Background: In recent years, innovative approaches utilizing Internet data have emerged in the field of syndromic surveillance. These novel methods aim to aid in the early prediction of epidemics across various scenarios and diseases. It has been observed that these systems demonstrate remarkable accuracy in monitoring outbreaks even before they become apparent in the general population. Therefore, they serve as valuable complementary tools to augment existing methodologies. In this study, we aimed to investigate the spatiotemporal distribution of migraine in China by leveraging Baidu Index (BI) data. Methods: Migraine-related BI data from January 2014 to December 2022 were leveraged, covering 301 city-level areas from 31 provincial-level regions by using the keyword “migraine (偏头痛)”. Prevalence data from the Global Burden of Disease study (GBD) were attracted to ensure the reliability of utilizing migraine-related BI data for research. Comprehensive analytical methods were then followed to investigate migraine’s spatiotemporal distribution. The Seasonal-Trend decomposition procedure based on Loess (STL) was used to identify the temporal distribution. Spatial distribution was explored using the Getis-Ord Gi^* statistic, standard deviation ellipse analysis, Moran’s Index, and Ordinary Kriging. The top eight migraine-related search terms were analyzed through the Demand Graph feature in the Baidu Index platform to understand the public’s concerns related to migraine. Results: A strong association was observed between migraine-related BI and the prevalence data of migraine from GBD with a Spearman correlation coefficient of 0.983 (P = 4.96 × 10^− 5). The overall trend of migraine-related BI showed a gradual upward trend over the years with a sharp increase from 2017 to 2019. Seasonality was observed and the peak period occurred in spring nationwide. The middle-lower reaches of the Yangtze River were found to be hotspots, while the eastern coastal areas had the highest concentration of migraine-related BI, with a gradual decrease towards the west. The most common search term related to migraine was “How to treat migraine quickly and effectively (偏头痛怎么办最快最有效的方法)”. Conclusions: This study reveals important findings on migraine distribution in China, underscoring the urgent need for effective prevention and management strategies.

Background: The relationship between urine output (UO) and severe-stage progression in the early phase of acute kidney injury (AKI) remains unclear. This study aimed to investigate the relationship between early-phase UO^6−12h [UO within 6 h after diagnosis of stage 1 AKI by Kidney Disease: Improving Global Outcomes (KDIGO) UO criteria] and severe-stage progression of AKI and to identify a reference value of early-phase UO^6−12h for guiding initial therapy in critical care.Methods: Adult patients with UO < 0.5 ml/kg/h for the first 6 h after intensive care unit (ICU) admission (meeting stage 1 AKI by UO) and UO^6−12h ≥ 0.5 ml/kg/h were identified from the Medical Information Mart for Intensive Care (MIMIC) III database. The primary outcome was progression to stage 2/3 AKI by UO. After other variables were adjusted through multivariate analysis, generalized additive model (GAM) was used to visualize the relationship between early-phase UO^6−12h and progression to stage 2/3 AKI by UO. A two-piecewise linear regression model was employed to identify the inflection point of early-phase UO^6−12h above which progression risk significantly leveled off. Sensitivity and subgroup analyses were performed to assess the robustness of our findings.Results: Of 2,984 individuals, 1,870 (62.7%) with KDIGO stage 1 UO criteria progressed to stage 2/3 AKI. In the multivariate analysis, early-phase UO^6−12h showed a significant association with progression to stage 2/3 AKI by UO (odds ratio, 0.40; 95% confidence interval, 0.34–0.46; p < 0.001). There was a non-linear relationship between early-phase UO^6−12h and progression of AKI. Early-phase UO^6−12h of 1.1 ml/kg/h was identified as the inflection point, above which progression risk significantly leveled off (p = 0.780). Patients with early-phase UO^6−12h ≥ 1.1 ml/kg/h had significantly shorter length of ICU stay (3.82 vs. 4.17 days, p < 0.001) and hospital stay (9.28 vs. 10.43 days, p < 0.001) and lower 30-day mortality (11.05 vs. 18.42%, p < 0.001). The robustness of our findings was confirmed by sensitivity and subgroup analyses.Conclusions: Among early-stage AKI patients in critical care, there was a non-linear relationship between early-phase UO^6−12h and progression of AKI. Early-phase UO^6−12h of 1.1 ml/kg/h was the inflection point above which progression risk significantly leveled off.

Background: Fibromyalgia, insomnia, depression, and anxiety share common clinical comorbidities, but their underlying genetic architecture and mechanism remain unclear. Methods: We conducted phenotype-specific Genome-wide association study (GWAS) meta-analyses for fibromyalgia, insomnia, depression, and anxiety, respectively. Genomic structural equation modeling was employed to identify a shared genetic factor (mvFibroPsych). Lead SNPs and associated genes were annotated using Functional Mapping and Annotation (FUMA), followed by gene-set and tissue enrichment analyses. The Latent Causal Variable (LCV) method was utilized to identify modifiable risk factors and phenotypes influenced by mvFibroPsych. Additionally, brain-wide and proteome-wide Mendelian randomization (MR) analyses were applied to explore brain regions and biomarkers associated with mvFibroPsych. Multi-layer molecular quantitative trait locus (QTL) analyses were conducted for mechanistic insights into mvFibroPsych. Results: Strong genetic correlations were observed among the four phenotypes (r _g = 0.55–0.84), with excellent model fit for the common factor [comparative fit index (CFI) = 0.999, standardized root mean square residual (SRMR) = 0.015]. The mvFibroPsych GWAS identified 49 lead SNPs across 43 loci, including 32 novel loci. Gene prioritization revealed 342 protein-coding genes, and pathway analysis indicated enrichment in synaptic function pathway. LCV identified 133 phenotypes causally linked to mvFibroPsych. Brain-wide MR found fractional anisotropy in the splenium of the corpus callosum to be inversely associated with mvFibroPsych. Proteome-wide MR identified five proteins significantly associated with mvFibroPsych, while multi-layer brain QTL analysis prioritized CD40 as a potential target. Conclusions: This study provides strong evidence for a shared genetic factor underlying fibromyalgia, insomnia, depression, and anxiety, linked to synaptic function, brain structure integrity, and neuroinflammatory pathways.