Distinct associations of intraperitoneal and retroperitoneal visceral adipose tissues with metabolic syndrome and its components

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.

Cellular Senescence and Extracellular Vesicles in the Pathogenesis and Treatment of Obesity-A Narrative Review

This narrative review explores the pathophysiology of obesity, cellular senescence, and exosome release. When exposed to excessive nutrients, adipocytes develop mitochondrial dysfunction and generate reactive oxygen species with DNA damage. This triggers adipocyte hypertrophy and hypoxia, inhibition of adiponectin secretion and adipogenesis, increased endoplasmic reticulum stress and maladaptive unfolded protein response, metaflammation, and polarization of macrophages. Such feed-forward cycles are not resolved by antioxidant systems, heat shock response pathways, or DNA repair mechanisms, resulting in transmissible cellular senescence via autocrine, paracrine, and endocrine signaling. Senescence can thus affect preadipocytes, mature adipocytes, tissue macrophages and lymphocytes, hepatocytes, vascular endothelium, pancreatic β cells, myocytes, hypothalamic nuclei, and renal podocytes. The senescence-associated secretory phenotype is closely related to visceral adipose tissue expansion and metaflammation; inhibition of SIRT-1, adiponectin, and autophagy; and increased release of exosomes, exosomal micro-RNAs, pro-inflammatory adipokines, and saturated free fatty acids. The resulting hypernefemia, insulin resistance, and diminished fatty acid β-oxidation lead to lipotoxicity and progressive obesity, metabolic syndrome, and physical and cognitive functional decline. Weight cycling is related to continuing immunosenescence and exposure to palmitate. Cellular senescence, exosome release, and the transmissible senescence-associated secretory phenotype contribute to obesity and metabolic syndrome. Targeted therapies have interrelated and synergistic effects on cellular senescence, obesity, and premature aging.

Trunk muscle quality and quantity are associated with renal volume in nondiabetic people

Background

Renal disease is a major problem in terms of community health and the economy. Skeletal muscle is involved in crosstalk with the kidney. We therefore investigated the relationship between muscle quality and quantity, and renal parenchymal volume (RPV).

Methods

The association between the parameters of skeletal muscle and RPV/body surface area (BSA) was analyzed by computed tomography in 728 middle-aged participants without kidney disease or diabetes mellitus in a cross-sectional study. A retrospective cohort study of 68 participants was undertaken to analyze the association between changes in RPV/BSA and muscle parameters. Parameter change was calculated as follows: parameter at the follow-up examination/parameter at the baseline examination. The normal attenuation muscle (NAM) and low attenuation muscle (LAM) were identified by Hounsfield Unit thresholds of +30 to +150, and -29 to +29, respectively.

Results

Positive correlations were found between estimated glomerular filtration rate and RPV/BSA (r = 0.451, P < .0001). Multiple regression analyses revealed that the NAM index was positively related to RPV/BSA (β = 0.458, P < .0001), whereas the LAM index was negatively related to RPV/BSA (β = -0.237, P < .0001). In this cohort study, a change in the LAM index was independently associated with a change in RPV/BSA (β = -0.349, P = .0032).

Conclusion

Both trunk muscle quantity and quality were associated with renal volume related to renal function in nondiabetic people. An increase in low quality muscle volume might be related to a decrease in renal volume.

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.

Automatic segmentation of large-scale CT image datasets for detailed body composition analysis

BACKGROUND

Body composition (BC) is an important factor in determining the risk of type 2-diabetes and cardiovascular disease. Computed tomography (CT) is a useful imaging technique for studying BC, however manual segmentation of CT images is time-consuming and subjective. The purpose of this study is to develop and evaluate fully automated segmentation techniques applicable to a 3-slice CT imaging protocol, consisting of single slices at the level of the liver, abdomen, and thigh, allowing detailed analysis of numerous tissues and organs.

METHODS

The study used more than 4000 CT subjects acquired from the large-scale SCAPIS and IGT cohort to train and evaluate four convolutional neural network based architectures: ResUNET, UNET++, Ghost-UNET, and the proposed Ghost-UNET++. The segmentation techniques were developed and evaluated for automated segmentation of the liver, spleen, skeletal muscle, bone marrow, cortical bone, and various adipose tissue depots, including visceral (VAT), intraperitoneal (IPAT), retroperitoneal (RPAT), subcutaneous (SAT), deep (DSAT), and superficial SAT (SSAT), as well as intermuscular adipose tissue (IMAT). The models were trained and validated for each target using tenfold cross-validation and test sets.

RESULTS

The Dice scores on cross validation in SCAPIS were: ResUNET 0.964 (0.909-0.996), UNET++ 0.981 (0.927-0.996), Ghost-UNET 0.961 (0.904-0.991), and Ghost-UNET++ 0.968 (0.910-0.994). All four models showed relatively strong results, however UNET++ had the best performance overall. Ghost-UNET++ performed competitively compared to UNET++ and showed a more computationally efficient approach.

CONCLUSION

Fully automated segmentation techniques can be successfully applied to a 3-slice CT imaging protocol to analyze multiple tissues and organs related to BC. The overall best performance was achieved by UNET++, against which Ghost-UNET++ showed competitive results based on a more computationally efficient approach. The use of fully automated segmentation methods can reduce analysis time and provide objective results in large-scale studies of BC.

Risk factors for postoperative sympathetic chain dysfunction following oblique lateral lumbar interbody fusion: a multivariate analysis

PURPOSE

Postoperative sympathetic chain dysfunction (PSCD) was a relatively common complication after anterior lumbar interbody fusion due to the manipulation adjacent to the lumbar sympathetic chain (LSC). This study aimed to investigate the incidence of PSCD and identify its related independent risk factors after oblique lateral lumbar interbody fusion (OLIF) surgery.

METHODS

PSCD was defined as either of the following in the affected lower limb compared to the contralateral: (1) increase in skin temperature by 1 ºC or more, (2) reduced skin perspiration, (3) limb swelling or skin discoloration. Consecutive patients who underwent OLIF at L4/5 level from February 2018 and May 2022 at a single institution were retrospectively reviewed and divided into two groups: patients with PSCD and patients without PSCD. Binary logistic regression analyses were performed on patients' demographic, comorbidities, radiological datum and perioperative factors to identify independent risk factors for PSCD.

RESULTS

Twelve (5.7%) of 210 patients experienced PSCD following OLIF surgery. Multivariate logistic regression analysis identified the identification of lumbar dextroscoliosis (OR = 7.907, P = 0.012) and the presence of "tear-drop" psoas (OR = 7.216, P = 0.011) as independent risk factors for the PSCD following OLIF.

CONCLUSION

This study identified the lumbar dextroscoliosis and the "tear-drop" psoas as independent risk factors for the development of PSCD after OLIF. Spine alignment examination and the morphological identification of psoas major muscle should be highly noticed for the PSCD prevention following OLIF.

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.

Effect of visceral adipose tissue on the accuracy of preoperative T-staging of gastric cancer

BACKGROUND

Due to the anatomical characteristics of the tumor and the specific variables of the patients, the accuracy of preoperative T-staging of gastric cancer needs to be further improved. This study investigated the effect of visceral adipose tissue (VAT) on the accuracy of clinical T-staging of gastric cancer.

METHODS

The clinical data of 455 patients who underwent gastrectomy from January 2013 to December 2018 were analyzed retrospectively. Taking the postoperative pathological results as the reference standard, the patients were divided into accurate staging group and mistaken staging group according to the comparison of clinical T stage (cT) and pathological T stage (pT). The individual characteristics of the two groups were compared, including visceral fat content at L2/L3 level calculated on computed tomography, age, sex, tumor size, tumor location (cardia, stomach body, stomach antrum), and degree of differentiation. Multivariate logistic regression was used to determine the independent factors affecting the accuracy of cT staging.

RESULTS

Among the 455 patients, 355 patients (78.0 %) had accurate preoperative cT staging and 100 patients (22.0 %) had inaccurate preoperative cT staging. The average area of VAT in the accurate staging group was (129.8 ± 72.6) cm² and that in the mistaken staging group was (74.6 ± 61.6) cm² (P < 0.001). The optimal cut-off value of VAT was 97.8 cm² calculated according to the Yoden index. Multivariate logistic regression analysis showed that VAT, tumor location and tumor size were independent predictors of cT accuracy.

CONCLUSIONS

Patients with lower visceral fat content (<97.8 cm²) based on L2/L3 level had a higher risk of false staging in preoperative clinical T staging.

Aerobic training reduces pancreatic fat content and improves β-cell function: A randomized controlled trial using IDEAL-IQ magnetic resonance imaging

AIMS

To explore the effects of six months of moderate-intensity aerobic exercise on pancreatic fat content and its impact on β-cell function.

MATERIALS AND METHODS

A total of 106 patients with type 2 diabetes mellitus were randomized to either a moderate-intensity aerobic training group (three times a week, including 5 min warm-up, 50 min aerobic dancing, and 5 min relaxation, n = 53) or control group (n = 53) with 6-month intervention. The primary endpoint was change in pancreatic fat content. An intention-to-treat analysis was conducted.

RESULTS

Eighty-six patients completed the study with 43 patients in the aerobic training group. The average age, HbA1c, and pancreatic fat content for all participants (106 patients) were 66.39 ± 5.59 years, 7.05 ± 1.24%, and 10.35 ± 9.20%, respectively. Nearly half (49.06%) of patients were males. Subjects in the aerobic training group saw a significant reduction in pancreatic fat content when compared to controls (p = 0.001). In logistic regression models containing age, diabetes duration, change in BMI, smoking/drinking status, changes in lipid indices, and other abdominal fat content, only reduction in pancreatic fat content (p < 0.05) was an independent protective factor for β-cell function and HbA1c.

CONCLUSIONS

Six months of moderate-intensity aerobic training significantly reduced the pancreatic fat content. The reduction of pancreatic fat content was an independent protective factor for β-cell function and HbA1c.