Detection Of Insulin Resistance Research Articles

Study Of HbA1c As A Reliable Indicator For Metabolic Syndrome In Non Diabetic Patients

With the metabolic syndrome and diabetes mellitus increase in the recent decade, the importance of early detection of insulin resistance is essential. However, a simple method is not currently available for precise measurements. Therefore the aim of this study was to elucidate the association of HbA 1c with metabolic syndrome as a constellation of cardiovascular risk factors. The study population consisted of 45 subjects with metabolic syndrome and 45 free of metabolic syndrome (control group).Total cholesterol, triglycerides, glucose, HbA 1c , body mass index (BMI), waist circumference (WC), systolic and diastolic blood pressure were measured in both groups. HbA 1c levels are found much more in MS group than the control group, 8,7% and 6,2%, respectively. The sensitivity and specificity of HbA 1c is significantly higher in metabolic syndrome patients, 86,7% and 46,67%, respectively. Additionally, subjects with metabolic syndrome exhibited significantly higher blood glucose, triglyceride, systolic/diastolic blood pressure and cholesterol. Our results suggest that HbA 1c may be a marker for metabolic syndrome and may identify in a certain degree insulin resistance subjects.

Homeostasis model assessment to detect insulin resistance and identify patients at high risk of breast cancer development: National Cancer Institute of Naples experience

BackgroundMetabolic Syndrome (MS) has been correlated to breast carcinogenesis. MS is common in the general population (34%) and increases with age and body mass index. Although the link between obesity, MS and hormone related cancer incidence is now widely recognized, the molecular mechanisms at the basis of such increase are still poorly characterized. A crucial role is supposed to be played by the altered insulin signalling, occurring in obese patients, which fuels cancer cell growth, proliferation and survival. Therefore we focused specifically on insulin resistance to investigate clinically the potential role of insulin in breast carcinogenesis.Methods975 patients were enrolled and the association between MS, insulin resistance, and breast cancer was evaluated. Women were stratified by age and menopausal status. Insulin resistance was measured through the Homeostasis Model Assessment score (HOMA-IR). The cut off value to define insulin resistance was HOMA-IR ≥ 2.50.ResultsHigher prevalence of MS (35%) was found among postmenopausal women with breast cancer compared to postmenopausal healthy women (19%) [OR 2.16]. A broad range of BMI spanning 19–48 Kg/m2 was calculated. Both cases and controls were characterized by BMI ≥ 25 Kg/m2 (58% of cases compared to 61% of controls). Waist circumference >88 cm was measured in 53% of cases - OR 1.58- (95% CI 0.8-2.8) and in 46% of controls. Hyperinsulinemia was detected in 7% of cases – OR 2.14 (95% CI 1.78-2.99) and only in 3% of controls. HOMA-IR score was elevated in 49% of cases compared to 34% of controls [OR 1.86], suggesting that insulin resistance can nearly double the risk of breast cancer development. Interestingly 61% of women operated for breast cancer (cases) with HOMA-IR ≥ 2.5 presented subclinical insulin resistance with fasting plasma glucose levels and fasting plasma insulin levels in the normal range. Both android fat distribution and insulin resistance correlated to MS in the subgroup of postmenopausal women affected by breast cancer.ConclusionsOur results further support the hypothesis that MS, in particular insulin resistance and abdominal fat, can be considered as risk factors for developing breast cancer after menopause. We suggest that HOMA-IR, rather than fasting plasma glucose and fasting plasma insulin levels alone, could be a valuable tool to identify patients with subclinical insulin resistance, which could be relevant for primary prevention and for high risk patient screening.

Insulin resistance, serum insulin and HOMA-R

The authors really appreciate the comments by Tomoyuki Kawada on their paper entitled ‘‘The effect of etanercept on hepatic fibrosis risk in patients with non-alcoholic fatty liver disease, metabolic syndrome and psoriasis’’ [1]. For Kawada [2], HOMA-R and serum insulin show equivalent efficacy in detecting insulin resistance, whereas it may be more difficult to calculate HOMA-R in subjects with increased fasting plasma glucose. Therefore, the homeostasis model assessment for insulin resistance could be substituted simply by serum insulin, with no need for data on plasma glucose [2]. This is a sharable point of view, however, the study reported by the author refers specifically to a wide male population without a current history of treatment for diabetes, hypertension, dyslipidemia, hyperuricemia, coronary, and/or cerebrovascular disease. Thus, when Kawada stated that both HOMA-R and serum insulin are equivalent in detecting insulin resistance, he specifically refers to apparently healthy subjects. However, conversely to population with no remarkable medical conditions considered by Kawada, our case series includes subjects with psoriasis, metabolic syndrome and NAFLD. In our opinion, in such a setting of patients, more than one marker of insulin resistance should be taken into consideration. Regarding the second query, focusing on the use of QUICKI and HOMA indexes in our study, we have to specify that in our retrospective analysis we selected patients with concomitant psoriasis, metabolic syndrome and NAFLD who had received etanercept or PUVA, and did not base our selections exclusively on FPG values. It’s true that in our retrospective analysis, the FPG values of included patients ranged from 73 to 122 mg/dl, but this was simply a matter of chance since we did not preventively exclude patients with FPG [ 140 mg/dl, as speculated by Kawada. Therefore, given FPG values \140 mg/dl, we deliberately decided to use both HOMA and QUICKI [3]. One of the limiting factors of our study was precisely that we did have not patients with FPG [ 140 mg/dl, because of its retrospective nature. Finally, the authors acknowledge the suggestion to directly assess the risk of hepatic fibrosis in further advanced studies.

The Association of the Triglyceride-to-HDL Cholesterol Ratio with Insulin Resistance in White European and South Asian Men and Women

IntroductionThere is recent interest surrounding the use of the triglyceride-to-HDL cholesterol ratio as a surrogate marker of insulin resistance in clinical practice, as it may identify people at high risk of developing diabetes or its complications. However, it has been suggested using this lipid ratio may not be appropriate for measuring insulin resistance in African-Americans, particularly women. We investigated if this inconsistency extended to South Asian women in a UK multi-ethnic cohort of White Europeans and South Asians.MethodsCross-sectional analysis was done of 729 participants from the ADDITION-Leicester study from 2005 to 2009. The association between tertiles of triglyceride-to-HDL cholesterol ratio to fasting insulin, homeostatic model of assessment for insulin resistance (HOMA1-IR), quantitative insulin sensitivity check index (QUICKI) and glucose: insulin ratio was examined with adjustment for confounding variables.ResultsIncremental tertiles of the triglyceride-to-HDL cholesterol ratio demonstrated a significant positive association with levels of fasting insulin, HOMA1-IR, glucose: insulin ratio and a negative association with QUICKI in White European men (n = 255) and women (n = 250) and South Asian men (n = 124) (all p<0.05), but not South Asian women (n = 100). A significant interaction was demonstrated between sex and triglyceride-to-HDL cholesterol ratio tertiles in South Asians only (p<0.05). The area under the receiver operating characteristic curve for triglyceride-to-HDL cholesterol ratio to detect insulin resistance, defined as the cohort HOMA1-IR≥75th percentile (3.08), was 0.74 (0.67 to 0.81), 0.72 (0.65 to 0.79), 0.75 (0.66 to 0.85) and 0.67 (0.56 to 0.78) in White European men and women, South Asian men and women respectively. The optimal cut-points for detecting insulin resistance were 0.9–1.7 in mmol/l (2.0–3.8 in mg/dl) for the triglyceride-to-HDL ratio.ConclusionIn South Asian women the triglyceride-to-HDL cholesterol ratio was not associated with insulin resistance; therefore there may be limitations in its use as a surrogate marker in this group.

Detecting Insulin Resistance in Pakistani Subjects by Fasting Blood Samples

Background: Insulin Resistance has been identified as an independent risk factor for a number of chronic diseases such as diabetes and cardiovascular diseases. Thus identifying insulin resistant cases would help to better prevent the progression of these diseases in such individuals.

Role of surrogate markers in assessment of insulin resistance in females with thyroid disorders

IntroductionThyroid status is known to affect insulin sensitivity although conflicting data have been reported regarding hypothyroid and hyperthyroid states. Assessment of insulin resistance is difficult, therefore we compared homeostasis model assessment (HOMA), glucose to insulin (G:I) ratio, glucose to C-peptide (G:C) ratio and ferritin of hypothyroid and hyperthyroid subjects with euthyroid, euglycemic subjects. MethodsThe study group comprised of 40 hypothyroid, 25 hyperthyroid female subjects and 40 euthyroid controls. Serum samples of all the patients were assayed for thyroid profile, glucose, Insulin, C-peptide and ferritin. Homeostasis model of assessment (HOMA-IR), (G:I), (G:C) and ferritin were employed as surrogate measures to assess the level of insulin resistance. The area under the curves for the surrogate markers was determined from the receiver operating characteristics (ROC) curves for the hypothyroid and hyperthyroid patients. ResultsPatients with hypothyroidism demonstrated insulin resistance as observed by the higher HOMA-IR and G:I ratio as compared to the controls whereas insulin resistance was not detected in the hyperthyroid patients in our study. ConclusionThyroid dysfunction attributes to deranged glucose metabolism. Assessment of the surrogate markers might prove to be beneficial in detecting insulin resistance at the incipient stages and subsequent management.

Prediction of insulin resistance with anthropometric measures: lessons from a large adolescent population

ObjectiveThe aim of this study was to describe the minimum number of anthropometric measures that will optimally predict insulin resistance (IR) and to characterize the utility of these measures among obese and nonobese adolescents.Research design and methodsSix anthropometric measures (selected from three categories: central adiposity, weight, and body composition) were measured from 1298 adolescents attending two New York City public high schools. Body composition was determined by bioelectric impedance analysis (BIA). The homeostatic model assessment of IR (HOMA-IR), based on fasting glucose and insulin concentrations, was used to estimate IR. Stepwise linear regression analyses were performed to predict HOMA-IR based on the six selected measures, while controlling for age.ResultsThe stepwise regression retained both waist circumference (WC) and percentage of body fat (BF%). Notably, BMI was not retained. WC was a stronger predictor of HOMA-IR than BMI was. A regression model using solely WC performed best among the obese II group, while a model using solely BF% performed best among the lean group. Receiver operator characteristic curves showed the WC and BF% model to be more sensitive in detecting IR than BMI, but with less specificity.ConclusionWC combined with BF% was the best predictor of HOMA-IR. This finding can be attributed partly to the ability of BF% to model HOMA-IR among leaner participants and to the ability of WC to model HOMA-IR among participants who are more obese. BMI was comparatively weak in predicting IR, suggesting that assessments that are more comprehensive and include body composition analysis could increase detection of IR during adolescence, especially among those who are lean, yet insulin-resistant.

Comment on "The Changes of Blood Glucose Control and Lipid Profiles after Short-Term Smoking Cessation in Healthy Males"

Sir, Lee et al.1 reported the interesting finding that smoking cessation in healthy males was associated with a significant increase of insulin resistance, and speculated that the increase of the body weight following smoking cessation might contribute to the worsening of insulin resistance. They dealt with a small number of subjects and the follow-up period was only 2-months. As opposite results, they cited one report of increase of insulin resistance in 63 normoglycaemic smokers as compared to 21 non-smokers and 35 former smokers with normoglycaemia.2 Cena et al.3 suggest that nicotine, carbon monoxide, and other metabolites derived from nicotine may play important roles in insulin resistance. On this point, Eliasson et al.4 reported that the degree of insulin sensitivity was negatively correlated with the extent of nicotine use, as measured by the plasma cotinine level, in long-trem nicotine gum users (n=20, r=-0.469, p=0.034). In order to clarify these associations, the salivary cotinine and nicotine concentrations of 180 male smokers were measured. The mean age±standard deviation (range) of the subjects was 42.8±6.2 (33-58). Subjects at a workplace with a current history of treatment for diabetes and/or subjects whose fasting plasma glucose level was ≥140 mg/dL were excluded from the analysis. Sampling of saliva was conducted using Salisoft® (Assist Co. Ltd., Tokyo). The analyses were performed by high-performance liquid chromatography using CAPCELL PAK MG II C18 (Shiseido Co. Ltd., Tokyo) columns under the 195temperature of 50℃ and wavelength of 262 nm. The detection limits for nicotine and cotinine were 5 ng/mL and 10 ng/mL, respectively. As an indicator of insulin resistance, the homeostasis model assessment for insulin resistance (HOMA-IR)5 was calculated as follows: HOMA-IR=(Fasting plasma glucose×Fasting serum insulin)/405. Units of glucose and insulin used for the calculation of HOMA-R were mg/dL and µIU/mL, respectively. Bernert et al.6 reported a regression line of salivary cotinine against the serum cotinine as Log10 (salivary cotinine)=0.963×Log10 (serum cotinine)+0.127, with the square value of the correlation coefficient of 0.997. The main finding was that the log-transformed salivary cotinine and log-transformed salivary nicotine concentrations had no significant relationship with the log-transformed HOMA-IR. The partial correlation coefficients after adjustments for the age and body mass index were -0.056 for salivary cotinine and -0.047 for salivary nicotine, respectively. Thus, it was found using biological indicators of tobacco use, that the relationship between smoking and insulin resistance was weak after adjustment for age and body mass index. Although HOMA-IR is only a substitute indicator of insulin resistance ideally determined by the glucose clamp method, which was adopted by Eliasson et al.4 it is a simple and reliable method to detect insulin resistance in subjects without insulin depletion or insulin treatment. Numerous factors can affect insulin resistance, and I recommend that the net effect of smoking on insulin resistance should be examined in further detail.

Coexistence of non-alcoholic fatty liver disease with elevated alanine aminotransferase is associated with insulin resistance in young Han males

Elevated alanine aminotransferase (ALT) and non-alcoholic fatty liver disease (NAFLD) have been associated with insulin resistance (IR). The objective of this study was to determine whether elevated ALT and NAFLD were useful for early and definitive determination of IR and to compare their coexistence with conventional risk factors in young Han males. Anthropometric and metabolic measurements were assessed in 216 young Han males (19.3 ± 0.8 years). NAFLD was diagnosed using ultrasonography, and the homeostasis model assessment of insulin resistance (HOMA-IR) served as an index of IR. Subjects in the fourth HOMA-IR quartile had a significantly higher frequency of abdominal obesity, elevated blood pressure and ALT, and NAFLD compared to subjects in quartiles 1-3 (P < 0.05). Using multivariate logistic regression analysis, risk of IR increased by 2.33-fold (95% CI: 1.00-5.42, P = 0.050) in participants with NAFLD. In addition, the coexistence of NAFLD and elevated ALT were significantly associated with HOMA-IR and participants with both elevated ALT and NAFLD had a 4.65-fold (95% CI: 1.55-13.97, P = 0.006) increased risk of IR. Coexistence of NAFLD and elevated ALT are associated with IR in young Han males and might be useful for early detection of IR as compared to conventional risk factors.

Acanthosis Nigricans as a Clinical Marker to Detect Insulin Resistance in Caucasian Children From West Virginia

The accuracy of acanthosis nigrcans (AN) as a dermatological clinical marker to predict insulin resistance (IR) has not been well established in children. A cohort of obese Caucasian children was prospectively recruited. Demographic data, body mass index values, and laboratory data were compared for the presence or absence of AN. A total of 76 children participated. In all, 46 (60.5%) children had AN, and 34 (44.7%) children were positive for IR (>3.16); 25 (32.9%) children were positive for both AN and IR. Sensitivity, specificity, positive and negative predictive values, and accuracy level for AN to detect IR in the obese children who participated in this study were 73.5%, 50%, 54.3%, 70%, and 49%, respectively. The correlation between insulin and fasting glucose levels in AN-negative or AN-positive patients was low (R (2) = 13% to 17%). Acanthosis nigricans was only a surrogate marker for IR. It is concluded that IR should be examined in every obese West Virginian child irrespective of his or her AN status.

Detection of insulin resistance in overweight and obese individuals

Background and objectives: This study was designed mainly to determine fasting serum insulin level and other parameters related to obesity, since Type II diabetes mellitus is the most prevalent problem associated with obesity. In Kurdish region, no previous studies about insulin essay in overweight and obese individuals have been executed. Measuring insulin level in overweight and obese individuals can give information about the body's sensitivity to insulin. Methods: Fasting blood samples were derived from (60) overweight individuals (30 males + 30 females) and from (30) normal weight individuals and were analyzed for insulin level by using an immuno assay method (ELISA method) whereas fasting blood glucose, Triglyceride(TG), Total Cholesterol(TC). High Density Lipoprotein(HDL), were estimated spectrophotometrically. Results: From total (60) overweight individuals 76.6% have insulin resistance. From this percent about 52% of those individuals were with family history of diabetes and 24% were with family history of obesity. Conclusion: Three relatively simple metabolic markers can identify overweight and obese individuals who are sufficiently insulin resistant to be at increased risk for various adverse outcomes are (TG level, ratio of TG to HDL, and insulin level).

Comparison between surrogate indexes of insulin sensitivity/resistance and hyperinsulinemic euglycemic glucose clamps in rhesus monkeys.

The euglycemic glucose clamp is the reference method for assessing insulin sensitivity in humans and animals. However, clamps are ill-suited for large studies because of extensive requirements for cost, time, labor, and technical expertise. Simple surrogate indexes of insulin sensitivity/resistance including quantitative insulin-sensitivity check index (QUICKI) and homeostasis model assessment (HOMA) have been developed and validated in humans. However, validation studies of QUICKI and HOMA in both rats and mice suggest that differences in metabolic physiology between rodents and humans limit their value in rodents. Rhesus monkeys are a species more similar to humans than rodents. Therefore, in the present study, we evaluated data from 199 glucose clamp studies obtained from a large cohort of 86 monkeys with a broad range of insulin sensitivity. Data were used to evaluate simple surrogate indexes of insulin sensitivity/resistance (QUICKI, HOMA, Log HOMA, 1/HOMA, and 1/Fasting insulin) with respect to linear regression, predictive accuracy using a calibration model, and diagnostic performance using receiver operating characteristic. Most surrogates had modest linear correlations with SI(Clamp) (r ≈ 0.4-0.64) with comparable correlation coefficients. Predictive accuracy determined by calibration model analysis demonstrated better predictive accuracy of QUICKI than HOMA and Log HOMA. Receiver operating characteristic analysis showed equivalent sensitivity and specificity of most surrogate indexes to detect insulin resistance. Thus, unlike in rodents but similar to humans, surrogate indexes of insulin sensitivity/resistance including QUICKI and log HOMA may be reasonable to use in large studies of rhesus monkeys where it may be impractical to conduct glucose clamp studies.

Assessment of insulin resistance in normoglycemic young adults

Detection of Insulin resistance (IR) in normoglycemic young subjects before the onset of Impaired Glucose Tolerance (IGT) is of importance as it affords implementation of preventive measures in such high risk subject. Very few studies have specifically evaluated for the presence of IR in younger age group with normal glucose tolerance. The gold standard for investigating and quantifying insulin resistance is the "hyperinsulinemic euglycemic clamp," the complicated nature of the "clamp" technique, alternatives have been sought to simplify the measurement of insulin resistance. The oral glucose tolerance test (OGTT) is one of the most commonly used methods to evaluate whole body glucose tolerance in vivo. IR & IS values of HOMA-IR, ISI 0-120, QUICKIE mathematical models derived from OGTT have been shown to produce equivalent results as in Euglycemic clamp technique we hypothesized that normoglycemic young adult who are siblings of type II diabetics (SD) probably have higher IR values than the siblings of non diabetics as they are genetically predisposed. In this study 79 normal young adult volunteers, 40 subjects with family history of diabetes (SD) and 39 subjects without family history of diabetes (SND), in the age range of 18 to 25 years were evaluated for Insulin resistance. Standard (75 g) OGTT was performed on all the study subjects after an overnight fast. Fasting (basal), 30, 120 min venous plasma glucose & Corresponding specific insulin concentration was determined by radioimmuno assay (RIA) using a human specific antibody RIA kit. In each subject, the degree of insulin resistance was estimated by various parameters of Insulin resistance & sensitivity that were calculated using physiological mathematical models like HOMA-IR, ISI0-120, IGI, QUICKIE and their formulas derived from OGTT. The mean age of the study population was 19.01 (18 to 25 years), Male: 33 (41.3%) and Female: 47 (58.8%). The normoglycemic subjects were categorized as Siblings of Diabetics (SD n = 40) and siblings of non-diabetics(SND n = 39). Both the groups were matched by physical, clinical and routine laboratory parameters and were not statistically significant different. Siblings of diabetics had a statistically significant higher values of insulin at baseline, 30 min & 120 minutes and glucose at 30 minutes. Siblings of diabetics had a significantly higher insulin resistance and lower insulin sensitivity indices as in HOMA-IR (2.01527, p < 0.017), ISIO-120 (56.27, p < 0.002) and I0/G0 ratio (0.122381, p < 0.012) and a trend towards significance for QUICKIE(0.29578, p < 0.056). Detection of insulin resistance in normoglycemic young adult subjects in pre-disease state is feasible using mathematical models like HOMA IR, ISIO-120 from OGTT. OGTT is simple and a generally acceptable test. Siblings of diabetics had higher Insulin resistance values and lower Insulin sensitivity values. Physiological models like HOMA IR, QUICKIE, I0/G0 ratio, IGI, ISIO-120 are simple & cost effective method for screening Insulin resistance. Diagnosis of Insulin resistance in pre-disease state allows initiation of preventive measures like life style modification, diet & exercise, thereby preventing the high risk subjects from progressing to disease state.

HOMA‐IR and QUICKI: decide on a general standard instead of making further comparisons

To limit further comparisons between the two fasting indices Homeostasis Model Assessment for Insulin Resistance (HOMA-IR) and Quantitative Insulin Sensitivity Check Index (QUICKI), and to examine their robustness in assessing insulin sensitivity. A total of 191 obese children and adolescents (age 13.9 ± 2.9 years, BMI SDS 6.1 ± 1.6), who had undergone a Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT), were included. Receiver operating characteristic curve (ROC) analysis was used to compare indices in detecting insulin resistance and Bland-Altman plots to investigate agreement between three consecutive fasting samples when compared to using single samples. ROC analysis showed that the diagnostic accuracy was identical for QUICKI and HOMA-IR [area under the curve (AUC) boys 0.80, 95%CI 0.70-0.89; girls 0.80, 0.71-0.88], while insulin had a nonsignificantly lower AUC (boys 0.76, 0.66-0.87; girls 0.75, 0.66-0.84). Glucose did not perform better than chance as a diagnostic test (boys 0.47, 0.34-0.60; girls 0.57, 0.46-0.68). Indices varied with consecutive sampling, mainly attributable to fasting insulin variations (mean maximum difference in HOMA-IR -0.8; -0.9 to -0.7). Using both HOMA-IR and QUICKI in further studies is superfluous as these indices function equally well as predictors of the FSIVGTT sensitivity index. Focus should be on establishing a general standard for research and clinical purposes.

Resistência à insulina e síndrome metabólica em pacientes ambulatoriais com transtorno do humor bipolar

Background: Bipolar disorder (BD) is associated with significant morbidity and mortality from metabolic diseases. There is a paucity of data regarding insulin resistance (IR) and its relationship with the metabolic syndrome (MS) in bipolar patients. Objective: To evaluate the prevalence of both IR and MS in BD outpatients and to assess clinical criteria associated with IR. Method: Cross-sectional study in 65 DSM-IV-TR BD patients consecutively assessed at the Bipolar Disorder Program at Hospital de Clinicas de Porto Alegre, Brazil. IR was diagnosed by the homeostatic model assessment – insulin resistance (HOMA-IR) and MS was diagnosed using three different definitions: National Cholesterol Educational Program – Adult Treatment Panel III (NCEP-ATP III); NCEP-ATP III modified criteria and International Diabetes Federation. Results: IR was present in 43.1% of the sample (women 40%, men 44.4%). The prevalence of MS defined by the NCEP-ATP III criteria was 32.3%, NCEP-ATP III modified was 40% and IDF was 41.5%. NCEP-ATP III modified criteria showed the best tradeoff between sensitivity (78.6%) and specificity (89.2%) to detect insulin resistance. Waist circumference was the clinical parameter most associated with IR. Discussion: Current MS criteria may provide reasonable sensitivity and specificity for the detection of IR in BD patients. Abdominal obesity is closely related to IR in this patient population.

Basal Insulin Resistance and Secretion in Nigerians With Type 2 Diabetes Mellitus

The objective of this study was to estimate basal insulin resistance (IR) and insulin secretion (IS) in Nigerians with type 2 diabetes mellitus (T2DM). The homeostasis model assessment (HOMA) method was used to estimate basal IR and IS in 146 Nigerians with T2DM and in 33 controls at the University of Nigeria Teaching Hospital (UNTH), Enugu, Nigeria. Correlations and multiple regression analysis between Box-Cox-transformed IR and log-transformed IS and anthropometric indices were carried out. IR and reduced IS were present, respectively, in 139 (95.5%) and 109 (74.7%) of the diabetic subjects and in 25 (75.8%) and 4 (12.1%) of the controls. In the diabetic subjects, age at diagnosis, duration of diabetes, waist circumference (WC), and body mass index (BMI) correlated significantly with IR (r = -0.2399, P = 0.0035; r = 0.1993, P = 0.0166; r = 0.2267, P = 0.0059; r = 0.2082, P = 0.0120; respectively), whereas duration of diabetes, WC, and BMI correlated significantly with IS (r = -0.2166, P = 0.0091; r = 0.3062, P = 0.0002; r = 0.2746, P = 0.0008; respectively). Age at diagnosis, WC, and duration of diabetes were significant predictors of IR (beta = -0.0161, P < 0.001; beta = 0.0121, P = 0.002; beta = 0.0138, P = 0.042; respectively), whereas duration of diabetes and WC significantly predicted IS (beta = -0.0159, P = 0.025; beta = 0.0155, P < 0.001). This study shows that both IR and reduced IS are major features of T2DM in Nigerians and that WC consistently correlated and predicted IR. WC measurement is simple and ideal in resource-poor settings for the detection of IR and abdominal obesity. The apparent rarity of coronary heart disease (CHD) in black Africans with T2DM despite a high prevalence of IR warrants further investigation.

The Δ9 Fatty Acid Desaturation Index as a Predictor of Metabolic Disease

There is a tremendous need for improved biomarkers that can be used in the clinical setting to aid in the early detection of metabolic diseases. Of particular interest is the metabolic syndrome, or insulin resistance syndrome, a condition that is often clinically diagnosed as a cluster of risk factors including abdominal obesity, hypertriglyceridemia, low HDL cholesterol, hypertension, impaired glucose tolerance, and increased fasting glucose (1)(2). This constellation of phenotypes greatly increases the risk for the development of chronic conditions such as diabetes and several cardiovascular diseases. The root cause of the metabolic syndrome is insulin resistance, a condition in which the pancreas increases insulin secretion to compensate for the decreased ability of insulin-sensitive tissues to perform insulin-stimulated glucose uptake to maintain euglycemia (1). Therefore, the development of new tests that focus on the early detection of insulin resistance could help identify at-risk patients so that the benefits of lifestyle and pharmacological interventions can be maximized and progression to overt metabolic disease can be prevented. Insulin resistance is not limited to defects in carbohydrate metabolism, but is also associated with major perturbations in lipid metabolism (2). For example, adipocytes increase the release of free fatty acids (FFA)1 into the circulation, resulting in subsequent FFA uptake by the liver and other peripheral tissues, processes that may further exacerbate insulin resistance (2)(3). Although hepatic insulin resistance contributes to hyperglycemia by causing an inappropriate elevation in hepatic glucose production, the branch of insulin signaling responsible for stimulating de novo fatty acid biosynthesis in the liver remains intact (2)(4). Additionally, high-carbohydrate diets, especially those enriched in rapidly absorbed refined carbohydrates such as glucose and fructose, are able to potently stimulate de novo lipogenesis (DNL) (5). The effects of insulin and dietary carbohydrate occur via the transcriptional activation of …

Optimal threshold of homeostasis model assessment for insulin resistance in an Iranian population: The implication of metabolic syndrome to detect insulin resistance

We assessed the threshold of homeostasis model assessment (HOMA) values to determine insulin resistance (IR) in a sample of Iranians and examined the associations of IR with metabolic syndrome (MetS). Study comprised 1327 non-diabetic and non-hypertensive subjects (438 men, 838 women; aged 20–77 years) incurred in four different locations (Tehran, Iran), 2005–2008. Lower limit of the top quintile of HOMA values in subjects without any metabolic abnormality was considered as the threshold of IR. This threshold was 1.8 (1.7 men; 1.8 women). Overall, 41.1% (36.3% men; 41.5% women) of subjects had IR. HOMA cut-off to determine MetS was 1.95 for ATPIII definition, and 1.85 for IDF. IR associated MetS (ATPIII: odds ratio (OR) = 2.9, 95% CI = 2.2–3.9, p < 0.05; IDF: OR = 2.94, 95% CI: 2.3–3.8, p < 0.05). Sensitivity of MetS to detect IR was 22.4% for IDF and 36.2% for ATPIII criteria. In multivariate models, HOMA was predicted by waist circumference, and inversely by age and serum HDL-cholesterol. In this study, the cut point of HOMA to detect IR was lower than other populations. IR is an unyielding correlate of MetS; but definitions of MetS are insensitive measures of IR in our population.

Threshold value of subepicardial adipose tissue to detect insulin resistance in obese children

Until now, the association between subepicardial adipose tissue (SAT), insulin resistance and intima-media thickness (IMT) has not been evaluated in obese children. In this study, we evaluated whether echocardiographic SAT is related to insulin resistance and IMT in obese children. A total of 46 obese subjects (10.2+/-2.5 years of age, 25 male patients) and 30 age- and gender-matched lean subjects (10.8+/-3.1 years of age, 13 male patients) were included in this study. The criterion for diagnosing obesity was defined as the body mass index (BMI) being over 97% percentile of the same gender and age. Serum triglyceride (TG), low- and high-density lipoprotein, cholesterol, glucose and insulin levels were measured during the fasting state. Each subject underwent a transthoracic echocardiogram and the SAT thickness was measured during end-diastole from the parasternal long-axis views. The obese subjects had significantly higher SAT thickness and IMT values compared with the subjects in the control group (5.7+/-1.4 vs 3.0+/-0.7 mm, 0.78+/-0.15 vs 0.51+/-0.11 mm, P=0.001, respectively). Simple linear regression analysis showed no significant correlation between SAT and insulin resistance (r=0.170, P=0.253), whereas there was significant correlation between SAT and BMI, age and IMT (r=0.625, P=0.02, r=0.589, P=0.001, r=0.343, P=0.02, respectively). As an optimal cutoff point, a SAT thickness of 4.1 mm determined insulin resistance with 90% sensitivity and 61% specificity. Our study showed that SAT was significantly correlated with age, BMI and IMT, but not insulin resistance. However, our findings suggest that a 4.1 mm cutoff of SAT thickness might be used as a simple, inexpensive and non-invasive screening method because of its ability to predict insulin resistance with high sensitivity in obese children.

IGF-I/IGFBP-3 ratio: a mechanistic insight into the metabolic syndrome

Recent reports suggest that IGF (insulin-like growth factor)-I and IGFBP-3 (IGF-binding protein-3) have independent and opposing mechanistic effects on insulin. The aim of the present study was to assess the relationship between the IGF-I/IGFBP-3 ratio and the metabolic syndrome. We examined 3281 subjects (1463 men and 1818 women, aged 20-49 years), otherwise healthy adults, who participated in NHANES III (Third National Health and Nutrition Examination Survey), which has released measurements of IGF-I and IGFBP-3. Insulin resistance was estimated using the computer HOMA2 (homoeostatic model assessment 2) model. The updated ATP-III (Adult Treatment Panel III) definition of the metabolic syndrome was used. We applied adjusted logistic and linear regression models. After adjusting for age and race, men and women in the lowest quartile of the IGF-I/IGFBP-3 ratio were 3-fold more likely to meet the ATP-III definition of the metabolic syndrome and twice as likely to be insulin-resistant. Mean values of the IGF-I/IGFBP-3 ratio decreased significantly as the number of metabolic syndrome components increased (P<0.0001, as determined by ANOVA). The area under the ROC (receiver operating characteristic) curve for detecting insulin resistance using the IGF-I/IGFBP-3 ratio was 0.760, significantly improving upon either protein alone (P=0.01). In conclusion, the IGF-I/IGFBP-3 ratio is significantly associated with the metabolic syndrome. Calculating the ratio of these two proteins may provide insight into the metabolic syndrome clustering phenomenon.