Prevalence, Agreement, and Comparative Classification of Metabolic Syndrome Definitions Among Rural and Urban Adults in Kumasi, Ghana

Prevalence, Agreement, and Comparative Classification of Metabolic Syndrome Definitions Among Rural and Urban Adults in Kumasi, Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prevalence, Agreement, and Comparative Classification of Metabolic Syndrome Definitions Among Rural and Urban Adults in Kumasi, Ghana Emmanuel Omari¹, Priscilla H. Mensah², Laura L. Issaka³, Jennifer A. Baafi⁴, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9360097/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Sub-Saharan Africa is undergoing an epidemiological transition associated with urbanization, dietary change, and reduced physical activity, contributing to an increasing burden of cardiometabolic disorders. Estimates of metabolic syndrome (MetS) vary because diagnostic criteria differ. In Ghana, few studies have directly compared commonly used MetS definitions within a single population. This study assessed MetS prevalence among rural and urban adults in Kumasi, Ghana using the World Health Organization (WHO 1999), National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III 2005), and Joint Interim Statement (JIS 2009) criteria, and examined agreement between these definitions. Methods We conducted a facility-based cross-sectional study among 200 adults recruited from diagnostic laboratories in rural and urban districts of the Ashanti Region. MetS was classified using WHO, NCEP-ATP III, and JIS criteria. Differences in prevalence by residence were examined using chi-square tests. Agreement across definitions was assessed using Cohen’s kappa and McNemar’s test. Pairwise diagnostic classification performance was evaluated using contingency tables. Results MetS prevalence ranged from 10.0% using WHO criteria to 16.0% using JIS criteria, with NCEP-ATP III yielding 12.0%. No significant rural–urban differences were observed under any definition (all p > 0.05). Agreement between definitions ranged from κ = 0.78 to 0.92. Across pairwise comparisons, sensitivity ranged from 75.0% to 93.8% and specificity from 97.0% to 99.0%. Conclusion MetS prevalence varied by diagnostic definition but was similar between rural and urban participants. The JIS criteria identified the highest prevalence, suggesting greater sensitivity for detecting clustered cardiometabolic risk. Metabolic syndrome Diagnostic criteria Cardiometabolic risk Rural–urban differences Epidemiologic transition Sub-Saharan Africa Ghana INTRODUCTION Metabolic syndrome (MetS) describes a clustering of interrelated cardiometabolic abnormalities, including central obesity, elevated blood pressure, dyslipidemia, and impaired glucose regulation, that collectively increase the risk of type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and premature mortality ( 1 , 2 ). The coexistence of these metabolic abnormalities reflects shared pathophysiological mechanisms such as insulin resistance and chronic low-grade inflammation( 3 ). Globally, it is estimated that approximately 20–25% of adults meet criteria for metabolic syndrome, although prevalence varies substantially across populations depending on demographic characteristics, lifestyle factors, and diagnostic definitions ( 1 , 4 ). As non-communicable diseases (NCDs) continue to rise worldwide, metabolic syndrome has become an important marker of cardiometabolic risk and a useful framework for identifying individuals at elevated risk for future chronic diseases. Sub-Saharan Africa is undergoing an epidemiological transition characterized by changes in lifestyle, dietary patterns, and physical activity. Increasing urbanization, shifts toward energy-dense diets, and declining occupational physical activity have contributed to rising levels of obesity, hypertension, and diabetes across the region ( 5 , 6 ). Historically, cardiometabolic risk factors were more commonly observed in urban populations due to greater exposure to sedentary lifestyles and processed foods ( 7 ). However, recent evidence suggests that rural populations are increasingly experiencing similar behavioral and environmental risk factors, contributing to a narrowing rural–urban gap in cardiometabolic disease risk in several African settings ( 5 , 8 ). In Ghana, reported prevalence estimates of metabolic syndrome vary considerably across studies, reflecting differences in study populations and diagnostic criteria. Previous studies conducted among urban adults, clinical populations, and specific demographic groups have reported prevalence estimates ranging from approximately 10% to over 20%( 9 , 10 ). Studies conducted among clinical populations, including individuals with type 2 diabetes, have reported even higher prevalence estimates ( 10 , 11 ). Much of the existing literature, however, has focused on urban populations or specific patient groups rather than community-based samples. Consequently, relatively few studies have directly compared rural and urban populations within the same study framework. Understanding potential rural–urban differences in metabolic syndrome prevalence is important for informing population-based prevention strategies in countries experiencing rapid socioeconomic and lifestyle transitions. Another challenge in metabolic syndrome research is the lack of a universally accepted diagnostic definition. Several organizations have proposed criteria for identifying metabolic syndrome, including the World Health Organization (WHO), the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), and the Joint Interim Statement (JIS) ( 1 , 12 , 13 ). These definitions differ in their diagnostic thresholds and in the relative emphasis placed on specific metabolic components. For example, the WHO definition requires evidence of glucose dysregulation or insulin resistance as a prerequisite condition( 12 ), whereas the NCEP-ATP III and JIS definitions classify metabolic syndrome based on the presence of any three abnormal metabolic components ( 13 ). As a result, prevalence estimates and case classification may vary depending on the criteria applied ( 1 , 3 ). This variation in diagnostic frameworks has important implications for epidemiologic surveillance and public health planning, particularly in low- and middle-income countries where consistent diagnostic criteria are needed to accurately monitor cardiometabolic risk and guide prevention strategies. Kumasi, Ghana’s second-largest city and a major commercial hub in the Ashanti Region, represents a population undergoing rapid urbanization and lifestyle change ( 14 ). The city and its surrounding districts include both urban and rural communities that experience varying levels of socioeconomic development and exposure to modern lifestyle risk factors associated with the ongoing epidemiologic and nutrition transitions in sub-Saharan Africa ( 5 , 6 , 15 ). Examining metabolic syndrome within this context provides an opportunity to assess cardiometabolic risk clustering in populations experiencing ongoing demographic and nutritional transitions. Therefore, the present study aimed to ( 1 ) determine the prevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana using the WHO (1999), NCEP-ATP III (2005), and JIS (2009) diagnostic criteria; and ( 2 ) evaluate the level of agreement and comparative classification characteristics between these definitions. Understanding how different diagnostic frameworks classify metabolic syndrome within the same population may help inform cardiometabolic surveillance and prevention strategies in Ghana and similar settings undergoing epidemiologic transition. METHODS Study Design and Setting A cross-sectional study was conducted between March and June 2020 in the Ashanti Region of Ghana. Participants were recruited from diagnostic laboratories located in two districts representing urban and rural settings. The urban site was the Bantama Sub-Metropolitan District within the Kumasi Metropolitan Area, while the rural site was Atwima Nwabiagya District. Recruitment occurred in four diagnostic laboratories: Medilab Diagnostic Services Ltd and Synlab Laboratory Centre in Bantama, and Barekese Health Centre Laboratory and Asuofua Government Hospital Laboratory in Atwima Nwabiagya. These facilities include both private and public laboratories that provide routine biochemical testing services to the public and receive clients from both rural and urban communities. Individuals attending the laboratories may be referred by physicians or may present voluntarily for routine health screening and metabolic assessment. Participants from both rural and urban areas were recruited using similar procedures and eligibility criteria, allowing for internal comparison between residence groups. Study Population and Sampling Adults aged 18 years and older who attended the selected diagnostic laboratories during the study period were eligible to participate. Eligible participants were recruited consecutively during routine laboratory hours after providing written informed consent. Consecutive recruitment was used to ensure inclusion of individuals attending the laboratories for routine biochemical testing during the study period. Individuals who were pregnant or critically ill at the time of recruitment were excluded from the study. Sample Size Determination The required sample size was estimated using Cochran’s formula for prevalence studies ( 16 ). Assuming a metabolic syndrome prevalence of 16% based on previous studies conducted in Ghana, a 95% confidence level, and a 5% margin of error, the minimum calculated sample size was 206 participants. Due to logistical constraints and participant availability during the study period, a total of 200 participants were ultimately recruited, comprising 100 individuals from rural settings and 100 from urban settings. This sample size was considered adequate for estimating metabolic syndrome prevalence and performing comparative analyses between groups. Data Collection Data were collected using a structured interviewer-administered questionnaire by trained research assistants. The questionnaire captured sociodemographic information including age, sex, marital status, education level, occupation, and income. Information on lifestyle behaviors such as alcohol consumption, smoking status, and physical activity was also collected. Self-reported chronic disease was defined as a previous diagnosis made by a physician or other qualified health professional. Participants were asked whether they had ever been diagnosed with specific conditions including hypertension, diabetes mellitus, or other chronic noncommunicable diseases and whether they were currently receiving treatment. Physical activity was assessed based on participants’ self-reported engagement in moderate-to-vigorous physical activity during a typical week. Activities such as brisk walking, jogging, running, or other aerobic activities were considered moderate-to-vigorous intensity. Participants reporting at least 150 minutes of moderate-to-vigorous physical activity per week were classified as physically active, consistent with World Health Organization recommendations for adult physical activity ( 17 ). Anthropometric Measurements Anthropometric measurements were obtained using standardized procedures. Body weight was measured using a calibrated digital weighing scale with participants wearing light clothing and no footwear. Height was measured using a stadiometer, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist circumference (WC) was measured to the nearest 0.1 cm using a non-stretchable measuring tape at the midpoint between the lowest rib and the iliac crest. Hip circumference was measured at the widest portion of the buttocks, and waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference. Blood Pressure Measurement Blood pressure was measured using a digital electronic Medline Blood Pressure Monitor (Model BA-803, Medline Industries, China). Measurements were obtained with participants seated after at least five minutes of rest. Two readings were taken at 5-minute intervals, and the average was recorded. If readings were inconsistent, additional measurements were obtained and averaged. This approach was used to reduce measurement variability and improve reliability. Biochemical Measurements Venous blood samples were collected after an overnight fast of 8 to 12 hours. Blood for glucose analysis was collected into sodium fluoride tubes and centrifuged at 3000 rpm for 10 minutes to obtain plasma. Serum samples were used for lipid profile analyses. Fasting blood glucose was measured using the Glucose Oxidase-Peroxidase (GOD-POD) enzymatic method. Liquizone Glucose-MR reagent (Lot GLU1801, India) was used according to manufacturer instructions. Samples were incubated at 37°C, and absorbance was measured at 590 nm using a spectrophotometer with a 1-cm light path cuvette. Serum triglycerides were measured using the Glycerol-3-Phosphate Oxidase-Peroxidase (GPO-POD) enzymatic method, performed on a Biochemistry Analyzer (Model YSTE-21A, China). Absorbance was read at 505 nm, and concentration was determined based on the intensity of the enzymatic colorimetric reaction. High-density lipoprotein cholesterol was determined using the phosphotungstic acid precipitation method in the presence of magnesium ions. Following precipitation of LDL, the HDL fraction in the supernatant was measured enzymatically using MEDSOURCE OZONE Biomedical Pvt. Ltd reagents. Low-density lipoprotein cholesterol was calculated using the Friedewald Eq. (1972) (18). This calculation was applied when triglyceride concentrations were below 4.5 mmol/L, consistent with standard laboratory practice in epidemiologic studies. Definition of Metabolic Syndrome Metabolic syndrome was defined using three widely applied diagnostic criteria: the World Health Organization (WHO 1999), the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III 2005), and the Joint Interim Statement (JIS 2009) ( 1 , 12 , 13 ). Because fasting insulin and microalbuminuria were not measured, the WHO (1999) criteria were operationalized using a modified approach in which glucose abnormality (fasting plasma glucose ≥ 6.1 mmol/L or previously diagnosed diabetes) was used as a surrogate indicator of insulin resistance ( 19 ). Participants were classified as having metabolic syndrome under the modified WHO criteria if glucose abnormality was present along with at least two of the following components: central obesity defined by WHR (> 0.90 for men or > 0.85 for women) or BMI > 30 kg/m², elevated blood pressure (≥ 140/90 mmHg), or dyslipidemia (triglycerides ≥ 1.7 mmol/L or reduced HDL cholesterol). The NCEP-ATP III criteria defined metabolic syndrome as the presence of any three or more of the following components: fasting plasma glucose ≥ 5.6 mmol/L or treatment for diabetes, blood pressure ≥ 130/85 mmHg or antihypertensive treatment, triglycerides ≥ 1.7 mmol/L, HDL cholesterol < 1.0 mmol/L in men or < 1.3 mmol/L in women, and waist circumference ≥ 102 cm in men or ≥ 88 cm in women ( 13 ). The JIS criteria defined metabolic syndrome as the presence of any three or more of the following components: waist circumference ≥ 94 cm in men or ≥ 80 cm in women, fasting plasma glucose ≥ 5.6 mmol/L or treatment for diabetes, blood pressure ≥ 130/85 mmHg or antihypertensive treatment, triglycerides ≥ 1.7 mmol/L, or HDL cholesterol < 1.0 mmol/L in men or < 1.3 mmol/L in women( 1 ). These waist circumference thresholds were applied because population-specific cutoffs for sub-Saharan African populations have not yet been firmly established. In the absence of validated African-specific thresholds, several epidemiological studies conducted in sub-Saharan Africa have adopted the European cutoffs (≥ 94 cm for men and ≥ 80 cm for women) recommended in the harmonized metabolic syndrome definition, as these thresholds are considered more appropriate for populations of African descent than Asian-specific cutoffs( 9 , 20 ). Statistical Analysis All statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Continuous variables were summarized as means and standard deviations, while categorical variables were presented as frequencies and percentages. Differences between rural and urban participants were evaluated using independent-samples t -tests for continuous variables and chi-square tests for categorical variables. The prevalence of metabolic syndrome was calculated separately according to the WHO, 1999, NCEP-ATP III, 2005, and JIS, 2009 criteria. Differences in prevalence between rural and urban participants were assessed using chi-square tests. Agreement between the diagnostic definitions was evaluated using Cohen’s kappa statistic (κ) with 95% confidence intervals to quantify agreement beyond chance. Contingency tables were constructed to compare classifications between diagnostic definitions, and McNemar’s test was used to examine marginal differences between paired definitions. To further evaluate classification performance across diagnostic frameworks, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A sensitivity analysis was conducted excluding participants with previously diagnosed diabetes to assess whether the presence of known diabetes influenced agreement and classification characteristics across the diagnostic definitions. Statistical significance was defined as a two-sided p-value < 0.05. RESULTS The study included 200 adults, with equal representation from rural (n = 100) and urban (n = 100) communities. The mean age did not differ significantly between rural and urban groups (p = 0.15). Women were more represented in the rural group than in the urban group (69.0% vs. 54.0%), although this difference was not statistically significant (p = 0.19). Sociodemographic characteristics were generally comparable between residence groups. Marital status, educational attainment, physical activity status, alcohol consumption, smoking status, self-reported chronic disease, and monthly income did not differ significantly between rural and urban participants (all p > 0.05) (Table 1 ). Table 1 Baseline sociodemographic, lifestyle, and health characteristics of participants by residence (N = 200) Variable Rural (n = 100) Urban (n = 100) p-value Age, years (mean ± SD) 40.5 ± 13.8 43.5 ± 14.5 0.15 Sex, n (%) 0.190 Male 31 (31.0) 46 (46.0) Female 69 (69.0) 54 (54.0) Marital status, n (%) 0.088 Married 41 (41.0) 56 (56.0) Single 42 (42.0) 31 (31.0) Divorced/Widowed 17 (17.0) 13 (13.0) Educational level, n (%) 0.894 Basic 37 (37.0) 33 (33.0) Secondary 15 (15.0) 18 (18.0) Tertiary 35 (35.0) 39 (39.0) Other 13 (13.0) 10 (10.0) Physical activity, n (%) 0.204 Active 42 (42.0) 51 (51.0) Inactive 58 (58.0) 49 (49.0) Alcohol consumption, n (%) 0.303 Yes 16 (16.0) 11 (11.0) No 84 (84.0) 89 (89.0) Current smoking, n (%) 0.157 Yes 0 (0.0) 2 (2.0) No 100 (100.0) 98 (98.0) Self-reported chronic disease, n (%) 0.314 Yes 36 (36.0) 49 (49.0) No 64 (64.0) 51 (51.0) Average monthly income, n (%) 0.090 ≤ 1000 69 (69.0) 68 (68.0) > 1000 31 (31.0) 32 (32.0) P-values derived from chi-square tests (or Fisher’s exact test where appropriate); age compared using independent-samples t-test Anthropometric, clinical, and biochemical characteristics of participants are presented in Table 2 . BMI and WC did not differ significantly between rural and urban participants (p = 0.276 and p = 0.105, respectively). However, WHR was significantly higher among urban participants compared with rural participants (0.91 ± 0.09 vs. 0.88 ± 0.07; p = 0.008). Systolic and diastolic blood pressure levels were similar between the two groups (p = 0.082 and p = 0.289, respectively). Fasting blood glucose and HDL cholesterol levels also did not differ significantly by residence (p = 0.366 and p = 0.571, respectively). In contrast, triglyceride concentrations were lower among urban participants (p = 0.049), whereas LDL cholesterol and total cholesterol were significantly higher in urban participants (both p < 0.001). Table 2 Anthropometric, clinical, and biochemical characteristics of participants by residence Parameter Rural (mean ± SD) Urban (mean ± SD) p-value Body mass index (kg/m²) 25.77 ± 5.80 26.73 ± 6.59 0.276 Waist circumference (cm) 90.08 ± 13.75 93.25 ± 13.95 0.105 Waist-to-hip ratio 0.88 ± 0.07 0.91 ± 0.09 0.008 Systolic blood pressure (mmHg) 129.80 ± 22.90 135.25 ± 23.48 0.082 Diastolic blood pressure (mmHg) 82.62 ± 18.13 80.23 ± 13.81 0.289 Fasting blood glucose (mmol/L) 4.94 ± 1.77 5.22 ± 2.56 0.366 Triglycerides (mmol/L) 1.15 ± 0.49 1.02 ± 0.44 0.049 HDL cholesterol (mmol/L) 1.59 ± 0.25 1.57 ± 0.29 0.571 LDL cholesterol (mmol/L) 2.84 ± 1.02 3.82 ± 1.03 < 0.001 Total cholesterol (mmol/L) 4.93 ± 0.93 5.86 ± 0.92 < 0.001 Values are mean ± standard deviation. P-values were derived from independent-samples t-tests. HDL, high-density lipoprotein; LDL, low-density lipoprotein. The prevalence of individual metabolic syndrome components by residence is presented in Table 3 . Central obesity defined using waist circumference criteria was similar in rural and urban participants (49.0% vs. 48.0%; p = 0.879). Likewise, central obesity defined using WHR criteria did not differ significantly between rural and urban groups (49.0% vs. 52.0%; p = 0.104). Elevated blood pressure (≥ 130/85 mmHg) was more common in urban than rural participants (57.0% vs. 48.0%), but the difference was not statistically significant (p = 0.189). Elevated fasting glucose (≥ 5.6 mmol/L) was also similar between groups (15.0% rural vs. 17.0% urban; p = 0.701). Reduced HDL cholesterol was identical in both groups (p = 1.000). Elevated triglycerides (≥ 1.7 mmol/L) were more frequent in rural than urban participants (15.0% vs. 7.0%), although this difference did not reach statistical significance (p = 0.071). Table 3 Prevalence of individual metabolic syndrome components by residence Variable Rural Urban p-value Measured metabolic syndrome components Central obesity (WC criteria*) 49 (49.0) 48 (48.0) 0.879 Central obesity (WHR criteria†) 49 (49.0) 52 (52.0) 0.104 Elevated blood pressure (≥ 130/85 mmHg‡) 48 (48.0) 57 (57.0) 0.189 Elevated fasting glucose (≥ 5.6 mmol/L§) 15 (15.0) 17 (17.0) 0.701 Reduced HDL-C (sex-specific¶) 7 (7.0) 7 (7.0) 1.000 Elevated triglycerides (≥ 1.7 mmol/L) 15 (15.0) 7 (7.0) 0.071 Values are presented as n (%). P-values derived from chi-square tests (or Fisher’s exact test where appropriate). *Central obesity (WC): waist circumference ≥ 94 cm (men) and ≥ 80 cm (women). †Central obesity (WHR): waist-to-hip ratio ≥ 0.90 (men) and ≥ 0.85 (women). ‡Elevated blood pressure defined as systolic ≥ 130 mmHg and/or diastolic ≥ 85 mmHg. §Elevated fasting glucose defined as fasting plasma glucose ≥ 5.6 mmol/L. ¶Reduced HDL-C defined as < 1.0 mmol/L (men) and < 1.3 mmol/L (women). The prevalence of metabolic syndrome varied according to the diagnostic definition applied (Table 4 ). Using the WHO (1999) criteria, metabolic syndrome prevalence was 9.0% among rural participants and 11.0% among urban participants (p = 0.637). Using the NCEP-ATP III (2005) criteria, prevalence was 11.0% in rural participants and 13.0% in urban participants (p = 0.661). Under the JIS (2009) criteria, prevalence was 18.0% in rural participants and 14.0% in urban participants (p = 0.441). No statistically significant rural–urban differences were observed under any diagnostic definition. Table 4 Prevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana according to WHO (1999), NCEP-ATP III (2005), and JIS (2009) criteria Definition Rural % (95% CI) Urban % (95% CI) p-value† WHO (1999)* 9.0 (3.4–14.6) 11.0 (4.9–17.1) 0.637 NCEP-ATP III (2005) 11.0 (4.9–17.1) 13.0 (6.4–19.6) 0.661 JIS (2009) 18.0 (10.5–25.5) 14.0 (7.2–20.8) 0.441 * The WHO (1999) definition was operationalized using glucose abnormality (fasting plasma glucose ≥ 6.1 mmol/L or previously diagnosed diabetes) as a surrogate indicator of insulin resistance. † P-values were derived from chi-square tests comparing metabolic syndrome prevalence between rural and urban participants. Agreement and comparative classification characteristics between metabolic syndrome definitions are presented in Table 5 . Agreement between the NCEP-ATP III and JIS criteria was high (κ = 0.85, 95% CI: 0.76–0.94), indicating almost perfect agreement. The NCEP-ATP III criteria demonstrated sensitivity of 93.8% and specificity of 98.8% relative to the JIS definition, with positive and negative predictive values of 90.6% and 99.4%, respectively. McNemar’s test indicated a statistically significant difference in marginal classification between these definitions (p = 0.008). Comparison between the NCEP-ATP III and modified WHO definitions also demonstrated very high agreement (κ = 0.92, 95% CI: 0.86–0.98), indicating high agreement. Sensitivity and specificity of the NCEP-ATP III definition relative to the WHO criteria were 88.0% and 99.0%, respectively, with PPV and NPV values of 95.0% and 97.6%. Agreement between the modified WHO and JIS definitions was slightly lower but remained substantial (κ = 0.78, 95% CI: 0.67–0.89). The WHO criteria demonstrated sensitivity of 75.0% and specificity of 97.0% relative to the JIS definition, with PPV of 80.0% and NPV of 96.4%. McNemar’s test indicated a significant difference in classification between the WHO and JIS definitions (p < 0.001). Results were similar after excluding participants with previously diagnosed diabetes (n = 20) (Supplementary Table S1 ). Agreement between NCEP-ATP III and JIS remained high (κ = 0.93; 95% CI: 0.88–0.98), while agreement involving the WHO definition was lower (WHO vs NCEP-ATP III: κ = 0.65; 95% CI: 0.52–0.78; WHO vs JIS: κ = 0.62; 95% CI: 0.49–0.75). Table 5 Agreement and comparative classification characteristics between metabolic syndrome definitions Comparison N κ (95% CI) McNemar’s p-value Sensitivity (%) Specificity (%) PPV (%) NPV (%) NCEP-ATP III vs JIS 200 0.85 (0.76–0.94) 0.008 93.8 98.8 90.6 99.4 NCEP-ATP III vs WHO 200 0.92 (0.86–0.98) 0.125 88.0 99.0 95.0 97.6 WHO vs JIS 200 0.78 (0.67–0.89) < 0.001 75.0 97.0 80.0 96.4 DISCUSSION This study examined the prevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana using three commonly applied diagnostic criteria—the WHO (1999), NCEP-ATP III (2005), and JIS, 2009 definitions—and evaluated the level of agreement between these frameworks. Metabolic syndrome prevalence ranged from 10.0% to 16.0% depending on the diagnostic criteria used, with the highest prevalence observed under the JIS definition. Agreement between definitions was substantial to near-perfect, indicating that although the frameworks differ in diagnostic structure and thresholds, they largely identify overlapping individuals within this population. No statistically significant rural–urban differences in metabolic syndrome prevalence were observed. Collectively, these findings suggest that the burden of metabolic syndrome in this population is moderate and relatively comparable across rural and urban settings, while methodological differences between diagnostic criteria primarily influence the magnitude of reported prevalence. The prevalence estimates observed in this study are broadly consistent with findings reported in Ghana and other sub-Saharan African populations. Previous studies conducted in Ghana have reported metabolic syndrome prevalence ranging from approximately 10% to over 20%, depending on the study population and diagnostic criteria applied ( 8 , 9 , 21 ). Similar variability has been documented across sub-Saharan Africa, where differences in population characteristics, lifestyle patterns, and diagnostic definitions contribute to variation in reported prevalence( 22 ). The estimates observed in the present study therefore align with regional evidence suggesting that metabolic syndrome represents an emerging but heterogeneous cardiometabolic risk pattern in many African populations undergoing epidemiologic transition. Consistent with previous comparative studies, the JIS criteria produced the highest prevalence estimate, followed by the NCEP-ATP III and WHO definitions. This pattern reflects structural differences between diagnostic frameworks. The JIS definition employs harmonized thresholds and does not require insulin resistance or glucose abnormalities as a prerequisite condition, thereby capturing a broader group of individuals with clustered metabolic risk factors ( 1 ). In contrast, the WHO definition requires evidence of glucose dysregulation or insulin resistance in addition to other metabolic abnormalities, which generally results in lower prevalence estimates ( 12 ). These diagnostic differences explain why harmonized definitions such as JIS often identify a larger proportion of individuals with metabolic syndrome in population-based studies. No significant rural–urban differences in metabolic syndrome prevalence were observed. Historically, cardiometabolic risk factors in sub-Saharan Africa have been more prevalent in urban populations due to greater exposure to sedentary lifestyles, dietary transitions, and other behavioral risk factors associated with urbanization ( 5 , 6 ). However, recent evidence suggests that rural populations are increasingly experiencing similar lifestyle changes, including shifts toward energy-dense diets and reduced physical activity ( 15 , 23 ). The absence of significant rural–urban differences in this study may therefore reflect the ongoing epidemiologic transition occurring within Ghana, where environmental and behavioral risk factors for cardiometabolic disease are becoming more widespread across both rural and urban settings. Agreement analysis demonstrated substantial to near-perfect concordance between diagnostic definitions, particularly between the NCEP-ATP III and WHO definitions (κ = 0.92) and between NCEP-ATP III and JIS (κ = 0.85). These findings are consistent with previous studies reporting strong agreement between NCEP and JIS definitions because these frameworks share similar thresholds for most metabolic components, including blood pressure, triglycerides, HDL cholesterol, and fasting glucose ( 1 , 3 ). The slightly lower agreement observed between WHO and JIS definitions likely reflects structural differences inherent to the WHO framework, including the requirement for glucose abnormalities or insulin resistance ( 3 ). Similar comparative studies have also shown that although different definitions may yield varying prevalence estimates, they largely identify overlapping groups of individuals with clustered cardiometabolic risk factors ( 24 , 25 ). The findings of this study have important implications for cardiometabolic surveillance and public health planning in Ghana. First, the moderate prevalence of metabolic syndrome observed across both rural and urban populations indicates that clustered cardiometabolic risk is not confined to urban settings and should be addressed through population-wide prevention strategies. Screening and early detection initiatives should therefore be integrated into primary health care systems serving both rural and urban communities. Second, the variability in prevalence estimates across diagnostic definitions highlights the importance of standardized criteria for national surveillance and research comparability. Differences of up to six percentage points between WHO and JIS estimates in this study illustrate how the choice of diagnostic framework can influence estimates of disease burden and potentially affect health policy planning and resource allocation. Although the high agreement observed between definitions suggests that they identify largely overlapping high-risk populations, consistent application of a standardized definition would facilitate comparability across studies and over time. This study has several strengths. First, it directly compared three widely used metabolic syndrome definitions within the same population, allowing standardized evaluation of prevalence, agreement, and classification characteristics. The inclusion of both rural and urban participants also enabled assessment of potential geographic differences in cardiometabolic risk within a Ghanaian population undergoing epidemiologic transition. In addition, standardized anthropometric and biochemical measurements strengthened the internal validity of the study. However, several limitations should be acknowledged. First, the facility-based sampling approach may limit the generalizability of the findings, as individuals attending diagnostic laboratories may differ from the broader population in health status or healthcare-seeking behavior. Second, the relatively modest sample size may have limited statistical power to detect small rural–urban differences in metabolic syndrome prevalence. Third, the WHO definition was applied using a modified approach in which fasting glucose abnormality was used as a proxy for insulin resistance due to the absence of direct measures of insulin resistance, which may affect comparability with studies applying the original WHO criteria. Future research should incorporate community-based sampling to generate population-representative estimates and to better characterize rural–urban differences in metabolic risk. Longitudinal studies are also needed to assess the predictive validity of the WHO, NCEP-ATP III, and JIS definitions for incident diabetes and cardiovascular disease, which cannot be evaluated in a cross-sectional design. In addition, validation of waist circumference thresholds specific to African populations remains a critical research priority, as the use of non-African cutoffs may influence case classification and comparability across studies. In conclusion, this study demonstrates that the prevalence of metabolic syndrome among adults in Kumasi varies according to the diagnostic criteria applied, with lower estimates observed under the WHO definition and higher estimates under the JIS criteria. Despite these differences, substantial agreement was observed between diagnostic frameworks, indicating that they largely identify overlapping groups of individuals with elevated cardiometabolic risk. Importantly, no significant rural–urban disparities were detected, suggesting that metabolic syndrome is not confined to urban populations in this setting. These findings highlight the importance of standardized diagnostic criteria for metabolic syndrome surveillance and emphasize the need for expanded cardiometabolic risk screening across both rural and urban communities in Ghana as the country continues to undergo epidemiologic transition. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Committee on Human Research, Publication and Ethics (CHRPE) of the School of Medical Sciences, Kwame Nkrumah University of Science and Technology (KNUST) and Komfo Anokye Teaching Hospital, Kumasi, Ghana (Ref: CHRPE/AP/171/20; approved 1 June 2020). Written informed consent was obtained from all participants prior to enrollment, and the study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are not publicly available due to ongoing analyses but are available from the corresponding author on reasonable request, Competing interests The authors declare that they have no competing interests. Funding This research received no external funding. Authors’ contributions E.O. conceived and designed the study. D.O.M. performed the statistical analysis and drafted the manuscript. L.L.I., J.A.B., and P.A. contributed to data collection and data management. P.H.M. assisted with manuscript drafting and revision. All authors contributed to interpretation of the results, critically reviewed the manuscript, and approved the final version. References Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. Grundy SM. Metabolic Syndrome Pandemic. Arteriosclerosis. Thromb Vascular Biology. 2008;28(4):629–36. Kassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9(1):48. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20(2):12. Dalal S, Beunza JJ, Volmink J, Adebamowo C, Bajunirwe F, Njelekela M, et al. Non-communicable diseases in sub-Saharan Africa: what we know now. Int J Epidemiol. 2011;40(4):885–901. Agyemang C, Boatemaa S, Frempong GA, de-Graft Aikins A. Obesity in sub-saharan Africa. Springer International Publishing Switzerland. 2015; 2015. Addo J, Smeeth L, Leon DA. Hypertension in sub-saharan Africa: a systematic review. Hypertension. 2007;50(6):1012–8. Agyemang C. Rural and urban differences in blood pressure and hypertension in Ghana, West Africa. Public Health. 2006;120(6):525–33. Ofori-Asenso R, Agyeman AA, Laar A. Metabolic syndrome in apparently healthy Ghanaian adults: A systematic review and meta-analysis. Int J chronic Dis. 2017;2017(1):2562374. Nsiah K, Shang VO, Boateng KA, Mensah F. Prevalence of metabolic syndrome in type 2 diabetes mellitus patients. Int J Appl Basic Med Res. 2015;5(2):133–8. Agyemang-Yeboah F, Eghan BAJ, Annani-Akollor ME, Togbe E, Donkor S, Oppong Afranie B. Evaluation of metabolic syndrome and its associated risk factors in type 2 diabetes: A descriptive Cross-Sectional study at the komfo anokye teaching hospital, kumasi. Ghana BioMed Res Int. 2019;2019(1):4562904. World Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: Report of a WHO consultation. Part 1: Diagnosis and classification of diabetes mellitus. Geneva: World Health Organization; 1999. Expert Panel on Detection E, Adults ToHBCi. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486–97. Ghana Statistical Service. 2021 Population and Housing Census: General Report Volume 3A: Population of Regions and Districts. Accra, Ghana: Ghana Statistical Service; 2021. Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3–21. Cochran WG. Sampling techniques. Wiley; 1977. World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. Goldfine AB, Gerwien RW, Kolberg JA, O'Shea S, Hamren S, Hein GP, et al. Biomarkers in fasting serum to estimate glucose tolerance, insulin sensitivity, and insulin secretion. Clin Chem. 2011;57(2):326–37. Motala AA, Esterhuizen T, Pirie FJ, Omar MAK. The Prevalence of Metabolic Syndrome and Determination of the Optimal Waist Circumference Cutoff Points in a Rural South African Community. Diabetes Care. 2011;34(4):1032–7. Obirikorang C, Osakunor DNM, Anto EO, Amponsah SO, Adarkwa OK. Obesity and cardio-metabolic risk factors in an urban and rural population in the Ashanti Region-Ghana: a comparative cross-sectional study. PLoS ONE. 2015;10(6):e0129494. Faijer-Westerink HJ, Kengne AP, Meeks KA, Agyemang C. Prevalence of metabolic syndrome in sub-Saharan Africa: A systematic review and meta-analysis. Nutr Metabolism Cardiovasc Dis. 2020;30(4):547–65. Schmidhuber J, Shetty P. The nutrition transition to 2030. Why developing countries are likely to bear the major burden. Acta agriculturae scand Sect c. 2005;2(3–4):150–66. Nilsson P, Engström G, Hedblad B. The metabolic syndrome and incidence of cardiovascular disease in non-diabetic subjects—a population‐based study comparing three different definitions. Diabet Med. 2007;24(5):464–72. Ford ES. Risks for All-Cause Mortality, Cardiovascular Disease, and Diabetes Associated With the Metabolic Syndrome: A summary of the evidence. Diabetes Care. 2005;28(7):1769–78. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 16 May, 2026 Reviewers agreed at journal 08 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Editor invited by journal 16 Apr, 2026 Editor assigned by journal 11 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 08 Apr, 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. 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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-9360097","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637551814,"identity":"52b634ec-e170-485c-8e82-e3088a02c1f1","order_by":0,"name":"Emmanuel Omari¹","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYNACAxsIzQNiE6kljWQtDIdJ0MLP3p34uaLgfOKGGwmMD962MdhtJ6RFsufsZskzBrdBWpgN57YxJO9sIKDF4EbuBskGg9vGkjMS2KR5gVoMDhDQYn8jd/PPBoNzIC3sv4nSYiCRuw1oywE5fokENmagFjuCWiTOnN1m2WCQLMfP87BZcs45iQSCWvjbezffbPhjx8PGnnzww5syG3uCWpAAYwPI1sQG4nVAgT3JOkbBKBgFo2DYAwAE3j2xGOYygAAAAABJRU5ErkJggg==","orcid":"","institution":"Knutsford University","correspondingAuthor":true,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Omari¹","suffix":""},{"id":637551816,"identity":"e51f37a6-b46b-4763-800d-aca4ecfbd117","order_by":1,"name":"Priscilla H. Mensah²","email":"","orcid":"","institution":"Sampa Government Hospital","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"H.","lastName":"Mensah²","suffix":""},{"id":637551819,"identity":"5d82a2b5-eeb3-48a1-a3b2-1a17efed8a6a","order_by":2,"name":"Laura L. Issaka³","email":"","orcid":"","institution":"Sunyani Technical University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"L.","lastName":"Issaka³","suffix":""},{"id":637551821,"identity":"8c646197-e7ac-412f-b909-45629e7f960a","order_by":3,"name":"Jennifer A. Baafi⁴","email":"","orcid":"","institution":"Akenten Appiah-Menka University of Skills Training and Entrepreneurial Development","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"A.","lastName":"Baafi⁴","suffix":""},{"id":637551823,"identity":"24ceba0e-b0e8-42fd-a42c-238ae72c025a","order_by":4,"name":"Priscilla Achaab⁵","email":"","orcid":"","institution":"Dar-Bem Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Priscilla","middleName":"","lastName":"Achaab⁵","suffix":""},{"id":637551825,"identity":"84316dd4-9c65-4b3e-bf19-292bc6a55eb4","order_by":5,"name":"Daniel O. Mensah²","email":"","orcid":"","institution":"Sampa Government Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"O.","lastName":"Mensah²","suffix":""}],"badges":[],"createdAt":"2026-04-08 17:54:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9360097/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9360097/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109204834,"identity":"cec0bb92-2038-4521-9fcd-427babed7c45","added_by":"auto","created_at":"2026-05-13 15:02:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":281633,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9360097/v1/27d0a97e-54c7-4bff-81c3-a0339b135278.pdf"},{"id":109120715,"identity":"555b463a-415e-4b9b-97a3-5aaf66ede7f0","added_by":"auto","created_at":"2026-05-12 17:12:16","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":17361,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-9360097/v1/db0b7d2b662ed4a7f276d96e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prevalence, Agreement, and Comparative Classification of Metabolic Syndrome Definitions Among Rural and Urban Adults in Kumasi, Ghana","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eMetabolic syndrome (MetS) describes a clustering of interrelated cardiometabolic abnormalities, including central obesity, elevated blood pressure, dyslipidemia, and impaired glucose regulation, that collectively increase the risk of type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and premature mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The coexistence of these metabolic abnormalities reflects shared pathophysiological mechanisms such as insulin resistance and chronic low-grade inflammation(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Globally, it is estimated that approximately 20\u0026ndash;25% of adults meet criteria for metabolic syndrome, although prevalence varies substantially across populations depending on demographic characteristics, lifestyle factors, and diagnostic definitions (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). As non-communicable diseases (NCDs) continue to rise worldwide, metabolic syndrome has become an important marker of cardiometabolic risk and a useful framework for identifying individuals at elevated risk for future chronic diseases.\u003c/p\u003e \u003cp\u003eSub-Saharan Africa is undergoing an epidemiological transition characterized by changes in lifestyle, dietary patterns, and physical activity. Increasing urbanization, shifts toward energy-dense diets, and declining occupational physical activity have contributed to rising levels of obesity, hypertension, and diabetes across the region (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Historically, cardiometabolic risk factors were more commonly observed in urban populations due to greater exposure to sedentary lifestyles and processed foods (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, recent evidence suggests that rural populations are increasingly experiencing similar behavioral and environmental risk factors, contributing to a narrowing rural\u0026ndash;urban gap in cardiometabolic disease risk in several African settings (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Ghana, reported prevalence estimates of metabolic syndrome vary considerably across studies, reflecting differences in study populations and diagnostic criteria. Previous studies conducted among urban adults, clinical populations, and specific demographic groups have reported prevalence estimates ranging from approximately 10% to over 20%(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Studies conducted among clinical populations, including individuals with type 2 diabetes, have reported even higher prevalence estimates (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Much of the existing literature, however, has focused on urban populations or specific patient groups rather than community-based samples. Consequently, relatively few studies have directly compared rural and urban populations within the same study framework. Understanding potential rural\u0026ndash;urban differences in metabolic syndrome prevalence is important for informing population-based prevention strategies in countries experiencing rapid socioeconomic and lifestyle transitions.\u003c/p\u003e \u003cp\u003eAnother challenge in metabolic syndrome research is the lack of a universally accepted diagnostic definition. Several organizations have proposed criteria for identifying metabolic syndrome, including the World Health Organization (WHO), the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III), and the Joint Interim Statement (JIS) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). These definitions differ in their diagnostic thresholds and in the relative emphasis placed on specific metabolic components. For example, the WHO definition requires evidence of glucose dysregulation or insulin resistance as a prerequisite condition(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), whereas the NCEP-ATP III and JIS definitions classify metabolic syndrome based on the presence of any three abnormal metabolic components (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). As a result, prevalence estimates and case classification may vary depending on the criteria applied (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This variation in diagnostic frameworks has important implications for epidemiologic surveillance and public health planning, particularly in low- and middle-income countries where consistent diagnostic criteria are needed to accurately monitor cardiometabolic risk and guide prevention strategies.\u003c/p\u003e \u003cp\u003eKumasi, Ghana\u0026rsquo;s second-largest city and a major commercial hub in the Ashanti Region, represents a population undergoing rapid urbanization and lifestyle change (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The city and its surrounding districts include both urban and rural communities that experience varying levels of socioeconomic development and exposure to modern lifestyle risk factors associated with the ongoing epidemiologic and nutrition transitions in sub-Saharan Africa (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Examining metabolic syndrome within this context provides an opportunity to assess cardiometabolic risk clustering in populations experiencing ongoing demographic and nutritional transitions.\u003c/p\u003e \u003cp\u003eTherefore, the present study aimed to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) determine the prevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana using the WHO (1999), NCEP-ATP III (2005), and JIS (2009) diagnostic criteria; and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) evaluate the level of agreement and comparative classification characteristics between these definitions. Understanding how different diagnostic frameworks classify metabolic syndrome within the same population may help inform cardiometabolic surveillance and prevention strategies in Ghana and similar settings undergoing epidemiologic transition.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted between March and June 2020 in the Ashanti Region of Ghana. Participants were recruited from diagnostic laboratories located in two districts representing urban and rural settings. The urban site was the Bantama Sub-Metropolitan District within the Kumasi Metropolitan Area, while the rural site was Atwima Nwabiagya District. Recruitment occurred in four diagnostic laboratories: Medilab Diagnostic Services Ltd and Synlab Laboratory Centre in Bantama, and Barekese Health Centre Laboratory and Asuofua Government Hospital Laboratory in Atwima Nwabiagya. These facilities include both private and public laboratories that provide routine biochemical testing services to the public and receive clients from both rural and urban communities. Individuals attending the laboratories may be referred by physicians or may present voluntarily for routine health screening and metabolic assessment. Participants from both rural and urban areas were recruited using similar procedures and eligibility criteria, allowing for internal comparison between residence groups.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population and Sampling\u003c/h3\u003e\n\u003cp\u003eAdults aged 18 years and older who attended the selected diagnostic laboratories during the study period were eligible to participate. Eligible participants were recruited consecutively during routine laboratory hours after providing written informed consent. Consecutive recruitment was used to ensure inclusion of individuals attending the laboratories for routine biochemical testing during the study period. Individuals who were pregnant or critically ill at the time of recruitment were excluded from the study.\u003c/p\u003e\n\u003ch3\u003eSample Size Determination\u003c/h3\u003e\n\u003cp\u003eThe required sample size was estimated using Cochran\u0026rsquo;s formula for prevalence studies (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Assuming a metabolic syndrome prevalence of 16% based on previous studies conducted in Ghana, a 95% confidence level, and a 5% margin of error, the minimum calculated sample size was 206 participants. Due to logistical constraints and participant availability during the study period, a total of 200 participants were ultimately recruited, comprising 100 individuals from rural settings and 100 from urban settings. This sample size was considered adequate for estimating metabolic syndrome prevalence and performing comparative analyses between groups.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured interviewer-administered questionnaire by trained research assistants. The questionnaire captured sociodemographic information including age, sex, marital status, education level, occupation, and income. Information on lifestyle behaviors such as alcohol consumption, smoking status, and physical activity was also collected. Self-reported chronic disease was defined as a previous diagnosis made by a physician or other qualified health professional. Participants were asked whether they had ever been diagnosed with specific conditions including hypertension, diabetes mellitus, or other chronic noncommunicable diseases and whether they were currently receiving treatment. Physical activity was assessed based on participants\u0026rsquo; self-reported engagement in moderate-to-vigorous physical activity during a typical week. Activities such as brisk walking, jogging, running, or other aerobic activities were considered moderate-to-vigorous intensity. Participants reporting at least 150 minutes of moderate-to-vigorous physical activity per week were classified as physically active, consistent with World Health Organization recommendations for adult physical activity (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eAnthropometric Measurements\u003c/h3\u003e\n\u003cp\u003eAnthropometric measurements were obtained using standardized procedures. Body weight was measured using a calibrated digital weighing scale with participants wearing light clothing and no footwear. Height was measured using a stadiometer, and body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared. Waist circumference (WC) was measured to the nearest 0.1 cm using a non-stretchable measuring tape at the midpoint between the lowest rib and the iliac crest. Hip circumference was measured at the widest portion of the buttocks, and waist-to-hip ratio (WHR) was calculated as waist circumference divided by hip circumference.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBlood Pressure Measurement\u003c/h2\u003e \u003cp\u003eBlood pressure was measured using a digital electronic Medline Blood Pressure Monitor (Model BA-803, Medline Industries, China). Measurements were obtained with participants seated after at least five minutes of rest. Two readings were taken at 5-minute intervals, and the average was recorded. If readings were inconsistent, additional measurements were obtained and averaged. This approach was used to reduce measurement variability and improve reliability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiochemical Measurements\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were collected after an overnight fast of 8 to 12 hours. Blood for glucose analysis was collected into sodium fluoride tubes and centrifuged at 3000 rpm for 10 minutes to obtain plasma. Serum samples were used for lipid profile analyses. Fasting blood glucose was measured using the Glucose Oxidase-Peroxidase (GOD-POD) enzymatic method. Liquizone Glucose-MR reagent (Lot GLU1801, India) was used according to manufacturer instructions. Samples were incubated at 37\u0026deg;C, and absorbance was measured at 590 nm using a spectrophotometer with a 1-cm light path cuvette. Serum triglycerides were measured using the Glycerol-3-Phosphate Oxidase-Peroxidase (GPO-POD) enzymatic method, performed on a Biochemistry Analyzer (Model YSTE-21A, China). Absorbance was read at 505 nm, and concentration was determined based on the intensity of the enzymatic colorimetric reaction. High-density lipoprotein cholesterol was determined using the phosphotungstic acid precipitation method in the presence of magnesium ions. Following precipitation of LDL, the HDL fraction in the supernatant was measured enzymatically using MEDSOURCE OZONE Biomedical Pvt. Ltd reagents. Low-density lipoprotein cholesterol was calculated using the Friedewald Eq.\u0026nbsp;(1972) (18). This calculation was applied when triglyceride concentrations were below 4.5 mmol/L, consistent with standard laboratory practice in epidemiologic studies.\u003c/p\u003e\n\u003ch3\u003eDefinition of Metabolic Syndrome\u003c/h3\u003e\n\u003cp\u003eMetabolic syndrome was defined using three widely applied diagnostic criteria: the World Health Organization (WHO 1999), the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III 2005), and the Joint Interim Statement (JIS 2009) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Because fasting insulin and microalbuminuria were not measured, the WHO (1999) criteria were operationalized using a modified approach in which glucose abnormality (fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L or previously diagnosed diabetes) was used as a surrogate indicator of insulin resistance (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Participants were classified as having metabolic syndrome under the modified WHO criteria if glucose abnormality was present along with at least two of the following components: central obesity defined by WHR (\u0026gt;\u0026thinsp;0.90 for men or \u0026gt;\u0026thinsp;0.85 for women) or BMI\u0026thinsp;\u0026gt;\u0026thinsp;30 kg/m\u0026sup2;, elevated blood pressure (\u0026ge;\u0026thinsp;140/90 mmHg), or dyslipidemia (triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L or reduced HDL cholesterol). The NCEP-ATP III criteria defined metabolic syndrome as the presence of any three or more of the following components: fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or treatment for diabetes, blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or antihypertensive treatment, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L, HDL cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L in men or \u0026lt;\u0026thinsp;1.3 mmol/L in women, and waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;102 cm in men or \u0026ge;\u0026thinsp;88 cm in women (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). The JIS criteria defined metabolic syndrome as the presence of any three or more of the following components: waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;94 cm in men or \u0026ge;\u0026thinsp;80 cm in women, fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L or treatment for diabetes, blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg or antihypertensive treatment, triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;1.7 mmol/L, or HDL cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L in men or \u0026lt;\u0026thinsp;1.3 mmol/L in women(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). These waist circumference thresholds were applied because population-specific cutoffs for sub-Saharan African populations have not yet been firmly established. In the absence of validated African-specific thresholds, several epidemiological studies conducted in sub-Saharan Africa have adopted the European cutoffs (\u0026ge;\u0026thinsp;94 cm for men and \u0026ge;\u0026thinsp;80 cm for women) recommended in the harmonized metabolic syndrome definition, as these thresholds are considered more appropriate for populations of African descent than Asian-specific cutoffs(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Continuous variables were summarized as means and standard deviations, while categorical variables were presented as frequencies and percentages. Differences between rural and urban participants were evaluated using independent-samples \u003cem\u003et\u003c/em\u003e-tests for continuous variables and chi-square tests for categorical variables. The prevalence of metabolic syndrome was calculated separately according to the WHO, 1999, NCEP-ATP III, 2005, and JIS, 2009 criteria. Differences in prevalence between rural and urban participants were assessed using chi-square tests. Agreement between the diagnostic definitions was evaluated using Cohen\u0026rsquo;s kappa statistic (κ) with 95% confidence intervals to quantify agreement beyond chance. Contingency tables were constructed to compare classifications between diagnostic definitions, and McNemar\u0026rsquo;s test was used to examine marginal differences between paired definitions. To further evaluate classification performance across diagnostic frameworks, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. A sensitivity analysis was conducted excluding participants with previously diagnosed diabetes to assess whether the presence of known diabetes influenced agreement and classification characteristics across the diagnostic definitions. Statistical significance was defined as a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eThe study included 200 adults, with equal representation from rural (n\u0026thinsp;=\u0026thinsp;100) and urban (n\u0026thinsp;=\u0026thinsp;100) communities. The mean age did not differ significantly between rural and urban groups (p\u0026thinsp;=\u0026thinsp;0.15). Women were more represented in the rural group than in the urban group (69.0% vs. 54.0%), although this difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.19). Sociodemographic characteristics were generally comparable between residence groups. Marital status, educational attainment, physical activity status, alcohol consumption, smoking status, self-reported chronic disease, and monthly income did not differ significantly between rural and urban participants (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline sociodemographic, lifestyle, and health characteristics of participants by residence (N\u0026thinsp;=\u0026thinsp;200)\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=\"char\" char=\".\" 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\u003eRural (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban (n\u0026thinsp;=\u0026thinsp;100)\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, years (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (41.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (56.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced/Widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (37.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInactive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol consumption, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (89.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent smoking, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (98.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSelf-reported chronic disease, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage monthly income, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68 (68.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eP-values derived from chi-square tests (or Fisher\u0026rsquo;s exact test where appropriate); age compared using independent-samples t-test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAnthropometric, clinical, and biochemical characteristics of participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. BMI and WC did not differ significantly between rural and urban participants (p\u0026thinsp;=\u0026thinsp;0.276 and p\u0026thinsp;=\u0026thinsp;0.105, respectively). However, WHR was significantly higher among urban participants compared with rural participants (0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09 vs. 0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07; p\u0026thinsp;=\u0026thinsp;0.008). Systolic and diastolic blood pressure levels were similar between the two groups (p\u0026thinsp;=\u0026thinsp;0.082 and p\u0026thinsp;=\u0026thinsp;0.289, respectively). Fasting blood glucose and HDL cholesterol levels also did not differ significantly by residence (p\u0026thinsp;=\u0026thinsp;0.366 and p\u0026thinsp;=\u0026thinsp;0.571, respectively). In contrast, triglyceride concentrations were lower among urban participants (p\u0026thinsp;=\u0026thinsp;0.049), whereas LDL cholesterol and total cholesterol were significantly higher in urban participants (both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eAnthropometric, clinical, and biochemical characteristics of participants by residence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003cp\u003e(mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\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\u003eBody mass index (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e25.77\u0026thinsp;\u0026plusmn;\u0026thinsp;5.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e26.73\u0026thinsp;\u0026plusmn;\u0026thinsp;6.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e90.08\u0026thinsp;\u0026plusmn;\u0026thinsp;13.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e93.25\u0026thinsp;\u0026plusmn;\u0026thinsp;13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-hip ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e129.80\u0026thinsp;\u0026plusmn;\u0026thinsp;22.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e135.25\u0026thinsp;\u0026plusmn;\u0026thinsp;23.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e82.62\u0026thinsp;\u0026plusmn;\u0026thinsp;18.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e80.23\u0026thinsp;\u0026plusmn;\u0026thinsp;13.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;2.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. P-values were derived from independent-samples t-tests. HDL, high-density lipoprotein; LDL, low-density lipoprotein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of individual metabolic syndrome components by residence is presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Central obesity defined using waist circumference criteria was similar in rural and urban participants (49.0% vs. 48.0%; p\u0026thinsp;=\u0026thinsp;0.879). Likewise, central obesity defined using WHR criteria did not differ significantly between rural and urban groups (49.0% vs. 52.0%; p\u0026thinsp;=\u0026thinsp;0.104). Elevated blood pressure (\u0026ge;\u0026thinsp;130/85 mmHg) was more common in urban than rural participants (57.0% vs. 48.0%), but the difference was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.189). Elevated fasting glucose (\u0026ge;\u0026thinsp;5.6 mmol/L) was also similar between groups (15.0% rural vs. 17.0% urban; p\u0026thinsp;=\u0026thinsp;0.701). Reduced HDL cholesterol was identical in both groups (p\u0026thinsp;=\u0026thinsp;1.000). Elevated triglycerides (\u0026ge;\u0026thinsp;1.7 mmol/L) were more frequent in rural than urban participants (15.0% vs. 7.0%), although this difference did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.071).\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\u003ePrevalence of individual metabolic syndrome components by residence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasured metabolic syndrome components\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity (WC criteria*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity (WHR criteria\u0026dagger;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated blood pressure (\u0026ge;\u0026thinsp;130/85 mmHg\u0026Dagger;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (57.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated fasting glucose (\u0026ge;\u0026thinsp;5.6 mmol/L\u0026sect;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReduced HDL-C (sex-specific\u0026para;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated triglycerides (\u0026ge;\u0026thinsp;1.7 mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues are presented as n (%). P-values derived from chi-square tests (or Fisher\u0026rsquo;s exact test where appropriate).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*Central obesity (WC): waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;94 cm (men) and \u0026ge;\u0026thinsp;80 cm (women).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026dagger;Central obesity (WHR): waist-to-hip ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.90 (men) and \u0026ge;\u0026thinsp;0.85 (women).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026Dagger;Elevated blood pressure defined as systolic\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg and/or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026sect;Elevated fasting glucose defined as fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;5.6 mmol/L.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026para;Reduced HDL-C defined as \u0026lt;\u0026thinsp;1.0 mmol/L (men) and \u0026lt;\u0026thinsp;1.3 mmol/L (women).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of metabolic syndrome varied according to the diagnostic definition applied (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Using the WHO (1999) criteria, metabolic syndrome prevalence was 9.0% among rural participants and 11.0% among urban participants (p\u0026thinsp;=\u0026thinsp;0.637). Using the NCEP-ATP III (2005) criteria, prevalence was 11.0% in rural participants and 13.0% in urban participants (p\u0026thinsp;=\u0026thinsp;0.661). Under the JIS (2009) criteria, prevalence was 18.0% in rural participants and 14.0% in urban participants (p\u0026thinsp;=\u0026thinsp;0.441). No statistically significant rural\u0026ndash;urban differences were observed under any diagnostic definition.\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\u003ePrevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana according to WHO (1999), NCEP-ATP III (2005), and JIS (2009) criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban % (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u0026dagger;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO (1999)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0 (3.4\u0026ndash;14.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.0 (4.9\u0026ndash;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.637\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCEP-ATP III (2005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.0 (4.9\u0026ndash;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.0 (6.4\u0026ndash;19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJIS (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.0 (10.5\u0026ndash;25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0 (7.2\u0026ndash;20.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* The WHO (1999) definition was operationalized using glucose abnormality (fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;6.1 mmol/L or previously diagnosed diabetes) as a surrogate indicator of insulin resistance. \u0026dagger; P-values were derived from chi-square tests comparing metabolic syndrome prevalence between rural and urban participants.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAgreement and comparative classification characteristics between metabolic syndrome definitions are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Agreement between the NCEP-ATP III and JIS criteria was high (κ\u0026thinsp;=\u0026thinsp;0.85, 95% CI: 0.76\u0026ndash;0.94), indicating almost perfect agreement. The NCEP-ATP III criteria demonstrated sensitivity of 93.8% and specificity of 98.8% relative to the JIS definition, with positive and negative predictive values of 90.6% and 99.4%, respectively. McNemar\u0026rsquo;s test indicated a statistically significant difference in marginal classification between these definitions (p\u0026thinsp;=\u0026thinsp;0.008). Comparison between the NCEP-ATP III and modified WHO definitions also demonstrated very high agreement (κ\u0026thinsp;=\u0026thinsp;0.92, 95% CI: 0.86\u0026ndash;0.98), indicating high agreement. Sensitivity and specificity of the NCEP-ATP III definition relative to the WHO criteria were 88.0% and 99.0%, respectively, with PPV and NPV values of 95.0% and 97.6%. Agreement between the modified WHO and JIS definitions was slightly lower but remained substantial (κ\u0026thinsp;=\u0026thinsp;0.78, 95% CI: 0.67\u0026ndash;0.89). The WHO criteria demonstrated sensitivity of 75.0% and specificity of 97.0% relative to the JIS definition, with PPV of 80.0% and NPV of 96.4%. McNemar\u0026rsquo;s test indicated a significant difference in classification between the WHO and JIS definitions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Results were similar after excluding participants with previously diagnosed diabetes (n\u0026thinsp;=\u0026thinsp;20) (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Agreement between NCEP-ATP III and JIS remained high (κ\u0026thinsp;=\u0026thinsp;0.93; 95% CI: 0.88\u0026ndash;0.98), while agreement involving the WHO definition was lower (WHO vs NCEP-ATP III: κ\u0026thinsp;=\u0026thinsp;0.65; 95% CI: 0.52\u0026ndash;0.78; WHO vs JIS: κ\u0026thinsp;=\u0026thinsp;0.62; 95% CI: 0.49\u0026ndash;0.75).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAgreement and comparative classification characteristics between metabolic syndrome definitions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComparison\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eκ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMcNemar\u0026rsquo;s p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePPV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCEP-ATP III vs JIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.76\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.008\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e99.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNCEP-ATP III vs WHO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.86\u0026ndash;0.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e99.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO vs JIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.78 (0.67\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e96.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study examined the prevalence of metabolic syndrome among rural and urban adults in Kumasi, Ghana using three commonly applied diagnostic criteria\u0026mdash;the WHO (1999), NCEP-ATP III (2005), and JIS, 2009 definitions\u0026mdash;and evaluated the level of agreement between these frameworks. Metabolic syndrome prevalence ranged from 10.0% to 16.0% depending on the diagnostic criteria used, with the highest prevalence observed under the JIS definition. Agreement between definitions was substantial to near-perfect, indicating that although the frameworks differ in diagnostic structure and thresholds, they largely identify overlapping individuals within this population. No statistically significant rural\u0026ndash;urban differences in metabolic syndrome prevalence were observed. Collectively, these findings suggest that the burden of metabolic syndrome in this population is moderate and relatively comparable across rural and urban settings, while methodological differences between diagnostic criteria primarily influence the magnitude of reported prevalence.\u003c/p\u003e \u003cp\u003eThe prevalence estimates observed in this study are broadly consistent with findings reported in Ghana and other sub-Saharan African populations. Previous studies conducted in Ghana have reported metabolic syndrome prevalence ranging from approximately 10% to over 20%, depending on the study population and diagnostic criteria applied (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Similar variability has been documented across sub-Saharan Africa, where differences in population characteristics, lifestyle patterns, and diagnostic definitions contribute to variation in reported prevalence(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The estimates observed in the present study therefore align with regional evidence suggesting that metabolic syndrome represents an emerging but heterogeneous cardiometabolic risk pattern in many African populations undergoing epidemiologic transition.\u003c/p\u003e \u003cp\u003eConsistent with previous comparative studies, the JIS criteria produced the highest prevalence estimate, followed by the NCEP-ATP III and WHO definitions. This pattern reflects structural differences between diagnostic frameworks. The JIS definition employs harmonized thresholds and does not require insulin resistance or glucose abnormalities as a prerequisite condition, thereby capturing a broader group of individuals with clustered metabolic risk factors (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In contrast, the WHO definition requires evidence of glucose dysregulation or insulin resistance in addition to other metabolic abnormalities, which generally results in lower prevalence estimates (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). These diagnostic differences explain why harmonized definitions such as JIS often identify a larger proportion of individuals with metabolic syndrome in population-based studies.\u003c/p\u003e \u003cp\u003eNo significant rural\u0026ndash;urban differences in metabolic syndrome prevalence were observed. Historically, cardiometabolic risk factors in sub-Saharan Africa have been more prevalent in urban populations due to greater exposure to sedentary lifestyles, dietary transitions, and other behavioral risk factors associated with urbanization (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, recent evidence suggests that rural populations are increasingly experiencing similar lifestyle changes, including shifts toward energy-dense diets and reduced physical activity (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The absence of significant rural\u0026ndash;urban differences in this study may therefore reflect the ongoing epidemiologic transition occurring within Ghana, where environmental and behavioral risk factors for cardiometabolic disease are becoming more widespread across both rural and urban settings.\u003c/p\u003e \u003cp\u003eAgreement analysis demonstrated substantial to near-perfect concordance between diagnostic definitions, particularly between the NCEP-ATP III and WHO definitions (κ\u0026thinsp;=\u0026thinsp;0.92) and between NCEP-ATP III and JIS (κ\u0026thinsp;=\u0026thinsp;0.85). These findings are consistent with previous studies reporting strong agreement between NCEP and JIS definitions because these frameworks share similar thresholds for most metabolic components, including blood pressure, triglycerides, HDL cholesterol, and fasting glucose (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). The slightly lower agreement observed between WHO and JIS definitions likely reflects structural differences inherent to the WHO framework, including the requirement for glucose abnormalities or insulin resistance (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Similar comparative studies have also shown that although different definitions may yield varying prevalence estimates, they largely identify overlapping groups of individuals with clustered cardiometabolic risk factors (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings of this study have important implications for cardiometabolic surveillance and public health planning in Ghana. First, the moderate prevalence of metabolic syndrome observed across both rural and urban populations indicates that clustered cardiometabolic risk is not confined to urban settings and should be addressed through population-wide prevention strategies. Screening and early detection initiatives should therefore be integrated into primary health care systems serving both rural and urban communities. Second, the variability in prevalence estimates across diagnostic definitions highlights the importance of standardized criteria for national surveillance and research comparability. Differences of up to six percentage points between WHO and JIS estimates in this study illustrate how the choice of diagnostic framework can influence estimates of disease burden and potentially affect health policy planning and resource allocation. Although the high agreement observed between definitions suggests that they identify largely overlapping high-risk populations, consistent application of a standardized definition would facilitate comparability across studies and over time.\u003c/p\u003e \u003cp\u003eThis study has several strengths. First, it directly compared three widely used metabolic syndrome definitions within the same population, allowing standardized evaluation of prevalence, agreement, and classification characteristics. The inclusion of both rural and urban participants also enabled assessment of potential geographic differences in cardiometabolic risk within a Ghanaian population undergoing epidemiologic transition. In addition, standardized anthropometric and biochemical measurements strengthened the internal validity of the study. However, several limitations should be acknowledged. First, the facility-based sampling approach may limit the generalizability of the findings, as individuals attending diagnostic laboratories may differ from the broader population in health status or healthcare-seeking behavior. Second, the relatively modest sample size may have limited statistical power to detect small rural\u0026ndash;urban differences in metabolic syndrome prevalence. Third, the WHO definition was applied using a modified approach in which fasting glucose abnormality was used as a proxy for insulin resistance due to the absence of direct measures of insulin resistance, which may affect comparability with studies applying the original WHO criteria.\u003c/p\u003e \u003cp\u003eFuture research should incorporate community-based sampling to generate population-representative estimates and to better characterize rural\u0026ndash;urban differences in metabolic risk. Longitudinal studies are also needed to assess the predictive validity of the WHO, NCEP-ATP III, and JIS definitions for incident diabetes and cardiovascular disease, which cannot be evaluated in a cross-sectional design. In addition, validation of waist circumference thresholds specific to African populations remains a critical research priority, as the use of non-African cutoffs may influence case classification and comparability across studies.\u003c/p\u003e \u003cp\u003eIn conclusion, this study demonstrates that the prevalence of metabolic syndrome among adults in Kumasi varies according to the diagnostic criteria applied, with lower estimates observed under the WHO definition and higher estimates under the JIS criteria. Despite these differences, substantial agreement was observed between diagnostic frameworks, indicating that they largely identify overlapping groups of individuals with elevated cardiometabolic risk. Importantly, no significant rural\u0026ndash;urban disparities were detected, suggesting that metabolic syndrome is not confined to urban populations in this setting. These findings highlight the importance of standardized diagnostic criteria for metabolic syndrome surveillance and emphasize the need for expanded cardiometabolic risk screening across both rural and urban communities in Ghana as the country continues to undergo epidemiologic transition.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Committee on Human Research, Publication and Ethics (CHRPE) of the School of Medical Sciences, Kwame Nkrumah University of Science and Technology (KNUST) and Komfo Anokye Teaching Hospital, Kumasi, Ghana (Ref: CHRPE/AP/171/20; approved 1 June 2020). Written informed consent was obtained from all participants prior to enrollment, and the study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are not publicly available due to ongoing analyses but are available from the corresponding author on reasonable request,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.O. conceived and designed the study. D.O.M. performed the statistical analysis and drafted the manuscript. L.L.I., J.A.B., and P.A. contributed to data collection and data management. P.H.M. assisted with manuscript drafting and revision. All authors contributed to interpretation of the results, critically reviewed the manuscript, and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: A joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity. Circulation. 2009;120(16):1640\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrundy SM. Metabolic Syndrome Pandemic. Arteriosclerosis. Thromb Vascular Biology. 2008;28(4):629\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassi E, Pervanidou P, Kaltsas G, Chrousos G. Metabolic syndrome: definitions and controversies. BMC Med. 2011;9(1):48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20(2):12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalal S, Beunza JJ, Volmink J, Adebamowo C, Bajunirwe F, Njelekela M, et al. Non-communicable diseases in sub-Saharan Africa: what we know now. Int J Epidemiol. 2011;40(4):885\u0026ndash;901.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgyemang C, Boatemaa S, Frempong GA, de-Graft Aikins A. Obesity in sub-saharan Africa. Springer International Publishing Switzerland. 2015; 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAddo J, Smeeth L, Leon DA. Hypertension in sub-saharan Africa: a systematic review. Hypertension. 2007;50(6):1012\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgyemang C. Rural and urban differences in blood pressure and hypertension in Ghana, West Africa. Public Health. 2006;120(6):525\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOfori-Asenso R, Agyeman AA, Laar A. Metabolic syndrome in apparently healthy Ghanaian adults: A systematic review and meta-analysis. Int J chronic Dis. 2017;2017(1):2562374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNsiah K, Shang VO, Boateng KA, Mensah F. Prevalence of metabolic syndrome in type 2 diabetes mellitus patients. Int J Appl Basic Med Res. 2015;5(2):133\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgyemang-Yeboah F, Eghan BAJ, Annani-Akollor ME, Togbe E, Donkor S, Oppong Afranie B. Evaluation of metabolic syndrome and its associated risk factors in type 2 diabetes: A descriptive Cross-Sectional study at the komfo anokye teaching hospital, kumasi. Ghana BioMed Res Int. 2019;2019(1):4562904.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Definition, diagnosis and classification of diabetes mellitus and its complications: Report of a WHO consultation. Part 1: Diagnosis and classification of diabetes mellitus. Geneva: World Health Organization; 1999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eExpert Panel on Detection E, Adults ToHBCi. Executive Summary of the Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001;285(19):2486\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhana Statistical Service. 2021 Population and Housing Census: General Report Volume 3A: Population of Regions and Districts. Accra, Ghana: Ghana Statistical Service; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012;70(1):3\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCochran WG. Sampling techniques. Wiley; 1977.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499\u0026ndash;502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoldfine AB, Gerwien RW, Kolberg JA, O'Shea S, Hamren S, Hein GP, et al. Biomarkers in fasting serum to estimate glucose tolerance, insulin sensitivity, and insulin secretion. Clin Chem. 2011;57(2):326\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMotala AA, Esterhuizen T, Pirie FJ, Omar MAK. The Prevalence of Metabolic Syndrome and Determination of the Optimal Waist Circumference Cutoff Points in a Rural South African Community. Diabetes Care. 2011;34(4):1032\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObirikorang C, Osakunor DNM, Anto EO, Amponsah SO, Adarkwa OK. Obesity and cardio-metabolic risk factors in an urban and rural population in the Ashanti Region-Ghana: a comparative cross-sectional study. PLoS ONE. 2015;10(6):e0129494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaijer-Westerink HJ, Kengne AP, Meeks KA, Agyemang C. Prevalence of metabolic syndrome in sub-Saharan Africa: A systematic review and meta-analysis. Nutr Metabolism Cardiovasc Dis. 2020;30(4):547\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmidhuber J, Shetty P. The nutrition transition to 2030. Why developing countries are likely to bear the major burden. Acta agriculturae scand Sect c. 2005;2(3\u0026ndash;4):150\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNilsson P, Engstr\u0026ouml;m G, Hedblad B. The metabolic syndrome and incidence of cardiovascular disease in non-diabetic subjects\u0026mdash;a population‐based study comparing three different definitions. Diabet Med. 2007;24(5):464\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFord ES. Risks for All-Cause Mortality, Cardiovascular Disease, and Diabetes Associated With the Metabolic Syndrome: A summary of the evidence. Diabetes Care. 2005;28(7):1769\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, Diagnostic criteria, Cardiometabolic risk, Rural–urban differences, Epidemiologic transition, Sub-Saharan Africa, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-9360097/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9360097/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSub-Saharan Africa is undergoing an epidemiological transition associated with urbanization, dietary change, and reduced physical activity, contributing to an increasing burden of cardiometabolic disorders. Estimates of metabolic syndrome (MetS) vary because diagnostic criteria differ. In Ghana, few studies have directly compared commonly used MetS definitions within a single population. This study assessed MetS prevalence among rural and urban adults in Kumasi, Ghana using the World Health Organization (WHO 1999), National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III 2005), and Joint Interim Statement (JIS 2009) criteria, and examined agreement between these definitions.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a facility-based cross-sectional study among 200 adults recruited from diagnostic laboratories in rural and urban districts of the Ashanti Region. MetS was classified using WHO, NCEP-ATP III, and JIS criteria. Differences in prevalence by residence were examined using chi-square tests. Agreement across definitions was assessed using Cohen\u0026rsquo;s kappa and McNemar\u0026rsquo;s test. Pairwise diagnostic classification performance was evaluated using contingency tables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMetS prevalence ranged from 10.0% using WHO criteria to 16.0% using JIS criteria, with NCEP-ATP III yielding 12.0%. No significant rural\u0026ndash;urban differences were observed under any definition (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Agreement between definitions ranged from κ\u0026thinsp;=\u0026thinsp;0.78 to 0.92. Across pairwise comparisons, sensitivity ranged from 75.0% to 93.8% and specificity from 97.0% to 99.0%.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMetS prevalence varied by diagnostic definition but was similar between rural and urban participants. The JIS criteria identified the highest prevalence, suggesting greater sensitivity for detecting clustered cardiometabolic risk.\u003c/p\u003e","manuscriptTitle":"Prevalence, Agreement, and Comparative Classification of Metabolic Syndrome Definitions Among Rural and Urban Adults in Kumasi, Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 17:11:31","doi":"10.21203/rs.3.rs-9360097/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-16T09:02:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"175524640163770001414946537146093425048","date":"2026-05-08T05:37:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163597267946802435317660980348805033128","date":"2026-05-07T06:32:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225032045278307874763378350184349546144","date":"2026-05-06T06:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-04T05:22:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-16T05:05:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-11T12:47:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-11T12:46:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-04-08T17:44:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"665746ee-a7cb-41bd-9bf3-970c0bb7fae1","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-16T09:02:34+00:00","index":54,"fulltext":""},{"type":"reviewerAgreed","content":"175524640163770001414946537146093425048","date":"2026-05-08T05:37:17+00:00","index":52,"fulltext":""},{"type":"reviewerAgreed","content":"163597267946802435317660980348805033128","date":"2026-05-07T06:32:53+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"225032045278307874763378350184349546144","date":"2026-05-06T06:51:46+00:00","index":35,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-04T05:22:31+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T17:11:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 17:11:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9360097","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9360097","identity":"rs-9360097","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}