, with 37.8% (n=68) meeting study criteria for severe or severe at-risk profiles.

Factors associated with overweight/obesity among study participants: in univariable analysis, adolescents aged 13-16 years had lower odds of overweight/obesity compared to those aged 17-19 years (OR=0.32, 95% CI: 0.12-0.84, p=0.020) (Table 4). Family history of diabetes was associated with higher odds (OR=2.29, 95% CI: 1.00-5.23, p=0.050). Age 10-12 years showed a similar trend but was not statistically significant (OR=0.33, 95% CI: 0.10-1.07, p=0.065). Sex, dysglycaemia, physical inactivity, hypertension, smoking, alcohol use, and poor sleep were not significant in univariable models. In multivariable analysis, adjusting for age, sex, family history of diabetes, and dysglycaemia, adolescents aged 13-16 years remained less likely to be overweight/obese compared to those aged 17-19 years (AOR=0.33, 95% CI: 0.12-0.90, p=0.031). Family history of diabetes was not statistically significant after adjustment (AOR=2.37, 95% CI: 0.99-5.65, p=0.052).

Factors associated with dysglycaemia among study participants: in univariate analysis, adolescents aged 13-16 years had significantly lower odds of dysglycaemia compared to those aged 17-19 years, while physical inactivity was associated with increased odds (Table 5). In multivariable analysis, physical inactivity remained an independent predictor of dysglycaemia, and the association with age 13-16 years approached statistical significance. No other covariates retained significance after adjustment.

Factors associated with elevated blood pressure among study participants: prevalence was highest among those aged 17-19 years (16.9%); however, statistically significant associations were not observed between hypertensive status and age group, sex, physical activity, alcohol use, smoking, or family history of hypertension (all p > 0.05) (Table 6).

Discussion     

This hospital-based cross-sectional study assessed the cardiovascular disease risk profile and its determinants among adolescents attending a Family Medicine Clinic in a Nigerian Teaching Hospital, Benin City. A high burden of cardiometabolic risk, with 85.6% of participants having at least one cardiovascular disease risk factor and 37.8% meeting the study criteria for severe or severe at-risk (established CVD risk) profiles. Hypertension was present in 11.1% of adolescents, while 42.8% were pre-diabetic and 3.3% diabetic, making dysglycaemia 46.1%. Although 48.3% of participants had normal BMI, 15.0% were overweight/obese and 36.7% were underweight. In multivariable analysis, adolescents aged 13-16 years had significantly lower odds of overweight/obesity compared to those aged 17-19 years, while family history of diabetes was not statistically significant after adjustment. Physical inactivity emerged as an independent predictor of dysglycaemia, whereas age 13-16 years showed a trend toward lower odds of dysglycaemia that approached significance. No sociodemographic or lifestyle factors, including sex, smoking, alcohol use, poor sleep, or family history of hypertension, were significantly associated with elevated blood pressure. These findings reveal a substantial and alarming burden of both clinical and subclinical CVD risk factors among adolescents in this clinical setting. The 85.6% prevalence of ≥1 CVD risk factor is considerably higher than the 68.9% prevalence of ≥2 risk factors reported by Jardim et al. among Brazilian school adolescents aged 12-17 years using office and home blood pressure measurements [8]. Odunaiya et al. also reported a high prevalence and clustering of modifiable CVD risk factors among rural adolescents aged 15 - 18 years in Southwest Nigeria, with 72% having between two and four risk factors and distinct clustering patterns identified [9]. Direct comparison is precluded by differing age range, study design, risk factor thresholds, and measurement protocols. However, this elevated prevalence likely reflects methodological and population differences. First, our hospital-based sample of adolescents aged 10-19 years differs from school-based studies of younger cohorts by Jardim et al. (11-17 years) and Odunaiya et al. (15-18 years) [8,9]. Second, diagnostic criteria varied across studies. The composite cardiovascular risk profile used here revealed only 14.4% of participants as healthy, with 37.8% classified as severe/severe at-risk. This classification was developed for descriptive purposes in this study and is not a validated or standardized cardiovascular risk score. It assigned equal weight to heterogeneous variables including smoking, poor sleep, obesity, diabetes, hypertension, and family history, and should be interpreted as risk clustering rather than predictive of future cardiovascular events. The 37.8% prevalence of severe/severe at-risk profiles (established CVD risk factors) in this study is lower than the 53.2% prevalence of established CVD risk reported by Rouberte et al. but exceeds the 23% and 3.5% reported by Vohra et al. and Okpokowuruk et al., respectively [23-25]. These variations are attributable to differences in study design, diagnostic tools, sample size, and our inclusion of family history as a defining component across all risk categories.

The co-occurrence of 36.7% underweight with 42.8% pre-diabetes represents an apparent nutritional paradox. The underweight prevalence aligns with the 0.3%-73.3% range reported in a systematic review of 51 Nigerian adolescent studies, and the 31% prevalence in the 2025 State of the Health of the Nation Report, though it exceeds the 29.0% reported by Adeomi et al. [26-28]. While this prevalence is higher than some earlier community-based reports, recent studies in sub-Saharan Africa are reporting alarmingly high rates of dysglycaemia among adolescents and young adults. For instance, a 2024 community-based study in Eastern Sudan reported a combined pre-diabetes and diabetes mellitus (DM) prevalence of 32.6% among adolescents aged 10-19 years using HbA1c [29]. In Côte d'Ivoire, a population-based study of 1572 children and adolescents aged 2-19 years reported 14.5% impaired fasting glucose and 0.4% DM using capillary fasting glucose per International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines [30]. Within Nigeria, a study among secondary school adolescents in Osogbo reported a pre-diabetes prevalence of 9.4% [31]. It is important to note that our study was hospital-based among adolescents attending the Family Medicine Clinic of the University of Benin Teaching Hospital. Hospital populations often have a higher burden of risk factors compared to community samples. The higher rate in our urban Benin City sample may reflect the advanced stage of the nutrition transition, sedentary lifestyle, and increasing obesity in urban Edo State compared to other settings. Conversely, the 46.1% prevalence of prediabetes and diabetes mellitus exceeds the 2.4% with impaired fasting glucose reported by Jaja et al. using laboratory venous sampling among Port Harcourt school adolescents aged 10-19 years [32]. This likely reflects methodological differences, including our use of single point-of-care capillary measurement versus laboratory confirmation and our hospital-based versus school-based setting. First, capillary fasting blood glucose was measured using an Accu-Chek Active glucometer (Roche Diagnostics), which is ISO 15197:2013 certified for accuracy; however, confirmatory venous plasma glucose or HbA1c was not done for pre-diabetes, which can overestimate prevalence. This is in line with ADA 2024 and ISPAD 2022 guidelines; a diagnosis of diabetes requires confirmation on a separate day, which we did for all participants with diabetic-range fasting blood sugar (FBS). For prediabetes, guidelines permit a single abnormal result to identify adolescents "at risk" and to initiate lifestyle intervention, with confirmatory testing recommended at follow-up in 6-12 months [33]. Second, fasting status was self-reported and was verified by participant and caregiver interviews who were present at the time of data collection. Third, our hospital-based sample may over-represent acute illness and stress hyperglycaemia.

The coexistence of underweight and dysglycaemia suggests the "double burden" of malnutrition described in low- and middle-income countries, or the "thin-fat" phenotype characterized by low BMI but increased visceral adiposity and insulin resistance [34-36]. As this was a hospital-based sample, participants may have had underlying illnesses or food insecurity predisposing to both underweight and glucose dysregulation.

The 11.1% prevalence of hypertension observed here exceeds the 3.5% reported by Okpokowuruk et al. among school children aged 3-17 years in semi-urban Uyo using 4^th task force criteria, but aligns with the 10.0% systolic hypertension documented by Vohra et al. in hospital-based adolescents aged 10-18 years in India [24,25]. The co-occurrence of underweight, elevated screening glucose, and elevated blood pressure in this hospital-based sample suggests concurrent undernutrition and cardiometabolic risk among adolescents. Further community-based studies are needed to clarify these relationships. These findings suggest that both undernutrition and dysglycaemia merit attention in this population, as underweight in adolescence has been linked to impaired growth, suboptimal academic outcomes, and reduced immunity, whereas overweight/obesity are associated with early development of cardiovascular risk factors such as hypertension and insulin resistance. Incorporating routine BMI assessment into school health services and family medicine clinics may support earlier identification of at-risk adolescents. School- and family-based health education initiatives that encourage balanced diets and regular physical activity could be beneficial, particularly when adapted to local food availability and cultural contexts.

This study has several strengths. First, it addresses an important gap by providing data on the cardiovascular disease risk profile of adolescents attending a Family Medicine Clinic in South-South Nigeria, a population underrepresented in existing literature. Second, the use of a comprehensive assessment that included anthropometric, blood pressure, and point-of-care fasting glucose measurements allowed for simultaneous evaluation of a dual burden of malnutrition and emerging cardiometabolic risk. Third, application of a study-specific composite classification identified participants with severe/severe at-risk cardiometabolic profiles, indicating a practical framework for risk stratification in resource-limited primary care settings; though not validated, it was intended for simplicity of risk cluster description. Fourth, data collection was conducted by trained personnel using standardized procedures and calibrated equipment, enhancing measurement reliability. Finally, the inclusion of adolescents aged 10-19 years enabled assessment across early to late adolescence, a critical period for the development of lifelong cardiovascular risk behaviours and phenotypes. Several limitations should be considered when interpreting these findings. First, the cross-sectional design precludes causal inferences regarding associations between risk factors and cardiometabolic outcomes.

Second, the hospital-based sampling at a single tertiary centre limits generalizability to community adolescents and may overestimate prevalence due to healthcare-seeking bias. Third, blood pressure and glucose were measured on a single occasion using office protocols without home or ambulatory confirmation, potentially misclassifying pre-hypertension and pre-diabetes status. Fourth, dietary assessment relied on an unvalidated binary classification susceptible to exposure misclassification and recall bias, and physical activity was measured using IPAQ, which has limited validation in younger adolescents aged 10-14 years compared to adolescent-specific instruments. Fifth, potential confounding by pubertal stage, socioeconomic status, and urban versus rural residence was not assessed. Sixth, this study did not assess several other important cardiometabolic risk factors, including waist circumference, sedentary screen time, and psychosocial stress. The absence of these variables limits the ability to fully characterize cardiovascular risk in this adolescent population and to adjust for potential confounding in the multivariable models. Future studies should incorporate these factors to provide a more comprehensive risk profile. Finally, self-reported behaviours, including smoking, alcohol use, and family history, are subject to social desirability and recall bias. Future community-based, longitudinal studies incorporating validated dietary tools, adolescent-specific activity measures, Tanner staging, and repeated clinical measurements are required to clarify these relationships.

Conclusion     

This hospital-based cross-sectional study of adolescents aged 10-19 years in Benin City revealed a substantial burden of cardiovascular disease risk, with 85.6% of participants having at least one risk factor and 37.8% meeting study criteria for severe/severe at-risk cardiometabolic profiles. A dual burden of malnutrition was evident, with 36.7% underweight and 15.0% overweight/obesity, while point-of-care glucose screening identified an alarming rate (46.1%) of dysglycaemia. Age 10-12 years was independently associated with increased odds of underweight, whereas physical inactivity was a predictor of dysglycaemia. Our findings support consideration of selective screening for glucose abnormalities in adolescents with risk factors such as overweight/obesity or family history of diabetes, in line with current paediatric guidelines. These findings also support the need for community-based studies, confirmatory laboratory testing with plasma glucose and HbA1c, and the need to integrate adolescent health services that address both undernutrition, physical inactivity, and emerging cardiometabolic risk through routine public health education in schools, screening and targeted lifestyle interventions in primary care settings.

Competing interests     

The authors declare no competing interests.

Authors' contributions     

Conception and study design and data collection: Roseline Egbe Adah; data analysis and interpretation and manuscript drafting and revision: Roseline Egbe Adah and Osazee Toyin Obazee. All the authors read and approved the final version of this manuscript.

Acknowledgments     

The authors gratefully acknowledge the support of the supervisors, staff, and management of the University of Benin Teaching Hospital, Benin, during data collection. We also thank the adolescents who participated in this study for their time and cooperation.

Tables     

Table 1: sociodemographic characteristics of study participants, recruited from the Family Medicine Clinic of the University of Benin Teaching Hospital (Nigeria), from June 2023 to August 2023 (N=180)

Table 2: distribution of anthropometric characteristics and lifestyle behaviours among study participants aged 10-19 years, recruited from the Family Medicine Clinic, University of Benin Teaching Hospital, Benin City, Nigeria, from June 2023 to August 2023 (N=180)

Table 3: clinical parameters and prevalence of cardiovascular disease risk factors among study participants, recruited from the Family Medicine Clinic, University of Benin Teaching Hospital, Benin City, Nigeria, from June 2023 to August 2023 (N=180)

Table 4: univariate and multivariable logistic regression models showing the association between sociodemographic and clinical factors and overweight/obesity among study participants aged 10-19 years, recruited from the Family Medicine Clinic, University of Benin Teaching Hospital, Benin City, Nigeria, from June 2023 to August 2023 (N=180)

Table 5: univariate and multivariable logistic regression models showing the association between sociodemographic and lifestyle factors and dysglycaemia (pre-diabetes and diabetes combined) among study participants aged 10-19 years, recruited from the Family Medicine Clinic, University of Benin Teaching Hospital, Benin City, Nigeria, from June 2023 to August 2023 (N=180)

Table 6: relationship between associated risk factors and hypertensive status among study participants aged 10-19 years, recruited from the Family Medicine Clinic, University of Benin Teaching Hospital, Benin City, Nigeria, from June 2023 to August 2023 (N=180)

References