Temporal relationships between blood glucose, lipids and BMI, and their impacts on atherosclerosis: a prospective cohort study.

OBJECTIVES: This study aimed to explore the temporal relationship between blood glucose, lipids and body mass index (BMI), and their impacts on atherosclerosis (AS). DESIGN: A prospective cohort study was designed. SETTING AND PARTICIPANTS: A total of 2659 subjects from Harbin Cohort Study on Diet, Nutrition and Chronic Non-communicable Diseases, and aged from 20 to 74 years were included. PRIMARY AND SECONDARY OUTCOME MEASURES: Body weight, height, fasting blood glucose (FBG) and 2-hour postprandial glucose (2-h PG), blood lipids including total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-c) and high-density lipoprotein cholesterol (HDL-c) were measured at baseline and follow-up. Brachial ankle pulse wave velocity (baPWV) was examined at follow-up as a marker of AS risk. Logistic regression analysis, cross-lagged path analysis and mediation analysis were performed to explore the temporal relationships between blood glucose, lipids and BMI, and their impacts on AS risk. RESULTS: Logistic regression analysis indicated that increased FBG, 2-h PG, TC, TG, LDL-c and BMI were positively associated with AS risk, while increased HDL-c was negatively associated with AS risk. The path coefficients from baseline blood parameters to the follow-up BMI were significantly greater than those from baseline BMI to the follow-up blood parameters. Mediation analysis suggested that increased FBG, 2-h PG, TC, TG and LDL-c could increase AS risk via increasing BMI, the effect intensity from strong to weak was LDL-c>TC>TG>FBG>2 h PG, while increased HDL-c could decrease AS risk via decreasing BMI. CONCLUSIONS: Changes in blood glucose and lipids could cause change in BMI, which mediated the impacts of blood glucose and lipids on AS risk. These results highlight the importance and provide support for the early and comprehensive strategies of AS prevention and control.

A multi-ancestry cerebral cortex transcriptome-wide association study identifies genes associated with smoking behaviors.

Transcriptome-wide association studies (TWAS) have provided valuable insight in identifying genes that may impact cigarette smoking. Most of previous studies, however, mainly focused on European ancestry. Limited TWAS studies have been conducted across multiple ancestries to explore genes that may impact smoking behaviors. In this study, we used cis-eQTL data of cerebral cortex from multiple ancestries in MetaBrain, including European, East Asian, and African samples, as reference panels to perform multi-ancestry TWAS analyses on ancestry-matched GWASs of four smoking behaviors including smoking initiation, smoking cessation, age of smoking initiation, and number of cigarettes per day in GWAS & Sequencing Consortium of Alcohol and Nicotine use (GSCAN). Multiple-ancestry fine-mapping approach was conducted to identify credible gene sets associated with these four traits. Enrichment and module network analyses were further performed to explore the potential roles of these identified gene sets. A total of 719 unique genes were identified to be associated with at least one of the four smoking traits across ancestries. Among those, 249 genes were further prioritized as putative causal genes in multiple ancestry-based fine-mapping approach. Several well-known smoking-related genes, including PSMA4, IREB2, and CHRNA3, showed high confidence across ancestries. Some novel genes, e.g., TSPAN3 and ANK2, were also identified in the credible sets. The enrichment analysis identified a series of critical pathways related to smoking such as synaptic transmission and glutamate receptor activity. Leveraging the power of the latest multi-ancestry GWAS and eQTL data sources, this study revealed hundreds of genes and relevant biological processes related to smoking behaviors. These findings provide new insights for future functional studies on smoking behaviors.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Vitamin D status in children and its association with glucose metabolism in northern China: a combination of a cross-sectional and retrospective study.

OBJECTIVES: This study aimed to explore the vitamin D status of children in northern China and the association between vitamin D and glucose metabolism. DESIGN: Cross-sectional study was conducted among child participants and retrospective study designs were conducted among adult participants. SETTING AND PARTICIPANTS: Both studies were recruited from Harbin, 326 children were included in children's study, 8469 adults were included in adult study. PRIMARY AND SECONDARY OUTCOME MEASURES: Physical examination, lifestyle and dietary habit data were recorded in all the participants. Serum insulin, glucose, 25(OH)D3 concentrations in children and serum glucose and lipids levels in adults were measured. Rickets history was also investigated in adults, which was used to define vitamin D deficiency in childhood. The associations were tested by linear regression and binary logistic regression. RESULT: In the children's study, only 10.7% of participants were vitamin D sufficient (≥30 ng/mL). Inverse correlations between serum 25(OH)D3 concentration and fasting insulin and homeostasis model assessment - insulin resistance (HOMA-IR) were found, and children with lower serum 25(OH)D3 concentrations were likely to have insulin resistance (IR) (OR: 0.955, 95% CI: 0.917 to 0.995, p value: 0.027). In an adult study, rickets in childhood increased the risk of type 2 diabetes in male participants (OR=1.414, 95% CI=1.013 to 1.972; p value=0.042), but this result was not observed in female participants. CONCLUSION: Our findings suggest that vitamin D deficiency is widespread in northern China. Vitamin D deficiency in childhood was associated with IR and increased the risk of type 2 diabetes in male adults.

The association of dietary flavonoids, magnesium and their interactions with the metabolic syndrome in Chinese adults: a prospective cohort study.

The aim was to systematically analyse the association of the specific flavonoids, Mg and their interactions from different food sources with the metabolic syndrome (MetS) and its components in a cohort study. A total of 6417 participants aged 20 to 74 years from the Harbin Cohort Study on Diet, Nutrition and Chronic Non-communicable Diseases were included. Multivariate logistic regression analyses, forest plot and restricted cubic spline were performed in the study. After a 5·3-year follow-up, 1283 incident cases of the MetS were reported. Those with a higher total flavonoid intake had a lower risk of the MetS (fourth v. first quartile, relative risk (RR) 0·58; 95 % CI 0·37, 0·93; P = 0·024) and central obesity (RR 0·56; 95 % CI 0·33, 0·95; P = 0·032). Further analysis showed that the specific flavonoids quercetin, kaempferol, isorhamnetin, luteolin, and flavonoids from fruits, potatoes and legumes had the similar associations with risk of the MetS and central obesity (P < 0·05 for all). A higher intake of total flavonoids, quercetin and luteolin combined with a high level of Mg was more strongly associated with a lower risk of the MetS (RR 0·60; 95 % CI 0·45, 0·81 for total; RR 0·61; 95 % CI 0·45, 0·82 for quercetin; RR 0·52; 95 % CI 0·38, 0·71 for luteolin; all Pfor interaction < 0·01). Dose-response effects showed an L-shaped curve between the total intake of five flavonoids and the risk of the MetS. A higher flavonoid intake is associated with a lower risk of the MetS and central obesity; their combination with Mg helps to strengthen their negative association with the MetS.

Dietary Flavonoids, Copper Intake, and Risk of Metabolic Syndrome in Chinese Adults.

The effects of flavonoids and copper (Cu) on metabolic syndrome (MetS) have been investigated separately, but no information exists about the joint associations between flavonoids and Cu on the risk of MetS in population studies. In this cross-sectional study, a total of 9108 people aged 20⁻75 years from the Harbin Cohort Study on Diet, Nutrition, and Chronic Non-Communicable Diseases (HDNNCDS) were included. Flavonoid intakes were calculated based on the flavonoid database created in our laboratory. Cu and other nutrient intakes were estimated using the Chinese Food Composition Table. Among all study subjects, a total of 2635 subjects (28.9%) met the diagnostic criteria for inclusion in the MetS group. Total flavonoids (fourth vs. first quartile, odds ratio (OR): 0.77, 95% confidence interval (CI) 0.66⁻0.90, Ptrend = 0.002) and Cu (OR 0.81, 90% CI: 0.70⁻0.94, Ptrend = 0.020) were inversely associated with the risk of MetS after adjusting for potential confounders. Higher flavonoid intake was more strongly associated with a lower risk of MetS with high levels of Cu intake (Pinteraction = 0.008). Dose⁻response effects showed an L-shaped curve between the total intake of five flavonoids and the risk of MetS. These results suggest that higher flavonoid intake is associated with a lower risk of MetS, especially under high levels of Cu intake.