Background: Growing evidence has shown that gut microbiome composition is associated with breast cancer (BC), but the causality remains unknown. We aimed to investigate the link between BC prognosis and the gut microbiome at various oestrogen receptor (ER) statuses. Methods: We performed a genome-wide association study (GWAS) to analyse the gut microbiome of BC patients, the dataset for which was collected by the Breast Cancer Association Consortium (BCAC). The analysis was executed mainly via inverse variance weighting (IVW); the Mendelian randomization (MR) results were verified by heterogeneity tests, sensitivity analysis, and pleiotropy analysis. Results: Our findings identified nine causal relationships between the gut microbiome and total BC cases, with ten and nine causal relationships between the gut microbiome and ER-negative (ER-) and ER-positive (ER+) BC, respectively. The family Ruminococcaceae and genus Parabacteroides were most apparent among the three categories. Moreover, the genus Desulfovibrio was expressed in ER- BC and total BC, whereas the genera Sellimonas, Adlercreutzia and Rikenellaceae appeared in the relationship between ER + BC and total BC. Conclusion: Our MR inquiry confirmed that the gut microbiota is causally related to BC. This further explains the link between specific bacteria for prognosis of BC at different ER statuses. Considering that potential weak instrument bias impacts the findings and that the results are limited to European females due to data constraints, further validation is crucial.

Introduction: The aim of the study was to examine the association between frailty and osteoarthritis.Material and methods: We conducted a cross-sectional study in the National Health and Nutrition Examination Survey, while logistic regression was used to explore the association of the two. Mendelian randomization (MR) study was used to explore the causal relationship between the two.Results: In the cross-sectional study, logistic regression analysis showed that odds ratio (OR) (95% CI) value was 1.07 (1.05, 1.08). In the MR study, the inverse-variance weighting (IVW) results showed OR (95% CI) value of 1.69 (1.01–2.83).Conclusions: There is both a correlation and a causal relationship between frailty and osteoarthritis, and frailty may be a potentially better response than age to osteoarthritis.

To explore the association between lipid markers and osteoarthritis (OA). First, the National Health and Nutrition Examination Survey (NHANES) database was used to screen participants with lipid markers, OA and relevant covariates, and logistic regression was used to analyze the association between lipid markers and OA; Then, under the theoretical framework of Mendelian randomization (MR), two-sample MR was performed using GWAS data of lipid markers and OA to explore the causal association between the two, which was analyzed by inverse variance weighting (IVW) method. Heterogeneity test, sensitivity analysis and pleiotropy analysis were also performed. The NHANES database screened a total of 3706 participants, of whom 836 had OA and 2870 did not have OA. When lipid markers were used as continuous variables, multivariate logistic results showed an association between HDL, LDL and OA (HDL, OR (95%):1.01 (1.00, 1.01); LDL, OR (95%):1.00 (0.99, 1.00)). When lipid markers were used as categorical variables, multivariate logistic results showed the fourth quartile result of 0.713 (0.513, 0.992) for LDL relative to the first quartile. In MR study, the results of the IVW method for TG, TL, HDL and LDL showed OR (95% CI) of 1.06 (0.97–1.16), 0.95 (0.85–1.06), 0.94 (0.86–1.02) and 0.89 (0.80–0.998) with P-values of 0.21, 0.37. 013, 0.046. The heterogeneity tests and multiplicity analyses showed P-values greater than 0.05, and sensitivity analyses showed no abnormal single nucleotide polymorphisms. Through NHANES database and MR analyses, LDL was found to be a protective factor for OA, while HDL still needs further study. Our results provide new biomarkers for preventive and therapeutic strategies for OA.

BACKGROUND: Low back pain is highly prevalent and the main cause of years lived with disability, but data on the burden and trends of low back pain (LBP) in adolescents and young adults (AYAs) are sparse. OBJECTIVE: To assess trends in the burden of LBP among AYAs aged 15–39 years at the global, regional and national levels from 1990 to 2019. METHODS: Data from the Global Burden of Disease (GBD) 2019 were used to analyze incidence, prevalence and Disability-adjusted life year (DALY) due to LBP at global, regional, and national levels. Joinpoint regression analysis calculated the average annual percentage changes (AAPC). Then analyse the association between incidence, prevalence and DALYs and socioeconomic development using the GBD Socio-demographic Index (SDI). Finally, projections were made until 2030 and calculated in Nordpred. RESULTS: The incidence, prevalence and DALYs rates (95%UI) were 2252.78 (1809.47–2784.79), 5473.43 (4488.62–6528.15) and 627.66 (419.71–866.97) in 2019, respectively. From 1990 to 2019, the incidence, prevalence, and DALYs rates AAPC (95%CI) were -0.49 (-0.56 to -0.42), -0.58 (-0.65 to -0.51) and -0.57 (-0.64 to -0.5), respectively. Incidence, prevalence, and DALYs rates in South Asia, East Asia, High-income North America, Western Europe, and Australasia decreased with SDI. Incidence, prevalence, and DALYs rates in Central Asia, Central Europe, and Eastern Europe decreased and then increased with SDI. At the national level, the incidence, prevalence, and DALYs rates are high in the United States and low in India and China. From the 2020 to 2030, most regions is predicted to decline. CONCLUSION: LBP in AYAs is a major global public problem with a high burden. There are large differences in incidence, prevalence and DALYs across SDIs, regions and countries. there is still a need to focus on LBP in AYAs and tailor interventions to reduce the future burden of this condition.

Background: As the spreading of the COVID-19 around the global, we investigated the characteristics and changes of symptoms in COVID-19 patients.

Background: To analyse the causal associations of different physical measures with osteoarthritis knee (KOA). Methods: Exposure factors (weight, body mass index (BMI), body fat percentage, waist circumference, hip circumference, waist–hip ratio (WHR), and basal metabolic rate (BMR)), and outcome factor KOA were analyzed by inverse-variance weighted (IVW) method, along with heterogeneity test, sensitivity and pleiotropy analyses. Meta-analysis was used to combine the effect values of IVW methods in different data sources. Results: Weight, BMI, body fat percentage, waist circumference, hip circumference and BMR analyses showed causal association with increased KOA risk, while WHR analysis indicated a reduction of the incidence of KOA. P-value for all the results was less than 0.05 and F-value large than 20. All results were negative for heterogeneity tests and sensitivity analyses, and there was pleiotropy in weight and BMR. Meta-analysis results showed that the results of Odds Ratios (95% Confidence Intervals) for Weight (1.43(1.35–1.51)), BMI (1.40(1.10–1.78)), body fat percentage (1.56(1.44–1.68)), waist circumference (1.40(1.10–1.78)), hip circumference (1.37(1.30–1.44)), WHR (0.86(0.71–1.04)) and BMR (1.36(1.27–1.46) were consistent with the ones by Mendelian randomization analyses. Conclusions: Body fat percentage may be a better indicator of KOA than BMI. In addition, weight and BMR may have a causal effect in KOA, but WHR does not have a causal relationship. BMI, body fat percentage, waist circumference, and hip circumference has a causal effect on KOA.