Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects

Near-infrared reactance spectroscopy-derived visceral adipose tissue for the assessment of metabolic syndrome in a multi-ethnic sample of young adults

OBJECTIVES

Visceral adipose tissue (VAT) is highly associated with metabolic syndrome (MetS), which is rapidly increasing in young adults. However, accessible VAT measurement methods are limited, restricting the use of VAT in early detection. This cross-sectional study sought to determine if near-infrared reactance spectroscopy (NIRS)-derived VAT (VATNIRS) was associated with MetS in a multi-ethnic sample of young adults.

METHODS

A total of 107 male and female (F:62, M:45) participants (age: 23.0 ± 4.3y; BMI: 27.1 ± 6.6 kg/m2) completed measurements of fasting blood pressure, blood glucose (FBG), blood lipids, and anthropometric assessments including waist circumference and VATNIRS. MetS severity (MetSindex) was calculated from the aforementioned risk factors using sex and race-specific equations.

RESULTS

VATNIRS was higher in participants with, and at risk for, MetS compared to those with lower risks (all p < .001). VATNIRS was positively associated with MetSindex for all groups (all p < .001). VATNIRS showed positive associations with systolic (SBP), diastolic (DBP), and mean arterial pressure (MAP), LDL-C and LDL-C-related biomarkers, and FBG; and negative associations with HDL-C and HDL-C-to-total cholesterol ratio (all p < .050). Associations between VATNIRS and blood pressure for females, and LDL-C and LDL-C-related biomarkers for males, were nonsignificant (all p > .050). VATNIRS was positively associated with DBP in African-American participants, and SBP in White participants, resulting in positive associations with MAP for both groups (all p < .050).

CONCLUSIONS

VATNIRS is associated with MetS and individual MetS risks factors in a multi-ethnic sample of young adults; providing a noninvasive, cost-effective, portable, and accessible method that may assist in the early detection of MetS and other cardiometabolic abnormalities.

LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning

Postoperative complications of radical gastrectomy seriously affect postoperative recovery and require accurate risk prediction. Therefore, this study aimed to develop a prediction model specifically tailored to guide perioperative clinical decision-making for postoperative complications in patients with gastric cancer. A retrospective analysis was conducted on patients who underwent radical gastrectomy at the First Affiliated Hospital of Nanjing Medical University between April 2022 and June 2023. A total of 166 patients were enrolled. Patient demographic characteristics, laboratory examination results, and surgical pathological features were recorded. Preoperative abdominal CT scans were used to segment the visceral fat region of the patients through 3Dslicer, a 3D Convolutional Neural Network (3D-CNN) to extract image features and the LASSO regression model was employed for feature selection. Moreover, an ensemble learning strategy was adopted to train the features and predict postoperative complications of gastric cancer. The prediction performance of the LGBM (Light Gradient Boosting Machine), XGB (XGBoost), RF (Random Forest), and GBDT (Gradient Boosting Decision Tree) models was evaluated through fivefold cross-validation. This study successfully constructed a model for predicting early complications following radical gastrectomy based on the optimal algorithm, LGBM. The LGBM model yielded an AUC value of 0.9232 and an accuracy of 87.28% (95% CI, 75.61-98.95%), surpassing the performance of other models. Through ensemble learning and integration of perioperative clinical data and visceral fat radiomics, a predictive LGBM model was established. This model has the potential to facilitate individualized clinical decision-making and the early recovery of patients with gastric cancer post-surgery.

Female-specific pancreatic cancer survival from CT imaging of visceral fat implicates glutathione metabolism in solid tumors

RATIONALE AND OBJECTIVES

To identify if body composition, assessed with preoperative CT-based visceral fat ratio quantification as well as tumor metabolic gene expression, predicts sex-dependent overall survival (OS) in patients with pancreatic ductal adenocarcinoma (PDAC).

MATERIALS AND METHODS

This was a retrospective analysis of preoperative CT in 98 male and 107 female patients with PDAC. Relative visceral fat (rVFA; visceral fat normalized to total fat) was measured automatically using software and corrected manually. Median and optimized rVFA thresholds were determined according to published methods. Kaplan Meier and log-rank tests were used to estimate OS. Multivariate models were developed to identify interactions between sex, rVFA, and OS. Unsupervised gene expression analysis of PDAC tumors from The Cancer Genome Atlas (TCGA) was performed to identify metabolic pathways with similar survival patterns to rVFA.

RESULTS

Optimized preoperative rVFA threshold of 38.9% predicted significantly different OS in females with a median OS of 15 months (above threshold) vs 24 months (below threshold; p = 0.004). No significant threshold was identified in males. This female-specific significance was independent of age, stage, and presence of chronic pancreatitis (p = 0.02). Tumor gene expression analysis identified female-specific stratification from a five-gene signature of glutathione S-transferases. This was observed for PDAC as well as clear cell renal carcinoma and glioblastoma.

CONCLUSION

CT-based assessments of visceral fat can predict pancreatic cancer OS in females. Glutathione S-transferase expression in tumors predicts female-specific OS in a similar fashion.

Cushing Syndrome Is Associated With Gut Microbial Dysbiosis and Cortisol-Degrading Bacteria

CONTEXT

Cushing syndrome (CS) is a severe endocrine disease characterized by excessive secretion of cortisol with multiple metabolic disorders. While gut microbial dysbiosis plays a vital role in metabolic disorders, the role of gut microbiota in CS remains unclear.

OBJECTIVE

The objective of this work is to examine the alteration of gut microbiota in patients with CS.

METHODS

We performed shotgun metagenomic sequencing of fecal samples from 78 patients with CS and 78 healthy controls matched for age and body mass index. Furthermore, we verify the cortisol degradation capacity of Ruminococcus gnavus in vitro and identify the potential metabolite by LC-MC/MS.

RESULTS

We observed significant differences in microbial composition between CS and controls in both sexes, with CS showing reduced Bacteroidetes (Bacteroides vulgatus) and elevated Firmicutes (Erysipelotrichaceae_bacterium_6_1_45) and Proteobacteria (Enterobacter cloacae). Despite distinct causes of hypercortisolism in ACTH-dependent and ACTH-independent CS, we found no significant differences in metabolic profiles or gut microbiota between the 2 subgroups. Furthermore, we identified a group of gut species, including R. gnavus, that were positively correlated with cortisol levels in CS. These bacteria were found to harbor cortisol-degrading desAB genes and were consistently enriched in CS. Moreover, we demonstrated the efficient capacity of R. gnavus to degrade cortisol to 11-oxygenated androgens in vitro.

CONCLUSION

This study provides evidence of gut microbial dysbiosis in patients with CS and identifies a group of CS-enriched bacteria capable of degrading cortisol. These findings highlight the potential role of gut microbiota in regulating host steroid hormone levels, and consequently host health.

Prediction of Hepatic Encephalopathy After Transjugular Intrahepatic Portosystemic Shunt Based on CT Radiomic Features of Visceral Adipose Tissue

RATIONALE AND OBJECTIVES

To evaluate the performance and clinical utility of CT radiomic features of visceral adipose tissue (VAT) in the prediction of hepatic encephalopathy (HE) after transjugular intrahepatic portosystemic shunt (TIPS).

MATERIALS AND METHODS

This multi-center study was retrospectively designed. Patients with cirrhosis who underwent TIPS were recruited from January 2015 to December 2020. Pre-TIPS contrast-enhanced CT images were collected for VAT segmentation and radiomic feature extraction. Least absolute shrinkage and selection operator regression with ten-fold cross-validation was performed to reduce dimension. Logistic regression with regularization, support vector machine, and random forest were used for model construction.

RESULTS

A total of 130 patients (90 men; mean age, 54 ± 11 years) were finally enrolled. The cohort was split into 85 patients for the training set (58 men; mean age, 53 ± 12 years) with 19 HE, 21 patients for the internal test set (17 men; mean age, 53 ± 11 years) with 5 HE, and 24 patients for the external test set (15 men; mean age, 55 ± 11 years). Ten radiomic features and C-reactive protein constituted radiomic-clinical models with the best performance. The average area under the receiver operating characteristic curve is 0.97 in the training set and 0.84 in the test sets. For a fixed sensitivity of 0.90, the specificity and negative predictive value of the model is 0.63 and 1.00, respectively; while for a fixed specificity of 0.90, the sensitivity and positive predictive value is 0.60 and 0.75, respectively.

CONCLUSION

Machine learning models based on CT radiomic features extracted from VAT can predict post-TIPS HE with satisfactory performance.

CLINICAL RELEVANCE STATEMENT

Our machine learning models based on CT radiomic features of visceral adipose tissue in patients with cirrhosis may assist in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt, indicating its potential in patient selection and clinical decision-making.

KEY POINTS

Radiomics of visceral adipose tissue provide great help in predicting hepatic encephalopathy after transjugular intrahepatic portosystemic shunt. The clinical-radiomic models showed satisfactory performance with an average area under the receiver operating characteristic curve of 0.84. The model can hypothetically provide 90% sensitivity and 100% negative predictive value for guiding patients who are considering transjugular intrahepatic portosystemic shunt.

MHO or MUO? White adipose tissue remodeling

In this review, we delve into the intricate relationship between white adipose tissue (WAT) remodeling and metabolic aspects in obesity, with a specific focus on individuals with metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). WAT is a highly heterogeneous, plastic, and dynamically secreting endocrine and immune organ. WAT remodeling plays a crucial role in metabolic health, involving expansion mode, microenvironment, phenotype, and distribution. In individuals with MHO, WAT remodeling is beneficial, reducing ectopic fat deposition and insulin resistance (IR) through mechanisms like increased adipocyte hyperplasia, anti-inflammatory microenvironment, appropriate extracellular matrix (ECM) remodeling, appropriate vascularization, enhanced WAT browning, and subcutaneous adipose tissue (SWAT) deposition. Conversely, for those with MUO, WAT remodeling leads to ectopic fat deposition and IR, causing metabolic dysregulation. This process involves adipocyte hypertrophy, disrupted vascularization, heightened pro-inflammatory microenvironment, enhanced brown adipose tissue (BAT) whitening, and accumulation of visceral adipose tissue (VWAT) deposition. The review underscores the pivotal importance of intervening in WAT remodeling to hinder the transition from MHO to MUO. This insight is valuable for tailoring personalized and effective management strategies for patients with obesity in clinical practice.

Radiomics signature of osteoarthritis: Current status and perspective

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis.

The translational potential of this article

Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Nomograms Based on MRI Radiomics for Differential Diagnosis and Predicting BRAFV600E Expression in Pleomorphic Xanthoastrocytoma and Ganglioglioma

RATIONALE AND OBJECTIVES

This study was designed to investigate the value of nomograms based on MRI radiomics and clinical semantic features in identifying pleomorphic xanthoastrocytoma (PXA) and ganglioglioma (GG) as well as predicting BRAFV600E expression.

MATERIALS AND METHODS

This study included 265 patients histologically diagnosed with PXA (n = 113) and GG (n = 152). T1WI, T2WI, and CET1 sequences were utilized to extract radiomics features. Univariate analysis, Spearman correlation analysis, and the least absolute shrinkage and selection operator were used for dimensionality reduction and feature selection. Following this, logistic regression was utilized to establish the radiomics model. Univariate and multivariate analyses of clinical semantic features were applied, and clinical models were constructed. The nomograms were established by merging radiomics and clinical features. Furthermore, ROC curve analysis was used for examining the model performance, whereas the decision curve analysis (DCA) examined the clinical utility of the nomograms.

RESULTS

Nomograms achieved the best predictive efficacy compared to clinical and radiomics models alone. Concerning the differentiation between PXA and GG, the area under the curve (AUC) values of the nomogram were 0.879 (0.828-0.930) and 0.887 (0.805-0.969) for the training and testing cohorts, respectively. For predicting BRAFV600E expression, the AUC values of the nomogram were 0.873 (0.811-0.936) and 0.851 (0.740-0.963) for the training and testing cohorts, respectively. DCA confirmed the clinical utility of the nomograms.

CONCLUSION

Nomograms based on radiomics and clinical semantic features were noninvasive tools for differential diagnosis of PXA and GG and predicting BRAFV600E expression, which may be helpful for assessing patient prognosis and developing individualized treatment strategies.

Nomograms combining computed tomography-based body composition changes with clinical prognostic factors to predict survival in locally advanced cervical cancer patients

OBJECTIVE

To explore the value of body composition changes (BCC) measured by quantitative computed tomography (QCT) for evaluating the survival of patients with locally advanced cervical cancer (LACC) underwent concurrent chemoradiotherapy (CCRT), nomograms combined BCC with clinical prognostic factors (CPF) were constructed to predict overall survival (OS) and progression-free survival (PFS).

METHODS

Eighty-eight patients with LACC were retrospectively selected. All patients underwent QCT scans before and after CCRT, bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA) were measured from two sets of computed tomography (CT) images, and change rates of these were calculated.

RESULTS

Multivariate Cox regression analysis showed ΔBMD, ΔSFA, SCC-Ag, LNM were independent factors for OS (HR = 3.560, 5.870, 2.702, 2.499, respectively, all P < 0.05); ΔPMA, SCC-Ag, LNM were independent factors for PFS (HR = 2.915, 4.291, 2.902, respectively, all P < 0.05). Prognostic models of BCC combined with CPF had the highest predictive performance, and the area under the curve (AUC) for OS and PFS were 0.837, 0.846, respectively. The concordance index (C-index) of nomograms for OS and PFS were 0.834, 0.799, respectively. Calibration curves showed good agreement between the nomograms' predictive and actual OS and PFS, decision curve analysis (DCA) showed good clinical benefit of nomograms.

CONCLUSION

CT-based body composition changes and CPF (SCC-Ag, LNM) were associated with survival in patients with LACC. The prognostic nomograms combined BCC with CPF were able to predict the OS and PFS in patients with LACC reliably.

Human RSPO1 Mutation Represses Beige Adipocyte Thermogenesis and Contributes to Diet-Induced Adiposity

Recent genetic evidence has linked WNT downstream mutations to fat distribution. However, the roles of WNTs in human obesity remain unclear. Here, the authors screen all Wnt-related paracrine factors in 1994 obese cases and 2161 controls using whole-exome sequencing (WES) and identify that 12 obese patients harbor the same mutations in RSPO1 (p.R219W/Q) predisposing to human obesity. RSPO1 is predominantly expressed in visceral fat, primarily in the fibroblast cluster, and is increased with adiposity. Mice overexpressing human RSPO1 in adipose tissues develop obesity under a high-fat diet (HFD) due to reduced brown/beige fat thermogenesis. In contrast, Rspo1 ablation resists HFD-induced adiposity by increasing thermogenesis. Mechanistically, RSPO1 overexpression or administration significantly inhibits adipocyte mitochondrial respiration and thermogenesis via LGR4-Wnt/β-catenin signaling pathway. Importantly, humanized knockin mice carrying the hotspot mutation (p.R219W) display suppressed thermogenesis and recapitulate the adiposity feature of obese carriers. The mutation disrupts RSPO1's electrostatic interaction with the extracellular matrix, leading to excessive RSPO1 release that activates LGR4-Wnt/β-catenin signaling and attenuates thermogenic capacity in differentiated beige adipocytes. Therefore, these findings identify that gain-of-function mutations and excessive expression of RSPO1, acting as a paracrine Wnt activator, suppress fat thermogenesis and contribute to obesity in humans.

Volumetric visceral fat machine learning phenotype on CT for differential diagnosis of inflammatory bowel disease

OBJECTIVES

To investigate whether volumetric visceral adipose tissue (VAT) features extracted using radiomics and three-dimensional convolutional neural network (3D-CNN) approach are effective in differentiating Crohn's disease (CD) and ulcerative colitis (UC).

METHODS

This retrospective study enrolled 316 patients (mean age, 36.25 ± 13.58 [standard deviation]; 219 men) with confirmed diagnosis of CD and UC who underwent CT enterography between 2012 and 2021. Volumetric VAT was semi-automatically segmented on the arterial phase images. Radiomics analysis was performed using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm. We developed a 3D-CNN model using VAT imaging data from the training cohort. Clinical covariates including age, sex, modified body mass index, and disease duration that impact VAT were added to the machine learning model for adjustment. The model's performance was evaluated on the testing cohort separating from the model's development process by its discrimination and clinical utility.

RESULTS

Volumetric VAT radiomics analysis with LASSO had the highest AUC value of 0.717 (95% CI, 0.614-0.820), though difference of diagnostic performance among the 3D-CNN model (AUC = 0.693; 95% CI, 0.587-0.798) and radiomics analysis with PCA (AUC = 0.662; 95% CI, 0.548-0.776) and LASSO have not reached statistical significance (all p > 0.05). The radiomics score was higher in UC than in CD on the testing cohort (mean ± SD, UC 0.29 ± 1.05 versus CD -0.60 ± 1.25; p < 0.001). The LASSO model with adjustment of clinical covariates reached an AUC of 0.775 (95%CI, 0.683-0.868).

CONCLUSION

The developed volumetric VAT-based radiomics and 3D-CNN models provided comparable and effective performance for the characterization of CD from UC.

KEY POINTS

• High-output feature data extracted from volumetric visceral adipose tissue on CT enterography had an effective diagnostic performance for differentiating Crohn's disease from ulcerative colitis. • With adjustment of clinical covariates that cause difference in volumetric visceral adipose tissue, adjusted clinical machine learning model reached stronger performance when distinguishing Crohn's disease patients from ulcerative colitis patients.

Changes in abdominal fat depots after bariatric surgery are associated with improved metabolic profile

BACKGROUND AND AIMS

Obesity associated with a change in the quantity and quality of fat depots. Using computed tomography (CT), we analyzed abdominal fat depots in patients with obesity after bariatric surgery according to their metabolic health status.

METHODS AND RESULTS

We recruited 79 individuals with metabolically unhealthy obesity before bariatric surgery and compared them with age-sex matched healthy controls. The volume and fat attenuation index (FAI) of fat depots were measured using CT scans that were conducted prior to and a year after bariatric surgery. 'Metabolically healthy' was defined as having no hypertension, normal fasting glucose and a waist-to-hip ratio of <1.05 for men and <0.95 for women. Individuals who achieved a metabolic health status conversion (MHC) (n = 29, 37%)-from unhealthy to healthy-were younger (p < 0.001) as compared to individuals without MHC. Pre-surgery BMI and reduction of BMI did not differ between the two groups (p = 0.099, p = 0.5730). Bariatric surgery reduced the volume and increased the FAI of fat depots. Baseline lower abdominal periaortic adipose tissue (AT) volume (p = 0.014) and great percent reduction in renal sinus AT volume after surgery (p = 0.019) were associated with MHC after surgery. Increased intraperitoneal AT FAI (p = 0.031) was also associated with MHC.

CONCLUSION

MHC was not associated with improvement in general obesity, based on indicators such as reduction of BMI after surgery. Weight reduction induced specific abdominal fat depot changes measured by CT are positively associated with MHC.

Establishment of a Nomogram Based on Inflammatory Response-Related Methylation Sites in Intraoperative Visceral Adipose Tissue to Predict EWL% at One Year After LSG

Background

Laparoscopic sleeve gastrectomy (LSG) is considered as an effective bariatric and metabolic surgery for patients with severe obesity. Chronic low-grade inflammation of adipose tissue is associated with obesity and obesity-related complications.

Objective

This study intends to establish a nomogram based on inflammatory response-related methylation sites in intraoperative visceral adipose tissue (VAT) to predict excess weight loss (EWL)% at one-year after LSG.

Methods

Based on EWL% at one-year after LSG, patients were divided into two groups: the satisfied group (group-A, EWL%≥50%) and the unsatisfied group (group-B, EWL%<50%). Next, we defined genes corresponding to the methylation sites in the 850 K methylation microarray as methylation-related genes (MRGs). We then took the intersection of MRGs and inflammatory response-related genes. After that, inflammatory response-related methylation sites were identified based on overlapping genes. Moreover, difference analysis was carried out to obtain inflammatory response-related differentially methylated sites (IRRDMSs) between group-A and group-B. LASSO analysis was used to identify the hub methylation sites. Finally, we developed a nomogram based on the hub methylation sites.

Results

There were 26 patients in the study, with 13 in group-A and 13 in group-B. After data filtering and difference analysis, 200 IRRDMSs were identified (143 hypermethylated sites and 57 hypomethylated sites). Then, we identified three hub methylation sites (cg03610073, cg03208951, and cg18746357) by LASSO analysis and built a predictive nomogram (Area under the curve=0.953).

Conclusion

The predictive nomogram based on three inflammatory-related methylation sites (cg03610073, cg03208951, and cg18746357) in intraoperative visceral adipose tissue can predict one-year EWL% after LSG effectively.

Effects of lactoferrin on intestinal flora of metabolic disorder mice

To study the mechanism of lactoferrin (LF) regulating metabolic disorders in nutritionally obese mice through intestinal microflora. Twenty-one male C57BL/6 mice were randomly divided into 3 groups: control group, model group and LF treatment group. The mice in control group were fed with maintenance diet and drank freely. The mice in model group were fed with high fat diet and drank freely. The mice in LF treatment group were fed with high fat diet and drinking water containing 2% LF freely. Body weight was recorded every week. Visceral fat ratio was measured at week 12. Blood glucose and serum lipid level were detected by automatic biochemical analyzer. The gut microbiota of mice was examined using 16 s rRNA sequencing method. LF treatment significantly reduced the levels of visceral adipose ratio, blood glucose, triglyceride, total cholesterol and low-density lipoprotein cholesterol (LDL-C) in high-fat diet mice (p < 0.05). It can be seen that drinking water with 2% LF had a significant impact on metabolic disorders. At the same time, the Firmicutes/Bacteroidetes ratio(F/B) of LF treated mice was decreased. The abundance of Deferribacteres, Oscillibacter, Butyricicoccus, Acinetobacter and Mucispirillum in LF treatment group were significantly decreased, and the abundance of Dubosiella was significantly increased (p < 0.05). In the LF-treated group, the expression levels of glucose metabolism genes in gut microbiota were increased, and the expression levels of pyruvate metabolism genes were decreased. It can be seen that metabolic disorders were related to intestinal flora. In conclusion, LF regulates metabolic disorders by regulating intestinal flora.

GREM2 is associated with human central obesity and inhibits visceral preadipocyte browning

Background

Methods

Findings

Interpretation

Funding

Associations of body shapes with insulin resistance and cardiometabolic risk in middle-aged and elderly Chinese

Background

We aimed to define refined body shapes by using multiple anthropometric traits that represent fat distribution, and evaluate their associations with risk of insulin resistance (IR) and cardiometabolic disorders in a Chinese population.

Methods

We performed a cross-sectional analysis in 6570 community-based participants aged ≥ 40 years. Four body circumferences (neck, waist, hip, and thigh) and their ratios were put simultaneously into an open-source Waikato Environment for Knowledge Analysis platform to select the worthiest indicators in determining IR. The ratio of the top 3 fat distribution indicators was used to define the refined body shapes.

Results

We defined 8 distinct body shapes based on sex-specific combinations of waist-to-hip ratio (WHR), waist-to-thigh ratio (WTR), and waist-to-neck ratio (WNR), which differed in participants’ distribution and risk of IR and related cardiometabolic disorders. In women, as compared to the low WHR-low WTR-low WNR shape, all body shapes were significantly associated with IR and related cardiometabolic disorders; while in men, the low WHR-high WTR-high WNR shape and the higher WHR related shapes were significantly associated with IR and related cardiometabolic disorders. Stratified by WHR, the results were consistent in women; however, no significant associations were detected in men.

Conclusions

We defined 8 distinct body shapes by taking WHR, WTR, and WNR, simultaneously into account, which differed in association with the risk of IR and related cardiometabolic disorders in women. This study suggests that body shapes defined by multiple anthropometric traits could provide a useful, convenient, and easily available method for identifying cardiometabolic risk.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12986-021-00629-1.