Background: Chronic pain poses a significant public health challenge. We present the global and regional data on Prevalence, Incidence and Years Lived with Disability (YLDs) for Chronic pain from the Global burden of disease (GBD) study 2019 data and analyze their associations with Socio-demographic index (SDI), age, and gender, and the future trends from 2020 to 2035. Methods: Regional trends in the burden of chronic pain and its association with age, gender, and SDI were assessed from 1990 to 2019. Joinpoint analysis was employed to describe trends in chronic pain burden across different SDI regions. Additionally, the Bayesian Age-Period-Cohort model (BAPC) was used for predicting future trends. Age-standardized rates (ASRs) of prevalence, incidence, and YLDs were employed to quantify the burden of chronic pain. Results: Between 1990 and 2019, a significant increase was observed in global prevalence and YLDs rates of chronic pain. Higher rates were found among females, whereas a faster rise was noted among males. Notably, Low Back Pain (LBP) and Migraine accounted for predominant YLDs globally, particularly among those aged 75 and above. A notable prevalence of Tension-type Headache (TTH) was observed among younger populations. Furthermore, ASRs for chronic pain were highest in high-SDI regions. Projections suggest an increase in headache ASRs globally for both genders from 2020 to 2035. Conclusion: From 1990 to 2019, the global burden of chronic pain increased significantly, with projections indicating a continued rise in headache burden over the next 15 years, underscoring the need for heightened attention to these issues.

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.

The current body of research points to a notable correlation between an imbalance in gut microbiota and the development of type 2 diabetes mellitus (T2D) as well as its consequential ailment, coronary artery disease (CAD). The complexities underlying the association, especially in the context of diabetic coronary artery disease (DCAD), are not yet fully understood, and the causal links require further clarification. In this study, a bidirectional Mendelian randomization (MR) methodology was utilized to explore the causal relationships between gut microbiota, T2D, and CAD. By analyzing data from the DIAGRAM, GERA, UKB, FHS, and mibioGen cohorts and examining GWAS databases, we sought to uncover genetic variants linked to T2D, CAD, and variations in gut microbiota and metabolites, aiming to shed light on the potential mechanisms connecting gut microbiota with DCAD. Our investigation uncovered a marked causal link between the presence of Oxalobacter formigenes and an increased incidence of both T2D and CAD. Specifically, a ten-unit genetic predisposition towards T2D was found to be associated with a 6.1% higher probability of an increase in the Oxalobacteraceae family's presence (β = 0.061, 95% CI = 0.002-0.119). In a parallel finding, an augmented presence of Oxalobacter was related to an 8.2% heightened genetic likelihood of CAD (β = 0.082, 95% CI = 0.026-0.137). This evidence indicates a critical pathway by which T2D can potentially raise the risk of CAD via alterations in gut microbiota. Additionally, our analyses reveal a connection between CAD risk and Methanobacteria, thus providing fresh perspectives on the roles of TMAO and carnitine in the etiology of CAD. The data also suggest a direct causal relationship between increased levels of certain metabolites — proline, lysophosphatidylcholine, asparagine, and salicylurate — and the prevalence of both T2D and CAD. Sensitivity assessments reinforce the notion that changes in Oxalobacter formigenes could pose a risk for DCAD. There is also evidence to suggest that DCAD may, in turn, affect the gut microbiota's makeup. Notably, a surge in serum TMAO levels in individuals with CAD, coinciding with a reduced presence of methanogens, has been identified as a potentially significant factor for future examination.

Purpose: Many observational studies have revealed intriguing connections between multisite chronic pain (MCP) and psychiatric disorders. Yet, the critical question of causality remains elusive. Our aim was to systematically evaluate the causal relationships between MCP and 11 psychiatric disorder subtypes while identifying potential mediators. Methods: We adopted a three-stage two-sample bi-directional Mendelian randomization (MR) analysis to ensure the robustness and reliability of our findings. MCP summary data was sourced from a genome-wide association study with 387,649 individuals in the UK Biobank. The discovery stage used summary statistics mainly from the Psychiatric Genomics Consortium, while validation involved MR analysis with data from the FinnGen consortium. Meta-analysis was conducted to pool MR estimates from the discovery and validation stages. Results: Positive causal relationships were found between MCP and 7 psychiatric disorder subtypes, including depression, panic disorder, anxiety disorders, insomnia, attention deficit hyperactivity disorder, post-traumatic stress disorder, and bipolar disorder, with combined odds ratios ranging from 1.361 ( P < 0.00001) to 3.322 ( P < 0.0045). Reverse MR revealed bidirectional causal associations between depression and MCP, along with anxiety disorders and MCP ( P < 0.05). Additionally, causal links were identified between MCP and smoking initiation, physical activity, body mass index, and changes in human blood cells. Multivariable MR analysis indicated these factors may mediate the relationship between MCP and psychiatric disorders. Conclusion: Our findings provide novel insights into the involvement of MCP in psychiatric disorders, highlighting potential modifiable risk factors to reduce their incidence.