Prevalence and Clustering of Cardiometabolic Risk Factors among Children and Adolescents in North-Central Nigeria

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Understanding age-related patterns of cardiometabolic risk clustering is essential for informing early prevention strategies. Methods We conducted a cross-sectional study among children and adolescents aged 6–19 years who participated in community health outreach programmes in semi-urban communities of North-Central Nigeria between 2019 and 2023. Anthropometric measurements, blood pressure, and random blood glucose were assessed using standardized protocols. Age- and sex-and height- appropriate definitions were applied for cardiometabolic risk factors. Cardiometabolic risk clustering was defined as the presence of two or more predefined risk factors. Prevalence estimates were described overall and by age group, and logistic regression was used to examine demographic factors associated with risk clustering. Results A total of 263 participants were included (95 aged 6 − 12 years and 168 aged 13 − 19 years). Overweight or obesity was present in 18.3% of participants, central obesity in 11.8%, elevated blood pressure in 58.2%, and dysglycaemia in 19.8%. Two or more cardiometabolic risk factors were observed in 26.6% of participants, occurring in 22.1% of children aged 6 − 12 years and 29.2% of adolescents aged 13 − 19 years. In regression analyses, adolescents had higher odds of cardiometabolic risk clustering than younger children, although this association was not statistically significant. Sex was not associated with risk clustering. Conclusions Cardiometabolic risk factors are common and frequently clustered among children and adolescents in North-Central Nigeria, with clustering evident from early childhood. These findings highlight the need for early, integrated, and population-based strategies for cardiometabolic disease prevention in paediatric populations. Cardiometabolic risk Risk-factor clustering Children Adolescents Hypertension Obesity Dysglycaemia Blood pressure Nigeria Sub-Saharan Africa 1. Introduction Cardiometabolic diseases, including type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVDs), are major causes of morbidity and mortality worldwide. Increasing evidence indicates that the foundations of these conditions are established during childhood and adolescence, with early-life exposure to overweight, obesity, elevated blood pressure, and dysglycaemia predisposing to cardiometabolic disease in adulthood. 1 In recent decades, the prevalence of these risk factors among children and adolescents has risen substantially, driven by urbanisation, dietary transitions, reduced physical activity, and socioeconomic change. 2 – 4 Once largely confined to high-income countries, this growing cardiometabolic burden is now increasingly concentrated in low- and middle-income countries (LMICs), where the pace of nutritional and lifestyle transition has been most rapid. 2 , 4 , 5 Cardiometabolic risk factors in early life rarely occur in isolation and frequently cluster within individuals, reflecting shared biological, behavioural, and environmental influences. 6 , 7 The presence of multiple cardiometabolic abnormalities in childhood has been associated with early vascular dysfunction, insulin resistance, and subclinical atherosclerosis. 8 – 10 These alterations may persist into adulthood and increase the risk of future cardiovascular and metabolic disease. Despite increasing global attention to childhood cardiometabolic health, evidence from sub-Saharan Africa remains limited. In Nigeria, most available studies have examined individual risk factors such as obesity, hypertension, or impaired glucose regulation, often in narrow age groups or localised settings. 11 , 12 Data describing the co-occurrence and clustering of cardiometabolic risk factors among children and adolescents are scarce, particularly in North-Central Nigeria, a region undergoing rapid demographic, nutritional, and lifestyle transitions. This study therefore aimed to describe the prevalence and clustering of cardiometabolic risk factors among children and adolescents aged 6–19 years in North-Central Nigeria. 2. Methods 2.1 Study Design and Setting A cross-sectional study was conducted between 2021 and 2023 during community health outreach programmes in semi-urban communities of Okene and Lokoja (Kogi State); as well as Gwagwalada (FCT, Abuja) in North-Central Nigeria. Recruitment occurred intermittently during outreach activities. 2.2 Study Population Children and adolescents aged 6–19 years attending outreach screenings were eligible. Apparently healthy participants were included, while acutely ill children and those without parental consent were excluded. Written informed consent and age-appropriate assent were obtained. 2.3 Sampling Method and Sample Size Convenience sampling was used, and all eligible attendees during the study period were enrolled. No formal sample size calculation was performed. 2.4 Data Collection and Measurements Trained personnel administered structured questionnaires and performed standardized clinical and anthropometric assessments at outreach venues. Blood pressure was measured after five minutes’ rest using a calibrated digital sphygmomanometer, with two readings averaged. 13 Weight, height, and waist circumference were measured using standard techniques, and body mass index (BMI) was calculated. 14 Random blood glucose was assessed by finger-prick using an Accu-Chek® glucometer, with standard quality-control procedures observed. 15 2.5 Definition of Cardiometabolic Risk Factors Risk factors were defined using age- and sex-specific criteria. Overweight and obesity were defined using World Health Organization (WHO) BMI-for-age z-scores for ages 5–19 years, with BMI-for-age > + 1 standard deviation indicating overweight and > + 2 standard deviations indicating obesity. 16 , 17 Central obesity was defined as waist circumference ≥ 90th percentile for age and sex. 18 Raised blood pressure was defined as any blood pressure above the normal range for age according to the 2017 American Academy of Pediatrics guideline: 13 systolic and/or diastolic blood pressure ≥ 90th percentile in children aged < 13 years, or ≥ 120/80 mmHg in adolescents aged ≥ 13 years. Dysglycaemia was defined as random blood glucose ≥ 11.1 mmol/L and used as a screening indicator. 19 A composite cardiometabolic risk score was created by summing all risk factors with equal weighting; this unvalidated score was used for exploratory analyses. Cardiometabolic risk-factor clustering was defined as the presence of ≥ 2 risk factors. 9 2.6 Statistical Analysis Data were analysed using Stata version 14 (StataCorp, College Station, TX, USA). Continuous variables were summarised as medians (IQR) and categorical variables as frequencies and percentages. Baseline characteristics and prevalence of cardiometabolic risk factors were described overall and by age group. Logistic regression was used to examine factors associated with cardiometabolic risk clustering (≥ 2 risk factors), with age group and sex included a priori. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs), and statistical significance was set at p < 0.05. 3. Results 3.1 Study Population The combined median age was 15.0 years (IQR 11.0–18.0), with medians of 9.0 years (IQR 7.0–11.0) in children and 17.0 years (IQR 15.0–18.0) in adolescents. Females constituted 61.6% of participants, representing 49.5% of children and 68.5% of adolescents. The overall median BMI was 20.1 kg/m² (IQR 16.9–24.0), with medians of 16.7 kg/m² (IQR 14.2–20.7) in children and 21.7 kg/m² (IQR 19.0–24.3) in adolescents. The overall median waist circumference was 67.0 cm (IQR 58.0–75.0), with medians of 55.0 cm (IQR 52.0–64.0) in children and 70.0 cm (IQR 64.0–77.0) in adolescents. Median systolic blood pressure was 110.0 mmHg (IQR, 100.0–130.0) overall and was identical in both age groups. Median diastolic blood pressure was 70.0 mmHg (IQR, 70.0–80.0) overall, with higher values in children (80.0 mmHg; IQR, 70.0–90.0) than in adolescents (70.0 mmHg; IQR, 70.0–80.0). Median random blood glucose was 5.9 mmol/L (IQR, 5.1–7.0) overall, with similar values in children and adolescents. 3.2 Prevalence of Individual Cardiometabolic Risk Factors Overall, raised blood pressure was the most prevalent risk factor (58.2%; 95% CI, 52.0–64.2), followed by dysglycaemia (19.8%; 95% CI, 15.1–25.1), overweight/obesity (18.3%; 95% CI, 13.7–23.5), and central obesity (11.8%; 95% CI, 8.2–16.3) (Table 2 ). Among children, prevalences were 64.2% for raised blood pressure, 16.8% for overweight/obesity, 13.7% for dysglycaemia, and 7.4% for central obesity. Among adolescents, corresponding prevalences were 54.8%, 19.0%, 23.2%, and 14.3%, respectively. 3.3 Distribution of Cardiometabolic Risk-Factor Counts Overall, 26.2% of participants had no cardiometabolic risk factors, 47.2% had one, and 26.6% had two or more (Table 3 ). Among children aged 6–12 years, 25.3% had no risk factors, 52.6% had one, and 22.1% had clustered risk factors. Corresponding proportions among adolescents were 26.7%, 44.1%, and 29.2%, respectively. Risk clustering (≥ 2 factors) was more frequent in adolescents than in children (29.2% vs. 22.1%) and in females than males (28.4% vs. 23.8%). Three or more risk factors were uncommon, occurring in fewer than 7% of participants in any subgroup. 3.4 Factors Associated with Cardiometabolic Risk-Factor Clustering In logistic regression analysis (Table 4 ), adolescents aged 13–19 years had higher odds of cardiometabolic risk clustering than children aged 6–12 years, but this association was not statistically significant (OR 1.39; 95% CI 0.78–2.48; p = 0.261). Sex was also not associated with clustering (OR for males vs females 0.83; 95% CI 0.47–1.49; p = 0.535). 4. Discussion Cardiometabolic risk factors were common and frequently clustered among children and adolescents in North-Central Nigeria. More than one-quarter had two or more concurrent risk factors, with clustering more frequent among adolescents but already evident in children aged 6–12 years. The lack of independent associations with age group or sex suggests that cardiometabolic vulnerability is established early and broadly distributed in this population. Raised blood pressure was the most prevalent abnormality in this study, affecting more than half of participants, a burden substantially higher than that reported in most Nigerian paediatric studies. School-based surveys have reported prevalences of 9.1% to 22.7% in comparable age groups. 11 , 20 In Kogi State, the same region as this study, Ejike et al. reported higher mean systolic blood pressure in urban than non-urban adolescents, although prevalence estimates were not provided. 21 While global analyses show rising childhood blood pressure, 2,22 the magnitude observed here suggests a particularly high-risk profile. Because childhood blood pressure tracks into adulthood and predicts later cardiovascular disease, 8,10 this finding indicates a substantial future cardiovascular burden. Overweight and obesity occurred at levels comparable to those reported in high-income countries and within the range described in urbanising African and Nigerian populations. 5 , 12 , 23 This convergence indicates that semi-urban North-Central Nigeria is already experiencing adiposity patterns similar to higher-income and rapidly urbanising settings, reflecting shared dietary transition, physical inactivity, and expanding urbanisation. Central obesity, although less prevalent than general overweight, was more frequent among adolescents. This pattern is consistent with evidence that pubertal visceral fat accumulation predicts later insulin resistance and subclinical atherosclerosis independent of body mass index. 9 , 10 The higher prevalence among adolescents in this cohort therefore suggests early emergence of a risk phenotype likely to persist into adulthood. 24 Dysglycaemia, particularly among adolescents, further supports this trajectory. Studies from Nigeria and South Africa link early glucose disturbances to adiposity and sedentary behaviour, 25,26 with pubertal insulin resistance providing a plausible mechanism for the higher prevalence observed in this age group. A central contribution of this study is the high prevalence of cardiometabolic risk clustering. The proportion with two or more concurrent risk factors is comparable to paediatric cohorts in Europe, North America, and Asia, where clustering predicts adult metabolic syndrome, atherosclerosis, and T2DM. 9 , 10 , 27 The presence of clustering in both children and adolescents supports a life-course model in which cardiometabolic risk begins early and tracks over time, reflecting shared pathways such as insulin resistance and low-grade inflammation, amplified by urbanisation-related exposures. 28 , 29 Importantly, clustering was already present in more than one-fifth of children aged 6–12 years, indicating that adverse risk trajectories may begin before adolescence. 10 , 28 The absence of a clear sex difference suggests that adiposity and pubertal stage may be more important determinants of early risk aggregation than sex alone. 18 These findings support integrated screening of multiple cardiometabolic risk factors in paediatric populations, as single–risk-factor approaches may miss children at highest cumulative risk. Community outreach and school-based programmes provide practical platforms for early identification and prevention. Incorporating paediatric cardiometabolic assessment into broader noncommunicable disease strategies, using a life-course approach, is essential to reduce future cardiovascular burden. 5. Strengths and Limitations Strengths of this study include the community-based design, inclusion of both children and adolescents, and the use of age- and sex-specific definitions of cardiometabolic risk factors. Limitations include the cross-sectional design, outreach-based measurements, use of random blood glucose instead of fasting measures, absence of lipid profiling (HDL and triglycerides), lack of local waist circumference reference values, and convenience sampling, which may limit causal inference and generalisability. 6. Conclusion Cardiometabolic risk factors are common and frequently clustered among children and adolescents in North-Central Nigeria. Early clustering highlights the need for integrated, life-course prevention strategies centred on early detection, school-based interventions, and supportive policy environments. Abbreviations Abbreviation Definition B BMI Body Mass Index CI Confidence Interval CVD Cerebrovascular Disease EC Ethics Committee FCT Federal Capital Territory GMC Gwagwalada Munincipal Council HDL High Density Lipoprotein IQR Interquartile Range KSMH Kogi State Ministry of Health LMICs Lower and Middle Income Countries OR Odds Ratio T2DM Type 2 Diabetes Mellitus USA United State of America WHO World Health Organization Declarations Ethics approval and consent to participate The study was conducted in accordance with the Declaration of Helsinki and relevant national and institutional guidelines for medical research involving human participants. Ethical approval was obtained from the Kogi State Ministry of Health Research Ethics Committee (KSMH/EC/045/2019) and the Gwagwalada Municipal Council Health Research Ethics Unit (GMC/RES/077/2023). Written informed consent was obtained from parents or legal guardians of all participating children and adolescents, and age-appropriate assent was obtained from the participants. Participation was voluntary, and confidentiality of participant information was strictly maintained. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form, including individual details, images, or videos. Competing interests The authors declare that they have no competing interests. Funding The medical screening activities for this study were supported by the Mark Anumah Medical Mission. The funder had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript. Author Contribution **B.O.B.-O.** conceived and designed the study, coordinated data collection, performed data analysis, and drafted the manuscript. **J.O.O.** and **R.Z.-A.** contributed to study coordination, data collection, data analysis, and critical revision of the manuscript. **A.O.S** ., **H.S** ., and **M.A.O.** contributed to data interpretation and manuscript writing and editing. **P.B** . conducted the literature review, assisted with data analysis, and reviewed the manuscript. All authors read and approved the final manuscript and take responsibility for the integrity of the work. Acknowledgements The authors gratefully acknowledge the study participants, community leaders, and local health workers in the participating semi-urban communities in Nigeria for their cooperation and support during the conduct of this study. Data Availability The datasets generated and/or analysed during the current study are not publicly available due to concerns regarding participant privacy and confidentiality but are available from the corresponding author on reasonable request. References Drozdz D, Alvarez-Pitti J, Wójcik M, Borghi C, Gabbianelli R, Mazur A, et al. Obesity and cardiometabolic risk factors: from childhood to adulthood. Nutrients. 2021;13(11):4176. https://doi.org/10.3390/nu13114176 . NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. Lancet. 2024;403(10431):1027–50. https://doi.org/10.1016/S0140-6736(23)02750-2 . Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3–21. https://doi.org/10.1111/j.1753-4887.2011.00456.x . 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Tables Table 1 Baseline Characteristics of Children and Adolescents (N = 263) Variable Overall (N = 263) 6–12 yrs (n = 95) 13–19 yrs (n = 168) Age, yrs, median (IQR) 15.0 (11.0–18.0) 9.0 (7.0–11.0) 17.0 (15.0–18.0) Sex, n (%) Female Male 162 (61.6) 101 (38.4) 47 (49.5) 48 (50.5) 115 (68.5) 53 (31.5) BMI (kg/m²), median (IQR) 20.1 (16.9–24.0) 16.7 (14.2–20.7) 21.7 (19.0–24.3) Waist circumference (cm), median (IQR) 67.0 (58.0–75.0) 55.0 (52.0–64.0) 70.0 (64.0–77.0) Systolic BP (mmHg), median (IQR) 110.0 (100.0–130.0) 110.0 (100.0–120.0) 110.0 (100.0–130.0) Diastolic BP (mmHg), median (IQR) 70.0 (70.0–80.0) 80.0 (70.0–90.0) 70.0 (70.0–80.0) RBS (mmol/L), median (IQR) 5.9 (5.1–7.0) 6.1 (5.2–6.8) 5.9 (5.0–7.5) Values are median (IQR) or n (%). No formal comparisons were performed. BMI, body mass index; BP, blood pressure; WC, waist circumference; RBS, random blood sugar. Table 2 Prevalence of Individual Cardiometabolic Risk Factors by Age Group (N = 263) Risk Factor Overall, n (%) [95% CI] 6 − 12 yrs (n = 95), n (%) [95% CI] 13–19 yrs (n = 168), n (%) [95% CI] Overweight/obesity (BMI-for-age ≥ 85th percentile) 48 (18.3) [13.7–23.5] 16 (16.8) [9.9 − 25.9] 32 (19.0) [13.8–25.8] Central obesity (WC ≥ 90th percentile) 31 (11.8) [8.2–16.3] 7 (7.4) [3.0–14.6] 24 (14.3) [9.4–20.5] Raised blood pressure† 153 (58.2) [52.0–64.2] 61 (64.2) [53.7–73.8] 92 (54.8) [46.9–62.4] Dysglycaemia‡ 52 (19.8) [15.1–25.1] 13 (13.7) [7.5–22.3] 39 (23.2) [17.1–30.3] † Raised BP defined using age-, sex-, and height-specific percentiles. ‡ Dysglycaemia defined as random blood glucose ≥ 11.1 mmol/L. CI = confidence interval; WC = waist circumference. Table 3 Distribution of Cardiometabolic Risk-Factor Counts by Age Group and Sex (N = 263) Number of Risk Factors Overall, n (%) 6–12 yrs (n = 95), n (%) 13–19 yrs (n = 168), n(%) Female, n (%) Male, n (%) 0 69 (26.2) 24 (25.3) 45 (26.7) 42 (25.9) 27 (26.7) 1 124 (47.2) 50 (52.6) 74 (44.1) 74 (45.7) 50 (49.5) 2 53 (20.2) 16 (16.8) 37 (22.0) 31 (19.1) 22 (21.8) 3 14 (5.3) 5 (5.3) 9 (5.4) 12 (7.4) 2 (2.0) 4 3 (1.1) 0 (0.0) 3 (1.8) 3 (1.9) 0 (0.0) Cluster ≥ 2 70 (26.6) 21 (22.1) 49 (29.2) 46 (28.4) 24 (23.8) Values are presented descriptively. Cardiometabolic risk count represents the number of predefined risk factors present (range 0–4). . Table 4 Factors Associated with Cardiometabolic Risk-Factor Clustering (≥ 2 Risk Factors) among Children and Adolescents (N = 263) Predictor OR 95% CI p-value Age group (13–19 vs 6–12 yrs) 1.39 0.78–2.48 0.261 Sex (Male vs Female) 0.83 0.47–1.49 0.535 Model notes: Outcome was cardiometabolic risk clustering (≥ 2 risk factors). Model adjusted for age group and sex. OR = odds ratio; CI = confidence interval. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 06 Mar, 2026 Editor assigned by journal 18 Feb, 2026 Editor invited by journal 12 Feb, 2026 Submission checks completed at journal 11 Feb, 2026 First submitted to journal 11 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8712791","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602967782,"identity":"c6dfefa8-ecb6-418b-b731-9cabcfc9ea19","order_by":0,"name":"Beatrice Ohunene Bello-Ovosi","email":"","orcid":"","institution":"Kaduna State University/Barau Dikko Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Beatrice","middleName":"Ohunene","lastName":"Bello-Ovosi","suffix":""},{"id":602967783,"identity":"294dcd3b-5fc0-4c76-b62e-3a2c50f8e0b8","order_by":1,"name":"Joseph Ogirima Ovosi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYBACxgYIzcMvAaYlZIjXIjkDzJbgId46gxsQ7YS1MLc3H3zMU1EnY3y7+fijGzUWPAzsh49uwOuwnmPJxjxnDvOY3TmW2JxzDOgwnrS0G3i1zMgxk5zZdoDH7EaOYXMOG1CLBJCNX0v+N6CWOh7jGSAt/4jSAjT5Yxszj4EEUEtuGzFaeo4ZG3wA+kXiRlri7Nw+CR42Qn4xbG9++CChos6ef0bygc853+rk+NkPH8OvpQFdhA2fchCQJ6RgFIyCUTAKRgEDAEpmRI45F4WXAAAAAElFTkSuQmCC","orcid":"","institution":"Air Force Institute of Technology","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"Ogirima","lastName":"Ovosi","suffix":""},{"id":602967784,"identity":"da9abde9-9d80-4dbc-9673-528ea8282e08","order_by":2,"name":"Ramatu Aliyu-Zubair","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Ramatu","middleName":"","lastName":"Aliyu-Zubair","suffix":""},{"id":602967785,"identity":"0eb5da00-2fb7-485f-b409-dc83abac6f9f","order_by":3,"name":"Abdulrahaman Ozovehe Shehu","email":"","orcid":"","institution":"National Eye Center, Kaduna","correspondingAuthor":false,"prefix":"","firstName":"Abdulrahaman","middleName":"Ozovehe","lastName":"Shehu","suffix":""},{"id":602967786,"identity":"489bbeed-aaec-43d7-9d1e-0ba239cad5fc","order_by":4,"name":"Hadiza Sani","email":"","orcid":"","institution":"Kaduna State University/Barau Dikko Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hadiza","middleName":"","lastName":"Sani","suffix":""},{"id":602967789,"identity":"f062d131-a309-4de4-971c-d0c3015ac014","order_by":5,"name":"Modupe Arinola Ogunsina","email":"","orcid":"","institution":"Federal Medical Center, Keffi","correspondingAuthor":false,"prefix":"","firstName":"Modupe","middleName":"Arinola","lastName":"Ogunsina","suffix":""},{"id":602967791,"identity":"f6794c67-10c1-48cb-a82c-bafb0168b994","order_by":6,"name":"Precious Oyiza Bawa","email":"","orcid":"","institution":"061 Aeromedical Center, kaduna","correspondingAuthor":false,"prefix":"","firstName":"Precious","middleName":"Oyiza","lastName":"Bawa","suffix":""}],"badges":[],"createdAt":"2026-01-27 16:39:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8712791/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8712791/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780249,"identity":"613b8f9c-7842-40b8-8af0-cdddd12956a8","added_by":"auto","created_at":"2026-03-17 07:51:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":863153,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8712791/v1/4873466f-bc90-4760-97ac-6bfb41382290.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence and Clustering of Cardiometabolic Risk Factors among Children and Adolescents in North-Central Nigeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCardiometabolic diseases, including type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVDs), are major causes of morbidity and mortality worldwide. Increasing evidence indicates that the foundations of these conditions are established during childhood and adolescence, with early-life exposure to overweight, obesity, elevated blood pressure, and dysglycaemia predisposing to cardiometabolic disease in adulthood.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In recent decades, the prevalence of these risk factors among children and adolescents has risen substantially, driven by urbanisation, dietary transitions, reduced physical activity, and socioeconomic change.\u003csup\u003e\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Once largely confined to high-income countries, this growing cardiometabolic burden is now increasingly concentrated in low- and middle-income countries (LMICs), where the pace of nutritional and lifestyle transition has been most rapid.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCardiometabolic risk factors in early life rarely occur in isolation and frequently cluster within individuals, reflecting shared biological, behavioural, and environmental influences.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e The presence of multiple cardiometabolic abnormalities in childhood has been associated with early vascular dysfunction, insulin resistance, and subclinical atherosclerosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e These alterations may persist into adulthood and increase the risk of future cardiovascular and metabolic disease.\u003c/p\u003e \u003cp\u003eDespite increasing global attention to childhood cardiometabolic health, evidence from sub-Saharan Africa remains limited. In Nigeria, most available studies have examined individual risk factors such as obesity, hypertension, or impaired glucose regulation, often in narrow age groups or localised settings.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Data describing the co-occurrence and clustering of cardiometabolic risk factors among children and adolescents are scarce, particularly in North-Central Nigeria, a region undergoing rapid demographic, nutritional, and lifestyle transitions. This study therefore aimed to describe the prevalence and clustering of cardiometabolic risk factors among children and adolescents aged 6\u0026ndash;19 years in North-Central Nigeria.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Setting\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted between 2021 and 2023 during community health outreach programmes in semi-urban communities of Okene and Lokoja (Kogi State); as well as Gwagwalada (FCT, Abuja) in North-Central Nigeria. Recruitment occurred intermittently during outreach activities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Population\u003c/h2\u003e \u003cp\u003eChildren and adolescents aged 6\u0026ndash;19 years attending outreach screenings were eligible. Apparently healthy participants were included, while acutely ill children and those without parental consent were excluded. Written informed consent and age-appropriate assent were obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Sampling Method and Sample Size\u003c/h2\u003e \u003cp\u003eConvenience sampling was used, and all eligible attendees during the study period were enrolled. No formal sample size calculation was performed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Collection and Measurements\u003c/h2\u003e \u003cp\u003eTrained personnel administered structured questionnaires and performed standardized clinical and anthropometric assessments at outreach venues. Blood pressure was measured after five minutes\u0026rsquo; rest using a calibrated digital sphygmomanometer, with two readings averaged.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Weight, height, and waist circumference were measured using standard techniques, and body mass index (BMI) was calculated.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Random blood glucose was assessed by finger-prick using an Accu-Chek\u0026reg; glucometer, with standard quality-control procedures observed.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Definition of Cardiometabolic Risk Factors\u003c/h2\u003e \u003cp\u003eRisk factors were defined using age- and sex-specific criteria. Overweight and obesity were defined using World Health Organization (WHO) BMI-for-age z-scores for ages 5\u0026ndash;19 years, with BMI-for-age\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;1 standard deviation indicating overweight and \u0026gt;\u0026thinsp;+\u0026thinsp;2 standard deviations indicating obesity.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Central obesity was defined as waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90th percentile for age and sex.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eRaised blood pressure was defined as any blood pressure above the normal range for age according to the 2017 American Academy of Pediatrics guideline:\u003csup\u003e13\u003c/sup\u003e systolic and/or diastolic blood pressure \u0026ge;\u0026thinsp;90th percentile in children aged\u0026thinsp;\u0026lt;\u0026thinsp;13 years, or \u0026ge;\u0026thinsp;120/80 mmHg in adolescents aged\u0026thinsp;\u0026ge;\u0026thinsp;13 years.\u003c/p\u003e \u003cp\u003eDysglycaemia was defined as random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L and used as a screening indicator.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e A composite cardiometabolic risk score was created by summing all risk factors with equal weighting; this unvalidated score was used for exploratory analyses. Cardiometabolic risk-factor clustering was defined as the presence of \u0026ge;\u0026thinsp;2 risk factors.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were analysed using Stata version 14 (StataCorp, College Station, TX, USA). Continuous variables were summarised as medians (IQR) and categorical variables as frequencies and percentages.\u003c/p\u003e \u003cp\u003eBaseline characteristics and prevalence of cardiometabolic risk factors were described overall and by age group. Logistic regression was used to examine factors associated with cardiometabolic risk clustering (\u0026ge;\u0026thinsp;2 risk factors), with age group and sex included a priori. Results are presented as odds ratios (ORs) with 95% confidence intervals (CIs), and statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Study Population\u003c/h2\u003e \u003cp\u003eThe combined median age was 15.0 years (IQR 11.0\u0026ndash;18.0), with medians of 9.0 years (IQR 7.0\u0026ndash;11.0) in children and 17.0 years (IQR 15.0\u0026ndash;18.0) in adolescents. Females constituted 61.6% of participants, representing 49.5% of children and 68.5% of adolescents.\u003c/p\u003e \u003cp\u003eThe overall median BMI was 20.1 kg/m\u0026sup2; (IQR 16.9\u0026ndash;24.0), with medians of 16.7 kg/m\u0026sup2; (IQR 14.2\u0026ndash;20.7) in children and 21.7 kg/m\u0026sup2; (IQR 19.0\u0026ndash;24.3) in adolescents. The overall median waist circumference was 67.0 cm (IQR 58.0\u0026ndash;75.0), with medians of 55.0 cm (IQR 52.0\u0026ndash;64.0) in children and 70.0 cm (IQR 64.0\u0026ndash;77.0) in adolescents.\u003c/p\u003e \u003cp\u003eMedian systolic blood pressure was 110.0 mmHg (IQR, 100.0\u0026ndash;130.0) overall and was identical in both age groups. Median diastolic blood pressure was 70.0 mmHg (IQR, 70.0\u0026ndash;80.0) overall, with higher values in children (80.0 mmHg; IQR, 70.0\u0026ndash;90.0) than in adolescents (70.0 mmHg; IQR, 70.0\u0026ndash;80.0). Median random blood glucose was 5.9 mmol/L (IQR, 5.1\u0026ndash;7.0) overall, with similar values in children and adolescents.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prevalence of Individual Cardiometabolic Risk Factors\u003c/h2\u003e \u003cp\u003eOverall, raised blood pressure was the most prevalent risk factor (58.2%; 95% CI, 52.0\u0026ndash;64.2), followed by dysglycaemia (19.8%; 95% CI, 15.1\u0026ndash;25.1), overweight/obesity (18.3%; 95% CI, 13.7\u0026ndash;23.5), and central obesity (11.8%; 95% CI, 8.2\u0026ndash;16.3) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong children, prevalences were 64.2% for raised blood pressure, 16.8% for overweight/obesity, 13.7% for dysglycaemia, and 7.4% for central obesity. Among adolescents, corresponding prevalences were 54.8%, 19.0%, 23.2%, and 14.3%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Distribution of Cardiometabolic Risk-Factor Counts\u003c/h2\u003e \u003cp\u003eOverall, 26.2% of participants had no cardiometabolic risk factors, 47.2% had one, and 26.6% had two or more (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among children aged 6\u0026ndash;12 years, 25.3% had no risk factors, 52.6% had one, and 22.1% had clustered risk factors. Corresponding proportions among adolescents were 26.7%, 44.1%, and 29.2%, respectively.\u003c/p\u003e \u003cp\u003eRisk clustering (\u0026ge;\u0026thinsp;2 factors) was more frequent in adolescents than in children (29.2% vs. 22.1%) and in females than males (28.4% vs. 23.8%). Three or more risk factors were uncommon, occurring in fewer than 7% of participants in any subgroup.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Factors Associated with Cardiometabolic Risk-Factor Clustering\u003c/h2\u003e \u003cp\u003eIn logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), adolescents aged 13\u0026ndash;19 years had higher odds of cardiometabolic risk clustering than children aged 6\u0026ndash;12 years, but this association was not statistically significant (OR 1.39; 95% CI 0.78\u0026ndash;2.48; p\u0026thinsp;=\u0026thinsp;0.261). Sex was also not associated with clustering (OR for males vs females 0.83; 95% CI 0.47\u0026ndash;1.49; p\u0026thinsp;=\u0026thinsp;0.535).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCardiometabolic risk factors were common and frequently clustered among children and adolescents in North-Central Nigeria. More than one-quarter had two or more concurrent risk factors, with clustering more frequent among adolescents but already evident in children aged 6\u0026ndash;12 years. The lack of independent associations with age group or sex suggests that cardiometabolic vulnerability is established early and broadly distributed in this population.\u003c/p\u003e \u003cp\u003eRaised blood pressure was the most prevalent abnormality in this study, affecting more than half of participants, a burden substantially higher than that reported in most Nigerian paediatric studies. School-based surveys have reported prevalences of 9.1% to 22.7% in comparable age groups.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e In Kogi State, the same region as this study, Ejike et al. reported higher mean systolic blood pressure in urban than non-urban adolescents, although prevalence estimates were not provided.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e While global analyses show rising childhood blood pressure,\u003csup\u003e2,22\u003c/sup\u003e the magnitude observed here suggests a particularly high-risk profile. Because childhood blood pressure tracks into adulthood and predicts later cardiovascular disease,\u003csup\u003e8,10\u003c/sup\u003e this finding indicates a substantial future cardiovascular burden.\u003c/p\u003e \u003cp\u003eOverweight and obesity occurred at levels comparable to those reported in high-income countries and within the range described in urbanising African and Nigerian populations.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This convergence indicates that semi-urban North-Central Nigeria is already experiencing adiposity patterns similar to higher-income and rapidly urbanising settings, reflecting shared dietary transition, physical inactivity, and expanding urbanisation.\u003c/p\u003e \u003cp\u003eCentral obesity, although less prevalent than general overweight, was more frequent among adolescents. This pattern is consistent with evidence that pubertal visceral fat accumulation predicts later insulin resistance and subclinical atherosclerosis independent of body mass index.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The higher prevalence among adolescents in this cohort therefore suggests early emergence of a risk phenotype likely to persist into adulthood.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eDysglycaemia, particularly among adolescents, further supports this trajectory. Studies from Nigeria and South Africa link early glucose disturbances to adiposity and sedentary behaviour,\u003csup\u003e25,26\u003c/sup\u003e with pubertal insulin resistance providing a plausible mechanism for the higher prevalence observed in this age group.\u003c/p\u003e \u003cp\u003eA central contribution of this study is the high prevalence of cardiometabolic risk clustering. The proportion with two or more concurrent risk factors is comparable to paediatric cohorts in Europe, North America, and Asia, where clustering predicts adult metabolic syndrome, atherosclerosis, and T2DM.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The presence of clustering in both children and adolescents supports a life-course model in which cardiometabolic risk begins early and tracks over time, reflecting shared pathways such as insulin resistance and low-grade inflammation, amplified by urbanisation-related exposures.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eImportantly, clustering was already present in more than one-fifth of children aged 6\u0026ndash;12 years, indicating that adverse risk trajectories may begin before adolescence.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e The absence of a clear sex difference suggests that adiposity and pubertal stage may be more important determinants of early risk aggregation than sex alone.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThese findings support integrated screening of multiple cardiometabolic risk factors in paediatric populations, as single\u0026ndash;risk-factor approaches may miss children at highest cumulative risk. Community outreach and school-based programmes provide practical platforms for early identification and prevention. Incorporating paediatric cardiometabolic assessment into broader noncommunicable disease strategies, using a life-course approach, is essential to reduce future cardiovascular burden.\u003c/p\u003e"},{"header":"5. Strengths and Limitations","content":"\u003cp\u003eStrengths of this study include the community-based design, inclusion of both children and adolescents, and the use of age- and sex-specific definitions of cardiometabolic risk factors. Limitations include the cross-sectional design, outreach-based measurements, use of random blood glucose instead of fasting measures, absence of lipid profiling (HDL and triglycerides), lack of local waist circumference reference values, and convenience sampling, which may limit causal inference and generalisability.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eCardiometabolic risk factors are common and frequently clustered among children and adolescents in North-Central Nigeria. Early clustering highlights the need for integrated, life-course prevention strategies centred on early detection, school-based interventions, and supportive policy environments.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDefinition\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eB \u0026nbsp; \u0026nbsp; BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthics Committee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFederal Capital Territory\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGMC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGwagwalada Munincipal Council\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHigh Density Lipoprotein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eInterquartile Range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKSMH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKogi State Ministry of Health\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLMICs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLower and Middle Income Countries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eType 2 Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUnited State of America\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWorld Health Organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e The study was conducted in accordance with the Declaration of Helsinki and relevant national and institutional guidelines for medical research involving human participants. Ethical approval was obtained from the Kogi State Ministry of Health Research Ethics Committee (KSMH/EC/045/2019) and the Gwagwalada Municipal Council Health Research Ethics Unit (GMC/RES/077/2023). Written informed consent was obtained from parents or legal guardians of all participating children and adolescents, and age-appropriate assent was obtained from the participants. Participation was voluntary, and confidentiality of participant information was strictly maintained.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form, including individual details, images, or videos.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe medical screening activities for this study were supported by the Mark Anumah Medical Mission. The funder had no role in the study design, data collection, analysis, interpretation of data, or writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e**B.O.B.-O.** conceived and designed the study, coordinated data collection, performed data analysis, and drafted the manuscript. **J.O.O.** and **R.Z.-A.** contributed to study coordination, data collection, data analysis, and critical revision of the manuscript. **A.O.S** ., **H.S** ., and **M.A.O.** contributed to data interpretation and manuscript writing and editing. **P.B** . conducted the literature review, assisted with data analysis, and reviewed the manuscript. All authors read and approved the final manuscript and take responsibility for the integrity of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e The authors gratefully acknowledge the study participants, community leaders, and local health workers in the participating semi-urban communities in Nigeria for their cooperation and support during the conduct of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to concerns regarding participant privacy and confidentiality but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDrozdz D, Alvarez-Pitti J, W\u0026oacute;jcik M, Borghi C, Gabbianelli R, Mazur A, et al. Obesity and cardiometabolic risk factors: from childhood to adulthood. 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Cham (Switzerland): Springer; 2016. pp. 41\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-319-12125-3_5-1\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-12125-3_5-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMaffeis C, Pietrobelli A, Grezzani A, Provera S, Tat\u0026ograve; L. Waist circumference and cardiovascular risk factors in prepubertal children. Obes Res. 2001;9(3):179\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/oby.2001.19\u003c/span\u003e\u003cspan address=\"10.1038/oby.2001.19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabageh AO, Ojofeitimi EO. Prevalence of obesity among adolescents in Ile-Ife, State O. Nigeria using body mass index and waist\u0026ndash;hip ratio: a comparative study. Niger Med J. 2013;54(3):153\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/0300-1652.114566\u003c/span\u003e\u003cspan address=\"10.4103/0300-1652.114566\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeer N, Steyn K, Lombard C, Lambert EV, Vythilingum B, Levitt NS. Rising diabetes prevalence among urban-dwelling Black South Africans. PLoS ONE. 2012;7(9):e43336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0043336\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0043336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the Third National Health and Nutrition Examination Survey, 1988\u0026ndash;1994. Arch Pediatr Adolesc Med. 2003;157(8):821\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/archpedi.157.8.821\u003c/span\u003e\u003cspan address=\"10.1001/archpedi.157.8.821\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJuonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. 2011;365(20):1876\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa1010112\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1010112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDwyer T, Sun C, Magnussen CG, Raitakari OT, Schork NJ, Venn A, et al. Cohort profile: the International Childhood Cardiovascular Cohort (i3C) Consortium. Int J Epidemiol. 2013;42(1):86\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dys004\u003c/span\u003e\u003cspan address=\"10.1093/ije/dys004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":" \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Children and Adolescents (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;12 yrs (n\u0026thinsp;=\u0026thinsp;95)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026ndash;19 yrs (n\u0026thinsp;=\u0026thinsp;168)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, yrs, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.0 (11.0\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.0 (7.0\u0026ndash;11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.0 (15.0\u0026ndash;18.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (61.6)\u003c/p\u003e \u003cp\u003e101 (38.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47 (49.5)\u003c/p\u003e \u003cp\u003e48 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e115 (68.5)\u003c/p\u003e \u003cp\u003e53 (31.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.1 (16.9\u0026ndash;24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.7 (14.2\u0026ndash;20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.7 (19.0\u0026ndash;24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0 (58.0\u0026ndash;75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.0 (52.0\u0026ndash;64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.0 (64.0\u0026ndash;77.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.0 (100.0\u0026ndash;130.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110.0 (100.0\u0026ndash;120.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.0 (100.0\u0026ndash;130.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.0 (70.0\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.0 (70.0\u0026ndash;90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70.0 (70.0\u0026ndash;80.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBS (mmol/L), median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9 (5.1\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1 (5.2\u0026ndash;6.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9 (5.0\u0026ndash;7.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eValues are median (IQR) or n (%). No formal comparisons were performed. BMI, body mass index; BP, blood pressure; WC, waist circumference; RBS, random blood sugar.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of Individual Cardiometabolic Risk Factors by Age Group (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 \u0026minus;\u0026thinsp;12 yrs (n\u0026thinsp;=\u0026thinsp;95), n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026ndash;19 yrs (n\u0026thinsp;=\u0026thinsp;168), n (%) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/obesity (BMI-for-age \u0026ge;\u0026thinsp;85th percentile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (18.3) [13.7\u0026ndash;23.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (16.8) [9.9 \u0026minus;\u0026thinsp;25.9]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32 (19.0) [13.8\u0026ndash;25.8]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity (WC\u0026thinsp;\u0026ge;\u0026thinsp;90th percentile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (11.8) [8.2\u0026ndash;16.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (7.4) [3.0\u0026ndash;14.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (14.3) [9.4\u0026ndash;20.5]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRaised blood pressure\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153 (58.2) [52.0\u0026ndash;64.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (64.2) [53.7\u0026ndash;73.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92 (54.8) [46.9\u0026ndash;62.4]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDysglycaemia\u0026Dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (19.8) [15.1\u0026ndash;25.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (13.7) [7.5\u0026ndash;22.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (23.2) [17.1\u0026ndash;30.3]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003e\u0026dagger; Raised BP defined using age-, sex-, and height-specific percentiles. \u0026Dagger; Dysglycaemia defined as random blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/L. CI\u0026thinsp;=\u0026thinsp;confidence interval; WC\u0026thinsp;=\u0026thinsp;waist circumference.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of Cardiometabolic Risk-Factor Counts by Age Group and Sex (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Risk Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOverall, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026ndash;12 yrs (n\u0026thinsp;=\u0026thinsp;95), n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u0026ndash;19 yrs (n\u0026thinsp;=\u0026thinsp;168), n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFemale, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale, n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (25.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (25.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (26.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (47.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74 (45.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (49.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (21.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCluster\u0026thinsp;\u0026ge;\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46 (28.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (23.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eValues are presented descriptively. Cardiometabolic risk count represents the number of predefined risk factors present (range 0\u0026ndash;4).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors Associated with Cardiometabolic Risk-Factor Clustering (\u0026ge;\u0026thinsp;2 Risk Factors) among Children and Adolescents (N\u0026thinsp;=\u0026thinsp;263)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group (13\u0026ndash;19 vs 6\u0026ndash;12 yrs)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;2.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47\u0026ndash;1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eModel notes: Outcome was cardiometabolic risk clustering (\u0026ge;\u0026thinsp;2 risk factors). Model adjusted for age group and sex. OR\u0026thinsp;=\u0026thinsp;odds ratio; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cardiometabolic risk, Risk-factor clustering, Children, Adolescents, Hypertension, Obesity, Dysglycaemia, Blood pressure, Nigeria, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-8712791/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8712791/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCardiometabolic diseases have their origins early in life, yet data on the co-occurrence and clustering of cardiometabolic risk factors among children and adolescents in sub-Saharan Africa remain limited. Understanding age-related patterns of cardiometabolic risk clustering is essential for informing early prevention strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003e We conducted a cross-sectional study among children and adolescents aged 6\u0026ndash;19 years who participated in community health outreach programmes in semi-urban communities of North-Central Nigeria between 2019 and 2023. Anthropometric measurements, blood pressure, and random blood glucose were assessed using standardized protocols. Age- and sex-and height- appropriate definitions were applied for cardiometabolic risk factors. Cardiometabolic risk clustering was defined as the presence of two or more predefined risk factors. Prevalence estimates were described overall and by age group, and logistic regression was used to examine demographic factors associated with risk clustering.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 263 participants were included (95 aged 6 \u0026minus;\u0026thinsp;12 years and 168 aged 13 \u0026minus;\u0026thinsp;19 years). Overweight or obesity was present in 18.3% of participants, central obesity in 11.8%, elevated blood pressure in 58.2%, and dysglycaemia in 19.8%. Two or more cardiometabolic risk factors were observed in 26.6% of participants, occurring in 22.1% of children aged 6 \u0026minus;\u0026thinsp;12 years and 29.2% of adolescents aged 13 \u0026minus;\u0026thinsp;19 years. In regression analyses, adolescents had higher odds of cardiometabolic risk clustering than younger children, although this association was not statistically significant. Sex was not associated with risk clustering.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCardiometabolic risk factors are common and frequently clustered among children and adolescents in North-Central Nigeria, with clustering evident from early childhood. These findings highlight the need for early, integrated, and population-based strategies for cardiometabolic disease prevention in paediatric populations.\u003c/p\u003e","manuscriptTitle":"Prevalence and Clustering of Cardiometabolic Risk Factors among Children and Adolescents in North-Central Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 15:52:10","doi":"10.21203/rs.3.rs-8712791/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"110376551899340280267012428278764025276","date":"2026-03-18T08:19:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-06T08:19:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-18T12:52:38+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-12T05:34:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-11T18:43:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-02-11T18:39:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"34c1844b-4fd2-4c5d-9249-4cf905bc0dd0","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T15:52:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 15:52:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8712791","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8712791","identity":"rs-8712791","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}