Association of Metabolically Healthy Obesity and Risk of Cardiovascular Disease Among Adults in China: A Retrospective Cohort Study

Obesity Phenotypes Causally Affect Cardiac MRI Structure and Induced Non-ischaemic Cardiomyopathy

Background

The growing obesity epidemic highlights the need to understand how various obesity phenotypes affect myocardial structure and increase non-ischaemic cardiomyopathy (NICM) incidence. The aim of this study was to investigate the causal effect of eight obesity-related traits on NICM and 16 cardiac MRI parameters. Potential mediators between obesity and NICM were also investigated.

Methods

Two-sample Mendelian randomisation was used to explore the causal relationship between eight obesity-related traits and NICM and assess their impact on cardiac MRI indicators. The study also used validation dataset analysis and multivariable Mendelian randomisation to ensure robustness, and mediation Mendelian randomisation analysis to identify metabolic markers as potential mediators.

Results

All eight obesity-related traits demonstrated a causal relationship with NICM, with the relationship between BMI and NICM persisting after adjustment for LDL cholesterol, urate level and hypertension (HTN). These traits also influenced arterial and cardiac structure and function, especially with regard to left ventricular mass. HTN was identified as a significant mediator, with a mediation effect ratio of 31%.

Conclusion

There is a robust causal association between obesity and NICM, and with abnormalities in myocardial structure and function. HTN emerges as a pivotal mediator in the obesity-NICM pathway, underscoring the critical role of managing obesity and HTN in preventing NICM progression.

Metabolically healthy across body mass index categories in relation to serum Klotho levels: a population-based study

Background

Metabolic health status and body mass index (BMI) are both key predictors of aging-related diseases and premature mortality. However, the relationship between metabolically distinct phenotypes, classified by BMI categories, and serum Klotho levels-a biomarker of aging-remains poorly understood. This study aimed to investigate the association between metabolically healthy phenotypes and serum Klotho levels among middle and aged adults.

Methods

A total of 11,413 participants were included in this study. Participants were categorized into phenotypes according to metabolic disorders and BMI: metabolically healthy and normal weight (MH-NW), metabolically healthy and overweight (MH-OW), metabolically healthy and obesity (MHO), metabolically unhealthy and normal weight (MU-NW), metabolically unhealthy and overweight (MU-OW), and metabolically unhealthy and obesity (MUO). Weighted multivariate linear regression models were performed to estimate the association between metabolically healthy phenotypes and Klotho levels.

Results

After adjusting for potential confounders, participants in the MH-OW, MU-OW, and MUO groups had significantly lower Klotho levels compared to the MH-NW group, with estimated percentage changes (95%CIs) at -5.4% (-9.2% to -1.4%), -5.0% (-8.3% to -1.5%), and -5.7% (-8.7% to -2.5%), respectively. Additionally, reduced Klotho levels were more pronounced among females with MU-OW and MUO phenotypes, showing estimated percentage changes of -6.6% (-11.1% to -1.9) and -8.4% (-12.7% to -3.8%), respectively.

Conclusion

This large population-based study found that Klotho levels vary according to metabolically healthy status across BMI categories, with metabolically unhealthy phenotypes exhibiting notably lower levels. These findings highlight the influence of metabolic abnormalities and body fatness on the aging process.

PM2.5, component cause of severe metabolically abnormal obesity: An in silico, observational and analytical study

Obesity is currently one of the most alarming pathological conditions due to the progressive increase in its prevalence. In the last decade, it has been associated with fine particulate matter suspended in the air (PM2.5). The purpose of this study was to explore the mechanistic interaction of PM2.5 with a high-fat diet (HFD) through the differential regulation of transcriptional signatures, aiming to identify the association of these particles with metabolically abnormal obesity. The research design was observational, using bioinformatic methods and an explanatory approach based on Rothman's causal model. We propose three new transcriptional signatures in murine adipose tissue. The sum of transcriptional differences between the group exposed to an HFD and PM2.5, compared to the control group, were 0.851, 0.265, and -0.047 (p > 0.05). The HFD group increased body mass by 20% with two positive biomarkers of metabolic impact. The group exposed to PM2.5 maintained a similar weight to the control group but exhibited three positive biomarkers. Enriched biological pathways (p < 0.05) included PPAR signaling, small molecule transport, adipogenesis genes, cytokine-cytokine receptor interaction, and HIF-1 signaling. Transcriptional regulation predictions revealed CpG islands and common transcription factors. We propose three new transcriptional signatures: FAT-PM2.5-CEJUS, FAT-PM2.5-UP, and FAT-PM2.5-DN, whose transcriptional regulation profile in adipocytes was statistically similar by dietary intake and HFD and exposure to PM2.5 in mice; suggesting a mechanistic interaction between both factors. However, HFD-exposed murines developed moderate metabolically abnormal obesity, and PM2.5-exposed murines developed severe abnormal metabolism without obesity. Therefore, in Rothman's terms, it is concluded that HFD is a sufficient cause of the development of obesity, and PM2.5 is a component cause of severe abnormal metabolism of obesity. These signatures would be integrated into a systemic biological process that would induce transcriptional regulation in trans, activating obesogenic biological pathways, restricting lipid mobilization pathways, decreasing adaptive thermogenesis and angiogenesis, and altering vascular tone thus inducing a severe metabolically abnormal obesity.

Association between metabolic syndrome and myocardial infarction among patients with excess body weight: a systematic review and meta-analysis

BACKGROUND

Cardiovascular diseases (CVDs) are a major cause of morbidity and mortality worldwide. Controversial views exist over the effects of metabolically unhealthy obesity phenotypes on CVDs. This study aimed to perform a meta-analysis to assess the association between metabolic syndrome and myocardial infarction (MI) among individuals with excess body weight (EBW).

METHODS

We searched PubMed/Medline, Scopus, and Web of Science databases as of December 9, 2023. Cohort studies involving patients with overweight or obesity that reported the relevant effect measures for the association between metabolic syndrome and MI were included. We excluded studies with incomplete or unavailable original data, reanalysis of previously published data, and those that did not report the adjusted effect sizes. We used the Newcastle Ottawa Scale for quality assessment. Random-effect model meta-analysis was performed. Publication bias was assessed by Begg's test.

RESULTS

Overall, nine studies comprising a total of 61,104 participants were included. There was a significant positive association between metabolic syndrome and MI among those with obesity (hazard ratio (HR): 1.68; 95% confidence interval (CI): 1.27, 2.22). Subgroup analysis showed higher HRs for obesity (1.72; 1.03, 2.88) than overweight (1.58; 1.-13-2.21). Meta-regression revealed no significant association between nationality and risk of MI (p = 0.75). All studies had high qualities. There was no significant publication bias (p = 0.42).

CONCLUSIONS

Metabolic syndrome increased the risk of MI in those with EBW. Further studies are recommended to investigate other risk factors of CVDs in EBW, in order to implement preventive programs to reduce the burden of CVD in obesity.