Multiple events case-control study in a prospective cohort to identify systemic, cellular, and molecular biomarkers of obesity-induced accelerated aging in 30-years-olds: the ObAGE study protocol

Guideline for designing microbiome studies in neoplastic diseases

Oncobiosis has emerged as a key contributor to the development, and modulator of the treatment efficacy of cancer. Hereby, we review the modalities through which the oncobiome can support the progression of tumors, and the emerging therapeutic opportunities they present. The review highlights the inherent challenges and limitations faced in sampling and accurately characterizing oncobiome. Additionally, the review underscores the critical need for the standardization of microbial analysis techniques and the consistent reporting of microbiome data. We provide a suggested metadata set that should accompany microbiome datasets from oncological settings so that studies remain comparable and decipherable.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

The Association between Cardio-metabolic and hepatic indices and anthropometric measures with metabolically obesity phenotypes: a cross-sectional study from the Hoveyzeh Cohort Study

BACKGROUND

This study aimed to compare the cardio-metabolic, anthropometric, and liver function indices among metabolic obesity phenotypes.

METHODS

In this cross-sectional study, which was carried out in Hoveyzeh, Khuzestan Province, Iran, a total of 7,464 individuals (male: 2859, female: 4605), were recruited and classified into four groups, based on Body Mass Index (obese, BMI ≥ 30 kg/m²; non-obese, BMI = 18.5-29.9 kg/m²) and the National Cholesterol Education Program and Adult Treatment Panel (NCEP ATP) III criteria (Healthy group, ≤ 1 of the criteria; Unhealthy group, ≥ 2 of the criteria), as follows: Metabolically Healthy Non-Obese (MHNO, 28.14%), Metabolically Unhealthy Non-Obese (MUNO, 33.06%), Metabolically Healthy Obese (MHO, 6.54%), and Metabolically Unhealthy Obese (MUO, 32.26%). Anthropometric indices (Waist/Hip Ratio (WHR), Waist/Height Ratio (WHtR), Body Adiposity Index (BAI), Visceral Adiposity Index (VAI), and Weight adjusted Waist Index (WWI)), cardio-metabolic indices (Atherogenic Index of Plasma (AIP), Lipid Accumulation Product (LAP), Cardio-Metabolic Index (CMI), Lipoprotein Combine Index (LCI), Triglyceride-Glucose (TyG), TyG-BMI, TyG-WC, and Thrombolysis In Myocardial Infarction (TIMI) risk index), and hepatic indices (Hepatic Steatosis Index (HSI) and ALD/NAFLD index (ANI)) were calculated and compared between the groups.

RESULTS

WHR,VAI, AIP, LAP, CMI, LCI, TyG, and TIMI risk index values were significantly higher in the MUNO phenotype compared to the MHO phenotype (WHR: 0.97 vs. 0.95; VAI: 3.16 vs. 1.33; AIP: 0.58 vs. 0.25; LAP: 78.87 vs. 55.79; CMI: 2.69 vs. 1.25; LCI: 27.91 vs. 12.11; TyG: 9.21 vs. 8.41; TIMI: 18.66 vs. 15.63; p < 0.001). The highest and lowest HSI and ANI values were detected in the MUO phenotype. After adjustment for age, sex, physical activity, and years of education, VAI showed the highest Odds Ratio for MUNO (OR: 5.65; 95% CI: 5.12, 6.24) and MUO (OR: 5.40; 95% CI: 5.89, 5.95) compared to the MHNO phenotypes (p < 0.001). The ANI indices was associated with a reduced risk of MUO (OR: 0.76; 95% CI: 0.75-0.78), MUNO (OR: 0.88; 95% CI: 0.87-0.90), and MHO (OR: 0.79; 95% CI: 0.77-0.81) phenotypes (p < 0.001).

CONCLUSION

MUNO phenotype was exposed to a higher risk of cardiovascular disease compared to the MHO phenotype. VAI was found to be the optimal index for cardiovascular risk assessment.

The epigenetic aging, obesity, and lifestyle

The prevalence of obesity has dramatically increased worldwide over the past decades. Aging-related chronic conditions, such as type 2 diabetes and cardiovascular disease, are more prevalent in individuals with obesity, thus reducing their lifespan. Epigenetic clocks, the new metrics of biological age based on DNA methylation patterns, could be considered a reflection of the state of one's health. Several environmental exposures and lifestyle factors can induce epigenetic aging accelerations, including obesity, thus leading to an increased risk of age-related diseases. The insight into the complex link between obesity and aging might have significant implications for the promotion of health and the mitigation of future disease risk. The present narrative review takes into account the interaction between epigenetic aging and obesity, suggesting that epigenome may be an intriguing target for age-related physiological changes and that its modification could influence aging and prolong a healthy lifespan. Therefore, we have focused on DNA methylation age as a clinical biomarker, as well as on the potential reversal of epigenetic age using a personalized diet- and lifestyle-based intervention.