The effect of obesity on chronic diseases in USA: a flexible copula approach

Using interpretable machine learning methods to identify the relative importance of lifestyle factors for overweight and obesity in adults: pooled evidence from CHNS and NHANES

BACKGROUND

Overweight and obesity pose a huge burden on individuals and society. While the relationship between lifestyle factors and overweight and obesity is well-established, the relative contribution of specific lifestyle factors remains unclear. To address this gap in the literature, this study utilizes interpretable machine learning methods to identify the relative importance of specific lifestyle factors as predictors of overweight and obesity in adults.

METHODS

Data were obtained from 46,057 adults in the China Health and Nutrition Survey (2004-2011) and the National Health and Nutrition Examination Survey (2007-2014). Basic demographic information, self-reported lifestyle factors, including physical activity, macronutrient intake, tobacco and alcohol consumption, and body weight status were collected. Three machine learning models, namely decision tree, random forest, and gradient-boosting decision tree, were employed to predict body weight status from lifestyle factors. The SHapley Additive exPlanation (SHAP) method was used to interpret the prediction results of the best-performing model by determining the contributions of specific lifestyle factors to the development of overweight and obesity in adults.

RESULTS

The performance of the gradient-boosting decision tree model outperformed the decision tree and random forest models. Analysis based on the SHAP method indicates that sedentary behavior, alcohol consumption, and protein intake were important lifestyle factors predicting the development of overweight and obesity in adults. The amount of alcohol consumption and time spent sedentary were the strongest predictors of overweight and obesity, respectively. Specifically, sedentary behavior exceeding 28-35 h/week, alcohol consumption of more than 7 cups/week, and protein intake exceeding 80 g/day increased the risk of being predicted as overweight and obese.

CONCLUSION

Pooled evidence from two nationally representative studies suggests that recognizing demographic differences and emphasizing the relative importance of sedentary behavior, alcohol consumption, and protein intake are beneficial for managing body weight status in adults. The specific risk thresholds for lifestyle factors observed in this study can help inform and guide future research and public health actions.

Follistatin as a Potential Biomarker for Identifying Metabolically Healthy and Unhealthy Obesity: A Cross-Sectional Study

Metabolically healthy obesity (MHO) refers to obese individuals with a favorable metabolic profile, without severe metabolic abnormalities. This study aimed to investigate the potential of follistatin, a regulator of metabolic balance, as a biomarker to distinguish between metabolically healthy and unhealthy obesity. This cross-sectional study included 30 metabolically healthy and 32 metabolically unhealthy individuals with obesity. Blood samples were collected to measure the follistatin levels using an enzyme-linked immunosorbent assay (ELISA). While follistatin did not significantly differentiate between metabolically healthy (median 41.84 [IQR, 37.68 to 80.09]) and unhealthy (median 42.44 [IQR, 39.54 to 82.55]) individuals with obesity (p = 0.642), other biochemical markers, such as HDL cholesterol, triglycerides, C-peptide, and AST, showed significant differences between the two groups. Insulin was the most significant predictor of follistatin levels, with a coefficient of 0.903, followed by C-peptide, which exerted a negative influence at -0.624. Quantile regression analysis revealed nuanced associations between the follistatin levels and metabolic parameters in different quantiles. Although follistatin may not serve as a biomarker for identifying MHO and metabolically unhealthy obesity, understanding the underlying mechanisms that contribute to metabolic dysfunction could provide personalized strategies for managing obesity and preventing associated complications.

Correlation analysis of diabetes based on Copula

Introduction

The ratio of Triglyceride (TG) to high-density lipoprotein cholesterol (HDL-C) is a crucial indicator for diabetes diagnosis.

Methods

This study utilizes the Copula function to model and fit the non-linear correlation among fasting blood glucose (Glu), glycosylated hemoglobin (HbA1C), and TG/HDL-C in patients with diabetes. The Copula function chosen for this study includes the two-dimensional Archimedes and Elliptical distribution family, as well as the multidimensional Vine Copula function, for fitting the data. The evaluation of the fitting effect is performed using the mean absolute error (MAE) and mean square error (MSE).

Results

The results indicate that the Clayton Copula exhibits the highest effectiveness in fitting the pairwise relationship between Glu and TG/HDL-C, as well as HbA1C and TG/HDL-C, displaying the smallest fitting error. Additionally, the Vine Copula function produces a satisfactory fit for the relationship among all three indicators. Compared to linear analysis methods, the Copula function more accurately depicts the correlation among these three types of indicators.

Discussion

Moreover, our findings indicate a stronger correlation in the lower tail between Glu and HbA1C, as well as TG/HDL-C, suggesting that the Copula function provides greater accuracy and applicability in depicting the relationship among these indicators. As a result, it can offer a more precise auxiliary diagnosis and serve as a valuable reference in clinical judgment.

Early Home Visits and Health Outcomes in Low-Income Mothers and Offspring: 18-Year Follow-Up of a Randomized Clinical Trial

Importance

Individuals with low income may have heightened rates of obesity and hypertension.

Objective

To determine whether prenatal and infancy home visitation by nurses reduces maternal and offspring obesity and hypertension.

Design, Setting, and Participants

This randomized clinical trial of prenatal and infancy nurse home visitation in a public health care system in Memphis, Tennessee, enrolled 742 women with no previous live births and at least 2 sociodemographic risk factors (unmarried, <12 years of education, unemployed) from June 1, 1990, through August 31, 1991. At registration during pregnancy, 727 mothers (98%) were unmarried, and 631 (85%) lived below the federal poverty level. At offspring ages 12 and 18 years, maternal and offspring obesity and hypertension were assessed by staff masked to treatment. The data analysis was performed from July 1, 2021, to October 31, 2023.

Interventions

Women assigned to the control group received free transportation for prenatal care and child developmental screening and referral at child ages 6, 12, and 24 months. Women assigned to nurse visitation received transportation and screening plus prenatal and infant and toddler nurse home visits.

Main Outcomes and Measures

Obesity and hypertension among mothers and their offspring at child ages 12 and 18 years, although not hypothesized in the original trial design, were analyzed using post-double selection lasso method.

Results

Of the 742 participants randomized (mean [SD] age, 18.1 [3.2] years), interviews were completed with 594 mothers and 578 offspring at child age 12 years and 618 mothers and 629 offspring at child age 18 years. Obesity was assessed for 576 offspring at age 12 years and 605 at age 18 years and for 563 and 598 mothers at child ages 12 and 18 years, respectively. Blood pressure was assessed for 568 offspring aged 12 years and 596 aged 18 years and 507 and 592 mothers at child ages 12 and 18 years, respectively. There were no overall treatment-control differences in offspring obesity or hypertension at ages 12 and 18 years combined, although nurse-visited female offspring, compared with controls, had a lower prevalence of obesity (adjusted relative risk [ARR], 0.449; 95% CI, 0.234-0.858; P = .003) and severe obesity (ARR, 0.185; 95% CI, 0.046-0.748; P < .001). There were reductions at ages 12 and 18 years combined for stage 1 and stage 2 hypertension for nurse-visited vs control group mothers, with differences limited to mothers of females (stage 1: ARR, 0.613 [95% CI, 0.440-0.855; P = .001]; stage 2: ARR, 0.217 [95% CI, 0.081-0.582; P < .001]). For both obesity and hypertension outcomes, there was no intervention effect among male offspring or the mothers of males. Self-reported maternal health aligned with program effects on hypertension.

Conclusions and Relevance

In this clinical trial follow-up at offspring ages 12 and 18, nurse-visited female offspring had lower rates of obesity and mothers of females had lower rates of hypertension than control-group counterparts. These findings suggest that risks for chronic disease among mothers of females and their female offspring who live in extreme poverty may be prevented with prenatal and infant and toddler home visitations by nurses.

Trial Registration

ClinicalTrials.gov Identifier: NCT00708695.

Anti-obesity effects of olivetol in adult zebrafish model induced by short-term high-fat diet

Obesity is a complex disease caused by various factors, and synthetic drugs used to treat it can have side effects. Natural compounds, such as olivetol, could be a promising alternative. Olivetol is a substance found in certain lichen species and has anti-inflammatory and anti-cancer properties. In this study, researchers conducted in-silico molecular docking studies and found that olivetol had significant binding affinity with receptors involved in obesity. They also investigated the effects of olivetol on a diet-induced obese zebrafish model and found that high doses of olivetol reduced excessive fat accumulation and triglyceride and lipid accumulation. The low dose of olivetol showed a significant reduction in liver enzymes' levels. However, the high dose of olivetol resulted in a significant increase in HMG-CoA levels. These results suggest that olivetol may be a promising anti-obesity agent for the treatment of hyperlipidemia-related disorders, but further research is necessary to understand its full effects on the body.

miR-383-5p Regulates Preadipocyte Proliferation and Differentiation by Targeting RAD51AP1

Obesity has become a major health problem worldwide, and increasing evidence supports the importance of microRNAs (miRNAs) in its pathogenesis. Recently, we found that miR-383-5p_1 is highly expressed in the perirenal fat of high-fat-fed rabbits, but it is not yet known whether miR-383-5p is involved in lipid metabolism. Here, we used transcriptome sequencing technology to screen 1642 known differentially expressed genes between miR-383-5p mimic groups and miR-383-5p negative control groups. Gene Ontology Resource (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched in the pathway related to lipid metabolism, and glycine biosynthesis, the NOD receptor signal pathway and nonalcoholic fatty liver were significantly enriched. Afterwards, our research results indicated that miR-383-5p can promote the proliferation and differentiation of rabbit preadipocytes, and there is a direct targeting relationship with RAD51AP1. Mechanistically, miR-383-5p directly interacts with the lipid metabolism and participates in adipogenesis and lipid accumulation by targeting RAD51AP1. In conclusion, our data highlight a physiological role for miRNA in lipid metabolism and suggest the miR-383-5p/RAD51AP1 axis may represent a potential mechanism for controlling lipid accumulation in obesity.