Kidney Dysfunction and its Determinants among Type 2 Diabetes Mellitus Patients; A Cross-sectional Study in the Obuasi East Municipality of Ghana

Kidney Dysfunction and its Determinants among Type 2 Diabetes Mellitus Patients; A Cross-sectional Study in the Obuasi East Municipality of Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Kidney Dysfunction and its Determinants among Type 2 Diabetes Mellitus Patients; A Cross-sectional Study in the Obuasi East Municipality of Ghana Prince Adoba, Dennis Balijai, Jeffery B. Amakye, Francis A. Amponsah, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6630356/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background : The prevalence of type 2 diabetes mellitus is rising globally, leading to an increase in diabetic kidney disease. About 20-40% of diabetic patients develop chronic kidney disease, which poses a burden on patient management. Kidney dysfunction can be prevented if the predisposing factors are identified and controlled. Also, effective management interventions can be instituted if detected early. Objective : This cross-sectional study assessed kidney dysfunction and its determinants among type 2 diabetics. Methods : Two hundred and four (204) participants were recruited. Blood samples were collected, serum creatinine measured and the estimated glomerular filtration rate (eGFR) calculated. Data on demographics, medical history, and lifestyle factors, were collected through structured questionnaires. Results : The prevalence of kidney dysfunction was 32.35%, with 23.5% presenting hyperfiltration and 8.82% showing decreased GFR (<90 mL/min/1.73m²). Sex, history of alcohol consumption, and treatment with glimepiride or tamsulosin were associated with abnormal GFR (p<0.05). Male patients had significantly higher odds of developing low GFR, while patients on glimepiride had significantly higher odds of developing high GFR. However, past alcohol consumers showed lower odds of developing kidney dysfunction. Conclusion : These findings underline the need for targeted interventions to manage and prevent the progression of kidney dysfunction to chronic kidney disease in this population. Type 2 diabetes mellitus kidney dysfunction estimated glomerular filtration rate (eGFR) hyperfiltration anti-diabetics Figures Figure 1 Introduction Diabetes mellitus is a chronic metabolic condition characterized by hyperglycemia resulting from impaired insulin synthesis, utilization, or both ( 1 ). It is among the four disease groups that account for approximately 80% of all non-communicable disease (NCD)-related deaths and a significant contributor to Ghana's NCD burden ( 2 , 3 ). Global diabetes prevalence reached 537 million people in 2021 ( 4 ), with type 2 diabetes mellitus accounting for over 90% of all cases ( 5 ). In Ghana, WHO reports indicate that an estimated 2.4 million people are living with diabetes ( 6 ). Long-term poorly-controlled type 2 diabetes mellitus affects nearly every organ system in the body. Diabetic kidney disease, one of its major complications, develops in 30–40% of diabetics and can progress to renal failure (7,8). In Ghana, diabetes mellitus is one of the major causes of kidney diseases with a prevalence of 12.5% (9,10). The progression of diabetic kidney dysfunction to renal failure not only increases morbidity and mortality but also poses a burden on the healthcare system, particularly in low- and middle-income countries where most patients cannot afford renal replacement therapies ( 11 ). Diabetic kidney disease and its complications can be prevented or managed through early detection and treatment. However, the diagnostic guidelines used by most laboratories in sub-Saharan African nations, particularly Ghana, lack sufficient sensitivity to detect early stages of kidney dysfunction ( 12 ). This diagnostic limitation emphasizes the need to identify determinants of kidney dysfunction among diabetic patients to develop targeted interventions for diabetes management. While studies investigating these determinants have been conducted in some Sub-Saharan African countries ( 13 – 15 ), data specific to Ghana, especially Obuasi East municipality, remain limited, hindering effective management. This study addressed this gap by assessing kidney dysfunction and its determinants among type 2 diabetic patients at Bryant Mission Hospital, Obuasi. By providing specific data on the Ghanaian population, this research will help develop targeted management and preventive interventions, ultimately improving patient outcomes. Materials and Methods Study Design and Site A cross-sectional study was conducted at Bryant Mission Hospital, Obuasi, Ghana. Bryant Mission Hospital (BMH) is a member of the Christian Health Association of Ghana (CHAG) located at Boete in the Obuasi East district of the Ashanti region of Ghana. The hospital has a well-established diabetic clinic providing healthcare to people in Obuasi East and its environs. Study Population Two hundred and four (204) diagnosed type 2 diabetes mellitus patients presenting to the hospital for management were conveniently recruited. Socio-demographic and Clinical Data A structured questionnaire was used to collect data on age, sex, ethnicity, marital status, occupation, residence, history of smoking, alcohol intake, and clinical data such as family history and medications used. Anthropometric Measurements The participants' weight was measured using a weighing scale (Hangzhou Kimislab Co., Ltd, China), and their height was measured using an AnthroFlex wall-mounted stadiometer (USA). Blood Pressure Measurement Blood pressure was measured using a Rossmax X3 BT automatic blood pressure monitor (Switzerland) after participants had rested for at least 15 minutes. Blood Sample Collection Venous blood (4ml) was taken from each participant after an overnight fast into a serum separator tube (SST), centrifuged within 30 mins at 3000 rpm for 10 minutes, and serum separated for analysis. Biochemical Tests Fasting Blood Glucose Estimation Fasting blood glucose was estimated using a OneTouch Select Plus Flex glucometer (LifeScan, Inc) after 8–12 hours of overnight fasting. Serum Creatinine Estimation Serum creatinine was measured using a Mindray BS-240 automated chemistry analyzer based on the sarcosine oxidase enzymatic method. Calibrators, reagents, and controls were obtained from the manufacturer, and all procedures were performed according to the manufacturer's instructions. The Chronic Kidney Disease Epidemiology Collaboration 2021 equation was used to calculate the eGFR from the creatinine levels and then staged using KDIGO GFR categories (16,17). Statistical Data Analysis Data collected with questionnaires were screened for completeness and errors. Statistical data was analyzed using the SPSS v26 (Chicago, USA) and GraphPad Prism. Categorical variables were presented as percentages and compared with chi-square. Continuous variables were presented as mean ± standard deviation (SD). Analyses of the association of the determinants with kidney dysfunction were done using multinomial logistic regression analysis. A p-value < 0.05 was considered as significant. Results The study included 204 participants, with a higher proportion being females. No significant difference was observed between the mean ages of the males and females (P = 0.493). The majority of the participants were aged 51 years and above. A high percentage had a lower level of education. The majority of the participants had hypertension with 12% having neuropathy. Most participants resided in urban areas and were employed in the informal sector. Although FBS and BMI were comparable between males and females (P > 0.05), serum creatinine was higher among males than females (P = 0.001). [Table 1 ] Table 1 General characteristics of study participants Total Male Female Variables (N = 204) (N = 48) (N = 156) P-value Mean Age (± SD) 60.8 ± 11.7 62.6 ± 8.8 60.3 ± 12.5 0.493 Age group 0.410 31–40 years 15(7.4) 0 (0.0) 15 (9.6) 41–50 years 24 (11.8) 3 (6.3) 21(13.5) 51–60 years 60 (29.4) 21 (43.8) 39 (25.0) 61–70 years 63 (30.9) 12 (25.0) 51 (32.7) ≥ 70 years 42 (20.6) 12(25.0) 30 (19.2) Occupation 0.040 * Unemployed 51 (25.0) 7 (43.8) 30 (19.2) Formal 12(5.9) 6 (12.5) 18 (3.8) Informal 141 (69.1) 63 (43.8) 120 (76.9) Residence 0.809 Rural 21 (10.3) 3 (6.3) 18 (11.5) Peri-urban 33 (16.2) 9 (18.8) 24 (15.4) Urban 150 (73.5) 36 (75.0) 114 (73.1) Education 0.039* No formal education 66 (32.4) 6 (12.5) 60 (38.5) Primary 27(13.2) 0 (0.0) 27 (17.3) JHS 84 (41.2) 33 (68.8) 51 (32.7) SHS 6 (2.9) 3 (6.3) 3 (1.9) Tertiary 21(10.3) 6 (12.5) 15 (9.6) Ethnicity 0.301 Akan 183 (89.7) 48 (100.0) 155 (86.5) Ewe 6 (2.9) 0 (0.0) 6 (3.8) Mole-Dagbani 15 (7.4) 0 (0.0) 15 (9.6) BP grades 0.133 Normal 54 (26.5) 3 (6.3) 51 (32.7) Elevated 36 (17.6) 12 (25.0) 24 (15.4) Stage 1 HPT 45 (22.1) 9 (18.8) 36 (23.1) Stage 2 HPT 69 (33.8) 24 (50.0) 45 (28.8) Comorbidities Hypertension 150 (73.5) 33 (68.8) 117 (75.0) 0.620 Neuropathy 24 (11.8) 6 (12.5) 18 (11.5) 0.917 FBS (mmol/l) 10.58 ± 5.06 9.74 ± 4.18 10.83 ± 5.31 0.452 BMI (Kg/m 2 ) 25.07 ± 4.95 23.89 ± 3.82 25.44 ± 5.22 0.277 Creatinine (µmol/l) 45.34 ± 19.02 58.51 ± 27.20 41.28 ± 13.66 0.001 * eGFR (mL/min/1.72m 2 ) 110 ± 19 101 ± 20 113 ± 17 0.025 * N = number of participants, SD = standard deviation, *=Statistical significance based on P-value < 0.05 Figure 1 illustrates the GFR stages of the participants. Hyperfiltration (high GFR) was present in 23.5% while low GFR was found in 8.9% of the participants The prevalence of kidney dysfunction was high with low GFR accounting for 8.8% and high GFR accounting for 23.5%. More males had low GFR compared to females (66.7% vs 33.3%). Among the participants with low GFR levels, 50% had good glycaemic control, and almost all were hypertensive. Additionally, half of the participants with low GFR were past alcohol consumers. In contrast to the low GFR findings, more females had higher GFR than males (87.5% vs 12.5%). A greater proportion of participants with high GFR (93.8%) had poor glycemic control. Sex, history of alcohol consumption, and treatment with glimepiride or tamsulosin were significantly associated with abnormal GFR. (Tables 2 and 3 ) Table 2 Socio-demographic characteristics among participants in relation to GFR grade GFR Low Normal High Variables (N = 18) (N = 138) (N = 48) P-value Mean age (± SD) 67.2 ± 11.2 61.2 ± 11.5 57.4 ± 11.9 0.209 Age group 0.938 31–40 years 0 (0.0) 9 (6.5) 6 (12.5) 41–50 years 0 (0.0) 18(13.0) 6 (12.5) 51–60 years 6 (33.3) 39 (28.3) 15 (31.3) 61–70 years 6(33.3) 42 (30.4) 15 (31.3) ≥ 70 years 6(33.3) 30 (21.7) 6 (12.5) Sex 0.025* Male 12 (66.7) 30 (21.7) 6 (12.5) Female 6 (33.3) 108 (78.3) 42 (87.5) Occupation 0.050 Unemployed 12 (66.7) 27 (19.6) 12 (25.0) Formal 3 (16.7) 9 (6.5) 0 (0.0) Informal 3 (16.7) 102 (73.9) 36 (75.0) Residence 0.160 Rural 3 (16.7) 9 (6.5) 9 (18.8) Peri-urban 6 (33.3) 27 (19.6) 0 (0.0) Urban 9 (50.0) 102 (73.9) 39 (81.3) Education 0.018* No formal education 6 (33.3) 42 (30.4) 18 (37.5) SHS 3 (16.7) 3 (2.2) 0 (0.0) Tertiary 6 (33.3) 12 (8.7) 3 (6.3) Ethnicity 0.718 Akan 18 (100.0) 123 (89.1) 42 (87.5) Ewe 0 (0.0) 6 (4.3) 0 (0.0) Mole-Dagbani 0 (0.0) 9 (6.5) 6(12.5) Table 3 Clinical characteristics and medication history among participants in relation to GFR grades Low GFR (N = 18) Normal GFR (N = 138) High GFR (N = 48) P-value FBS control 0.056 Good 9 (50.0) 24(17.4) 3 (6.3) Poor 9 (50.0) 114 (82.6) 45 (93.8) Comorbidities Hypertension 15 (83.3) 96 (69.6) 39 (81.3) 0.561 Neuropathy 3 (16.7) 18(13.0) 3 (6.3) 0.712 Previous smoking 0 (0.0) 6 (4.3) 0 (0.0) 0.611 Current alcohol intake 3 (16.7) 18 (13.0) 3 (6.3) 0.712 Previous alcohol intake 9 (50.0) 96 (69.6) 9 (18.8) 0.002 * Family History of diabetes 9 (50.0) 75 (54.3) 33 (68.8) 0.562 Anti-hypertensives Amlodipine 15 (50.0) 45 (40.5) 12 (25.0) 0.526 Lisinopril 6 (33.3) 51 (44.7) 12 (25.0) 0.457 Losartan 3 (16.7) 6 (5.3) 3 (6.3) 0.587 Nifedipine 3 (16.7) 21 (18.4) 9 (18.8) 0.959 Lipid-lowering Atorvastatin 6 (33.3) 45 (39.5) 18 (40.0) 0.956 Anti-diabetics Metformin 15 (83.3) 99 (71.7) 42 (87.5) 0.748 Glibenclamide 9 (50.0) 33 (23.9) 6 (12.5) 0.213 Pioglitazone 0 (0.0) 15(10.9) 3 (6.7) 0.535 Glimepiride 3 (16.7) 15 (10.9) 24 (50.0) 0.006 * Other Drugs Tamsulosin 3 (16.7) 0 (0.0) 0 (0.0) 0.011 * Diazepam 0 (0.0) 6 (4.3) 0 (0.0) 0.564 FBS (mmol/l) 10.33 ± 5.62 10.21 ± 5.21 11.71 ± 4.53 0.596 Creatinine (µmol/l) 80.37 ± 30.18 46.22 ± 12.36 29.67 ± 9.95 < 0.001 * eGFR (mL/min/1.72m 2 ) 74.65 ± 16.85 106.74 ± 7.61 133.45 ± 12.90 < 0.001 * FBS: Fasting blood sugar Upon a multinomial regression analysis, males were more likely to develop low GFR, with those on glimepiride more likely to develop high GFR. However, past alcohol consumers showed significantly lower odds of developing high GFR. (Tables 4 and 5 ) Table 4 Multinomial regression analysis of socio-demographic and clinical factors associated with GFR Low . High Variables OR (95% CI) P-value OR (95% CI) P-value Sex Male 7.200 (1.148–45.167) 0.035* 0.514 (0.100-2.648) 0.426 Female 1 1 Residence Rural 1 1 Peri-urban 0.667 (0.043–10.253) 0.771 - - Urban 0.265 (0.021–3.399) 0.307 0.382 (0.068–2.142) 0.274 Education No formal education 1 1 Primary 2.333 (0.156–34.894) 0.539 3.889 (0.695–21.749) 0.122 JHS - - 0.389 (0.093–1.619) 0.194 SHS 7.000 (0.302-162.202) 0.225 - - Tertiary 3.500 (0.368–33.308) 0.276 0.583 (0.053–6.372) 0.659 Comorbidities Hypertension 2.187 (0.234–20.486) 0.493 1.896 (0.466–7.717) 0.372 Neuropathy 1.333 (0.132–13.461) 0.807 0.444 (0.049–4.005) 0.470 Family History 0.840 (0.153–4.608) 0.841 1.848 (0.553–6.172) 0.318 Previous alcohol intake 0.438 (0.078–2.441) 0.346 0.101 (0.025–0.411) 0.001 * Current alcohol intake 1.333 (0.132–13.461) 0.807 0.444 (0.049–4.005) 0.470 BMI Underweight 2.333 (0.179–30.371) 0.518 1.167 (0.102–13.362) Normal 1 1 Overweight 0.500 (0.047–5.306) 0.565 1.500 (0.401–5.605) 0.547 Obese 0.875 (0.079–9.696) 0.913 1.313 (0.263–6.549) 0.740 Table 5 Multinomial logistic regression of drug history and GFR stages Low High Drugs OR (95% CI) P-value OR (95% CI) P-value Anti-hypertensives Amlodipine 1.467 (0.260–8.270) 0.664 0.533 (0.143–1.995) 0.350 Lisinopril 0.618 (0.101–3.789) 0.603 0.449 (0.121–1.666) 0.231 Losartan 3.600 (0.274–47.333) 0.330 1.286 (0.108–15.331) 0.842 Nifedipine 0.886 (0.089–8.820) 0.918 1.208 (0.265–5.508) 0.807 Lipid-lowering Atorvastatin 0.767 (0.124–4.721) 0.775 1.022 (0.302–3.465) 0.972 Anti-diabetics Metformin 0.758 (0.073–7.896) 0.816 2.121 (0.227–19.849) 0.510 Glibenclamide 2.455 (0.428–14.084) 0.314 0.378 (0.073–1.957) 0.246 Pioglitazone - - 0.471 (0.050–4.411) 0.510 Glimepiride 1.360 (0.131–14.165) 0.797 7.771 (1.951–30.956) 0.004 * Other drugs Acetylsalicylic acid - - 1.795 (0.269–11.989) 0.546 Diclofenac - - 2.667 (0.669–10.632) 0.165 Tamsulosin - - - - Vitamin B 0.326 (0.053–2.008) 0.227 0.435 (0.128–1.474) 0.181 Nocte 2.222 (0.363–13.618) 0.388 0.556 (0.159–1.936) 0.356 OR = Odds Ratio; CI = Confidence Interval Discussion The study found a high prevalence of kidney dysfunction among the study participants with 23.5% of the participants having high GFR and 8.85% having low GFR. Although kidney dysfunction among diabetics especially among those with lower GFR is known to vary across the world, the prevalence of 8.85% from this study is comparatively lower than the global average of 27% (18). This could be attributed to factors such as access to healthcare services, good management of diabetes by the hospital, good lifestyle modification and adherence to medication by the patients. Comparing other regional studies to the findings from this study, showed a relatively higher prevalence of 24.7% in Africa ( 19 ), 28.96% in Middle East regions (20), 30.6% in Iran ( 21 ), and 31% in Asia. Also, studies carried out in Ghana, in the southwestern part saw a similar trend with a 27% prevalence ( 22 ) and a 16.1% prevalence was recorded in another multicenter study conducted in Ghana ( 11 ). Notably, 23.5% of the study participants exhibited high GFR. Hyperfiltration is the preclinical stage of diabetic kidney disease and it is a risk factor for the acceleration of renal dysfunction and CKD progression among diabetics ( 23 ). The prevalence recorded in this study is comparable to the average prevalence of 6–75% among type 2 diabetics (24,25). Like low GFR, hyperfiltration is a determinant of the early onset of diabetic kidney dysfunction and needs to be screened for during the course of the disease (24,26). Males had significantly higher odds of developing low GFR compared to females. This finding is similar to observations in other studies where being a male was a risk factor for developing kidney impairment ( 11 , 27 ). This trend could be attributed to the fact that men tend to adopt more unhealthy lifestyle choices, which can elevate their risk of experiencing kidney dysfunction and do not report for treatment early. Additionally, elevated levels of testosterone in men have been speculated to decline kidney function by inducing podocyte damage. Also, estrogen which is in higher levels in women offers some form of “protection” to the kidney, which may explain why males tend to develop kidney dysfunction compared to females ( 28 – 31 ). Glomerular hyperfiltration has also been reported to be a direct pathogenic link between obesity and nephropathy ( 32 ). Interestingly, glimepiride which is a part of the sulphonylurea class of antidiabetic drugs is known to cause weight gain ( 33 ) and diabetes patients who are overweight or obese are at great risk of developing kidney impairment or dysfunction ( 15 ). As recorded in this study, patients on glimepiride had significantly higher odds of developing hyperfiltration (high GFR). Participants who previously consumed alcohol had lower odds of developing high GFR, a finding that aligns with studies conducted in Korea and Zimbabwe (34,35). This observation supports research, indicating that low to moderate alcohol consumption is associated with a reduced risk of kidney disease, although the underlying protective mechanisms remain unclear (36,37). These results imply that moderate alcohol intake may not negatively impact kidney function. On the contrary, previous studies found variables like hypertension ( 38 ), educational level ( 11 ), and obesity ( 15 ) to be associated with kidney impairment, unlike the observations in this study. Possible reasons could be differences in the study population, regional variation, and differences in the study design. Although the study is limited by the sample size during the study period, to the best of our knowledge, it is the first to be conducted among type 2 diabetes mellitus patients in the Obuasi East municipality. Conclusion The prevalence of kidney dysfunction (high and low GFR) among the study population was high. The majority with kidney dysfunction had hyperfiltration, which is an independent predictor of the preclinical stage of the progression of diabetic kidney disease, with about 9% having low GFR. Educational level, sex, previous alcohol intake, and patients on glimepiride and tamsulosin were associated with abnormal GFR. Males had higher odds of developing low GFR and patients on glimepiride had higher odds of developing high GFR. These findings indicate the need for targeted interventions in diabetes management to identify and prevent the progression of kidney dysfunction. Abbreviations GFR: Glomerular filtration rate eGFR: Estimated glomerular filtration rate NCD: Non-communicable disease CHAG: Christian Health Association of Ghana KDIGO: Kidney Disease: Improving Global Outcomes BMI: Body Mass Index CKD: Chronic kidney disease FSB: Fasting blood sugar Declarations Ethical approval and consent to participate Ethical approval was obtained from the Committee on Human Research and Publication Ethics (CHRPE) at Kwame Nkrumah University of Science and Technology (CHRPE/AP/715/24) and the authorities at the Bryant Mission Hospital. Participants signed a written consent form before commencing the study. Consent to participate Participants signed an informed written consent form before collecting their data and samples. Consent for publication Not applicable Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that there are no competing interests Funding The study was self-financed by the authors. Authors' contributions PA, DB, JBA, SBD, JM and RKDE conceived of the study and participated in its design and coordination. PA, DB, JBA, SBD, JM, FAA, IA, CT and TSD were involved in the recruitment of participants, data collection and analysis of samples. PA, DB, GKA and DKS drafted the manuscript. PA, DB, FAA, IA and RKDE provided analytic and statistical support. All authors read and approved the final manuscript. Acknowledgement The authors express sincere gratitude to the management and staff of Bryant Mission Hospital, Obuasi for their invaluable support during this research. We also extend our appreciation to the patients who voluntarily participated in this study. References Sapra A, Bhandari P. Diabetes. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2024 Mar 18]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK551501/ Boakye H, Atabila A, Hinneh T, Ackah M, Ojo-Benys F, Bello AI. The prevalence and determinants of non-communicable diseases among Ghanaian adults: A survey at a secondary healthcare level. PLOS ONE. 2023 Feb 8;18(2):e0281310. W.H.O. Non communicable diseases [Internet]. 2023 [cited 2024 Jul 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases IDF. IDF Diabetes Atlas [Internet]. 2024 [cited 2024 Mar 18]. Available from: https://diabetesatlas.org/ Khan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes – Global Burden of Disease and Forecasted Trends: J Epidemiol Glob Health. 2019;10(1):107. WHO. WHO | Regional Office for Africa. 2024 [cited 2024 Mar 1]. Ghana on the offensive against diabetes. Available from: https://www.afro.who.int/countries/ghana/news/ghana-offensive-against-diabetes Tomofuji, Y., Suzuki, K., Kishikawa, T., Shojima, N., Hosoe, J., Inagaki, K., et al., 2023. Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes. Commun. Med. 3, 5. https://doi.org/10.1038/s43856-022-00231-3 Tu C, Wang L, Wei L, Jiang Z. The role of circular RNA in Diabetic Nephropathy. Int J Med Sci. 2022 May 20;19(5):916–23. Amoako YA, Laryea DO, Bedu-Addo G, Andoh H, Awuku YA. Clinical and demographic characteristics of chronic kidney disease patients in a tertiary facility in Ghana. Pan Afr Med J. 2014;18:274. Annani-Akollor, M.E., Addai-Mensah, O., Fondjo, L.A., Sallah, L., Owiredu, E.-W., Acheampong, E., et al., 2019. Predominant Complications of Type 2 Diabetes in Kumasi: A 4-Year Retrospective Cross-Sectional Study at a Teaching Hospital in Ghana. Medicina (Mex.) 55, 125. https://doi.org/10.3390/medicina55050125 Tannor EK, Sarfo FS, Mobula LM, Sarfo-Kantanka O, Adu-Gyamfi R, Plange-Rhule J. Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross-sectional study. J Clin Hypertens. 2019;21(10):1542–50. Siddiqui K, George TP, Joy SS, Alfadda AA. Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes. Front Endocrinol [Internet]. 2022 Dec 9 [cited 2024 Jul 9];13. Available from: https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.1079725/full Al-Momany A, Almomani EY, Almomani HY, Al-Azzam SI, Qablan A. Evaluating kidney function and the associated risk factors among patients with type 2 diabetes mellitus: a cross-sectional study at a tertiary hospital in Jordan. BMJ Open. 2023 Oct 1;13(10):e073536. Mariye Zemicheal, T., Bahrey Tadesse, D., Tasew Atalay, H., Teklay Weldesamuel, G., Gebremichael, G.B., Tesfay, H.N., et al., 2020. Determinants of Diabetic Nephropathy among Diabetic Patients in General Public Hospitals of Tigray, Ethiopia, 2018/19. Int. J. Endocrinol. 2020, 6396483. https://doi.org/10.1155/2020/6396483 Tesfahun E, Mulatu K, Deresse T, Mulugeta B. Determinants of diabetic nephropathy among diabetic patients attending in Tikur Anbesa specialized hospital, Addis Abeba, Ethiopia. Clin Epidemiol Glob Health. 2024 Jan 1;25:101481. Delgado, C., Baweja, M., Crews, D.C., Eneanya, N.D., Gadegbeku, C.A., Inker, L.A., et al., 2022. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Am. J. Kidney Dis. 79, 268-288.e1. https://doi.org/10.1053/j.ajkd.2021.08.003 Stevens, P.E., Ahmed, S.B., Carrero, J.J., Foster, B., Francis, A., Hall, R.K., et al., 2024. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 105, S117–S314. https://doi.org/10.1016/j.kint.2023.10.018 Fenta, E.T., Eshetu, H.B., Kebede, N., Bogale, E.K., Zewdie, A., et al., 2023. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol. Metab. Syndr. 15, 245. https://doi.org/10.1186/s13098-023-01202-x ElHafeez SA, Bolignano D, D’Arrigo G, Dounousi E, Tripepi G, Zoccali C. Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: a systematic review. BMJ Open. 2018 Jan 1;8(1):e015069. Naser, A.Y., Alwafi, H., Alotaibi, B., Salawati, E., Samannodi, M., Alsairafi, Z., et al., 2021. Prevalence of Chronic Kidney Diseases in Patients with Diabetes Mellitus in the Middle East: A Systematic Review and Meta-Analysis. Int. J. Endocrinol. 2021, 4572743. https://doi.org/10.1155/2021/4572743 Rabieenia E, Jalali R, Mohammadi M. Prevalence of nephropathy in patients with type 2 diabetes in Iran: A systematic review and meta-analysis based on geographic information system (GIS). Diabetes Metab Syndr. 2020;14(5):1543–50. Ephraim R, Biekpe S, Sakyi S, Adoba P, Agbodzakey H, Anto E. Prevalence of Chronic Kidney Disease among the High Risk Population in South-Western Ghana; A Cross Sectional Study. Can J Kidney Health Dis. 2015 Dec 1;2. Yang Y, Xu G. Update on Pathogenesis of Glomerular Hyperfiltration in Early Diabetic Kidney Disease. Front Endocrinol. 2022 May 19;13:872918. Moriconi, D., Sacchetta, L., Chiriacò, M., Nesti, L., Forotti, G., Natali, A., et al., 2023. Glomerular Hyperfiltration Predicts Kidney Function Decline and Mortality in Type 1 and Type 2 Diabetes: A 21-Year Longitudinal Study. Diabetes Care 46, 845–853. https://doi.org/10.2337/dc22-2003 Tonneijck, L., Muskiet, M.H.A., Smits, M.M., van Bommel, E.J., Heerspink, H.J.L., van Raalte, D.H., et al., 2017. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J. Am. Soc. Nephrol. JASN 28, 1023–1039. https://doi.org/10.1681/ASN.2016060666 Oh SW, Yang JH, Kim MG, Cho WY, Jo SK. Renal hyperfiltration as a risk factor for chronic kidney disease: A health checkup cohort study. PLOS ONE. 2020 Sep 3;15(9):e0238177. Gheith O, Farouk N, Nampoory N, Halim MA, Al-Otaibi T. Diabetic kidney disease: world wide difference of prevalence and risk factors. J Nephropharmacology. 2015 Oct 9;5(1):49–56. Cristy Lytal. ScienceDaily. 2023 [cited 2024 Jul 27]. Why are male kidneys more vulnerable to disease than female kidneys? Mouse study points to testosterone. Available from: https://www.sciencedaily.com/releases/2023/09/230905124856.htm Doublier, S., Lupia, E., Catanuto, P., Periera-Simon, S., Xia, X., et al., 2010. Testosterone and 17β-estradiol have opposite effects on podocyte apoptosis that precedes glomerulosclerosis in female estrogen receptor knockout mice. Kidney Int. 79, 404. https://doi.org/10.1038/ki.2010.398 Filler G, Ramsaroop A, Stein R, Grant C, Marants R, So A, et al. Is Testosterone Detrimental to Renal Function? Kidney Int Rep. 2016 Jul 21;1(4):306. NKF. National Kidney Foundation. 2020 [cited 2024 Jul 27]. Kidney Failure Risk Factor: Gender (Sex). Available from: https://www.kidney.org/content/kidney-failure-risk-factor-gender-sex Basolo A, Salvetti G, Giannese D, Genzano SB, Ceccarini G, Giannini R, et al. Obesity, Hyperfiltration, and Early Kidney Damage: A New Formula for the Estimation of Creatinine Clearance. J Clin Endocrinol Metab. 2023 Nov 17;108(12):3280–6. Pistrosch F, Passauer J, Herbrig K, Schwanebeck U, Gross P, Bornstein SR. Effect of thiazolidinedione treatment on proteinuria and renal hemodynamic in type 2 diabetic patients with overt nephropathy. Horm Metab Res Horm Stoffwechselforschung Horm Metab. 2012 Nov;44(12):914–8. Ahn, J.H., Yu, J.H., Ko, S.-H., Kwon, H.-S., Kim, D.J., et al., 2014. Prevalence and Determinants of Diabetic Nephropathy in Korea: Korea National Health and Nutrition Examination Survey. Diabetes Metab. J. 38, 109–119. https://doi.org/10.4093/dmj.2014.38.2.109 Tapera S. Prevalence and Risk Factors for Diabetes Chronic Complications in Harare, Zimbabwe, 2014 [Internet] [masters]. University of Zimbabwe; 2014 [cited 2024 Jul 22]. Available from: http://thesisbank.jhia.ac.ke/3861/ Cheungpasitporn, W., Thongprayoon, C., Kittanamongkolchai, W., Brabec, B.A., O’Corragain, O.A.,et al., 2015. High alcohol consumption and the risk of renal damage: a systematic review and meta-analysis. QJM Int. J. Med. 108, 539–548. https://doi.org/10.1093/qjmed/hcu247 Li Y, Zhu B, Song N, Shi Y, Fang Y, Ding X. Alcohol consumption and its association with chronic kidney disease: Evidence from a 12-year China health and Nutrition Survey. Nutr Metab Cardiovasc Dis. 2022 Jun 1;32(6):1392–401. Berhanu A. Prevalence and Predictors of Diabetic Kidney Disease among Diabetes Patients in Diabetic Clinic of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: A Cross Sectional Study [Internet]. Addis Abeba University; 2021 [cited 2024 Jul 27]. Available from: http://etd.aau.edu.et/handle/123456789/30369 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 10 Jun, 2025 Editor invited by journal 15 May, 2025 Editor assigned by journal 12 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 09 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6630356","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":460002082,"identity":"52d3fd3e-2d84-4be1-af2f-b30b5f534f75","order_by":0,"name":"Prince Adoba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYFCCBDYQyQPEjA9ADD5StDAbgBhsxGoBATYJMElIA3978rEHP3fckzFnP3ys8muOnQwbA/PDRzfwaJE48yzdsPdMMY9lT1rabdltyUCHsRkb5+Cz5kaOmQRvWwKPwQ0es9uS25iBWnjYpPFpkQdqkfwL1sL/rVhyWz1hLQZALdJQW9gYP247TFiLIdAvxrJngFrOpBlLM247zsPGTMAvcseTjz18uyPB3uD44Ycff26rtudnb374GK/3QYCxAUIz84BJQsqRtTD+IEb1KBgFo2AUjDgAAAeHRIqw2RVbAAAAAElFTkSuQmCC","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Prince","middleName":"","lastName":"Adoba","suffix":""},{"id":460002084,"identity":"c8b77f55-1998-4f2a-9780-cba39f7142ab","order_by":1,"name":"Dennis Balijai","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dennis","middleName":"","lastName":"Balijai","suffix":""},{"id":460002089,"identity":"24be8c48-9375-420c-ac10-f2f2297fb930","order_by":2,"name":"Jeffery B. Amakye","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jeffery","middleName":"B.","lastName":"Amakye","suffix":""},{"id":460002090,"identity":"eb0d883b-735e-4951-a396-93ac4485172e","order_by":3,"name":"Francis A. Amponsah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Francis","middleName":"A.","lastName":"Amponsah","suffix":""},{"id":460002091,"identity":"78a43d3e-fcc8-497d-bf2b-3dcd27754259","order_by":4,"name":"Isaac Acheampong","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Isaac","middleName":"","lastName":"Acheampong","suffix":""},{"id":460002092,"identity":"8a3d3073-d928-4fb3-a9b2-37ed5e0e007b","order_by":5,"name":"Solomon Bernard Dodoo","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Solomon","middleName":"Bernard","lastName":"Dodoo","suffix":""},{"id":460002093,"identity":"92e207e7-968c-4325-92b6-bbe119c427aa","order_by":6,"name":"Joseph Mensah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Mensah","suffix":""},{"id":460002094,"identity":"43afea30-0107-4708-b562-a7390222b5bf","order_by":7,"name":"Cephas Tetteh","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Cephas","middleName":"","lastName":"Tetteh","suffix":""},{"id":460002095,"identity":"b9750971-507d-4f9d-bc47-91c976af763b","order_by":8,"name":"Glenn K. Appenteng","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Glenn","middleName":"K.","lastName":"Appenteng","suffix":""},{"id":460002096,"identity":"93e37f63-32e8-498e-8f9b-4f5d9b1f47e5","order_by":9,"name":"Daniel K. Somuah","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"K.","lastName":"Somuah","suffix":""},{"id":460002097,"identity":"55152487-c1ca-420e-aa29-dfbb24dcf430","order_by":10,"name":"Tawiah Setsofia Dzata","email":"","orcid":"","institution":"Bryant Mission Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tawiah","middleName":"Setsofia","lastName":"Dzata","suffix":""},{"id":460002098,"identity":"f9973054-7f79-4c9b-975c-fee5a3c7d930","order_by":11,"name":"Richard K.D. Ephraim","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"K.D.","lastName":"Ephraim","suffix":""}],"badges":[],"createdAt":"2025-05-09 16:23:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6630356/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6630356/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84341039,"identity":"0d4df2d5-ad33-40ad-9ac0-d066bd827427","added_by":"auto","created_at":"2025-06-10 18:27:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":189912,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGFR Staging among study participants.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6630356/v1/f006d85281800ac74bfaca5a.png"},{"id":84341869,"identity":"91f18ad1-71be-4715-96f0-8a3f3ef55791","added_by":"auto","created_at":"2025-06-10 18:43:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1797104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6630356/v1/3cc2009c-0caf-449c-bd8d-23df8335e2f0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Kidney Dysfunction and its Determinants among Type 2 Diabetes Mellitus Patients; A Cross-sectional Study in the Obuasi East Municipality of Ghana","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiabetes mellitus is a chronic metabolic condition characterized by hyperglycemia resulting from impaired insulin synthesis, utilization, or both (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). It is among the four disease groups that account for approximately 80% of all non-communicable disease (NCD)-related deaths and a significant contributor to Ghana's NCD burden (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Global diabetes prevalence reached 537\u0026nbsp;million people in 2021 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), with type 2 diabetes mellitus accounting for over 90% of all cases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In Ghana, WHO reports indicate that an estimated 2.4\u0026nbsp;million people are living with diabetes (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLong-term poorly-controlled type 2 diabetes mellitus affects nearly every organ system in the body. Diabetic kidney disease, one of its major complications, develops in 30\u0026ndash;40% of diabetics and can progress to renal failure (7,8). In Ghana, diabetes mellitus is one of the major causes of kidney diseases with a prevalence of 12.5% (9,10). The progression of diabetic kidney dysfunction to renal failure not only increases morbidity and mortality but also poses a burden on the healthcare system, particularly in low- and middle-income countries where most patients cannot afford renal replacement therapies (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiabetic kidney disease and its complications can be prevented or managed through early detection and treatment. However, the diagnostic guidelines used by most laboratories in sub-Saharan African nations, particularly Ghana, lack sufficient sensitivity to detect early stages of kidney dysfunction (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This diagnostic limitation emphasizes the need to identify determinants of kidney dysfunction among diabetic patients to develop targeted interventions for diabetes management. While studies investigating these determinants have been conducted in some Sub-Saharan African countries (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e15\u003c/span\u003e), data specific to Ghana, especially Obuasi East municipality, remain limited, hindering effective management.\u003c/p\u003e \u003cp\u003eThis study addressed this gap by assessing kidney dysfunction and its determinants among type 2 diabetic patients at Bryant Mission Hospital, Obuasi. By providing specific data on the Ghanaian population, this research will help develop targeted management and preventive interventions, ultimately improving patient outcomes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Site\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted at Bryant Mission Hospital, Obuasi, Ghana. Bryant Mission Hospital (BMH) is a member of the Christian Health Association of Ghana (CHAG) located at Boete in the Obuasi East district of the Ashanti region of Ghana. The hospital has a well-established diabetic clinic providing healthcare to people in Obuasi East and its environs.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eTwo hundred and four (204) diagnosed type 2 diabetes mellitus patients presenting to the hospital for management were conveniently recruited.\u003c/p\u003e\n\u003ch3\u003eSocio-demographic and Clinical Data\u003c/h3\u003e\n\u003cp\u003eA structured questionnaire was used to collect data on age, sex, ethnicity, marital status, occupation, residence, history of smoking, alcohol intake, and clinical data such as family history and medications used.\u003c/p\u003e\n\u003ch3\u003eAnthropometric Measurements\u003c/h3\u003e\n\u003cp\u003eThe participants' weight was measured using a weighing scale (Hangzhou Kimislab Co., Ltd, China), and their height was measured using an AnthroFlex wall-mounted stadiometer (USA).\u003c/p\u003e\n\u003ch3\u003eBlood Pressure Measurement\u003c/h3\u003e\n\u003cp\u003eBlood pressure was measured using a Rossmax X3 BT automatic blood pressure monitor (Switzerland) after participants had rested for at least 15 minutes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBlood Sample Collection\u003c/h2\u003e \u003cp\u003eVenous blood (4ml) was taken from each participant after an overnight fast into a serum separator tube (SST), centrifuged within 30 mins at 3000 rpm for 10 minutes, and serum separated for analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eBiochemical Tests\u003c/h3\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFasting Blood Glucose Estimation\u003c/h2\u003e \u003cp\u003eFasting blood glucose was estimated using a OneTouch Select Plus Flex glucometer (LifeScan, Inc) after 8\u0026ndash;12 hours of overnight fasting.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSerum Creatinine Estimation\u003c/h2\u003e \u003cp\u003eSerum creatinine was measured using a Mindray BS-240 automated chemistry analyzer based on the sarcosine oxidase enzymatic method. Calibrators, reagents, and controls were obtained from the manufacturer, and all procedures were performed according to the manufacturer's instructions. The Chronic Kidney Disease Epidemiology Collaboration 2021 equation was used to calculate the eGFR from the creatinine levels and then staged using KDIGO GFR categories (16,17).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Data Analysis\u003c/h2\u003e \u003cp\u003eData collected with questionnaires were screened for completeness and errors. Statistical data was analyzed using the SPSS v26 (Chicago, USA) and GraphPad Prism. Categorical variables were presented as percentages and compared with chi-square. Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Analyses of the association of the determinants with kidney dysfunction were done using multinomial logistic regression analysis. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered as significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study included 204 participants, with a higher proportion being females. No significant difference was observed between the mean ages of the males and females (P\u0026thinsp;=\u0026thinsp;0.493). The majority of the participants were aged 51 years and above. A high percentage had a lower level of education. The majority of the participants had hypertension with 12% having neuropathy. Most participants resided in urban areas and were employed in the informal sector. Although FBS and BMI were comparable between males and females (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), serum creatinine was higher among males than females (P\u0026thinsp;=\u0026thinsp;0.001). [Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral characteristics of study participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;156)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean Age (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (20.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e141 (69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e120 (76.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e114 (73.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84 (41.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e183 (89.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (86.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (3.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMole-Dagbani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBP grades\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (32.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 1 HPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage 2 HPT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (33.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBS (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.58\u0026thinsp;\u0026plusmn;\u0026thinsp;5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.74\u0026thinsp;\u0026plusmn;\u0026thinsp;4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (Kg/m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.07\u0026thinsp;\u0026plusmn;\u0026thinsp;4.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.89\u0026thinsp;\u0026plusmn;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.51\u0026thinsp;\u0026plusmn;\u0026thinsp;27.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.28\u0026thinsp;\u0026plusmn;\u0026thinsp;13.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR (mL/min/1.72m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110\u0026thinsp;\u0026plusmn;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eN\u0026thinsp;=\u0026thinsp;number of participants, SD\u0026thinsp;=\u0026thinsp;standard deviation, *=Statistical significance based on P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the GFR stages of the participants. Hyperfiltration (high GFR) was present in 23.5% while low GFR was found in 8.9% of the participants\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of kidney dysfunction was high with low GFR accounting for 8.8% and high GFR accounting for 23.5%. More males had low GFR compared to females (66.7% vs 33.3%). Among the participants with low GFR levels, 50% had good glycaemic control, and almost all were hypertensive. Additionally, half of the participants with low GFR were past alcohol consumers. In contrast to the low GFR findings, more females had higher GFR than males (87.5% vs 12.5%). A greater proportion of participants with high GFR (93.8%) had poor glycemic control. Sex, history of alcohol consumption, and treatment with glimepiride or tamsulosin were significantly associated with abnormal GFR. (Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocio-demographic characteristics among participants in relation to GFR grade\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGFR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMean age (\u0026plusmn;\u0026thinsp;SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.4\u0026thinsp;\u0026plusmn;\u0026thinsp;11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;50 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (28.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;70 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.025*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 (21.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108 (78.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOccupation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (73.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.018*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (30.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.718\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123 (89.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEwe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMole-Dagbani\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics and medication history among participants in relation to GFR grades\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow GFR\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal GFR\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;138)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHigh GFR\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBS control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24(17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45 (93.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39 (81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18(13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious smoking\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent alcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious alcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96 (69.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History of diabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75 (54.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (68.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.562\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-hypertensives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmlodipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (40.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLisinopril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLosartan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.587\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNifedipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid-lowering\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtorvastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-diabetics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (83.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (71.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42 (87.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlibenclamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePioglitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.535\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlimepiride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther Drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamsulosin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiazepam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFBS (mmol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.33\u0026thinsp;\u0026plusmn;\u0026thinsp;5.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.71\u0026thinsp;\u0026plusmn;\u0026thinsp;4.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCreatinine (\u0026micro;mol/l)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.37\u0026thinsp;\u0026plusmn;\u0026thinsp;30.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.22\u0026thinsp;\u0026plusmn;\u0026thinsp;12.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.67\u0026thinsp;\u0026plusmn;\u0026thinsp;9.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eeGFR (mL/min/1.72m\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.65\u0026thinsp;\u0026plusmn;\u0026thinsp;16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.74\u0026thinsp;\u0026plusmn;\u0026thinsp;7.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133.45\u0026thinsp;\u0026plusmn;\u0026thinsp;12.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eFBS: Fasting blood sugar\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUpon a multinomial regression analysis, males were more likely to develop low GFR, with those on glimepiride more likely to develop high GFR. However, past alcohol consumers showed significantly lower odds of developing high GFR. (Tables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial regression analysis of socio-demographic and clinical factors associated with GFR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.200 (1.148\u0026ndash;45.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.035*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.514 (0.100-2.648)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eResidence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeri-urban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.667 (0.043\u0026ndash;10.253)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.265 (0.021\u0026ndash;3.399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382 (0.068\u0026ndash;2.142)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.333 (0.156\u0026ndash;34.894)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.889 (0.695\u0026ndash;21.749)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.389 (0.093\u0026ndash;1.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSHS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.000 (0.302-162.202)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.500 (0.368\u0026ndash;33.308)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.583 (0.053\u0026ndash;6.372)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.187 (0.234\u0026ndash;20.486)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.896 (0.466\u0026ndash;7.717)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeuropathy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.333 (0.132\u0026ndash;13.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444 (0.049\u0026ndash;4.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily History\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.840 (0.153\u0026ndash;4.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.848 (0.553\u0026ndash;6.172)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrevious alcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438 (0.078\u0026ndash;2.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.101 (0.025\u0026ndash;0.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCurrent alcohol intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.333 (0.132\u0026ndash;13.461)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.444 (0.049\u0026ndash;4.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.333 (0.179\u0026ndash;30.371)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.167 (0.102\u0026ndash;13.362)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.500 (0.047\u0026ndash;5.306)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.500 (0.401\u0026ndash;5.605)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObese\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.875 (0.079\u0026ndash;9.696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.313 (0.263\u0026ndash;6.549)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression of drug history and GFR stages\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAnti-hypertensives\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmlodipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.467 (0.260\u0026ndash;8.270)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.533 (0.143\u0026ndash;1.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLisinopril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.618 (0.101\u0026ndash;3.789)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.449 (0.121\u0026ndash;1.666)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLosartan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.600 (0.274\u0026ndash;47.333)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.286 (0.108\u0026ndash;15.331)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNifedipine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.886 (0.089\u0026ndash;8.820)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.208 (0.265\u0026ndash;5.508)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLipid-lowering\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtorvastatin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.767 (0.124\u0026ndash;4.721)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.022 (0.302\u0026ndash;3.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.972\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAnti-diabetics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.758 (0.073\u0026ndash;7.896)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.121 (0.227\u0026ndash;19.849)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlibenclamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.455 (0.428\u0026ndash;14.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.378 (0.073\u0026ndash;1.957)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePioglitazone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.471 (0.050\u0026ndash;4.411)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlimepiride\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.360 (0.131\u0026ndash;14.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.771 (1.951\u0026ndash;30.956)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOther drugs\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetylsalicylic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.795 (0.269\u0026ndash;11.989)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiclofenac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.667 (0.669\u0026ndash;10.632)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTamsulosin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.326 (0.053\u0026ndash;2.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.435 (0.128\u0026ndash;1.474)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNocte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.222 (0.363\u0026ndash;13.618)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.556 (0.159\u0026ndash;1.936)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eOR\u0026thinsp;=\u0026thinsp;Odds Ratio; CI\u0026thinsp;=\u0026thinsp;Confidence Interval\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study found a high prevalence of kidney dysfunction among the study participants with 23.5% of the participants having high GFR and 8.85% having low GFR.\u003c/p\u003e \u003cp\u003eAlthough kidney dysfunction among diabetics especially among those with lower GFR is known to vary across the world, the prevalence of 8.85% from this study is comparatively lower than the global average of 27% (18). This could be attributed to factors such as access to healthcare services, good management of diabetes by the hospital, good lifestyle modification and adherence to medication by the patients. Comparing other regional studies to the findings from this study, showed a relatively higher prevalence of 24.7% in Africa (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e19\u003c/span\u003e), 28.96% in Middle East regions (20), 30.6% in Iran (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and 31% in Asia. Also, studies carried out in Ghana, in the southwestern part saw a similar trend with a 27% prevalence (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and a 16.1% prevalence was recorded in another multicenter study conducted in Ghana (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Notably, 23.5% of the study participants exhibited high GFR. Hyperfiltration is the preclinical stage of diabetic kidney disease and it is a risk factor for the acceleration of renal dysfunction and CKD progression among diabetics (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e23\u003c/span\u003e). The prevalence recorded in this study is comparable to the average prevalence of 6\u0026ndash;75% among type 2 diabetics (24,25). Like low GFR, hyperfiltration is a determinant of the early onset of diabetic kidney dysfunction and needs to be screened for during the course of the disease (24,26).\u003c/p\u003e \u003cp\u003eMales had significantly higher odds of developing low GFR compared to females. This finding is similar to observations in other studies where being a male was a risk factor for developing kidney impairment (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e27\u003c/span\u003e). This trend could be attributed to the fact that men tend to adopt more unhealthy lifestyle choices, which can elevate their risk of experiencing kidney dysfunction and do not report for treatment early. Additionally, elevated levels of testosterone in men have been speculated to decline kidney function by inducing podocyte damage. Also, estrogen which is in higher levels in women offers some form of \u0026ldquo;protection\u0026rdquo; to the kidney, which may explain why males tend to develop kidney dysfunction compared to females (\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR19\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGlomerular hyperfiltration has also been reported to be a direct pathogenic link between obesity and nephropathy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Interestingly, glimepiride which is a part of the sulphonylurea class of antidiabetic drugs is known to cause weight gain (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e33\u003c/span\u003e) and diabetes patients who are overweight or obese are at great risk of developing kidney impairment or dysfunction (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e15\u003c/span\u003e). As recorded in this study, patients on glimepiride had significantly higher odds of developing hyperfiltration (high GFR).\u003c/p\u003e \u003cp\u003eParticipants who previously consumed alcohol had lower odds of developing high GFR, a finding that aligns with studies conducted in Korea and Zimbabwe (34,35). This observation supports research, indicating that low to moderate alcohol consumption is associated with a reduced risk of kidney disease, although the underlying protective mechanisms remain unclear (36,37). These results imply that moderate alcohol intake may not negatively impact kidney function.\u003c/p\u003e \u003cp\u003eOn the contrary, previous studies found variables like hypertension (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e38\u003c/span\u003e), educational level (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e11\u003c/span\u003e), and obesity (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e15\u003c/span\u003e) to be associated with kidney impairment, unlike the observations in this study. Possible reasons could be differences in the study population, regional variation, and differences in the study design.\u003c/p\u003e \u003cp\u003eAlthough the study is limited by the sample size during the study period, to the best of our knowledge, it is the first to be conducted among type 2 diabetes mellitus patients in the Obuasi East municipality.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prevalence of kidney dysfunction (high and low GFR) among the study population was high. The majority with kidney dysfunction had hyperfiltration, which is an independent predictor of the preclinical stage of the progression of diabetic kidney disease, with about 9% having low GFR. Educational level, sex, previous alcohol intake, and patients on glimepiride and tamsulosin were associated with abnormal GFR. Males had higher odds of developing low GFR and patients on glimepiride had higher odds of developing high GFR. These findings indicate the need for targeted interventions in diabetes management to identify and prevent the progression of kidney dysfunction.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGFR: Glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eeGFR: Estimated glomerular filtration rate\u003c/p\u003e\n\u003cp\u003eNCD: Non-communicable disease\u003c/p\u003e\n\u003cp\u003eCHAG: Christian Health Association of Ghana\u003c/p\u003e\n\u003cp\u003eKDIGO: Kidney Disease: Improving Global Outcomes\u003c/p\u003e\n\u003cp\u003eBMI: Body Mass Index\u003c/p\u003e\n\u003cp\u003eCKD: Chronic kidney disease\u003c/p\u003e\n\u003cp\u003eFSB: Fasting blood sugar\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Committee on Human Research and Publication Ethics (CHRPE) at Kwame Nkrumah University of Science and Technology (CHRPE/AP/715/24) and the authorities at the Bryant Mission Hospital. \u0026nbsp; Participants signed a written consent form before commencing the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants signed an informed written consent form before collecting their data and samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was self-financed by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePA, DB, JBA, SBD, JM and RKDE\u0026nbsp;conceived of the study and participated in its design and coordination. PA, DB, JBA, SBD, JM, FAA, IA, CT and TSD\u0026nbsp;were involved in the recruitment of participants, data collection and analysis of samples.\u0026nbsp;PA, DB, GKA and DKS drafted the manuscript. PA, DB, FAA, IA and RKDE provided analytic and statistical support. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express sincere gratitude to the management and staff of Bryant Mission Hospital, Obuasi for their invaluable support during this research. We also extend our appreciation to the patients who voluntarily participated in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSapra A, Bhandari P. Diabetes. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2024 Mar 18]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK551501/\u003c/li\u003e\n\u003cli\u003eBoakye H, Atabila A, Hinneh T, Ackah M, Ojo-Benys F, Bello AI. The prevalence and determinants of non-communicable diseases among Ghanaian adults: A survey at a secondary healthcare level. PLOS ONE. 2023 Feb 8;18(2):e0281310.\u003c/li\u003e\n\u003cli\u003eW.H.O. Non communicable diseases [Internet]. 2023 [cited 2024 Jul 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases\u003c/li\u003e\n\u003cli\u003eIDF. IDF Diabetes Atlas [Internet]. 2024 [cited 2024 Mar 18]. Available from: https://diabetesatlas.org/\u003c/li\u003e\n\u003cli\u003eKhan MAB, Hashim MJ, King JK, Govender RD, Mustafa H, Al Kaabi J. Epidemiology of Type 2 Diabetes \u0026ndash; Global Burden of Disease and Forecasted Trends: J Epidemiol Glob Health. 2019;10(1):107.\u003c/li\u003e\n\u003cli\u003eWHO. WHO | Regional Office for Africa. 2024 [cited 2024 Mar 1]. Ghana on the offensive against diabetes. Available from: https://www.afro.who.int/countries/ghana/news/ghana-offensive-against-diabetes\u003c/li\u003e\n\u003cli\u003eTomofuji, Y., Suzuki, K., Kishikawa, T., Shojima, N., Hosoe, J., Inagaki, K., et al., 2023. Identification of serum metabolome signatures associated with retinal and renal complications of type 2 diabetes. Commun. Med. 3, 5. https://doi.org/10.1038/s43856-022-00231-3\u003c/li\u003e\n\u003cli\u003eTu C, Wang L, Wei L, Jiang Z. The role of circular RNA in Diabetic Nephropathy. Int J Med Sci. 2022 May 20;19(5):916\u0026ndash;23.\u003c/li\u003e\n\u003cli\u003eAmoako YA, Laryea DO, Bedu-Addo G, Andoh H, Awuku YA. Clinical and demographic characteristics of chronic kidney disease patients in a tertiary facility in Ghana. Pan Afr Med J. 2014;18:274.\u003c/li\u003e\n\u003cli\u003eAnnani-Akollor, M.E., Addai-Mensah, O., Fondjo, L.A., Sallah, L., Owiredu, E.-W., Acheampong, E., et al., 2019. Predominant Complications of Type 2 Diabetes in Kumasi: A 4-Year Retrospective Cross-Sectional Study at a Teaching Hospital in Ghana. Medicina (Mex.) 55, 125. https://doi.org/10.3390/medicina55050125\u003c/li\u003e\n\u003cli\u003eTannor EK, Sarfo FS, Mobula LM, Sarfo-Kantanka O, Adu-Gyamfi R, Plange-Rhule J. Prevalence and predictors of chronic kidney disease among Ghanaian patients with hypertension and diabetes mellitus: A multicenter cross-sectional study. J Clin Hypertens. 2019;21(10):1542\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eSiddiqui K, George TP, Joy SS, Alfadda AA. Risk factors of chronic kidney disease among type 2 diabetic patients with longer duration of diabetes. Front Endocrinol [Internet]. 2022 Dec 9 [cited 2024 Jul 9];13. Available from: https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2022.1079725/full\u003c/li\u003e\n\u003cli\u003eAl-Momany A, Almomani EY, Almomani HY, Al-Azzam SI, Qablan A. Evaluating kidney function and the associated risk factors among patients with type 2 diabetes mellitus: a cross-sectional study at a tertiary hospital in Jordan. BMJ Open. 2023 Oct 1;13(10):e073536.\u003c/li\u003e\n\u003cli\u003eMariye Zemicheal, T., Bahrey Tadesse, D., Tasew Atalay, H., Teklay Weldesamuel, G., Gebremichael, G.B., Tesfay, H.N., et al., 2020. Determinants of Diabetic Nephropathy among Diabetic Patients in General Public Hospitals of Tigray, Ethiopia, 2018/19. Int. J. Endocrinol. 2020, 6396483. https://doi.org/10.1155/2020/6396483\u003c/li\u003e\n\u003cli\u003eTesfahun E, Mulatu K, Deresse T, Mulugeta B. Determinants of diabetic nephropathy among diabetic patients attending in Tikur Anbesa specialized hospital, Addis Abeba, Ethiopia. Clin Epidemiol Glob Health. 2024 Jan 1;25:101481.\u003c/li\u003e\n\u003cli\u003eDelgado, C., Baweja, M., Crews, D.C., Eneanya, N.D., Gadegbeku, C.A., Inker, L.A., et al., 2022. A Unifying Approach for GFR Estimation: Recommendations of the NKF-ASN Task Force on Reassessing the Inclusion of Race in Diagnosing Kidney Disease. Am. J. Kidney Dis. 79, 268-288.e1. https://doi.org/10.1053/j.ajkd.2021.08.003\u003c/li\u003e\n\u003cli\u003eStevens, P.E., Ahmed, S.B., Carrero, J.J., Foster, B., Francis, A., Hall, R.K., et al., 2024. KDIGO 2024 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease. Kidney Int. 105, S117\u0026ndash;S314. https://doi.org/10.1016/j.kint.2023.10.018\u003c/li\u003e\n\u003cli\u003eFenta, E.T., Eshetu, H.B., Kebede, N., Bogale, E.K., Zewdie, A., et al., 2023. Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol. Metab. Syndr. 15, 245. https://doi.org/10.1186/s13098-023-01202-x\u003c/li\u003e\n\u003cli\u003eElHafeez SA, Bolignano D, D\u0026rsquo;Arrigo G, Dounousi E, Tripepi G, Zoccali C. Prevalence and burden of chronic kidney disease among the general population and high-risk groups in Africa: a systematic review. BMJ Open. 2018 Jan 1;8(1):e015069.\u003c/li\u003e\n\u003cli\u003eNaser, A.Y., Alwafi, H., Alotaibi, B., Salawati, E., Samannodi, M., Alsairafi, Z., et al., 2021. Prevalence of Chronic Kidney Diseases in Patients with Diabetes Mellitus in the Middle East: A Systematic Review and Meta-Analysis. Int. J. Endocrinol. 2021, 4572743. https://doi.org/10.1155/2021/4572743\u003c/li\u003e\n\u003cli\u003eRabieenia E, Jalali R, Mohammadi M. Prevalence of nephropathy in patients with type 2 diabetes in Iran: A systematic review and meta-analysis based on geographic information system (GIS). Diabetes Metab Syndr. 2020;14(5):1543\u0026ndash;50.\u003c/li\u003e\n\u003cli\u003eEphraim R, Biekpe S, Sakyi S, Adoba P, Agbodzakey H, Anto E. Prevalence of Chronic Kidney Disease among the High Risk Population in South-Western Ghana; A Cross Sectional Study. Can J Kidney Health Dis. 2015 Dec 1;2.\u003c/li\u003e\n\u003cli\u003eYang Y, Xu G. Update on Pathogenesis of Glomerular Hyperfiltration in Early Diabetic Kidney Disease. Front Endocrinol. 2022 May 19;13:872918.\u003c/li\u003e\n\u003cli\u003eMoriconi, D., Sacchetta, L., Chiriac\u0026ograve;, M., Nesti, L., Forotti, G., Natali, A., et al., 2023. Glomerular Hyperfiltration Predicts Kidney Function Decline and Mortality in Type 1 and Type 2 Diabetes: A 21-Year Longitudinal Study. Diabetes Care 46, 845\u0026ndash;853. https://doi.org/10.2337/dc22-2003\u003c/li\u003e\n\u003cli\u003eTonneijck, L., Muskiet, M.H.A., Smits, M.M., van Bommel, E.J., Heerspink, H.J.L., van Raalte, D.H., et al., 2017. Glomerular Hyperfiltration in Diabetes: Mechanisms, Clinical Significance, and Treatment. J. Am. Soc. Nephrol. JASN 28, 1023\u0026ndash;1039. https://doi.org/10.1681/ASN.2016060666\u003c/li\u003e\n\u003cli\u003eOh SW, Yang JH, Kim MG, Cho WY, Jo SK. Renal hyperfiltration as a risk factor for chronic kidney disease: A health checkup cohort study. PLOS ONE. 2020 Sep 3;15(9):e0238177.\u003c/li\u003e\n\u003cli\u003eGheith O, Farouk N, Nampoory N, Halim MA, Al-Otaibi T. Diabetic kidney disease: world wide difference of prevalence and risk factors. J Nephropharmacology. 2015 Oct 9;5(1):49\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eCristy Lytal. ScienceDaily. 2023 [cited 2024 Jul 27]. Why are male kidneys more vulnerable to disease than female kidneys? Mouse study points to testosterone. Available from: https://www.sciencedaily.com/releases/2023/09/230905124856.htm\u003c/li\u003e\n\u003cli\u003eDoublier, S., Lupia, E., Catanuto, P., Periera-Simon, S., Xia, X., et al., 2010. Testosterone and 17\u0026beta;-estradiol have opposite effects on podocyte apoptosis that precedes glomerulosclerosis in female estrogen receptor knockout mice. Kidney Int. 79, 404. https://doi.org/10.1038/ki.2010.398\u003c/li\u003e\n\u003cli\u003eFiller G, Ramsaroop A, Stein R, Grant C, Marants R, So A, et al. Is Testosterone Detrimental to Renal Function? Kidney Int Rep. 2016 Jul 21;1(4):306.\u003c/li\u003e\n\u003cli\u003eNKF. National Kidney Foundation. 2020 [cited 2024 Jul 27]. Kidney Failure Risk Factor: Gender (Sex). Available from: https://www.kidney.org/content/kidney-failure-risk-factor-gender-sex\u003c/li\u003e\n\u003cli\u003eBasolo A, Salvetti G, Giannese D, Genzano SB, Ceccarini G, Giannini R, et al. Obesity, Hyperfiltration, and Early Kidney Damage: A New Formula for the Estimation of Creatinine Clearance. J Clin Endocrinol Metab. 2023 Nov 17;108(12):3280\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003ePistrosch F, Passauer J, Herbrig K, Schwanebeck U, Gross P, Bornstein SR. Effect of thiazolidinedione treatment on proteinuria and renal hemodynamic in type 2 diabetic patients with overt nephropathy. Horm Metab Res Horm Stoffwechselforschung Horm Metab. 2012 Nov;44(12):914\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eAhn, J.H., Yu, J.H., Ko, S.-H., Kwon, H.-S., Kim, D.J., et al., 2014. Prevalence and Determinants of Diabetic Nephropathy in Korea: Korea National Health and Nutrition Examination Survey. Diabetes Metab. J. 38, 109\u0026ndash;119. https://doi.org/10.4093/dmj.2014.38.2.109\u003c/li\u003e\n\u003cli\u003eTapera S. Prevalence and Risk Factors for Diabetes Chronic Complications in Harare, Zimbabwe, 2014 [Internet] [masters]. University of Zimbabwe; 2014 [cited 2024 Jul 22]. Available from: http://thesisbank.jhia.ac.ke/3861/\u003c/li\u003e\n\u003cli\u003eCheungpasitporn, W., Thongprayoon, C., Kittanamongkolchai, W., Brabec, B.A., O\u0026rsquo;Corragain, O.A.,et al., 2015. High alcohol consumption and the risk of renal damage: a systematic review and meta-analysis. QJM Int. J. Med. 108, 539\u0026ndash;548. https://doi.org/10.1093/qjmed/hcu247\u003c/li\u003e\n\u003cli\u003eLi Y, Zhu B, Song N, Shi Y, Fang Y, Ding X. Alcohol consumption and its association with chronic kidney disease: Evidence from a 12-year China health and Nutrition Survey. Nutr Metab Cardiovasc Dis. 2022 Jun 1;32(6):1392\u0026ndash;401.\u003c/li\u003e\n\u003cli\u003eBerhanu A. Prevalence and Predictors of Diabetic Kidney Disease among Diabetes Patients in Diabetic Clinic of Tikur Anbessa Specialized Hospital, Addis Ababa, Ethiopia: A Cross Sectional Study [Internet]. Addis Abeba University; 2021 [cited 2024 Jul 27]. Available from: http://etd.aau.edu.et/handle/123456789/30369\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Type 2 diabetes mellitus, kidney dysfunction, estimated glomerular filtration rate (eGFR), hyperfiltration, anti-diabetics","lastPublishedDoi":"10.21203/rs.3.rs-6630356/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6630356/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: The prevalence of type 2 diabetes mellitus is rising globally, leading to an increase in diabetic kidney disease. About 20-40% of diabetic patients develop chronic kidney disease, which poses a burden on patient management. Kidney dysfunction can be prevented if the predisposing factors are identified and controlled. Also, effective management interventions can be instituted if detected early.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This cross-sectional study assessed kidney dysfunction and its determinants among type 2 diabetics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Two hundred and four (204) participants were recruited. Blood samples were collected, serum creatinine measured and the estimated glomerular filtration rate (eGFR) calculated. Data on demographics, medical history, and lifestyle factors, were collected through structured questionnaires.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The prevalence of kidney dysfunction was 32.35%, with 23.5% presenting hyperfiltration and 8.82% showing decreased GFR (\u0026lt;90 mL/min/1.73m²). Sex, history of alcohol consumption, and treatment with glimepiride or tamsulosin were associated with abnormal GFR (p\u0026lt;0.05). Male patients had significantly higher odds of developing low GFR, while patients on glimepiride had significantly higher odds of developing high GFR. However, past alcohol consumers showed lower odds of developing kidney dysfunction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: These findings underline the need for targeted interventions to manage and prevent the progression of kidney dysfunction to chronic kidney disease in this population.\u003c/p\u003e","manuscriptTitle":"Kidney Dysfunction and its Determinants among Type 2 Diabetes Mellitus Patients; A Cross-sectional Study in the Obuasi East Municipality of Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 18:27:34","doi":"10.21203/rs.3.rs-6630356/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"282851565102873250884838121642952614188","date":"2025-06-24T14:45:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T16:36:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56464737127388830903875329731995089886","date":"2025-06-12T14:39:41+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-10T14:07:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-15T12:02:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-12T10:10:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-12T10:06:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2025-05-09T16:19:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"299598b6-a6cb-46ff-acc1-d40d99d3c716","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-10T18:27:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 18:27:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6630356","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6630356","identity":"rs-6630356","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}