Inter and intra-reliability of ultrasonography for the measurement of abdominal subcutaneous & visceral adipose tissue thickness at 12 weeks gestation

Subcutaneous fat thickness predicts postoperative seroma following laparoscopic total extra-peritoneal hernioplasty

PURPOSE

Seroma formation is the most common cause of morbidity associated with laparoscopic inguinal hernia repair. This study aimed to examine the relationship between the thickness of subcutaneous fat (TSF) and the risk of postoperative seroma.

METHODS

We reviewed data from a prospective cohort of 229 male patients who underwent laparoscopic total extra-peritoneal (TEP) hernioplasty for indirect inguinal hernia between August 2018 and July 2021. The TSF was assessed using preoperative ultrasound images. The risk factors for postoperative seroma were determined using univariate and multivariate logistic regression models.

RESULTS

Postoperative seromas occurred in 26 patients (11.4%). The factors associated with postoperative seroma included longer hernia duration, larger hernia defects, extension into the scrotum, and greater TSF (P < 0.05). In multivariate analysis, a greater TSF was independently associated with a greater risk of postoperative seroma (per 1 mm: odd ratio [OR] 1.105, 95% confidence interval [CI] 1.048-1.165, P < 0.001; TSF ≥ 26.0 mm: OR 7.033, 95% CI 2.485-19.901, P < 0.001). Similar results were obtained in the subgroup analysis. The area under the curve of TSF for predicting seroma formation was 0.703 (95% CI 0.601-0.806).

CONCLUSION

Ultrasound-derived TSF may be a promising prognostic factor for postoperative seroma in patients undergoing laparoscopic TEP repair. Further validation is required and then this parameter can be used to improve decision-making process.

Study of How Adiposity in Pregnancy has an Effect on outcomeS (SHAPES): protocol for a prospective cohort study

INTRODUCTION

Maternal obesity increases the risk of multiple maternal and infant pregnancy complications, such as gestational diabetes and pre-eclampsia. Current UK guidelines use body mass index (BMI) to identify which women require additional care due to increased risk of complications. However, BMI may not accurately predict which women will develop complications during pregnancy as it does not determine amount and distribution of adipose tissue. Some adiposity measures (eg, waist circumference, ultrasound measures of abdominal visceral fat) can better identify where body fat is stored, which may be useful in predicting those women who need additional care.

METHODS AND ANALYSIS

This prospective cohort study (SHAPES, Study of How Adiposity in Pregnancy has an Effect on outcomeS) aims to evaluate the prognostic performance of adiposity measures (either alone or in combination with other adiposity, sociodemographic or clinical measures) to estimate risk of adverse pregnancy outcomes. Pregnant women (n=1400) will be recruited at their first trimester ultrasound scan (11+2-14+1 weeks') at Newcastle upon Tyne National Health Service Foundation Trust, UK. Early pregnancy adiposity measures and clinical and sociodemographic data will be collected. Routine data on maternal and infant pregnancy outcomes will be collected from routine hospital records. Regression methods will be used to compare the different adiposity measures with BMI in terms of their ability to predict pregnancy complications. If no individual measure performs better than BMI, multivariable models will be developed and evaluated to identify the most parsimonious model. The apparent performance of the developed model will be summarised using calibration, discrimination and internal validation analyses.

ETHICS AND DISSEMINATION

Ethical favourable opinion has been obtained from the North East: Newcastle & North Tyneside 1 Research Ethics Committee (REC reference: 22/NE/0035). All participants provide informed consent to take part in SHAPES. Planned dissemination includes peer-reviewed publications and additional dissemination appropriate to target audiences, including policy briefs for policymakers, media/social-media coverage for public and conferences for research TRIAL REGISTRATION NUMBER: ISRCTN82185177.

Evaluating the Adipose Tissue Depth as a Predictor Factor for Gestational Diabetes in Later Pregnancy-A Systematic Review

The increasing prevalence of gestational diabetes mellitus (GDM) requires non-invasive and precise techniques for evaluating the predisposing risk factors such as visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). According to PRISMA, we developed a systematic review and searched after "visceral adipose tissue AND gestational diabetes" and identified 221 articles on the MEDLINE and Word of Science databases. After assessing them for inclusion criteria and two researchers screened them, 11 relevant articles were included. Although evidence is conflicting, more studies favor using US-determined VAT in GDM prediction. VAT may be more valuable than body mass index or SAT in predicting GDM. VAT can represent an additive factor to the prediction tool of the risk of developing GDM when used in conjunction with other anthropometric or biological parameters or maternal risk factors. US measurements are heterogeneous given different evaluation techniques, cut-off values and inter-operator variation. A significant limitation is the lack of a gold standard to identify GDM confidently. Pregnant women may benefit from early monitoring and preventive care if classified as high risk for GDM early in the gestational period. US-measured VAT during the first trimester of pregnancy seems a valuable and inexpensive screening approach to predict GDM development later in pregnancy, either by itself or if used in conjunction with other clinical and biological parameters.

The Association Between Body Fat Index and Gestational Diabetes Mellitus: A Prospective Cohort Study

BACKGROUND

 Body mass index (BMI) has commonly been used to evaluate the risk of gestational diabetes mellitus (GDM), but BMI does not always represent body fat mass distribution. Body fat index (BFI), which includes the measurement of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT), has been suggested to be a better predictor for GDM than BMI.

OBJECTIVE

The objective of this study is to compare the risk of GDM among pregnant females with BFI of >0.5 and ≤0.5.

METHODS

Maternal abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) thickness were measured by ultrasonography before 14 weeks of gestation, and BFI was calculated (VAT×SAT/height). The study group was 160 females with BFI of >0.5, and the comparison group was 80 females with BFI of ≤0.5. All females received GDM screening during the first antenatal visit and at 24-28 weeks of gestation. The rate of GDM was compared between the two groups. The correlation between BFI and BMI and their diagnostic ability for GDM were evaluated. Logistic regression analysis was performed to determine the independent associated factors for GDM.

RESULTS

Females with BFI of >0.5 were significantly older (p=0.033) and had higher body mass index (BMI) (p<0.001) and were more likely to be overweight or obese (p<0.001). BFI correlated well with BMI (correlation coefficient of 0.736, p<0.001). GDM was significantly more common in females with BFI of >0.5 (24.4% versus 11.3%, p=0.017). The diagnostic ability for GDM between BFI and BMI was similar (areas under receiver operating characteristic {ROC} curves of 0.641 and 0.646, respectively). Significant independent risk factors for GDM were a BFI of >0.5 and a BMI of ≥25 kg/m² (adjusted odds ratio {OR}, 3.8; 95% confidence interval {CI}, 1.5-9.2), age of ≥30 years (adjusted OR, 2.8; 95% CI, 1.2-6.4), and family history of diabetes mellitus (DM) (adjusted OR, 4.0; 95% CI, 1.9-8.3).

CONCLUSION

Females with BFI of >0.5 were significantly more likely to have GDM. The diagnostic ability of BFI and BMI for GDM was comparable. Females with BFI of >0.5 and BMI of ≥25 kg/m² have an increased risk for GDM.

A risk-prediction model using parameters of maternal body composition to identify gestational diabetes mellitus in early pregnancy

BACKGROUND

Accurate early risk-prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated maternal risk-factors and parameters of body-composition to develop a prediction model for GDM in early gestation.

METHODS

A prospective observational study was undertaken. Pregnant women aged between 18 and 50 y of age with gestational age between 10 and 16 weeks were included in the study. Women aged ≤18 y, twin-pregnancies, known foetal anomaly or pre-existing condition affecting oedema status were excluded. 8-point-skinfold thickness (SFT), mid-upper-arm-circumference (MUAC), waist, hip, weight and ultrasound measurements of subcutaneous (SAT) and visceral abdominal-adipose (VAT) were measured. Oral-glucose-tolerance-test (OGTT) for GDM diagnosis was undertaken at 28 weeks gestation. Binomial logistic-regression models were used to predict GDM. ROC-analysis determined discrimination and concordance of model and individual variables.

RESULTS

188 women underwent OGTT at ~28 weeks gestation. 20 women developed GDM. BMI (24.7 kg m^-2 (±6.1), 29.9 kg m^-2 (±7.8), p = 0.022), abdominal SAT(1.32 cm (CI 1.31, 1.53), 1.99 cm (CI 1.64, 2.31), p = 0.027), abdominal VAT(0.78 cm (CI 0.8, 0.96), 1.41 cm (CI 1.11, 1.65), p = 0.002), truncal SFT (84.8 mm (CI 88.2, 101.6), 130.4 mm (CI 105.1, 140.1), p = 0.010), waist (79.8 cm (CI 80.3, 84.1), 90.3 cm (CI 85.9, 96.2), p = 0.006) and gluteal hip (94.3 cm (CI 93.9, 98.0), 108.6 cm (CI 99.9, 111.6), p = 0.023) were higher in GDM vs. non-GDM. After screening variables for inclusion into the multivariate model, family history of diabetes, previous perinatal death, overall insulin resistant condition, abdominal SAT and VAT, 8-point SFT, MUAC and weight were included. The combined multivariate prediction model achieved an excellent level of discrimination, with an AUC of 0.860 (CI 0.774, 0.945) for GDM.

CONCLUSIONS

An early gestation risk prediction model, incorporating known risk-factors, and parameters of body-composition, accurately identify pregnant women in their first-trimester who developed GDM later on in gestation. This methodology could be used clinically to identify at-risk pregnancies, and target specific treatment through referred services to those mothers who would most benefit.