Although observational studies have shown positive associations between body mass index (BMI) and the risk of atrial fibrillation (AF), the causal relationship is still uncertain owing to the susceptibility to confounding and reverse causation. This study aimed to examine the potential causality of BMI on AF by conducting a two-sample Mendelian randomization (TSMR) study. Methods: The independent genetic variants associated with BMI (n = 303) at the genome-wide significant level were derived as instrumental variables (IV) from the Genetic Investigation of Anthropometric Traits (GIANT) consortium consisting of 681,275 individuals of European ancestry. We then derived the outcome data from a GWAS meta-analysis comprised of 60,620 cases and 970,216 controls of European ancestry. The TSMR analyses were performed in five methods, namely inverse variance weighted (IVW) method, MR-Egger regression, the weighted median estimator (WME), the generalized summary data-based Mendelian randomization (GSMR), and the robust adjusted profile score (RAPS), to investigate whether BMI was causally associated with the risk of AF. Results: We found a genetically determined 1–standard deviation (SD) increment of BMI causally increased a 42.5% risk of AF (OR = 1.425; 95% CI, 1.346 to 1.509) based on the IVW method, which was consistent with the results of MR-Egger regression, WME, GSMR, as well as RAPS. The Mendelian randomization assumptions did not seem to be violated. Conclusion: This study provides evidence that higher BMI causally increased the risk of AF, suggesting control of BMI and obesity for prevention of AF.

We performed univariable and multivariable Mendelian randomization (MR) analysis to evaluate the association between blood lipids and risk of atrial fibrillation (AF), including low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), Apolipoprotein A1, and Apolipoprotein B. Methods: Data on the single nucleotide polymorphisms (SNPs) related to blood lipids were obtained from the UK Biobank study with more than 300,000 subjects of White British European ancestry, and data for AF were from the latest meta-analysis of Genome-wide association study (GWASs) with six independent cohorts with more than 1,000,000 subjects of European ancestry. The univariable MR analysis was conducted to explore whether genetic evidence of individual lipid-related traits was significantly associated with AF risks and multivariable MR analysis with three models was performed to assess the independent effects of lipid-related traits. Results: The IVW estimate showed that genetically predicted LDL-C (OR: 1.016, 95% CI: 0.962–1.073, p = 0.560), HDL-C (OR: 0.951, 95% CI: 0.895–1.010, p = 0.102), TG (OR: 0.961, 95% CI: 0.889–1.038, p = 0.313), Apolipoprotein A1 (OR: 0.978, 95% CI: 0.933–1.025, p = 0.356), and Apolipoprotein B (OR: 1.008, 95% CI: 0.959–1.070, p = 0.794) were not causally associated with the risk of AF. Sample mode (OR: 0.852, 95% CI: 0.731–0.993, p = 0.043) and weighted mode (OR: 0.907, 95% CI: 0.841–0.979, p = 0.013) showed that a 1-unit increase in TG (mmol/L) was causally associated with a 14.8% and 9.3% relative decrease in AF risk, respectively. The multivariable MR analysis with model 1, 2, and 3 indicated that TG, LDL-C, HDL-C, Apolipoprotein A1, and Apolipoprotein B were not associated with the lower risk for AF. Conclusions: Our multivariable Mendelian randomization analysis (MVMR) finding suggested no genetic evidence of lipid traits was significantly associated with AF risk. Furthermore, more work is warranted to confirm the potential association between lipid traits and AF risks.

Mendelian randomization (MR) analysis was performed to explore the effect of psoriasis on lipid metabolism traits and myocardial infarction (MI) risk and to analyze the proportion of the mediatory effect of lipid metabolism traits. Publicly accessible summary-level data for psoriasis, lipid metabolism traits, and MI were provided by the genome-wide association studies (GWASs) of the FinnGen Biobank, UK Biobank, and CARDIoGRAMplusC4D, respectively. A two-sample MR was carried out to evaluate the association of psoriasis with lipid metabolism traits and MI. Furthermore, the current research focused on determining if the impact of psoriasis on MI is mediated by lipid metabolism traits. The outcomes of the random effect inverse-variance-weighted (IVW) technique indicated a substantial link between genetically predicted psoriasis and a higher risk of low-density lipoprotein (LDL) cholesterol (OR: 1.006, 95% CI: 1.005–1.007, p = 0.024), apolipoprotein B (OR: 1.018, 95% CI: 1.010–1.026, p = 0.015), lipoprotein A (OR: 1.006, 95% CI: 1.002–1.010, p = 0.039), and MI (OR: 1.066, 95% CI: 1.014–1.121, p = 0.012). The percentages of the mediatory effect of LDL cholesterol, apolipoprotein B, and lipoprotein A under psoriasis conditions on MI risk was 7.4%, 10.2%, and 4.1%, respectively. Psoriasis was causally linked to an elevated risk of lipid metabolism levels and MI. This study further demonstrated that LDL cholesterol, apolipoprotein B, and lipoprotein A mediated the effect of psoriasis on MI risk. And timely lipid-lowering treatment should be given to MI patients.

INPLASY2020100095 The longitudinal trend of prevalence of cerebral palsy among Chinese children and adolescents from 1988 to 2020: a systematic review and meta-analysis Keywords: Cerebral palsy; Children; Prevalence; Meta-analysis; China. Download PDF – 2020100095

Background: The relationship between weekend catch‐up sleep (WCS) and weight and obesity remains controversial. The aim of this systematic review and meta‐analysis was to determine the association between WCS and obesity and to explore the dose–response effect between these two factors. Methods: We conducted a meta‐analysis with a comprehensive search of electronic databases from inception. Heterogeneity was assessed using the Chi‐squared (Q) test and I ² statistic. Different subgroup analyses will be conducted, such as gender, age, WCS durations, weekday sleep deprivation, and obesity definition. Leave‐one‐out sensitivity analyses were performed by removing a single study each time, to assess whether the results of this meta‐analysis were robust. Publication bias was explored using the funnel plot. The potential linear or nonlinear dose–response effect was evaluated using a restricted cubic splines regression model. Results: A total of 11 studies, comprising a total of 149,795 participants, were included. The pooled analysis using a random‐effects model demonstrated a significant association between WCS and reduced obesity risk (OR = 0.79, 95% CI: 0.70–0.89, p < 0.001). In subgroup analysis by weekday sleep deprivation, an association was observed among three subgroups with weekday sleep deprivation (OR = 0.73, 95% CI: 0.55–0.97, p = 0.027), while no clear correlation was shown among three subgroups without weekday sleep deprivation (OR = 0.90, 95% CI: 0.78–1.06, p = 0.206). Moreover, obesity was associated with 0 < WCS< 1 h (OR = 0.85, 95% CI: 0.77–0.94, p = 0.001) and 1 < WCS< 2 h (OR = 0.85, 95% CI: 0.78–0.94, p = 0.001) when compared with the reference group. Furthermore, WCS was associated with those BMI above 25 kg/m ² criteria (OR = 0.77, 95% CI: 0.60–0.99, p = 0.042) and BMI z‐score > 95th percentile criteria: (OR = 0.81, 95% CI: 0.66–0.99, p = 0.040), whereas no significant association was observed in studies using BMI above 30 kg/m ² criteria (OR = 1.03, 95% CI: 0.98–1.06, p = 0.417). When stratified by age, an association was observed among adults (OR = 0.73, 95% CI: 0.55–0.97, p = 0.030) and adolescents (OR = 0.77, 95% CI: 0.65–0.91, p = 0.002), whereas no clear association was seen among children (OR = 0.82, 95% CI: 0.66–1.01, p = 0.073). A nonlinear relationship was observed between WCS and the risk of obesity, where moderate WCS may reflect an attempt to compensate for sleep debt, and excessive or irregular sleep extension was associated with higher odds of obesity. Conclusions: WCS may be associated with a lower risk of overweight or obesity. More prospective studies incorporating objective sleep metrics are needed to verify the dose–response association and underlying biological pathways.