Correlation analysis of dynamic changes of abdominal fat during rapid weight loss after bariatric surgery: A prospective magnetic resonance imaging study.

OBJECTIVE: The factors related to the changes in the liver and abdominal adipose tissue during the rapid weight loss after bariatric surgery remain uncertain. METHODS: This study included 44 participants who had undergone sleeve gastrectomy. The study aimed to analyze changes and correlations of body weight (BW), laboratory tests, and magnetic resonance imaging (MRI) indicators of the liver and abdominal adipose tissue conducted before and after bariatric surgery at 1, 3, and 6 months. RESULTS: Following a rapid weight loss within 6 months of surgery, there was a concurrent decrease in blood glucose, blood lipids, and fat content of the liver and abdomen and the changes showed a correlation. The change of BW (ΔBW) was positively correlated with the change of hepatic proton density fat fraction (ΔPDFF) in one and three months after surgery and was positively correlated with the change of abdominal visceral fat area (ΔAVFA) in six months after surgery, (P<0.05). In one month after surgery, ΔPDFF was positively correlated with the change of aspartate aminotransferase (ΔAST), change of alanine aminotransferase (ΔALT), and change of triglyceride glucose (ΔTYG) index (P<0.05). ΔPDFF was positively correlated with the change of hepatic native T1 values (P<0.001) and was moderately negatively correlated with the change of hepatic apparent diffusion coefficient (ΔADC) values in three months after surgery (P<0.05). CONCLUSION: ΔBW can serve as an indirect indicator for evaluating changes in liver fat fraction at 1 and 3 months after bariatric surgery and indicative of changes in visceral fat 6 months after surgery. ΔPDFF was positively correlated with ΔAST, ΔALT and ΔTYG index in 1 months after surgery.

Evaluating Sex-specific Differences in Abdominal Fat Volume and Proton Density Fat Fraction at MRI Using Automated nnU-Net-based Segmentation.

Sex-specific abdominal organ volume and proton density fat fraction (PDFF) in people with obesity during a weight loss intervention was assessed with automated multiorgan segmentation of quantitative water-fat MRI. An nnU-Net architecture was employed for automatic segmentation of abdominal organs, including visceral and subcutaneous adipose tissue, liver, and psoas and erector spinae muscle, based on quantitative chemical shift-encoded MRI and using ground truth labels generated from participants of the Lifestyle Intervention (LION) study. Each organ's volume and fat content were examined in 127 participants (73 female and 54 male participants; body mass index, 30-39.9 kg/m2) and in 81 (54 female and 32 male participants) of these participants after an 8-week formula-based low-calorie diet. Dice scores ranging from 0.91 to 0.97 were achieved for the automatic segmentation. PDFF was found to be lower in visceral adipose tissue compared with subcutaneous adipose tissue in both male and female participants. Before intervention, female participants exhibited higher PDFF in subcutaneous adipose tissue (90.6% vs 89.7%; P < .001) and lower PDFF in liver (8.6% vs 13.3%; P < .001) and visceral adipose tissue (76.4% vs 81.3%; P < .001) compared with male participants. This relation persisted after intervention. As a response to caloric restriction, male participants lost significantly more visceral adipose tissue volume (1.76 L vs 0.91 L; P < .001) and showed a higher decrease in subcutaneous adipose tissue PDFF (2.7% vs 1.5%; P < .001) than female participants. Automated body composition analysis on quantitative water-fat MRI data provides new insights for understanding sex-specific metabolic response to caloric restriction and weight loss in people with obesity. Keywords: Obesity, Chemical Shift-encoded MRI, Abdominal Fat Volume, Proton Density Fat Fraction, nnU-Net ClinicalTrials.gov registration no. NCT04023942 Supplemental material is available for this article. Published under a CC BY 4.0 license.

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.

Inflammatory markers in prepubertal children and their associations with abdominal fat.

OBJECTIVE: To evaluate the association between inflammatory markers and abdominal fat assessed by ultrasound in prepubertal children with and without excess weight. METHODS: A cross-sectional study involving 241 prepubertal children, 156 with obesity, 37 with overweight, and 48 with normal weight, aged five to ten years, who were followed at a research unit on Childhood Obesity from a teaching hospital belonging to a public health system. The concentration of interleukin-6, tumor necrosis factor-α and C-reactive protein were assessed and regression analyses, considering outcome variables such as abdominal wall and intra-abdominal fat thickness measured by ultrasound, were performed. RESULTS: The findings highlighted an association between abdominal fat and inflammatory markers, even in children at this young age group. Subcutaneous fat showed a stronger association with inflammatory biomarkers compared to intra-abdominal fat when performing logistic regression, with a positive association between tumor necrosis factor-α and abdominal wall thickness equal to or greater than the 75th percentile in adjusted logistic regression (OR: 18.12; CI 95 %: 1.57: 209.55). CONCLUSIONS: Abdominal wall fat, in contrast to what is often observed in adults, appears to have a greater impact on chronic inflammation related to excessive weight in very young children.

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.

Effect of Liraglutide in Different Abdominal Fat Layers Measured by Ultrasound: The Importance of Perirenal Fat Reduction.

INTRODUCTION: Ultrasonography (US) in patients with obesity allows us to measure different layers of abdominal fat (superficial subcutaneous, deep subcutaneous, preperitoneal, omental, and perirenal), not assessable by DEXA or CT scan. Omental and perirenal fat depots are considered predictors of metabolic complications. Liraglutide is particularly effective in reducing weight in patients with insulin-resistance, but its direct impact on each abdominal fat layer is unknown. METHODS: We measured, at the L4 level, all 5 abdominal fat depots in 860 patients with obesity (72.8% women, mean age 56.6 ± 1.5 years, BMI 34.4 ± 4.7 kg/m2, body fat 47 ± 2%, abdominal circumference 105.8 ± 3 cm), before and after 6 months of liraglutide treatment. Laboratory tests for glucose, insulin, and lipid profile were routinely done. T-student was used to compare intraindividual differences. RESULTS: Weight loss was 7.5 ± 2.8 kg (7.96% from baseline), with no differences by sex/age/BMI. Greater loss was observed in patients with higher dosages and NAFLD. All US-measured fat layers showed a significant reduction (p < 0.05) at 6th months. Preperitoneal fat showed a -26 ± 5.5% reduction and 46% of the patients went below metabolic syndrome (MS) risk cut-off values. Omental fat was reduced by -17.8 ± 5% (67% of the patients below MS risk) and perirenal fat by -22.4 ± 4.4% (56% of the patients below MS). Both omental and perirenal fat reduction correlated with total and LDL cholesterol. Higher perirenal fat reduction (-28%) was seen among patients with obesity and hypertension. Perirenal fat also correlated with blood pressure reduction. CONCLUSION: Liraglutide induces greater fat loss in the layers involved with MS. However, the maximal reduction is seen at perirenal fat, which has been recently related with hypertension and could play an important role in modulating kidney's expansion and intraglomerular pressure. US is a reproducible clinical tool to assess pathologic fat depots in patients living with obesity.

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.

Comparison of abdominal adipose tissue versus body mass index (BMI) as a predictor of complications and survival in liver transplantation.

INTRODUCTION: Because of the obesity epidemic, more obese patients are on liver transplant (LT) waiting lists. The diseases associated with obesity may increase complications and limit survival after LT. However, there is no established measure or cut-off point to determine this impact and aid decision making. The aim of the present study is to evaluate obesity in patients undergoing LT via BMI and CT-based measurement of adipose tissue (AAT). These parameters will be used to predict the risk of postoperative complications and 5-year survival. METHODS: A retrospective, single-center study was carried out at a tertiary Spanish hospital, including all patients who received LT between January 2012 and July 2019 (n = 164). The patients were adults who underwent LT using the 'piggyback' technique, preserving the recipient vena cava. Visceral adipose tissue (VAT) and BMI were calculated to examine correlations with postoperative complications and 5-year survival. RESULTS: No significant association was found between postoperative complications by Comprehensive Complication Index, BMI, AAT/height, subcutaneous fat/height and VAT/height. Kaplan-Meier curves for 5-year survival compared LT recipients with BMI < 30.45 versus ≥30.45, with an estimated survival of 58.97 months versus 43.11 months, respectively (P < .001) (Fig. 3) and for LT recipients with an AAT/height <27.35 mm versus ≥27.35 mm, with an estimated survival of 57.69 months versus 46.34 months (P = .001). CONCLUSIONS: This study does not show a higher rate of postoperative complications in obese patients. There is a significantly lower long-term survival in patients with AAT/height ≥27.35 mm and BMI ≥ 30.45. BMI is a valid estimate of obesity and is predictive of survival.

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.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs.

OBJECTIVE: Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs. MATERIALS AND METHODS: 920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC). RESULTS: ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis. DISCUSSION: ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

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 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.

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 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.

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.

Ultrasonographic abdominal adipose tissue thickness for the prediction of gestational diabetes mellitus: A meta-analysis.

Obesity has been linked to the risk of gestational diabetes mellitus (GDM). The meta-analysis aimed to assess the predictive role of ultrasonographic measurements of the abdominal adipose tissue thickness for GDM in pregnant women. Cohort studies evaluating the association between abdominal subcutaneous and/or visceral adipose thickness (SAT and/or VAT) and subsequent risk of GDM were retrieved from PubMed, Embase, and Web of Science databases. Only studies with SAT/VAT measured before the diagnosis of GDM were included. Random-effects models incorporating the influence of potential heterogeneity were used to pool the results. A total of 13 studies involving 5616 pregnant women were included. Pooled results showed that both a high abdominal SAT (odds ratio [OR] for per 1-cm increment: 1.23, 95% confidence interval [CI]: 1.07 to 1.41, P = 0.003, I2 = 13%; OR for high versus low category: 3.42, 95% CI: 2.31 to 5.07, P < 0.001, I2 = 0%) and VAT (OR for per 1-cm increment: 1.54, 95% CI: 1.16 to 2.06, P = 0.003, I2 = 63%; OR for high versus low category: 5.73, 95% CI: 3.39 to 9.77, P < 0.001, I2 = 31%) at early stages of pregnancy were associated with a higher subsequent risk of GDM. Subgroup analysis based on study design, timing of ultrasound examination, GDM diagnostic criteria, and study quality score showed consistent results. In conclusion, ultrasound-measured abdominal adipose tissue thickness may be useful for predicting the subsequent risk of GDM in pregnant women.

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.