Better Predictor Of Insulin Resistance Research Articles

Abdominal Obesity in Women with Polycystic Ovary Syndrome and Its Relationship with Diet, Physical Activity and Insulin Resistance: A Pilot Study.

Abdominal obesity is a common feature of women with polycystic ovary syndrome (PCOS), and it is known to exacerbate insulin resistance (IR). Improper dietary and physical activity patterns are crucial environmental factors involved in the development of obesity, and they can significantly influence the central deposition of adipose tissue. Therefore, in this cross-sectional study, we aimed to evaluate the relationship between abdominal adiposity (measured by VAT (visceral adipose tissue), SAT (subcutaneous adipose tissue), VAT/SAT ratio (visceral to subcutaneous fat ratio), and WHR (waist-to-hip ratio)) and the prevalence and odds ratios of IR (measured by the homeostatic model assessment of insulin resistance (HOMA-IR), the homeostatic model assessment-adiponectin (HOMA-AD) and leptin to adiponectin ratio (L/A ratio)) in 56 PCOS women. Furthermore, we investigated the relationship between these abdominal obesity indices and diet and physical activity. An original food frequency questionnaire and Actigraph GT3X-BT were used to assess adherence to the diet recommended in IR and the level of physical activity, respectively. We observed a higher prevalence of IR among women with higher VAT, VAT/SAT, and WHR values compared to women with normal values of those abdominal obesity indices. Moreover, VAT/SAT seemed to be the best predictor of IR measured by HOMA-IR and HOMA-AD. However, VAT appeared to be the best and strongest predictor of IR measured by the L/A ratio. We also observed that higher adherence to the diet recommended in IR and higher levels of vigorous physical activity were associated with lower values of central fat accumulation indices and a greater chance of their normal values. Our findings indicate that central obesity increases the odds of IR and supports the beneficial role of diet and physical activity in the management of abdominal obesity in PCOS women.

Lipid accumulation product and visceral adiposity index: two indices to predict metabolic syndrome and insulin resistance in chronic kidney disease patients.

Objective. Chronic kidney disease (CKD), metabolic syndrome (MetS) and insulin resistance (IR) are the major health problems associated with the increasing risk of cardiovascular and cerebrovascular complications. Methods. This cross-sectional study included 209 CKD patients of stage (3-5) on conservative treatment to assess the usage of lipid accumulation product (LAP) and visceral adiposity index (VAI) to predict both MetS and IR in CKD patients. Results. In males, from the anthropometric measurements, LAP was the best predictor of MetS with 94.4% sensitivity and 77.8% specificity. VAI was the next one with 83.3% sensitivity and 69.4% specificity. The same results were obtained in females. The receiver operating characteristic (ROC) curve showed LAP as the best predictor of MetS with the highest 92.6% sensitivity and 60.6% specificity followed by VAI with 83.6% sensitivity and 83.6% specificity. In addition, LAP was a good predictor of IR with more than 70% sensitivity in both males and females. VAI as a predictor of IR showed 62.2% sensitivity in males and 69.9% in females. Conclusion. The present data indicate that both LAP and VAI can serve as predictors of MetS and IR in CKD patients, whereas LAP is the best anthropometric measure to predict MetS and LAP is more sensitive and specific than VAI in IR predicting in both males and females.

Relationship of Body Mass Index and Waist Hip Ratio with Insulin Resistance in Polycystic Ovarian Syndrome Patients

Background: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy among young women. Insulin resistance is a key feature in the pathogenesis of PCOS. Obesity, which is frequently associated with PCOS women, seems to amplify the degree of insulin resistance. The aim of the study is to investigate the relationship of insulin resistance with obesity in PCOS patients.
 Materials and methods: This was a hospital based cross-sectional study comprising fifty (50) diagnosed PCOS patients aged between 20 and 40 years. This study was carried out in the Department of Biochemistry, Chittagong Medical College (CMC) and inpatients, outpatient Department of Gynaecology and Obstetrics, Chittagong Medical College Hospital (CMCH). Samples were taken by non-probability purposive sampling. Results: The percentage of Insulin Resistance (IR) was 60% in this study. Mean age of PCOS patients was 30.6±0.65years. Obese PCOS patients with insulin resistance were 46% and overweight PCOS patients with IR were 48% in this study. There were also significant positive association of IR with increased BMI and waist hip ratio. Additionally 06% non-obese PCOS patients showed IR in this study cases.
 Conclusions: This study suggested that abdominal obesity was a good predictor of IR among PCOS patients. So, waist circumference or waist hip ratio may be used as a screening tool for IR risk assessment among PCOS patients as an inexpensive, noninvasive and easy-to-detect marker. Hence early diagnosis and proper preventive management of these patients will reduce the reproductive complications.
 IAHS Medical Journal Vol 4(2), December 2021; 3-7

Neck circumference is a better correlate of insulin resistance markers than other standard anthropometric indices in patients presenting severe obesity

BackgroundPrevious studies have demonstrated stronger associations between metabolic alterations and neck circumference (NC) than with body mass index (BMI) or waist circumference (WC). However, most of these studies were performed in individuals presenting overweight or mild obesity. ObjectiveTo determine which adiposity index among BMI, WC, NC and fat mass (FM) can best predict metabolic alterations in men and women presenting severe obesity. MethodsAnthropometric and plasma biochemical parameters were measured in 81 participants presenting severe obesity (19 men, 62 women; age: 44.5 ± 8.9 years; BMI: 43.5 ± 4.1 kg/m2). Multiple linear regressions were used to determine the best predictors of metabolic alterations among each adiposity index. ResultsNC was positively correlated with fasting insulin concentrations, C-peptide concentrations and HOMA-IR values and negatively correlated with HDL-C concentrations. NC was the best predictor of glucose homeostasis indices and HDL-C concentrations in models also including sex, BMI, WC, and FM. The ROC curve analysis indicated that a NC ≥ 37.8 cm best predicted type 2 diabetes. ConclusionsNC seems a better predictor of insulin resistance and lower HDL-C concentrations in patients presenting severe obesity compared to other standard anthropometric indices, and particularly in women. The small sample size in men prevent us to draw clear conclusions. NC could be useful in targeting patients with metabolic alterations who could benefit from medical or surgical treatment of obesity.

Body mass index is a reliable predictor of insulin resistance than body fat percentage

Background: Obesity leads to insulin resistance (IR) which is the core pathophysiology of lifestyle diseases such as Type 2 diabetes mellitus and hypertension. Commonly used indices determining obesity and therefore IR are body mass index (BMI) and body fat percentage (BF%). BMI and BF% are, therefore, predictors of IR. However, there is contention as to which between BMI and BF% is a better predictor of IR. Aims and Objectives: This study aims at finding out which is a better predictor of IR between BMI and BF%. Materials and methods: This is an observational cross-sectional study conducted on 50 urban sedentary adults. After obtaining informed consent for the study, fasting blood sample was collected from selected participants for estimating fasting blood glucose and plasma insulin which are useful in deriving homeostatic model assessment IR for IR. This was followed by the measuring of anthropometric data from which BMI was derived. BF% was measured by dual X-ray Absorbsiometry. Results: Analysis and comparison of variables revealed a meaningful relationship between BMI and IR than with BF%. Pearson’s correlation also demonstrated a stronger association between BMI and IR than with BF%. Conclusion: BMI is a better and a reliable predictor of IR than BF%.

Anthropometric features in predicting insulin resistance among non-menopausal Indonesian adult females.

The prevalence of obesity is increasing worldwide in high, low, and middle-income countries such as Indonesia. Obesity rate is higher in females in Indonesia. Obesity has important contribution in the occurrence of insulin resistance (IR) and type 2 diabetes mellitus. Several anthropometric measurements such as waist circumference (WC), body mass index (BMI), body mass (BM), total body fat percentage (Fat%) and visceral fat (VF) are related to IR. This study aimed to investigate which of those measurements could be used as a better predictor of IR in non-menopausal Indonesian adult females. Total of 80 non-menopausal Indonesian adult females ranging from 21 to 40 years were recruited in this study. Insulin resistance was measured by using Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) equation. Subjects with HOMA-IR index >75th percentile with cut-off 2.74 were defined as IR. Waist circumference, BMI and BM were measured, while TF and VF were measured by bioelectrical impedance analysis (BIA). HOMA-IR had significant correlation with WC (r = 0.563, p < 0.001), BMI (r = 0.537, p < 0.001), BM (r = 0.515, p < 0.001), VF (r = 0.515, p < 0.001), Fat% (r = 0.490, p < 0.001). The area under curve of VF (0.809), BMI (0.807), WC (0.805), and BM (0.799) are slightly larger than and Fat% (0.766). Insulin resistance had strong correlation with all anthropometric measurements, but the correlation was less significant with Fat%.

Wrist circumference: A new marker for insulin resistance in African women with polycystic ovary syndrome.

BACKGROUNDInsulin resistance (IR) is the main complication found in 35%-80% of women with polycystic ovary syndrome (PCOS). However, there is no definite consensus regarding which marker to use for its assessment in PCOS women. Research has shown that hyperinsulinemia is correlated with increased bone mass. Given that most women with PCOS are insulin resistant, which is independent from body fat and characterized by hyperinsulinemia, it could be hypothesized that there would be an increased bone mass in the patient as a result. Subsequently, increased bone mass could be measured using the wrist circumference method.AIMTo assess the wrist circumference as an easy-to-detect marker of IR in Congolese women with PCOS.METHODSSeventy-two Congolese women with PCOS and seventy-one controls from the same ethnic group, were enrolled in the study (mean age 24.33 ± 5.36 years). Fasting biochemical parameters, and the Homeostasis Model Assessment of insulin resistance (HOMA-IR) and body composition were evaluated. The non-dominant wrist circumference was measured manually, as was the waist circumference (WC), hip circumference, height and weight. Calculated measures included evaluation of body mass index (BMI), Waist-to-Height (WHtR) and Waist-to-hip ratio (WHR). In addition, body composition was assessed by Bioelectrical Impedance Analysis using a body fat analyzer.RESULTSThe non-dominant wrist circumference was more closely correlated with HOMA-IR (r = 0.346; P = 0.003) and was the best anthropometrical marker correlated with IR (P = 0.011 ) compared with other anthropometrical markers in women with PCOS: Dominant Wrist Circumference (r = 0.315; P = 0.007), Waist Circumference (WC) (r = 0.259; P = 0.028), BMI (r = 0.285; P = 0.016), WHR (r = 0.216; P = 0,068) and WHtR (r = 0.263; P = 0.027). The diagnostic accuracy of the non-dominant wrist circumference for the presence or absence of IR using Receiver-operating characteristic (ROC) curve analysis showed that the area under the ROC curve was 0.72. A cutoff value for the non-dominant wrist circumference of 16.3 cm was found to be the best predictor of IR in Congolese women with PCOS.CONCLUSIONNon-dominant wrist circumference is, to date, the best anthropometrical marker of IR in Sub-Saharan African women with PCOS. It could be suggested as an easy-to-detect marker for assessing IR.

P114: B lymphocytes may be a good predictor of insulin resistance in women with gestational diabetes mellitus

P114: B lymphocytes may be a good predictor of insulin resistance in women with gestational diabetes mellitus

Body Mass, Total Body Fat Percentage, and Visceral Fat Level Predict Insulin Resistance Better Than Waist Circumference and Body Mass Index in Healthy Young Male Adults in Indonesia.

The incidence of obesity which leads to insulin resistance (IR) and metabolic disorder is increasing in developing countries, including Indonesia. Male adults have a higher risk of abdominal obesity than females. This is associated with cardiometabolic disorders. Several anthropometric measurements have been proposed to predict IR. The aim of this study was to investigate whether body mass, body mass index (BMI), waist circumference (WC), body fat percentage (BF) or visceral fat level (VF) could become a better predictor of IR in healthy young male adults. A total of 140 healthy young male adults ranging from 18–25 years were recruited in the study. Insulin resistance was measured by calculating their Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). Subjects with a HOMA-IR value ≥75th percentile, with cut off 3.75, were defined as IR. Anthropometric measurements including body weight, BMI, and WC were performed, whereas BF and VC were measured using bioelectrical impedance analysis (BIA). IR had a strong correlation with body weight, BMI, WC, BF, and VF. In the area under the curve of body mass, BF and VF were slightly greater than WC and BMI. Anthropometric measurements correlated strongly with IR but body weight, BF, VF had a stronger correlation than WC and BMI in healthy young male adults.

Determination of Insulin Resistance and Beta-Cell Function Using Homeostatic Model Assessment in Type 2 Diabetic Patients at Diagnosis

Objective: Despite the increasing incidence of type 2 diabetes mellitus (T2DM) in South Asian countries, there have been no studies related to insulin resistance (IR) and beta-cell function (BCF) in Nepal. Measurement of both of these parameters at T2DM diagnosis can be a potential tool in evaluation, risk stratification and monitoring treatment. In this study, we used C-peptide modified homeostatic model assessment (both HOMA1 and HOMA2) and correlated the obtained IR with cardiovascular risk factors. We also intended to find out whether reduced insulin sensitivity or beta-cell failure predominates in new T2DM cases. Also there has been some dilemma in using either body mass index (BMI) or waist-hip ratio (WHR) as a better predictor of IR in our population. Lipoprotein ratios TC/ HDL and TG/HDL also needed evaluation in this regard.Results: Participants were sixty newly diagnosed T2DM patients visiting Tribhuvan University Teaching Hospital (TUTH), Nepal. The mean IR and beta-cell function were HOMA1IR=4.91 ± 1.62; HOMA2IR=2.61 ± 1.06 and HOMA1%B=40.28 ± 23.64; HOMA2%B=47.10 ± 24.67 respectively. Both HOMA1 and HOMA2 showed greater reduction in insulin sensitivity than beta-cell function at diagnosis. ROC curves analysis showed WHR and TC/HDL ratio as better predictors of IR.

Assessment of regional body fat and body mass index with glycemic status in the middle-aged offspring of type 2 diabetic parents - A cross-sectional study

Background: Genetic predisposition is a key factor in developing insulin resistance in prone individuals. Sedentary lifestyle, lack of regular physical exercise, excessive intake of junk foods, and stress are proved to be important factor in the development of obesity. Assessing occurrence of insulin resistance earlier, an important component of metabolic syndrome can prevent the morbidity in the subjects. Aims and Objectives: This study aims to correlate body mass index (BMI), visceral fat, and subcutaneous fat individually with glycemic status of the subject and assess the better predictor of insulin resistance. Materials and Methods: Detailed questionnaire concerned with the study and informed consent was obtained from the subjects. The study included 100 middle-aged non-diabetic offspring of diabetic parents. The mean age of diabetic subjects was 39.4 years. Age - 35–45 years, subjects with BMI in the range between 18.5 and 30 kg/m2 were included in the study while diabetics, hypertensives, alcoholics, smokers, and subjects with any other recent illness were excluded from the study. Visceral fat and subcutaneous fat were measured using BC-601 TANITA bioimpedance analyzer. Fasting blood sugar was measured using single prick glucometer technique. Results: Pearson correlation was applied. Fasting blood sugar showed significant positive correlation with visceral fat and subcutaneous fat with r value of + 0.329 and + 0.464, respectively. Fasting blood sugar did not correlate with BMI and has an r value of + 0.050 only. Conclusion: Visceral fat and subcutaneous fat remain a better predictor than BMI in monitoring the preponderance to insulin resistance.

Body mass index and waist circumference are better predictors of insulin resistance than total body fat percentage in middle-aged and elderly Taiwanese.

The incidence of diabetes mellitus is rising worldwide, and prediabetic screening for insulin resistance (IR) has become ever more essential. This study aimed to investigate whether body mass index (BMI), waist circumference (WC), or body fat percentage (BF%) could be a better predictor of IR in a middle-aged and elderly population. In this cross-sectional, community-based study, 394 individuals (97 with IR and 297 without IR) were enrolled in the analysis. IR was measured by homeostasis model assessment (HOMA-IR), and subjects with HOMA-IR value ≧75th percentile were defined as being IR. Associations between IR and BMI, WC and BF% were evaluated by t test, chi square, Pearson correlation, logistic regression, and receiver operating characteristic (ROC) curves. A total of 394 community-dwelling, middle-aged, and elderly persons were enrolled; 138 (35%) were male, and 256 were female (65%). The mean age was 64.41 ± 8.46 years. A significant association was identified between BMI, WC, BF%, and IR, with Pearson correlation coefficients of 0.437 (P < .001), 0.412 (P < .001), and 0.361 (P < .001), respectively. Multivariate logistic regression revealed BMI (OR = 1.31; 95% CI = 1.20–1.42), WC (OR = 1.13; 95% CI = 1.08–1.17), and BF% (OR = 1.17; 95% CI = 1.11–1.23) to be independent predictors of IR. The area under curves of BMI and WC, 0.749 and 0.745 respectively, are greater than that of BF% 0.687. BMI and WC were more strongly associated with IR than was BF%. Excess body weight and body fat distribution were more important than total body fat in predicting IR.

Could “a body shape index” and “waist to height ratio” predict insulin resistance and metabolic syndrome in polycystic ovary syndrome?

ObjectiveTo investigate whether a body shape index (ABSI) and waist to height ratio (WHtR) could predict insulin resistance (IR) and metabolic syndrome (MetS) in women with polycystic ovary syndrome (PCOS) compared to healthy women. Study designIn a population-based study a cohort of 754 reproductive-aged women including 704 eumenorrheic non-hirsute subjects and 50 PCOS women selected according to the national institutes of health’s (NIH) criteria. The ability of ABSI and WHtR for the prediction of IR was estimated by the homeostasis model and metabolic syndrome according to the joint interim statement criteria. ResultsAge and BMI adjusted prevalence of IR and MetS in PCOS women vs. healthy controls were 34% vs. 26%, P=0.041 and 15% vs. 14%, P=0.917, respectively. Mean (SD) of ABSI in PCOS women and healthy women were 0.76 (0.05) and 0.76 (0.053), respectively (P=0.363). The area under curve (CI 95%) of WHtR for predicting IR and MetS among PCOS women vs. healthy women were 0.751 (0.60–0.89) vs. 0.69 (0.65–0.73) and 0.902 (0.81–0.98) vs. 0.802 (0.76–0.83), respectively. As such, the area under curve (CI 95%) of ABSI for ROC curve analysis for predicting IR and MetS among PCOS women vs. healthy women were 0.482 (0.31–0.64) vs. 0.537 (0.49–0.58) and 0.538 (0.35–0.72) vs. 0.584 (0.60–0.69), respectively. ConclusionsThese findings suggested that WHtR but not ABSI were a good predictor of IR and MetS among PCOS and healthy women. WHtR may be proposed as a screening tool for IR and MetS risk assessment among PCOS women as a sensitive, inexpensive, noninvasive, simple to assess and easy to calculate measurement tools.

Concentrations of omentin and vaspin versus insulin resistance in obese individuals.

Omentin and vaspin are adipokines manifesting a potentially protective action against obesity-associated metabolic disturbances. Evaluation of relationship between serum concentrations of omentin and vaspin on one hand and indices of insulin resistance and anthropometric parameters in obese individuals on the other. The studies were conducted on 64 individuals. The investigated group (37 obese patients) included the subgroup with normal glucose tolerance (NGT) and with abnormal glucose tolerance (AGT). The control group (n=27) included healthy individuals with normal body weight. In all participants anthropometric analyses and biochemical tests, including estimation of omentin and vaspin concentrations were performed, and insulin resistance by HOMA-IR was evaluated. Concentrations of examined adipokines manifested no significant differences between the examined groups. Median values of the index defining ratio between studied adipokine and degree of insulin resistance, i.e. omentin/HOMA-IR, proved to be different in the investigated and the control group while no such difference could be noted in cases of vaspin/HOMA-IR indices. In the studied population a negative relationship was detected between serum concentration of omentin and systolic blood pressure (p<0.04). Values of omentin/HOMA-IR index manifested a correlation with values of most anthropometric parameters (p<0.0001), blood pressure (p<0.0001) concentrations of TG (p<000.1) and HDL (p<0.0001), ISIbasal (p<0.00001), ISIgly (p<0.0001), Quicki (p<0.00001) and fasting insulinaemia (p<0.00001). In the case of vaspin/HOMA-IR index only its positive relationship with HDL concentration was noted (p<0.05). In context of date of correlation, multiple regression and values of area of under receiver operating characteristics curve omentin, as compared to vaspin, seems to provide a better predictor of insulin resistance in obese individuals.

Relationship of Early Pregnancy Waist-to-Hip Ratio versus Body Mass Index with Gestational Diabetes Mellitus and Insulin Resistance.

To determine the risk of gestational diabetes mellitus (GDM) and insulin resistance (IR) in obesity defined by body mass index (BMI), waist-to-hip ratio (WHR), or both combined. Secondary analysis of a randomized multicenter trial of antioxidant supplementation versus placebo in nulliparous low-risk women to prevent pregnancy associated hypertension. Women between 9 and 16 weeks with data for WHR and BMI were analyzed for GDM (n = 2,300). Those with fasting glucose and insulin between 22 and 26 weeks (n = 717) were analyzed for IR by homeostatic model assessment of IR (normal, ≤ 75th percentile). WHR and BMI were categorized as normal (WHR, < 0.80; BMI, < 25 kg/m(2)); overweight (WHR, 0.8-0.84; BMI, 25-29.9 kg/m(2)); and obese (WHR, ≥ 0.85; BMI ≥ 30 kg/m(2)). Receiver operating characteristic curves and logistic regression models were used. Compared with normal, the risks of GDM or IR were higher in obese by BMI or WHR. The subgroup with obesity by WHR but not by BMI had no increased risk of GDM. BMI was a better predictor of IR (area under the curve [AUC]: 0.71 [BMI], 0.65 [WHR], p = 0.03) but similar to WHR for GDM (AUC: 0.68 [BMI], 0.63 [WHR], p = 0.18). Increased WHR and BMI in early pregnancy are associated with IR and GDM. BMI is a better predictor of IR compared with WHR. Adding WHR to BMI does not improve its ability to detect GDM or IR.

Relationship between leptin and body mass and metabolic syndrome in an adult population

ObjectiveTo analyze the relationship between leptin and obesity expressed as body mass index (BMI) and certain components of the metabolic syndrome (MS) in an adult population. MethodsThe study included 103 subjects, 42 men and 61 women, aged over 30 years, clinically defined as non-diabetic but with personal or family history of cardiovascular disease. All subjects underwent fasting blood measurement of leptin, insulin, glucose, glucose after ingestion of 75g glucose, HDL cholesterol and triglycerides, and insulin resistance (IR) and BMI were calculated. ResultsBMI as an index of overall adiposity was strongly associated with serum leptin. BMI rose as serum leptin levels increased from the first to the third tertile; the correlation between leptin and BMI was strong, r=0.524 in men and r=0.603 in women, with high statistical significance (p<0.001); BMI was the best predictor of hyperleptinemia on ROC analysis, with area under the curve (AUC)=0.81 in men and 0.84 in women.The association between leptin and obesity (BMI≥30kg/m2) showed high odds ratios (OR) in both sexes (10.11 in men, 6.00 in women) on univariate regression analysis and 9.30 in men and 8.21 in women on multivariate regression analysis.Hyperinsulinemia and IR strongly influenced hyperleptinemia. Leptin was the best predictor of IR in both sexes (AUC=0.89 in men and 0.85 in women), and IR in men (AUC=0.79) and hyperinsulinemia in women (AUC=0.78) were the best predictors of hyperleptinemia after BMI. The correlations between leptin and IR, and leptin and insulinemia, were strong in both sexes.With regard to MS components, increased serum levels of the study variables were observed as leptin concentrations rose from the first to the third tertile (with the exception of HDL cholesterol, which decreased). ConclusionElevated serum leptin, particularly in obese individuals, should be taken as a warning sign of energy imbalance, poor diet, hyperinsulinemia, insulin resistance, or changes in other metabolic risk factors that are strongly associated with cardiovascular disease and type 2 diabetes.

Relação entre a leptina, a massa corporal e a síndrome metabólica numa amostra da população adulta

ObjectiveTo analyze the relationship between leptin and obesity expressed as body mass index (BMI) and certain components of the metabolic syndrome (MS) in an adult population. MethodsThe study included 103 subjects, 42 men and 61 women, aged over 30 years, clinically defined as non-diabetic but with personal or family history of cardiovascular disease. All subjects underwent fasting blood measurements of leptin, insulin, glucose, glucose after ingestion of 75g glucose, HDL cholesterol and triglycerides, and insulin resistance (IR) and BMI were calculated. ResultsBMI as an index of overall adiposity was strongly associated with serum leptin. BMI rose as serum leptin levels increased from the first to the third tertile; the correlation between leptin and BMI was strong, r=0.524 in men and r=0.603 in women, with high statistical significance (p<0.001); BMI was the best predictor of hyperleptinemia on ROC analysis, with area under the curve (AUC)=0.81 in men and 0.84 in women.The association between leptin and obesity (BMI ≥30kg/m2) showed high odds ratios (OR) in both sexes (10.11 in men, 6.00 in women) on univariate regression analysis and 9.30 in men and 8.21 in women on multivariate regression analysis.Hyperinsulinemia and IR strongly influenced hyperleptinemia. Leptin was the best predictor of IR in both sexes (AUC=0.89 in men and 0.85 in women), and IR in men (AUC=0.79) and hyperinsulinemia in women (AUC=0.78) were the best predictors of hyperleptinemia after BMI. The correlations between leptin and IR, and leptin and insulinemia, were strong in both sexes.With regard to MS components, increased serum levels of the study variables were observed as leptin concentrations rose from the first to the third tertile (with the exception of HDL cholesterol, which decreased). ConclusionElevated serum leptin, particularly in obese individuals, should be taken as a warning sign of energy imbalance, poor diet, hyperinsulinemia, insulin resistance, or changes in other metabolic risk factors that are strongly associated with cardiovascular disease and type 2 diabetes.

Vitamin D as a predictor of insulin resistance in Polycystic Ovarian Syndrome

IntroductionPolycystic Ovary Syndrome (PCOS) is the most common endocrinological disorder in women in the reproductive age group. The salient features of this condition include hyperandrogenic features, infertility and insulin resistance among others. Mechanisms behind these features are a matter of debate. Vitamin D has been implicated lately in the etiology of many disorders. The aim of our study was to assess the role of vitamin D as an etiological and predictive factor in PCOS. Materials and methodsThe study comprised 60 proven cases of PCOS diagnosed on the basis of Rotterdam criteria. The parameters assessed include HOMA-IR, vitamin D besides the routine anthropometric and biochemical parameters. ResultsThe study population was divided into 3 groups according to vitamin D status. Insulin resistance was most severe in the sub group with vitamin D deficiency. Multiple regression analysis established the role of vitamin D as the best predictor of insulin resistance in our study. ConclusionVitamin D has an important role in the pathogenesis of insulin resistance in PCOS.

Waist circumference is a better predictor than body mass index of insulin resistance in type 2 diabetes

Summay: Insulin resistance is an important pathogenic factor in type 2 diabetes patents. An easy and efficiency measurement predicting insulin resistance; which can be done easily by type 2 patients is desired. To examine whether waist circumference is a better predictor of insulin resistance in type 2 diabetes than body mass index (BMI). From a population of 1356 registered diabetic patients, 144 who met (1) aged between 30 and 75 years, (2) being Chinese, (3) having had type 2 diabetes for more than one year, and (4) having been taking gliclazide and metformin for more than 6 months were enrolled in this study. The main outcome evaluated is the associations of HOMA insulin resistance index (HOMA index); which were assessed using multiple linear regression analysis. The coefficients of multiple regression analysis with stepwise model showed that waist circumference (β = 0.35, p < 0.001) but not BMI (β = 0.01, p = 0.94), adiponectin (β = -0.25, p = 0.04) and hemoglobulin A1c% (HbA1c) (β = 0.25, p = 0.01) were the main predictors of HOMA index. These initial findings indicate that waist circumference is a better predictor of insulin resistance in type 2 diabetes than BMI.

Cellular mechanism of insulin resistance in nonalcoholic fatty liver disease

Insulin resistance is associated with nonalcoholic fatty liver disease (NAFLD) and is a major factor in the pathogenesis of type 2 diabetes. The development of hepatic insulin resistance has been ascribed to multiple causes, including inflammation, endoplasmic reticulum (ER) stress, and accumulation of hepatocellular lipids in animal models of NAFLD. However, it is unknown whether these same cellular mechanisms link insulin resistance to hepatic steatosis in humans. To examine the cellular mechanisms that link hepatic steatosis to insulin resistance, we comprehensively assessed each of these pathways by using flash-frozen liver biopsies obtained from 37 obese, nondiabetic individuals and correlating key hepatic and plasma markers of inflammation, ER stress, and lipids with the homeostatic model assessment of insulin resistance index. We found that hepatic diacylglycerol (DAG) content in cytoplasmic lipid droplets was the best predictor of insulin resistance (R = 0.80, P < 0.001), and it was responsible for 64% of the variability in insulin sensitivity. Hepatic DAG content was also strongly correlated with activation of hepatic PKCε (R = 0.67, P < 0.001), which impairs insulin signaling. In contrast, there was no significant association between insulin resistance and other putative lipid metabolites or plasma or hepatic markers of inflammation. ER stress markers were only partly correlated with insulin resistance. In conclusion, these data show that hepatic DAG content in lipid droplets is the best predictor of insulin resistance in humans, and they support the hypothesis that NAFLD-associated hepatic insulin resistance is caused by an increase in hepatic DAG content, which results in activation of PKCε.