Presence of Cutaneous Signs of Insulin Resistance with Central Obesity (CO-CSIR) in Asian Indians is a sensitive physical sign of Metabolic Syndrome (MetS)

Presence of Cutaneous Signs of Insulin Resistance with Central Obesity (CO-CSIR) in Asian Indians is a sensitive physical sign of Metabolic Syndrome (MetS) | 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 Presence of Cutaneous Signs of Insulin Resistance with Central Obesity (CO-CSIR) in Asian Indians is a sensitive physical sign of Metabolic Syndrome (MetS) Anamika Gora, Pradeep Tiwari, Aditya Saxena, Rajendra Mandia, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4340896/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim and objectives : To investigate cutaneous sign of insulin resistance, acanthosis nigricans (AN) and acrochordon (AC) in individual of central obesity (CO-CSIR) as a physical sign for prediction of metabolic syndrome (MetS) and the underlying adipose tissue pathology and the consequent pathophysiological trait in Asian Indians. Methods: Design: Single center cross sectional study. Study subjects: 371 (aged 51.7±12.4; M: F ratio 210:161). Following parameters were investigated: Physical signs: cutaneous signs of insulin resistance, BMI, WC, HC, WHR, blood pressure. Biochemical parameters: FBG, lipid profile, HbA1c, HOMA-β. HOMA-IR. Radiological parameters: Abdominal visceral, subcutaneous and ectopic liver fat by MRI. Molecular Parameters: Genome wide transcription profile of adipose tissue biopsies in 85 individuals undergoing surgery for other indications. Results: AN, AC and both were present respectively 50(13.3%), 27(7.2%) and 75(20.2%) individuals and they absent 216 individuals. Presence of AN and AC were associated with significantly higher BMI (6.4 X10 -5 ), W:H ratio (0.04), WC (9.5 X 10 -7 ), HOMA-IR (0.0002), glucose (1.11 x 10 -10 ) and prevalence of T2D (100%) and MetS (83%). AC as compared to AN was associated with more ectopic fat and higher IR.CO-CSIR was found to be the best physical sign of MetS (94.8% sensitivity,57.5 % specificity, 86.4 precision with 95.1F1 score). MetS negative CO-CSIR individuals show high IR, ectopic fat deposition, hyperglycemia and prevalence of T2D. Conclusion: CO-CSIR a promising physical sign of MetS and the underlying adipose tissue driven dysmetabolism in Asian Indians. Acanthosis nigricans Acrochordon Asian Indians Central obesity Insulin Resistance Metabolic Syndrome Introduction Metabolic syndrome (MetS) is a cluster of certain cardiovascular risk factors, including obesity, dyslipidemia, hypertension, and glucose intolerance. The accumulation of fat, particularly in the central and visceral compartments, is the cornerstone structural feature of MetS [ 2 – 3 ]. Pathophysiologically, it is associated with altered adipose tissue function, and insulin resistance (IR). In other words, MetS is associated with altered quality of adipose tissue, in addition to its quantity and distribution. [ 1 ]. Obesity related insulin resistance (IR) has traditional been considered to be the major underlying pathophysiological defect of MetS. [ 6 – 8 ]. However, Asian Indians have been shown to be more insulin resistant at any given body mass index (BMI). For any given BMI, they run a higher risk of developing type 2 diabetes and cardiovascular disease as compared to their Caucasian counterparts [ 9 – 10 ]. The clinical importance of this pathophysiology lies in the fact that type-2 diabetes mellitus (T2DM), and MetS clusters with predominantly central obesity (clinically assessed as waist-to-hip ratio) instead of generalized obesity (clinically assessed as BMI) in this population. Recently we conducted gene expression profiling of adipose tissue in Asian Indian diabetics and non-diabetics and found that T2D is associated with not only pathologically altered quality of adipose tissue at molecular level, but several modules of co-expressed genes showed significant correlation with intermediate traits of T2D and MetS. Therefore, these results strengthen the belief that it is indeed the sick fat or adiposopathy, which is a major factor in the causation ofMetS and diabetes and it affects both—peripheral, as well as visceral adipose tissue compartments [ 11 ]. Keeping in view the considerable magnitude of the problem and the heightened risk of vascular-metabolic morbidity and mortality, diagnosis of MetS and its underlying adipose tissue pathology in clinical practice as well as at the community level is the need of the hour. It is particularly more important for Asian Indians, because body mass index (BMI) criteria of obesity might not truly reflect MetS and adipose tissue pathology. Several definitions of MetS have been described, and those recommended by National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) [ 4 ] and International Diabetes Federation [ 5 ] are the commonly used. However, the precise cut-off values of the anthropometric parameter of central-fat deposition as well as parameters of clustering diseases could not be determined as a consensus. Therefore, clinical diagnosis of MetS still remains to be a challenge. An alternative strategy could be to predict MetS and underlying adipose tissue pathology on the basis of physical signs and further investigate the different facets of adipose tissue pathology driven dysmetabolism and the clustering diseases. There are several physical signs that show association with MetS and IR. Anthropometric parameters like BMI, WC and W:H ratio primarily estimates body fat content and its distribution in central versus peripheral compartments. There are also two cutaneous signs of insulin resistance. Several previous studies have found their association with insulin resistance and the underlying pathophysiological mechanisms of insulin resistance [ 15 ]. Acanthosis Nigricans (AN) is a dermatological disorder characterized by symmetrical plaques with hyperpigmentation and hyperkeratosis. It is usually observed on the posterior neck, axilla, and groin, but may also be seen on the elbows, knuckles, and knees [ 16 ]. Acrochordon (skin tags, AC) are brown or skin-colored common tumors 1mm-1cm in diameter, small soft and pedunculated, usually occurring on the neck, armpits, and groin [ 17 ]. Very few studies, including some of them in Asian Indians, have demonstrated the importance of acrochordon (AC) and acanthosis nigricans (AN) as a clinical sign of IR and MetS. In the context of the descriptive facts of association between these anthropometric parameters and cutaneous signs with anatomical facet of body fat distribution and functional defects of insulin resistance respectively, a very pertinent question is that if they can be used clinically as a physical sign to predict presence and absence of MetS. Another question is that- Do they show any association with pathological changes in adipose tissue and consequent pathophysiological mechanisms of MetS? Our hypothesis is that - presence of these cutaneous signs, particularly in the context of anthropometric parameters of predominantly central deposition of adipose tissue can serve as a simple clinical tool to suspect presence of MetS and adipose tissue pathology. Therefore, in present study we explore sensitivity and specificity of these cutaneous sign in predicting presence and absence of MetS in Asian Indian particularly in contest of central obesity assessed as waist to hip ratio. Secondly, we also investigated whether these cutaneous signs show any association with pathological changes in adipose tissue. Material & Methods Study Subjects This study is an analytical cross-sectional study conducted on the individuals attending S.M.S. Medical College Hospital, Jaipur India from 2013 to 2023 and they had participated in research projects on adiposopathy. assessment in this population and all these studies shared common protocol and clinical, biochemical and radiological assessment methods. The institutional ethics committee of SMS Medical College approved these studies. Informed written consent was taken before participation in this study. All methods were performed as per the relevant guidelines and regulations. Total 371 individuals having either or both cutaneous signs of insulin resistance (acanthosis nigricans and acrochordon) and without cutaneous sign were included in the present study. The inclusion criteria in all these studies were common and shared uniform clinical, biochemical and radiological assessment methodology. The participants were recruited in two groups: (1) T2D diagnosed as per American Diabetes Association (ADA) standards and, (2) normal glucose tolerant individuals without T2D in another arm (confirmed by Oral Glucose Tolerance Test -OGTT). The exclusion criteria were the presence of infection, malignancy, and drugs affecting body fat such as thiazolidinediones and glucocorticoids. For the present study, we re-assigned these participants into metabolic syndrome -positive (MetS) and -negative groups as per the NCEP III criteria, i.e. , if at least three out of five measured MetS traits of individual were found positive - waist circumference, triglycerides, HDL, fasting glucose, and blood pressure. Clinical evaluation Subjects underwent comprehensive clinical evaluation including detailed clinical history, physical examination that included cutaneous signs of insulin resistance and anthropometric measurements (height, weight, body mass index [BMI], waist (WC) and hip (HC) circumferences, waist-to-hip circumference ratio (WHR). Supine blood pressure was measured using mercury sphygmomanometer (BPMR-120 Diamond delux, Industrial electronic and allied products, Maharashtra, India) after 10 minutes of rest. Assessment of cutaneous signs AN and AC were diagnosed clinically during a physical examination. The diagnostic criteria for AN were the presence of thick, rough, irregular wrinkles and brown pigmentation of the skin around the neck and armpits. Whereas the diagnostic criteria for AC was the presence of small soft and pedunculated protrusions occurring on the neck & armpits. Anthropometric Measurements Weight was measured using a standard balance beam scale, and height was measured using a stadiometer. Body mass index was calculated from the ratio of body weight in kg to height in square meters and expressed as kg/m 2 . Waist circumference was measured using a non-stretchable flexible tape measure at the site of maximum circumference midway between the lower ribs and the anterior superior iliac spine. Hip-circumference was measured over the greatest protrusion of the gluteal muscles. Biochemical Investigations A venous blood sample was obtained after an overnight fast of at least 8 hours. Biochemical measurements included Fasting blood glucose (FBG), Lipid profile including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein (VLDL) using were measured on Kopran AU/400 (Olympus corporation, Shinjuku, Tokyo, Japan) fully automated analyzer. Serum insulin was measured using a chemiluminescent immunometric assay (Immulite 2000 machine, SiemensHealthineers AG, Erlangen, Germany). HbA1c was measured by turbidimetry method using BioSystems (Biosystems, S.A. Barcelona, Spain) kits. HOMA-β wascalculatedusing the followingformula[ 28 ] 360 x [Insulin in µU/ml]/ ([Glucose in mg/dL] − 63) HOMA-IR is a measure of insulin resistance, and was calculated using the following formula: [ 28 ] {[Glucose in mg/dL] x [Insulin in µU/ml]}/ 405 Determination of Metabolic syndrome parameter According to the NCEP ATP III[ 4 ] definition, metabolic syndrome is present if three or more of the following five criteria are met: 1) increased waist circumference (≥ 90 cms for men, ≥ 80 cms for women); 2) elevated triglycerides (≥ 150 mg/dl); 3) low HDL cholesterol (< 40 mg/dl in men, < 50 mg/dl in women); 4) hypertension (≥ 130/≥85 mmHg); and 5) fasting glucose (≥ 100 mg/dl). In addition, existing drug treatment for dyslipidemia, dysglycemia, raised blood pressure would also be qualifying criteria. Radiological investigations Abdominal fat content and distribution among VAT, SAT and ectopic hepatic compartments were estimated by MRI. MRI scan procedure: The MRI scans were done at the S.M.S, hospital in the department of radiology, using 3 tesla Philips Ingenia Machine. The single investigator who interpreted the scans on Osirix software was unaware of the clinical status of the study subjects. A single scan (3 mm) of the abdomen was done at the level of L4-L5 vertebrae and analyzed for a cross-sectional area of adipose tissue, which was expressed in centimeters squared. The parameters studied included VAT and SAT. VAT, which represents intra-abdominal omental fat (without ectopic fat) was distinguished from SAT by tracing along the fascial plane defining the internal abdominal wall and the area was calculated in centimeters square. Ectopic liver fat was measured using liver intensity on Osirix software using Dixon method (Liver Fat percentage = 100X ((Signal intensity liver/signal intensity spleen) on in phase T1- (signal intensity liver/signal intensity spleen) on out phase T1)/2x (signal intensity liver/signal intensity spleen) on in phase T1). Transcriptional Profiling of adipose tissue Out of 371 participants, 33 individuals (23 MetS: 10 Non-MetS) and 52 individuals (36 MetS: 16 Non-MetS) respectively had undergone either abdominal or femur bone surgery for the primary indication other than this study. Adipose tissue biopsies were obtained from abdominal (one visceral and one subcutaneous each) and thigh (peripheral subcutaneous) fat compartments respectively. These biopsy samples were then subjected to gene-expression microarray analysis using Affymetrix PrimeView chips. A total of 118 gene-expression datasets were generated out from these biopsies. These samples were among 140 samples that we have earlier submitted to NCBI GEO database with accession number GSE78721. Statistical and Bioinformatics analysis To test the difference between groups, we used Student’s t-test and ANOVA in Microsoft Excel and a P -value of 0.05 was taken as statistically significant. Sensitivity – specificity analysis was done in python using libraries - numpy, matplotlib, and sklearn. First a confusion matrix was created based on actual, and predicted labels and then we calculated specific metrics from this matrix to estimate sensitivity, specificity, precision, and F1 score using following formulas. (TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative). Sensitivity (Recall) = TP / (TP + FN) Specificity = TN / (TN + FP) Precision = TP / (TP + FP) F1-Score = 2 X (Precision X Sensitivity) / (Precision + Sensitivity) Low-level analysis of microarray datasets was carried out using Bioconductor packages in R: gcrma , and genefilter . As the annotation package for the prime-view was not available, it was created using human.db0 , and AnnotationForge package using prime-view annotation file. To relate the normalized expression values of genes with the presence/absence status of cutaneous signs, T2D, and MetS, we used a system biology method - Weighted Gene Correlation Network Analysis (WGCNA) which identifies modules of co-expressed genes and define these modules in separate colors as visual aid. It also estimates Module Eigengene (ME) as the first principal component of the expression matrix of the corresponding module and provides functionalities to relate these MEs with external traits. WGCNA was carried out as follows -The normalized expression matrix (containing 118 datasets) genes were matched with the corresponding intermediate traits and a single step network construction and module detection was used by selecting soft threshold power β = 15 to ensure scale-free topology of the network. MEs were then used to relate each module with selected traits. We separately carried out WGCNA in femoral, visceral, and subcutaneous fat microarray datasets for AC, AN, T2D, and MetS. We then selected microarray samples of only those individuals who have elevated W:H ratio and carried out differential gene expression analysis between cutaneous-sign positive (CP) datasets and negative (CN) datasets in each depot using limma and further look for enriched KEGG pathways using WebGeStalt tool [ 18 ] for DE genes ( P < 0.05). depot-wise count was- femoral fat (17 CP: 32CN), subcutaneous (10 CP:22CN), and visceral subcutaneous (10 CP:22CN). Results Out of 371 participants 215,235 and 185 persons respectively had MetS, T2DM and hypertension. The cutaneous signs of AN, AC and both were present respectively 50, 27 and 75 individuals. Comparison of clinical, biochemical and abdominal fat distribution parameters according to presence and absence of cutaneous signs We carried out ANOVA across 216 cutaneous sign negative individuals (136 NGT:80 T2D), 50ANpositive (all T2D) and 27 AC positive individuals (all T2D) and results shown that there exists extremely significant difference among these groups except for subcutaneous fat and HDL. Both the cutaneous signs were associated with high insulin resistance, higher BMI, central fat deposition, more ectopic fat deposition and larger number of components of MetS. AC as compared to AN was found to be associated with more ectopic fat and higher insulin resistance (Table 1 ). Table 1 Comparison of three groups using Analysis of Variance test. Characteristic Mean (No cutaneous sign) Mean (Acanthosis Nigricans +) Mean (Acrochordon +) F Score P -value BMI 22.9 25.9 25.3 14.1 1.40174E-06 Waist Circumference 86.9 95.1554 91.9 17.5 6.1935E-08 W: H ratio 0.94 0.97 0.98 5.6 0.004082 Glucose 123.3 193.7 187.4 35.3 1.89938E-14 Total Cholesterol 176.6 187.06 200.8 4.2 0.015628275 Triglycerides 145.5 168.778 188.8 3.7 0.025671277 HDL 43.9 43.6 42.9 0.2 0.810975945 HOMA-B 111.8 34.1 66.05 3.1 0.042793228 HOMA-IR 2.6 4.6 7.9 18.1 3.62064E-08 Visceral Fat 135.09 174.85 113.13 4.46 0.01 Subcutaneous Fat 131.80 150.96 132.87 1.09 0.34 Ectopic Liver Fat 6.27 9.40 12.15 10.77 0.00005 No. of MetS Components 2.2 3.4 3.8 33.9 5.48672E-14 Comparison of clinical, biochemical and abdominal fat distribution parameters according to presence and absence of MetS First, we assessed – if there exist significant difference between cutaneous sign positive and cutaneous sign negative groups in all the clinical parameters (Table 2 ). Prevalence of T2D was 53% in negative group and 100% among positive group. Similarly, prevalence of MetS was 51% in negative group and 83% among positive group. Table 2 Comparison between cutaneous sign positive and cutaneous sign negative using t-test Characteristic Mean (variance) cutaneous sign negative Mean (variance) cutaneous sign positive P –Value (T- statistics) BMI 23.66 25.76 6.46 x 10 − 5 (-4.04) Waist Circumference 88.78 94.88 9.57 x 10 − 7 (-4.98) W:H 0.95 0.97 0.04 (-2.00) Fasting Glucose 141.22 203.11 1.11 x 10 − 10 (-6.64) HOMA-R 3.47 6.20 0.0002 (-3.67) Triglycerides 153.50 173.09 0.08 (-1.75) Visceral Fat 141.00 151.06 Ectopic Liver fat 7.10 7.75 We further carried out t-Test between non- MetS group (all 20 T2D) and MetS Score between 3–5 group (32 NGT: 183 T2D) and results shown that there exists extremely significant difference among these groups except for visceral fat. (Table 3 ) Table 3 Comparison between groups MetS score zero and MetS score between 3–5 groups using t-test Characteristic Mean (variance) MetS Score 0 Mean (variance) MetS Score 3–5 P –Value (T- statistics) Acanthosis Nigricans 0.00 (0.00) 0.51 (0.25) 5.21891E-35 (-14.83) Acrochordons 0.00 (0.00) 0.41 (0.24) 1.45345E-26 (-12.18) BMI 21.90 (8.66) 25.24 (19.51) 3.99545E-05 (-4.61) waist Circumference 81.72 (21.65) 94.18 (76.44) 3.03922E-12 (-10.4) W:H 0.90 (0.00) 0.98 (0.01) 8.34737E-10 (-7.71) Insulin 6.57 (27.64) 12.37 (150.39) 0.000117524 (-4.02) Fasting Glucose 83.70 (67.17) 178.47 (6168.03) 4.99826E-42 (-16.74) T. Cholesterol 153.02 (891.21) 192.52 (2311.72) 5.34361E-06 (-5.31) Triglycerides 99.67 (678.30) 180.58 (9299.32) 9.18085E-15 (-9.21) HDL 49.21 (56.46) 42.50 (72.55) 0.000471437 (3.77) LDL 82.75 (815.88) 106.10 (1448.85) 0.001129538 (-3.39) VLDL 20.05 (24.76) 38.31 (520.76) 7.96199E-17 (-9.55) HOMA- B 119.67 (6794.11) 63.48 (6437.72) 0.003945245 (2.92) HOMA-R 1.39 (1.42) 5.66 (51.49) 5.12476E-13 (-7.67) Visceral Fat 145.93 (525.97) 147.69 (4623.57) 0.433852267 (-0.17) Subcutaneous fat 124.35 (243.28) 158.69 (7043.77) 0.001271515 (-3.12) Ectopic Liver fat 5.44 (3.27) 8.97 (20.96) 1.96E-05 (-4.51) Comparison of clinical, biochemical and abdominal fat distribution parameters according to normal or elevated W:H ratio We further carried out t-Test between Normal W:H ratio (27 NGT: 13 T2D) and Elevated W:H ratio (109 NGT: 220 T2D) groups and results shown that there exists extremely significant difference among these groups except for visceral fat, subcutaneous fat and HDL. (Table 3 ) Comparison of clinical, biochemical and abdominal fat distribution parameters according to combined presence of either cutaneous sign plus elevated W:H ratio We further carried out t-Test between Normal W:H ratio and no cutaneous sign present (27 NGT: 7 T2D) and Elevated W:H ratio and either one or both cutaneous sign present (all 147 T2D) groups and results shown that there exists extremely significant difference among these groups except for visceral fat, subcutaneous fat and HDL. (Table 4 ) Table 4 Comparison between groups Normal W:H ratio and Elevated W:H ratio groups using t-test. Characteristic Mean (variance) Normal W:H Mean (variance) Elevated W:H ratio P –Value (T- statistics) Acanthosis Nigricans % 0.142 (0.12) 0.37 (0.23) 0.0001 (-3.7) Acrochordons % 0.14 (0.12) 0.3 (0.214) 0.005 (-2.6) BMI 22.8 (14.9) 24.2 (17.4) 0.01 (-2.2) Glucose 130.1 (4564.49) 157.3 (6083.06) 0.009 (-2.4) Insulin 6.9 (58.19) 10.17 (115.3) 0.008 (-2.4) T. Cholesterol 156.6 (2353.09) 187.5(2042.3) 0.0001 (-3.9) Triglycerides 128.16 (6096.08) 161.3 (7831.2) 0.006 (-2.5) VLDL 26.3(279.8) 34.5(434.4) 0.002(-2.9) HDL 42.8 (53.3) 43.8 (72.1) 0.18 (-0.8) LDL 87.8 (1537.4) 104.6 (1252.3) 0.005(-2.6) HOMA- B 12.45(872.8) 33.6 (5025.02) 0.0002 (-3.5) HOMA-R 1.24 (7.19) 2.7 (32.2) 0.0019 (-2.9) Ectopic liver fat 5.2 (2.2) 7.52 (16.5) 2.21882E-05 (-4.3) Subcutaneous fat 131.5 (1349.5) 141.36(4807.7) 0.164 (-0.98) Visceral Fat 145.3(825.6) 141.8(4219.4) 0.3(0.4) Components of MetS present 1.8 (1.6) 2.9 (1.6) 2.3417727E-06 (-5.1) Sensitivity – Specificity Analysis We attempted a couple of sensitivity – specificity analysis across various anthropometric, glycemic, and cutaneous phenotypes to ascertain their association with MetS (Table 5 ). Table 5 Comparison between groups Normal W:H ratio and no cutaneous sign present groupand ‘Elevated W:H ratio and either one or both cutaneous sign present’ group using t-test Characteristic Mean (variance) Normal W:H ratio and no cutaneous sigh Mean (variance) Elevated W:H ratio and any cutaneous sign P –Value (T- statistics) BMI 22.11 (13.21) 25.73 (19.62) 2.71642E-06(-5.01) Fasting Glucose 111.85 (2599.64) 196.77 (6371.38) 1.59903E-11(-7.76) Insulin 6.55 (44.63) 11.78 (150.14) 0.000460705(-3.42) T. Cholesterol 149.13 (2074.72) 194.63 (2262.59) 1.74033E-06(-5.21) Triglycerides 129.31 (6753.09) 177.23 (7179.84) 0.001831042(-3.05) LDL 80.51 (1200.41) 111.81 (1446.29) 1.09569E-05(-4.66) VLDL 26.64 (315.40) 36.45 (314.24) 0.002753376(-2.90) HDL 42.39 (3.06) 43.51 (82.51) 0.224130267(-0.76) HOMA- B 12.66 (1061.42) 27.37 (2803.91) 0.020634814(-2.07) HOMA-R 1.02 (8.36) 3.91 (55.67) 0.000178363(-3.66) Subcutaneous fat 132.47 (1395.43) 157.86 (8320.92) 0.056031393(-1.61) Visceral Fat 142.14 (653.33) 155.98 (3576.49) 0.101437911(-1.29) Liver % fat sign 5.24 (2.30) 9.17 (22.27) 9.18466E-06(-4.73) MetS 0.21 (0.17) 0.86 (0.12) 2.23969E-11(-8.67) Number of components of MetS 0.47 (0.25) 3.6 (0.9) 1.9307958E-46 (-26.6) Body mass index (BMI) is not a complete measure of metabolic health, and our analysis concluded that BMI > 27.5 or even > 30 correspond to only 27% and 10% sensitivity respectively in predicting MetS status. The other two measures of insulin resistance – cutaneous signs and HOMA-IR (a value > 2 in male and > 3 in female) give sensitivity values 30% and 62% respectively. Conventionally elevated waist circumference (> 80 cm. in female and > 90 cm. in male) is considered as one of the components for scoring MetS. We, however found elevated W:H ratio (> 0.85 in female and > 0.90 in male), a better denominator of MetS (87% vs. 94% sensitivity in predicting MetS). Presence of either one or both cutaneous signs along with elevated W:H ratio surpass all the previously studied parameters with 94.8% sensitivity and a modest specificity of 57.4%. It also produces the highest F1 score that combines the precision and recall scores of a model. In other words, presence of cutaneous signs of insulin resistance in an individual with central obesity (CO-CSIR) is the most sensitive bed-side predictor of MetS. Though, its specificity is moderate, but the individuals have this bed-side signs, CO-CSIR, when compared to those without it, as shown in the Table 5 , they had not only had high insulin resistance, but also had higher triglyceride and glucose values. Therefore, had dysmetabolism. We further carried out t-Test between Normal W:H ratio andno cutaneous sign present’ (23 NGT: 4 T2D) and ‘Elevated W:H ratio and either one or both cutaneous sign present’(all 20 T2D) groups within MetS negative group and results shown that there exists extremely significant difference among these groups except for triglycerides, insulin, visceral, and ectopic fat. (Table 6 ) Table 6 Sensitivity and specificity analysis of anthropometric parameters, cutaneous signs and HOMA-IR in predicting MetS Evaluation Metric BMI-30 BMI-27.5 AC/AN HOMA-IR Waist Circumference W:H Ratio AC/AN + W:H Sensitivity (True Positive Rate) 0.1 0.27 0.30 0.62 0.87 0.94 94.8 Specificity (True Negative Rate) 0.98 0.94 0.92 0.78 0.65 0.19 57.4 Precision 0.88 0.85 0.83 0.8 0.78 0.62 86.4 F1 Score 0.18 0.41 0.44 0.7 0.82 0.75 95.1 Table 7 Comparison between MetS - negative groups ‘Normal W:H ratio and No cutaneous signs’ and ‘Elevated W:H ratio and cutaneous sign present’ groups using t-test Characteristic Mean (variance) Normal W:H + No cutaneous sign Mean (variance) Elevated W:H ratio + cutaneous sign P –Value (T- statistics) BMI 21.55(9.78) 24.18(7.75) 0.00(-3.03) Triglycerides 108.67(1408.89) 130.31(2920.87) 0.07(-1.54) T. Cholesterol 141.59(1543.37) 181.58(1540.40) 0.00(-3.45) HDL 43.29(55.97) 47.37(60.47) 0.04(-1.81) LDL 78.83(1099.68) 107.33(1108.21) 0.00(-2.88) VLDL 21.66(58.54) 26.42(123.03) 0.05(-1.65) Fasting Glucose 103.33(1891.46) 191.90(4880.73) 0.00(-5.00) Insulin 5.69(21.05) 5.49(22.23) 0.44(0.15) HOMA-R 0.41(0.89) 2.06(6.42) 0.01(-2.76) HOMA- B 5.41(126.14) 13.74(189.30) 0.02(-2.21) Visceral Fat 147.19(390.99) 162.32(820.02) 0.24(-0.87) Subcutaneous fat 128.58(407.72) 102.86(221.45) 0.04(2.62) Ectopic liver fat 5.22(2.71) 5.85(9.24) 0.38(-0.35) Gene Expression Microarray Analysis Limma analysis identified 205 DEGs in subcutaneous fat, 332 DEGs in femoral fat, and 818 DEGs in visceral depot ( P < 0.05). Number of DEGs may indicate the relative significance of cutaneous signs. WebGstalt analysis further reports KEGG pathways enriched by these DEGs (See Supplementary File). In subcutaneous and visceral depots, various inflammation-related pathways were found enriched: ‘ Leukocytetrans-endothelial migration’, ‘Chemokine signaling pathway’, ‘Phagosome’ , and ‘Osteoclast differentiation’ , indicating adipose pathology. We also observed various infection (‘ Staphylococcus aureus infection’, ‘Tuberculosis’, ‘Amoebiasis’, ‘Leishmaniasis ’ etc.) and immune system-related pathways (‘ Type I diabetes mellitus’,’Rheumatoidarthritis’,’Graft-versus-host diseases ’) which support our view. Peripheral fat mainly enriched some metabolic and cancer-related pathways. WGCNA Analysis In visceral fat, three modules brown, blue, and grey have shown significant negative correlation and one module – turquoise shown positive correlation with AC and MetS (shown in Fig. 1 ). Directionality of correlation in these four modules indicate that both these traits are indeed associative with each other. We therefore assume, gene-expression in visceral fat is indeed the probable denominator of MetS and somehow links occurrence of skin-tags with clinical diseases. We also gone through the pathway enrichment analysis of statistically significant modules for each depot and this analysis is presented in supplementary file. Discussion The cutaneous signs of insulin resistance and the anthropometric measures of obesity, particularly those assessing central obesity are well known for their association with high insulin resistance. They also cluster with MetS and its constituent diseases. However, from the clinical practice point of view the most pertinent question is whether these physical signs can be used for the bedside prediction of MetS in the Asian Indians who show thin fat phenotype. The findings of the present study support the view that presence of any cutaneous sign of insulin resistance in an individual with higher waist to hip ratio (CO-CSIR) is the most sensitive and the best physical sign for the prediction of MetS among the several parameters investigated in this study including the BMI and waist circumference. Though specificity of CO-CSIR in predicting MetS is moderate, but the non-MetS individuals positive for CO-CSIR had high insulin resistance, impaired pancreatic beta cell function, dyslipidemia and hyperglycemia. They also had higher prevalence of T2D and other clustering diseases of MetS. Moreover, the modules of co-expressed genes in the visceral adipose tissue showing association with MetS also showed an association with AC. In other words, both AC and MetS share similar qualitative molecular traits in visceral adipose tissue. Taken together, these findings suggest that CO-CSIR is not only a sensitive physical sign of MetS, but even among those who do not meet the clinical criteria of MetS, it reflectspresence of pathologic adipose tissue and the consequent pathophysiological mechanisms and pathways of MetS. Therefore, CO-CSIR could also serve as a physical sign of “adiposopathy” even among those who does not meet the diagnostic criteria of MetS. Several previous studies from India have investigated association between these cutaneous signs of IR in isolation with different facets of MetS like anthropometric measures of obesity (i.e., BMI [ 32 ], waist circumference [ 37 , 38 ], W:H ratio), HOMA-IR [ 29 , 30 , 31 , 33 ], lipid parameters, diabetes and other diseases clustering with MetS [ 34 , 35 , 36 ]. They all find an association between these cutaneous signs and the facets of MetS they investigated. To the best of our knowledge, no previous study has compared both the cutaneous signs togetherfor their association with HOMA-IR or MetS. We observed here that AC as compared to AN was found to be associated with higher degree of insulin resistance, more ectopic fat. Thus, suggesting that AC indicates more severe degree of the pathophysiological mechanisms of MetS. Moreover, as mentioned previously it is AC, not the AN which showed association with both MetS and molecular qualitative traits of adiposopathy in visceral adipose tissue. To the best of our knowledge, the concept of CO-CSIR, the bedside physical sign of MetS and adipose tissue centric dysmetabolism (otherwise so called adiposopathy) presented in this study has not been reported in any previous study from India. Does these two clinical signs, i.e. , W; H ratio and AC / AN represent two different facets of the pathophysiological mechanism of MetS or else, cannot be answered by this cross-sectional study. However, results of present study support the notion that presence of cutaneous signs of IR in individuals with central obesity is associated with higher degree of dysfunction in several facets of adipose tissue pathology (adiposopathy) driven mechanisms of MetS, i.e. ectopic fat, HOMA-IR, beta cell dysfunction, hyperglycemia etc. as well as higher degree of W:H ratio itself. In other words, CO-CSIR reflects higher degree of adiposopathy as compared increase W:H ratio alone. The other facet of the coin is that, while keeping apart these potential adipose centric pathophysiological mechanisms of MetS, what is more important clinically is that both of these physical signs can be easily measured bed side and in general practice. However, there are several limitations of the present study that it is a cross sectional and single hospital based cross sectional study. Therefore, there is need for large scale prospective studies. Moreover, molecular insight into the shared mechanisms and pathways between these physical signs and clustering diseases would be of paramount importance in establishing CO-CSIR as physical sign of MetS as well as the adipose dysfunction driven dysmetabolism (so called adiposopathy). MetS has been a subject of controversy, both in terms of its utility as CV risk predictor as well as the precise cut off values of the parameters of its clustering diseases and anthropometric measurements in its diagnosis. Secondly, despite so much of research in this field, is MetS still remains away from a diagnostic entity in common prescription. We have presented here the concept of a “physical sign”, i.e., CO-CSIR for the bed side sign for recognition of not only the cluster of diseases, that otherwise constitutes the MetS, but also adipose tissue dysfunction and the consequent pathophysiologic mechanisms of MetS like HOMA-IR, ectopic fat, dyslipidemia as well as impaired beta cell function (i.e., adiposopathy). In the light of the concept that adiposopathy as a complex genetic disease with MetS as its clinical manifestation, CO-CSIR could potentially be as a physical sign of this disease. However, an important question still remains unanswered that in non-MetS subjects the CO-CSIR represent early stage in its natural history or incompletely developed MetS? This question cannot be answered by this cross-sectional study. Therefore, there is need of longitudinal studies to understand the prognostic clinical importance of non-MetS CO-CSIR condition. Limitation This is a single center clinical based study, not community-based study. We recruited patient as patient having diabetes and non-diabetic and did not include patient of impaired glucose intolerance. As metabolic syndrome is not routine finding a place in common prescription. Hence it is not possible to recruit patient as having metabolic syndrome and not having metabolic syndrome. Conclusion Presence of cutaneous sign of insulin resistance in a individual with central obesity (CO-CSIR) is a sensitive physical sign of metabolic syndrome in Asian Indian, though its specificity is moderate but this is associated with underlying pathophysiological mechanism of metabolic syndrome like insulin resistance ectopic fat deposition, hyperglycemia, hyperinsulinemia etc. Hence CO-CSIR seems to be very potential physical sign of adipose tissue driven dysmetabolism so called adiposopathy. Declarations Acknowledgements Financial grant received from the Indian Council of Medical Research, Government of India is gratefully acknowledged (grant No. 5/4/8/2012-RMC). We gratefully acknowledge the Research Society for the Study of Diabetes in India (RSSDI) for providing partial funding and also acknowledge Metabolism and Molecular Research Society (MMRS), Jaipur and Principal, SMS Medical College, and hospital, Jaipur (Rajasthan) India for providing necessary infrastructural facility to carry out this study. Statement of Ethics The institutional ethics committee of SMS Medical College, Jaipur approved the study. Written informed consent was obtained from participants (or their parent/legal guardian) before participation in this study following the Institutional ethics committee guidelines. Conflict of Interest Statement The authors declare no conflict of interest. Funding Sources This study was financially supported by the Metabolic and Molecular Research Society (MMRS) (Jaipur) and Indian Council of Medical Research (ICMR), Government of India through grant no. 5/4/8/2012-RMC and Research Society for the Study of Diabetes in India (RSSDI) grant no. RSSDI/HQ/Grants/2018/4647. Author Contributions SKM, AG were involved in the clinical diagnosis and management of the patient. AG and PT were involved in biochemical characterization of the samples. AG, RM, SG and RKL were involved in collecting samples. AS performed the bioinformatics analysis. PT and AS were involved in data analysis. AG and SKM wrote the initial draft. SKM. was involved in leading the investigations, recruiting the cohorts, and conceptualization of the project. All authors have read and agreed to the published version of the manuscript. Data Availability Statement Original data generated and analyzed for the manuscript are included in this published article. 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Nicotinamide: oversight of metabolic dysfunction through SIRT1, mTOR, and clock genes. Current neurovascular research. 2020;17(5):765–83. Restrepo BI, Twahirwa M, Rahbar MH, Schlesinger LS. Phagocytosis via complement or Fc-gamma receptors is compromised in monocytes from type 2 diabetes patients with chronic hyperglycemia. PloS one. 2014;9(3):e92977. Fountas A, Diamantopoulos LN, Tsatsoulis A. Tyrosine kinase inhibitors and diabetes: a novel treatment paradigm? Trends in Endocrinology & Metabolism. 2015;26(11):643–56. Li X, Sun F, Lu J, Zhang J, Wang J, Zhu H, Gu M, Ma J. Osteoclasts may affect glucose uptake-related insulin resistance by secreting resistin. Diabetes, Metabolic Syndrome and Obesity. 2021 Jul 31:3461–70. Samario-Román J, Larqué C, Pánico P, Ortiz-Huidobro RI, Velasco M, Escalona R, Hiriart M. NGF and its role in immunoendocrine communication during metabolic syndrome. International Journal of Molecular Sciences. 2023;24(3):1957. Bani D, Pini A, Ka-Sheng Yue S. Relaxin, insulin and diabetes: an intriguing connection. Current Diabetes Reviews. 2012;8(5):329–35. Gehart H, Kumpf S, Ittner A, Ricci R. MAPK signalling in cellular metabolism: stress or wellness?. EMBO reports. 2010;11(11):834–40. Saxton RA, Sabatini DM. mTOR signaling in growth, metabolism, and disease. Cell. 2017;168(6):960–76. Matthews DR, et al. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–419. doi: 10.1007/BF00280883 Singh SK, Agrawal NK, Vishwakarma AK. Association of Acanthosis Nigricans and Acrochordon with Insulin Resistance: A Cross-Sectional Hospital-Based Study from North India. Indian J Dermatol. 2020 Mar-Apr;65(2):112–117. Puri N. A study of pathogenesis of acanthosis nigricans and its clinical complication. Indian J Dermatol. 2011;56:678–83. Bhagyanathan M, Dhayanithy D, Parambath VA, Bijayraj R. Acanthosis nigricans: A screening test for insulin resistance-An important risk factor for diabetes mellitus type-2. J Family Med Prim Care. 2017;6:43–6. Vijayan AP, Varma KK, Bhagyanathan M, Dinesh KB, Divianath KR, Bijayraj R. Three physical markers of insulin resistance (body mass index, waist circumference and acanthosis nigricans): A cross-sectional study among children in South India. Med Pract Rev. 2011;2:37–43. Venkatswami S, Anandam S. Acanthosis nigricans: A flag for insulin resistance. J EndocrinolMetab Diabetes South Afr. 2014;19:68–74. Choudhary S, Srivastava A, Saoji V, Singh A, Verma I, Dhande S. Association of acanthosis nigricans with metabolic syndrome - An analytic cross-sectional study. An Bras Dermatol. 2023 Jul-Aug;98(4):460–465. Naidu, B. T. K., &Baddireddy, K. (2020). Metabolic syndrome in south Indian population with cutaneous signss: a hospital-based case control study. International Journal of Research in Medical Sciences, 8(10), 3682–3686. Sherin N, Khader A, Binitha MP, George B. Acrochordon as a marker of metabolic syndrome – A cross-sectional study from South India. J Skin Sex Transm Dis 2023;5:40–6. Tripathy, Tapaswini; Singh, Bhabani S.T.P.; Kar, Bikash R.. Association of Cutaneous signs with Metabolic Syndrome and its Components: A Case–control Study from Eastern India. Indian Dermatology Online Journal 10(3):p 284–287, May–Jun 2019. Shah R, Jindal A, Patel N. Acrochordons as a cutaneous sign of metabolic syndrome: a case-control study. Ann Med Health Sci Res. 2014;4(2):202–5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4340896","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":298944753,"identity":"d37b1cef-3e25-494a-be36-7798f539d1cc","order_by":0,"name":"Anamika Gora","email":"","orcid":"","institution":"Sawai Man Singh (SMS) Medical College","correspondingAuthor":false,"prefix":"","firstName":"Anamika","middleName":"","lastName":"Gora","suffix":""},{"id":298944756,"identity":"396922a8-e25e-44d1-834f-2da940801b91","order_by":1,"name":"Pradeep Tiwari","email":"","orcid":"","institution":"Sawai Man Singh (SMS) Medical College","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"","lastName":"Tiwari","suffix":""},{"id":298944759,"identity":"2d6bdaa7-9883-46bb-8db4-0d4b9381dea3","order_by":2,"name":"Aditya Saxena","email":"","orcid":"","institution":"GLA University","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Saxena","suffix":""},{"id":298944762,"identity":"5fae614d-b985-456b-a743-564465a8cc66","order_by":3,"name":"Rajendra Mandia","email":"","orcid":"","institution":"SMS Medical College and attached hospital","correspondingAuthor":false,"prefix":"","firstName":"Rajendra","middleName":"","lastName":"Mandia","suffix":""},{"id":298944765,"identity":"d4fc98cd-67ab-4e21-a3dd-b64912fd42e6","order_by":4,"name":"Shalu Gupta","email":"","orcid":"","institution":"SMS Medical College and attached hospital","correspondingAuthor":false,"prefix":"","firstName":"Shalu","middleName":"","lastName":"Gupta","suffix":""},{"id":298944768,"identity":"ba53fa03-eea2-4869-a60b-04d7d8ebe682","order_by":5,"name":"Ravinder Kumar Lamoria","email":"","orcid":"","institution":"SMS Medical College and attached hospital Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Ravinder","middleName":"Kumar","lastName":"Lamoria","suffix":""},{"id":298944773,"identity":"6a11fa84-ba07-412d-a89b-c229b22089b1","order_by":6,"name":"Sandeep Kumar Mathur","email":"data:image/png;base64,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","orcid":"","institution":"Sawai Man Singh (SMS) Medical College","correspondingAuthor":true,"prefix":"","firstName":"Sandeep","middleName":"Kumar","lastName":"Mathur","suffix":""}],"badges":[],"createdAt":"2024-04-29 07:12:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4340896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4340896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57189061,"identity":"2f3246ff-e64a-40ac-9124-da244f521365","added_by":"auto","created_at":"2024-05-27 06:47:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1068218,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4340896/v1/e7d27e26-bea7-4cfe-a2d2-2a939b5f8dc3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Presence of Cutaneous Signs of Insulin Resistance with Central Obesity (CO-CSIR) in Asian Indians is a sensitive physical sign of Metabolic Syndrome (MetS)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMetabolic syndrome (MetS) is a cluster of certain cardiovascular risk factors, including obesity, dyslipidemia, hypertension, and glucose intolerance. The accumulation of fat, particularly in the central and visceral compartments, is the cornerstone structural feature of MetS [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Pathophysiologically, it is associated with altered adipose tissue function, and insulin resistance (IR). In other words, MetS is associated with altered quality of adipose tissue, in addition to its quantity and distribution. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Obesity related insulin resistance (IR) has traditional been considered to be the major underlying pathophysiological defect of MetS. [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, Asian Indians have been shown to be more insulin resistant at any given body mass index (BMI). For any given BMI, they run a higher risk of developing type 2 diabetes and cardiovascular disease as compared to their Caucasian counterparts [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The clinical importance of this pathophysiology lies in the fact that type-2 diabetes mellitus (T2DM), and MetS clusters with predominantly central obesity (clinically assessed as waist-to-hip ratio) instead of generalized obesity (clinically assessed as BMI) in this population.\u003c/p\u003e \u003cp\u003eRecently we conducted gene expression profiling of adipose tissue in Asian Indian diabetics and non-diabetics and found that T2D is associated with not only pathologically altered quality of adipose tissue at molecular level, but several modules of co-expressed genes showed significant correlation with intermediate traits of T2D and MetS. Therefore, these results strengthen the belief that it is indeed the sick fat or adiposopathy, which is a major factor in the causation ofMetS and diabetes and it affects both\u0026mdash;peripheral, as well as visceral adipose tissue compartments [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKeeping in view the considerable magnitude of the problem and the heightened risk of vascular-metabolic morbidity and mortality, diagnosis of MetS and its underlying adipose tissue pathology in clinical practice as well as at the community level is the need of the hour. It is particularly more important for Asian Indians, because body mass index (BMI) criteria of obesity might not truly reflect MetS and adipose tissue pathology. Several definitions of MetS have been described, and those recommended by National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and International Diabetes Federation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] are the commonly used. However, the precise cut-off values of the anthropometric parameter of central-fat deposition as well as parameters of clustering diseases could not be determined as a consensus. Therefore, clinical diagnosis of MetS still remains to be a challenge. An alternative strategy could be to predict MetS and underlying adipose tissue pathology on the basis of physical signs and further investigate the different facets of adipose tissue pathology driven dysmetabolism and the clustering diseases. There are several physical signs that show association with MetS and IR. Anthropometric parameters like BMI, WC and W:H ratio primarily estimates body fat content and its distribution in central versus peripheral compartments. There are also two cutaneous signs of insulin resistance. Several previous studies have found their association with insulin resistance and the underlying pathophysiological mechanisms of insulin resistance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Acanthosis Nigricans (AN) is a dermatological disorder characterized by symmetrical plaques with hyperpigmentation and hyperkeratosis. It is usually observed on the posterior neck, axilla, and groin, but may also be seen on the elbows, knuckles, and knees [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Acrochordon (skin tags, AC) are brown or skin-colored common tumors 1mm-1cm in diameter, small soft and pedunculated, usually occurring on the neck, armpits, and groin [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Very few studies, including some of them in Asian Indians, have demonstrated the importance of acrochordon (AC) and acanthosis nigricans (AN) as a clinical sign of IR and MetS.\u003c/p\u003e \u003cp\u003eIn the context of the descriptive facts of association between these anthropometric parameters and cutaneous signs with anatomical facet of body fat distribution and functional defects of insulin resistance respectively, a very pertinent question is that if they can be used clinically as a physical sign to predict presence and absence of MetS. Another question is that- Do they show any association with pathological changes in adipose tissue and consequent pathophysiological mechanisms of MetS? Our hypothesis is that - presence of these cutaneous signs, particularly in the context of anthropometric parameters of predominantly central deposition of adipose tissue can serve as a simple clinical tool to suspect presence of MetS and adipose tissue pathology. Therefore, in present study we explore sensitivity and specificity of these cutaneous sign in predicting presence and absence of MetS in Asian Indian particularly in contest of central obesity assessed as waist to hip ratio. Secondly, we also investigated whether these cutaneous signs show any association with pathological changes in adipose tissue.\u003c/p\u003e"},{"header":"Material \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eThis study is an analytical cross-sectional study conducted on the individuals attending S.M.S. Medical College Hospital, Jaipur India from 2013 to 2023 and they had participated in research projects on adiposopathy. assessment in this population and all these studies shared common protocol and clinical, biochemical and radiological assessment methods. The institutional ethics committee of SMS Medical College approved these studies. Informed written consent was taken before participation in this study. All methods were performed as per the relevant guidelines and regulations. Total 371 individuals having either or both cutaneous signs of insulin resistance (acanthosis nigricans and acrochordon) and without cutaneous sign were included in the present study. The inclusion criteria in all these studies were common and shared uniform clinical, biochemical and radiological assessment methodology. The participants were recruited in two groups: (1) T2D diagnosed as per American Diabetes Association (ADA) standards and, (2) normal glucose tolerant individuals without T2D in another arm (confirmed by Oral Glucose Tolerance Test -OGTT). The exclusion criteria were the presence of infection, malignancy, and drugs affecting body fat such as thiazolidinediones and glucocorticoids. For the present study, we re-assigned these participants into metabolic syndrome -positive (MetS) and -negative groups as per the NCEP III criteria, \u003cem\u003ei.e.\u003c/em\u003e, if at least three out of five measured MetS traits of individual were found positive - waist circumference, triglycerides, HDL, fasting glucose, and blood pressure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eClinical evaluation\u003c/h2\u003e \u003cp\u003eSubjects underwent comprehensive clinical evaluation including detailed clinical history, physical examination that included cutaneous signs of insulin resistance and anthropometric measurements (height, weight, body mass index [BMI], waist (WC) and hip (HC) circumferences, waist-to-hip circumference ratio (WHR). Supine blood pressure was measured using mercury sphygmomanometer (BPMR-120 Diamond delux, Industrial electronic and allied products, Maharashtra, India) after 10 minutes of rest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of cutaneous signs\u003c/h2\u003e \u003cp\u003eAN and AC were diagnosed clinically during a physical examination. The diagnostic criteria for AN were the presence of thick, rough, irregular wrinkles and brown pigmentation of the skin around the neck and armpits. Whereas the diagnostic criteria for AC was the presence of small soft and pedunculated protrusions occurring on the neck \u0026amp; armpits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eAnthropometric Measurements\u003c/h2\u003e \u003cp\u003eWeight was measured using a standard balance beam scale, and height was measured using a stadiometer. Body mass index was calculated from the ratio of body weight in kg to height in square meters and expressed as kg/m\u003csup\u003e2\u003c/sup\u003e. Waist circumference was measured using a non-stretchable flexible tape measure at the site of maximum circumference midway between the lower ribs and the anterior superior iliac spine. Hip-circumference was measured over the greatest protrusion of the gluteal muscles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBiochemical Investigations\u003c/h2\u003e \u003cp\u003eA venous blood sample was obtained after an overnight fast of at least 8 hours. Biochemical measurements included Fasting blood glucose (FBG), Lipid profile including total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein (VLDL) using were measured on Kopran AU/400 (Olympus corporation, Shinjuku, Tokyo, Japan) fully automated analyzer. Serum insulin was measured using a chemiluminescent immunometric assay (Immulite 2000 machine, SiemensHealthineers AG, Erlangen, Germany). HbA1c was measured by turbidimetry method using BioSystems (Biosystems, S.A. Barcelona, Spain) kits. HOMA-β wascalculatedusing the followingformula[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e360 x [Insulin in \u0026micro;U/ml]/ ([Glucose in mg/dL] \u0026minus;\u0026thinsp;63)\u003c/h2\u003e \u003cp\u003eHOMA-IR is a measure of insulin resistance, and was calculated using the following formula: [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e{[Glucose in mg/dL] x [Insulin in \u0026micro;U/ml]}/ 405\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eDetermination of Metabolic syndrome parameter\u003c/h2\u003e \u003cp\u003eAccording to the NCEP ATP III[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] definition, metabolic syndrome is present if three or more of the following five criteria are met: 1) increased waist circumference (\u0026ge;\u0026thinsp;90 cms for men, \u0026ge;\u0026thinsp;80 cms for women); 2) elevated triglycerides (\u0026ge;\u0026thinsp;150 mg/dl); 3) low HDL cholesterol (\u0026lt;\u0026thinsp;40 mg/dl in men, \u0026lt;\u0026thinsp;50 mg/dl in women); 4) hypertension (\u0026ge;\u0026thinsp;130/\u0026ge;85 mmHg); and 5) fasting glucose (\u0026ge;\u0026thinsp;100 mg/dl). In addition, existing drug treatment for dyslipidemia, dysglycemia, raised blood pressure would also be qualifying criteria.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRadiological investigations\u003c/h2\u003e \u003cp\u003eAbdominal fat content and distribution among VAT, SAT and ectopic hepatic compartments were estimated by MRI. MRI scan procedure: The MRI scans were done at the S.M.S, hospital in the department of radiology, using 3 tesla Philips Ingenia Machine. The single investigator who interpreted the scans on Osirix software was unaware of the clinical status of the study subjects. A single scan (3 mm) of the abdomen was done at the level of L4-L5 vertebrae and analyzed for a cross-sectional area of adipose tissue, which was expressed in centimeters squared. The parameters studied included VAT and SAT. VAT, which represents intra-abdominal omental fat (without ectopic fat) was distinguished from SAT by tracing along the fascial plane defining the internal abdominal wall and the area was calculated in centimeters square. Ectopic liver fat was measured using liver intensity on Osirix software using Dixon method (Liver Fat percentage\u0026thinsp;=\u0026thinsp;100X ((Signal intensity liver/signal intensity spleen) on in phase T1- (signal intensity liver/signal intensity spleen) on out phase T1)/2x (signal intensity liver/signal intensity spleen) on in phase T1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional Profiling of adipose tissue\u003c/h2\u003e \u003cp\u003eOut of 371 participants, 33 individuals (23 MetS: 10 Non-MetS) and 52 individuals (36 MetS: 16 Non-MetS) respectively had undergone either abdominal or femur bone surgery for the primary indication other than this study. Adipose tissue biopsies were obtained from abdominal (one visceral and one subcutaneous each) and thigh (peripheral subcutaneous) fat compartments respectively. These biopsy samples were then subjected to gene-expression microarray analysis using Affymetrix PrimeView chips. A total of 118 gene-expression datasets were generated out from these biopsies. These samples were among 140 samples that we have earlier submitted to NCBI GEO database with accession number GSE78721.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical and Bioinformatics analysis\u003c/h2\u003e \u003cp\u003eTo test the difference between groups, we used Student\u0026rsquo;s t-test and ANOVA in Microsoft Excel and a \u003cem\u003eP\u003c/em\u003e-value of 0.05 was taken as statistically significant.\u003c/p\u003e \u003cp\u003eSensitivity \u0026ndash; specificity analysis was done in python using libraries - numpy, matplotlib, and sklearn. First a confusion matrix was created based on actual, and predicted labels and then we calculated specific metrics from this matrix to estimate sensitivity, specificity, precision, and F1 score using following formulas. (TP: True Positive, TN: True Negative, FP: False Positive, FN: False Negative).\u003c/p\u003e \u003cp\u003eSensitivity (Recall)\u0026thinsp;=\u0026thinsp;TP / (TP\u0026thinsp;+\u0026thinsp;FN)\u003c/p\u003e \u003cp\u003eSpecificity\u0026thinsp;=\u0026thinsp;TN / (TN\u0026thinsp;+\u0026thinsp;FP)\u003c/p\u003e \u003cp\u003ePrecision\u0026thinsp;=\u0026thinsp;TP / (TP\u0026thinsp;+\u0026thinsp;FP)\u003c/p\u003e \u003cp\u003eF1-Score\u0026thinsp;=\u0026thinsp;2 X (Precision X Sensitivity) / (Precision\u0026thinsp;+\u0026thinsp;Sensitivity)\u003c/p\u003e \u003cp\u003eLow-level analysis of microarray datasets was carried out using Bioconductor packages in R: \u003cem\u003egcrma\u003c/em\u003e, and \u003cem\u003egenefilter\u003c/em\u003e. As the annotation package for the prime-view was not available, it was created using \u003cem\u003ehuman.db0\u003c/em\u003e, and \u003cem\u003eAnnotationForge\u003c/em\u003epackage using prime-view annotation file.\u003c/p\u003e \u003cp\u003eTo relate the normalized expression values of genes with the presence/absence status of cutaneous signs, T2D, and MetS, we used a system biology method - Weighted Gene Correlation Network Analysis (WGCNA) which identifies modules of co-expressed genes and define these modules in separate colors as visual aid. It also estimates Module Eigengene (ME) as the first principal component of the expression matrix of the corresponding module and provides functionalities to relate these MEs with external traits.\u003c/p\u003e \u003cp\u003eWGCNA was carried out as follows -The normalized expression matrix (containing 118 datasets) genes were matched with the corresponding intermediate traits and a single step network construction and module detection was used by selecting soft threshold power β\u0026thinsp;=\u0026thinsp;15 to ensure scale-free topology of the network. MEs were then used to relate each module with selected traits. We separately carried out WGCNA in femoral, visceral, and subcutaneous fat microarray datasets for AC, AN, T2D, and MetS.\u003c/p\u003e \u003cp\u003eWe then selected microarray samples of only those individuals who have elevated W:H ratio and carried out differential gene expression analysis between cutaneous-sign positive (CP) datasets and negative (CN) datasets in each depot using \u003cem\u003elimma\u003c/em\u003e and further look for enriched KEGG pathways using WebGeStalt tool [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] for DE genes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). depot-wise count was- femoral fat (17 CP: 32CN), subcutaneous (10 CP:22CN), and visceral subcutaneous (10 CP:22CN).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eOut of 371 participants 215,235 and 185 persons respectively had MetS, T2DM and hypertension. The cutaneous signs of AN, AC and both were present respectively 50, 27 and 75 individuals.\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of clinical, biochemical and abdominal fat distribution parameters according to presence and absence of cutaneous signs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe carried out ANOVA across 216 cutaneous sign negative individuals (136 NGT:80 T2D), 50ANpositive (all T2D) and 27 AC positive individuals (all T2D) and results shown that there exists extremely significant difference among these groups except for subcutaneous fat and HDL. Both the cutaneous signs were associated with high insulin resistance, higher BMI, central fat deposition, more ectopic fat deposition and larger number of components of MetS. AC as compared to AN was found to be associated with more ectopic fat and higher insulin resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of three groups using Analysis of Variance test.\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (No cutaneous sign)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(Acanthosis Nigricans +)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e(Acrochordon +)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40174E-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e86.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.1554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.1935E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW: H ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e193.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e187.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.89938E-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e200.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015628275\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e168.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025671277\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.810975945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.042793228\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\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.62064E-08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e113.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e150.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e132.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic Liver Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of MetS Components\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.48672E-14\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\u003eComparison of clinical, biochemical and abdominal fat distribution parameters according to presence and absence of MetS\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFirst, we assessed \u0026ndash; if there exist significant difference between cutaneous sign positive and cutaneous sign negative groups in all the clinical parameters (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Prevalence of T2D was 53% in negative group and 100% among positive group. Similarly, prevalence of MetS was 51% in negative group and 83% among positive group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between cutaneous sign positive and cutaneous sign negative using t-test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003ecutaneous sign negative\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003ecutaneous sign positive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP \u0026ndash;Value\u003c/p\u003e \u003cp\u003e(T- statistics)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.46 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (-4.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.57 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e (-4.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW:H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04 (-2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e (-6.64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002 (-3.67)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e173.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08 (-1.75)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic Liver fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe further carried out t-Test between non- MetS group (all 20 T2D) and MetS Score between 3\u0026ndash;5 group (32 NGT: 183 T2D) and results shown that there exists extremely significant difference among these groups except for visceral fat. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between groups MetS score zero and MetS score between 3\u0026ndash;5 groups using t-test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eMetS Score 0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eMetS Score 3\u0026ndash;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP \u0026ndash;Value\u003c/p\u003e \u003cp\u003e(T- statistics)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcanthosis Nigricans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.51 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.21891E-35 (-14.83)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcrochordons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45345E-26 (-12.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.90 (8.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.24 (19.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.99545E-05 (-4.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewaist Circumference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e81.72 (21.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94.18 (76.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.03922E-12 (-10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW:H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.34737E-10 (-7.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.57 (27.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.37 (150.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000117524 (-4.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.70 (67.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e178.47 (6168.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.99826E-42 (-16.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT. Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153.02 (891.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192.52 (2311.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34361E-06 (-5.31)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99.67 (678.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180.58 (9299.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.18085E-15 (-9.21)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49.21 (56.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.50 (72.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000471437 (3.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82.75 (815.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106.10 (1448.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001129538 (-3.39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.05 (24.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38.31 (520.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.96199E-17 (-9.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA- B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119.67 (6794.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.48 (6437.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003945245 (2.92)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.39 (1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.66 (51.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.12476E-13 (-7.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145.93 (525.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147.69 (4623.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.433852267 (-0.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124.35 (243.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e158.69 (7043.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001271515 (-3.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic Liver fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.44 (3.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.97 (20.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96E-05 (-4.51)\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\u003eComparison of clinical, biochemical and abdominal fat distribution parameters according to normal or elevated W:H ratio\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe further carried out t-Test between Normal W:H ratio (27 NGT: 13 T2D) and Elevated W:H ratio (109 NGT: 220 T2D) groups and results shown that there exists extremely significant difference among these groups except for visceral fat, subcutaneous fat and HDL. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cb\u003eComparison of clinical, biochemical and abdominal fat distribution parameters according to combined presence of either cutaneous sign plus elevated W:H ratio\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe further carried out t-Test between Normal W:H ratio and no cutaneous sign present (27 NGT: 7 T2D) and Elevated W:H ratio and either one or both cutaneous sign present (all 147 T2D) groups and results shown that there exists extremely significant difference among these groups except for visceral fat, subcutaneous fat and HDL. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between groups Normal W:H ratio and Elevated W:H ratio groups using t-test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eNormal W:H\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eElevated W:H ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e \u0026ndash;Value\u003c/p\u003e \u003cp\u003e(T- statistics)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcanthosis Nigricans %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.142 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37 (0.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001 (-3.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcrochordons %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3 (0.214)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005 (-2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.8 (14.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.2 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01 (-2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.1 (4564.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.3 (6083.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009 (-2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.9 (58.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.17 (115.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008 (-2.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT. Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156.6 (2353.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e187.5(2042.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001 (-3.9)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.16 (6096.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e161.3 (7831.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006 (-2.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.3(279.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34.5(434.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002(-2.9)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.8 (53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.8 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18 (-0.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87.8 (1537.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104.6 (1252.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005(-2.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA- B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.45(872.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.6 (5025.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0002 (-3.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (7.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0019 (-2.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic liver fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.2 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.52 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21882E-05 (-4.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e131.5 (1349.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.36(4807.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.164 (-0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e145.3(825.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141.8(4219.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3(0.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComponents of MetS present\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.8 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.3417727E-06 (-5.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity \u0026ndash; Specificity Analysis\u003c/h2\u003e \u003cp\u003eWe attempted a couple of sensitivity \u0026ndash; specificity analysis across various anthropometric, glycemic, and cutaneous phenotypes to ascertain their association with MetS (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between groups Normal W:H ratio and no cutaneous sign present groupand \u0026lsquo;Elevated W:H ratio and either one or both cutaneous sign present\u0026rsquo; group using t-test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eNormal W:H ratio and no cutaneous sigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eElevated W:H ratio and any cutaneous sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP \u0026ndash;Value\u003c/p\u003e \u003cp\u003e(T- statistics)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.11 (13.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.73 (19.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.71642E-06(-5.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.85 (2599.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e196.77 (6371.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.59903E-11(-7.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.55 (44.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.78 (150.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000460705(-3.42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT. Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.13 (2074.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e194.63 (2262.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74033E-06(-5.21)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e129.31 (6753.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177.23 (7179.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001831042(-3.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.51 (1200.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e111.81 (1446.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.09569E-05(-4.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.64 (315.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.45 (314.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002753376(-2.90)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.39 (3.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.51 (82.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224130267(-0.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA- B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.66 (1061.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.37 (2803.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020634814(-2.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (8.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.91 (55.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000178363(-3.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132.47 (1395.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e157.86 (8320.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056031393(-1.61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142.14 (653.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e155.98 (3576.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101437911(-1.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiver % fat sign\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.24 (2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.17 (22.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.18466E-06(-4.73)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.23969E-11(-8.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of components of MetS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.47 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.6 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9307958E-46 (-26.6)\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\u003eBody mass index (BMI) is not a complete measure of metabolic health, and our analysis concluded that BMI\u0026thinsp;\u0026gt;\u0026thinsp;27.5 or even \u0026gt;\u0026thinsp;30 correspond to only 27% and 10% sensitivity respectively in predicting MetS status. The other two measures of insulin resistance \u0026ndash; cutaneous signs and HOMA-IR (a value\u0026thinsp;\u0026gt;\u0026thinsp;2 in male and \u0026gt;\u0026thinsp;3 in female) give sensitivity values 30% and 62% respectively.\u003c/p\u003e \u003cp\u003eConventionally elevated waist circumference (\u0026gt;\u0026thinsp;80 cm. in female and \u0026gt;\u0026thinsp;90 cm. in male) is considered as one of the components for scoring MetS. We, however found elevated W:H ratio (\u0026gt;\u0026thinsp;0.85 in female and \u0026gt;\u0026thinsp;0.90 in male), a better denominator of MetS (87% vs. 94% sensitivity in predicting MetS).\u003c/p\u003e \u003cp\u003ePresence of either one or both cutaneous signs along with elevated W:H ratio surpass all the previously studied parameters with 94.8% sensitivity and a modest specificity of 57.4%. It also produces the highest F1 score that combines the precision and recall scores of a model. In other words, presence of cutaneous signs of insulin resistance in an individual with central obesity (CO-CSIR) is the most sensitive bed-side predictor of MetS. Though, its specificity is moderate, but the individuals have this bed-side signs, CO-CSIR, when compared to those without it, as shown in the Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, they had not only had high insulin resistance, but also had higher triglyceride and glucose values. Therefore, had dysmetabolism.\u003c/p\u003e \u003cp\u003eWe further carried out t-Test between Normal W:H ratio andno cutaneous sign present\u0026rsquo; (23 NGT: 4 T2D) and \u0026lsquo;Elevated W:H ratio and either one or both cutaneous sign present\u0026rsquo;(all 20 T2D) groups within MetS negative group and results shown that there exists extremely significant difference among these groups except for triglycerides, insulin, visceral, and ectopic fat. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\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 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity and specificity analysis of anthropometric parameters, cutaneous signs and HOMA-IR in predicting MetS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvaluation Metric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI-30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI-27.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAC/AN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHOMA-IR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWaist Circumference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eW:H Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAC/AN\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003eW:H\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003cp\u003e(True Positive Rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003cp\u003e(True Negative Rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e57.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF1 Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95.1\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=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison between MetS - negative groups \u0026lsquo;Normal W:H ratio and No cutaneous signs\u0026rsquo; and \u0026lsquo;Elevated W:H ratio and cutaneous sign present\u0026rsquo; groups using t-test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eNormal W:H\u0026thinsp;+\u0026thinsp;No cutaneous sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (variance)\u003c/p\u003e \u003cp\u003eElevated W:H ratio\u0026thinsp;+\u0026thinsp;cutaneous sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP \u0026ndash;Value\u003c/p\u003e \u003cp\u003e(T- statistics)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.55(9.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.18(7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(-3.03)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.67(1408.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e130.31(2920.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07(-1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT. Cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141.59(1543.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181.58(1540.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(-3.45)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.29(55.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.37(60.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04(-1.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.83(1099.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107.33(1108.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(-2.88)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVLDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.66(58.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.42(123.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05(-1.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Glucose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.33(1891.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191.90(4880.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00(-5.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsulin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.69(21.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.49(22.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44(0.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA-R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.41(0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.06(6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01(-2.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHOMA- B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.41(126.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.74(189.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02(-2.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisceral Fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147.19(390.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e162.32(820.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24(-0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubcutaneous fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128.58(407.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e102.86(221.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.04(2.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEctopic liver fat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.22(2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.85(9.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38(-0.35)\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=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eGene Expression Microarray Analysis\u003c/h2\u003e \u003cp\u003eLimma analysis identified 205 DEGs in subcutaneous fat, 332 DEGs in femoral fat, and 818 DEGs in visceral depot (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Number of DEGs may indicate the relative significance of cutaneous signs. WebGstalt analysis further reports KEGG pathways enriched by these DEGs (See Supplementary File).\u003c/p\u003e \u003cp\u003eIn subcutaneous and visceral depots, various inflammation-related pathways were found enriched: \u0026lsquo;\u003cem\u003eLeukocytetrans-endothelial migration\u0026rsquo;, \u0026lsquo;Chemokine signaling pathway\u0026rsquo;, \u0026lsquo;Phagosome\u0026rsquo;\u003c/em\u003e, and\u003cem\u003e\u0026lsquo;Osteoclast differentiation\u0026rsquo;\u003c/em\u003e, indicating adipose pathology. We also observed various infection (\u0026lsquo;\u003cem\u003eStaphylococcus aureus infection\u0026rsquo;, \u0026lsquo;Tuberculosis\u0026rsquo;, \u0026lsquo;Amoebiasis\u0026rsquo;, \u0026lsquo;Leishmaniasis\u003c/em\u003e\u0026rsquo; etc.) and immune system-related pathways (\u0026lsquo;\u003cem\u003eType I diabetes mellitus\u0026rsquo;,\u0026rsquo;Rheumatoidarthritis\u0026rsquo;,\u0026rsquo;Graft-versus-host diseases\u003c/em\u003e\u0026rsquo;) which support our view. Peripheral fat mainly enriched some metabolic and cancer-related pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA Analysis\u003c/h2\u003e \u003cp\u003eIn visceral fat, three modules brown, blue, and grey have shown significant negative correlation and one module \u0026ndash; turquoise shown positive correlation with AC and MetS (shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Directionality of correlation in these four modules indicate that both these traits are indeed associative with each other.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe therefore assume, gene-expression in visceral fat is indeed the probable denominator of MetS and somehow links occurrence of skin-tags with clinical diseases. We also gone through the pathway enrichment analysis of statistically significant modules for each depot and this analysis is presented in supplementary file.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe cutaneous signs of insulin resistance and the anthropometric measures of obesity, particularly those assessing central obesity are well known for their association with high insulin resistance. They also cluster with MetS and its constituent diseases. However, from the clinical practice point of view the most pertinent question is whether these physical signs can be used for the bedside prediction of MetS in the Asian Indians who show thin fat phenotype. The findings of the present study support the view that presence of any cutaneous sign of insulin resistance in an individual with higher waist to hip ratio (CO-CSIR) is the most sensitive and the best physical sign for the prediction of MetS among the several parameters investigated in this study including the BMI and waist circumference. Though specificity of CO-CSIR in predicting MetS is moderate, but the non-MetS individuals positive for CO-CSIR had high insulin resistance, impaired pancreatic beta cell function, dyslipidemia and hyperglycemia. They also had higher prevalence of T2D and other clustering diseases of MetS. Moreover, the modules of co-expressed genes in the visceral adipose tissue showing association with MetS also showed an association with AC. In other words, both AC and MetS share similar qualitative molecular traits in visceral adipose tissue. Taken together, these findings suggest that CO-CSIR is not only a sensitive physical sign of MetS, but even among those who do not meet the clinical criteria of MetS, it reflectspresence of pathologic adipose tissue and the consequent pathophysiological mechanisms and pathways of MetS. Therefore, CO-CSIR could also serve as a physical sign of \u0026ldquo;adiposopathy\u0026rdquo; even among those who does not meet the diagnostic criteria of MetS.\u003c/p\u003e \u003cp\u003eSeveral previous studies from India have investigated association between these cutaneous signs of IR in isolation with different facets of MetS like anthropometric measures of obesity (i.e., BMI [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], waist circumference [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], W:H ratio), HOMA-IR [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], lipid parameters, diabetes and other diseases clustering with MetS [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. They all find an association between these cutaneous signs and the facets of MetS they investigated. To the best of our knowledge, no previous study has compared both the cutaneous signs togetherfor their association with HOMA-IR or MetS. We observed here that AC as compared to AN was found to be associated with higher degree of insulin resistance, more ectopic fat. Thus, suggesting that AC indicates more severe degree of the pathophysiological mechanisms of MetS. Moreover, as mentioned previously it is AC, not the AN which showed association with both MetS and molecular qualitative traits of adiposopathy in visceral adipose tissue.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, the concept of CO-CSIR, the bedside physical sign of MetS and adipose tissue centric dysmetabolism (otherwise so called adiposopathy) presented in this study has not been reported in any previous study from India. Does these two clinical signs, \u003cem\u003ei.e.\u003c/em\u003e, W; H ratio and AC / AN represent two different facets of the pathophysiological mechanism of MetS or else, cannot be answered by this cross-sectional study. However, results of present study support the notion that presence of cutaneous signs of IR in individuals with central obesity is associated with higher degree of dysfunction in several facets of adipose tissue pathology (adiposopathy) driven mechanisms of MetS, i.e. ectopic fat, HOMA-IR, beta cell dysfunction, hyperglycemia etc. as well as higher degree of W:H ratio itself. In other words, CO-CSIR reflects higher degree of adiposopathy as compared increase W:H ratio alone. The other facet of the coin is that, while keeping apart these potential adipose centric pathophysiological mechanisms of MetS, what is more important clinically is that both of these physical signs can be easily measured bed side and in general practice. However, there are several limitations of the present study that it is a cross sectional and single hospital based cross sectional study. Therefore, there is need for large scale prospective studies. Moreover, molecular insight into the shared mechanisms and pathways between these physical signs and clustering diseases would be of paramount importance in establishing CO-CSIR as physical sign of MetS as well as the adipose dysfunction driven dysmetabolism (so called adiposopathy).\u003c/p\u003e \u003cp\u003eMetS has been a subject of controversy, both in terms of its utility as CV risk predictor as well as the precise cut off values of the parameters of its clustering diseases and anthropometric measurements in its diagnosis. Secondly, despite so much of research in this field, is MetS still remains away from a diagnostic entity in common prescription. We have presented here the concept of a \u0026ldquo;physical sign\u0026rdquo;, i.e., CO-CSIR for the bed side sign for recognition of not only the cluster of diseases, that otherwise constitutes the MetS, but also adipose tissue dysfunction and the consequent pathophysiologic mechanisms of MetS like HOMA-IR, ectopic fat, dyslipidemia as well as impaired beta cell function (i.e., adiposopathy). In the light of the concept that adiposopathy as a complex genetic disease with MetS as its clinical manifestation, CO-CSIR could potentially be as a physical sign of this disease. However, an important question still remains unanswered that in non-MetS subjects the CO-CSIR represent early stage in its natural history or incompletely developed MetS? This question cannot be answered by this cross-sectional study. Therefore, there is need of longitudinal studies to understand the prognostic clinical importance of non-MetS CO-CSIR condition.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eThis is a single center clinical based study, not community-based study. We recruited patient as patient having diabetes and non-diabetic and did not include patient of impaired glucose intolerance. As metabolic syndrome is not routine finding a place in common prescription. Hence it is not possible to recruit patient as having metabolic syndrome and not having metabolic syndrome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePresence of cutaneous sign of insulin resistance in a individual with central obesity (CO-CSIR) is a sensitive physical sign of metabolic syndrome in Asian Indian, though its specificity is moderate but this is associated with underlying pathophysiological mechanism of metabolic syndrome like insulin resistance ectopic fat deposition, hyperglycemia, hyperinsulinemia etc. Hence CO-CSIR seems to be very potential physical sign of adipose tissue driven dysmetabolism so called adiposopathy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinancial grant received from the Indian Council of Medical Research, Government of India\u003c/p\u003e\n\u003cp\u003eis gratefully acknowledged (grant No. 5/4/8/2012-RMC). We gratefully acknowledge the Research Society for the Study of Diabetes in India (RSSDI) for providing partial funding and also acknowledge Metabolism and Molecular Research Society (MMRS), Jaipur and Principal, SMS Medical College, and hospital, Jaipur (Rajasthan) India for providing necessary infrastructural facility to carry out this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatement of Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional ethics committee of SMS Medical College, Jaipur approved the study. Written informed consent was obtained from participants (or their parent/legal guardian) before participation in this study following the Institutional ethics committee guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by the Metabolic and Molecular Research Society (MMRS) (Jaipur) and Indian Council of Medical Research (ICMR), Government of India through grant no. 5/4/8/2012-RMC and Research Society for the Study of Diabetes in India (RSSDI) grant no. RSSDI/HQ/Grants/2018/4647.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSKM, AG were involved in the clinical diagnosis and management of the patient. AG and PT were involved in biochemical characterization of the samples. \u0026nbsp;AG, RM, SG and RKL were involved in collecting samples. AS performed the bioinformatics analysis. PT and AS were involved in data analysis. AG and SKM wrote the initial draft. SKM. was involved in leading the investigations, recruiting the cohorts, and conceptualization of the project. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOriginal data generated and analyzed for the manuscript are included in this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eEnas EA, Mohan V, Deepa M, Farooq S, Pazhoor S, Chennikkara H. The metabolic syndrome and dyslipidemia among Asian Indians: A population with high rates of diabetes and premature coronary artery disease. 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Ann Med Health Sci Res. 2014;4(2):202\u0026ndash;5\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acanthosis nigricans, Acrochordon, Asian Indians, Central obesity, Insulin Resistance, Metabolic Syndrome","lastPublishedDoi":"10.21203/rs.3.rs-4340896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4340896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim and objectives\u003c/strong\u003e: To investigate cutaneous sign of insulin resistance, acanthosis nigricans (AN) and acrochordon (AC) in individual of central obesity (CO-CSIR) as a physical sign for prediction of metabolic syndrome (MetS) and the underlying adipose tissue pathology and the consequent pathophysiological trait in Asian Indians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eDesign: Single center cross sectional study. Study subjects: 371 (aged 51.7±12.4; M: F ratio 210:161). Following parameters were investigated: Physical signs: cutaneous signs of insulin resistance, BMI, WC, HC, WHR, blood pressure. Biochemical parameters: FBG, lipid profile, HbA1c, HOMA-β. HOMA-IR. Radiological parameters: Abdominal visceral, subcutaneous and ectopic liver fat by MRI. Molecular Parameters: Genome wide transcription profile of adipose tissue biopsies in 85 individuals undergoing surgery for other indications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAN, AC and both were present respectively 50(13.3%), 27(7.2%) and 75(20.2%) individuals and they absent 216 individuals. Presence of AN and AC were associated with significantly higher BMI (6.4 X10\u003csup\u003e-5\u003c/sup\u003e), W:H ratio (0.04), WC (9.5 X 10\u003csup\u003e-7\u003c/sup\u003e), HOMA-IR (0.0002), glucose (1.11 x 10\u003csup\u003e-10\u003c/sup\u003e) and prevalence of T2D (100%) and MetS (83%). AC as compared to AN was associated with more ectopic fat and higher IR.CO-CSIR was found to be the best physical sign of MetS (94.8% sensitivity,57.5 % specificity, 86.4 precision with 95.1F1 score). MetS negative CO-CSIR individuals show high IR, ectopic fat deposition, hyperglycemia and prevalence of T2D.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e CO-CSIR a promising physical sign of MetS and the underlying adipose tissue driven dysmetabolism in Asian Indians.\u003c/p\u003e","manuscriptTitle":"Presence of Cutaneous Signs of Insulin Resistance with Central Obesity (CO-CSIR) in Asian Indians is a sensitive physical sign of Metabolic Syndrome (MetS)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-07 19:12:18","doi":"10.21203/rs.3.rs-4340896/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"19f48836-9fac-4528-8d32-4b96df08fd8d","owner":[],"postedDate":"May 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-27T06:39:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-07 19:12:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4340896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4340896","identity":"rs-4340896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}