Single Point Insulin Sensitivity Estimator (SPISE), a novel score to evaluate insulin sensitivity, is predictive in anthropometry specified adults with Type 2 Diabetes Mellitus

Single Point Insulin Sensitivity Estimator (SPISE), a novel score to evaluate insulin sensitivity, is predictive in anthropometry specified adults with Type 2 Diabetes Mellitus | 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 Single Point Insulin Sensitivity Estimator (SPISE), a novel score to evaluate insulin sensitivity, is predictive in anthropometry specified adults with Type 2 Diabetes Mellitus Elakiya K., Jayanthi R, Srinivasan A.R., Mohamed Hanifah A. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8746166/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Purpose The study was aimed at the assessment of non-insulin-based markers as alternatives to the conventional insulin-based indices in anthropometry specified type 2 Diabetes mellitus. The outcome was targeted towards the utility of such indices in diabetes mellitus and associated metabolic derangements centering around insulin resistance. Methods Fasting venous blood samples were utilized for biochemical analyses. The parameters included plasma glucose, insulin, lipid profile {triglycerides, HDL cholesterol, total cholesterol and LDL cholesterol (Friedwald equation)}. Uric acid, liver enzymes, renal profile, electrolytes, and thyroid function tests were also evaluated. Appropriate statistical analysis, as deemed fit for normal and skewed data were undertaken. Results Across the BMI categories, significant differences were observed in several insulin-resistance indices. Quantitative Insulin Sensitivity Check Index (QUICKI) values were lowest in the obese group and highest in the normal-weight group (p < 0.0001). Single Point Insulin Sensitivity Estimator (SPISE) values also showed reduction across normal, overweight, and obese groups (p < 0.0001). The TyG index demonstrated a significant increase with rising BMI (p = 0.0002). With reference to utility of SPISE as a predictor in anthropometry specified groups, it demonstrated a near-perfect discriminatory performance with an AUC of 0.99 (95% CI: 0.97–1.00, p < 0.0001). A cut-off value of < 5.55 yielded a sensitivity of 92.5% and specificity of 93.3%. Conclusions Single Point Insulin Sensitivity Estimator (SPISE), independent of insulin possesses greater sensitivity and specificity, in comparison to the conventional Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) in anthropometry specified diabetic population. Obesity T2DM Insulin resistance SPISE HOMA IR TyG Figures Figure 1 Figure 2 1. Introduction Insulin resistance (IR) continues to be the pivotal aberration, with reference to the pathophysiology of type 2 diabetes mellitus (T2DM). IR contributes immensely to organ dysfunction, including cardiometabolic disease progression. The global burden of T2DM has risen sharply, since the past decade, with South Asian populations portraying disproportionately higher susceptibility. This is attributed to the vicious conglomerate of genetic, metabolic, and lifestyle factors [ 1 ]. India, in particular, has been witnessing a steep increase in the prevalence of DM, with the ICMR-INDIAB study duly documenting the substantial rates of both diabetes and prediabetes across multiple states of India [ 2 ]. A startling observation is that individuals of the South Asian lineage develop IR at a relatively younger age and lower BMI values, in comparison to the Western populations. This calls for the immediate need for promulgating region-specific metabolic markers that would vividly capture early metabolic risk [ 3 ]. However, the fact remains that these markers must necessarily be sensitive, specific, reliable, feasible and also economically viable. Besides, even the health care establishments that are not tertiary in nature must be able to facilitate the utility of such biomarkers. Conventional assessment of IR relies on the direct or indirect insulin measurements, such as fasting insulin, HOMA-IR, or the euglycemic hyperinsulinemic clamp. Although the clamp technique continues to be the gold standard, it is not pragmatic and feasible in routine clinical settings due to its high cost, gross technical demands, and most importantly, the need for qualified and trained personnel [ 4 ]. Having said that, surrogate markers such as fasting insulin and HOMA-IR are hampered by inter-assay variability, low standardization across laboratories, and attenuated reliability in populations endowed with varying beta-cell reserve[ 5 ]. QUICKI, though analytically useful, also depends on the quantitation of fasting insulin and therefore shares the same limitations in low-resource settings. These limitations and demerits have prompted the scientific community to search for alternative indices of insulin resistance that are simple, cost-effective, independent of insulin assays and at the same time reliable and reproducible. In recent years, non-insulin-based indices have been receiving wide attention. Among these, the Triglyceride-Glucose (TyG) index has emerged as one of the strongest and most reproducible markers of IR. TyG, derived from fasting triglycerides and fasting glucose has shown strong correlation with clamp-measured insulin sensitivity. Besides, it has unequivocally demonstrated the value in envisaging the impending adverse cardiometabolic outcomes. Comprehensive cohort studies have delineated the fact that higher TyG values are associated with enhanced cardiovascular disease risk and all-cause mortality in young adults [ 6 ]. Furthermore, the studies have linked the index with accelerated progression of cardinal events including coronary artery calcification and that irrespective of baseline atherosclerotic burden [ 7 ]. In view of the afore-mentioned attributes, TyG holds immense promise in launching large-scale screening, even in primary care settings. Dyslipidemia-derived markers are also gaining impetus as surrogate indicators of metabolic dysfunction. The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio reflects both hepatic overproduction of triglyceride-rich lipoproteins and impaired HDL-mediated reverse cholesterol transport. In addition, TG/HDL-C is also considered as a surrogate marker of small dense LDL. Elevated TG/HDL-C values possess a nexus with IR, visceral adiposity, and metabolic syndrome. The SPISE (Single Point Insulin Sensitivity Estimator) index fortifies this concept even further by integrating BMI, HDL-C, and triglycerides into a single validated formula. SPISE has been shown to depict insulin sensitivity in children and adults without calling for insulin measurements [ 8 ]. By virtue of retaining the anthropometry index and lipid fractions, SPISE thus confers a much more reliable and broader feasible picture on the metabolic profile of insulin sensitivity. Uric acid, the endogenously synthesized antioxidant has also been recognized as a key player in the realms of metabolic stress. Uric acid levels are associated with oxidative stress, endothelial dysfunction, and impaired insulin signaling [ 9 ]. The uric acid/HDL-C ratio has been examined as an integrative marker that would reflect both pro-oxidative burden and reduced antioxidant capacity. Studies on individuals with T2DM have revealed that this ratio is emphatically associated with metabolic syndrome and cardiometabolic abnormalities [ 10 ]. Given its simplicity and widespread availability, the uric acid/HDL-C ratio is rapidly emerging as a pragmatic, economically viable and augmentative marker for monitoring IR and its associated complications. In view of these considerations, these non-insulin-based markers offer promising alternatives to conventional insulin-based indices, particularly in those settings and establishments, where the measurements of insulin and C-peptide are hampered by constraints related to inherent logistic issues. Need for the present study: India’s diverse and high-burden population with T2DM provides a unique opportunity to evaluate these indices in the routine clinical practice. As a corollary, the utility and distribution of TyG, TG/HDL-C ratio, SPISE, and uric acid/HDL-C ratio in individuals with T2DM might facilitate modalities related to the timely detection of IR, besides augmenting risk stratification, and intervention strategies tailored to cater to the demands of the South Asian phenotypes, in general and Indians, in particular. The present study is a humble effort aimed at the evaluation of these surrogate markers of IR in individuals with T2DM, who had attended the clinics. Though our establishment is a tertiary healthcare set up in South India, we began the study in all earnestness with the intention that the projected outcome would cater to the needs of the continuum (primary and secondary health care establishments), in terms of feasibility, reliability and utility. By resorting to the use of parameters that are relatively inexpensive and easily quantifiable, reproducible, and independent of insulin measurement, the study aims to offer practical insights into the future that would pave the way for wider adoption of such indices in routine use. 2. Materials and Methods 2.1. Study Design and Setting This study was conducted in the Department of Biochemistry, in association with the Department of General Medicine at the outpatient clinics of a tertiary healthcare setup in Pondicherry located in the Union territory of Puducherry, South India. The tertiary-care teaching hospital serves a mixed population comprising urban, semi-urban and rural drawn from Puducherry and neighbouring districts of Tamil Nadu. 2.2. Consent to participate in the study and Ethics Approval (i). Patient (Subject) Information sheet was prepared in English and vernacular language (Tamil). Care was taken to explain in detail the objectives of the study, expected study outcome and risks, if any, pertaining to the voluntary participation in the study. Informed written consent was obtained from all the study participants. (ii). The study protocol, complete in all aspects was reviewed and subsequently approved by the Institutional Ethics Committee, in accordance with the Declaration of Helsinki (vide Project No: MGMCRI/2025/02/IHEC/97 dt. 16-07-2025). The study was begun, only following the completion of the mandatory procedure, as outlined above in (i). and (ii). Clinical trial number: Not applicable 2.3. Sample Size A total of 100 subjects (n = 100) with T2DM were included. The study had included both the genders. Previous studies on surrogate insulin-resistance indices had demonstrated adequate analytical power with the sample size ranging between 80 and 120. Given the exploratory aim of comparing multiple indices across anthropometry (BMI) defined subgroups, a sample size of 100 provided sufficient variability for facilitating subgroup and correlation analyses. 2.4. Study Population Adults with T2DM were included in the study. The study participants were segregated into three groups, based on the Asian criteria for BMI: non-obese (normal), overweight, and obese. 2.5. Exclusion Criteria Participants were promptly excluded, if they had type 1 diabetes, secondary forms of diabetes (e.g., pancreatitis, endocrine disorders), or pregnancy/lactation. Individuals with comorbidities, namely Thyroid dysfunction, renal disease, hepatic dysfunction, acute infections, recent hospitalization, other organ dysfunction or active inflammatory conditions were excluded. Those receiving medications known to significantly alter insulin sensitivity were also not included. 2.6. Data collection procedure and quantitation of biochemical analytes Demographic details, duration of diabetes mellitus, and treatment history were duly documented. Height and weight were recorded, based on established procedures, and BMI was calculated as weight (kg)/height (m²), in accordance with the WHO Asian-specific cut-offs [ 11 ]. Fasting venous blood samples were analyzed for biochemical analytes at the comprehensive, Central Clinical Laboratory using an automated Biochemistry analyzer (Roche Cobas 6000), with standard internal quality controls. The parameters included plasma glucose, insulin, lipid profile (triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol), serum uric acid, liver enzymes, renal profile, electrolytes, and thyroid function tests. The Central Clinical Laboratory at our tertiary health care set up is a participating laboratory in Quality assessment, enabled under the aegis of CMCH-ACBI External Quality Assessment Scheme. 2.7. Derived Metabolic Indices The following insulin resistance and metabolic indices were computed using established formulae: HOMA-IR, QUICKI, TyG index, and SPISE index. Additional ratios, including the triglyceride-to-HDL cholesterol ratio and the uric acid-to-HDL cholesterol ratio were also calculated, as these measures also figure as useful markers of metabolic dysfunction in T2DM [ 6 – 10 ]. 2.8. Primary and Secondary Outcomes The primary outcome was the assessment of insulin resistance using multiple indices (HOMA-IR, QUICKI, TyG, and SPISE) and comparison across BMI categories, based on Asian-specific thresholds. The secondary outcomes included the evaluation of associations between each index and cardio metabolic parameters, namely triglycerides, HDL cholesterol, uric acid, the triglyceride-to-HDL ratio, as well as the uric acid-to-HDL ratio. These were calculated in order to elicit as to which index would best reflect the underlying metabolic derangements, in terms of reliability and feasibility. 2.9. Statistical Analysis Data were entered into a datasheet and analyzed using GraphPad Prism and SPSS. The normality of continuous variables was assessed using the Shapiro–Wilk test. Comparisons across BMI groups were performed using ANOVA for normally distributed variables and the Kruskal–Wallis test for skewed data. Categorical variables were analyzed using the chi-square test. Correlations between insulin-resistance indices and metabolic variables were evaluated using Pearson or Spearman coefficients, as deemed appropriate. A p-value < 0.05 was considered statistically significant. 3. Results 3.1. Baseline characteristics of the study population Baseline characteristics of the study cohort are summarized in Table 1 A. The mean age was 50.3 ± 9.9 years, and the average duration of T2DM was 4.8 ± 3.5 years. Participants predominantly exhibited an overweight–obese phenotype, with a mean BMI of 26.2 ± 3.4 kg/m² and elevated central adiposity indices (mean waist circumference 93.0 ± 6.4 cm; WHtR 0.59 ± 0.04). Glycemic parameters were above target (mean FBS 159 mg/dL; HbA1c 8.0 ± 1.9%). Lipid values depicted modest dyslipidemia. Electrolytes, renal, hepatic, and thyroid profile remained within clinically acceptable ranges. The study population comprised 39 males and 61 females, indicating a higher representation of women in the sample. A total of 60 participants reported a known history of diabetes mellitus, while 40 had no prior diagnosis (Table 1 B). Table 1 A: Baseline characteristics of the study population (n = 100) Variable Mean Standard Deviation Duration of diabetes (in years) 4.84 3.495 Age (in years) 50.36 9.862 Weight (in Kg) 63.95 8.293 Height (in cm) 156.2 6.616 BMI (kg/m²) 26.24 3.367 Waist circumference (in cm) 93.02 6.435 Hip circumference (in cm) 99.15 5.715 Waist-to-hip ratio (WHR) 0.939 0.049 Waist-to-height ratio (WHtR) 0.601 0.039 Fasting blood glucose (mg/dL) 159.1 46.86 Postprandial glucose (mg/dL) 226.7 50.86 HbA1c (%) 8.028 2.134 Urea (mg/dL) 24.61 9.461 Creatinine (mg/dL) 1.026 0.379 Total cholesterol (mg/dL) 203.5 37.04 Triglycerides (mg/dL) 167.5 77.17 HDL cholesterol (mg/dL) 40.6 7.121 LDL cholesterol (mg/dL) 130.7 31.9 VLDL cholesterol (mg/dL) 33.53 15.42 Total Triodothyronine [T3] (ng/mL) 2.652 0.670 Total Thyroxine [T4] (µg/dL) 1.206 0.291 TSH (µIU/mL) 4.85 7.38 Insulin (µIU/mL) 24.17 21.58 Homeostatic model assessment for Insulin Resistance (HOMA-IR) 9.841 12.27 Zinc (µg/dL) 39.53 23.24 Cortisol (µg/dL) 13.97 7.88 Sodium (mmol/L) 140.2 3.578 Potassium (mmol/L) 5.316 5.214 Chloride (mmol/L) 102.8 3.68 Calcium (mg/dL) 9.484 0.855 Magnesium (mg/dL) 1.675 0.426 Uric acid (mg/dL) 4.357 1.134 Total protein (g/dL) 7.848 0.579 Albumin (g/dL) 4.474 0.341 Globulin (g/dL) 3.365 0.588 A/G ratio 1.377 0.307 Total bilirubin (mg/dL) 0.531 0.265 Direct bilirubin (mg/dL) 0.187 0.058 AST (U/L) 21.75 9.124 ALT (U/L) 27.08 14.36 ALP (U/L) 84.15 24.68 Table 1 B. Distribution of categorical variables in the given study population (n = 100) Variable Category Frequency (n) Gender Male 39 Female 61 History of diabetes mellitus Yes 60 No 40 3.2. Anthropometric variation across BMI groups Anthropometry specified comparisons across BMI categories are presented in Table 2 . Weight (p < 0.0001), BMI (p < 0.0001), waist circumference (p = 0.0036), hip circumference (p = 0.0019), and WHtR (p < 0.0001) revealed statistical significance among the groups. Height depicted a modest significance (p = 0.0065), primarily attributed to lower mean height in the obese group. Age and duration of diabetes mellitus did not reveal significant variation. Table 2 Anthropometric characteristics of T2DM patients categorized on the basis of BMI Variable Non-obese [Normal] (BMI < 23 kg/m²) Overweight (BMI 23–27.49 kg/m²) Obese (BMI ≥ 27.5 kg/m²) p-value Duration of diabetes mellitus (in years) 5.27 ± 2.22 5.18 ± 4.35 4.30 ± 2.73 0.520 Age (in years) 50.20 ± 8.74 50.33 ± 11.03 50.45 ± 9.07 0.996 Weight (in kg) 54.93 ± 6.20ᵃᵇ 61.84 ± 5.19ᵃᶜ 69.70 ± 7.75ᵇᶜ < 0.0001 Height (in cm) 158.4 ± 7.11 157.5 ± 5.35ᶜ 153.9 ± 7.16ᶜ 0.0065 BMI (kg/m²) 21.83 ± 1.11ᵃᵇ 24.89 ± 1.41ᵃᶜ 29.41 ± 2.49ᵇᶜ < 0.0001 Waist circumference (in cm) 88.33 ± 6.76ᵇ 92.81 ± 6.21 95.01 ± 5.71ᵇ 0.0036 Hip circumference (in cm) 95.62 ± 6.04ᵇ 98.81 ± 5.76 100.90 ± 4.94ᵇ 0.0019 Waist–hip ratio (WHR) 0.920 ± 0.041 0.944 ± 0.050 0.940 ± 0.050 0.244 Waist–height ratio (WHtR) 0.553 ± 0.052ᵃᵇ 0.604 ± 0.021ᵃ 0.615 ± 0.036ᵇ < 0.0001 ᵃ Normal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant 3.3. Glycemic and Insulin-Resistance indices The key glycemic and insulin-resistance indices across BMI groups are portrayed in Table 3 . Fasting glucose (p < 0.0001) and PPBS (p = 0.0081) were significantly higher in the obese group compared with non-obese (normal). Insulin (p = 0.0231) and HOMA-IR (p < 0.0001) depicted pronounced significance, as evidenced by progressive increase with BMI, thereby reflecting worsening insulin resistance. HbA1c did not differ significantly (p = 0.4961). Total cholesterol showed a modest difference (p = 0.0362), while triglycerides, HDL, LDL, and VLDL did not exhibit significance. Table 3 Glycaemic and Insulin Resistance indices across BMI Groups in T2DM Patients Variable Non-obese [Normal] (BMI < 23 kg/m²) Overweight (BMI 23–27.49 kg/m²) Obese (BMI ≥ 27.5 kg/m²) p-value FBS (mg/dL) 136.4 ± 24.35ᵇ 145.3 ± 47.74ᶜ 183.2 ± 42.18ᵇᶜ < 0.0001 PPBS (mg/dL) 203.8 ± 52.66ᵇ 216.3 ± 50.43ᶜ 246.9 ± 44.24ᵇᶜ 0.0081 HbA1c (%) 7.43 ± 1.22 8.04 ± 2.32 8.24 ± 2.18 0.4961 Insulin (µIU/mL) 17.27 ± 6.07ᵇ 21.87 ± 9.56 24.44 ± 8.61ᵇ 0.0231 HOMA-IR 5.82 ± 2.38ᵇ 7.47 ± 3.54ᶜ 11.27 ± 5.17ᵇᶜ < 0.0001 Total cholesterol (mg/dL) 203.2 ± 41.88 196.4 ± 40.98ᶜ 211.6 ± 28.87ᶜ 0.0362 Triglycerides (mg/dL) 143.0 ± 59.02 157.8 ± 58.72 187.7 ± 96.01 0.1134 HDL (mg/dL) 40.87 ± 10.60 39.34 ± 6.19 41.91 ± 6.45 0.2172 LDL (mg/dL) 133.7 ± 36.52 127.8 ± 34.26 133.0 ± 27.61 0.3678 VLDL (mg/dL) 28.67 ± 11.76 31.62 ± 11.69 37.50 ± 19.25 0.1270 FBS: Fasting Blood Sugar PPBS: Post Prandial Blood sugar HbA1c: Glycated hemoglobin HOMA-IR: Homeostatic Model Assessment for Insulin Resistance HDL: High-Density Lipoprotein LDL: Low- Density VLDL: Very Low- Density Lipoprotein ᵃ Normal vs Overweight significant: ᵇ Normal vs Obese significant: ᶜ Overweight vs Obese significant 3.4. Renal and hepatic profile The biochemical indicators of renal and hepatic function, across BMI categories are detailed in Table 4 . With reference to Table 4 the following parameters did not reveal significance:- Urea, creatinine, total protein, albumin, globulin, A/G ratio, and bilirubin However, the enzymes showed statistically significant differences, as evidenced by AST (p = 0.0263) and ALT (p = 0.0158) that were higher in the obese group when compared to the overweight subjects. ALP did not reveal significance. Table 4 Biochemical indicators of renal and hepatic function across BMI Groups in T2DM Patients Variable Non-obese [Normal] (BMI < 23 kg/m²) Overweight (BMI 23–27.49 kg/m²) Obese (BMI ≥ 27.5 kg/m²) p-value Urea (mg/dL) 24.27 ± 5.91 23.65 ± 8.81 25.83 ± 11.16 0.603 Creatinine (mg/dL) 0.913 ± 0.141 1.029 ± 0.482 1.065 ± 0.297 0.119 Total protein (g/dL) 7.91 ± 0.55 7.72 ± 0.56 7.95 ± 0.59 0.082 Albumin (g/dL) 4.47 ± 0.30 4.46 ± 0.30 4.49 ± 0.40 0.738 Globulin (g/dL) 3.44 ± 0.69 3.25 ± 0.53 3.47 ± 0.60 0.338 A/G ratio 1.34 ± 0.34 1.42 ± 0.30 1.34 ± 0.30 0.576 Total bilirubin (mg/dL) 0.593 ± 0.365 0.496 ± 0.192 0.548 ± 0.293 0.798 Direct bilirubin (mg/dL) 0.207 ± 0.080 0.184 ± 0.052 0.183 ± 0.055 0.582 AST (U/L) 20.21 ± 3.09 19.46 ± 7.25 ᶜ 23.41 ± 8.15 ᶜ 0.0263 ALT (U/L) 23.43 ± 3.18 22.56 ± 10.60 ᶜ 31.35 ± 15.78 ᶜ 0.0158 ALP (U/L) 84.07 ± 23.19 87.11 ± 25.99 80.93 ± 23.93 0.746 ᵃ Normal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant. 3.5. Electrolytes, micronutrients, and thyroid profile With the exception of Thyroxine [T4] that differed significantly across BMI categories (p = 0.0129), wherein obese participants showed higher values compared to the overweight group, other parameters did not depict any significance (Table 5 ). Table 5 Electrolyte, micronutrient, and thyroid profile across BMI Groups in T2DM patients Variable Non-obese [Normal] (BMI < 23 kg/m²) Overweight (BMI 23–27.49 kg/m²) Obese (BMI ≥ 27.5 kg/m²) p-value Sodium (mEq/L) 141.2 ± 2.78 139.9 ± 3.89 140.3 ± 3.49 0.2723 Potassium (mEq/L) 4.55 ± 0.27 4.61 ± 0.41 4.53 ± 0.40 0.7303 Chloride (mEq/L) 103.7 ± 2.90 102.8 ± 3.97 102.5 ± 3.63 0.4428 Calcium (mg/dL) 9.52 ± 0.74 9.38 ± 0.84 9.59 ± 0.92 0.2369 Magnesium (mg/dL) 1.607 ± 0.240 1.638 ± 0.341 1.743 ± 0.547 0.6172 Zinc (µg/dL) 36.09 ± 11.68 41.96 ± 23.91 34.69 ± 18.41 0.4248 T3 (ng/mL) 2.413 ± 0.959 2.624 ± 0.640 2.773 ± 0.558 0.5402 T4 (µg/dL) 1.087 ± 0.325 1.169 ± 0.282ᶜ 1.310 ± 0.247ᶜ 0.0129 TSH (µIU/mL) 3.007 ± 1.322 3.187 ± 2.052 3.157 ± 2.358 0.6424 Cortisol (µg/dL) 14.26 ± 6.82 13.20 ± 8.46 14.73 ± 7.68 0.5715 ᵃ Normal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant. 3.6. Insulin-Resistance indices across BMI Groups Across the BMI categories, significant differences were observed in several insulin-resistance indices, as shown in Fig. 1 (A-F). QUICKI values were lowest in the obese group and highest in the normal-weight group (p < 0.0001). SPISE values also showed a stepwise reduction across normal, overweight, and obese groups (p < 0.0001). The TyG index demonstrated a significant increase with rising BMI (p = 0.0002). In contrast, TG/HDL and uric acid/HDL ratios did not differ significantly between the three BMI groups (p = 0.23 and p = 0.71, respectively). An additional parameter, namely De ritis ratio (AST/ALT) was also included that remained comparable across groups. The distribution pattern of composite metabolic indices, as a function of normal (non-obese), overweight, and obese BMI groups in patients with type 2 diabetes mellitus is depicted in Fig. 1 . With reference to Fig. 1 , each jittered scatter plot displays individual patient values with overlaid group means and standard deviations, illustrating progressive deterioration in insulin-sensitivity–related indices with increasing adiposity. Obese participants exhibited markedly lower QUICKI and SPISE values and higher TyG indices compared with the normal BMI group, reflecting a shift toward greater insulin resistance. In contrast, TG/HDL, uric acid/HDL, and AST/ALT ratios did not differ significantly between groups, though trends toward metabolic worsening were observable. 3.7. Distribution of categorical variables In this study, no significant association was observed between gender and BMI category. Among the 100 participants, males and females were similarly distributed across the normal (non-obese), overweight, and obese groups, and the chi-square test confirmed the absence of a statistically meaningful relationship (χ² = 1.17; df = 2; p = 0.556). Likewise, the association between family history of diabetes mellitus and BMI category was not statistically significant. Although participants with a positive history appeared more frequently in the overweight and obese groups, chi-square analysis demonstrated that this distribution did not differ significantly from those without a family history (χ² = 0.13; df = 2; p = 0.937). These findings indicate that within this cohort, neither gender nor family history of diabetes showed a significant relationship with BMI classification. 3.8. Correlation patterns between metabolic variables and Insulin-Resistance indices Spearman analysis revealed unequivocal and biologically consistent associations among adiposity, glycaemic parameters, lipid markers, and the insulin-resistance indices (Table 6 ). BMI displayed statistically significant positive correlation with HOMA-IR and negative correlation with QUICKI, TyG and SPISE. Waist circumference exhibited statistically significant positive correlation with HOMA-IR and negative correlation with QUICKI and SPISE. Waist Hip Ratio depicted positive correlation with TyG. Waist Height Ratio exhibited positive correlation with HOMA-IR and negative correlation with QUICKI and SPISE. These results are shown in Table 6 . Table 6 Spearman Correlation between Insulin-Resistance Markers and Metabolic Variables Variable INSULIN (µIU/mL) HOMA-IR QUICKI TyG SPISE BMI 0.27 (0.01) 0.50 (< 0.0001 ) –0.50 (< 0.0001) 0.45 (< 0.0001) –0.88 (< 0.0001) Waist (cm) 0.58 (< 0.0001 ) 0.51 (< 0.0001 ) –0.50 (< 0.0001 ) 0.24 (0.02) –0.45 (0.001) Waist Hip Ratio 0.17 (0.08) 0.18 (0.07) –0.17 (0.08) 0.28 (0.01) –0.18 (0.08) WHtR 0.36 (0.0003 ) 0.35 (0.0003) –0.34 ( 0.0004) 0.11 (0.28) –0.48 (< 0.0001 ) FBS (mg/dl) –0.01 (0.95) 0.46 (< 0.0001 ) –0.47 (< 0.0001 ) 0.70 (< 0.0001 ) –0.58 (< 0.0001 ) PPBS (mg/dl) 0.05 (0.59) 0.31 (0.002) –0.31 (0.002) 0.56 (< 0.0001 ) –0.43 (< 0.0001 ) HbA1c (%) –0.04 (0.72) 0.20 (0.046) –0.20 (0.046) 0.47 (< 0.0001) –0.28 (0.005) Triglycerides 0.11 (0.29) 0.27 (0.007) –0.27 (0.007) 0.86 (< 0.0001) –0.61 (< 0.0001) HDL 0.00 (0.99) –0.06 (0.52) 0.07 (0.50) –0.27 (0.007) 0.14 (0.16) T4 0.01 (0.93) 0.14 (0.15) –0.15 (0.15) 0.22 (0.03) –0.27 (0.007) TG/HDL 0.07 (0.47) 0.22 (0.03) –0.22 (0.03) 0.80 (< 0.0001) –0.56 (< 0.0001) Uric acid/HDL 0.11 (0.27) 0.09 (0.37) –0.09 (0.38) 0.05 (0.65) –0.06 (0.58) AST/ALT –0.05 (0.61) –0.11 (0.26) 0.11 (0.26) –0.18 (0.07) 0.13 (0.21) INSULIN (µIU/mL) — 0.85 (< 0.0001) –0.84 (< 0.0001) 0.08 (0.44) –0.23 (0.02) HOMA-IR 0.85 (< 0.0001) — –1.00 (< 0.0001) 0.43 (< 0.0001) –0.50 (< 0.0001) QUICKI –0.84 (< 0.0001) –1.00 (< 0.0001) — –0.43 (< 0.0001) 0.50 (< 0.0001) TyG 0.08 (0.44) 0.43 (< 0.0001) –0.43 (< 0.0001) — –0.73 (< 0.0001) SPISE –0.23 (0.02) –0.50 (< 0.0001 ) 0.50 (< 0.0001 ) –0.73 (< 0.0001) — Waist Height Ratio showed significant positive correlations with fasting insulin (r = 0.27 and r = 0.58) and HOMA-IR (r = 0.50 and r = 0.51), and corresponding negative correlations with QUICKI (r = − 0.50 for both). WHtR showed a similar pattern, indicating that central adiposity was closely linked with greater insulin resistance even within this T2DM cohort. The IR indices demonstrated strong internal coherence. HOMA-IR and QUICKI were perfectly inversely correlated (r = − 1.00). Both showed moderate correlations with TyG (r = 0.43 and r = − 0.43) and SPISE (r = − 0.50 and r = 0.50). SPISE correlated most strongly with BMI (r = − 0.88), followed by waist and WHtR, reflecting its sensitivity to adiposity-related metabolic dysfunction. Glycaemic parameters also correlated with IR indices. Fasting glucose correlated with HOMA-IR (r = 0.46) and QUICKI (r = − 0.47), and post-prandial glucose showed similar moderate associations. TyG demonstrated the expected strong correlation with fasting glucose (r = 0.70). Among lipids, triglycerides correlated positively with HOMA-IR and strongly with TyG (r = 0.86), and negatively with QUICKI and SPISE. TG/HDL showed a comparable pattern. HDL, uric acid/HDL, and AST/ALT did not show meaningful correlations with the major IR indices. Overall, anthropometric measures, glycaemic variables, and triglyceride-related lipid markers showed the strongest relationships with the insulin-resistance indices, with SPISE and TyG emerging as robust surrogate markers in obesity-linked insulin resistance. 3.9. ROC analysis: Since our results have fortified the fact that SPISE is a robust surrogate marker of insulin resistance, we embarked upon the study further to explore the utility of SPISE as a predictor in anthropometry specified groups of T2DM. To enable this, the Receiver operating characteristic (ROC) analysis was performed for evaluating the ability of SPISE and HOMA-IR to discriminate obese from normal (non-obese) individuals. SPISE demonstrated near-perfect discriminatory performance with an AUC of 0.99 (95% CI: 0.97–1.00, p < 0.0001). A cut-off value of < 5.55 yielded a sensitivity of 92.5% and specificity of 93.3%. HOMA-IR also showed significant discriminatory ability with an AUC of 0.85 (95% CI: 0.74–0.95, p 7.10, HOMA-IR demonstrated a sensitivity of 80.0% and specificity of 86.7%. However, SPISE showed superior discrimination compared with HOMA-IR for identifying obesity-associated insulin resistance. The results are depicted in Table 7 and Fig. 2 . Table 7 ROC characteristics of SPISE and HOMA-IR for discriminating obesity Index AUC (95% CI) Cut-off Sensitivity (%) Specificity (%) p-value SPISE 0.99 (0.97–1.00) < 5.55 92.5 93.3 7.10 80.0 86.7 < 0.0001 Receiver operating characteristic (ROC) analysis illustrating the ability of (A) the Single Point Insulin Sensitivity Estimator (SPISE) and (B) HOMA-IR to discriminate obese from normal (non-obese) individuals with T2DM. SPISE demonstrated near-perfect discriminatory performance (AUC = 0.99), whereas HOMA-IR showed good discriminatory ability (AUC = 0.845). The diagonal reference line represents no discrimination 4. Discussion This study explored vistas as to how anthropometric measures, biochemical markers, and the commonly used insulin-resistance (IR) indices behave across BMI categories in South Indian adults with T2DM. Despite similar age and duration of diabetes across groups, obesity was associated with higher fasting and post-prandial glucose, elevated fasting insulin and HOMA-IR, and lower QUICKI and SPISE. TyG also increased progressively with BMI. Correlation patterns showed consistent clustering: adiposity measures, glycaemia, triglycerides, TyG, and SPISE strongly inter-related, whereas uric-acid–based ratios and AST/ALT contributed little. Overall, simple fasting-derived indices appeared to capture much of the IR burden linked to adiposity in this cohort. Having said that, it must be emphasized that several of these revelations had been documented by earlier workers. However, an important aspect that has emerged from this study is the relative superiority of SPISE over the time-tested HOMA-IR in discriminating obese T2DM from normal and overweight, on the basis of insulin resistance. The feasibility and reliability of SPISE index centres around the fact that it is a simple, cost-effective tool that could be utilized in routine clinical practice and large-scale epidemiological studies for quantitating insulin sensitivity, without having to resort to direct insulin measurements. This is an important outcome of our study. The gradients observed for BMI, waist circumference, and waist-to-height ratio align with previously generated evidences emphasizing the high metabolic risk associated with central adiposity in South Asians. 3,4 Even within established T2DM, increasing adiposity corresponded with higher insulin resistance and poorer QUICKI and SPISE values. Waist-to-height ratio behaved comparably to BMI and waist circumference, supporting its use as a practical screening tool in Indian populations, where visceral adiposity is a commonly observed predisposing factor. The behaviour of TyG in our cohort is consistent with the existing reports, especially with reference to the demonstrated utility of the index as a surrogate marker for IR[ 11 ]. It is noteworthy to mention that research carried out on the populations (adolescents) of China and Brazil showed the ability of TyG to reveal metabolically unhealthy phenotypes [ 12 , 13 ]. Data obtained from meta-analysis point to the fact that TyG is an established marker linked to higher cardiometabolic and future risk for Diabetes[ 9 ]. As per the data from this study, TyG correlated strongly with glycaemia, triglycerides, HOMA-IR, and QUICKI, indicating that it retains clinical utility even among individuals with established T2DM, thereby implying that glucotoxicity paves the way for lipotoxicity culminating in glucolipotoxicity, synonymous with dyslipidemia, decreased insulin sensitivity and enhanced insulin resistance. The TyG index thus reflects the underlying process of glucolipotoxicity, where chronic elevation of both glucose and triglyceride-rich lipoproteins enhance circulating free fatty acids, linked to oxidative stress, and injury to insulin-sensitive tissues[ 1 , 5 ]. It is in the fitness of things to state that TyG is not merely a statistical surrogate, but an integrated, well-rounded marker of combined glucose–lipid burden. The strong correlations that have been observed in the present study among TyG, fasting glucose, triglycerides, and HOMA-IR are consistent with this documented mechanism. SPISE, derived without insulin measurements, also performed well. Previous studies have shown strong correlations between SPISE and clamp-derived insulin sensitivity, as well as classical IR indices[ 8 , 14 ]. Longitudinal research in adolescents had demonstrated that lower SPISE predicts development of metabolic syndrome and impaired glucose regulation[ 15 , 16 ]. In our study, SPISE declined steadily across BMI categories and demonstrated expected inverse relationships with adiposity, glycaemia, triglycerides, and TyG. These findings reinforce its potential as a cost-effective alternative to insulin-based indices in routine care. However, citing our present study, it must be mentioned that SPISE scores over HOMA-IR in delineating obese from non-obese (normal) and overweight patients with T2DM and hence adds to the existing global armamentarium as a predictor of promise. Although BMI remains the most commonly used anthropometric index, it is well recognized that many individuals, particularly in South Asian populations, might have normal BMI, despite significant central adiposity and metabolic risk, which limits its reliability as a sole indicator of insulin resistance. Ethnic differences in body composition and fat distribution contribute to higher cardiometabolic risk observed at lower BMI levels in South Asians[ 17 ]. Nevertheless, BMI remains clinically useful due to its simplicity and reproducibility, and in the present study it was therefore interpreted alongside metabolically informative indices. HOMA-IR has long been accepted as a surrogate marker of insulin resistance and correlates with direct measures of insulin sensitivity[ 18 ]. However, its dependence on fasting insulin measurement restricts routine clinical use in many laboratories, thereby prompting us to look out for alternative non-insulin-based indices. In this context, the TyG index and the TG/HDL-C ratio have emerged as practical alternatives. The TyG index reflects the combined burden of hyperglycaemia and hypertriglyceridemia and is strongly associated with insulin resistance and cardiometabolic risk[ 19 ]. The TG/HDL-C ratio also correlates closely with insulin resistance and has been proposed as a surrogate biomarker of dyslipidaemia-related metabolic risk [ 20 ]. Furthermore, TG/HDL ratio functions as the surrogate for small dense LDL (sdLDL). Quite recently, the Single Point Insulin Sensitivity Estimator (SPISE) has been receiving prominence as a reliable indirect marker of insulin sensitivity that does not require insulin measurement. Based on BMI, triglycerides, and HDL-C, SPISE demonstrates good diagnostic performance for cardiometabolic risk and emerging dysglycaemia [ 21 – 23 ]. Taken together, the combined evaluation of SPISE, TyG, TG/HDL-C ratio, and HOMA-IR provides a broader and more objective assessment of insulin resistance and insulin sensitivity across body phenotypes and may support individualized, anthropometry-guided therapeutic strategies. Uric-acid–related markers and AST/ALT ratios, although previously reported to be associated with metabolic risk and NAFLD (MASLD) in various populations[ 10 , 24 , 25 ], did not differentiate BMI groups in this study and showed weak correlations with primary IR indices. This likely reflects limited variability observed within a cohort entirely composed essentially of of individuals with T2DM. Micronutrient markers such as magnesium and zinc also did not vary significantly across BMI classes, though both have documented associations with insulin resistance and oxidative stress[ 26 , 27 ]. Their limited contribution here may be attributable to dietary patterns or the absence of marked deficiencies in the sample. Although T4 differed modestly across BMI categories, the values remained within normal limits (clinically euthyroid) and showed no meaningful association with insulin-resistance indices. The internal consistency of the correlation matrix provides confidence in the comparative performance of the indices studied. HOMA-IR and QUICKI showed expected inverse relationships, and TyG and SPISE, both insulin-independent, tracked closely with the traditional insulin-based measures. External validation work has consistently shown similar patterns across diverse populations [ 8 , 9 , 11 – 16 ]. The concordant behaviour of these indices suggests they may be valuable tools in the Indian clinical settings where insulin assays or dynamic testing are not routinely feasible. Relying solely on insulin-based indices such as HOMA-IR or QUICKI is often impractical in routine Indian hospital settings, thereby rendering fasting-derived surrogates, namely TyG and SPISE as feasible alternatives. The ROC analysis further strengthened the comparative findings, showing that SPISE provided near-perfect discrimination between obese and normal-weight individuals, outperforming HOMA-IR. Although HOMA-IR demonstrated good diagnostic accuracy, its reliance on fasting insulin limits routine applicability. The superior performance of SPISE, as per this study coupled with its insulin-independent status, highlights its clinical utility as a pragmatic and scalable alternative for identifying obesity-associated insulin resistance in routine and resource-limited settings. Another point that would acquire relevance is the fact that in obesity, there is down regulation of insulin receptors. Since SPISE shows excellent discrimination in segregating obese patients with T2DM, it would be in the fitness of things to opine that the utility of SPISE can also be indirectly perceived from the standpoint of insulin receptors. Limitations of the study The sample size for the study was modest and the study subjects were drawn from a single centre, thereby limiting generalizability. Comparison of the surrogate indices of insulin sensitivity/insulin resistance was not performed with the gold-standard technique, namely the euglycaemic clamp. Residual confounding factors are also possible. Despite these constraints, the findings underscore that simple fasting-based surrogate indices, especially TyG and SPISE, parallel insulin-based measures and meaningfully reflect obesity-related insulin resistance. Their ease of calculation and reliance on routinely available tests make them attractive for use in resource-limited settings. Salient points that have emerged from the study: - TyG and SPISE tracked obesity-related insulin resistance in South Indian adults with T2DM. SPISE scored over the time tested HOMA-IR in discriminating obese patients with T2DM Insulin-independent indices performed comparably to HOMA-IR and QUICKI across BMI groups. TyG correlated strongly with glycaemia, triglycerides, and insulin-based IR measures. SPISE declined with rising BMI and reflected combined adiposity and metabolic burden. Uric acid–based and AST/ALT ratios showed weak links to insulin resistance in this cohort. Suggestions for future studies We strongly suggest that larger and more comprehensive longitudinal studies are warranted that would also take into consideration imaging and vascular outcomes. By doing so, we would be in a position to refine the cut-offs. This would also enable us to overcome the confounding factors. Such studies would possibly culminate in upholding the clinical utility of TyG and SPISE in predicting long-term metabolic risks associated with organ dysfunction. Novelty of the study Despite the aforementioned constraints and limitations, the findings of the present study underscore that the promulgation of rapid and simple fasting-based surrogate indices, especially TyG and SPISE would parallel insulin-based measures and meaningfully reflect insulin resistance, especially in anthropometry specified population with T2DM. The ease of calculation and reliance on routinely available tests render them attractive for use in resource-limited settings. Computing SPISE index on the basis of BMI, TyG and HDL is easily accomplishable even in centers not possessing tertiary care facilities. Hence, SPISE index holds promise, by virtue of being a single point indicator of insulin sensitivity. Our laboratory is also in the process of generating further evidences to suggest novel parameters that portray relationship with SPISE index. However, these results would be communicated separately, upon completion of the analysis and objectivized interpretation. 5. Conclusion In this cohort of adults with T2DM drawn from a tertiary healthcare centre in South India, obesity and central adiposity were strongly linked to higher fasting insulin, HOMA-IR, and TyG, and to lower QUICKI and SPISE values, while conventional lipid fractions and uric-acid–based ratios added little additional information. TyG and SPISE, calculated from routinely available fasting glucose, triglycerides, HDL cholesterol, and BMI, closely paralleled insulin-based indices and reflected the underlying obesity-related insulin resistance. These findings support the use of TyG and SPISE as practical, low-cost surrogate markers of insulin resistance in routine care, particularly in settings where insulin assays or dynamic tests are not feasible. A significant aspect of the present study is the utility of predictive capacity of SPISE that is superior to the time-tested surrogate marker of Insulin resistance, namely HOMA-IR. Larger, longitudinal studies are needed to establish optimal cut-offs and to create evidences as to whether such indices would help improve the prediction of cardiometabolic outcome and impending organ dysfunction in patients with T2DM. Declarations Funding Declaration: The authors declare that they did not receive any funds from any agency or organization Author Contribution EK conducted the study in her capacity as the Principal investigator, under the direct supervision/guidance of SAR, aided by JR (co-supervisor). MHA, in his capacity as the Professor of General Medicine and i/c, Diabetic clinic added valuable inputs from the perspectives of endocrinology and internal medicine. Statistical analysis was completed by EK,with inputs from JR and SAR. SAR critically reviewed the manuscript finally after all the other co-authors had reviewed. Acknowledgement The authors thank the Chancellor of Sri Balaji Vidyapeeth- Mr.M.K.Rajagopalan , Vice Chancellor Dr.N.R.Biswas and Director Research Dr.K.S.Reddy. References DeFronzo RA, Ferrannini E, Groop L et al (2015) Type 2 diabetes mellitus. Nat Rev Dis Primers 1:15019. 10.1038/nrdp.2015.19 Anjana RM, Deepa M, Pradeepa R et al (2017) Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR–INDIAB study. Lancet Diabetes Endocrinol 5(8):585–596 Unnikrishnan R, Anjana RM, Mohan V (2014) Diabetes in South Asians: is the phenotype different? Curr Diab Rep 14(1):518 Ashwell M, Gunn P, Gibson S (2012) Waist-to-height ratio is a better screening tool than waist circumference and BMI. BMJ Open 2:e000650 Mahajan R (2017) Insulin resistance: quest for surrogate markers. Int J Appl Basic Med Res 7(3):149–150 Cho YK, Han KD, Kim HS, Jung CH, Park JY, Lee WJ (2022) TyG index and future cardiovascular disease: a national cohort study. J Lipid Atheroscler 11(2):178–186 Won KB, Park EJ, Han D et al (2020) Triglyceride-glucose index predicts progression of coronary artery calcification. Cardiovasc Diabetol 19:34 Paulmichl K, Hatunic M, Højlund K et al Modification and validation of the triglyceride-to-HDL cholesterol ratio as a surrogate of insulin sensitivity: the Single Point Insulin Sensitivity Estimator (SPISE) Li S, Guo B, Chen H et al (2019) TyG index and cardiovascular events: a systematic review and meta-analysis. Cardiovasc Diabetol 18:99 Kocak MZ, Aktas G, Erkus E et al (2019) Uric acid/HDL-C ratio as a predictor of metabolic syndrome in T2DM. Rev Assoc Med Bras 65(1):9–15 Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F (2008) The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord 6(4):299–304. 10.1089/met.2008.0034 Yu X, Wang L, Zhang W et al (2019) Fasting triglycerides and glucose index is more suitable for the identification of metabolically unhealthy individuals in the Chinese adult population. J Diabetes Investig 10(4):1050–1057. 10.1111/jdi.12975 Reckziegel MB, Nepomuceno P, Machado T et al (2023) The triglyceride-glucose index as an indicator of insulin resistance and cardiometabolic risk in Brazilian adolescents. Arch Endocrinol Metab 67(2):153–161. 10.20945/2359-3997000000506 Cederholm J, Zethelius B (2019) SPISE and other fasting indexes of insulin resistance: risks of coronary heart disease or type 2 diabetes. Ups J Med Sci 124(4):265–272. 10.1080/03009734.2019.1680583 Barchetta I, Guglielmi C, Bertoccini L et al (2022) The single-point insulin sensitivity estimator (SPISE) index is a strong predictor of abnormal glucose metabolism in overweight/obese children: a long-term follow-up study. J Endocrinol Invest 45(1):43–51. 10.1007/s40618-021-01612-6 Correa-Burrows P, Matamoros M, de Toro V et al (2023) A single-point insulin sensitivity estimator (SPISE) of 5.4 is a good predictor of both metabolic syndrome and insulin resistance in adolescents with obesity. Front Endocrinol (Lausanne) 14:1078949. 10.3389/fendo.2023.1078949 Misra A, Shrivastava U (2013) Obesity and dyslipidemia in South Asians: implications for cardiovascular risk. Nutrients 5(7):2708–2733 Wallace TM, Levy JC, Matthews DR (2004) Use and abuse of HOMA modeling. Diabetes Care 27(6):1487–1495 Wang S, Shi J, Peng Y et al (2021) Stronger association of triglyceride glucose index than the HOMA-IR with arterial stiffness in patients with type 2 diabetes: a real-world single-centre study. Cardiovasc Diabetol 20(1):82. 10.1186/s12933-021-01274-x Baneu P, Văcărescu C, Drăgan SR et al (2024) The triglyceride/HDL ratio as a surrogate biomarker for insulin resistance. Biomedicines 12(7):1493. 10.3390/biomedicines12071493 Correa-Burrows P, Blanco E, Gahagan S, Burrows R (2020) Validity assessment of the single-point insulin sensitivity estimator (SPISE) for diagnosis of cardiometabolic risk in post-pubertal Hispanic adolescents. Sci Rep 10(1):14399 Stein R, Koutny F, Riedel J et al (2023) Single point insulin sensitivity estimator (SPISE) as a prognostic marker for emerging dysglycemia in children with overweight or obesity. Metabolites 13(1):100 Song K, Lee E, Lee HS et al (2025) Comparison of SPISE and METS-IR and other markers to predict insulin resistance and elevated liver transaminases in children and adolescents. Diabetes Metab J 49(2):264–274. 10.4093/dmj.2024.0302 Li Q, Yang Z, Lu B et al (2011) Serum uric acid level and its association with metabolic syndrome and carotid atherosclerosis in patients with type 2 diabetes. Cardiovasc Diabetol 10:72. 10.1186/1475-2840-10-72 Elhence A, Aggarwal A, Kumar A et al (2022) Prevalence of non-alcoholic fatty liver disease in India: a systematic review and meta-analysis. J Clin Exp Hepatol 12(5):1329–1340. 10.1016/j.jceh.2022.02.005 Olechnowicz J, Tinkov A, Skalny A, Suliburska J (2018) Zinc status is associated with inflammation, oxidative stress, lipid, and glucose metabolism. J Physiol Sci 68(1):19–31. 10.1007/s12576-017-0571-7 Li W, Jiang Y, Zhang S et al (2022) Serum magnesium and the risk of prediabetes and type 2 diabetes: a large prospective cohort study and dose–response meta-analysis. Nutrients 14(9):1799. 10.3390/nu14091799 Additional Declarations No competing interests reported. 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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-8746166","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609819340,"identity":"7ac46657-fe5b-40ab-a591-61b65dd8f9b0","order_by":0,"name":"Elakiya K.","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Elakiya","middleName":"","lastName":"K.","suffix":""},{"id":609819341,"identity":"842ad3a9-a52d-478f-ad6b-370cee0db9be","order_by":1,"name":"Jayanthi R","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Jayanthi","middleName":"","lastName":"R","suffix":""},{"id":609819342,"identity":"c8265913-6e0a-47a3-943a-918e3e483bef","order_by":2,"name":"Srinivasan A.R.","email":"data:image/png;base64,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","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Srinivasan","middleName":"","lastName":"A.R.","suffix":""},{"id":609819343,"identity":"b0d35c75-745f-48b6-8580-db76e5f1a593","order_by":3,"name":"Mohamed Hanifah A.","email":"","orcid":"","institution":"Mahatma Gandhi Medical College and Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Mohamed","middleName":"Hanifah","lastName":"A.","suffix":""}],"badges":[],"createdAt":"2026-01-31 03:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8746166/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8746166/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105214988,"identity":"ff4048aa-fb9f-4b82-91d0-547a538b678c","added_by":"auto","created_at":"2026-03-23 14:27:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":164437,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution pattern of composite metabolic indices across normal (non-obese), overweight, and obese BMI groups in patients with type 2 diabetes mellitus \u0026nbsp;\u0026nbsp;(A) QUICKI (B) SPISE (C) TyG index (D) TG/HDL ratio (E) Uric acid/HDL ratio (F) AST/ALT ratio (De ritis) Group comparisons were performed using the Kruskal – Wallis test followed by Dunn’s post-hoc analysis. * denote p \u0026lt; 0.05 which was considered statistically significant\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8746166/v1/3fe03ee893a729d41a0fca13.png"},{"id":105214997,"identity":"e15e81f3-047b-4533-95bd-b02f414f6261","added_by":"auto","created_at":"2026-03-23 14:27:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89452,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for SPISE and HOMA-IR\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8746166/v1/2a70d07617b67a0484898dc8.png"},{"id":105564424,"identity":"0c72913d-3aa5-442a-a054-b37e88a41cf7","added_by":"auto","created_at":"2026-03-27 12:49:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1958196,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8746166/v1/142086ef-0923-458a-a57e-37774f572dc1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eSingle Point Insulin Sensitivity Estimator (SPISE), a novel score to evaluate insulin sensitivity, is predictive in anthropometry specified adults with Type 2 Diabetes Mellitus\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInsulin resistance (IR) continues to be the pivotal aberration, with reference to the pathophysiology of type 2 diabetes mellitus (T2DM). IR contributes immensely to organ dysfunction, including cardiometabolic disease progression.\u003c/p\u003e \u003cp\u003eThe global burden of T2DM has risen sharply, since the past decade, with South Asian populations portraying disproportionately higher susceptibility. This is attributed to the vicious conglomerate of genetic, metabolic, and lifestyle factors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. India, in particular, has been witnessing a steep increase in the prevalence of DM, with the ICMR-INDIAB study duly documenting the substantial rates of both diabetes and prediabetes across multiple states of India [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A startling observation is that individuals of the South Asian lineage develop IR at a relatively younger age and lower BMI values, in comparison to the Western populations. This calls for the immediate need for promulgating region-specific metabolic markers that would vividly capture early metabolic risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, the fact remains that these markers must necessarily be sensitive, specific, reliable, feasible and also economically viable. Besides, even the health care establishments that are not tertiary in nature must be able to facilitate the utility of such biomarkers.\u003c/p\u003e \u003cp\u003eConventional assessment of IR relies on the direct or indirect insulin measurements, such as fasting insulin, HOMA-IR, or the euglycemic hyperinsulinemic clamp. Although the clamp technique continues to be the gold standard, it is not pragmatic and feasible in routine clinical settings due to its high cost, gross technical demands, and most importantly, the need for qualified and trained personnel [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHaving said that, surrogate markers such as fasting insulin and HOMA-IR are hampered by inter-assay variability, low standardization across laboratories, and attenuated reliability in populations endowed with varying beta-cell reserve[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. QUICKI, though analytically useful, also depends on the quantitation of fasting insulin and therefore shares the same limitations in low-resource settings. These limitations and demerits have prompted the scientific community to search for alternative indices of insulin resistance that are simple, cost-effective, independent of insulin assays and at the same time reliable and reproducible.\u003c/p\u003e \u003cp\u003eIn recent years, non-insulin-based indices have been receiving wide attention. Among these, the Triglyceride-Glucose (TyG) index has emerged as one of the strongest and most reproducible markers of IR. TyG, derived from fasting triglycerides and fasting glucose has shown strong correlation with clamp-measured insulin sensitivity. Besides, it has unequivocally demonstrated the value in envisaging the impending adverse cardiometabolic outcomes. Comprehensive cohort studies have delineated the fact that higher TyG values are associated with enhanced cardiovascular disease risk and all-cause mortality in young adults [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, the studies have linked the index with accelerated progression of cardinal events including coronary artery calcification and that irrespective of baseline atherosclerotic burden [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In view of the afore-mentioned attributes, TyG holds immense promise in launching large-scale screening, even in primary care settings.\u003c/p\u003e \u003cp\u003eDyslipidemia-derived markers are also gaining impetus as surrogate indicators of metabolic dysfunction. The triglyceride-to-HDL cholesterol (TG/HDL-C) ratio reflects both hepatic overproduction of triglyceride-rich lipoproteins and impaired HDL-mediated reverse cholesterol transport. In addition, TG/HDL-C is also considered as a surrogate marker of small dense LDL. Elevated TG/HDL-C values possess a nexus with IR, visceral adiposity, and metabolic syndrome. The SPISE (Single Point Insulin Sensitivity Estimator) index fortifies this concept even further by integrating BMI, HDL-C, and triglycerides into a single validated formula. SPISE has been shown to depict insulin sensitivity in children and adults without calling for insulin measurements [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. By virtue of retaining the anthropometry index and lipid fractions, SPISE thus confers a much more reliable and broader feasible picture on the metabolic profile of insulin sensitivity.\u003c/p\u003e \u003cp\u003eUric acid, the endogenously synthesized antioxidant has also been recognized as a key player in the realms of metabolic stress. Uric acid levels are associated with oxidative stress, endothelial dysfunction, and impaired insulin signaling [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The uric acid/HDL-C ratio has been examined as an integrative marker that would reflect both pro-oxidative burden and reduced antioxidant capacity. Studies on individuals with T2DM have revealed that this ratio is emphatically associated with metabolic syndrome and cardiometabolic abnormalities [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Given its simplicity and widespread availability, the uric acid/HDL-C ratio is rapidly emerging as a pragmatic, economically viable and augmentative marker for monitoring IR and its associated complications.\u003c/p\u003e \u003cp\u003eIn view of these considerations, these non-insulin-based markers offer promising alternatives to conventional insulin-based indices, particularly in those settings and establishments, where the measurements of insulin and C-peptide are hampered by constraints related to inherent logistic issues.\u003c/p\u003e \u003cp\u003eNeed for the present study: India\u0026rsquo;s diverse and high-burden population with T2DM provides a unique opportunity to evaluate these indices in the routine clinical practice. As a corollary, the utility and distribution of TyG, TG/HDL-C ratio, SPISE, and uric acid/HDL-C ratio in individuals with T2DM might facilitate modalities related to the timely detection of IR, besides augmenting risk stratification, and intervention strategies tailored to cater to the demands of the South Asian phenotypes, in general and Indians, in particular.\u003c/p\u003e \u003cp\u003eThe present study is a humble effort aimed at the evaluation of these surrogate markers of IR in individuals with T2DM, who had attended the clinics. Though our establishment is a tertiary healthcare set up in South India, we began the study in all earnestness with the intention that the projected outcome would cater to the needs of the continuum (primary and secondary health care establishments), in terms of feasibility, reliability and utility. By resorting to the use of parameters that are relatively inexpensive and easily quantifiable, reproducible, and independent of insulin measurement, the study aims to offer practical insights into the future that would pave the way for wider adoption of such indices in routine use.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Design and Setting\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Department of Biochemistry, in association with the Department of General Medicine at the outpatient clinics of a tertiary healthcare setup in Pondicherry located in the Union territory of Puducherry, South India. The tertiary-care teaching hospital serves a mixed population comprising urban, semi-urban and rural drawn from Puducherry and neighbouring districts of Tamil Nadu.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Consent to participate in the study and Ethics Approval\u003c/h2\u003e \u003cp\u003e(i). Patient (Subject) Information sheet was prepared in English and vernacular language (Tamil). Care was taken to explain in detail the objectives of the study, expected study outcome and risks, if any, pertaining to the voluntary participation in the study. Informed written consent was obtained from all the study participants.\u003c/p\u003e \u003cp\u003e(ii). The study protocol, complete in all aspects was reviewed and subsequently approved by the Institutional Ethics Committee, in accordance with the Declaration of Helsinki (vide Project No: MGMCRI/2025/02/IHEC/97 dt. 16-07-2025).\u003c/p\u003e \u003cp\u003eThe study was begun, only following the completion of the mandatory procedure, as outlined above in (i). and (ii).\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical trial number: Not applicable\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample Size\u003c/h2\u003e \u003cp\u003eA total of 100 subjects (n\u0026thinsp;=\u0026thinsp;100) with T2DM were included. The study had included both the genders. Previous studies on surrogate insulin-resistance indices had demonstrated adequate analytical power with the sample size ranging between 80 and 120. Given the exploratory aim of comparing multiple indices across anthropometry (BMI) defined subgroups, a sample size of 100 provided sufficient variability for facilitating subgroup and correlation analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Study Population\u003c/h2\u003e \u003cp\u003eAdults with T2DM were included in the study. The study participants were segregated into three groups, based on the Asian criteria for BMI: non-obese (normal), overweight, and obese.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Exclusion Criteria\u003c/h2\u003e \u003cp\u003eParticipants were promptly excluded, if they had type 1 diabetes, secondary forms of diabetes (e.g., pancreatitis, endocrine disorders), or pregnancy/lactation. Individuals with comorbidities, namely Thyroid dysfunction, renal disease, hepatic dysfunction, acute infections, recent hospitalization, other organ dysfunction or active inflammatory conditions were excluded. Those receiving medications known to significantly alter insulin sensitivity were also not included.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Data collection procedure and quantitation of biochemical analytes\u003c/h2\u003e \u003cp\u003eDemographic details, duration of diabetes mellitus, and treatment history were duly documented. Height and weight were recorded, based on established procedures, and BMI was calculated as weight (kg)/height (m\u0026sup2;), in accordance with the WHO Asian-specific cut-offs [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFasting venous blood samples were analyzed for biochemical analytes at the comprehensive, Central Clinical Laboratory using an automated Biochemistry analyzer (Roche Cobas 6000), with standard internal quality controls. The parameters included plasma glucose, insulin, lipid profile (triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol), serum uric acid, liver enzymes, renal profile, electrolytes, and thyroid function tests.\u003c/p\u003e \u003cp\u003eThe Central Clinical Laboratory at our tertiary health care set up is a participating laboratory in Quality assessment, enabled under the aegis of \u003cem\u003eCMCH-ACBI External Quality Assessment Scheme.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Derived Metabolic Indices\u003c/h2\u003e \u003cp\u003eThe following insulin resistance and metabolic indices were computed using established formulae: HOMA-IR, QUICKI, TyG index, and SPISE index. Additional ratios, including the triglyceride-to-HDL cholesterol ratio and the uric acid-to-HDL cholesterol ratio were also calculated, as these measures also figure as useful markers of metabolic dysfunction in T2DM [\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Primary and Secondary Outcomes\u003c/h2\u003e \u003cp\u003eThe primary outcome was the assessment of insulin resistance using multiple indices (HOMA-IR, QUICKI, TyG, and SPISE) and comparison across BMI categories, based on Asian-specific thresholds.\u003c/p\u003e \u003cp\u003eThe secondary outcomes included the evaluation of associations between each index and cardio metabolic parameters, namely triglycerides, HDL cholesterol, uric acid, the triglyceride-to-HDL ratio, as well as the uric acid-to-HDL ratio. These were calculated in order to elicit as to which index would best reflect the underlying metabolic derangements, in terms of reliability and feasibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Statistical Analysis\u003c/h2\u003e \u003cp\u003eData were entered into a datasheet and analyzed using GraphPad Prism and SPSS. The normality of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test. Comparisons across BMI groups were performed using ANOVA for normally distributed variables and the Kruskal\u0026ndash;Wallis test for skewed data. Categorical variables were analyzed using the chi-square test. Correlations between insulin-resistance indices and metabolic variables were evaluated using Pearson or Spearman coefficients, as deemed appropriate. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Baseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the study cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA. The mean age was 50.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.9 years, and the average duration of T2DM was 4.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 years. Participants predominantly exhibited an overweight\u0026ndash;obese phenotype, with a mean BMI of 26.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4 kg/m\u0026sup2; and elevated central adiposity indices (mean waist circumference 93.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4 cm; WHtR 0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04). Glycemic parameters were above target (mean FBS 159 mg/dL; HbA1c 8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9%). Lipid values depicted modest dyslipidemia. Electrolytes, renal, hepatic, and thyroid profile remained within clinically acceptable ranges. The study population comprised 39 males and 61 females, indicating a higher representation of women in the sample. A total of 60 participants reported a known history of diabetes mellitus, while 40 had no prior diagnosis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eA: Baseline characteristics of the study population (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (in Kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-hip ratio (WHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-height ratio (WHtR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting blood glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e159.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostprandial glucose (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e203.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Triodothyronine [T3] (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Thyroxine [T4] (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeostatic model assessment for Insulin Resistance (HOMA-IR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.578\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.214\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e102.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobulin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.588\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/G ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.124\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\u003e27.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eB. Distribution of categorical variables in the given study population (n\u0026thinsp;=\u0026thinsp;100)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of diabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Anthropometric variation across BMI groups\u003c/h2\u003e \u003cp\u003eAnthropometry specified comparisons across BMI categories are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), waist circumference (p\u0026thinsp;=\u0026thinsp;0.0036), hip circumference (p\u0026thinsp;=\u0026thinsp;0.0019), and WHtR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) revealed statistical significance among the groups. Height depicted a modest significance (p\u0026thinsp;=\u0026thinsp;0.0065), primarily attributed to lower mean height in the obese group. Age and duration of diabetes mellitus did not reveal significant variation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnthropometric characteristics of T2DM patients categorized on the basis of BMI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese [Normal] (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight (BMI 23\u0026ndash;27.49 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDuration of diabetes mellitus (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.27\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (in years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e50.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e50.33\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e50.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (in kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e54.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.20ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e61.84\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19ᵃᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e69.70\u0026thinsp;\u0026plusmn;\u0026thinsp;7.75ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e158.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e157.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.35ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e153.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.16ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e21.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.11ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e24.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.41ᵃᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e29.41\u0026thinsp;\u0026plusmn;\u0026thinsp;2.49ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e88.33\u0026thinsp;\u0026plusmn;\u0026thinsp;6.76ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e92.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e95.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.71ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0036\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (in cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e95.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.04ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e98.81\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e100.90\u0026thinsp;\u0026plusmn;\u0026thinsp;4.94ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0019\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist\u0026ndash;hip ratio (WHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.920\u0026thinsp;\u0026plusmn;\u0026thinsp;0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.944\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.940\u0026thinsp;\u0026plusmn;\u0026thinsp;0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist\u0026ndash;height ratio (WHtR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.553\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052ᵃᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.604\u0026thinsp;\u0026plusmn;\u0026thinsp;0.021ᵃ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.615\u0026thinsp;\u0026plusmn;\u0026thinsp;0.036ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eᵃ Normal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Glycemic and Insulin-Resistance indices\u003c/h2\u003e \u003cp\u003eThe key glycemic and insulin-resistance indices across BMI groups are portrayed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Fasting glucose (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and PPBS (p\u0026thinsp;=\u0026thinsp;0.0081) were significantly higher in the obese group compared with non-obese (normal). Insulin (p\u0026thinsp;=\u0026thinsp;0.0231) and HOMA-IR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) depicted pronounced significance, as evidenced by progressive increase with BMI, thereby reflecting worsening insulin resistance. HbA1c did not differ significantly (p\u0026thinsp;=\u0026thinsp;0.4961). Total cholesterol showed a modest difference (p\u0026thinsp;=\u0026thinsp;0.0362), while triglycerides, HDL, LDL, and VLDL did not exhibit significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGlycaemic and Insulin Resistance indices across BMI Groups in T2DM Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese [Normal] (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight (BMI 23\u0026ndash;27.49 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e136.4\u0026thinsp;\u0026plusmn;\u0026thinsp;24.35ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e145.3\u0026thinsp;\u0026plusmn;\u0026thinsp;47.74ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e183.2\u0026thinsp;\u0026plusmn;\u0026thinsp;42.18ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPBS (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e203.8\u0026thinsp;\u0026plusmn;\u0026thinsp;52.66ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e216.3\u0026thinsp;\u0026plusmn;\u0026thinsp;50.43ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e246.9\u0026thinsp;\u0026plusmn;\u0026thinsp;44.24ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0081\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e8.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.27\u0026thinsp;\u0026plusmn;\u0026thinsp;6.07ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e21.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e24.44\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0231\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38ᵇ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.54ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e11.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.17ᵇᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e203.2\u0026thinsp;\u0026plusmn;\u0026thinsp;41.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e196.4\u0026thinsp;\u0026plusmn;\u0026thinsp;40.98ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e211.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.87ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0362\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e143.0\u0026thinsp;\u0026plusmn;\u0026thinsp;59.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e157.8\u0026thinsp;\u0026plusmn;\u0026thinsp;58.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e187.7\u0026thinsp;\u0026plusmn;\u0026thinsp;96.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1134\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e40.87\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e39.34\u0026thinsp;\u0026plusmn;\u0026thinsp;6.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e41.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e133.7\u0026thinsp;\u0026plusmn;\u0026thinsp;36.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e127.8\u0026thinsp;\u0026plusmn;\u0026thinsp;34.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e133.0\u0026thinsp;\u0026plusmn;\u0026thinsp;27.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3678\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e28.67\u0026thinsp;\u0026plusmn;\u0026thinsp;11.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31.62\u0026thinsp;\u0026plusmn;\u0026thinsp;11.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e37.50\u0026thinsp;\u0026plusmn;\u0026thinsp;19.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFBS: Fasting Blood Sugar PPBS: Post Prandial Blood sugar HbA1c: Glycated hemoglobin HOMA-IR: Homeostatic Model Assessment for Insulin Resistance HDL: High-Density Lipoprotein LDL: Low- Density VLDL: Very Low- Density Lipoprotein\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eᵃ Normal vs Overweight significant: ᵇ Normal vs Obese significant: ᶜ Overweight vs Obese significant\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Renal and hepatic profile\u003c/h2\u003e \u003cp\u003eThe biochemical indicators of renal and hepatic function, across BMI categories are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWith reference to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e the following parameters did not reveal significance:- Urea, creatinine, total protein, albumin, globulin, A/G ratio, and bilirubin\u003c/p\u003e \u003cp\u003eHowever, the enzymes showed statistically significant differences, as evidenced by AST (p\u0026thinsp;=\u0026thinsp;0.0263) and ALT (p\u0026thinsp;=\u0026thinsp;0.0158) that were higher in the obese group when compared to the overweight subjects.\u003c/p\u003e \u003cp\u003eALP did not reveal significance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBiochemical indicators of renal and hepatic function across BMI Groups in T2DM Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese [Normal] (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight (BMI 23\u0026ndash;27.49 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e24.27\u0026thinsp;\u0026plusmn;\u0026thinsp;5.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e23.65\u0026thinsp;\u0026plusmn;\u0026thinsp;8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e25.83\u0026thinsp;\u0026plusmn;\u0026thinsp;11.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.913\u0026thinsp;\u0026plusmn;\u0026thinsp;0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.065\u0026thinsp;\u0026plusmn;\u0026thinsp;0.297\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e7.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobulin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA/G ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.593\u0026thinsp;\u0026plusmn;\u0026thinsp;0.365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.496\u0026thinsp;\u0026plusmn;\u0026thinsp;0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.548\u0026thinsp;\u0026plusmn;\u0026thinsp;0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.207\u0026thinsp;\u0026plusmn;\u0026thinsp;0.080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.184\u0026thinsp;\u0026plusmn;\u0026thinsp;0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e0.183\u0026thinsp;\u0026plusmn;\u0026thinsp;0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.21\u0026thinsp;\u0026plusmn;\u0026thinsp;3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e19.46\u0026thinsp;\u0026plusmn;\u0026thinsp;7.25\u003cb\u003eᶜ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e23.41\u0026thinsp;\u0026plusmn;\u0026thinsp;8.15\u003cb\u003eᶜ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0263\u003c/b\u003e\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=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e23.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.56\u0026thinsp;\u0026plusmn;\u0026thinsp;10.60\u003cb\u003eᶜ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e31.35\u0026thinsp;\u0026plusmn;\u0026thinsp;15.78\u003cb\u003eᶜ\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALP (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e84.07\u0026thinsp;\u0026plusmn;\u0026thinsp;23.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e87.11\u0026thinsp;\u0026plusmn;\u0026thinsp;25.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e80.93\u0026thinsp;\u0026plusmn;\u0026thinsp;23.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eᵃ Normal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Electrolytes, micronutrients, and thyroid profile\u003c/h2\u003e \u003cp\u003eWith the exception of Thyroxine [T4] that differed significantly across BMI categories (p\u0026thinsp;=\u0026thinsp;0.0129), wherein obese participants showed higher values compared to the overweight group, other parameters did not depict any significance (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eElectrolyte, micronutrient, and thyroid profile across BMI Groups in T2DM patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-obese [Normal] (BMI\u0026thinsp;\u0026lt;\u0026thinsp;23 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverweight (BMI 23\u0026ndash;27.49 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;27.5 kg/m\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e141.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e139.9\u0026thinsp;\u0026plusmn;\u0026thinsp;3.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e140.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e4.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e4.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7303\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChloride (mEq/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e103.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e102.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e102.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMagnesium (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.607\u0026thinsp;\u0026plusmn;\u0026thinsp;0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.638\u0026thinsp;\u0026plusmn;\u0026thinsp;0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.743\u0026thinsp;\u0026plusmn;\u0026thinsp;0.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZinc (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e36.09\u0026thinsp;\u0026plusmn;\u0026thinsp;11.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.96\u0026thinsp;\u0026plusmn;\u0026thinsp;23.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e34.69\u0026thinsp;\u0026plusmn;\u0026thinsp;18.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e2.413\u0026thinsp;\u0026plusmn;\u0026thinsp;0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.624\u0026thinsp;\u0026plusmn;\u0026thinsp;0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.773\u0026thinsp;\u0026plusmn;\u0026thinsp;0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4 (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.087\u0026thinsp;\u0026plusmn;\u0026thinsp;0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.169\u0026thinsp;\u0026plusmn;\u0026thinsp;0.282ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e1.310\u0026thinsp;\u0026plusmn;\u0026thinsp;0.247ᶜ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0129\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSH (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.007\u0026thinsp;\u0026plusmn;\u0026thinsp;1.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.187\u0026thinsp;\u0026plusmn;\u0026thinsp;2.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.157\u0026thinsp;\u0026plusmn;\u0026thinsp;2.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6424\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCortisol (\u0026micro;g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e14.26\u0026thinsp;\u0026plusmn;\u0026thinsp;6.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;8.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e14.73\u0026thinsp;\u0026plusmn;\u0026thinsp;7.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eᵃ \u003cb\u003eNormal vs Overweight significant; ᵇ Normal vs Obese significant; ᶜ Overweight vs Obese significant.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Insulin-Resistance indices across BMI Groups\u003c/h2\u003e \u003cp\u003eAcross the BMI categories, significant differences were observed in several insulin-resistance indices, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A-F). QUICKI values were lowest in the obese group and highest in the normal-weight group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). SPISE values also showed a stepwise reduction across normal, overweight, and obese groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The TyG index demonstrated a significant increase with rising BMI (p\u0026thinsp;=\u0026thinsp;0.0002). In contrast, TG/HDL and uric acid/HDL ratios did not differ significantly between the three BMI groups (p\u0026thinsp;=\u0026thinsp;0.23 and p\u0026thinsp;=\u0026thinsp;0.71, respectively). An additional parameter, namely De ritis ratio (AST/ALT) was also included that remained comparable across groups.\u003c/p\u003e \u003cp\u003eThe distribution pattern of composite metabolic indices, as a function of normal (non-obese), overweight, and obese BMI groups in patients with type 2 diabetes mellitus is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith reference to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, each jittered scatter plot displays individual patient values with overlaid group means and standard deviations, illustrating progressive deterioration in insulin-sensitivity\u0026ndash;related indices with increasing adiposity. Obese participants exhibited markedly lower QUICKI and SPISE values and higher TyG indices compared with the normal BMI group, reflecting a shift toward greater insulin resistance. In contrast, TG/HDL, uric acid/HDL, and AST/ALT ratios did not differ significantly between groups, though trends toward metabolic worsening were observable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Distribution of categorical variables\u003c/h2\u003e \u003cp\u003eIn this study, no significant association was observed between gender and BMI category. Among the 100 participants, males and females were similarly distributed across the normal (non-obese), overweight, and obese groups, and the chi-square test confirmed the absence of a statistically meaningful relationship (χ\u0026sup2; = 1.17; df\u0026thinsp;=\u0026thinsp;2; p\u0026thinsp;=\u0026thinsp;0.556). Likewise, the association between family history of diabetes mellitus and BMI category was not statistically significant. Although participants with a positive history appeared more frequently in the overweight and obese groups, chi-square analysis demonstrated that this distribution did not differ significantly from those without a family history (χ\u0026sup2; = 0.13; df\u0026thinsp;=\u0026thinsp;2; p\u0026thinsp;=\u0026thinsp;0.937). These findings indicate that within this cohort, neither gender nor family history of diabetes showed a significant relationship with BMI classification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Correlation patterns between metabolic variables and Insulin-Resistance indices\u003c/h2\u003e \u003cp\u003eSpearman analysis revealed unequivocal and biologically consistent associations among adiposity, glycaemic parameters, lipid markers, and the insulin-resistance indices (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e). BMI displayed statistically significant positive correlation with HOMA-IR and negative correlation with QUICKI, TyG and SPISE. Waist circumference exhibited statistically significant positive correlation with HOMA-IR and negative correlation with QUICKI and SPISE. Waist Hip Ratio depicted positive correlation with TyG. Waist Height Ratio exhibited positive correlation with HOMA-IR and negative correlation with QUICKI and SPISE. These results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eSpearman Correlation between Insulin-Resistance Markers and Metabolic Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINSULIN (\u0026micro;IU/mL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQUICKI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPISE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.27 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.50 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.50 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.45 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.88 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.58 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.51 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.50 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.45 (0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Hip Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.17 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.18 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.17 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.18 (0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHtR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36 \u003cb\u003e(0.0003\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.35 \u003cb\u003e(0.0003)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.34 (\u003cb\u003e0.0004)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11 (0.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.48 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFBS (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.01 (0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.46 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.47 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.58 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePPBS (mg/dl)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 (0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.31 (0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.43 (\u0026lt;\u0026thinsp;\u003cb\u003e0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.04 (0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.20 (0.046)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.47 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.28 (0.005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.27 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.61 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.06 (0.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07 (0.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.27 (0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14 (0.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.15 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.22 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.27 (0.007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.22 (0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.56 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid/HDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11 (0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.09 (0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.09 (0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.06 (0.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST/ALT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.05 (0.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.11 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11 (0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.18 (0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13 (0.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINSULIN (\u0026micro;IU/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.84 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.08 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.23 (0.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;1.00 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.50 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQUICKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.84 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;1.00 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.43 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.08 (0.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.43 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;0.43 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026ndash;0.73 (\u0026lt;\u0026thinsp;0.0001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSPISE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.23 (0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.50 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001\u003c/b\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;0.73 \u003cb\u003e(\u0026lt;\u0026thinsp;0.0001)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWaist Height Ratio showed significant positive correlations with fasting insulin (r\u0026thinsp;=\u0026thinsp;0.27 and r\u0026thinsp;=\u0026thinsp;0.58) and HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.50 and r\u0026thinsp;=\u0026thinsp;0.51), and corresponding negative correlations with QUICKI (r = \u0026minus;\u0026thinsp;0.50 for both). WHtR showed a similar pattern, indicating that central adiposity was closely linked with greater insulin resistance even within this T2DM cohort.\u003c/p\u003e \u003cp\u003eThe IR indices demonstrated strong internal coherence. HOMA-IR and QUICKI were perfectly inversely correlated (r = \u0026minus;\u0026thinsp;1.00). Both showed moderate correlations with TyG (r\u0026thinsp;=\u0026thinsp;0.43 and r = \u0026minus;\u0026thinsp;0.43) and SPISE (r = \u0026minus;\u0026thinsp;0.50 and r\u0026thinsp;=\u0026thinsp;0.50). SPISE correlated most strongly with BMI (r = \u0026minus;\u0026thinsp;0.88), followed by waist and WHtR, reflecting its sensitivity to adiposity-related metabolic dysfunction.\u003c/p\u003e \u003cp\u003eGlycaemic parameters also correlated with IR indices. Fasting glucose correlated with HOMA-IR (r\u0026thinsp;=\u0026thinsp;0.46) and QUICKI (r = \u0026minus;\u0026thinsp;0.47), and post-prandial glucose showed similar moderate associations. TyG demonstrated the expected strong correlation with fasting glucose (r\u0026thinsp;=\u0026thinsp;0.70).\u003c/p\u003e \u003cp\u003eAmong lipids, triglycerides correlated positively with HOMA-IR and strongly with TyG (r\u0026thinsp;=\u0026thinsp;0.86), and negatively with QUICKI and SPISE. TG/HDL showed a comparable pattern. HDL, uric acid/HDL, and AST/ALT did not show meaningful correlations with the major IR indices.\u003c/p\u003e \u003cp\u003eOverall, anthropometric measures, glycaemic variables, and triglyceride-related lipid markers showed the strongest relationships with the insulin-resistance indices, with SPISE and TyG emerging as robust surrogate markers in obesity-linked insulin resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.9. ROC analysis:\u003c/h2\u003e \u003cp\u003eSince our results have fortified the fact that SPISE is a robust surrogate marker of insulin resistance, we embarked upon the study further to explore the utility of SPISE as a predictor in anthropometry specified groups of T2DM. To enable this, the Receiver operating characteristic (ROC) analysis was performed for evaluating the ability of SPISE and HOMA-IR to discriminate obese from normal (non-obese) individuals. SPISE demonstrated near-perfect discriminatory performance with an AUC of 0.99 (95% CI: 0.97\u0026ndash;1.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A cut-off value of \u0026lt;\u0026thinsp;5.55 yielded a sensitivity of 92.5% and specificity of 93.3%. HOMA-IR also showed significant discriminatory ability with an AUC of 0.85 (95% CI: 0.74\u0026ndash;0.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). At an optimal cut-off of \u0026gt;\u0026thinsp;7.10, HOMA-IR demonstrated a sensitivity of 80.0% and specificity of 86.7%. However, SPISE showed superior discrimination compared with HOMA-IR for identifying obesity-associated insulin resistance. The results are depicted in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003eROC characteristics of SPISE and HOMA-IR for discriminating obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCut-off\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSPISE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;5.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.85 (0.74\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eReceiver operating characteristic (ROC) analysis illustrating the ability of (A) the Single Point Insulin Sensitivity Estimator (SPISE) and (B) HOMA-IR to discriminate obese from normal (non-obese) individuals with T2DM. SPISE demonstrated near-perfect discriminatory performance (AUC\u0026thinsp;=\u0026thinsp;0.99), whereas HOMA-IR showed good discriminatory ability (AUC\u0026thinsp;=\u0026thinsp;0.845). The diagonal reference line represents no discrimination\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study explored vistas as to how anthropometric measures, biochemical markers, and the commonly used insulin-resistance (IR) indices behave across BMI categories in South Indian adults with T2DM. Despite similar age and duration of diabetes across groups, obesity was associated with higher fasting and post-prandial glucose, elevated fasting insulin and HOMA-IR, and lower QUICKI and SPISE. TyG also increased progressively with BMI. Correlation patterns showed consistent clustering: adiposity measures, glycaemia, triglycerides, TyG, and SPISE strongly inter-related, whereas uric-acid\u0026ndash;based ratios and AST/ALT contributed little. Overall, simple fasting-derived indices appeared to capture much of the IR burden linked to adiposity in this cohort.\u003c/p\u003e \u003cp\u003eHaving said that, it must be emphasized that several of these revelations had been documented by earlier workers. However, an important aspect that has emerged from this study is the relative superiority of SPISE over the time-tested HOMA-IR in discriminating obese T2DM from normal and overweight, on the basis of insulin resistance. The feasibility and reliability of SPISE index centres around the fact that it is a simple, cost-effective tool that could be utilized in routine clinical practice and large-scale epidemiological studies for quantitating insulin sensitivity, without having to resort to direct insulin measurements. This is an important outcome of our study.\u003c/p\u003e \u003cp\u003eThe gradients observed for BMI, waist circumference, and waist-to-height ratio align with previously generated evidences emphasizing the high metabolic risk associated with central adiposity in South Asians.\u003csup\u003e3,4\u003c/sup\u003e Even within established T2DM, increasing adiposity corresponded with higher insulin resistance and poorer QUICKI and SPISE values. Waist-to-height ratio behaved comparably to BMI and waist circumference, supporting its use as a practical screening tool in Indian populations, where visceral adiposity is a commonly observed predisposing factor.\u003c/p\u003e \u003cp\u003eThe behaviour of TyG in our cohort is consistent with the existing reports, especially with reference to the demonstrated utility of the index as a surrogate marker for IR[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. It is noteworthy to mention that research carried out on the populations (adolescents) of China and Brazil showed the ability of TyG to reveal metabolically unhealthy phenotypes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Data obtained from meta-analysis point to the fact that TyG is an established marker linked to higher cardiometabolic and future risk for Diabetes[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As per the data from this study, TyG correlated strongly with glycaemia, triglycerides, HOMA-IR, and QUICKI, indicating that it retains clinical utility even among individuals with established T2DM, thereby implying that glucotoxicity paves the way for lipotoxicity culminating in glucolipotoxicity, synonymous with dyslipidemia, decreased insulin sensitivity and enhanced insulin resistance.\u003c/p\u003e \u003cp\u003eThe TyG index thus reflects the underlying process of glucolipotoxicity, where chronic elevation of both glucose and triglyceride-rich lipoproteins enhance circulating free fatty acids, linked to oxidative stress, and injury to insulin-sensitive tissues[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is in the fitness of things to state that TyG is not merely a statistical surrogate, but an integrated, well-rounded marker of combined glucose\u0026ndash;lipid burden. The strong correlations that have been observed in the present study among TyG, fasting glucose, triglycerides, and HOMA-IR are consistent with this documented mechanism.\u003c/p\u003e \u003cp\u003eSPISE, derived without insulin measurements, also performed well. Previous studies have shown strong correlations between SPISE and clamp-derived insulin sensitivity, as well as classical IR indices[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Longitudinal research in adolescents had demonstrated that lower SPISE predicts development of metabolic syndrome and impaired glucose regulation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In our study, SPISE declined steadily across BMI categories and demonstrated expected inverse relationships with adiposity, glycaemia, triglycerides, and TyG. These findings reinforce its potential as a cost-effective alternative to insulin-based indices in routine care. However, citing our present study, it must be mentioned that SPISE scores over HOMA-IR in delineating obese from non-obese (normal) and overweight patients with T2DM and hence adds to the existing global armamentarium as a predictor of promise.\u003c/p\u003e \u003cp\u003eAlthough BMI remains the most commonly used anthropometric index, it is well recognized that many individuals, particularly in South Asian populations, might have normal BMI, despite significant central adiposity and metabolic risk, which limits its reliability as a sole indicator of insulin resistance. Ethnic differences in body composition and fat distribution contribute to higher cardiometabolic risk observed at lower BMI levels in South Asians[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, BMI remains clinically useful due to its simplicity and reproducibility, and in the present study it was therefore interpreted alongside metabolically informative indices.\u003c/p\u003e \u003cp\u003eHOMA-IR has long been accepted as a surrogate marker of insulin resistance and correlates with direct measures of insulin sensitivity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, its dependence on fasting insulin measurement restricts routine clinical use in many laboratories, thereby prompting us to look out for alternative non-insulin-based indices.\u003c/p\u003e \u003cp\u003eIn this context, the TyG index and the TG/HDL-C ratio have emerged as practical alternatives. The TyG index reflects the combined burden of hyperglycaemia and hypertriglyceridemia and is strongly associated with insulin resistance and cardiometabolic risk[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The TG/HDL-C ratio also correlates closely with insulin resistance and has been proposed as a surrogate biomarker of dyslipidaemia-related metabolic risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Furthermore, TG/HDL ratio functions as the surrogate for small dense LDL (sdLDL).\u003c/p\u003e \u003cp\u003eQuite recently, the Single Point Insulin Sensitivity Estimator (SPISE) has been receiving prominence as a reliable indirect marker of insulin sensitivity that does not require insulin measurement. Based on BMI, triglycerides, and HDL-C, SPISE demonstrates good diagnostic performance for cardiometabolic risk and emerging dysglycaemia [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Taken together, the combined evaluation of SPISE, TyG, TG/HDL-C ratio, and HOMA-IR provides a broader and more objective assessment of insulin resistance and insulin sensitivity across body phenotypes and may support individualized, anthropometry-guided therapeutic strategies.\u003c/p\u003e \u003cp\u003eUric-acid\u0026ndash;related markers and AST/ALT ratios, although previously reported to be associated with metabolic risk and NAFLD (MASLD) in various populations[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], did not differentiate BMI groups in this study and showed weak correlations with primary IR indices. This likely reflects limited variability observed within a cohort entirely composed essentially of of individuals with T2DM.\u003c/p\u003e \u003cp\u003eMicronutrient markers such as magnesium and zinc also did not vary significantly across BMI classes, though both have documented associations with insulin resistance and oxidative stress[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Their limited contribution here may be attributable to dietary patterns or the absence of marked deficiencies in the sample. Although T4 differed modestly across BMI categories, the values remained within normal limits (clinically euthyroid) and showed no meaningful association with insulin-resistance indices.\u003c/p\u003e \u003cp\u003eThe internal consistency of the correlation matrix provides confidence in the comparative performance of the indices studied. HOMA-IR and QUICKI showed expected inverse relationships, and TyG and SPISE, both insulin-independent, tracked closely with the traditional insulin-based measures. External validation work has consistently shown similar patterns across diverse populations [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The concordant behaviour of these indices suggests they may be valuable tools in the Indian clinical settings where insulin assays or dynamic testing are not routinely feasible. Relying solely on insulin-based indices such as HOMA-IR or QUICKI is often impractical in routine Indian hospital settings, thereby rendering fasting-derived surrogates, namely TyG and SPISE as feasible alternatives.\u003c/p\u003e \u003cp\u003eThe ROC analysis further strengthened the comparative findings, showing that SPISE provided near-perfect discrimination between obese and normal-weight individuals, outperforming HOMA-IR. Although HOMA-IR demonstrated good diagnostic accuracy, its reliance on fasting insulin limits routine applicability. The superior performance of SPISE, as per this study coupled with its insulin-independent status, highlights its clinical utility as a pragmatic and scalable alternative for identifying obesity-associated insulin resistance in routine and resource-limited settings. Another point that would acquire relevance is the fact that in obesity, there is down regulation of insulin receptors. Since SPISE shows excellent discrimination in segregating obese patients with T2DM, it would be in the fitness of things to opine that the utility of SPISE can also be indirectly perceived from the standpoint of insulin receptors.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations of the study\u003c/strong\u003e \u003cp\u003eThe sample size for the study was modest and the study subjects were drawn from a single centre, thereby limiting generalizability. Comparison of the surrogate indices of insulin sensitivity/insulin resistance was not performed with the gold-standard technique, namely the euglycaemic clamp. Residual confounding factors are also possible.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eDespite these constraints, the findings underscore that simple fasting-based surrogate indices, especially TyG and SPISE, parallel insulin-based measures and meaningfully reflect obesity-related insulin resistance. Their ease of calculation and reliance on routinely available tests make them attractive for use in resource-limited settings.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSalient points that have emerged from the study: -\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eTyG and SPISE tracked obesity-related insulin resistance in South Indian adults with T2DM.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSPISE scored over the time tested HOMA-IR in discriminating obese patients with T2DM\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInsulin-independent indices performed comparably to HOMA-IR and QUICKI across BMI groups.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTyG correlated strongly with glycaemia, triglycerides, and insulin-based IR measures.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSPISE declined with rising BMI and reflected combined adiposity and metabolic burden.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUric acid\u0026ndash;based and AST/ALT ratios showed weak links to insulin resistance in this cohort.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSuggestions for future studies\u003c/strong\u003e \u003cp\u003eWe strongly suggest that larger and more comprehensive longitudinal studies are warranted that would also take into consideration imaging and vascular outcomes. By doing so, we would be in a position to refine the cut-offs. This would also enable us to overcome the confounding factors. Such studies would possibly culminate in upholding the clinical utility of TyG and SPISE in predicting long-term metabolic risks associated with organ dysfunction.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNovelty of the study\u003c/strong\u003e \u003cp\u003eDespite the aforementioned constraints and limitations, the findings of the present study underscore that the promulgation of rapid and simple fasting-based surrogate indices, especially TyG and SPISE would parallel insulin-based measures and meaningfully reflect insulin resistance, especially in anthropometry specified population with T2DM. The ease of calculation and reliance on routinely available tests render them attractive for use in resource-limited settings. Computing SPISE index on the basis of BMI, TyG and HDL is easily accomplishable even in centers not possessing tertiary care facilities. Hence, SPISE index holds promise, by virtue of being a single point indicator of insulin sensitivity.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eOur laboratory is also in the process of generating further evidences to suggest novel parameters that portray relationship with SPISE index. However, these results would be communicated separately, upon completion of the analysis and objectivized interpretation.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this cohort of adults with T2DM drawn from a tertiary healthcare centre in South India, obesity and central adiposity were strongly linked to higher fasting insulin, HOMA-IR, and TyG, and to lower QUICKI and SPISE values, while conventional lipid fractions and uric-acid\u0026ndash;based ratios added little additional information. TyG and SPISE, calculated from routinely available fasting glucose, triglycerides, HDL cholesterol, and BMI, closely paralleled insulin-based indices and reflected the underlying obesity-related insulin resistance. These findings support the use of TyG and SPISE as practical, low-cost surrogate markers of insulin resistance in routine care, particularly in settings where insulin assays or dynamic tests are not feasible. A significant aspect of the present study is the utility of predictive capacity of SPISE that is superior to the time-tested surrogate marker of Insulin resistance, namely HOMA-IR. Larger, longitudinal studies are needed to establish optimal cut-offs and to create evidences as to whether such indices would help improve the prediction of cardiometabolic outcome and impending organ dysfunction in patients with T2DM.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cb\u003eFunding Declaration: The authors declare that they did not receive any funds from any agency or organization\u003c/b\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEK conducted the study in her capacity as the Principal investigator, under the direct supervision/guidance of SAR, aided by JR (co-supervisor). MHA, in his capacity as the Professor of General Medicine and i/c, Diabetic clinic added valuable inputs from the perspectives of endocrinology and internal medicine. Statistical analysis was completed by EK,with inputs from JR and SAR. SAR critically reviewed the manuscript finally after all the other co-authors had reviewed.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors thank the Chancellor of Sri Balaji Vidyapeeth- Mr.M.K.Rajagopalan , Vice Chancellor Dr.N.R.Biswas and Director Research Dr.K.S.Reddy.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDeFronzo RA, Ferrannini E, Groop L et al (2015) Type 2 diabetes mellitus. Nat Rev Dis Primers 1:15019. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrdp.2015.19\u003c/span\u003e\u003cspan address=\"10.1038/nrdp.2015.19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnjana RM, Deepa M, Pradeepa R et al (2017) Prevalence of diabetes and prediabetes in 15 states of India: results from the ICMR\u0026ndash;INDIAB study. 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Nutrients 14(9):1799. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu14091799\u003c/span\u003e\u003cspan address=\"10.3390/nu14091799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-internal-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Internal Medicine](https://ejim.springeropen.com/)","snPcode":"43162","submissionUrl":"https://submission.springernature.com/new-submission/43162/3","title":"The Egyptian Journal of Internal Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Obesity T2DM Insulin resistance SPISE HOMA IR TyG","lastPublishedDoi":"10.21203/rs.3.rs-8746166/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8746166/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe study was aimed at the assessment of non-insulin-based markers as alternatives to the conventional insulin-based indices in anthropometry specified type 2 Diabetes mellitus. The outcome was targeted towards the utility of such indices in diabetes mellitus and associated metabolic derangements centering around insulin resistance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFasting venous blood samples were utilized for biochemical analyses. The parameters included plasma glucose, insulin, lipid profile {triglycerides, HDL cholesterol, total cholesterol and LDL cholesterol (Friedwald equation)}. Uric acid, liver enzymes, renal profile, electrolytes, and thyroid function tests were also evaluated. Appropriate statistical analysis, as deemed fit for normal and skewed data were undertaken.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAcross the BMI categories, significant differences were observed in several insulin-resistance indices. Quantitative Insulin Sensitivity Check Index (QUICKI) values were lowest in the obese group and highest in the normal-weight group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Single Point Insulin Sensitivity Estimator (SPISE) values also showed reduction across normal, overweight, and obese groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The TyG index demonstrated a significant increase with rising BMI (p\u0026thinsp;=\u0026thinsp;0.0002). With reference to utility of SPISE as a predictor in anthropometry specified groups, it demonstrated a near-perfect discriminatory performance with an AUC of 0.99 (95% CI: 0.97\u0026ndash;1.00, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). A cut-off value of \u0026lt;\u0026thinsp;5.55 yielded a sensitivity of 92.5% and specificity of 93.3%.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSingle Point Insulin Sensitivity Estimator (SPISE), independent of insulin possesses greater sensitivity and specificity, in comparison to the conventional Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) in anthropometry specified diabetic population.\u003c/p\u003e","manuscriptTitle":"Single Point Insulin Sensitivity Estimator (SPISE), a novel score to evaluate insulin sensitivity, is predictive in anthropometry specified adults with Type 2 Diabetes Mellitus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-23 14:25:31","doi":"10.21203/rs.3.rs-8746166/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-19T05:15:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-06T22:06:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-04T09:49:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Egyptian Journal of Internal Medicine","date":"2026-01-31T03:45:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-egyptian-journal-of-internal-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Egyptian Journal of Internal Medicine](https://ejim.springeropen.com/)","snPcode":"43162","submissionUrl":"https://submission.springernature.com/new-submission/43162/3","title":"The Egyptian Journal of Internal Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d09331e1-3021-4b25-99a6-f16d31dcbe22","owner":[],"postedDate":"March 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T14:25:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-23 14:25:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8746166","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8746166","identity":"rs-8746166","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}