Associations of Metabolic/Obesity Phenotypes with Insulin Resistance and C-Reactive Protein: Results from the CNTR Study

The triglycerides and glucose index is more strongly associated with metabolically healthy obesity phenotype than the lipid and obesity indices

PURPOSE

The triglycerides and glucose (TyG) index is a reliable biomarker for estimating insulin resistance; however, evidence regarding the use of the TyG index in individuals with metabolically healthy obesity (MHO) is scarce. Thus, we examined the association between the TyG index and the MHO phenotype.

METHODS

Apparently healthy men and women aged 18 years or more with obesity (body mass index [BMI] ≥ 30 kg/m2) were allocated into the following groups: MHO and metabolically unhealthy obesity (MUO). The MHO phenotype was defined by obesity and the absence of the following metabolic disorders: elevated triglyceride concentrations, elevated glucose levels, elevated blood pressure, and low HDL-C. The MUO was defined by individuals with obesity and at least one of the aforementioned cardiovascular risk factors.

RESULTS

A total 827 individuals, 605 (73.1%) women and 222 (26.9%) men were enrolled and allocated into the MHO (n = 104) and MUO (n = 723) groups. The adjusted regression analysis by age, sex, BMI, and waist circumference showed that fasting glucose (OR = 0.90; 95% CI: 0.88-0.93), and triglycerides (OR = 0.97; 95% CI: 0.96-0.98), as well as the triglycerides/HDL-C (OR = 0.18; 95% CI: 0.13-0.26), lipid accumulation product (OR = 0.95; 95% CI: 0.93-0.96), visceral adipose index (OR = 0.38; 95% CI: 0.31-0.46), and TyG index (OR = 0.001; 95% CI: 0.000-0.004) are inversely associated with the MHO, while the HDL-C (OR = 1.10; 95% CI: 1.07-1.12) had a direct association.

CONCLUSIONS

Our results show that the TyG index is more strongly associated with the MHO phenotype than the lipid and obesity indices.

Shared and Distinct Gut Microbial Profiles in Saudi Women with Metabolically Healthy and Unhealthy Obesity

BACKGROUND

Mounting evidence suggests a pivotal role for the gut microbiome in energy disequilibrium characteristic of obesity. The clinical utility of microbial profiling for the distinction between metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) remains ill-defined. We aim to probe microbial composition and diversity in young adult Saudi females with MHO and MUO. This observational study included anthropometric and biochemical measurements and shotgun sequencing of stool DNA for 92 subjects. α- and β-diversity metrics were calculated to determine the richness and variability in microbial communities, respectively. Results showed that Bacteroides and Bifidobacterium merycicum were less abundant in MUO compared to healthy and MHO groups. BMI was negatively correlated with B. adolescentis, B. longum, and Actinobacteria in MHO, while being positively correlated with Bacteroides thetaiotaomicron in both MHO and MUO. Positive correlations between waist circumference and B. merycicum and B. thetaiotaomicron were observed in MHO and MUO, respectively. Compared to MHO and MUO groups, higher α-diversity was detected in healthy individuals who also had higher β-diversity compared to those with MHO. We conclude that modulation of the gut microbiome cohorts through prebiotics, probiotics, and fecal microbiota transplantation may be a promising preventive and therapeutic approach to obesity-associated disease.

Diet-Related Inflammation Is Associated with Worse COVID-19 Outcomes in the UK Biobank Cohort

Diet, the most important modulator of inflammatory and immune responses, may affect COVID-19 incidence and disease severity. Data from 196,154 members of the UK biobank had at least one 24 h dietary recall. COVID-19 outcomes were based on PCR testing, hospital admissions, and death certificates. Adjusted Poisson regression analyses were performed to estimate the risk ratios (RR) and their 95% confidence intervals (CI) for dietary inflammatory index (DII)/energy-adjusted DII (E-DII) scores. Models were adjusted for sociodemographic factors, comorbidities, smoking status, physical activity, and sleep duration. Between January 2020 and March 2021, there were 11,288 incident COVID-19 cases, 1270 COVID-19-related hospitalizations, and 315 COVID-19-related deaths. The fully adjusted model showed that participants in the highest (vs. lowest) DII/E-DII quintile were at 10-17% increased risk of COVID-19 (DII: RR _{Q5 vs}. _Q1 = 1.10, 95% CI 1.04-1.17, P_trend < 0.001; E-DII: RR _{Q5 vs}. _Q1 = 1.17, 95% CI 1.10-1.24, P_trend < 0.001) and ≈40% higher risk was observed for disease severity (DII: RR _{Q5 vs}. _Q1 = 1.40, 95% CI 1.18-1.67, P_trend < 0.001; E-DII: RR _{Q5 vs}. _Q1 = 1.39, 95% CI 1.16-1.66, P_trend < 0.001). There was a 43% increased risk of COVID-19-related death in the highest DII quintile (RR _{Q5 vs}. _Q1 = 1.43, 95% CI 1.01-2.01, P_trend = 0.04). About one-quarter of the observed positive associations between DII and COVID-19-related outcomes were mediated by body mass index (25.8% for incidence, 21.6% for severity, and 19.8% for death). Diet-associated inflammation increased the risk of COVID-19 infection, severe disease, and death.

Insulin resistance mediates obesity-related risk of cardiovascular disease: a prospective cohort study

BACKGROUND

The mechanisms linking obesity to cardiovascular disease (CVD) are still not clearly defined. Individuals who are overweight or obese often develop insulin resistance, mediation of the association between obesity and CVD through the insulin resistance seems plausible and has not been investigated. This study aimed to evaluate whether and to what extend the effect of general and central obesity on cardiovascular disease (CVD) is mediated by insulin resistance.

METHODS

A total of 94,136 participants without CVD at baseline were recruited from the Kailuan study. Insulin resistance was evaluated by the triglyceride-glucose (TyG) index, calculating as ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Mediation analysis using a new 2-stage regression method for survival data proposed by Valeri and VanderWeele was to explore the mediating effects of the TyG index on the association between obesity and CVD.

RESULTS

During a median follow-up of 13.01 years, we identified 7327 cases of CVD. Mediation analyses showed that 47.81% of the total association (hazard ratio [HR], 1.18; 95% confidence interval [CI], 1.12-1.24) between overweight and CVD was mediated through the TyG index (HR [indirect association], 1.07; 95% CI, 1.07-1.09), and the proportion mediated was 37.94% for general obesity. For central obesity, analysis by waist circumference, waist/hip, and waist/height categories yielded an attenuated proportion mediated of 32.01, 35.02, and 31.06% for obesity, taken normal weight as reference.

CONCLUSIONS

The association between obesity and CVD was mediated by TyG index, suggesting proper control of insulin resistance can be effective to reduce the effects of obesity on CVD.

Obesity phenotypes related to musculoskeletal disorders; a cross-sectional study from RaNCD cohort

BACKGROUND

This study was evaluated the association between obesity phenotypes and risk of lower torso musculoskeletal disorders including low back pain (LBP), low back stiffness (LBS), arthralgia, and joint stiffness in Ravansar non-communicable diseases (RaNCD) cohort study.

METHODS

In this cross-sectional study, 6940 adults were examined for the presence of lower torso musculoskeletal disorders by a physician. Obesity phenotypes including metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO) were defined based on the International Diabetes Federation, as well as, body mass index > 30 kg/m2. Metabolically unhealthy non-obesity (MUNO) phenotype was considered as unhealthy metabolic without obesity.

RESULTS

The prevalence of LBP, LBS, arthralgia, and joint stiffness in MHO, MUO, and MUNO were significantly higher than in healthy participants compared to obesity phenotypes. Logistic regression showed that MHO phenotype was significantly increased with risk of LBP (OR: 1.19, CI 95%: 1.01-1.41), LBS (OR: 1.44, CI 95%: 1.12-1.86), arthralgia (OR: 1.54, CI 95%: 1.33-1.78), and joint stiffness (OR: 1.84, CI 95%: 1.35-2.52). Moreover, MUO phenotype was positively associated with risk of LBS (OR: 1.46, CI 95%: 1.09-1.94) and arthralgia (OR: 1.66, CI 95%: 1.41-1.96). In addition, MUNO phenotype was associated with a higher risk of arthralgia (OR: 1.21, CI 95%: 1.06-1.37).

CONCLUSION

All three phenotypes, MHO, MUO and MUNO were significantly increased the risk of arthralgia. However, MHO phenotype was significantly associated with a higher risk of all examined lower torso musculoskeletal disorders in the current study.