Besides LDL-cholesterol, local vascular inflammation plays a key role in atherogenesis. Efficient therapies to treat the inflammatory component of the disease have not been established. The discovery of specialized inflammation-resolving mediators, such as resolvins may provide new opportunities for treatment. This study examines whether the ω-3 fatty acid eicosapentaenoic acid-derived resolvin E1 (RvE1), can reduce atherosclerosis, when administered alone or in combination with a cholesterol-lowering statin. ApoE*3Leiden mice were fed a hypercholesterolemic diet for 9 weeks and subsequently treated with RvE1-low (1 mg/kg/day), RvE1-high (5 mg/kg/day), atorvastatin (1.5 mg/kg/day) or the combination of atorvastatin and RvE1-low for the following 16 weeks. RvE1-low and RvE1-high reduced atherosclerotic lesion size to the same extent (-35%; p < 0.05), attenuated the formation of severe lesions, also seen as a proportional increase in the presence of mild lesions, but did not alter plasma cholesterol levels. Cholesterol-lowering atorvastatin reduced atherosclerosis (-27%, p < 0.05), and the combination of RvE1 and atorvastatin further attenuated lesion size (-51%, p < 0.01) and increased the content of mild lesions. RvE1 did not affect plasma SAA, E-selectin, VCAM-1 or MCP-1 but did reduce plasma EPHX4 and down-regulated the local expression of pro-atherogenic genes in the aortae, (e.g. Cd74, Cd44, Ccl2, Ccr5 and Adam17) and significantly inactivated IFN-γ (p < 0.001) and TNF-α (p < 0.001) signalling pathways. RvE1 attenuates atherogenesis both alone and on top of a statin. The local effects of RvE1 are demonstrated by the modulated aortic expression of genes involved in inflammatory and immune responses, without altering plasma cholesterol or circulating SAA.

Background: Previous genome-wide association studies for type 2 diabetes susceptibility genes have confirmed that a common variant, rs9939609, in the fat mass and obesity associated (FTO) gene region is associated with body mass index (BMI) in European children and adults. A significant association of the same risk allele has been described in Asian adult populations, but the results are conflicting. In addition, no replication studies have been conducted in children and adolescents of Asian ancestry.Methods: A population-based survey was carried out among 3503 children and adolescents (6-18 years of age) in Beijing, China, including 1229 obese and 2274 non-obese subjects. We investigated the association of rs9939609 with BMI and the risk of obesity. In addition, we tested the association of rs9939609 with weight, height, waist circumference, waist-to-height ratio, fat mass percentage, birth weight, blood pressure and related metabolic traits.Results: We found significant associations of rs9939609 variant with weight, BMI, BMI standard deviation score (BMI-SDS), waist circumference, waist-to-height ratio, and fat mass percentage in children and adolescents (pfor trend = 3.29 × 10^-5, 1.39 × 10^-6, 3.76 × 10^-6, 2.26 × 10^-5, 1.94 × 10^-5, and 9.75 × 10^-5, respectively). No significant associations were detected with height, birth weight, systolic and diastolic blood pressure and related metabolic traits such as total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol and fasting plasma glucose (allp> 0.05). Each additional copy of the rs9939609 A allele was associated with a BMI increase of 0.79 [95% Confidence interval (CI) 0.47 to 1.10] kg/m², equivalent to 0.25 (95%CI 0.14 to 0.35) BMI-SDS units. This rs9939609 variant is significantly associated with the risk of obesity under an additive model [Odds ratio (OR) = 1.29, 95% CI 1.11 to 1.50] after adjusting for age and gender. Moreover, an interaction between theFTOrs9939609 genotype and physical activity (p< 0.001) was detected on BMI levels, the effect of rs9939609-A allele on BMI being (0.95 ± 0.10), (0.77 ± 0.08) and (0.67 ± 0.05) kg/m², for subjects who performed low, moderate and severe intensity physical activity.Conclusion: TheFTOrs9939609 variant is strongly associated with BMI and the risk of obesity in a population of children and adolescents in Beijing, China.

Objective: Rheumatoid arthritis and Ankylosing spondylitis are two common autoimmune inflammatory rheumatic diseases that negatively affect activities of daily living and can lead to structural and functional disability, reduced quality of life. Here, this study utilized Fourier transform infrared (FTIR) spectroscopy on dried serum samples and achieved early diagnosis of rheumatoid arthritis and ankylosing spondylitis based on deep learning models. Method: A total of 243 dried serum samples were collected in this study, including 81 samples each from ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Three multi-scale convolutional modules with different specifications were designed based on the multi-scale convolutional neural network (MSCNN) to effectively fuse the local features to enhance the generalization ability of the model. The FTIR was then combined with the MSCNN model to achieve a non-invasive, fast, and accurate diagnosis of ankylosing spondylitis, rheumatoid arthritis, and healthy controls. Results: Spectral analysis shows that the curves and waveforms of the three spectral graphs are similar. The main differences are distributed in the spectral regions of 3300–3250 cm⁻¹, 3000–2800 cm⁻¹, 1750–1500 cm⁻¹, and 1500–1300 cm⁻¹ , which represent: Amides , fatty acids, cholesterol, proteins with a carboxyl group, amide II, free amino acids, and polysaccharides. Four classification models, namely artificial neural network (ANN), convolutional neural network (CNN), improved AlexNet model, and multi-scale convolutional neural network (MSCNN) were established. Through comparison, it was found that the diagnostic AUC value of the MSCNN model was 0.99, and the accuracy rate was as high as 0.93, which was much higher than the other three models. Conclusion: The study demonstrated the superiority of MSCNN in distinguishing ankylosing spondylitis from rheumatoid arthritis and healthy controls. FTIR may become a rapid, sensitive, and non-invasive means of diagnosing rheumatism.

Background. Genome-wide association studies have found an obesity-related single-nucleotide polymorphism rs10938397 near the glucosamine-6-phosphate deaminase 2 gene (GNPDA2) encoding, an enzyme that catalyzes the deamination of the glucosamine-6-phosphate involved in the hexosamine signaling pathway, but the molecular mechanisms underlying the missing link between GNPDA2 and obesity remain elusive. Methods. As obesity is accompanied by an increase in the size and the number of adipocytes, the present study investigates the possible mechanism of the GNPDA2 in adipogenesis using GeneChip® Human Transcriptome Array 2.0 in human adipose-derived mesenchymal stem cells. Results. We found that overexpression of GNPDA2 enhanced accumulation of lipid droplets, and knocking down the gene decreased accumulation of lipid droplets. GO term enrichment analysis indicated that most differentially expressed genes (DEGs) affected by deficiency of GNPDA2 have functions to lipid and glucose metabolism. Further KEGG enrichment analysis showed that the greatest proportion of DEGs are involved in thermogenesis, peroxisome proliferator-activated receptor (PPAR) signaling pathway, carbon metabolism, and fatty acid metabolism including fatty acid degradation, elongation, and biosynthesis. Conclusion. These findings suggest that GNPDA2 may be a critical gene for lipid and glucose metabolism, and the expression level of GNPDA2 alters the transcriptome profile of human adipose-derived mesenchymal stem cells.

The objective of this study is to explore the associations between obesity, body composition, and the self-reported risk of obstructive sleep apnea (OSA) and to examine whether the risk of OSA is related to metabolic abnormalities in children and adolescents aged 6–17 years. Utilizing data from the 2022 to 2023 Beijing Children and Adolescents Health Cohort baseline survey, 5000 school-aged participants were analyzed. OSA risk was assessed via the Pediatric Sleep Questionnaire, with anthropometric and body composition measurements taken. Metabolic markers included blood pressure, lipid levels, blood glucose, and uric acid. Associations were analyzed using logistic regression and generalized linear models. Results showed that 88.6% were low-risk and 11.4% were high-risk for OSA. Overweight (aOR 1.53, 95% CI 1.22–1.92), obesity (aOR 1.94, 95% CI 1.57–2.40), and abdominal obesity (aOR 1.59, 95% CI 1.31–1.93) significantly increased OSA risk. High fat mass was a critical factor, while muscle mass was not, especially in those who were overweight and obese. Associations of OSA risk with metabolic abnormalities were non-significant after adjusting for BMI. Our research highlights the significant associations of obesity and body composition with OSA risk, with child BMI influencing the relationship between OSA and metabolic abnormalities. Future research should explore causative relationships and the enduring impacts of OSA on metabolic health in children.