ObjectiveThe important contribution of dietary triggers to migraine pathogenesis has been recognized. However, the potential causal roles of many dietary habits on the risk of migraine in the whole population are still under debate. The objective of this study was to determine the potential causal association between dietary habits and the risk of migraine (and its subtypes) development, as well as the possible mediator roles of migraine risk factors.MethodsBased on summary statistics from large-scale genome-wide association studies, we conducted two-sample Mendelian randomization (MR) and bidirectional MR to investigate the potential causal associations between 83 dietary habits and migraine and its subtypes, and network MR was performed to explore the possible mediator roles of 8 migraine risk factors.ResultsAfter correcting for multiple testing, we found evidence for associations of genetically predicted coffee, cheese, oily fish, alcohol (red wine), raw vegetables, muesli, and wholemeal/wholegrain bread intake with decreased risk of migraine, those odds ratios ranged from 0.78 (95% CI: 0.63–0.95) for overall cheese intake to 0.61 (95% CI: 0.47–0.80) for drinks usually with meals among current drinkers (yes + it varies vs. no); while white bread, cornflakes/frosties, and poultry intake were positively associated with the risk of migraine. Additionally, genetic liability to white bread, wholemeal/wholegrain bread, muesli, alcohol (red wine), cheese, and oily fish intake were associated with a higher risk of insomnia and (or) major depression disorder (MDD), each of them may act as a mediator in the pathway from several dietary habits to migraine. Finally, we found evidence of a negative association between genetically predicted migraine and drinking types, and positive association between migraine and cups of tea per day.SignificanceOur study provides evidence about association between dietary habits and the risk of migraine and demonstrates that some associations are partly mediated through one or both insomnia and MDD. These results provide new insights for further nutritional interventions for migraine prevention.

ObjectiveType 2 diabetes is more common in adults, but is becoming the major concern in children and adolescent recently. This study aimed to provide additional pharmaceutical management for children and adolescents with type 2 diabetes by assessing the efficacy and safety of several glucose-lowering drugs.MethodsSearches were performed in PubMed, Medline, Ovid, Cochrane Controlled Register of Trials (CENTRAL), and ClinicalTrials.gov that reported the efficacy and safety of drugs for children and adolescents with type 2 diabetes. Pooled effects were calculated by frequentist fixed effects network meta-analyses and additive network meta-analyses.ResultsA total of 12 trials assessing eight glucose-lowering drugs were included, which compose of seven trials with monotherapy and five trials with combination therapies. Network meta-analysis results showed compared to placebo, saxagliptin+metformin (mean difference (MD) -1.91% [-2.85%, -0.97%]), liraglutide+metformin (MD -1.45% [-1.65%, -1.26%]), and liraglutide (MD -0.90% [-1.35%, -0.45%]) were the top 3 drugs that significantly reduced hemoglobin A1c (HbA1c). Sitagliptin+metformin, dapagliflozin, exenatide-2mcg, linagliptin-5mg, metformin, exenatide-5/10mcg, glimepiride, and sitagliptin also showed significant reduction in HbA1c. There were no significant differences between treatments in the incidence of adverse events, except that liraglutide+metformin had significant adverse effect such as abdominal pain. In addition, dapagliflozin, sitagliptin+metformin, and saxagliptin+metformin showed better efficacy compared with FDA-approved drugs.ConclusionsThe top 10 treatments of type 2 diabetes in children and adolescents aged 10–17 years were saxagliptin+metformin, liraglutide+metformin, liraglutide, dapagliflozin, exenatide–2 mcg, sitagliptin+metformin, linagliptin–5 mg, linagliptin–1 mg, metformin, and exenatide–5/10 mcg.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=284897, identifier CRD42021284897.

Atmospheric transmittance can critically affect the accuracy of measuring infrared characteristics exhibited by targets. On the whole, the existing measurement of atmospheric transmittance has complied with the engineering calculation results of the MODTRAN software by applying several vital parameters (e.g., temperatures, air pressures and water-vapor content). In general, the error of such a method exceeds 20%, and it is significantly impacted by local weather. In this study, a ratio correction method was adopted to decrease the error in measuring atmospheric transmittance. The correction factor was determined by comparing the directly measured value from the infrared images of reference blackbody at different temperatures with the calculated value of the MODTRAN. Subsequently, the correction factor could be exploited to correct atmospheric transmittance. The experiment for measuring infrared radiation was performed, and the radiance inversion error was reduced by more than 10% after the correction of atmospheric transmittance. Furthermore, the correction factor calculated from LWIR images could be extrapolated to other bands. Besides, the inversion accuracy of the infrared radiation characteristics significantly increased. Thus, the multi-band applicability of the correction method was verified.

Background: Dietary habit plays an important role in the composition and function of gut microbiota which possibly manipulates host eating behavior. Gut microflora and nutritional imbalance are associated with telomere length (TL). However, the causality among them remains unclear. Methods: Firstly, we calculate the significance threshold based on genetic correlations. Then we perform bi-directional Mendelian Randomization (MR) analyses among 82 food intakes (FIs) (UK Biobank, N=455,146), 95 gut microbial traits (Flemish Gut Flora Project, N=2,223) and TL (genome-wide meta-analysis from 15 cohorts, N=37,684) using summary-level data from large genome-wide association studies. Fixed-effect inverse variance weighting is the main analysis method and the other eight two-sample MR methods and three sensitivity analyses are performed. Finally, GO enrichment analyses are used to investigate the bio-function. Results: Several bi-directional causal relationships among gut microbiota, FIs and TL are obtained by two-sample MR. Overall, we find suggestive evidence of three main causal pathways among them. Drinking more glasses of water per day is able to affect the habit of eating dried fruit through the host gut microbiota (Barnesiella). The change of one gut microbiota taxon (Collinsella) in the host causally influences another gut microbiota taxon (Lactonccus) through the diet habits (intake of oil-based spread). Additionally, the TL alters the habits of drinking ground coffee and further affects the gut microbiota (Acidaminococcaceae). GO enrichment analysis further confirmed the MR results. Conclusion: TL has an impact on diet habits and gut microbiota and there are bi-directional relationships between diet habits and gut microbiota.

Background: Dietary habit plays an important role in the composition and function of gut microbiota which possibly manipulates host eating behavior. Gut microflora and nutritional imbalance are associated with telomere length (TL). However, the causality among them remains unclear. We aim to explore the causal pathways among gut microbiota, food intake (FI) and TL. Results: Firstly, we calculate the significance threshold based on genetic correlations.Then we perform bi-directional Mendelian Randomization (MR) analyses among 82 FIs (UK Biobank, N=455,146), 95 gut microbial traits (Flemish Gut Flora Project, N=2,223) and TL (genome-wide meta-analysis from 15 cohorts, N=37,684) using summary-level data from large genome-wide association studies. Fixed-effect inverse variance weighting is the main analysis method and the other eight two-sample MR methods and three sensitivity analyses are performed. Several bi-directional causal relationships among gut microbiota, FIs and TL are obtained by two-sample MR. Overall, we find suggestive evidence of three main causal pathways among them. Drinking more glasses of water per day is able to affect the habit of eating dried fruit through the host gut microbiota (Barnesiella). The change of one gut microbiota taxon (Collinsella) in the host causally influences another gut microbiota taxon (Lactonccus) through the diet habits (intake of oil-based spread). Additionally, the TL alters the habits of drinking ground coffee and further affects the gut microbiota (Acidaminococcaceae). Finally, GO enrichment analyses are used to investigate the bio-function and confirm the MR results. Conclusions: TL has an impact on diet habits and gut microbiota and there are bi-directional relationships between diet habits and gut microbiota.