To investigate the pollution status of lead, cadmium, total mercury, total arsenic, total chromium, manganese, nickel, aluminum, copper on sale vegetables in Taizhou City, and to assess the health risks to people.

According to the requirements of relevant national standards, detect the contents of metals and metalloids in four types of commercial vegetable samples collected from 2019 to 2023. The pollution status evaluated by single factor pollution index (P_i) and Nemerow composite pollution index (P_M) method, the health risks were evaluated by both target hazard quotient (THQ)and total target hazard quotient (TTHQ).

A total of 419 samples of four types of vegetables were collected, manganese and copper had the highest detection rate with both of 100.0%, the others from high to low were nickel (89.17%)>aluminum (87.26%)>cadmium (85.20%)>total arsenic (52.03%)>lead (35.32%)>total chromium (30.55%)>total mercury (2.39%). The overall cadmium exceeding rate was 2.63% and all concentrated in root vegetables, the other elements did not exceed the limit. The P_i and P_M of lead, cadmium, total mercury, total arsenic, total chromium in different types of vegetables were all did not exceed 0.7. The TTHQ of four types of vegetables for adults were all less than 1.0, but the TTHQ were more than 1.0 while children consumed root and leafy vegetables.

The overall pollution levels of nine metals and metalloids in four categories of vegetables in Taizhou city were low, and the health risk level of these metals and metalloids in residents was acceptable. However, intake of root and leafy vegetables may have negative effects on human health in children.

To explore the application of integrated clustering methods in identifying low-grade glioma subtypes and prognostic prediction.

A comprehensive clustering algorithm (MOVICS), which pools ten clustering algorithms, was used to integrate the multi-omics data of LGG patients downloaded from TCGA to obtain cluster subtypes; prognostic factors of LGG were analyzed by multifactorial Cox regression. A random forest classification prediction model was constructed using mRNA data to evaluate the classification performance and externally validated with the CGGA dataset.

LGG patients were clustered into two subtypes, and the difference in survival between the two groups was statistically significant (χ²=54.410, P<0.001). The results of multifactorial Cox regression analysis showed that age (HR=1.053，95%CI: 1.037-1.069), cancer grade (HR=2.733，95%CI: 1.836-4.069) and cluster typing (HR=3.210，95%CI: 2.216-4.650) were all prognostic factors for LGG, and the results of Nomogram plots, calibration curves and ROC curves indicated good predictive performance of the model. The average prediction accuracy of the ten-fold cross-validated RF model was 87.81%, and the C-indexes of the training set, the internal validation set, and the two external validation sets were 0.717, 0.721, 0.574, and 0.572, and the Brier scores were 0.044, 0.066, 0.179, and 0.128, and the differences in the survival of the two external validation datasets were all statistically significance (P<0.05).

The comprehensive clustering method can effectively identify LGG subtypes, which are prognostic factors for LGG patients, and has been validated in an external dataset, CGGA, which can provide an important theoretical basis for clinical personalized treatment of LGG.

To understand the Prevalence of abnormal glucose metabolism and its influencing factors in Kunming, and to provide scientific basis for the prevention and treatment of diabetes.

From August to November 2020, 6332 eligible residents from 32 administrative villages/neighborhood committees in 4 counties (districts) of Kunming City were selected as survey subjects, physical examinations, and laboratory tests. Multivariate logistic regression was used to analyze the influencing factors of abnormal glucose metabolism.

The prevalence of abnormal glucose metabolism in adults in Kunming was 26.93% (PDM 12.86%, diabetes 14.07%). Multivariate logistic regression analysis showed that ≥60 years old (OR=2.408, 95%CI:1.468-3.951), overweight (OR=1.348, 95%CI:1.063-1.709), central obesity (OR=1.740, 95%CI:1.374-2.203), hypertension (OR=1.301, 95%CI:1.020-1.660) and dyslipidemia (OR=1.407, 95%CI:1.160-1.706) were associated with the risk of PDM. 40-49 years old (OR=3.230，95%CI:1.942-5.373), 50 - 59 years old (OR=5.229，95%CI：3.229-8.467),≥60 years old (OR=8.924，95%CI:5.515-14.438), low body weight (OR=1.065，95%CI：1.032-2.498), obesity (OR=1.540，95%CI:1.180-2.009), central obesity (OR=1.565, 95%CI:1.288-1.901), hypertension (OR=1.482, 95%CI:1.264-1.737) and dyslipidemia (OR=1.677, 95%CI:1.437-1.958). Family history of diabetes (OR=3.926, 95%CI:3.179-4.850) and insufficient physical activity (OR=1.473, 95%CI:1.259-1.725) were associated with the risk of diabetes. Physical labor (OR=0.581, 95%CI:0.387-0.873) was associated with a lower risk of diabetes mellitus. There were statistically significant.

The prevalence of abnormal glucose metabolism in adult residents of Kunming City is high. Age, overweight and obesity, history of hypertension and dyslipidemia, family history of diabetes, and lack of physical activity are risk factors for abnormal glucose metabolism. Prevention and control should be focused on these factors.

To assess the disease burden of migraine in China from 1990 to 2019 and predict the changing trends in disease burden from 2020 to 2030, which may provide reliable data support for public health decision-making.

The study utilized data from the Global Burden of Disease 2019 (GBD2019) database and analyzed the age-standardized incidence rate (ASIR) and age-standardized DALY rate of migraine in China from 1990 to 2019. Estimation of annual percentage change (EAPC) models and Bayesian age-period-cohort (BAPC) analysis methods were employed to reveal changes and trends.

From 1990 to 2019, both the ASIR and age-standardized DALY rate of migraine in China showed an upward trend, with EAPCs of 0.24% (95%CI: 0.20%-0.29%) and 0.27% (95%CI: 0.22%-0.32%), respectively. The highest ASIR for migraine was observed in the age group of 10-15 years, followed by a fluctuating decline, and then an increase in ASIR in the 25-30 age group. The age-standardized DALY rate peaked in the 40-45 age group. Disease burden indicators for females exceeded those for males during the same period. According to the BAPC model predictions, the ASIR for migraine in the entire population of China was expected to increase from 2020 to 2030, with an annual growth rate of 0.88%. The annual growth rates of ASIR for males and females were projected to be 1.21% and 0.64%, respectively.

The burden of migraine in China remains quite severe, and there should be a focus on adolescents and young to middle-aged populations. Customized prevention and intervention strategies are crucial for high-risk populations of different ages and genders.

To investigate the association between lifestyle and the incidence and development trajectory of cardiometabolic multimorbidity.

Based on the China Health and Retirement Longitudinal Study(CHARLS) from 2011 to 2020, a total of 8 202 individuals aged 45 years and older were included. A Multi-state Markov model (MSM) was applied to analyze the effects of lifestyle on the trajectories of cardiometabolic multimorbidity.

The disease progression trajectories mainly involved transitions from health to first cardiometabolic diseases (FCMD), health to cardiometabolic multimorbidity (CMM) and FCMD to CMM. During the follow-up period, 2 962 participants developed FCMD, 687 participants developed CMM, and 699 participants died. The results indicated that transitioning from health to FCMD, being overweight or obese, having longer nighttime sleep duration, and lacking a habit of daytime naps were positively associated with FCMD, which were 1.40(95%CI: 1.30-1.52) times, 1.16(95%CI: 1.02-1.32) times, and 1.21(95%CI: 1.06-1.39) times higher than those of healthy lifestyle people, respectively. During the transition from health to CMM, being overweight or obese was positively associated with the increased incidence of CMM (HR=1.42,95%CI: 1.06-1.90). Transitioning from FCMD to CMM, only overweight or obesity was observed as a risk factor (HR=1.44,95%CI: 1.17-1.78).

Lifestyle may increase the risk of cardiometabolic diseases and multimorbidity, emphasizing the importance of lifestyle interventions in health management practices.

To analyze the causal relationships between basal metabolic rate(BMR) and frailty index (FI) based on bidirectional two-sample mendelian randomization(MR).

The data of BMR (n=454 874) and FI (n=175 226) were extracted from the published genome-wide association studies (GWAS). By setting conditions with P<5×10^-8 as the screening criterion, the linkage disequilibrium coefficient set to 0.001 and the width of the linkage disequilibrium region of 10 000 kb, and single nucleotide polymorphisms (SNP) were screened out as the final instrumental variables. Five methods including MR-Egger regression, weighted median, Inverse variance weighted (IVW), simple mode and weighted mode were used for MR analysis, and IVW was used as the main analysis method, and β and 95%CI were used to demonstrate the causal relationship between BMR and FI. MR-Egger intercept and MR-PRESSO analysis were used to test the pleiotropy, and the heterogeneity was analyzed by Cochran Q test of MR-Egger. Leave-one-out test were performed to analyze the sensitivity. Finally, reverse MR analysis was used to verify the robustness of the results.

A total of 787 SNPs associated with BMR and 9 SNPs associated with FI were screened. The MR analysis showed a causal relationship between BMR and FI(Weighted median, β=0.105, 95%CI: 0.063-0.146, P<0.001; IVW, β=0.110, 95%CI: 0.808-0.140, P<0.001; weighted mode, β=0.145, 95%CI: 0.047-0.244, P=0.004). The screened SNPs did not have horizontal pleiotropy because the the MR-Egger intercept was 0.000 2 (P=0.374), Therefore, the MR was an effective method for causal inference in this study. The Cochran Q-test results showed Q=137.053, P=0.188, indicating no heterogeneity among the SNPs included in the MR analysis. And no outlier SNPs were found by MR-PRESSO analysis. The sensitivity analysis based on the leave-one-out method showed that the individual SNP did not affect the robustness of the MR analysis results. No causal relationship between FI and BMR was found in the reverse MR analysis(P>0.05).

Genetically predicted high BMR level is significantly associated with elevated FI, and there is no association in reverse direction, which may provide fresh ideas for proposing feasible interventions to manage frailty.

To investigate heterogeneous trajectory of sarcopenia and its associated factors among older adults with multimorbidity.

Based on the Asian Working Group for Sarcopenia 2019 Consensus, three waves of data in 2011, 2013, and 2015 from the China Health and Retirement Longitudinal Study were utilized. Latent growth mixture model and multinomial logistic regression were selected to identify heterogeneous sarcopenia trajectories and associated factors among older adults with multimorbidity.

Of the 1 254 participants, four heterogeneous trajectories of sarcopenia were identified: high initial but sharply declining trajectory (7.5%,n=93), low and stable change trajectory (64.0%, n=803), high and stable change trajectory (23.5%, n=295), low initial but sharply increasingtrajectory (5.0%, n=63). Disability, body mass index (BMI), cognition, sleep duration, smoking habits, and alcohol consumption were associated with different sarcopenia trajectories.

There are heterogenous sarcopenia trajectories among older adults with multimorbidity, targeted intervention regarding disability, BMI, cognition, sleep, smoking, and drinking should conducted to prevent sarcopenia.

To assess the potential causal relationship between gut microbiota and the risk of uterine fibroids using a Mendelian randomization approach.

We used the genome-wide association study (GWAS) statistics of gut microbiota published by the Netherlands Microbiome Project in 2022 (n=7 738) and the GWAS data of uterine fibroids from the Finnish R9 database (31 661 uterine fibroid patients and 179 209 controls). Inverse variance weighting (IVW), MR-Egger regression and weighted median method were used to study the causal relationship between intestinal flora and uterine fibroids. Sensitivity analyses were performed to test the reliability of the results of the Mendelian randomization analysis.

IVW results showed that Bacteroides (OR=1.045, 95%CI: 1.006-1.085), Ruminococcaceae (OR=1.119, 95%CI: 1.027-1.219), Roseobacter(OR=1.074, 95%CI: 1.004-1.149), Lactobacillus (OR=1.066, 95%CI: 1.017-1.118) and Firmicutes (OR=1.052, 95%CI: 1.003-1.102) were positively correlated with the incidence of uterine fibroids. No significant statistical difference was foundafter FDR correction (P>0.05).Bifidobacterium was negatively correlated with the incidence of uterine fibroids (OR=0.952,95%CI: 0.921-0.985). The causal association was statistically significant after FDR correction (P<0.05). No significant heterogeneity and horizontal pleiotropy were found in the sensitivity analysis.

Bifidobacterium may be a protective factor for the pathogenesis of uterine fibroids, while Bacteroides, Ruminococcaceae, Roseobacter, Lactobacillus and Firmicutes may be a potential risk factor for the pathogenesis of uterine fibroids. However, more studies are needed to verify the results. This study provides information about the microbiota that can be further studied, which is expected to improve the incidence and prognosis of uterine fibroids.

To evaluate the causal relationships between lipids (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol [HDL-C], triglyceride [TG], apolipoprotein A-I [ApoA-I], apolipoprotein B) and non-alcoholic fatty liver disease (NAFLD) through a two-sample Mendelian randomization (MR) study.

Summary datasets of genome-wide association studies of lipids and NAFLD in European ancestry were leveraged, and single nucleotide polymorphisms associated with lipids were selected as instrumental variables. Several univariable MR analyses were conducted, including inverse-variance weighting method, MR-Egger regression, weighted median estimation, MR pleiotropy residual sum and outlier method. A multivariable inverse-variance weighting method was further used to evaluate the independent causal effect of lipids on NAFLD.

Univariable inverse-variance weighting MR showed that genetically predicted higher HDL-C (OR=0.774, 95%CI: 0.708-0.845, P<0.001) and ApoA-I (OR=0.878, 95%CI: 0.796-0.968, P=0.009) were associated with a lower risk of NAFLD, while genetically predicted higher TG (OR=1.323, 95%CI: 1.201-1.458, P<0.001) was associated with a higher risk of NAFLD. The causal effects of HDL-C and TG on NAFLD remained consistent in multivariable MR when adjusting for other lipids, body mass index, and type-2 diabetes.

Our study supports a putative causal relationship between HDL-C, TG, and NAFLD.