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Anthropometric indicators of insulin resistance
Abstract
During recent years, there have been numerous published reports associating increased circumferences of certain regions of the human body with insulin resistance or increased risk of cardiovascular disease. In the present review, we summarize the findings and conclusions of some of these publications.
... These indicators, primarily calculated based on basic measurements of height, weight, waist circumference, and hip circumference, offer potential advantages in terms of accessibility and non-invasiveness, particularly in primary healthcare and epidemiological research settings (21)(22)(23). These anthropometric indicators will be beneficial for low-cost universal screening of IR and potential metabolic diseases (24,25). ...
Background and objectives
Metabolic disease has become a global health concern, and insulin resistance (IR) is a crucial underlying mechanism in various metabolic diseases. This study aims to compare the ability of seven anthropometric indicators in predicting IR in the Chinese population, and to find more sensitive and simple anthropometric indicator for early identification of IR.
Methods
This prospective cross-sectional study obtained participants’ medical history, anthropometric indicators, and serum samples from three hospitals in China. Various anthropometric indicators were calculated, including body mass index (BMI), Waist-to-hip ratio (WHR), waist-to-height ratio (WtHR), conicity index (CI), A Body Shape Index (ABSI), body roundness index (BRI), abdominal volume index (AVI). The evaluation of IR is performed using the homeostasis model assessment-insulin resistance (HOMA-IR). Logistic regression analysis examined the relationship between indicators and HOMA-IR. The ability of the anthropometric indicators to predict IR was analyzed using the receiver operating characteristic (ROC) curve. Additionally, a stratified analysis was performed to evaluate the ability of the indicators in different age and gender groups.
Results
The study included 1,592 adult subjects, with 531 in the non-IR group and 1,061 in the IR group. After adjusting for confounding factors, the anthropometric indicators showed a positive correlation with IR in the general population and across different genders and age groups (OR > 1, p < 0.05), except for ABSI. In the ROC curve analysis, WtHR and BRI had the highest AUC values of 0.711 for detecting IR. The optimal cut-off value for WtHR to diagnose IR was 0.53, while for BRI, it was 4.00. In the gender-stratified and age-stratified analysis, BMI, WtHR, BRI, and AVI all had AUC values >0.700 in females and individuals below 60.
Conclusion
WtHR and BRI demonstrated a better ability to predict IR in the overall study population, making them preferred indicators for screening IR, and gender and age are important considerations. In the stratified analysis of different genders or age, BMI, WtHR, BRI, and AVI are also suitable for detecting IR in women or individuals under 60 years old in this study.
Clinical trial registration
www.chictr.org.cn, ChiCTR2100054654.
... It is important to highlight that QUICKI, BMI (kg/m 2 ), total body fat (TBF %), AF (%), WC (cm), TyG-WHtR, WHtR, leptin levels, and LAR have been described as predictors of insulin sensitivity/resistance in young male adults, yielding similar results to those described in Table 1 in the individuals grouped into IR and non-IR in this study, findings that confirm the high diagnosis accuracy classification using the Matsuda index cutoff value as reference (4.03) when the computational approach is applied for IR discrimination (Table 1) (21,22,(37)(38)(39)(40)(41)(42)(43)(44). Additionally, anthropometric measurement, clinical features, leptin, lipid profile, glucose, and insulin levels during fasting at each point of the OGTT and surrogate indices of muscle and hepatic insulin sensitivity are described in IR and non-IR young individuals as described in Table 1. ...
Background
Overweight and obesity, high blood pressure, hyperglycemia, hyperlipidemia, and insulin resistance (IR) are strongly associated with non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular disease, stroke, and cancer. Different surrogate indices of IR are derived and validated with the euglycemic–hyperinsulinemic clamp (EHC) test. Thus, using a computational approach to predict IR with Matsuda index as reference, this study aimed to determine the optimal cutoff value and diagnosis accuracy for surrogate indices in non-diabetic young adult men.
Methods
A cross-sectional descriptive study was carried out with 93 young men (ages 18–31). Serum levels of glucose and insulin were analyzed in the fasting state and during an oral glucose tolerance test (OGTT). Additionally, clinical, biochemical, hormonal, and anthropometric characteristics and body composition (DEXA) were determined. The computational approach to evaluate the IR diagnostic accuracy and cutoff value using difference parameters was examined, as well as other statistical tools to make the output robust.
Results
The highest sensitivity and specificity at the optimal cutoff value, respectively, were established for the Homeostasis model assessment of insulin resistance index (HOMA-IR) (0.91; 0.98; 3.40), the Quantitative insulin sensitivity check index (QUICKI) (0.98; 0.96; 0.33), the triglyceride-glucose (TyG)-waist circumference index (TyG-WC) (1.00; 1.00; 427.77), the TyG-body mass index (TyG-BMI) (1.00; 1.00; 132.44), TyG-waist-to-height ratio (TyG-WHtR) (0.98; 1.00; 2.48), waist-to-height ratio (WHtR) (1.00; 1.00; 0.53), waist circumference (WC) (1.00; 1.00; 92.63), body mass index (BMI) (1.00; 1.00; 28.69), total body fat percentage (TFM) (%) (1.00; 1.00; 31.07), android fat (AF) (%) (1.00; 0.98; 40.33), lipid accumulation product (LAP) (0.84; 1.00; 45.49), leptin (0.91; 1.00; 16.08), leptin/adiponectin ratio (LAR) (0.84; 1.00; 1.17), and fasting insulin (0.91; 0.98; 16.01).
Conclusions
The computational approach was used to determine the diagnosis accuracy and the optimal cutoff value for IR to be used in preventive healthcare.
... The insulin resistance index was evaluated by the homeostasis model assessment (HOMA) and the HOMA-IR (homeostasis model assessment insulin resistance) index. HOMA-IR value >2.5 is IR positive [26]. HOMA-IR = FIns×PG 22.5 . ...
Background:
Insulin resistance (IR) is a major contributing factor to the pathogenesis of metabolic syndrome and type 2 diabetes mellitus (T2D). Adipocyte metabolism is known to play a crucial role in IR. Therefore, the aims of this study were to identify metabolism-related proteins that could be used as potential biomarkers of IR and to investigate the role of N6-methyladenosine (m6A) modification in the pathogenesis of this condition.
Methods:
RNA-seq data on human adipose tissue were retrieved from the Gene Expression Omnibus database. The differentially expressed genes of metabolism-related proteins (MP-DEGs) were screened using protein annotation databases. Biological function and pathway annotations of the MP-DEGs were performed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Key MP-DEGs were screened, and a protein-protein interaction (PPI) network was constructed using STRING, Cytoscape, MCODE, and CytoHubba. LASSO regression analysis was used to select primary hub genes, and their clinical performance was assessed using receiver operating characteristic (ROC) curves. The expression of key MP-DEGs and their relationship with m6A modification were further verified in adipose tissue samples collected from healthy individuals and patients with IR.
Results:
In total, 69 MP-DEGs were screened and annotated to be enriched in pathways related to hormone metabolism, low-density lipoprotein particle and carboxylic acid transmembrane transporter activity, insulin signaling, and AMPK signaling. The MP-DEG PPI network comprised 69 nodes and 72 edges, from which 10 hub genes (FASN, GCK, FGR, FBP1, GYS2, PNPLA3, MOGAT1, SLC27A2, PNPLA3, and ELOVL6) were identified. FASN was chosen as the key gene because it had the highest maximal clique centrality (MCC) score. GCK, FBP1, and FGR were selected as primary genes by LASSO analysis. According to the ROC curves, GCK, FBP1, FGR, and FASN could be used as potential biomarkers to detect IR with good sensitivity and accuracy (AUC = 0.80, 95% CI: 0.67-0.94; AUC = 0.86, 95% CI: 0.74-0.94; AUC = 0.83, 95% CI: 0.64-0.92; AUC = 0.78, 95% CI: 0.64-0.92). The expression of FASN, GCK, FBP1, and FGR was significantly correlated with that of IGF2BP3, FTO, EIF3A, WTAP, METTL16, and LRPPRC (p < 0.05). In validation clinical samples, the FASN was moderately effective for detecting IR (AUC = 0.78, 95% CI: 0.69-0.80), and its expression was positively correlated with the methylation levels of FASN (r = 0.359, p = 0.001).
Conclusion:
Metabolism-related proteins play critical roles in IR. Moreover, FASN and GCK are potential biomarkers of IR and may be involved in the development of T2D via their m6A modification. These findings offer reliable biomarkers for the early detection of T2D and promising therapeutic targets.
... Recently, there have been numerous published reports associating increased circumferences of certain regions of the human body with IR or increased risk of cardiovascular disease [16]. Some studies have analyzed the efficacy of anthropometric indicators in predicting IR as they are more economic and accessible. ...
Publications suggesting that thyroid nodule might be associated with insulin resistance (IR) and metabolic syndrome are quite interesting. In a very recent report, increased thyroid volume and nodule prevalence were also reported in patients with IR in an iodine-sufficient area []. The purpose of the work is to analyze the association between anthropometric indicators IR and IGF-1 in patients with nodular goiter. Materials and methods. During the study the authors examined 73 patients with euthyroid single-node (n = 34) and multinodular goiter (n = 39) aged 17 to 74 years (mean - (51.0 ± 10.6) years), determining WC, WC / HC, BMI, WHtR, ABSI, BFD, BRI, CI, AVI, BAI, IGF-1, TSH, fT4, fT3. Thyroid volume, its structure, number, size and location of foci was assessed by an ultrasonic complex Aloka SSD-1100 (Japan), using a linear sensor 7.5 MHz. Results and their discussion. In the total number of patients with nodular goiter IGF-1 is nonlinearly negatively associated with BMI (r = -0.30; P = 0.016), WC (r = -0.26; P = 0.036), WHtR (r = -0.30) ; P = 0.020), AVI (r = -0.27; P = 0.03), ABSI (r = -0.31; P = 0.015), nonlinear positive with BFD (r = 0.27; P = 0.033) ), BRI (r = 0.29; P = 0.02) and linearly positive with BAI (r = 0.36; P = 0.004); thyroid volume is linearly positively associated with age (r = 0.35; P = 0.009), nonlinearly positively with WC / HC (r = 0.43; P = 0.001), BFD (r = 0.26; P = 0.06 ) and CI (r = 0.31; P = 0.02). In patients with nodular goiter with BMI≥35 kg / m2 thyroid volume is linearly positively associated with BMI (r = 0.71; P = 0.049). In patients with nodular goiter with IRF-1 above the sex-age norm, thyroid volume is nonlinearly positively associated with WC / HC (r = 0.71; P = 0.01), BAI (r = 0.66; P = 0.03 ) and nonlinearly negative with BFD (r = -0.52; P = 0.01). It has been found that BAI explains 82.37% of the variance of IGF-1 in the general group and more than 90% of the variance of its level in groups of patients with nodular goiter with high IGF-1 with / without obesity. In patients with nodular goiter with high IGF-1 and obesity, the predictor of increased thyroid volume is BRI, which explains 81.14% of the variance of its volume. Conclusions: Patients with nodular goiter with IGF-1 level in blood above the sex-age norm have significantly higher values of anthropometric indicators IR (WHtR, ABSI, BFD and BAI) compared with patients with a normal level of this indicator; in patients with nodular goiter with II degree obesity and above, thyroid volume is significantly associated with BMI; BAI (R2 = 82.37%) is a predictor of increased levels of IGF-1 in blood of patients with nodular goiter, regardless of the obesity; BRI (R2 = 81.14%) is a predictor of increased thyroid volume in patients with nodular goiter with IGF -1 high level and obesity. Key words: nodular goiter, anthropometric indicators, insulin resistance
Objective: Recent studies on type 2 diabetes (T2D) have identified several novel biomarkers that demonstrate greater stability compared to traditional blood glucose indicators. This trial aimed to investigate the effect...
Introduction:
Obesity is one of the main risk factors for cardiovascular disease (CVD) and cardiometabolic disease (CMD). Many studies have developed cutoff points of anthropometric indices for predicting these diseases. The aim of this systematic review was to differentiate the screening potential of body mass index (BMI), waist circumference (WC), and waist-to-hip ratio (WHR) for adult CVD risk.
Methods:
We used relevant key words to search electronic databases to identify studies published up to 2019 that used receiver operating characteristic (ROC) curves for assessing the cut-off points of anthropometric indices. We used a random-effects model to pool study results and assessed between-study heterogeneity by using the I2 statistic and Cochran's Q test.
Results:
This meta-analysis included 38 cross-sectional and 2 cohort studies with 105 to 137,256 participants aged 18 or older. The pooled area under the ROC curve (AUC) value for BMI was 0.66 (95% CI, 0.63-0.69) in both men and women. The pooled AUC values for WC were 0.69 (95% CI, 0.67-0.70) in men and 0.69 (95% CI, 0.64-0.74) in women, and the pooled AUC values for WHR were 0.69 (95% CI, 0.66-0.73) in men and 0.71 (95% CI, 0.68-0.73) in women.
Conclusion:
Our findings indicated a slight difference between AUC values of these anthropometric indices. However, indices of abdominal obesity, especially WHR, can better predict CVD occurrence.
Background:
Recent studies have suggested that neck circumference (NC) is a supplemental screening measure for diagnosing metabolic complications and might be associated with glycemic parameters. The aim of the present study was to to evaluate the association between NC and glycemic parameters.
Methods:
We systematically searched the electronic databases (including MEDLINE, Scopus, EMBASE, and Google scholar) up to April 2018. Observational studies that reported correlation coefficient between NC and glycemic parameters were included in the analysis. A random effects model was used to estimate overall Fisher's Z and 95% confidence interval of glycemic parameters including fasting plasma glucose (FBG), serum fasting insulin level, homeostasis model assessment-estimated insulin resistance (HOMA-IR) and glycated hemoglobin (HbA1c).
Results:
A total of 21 studies (44,031 participants) were eligible for including in the systematic review and meta-analysis. Significant correlations were found between NC and FBG (Fisher's Z = 0.18; 95% CI 0.16, 0.21), serum fasting insulin level (Fisher's Z = 0.34; 95% CI 0.26, 0.41), HOMA-IR (Fisher's Z = 0.36; 95% CI 0.29, 0.43) and HbA1c (Fisher's Z = 0.14; 95% CI 0.09, 0.20). Meta-regression analysis showed that NC were marginally associated with FBG in a linear manner (β = 0.008, P = 0.09); but not related to serum fasting insulin level, HOMA-IR, and HbA1c.
Conclusions:
This meta-analysis of cross-sectional studies showed that NC was positively correlated with glycemic parameters including FBG, serum fasting insulin level, HOMA-IR, and HbA1c. Further investigations with prospective design are required to confirm these findings.
Background:
Recently, neck circumference (NC) has been used to predict the risk of cardiometabolic factors. This study aimed to perform a systematic review and meta-analysis to examine: (i) the sensitivity (SE) and specificity (SP) of NC to predict cardiometabolic risk factors and (ii) the association between NC and the risk of cardiometabolic parameters.
Methods:
A systematic search was conducted through PubMed/Medline, Institute of Scientific Information, and Scopus, until 2017 based on the search terms of metabolic syndrome (MetS) and cardio metabolic risk factors. Random-effect model was used to perform a meta-analysis and estimate the pooled SE, SP and correlation coefficient (CC).
Results:
A total of 41 full texts were selected for systematic review. The pooled SE of greater NC to predict MetS was 65% (95% CI 58, 72) and 77% (95% CI 55, 99) in adult and children, respectively. Additionally, the pooled SP was 66% (95% CI 60, 72) and 66% (95% CI 48, 84) in adult and children, respectively. According to the results of meta-analysis in adults, NC had a positive and significant correlation with fasting blood sugar (FBS) (CC: 0.16, 95% CI 0.13, 0.20), HOMA-IR (0.38, 95% CI 0.25, 0.50), total cholesterol (TC) (0.07 95% CI 0.02, 0.12), triglyceride (TG) concentrations (0.23, 95% CI 0.19, 0.28) and low density lipoprotein cholesterol (LDL-C) (0.14, 95% CI 0.07, 0.22). Among children, NC was positively associated with FBS (CC: 0.12, 95% CI 0.07, 0.16), TG (CC: 0.21, 95% CI 0.17, 0.25), and TC concentrations (CC: 0.07, 95% CI 0.02, 0.12). However, it was not significant for LDL-C.
Conclusion:
NC has a good predictive value to identify some cardiometabolic risk factors. There was a positive association between high NC and most cardiometabolic risk factors. However due to high heterogeneity, findings should be declared with caution.
Overweight and obese persons are at risk of a number of medical conditions which can lead to further morbidity and mortality. The primary objective of this study is to provide an estimate of the incidence of each co-morbidity related to obesity and overweight using a meta-analysis.
A literature search for the twenty co-morbidities identified in a preliminary search was conducted in Medline and Embase (Jan 2007). Studies meeting the inclusion criteria (prospective cohort studies of sufficient size reporting risk estimate based on the incidence of disease) were extracted. Study-specific unadjusted relative risks (RRs) on the log scale comparing overweight with normal and obese with normal were weighted by the inverse of their corresponding variances to obtain a pooled RR with 95% confidence intervals (CI).
A total of 89 relevant studies were identified. The review found evidence for 18 co-morbidities which met the inclusion criteria. The meta-analysis determined statistically significant associations for overweight with the incidence of type II diabetes, all cancers except esophageal (female), pancreatic and prostate cancer, all cardiovascular diseases (except congestive heart failure), asthma, gallbladder disease, osteoarthritis and chronic back pain. We noted the strongest association between overweight defined by body mass index (BMI) and the incidence of type II diabetes in females (RR = 3.92 (95% CI: 3.10-4.97)). Statistically significant associations with obesity were found with the incidence of type II diabetes, all cancers except esophageal and prostate cancer, all cardiovascular diseases, asthma, gallbladder disease, osteoarthritis and chronic back pain. Obesity defined by BMI was also most strongly associated with the incidence of type II diabetes in females (12.41 (9.03-17.06)).
Both overweight and obesity are associated with the incidence of multiple co-morbidities including type II diabetes, cancer and cardiovascular diseases. Maintenance of a healthy weight could be important in the prevention of the large disease burden in the future. Further studies are needed to explore the biological mechanisms that link overweight and obesity with these co-morbidities.
An upper body/visceral fat distribution in obesity is closely linked with metabolic complications, whereas increased lower body fat is independently predictive of reduced cardiovascular risk.
The measured functions of different fat depots with regards to fatty acid storage and release in health and obesity were reviewed. The adverse effects of experimentally increasing free fatty acid (FFA) concentrations on liver, muscle, pancreatic beta-cell, and endothelial function were noted.
The most dramatic abnormality in FFA metabolism is failure to suppress FFA concentrations/adipose tissue lipolysis normally in response to postprandial hyperinsulinemia. Upper body sc fat delivers the majority of FFA to the systemic circulation under postabsorptive and postprandial conditions. In upper body obesity, portal FFA concentrations resulting from both systemic and visceral adipose tissue lipolysis may be significantly greater than arterial FFA concentrations, exposing the liver to even greater amounts of FFA. Visceral fat also releases sufficient IL-6 to increase portal vein IL-6 concentrations, which can affect hepatic metabolism as well.
Lower body, upper body sc, and visceral fat depots have unique characteristics with regards to fatty acid metabolism. Selective dysregulation of these depots probably plays an important role with the metabolic complications of obesity.
Obesity represents a risk factor for insulin resistance, type 2 diabetes mellitus, and atherosclerosis. In addition, for any given amount of total body fat, an excess of visceral fat or fat accumulation in the liver and skeletal muscle augments the risk. Conversely, even in obesity, a metabolically benign fat distribution phenotype may exist.
In 314 subjects, we measured total body, visceral, and subcutaneous fat with magnetic resonance (MR) tomography and fat in the liver and skeletal muscle with proton MR spectroscopy. Insulin sensitivity was estimated from oral glucose tolerance test results. Subjects were divided into 4 groups: normal weight (body mass index [BMI] [calculated as weight in kilograms divided by height in meters squared], < 25.0), overweight (BMI, 25.0-29.9), obese-insulin sensitive (IS) (BMI, > or = 30.0 and placement in the upper quartile of insulin sensitivity), and obese-insulin resistant (IR) (BMI, > or = 30.0 and placement in the lower 3 quartiles of insulin sensitivity).
Total body and visceral fat were higher in the overweight and obese groups compared with the normal-weight group (P < .05); however, no differences were observed between the obese groups. In contrast, ectopic fat in skeletal muscle (P < .001) and particularly the liver (4.3% +/- 0.6% vs 9.5% +/- 0.8%) and the intima-media thickness of the common carotid artery (0.54 +/- 0.02 vs 0.59 +/- 0.01 mm) were lower and insulin sensitivity was higher (17.4 +/- 0.9 vs 7.3 +/- 0.3 arbitrary units) in the obese-IS vs the obese-IR group (P < .05). Unexpectedly, the obese-IS group had almost identical insulin sensitivity and the intima-media thickness was not statistically different compared with the normal-weight group (18.2 +/- 0.9 AU and 0.51 +/- 0.02 mm, respectively).
A metabolically benign obesity that is not accompanied by insulin resistance and early atherosclerosis exists in humans. Furthermore, ectopic fat in the liver may be more important than visceral fat in the determination of such a beneficial phenotype in obesity.
The vascular pedicles of Bichat's fat pad are numerous. The most important artery is the buccal artery, branch of the maxillary artery, which supplies the principal part and the six extensions of the buccal fat pad. Not only many other branches of the maxillary artery, but also branches of the facial artery and of the superficial temporal artery send thin vessels to cross the capsule and supply Bichat's fat pad, body and extensions, from periphery to center. The microcirculation of Bichat's fat pad shows a network almost similar with those of the other zones of white adipose tissue, but the capillary meshes are smaller and more tightened, the capillaries have a bigger diameter and several "basket-like" structures are to be find beneath the capsule.
Within the cheek, wedged between masseter and buccinator, is a biconvex pad of fatty tissue, the corpus adiposum buccae, or buccal fat pad (of Bichat). It contributes significantly to the prominence of the cheek of the newborn infant and is sometimes encountered in surgical procedures in the region of the ramus of the mandible or the maxillary tuberosity.
This paper reviews the history of the study of the buccal pad of fat, its anatomical location, blood supply, and comparative anatomy. We have also reviewed the pathology of the buccal pad of fat, including traumatic herniation. The fat pad is of interest surgically as it can be used as a free or pedicled graft to close maxillary defects after excision of tumors. © 1995 WiIey‐Liss, Inc.
To the Editor: An upper-body distribution of fat, especially with increased visceral fat, is more predictive of the metabolic complications of obesity than is the degree of overweight.1 Insulin resistance, type 2 diabetes mellitus, dyslipidemia, and hypertension are associated with upper-body and visceral obesity. We noted that patients with chubby facial cheeks tended to have upper-body obesity and hypothesized that cheek and visceral fat might accumulate in concert. To assess this observation, we measured cheek (buccal), visceral, and abdominal subcutaneous fat in 25 consecutive patients who underwent computed tomographic (CT) scanning of the head and abdomen for clinical purposes within . . .
Reports of relationships between measures of insulin sensitivity and measures of body fat and fat distribution suggest that abdominal fat accumulation is a predictor of insulin resistance. It has been previously suggested that facial fat (primarily in the cheeks and neck) is strongly associated with visceral abdominal fat accumulation. The facial fat is a rich vascular region, that seems to be metabolically active and resembles abdominal white adipose tissue. We, therefore, hypothesize that facial fat could be a good predictor of insulin resistance. Whether facial fat can be used as an accurate marker for insulin resistance remains to be determined.
The recent escalation of obesity from an individual health problem to a major public health issue reaching epidemic proportions has drawn attention to a constellation of abnormalities (abdominal obesity, hypertension and dyslipidaemia) collectively referred to as metabolic syndrome. As an indicator of insulin resistance and a harbinger of diabetes, this syndrome has been associated with major cardiovascular mortality and morbidity. Yet, the exact pathophysiological events leading to the development of metabolic syndrome remain unknown. We review some of the current literature on the pathogenesis of metabolic syndrome with an emphasis on the role of ectopic lipid accumulation.
Use and abuse of HOMA modeling
- T M Wallace
- J C Levy
- D R Mathews
La vascularisation du corps adipeux de la joue
- J L Kahn
- H Sick
- M Laude
- J G Koritke