Publications

Abstract Statins are thought to have positive effects on migraine but existing data are inconclusive. We aimed to evaluate the causal effect of such drugs on migraines using Mendelian randomization. We used four types of genetic instruments as proxies for HMG-CoA reductase inhibition. We included the expression quantitative trait loci of the HMG-CoA reductase gene and genetic variation within or near the HMG-CoA reductase gene region. Variants were associated with low-density lipoprotein cholesterol, apolipoprotein B, and total cholesterol. Genome-wide association study summary data for the three lipids were obtained from the UK Biobank. Comparable data for migraine were obtained from the International Headache Genetic Consortium and the FinnGen Consortium. Inverse variance weighting method was used for the primary analysis. Additional analyses included pleiotropic robust methods, colocalization, and meta-analysis. Genetically determined high expression of HMG-CoA reductase was associated with an increased risk of migraines (OR = 1.55, 95% CI 1.30–1.84, P = 6.87 × 10 −7 ). Similarly, three genetically determined HMG-CoA reductase-mediated lipids were associated with an increased risk of migraine. These conclusions were consistent across meta-analyses. We found no evidence of bias caused by pleiotropy or genetic confounding factors. These findings support the hypothesis that statins can be used to treat migraine.

Abstract Twin studies suggest that expressive vocabulary at ~24 months is modestly heritable. However, the genes influencing this early linguistic phenotype are unknown. Here we conduct a genome-wide screen and follow-up study of expressive vocabulary in toddlers of European descent from up to four studies of the EArly Genetics and Lifecourse Epidemiology consortium, analysing an early (15–18 months, ‘one-word stage’, N Total =8,889) and a later (24–30 months, ‘two-word stage’, N Total =10,819) phase of language acquisition. For the early phase, one single-nucleotide polymorphism (rs7642482) at 3p12.3 near ROBO2 , encoding a conserved axon-binding receptor, reaches the genome-wide significance level ( P =1.3 × 10 −8 ) in the combined sample. This association links language-related common genetic variation in the general population to a potential autism susceptibility locus and a linkage region for dyslexia, speech-sound disorder and reading. The contribution of common genetic influences is, although modest, supported by genome-wide complex trait analysis (meta-GCTA h 2 15–18-months =0.13, meta-GCTA h 2 24–30-months =0.14) and in concordance with additional twin analysis (5,733 pairs of European descent, h 2 24-months =0.20).

We previously reported that autologous-fecal-microbiota-transplantation (aFMT), following 6 m of lifestyle intervention, attenuated subsequent weight regain and insulin rebound for participants consuming a high-polyphenol green-Mediterranean diet. Here, we explored whether specific changes in the core (abundant) vs. non-core (low-abundance) gut microbiome taxa fractions during the weight-loss phase (0-6 m) were differentially associated with weight maintenance following aFMT. Eighty-two abdominally obese/dyslipidemic participants (age = 52 years; 6 m weightloss = -8.3 kg) who provided fecal samples (0 m, 6 m) were included. Frozen 6 m's fecal samples were processed into 1 g, opaque and odorless aFMT capsules. Participants were randomly assigned to receive 100 capsules containing their own fecal microbiota or placebo over 8 m-14 m in ten administrations (adherence rate > 90%). Gut microbiome composition was evaluated using shotgun metagenomic sequencing. Non-core taxa were defined as ≤ 66% prevalence across participants. Overall, 450 species were analyzed. At baseline, 13.3% were classified as core, and Firmicutes presented the highest core proportion by phylum. During 6 m weight-loss phase, abundance of non-core species changed more than core species (<i>P</i> < .0001). Subject-specific changes in core and non-core taxa fractions were strongly correlated (Jaccard Index; <i>r</i> = 0.54; <i>P</i> < .001). Following aFMT treatment, only participants with a low 6 m change in core taxa, and a high change in non-core taxa, avoided 8-14 m weight regain (aFMT = -0.58 ± 2.4 kg, corresponding placebo group = 3.18 ± 3.5 kg; <i>P</i> = .02). In a linear regression model, <i>low core/high non-core</i> 6 m change was the only combination that was significantly associated with attenuated 8-14 m weight regain (<i>P</i> = .038; <i>P</i> = .002 for taxa patterns/treatment intervention interaction). High change in non-core, low-abundance taxa during weight-loss might mediate aFMT treatment success for weight loss maintenance.<b>ClinicalTrials.gov:</b> NCT03020186.

Single-walled carbon nanotube (SWNT) probe microscopy tips were grown by a surface growth chemical vapor deposition method. Tips consisting of individual SWNTs (1.5–4 nm in diameter) and SWNT bundles (4–12 nm in diameter) have been prepared by design through variations in the catalyst and growth conditions. In addition to high-resolution imaging, these tips have been used to fabricate SWNT nanostructures by spatially controlled deposition of specific length segments of the nanotube tips.

Qibin Qi, 2011 Finalist and Presenting Author

Background: A healthy lifestyle is associated with a lower risk of premature death. Metabolic pathways of a healthy lifestyle and their association with mortality remain to be understood. This study aimed to identify the metabolomic profile of a healthy lifestyle score and examine its prospective association with all-cause and cause-specific mortality, including death from cardiovascular disease (CVD) and cancer. Methods: The population included 12,146 participants from the Nurses’ Health Study (NHS), NHS II and Health Professionals Follow-Up Study (HPFS)(83% women, 97% white, aged 55±9y). Plasma metabolites were profiled using high-throughput liquid chromatography mass-spectrometry at baseline (NHS:1989-1990; NHSII:1996-1999; HPFS:1993-1995). The healthy lifestyle score was computed by summing the total number of healthy lifestyle factors participants adhered to from validated questionnaires at baseline: healthy diet (Alternative Healthy Eating Index, upper 40%), moderate alcohol intake (women: 5-15 g/d; men: 5-30 g/d), moderate-to-vigorous physical activity (≥30min/d), never smoking and normal BMI (18.5-24.9kg/m 2 ). Deaths were ascertained with death certificates and medical records. The metabolite profile was identified using elastic net regressions with train test validation split (70-30%). Metabolic pathways were determined using Metabolite Set Enrichment Analysis (MSEA). Multivariable-adjusted Cox proportional hazards regressions were used to estimate hazard ratios and 95% confidence intervals (HR[CI]) per unit of score of the healthy lifestyle metabolite profile with mortality risk. Results: The identified profile included 88 metabolites and correlated with the healthy lifestyle score (Pearson r=0.43-0.44; p&lt;0.001). Triglyceride and diglyceride metabolite sets were inversely associated with the healthy lifestyle score, whereas cholesteryl ester and phosphatidylcholine plasmalogen sets were directly associated (p&lt;0.001). Among individual lifestyle factors, the profile was most strongly correlated with normal BMI (r pb =0.43; p&lt;0.001). Over 32y of follow-up, there were 3,851 deaths, including 749 deaths from CVD and 994 from cancer. Participants with a higher healthy lifestyle metabolite profile score had lower risk of all-cause (HR=0.79[0.73, 0.85]) and CVD mortality (HR=0.77[0.58, 0.95]), but not cancer (HR=0.91[0.77, 1.05]). Significant associations persisted after further adjustment for the healthy lifestyle score. Conclusions: In US adults, we identified a metabolite profile related to a healthy lifestyle largely reflecting lipid metabolism pathways. A higher metabolite score was associated with lower subsequent all-cause mortality risk, specifically from CVD. Findings provide novel insights into potential metabolic pathways underlying the association between a healthy lifestyle and lower premature mortality.

A systematic review is a rigorous process that involves identifying, selecting, and synthesizing available evidence pertaining to an a priori–defined research question. The resulting evidence base may be summarized qualitatively or through a quantitative analytic approach known as meta-analysis. Systematic review and meta-analysis (SRMAs) have risen in popularity across the scientific realm including diabetes research. Although well-conducted SRMAs are an indispensable tool in informing evidence-based medicine, the proliferation of SRMAs has led to many reviews of questionable quality and misleading conclusions. The objective of this article is to provide up-to-date knowledge and a comprehensive understanding of strengths and limitations of SRMAs. We first provide an overview of the SRMA process and offer ways to identify common pitfalls at key steps. We then describe best practices as well as evolving approaches to mitigate biases, improve transparency, and enhance rigor. We discuss several recent developments in SRMAs including individual-level meta-analyses, network meta-analyses, umbrella reviews, and prospective meta-analyses. Additionally, we outline several strategies that can be used to enhance quality of SRMAs and present key questions that authors, editors, and readers should consider in preparing or critically reviewing SRMAs.

<title>Abstract</title> Here, we present genome-wide metabolomic signatures for copy-number variants (CNV) and single nucleotide polymorphisms (SNP) in two Finnish cohorts - The Northern Finland Birth Cohort 1966 (NFBC 1966) and NFBC 1986. This work builds upon our earlier study of characterising common CNVs in the TSPAN8 gene. Here, we have carried out an analysis of CNVs in over 9,300 individuals and characterised their dosage effect (CNV-metabolomic QTL) on 228 plasma lipoproteins and metabolites. We have reported reference (normal physiology) metabolomic signatures for up-to ~ 2.6 million COVID-19 GWAS results from the GRASP database, including for outcomes related to COVID-19 death, severity, and hospitalisation. Furthermore, by analysing two exemplar genes for COVID-19 severity namely LZTFL1 and OAS1, both reported to have Neanderthal ancestry, we have reported here two additional candidate genes for COVID-19 severity biology, namely 1) NFIX, a gene related to viral (adenovirus) replication and hematopoietic stem cells and 2) ACSL1, a known candidate gene for sepsis and bacterial inflammation. Based on our results and current literature we hypothesise that 1) charge imbalance across the cellular membrane between cations (Fe²⁺, Mg²⁺ etc) and anions (e.g., ROS, hydroxide ion from cellular Fenton reactions, superoxide etc), 2) iron trafficking within and between different cell types e.g., macrophages and 3) systemic oxidative stress response (e.g., lipid peroxidation mediated inflammation), together could be of relevance in severe COVID-19 cases. To conclude, our unique atlas of univariate and multivariate metabolomic signatures for CNVs (~ 7.2 million signatures) and SNPs (~ 0.7 million signatures) with deep annotations of various multi omics data sets provide an important reference knowledge base for human metabolism and diseases.

Additional file 3: Table S3. Summary statistics comparing the 274 KORA-significant associations across the replication cohorts. Results are given as total number (proportion) or as p values calculated using the binomial distribution (n = number of valid models in the study), where the probability for a single Bernoulli trial is given as *p = 0.5, **0.05, ***0.05/274. The number of valid models is the number of pairs for which results were available.