How much abdominal fat do obese patients lose short term after laparoscopic sleeve gastrectomy? A quantitative study evaluated with MRI

The effectiveness of magnetic resonance imaging (MRI) iron corrected T1 in monitoring metabolic dysfunction-associated steatohepatitis in obesity following bariatric surgery and lifestyle modification: a prospective cohort study

Background

Bariatric surgery and lifestyle modification are important treatments for obesity, a risk factor for metabolic dysfunction-associated steatohepatitis (MASH). Studies have related weight reduction with changes in MASH, however, few have used imaging to investigate effects on liver health. We evaluated differences in liver response to obesity treatment using disease activity iron corrected T1 (cT1) and proton density fat fraction (PDFF) in patients with both obesity and metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

Thirty-four patients with obesity and MASLD were recruited between March 2019 to February 2022 from a tertiary hospital in this longitudinal study; 13 underwent laparoscopic sleeve gastrectomy (LSG) alongside intraoperative liver biopsy, and 21 underwent a 4-month lifestyle modification program (LMP). All patients had multi-parametric magnetic resonance imaging (MRI) at baseline and 4-months. Diagnostic accuracy to identify MASH was assessed using the area under receiver operating characteristic (AUROC) curve.

Results

Four (31%) of patients in the LSG group had MASH [non-alcoholic steatohepatitis (NAS) activity score ≥4] on liver biopsy. PDFF and cT1 correlated with the NAS activity score [r=0.81, 95% confidence interval (CI): 0.453 to 0.943, P<0.001] and (r=0.70, 95% CI: 0.228 to 0.907, P=0.008, respectively). There was good AUROC curve for cT1 (0.89, 95% CI: 0.67 to 1.00, P=0.031) and PDFF (0.83, 95% CI: 0.57 to 1.00, P=0.064) to identify MASH. At follow-up, weight reduction -22.8% (P=0.013) vs. -1.3% (P=0.262) resulted in cT1 reduction of -8.04% (864 ms, P=0.025) vs. -3.87% (907 ms, P=0.083) in the LSG vs. LMP group, respectively. Significant differences between interventions were observed for percentage PDFF decrease (-64.52% vs. -29.16%, P=0.001). Both biomarkers were significantly reduced in the LSG group (cT1 by -8.04%, P=0.025, PDFF by -64.52%, P=0.012), while only PDFF (-29.16%, P=0.012) was significantly reduced in the LMP group.

Conclusions

MRI biomarkers may have some utility to monitor MASH following intervention in patients with obesity allowing objective comparison between intervention strategies. Compared to LMP, LSG was more effective in improving liver health.

A Simple Way to Estimate a Difficult Sleeve Gastrectomy Prior to Operating

BACKGROUND

Today, bariatric procedures are common. These surgeries' difficulties are classified as patient- or surgical team-related and are estimated by body mass index (BMI). More efficient methods are needed to help surgeons. This study evaluated the effect of measuring patients' subcutaneous fat tissue thickness (SFT) and umbilicus-xiphoid (DXU) to anticipate surgical difficulties.

MATERIAL AND METHODS

This was a prospective retrospective data analysis study. Laparoscopic sleeve gastrectomy patients seen between May and October 2022 were included in the analysis and divided into three groups, according to a surgeon's assessment. All patients' SFT, DXU, rectus muscle thickness, total fat tissue amount (TFT), and operational time were recorded prospectively and analyzed.

RESULTS

In all, 151 patients were included in the study; of these, 124 (82.1%) were women and 27 (17.9%) were men. Their mean BMI value was 41.1 ± 6.2. Based on expert's opinion, we classified three groups: easy (n = 123, 81.5%), intermediate (n = 22, 14.6%), or difficult (n = 6, 4%). When the easy group was compared to the intermediate/difficult groups, we found that intermediate/difficult groups' SFT values were statistically significantly higher than the easy group (p = 0.000). Also, the intermediate/difficult group's TFT value was statistically significantly higher than the easy group (p = 0.000). We found no statistically significant differences between groups' DXU and rectus muscle thickness.

CONCLUSION

This is the first study to anticipate sleeve gastrectomy difficulty using SFT and TFT. This is an easy technique to apply and no additional costs. Anticipating difficulties based on these criteria can ensure necessary preparations are made and help avoid complications.

Changes in Lean Tissue Mass, Fat Mass, Biological Parameters and Resting Energy Expenditure over 24 Months Following Sleeve Gastrectomy

Sleeve gastrectomy (SG) induces weight loss but its effects on body composition (BC) are less well known. The aims of this longitudinal study were to analyse the BC changes from the acute phase up to weight stabilization following SG. Variations in the biological parameters related to glucose, lipids, inflammation, and resting energy expenditure (REE) were concomitantly analysed. Fat mass (FM), lean tissue mass (LTM), and visceral adipose tissue (VAT) were determined by dual-energy X-ray absorptiometry in 83 obese patients (75.9% women) before SG and 1, 12 and 24 months later. After 1 month, LTM and FM losses were comparable, whereas at 12 months the loss of FM exceeded that of LTM. Over this period, VAT also decreased significantly, biological parameters became normalized, and REE was reduced. For most of the BC, biological and metabolic parameters, no substantial variation was demonstrated beyond 12 months. In summary, SG induced a modification in BC changes during the first 12 months following SG. Although the significant LTM loss was not associated with an increase in sarcopenia prevalence, the preservation of LTM might have limited the reduction in REE, which is a longer-term weight-regain criterion.

Epicardial and pericardial fat analysis on CT images and artificial intelligence: a literature review

The present review summarizes the available evidence on artificial intelligence (AI) algorithms aimed to the segmentation of epicardial and pericardial adipose tissues on computed tomography (CT) images. Body composition imaging is a novel concept based on quantitative analysis of body tissues. Manual segmentation of medical images allows to obtain quantitative and qualitative data on several tissues including epicardial and pericardial fat. However, since manual segmentation requires a considerable amount of time, the analysis of adipose tissue compartments based on AI has been proposed as an automatic, reliable, accurate and fast tool. The literature research was performed on March 2021 using MEDLINE PubMed Central and "adipose tissue artificial intelligence", "adipose tissue deep learning" or "adipose tissue machine learning" as keywords for articles search. Relevant articles concerning epicardial adipose tissue, pericardial adipose tissue and AI were selected. The evaluation of adipose tissue compartments can provide additional information on the pathogenesis and prognosis of several diseases, including cardiovascular. AI can assist physicians to obtain important information, possibly improving the patient's quality of life and identifying patients at risk of developing variable disorders.

The Appropriateness Criteria of Abdominal Fat Measurement at the Level of the L1-L2 Intervertebral Disc in Patients With Obesity

BACKGROUND

In this study, we proposed to use MR images at L1-L2 (lumbar) intervertebral disc level to measure abdominal fat area in patients with obesity. The quantitative results would provide evidence for the individualized assessment of the severity of obesity.

METHODS

All patients in the IRB-approved database of Beijing Friendship Hospital who underwent bariatric surgery between November 2017 and November 2019 were recruited. We retrospectively reviewed upper abdominal magnetic resonance (MR) data before surgery. We analyzed the correlation and consistency of the area of abdominal subcutaneous adipose tissue (ASAT) and visceral adipose tissue (VAT) measured at the L1-L2 and L2-L3 levels on MR images. We randomly distributed the cases into prediction model training data and testing data at a ratio of 7:3.

RESULTS

Two hundred and forty-five subjects were included. The ASAT and VAT results within the L1-L2 and L2-L3 levels were very similar and highly correlated (male^ASAT: r=0.98, female^ASAT: r=0.93; male^VAT: r=0.91, female^VAT: r=0.88). There was no substantial systematic deviation among the results at the two levels, except for the ASAT results in males. The intraclass correlation coefficients (ICCs) were 0.91 and 0.93 for male^ASAT and female^ASAT, and 0.88 and 0.87 for male^VAT and female^VAT, respectively. The ASAT/VAT area at the L2-L3 level was well predicted. The coefficient β of linear regression that predicted L2-L3 ASAT from L1-L2 ASAT was 1.11 for males and 0.99 for females. The R-squares were 0.97 and 0.91, respectively. For VAT prediction, the coefficient β was 1.02 for males and 0.96 for females. The R-squares were 0.82 and 0.77, respectively.

CONCLUSION

For patients with obesity, the L1-L2 intervertebral disc level can be used as the substitution of L2-L3 level in abdominal fat measurement.