A neurobehavioral study on the efficacy of price interventions in promoting healthy food choices among low socioeconomic families

Identification of ion channel-related genes as diagnostic markers and potential therapeutic targets for osteoarthritis through bioinformatics and machine learning-based approaches

BACKGROUND

Osteoarthritis (OA) is a debilitating joint disorder characterized by the progressive degeneration of articular cartilage. Although the role of ion channels in OA pathogenesis is increasingly recognized, diagnostic markers and targeted therapies remain limited.

METHODS

In this study, we analyzed the GSE48556 dataset to identify differentially expressed ion channel-related genes (DEGs) in OA and normal controls. We employed machine learning algorithms, least absolute shrinkage and selection operator(LASSO), and support vector machine recursive feature elimination(SVM-RFE) to select potential diagnostic markers. Then the gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were performed to explore the potential diagnostic markers' involvement in biological pathways. Finally, weighted gene co-expression network analysis (WGCNA) was used to identify key genes associated with OA.

RESULTS

We identified a total of 47 DEGs, with the majority involved in transient receptor potential (TRP) pathways. Seven genes (CHRNA4, GABRE, HTR3B, KCNG2, KCNJ2, LRRC8C, and TRPM5) were identified as the best characteristic genes for distinguishing OA from healthy samples. We performed clustering analysis and identified two distinct subtypes of OA, C1, and C2, with differential gene expression and immune cell infiltration profiles. Then we identified three key genes (PPP1R3D, ZNF101, and LOC651309) associated with OA. We constructed a prediction model using these genes and validated it using the GSE46750 dataset, demonstrating reasonable accuracy and specificity.

CONCLUSIONS

Our findings provide novel insights into the role of ion channel-related genes in OA pathogenesis and offer potential diagnostic markers and therapeutic targets for the treatment of OA.

Association between environmental composite quality index score and obesity in children and their family: A cross-sectional study in northeast China

BACKGROUND

Childhood obesity has emerged as a severe public health problem worldwide. Most studies focused on the association between single environmental risk factors and obesity. Thus, we investigate the association between environmental composite quality index (ECQI) score and obesity in children and their families.

METHOD

The cross-sectional study was conducted among 2354 children and 1761 mothers in Fuxin City, Liaoning Province, in 2015. The information was collected by questionnaires, including environmental factors, heights, weights, and sociodemographic data. We quantitated thirteen indoor and outdoor risk factors and constructed the ECQI. There were 4 household environmental factors in the household air quality index (HAQI) and 9 variables to evaluate the outdoor environmental quality index (OEQI). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using the binary logistic regression model after adjusting the most common obesity risk factors.

RESULTS

A higher ECQI score was correlated with a higher risk of childhood obesity and family obesity. Comparing the ECQI score 0-2 to the ECQI score ≥5, ORs with childhood obesity was 1.73 (95%CI, 1.25-2.39, P for trend = 0.001), and 1.53 for family obesity (95%CI, 1.09-2.15; P for trend = 0.003) after adjusted confounding factors. Similarly, a significantly positive association was found between OEQI score, HAQI score, and childhood obesity, family obesity.

CONCLUSION

A higher ECQI score was associated with an increased risk of obesity in children and their families. Both HAQI score and OEQI score were associated to childhood obesity and family obesity. Further studies should elucidate the underlying mechanisms.