Prevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study

Prevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study | 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 of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study Shankar Ganesh M, Aravindhan S, Satheesh S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Previous surveys suggest that obesity, hypertension, and diabetes mellitus may be positively related to the development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Chronic kidney disease has become a worldwide health problem among aging populations. Hence, epidemiological information on middle-aged patients with metabolic syndrome is still lacking. Objectives The study aimed to assess the prevalence of metabolic syndrome among middle-aged patients and its association with chronic kidney disease. Methodology: The hospital-based cross-sectional study was carried out on 317 participants aged 40–59 years. All participants received a standardized personal interview, including a structured questionnaire, anthropometric measurements, and blood samples collected for laboratory testing. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III). The estimated glomerular filtration rate (eGFR) was calculated by using the Cockroft-Gault formula, which in turn is utilized to predict the stages of chronic kidney disease based on the eGFR range based on NKF-KDOQI. Result and discussion: We investigated the association between metabolic syndrome and chronic kidney disease (CKD) in 317 participants aged 40-59 years. We found that metabolic syndrome was prevalent in more than half of the participants (54.2%) and increased with the worsening of CKD stages. We also identified waist circumference, fasting blood sugar, and triglycerides as significant metabolic factors associated with CKD. Furthermore, we observed that longer durations of diabetes mellitus and hypertension, especially when combined, increased the risk of CKD. Conclusion: Our findings suggest that metabolic syndrome is a major contributor to CKD and that early detection and management of metabolic factors are essential to prevent kidney damage. Epidemiology Metabolic syndrome Chronic kidney disease Middle aged patients NCEP/ATP III NKF-KDOQI Introduction The escalating prevalence of obesity and diabetes, driven by shifts in dietary habits and lifestyle behaviors, is a significant global health concern. 1,2,3 This rise has been paralleled by an increase in metabolic syndrome, a condition that heightens the risk of mortality from chronic kidney disease (CKD), as reported by LaGuardia HA et al. 4 CKD, characterized by a gradual and progressive loss of renal function over at least three months, is a primary cause of end-stage renal disease (ESRD). 5 The global prevalence of CKD ranges from 8–16%, varying across different regions. 6 CKD is categorized into five stages based on the degree of kidney damage or the estimated glomerular filtration rate (eGFR). 7,8 Regrettably, the majority of patients remain unaware of their condition until it has advanced to a later stage. 9 Early cardiovascular disease (CVD), anemia, metabolic acidosis, and bone diseases are among the major complications associated with impaired kidney function. 10 The leading risk factors and predictors for CKD include impaired fasting plasma glucose, hypertension, and a high body mass index (BMI). 11 There is a growing body of evidence suggesting a link between obesity, hypertension, diabetes mellitus, and the development of CKD. 12,13 Systematic reviews and meta-analyses have indicated that metabolically unhealthy individuals are at an increased risk for CKD. 14,15 However, it is important to note that previous studies investigating the association between metabolic health status phenotypes and renal disorders have primarily focused on eGFR measurements to define CKD. 16 Given the contradictory findings and the scarcity of studies examining the association between CKD and metabolic syndrome in middle-aged patients, we aim to conduct cross-sectional studies to elucidate the relationship between CKD and metabolic health status. This research will contribute to a more comprehensive understanding of these complex health issues and inform future preventative and treatment strategies. Materials and methods Study Design and participants' characteristics This research was a cross-sectional, population-based study carried out in the medical wards of a tertiary care hospital in Erode. The participants comprised individuals who sought care at health centers in Erode. The study included individuals of all genders, aged 40–59 years, who were residents of Erode, willing to participate, and diagnosed with non-communicable diseases such as Diabetes Mellitus, Hypertension, Dyslipidemia, and Obesity. The exclusion criteria encompassed incomplete questionnaires, unwillingness to participate in the tests, and pre-existing renal diseases or other conditions that could potentially impact renal function. Ultimately, a total of 317 eligible middle-aged individuals were included in the study. The study was conducted by the Declaration of Helsinki and was approved by the Institutional Ethics Committee of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Before the commencement of the study, all participants provided their written informed consent. The research took into account a variety of characteristics of the participants, including their age, gender, place of residence, level of physical activity, dietary habits, and medical history. The age of the participants was divided into four categories: 40–44, 45–49, 50–54, and 55–59 years. The International Physical Activity Questionnaire (IPAQ) was used to classify physical activity levels as high, moderate, or low. 17 Dietary habits were categorized as healthy, moderate, or unhealthy based on the Healthy Eating Index (HEI) score. 18,19 The duration of conditions such as diabetes and hypertension was determined through direct patient interviews. Information on current smoking habits and alcohol consumption was also collected. Metabolic Syndrome Assessment Risk factors for renal failure, including hypertension, dyslipidemia, waist circumference, smoking, blood pressure, fasting blood glucose, lipid analysis, BUN/creatinine, and BMI were analyzed. Metabolic syndrome was diagnosed based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III). 20 Participants were diagnosed with metabolic syndrome if they met three or more of the following five criteria: waist circumference ≥ 90 cm (men) or ≥ 85 cm (women), fasting blood sugar ≥ 100 mg/dL, triglycerides > 150 mg, HDL –C level < 40 mg/dL (men) or < 50 mg/dL (women), and blood pressure ≥ 130/85 mmHg. The data from participants diagnosed with metabolic syndrome were then analyzed. Biochemical Analysis Blood samples were primarily drawn from the median cubital and cephalic veins after a minimum of 8 hours of fasting. The samples were refrigerated and sent to a diagnostic medical laboratory for analysis within 24 hours. The levels of triglycerides, HDL-C, and fasting blood glucose were measured using enzymatic methods on Lipid Biosensor (TamilNadu, India). 21 Chronic Kidney Disease Assessment The assessment of chronic kidney disease (CKD) was conducted using the estimated Glomerular Filtration Rate (eGFR), calculated with the Cockcroft-Gault formula. 22 This formula takes into account the patient’s creatinine levels, age, gender, and ethnicity: CrCl = 72×serum creatinine in mg/dL (140 − age in years) weight in kg [For females, the result is multiplied by a factor of 0.85]. Following the National Kidney Foundation- Kidney Disease Outcomes Quality Initiative (NKF-KDOQI), CKD was categorized into five stages: normal (stage 1), mildly decreased (stage 2), mildly to moderately decreased (stage 3a), moderately to severely decreased (stage 3b), and severely decreased (stage 4) (stage 1, eGFR ≥ 90 mL/min/1.73 m2; stage 2, 89 to 60 mL/min/1.73 m2; stage 3a, 59 to 45 mL/min/1.73 m2; stage 3b, 44 to 30 mL/min/1.73 m2; and stage 4, < 30 mL/min/1.73 m2, respectively). 23 Our research aimed to understand the effects of metabolic syndrome on the progression of renal function deterioration. To achieve this, we categorized participants into five distinct groups based on their renal function: normal, mildly decreased, mildly to moderately decreased, moderately to severely decreased, and severely decreased. We then proceeded to analyze various factors that could potentially influence the deterioration of renal function. Statistical Analysis Data were collated in an Excel spreadsheet and analyzed using SPSS version 27.0.1.0. The mean ± standard deviation (SD) for continuous variables was calculated, and the number of participants was expressed as a percentage for categorical variables. Hypothesis testing for continuous variables was performed using an independent t-test and One-way ANOVA to examine differences in the mean values between groups. For categorical variables, the Chi-squared test and X2-trend test were used to identify differences in proportions. Multiple logistic regression models were developed to investigate the association between the metabolic components of metabolic syndrome and chronic kidney disease (CKD). A p-value of less than 0.05 was considered statistically significant. Results Proportion of participants with chronic kidney disease According to Table 1 , more than half of the participants (54.2%) had metabolic syndrome, while the rest (45.8%) did not. Among those with metabolic syndrome, the distribution of chronic kidney disease (CKD) stages was as follows: Stage 2, n = 64 (95% CI: 38.3–55); Stage 3a, n = 41 (95% CI: 44.7–67.5); Stage 3b, n = 20 (95% CI: 57.5–90.6); Stage 4, n = 9 (95% CI: 59-104.6). The data indicate that the prevalence of metabolic syndrome increases with the worsening of CKD, especially in Stages 3b, 4, and 5. This implies a potential causal link between the severity of CKD and the occurrence of metabolic syndrome. The p-value confirms the statistical significance of the association between metabolic syndrome status and different glomerular filtration rate (GFR) levels. This highlights the need for regular screening and management of metabolic syndrome in CKD patients to prevent further deterioration of kidney function. Patient Demographics A total of 317 participants were categorized into four different age groups: 40–44 years (n = 25), 45–49 years (n = 78), 50–54 years (n = 90) and 55–59 years old (n = 124). The gender, BMI, waist circumference, physical activity level, alcohol consumption status, previous history of dyslipidemia, metabolic syndrome presence, and CKD stages were significantly correlated between different age groups (Table 2) . Association between Metabolic Syndrome and Chronic kidney disease The logistic regression analysis highlights several significant associations between certain metabolic factors and CKD (stages 3a to 5). It shows that for every unit increase in Waist Circumference (WC), there's a 3% decrease in the odds of having CKD, significant at a p-value of 0.007. On the other hand, each unit increase in Fasting Blood Sugar (FBS) and Triglycerides (TG) is associated with a 1% increase in the odds of having CKD, significant at p-values of 0.011 and 0.001, respectively. Moreover, individuals with metabolic syndrome have approximately 101% higher odds of having CKD (stages 3a to 5) compared to those without metabolic syndrome, as indicated by an Odds Ratio of 2.01. This association is statistically significant at the 5% significance level with a p-value of 0.004, providing strong evidence of a notable relationship between metabolic syndrome status and CKD (Table 3) . Interplay of DM and Hypertension duration in predictors of CKD Research into Chronic Kidney Disease (CKD) predictors highlights the significant impact of Diabetes Mellitus (DM) and Hypertension (HT) durations. Specifically, durations of 5–10 years and over 10 years for both DM and HT show high significance (OR > 1) with increased risks for CKD. For instance, 5–10 years of DM and HT exhibit an Odds Ratio (OR) of 11.08, and over 10 years show an OR of 15.28, indicating substantially heightened risks for CKD. However, the duration of 5–10 years and over 10 years for HT alone display low significance (OR < 1) with reduced likelihoods of CKD (OR: 0.035 and 0.103, respectively). These findings emphasize the critical role of DM and longer durations of both DM and HT in the onset and progression of CKD. Vigilant monitoring and effective management strategies are crucial for populations at risk to mitigate these potential risks. ( Table 4 ) Discussion This study’s primary findings indicate that metabolic syndrome independently contributes to the development of chronic kidney disease (CKD). The results affirm the efficacy of the modified ATP III guideline in diagnosing metabolic syndrome and predicting CKD incidence. In this cross-sectional study, we observed a significant positive correlation between the prevalence of CKD and Metabolic Syndrome. Furthermore, a decrease in the Glomerular Filtration Rate (GFR) was associated with Metabolic Syndrome. Our study adds to the growing body of evidence suggesting that individuals with Metabolic Syndrome are at an elevated risk of developing CKD. While previous studies have reported a 65% prevalence of metabolic syndrome among CKD patients, this association could be significantly influenced by hypertension. 24 Prior research has proposed that the link between metabolic syndrome and CKD may be weakened by diabetes mellitus and obesity. 25 Conversely, our study aligns with the view that Metabolic Syndrome is a crucial risk factor for secondary chronic kidney damage, including mild renal dysfunction, with diabetes and increased Body Mass Index (BMI) being the most significant risk factors. 26 The literature presents inconsistent results regarding the association between metabolic syndrome and CKD. Contrary to our study, a cross-sectional study on middle-aged men and women reported that metabolic syndrome increases the risk of CKD. Interpreting the results of this study requires considering several limitations. Due to the cross-sectional nature of this study, we cannot establish a causal relationship between CKD indicators and associated factors. Conclusion Our study demonstrated a strong positive correlation between the prevalence of Metabolic Syndrome and CKD. Moreover, we found that increased waist circumference, fasting blood glucose, and triglyceride levels were risk factors for CKD in the middle-aged population. Our results also showed that the duration of diabetes mellitus (DM) and hypertension (HT) for 5–10 years and more than 10 years were significantly associated with higher odds ratios (OR > 1) for CKD. These findings suggest that middle age is a critical stage for the development and progression of metabolic syndrome and CKD and that early detection and intervention are essential to prevent further complications. Declarations Ethics Approval and Consent to Participate Ethical approval for this study was granted by the Institutional Review Board of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Written informed consent was obtained from all participants before their inclusion in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Consent for Publication Individual consent for publication was obtained from all participants involved in the study. Participants were informed that the data collected would be used for research and potential publication, ensuring confidentiality and anonymity in the presentation of the research findings. Availability of Data and Materials The datasets generated and analyzed during the current study are available from the corresponding author, Dr. Satheesh S., Associate Professor at JKKN College of Pharmacy, on reasonable request. The data are not publicly available due to them containing information that could compromise the privacy of research participants. Competing Interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgments My sincere gratitude to Dr. S. Satheesh, Pharm.D., Associate Professor, Department of Pharmacy Practice, who supported and contributed to the creation of this research work. This job would not have been possible without her great support, advice, and encouragement. I would like to extend my sincere gratitude to Dr. N. Venkateswaramurthy, M.Pharm., Ph.D., Head of the Department, for his direction and experience in the preparation of this research. Lastly, my heartfelt gratitude goes to my family and friends for their endless encouragement throughout this project. Authors Contributions Study concept and design: Shankar Ganesh M; Acquisition of data: Aravindhan S; Analysis and interpretation of data: Satheesh S; Drafting of the manuscript: Shankar Ganesh M and Aravindhan S; Critical revision of the manuscript: Satheesh S; Statistical analysis: Shankar Ganesh M and Satheesh S; administrative, technical, or material support: Satheesh S; and study supervision: Satheesh S References Forouhi NG, Wareham NJ. Epidemiology of diabetes. Medicine . 2014;42(12):698-702. doi:https://doi.org/10.1016/j.mpmed.2014.09.007. Muoio DM, Newgard CB. Molecular and metabolic mechanisms of insulin resistance and β-cell failure in type 2 diabetes. 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Journal of the American Society of Nephrology . 2006;17(4_suppl_2):S81-S85. doi:https://doi.org/10.1681/asn.2005121332. Tables Table 1. Proportion of participants with chronic kidney disease CKD Stage Metabolic Syndrome Presence Metabolic Syndrome Absence P -value n prevalence 95% CI n prevalence 95% CI Stage 1 32 50.79 38.5-63.1 31 49.21 36.8-61.5 0.0011 Stage 2 64 46.7 38.3-55 73 53.2 44.9-61.6 Stage 3a 41 56.1 44.7-67.5 32 43.8 32.4-55.2 Stage 3b 20 74 57.5-90.6 7 25.9 9.4-42.4 Stage 4 9 81.8 59-104.6 2 18.18 4.61-40.97 Stage 5 6 100 100-100 0 0 0 Table 2. Demographics and biochemical characteristics Demographic and Clinical Data Age Range (in years) P -value 40-44 45-49 50-54 55-59 Mean ± SD Mean ± SD Mean ± SD Mean ± SD Age (in years) 42.12±1.07 46.8±1.19 52.3±1.22 56.9±1.26 <0.001 Male 18 (5.67) 34(10.7) 55(17.3) 59(18.6) 0.039 Female 8 (2.5) 43(13.5) 36(11.3) 64(20.1) Height 159.9±7.93 155.4±11.22 158.9±10.35 156.4±8.7 0.072 Weight 62.6±9.26 62.6±9.16 60.7±9.3 61.6±10 0.580 Body Mass Index 24.7±3.4 26.18±4.6 24.17±3.9 25.3±4.2 0.019 Waist Circumference 102.6±8.09 97.18±9.45 100.2±10.6 99.3±8.19 0.039 Systolic BP (mmHg) 133.69±21.15 137.7±23.7 141.3±27.7 141.6±28.6 0.436 Diastolic BP (mmHg) 84±11.52 86.8±11.37 89.3±15.9 88.16±14.3 0.334 Fasting Blood Sugar 161.9±48.25 152.9±60.9 154.4±54 154.4±61.9 0.926 High-Density Lipoprotein cholesterol 40.9±10.34 44.8±8.5 44.241±8.7 43.1±9.3 0.230 Post Prandial Blood Sugar 187±61.7 174±63.5 183±60.2 177.2±71.4 0.750 Low-Density Lipoprotein cholesterol 93.1±20.1 97.9±20.6 97±18.7 101±17.5 0.179 Very Low-Density Lipoprotein cholesterol 28.9±7.6 25.9±9.8 26.8±8.16 26.3±7.6 0.448 Blood Urea 41.5±38.1 36.7±28.8 34±7.3 34.7±18.4 0.482 Serum creatinine 1.42±1.54 1.18±0.79 1.2±0.9 1.23±0.8 0.694 eGFR 77.9±29.9 75.1±27 69.8±26.9 65.2±32.45 0.061 Residence, n (%) Rural 14 (53.8) 41 (53.2) 48 (52.7) 65 (52.8) 0.996 Urban 12 (46.15) 36 (46.7) 43 (47.2) 58 (47.15) Physical activity, n (%) High 4 (15.38) 23 (29.8) 21 (23) 17 (13.8) 0.030 Moderate 22 (84.6) 54 (70.1) 66 (72.5) 98 (79.67) Low 0 0 4 (4.4) 8 (6.5) Diet Intake, n (%) Healthy 10 (38.4) 29 (37.6) 19 (20.8) 36 (29.2) 0.263 Moderate 14 (53.8) 40 (51.9) 58 (63.7) 68 (55.2) Unhealthy 2 (7.6) 8 (10.39) 14 (15.3) 19 (15.4) Smoking, n (%) Yes 13 (50) 23 (29.8) 40 (43.9) 43 (34.9) 0.132 No 13 (50) 54 (70.1) 51 (56) 80 (65) Alcohol, n (%) Yes 15 (57.6) 25 (32.4) 41 (45) 42 (34.1) 0.049 No 11 (42.3) 52 (67.5) 50 (54.9) 81 (65.8) Diabetes Mellitus, n (%) Yes 19 (73) 50 (64.9) 71 (78) 89 (72.3) 0.311 No 7 (26.9) 27 (35) 20 (21.9) 34 (27.6} Hypertension, n (%) Yes 13 (50) 56 (72.7) 60 (65.9) 82 (66.6) 0.209 No 13 (50) 21 (27.2) 31 (34) 41 (33.3) Dyslipidemia, n (%) Yes 0 1 (1.3) 0 7 (5.6) 0.036 No 26 (100) 76 (98.7) 91 (100) 116 (94.3) Metabolic syndrome, n (%) Presence 16 (61.5) 31 (40.2) 54 (59.3) 71 (57.7) 0.042 Absence 10 (38.5) 46 (59.7) 37 (40.6) 52 (42.2) CKD stages, n (%) Stage 1 10 (38.4) 22 (28.5) 18 (19.7) 13 (10.5) 0.053 Stage 2 8 (30.7) 33 (42.8) 43 (47.2) 53 (43) Stage 3a 6 (23) 15 (19.4) 18 (19.7) 34 (27.6) Stage 3b 0 4 (5.1) 7 (7.6) 16 (13) Stage 4 1 (3.8) 1 (1.3) 4 (4.4) 5 (4) Stage 5 1 (3.8) 2 (2.6) 1 (1.1) 2 (1.6) Table 3. Association between Metabolic Syndrome and Chronic kidney disease Metabolic syndrome factors Odds Ratio 95% Confidence Interval P -value Waist Circumference 0.97 0.94-0.99 0.007 Systolic Blood Pressure 1.01 1.0-1.01 0.211 Diastolic Blood Pressure 1.01 0.99-1.02 0.431 Fasting Blood Sugar 1.01 1.0-1.01 0.011 Triglycerides 1.01 1.01-1.02 0.001 High-Density Lipoprotein-C 0.98 0.95-1.0 0.101 Metabolic Syndrome (Presence vs Absence) 2.01 1.25-3.21 0.004 Table 4. Interplay of DM and Hypertension duration in predictors of CKD Duration Duration of Hypertension OR (95% CI) 10 years Nil Duration of Diabetes Mellitus OR (95% CI) 10 years Zero counts 6.73 (0.86-52.755) 15.28 (4.81-48.5) 2.19 (0.81-5.95) Nil 7.85 (3.05-20.21) 0.035 (0.012-0.098) 0.103 (0.027-0.393) 6.26 (1.74-22.51) Additional Declarations The authors declare no competing interests. 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S","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0001-7179-1143","institution":"JKKN college of pharmacy","correspondingAuthor":true,"prefix":"","firstName":"Satheesh","middleName":"","lastName":"S","suffix":""}],"badges":[],"createdAt":"2024-03-02 14:37:52","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4006719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4006719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51974318,"identity":"e122a8e5-46dd-407b-86b7-31b4bb33f611","added_by":"auto","created_at":"2024-03-04 19:06:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":697497,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4006719/v1/bf405341-b175-4653-a303-be8a6c6d8b22.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003ePrevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe escalating prevalence of obesity and diabetes, driven by shifts in dietary habits and lifestyle behaviors, is a significant global health concern.\u003csup\u003e1,2,3\u003c/sup\u003e This rise has been paralleled by an increase in metabolic syndrome, a condition that heightens the risk of mortality from chronic kidney disease (CKD), as reported by LaGuardia HA et al.\u003csup\u003e4\u003c/sup\u003e CKD, characterized by a gradual and progressive loss of renal function over at least three months, is a primary cause of end-stage renal disease (ESRD).\u003csup\u003e5\u003c/sup\u003e The global prevalence of CKD ranges from 8\u0026ndash;16%, varying across different regions.\u003csup\u003e6\u003c/sup\u003e CKD is categorized into five stages based on the degree of kidney damage or the estimated glomerular filtration rate (eGFR).\u003csup\u003e7,8\u003c/sup\u003e Regrettably, the majority of patients remain unaware of their condition until it has advanced to a later stage.\u003csup\u003e9\u003c/sup\u003e Early cardiovascular disease (CVD), anemia, metabolic acidosis, and bone diseases are among the major complications associated with impaired kidney function.\u003csup\u003e10\u003c/sup\u003e The leading risk factors and predictors for CKD include impaired fasting plasma glucose, hypertension, and a high body mass index (BMI).\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThere is a growing body of evidence suggesting a link between obesity, hypertension, diabetes mellitus, and the development of CKD.\u003csup\u003e12,13\u003c/sup\u003e Systematic reviews and meta-analyses have indicated that metabolically unhealthy individuals are at an increased risk for CKD.\u003csup\u003e14,15\u003c/sup\u003e However, it is important to note that previous studies investigating the association between metabolic health status phenotypes and renal disorders have primarily focused on eGFR measurements to define CKD.\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven the contradictory findings and the scarcity of studies examining the association between CKD and metabolic syndrome in middle-aged patients, we aim to conduct cross-sectional studies to elucidate the relationship between CKD and metabolic health status. This research will contribute to a more comprehensive understanding of these complex health issues and inform future preventative and treatment strategies.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and participants' characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was a cross-sectional, population-based study carried out in the medical wards of a tertiary care hospital in Erode. The participants comprised individuals who sought care at health centers in Erode. The study included individuals of all genders, aged 40\u0026ndash;59 years, who were residents of Erode, willing to participate, and diagnosed with non-communicable diseases such as Diabetes Mellitus, Hypertension, Dyslipidemia, and Obesity.\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria encompassed incomplete questionnaires, unwillingness to participate in the tests, and pre-existing renal diseases or other conditions that could potentially impact renal function. Ultimately, a total of 317 eligible middle-aged individuals were included in the study. The study was conducted by the Declaration of Helsinki and was approved by the Institutional Ethics Committee of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Before the commencement of the study, all participants provided their written informed consent.\u003c/p\u003e\n\u003cp\u003eThe research took into account a variety of characteristics of the participants, including their age, gender, place of residence, level of physical activity, dietary habits, and medical history. The age of the participants was divided into four categories: 40\u0026ndash;44, 45\u0026ndash;49, 50\u0026ndash;54, and 55\u0026ndash;59 years. The International Physical Activity Questionnaire (IPAQ) was used to classify physical activity levels as high, moderate, or low.\u003csup\u003e17\u003c/sup\u003e Dietary habits were categorized as healthy, moderate, or unhealthy based on the Healthy Eating Index (HEI) score.\u003csup\u003e18,19\u003c/sup\u003e The duration of conditions such as diabetes and hypertension was determined through direct patient interviews. Information on current smoking habits and alcohol consumption was also collected.\u003c/p\u003e\n\u003ch3\u003eMetabolic Syndrome Assessment\u003c/h3\u003e\n\u003cp\u003eRisk factors for renal failure, including hypertension, dyslipidemia, waist circumference, smoking, blood pressure, fasting blood glucose, lipid analysis, BUN/creatinine, and BMI were analyzed. Metabolic syndrome was diagnosed based on the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III).\u003csup\u003e20\u003c/sup\u003e Participants were diagnosed with metabolic syndrome if they met three or more of the following five criteria: waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm (men) or \u0026ge;\u0026thinsp;85 cm (women), fasting blood sugar\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL, triglycerides\u0026thinsp;\u0026gt;\u0026thinsp;150 mg, HDL \u0026ndash;C level\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (men) or \u0026lt;\u0026thinsp;50 mg/dL (women), and blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85 mmHg. The data from participants diagnosed with metabolic syndrome were then analyzed.\u003c/p\u003e\n\u003ch3\u003eBiochemical Analysis\u003c/h3\u003e\n\u003cp\u003eBlood samples were primarily drawn from the median cubital and cephalic veins after a minimum of 8 hours of fasting. The samples were refrigerated and sent to a diagnostic medical laboratory for analysis within 24 hours. The levels of triglycerides, HDL-C, and fasting blood glucose were measured using enzymatic methods on Lipid Biosensor (TamilNadu, India).\u003csup\u003e21\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eChronic Kidney Disease Assessment\u003c/h3\u003e\n\u003cp\u003eThe assessment of chronic kidney disease (CKD) was conducted using the estimated Glomerular Filtration Rate (eGFR), calculated with the Cockcroft-Gault formula.\u003csup\u003e22\u003c/sup\u003e This formula takes into account the patient\u0026rsquo;s creatinine levels, age, gender, and ethnicity: CrCl\u0026thinsp;=\u0026thinsp;72\u0026times;serum creatinine in mg/dL (140\u0026thinsp;\u0026minus;\u0026thinsp;age in years) weight in kg\u003c/p\u003e\n\u003cp\u003e[For females, the result is multiplied by a factor of 0.85].\u003c/p\u003e\n\u003cp\u003eFollowing the National Kidney Foundation- Kidney Disease Outcomes Quality Initiative (NKF-KDOQI), CKD was categorized into five stages: normal (stage 1), mildly decreased (stage 2), mildly to moderately decreased (stage 3a), moderately to severely decreased (stage 3b), and severely decreased (stage 4) (stage 1, eGFR\u0026thinsp;\u0026ge;\u0026thinsp;90 mL/min/1.73 m2; stage 2, 89 to 60 mL/min/1.73 m2; stage 3a, 59 to 45 mL/min/1.73 m2; stage 3b, 44 to 30 mL/min/1.73 m2; and stage 4, \u0026lt;\u0026thinsp;30 mL/min/1.73 m2, respectively).\u003csup\u003e23\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur research aimed to understand the effects of metabolic syndrome on the progression of renal function deterioration. To achieve this, we categorized participants into five distinct groups based on their renal function: normal, mildly decreased, mildly to moderately decreased, moderately to severely decreased, and severely decreased. We then proceeded to analyze various factors that could potentially influence the deterioration of renal function.\u003c/p\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch3\u003eStatistical Analysis\u003c/h3\u003e\n\u003cp\u003eData were collated in an Excel spreadsheet and analyzed using SPSS version 27.0.1.0. The mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables was calculated, and the number of participants was expressed as a percentage for categorical variables. Hypothesis testing for continuous variables was performed using an independent t-test and One-way ANOVA to examine differences in the mean values between groups. For categorical variables, the Chi-squared test and X2-trend test were used to identify differences in proportions. Multiple logistic regression models were developed to investigate the association between the metabolic components of metabolic syndrome and chronic kidney disease (CKD). A p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eProportion of participants with chronic kidney disease\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccording to \u003cb\u003eTable\u0026nbsp;1\u003c/b\u003e, more than half of the participants (54.2%) had metabolic syndrome, while the rest (45.8%) did not. Among those with metabolic syndrome, the distribution of chronic kidney disease (CKD) stages was as follows: Stage 2, n\u0026thinsp;=\u0026thinsp;64 (95% CI: 38.3\u0026ndash;55); Stage 3a, n\u0026thinsp;=\u0026thinsp;41 (95% CI: 44.7\u0026ndash;67.5); Stage 3b, n\u0026thinsp;=\u0026thinsp;20 (95% CI: 57.5\u0026ndash;90.6); Stage 4, n\u0026thinsp;=\u0026thinsp;9 (95% CI: 59-104.6). The data indicate that the prevalence of metabolic syndrome increases with the worsening of CKD, especially in Stages 3b, 4, and 5. This implies a potential causal link between the severity of CKD and the occurrence of metabolic syndrome. The p-value confirms the statistical significance of the association between metabolic syndrome status and different glomerular filtration rate (GFR) levels. This highlights the need for regular screening and management of metabolic syndrome in CKD patients to prevent further deterioration of kidney function.\u003c/p\u003e\n\u003ch3\u003ePatient Demographics\u003c/h3\u003e\n\u003cp\u003eA total of 317 participants were categorized into four different age groups: 40\u0026ndash;44 years (n\u0026thinsp;=\u0026thinsp;25), 45\u0026ndash;49 years (n\u0026thinsp;=\u0026thinsp;78), 50\u0026ndash;54 years (n\u0026thinsp;=\u0026thinsp;90) and 55\u0026ndash;59 years old (n\u0026thinsp;=\u0026thinsp;124). The gender, BMI, waist circumference, physical activity level, alcohol consumption status, previous history of dyslipidemia, metabolic syndrome presence, and CKD stages were significantly correlated between different age groups \u003cb\u003e(Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eAssociation between Metabolic Syndrome and Chronic kidney disease\u003c/h3\u003e\n\u003cp\u003eThe logistic regression analysis highlights several significant associations between certain metabolic factors and CKD (stages 3a to 5). It shows that for every unit increase in Waist Circumference (WC), there's a 3% decrease in the odds of having CKD, significant at a p-value of 0.007. On the other hand, each unit increase in Fasting Blood Sugar (FBS) and Triglycerides (TG) is associated with a 1% increase in the odds of having CKD, significant at p-values of 0.011 and 0.001, respectively. Moreover, individuals with metabolic syndrome have approximately 101% higher odds of having CKD (stages 3a to 5) compared to those without metabolic syndrome, as indicated by an Odds Ratio of 2.01. This association is statistically significant at the 5% significance level with a p-value of 0.004, providing strong evidence of a notable relationship between metabolic syndrome status and CKD \u003cb\u003e(Table\u0026nbsp;3)\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eInterplay of DM and Hypertension duration in predictors of CKD\u003c/h3\u003e\n\u003cp\u003eResearch into Chronic Kidney Disease (CKD) predictors highlights the significant impact of Diabetes Mellitus (DM) and Hypertension (HT) durations. Specifically, durations of 5\u0026ndash;10 years and over 10 years for both DM and HT show high significance (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) with increased risks for CKD. For instance, 5\u0026ndash;10 years of DM and HT exhibit an Odds Ratio (OR) of 11.08, and over 10 years show an OR of 15.28, indicating substantially heightened risks for CKD. However, the duration of 5\u0026ndash;10 years and over 10 years for HT alone display low significance (OR\u0026thinsp;\u0026lt;\u0026thinsp;1) with reduced likelihoods of CKD (OR: 0.035 and 0.103, respectively). These findings emphasize the critical role of DM and longer durations of both DM and HT in the onset and progression of CKD. Vigilant monitoring and effective management strategies are crucial for populations at risk to mitigate these potential risks. (\u003cb\u003eTable\u0026nbsp;4\u003c/b\u003e)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study\u0026rsquo;s primary findings indicate that metabolic syndrome independently contributes to the development of chronic kidney disease (CKD). The results affirm the efficacy of the modified ATP III guideline in diagnosing metabolic syndrome and predicting CKD incidence.\u003c/p\u003e \u003cp\u003eIn this cross-sectional study, we observed a significant positive correlation between the prevalence of CKD and Metabolic Syndrome. Furthermore, a decrease in the Glomerular Filtration Rate (GFR) was associated with Metabolic Syndrome. Our study adds to the growing body of evidence suggesting that individuals with Metabolic Syndrome are at an elevated risk of developing CKD.\u003c/p\u003e \u003cp\u003eWhile previous studies have reported a 65% prevalence of metabolic syndrome among CKD patients, this association could be significantly influenced by hypertension.\u003csup\u003e24\u003c/sup\u003e Prior research has proposed that the link between metabolic syndrome and CKD may be weakened by diabetes mellitus and obesity.\u003csup\u003e25\u003c/sup\u003e Conversely, our study aligns with the view that Metabolic Syndrome is a crucial risk factor for secondary chronic kidney damage, including mild renal dysfunction, with diabetes and increased Body Mass Index (BMI) being the most significant risk factors.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe literature presents inconsistent results regarding the association between metabolic syndrome and CKD. Contrary to our study, a cross-sectional study on middle-aged men and women reported that metabolic syndrome increases the risk of CKD.\u003c/p\u003e \u003cp\u003eInterpreting the results of this study requires considering several limitations. Due to the cross-sectional nature of this study, we cannot establish a causal relationship between CKD indicators and associated factors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study demonstrated a strong positive correlation between the prevalence of Metabolic Syndrome and CKD. Moreover, we found that increased waist circumference, fasting blood glucose, and triglyceride levels were risk factors for CKD in the middle-aged population. Our results also showed that the duration of diabetes mellitus (DM) and hypertension (HT) for 5\u0026ndash;10 years and more than 10 years were significantly associated with higher odds ratios (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) for CKD. These findings suggest that middle age is a critical stage for the development and progression of metabolic syndrome and CKD and that early detection and intervention are essential to prevent further complications.\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 granted by the Institutional Review Board of JKKN College of Pharmacy (JKKNCP/IEC-CER/0522123/38). Written informed consent was obtained from all participants before their inclusion in the study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual consent for publication was obtained from all participants involved in the study. Participants were informed that the data collected would be used for research and potential publication, ensuring confidentiality and anonymity in the presentation of the research findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author, Dr. Satheesh S., Associate Professor at JKKN College of Pharmacy, on reasonable request. The data are not publicly available due to them containing information that could compromise the privacy of research participants.\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 did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMy sincere gratitude to Dr. S. Satheesh, Pharm.D., Associate Professor, Department of Pharmacy Practice, who supported and contributed to the creation of this research work. This job would not have been possible without her great support, advice, and encouragement. I would like to extend my sincere gratitude to Dr. N. Venkateswaramurthy, M.Pharm., Ph.D., Head of the Department, for his direction and experience in the preparation of this research. Lastly, my heartfelt gratitude goes to my family and friends for their endless encouragement throughout this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: Shankar Ganesh M; Acquisition of data: Aravindhan S; Analysis and interpretation of data: Satheesh S; Drafting of the manuscript: Shankar Ganesh M and Aravindhan S; Critical revision of the manuscript: Satheesh S; Statistical analysis: Shankar Ganesh M and Satheesh S; administrative, technical, or material support: Satheesh S; and study supervision: Satheesh S\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eForouhi NG, Wareham NJ. 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Assessment of Acute complications and Quality of Life in Hemodialysis patients with Chronic Kidney Disease. \u003cem\u003eResearch Journal of Pharmacy and Technology.\u003c/em\u003e 2021;14(5):2671-2675. doi:https://doi.org/10.52711/0974-360X.2021.00471.\u003c/li\u003e\n\u003cli\u003eCharytan DM, Fishbane S, Jolanta Małyszko, McCullough PA, Goldsmith D. Cardiorenal Syndrome and the Role of the Bone-Mineral Axis and Anemia. \u003cem\u003eAmerican Journal of Kidney Diseases.\u003c/em\u003e 2015;66(2):196-205. doi:https://doi.org/10.1053/j.ajkd.2014.12.016.\u003c/li\u003e\n\u003cli\u003eLevey AS, Atkins R, Coresh J, et al. Chronic kidney disease as a global public health problem: Approaches and initiatives \u0026ndash; a position statement from Kidney Disease Improving Global Outcomes. \u003cem\u003eKidney International\u003c/em\u003e. 2007;72(3):247-259. doi:https://doi.org/10.1038/sj.ki.5002343.\u003c/li\u003e\n\u003cli\u003eMiricescu D, Balan D, Tulin A, et al. Impact of adipose tissue in chronic kidney disease development (Review). \u003cem\u003eExperimental and Therapeutic Medicine\u003c/em\u003e. 2021;21(5). doi:https://doi.org/10.3892/etm.2021.9969.\u003c/li\u003e\n\u003cli\u003eMaric-Bilkan C. Obesity and Diabetic Kidney Disease. \u003cem\u003eMedical Clinics of North America\u003c/em\u003e. 2013;97(1):59-74.\u003c/li\u003e\n\u003cli\u003eLocatelli F, Pozzoni P, Del Vecchio L. Renal Manifestations in the Metabolic Syndrome. \u003cem\u003eJournal of the American Society of Nephrology.\u003c/em\u003e 2006;17(4_suppl_2):S81-S85. doi:https://doi.org/10.1681/asn.2005121332\u003c/li\u003e\n\u003cli\u003eChen S, Zhou S, Wu B, et al. Association between metabolically unhealthy overweight/obesity and chronic kidney disease: The role of inflammation. \u003cem\u003eDiabetes \u0026amp; Metabolism\u003c/em\u003e. 2014;40(6):423-430. doi:https://doi.org/10.1016/j.diabet.2014.08.005.\u003c/li\u003e\n\u003cli\u003eZimmet P, Alberti KGM, Kaufman F, et al. The metabolic syndrome in children and adolescents? an IDF consensus report. \u003cem\u003ePediatric Diabetes\u003c/em\u003e. 2007;8(5):299-306. doi https://doi.org/10.1111/j.1399-5448.2007.00271.x.\u003c/li\u003e\n\u003cli\u003eYu Z, Ye X, Wang J, et al. Associations of Physical Activity With Inflammatory Factors, Adipocytokines, and Metabolic Syndrome in Middle-Aged and Older Chinese People. \u003cem\u003eCirculation.\u003c/em\u003e 2009;119(23):2969-2977. doi:https://doi.org/10.1161/circulationaha.108.833574.\u003c/li\u003e\n\u003cli\u003eGuo X, Crockett P. Healthy Eating Index and Metabolic Syndrome ‐ Results from the NHANES 1999‐2002. \u003cem\u003eThe FASEB Journal\u003c/em\u003e. 2006;20(4). doi:https://doi.org/10.1096/fasebj.20.4.a577-c.\u003c/li\u003e\n\u003cli\u003eLafreniere J, Carbonneau E, Laramee C, et al. Is the Canadian Healthy Eating Index 2007 an Appropriate Diet Indicator of Metabolic Health? Insights from Dietary Pattern Analysis in the PREDISE Study. \u003cem\u003eNutrients\u003c/em\u003e. 2019;11(7):1597. doi:https://doi.org/10.3390/nu11071597.\u003c/li\u003e\n\u003cli\u003eGrundy SM, Cleeman JI, Merz CNB, et al. A Summary of Implications of Recent Clinical Trials for the National Cholesterol Education Program Adult Treatment Panel III Guidelines.\u003cem\u003e Arteriosclerosis, Thrombosis, and Vascular Biology\u003c/em\u003e. 2004;24(8):1329-1330. doi:https://doi.org/10.1161/01.atv.0000139012.45265.e0.\u003c/li\u003e\n\u003cli\u003eAhmadraji T, Gonzalez-Macia L, Killard AJ. A biosensor for the determination of high density lipoprotein cholesterol employing combined surfactant-derived selectivity and sensitivity enhancements. \u003cem\u003eAnalytical Methods\u003c/em\u003e. 2014;6(12):3975-3981. doi:https://doi.org/10.1039/C3AY42262C.\u003c/li\u003e\n\u003cli\u003eAli A, Asif N, Rais Z. Estimation of GFR by MDRD Formula and Its Correlation to Cockcroft-Gault Equation in Five Stages of Chronic Kidney Disease. \u003cem\u003eOpen Journal of Nephrology\u003c/em\u003e. 2013;03(01):37-40. doi:https://doi.org/10.4236/ojneph.2013.31006.\u003c/li\u003e\n\u003cli\u003eHass VM. Updated management of chronic kidney disease in patients with diabetes. \u003cem\u003eJournal of the American Academy of Physician Assistants.\u003c/em\u003e 2014;27(6):17-22. doi:https://doi.org/10.1097/01.jaa.0000447000.04339.f9.\u003c/li\u003e\n\u003cli\u003eTownsend RR, Anderson AH, Chen J, et al. Metabolic Syndrome, Components, and Cardiovascular Disease Prevalence in Chronic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study. \u003cem\u003eAmerican Journal of Nephrology\u003c/em\u003e. 2011;33(6):477-484. doi:https://doi.org/10.1159/000327618.\u003c/li\u003e\n\u003cli\u003eKatsiki N, Anagnostis P, Kotsa K, Goulis DG, Mikhailidis DP. Obesity, Metabolic Syndrome and the Risk of Microvascular Complications in Patients with Diabetes Mellitus. \u003cem\u003eCurrent Pharmaceutical Design\u003c/em\u003e. 2019;25(18):2051-2059. doi https://doi.org/10.2174/1381612825666190708192134.\u003c/li\u003e\n\u003cli\u003eLocatelli F, Pozzoni P, Del Vecchio L. Renal Manifestations in the Metabolic Syndrome. \u003cem\u003eJournal of the American Society of Nephrology\u003c/em\u003e. 2006;17(4_suppl_2):S81-S85. doi:https://doi.org/10.1681/asn.2005121332.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Proportion of participants with chronic kidney disease\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.827993254637438%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.919055649241145%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePresence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.436762225969645%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Syndrome\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbsence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.816188870151771%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.486238532110091%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3302752293578%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.26605504587156%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.256880733944953%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3302752293578%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eprevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.3302752293578%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.804713804713804%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.228956228956229%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.656565656565656%\" valign=\"top\"\u003e\n \u003cp\u003e50.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.141414141414142%\" valign=\"top\"\u003e\n \u003cp\u003e38.5-63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.0606060606060606%\" valign=\"top\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.656565656565656%\" valign=\"top\"\u003e\n \u003cp\u003e49.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.656565656565656%\" valign=\"top\"\u003e\n \u003cp\u003e36.8-61.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.794612794612794%\" rowspan=\"6\"\u003e\n \u003cp\u003e0.0011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.83011583011583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e46.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003e38.3-55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.94980694980695%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e44.9-61.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.83011583011583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 3a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e56.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003e44.7-67.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.94980694980695%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e32.4-55.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.83011583011583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 3b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003e57.5-90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.94980694980695%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e25.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e9.4-42.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.83011583011583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003e59-104.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.94980694980695%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e18.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e4.61-40.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.83011583011583%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.216216216216218%\" valign=\"top\"\u003e\n \u003cp\u003e100-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.94980694980695%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.953667953667953%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cstrong\u003eTable 2. Demographics and biochemical characteristics\u003c/strong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"651\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic and Clinical Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.38461538461539%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Range (in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.06766917293233%\"\u003e\n \u003cp\u003e\u003cstrong\u003e40-44\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003e\u003cstrong\u003e45-49\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.06015037593985%\"\u003e\n \u003cp\u003e\u003cstrong\u003e50-54\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.06015037593985%\"\u003e\n \u003cp\u003e\u003cstrong\u003e55-59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.06766917293233%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.81203007518797%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.06015037593985%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.06015037593985%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (in years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e42.12\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e46.8\u0026plusmn;1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e52.3\u0026plusmn;1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e56.9\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e18 (5.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e34(10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e55(17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e59(18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.911262798634812%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.43003412969283%\"\u003e\n \u003cp\u003e8 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.89419795221843%\"\u003e\n \u003cp\u003e43(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.38225255972696%\"\u003e\n \u003cp\u003e36(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.38225255972696%\"\u003e\n \u003cp\u003e64(20.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e159.9\u0026plusmn;7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e155.4\u0026plusmn;11.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e158.9\u0026plusmn;10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e156.4\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e62.6\u0026plusmn;9.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e62.6\u0026plusmn;9.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e60.7\u0026plusmn;9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e61.6\u0026plusmn;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody Mass Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e24.7\u0026plusmn;3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e26.18\u0026plusmn;4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e24.17\u0026plusmn;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e25.3\u0026plusmn;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e102.6\u0026plusmn;8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e97.18\u0026plusmn;9.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e100.2\u0026plusmn;10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e99.3\u0026plusmn;8.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e133.69\u0026plusmn;21.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e137.7\u0026plusmn;23.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e141.3\u0026plusmn;27.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e141.6\u0026plusmn;28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic BP (mmHg)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e84\u0026plusmn;11.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e86.8\u0026plusmn;11.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e89.3\u0026plusmn;15.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e88.16\u0026plusmn;14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting Blood Sugar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e161.9\u0026plusmn;48.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e152.9\u0026plusmn;60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e154.4\u0026plusmn;54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e154.4\u0026plusmn;61.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-Density Lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e40.9\u0026plusmn;10.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e44.8\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e44.241\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e43.1\u0026plusmn;9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost Prandial Blood Sugar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e187\u0026plusmn;61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e174\u0026plusmn;63.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e183\u0026plusmn;60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e177.2\u0026plusmn;71.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow-Density Lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e93.1\u0026plusmn;20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e97.9\u0026plusmn;20.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e97\u0026plusmn;18.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e101\u0026plusmn;17.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVery Low-Density Lipoprotein cholesterol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e28.9\u0026plusmn;7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e25.9\u0026plusmn;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e26.8\u0026plusmn;8.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e26.3\u0026plusmn;7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.448\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood Urea\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e41.5\u0026plusmn;38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e36.7\u0026plusmn;28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e34\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e34.7\u0026plusmn;18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.482\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSerum creatinine\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e1.42\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e1.18\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e1.2\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e1.23\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.694\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.76923076923077%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eeGFR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.615384615384617%\"\u003e\n \u003cp\u003e77.9\u0026plusmn;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.23076923076923%\"\u003e\n \u003cp\u003e75.1\u0026plusmn;27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e69.8\u0026plusmn;26.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76923076923077%\"\u003e\n \u003cp\u003e65.2\u0026plusmn;32.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.846153846153847%\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eResidence, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e14 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e41 (53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e48 (52.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e65 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUrban\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e12 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e36 (46.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e43 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e58 (47.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical activity,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e4 (15.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e23 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e21 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e17 (13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e22 (84.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e54 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e66 (72.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e98 (79.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e4 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e8 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiet Intake,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e10 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e29 (37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e19 (20.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e36 (29.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e14 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e40 (51.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e58 (63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e68 (55.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnhealthy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e2 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e8 (10.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e14 (15.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e19 (15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e13 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e23 (29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e40 (43.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e43 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e13 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e54 (70.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e51 (56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e80 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e15 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e25 (32.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e41 (45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e42 (34.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e11 (42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e52 (67.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e50 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e81 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e19 (73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e50 (64.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e71 (78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e89 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e7 (26.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e27 (35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e20 (21.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e34 (27.6}\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e13 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e56 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e60 (65.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e82 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e13 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e21 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e31 (34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e41 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDyslipidemia, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e7 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e26 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e76 (98.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e91 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e116 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic syndrome,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e16 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e31 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e54 (59.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e71 (57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e10 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e46 (59.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e37 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e52 (42.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.81874039938556%\" rowspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eCKD stages, n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.059907834101383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.589861751152075%\"\u003e\n \u003cp\u003e10 (38.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.2073732718894%\"\u003e\n \u003cp\u003e22 (28.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e18 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.746543778801843%\"\u003e\n \u003cp\u003e13 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.831029185867896%\" rowspan=\"6\"\u003e\n \u003cp\u003e0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e8 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e33 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e43 (47.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e53 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 3a\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e6 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e15 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e18 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e34 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 3b\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e4 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e7 (7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e16 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e1 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e1 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e4 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e5 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.286624203821656%\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.929936305732483%\"\u003e\n \u003cp\u003e1 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.019108280254777%\"\u003e\n \u003cp\u003e2 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e1 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.38216560509554%\"\u003e\n \u003cp\u003e2 (1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Association between Metabolic Syndrome and Chronic kidney disease\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"569\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic syndrome factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% Confidence Interval\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist Circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e0.94-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSystolic Blood Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e1.0-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiastolic Blood Pressure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e0.99-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eFasting Blood Sugar\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e1.0-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTriglycerides\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e1.01-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh-Density Lipoprotein-C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e0.95-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"42.00351493848858%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Syndrome (Presence vs Absence)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.926186291739896%\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.307557117750438%\"\u003e\n \u003cp\u003e1.25-3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.76274165202109%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Interplay of DM and Hypertension duration in predictors of CKD\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.021978021978022%\" colspan=\"2\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"71.97802197802197%\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of Hypertension OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.900763358778626%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 5 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.173027989821882%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-10 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.954198473282442%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 10 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.97201017811705%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.750915750915752%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of Diabetes Mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.271062271062272%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt; 5 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.483516483516482%\"\u003e\n \u003cp\u003e4.75 (1.92-11.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.399267399267398%\"\u003e\n \u003cp\u003e3.09 (0.98-9.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.681318681318682%\"\u003e\n \u003cp\u003e4.51 (0.57-35.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.413919413919412%\"\u003e\n \u003cp\u003e0.761 (0.315-1.841)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5-10 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e5.22 (2.03-13.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.652173913043477%\"\u003e\n \u003cp\u003e11.08 (4.65-26.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.17391304347826%\"\u003e\n \u003cp\u003e2.20 (0.67-7.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.043478260869566%\"\u003e\n \u003cp\u003e_\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 10 years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003eZero counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.652173913043477%\"\u003e\n \u003cp\u003e6.73 (0.86-52.755)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.17391304347826%\"\u003e\n \u003cp\u003e15.28 (4.81-48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.043478260869566%\"\u003e\n \u003cp\u003e2.19 (0.81-5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.565217391304348%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.565217391304348%\"\u003e\n \u003cp\u003e7.85 (3.05-20.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.652173913043477%\"\u003e\n \u003cp\u003e0.035 (0.012-0.098)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.17391304347826%\"\u003e\n \u003cp\u003e0.103 (0.027-0.393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.043478260869566%\"\u003e\n \u003cp\u003e6.26 (1.74-22.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"JKKN college of pharmacy","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metabolic syndrome, Chronic kidney disease, Middle aged patients, NCEP/ATP III, NKF-KDOQI","lastPublishedDoi":"10.21203/rs.3.rs-4006719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4006719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious surveys suggest that obesity, hypertension, and diabetes mellitus may be positively related to the development of chronic kidney disease (CKD). However, this association might be altered by metabolic syndrome. Chronic kidney disease has become a worldwide health problem among aging populations. Hence, epidemiological information on middle-aged patients with metabolic syndrome is still lacking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study aimed to assess the prevalence of metabolic syndrome among middle-aged patients and its association with chronic kidney disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe hospital-based cross-sectional study was carried out on 317 participants aged 40–59 years. All participants received a standardized personal interview, including a structured questionnaire, anthropometric measurements, and blood samples collected for laboratory testing. Metabolic syndrome was identified based on the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III). The estimated glomerular filtration rate (eGFR) was calculated by using the Cockroft-Gault formula, which in turn is utilized to predict the stages of chronic kidney disease based on the eGFR range based on NKF-KDOQI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult and discussion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated the association between metabolic syndrome and chronic kidney disease (CKD) in 317 participants aged 40-59 years. We found that metabolic syndrome was prevalent in more than half of the participants (54.2%) and increased with the worsening of CKD stages. We also identified waist circumference, fasting blood sugar, and triglycerides as significant metabolic factors associated with CKD. Furthermore, we observed that longer durations of diabetes mellitus and hypertension, especially when combined, increased the risk of CKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings suggest that metabolic syndrome is a major contributor to CKD and that early detection and management of metabolic factors are essential to prevent kidney damage.\u003c/p\u003e","manuscriptTitle":"Prevalence of Metabolic Syndrome among middle-aged patients and its association with chronic kidney disease: A Cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-04 18:58:45","doi":"10.21203/rs.3.rs-4006719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d3c8a583-0665-432d-a61d-746c9fa5e141","owner":[],"postedDate":"March 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29093578,"name":"Epidemiology"}],"tags":[],"updatedAt":"2024-03-04T18:58:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-04 18:58:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4006719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4006719","identity":"rs-4006719","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}