Non-HDL Cholesterol and Remnant Cholesterol Predict Different Components of the Metabolic Syndrome in Type 2 Diabetes Mellitus Patients in a Regional Hospital

Non-HDL Cholesterol and Remnant Cholesterol Predict Different Components of the Metabolic Syndrome in Type 2 Diabetes Mellitus Patients in a Regional Hospital | 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 Non-HDL Cholesterol and Remnant Cholesterol Predict Different Components of the Metabolic Syndrome in Type 2 Diabetes Mellitus Patients in a Regional Hospital Paul Nsiah, Samuel Acquah, Ansumana Sandy Bockarie, George Adjei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2696463/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Type 2 diabetes mellitus (T2DM) continues to increase in incidence within the ageing population of the globe. Patients with T2DM have a 2-4 times higher risk of experiencing an adverse cardiovascular event than their non-diabetic counterparts. Total cholesterol, low-density lipoprotein (LDL), triglycerides and high-density lipoprotein (HDL) cholesterol levels have been the routine biomarkers for lipid-based cardiovascular disease diagnostic and prognostic decisions in clinical practice. Recent evidence elsewhere suggests remnant cholesterol (RC) and Non-HDL cholesterol (Non-HDL-c) can serve as biomarkers with a higher predictive power for cardiovascular disease (CVD) than the aforementioned routine ones. In our context, there is limited information on the suitability and superiority of these emerging biomarkers for the assessment of CVD risk in T2DM. The current study therefore sought to examine the relationship between RC and non-HDL-c for predicting CVD in T2DM patients in the context of the obesity paradox. Apart from adiponectin level which was lower (P < 0.05), overweight/obese respondents exhibited higher (P < 0.05) mean levels for all the measured indices. Insulin resistance was independently predicted (R 2 = 0.951; adjusted R 2 = 0.951; P < 0.001) by RC, duration and fasting plasma glucose. However, Non-HDL-c predicted CVD risk (AOR = 4.31; P <0.001), hypertension (AOR = 2.24; P <0.001), resistin (AOR = 2.14; P <0.001) and adiponectin (AOR = -2.24; P <0.001) levels. Our findings point to different mechanisms by which RC and non-HDL-c contribute to the development of CVD. Introduction It is well known that people with diabetes have an elevated cardiovascular disease (CVD) risk. Diabetic patients have a two to four times higher risk of experiencing cardiovascular events than those without diabetes(Roman & Stoian, 2021), and their relative risk of dying from CVD is about twice as high (Mensah et al., 2017). Growing evidence suggests that dyslipidaemia contributes significantly to the excess risk of CVD (Hedayatnia et al., 2020 ). Common characteristic features of diabetic dyslipidaemia are the elevation of plasma triglycerides and triglyceride-rich very low-density lipoprotein (VLDL) choles terol, reduced high-density lipoprotein cholesterol (HDL-c), and an increased number of small dense low-density lipoprotein cholesterol (LDL-c) particles(Bonilha et al., 2021 ). Although LDL-c is not typically elevated in patients with diabetes, the changes in LDL-c composition that can accompany the disease make the LDL-c exceptionally atherogenic (Wadhera et al., 2016 ). Traditionally, patients are evaluated for dyslipidaemia with respect to lipids with total cholesterol (TC), LDL-c, HDL-c and triglycerides with attention to LDL-c values (Ridefelt et al., 2019 ). According to the National Cholesterol Education Program (NCEP), LDL-c is still considered the primary target of lipid-lowering therapy for CVDs (Aggarwal et al., 2021 ; Bonow, 2002 ). However, LDL-c does not include the participation of other lipoprotein fractions, such as lipoprotein a (Lp (a)) and the triglyceride-rich very low-density lipoprotein cholesterol (VLDL), which have been suggested to contribute to the development of atherosclerosis (Ridefelt et al., 2019 ). In addition, the prevalence of obesity and metabolic syndrome as well as diabetes, are rising. Identification of lipid predictors of CVD in diabetics is, therefore, very important. Two of the emerging risk factors are Non-HDL cholesterol and remnant cholesterol (RC). Non-HDL cholesterol (total cholesterol minus HDL-c) represents essentially the sum of all lipoproteins that have atherogenic properties (Aggarwal et al., 2021 ). Remnant cholesterol is the cholesterol content of triglyceride-rich lipoproteins (TRLs) that consists of VLDL, intermediate-density lipoproteins (IDL), and chylomicron remnants(Jørgensen et al., 2013 ). Epidemiological evidence has suggests that higher RC levels are significantly associated with the development of type 2 diabetes mellitus (T2DM) and contributes to the comorbidities such as hypertension (Sun et al., 2022). RC is calculated from a standard lipid profile as total cholesterol minus low-density lipoprotein (LDL) cholesterol minus high-density lipoprotein (HDL) cholesterol. A recent study on CVD risk assessment indicated that current high-density lipoprotein cholesterol–based risk calculations could lead to inaccurate risk assessment in Black adults (Zakai et al., 2022 ) (This raises a lot of issues about the estimation of cardiovascular disease risk scores. While several studies from different populations have reported that RC and Non-HDL-c (Zaka et al., 2020 ; Sun et al., 2022; Zheng et al., 2022 ) have more predictive power than LDL cholesterol in detecting CVD, very few studies have been done in sub-Saharan Africa on people with diabetes. The understanding of the role of obesity as a risk factor is still debated. The concept of the obesity paradox, which was first observed in coronary artery disease patients, hypothesizes that some individuals with normal weight could have worse outcomes than their overweight/obese counterparts (Gruberg et al., 2002 ). The role of non-routine lipid biomarkers in the obesity paradox has not been widely explored. Therefore, the current study was designed to compare the respective predictive values of LDL-c, Non-HDL-c and RC for CVD in diabetes patients in the context of obesity paradox. Materials And Methods Study Design, population and sampling A cross-sectional study was conducted at the diabetic clinic of Effia Nkwanta Regional Hospital, Takoradi, Ghana. In the Western Region, the Effia Nkwanta Regional Hospital serves as a hub for referrals to outlying healthcare facilities. Patients with diabetes can receive both general and specialty care at the diabetic clinic.This study involved 154 patients with type 2 diabetes who were randomly selected at the diabetes clinic of the Effia Nkwanta Regional Hospital. Blood Pressure and Anthropometric Measurements An experienced nurse at the diabetic clinic took the patient's blood pressure from the left upper arm using a mercury sphygmomanometer and a stethoscope. Prior to measurement, participants were instructed to rest for at least five minutes. The blood pressure reading was calculated as the average of two readings taken five minutes apart. Height and weight were measured for computation of body mass index (BMI). Height was measured barefoot using a wall-mounted ruler, to the nearest 0.1 cm. Weight was measured in light clothing to the nearest 0.1 kg with a weighing scale. BMI was computed by dividing the weight by the square of the height, BMI = BMI (kg/m 2 ) = weight/height 2 . Waist and hip circumferences were measured to the nearest 0.1 cm using a measuring tape. Waist was divided by hip to obtain the waist-to-hip ratio (WHR). Total body fat (%) was estimated using the Omron BF511 Body Composition Monitor (Omron Corporation, Japan). Sample Collection, Preparation and Biochemical Assays Five milliliters (5 ml) of venous blood sample was collected after an overnight fast. One (1) milliliter of blood was dispensed into tubes containing fluoride oxalate, and the remaining 4 ml of blood was dispensed into gel separator tubes. The sample in the fluoride oxalate was used for fasting plasma glucose (FPG) estimation. The gel separator tubes were placed in a centrifuge and spun at 3000 rpm for 5 min to obtain the serum. FPG was measured immediately, and the serum for the measurement of other biochemical variables was aliquoted and stored at − 20°C until analysis. Total cholesterol (TC), HDL-c, triglyceride (TG), LDL-c, FPG, alanine transaminase (ALT) and gamma-glutamyl transferase (GGT) were estimated using an automated chemistry analyzer (Selectra Pro S System, Elitech Group, France). Serum adiponectin, resistin and high sensitive C-reactive protein (hs-CRP) were determined by commercially available enzyme-linked immunosorbent assay (ELISA) test kits procured from DRG International, following manufacturer’s instructions (DRG International, NJ, USA). Ten-year cardiovascular disease risk score was assessed using the Framingham risk score. Insulin resistance was assessed by the triglyceride glucose index derived by the formula, TyG = ln[FBS(mg/dl) × TG (mg/dl)]/2 developed by Simental-Mendía et al. ( 2008 ). Ethical Consideration The study was approved by the Institutional Review Board of University of Cape Coast (UCCIRB). In addition, institutional approval was obtained from the Effia Nkwanta Regional Hospital. Furthermore, the conduct of the study was in strict adherence to the ethical standards of the Ghana Health Service (GHS) and the World Medical Association Declaration of Helsinki. Above all, written informed consent was obtained from each study participant, and strict confidentiality of participants’ information was maintained throughout the study. STATISTICAL ANALYSIS Data obtained were analyzed by Statistical Package for Social Sciences (SPSS) software version 17. Data are presented as mean ± standard deviation (SD) or percentages, where appropriate. Independent sample t-test was used to compare the mean levels of measured indices between weight groups and between sexes. Pearson correlation, stepwise linear and logistic regression analyses were performed. A p-value < 0.05 was considered statistically significant. Results There were 154 participants aged 40–78 years and made up of 91 females and 63 males. The mean age was 52 years. Generally, the mean values for the indices assessed were within the reference range, except for FPG, LDL cholesterol, Non-HDL-cholesterol, BMI and systolic blood pressure, which exceeded the optimal levels (Table 1 ). The majority (75%) of the respondents were hypertensive. When the various indices were analyzed by gender, there was no statistically significant (P > 0.05; Table 2 ) difference between the male and female respondents except percentage body fat (P < 0.001; Table 2 ), which was expectedly higher in females. Table 1 Clinical and biochemical characteristics of respondents Variable Mean ± SD Range Age (years) 52.20 ± 8.70 40–78 FPG (mmol/l) 8.30 ± 4.70 3.5–25.1 TC (mmol/l) 5.10 ± 1.00 3.1–7.8 TG (mmol/l) 1.20 ± 0.50 0.40–2.90 HDL-c (mmol/l) 1.20 ± 0.20 0.70–1.60 LDL-c (mmol/l) 3.10 ± 1.00 1.20–6.10 Non-HDL-c (mmol/L) 3.90 ± 1.00 1.60–6.90 RC (mmol/L) 0.54 ± 0.23 0.20–1.30 TyG Index 8.70 ± 0.80 7.40–10.70 BMI (kg/m 2 ) 26.90 ± 4.10 15.10–40.50 CVD risk (%) 14.30 ± 8.70 3.00–45.00 hs-CRP (mg/dL) 1.60 ± 0.80 0.11–3.80 Adiponectin (µg/ml) 7.70 ± 4.70 1.30–20.00 Resistin (ng/mL) 13.50 ± 3.70 4.80–22.30 Systolic BP (mmHg) 142 ± 16.00 102–197 Diastolic BP (mmHg) 88 ± 11.00 64–124 MAP(mmHg) 106 ± 12.00 78–146 FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT gamma-glutamyl transferase; TC = total cholesterol; TyG = triglyceride glucose index; * = significant p-value. Table 2 Clinical and biochemical characteristics of respondents by gender Parameter Male (63) Female (91) P-value Age (years) 52.2 ± 7.90 54.0 ± 9.07 0.217 Duration (years) 4.0 ± 3.10 4.6 ± 3.30 0.274 FPG (mmol/L) 7.8 ± 4.60 8.5 ± 4.80 0.372 TC (mmol/L) 5.1 ± 1.00 5.1 ± 1.00 0.932 HDL-c (mmol/L) 1.2 ± 0.20 1.2 ± 0.20 0.24 LDL-c (mmol/L) 3.4 ± 1.00 3.3 ± 1.00 0.576 TG (mmol/L) 1.1 ± 0.50 1.2 ± 0.50 0.321 TyG Index 8.66 ± 0.78 8.81 ± 0.74 0.212 RC (mmol/L) 0.52 ± 0.23 0.56 ± 0.23 0.344 Non-HDL (mmol/L) 3.8 ± 1.00 3.9 ± 1.10 0.754 BMI (Kg/m 2 ) 26.7 ± 3.90 27.0 ± 4.30 0.61 WHR 0.90 ± 0.05 0.89 ± 0.06 0.189 CVD Risk (%) 15.4 ± 10.30 13.6 ± 7.30 0.186 DBP (mmHg) 87.4 ± 11.50 88.2 ± 10.10 0.397 SBP (mmHg) 140.7 ± 15.80 143.0 ± 16.90 0.66 ALT (U/L) 24.5 ± 7.10 22.9 ± 8.00 0.181 GGT (U/L) 22.3 ± 7.10 22.7 ± 8.00 0.758 Adiponectin (µg/ml) 7.3 ± 4.85 7.9 ± 4.60 0.467 Resistin (ng/mL) 13.4 ± 3.40 13.6 ± 3.80 0.711 hs-CRP 1.6 ± 0.90 1.6 ± 0.80 0.842 Fat (%) 26.84 ± 8.97 33.2 ± 7.9 < 0.001* FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyil transferase; TC = total cholesterol; TyG = triglyceride glucose index; * = significant p-value. In relation to BMI, 2%, 18%, 24% and 56% of respondents were underweight, obese, normal weight and overweight, respectively. Respondents were grouped into underweight/normal weight and overweight/obese categories for comparison of the various indices (Table 3 ). Total cholesterol, LDL, Non-HDL cholesterol, blood pressure, hs-CRP, resistin and fat percentage levels were significantly (P 0.05; Table 3 ) between the two weight groups. Table 3 Clinical and biochemical characteristics of respondents by weight category Parameter Normal/underweight (40) Overweight/obese (114) P-value Age (years) 53.80 ± 10.13 53.09 ± 8.09 0.655 Duration (years) 4.04 ± 3.49 4.51 ± 3.09 0.482 FPG (mmol/L) 8.24 ± 4.75 8.26 ± 4.75 0.981 TC (mmol/L) 4.58 ± 0.74 5.22 ± 1.00 < 0.001* HDL-c (mmol/L) 1.19 ± 0.22 1.2 ± 0.20 0.774 LDL-c (mmol/L) 2.85 ± 0.78 3.47 ± 0.74 < 0.001* TyG Index 8.72 ± 0.76 8.76 ± 0.76 0.817 Triglycerides (mmol/L) 1.18 ± 0.59 1.19 ± 0.48 0.933 RC (mmol/L) 0.54 ± 0.27 0.54 ± 0.21 0.833 Non-HDL (mmol/L) 3.39 ± 0.81 4.02 ± 1.06 0.001* CVD Risk (%) 13.05 ± 7.96 14.78 ± 8.90 0.279 Diastolic BP (mmHg) 84.78 ± 9.93 88.93 ± 10.68 0.033* Systolic BP (mmHg) 137.63 ± 15.93 143.45 ± 16.65 0.047* ALT (U/L) 22.80 ± 6.87 23.81 ± 7.42 0.451 GGT (U/L) 22.3 ± 7.10 22.68 ± 7.85 0.617 Adiponectin (µg/ml) 9.14 ± 4.83 7.15 ± 4.58 0.021* Resistin (ng/mL) 11.56 ± 3.89 14.20 ± 3.66 < 0.001* hs-CRP 1.27 ± 0.70 1.72 ± 0.87 0.004* Fat (%) 23.58 ± 7.47 33.04 ± 8.03 < 0.001* FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; BMI = body mass index; TC = total cholesterol; * = significant p-value. When BMI-based data categorization was further disaggregated by sex, a similar trend was observed in terms of parameters that differed significantly in the two weight groups, but the specific indices differed according to sex (Tables 4 a and 4 b). In the female respondents, only total cholesterol, LDL-c, Non-HDL-c, hs-CRP and percentage body fat levels were significantly (P < 0.05; Table 4 a) higher in the overweight/obese group. However, in the male respondents, total cholesterol, LDL-c, Non-HDL-c, hs-CRP, resistin, diastolic blood pressure and percentage body fat levels were higher but adiponectin level was lower (P < 0.05; Table 4 b) in the overweight/obese group. Table 4 a: Clinical and biochemical characteristics that differed significantly in females by weight category Parameter Underweight/Normal (18) Overweight/Obese (73) P-value TC (mmol/L) 4.54 ± 0.81 5.17 ± 0.97 0.012 LDL-c (mmol/L) 2.78 ± 0.81 3.40 ± 0.96 0.013 Non-HDL-c (mmol/L) 3.29 ± 0.84 3.96 ± 1.03 0.012 hs-CRP 1.23 ± 0.64 1.68 ± 0.82 0.032 Fat (%) 27.12 ± 6.61 34.66 ± 7.51 < 0.001 LDL-c = low-density lipoprotein cholesterol; HDL-c = low-density lipoprotein cholesterol; hs-CRP = high sensitivity C-reactive protein; TC = total cholesterol; Non-HDL-c = non-HDL cholesterol; Fat (percentage body fat Table 4 b: Clinical and biochemical characteristics that differed significantly in males by weight category Parameter Underweight/Normal (16) Overweight/Obese (47) P-value TC (mmol/L) 4.51 ± 0.67 5.25 ± 1.03 0.01 LDL (mmol/L) 2.82 ± 0.71 3.55 ± 1.00 0.009 Non-HDL-c (mmol/L) 3.40 ± 0.80 4.05 ± 1.09 0.03 hs-CRP 1.13 ± 0.68 1.79 ± 0.93 0.012 Adiponectin 10.26 ± 4.93 6.33 ± 4.44 0.004 Resistin 11.05 ± 2.76 14.18 ± 3.28 0.001 Systolic BP (mmHg) 132.38 ± 9.93 143.55 ± 16.52 0.013 Fat (%) 20.00 ± 7.44 29.17 ± 8.28 < 0.001 FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase; TyG = triglyceride glucose index.; TC = total cholesterol Since the majority (75%) of respondents were hypertensive on appropriate treatment regimens, the various indices were compared between the hypertensive and normotensive groups regardless of gender and weight category to examine the effects of hypertension on the measured indices. As expected, the hypertensive group demonstrated significantly (P 0.05; Table 5 ) in the two groups. Table 5 Comparison of clinical and biochemical characteristics of respondents by hypertension status Parameter Normotensive (39) Hypertensive (115) P -value FPG (mmol/L) 7.90 ± 5.00 8.40 ± 4.70 0.58 TC (mmol/L) 4.50 ± 0.10 5.20 ± 0.10 < 0.001* HDL-c (mmol/L) 1.20 ± 0.19 1.20 ± 0.21 0.29 TG (mmol/L) 1.00 ± 0.50 1.30 ± 0.50 0.004* TyG index 8.50 ± 0.80 8.80 ± 0.70 0.02* LDL-c (mmol/L) 2.90 ± 0.80 3.50 ± 1.00 0.001* RC (mmol/L) 0.50 ± 0.20 0.60 ± 0.20 0.004* Non-HDL (mmol/L) 3.30 ± 0.10 4.00 ± 1.00 0.001* CVD risk (%) 8.30 ± 5.80 16.40 ± 8.50 < 0.001* hs-CRP (mg/dl) 1.00 ± 0.57 1.80 ± 0.80 < 0.001* Resistin (ng/ml) 11.30 ± 2.80 14.30 ± 3.60 < 0.001* Adiponectin(µg/ml) 11.40 ± 4.00 6.40 ± 4.20 < 0.001* BMI (kg/m 2 ) 24.60 ± 3.45 27.60 ± 4.10 < 0.001* WHR 0.86 ± 0.06 0.89 ± 0.05 0.416 WC (cm) 87.40 ± 10.40 92.90 ± 8.40 0.001* ALT (U/L) 22.20 ± 6.80 24.00 ± 7.40 0.17 GGT (U/L) 23.20 ± 8.20 22.30 ± 7.50 0.55 Duration (years) 2.80 ± 2.30 4.90 ± 3.30 0.002* Fat (%) 24.43 ± 6.78 32.66 ± 8.58 < 0.001 FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase; TyG = triglyceride glucose index; TC = total cholesterol; * = significant p-value. Although the mean levels of most of the measured indices appear to be within the acceptable reference ranges, the levels of biomarkers observed for a reasonable proportion of individuals in the entire sample and by BMI categorization fell outside the acceptable limits (Table 6 ). For instance, the prevalence of dyslipidaemia associated with the various components of the lipid profile in the entire respondents ranged from 18% for triglycerides to 74% for LDL-c (Table 6 ). In relation to weight groups, the overweight/obese group appear to consistently exhibit a relatively higher proportion of individuals with unfavourable levels of various cardiometabolic indices except for HDL-c, TyG and remnant cholesterol that the underweight/normal weight group seem to have a higher proportion of individuals with unfavourable levels. (Table 6 ). Table 6 Prevalence of selected cardiometabolic risk factors among respondents Parameter underweight/normal weight (34) Overweight/obese (120) Entire respondents (154) TC (≥ 5.2 mmol/l) 6 (17.65%) 56 (46.67%) 62 (40.26%) HDL-c (< 1.0 in males, 2.6 mmol/L) 22 (64.71%) 92 (76.67%) 114 (74.01%) Non-HDL (> 3.4 mmol/L) 14 (41.18%) 86 (71.67%) 99 (64.29%) RC (> 0.6 mmol/L) 15 (44.12%) 49 (40.83%) 64 (41.56%) TG (> 1.7mmol/l) 5 (14.71%) 23 (19.17%) 28 (18.18%) BP (> 140/90mmHg) 19 (53.44%) 96 (80%) 115 (74.68%) hs-CRP (> 1.0 mg/l) 16 (47.06%) 92 (76.67%) 108 (70.13%) Adiponectin ( 14.8 ng/L 5 (14.71%) 58 (48.33%) 63 (40.91%) CVD risk (> 10%) 22 (64.71%) 86 (71.67%) 108 (70.13%) TyG > 8 126 (81.82%) 28 (82.34%) 98 (81.67%) HDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; TC = total cholesterol; TG = triglycerides; TyG = triglyceride glucose index. In bivariate correlational analysis to examine linear relationships between biomarkers irrespective of gender and BMI in the entire sample, the usual expected linear relationships were observed among the measured indices. As indicated in Table 7 , the duration of diabetic condition exhibited a positive correlation with TyG, hs-CRP, CVD risk, blood pressure, ALT, percentage fat and triglyceride levels but a negative correlation with adiponectin. FPG correlated positively with total cholesterol, TyG, LDL-c, remnant cholesterol, Non-HDL-c and GGT. Additionally, total cholesterol correlated negatively with adiponectin but positively with resistin, hs-CRP, ALT and GGT. Adiponectin continued to demonstrate a negative correlation with Non-HDL and LDL, even as TyG showed a positive correlation with hs-CRP, resistin, ALT and GGT. Expectedly, LDL-c correlated positively with resistin, hs-CRP, ALT and GGT, with Non-HDL correlating positively with resistin, hs-CRP, ALT and GGT. Interestingly, no correlation was observed among hs-CRP, adiponectin, resistin, ALT and GGT in the current study. Table 7 Bivariate correlation among selected clinical and biochemical characteristics of respondents Parameter Correlation coefficient P -value Duration and TyG 0.222 0.014 Duration and hs-CRP 0.193 0.034 Duration and CVD risk 0.417 < 0.001 Duration and Adiponectin -0.234 0.009 Duration and systolic 0.25 0.005 Duration and diastolic 0.273 0.002 Duration and ALT 0.18 0.047 Duration and fat% 0.248 0.006 Duration and TG 0.179 0.048 FPG and cholesterol 0.341 < 0.001 FPG and TyG 0.813 < 0.001 FPG and LDL-c 0.297 < 0.001 FPG and RC, Non-HDL-c 0.394,0.364 < 0.001 FPG and GGT 0.256 0.001 TC and HS-CRP, resistin 0.596,0.558 < 0.001 TC and adiponectin -0.447 < 0.001 TC and ALT, GGT 0.234, 223 0.003, 0.005 TyG and hs-CRP, resistin 0.318, 0.25 < 0.001, 0.002 TyG and ALT, GGT 0.171, 0.272 0.034, 0.001 LDL-c and hs-hs-CRP, resistin 0.603, 0.558 < 0.001 LDL-c and adiponectin -0.459 < 0.001 LDL-c and ALT, GGT 0.212, 0.207 0.008, 0.01 Non-HDL-c and hs-CRP, resistin 0.648, 0.577 < 0.001 Non-HDL-c and adiponectin -0.474 < 0.001 Non-HDL-c and ALT, GGT 0.237, 0.226 0.003, 0.005 FPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase;TC = total cholesterol; TyG = triglyceride glucose index. In a stepwise linear regression analysis with TyG as dependent variable, remnant cholesterol, duration of diabetes ,and FPG were found to be independent predictors (R 2 = 0.951; adjusted R 2 = 0.951; P < 0.001) predictors of TyG with the model explaining about 95% of observed variation. Table 8 presents predictors of cardiovascular disease risk, adiponectin, resistin, hs-CRP and hypertension in the studied sample by multivariate logistic regression analyses. On average, the cardiovascular disease risk decreased by 2.41 for females compared with males (P = 0.003; Table 8 ) but increased by 7.88 for ages 50–59 years and 14.45 for ages 60 years and above compared to ages 40–49 years (P < 0.001; Table 8 ). For a unit increase in non-HDL-c, the risk of having a cardiovascular disease increased by 4.31 on average. Table 8 Predictors of selected clinical and biochemical outcome variables Variable AOR (95% CI) P-value CVD risk Sex Male 1 Female -2.41 (-3.98, -0.85) 0.003* Age 40–49 1 50–59 7.88 (6.14, 9.61) < 0.001* ≥ 60 14.45 (12.38, 16.52) < 0.001* RC 1.37 (-2.32, 5.06) 0.464 Non-HDL-c 4.31 (3.49, 5.13) < 0.001* Adiponectin Sex Male 1 Female 0.45 (-0.89, 1.79) 0.508 Age 40–49 1 50–59 -2.11 (-3.60, -0.63) 0.006* ≥ 60 -0.99 (-2.76, 0.79) 0.274 RC -2.54 (-3.42, 2.91) 0.874 Non-HDL -2.24 (-2.94, -1.54) < 0.001* Resistin Sex Male 1 Female 0.47 (-4.8, 1.41) 0.328 Age 40–49 1 50–59 0.58 (-0.46, 1.63) 0.273 ≥ 60 -1.81 (-3.06, -0.56) 0.005* RC 0.29 (-1.94, 2.51) 0.801 Non-HDL 2.14 (1.64, 2.64) < 0.001* Hs-hs-CRP Sex Male 1 Female -0.04 (-0.25, 0.16) 0.683 Age* 40–49 1 50–59 0.19 (-0.04, 0.41) 0.106 ≥ 60 0.41 (0.14, 0.68) 0.003* RC 0.53 (0.05, 1.02) 0.030* Non-HDL 0.48 (0.37, 0.59) < 0.001* Hypertension Sex Male 1 Female 1.27 (0.56, 2.87) 0.571 Age 40–49 1 50–59 3.02 (1.24, 7.37) 0.015* ≥ 60 6.24 (1.59, 24.56) 0.009* RC 3.23 (0.43, 24.33) 0.255 Non-HDL 2.24 (1.35, 3.72) 0.002* AOR = Adjusted odds ratio; RC = remnant cholesterol; HDL = low-density lipoprotein cholesterol; CVD = cardiovascular disease; hs-HS-CRP = high sensitivity C-reactive protein; * = significant p-value Adiponectin levels decreased with increased (P < 0.05; Table 8 ) Non-HDL levels and age 50–59 years only, whereas resistin levels decreased with increasing age at 60 years and above. With respect to hs-CRP, on average, participants showed decreased likelihood in its rise for a unit increase in Non-HDL, remnant cholesterol and age 60 years and above. The likelihood of hypertension in participants increased from 3-fold at age 50–59 years to about 6-fold at age 60 years and above compared to age 40–49 years (P < 0.05; Table 8 ). Above all, a unit rise in Non-HDL-c levels increased one’s risk of hypertension by approximately 2-fold (P = 0.002; Table 8 ). Discussion This cross-sectional study in 154 type 2 diabetes mellitus (T2DM) patients at Effia-Nkwanta Regional Hospital examined the predictive power of remnant cholesterol and non-HDL cholesterol in predicting the likelihood of developing cardiovascular disease in the context of obesity paradox. Our results showed that apart from the body fat level, which was higher in females, mean levels of all the other indices were comparable between the genders. The comparable mean levels of biomarkers between gender in the current study (Table 2 ) are in variance with findings from studies in Nigeria (Aderibigbe et al., 2018 ), China (Yin et al., 2021 ) and Spain (Ramírez-Morros et al., 2022 ) which reported gender variations in measured indices such as total cholesterol, LDL-c and BMI. It has been suggested that T2DM females are likely to be older with higher BMI due to the slow pace of visceral and ectopic fat accumulation, which may facilitate insulin resistance and subsequent development of T2DM (de Ritter et al., 2020 ; Ramírez-Morros et al., 2022 ). As a result, the female is likely to be exposed to less favourable circulating glucose levels for a relatively longer time than the male counterpart, increasing the risk of development of worsening CVD risk markers (de Ritter et al., 2020 ; Ramírez-Morros et al., 2022 ). The difference between the current work and the previous ones (Aderibigbe et al., 2018 ; Yin et al., 2021 ; Ramírez-Morros et al., 2022 ) could be ascribed to differences in sample characteristics such as age range, duration of diabetic condition, existing comorbidities, the extent of adherence to treatment regimen and related lifestyle activities. For instance, respondents in the previous studies were on average older with a longer duration of diabetes than those in the current study. Indeed, unfair treatment differences between genders coupled with culturally induced lifestyle factors that have an impact on treatment outcomes have been reported (de Ritter et al., 2020 ; Ramírez-Morros et al., 2022 ). The above reasons may partially explain the reduction in the odds of CVD risk in women compared to men observed in the current study (Table 8 ). This notwithstanding, females had higher body fat percentage than males in the current study. This observation consistent with sex-induced variation in fat metabolism where females have an overall higher fat content (Blaak, 2001 ; O’Sullivan, 2009 ; Wu & O’Sullivan, 2011 ) due to lower basal fat oxidation and efficient fat storage (Blaak, 2001 ; O’Sullivan, 2009 ) despite reduced food intake (Kant & Graubard, 2006 ) and increased tendency for fat-based energy during exercise (Tarnopolsky, 2008 ). When data was disaggregated by BMI categorization (Table 3 ) irrespective of gender, with a view to examining the existence of the obesity paradox in our sample, total cholesterol, LDL-c, non-HDL-c, blood pressure, HS-CRP, resistin and percentage body fat levels were higher, but adiponectin was lower in the overweight/obese group compared with the underweight/normal weight. The observed trend of mean levels of biomarkers between the weight groups in the entire sample, irrespective of gender, was generally observed in the gender-based weight categorization data (Tables 4 a and 4 b). However, diastolic blood pressure, resistin and adiponectin levels differed in the weight-based male respondents only (Table 4 b). Since the expected trend of overweight/obese respondents demonstrating higher levels of measured indices was observed, it implies that the obesity paradox could not be detected in this study. The obesity paradox has been reported in other conditions such as hypertension (Uretsky et al., 2007 ), T2DM (Doehner et al., 2012 ), and coronavirus disease 2019 (Lavie et al., 2021 ), where overweight/obese respondents demonstrated lower mortality numbers than their underweight/normal weight counterparts. Since cardiovascular disease mortality has been found to be associated with alterations in the levels of various biomarkers, it should be possible to examine the obesity paradox in relation to levels of relevant biomarkers as variables as was attempted in the current study. In terms of proportion of respondents with unfavourable levels of biomarkers, the overweight/obese respondents appeared to demonstrate higher a proportion of individuals for all the measured biomarkers except HDL-c and remnant cholesterol. Thus, in general, the overweight/obese respondents in the current study did not demonstrate any prognostic advantage over the underweight/normal weight counterpart to warrant any speculation of the existence of an obesity paradox in the current study. The relatively small sample size of the current study compared with previous ones (Gruberg et al., 2002 ; Uretsky et al., 2007 ; Doehner et al., 2012 ; Lavie et al., 2021 ), could be partly responsible for the difference in observation regarding the obesity paradox, a larger study will be necessary to fully examine the concept in the Ghanaian context. Hypertension remains a critical comorbidity in patients with T2DM, with 74.68% of respondents in the current study being hypertensive. In Ghana, the prevalence of hypertension in the general population has been reported to be 27%, with 65% of the affected unaware that they have the condition (Bosu & Bosu, 2021 ), implying that the high prevalence of hypertension in T2DM diabetes patients in the current report is expected. The prevalence observed in this study appears higher than a recent report in Ethiopia (37.5%) (Abdissa & Kene, 2020 ) but similar to one from Afghanistan (70.5%) (Naseri et al., 2022 ) and Jordan (74.6%) (Salameh et al., 2022 ). Both the difference and similarity in prevalence could be ascribed to variations in respondent’s characteristics. For instance, the Ethiopian study involved both types 1 and 2 diabetes mellitus patients who were relatively younger in age and duration of the condition, predominantly farmers and less overweight/obese. However, the current study and similar ones (Naseri et al., 2022 ; Salameh et al., 2022 ) involved only T2DM patients with longer duration of condition, older, less physically active and more overweight/obese. This is critical because the successful management of hypertension and diabetes is influenced heavily by obesity and other modifiable lifestyle factors. Indeed, diabetes and hypertension are closely linked to similar risk factors and pathogenic mechanisms through inflammation and oxidative stress. Thus, through inflammation and oxidative stress due to ineffective control of blood glucose level, as is normally the case for older T2DM patients, hypertension may develop, accounting for the high prevalence observed in the current study. Indeed, the mean fasting glucose level in the current study was 8.3 mmol/L as against the American Diabetes Association recommended range of 4-7.5 mmol/L (AMA, 2022), suggesting a suboptimal glucose control in our respondents, which may have favoured the development of hypertension. Although a large Japanese longitudinal study in a general adult population found high fasting glucose as an independent risk factor for hypertension (Kuwabara et al., 2019 ), the current study rather observed that age ≥ 60 years and non-HDL-c increased the odds of having hypertension. Additionally, an increase in non-HDL-c levels increased the risk of CVD events, and raised hs-CRP and resistin levels whilst lowering adiponectin levels in our study respondents. Above all, the mean Non-HDL-c level was higher in hypertensives than in normotensive respondents. These findings are generally in support of several studies that have reported a positive association between Non-HDL-c and hypertension, arterial stiffness and ischemic heart disease, and a cardiovascular event (de Oliveira Alvim et al., 2017 ; Zaka et al., 2020 ; Calling et al., 2021 ). Our findings also support the view non-HDL-c could be a reliable marker for the clinical management of dyslipidaemia in T2DM patients (Peters, 2008 ). This assertion is supported by the finding that non-HDL-c correlated negatively (r = -0.474; P < 0.001) with adiponectin but positively with resistin (r = 0.577; P < 0.001), hs-CRP (r = 0.648; P < 0.001) and FPG (r = 0.364; P < 0.001) suggesting that it promotes inflammation in a similar trend to LDL-c but with a slightly increased strength of the observed correlations in the current report. Therefore, targeting Non-HDL-c as a prognostic marker in clinical management of dyslipidaemia associated with T2DM is likely to be more reliable since its reduction connotes a reduction in inflammation and improved insulin sensitivity for a favourable lipid profile and glucose regulation, in line with the reported role of adiponectin (Yadav et al., 2013; Li et al., 2020 ). Another lipid index that is emerging as a biomarker for CDV in recent times is remnant cholesterol. Several studies from China have reported remnant cholesterol as a predictor of hypertension in adult populations with or without T2DM (Chen et al., 2022 ; Zheng et al., 2022 ). Another study has reported remnant cholesterol as a predictor of new-onset diabetes (Xie et al., 2022). Interestingly, the current study only observed that remnant cholesterol correlated positively and FPG (r = 0.394; P < 0.001) and could predict hs-CRP level in a logistic regression analysis. Remnant cholesterol together with duration of diabetic condition and FPG could independently (R 2 = 0.951; adjusted R 2 = 0.951; P < 0.001) predict TyG level in a stepwise linear regression analysis in our sample with the model explaining about 95% of the observed variation. Thus, unlike previous studies (Chen et al., 2022 ; Zheng et al., 2022 ), remnant cholesterol could not predict CVD and hypertension in this study. Differences in study design, sample size and respondents characteristics could be responsible for the variation in findings between the current study and the previous ones (Chen et al., 2022 ; Zheng et al., 2022 ). However, the predictive ability of remnant cholesterol for hs-CRP and TyG in the current report seems to provide an indirect corroboration to the findings of Xie et al. ( 2021 ) since insulin resistance and inflammation are critical factors for the development of diabetes mellitus. Therefore, any agent with the potential to initiate insulin resistance and inflammation in a sustained manner can potentiate the development of T2DM. Putting it all together, our findings point to a role for both remnant cholesterol and Non-HDL-c in the development of CVD through different mechanisms. Conclusion Non-HDL cholesterol measurement provides a single index of all the atherogenic, apolipoprotein (apo) B–containing lipoproteins, LDL, VLDL and intermediate density lipoprotein (IDL). Again, measurement of non-HDL cholesterol is more practical, reliable, and inexpensive and is accepted as a surrogate marker for apo B. Remnant cholesterol influences markers for inflammation as well as glucose control.Our study supports a growing evidence that Non-HDL cholesterol is a better treatment target than LDL cholesterol. However a larger study may be necessary to examine the real impact of obesity paradox in the development of CVD in T2DM in our setting. Declarations Email of corresponding author [email protected] Acknowledgement: The Effia Nkwanta Regional Hospital's patients, administration, and personnel are gratefully acknowledged by the writers. Ethical Approval The study protocol was reviewed and approved by the Institutional Review Board of University of Cape Coast (UCCIRB). In addition, institutional approval was obtained from the Effia Nkwanta Regional Hospital. Furthermore, the conduct of the study was in strict adherence to the ethical standards of the Ghana Health Service (GHS) and the World Medical Association Declaration of Helsinki. Consent to participate. Written informed consent was obtained from each study participant, and strict confidentiality of participants’ information was maintained throughout the study. Consent to publish Approval for publication of research findings, including participants’ details, was obtained before enrollment into the study. Authors' contributions Conceptualization: Paul Nsiah and Samuel Acquah Data curation: Paul Nsiah and Ebenezer Aniakwaa-Bonsu Formal analysis: Samuel Acquah, George Adjei, Paul Nsiah and Ebenezer Aniakwaa-Bonsu Investigation: Paul Nsiah, Samuel Acquah and Ansumana Sandy Bockarie Methodology: Paul Nsiah, Samuel Acquah and Oksana Debrah Project administration: Paul Nsiah. Resources: Paul Nsiah, Samuel Acquah and Ansumana Sandy Bockarie Supervision: Paul Nsiah. Validation: Eliezer Togbe and Paul Poku Sampene Ossei Writing original draft: Paul Nsiah and Samuel Acquah Writing review & editing: Paul Nsiah, Samuel Acuah, Ansumana Sandy Bockarie and Oksana Debrah Data availability statement The authors confirm that the data supporting the findings of this study are available. Disclosure statement No potential conflict of interest was reported by the authors. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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Journal of the American College of Cardiology , 80 (22), 2104–2115. https://doi.org/10.1016/j.jacc.2022.09.027 Zheng, X., Han, L., & Shen, S. (2022). Hypertension, remnant cholesterol and cardiovascular disease: evidence from the China health and retirement longitudinal study. Journal of Hypertension , 40 (11), 2292–2298. https://doi.org/10.1097/hjh.0000000000003259 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2696463","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":184943912,"identity":"b39cc909-76c9-495b-aa59-9114c29c2b03","order_by":0,"name":"Paul Nsiah","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYJCCA4wNNkCKsfEAKVrSQFoaiNcCVHwYopco1ebS7Q8P/Nxx3m5t+2GgLTU20QS1WM45Y3Cw98zt5G1nEoFajqXlNhDSYnAjh+EAb9vtZLMDQC1AFxKjJf3Bwb9t55LNzj8kWkuCwWHetgN2ZjeItcVyRo7BYdm25ASzG0BbEojxi7lE+uOPb9vs7M3Opz988KHGhgiHQelEsMoEQsqRtdgTo3gUjIJRMApGKAAAdY1NoVSSTnAAAAAASUVORK5CYII=","orcid":"","institution":"University of Cape Coast","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Nsiah","suffix":""},{"id":184943915,"identity":"b1d6e623-5985-4783-957c-08a24c472d26","order_by":1,"name":"Samuel Acquah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Acquah","suffix":""},{"id":184943918,"identity":"5f9c6a45-d22f-43ce-9b8a-cb7a2f936c5b","order_by":2,"name":"Ansumana Sandy Bockarie","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ansumana","middleName":"Sandy","lastName":"Bockarie","suffix":""},{"id":184943920,"identity":"ed31d309-7d0e-43ce-b340-1f7a7e2063ba","order_by":3,"name":"George Adjei","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Adjei","suffix":""},{"id":184943922,"identity":"cc3481f1-8a1f-4fdc-a3fe-f9a27ad4a48f","order_by":4,"name":"Ebenezer Aniakwaa-Bonsu","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ebenezer","middleName":"","lastName":"Aniakwaa-Bonsu","suffix":""},{"id":184943923,"identity":"854680fb-acad-45e1-8fbd-96c4dbc300f8","order_by":5,"name":"Eliezer Togbe","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eliezer","middleName":"","lastName":"Togbe","suffix":""},{"id":184943925,"identity":"8cbb09c1-e372-478f-926d-00b4cf3cc51a","order_by":6,"name":"Paul Poku Sampene Ossei","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paul","middleName":"Poku Sampene","lastName":"Ossei","suffix":""},{"id":184943927,"identity":"9b75821f-c7ec-4411-a080-daf81544341d","order_by":7,"name":"Oksana Debrah","email":"","orcid":"","institution":"University of Cape Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Oksana","middleName":"","lastName":"Debrah","suffix":""}],"badges":[],"createdAt":"2023-03-15 12:59:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2696463/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2696463/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":34657561,"identity":"9be75641-db8e-4aa1-ad39-8d7260011ed4","added_by":"auto","created_at":"2023-03-22 15:14:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2696463/v1/bacd974a-0a2f-48ac-b8aa-f447bcf0c271.pdf"},{"id":34657555,"identity":"a57378ac-29a9-45b4-9d5f-d46716a50182","added_by":"auto","created_at":"2023-03-22 15:14:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":508318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2696463/v1/3a6c2427-0787-4008-82cd-e1bdbf682cae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eNon-HDL Cholesterol and Remnant Cholesterol Predict Different Components of the Metabolic Syndrome in Type 2 Diabetes Mellitus Patients in a Regional Hospital\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIt is well known that people with diabetes have an elevated cardiovascular disease (CVD) risk. Diabetic patients have a two to four times higher risk of experiencing cardiovascular events than those without diabetes(Roman \u0026amp; Stoian, 2021), and their relative risk of dying from CVD is about twice as high (Mensah et al., 2017). Growing evidence suggests that dyslipidaemia contributes significantly to the excess risk of CVD (Hedayatnia et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCommon characteristic features of diabetic dyslipidaemia are the elevation of plasma triglycerides and triglyceride-rich very low-density lipoprotein (VLDL) choles terol, reduced high-density lipoprotein cholesterol (HDL-c), and an increased number of small dense low-density lipoprotein cholesterol (LDL-c) particles(Bonilha et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although LDL-c is not typically elevated in patients with diabetes, the changes in LDL-c composition that can accompany the disease make the LDL-c exceptionally atherogenic (Wadhera et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Traditionally, patients are evaluated for dyslipidaemia with respect to lipids with total cholesterol (TC), LDL-c, HDL-c and triglycerides with attention to LDL-c values (Ridefelt et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to the National Cholesterol Education Program (NCEP), LDL-c is still considered the primary target of lipid-lowering therapy for CVDs (Aggarwal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bonow, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, LDL-c does not include the participation of other lipoprotein fractions, such as lipoprotein a (Lp (a)) and the triglyceride-rich very low-density lipoprotein cholesterol (VLDL), which have been suggested to contribute to the development of atherosclerosis (Ridefelt et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the prevalence of obesity and metabolic syndrome as well as diabetes, are rising. Identification of lipid predictors of CVD in diabetics is, therefore, very important. Two of the emerging risk factors are Non-HDL cholesterol and remnant cholesterol (RC). Non-HDL cholesterol (total cholesterol minus HDL-c) represents essentially the sum of all lipoproteins that have atherogenic properties (Aggarwal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRemnant cholesterol is the cholesterol content of triglyceride-rich lipoproteins (TRLs) that consists of VLDL, intermediate-density lipoproteins (IDL), and chylomicron remnants(J\u0026oslash;rgensen et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Epidemiological evidence has suggests that higher RC levels are significantly associated with the development of type 2 diabetes mellitus (T2DM) and contributes to the comorbidities such as hypertension (Sun et al., 2022). RC is calculated from a standard lipid profile as total cholesterol minus low-density lipoprotein (LDL) cholesterol minus high-density lipoprotein (HDL) cholesterol. A recent study on CVD risk assessment indicated that current high-density lipoprotein cholesterol\u0026ndash;based risk calculations could lead to inaccurate risk assessment in Black adults (Zakai et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) (This raises a lot of issues about the estimation of cardiovascular disease risk scores.\u003c/p\u003e \u003cp\u003eWhile several studies from different populations have reported that RC and Non-HDL-c (Zaka et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al., 2022; Zheng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have more predictive power than LDL cholesterol in detecting CVD, very few studies have been done in sub-Saharan Africa on people with diabetes. The understanding of the role of obesity as a risk factor is still debated. The concept of the obesity paradox, which was first observed in coronary artery disease patients, hypothesizes that some individuals with normal weight could have worse outcomes than their overweight/obese counterparts (Gruberg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The role of non-routine lipid biomarkers in the obesity paradox has not been widely explored. Therefore, the current study was designed to compare the respective predictive values of LDL-c, Non-HDL-c and RC for CVD in diabetes patients in the context of obesity paradox.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design, population and sampling\u003c/h2\u003e \u003cp\u003e A cross-sectional study was conducted at the diabetic clinic of Effia Nkwanta Regional Hospital, Takoradi, Ghana. In the Western Region, the Effia Nkwanta Regional Hospital serves as a hub for referrals to outlying healthcare facilities. Patients with diabetes can receive both general and specialty care at the diabetic clinic.This study involved 154 patients with type 2 diabetes who were randomly selected at the diabetes clinic of the Effia Nkwanta Regional Hospital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBlood Pressure and Anthropometric Measurements\u003c/h2\u003e \u003cp\u003eAn experienced nurse at the diabetic clinic took the patient's blood pressure from the left upper arm using a mercury sphygmomanometer and a stethoscope. Prior to measurement, participants were instructed to rest for at least five minutes. The blood pressure reading was calculated as the average of two readings taken five minutes apart. Height and weight were measured for computation of body mass index (BMI). Height was measured barefoot using a wall-mounted ruler, to the nearest 0.1 cm. Weight was measured in light clothing to the nearest 0.1 kg with a weighing scale. BMI was computed by dividing the weight by the square of the height, BMI\u0026thinsp;=\u0026thinsp;BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;=\u0026thinsp;weight/height\u003csup\u003e2\u003c/sup\u003e. Waist and hip circumferences were measured to the nearest 0.1 cm using a measuring tape. Waist was divided by hip to obtain the waist-to-hip ratio (WHR). Total body fat (%) was estimated using the Omron BF511 Body Composition Monitor (Omron Corporation, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSample Collection, Preparation and Biochemical Assays\u003c/h2\u003e \u003cp\u003eFive milliliters (5 ml) of venous blood sample was collected after an overnight fast. One (1) milliliter of blood was dispensed into tubes containing fluoride oxalate, and the remaining 4 ml of blood was dispensed into gel separator tubes. The sample in the fluoride oxalate was used for fasting plasma glucose (FPG) estimation. The gel separator tubes were placed in a centrifuge and spun at 3000 rpm for 5 min to obtain the serum. FPG was measured immediately, and the serum for the measurement of other biochemical variables was aliquoted and stored at \u0026minus;\u0026thinsp;20\u0026deg;C until analysis. Total cholesterol (TC), HDL-c, triglyceride (TG), LDL-c, FPG, alanine transaminase (ALT) and gamma-glutamyl transferase (GGT) were estimated using an automated chemistry analyzer (Selectra Pro S System, Elitech Group, France). Serum adiponectin, resistin and high sensitive C-reactive protein (hs-CRP) were determined by commercially available enzyme-linked immunosorbent assay (ELISA) test kits procured from DRG International, following manufacturer\u0026rsquo;s instructions (DRG International, NJ, USA). Ten-year cardiovascular disease risk score was assessed using the Framingham risk score. Insulin resistance was assessed by the triglyceride glucose index derived by the formula, TyG\u0026thinsp;=\u0026thinsp;ln[FBS(mg/dl) \u0026times; TG (mg/dl)]/2 developed by Simental-Mend\u0026iacute;a et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003e The study was approved by the Institutional Review Board of University of Cape Coast (UCCIRB). In addition, institutional approval was obtained from the Effia Nkwanta Regional Hospital. Furthermore, the conduct of the study was in strict adherence to the ethical standards of the Ghana Health Service (GHS) and the World Medical Association Declaration of Helsinki. Above all, written informed consent was obtained from each study participant, and strict confidentiality of participants\u0026rsquo; information was maintained throughout the study.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cp\u003eData obtained were analyzed by Statistical Package for Social Sciences (SPSS) software version 17. Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or percentages, where appropriate. Independent sample t-test was used to compare the mean levels of measured indices between weight groups and between sexes. Pearson correlation, stepwise linear and logistic regression analyses were performed. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThere were 154 participants aged 40–78 years and made up of 91 females and 63 males. The mean age was 52 years. Generally, the mean values for the indices assessed were within the reference range, except for FPG, LDL cholesterol, Non-HDL-cholesterol, BMI and systolic blood pressure, which exceeded the optimal levels (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority (75%) of the respondents were hypertensive. When the various indices were analyzed by gender, there was no statistically significant (P \u0026gt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) difference between the male and female respondents except percentage body fat (P \u0026lt; 0.001; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which was expectedly higher in females.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\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\u003eClinical and biochemical characteristics of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean ± SD\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e52.20 ± 8.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40–78\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.30 ± 4.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5–25.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e5.10 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1–7.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.20 ± 0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40–2.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.20 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70–1.60\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.10 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20–6.10\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.90 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.60–6.90\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.54 ± 0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20–1.30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.70 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.40–10.70\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e26.90 ± 4.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.10–40.50\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD risk (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e14.30 ± 8.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00–45.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (mg/dL)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.60 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11–3.80\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (µg/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e7.70 ± 4.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.30–20.00\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin (ng/mL)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e13.50 ± 3.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.80–22.30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e142 ± 16.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102–197\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e88 ± 11.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64–124\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMAP(mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e106 ± 12.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78–146\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT gamma-glutamyl transferase; TC = total cholesterol; TyG = triglyceride glucose index; * = significant p-value.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003eClinical and biochemical characteristics of respondents by gender\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale (63)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale (91)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e52.2 ± 7.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e54.0 ± 9.07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.0 ± 3.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.6 ± 3.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e7.8 ± 4.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.5 ± 4.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e5.1 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e5.1 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.2 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.2 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.4 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.3 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.1 ± 0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.2 ± 0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.66 ± 0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.81 ± 0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.52 ± 0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.56 ± 0.23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.8 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.9 ± 1.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (Kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e26.7 ± 3.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e27.0 ± 4.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.90 ± 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.89 ± 0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD Risk (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e15.4 ± 10.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e13.6 ± 7.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e87.4 ± 11.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e88.2 ± 10.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e140.7 ± 15.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e143.0 ± 16.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e24.5 ± 7.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e22.9 ± 8.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e22.3 ± 7.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e22.7 ± 8.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.758\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (µg/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e7.3 ± 4.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e7.9 ± 4.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin (ng/mL)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e13.4 ± 3.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e13.6 ± 3.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.711\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.6 ± 0.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.6 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.842\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e26.84 ± 8.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e33.2 ± 7.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyil transferase; TC = total cholesterol; TyG = triglyceride glucose index; * = significant p-value.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn relation to BMI, 2%, 18%, 24% and 56% of respondents were underweight, obese, normal weight and overweight, respectively. Respondents were grouped into underweight/normal weight and overweight/obese categories for comparison of the various indices (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Total cholesterol, LDL, Non-HDL cholesterol, blood pressure, hs-CRP, resistin and fat percentage levels were significantly (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) higher in the overweight/obese group. Adiponectin level was significantly (P = 0.021; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) lower in the overweight/obese group, with the remaining indices being comparable (P \u0026gt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) between the two weight groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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 and biochemical characteristics of respondents by weight category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal/underweight (40)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight/obese (114)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e53.80 ± 10.13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e53.09 ± 8.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.04 ± 3.49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.51 ± 3.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.24 ± 4.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.26 ± 4.75\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.58 ± 0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e5.22 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.19 ± 0.22\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.2 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.85 ± 0.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.47 ± 0.74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.72 ± 0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.76 ± 0.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.18 ± 0.59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.19 ± 0.48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.54 ± 0.27\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.54 ± 0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.39 ± 0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.02 ± 1.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD Risk (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e13.05 ± 7.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e14.78 ± 8.90\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.279\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e84.78 ± 9.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e88.93 ± 10.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e137.63 ± 15.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e143.45 ± 16.65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e22.80 ± 6.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e23.81 ± 7.42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e22.3 ± 7.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e22.68 ± 7.85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (µg/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e9.14 ± 4.83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e7.15 ± 4.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin (ng/mL)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e11.56 ± 3.89\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e14.20 ± 3.66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.27 ± 0.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.72 ± 0.87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e23.58 ± 7.47\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e33.04 ± 8.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; BMI = body mass index; TC = total cholesterol; * = significant p-value.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWhen BMI-based data categorization was further disaggregated by sex, a similar trend was observed in terms of parameters that differed significantly in the two weight groups, but the specific indices differed according to sex (Tables\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). In the female respondents, only total cholesterol, LDL-c, Non-HDL-c, hs-CRP and percentage body fat levels were significantly (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) higher in the overweight/obese group. However, in the male respondents, total cholesterol, LDL-c, Non-HDL-c, hs-CRP, resistin, diastolic blood pressure and percentage body fat levels were higher but adiponectin level was lower (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) in the overweight/obese group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\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\u003ea: Clinical and biochemical characteristics that differed significantly in females by weight category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight/Normal (18)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight/Obese (73)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.54 ± 0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e5.17 ± 0.97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.78 ± 0.81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.40 ± 0.96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.29 ± 0.84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.96 ± 1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.23 ± 0.64\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.68 ± 0.82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e27.12 ± 6.61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e34.66 ± 7.51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eLDL-c = low-density lipoprotein cholesterol; HDL-c = low-density lipoprotein cholesterol; hs-CRP = high sensitivity C-reactive protein; TC = total cholesterol; Non-HDL-c = non-HDL cholesterol; Fat (percentage body fat\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb: Clinical and biochemical characteristics that differed significantly in males by weight category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnderweight/Normal (16)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight/Obese (47)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.51 ± 0.67\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e5.25 ± 1.03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.82 ± 0.71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.55 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.40 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.05 ± 1.09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.13 ± 0.68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.79 ± 0.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e10.26 ± 4.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e6.33 ± 4.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e11.05 ± 2.76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e14.18 ± 3.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic BP (mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e132.38 ± 9.93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e143.55 ± 16.52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e20.00 ± 7.44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e29.17 ± 8.28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase; TyG = triglyceride glucose index.; TC = total cholesterol\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eSince the majority (75%) of respondents were hypertensive on appropriate treatment regimens, the various indices were compared between the hypertensive and normotensive groups regardless of gender and weight category to examine the effects of hypertension on the measured indices. As expected, the hypertensive group demonstrated significantly (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) higher levels of all the measured indices except adiponectin which was lower than their normotensive counterparts with FPG, HDL-c and WHR being comparable (P \u0026gt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) in the two groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of clinical and biochemical characteristics of respondents by hypertension status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormotensive (39)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHypertensive (115)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e7.90 ± 5.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.40 ± 4.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e4.50 ± 0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e5.20 ± 0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.20 ± 0.19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.20 ± 0.21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.00 ± 0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.30 ± 0.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.50 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e8.80 ± 0.70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.90 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e3.50 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.50 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.60 ± 0.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL (mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e3.30 ± 0.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.00 ± 1.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD risk (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e8.30 ± 5.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e16.40 ± 8.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (mg/dl)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e1.00 ± 0.57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e1.80 ± 0.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin (ng/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e11.30 ± 2.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e14.30 ± 3.60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin(µg/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e11.40 ± 4.00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e6.40 ± 4.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e24.60 ± 3.45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e27.60 ± 4.10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e0.86 ± 0.06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e0.89 ± 0.05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC (cm)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e87.40 ± 10.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e92.90 ± 8.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e22.20 ± 6.80\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e24.00 ± 7.40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGGT (U/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e23.20 ± 8.20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e22.30 ± 7.50\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration (years)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e2.80 ± 2.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e4.90 ± 3.30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat (%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c2\"\u003e \u003cp\u003e24.43 ± 6.78\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\"±\" colname=\"c3\"\u003e \u003cp\u003e32.66 ± 8.58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase; TyG = triglyceride glucose index; TC = total cholesterol; * = significant p-value.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAlthough the mean levels of most of the measured indices appear to be within the acceptable reference ranges, the levels of biomarkers observed for a reasonable proportion of individuals in the entire sample and by BMI categorization fell outside the acceptable limits (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). For instance, the prevalence of dyslipidaemia associated with the various components of the lipid profile in the entire respondents ranged from 18% for triglycerides to 74% for LDL-c (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). In relation to weight groups, the overweight/obese group appear to consistently exhibit a relatively higher proportion of individuals with unfavourable levels of various cardiometabolic indices except for HDL-c, TyG and remnant cholesterol that the underweight/normal weight group seem to have a higher proportion of individuals with unfavourable levels. (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrevalence of selected cardiometabolic risk factors among respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunderweight/normal weight (34)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight/obese\u003c/p\u003e \u003cp\u003e(120)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEntire respondents (154)\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (≥ 5.2 mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (17.65%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (46.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62 (40.26%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c (\u0026lt; 1.0 in males, \u0026lt; 1.3 in females)/mmol/l\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (41.18%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (37.50%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59 (38.31%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c (\u0026gt; 2.6 mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (64.71%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (76.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114 (74.01%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL (\u0026gt; 3.4 mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (41.18%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (71.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (64.29%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC (\u0026gt; 0.6 mmol/L)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (44.12%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49 (40.83%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64 (41.56%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (\u0026gt; 1.7mmol/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (14.71%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (19.17%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28 (18.18%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP (\u0026gt; 140/90mmHg)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19 (53.44%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (80%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e115 (74.68%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP (\u0026gt; 1.0 mg/l)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (47.06%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92 (76.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (70.13%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdiponectin (\u0026lt; 8.0 µg/ml)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18 (52.94%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (65%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96 (62.34%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResistin \u0026gt; 14.8 ng/L\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (14.71%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (48.33%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63 (40.91%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCVD risk (\u0026gt; 10%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22 (64.71%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86 (71.67%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e108 (70.13%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG \u0026gt; 8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126 (81.82%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (82.34%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e98 (81.67%)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eHDL-c = high-density lipoprotein cholesterol; LDL-c low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; TC = total cholesterol; TG = triglycerides; TyG = triglyceride glucose index.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn bivariate correlational analysis to examine linear relationships between biomarkers irrespective of gender and BMI in the entire sample, the usual expected linear relationships were observed among the measured indices. As indicated in Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the duration of diabetic condition exhibited a positive correlation with TyG, hs-CRP, CVD risk, blood pressure, ALT, percentage fat and triglyceride levels but a negative correlation with adiponectin. FPG correlated positively with total cholesterol, TyG, LDL-c, remnant cholesterol, Non-HDL-c and GGT. Additionally, total cholesterol correlated negatively with adiponectin but positively with resistin, hs-CRP, ALT and GGT. Adiponectin continued to demonstrate a negative correlation with Non-HDL and LDL, even as TyG showed a positive correlation with hs-CRP, resistin, ALT and GGT. Expectedly, LDL-c correlated positively with resistin, hs-CRP, ALT and GGT, with Non-HDL correlating positively with resistin, hs-CRP, ALT and GGT. Interestingly, no correlation was observed among hs-CRP, adiponectin, resistin, ALT and GGT in the current study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBivariate correlation among selected clinical and biochemical characteristics of respondents\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation coefficient\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and TyG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and hs-CRP\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and CVD risk\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and Adiponectin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.234\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and systolic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and diastolic\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and ALT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and fat%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.248\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration and TG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG and cholesterol\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG and TyG\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG and LDL-c\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.297\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG and RC, Non-HDL-c\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.394,0.364\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG and GGT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC and HS-CRP, resistin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.596,0.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC and adiponectin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.447\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC and ALT, GGT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.234, 223\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003, 0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG and hs-CRP, resistin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.318, 0.25\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001, 0.002\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG and ALT, GGT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.171, 0.272\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034, 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c and hs-hs-CRP, resistin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.603, 0.558\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c and adiponectin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.459\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-c and ALT, GGT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.212, 0.207\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008, 0.01\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c and hs-CRP, resistin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.648, 0.577\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c and adiponectin\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.474\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c and ALT, GGT\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.237, 0.226\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003, 0.005\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFPG = fasting plasma glucose; HDL-c = high-density lipoprotein cholesterol; LDL-c = low-density lipoprotein cholesterol; RC = remnant cholesterol; CVD = cardiovascular disease; hs-hs-CRP = high sensitivity C-reactive protein; BP = blood pressure; MAP = mean arterial pressure; BMI = body mass index; TG = triglycerides; WC = waist circumference; WHR = waist-to-hip ratio; ALT = alanine amino transferase; GGT = gamma-glutamyl transferase;TC = total cholesterol; TyG = triglyceride glucose index.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn a stepwise linear regression analysis with TyG as dependent variable, remnant cholesterol, duration of diabetes ,and FPG were found to be independent predictors (R\u003csup\u003e2\u003c/sup\u003e = 0.951; adjusted R\u003csup\u003e2\u003c/sup\u003e = 0.951; P \u0026lt; 0.001) predictors of TyG with the model explaining about 95% of observed variation.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents predictors of cardiovascular disease risk, adiponectin, resistin, hs-CRP and hypertension in the studied sample by multivariate logistic regression analyses. On average, the cardiovascular disease risk decreased by 2.41 for females compared with males (P = 0.003; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e) but increased by 7.88 for ages 50–59 years and 14.45 for ages 60 years and above compared to ages 40–49 years (P \u0026lt; 0.001; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). For a unit increase in non-HDL-c, the risk of having a cardiovascular disease increased by 4.31 on average.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePredictors of selected clinical and biochemical outcome variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAOR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCVD risk\u003c/em\u003e\u003c/p\u003e \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\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\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e-2.41 (-3.98, -0.85)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.88 (6.14, 9.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.45 (12.38, 16.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.37 (-2.32, 5.06)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL-c\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.31 (3.49, 5.13)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAdiponectin\u003c/em\u003e\u003c/p\u003e \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\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\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e0.45 (-0.89, 1.79)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.11 (-3.60, -0.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.99 (-2.76, 0.79)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.54 (-3.42, 2.91)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-HDL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.24 (-2.94, -1.54)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eResistin\u003c/em\u003e\u003c/p\u003e \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\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\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e0.47 (-4.8, 1.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.328\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 (-0.46, 1.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.81 (-3.06, -0.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29 (-1.94, 2.51)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.801\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.14 (1.64, 2.64)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHs-hs-CRP\u003c/em\u003e\u003c/p\u003e \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\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\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e-0.04 (-0.25, 0.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.683\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge*\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19 (-0.04, 0.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41 (0.14, 0.68)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53 (0.05, 1.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48 (0.37, 0.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; 0.001*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHypertension\u003c/em\u003e\u003c/p\u003e \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\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\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\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\u003e1.27 (0.56, 2.87)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40–49\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50–59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.02 (1.24, 7.37)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≥ 60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.24 (1.59, 24.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23 (0.43, 24.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-HDL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.24 (1.35, 3.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eAOR = Adjusted odds ratio; RC = remnant cholesterol; HDL = low-density lipoprotein cholesterol; CVD = cardiovascular disease; hs-HS-CRP = high sensitivity C-reactive protein; * = significant p-value\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAdiponectin levels decreased with increased (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e) Non-HDL levels and age 50–59 years only, whereas resistin levels decreased with increasing age at 60 years and above. With respect to hs-CRP, on average, participants showed decreased likelihood in its rise for a unit increase in Non-HDL, remnant cholesterol and age 60 years and above. The likelihood of hypertension in participants increased from 3-fold at age 50–59 years to about 6-fold at age 60 years and above compared to age 40–49 years (P \u0026lt; 0.05; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Above all, a unit rise in Non-HDL-c levels increased one’s risk of hypertension by approximately 2-fold (P = 0.002; Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study in 154 type 2 diabetes mellitus (T2DM) patients at Effia-Nkwanta Regional Hospital examined the predictive power of remnant cholesterol and non-HDL cholesterol in predicting the likelihood of developing cardiovascular disease in the context of obesity paradox. Our results showed that apart from the body fat level, which was higher in females, mean levels of all the other indices were comparable between the genders. The comparable mean levels of biomarkers between gender in the current study (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) are in variance with findings from studies in Nigeria (Aderibigbe et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), China (Yin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Spain (Ramírez-Morros et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) which reported gender variations in measured indices such as total cholesterol, LDL-c and BMI. It has been suggested that T2DM females are likely to be older with higher BMI due to the slow pace of visceral and ectopic fat accumulation, which may facilitate insulin resistance and subsequent development of T2DM (de Ritter et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ramírez-Morros et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, the female is likely to be exposed to less favourable circulating glucose levels for a relatively longer time than the male counterpart, increasing the risk of development of worsening CVD risk markers (de Ritter et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ramírez-Morros et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The difference between the current work and the previous ones (Aderibigbe et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yin et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramírez-Morros et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) could be ascribed to differences in sample characteristics such as age range, duration of diabetic condition, existing comorbidities, the extent of adherence to treatment regimen and related lifestyle activities. For instance, respondents in the previous studies were on average older with a longer duration of diabetes than those in the current study. Indeed, unfair treatment differences between genders coupled with culturally induced lifestyle factors that have an impact on treatment outcomes have been reported (de Ritter et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ramírez-Morros et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The above reasons may partially explain the reduction in the odds of CVD risk in women compared to men observed in the current study (Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e8\u003c/span\u003e). This notwithstanding, females had higher body fat percentage than males in the current study. This observation consistent with sex-induced variation in fat metabolism where females have an overall higher fat content (Blaak, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; O’Sullivan, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Wu \u0026amp; O’Sullivan, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) due to lower basal fat oxidation and efficient fat storage (Blaak, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; O’Sullivan, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) despite reduced food intake (Kant \u0026amp; Graubard, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and increased tendency for fat-based energy during exercise (Tarnopolsky, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen data was disaggregated by BMI categorization (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) irrespective of gender, with a view to examining the existence of the obesity paradox in our sample, total cholesterol, LDL-c, non-HDL-c, blood pressure, HS-CRP, resistin and percentage body fat levels were higher, but adiponectin was lower in the overweight/obese group compared with the underweight/normal weight. The observed trend of mean levels of biomarkers between the weight groups in the entire sample, irrespective of gender, was generally observed in the gender-based weight categorization data (Tables \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). However, diastolic blood pressure, resistin and adiponectin levels differed in the weight-based male respondents only (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). Since the expected trend of overweight/obese respondents demonstrating higher levels of measured indices was observed, it implies that the obesity paradox could not be detected in this study. The obesity paradox has been reported in other conditions such as hypertension (Uretsky et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), T2DM (Doehner et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and coronavirus disease 2019 (Lavie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), where overweight/obese respondents demonstrated lower mortality numbers than their underweight/normal weight counterparts. Since cardiovascular disease mortality has been found to be associated with alterations in the levels of various biomarkers, it should be possible to examine the obesity paradox in relation to levels of relevant biomarkers as variables as was attempted in the current study. In terms of proportion of respondents with unfavourable levels of biomarkers, the overweight/obese respondents appeared to demonstrate higher a proportion of individuals for all the measured biomarkers except HDL-c and remnant cholesterol. Thus, in general, the overweight/obese respondents in the current study did not demonstrate any prognostic advantage over the underweight/normal weight counterpart to warrant any speculation of the existence of an obesity paradox in the current study. The relatively small sample size of the current study compared with previous ones (Gruberg et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Uretsky et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Doehner et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lavie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), could be partly responsible for the difference in observation regarding the obesity paradox, a larger study will be necessary to fully examine the concept in the Ghanaian context.\u003c/p\u003e\u003cp\u003eHypertension remains a critical comorbidity in patients with T2DM, with 74.68% of respondents in the current study being hypertensive. In Ghana, the prevalence of hypertension in the general population has been reported to be 27%, with 65% of the affected unaware that they have the condition (Bosu \u0026amp; Bosu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), implying that the high prevalence of hypertension in T2DM diabetes patients in the current report is expected. The prevalence observed in this study appears higher than a recent report in Ethiopia (37.5%) (Abdissa \u0026amp; Kene, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but similar to one from Afghanistan (70.5%) (Naseri et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Jordan (74.6%) (Salameh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Both the difference and similarity in prevalence could be ascribed to variations in respondent’s characteristics. For instance, the Ethiopian study involved both types 1 and 2 diabetes mellitus patients who were relatively younger in age and duration of the condition, predominantly farmers and less overweight/obese. However, the current study and similar ones (Naseri et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Salameh et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) involved only T2DM patients with longer duration of condition, older, less physically active and more overweight/obese. This is critical because the successful management of hypertension and diabetes is influenced heavily by obesity and other modifiable lifestyle factors. Indeed, diabetes and hypertension are closely linked to similar risk factors and pathogenic mechanisms through inflammation and oxidative stress. Thus, through inflammation and oxidative stress due to ineffective control of blood glucose level, as is normally the case for older T2DM patients, hypertension may develop, accounting for the high prevalence observed in the current study. Indeed, the mean fasting glucose level in the current study was 8.3 mmol/L as against the American Diabetes Association recommended range of 4-7.5 mmol/L (AMA, 2022), suggesting a suboptimal glucose control in our respondents, which may have favoured the development of hypertension. Although a large Japanese longitudinal study in a general adult population found high fasting glucose as an independent risk factor for hypertension (Kuwabara et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the current study rather observed that age ≥ 60 years and non-HDL-c increased the odds of having hypertension. Additionally, an increase in non-HDL-c levels increased the risk of CVD events, and raised hs-CRP and resistin levels whilst lowering adiponectin levels in our study respondents. Above all, the mean Non-HDL-c level was higher in hypertensives than in normotensive respondents. These findings are generally in support of several studies that have reported a positive association between Non-HDL-c and hypertension, arterial stiffness and ischemic heart disease, and a cardiovascular event (de Oliveira Alvim et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zaka et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Calling et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our findings also support the view non-HDL-c could be a reliable marker for the clinical management of dyslipidaemia in T2DM patients (Peters, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This assertion is supported by the finding that non-HDL-c correlated negatively (r = -0.474; P \u0026lt; 0.001) with adiponectin but positively with resistin (r = 0.577; P \u0026lt; 0.001), hs-CRP (r = 0.648; P \u0026lt; 0.001) and FPG (r = 0.364; P \u0026lt; 0.001) suggesting that it promotes inflammation in a similar trend to LDL-c but with a slightly increased strength of the observed correlations in the current report. Therefore, targeting Non-HDL-c as a prognostic marker in clinical management of dyslipidaemia associated with T2DM is likely to be more reliable since its reduction connotes a reduction in inflammation and improved insulin sensitivity for a favourable lipid profile and glucose regulation, in line with the reported role of adiponectin (Yadav et al., 2013; Li et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAnother lipid index that is emerging as a biomarker for CDV in recent times is remnant cholesterol. Several studies from China have reported remnant cholesterol as a predictor of hypertension in adult populations with or without T2DM (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Another study has reported remnant cholesterol as a predictor of new-onset diabetes (Xie et al., 2022). Interestingly, the current study only observed that remnant cholesterol correlated positively and FPG (r = 0.394; P \u0026lt; 0.001) and could predict hs-CRP level in a logistic regression analysis. Remnant cholesterol together with duration of diabetic condition and FPG could independently (R\u003csup\u003e2\u003c/sup\u003e = 0.951; adjusted R\u003csup\u003e2\u003c/sup\u003e = 0.951; P \u0026lt; 0.001) predict TyG level in a stepwise linear regression analysis in our sample with the model explaining about 95% of the observed variation. Thus, unlike previous studies (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), remnant cholesterol could not predict CVD and hypertension in this study. Differences in study design, sample size and respondents characteristics could be responsible for the variation in findings between the current study and the previous ones (Chen et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the predictive ability of remnant cholesterol for hs-CRP and TyG in the current report seems to provide an indirect corroboration to the findings of Xie et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) since insulin resistance and inflammation are critical factors for the development of diabetes mellitus. Therefore, any agent with the potential to initiate insulin resistance and inflammation in a sustained manner can potentiate the development of T2DM. Putting it all together, our findings point to a role for both remnant cholesterol and Non-HDL-c in the development of CVD through different mechanisms.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNon-HDL cholesterol measurement \u0026nbsp;provides a single index of all the atherogenic, apolipoprotein (apo) B\u0026ndash;containing lipoproteins, LDL, VLDL and intermediate density lipoprotein (IDL). Again, measurement of non-HDL cholesterol is more practical, reliable, and inexpensive and is accepted as a surrogate marker for apo B. Remnant cholesterol influences markers for inflammation as well as glucose control.Our study supports a growing evidence that Non-HDL cholesterol is a better treatment target than LDL cholesterol. However a larger study may be necessary to examine the real impact of obesity paradox in the development of CVD in T2DM in our setting.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEmail of corresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Effia Nkwanta Regional Hospital\u0026apos;s patients, administration, and personnel are gratefully acknowledged by the writers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the\u0026nbsp;Institutional Review Board of University of Cape Coast (UCCIRB). In addition, institutional approval was obtained from the Effia Nkwanta Regional Hospital. Furthermore, the conduct of the study was in strict adherence to the ethical standards of the Ghana Health Service (GHS) and the World Medical Association Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from each study participant, and strict confidentiality of participants\u0026rsquo; information was maintained throughout the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproval for publication of research findings, including participants\u0026rsquo; details, was obtained before enrollment into the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u0026nbsp;\u003c/strong\u003ePaul Nsiah and Samuel Acquah\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData curation:\u0026nbsp;\u003c/strong\u003ePaul Nsiah and Ebenezer Aniakwaa-Bonsu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFormal analysis:\u0026nbsp;\u003c/strong\u003eSamuel Acquah, George Adjei, Paul Nsiah and Ebenezer Aniakwaa-Bonsu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInvestigation:\u0026nbsp;\u003c/strong\u003ePaul Nsiah, Samuel Acquah and Ansumana Sandy Bockarie\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u0026nbsp;\u003c/strong\u003ePaul Nsiah, Samuel Acquah and Oksana Debrah\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProject administration:\u0026nbsp;\u003c/strong\u003ePaul Nsiah.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResources:\u0026nbsp;\u003c/strong\u003ePaul Nsiah, Samuel Acquah and Ansumana Sandy Bockarie\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupervision:\u0026nbsp;\u003c/strong\u003ePaul Nsiah.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation:\u0026nbsp;\u003c/strong\u003eEliezer Togbe and Paul Poku Sampene Ossei\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026nbsp; original draft:\u0026nbsp;\u003c/strong\u003ePaul Nsiah and Samuel Acquah\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWriting \u0026nbsp;review \u0026amp; editing:\u0026nbsp;\u003c/strong\u003ePaul Nsiah, Samuel Acuah, Ansumana Sandy Bockarie and Oksana Debrah\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that the data supporting the findings of this study are available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo potential conflict of interest was reported by the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdissa, D., Kene, K. 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(2022). Race-Dependent Association of High-Density Lipoprotein Cholesterol Levels with Incident Coronary Artery Disease. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e, \u003cem\u003e80\u003c/em\u003e(22), 2104\u0026ndash;2115. https://doi.org/10.1016/j.jacc.2022.09.027\u003c/li\u003e\n \u003cli\u003eZheng, X., Han, L., \u0026amp; Shen, S. (2022). Hypertension, remnant cholesterol and cardiovascular disease: evidence from the China health and retirement longitudinal study. \u003cem\u003eJournal of Hypertension\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(11), 2292\u0026ndash;2298. https://doi.org/10.1097/hjh.0000000000003259\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-2696463/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2696463/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes mellitus (T2DM) continues to increase in incidence within the ageing population of the globe. Patients with T2DM have a \u0026nbsp;2-4 times higher risk of experiencing an adverse cardiovascular event than their non-diabetic counterparts. Total cholesterol, low-density lipoprotein (LDL), triglycerides and high-density lipoprotein (HDL) cholesterol levels have been the routine biomarkers for lipid-based cardiovascular disease diagnostic and prognostic decisions in clinical practice. Recent evidence elsewhere suggests remnant cholesterol (RC) and Non-HDL cholesterol (Non-HDL-c) can serve as biomarkers with a higher predictive power for cardiovascular disease (CVD) than the aforementioned routine ones. In our context, there is limited information on the suitability and superiority of these emerging biomarkers for the assessment of CVD risk in T2DM. The current study therefore sought to examine the relationship between RC and non-HDL-c for predicting CVD in T2DM patients in the context of the obesity paradox. Apart from adiponectin level which was lower (P \u0026lt; 0.05), overweight/obese respondents exhibited higher (P \u0026lt; 0.05) mean levels for all the measured indices. Insulin resistance was independently predicted\u003cstrong\u003e \u003c/strong\u003e(R\u003csup\u003e2\u003c/sup\u003e = 0.951; adjusted R\u003csup\u003e2\u003c/sup\u003e = 0.951; P \u0026lt; 0.001)\u003cstrong\u003e \u003c/strong\u003eby RC, duration and fasting plasma glucose. However, Non-HDL-c predicted CVD risk (AOR = 4.31; P \u0026lt;0.001), hypertension (AOR = 2.24; P \u0026lt;0.001), resistin (AOR = 2.14; P \u0026lt;0.001) and adiponectin (AOR = -2.24; P \u0026lt;0.001) levels. Our findings point to different mechanisms by which RC and non-HDL-c contribute to the development of CVD.\u003c/p\u003e","manuscriptTitle":"Non-HDL Cholesterol and Remnant Cholesterol Predict Different Components of the Metabolic Syndrome in Type 2 Diabetes Mellitus Patients in a Regional Hospital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-03-22 15:13:57","doi":"10.21203/rs.3.rs-2696463/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c192bcb0-84db-4f1d-a5d8-cae12d1d8c07","owner":[],"postedDate":"March 22nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-22T15:14:00+00:00","versionOfRecord":[],"versionCreatedAt":"2023-03-22 15:13:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2696463","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2696463","identity":"rs-2696463","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}