Effect of visceral adipose tissue mass on coronary artery disease and heart failure: A Mendelian randomization study

Association of metabolic score for visceral fat with all-cause mortality, cardiovascular mortality, and cancer mortality: A prospective cohort study

AIM

Our study aimed to evaluate the association between the metabolic score for visceral fat (METS-VF) and mortality.

METHODS

We conducted a cohort study comprising 11,120 participants. We employed weighted multivariable Cox regression analysis to assess the relationship between METS-VF and mortality. Restricted cubic spline analyses were used to investigate potential non-linear associations. Receiver operating characteristic curves were used to evaluate the predictive value of METS-VF and other obesity-related indicators for mortality. Subgroup analysis and sensitivity analysis were performed to confirm the robustness of the results. Mendelian randomization analysis was utilized to assess potential causality.

RESULTS

Over a median follow-up duration of 83 months, a total of 1014 all-cause deaths, 301 cardiovascular deaths, and 262 cancer deaths occurred. For every 0.2-unit increase in METS-VF, the hazard ratios(HRs) of all-cause mortality, cardiovascular mortality, and cancer mortality were 1.13 [95% confidence interval (CI): 1.06, 1.20], 1.18 (95% CI: 1.06, 1.31), and 1.13 (95% CI: 1.03, 1.25), respectively. In addition, restricted cubic spline analyses revealed no significant non-linear associations between METS-VF and all-cause mortality, cardiovascular mortality, and cancer mortality. In multivariate Cox regression models, hazard ratios of all-cause mortality, cardiovascular mortality and cancer mortality were higher in the highest METS-VF group compared to the reference group. Subgroup and sensitivity analyses confirmed that our results were robust. Receiver operating characteristic curves indicated that METS-VF predicted mortality better than other obesity-related indicators. Mendelian randomization analysis confirmed significant causal relationships.

CONCLUSIONS

METS-VF was positively associated with all-cause mortality, cardiovascular mortality, and cancer mortality. These findings suggest that METS-VF could serve as a straightforward, reliable, and cost-effective marker for identifying individuals at high risk of mortality.

Association of visceral adipose tissue with gout: Observational and Mendelian randomization analyses

BACKGROUND

The causal relationship between visceral adipose tissue (VAT) and gout is still unclear. We aimed to examine the potential association between them using observational and Mendelian randomization (MR) analyses.

METHODS

In the observational analyses, a total of 11,967 participants (aged 39.5 ± 11.5 years) were included from the National Health and Nutrition Examination Survey. Logistic regression models were used to investigate the association between VAT mass and the risk of gout. In two-sample MR analyses, 211 VAT mass-related independent genetic variants (derived from genome-wide association studies in 325,153 UK biobank participants) were used as instrumental variables. The random-effects inverse-variance weighted (IVW) method was used as the primary analysis. Additional sensitivity analyses were also performed to validate our results.

RESULTS

Observational analyses found that an increase in VAT mass (per standard deviation) was associated with a higher risk of gout after controlling for confounding factors (odds ratio [OR] = 1.27, 95% confidence intervals [CI] = 1.11-1.45). The two-sample MR analyses demonstrated a causal relationship between increased VAT mass and the risk of gout in primary analyses (OR = 1.78, 95% CI = 1.57-2.03). Sensitivity analyses also showed similar findings, including MR-Egger, weighted median, simple mode, weighted mode, and leave-one-out analyses.

CONCLUSIONS

Observational analyses showed a robust association of VAT mass with the risk of gout. Meanwhile, MR analyses also provided evidence of a causal relationship between them. In summary, our findings suggested that targeted interventions for VAT mass may be beneficial to prevent gout.

Machine learning allows robust classification of visceral fat in women with obesity using common laboratory metrics

The excessive accumulation and malfunctioning of visceral adipose tissue (VAT) is a major determinant of increased risk of obesity-related comorbidities. Thus, risk stratification of people living with obesity according to their amount of VAT is of clinical interest. Currently, the most common VAT measurement methods include mathematical formulae based on anthropometric dimensions, often biased by human measurement errors, bio-impedance, and image techniques such as X-ray absorptiometry (DXA) analysis, which requires specialized equipment. However, previous studies showed the possibility of classifying people living with obesity according to their VAT through blood chemical concentrations by applying machine learning techniques. In addition, most of the efforts were spent on men living with obesity while little was done for women. Therefore, this study aims to compare the performance of the multilinear regression model (MLR) in estimating VAT and six different supervised machine learning classifiers, including logistic regression (LR), support vector machine and decision tree-based models, to categorize 149 women living with obesity. For clustering, the study population was categorized into classes 0, 1, and 2 according to their VAT and the accuracy of each MLR and classification model was evaluated using DXA-data (DXAdata), blood chemical concentrations (BLDdata), and both DXAdata and BLDdata together (ALLdata). Estimation error and R 2 were computed for MLR, while receiver operating characteristic (ROC) and precision-recall curves (PR) area under the curve (AUC) were used to assess the performance of every classification model. MLR models showed a poor ability to estimate VAT with mean absolute error ≥ 401.40 andR 2 ≤ 0.62 in all the datasets. The highest accuracy was found for LR with values of 0.57, 0.63, and 0.53 for ALLdata, DXAdata, and BLDdata, respectively. The ROC AUC showed a poor ability of both ALLdata and DXAdata to distinguish class 1 from classes 0 and 2 (AUC = 0.31, 0.71, and 0.85, respectively) as also confirmed by PR (AUC = 0.24, 0.57, and 0.73, respectively). However, improved performances were obtained when applying LR model to BLDdata (ROC AUC ≥ 0.61 and PR AUC ≥ 0.42), especially for class 1. These results seem to suggest that, while a direct and reliable estimation of VAT was not possible in our cohort, blood sample-derived information can robustly classify women living with obesity by machine learning-based classifiers, a fact that could benefit the clinical practice, especially in those health centres where medical imaging devices are not available. Nonetheless, these promising findings should be further validated over a larger population.

Mediation Mendelian randomisation study on the effects of shift work on coronary heart disease and traditional risk factors via gut microbiota

Background

Epidemiological evidence suggests that there is an increased risk of coronary heart disease (CHD) related to jobs involving shift work (JSW), but the causality of and mechanism underlying such a relationship remain unclear. Therefore, we aimed to explore the relationship between JSW and CHD, investigating both causality and potential mediating factors.

Methods

We performed univariate, multivariate, and mediation Mendelian randomisation (MR) analyses using data from large genome-wide association studies focussed on JSW and CHD, as well as data on some CHD risk factors (type 2 diabetes, hypertension, obesity, and lipids measurement) and 196 gut microbiota taxa. Single-nucleotide polymorphisms significantly associated with JSW acted as instrument variables. We used inverse-variance weighting as the primary method of analysis.

Results

Bidirectional MR analysis indicated a robust effect of JSW on increased CHD risk; however, the existence of CHD did not affect the choice of JSW. We identified a mediating effects of type 2 diabetes and hypertension in this relationship, accounting for 11.89% and 14.80% of the total effect of JSW on CHD, respectively. JSW were also causally associated with the risk of type 2 diabetes and hypertension and had an effect on nine microbial taxa. The mediating influence of the Eubacterium brachy group at the genus level explained 16.64% of the total effect of JSW on hypertension. We found limited evidence for the causal effect of JSW on obesity and lipids measurements.

Conclusions

Our findings suggest a causal effect of JSW on CHD, diabetes, and hypertension. We also found evidence for a significant connection between JSW and alterations in the gut microbiota. Considering that certain microbial taxa mediated the effect of JSW on hypertension risk, targeting gut microbiota through therapeutics could potentially mitigate high risks of hypertension and CHD associated with JSW.

Estrogen restores disordered lipid metabolism in visceral fat of prediabetic mice

BACKGROUND

Visceral obesity is increasingly prevalent among adolescents and young adults and is commonly recognized as a risk factor for type 2 diabetes. Estrogen [17β-estradiol (E2)] is known to offer protection against obesity via diverse me-chanisms, while its specific effects on visceral adipose tissue (VAT) remain to be fully elucidated.

AIM

To investigate the impact of E2 on the gene expression profile within VAT of a mouse model of prediabetes.

METHODS

Metabolic parameters were collected, encompassing body weight, weights of visceral and subcutaneous adipose tissues (VAT and SAT), random blood glucose levels, glucose tolerance, insulin tolerance, and overall body composition. The gene expression profiles of VAT were quantified utilizing the Whole Mouse Genome Oligo Microarray and subsequently analyzed through Agilent Feature Extraction software. Functional and pathway analyses were conducted employing Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, respectively.

RESULTS

Feeding a high-fat diet (HFD) moderately increased the weights of both VAT and SAT, but this increase was mitigated by the protective effect of endogenous E2. Conversely, ovariectomy (OVX) led to a significant increase in VAT weight and the VAT/SAT weight ratio, and this increase was also reversed with E2 treatment. Notably, OVX diminished the expression of genes involved in lipid metabolism compared to HFD feeding alone, signaling a widespread reduction in lipid metabolic activity, which was completely counteracted by E2 administration. This study provides a comprehensive insight into E2's local and direct protective effects against visceral adiposity in VAT at the gene level.

CONCLUSION

In conclusion, the present study demonstrated that the HFD-induced over-nutritional challenge disrupted the gene expression profile of visceral fat, leading to a universally decreased lipid metabolic status in E2 deficient mice. E2 treatment effectively reversed this condition, shedding light on the mechanistic role and therapeutic potential of E2 in combating visceral obesity.

Causal relationship between nutritional assessment phenotypes and heart failure: A Mendelian randomization study

Introduction

Malnutrition is strongly associated with heart failure (HF); however, the causal link remains unclear. We used Mendelian randomization (MR) to infer causal associations between different nutritional assessment phenotypes and HF and to analyze whether these associations were mediated by common HF risk factors.

Methods

Two-sample bidirectional MR was used to infer causal associations between nutritional assessment phenotypes and HF. Mutual influences between different nutritional assessment phenotypes and potential correlations were estimated using multivariate MR methods. Two-step MR was used to quantify the mediating effects of common HF risk factors on the causal associations.

Results

Three phenotypes were positively associated with the development of HF: waist circumference (WC) (odds ratio [OR] = 1.74; 95% confidence interval [CI], 1.60-1.90; P = 3.95 × 10-39), body mass index (BMI) (OR = 1.70; 95%CI, 1.60-1.80; P = 1.35 × 10-73), and whole body fat mass (WBFM) (OR = 1.54; 95%CI, 1.44-1.65; P = 4.82 × 10-37). Multivariate MR indicated that WBFM remained positively associated with HF after conditioning on BMI and WC (OR = 2.05; 95%CI, 1.27-3.31; P = 0.003). Three phenotypes were negatively correlated with the development of HF: usual walking pace (UWP) (OR = 0.40; 95%CI, 0.27-0.60; P = 8.41 × 10-6), educational attainment (EA) (OR = 0.73; 95%CI, 0.67-0.79; P = 2.27 × 10-13), and total cholesterol (TC) (OR = 0.90; 95%CI, 0.84-0.96; P = 4.22 × 10-3). There was a bidirectional causality between HF and UWP (Effect estimate = -0.03; 95%CI, -0.05 to -0.01; P = 1.95 × 10-3). Mediation analysis showed that common risk factors for HF (hypertension, coronary artery disease, cardiomyopathy, and valvular heart disease) mediated these causal associations (all P < 0.05).

Conclusions

BMI, WC, and WBFM are potential risk factors for HF, and the correlation between WBFM and HF was significantly stronger than that between BMI and WC, and HF. EA, UWP, and TC are potential protective factors against HF. Common risk factors for HF mediate these causal pathways. Early identification of potential risk or protective factors for HF patients from the dimension of nutritional status is expected to further improve patient outcomes.

Predicted visceral adiposity index in relation to risk of coronary heart disease and all-cause mortality: insights from NHANES

Background and aims

The Visceral Adiposity Index (VAI) is a straightforward and gender-specific marker that combines anthropometric measurements with lipid profiles. The objective of this study was to evaluate the relationship between VAI and coronary heart disease (CHD).

Methods and results

The study examined data collected from adults during the NHANES 1999-2018 cycle. The analyses were weighted, and multivariable logistic regression models were employed to investigate the association between VAI and CHD. Additionally, subgroup analyses stratified by age were conducted. To evaluate the impact of VAI levels on survival outcomes, the study utilized the Kaplan-Meier method and performed the log-rank test to evaluate the survival outcome of participants with different VAI levels. The study findings revealed a significant association between VAI and CHD, indicating a non-linear relationship where an increase in VAI was associated with an elevated risk of CHD. High levels of VAI were linked to an increased prevalence of CHD (Q4 vs Q1, OR 1.50, 95% CI 1.12-2.01, P=0.01). Additionally, higher levels of VAI were associated with a poorer overall prognosis in terms of survival outcomes. There were no statistically significant differences in survival outcomes among the population with CHD.

Conclusion

The results of this study highlighted a significant association between VAI and CHD, with a non-linear relationship observed. High VAI levels were associated with an increased risk of CHD and poor survival outcomes, emphasizing the importance of understanding and managing this risk factor, particularly in older age groups.

Comparison of computed tomography and dual-energy X-ray absorptiometry in the evaluation of body composition in patients with obesity

Objective

a) To evaluate the accuracy of the pre-existing equations (based on cm2 provided by CT images), to estimate in kilograms (Kg) the body composition (BC) in patients with obesity (PwO), by comparison with Dual-energy X-ray absorptiometry (DXA). b) To evaluate the accuracy of a new approach (based on both cm2 and Hounsfield Unit parameters provided by CT images), using an automatic software and artificial intelligence to estimate the BC in PwO, by comparison with DXA.

Methods

Single-centre cross-sectional study including consecutive PwO, matched by gender with subjects with normal BMI. All the subjects underwent BC assessment by Dual-energy X-ray absorptiometry (DXA) and skeletal-CT at L3 vertebrae. CT images were processed using FocusedON-BC software. Three different models were tested. Model 1 and 2, based on the already existing equations, estimate the BC in Kg based on the tissue area (cm2) in the CT images. Model 3, developed in this study, includes as additional variables, the tissue percentage and its average Hounsfield unit.

Results

70 subjects (46 PwO and 24 with normal BMI) were recruited. Significant correlations for BC were obtained between the three models and DXA. Model 3 showed the strongest correlation with DXA (r= 0.926, CI95% [0.835-0.968], p<0.001) as well as the best agreement based on Bland - Altman plots.

Conclusion

This is the first study showing that the BC assessment based on skeletal CT images analyzed by automatic software coupled with artificial intelligence, is accurate in PwO, by comparison with DXA. Furthermore, we propose a new equation that estimates both the tissue quantity and quality, that showed higher accuracy compared with those currently used, both in PwO and subjects with normal BMI.