Estimated glucose disposal rate (eGDR) is a measure of insulin sensitivity. While recent evidence suggests its role in cardiovascular risk assessment in Type 1 diabetes, its associations with cardiovascular disease (CVD), diabetic microvascular complications (DMC), and mortality across different populations remain unclear. We systematically searched Medline, EMBASE, Web of Science, and the Cochrane Library up to September 1st, 2024, following PRISMA guidelines. We examined associations between eGDR and CVD, DMC (including diabetic retinopathy, nephropathy, and peripheral neuropathy), and all-cause mortality using random-effects models. Secondary analysis assessed mean eGDR levels in diabetes populations. Nineteen observational studies (185,810 participants) examined clinical outcomes, while 50 studies reported mean eGDR values. In patients with Type 1 diabetes (T1DM), each 1-unit (mg/kg/min) increase in eGDR was associated with lower risks of CVD (HR 0.78; 95% CI 0.69–0.87; I2 = 68%) and all-cause mortality (HR 0.83; 95% CI 0.79–0.88; I2 = 0%). The association between eGDR and DMC in T1DM was not statistically significant (HR 0.86; 95% CI 0.72–1.03; I2 = 25%). In patients with Type 2 diabetes (T2DM), each 1-unit (mg/kg/min) increase in eGDR was associated with reduced all-cause mortality (HR 0.90; 95% CI 0.84–0.97; I2 = 62%). Similarly, in the general population, each 1-unit (mg/kg/min) increase in eGDR was associated with decreased mortality risk (HR 0.88; 95% CI 0.82–0.94; I2 = 48%). The pooled mean eGDR was higher in patients with T1DM (8.19 mg/kg/min; 95% CI 7.81–8.57; I2 = 99%) compared to those with T2DM (7.03 mg/kg/min; 95% CI 4.89–9.17; I2 = 100%). Higher eGDR levels were consistently associated with lower risks of CVD and mortality in T1DM, with similar associations observed for mortality in T2DM. In the general population, higher eGDR levels were associated with reduced mortality risk. The relationship between eGDR and DMC requires further investigation, particularly in T2DM. These findings suggest eGDR's potential utility as a risk assessment tool, though its clinical application may vary across different populations.

The mechanistic relationship between respiratory disorders and brain function remains poorly understood, despite growing evidence of cognitive and neurological manifestations in respiratory diseases. We aim to identify whether specific brain network connectivity patterns causally influence respiratory disease susceptibility, while respiratory conditions might reciprocally affect brain network architecture. We performed bidirectional Mendelian randomization (MR) analyses using genome-wide association studies (GWAS) of brain network connectivity from UK Biobank resting-state functional MRI (rs-fMRI) data (N = 31,453) and GWAS data from ten major respiratory conditions: chronic obstructive pulmonary disease (COPD), asthma, idiopathic pulmonary fibrosis (IPF), sleep apnea syndrome (SAS), lung squamous carcinoma (LUSC), lung adenocarcinoma (LUAD), small cell lung carcinoma (SCLC), hospitalized COVID-19, very severe COVID-19, and bronchiectasis. Five MR methods, inverse variance weighted (IVW) with multiplicative random-effect model, weighted median, weighted mode, MR Egger, and MR-robust adjusted profile score (MR-RAPS) were employed to ensure causal inference. In forward analysis, five respiratory disorders - asthma, IPF, SAS, LUSC, and very severe COVID-19 - showed significant causal associations (p < 1.31 ×10^-4) with 11 rs-fMRI phenotypes, spanning multiple brain networks including the central executive, subcortical-cerebellum, motor, limbic, attention, salience, visual, and default mode networks. In reverse analysis, twelve brain functional networks demonstrated genetic associations with eight respiratory conditions (COPD, asthma, IPF, SAS, LUSC, SCLC, hospitalized COVID-19, and very severe COVID-19), predominantly involving attention, salience, default mode, visual, and central executive networks. Our study provides preliminary genetic evidence suggesting potential causal relationships between brain network connectivity and respiratory disorders, contributing to our understanding of the lung-brain axis. While the identification of disease-specific network alterations offers promising insights, further clinical validation is needed before these findings can be translated into therapeutic interventions.

To elucidate the genetic and molecular mechanisms underlying psoriasis by employing an integrative multi-omics approach, using summary-data-based Mendelian randomization (SMR) to infer causal relationships among DNA methylation, gene expression, and protein levels in relation to psoriasis risk. We conducted SMR analyses integrating genome-wide association study (GWAS) summary statistics with methylation quantitative trait loci (mQTL), expression quantitative trait loci (eQTL), and protein quantitative trait loci (pQTL) data. Publicly available datasets were utilized, including psoriasis GWAS data from the European Molecular Biology Laboratory–European Bioinformatics Institute and the UK Biobank. Heterogeneity in dependent instruments (HEIDI) test and colocalization analyses were performed to identify shared causal variants, and multi-omics integration was employed to construct potential regulatory pathways. Our analyses identified significant causal associations between DNA methylation, gene expression, protein abundance, and psoriasis risk. We discovered two pathways involving the long non-coding RNA RP11-977G19.11 and apolipoprotein F (APOF). Methylation at sites cg26804944 and cg02705573 was negatively associated with RP11-977G19.11 expression. Reduced expression of RP11-977G19.11 was linked to increased APOF levels, which were positively associated with a higher risk of psoriasis. Methylation at sites cg00172967, cg00294382, and cg24773560 was positively associated with RP11-977G19.11 expression. Elevated expression of RP11-977G19.11 was associated with decreased APOF levels, reducing the risk of psoriasis. Colocalization analysis highlighted APOF as a key protein in psoriasis pathogenesis. Validation using skin tissue, EBV-transformed lymphocytes data and inflammation-related protein panels confirmed the associations of RP11-977G19.11 and APOF with psoriasis. Our multi-omics analysis provides preliminary evidence for potential molecular mechanisms in psoriasis pathogenesis. Through the integration of GWAS and molecular QTL data, we identify candidate pathways that may be relevant to disease biology. While these findings require extensive experimental validation, they offer a framework for future investigations into the molecular basis of psoriasis.

Alzheimer’s disease (AD) is a globally prevalent neurodegenerative disorder, the underlying causes and mechanisms of which remain elusive. The emerging interest in the potential connection between gut microbes and AD prompted our study to investigate this field through bibliometric analysis. To examine research trends over the past decade, we collected relevant data using search terms associated with gut microbiota and AD from the Web of Science Core Collection. Our analysis involved various tools, including R (version 4.2.2), VOSviewer (version 1.6.18), CiteSpace software (version 6.2.R1), and an online bibliometric platform. Our findings identified a total of 1170 articles published between 2012 and 2022, indicating a consistent growth of research interest in this area. Notably, China significantly contributed with 40.7% (374) of the publications, signifying its prominent role in this field. Among the journals, the Journal of Alzheimer’s Disease published the highest number of articles (57; 4.9%). In terms of author influence, Wang Y, with an H-index of 13, emerged as the most influential author. Additionally, Shanghai Jiaotong University was the most productive institution, accounting for 66 articles (5.6%). Through keyword analysis, we grouped high-frequency keywords into six clusters: gut microbiota, AD, neuroinflammation, gut-brain axis, oxidative stress, and chain fatty acids. Chain fatty acids, oxidative stress, and the gut-brain axis emerged as dominant research topics in the past five years. Recent studies have specifically focused on “nlrp3 inflammasome” and “clearance” (2020–2022), indicating shifting research interests within this field. This bibliometric analysis aims to provide insights into the evolving landscape of research on the gut microbiota and AD. Our results identify key research trends and themes while highlighting potential research gaps. The findings offer valuable perspectives for future investigations, advancing our understanding of AD and exploring potential therapeutic strategies.