AATCT-IDS: A benchmark Abdominal Adipose Tissue CT Image Dataset for image denoising, semantic segmentation, and radiomics evaluation.

BACKGROUND AND OBJECTIVE: The metabolic syndrome induced by obesity is closely associated with cardiovascular disease, and the prevalence is increasing globally, year by year. Obesity is a risk marker for detecting this disease. However, current research on computer-aided detection of adipose distribution is hampered by the lack of open-source large abdominal adipose datasets. METHODS: In this study, a benchmark Abdominal Adipose Tissue CT Image Dataset (AATCT-IDS) containing 300 subjects is prepared and published. AATCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. RESULTS: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATCT-IDS reveals three adipose distributions in the subject population. CONCLUSION: AATCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/AATTCT-IDS/23807256.

Optimizing CT Abdomen-Pelvis Scan Radiation Dose: Examining the Role of Body Metrics (Waist Circumference, Hip Circumference, Abdominal Fat, and Body Mass Index) in Dose Efficiency.

Objective: This study investigates the correlation between patient body metrics and radiation dose in abdominopelvic CT scans, aiming to identify significant predictors of radiation exposure. Methods: Employing a cross-sectional analysis of patient data, including BMI, abdominal fat, waist, abdomen, and hip circumference, we analyzed their relationship with the following dose metrics: the CTDIvol, DLP, and SSDE. Results: Results from the analysis of various body measurements revealed that BMI, abdominal fat, and waist circumference are strongly correlated with increased radiation doses. Notably, the SSDE, as a more patient-centric dose metric, showed significant positive correlations, especially with waist circumference, suggesting its potential as a key predictor for optimizing radiation doses. Conclusions: The findings suggest that incorporating patient-specific body metrics into CT dosimetry could enhance personalized care and radiation safety. Conclusively, this study highlights the necessity for tailored imaging protocols based on individual body metrics to optimize radiation exposure, encouraging further research into predictive models and the integration of these metrics into clinical practice for improved patient management.

Assessment of transabdominal fat volumes as a predictor of prognosis in patients undergoing transcatheter aortic valve replacement.

Transcatheter aortic valve replacement (TAVR) has emerged as a well-established treatment option for eligible patients with severe aortic stenosis. This study aimed to investigate the correlation between abdominal fat tissue volumes, measured using computed tomography (CT), and all-cause mortality in patients undergoing TAVR. The study included 258 consecutive patients who underwent TAVR at a single center between September 2017 and November 2020. During the preoperative preparation, CT scans were used to perform a semi-quantitative measurement of abdominal fat components. Body mass index (BMI) for each participant was calculated. The relationship between fat parameters and overall survival was determined using multivariable Cox proportional hazards models. Participants had a mean age of 76.8 ± 7.8 years, of whom 32.9% were male. The median follow-up period was 12 months, during which 38 patients (14.7%) died. Both the survivor and non-survivor groups showed comparable risk factors. Regarding transabdominal fat volume parameters, deceased individuals exhibited significantly lower values. However, no significant differences were observed in BMI and transabdominal area measurements. Among transabdominal fat parameters, only subcutaneous fat volume [adjusted Hazard Ratio (aHR) = 0.83, p = 0.045] and total fat volume (TFV) [aHR = 0.82, p = 0.007] were identified as significant predictors of reduced all-cause mortality. Furthermore, TFV demonstrated the highest discriminative performance with a threshold of ≤ 9.1 L (AUC = 0.751, p < 0.001, sensitivity 71.1%, specificity 70.9%). Preoperative CT-based abdominal fat volume parameters, particularly TFV, can serve as potential predictors of survival in patients undergoing TAVR.

Evaluation for Performance of Body Composition Index Based on Quantitative Computed Tomography in the Prediction of Metabolic Syndrome.

Objective: We aimed to evaluate the performance of predicting metabolic syndrome (MS) using body composition indices obtained by quantitative computed tomography (QCT). Methods: In this cross-sectional study, data were collected from 4745 adults who underwent QCT examinations at a Chongqing teaching hospital between July 2020 and March 2022. Visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), total abdominal fat (TAT), abdominal muscle tissue (AMT), and liver fat content (LFC) were measured at the L2-L3 disc level using specialized software, and the skeletal muscle index (SMI) were calculated. The correlations between body composition indicators were analyzed using the Pearson correlation analysis. Receiver operating characteristic (ROC) curve analysis and area under the curve (AUC) were used to assess these indicators' predictive potential for MS. Results: VAT and TAT exhibited the best predictive ability for MS, with AUCs of 0.797 [95% confidence interval (CI): 0.779-0.815] and 0.794 (95% CI: 0.775-0.812) in males, and 0.811 (95% CI: 0.785-0.836) and 0.802 (95% CI: 0.774-0.830) in females. The AUCs for VAT and TAT were the same but significantly higher than body mass index and other body composition measures. SAT also demonstrated good predictive power in females [AUC = 0.725 (95%CI: 0.692-0.759)] but fair power in males [AUC = 0.6673 (95%CI: 0.650-0.696)]. LFC showed average predictive ability, AMT showed average predictive ability in males but poor ability in females, and SMI had no predictive ability. Correlation analysis revealed a strong correlation between VAT and TAT (males: r = 0.95, females: r = 0.89). SAT was strongly correlated with TAT only in females (r = 0.89). In the male group, the optimal thresholds for VAT and TAT were 207.6 and 318.7 cm2, respectively; in the female group, the optimal thresholds for VAT and TAT were 128.0 and 269.4 cm2, respectively. Conclusions: VAT and TAT are the best predictors of MS. SAT and LFC can also be acceptable to make predictions, whereas AMT can only make predictions of MS in males.

Abdominal fat depots are related to lower cognitive functioning and brain volumes in middle-aged males at high Alzheimer's risk.

OBJECTIVE: High BMI, which poorly represents specific fat depots, is linked to poorer cognition and higher dementia risk, with different associations between sexes. This study examined associations of abdominal fat depots with cognition and brain volumes and whether sex modifies this association. METHODS: A total of 204 healthy middle-aged offspring of Alzheimer's dementia patients (mean age = 59.44, 60% females) underwent abdominal magnetic resonance imaging to quantify hepatic, pancreatic, visceral, and subcutaneous adipose tissue and to assess cognition and brain volumes. RESULTS: In the whole sample, higher hepatic fat percentage was associated with lower total gray matter volume (ß = -0.17, p < 0.01). Primarily in males, higher pancreatic fat percentage was associated with lower global cognition (males: ß = -0.27, p = 0.03; females: ß = 0.01, p = 0.93) executive function (males: ß = -0.27, p = 0.03; females: ß = 0.02, p = 0.87), episodic memory (males: ß = -0.28, p = 0.03; females: ß = 0.07, p = 0.48), and inferior frontal gyrus volume (males: ß = -0.28, p = 0.02; females: ß = 0.10, p = 0.33). Visceral and subcutaneous adipose tissue was inversely associated with middle frontal and superior frontal gyrus volumes in males and females. CONCLUSIONS: In middle-aged males at high Alzheimer's dementia risk, but not in females, higher pancreatic fat was associated with lower cognition and brain volumes. These findings suggest a potential sex-specific link between distinct abdominal fat with brain health.

Behind BMI: The Potential Indicative Role of Abdominal Ectopic Fat on Glucose Metabolism.

INTRODUCTION: The purpose of this study was to compare the difference in abdominal fat distribution between different metabolic groups and find the ectopic fat with the most risk significance. METHODS: A total of 98 subjects were enrolled; there were 53 cases in the normal glucose metabolism group and 45 cases in the abnormal glucose metabolism group. Chemical shift-encoded magnetic resonance imaging was applied for quantification of pancreatic fat fraction (PFF) and hepatic fat fraction (HFF), subcutaneous adipose tissue (SAT), and visceral adipose tissue (VAT). The correlation and the difference of fat distribution between different metabolism groups were analyzed. The receiver operating characteristic (ROC) curve was used to analyze the suggestive effect of different body fat fraction. RESULTS: Correlation analysis showed that body mass index (BMI) had the strongest correlation with fasting insulin (r = 0.473, p < 0.001), HOMA-IR (r = 0.363, p < 0.001), and C-reactive protein (r = 0.245, p < 0.05). Pancreatic fat has a good correlation with fasting blood glucose (r = 0.247, p < 0.05) and HbA1c (r = 0.363, p < 0.001). With the increase of BMI, PFF, VAT, and SAT showed a clear upward trend, but liver fat was distributed relatively more randomly. The pancreatic fat content in the abnormal glucose metabolism group is significantly higher than that in the normal group, and pancreatic fat is also a reliable indicator of abnormal glucose metabolism, especially in the normal and overweight groups (the area under the curve was 0.859 and 0.864, respectively). CONCLUSION: MR-based fat quantification techniques can provide additional information on fat distribution. There are differences in fat distribution among people with different metabolic status. People with more severe pancreatic fat deposition have a higher risk of glucose metabolism disorders.

Abdominal adipose tissue distribution assessed by computed tomography and mortality in hospitalised patients with COVID-19: a retrospective longitudinal cohort study.

BACKGROUND: Visceral adiposity has been associated with an increased risk of critical illness in COVID-19 patients. However, if it also associates to a poor survival is still not well established. The aim of the study was to assess the relationship between abdominal fat distribution and COVID-19 mortality. METHODS: In this six-month longitudinal cohort study, abdominal visceral (VAT) and subcutaneous adipose tissues (SAT) were measured by computed tomography in a cohort of 174 patients admitted to the emergency department with a diagnosis of COVID-19, during the first wave of pandemic. The primary exposure and outcome measures were VAT and SAT at hospital admission, and death at 30 and 180 days, respectively. RESULTS: Overall survival was not different according to VAT (p = 0.94), SAT (p = 0.32) and VAT/SAT ratio (p = 0.64). However, patients in the lowest SAT quartile (thickness ≤ 11.25 mm) had a significantly reduced survival compared to those with thicker SAT (77 vs. 94% at day 30; 74 vs. 91% at day 180, p = 0.01). Similarly, a thinner SAT was associated with lower survival in Intensive Care Unit (ICU) admitted patients, independently of sex or age (p = 0.02). The VAT/SAT ratio showed a non-linear increased risk of ICU admission, which plateaued out and tended for inversion at values greater than 1.9 (p = 0.001), although was not associated with increased mortality rate. CONCLUSIONS: In our cohort, visceral adiposity did not increase mortality in patients with COVID-19, but low SAT may be associated with poor survival.

Abdominal Fat is a Reliable Indicator of Lumbar Intervertebral Disc Degeneration than Body Mass Index.

OBJECTIVE: The objective of this study was to determine whether abdominal fat status correlates with low back pain (LBP) and lumbar intervertebral disc degeneration (IVDD) and to identify a new anthropometric index to predict the likelihood of developing LBP. METHODS: Patients with chronic low back pain admitted to the Affiliated Hospital of Southwest Medical University from June 2022 to May 2023 were collected as the experimental group. Volunteers without LBP from June 2022 to May 2023 were also recruited as the control group. They underwent lumbar spine magnetic resonance imaging and had their body mass index (BMI) measured. Abdominal parameters were measured on T2-weighted median sagittal magnetic resonance imaging at the L3/4 level: abdominal diameter, sagittal abdominal diameter (SAD), and subcutaneous abdominal fat thickness (SAFT). Each lumbar IVDD was assessed using the Pfirrmann grading system. The differences in abdominal parameters and BMI between the experimental and control groups were compared, and the correlations between abdominal parameters, BMI, LBP, and IVDD were analyzed. RESULTS: Abdominal diameter, SAD, and SAFT had moderate-to-strong correlations with BMI. SAD was significantly associated with severe IVDD at L4-L5 and L5-S1 levels with odds ratio of 3.201 (95% confidence interval [CI]: 1.850-5.539, P < 0.001) and 1.596 (95% CI: 1.072-2.378, P = 0.021), respectively. BMI had no significant association with severe IVDD. In women, SAFT and BMI were significantly correlated with LBP; in men, only SAFT was significantly correlated with LBP. Appropriate cutoff values for men and women were 1.52 cm (area under the curve = 0.702, 95% CI: 0.615-0.789, P < 0.001) and 1.97 cm (area under the curve = 0.740, 95% CI: 0.662-0.818, P < 0.001), respectively. Men and women with SAFT of >1.52 cm and >1.97 cm, respectively, had significantly higher rates of LBP. CONCLUSIONS: SAD could predict severe IVDD better than BMI. SAFT is a better predictor of LBP than BMI, especially in men, and reliably distinguished patients with LBP from asymptomatic subjects with reliable cutoff values for men and women.

Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms.

Diabetic kidney disease (DKD) is a significant healthcare burden worldwide that substantially increases the risk of kidney failure and cardiovascular events. To reduce the prevalence of DKD, extensive research is being conducted to determine the risk factors and consequently implement early interventions. Patients with type 2 diabetes mellitus (T2DM) are more likely to be obese. Abdominal adiposity is associated with a greater risk of kidney damage than general obesity. Abdominal adipose tissue can be divided into different fat depots according to the location and function, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PAT), and renal sinus adipose tissue (RSAT), which can be accurately measured by radiology techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). Abdominal fat depots may affect the development of DKD through different mechanisms, and radiologic abdominal adipose characteristics may serve as imaging indicators of DKD risk. This review will first describe the CT/MRI-based assessment of abdominal adipose depots and subsequently describe the current studies on abdominal adipose tissue and DKD development, as well as the underlying mechanisms in patients of T2DM with DKD.

Isthmin-1 (ISM1), a novel adipokine that reflects abdominal adipose tissue distribution in individuals with obesity.

BACKGROUND: The assessment of obesity-related health risks has traditionally relied on the Body Mass Index and waist circumference, but their limitations have propelled the need for a more comprehensive approach. The differentiation between visceral (VIS) and subcutaneous (SC) fat provides a finer-grained understanding of these risks, yet practical assessment methods are lacking. We hypothesized that combining the SC-VIS fat ratio with non-invasive biomarkers could create a valuable tool for obesity-related risk assessment. METHODS AND RESULTS: A clinical study of 125 individuals with obesity revealed significant differences in abdominal fat distribution measured by CT-scan among genders and distinct models of obesity, including visceral, subcutaneous, and the SC/VIS ratio. Stratification based on these models highlighted various metabolic changes. The SC/VIS ratio emerged as an excellent metric to differentiate metabolic status. Gene expression analysis identified candidate biomarkers, with ISM1 showing promise. Subsequent validation demonstrated a correlation between ISM1 levels in SC and plasma, reinforcing its potential as a non-invasive biomarker for fat distribution. Serum adipokine levels also correlated with the SC/VIS ratio. The Receiver Operating Characteristic analysis revealed ISM1's efficacy in discriminating individuals with favorable metabolic profiles based on adipose tissue distribution. Correlation analysis also suggested that ISM1 was involved in glucose regulation pathways. CONCLUSION: The study's results support the hypothesis that the SC-VIS fat ratio and its derived non-invasive biomarkers can comprehensively assess obesity-related health risks. ISM1 could predict abdominal fat partitioning and be a potential biomarker for evaluating obesity-related health risks.

Abdominal fat depots and their association with insulin resistance in patients with type 2 diabetes.

BACKGROUND: Asian-Indians show thin fat phenotype, characterized by predominantly central deposition of excess fat. The roles of abdominal subcutaneous fat (SAT), intra-peritoneal adipose tissue, and fat depots surrounding the vital organs (IPAT-SV) and liver fat in insulin resistance (IR), type-2 diabetes (T2D) and metabolic syndrome (MetS) in this population are sparsely investigated. AIMS AND OBJECTIVES: Assessment of liver fat, SAT and IPAT-SV by MRI in subjects with T2D and MetS; and to investigate its correlation with IR, specifically according to different quartiles of HOMA-IR. METHODS: Eighty T2D and the equal number of age sex-matched normal glucose tolerant controls participated in this study. Abdominal SAT, IPAT-SV and liver fat were measured using MRI. IR was estimated by the Homeostatic Model Assessment for Insulin Resistance (HOMA-IR). RESULTS: T2D and MetS subjects have higher quantity liver fat and IPAT-SV fat than controls (P = 9 x 10-4 and 4 x 10-4 for T2D and 10-4 and 9 x 10-3 for MetS subjects respectively). MetS subjects also have higher SAT fat mass (P = 0.012), but not the BMI adjusted SAT fat mass (P = 0.48). Higher quartiles of HOMA-IR were associated with higher BMI, W:H ratio, waist circumference, and higher liver fat mass (ANOVA Test P = 0.020, 0.030, 2 x 10-6 and 3 x 10-3 respectively with F-values 3.35, 3.04, 8.82, 4.47 respectively). In T2D and MetS subjects, HOMA-IR showed a moderately strong correlation with liver fat (r = 0.467, P < 3 x 10-5 and r = 0.493, P < 10-7), but not with SAT fat and IPAT-SV. However, in MetS subjects IPAT-SV fat mass showed borderline correlation with IR (r = 0.241, P < 0.05), but not with the BMI adjusted IPAT-SV fat mass (r = 0.13, P = 0.26). In non-T2D and non-MetS subjects, no such correlation was seen. On analyzing the correlation between the three abdominal adipose compartment fat masses and IR according to its severity, the correlation with liver fat mass becomes stronger with increasing quartiles of HOMA-IR, and the strongest correlation is seen in the highest quartile (r = 0.59, P < 10-3). On the other hand, SAT fat mass tended to show an inverse relation with IR with borderline negative correlation in the highest quartile (r = -0.284, P < 0.05). IPAT-SV fat mass did not show any statistically significant correlation with HOMA-IR, but in the highest quartile it showed borderline, but statistically insignificant positive correlation (P = 0.07). CONCLUSION: In individuals suffering from T2D and MetS, IR shows a trend towards positive and borderline negative correlation with liver fat and SAT fat masses respectively. The positive trend with liver fat tends to become stronger with increasing quartile of IR. Therefore, these findings support the theory that possibly exhaustion of protective compartment's capacity to store excess fat results in its pathological deposition in liver as ectopic fat.

Polycystic ovary syndrome and abdominal fat: is there a relationship?

OBJECTIVE: The aim of this study was to compare the distribution of fat tissue in non-obese women with polycystic ovary syndrome and those without the syndrome using dual-energy radiological densitometry. METHODS: This was a case-control study in which we enrolled women aged 14-39 years with polycystic ovary syndrome according to the Rotterdam criteria with a body mass index between 18.5 and 30 kg/m2. The control group comprised women with the same profile, but without polycystic ovary syndrome. Patients were treated at the Endocrinological Gynecology Outpatient Clinic of the Department of Obstetrics and Gynecology of the Irmandade da Santa Casa de Misericórdia de São Paulo between 2019 and 2022. Anthropometric measurements were taken and the assessment of body composition was performed using dual-energy radiological densitometry. RESULTS: The sample comprised 57 women: 37 in the polycystic ovary syndrome group and 20 in the control group. The mean age of the polycystic ovary syndrome group was 24.9 years (±6.9) with a mean body mass index of 60.8 kg/m2 (±8.5), and for the control group, it was 24.2 years (±6.9) with a mean body mass index of 58 kg/m2 (±8.4). Body composition was evaluated using dual-energy radiological densitometry and showed a higher value of trunk fat in the polycystic ovary syndrome group (44.1%, ±9.0) compared to the control group (35.2%, ±11.4), which was statistically significant (p=0.002). CONCLUSION: Our study showed that non-obese polycystic ovary syndrome patients have a higher concentration of abdominal fat, which is a risk factor for increased cardiovascular risk and insulin resistance.ClinicalTrials.gov ID: NCT02467751.

A fully convolutional neural network for comprehensive compartmentalization of abdominal adipose tissue compartments in MRI.

BACKGROUND: Existing literature has highlighted structural, physiological, and pathological disparities among abdominal adipose tissue (AAT) sub-depots. Accurate separation and quantification of these sub-depots are crucial for advancing our understanding of obesity and its comorbidities. However, the absence of clear boundaries between the sub-depots in medical imaging data has challenged their separation, particularly for internal adipose tissue (IAT) sub-depots. To date, the quantification of AAT sub-depots remains challenging, marked by a time-consuming, costly, and complex process. PURPOSE: To implement and evaluate a convolutional neural network to enable granular assessment of AAT by compartmentalization of subcutaneous adipose tissue (SAT) into superficial subcutaneous (SSAT) and deep subcutaneous (DSAT) adipose tissue, and IAT into intraperitoneal (IPAT), retroperitoneal (RPAT), and paraspinal (PSAT) adipose tissue. MATERIAL AND METHODS: MRI datasets were retrospectively collected from Singapore Preconception Study for Long-Term Maternal and Child Outcomes (S-PRESTO: 389 women aged 31.4 ± 3.9 years) and Singapore Adult Metabolism Study (SAMS: 50 men aged 28.7 ± 5.7 years). For all datasets, ground truth segmentation masks were created through manual segmentation. A Res-Net based 3D-UNet was trained and evaluated via 5-fold cross-validation on S-PRESTO data (N = 300). The model's final performance was assessed on a hold-out (N = 89) and an external test set (N = 50, SAMS). RESULTS: The proposed method enabled reliable segmentation of individual AAT sub-depots in 3D MRI volumes with high mean Dice similarity scores of 98.3%, 97.2%, 96.5%, 96.3%, and 95.9% for SSAT, DSAT, IPAT, RPAT, and PSAT respectively. CONCLUSION: Convolutional neural networks can accurately sub-divide abdominal SAT into SSAT and DSAT, and abdominal IAT into IPAT, RPAT, and PSAT with high accuracy. The presented method has the potential to significantly contribute to advancements in the field of obesity imaging and precision medicine.

Effects of the abdominal fat distribution on the relationship between exposure to air pollutants and thyroid hormones among Korean adult males.

BACKGROUND: Several significant associations between air pollution and thyroid function have been reported, but few studies have identified whether these associations differ by obesity, particularly its regional distribution. We assessed the relationship between ambient air pollution and thyroid hormone, and whether this relationship is modified by abdominal adiposity, as indicated by the waist circumference, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and visceral-to-subcutaneous fat ratio (VSR) in Korean men. METHODS: We included 2440 male adults in the final analysis and used each person's annual average exposure to four air pollutants: particulate matter with an aerodynamic diameter ≤ 10 µm (PM10), nitrogen dioxide, sulfur dioxide (SO2), and carbon monoxide (CO). Abdominal fat deposition was quantified by computed tomography. Serum thyrotropin (TSH) and free thyroxine (FT4) concentrations were measured for thyroid hormone. To evaluate the relationship between air pollution and thyroid hormone according to adiposity, we performed multiple linear regression analysis on the two subgroups stratified by abdominal fat level. RESULTS: Abdominal adiposity was significantly related to FT4 concentration. The exposures to air pollutants were associated with increased TSH and decreased FT4 concentrations. In stratified analysis using abdominal fat traits, ambient air pollution except for SO2 was significantly related to increased TSH and decreased FT4 concentrations in the high adiposity group (all p < 0.05), but not in the normal adiposity group. Among the air pollutants, PM10 showed an association with an increase of TSH concentration in all group with high adiposity, including high VAT, high SAT, and high VSR groups (all p < 0.05). In case of FT4, CO showed a similar pattern. Among the abdominal fat-related traits, the VSR in the high adiposity group had the largest effect on the relationship between exposure to air pollutants and thyroid hormone. CONCLUSIONS: This study suggests the first clue that the relationship between air pollution exposure and thyroid hormone differs according to abdominal fat distribution among Korean adult males.

Abdominal fat quantification using convolutional networks.

OBJECTIVES: To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance-accuracy, reliability, processing effort, and time-in comparison with an interactive reference method. MATERIALS AND METHODS: Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures. RESULTS: The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT. CONCLUSION: The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification. CLINICAL RELEVANCE STATEMENT: Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity. KEY POINTS: • This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. • Supervised deep learning-based methods using fully convolutional networks  were suited best. • Measures of accuracy were equal to or better than the operator-driven approach.

Lower abdominal adipose tissue cannabinoid type 1 receptor availability in young men with overweight.

OBJECTIVE: Cannabinoid type 1 receptors (CB1R) modulate feeding behavior and energy homeostasis, and the CB1R tone is dysgulated in obesity. This study aimed to investigate CB1R availability in peripheral tissue and brain in young men with overweight versus lean men. METHODS: Healthy males with high (HR, n = 16) or low (LR, n = 20) obesity risk were studied with fluoride 18-labeled FMPEP-d2 positron emission tomography to quantify CB1R availability in abdominal adipose tissue, brown adipose tissue, muscle, and brain. Obesity risk was assessed by BMI, physical exercise habits, and familial obesity risk, including parental overweight, obesity, and type 2 diabetes. To assess insulin sensitivity, fluoro-[18 F]-deoxy-2-D-glucose positron emission tomography during hyperinsulinemic-euglycemic clamp was performed. Serum endocannabinoids were analyzed. RESULTS: CB1R availability in abdominal adipose tissue was lower in the HR than in the LR group, whereas no difference was found in other tissues. CB1R availability of abdominal adipose tissue and brain correlated positively with insulin sensitivity and negatively with unfavorable lipid profile, BMI, body adiposity, and inflammatory markers. Serum arachidonoyl glycerol concentration was associated with lower CB1R availability of the whole brain, unfavorable lipid profile, and higher serum inflammatory markers. CONCLUSIONS: The results suggest endocannabinoid dysregulation already in the preobesity state.

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.

PURPOSE: Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. MATERIALS AND METHODS: This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. RESULTS: There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons. CONCLUSION: We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.

Differences in specific abdominal fat depots between metabolically healthy and unhealthy children with overweight/obesity: The role of cardiorespiratory fitness.

OBJECTIVES: Fat depots localization has a critical role in the metabolic health status of adults. Nevertheless, whether that is also the case in children remains under-studied. Therefore, the aims of this study were: (i) to examine the differences between metabolically healthy (MHO) and unhealthy (MUO) overweight/obesity phenotypes on specific abdominal fat depots, and (ii) to further explore whether cardiorespiratory fitness plays a major role in the differences between metabolic phenotypes among children with overweight/obesity. METHODS: A total of 114 children with overweight/obesity (10.6 ± 1.1 years, 62 girls) were included. Children were classified as MHO (n = 68) or MUO. visceral (VAT), abdominal subcutaneous (ASAT), intermuscular abdominal (IMAAT), psoas, hepatic, pancreatic, and lumbar bone marrow adipose tissues were measured by magnetic resonance imaging. Cardiorespiratory fitness was assessed using the 20 m shuttle run test. RESULTS: MHO children had lower VAT and ASAT contents and psoas fat fraction compared to MUO children (difference = 12.4%-25.8%, all p < 0.035). MUO-unfit had more VAT and ASAT content than those MUO-fit and MHO-fit (difference = 34.8%-45.3%, all p < 0.044). MUO-unfit shows also greater IMAAT fat fraction than those MUO-fit and MHO-fit peers (difference = 16.4%-13.9% respectively, all p ≤ 0.001). In addition, MHO-unfit presented higher IMAAT fat fraction than MHO-fit (difference = 13.4%, p < 0.001). MUO-unfit presented higher psoas fat fraction than MHO-fit (difference = 29.1%, p = 0.008). CONCLUSIONS: VAT together with ASAT and psoas fat fraction, were lower in MHO than in MUO children. Further, we also observed that being fit, regardless of metabolic phenotype, has a protective role over the specific abdominal fat depots among children with overweight/obesity.

Association between abdominal perivascular adipose tissue quantity and quality assessed by CT and cardiometabolic risk.

BACKGROUND & AIMS: Changes in the perivascular adipose tissue (PVAT) are associated with the risk of metabolic syndrome (MetS). We hypothesized that the quantity and quality of PVAT measured by computed tomography (CT) are associated with cardiometabolic risk. METHODS: This study analyzed the data of 505 participants (men, 72.7%) who underwent general health checkups, including abdominal and pelvic CT. We measured the volume and fat attenuation index (FAI) of the abdominal periaortic (APA) and renal sinus (RS) adipose tissues. Participants were categorized into three groups according to the number of MetS components they had based on the modified ATP III criteria (0, 1-2, and ≥3). RESULTS: Moving stepwise from the no MetS component group to the 1-2 components group to the ≥3 components group, all PVAT volumes increased and all PVAT FAIs decreased consistently. Greater PVAT volume was independently associated with greater prevalence of MetS components in the ≥3 components group (P = 0.002 for right RS, P = 0.027 for left RS, and P = 0.001 for APA), whereas lower FAI in all PVATs was associated with greater prevalence of MetS components in the 1-2 components group after adjusting for the corresponding adipose tissue volumes (P = 0.007 for right RS, P = 0.002 for left RS, and P = 0.001 for APA). CONCLUSION: Higher abdominal PVAT volume was independently associated with prevalent MetS. Moreover, lower abdominal PVAT FAI was associated with mild metabolic derangement. Image-based assessment of abdominal PVAT may be a potential biomarker for cardiometabolic risk.

Abdominal fat and muscle distributions in different stages of colorectal cancer.

BACKGROUND: The purpose of this study is to explore the difference of abdominal fat and muscle composition, especially subcutaneous and visceral adipose tissue, in different stages of colorectal cancer (CRC). MATERIALS AND METHODS: Patients were divided into 4 groups: healthy controls (patients without colorectal polyp), polyp group (patients with colorectal polyp), cancer group (CRC patients without cachexia), and cachexia group (CRC patients with cachexia). Skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT) were assessed at the third lumbar level on computed tomography images obtained within 30 days before colonoscopy or surgery. One-way ANOVA and linear regression were used to analyze the difference of abdominal fat and muscle composition in different stages of CRC. RESULTS: A total of 1513 patients were divided into healthy controls, polyp group, cancer group, and cachexia group, respectively. In the development of CRC from normal mucosa to polyp and cancer, the VAT area of the polyp group was significantly higher than that of the healthy controls both in male (156.32 ± 69.71 cm2 vs. 141.97 ± 79.40 cm2, P = 0.014) and female patients (108.69 ± 53.95 cm2 vs. 96.28 ± 46.70 cm2, P = 0.044). However, no significant differences were observed of SAT area between polyp group and healthy controls in both sexes. SAT area decreased significantly in the male cancer group compared with the polyp group (111.16 ± 46.98 cm2 vs. 126.40 ± 43.52 cm2, P = 0.001), while no such change was observed in female patients. When compared with healthy controls, the SM, IMAT, SAT, and VAT areas of cachexia group was significantly decreased by 9.25 cm2 (95% CI: 5.39-13.11 cm2, P < 0.001), 1.93 cm2 (95% CI: 0.54-3.32 cm2, P = 0.001), 28.84 cm2 (95% CI: 17.84-39.83 cm2, P < 0.001), and 31.31 cm2 (95% CI: 18.12-44.51 cm2, P < 0.001) after adjusting for age and gender. CONCLUSION: Abdominal fat and muscle composition, especially SAT and VAT, was differently distributed in different stages of CRC. It is necessary to pay attention to the different roles of subcutaneous and visceral adipose tissue in the development of CRC.