Temperature might increase the hospital admission risk for rheumatoid arthritis patients in Anqing, China: a time-series study

Chronic cold environment regulates rheumatoid arthritis through modulation of gut microbiota-derived bile acids

The pathologies of many diseases are influenced by environmental temperature. As early as the classical Roman age, people believed that exposure to cold weather was bad for rheumatoid arthritis (RA). However, there is no direct evidence supporting this notion, and the molecular mechanisms of the effects of chronic cold exposure on RA remain unknown. Here, in a temperature-conditioned environment, we found that chronic cold exposure aggravates collagen-induced arthritis (CIA) by increasing ankle swelling, bone erosion, and cytokine levels in rats. Furthermore, in chronic cold-exposed CIA rats, gut microbiota dysbiosis was identified, including a decrease in the differential relative abundance of the families Lachnospiraceae and Ruminococcaceae. We also found that an antibiotic cocktail suppressed arthritis severity under cold conditions. Notably, the fecal microbiota transplantation (FMT) results showed that transplantation of cold-adapted microbiota partly recapitulated the microbiota signature in the respective donor rats and phenocopied the cold-induced effects on CIA rats. In addition, cold exposure disturbed bile acid profiles, in particular decreasing gut microbiota-derived taurohyodeoxycholic acid (THDCA) levels. The perturbation of bile acids was also associated with activation of the TGR5-cAMP-PKA axis and NLRP3 inflammasome. Oral THDCA supplementation mitigated the arthritis exacerbation induced by chronic cold exposure. Our findings identify an important role of aberrant gut microbiota-derived bile acids in cold exposure-related RA, highlighting potential opportunities to treat cold-related RA by manipulating the gut microbiota and/or supplementing with THDCA.

Geospatial analysis of the hospitalisation rate of patients with rheumatoid arthritis in Hunan: a cross-sectional Chinese study

OBJECTIVE

Little is known about spatial variability of hospitalisation rate (HR) of patients with rheumatoid arthritis (RA) worldwide, especially in China.

METHODS

A cross-sectional study was conducted among patients with RA admitted to hospitals in Hunan Province. Global Moran's I and local indicators of spatial association were used to explore the geospatial pattern of the HR of patients with RA. Generalised estimating equation analysis and geographically weighted regression were used to identify the potential influencing factors of the HR of patients with RA.

RESULTS

There were a total of 11 599 admissions, and the average HR was 1.57 per 10 000 population in Hunan. We detected different cluster patterns of the HR among patients with RA by local indicators of spatial association. Age, ethnicity, average temperature, average temperature range, average rainfall, regions, gross domestic product per capita, and doctors and hospitals per 10 000 people were risk factors for the HR. However, only average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. The increase in hospitals increased the probability of HR from east to west in Hunan with a positive coefficient, while temperature decreases increased the risk of HR from south to north negatively. Similarly, the growth of gross domestic product per capita decreased the probability of HR from southwest to northeast.

CONCLUSION

A non-random spatial distribution of the HR of patients with RA was demonstrated in Hunan, and average temperature, gross domestic product per capita and hospitals per 10 000 people showed different regression coefficients on the HR in different counties. Our study indicated that spatial and geostatistics may be useful approaches for further study among patients with RA.

Rheumatoid Arthritis Has Won the Battle but Not the War: How Many Joints Will We Save Tomorrow?

Rheumatoid arthritis refers to joint diseases of unclear etiology whose final stages can lead to unbearable pain and complete immobility of the affected joints. As one of the most widely known diseases of the joints, it serves as a study target for a large number of research groups and pharmaceutical companies. Modern treatment with anti-inflammatory drugs, including janus kinase (JAK) inhibitors, monoclonal antibodies, and botanicals (polyphenols, glycosides, alkaloids, etc.) has achieved some success and hope for improving the course of the disease. However, existing drugs against RA have a number of side effects which push researchers to elaborate on more selective and effective drug candidates. The avant-garde of research, which aims to develop treatment of rheumatoid arthritis using antisense oligonucleotides along with nonsteroidal drugs and corticosteroids against inflammation, increases the chances of success and expands the arsenal of drugs. The primary goal in the treatment of this disease is to find therapies that allow patients with rheumatoid arthritis to move their joints without pain. The main purpose of this review is to show the victories and challenges for the treatment of rheumatoid arthritis and the tortuous but promising path of research that aims to help patients experience the joy of freely moving joints without pain.

Climate change and daily outpatient visits for dermatomyositis in Hefei, China: a time-series study

With the deepening of research on the correlation between meteorological factors and autoimmune diseases, the relationship between climate change and dermatomyositis (DM) has come to our attention. This study aimed to explore the short-term correlation between meteorological factors and DM outpatient visits. Daily records of hospital outpatient visits for DM, air pollutants, and meteorological factor data in Hefei from January 1, 2018 to December 31, 2021 were obtained. The mean temperature (MT), relative humidity (RH), diurnal temperature range (DTR), and temperature change between neighboring days (TCN) were used to quantify environmental temperature and humidity and their variations. And we performed a time series analysis using a generalized linear model (GLM) in combination with a distributed lag nonlinear model (DLNM). Furthermore, gender and age were further stratified for the analysis. The sensitivity analysis was also performed. A total of 4028 DM outpatient visits were recorded during this period. There were statistically significant associations of low temperature (5th, 1.5 °C), low RH (1st, 48.6%), high RH (99th, 99%), high DTR (75th, 12.6°c), and low TCN (10th, -2.7 °C) that were associated with risk of DM outpatient visits, with lag days of 30, 16, 16, 10, and 14, respectively. Moreover, women were more susceptible to high RH exposure and low TCN exposure, while the elderly were more susceptible to low temperature. This study concluded that exposure to low temperature, extreme RH, and temperature changes (especially high DTR and low TCN) was associated with an increased risk of DM outpatient visits.

Association between air pollution exposure and outpatient visits for dermatomyositis in a humid subtropical region of China: a time-series study

In recent years, a growing number of studies have found that air pollution plays critical roles in the onset and development of autoimmune diseases, but few studies have shown an association between air pollutants and dermatomyositis (DM). We sought to investigate the relationship between short-term exposure to air pollution and outpatient visits for DM and to quantify the burden of DM due to exposure to air pollutants in Hefei, China. Daily records of hospital outpatient visits for DM, air pollutants and meteorological factors data in Hefei from January 1, 2018 to December 31, 2021 were obtained. We used a distributed lag non-linear model (DLNM) in conjunction with a generalized linear model (GLM) to explore the association between air pollution and outpatient visits for DM, and conducted stratified analyses by gender, age and season. Moreover, we used attributable fraction (AF) and attributable number (AN) to reflect the burden of disease. A total of 4028 DM clinic visits were recorded during this period. High concentration nitrogen dioxide (NO2) exposure was associated with increased risk of DM outpatient visits (relative risk (RR) 1.063, 95% confidence interval (CI) 1.015-1.114, lag 0-5). Intriguingly, exposure to high concentration ozone (O3) was associated with reduced risk of outpatient visits for DM (RR 0.974, 95% CI 0. 0.954-0.993, lag 0-6). The results of stratified analyses showed that the cold season (vs. warm season) were more susceptible to outpatient visits for DM associated with NO2 and O3 exposure. In addition, we observed that an increased risk of DM outpatient visits was attributable to high concentration NO2 exposure, while high concentration O3 exposure was associated with a decreased risk of DM outpatient visits. This study provided a scientific basis for the etiology research and health protection of DM.

Climate and human health: a review of publication trends in the International Journal of Biometeorology

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Population-Based Disparities in U.S. Urban Heat Exposure from 2003 to 2018

Previous studies have shown, in the United States (U.S.), that communities of color are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have generally been cross-sectional and are therefore "snapshots" in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 to 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing on average through time for the examined tracts. However, based on raw observations the increase is only statistically significant for White and Black prevalent census tracts. There is a 1.25 K to ~2.00 K higher degree of thermal exposure on average for communities of color relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, by <6.00 K. Hispanic (>7.00 K) and Asian (<6.75 K) prevalent tracts were greatest in intensity in San Jose, CA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage witnessed a lowering of SUHI intensity. Throughout all modeled tracts, there is a statistically significant 0.01 K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity based on this portion of the analysis. Similarly, there is a statistically significant 0.01 K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and further urgency related to social, climatic, and environmental justice concerns.