Background Despite mounting evidence linking neurological diseases with climate change, the link between autism spectrum disorder (ASD) and global warming has yet to be explored. Aims To examine the relationship between the incidence of ASD and global warming from 1990 to 2019 and estimate the trajectory of ASD incidence from 2020 to 2100 globally. Method We extracted meteorological data from TerraClimate between 1990 and 2019. To estimate the association between global ASD incidence and temperature variation, we adopted a two-stage analysis strategy using a generalised additive regression model. Additionally, we projected future ASD incidence under four representative shared socioeconomic pathways (SSPs: 126, 245, 370 and 585) by bootstrapping. Results Between 1990 and 2019, the global mean incidence of ASD in children under 5 years old was 96.9 per 100 000. The incidence was higher in males (147.5) than in females (46.3). A 1.0 °C increase in the temperature variation was associated with a 3.0% increased risk of ASD incidence. The association was stronger in boys and children living in a low/low-middle sociodemographic index region, as well as in low-latitude areas. According to the SSP585 scenario, by 2100, the children living in regions between 10 and 20° latitude, particularly in Africa, will experience a 68.6% increase in ASD incidence if the association remains. However, the SSP126 scenario is expected to mitigate this increase, with a less than 10% increase in incidence across all latitudes. Conclusions Our study highlights the association between climate change and ASD incidence worldwide. Prospective studies are warranted to confirm the association.

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Tissue injury induces metabolic changes in stem cells, which likely modulate regeneration. Using a model of organ regeneration called wound-induced hair follicle neogenesis (WIHN), we identified skin-resident bacteria as key modulators of keratinocyte metabolism, demonstrating a positive correlation between bacterial load, glutamine metabolism, and regeneration. Specifically, through comprehensive multiomic analysis and single-cell RNA sequencing in murine skin, we show that bacterially induced hypoxia drives increased glutamine metabolism in keratinocytes with attendant enhancement of skin and hair follicle regeneration. In human skin wounds, topical broad-spectrum antibiotics inhibit glutamine production and are partially responsible for reduced healing. These findings reveal a conserved and coherent physiologic context in which bacterially induced metabolic changes improve the tolerance of stem cells to damage and enhance regenerative capacity. This unexpected proregenerative modulation of metabolism by the skin microbiome in both mice and humans suggests important methods for enhancing regeneration after injury.

RNA methylation normally inhibits the self-recognition and immunogenicity of RNA. As such, it is likely an important inhibitor of cancer immune recognition in the tumor microenvironment, but how N6-methyladenosine (m6A) affects prognosis and treatment response remains unknown. In eight independent melanoma cohorts (1,564 patients), the modification patterns of 21 m6A gene signatures were systematically correlated with the immune cell infiltration of melanoma tumor microenvironment. m6A modification patterns for each patient were quantified using the principal component analysis method, yielding an m6Ascore that reflects the abundance of m6A RNA modifications. Two different m6A modification patterns were observed in patients with melanoma, separated into high and low m6Ascores that correlated with survival and treatment response. Low m6Ascores were characterized by an immune-inflamed phenotype, with 61.1% 5-year survival. High m6Ascores were characterized by an immune-excluded phenotype, with 52.2% 5-year survival. Importantly, lower m6Ascores correlated with more sensitive anti-PD-1 and anti-CTLA4 treatment responses, with 90% of patients with low m6Ascore responding, whereas 10% of those with high m6Ascore nonresponding (in cohort GSE63557). At single-cell and spatial transcriptome resolution, m6Ascore reflects melanoma malignant progression, immune exhaustion, and resistance to immune checkpoint blockade therapy. Hence, the m6Ascore correlates to an important facet of tumor immune escape as a tool for personalized medicine to guide immunotherapy in patients with melanoma.

BACKGROUND:Asthma, the second leading cause of death from chronic respiratory diseases, is associated with climate change, especially temperature changes. It is currently unclear about the relationship between long-term temperature variability and the incidence of asthma on a global scale. METHODS:We used asthma incidence, demographic and socioeconomic data from the Global Burden of Disease (GBD) Results Database, and environmental and geographical statistics from TerraClimate between 1990 and 2019 to determine the association between maximum temperature variability and asthma incidence. We also predicted the incidence of heat-related asthma in the future (2020-2100) under four shared socioeconomic pathways (SSPs: 126, 245, 370, and 585). RESULTS:Between 1990 and 2019, the global median incidence of asthma was 402.0 per 100,000 with a higher incidence (median: 1380.3 per 100,000) in children under 10 years old. We found that every 1 °C increase in maximum temperature variability increased the risk of asthma globally by 5.0 %, and the effect was robust for individuals living in high-latitude areas or aged from 50 to 70 years. By 2100, the average incidence of asthma is estimated to be reduced by 95.55 %, 79.32 %, and 40.02 % under the SSP126, SSP245, and SSP370 scenarios, respectively, compared to the SSP585 at latitudes >60°. CONCLUSION:Our study provides evidence that maximum temperature variability is associated with asthma incidence. These findings suggest that implementing stricter mitigation and adaptation strategies may be importment in reducing asthma cases caused by climate change.

Background Particulate matter (PM) is detrimental to the respiratory and circulatory systems. However, no study has evaluated the lag effects of weekly exposure to fine PM during the period from preconception to delivery on the risk of hypertensive disorders of pregnancy (HDPs). Objective We set out to investigate the lag effect windows of PM on the risk of HDPs on a weekly scale. Methods Data from women with de novo HDPs and normotensive pregnant women who were part of the Peking University Retrospective Birth Cohort, based on the hospital information system of Tongzhou district, were obtained for this study. Meteorological data and data on exposure to fine PM were predicted by satellite remote sensing data based on maternal residential address. The de novo HDP group consisted of pregnant women who were diagnosed with gestational hypertension or preeclampsia. Fine PM was defined as PM2.5 and PM1. The gestational stage of participants was from preconception (starting 12 weeks before gestation) to delivery (before the 42nd gestational week). A distributed-lag nonlinear model (DLNM) was nested in a Cox regression model to evaluate the lag effects of weekly PM exposure on de novo HDP hazard by controlling the nonlinear relationship of exposure–reaction. Stratified analyses by employment status (employed or unemployed), education level (higher or lower), and parity (primiparity or multiparity) were performed. Results A total of 22,570 pregnant women (mean age 29.1 years) for whom data were available between 2013 and 2017 were included in this study. The prevalence of de novo HDPs was 6.7% (1520/22,570). Our findings showed that PM1 and PM2.5 were significantly associated with an elevated hazard of HDPs. Exposure to PM1 during the 5th week before gestation to the 6th gestational week increased the hazard of HDPs. A significant lag effect of PM2.5 was observed from the 1st week before gestation to the 6th gestational week. The strongest lag effects of PM1 and PM2.5 on de novo HDPs were observed at week 2 and week 6 (hazard ratio [HR] 1.024, 95% CI 1.007-1.042; HR 1.007, 95% CI 1.000-1.015, respectively, per 10 μg/m3 increase). The stratified analyses indicated that pregnant women who were employed, had low education, and were primiparous were more vulnerable to PM exposure for de novo HDPs. Conclusions Exposure to PM1 and PM2.5 was associated with the risk of de novo HDPs. There were significant lag windows between the preconception period and the first trimester. Women who were employed, had low education, and were primiparous were more vulnerable to the effects of PM exposure; more attention should be paid to these groups for early prevention of de novo HDPs.

The effects of fine particulate matter (PM) on de novo hypertensive disorders of pregnancy (HDP) were inconsistent during the first and second trimesters. This study aimed to assess the trimester-specific effects of PM2.5 and PM1 prior to diagnosis of de novo HDP. The exposure of fine PM was predicted by satellite remote sensing data according to maternal residential addresses. De novo HDP was defined as gestational hypertension and preeclampsia during the current pregnancy. A logistic regression model was performed to assess the association of PM2.5 and PM1 with HDP during the first and early second trimesters (0–13 weeks and 14–20 weeks). The generalized estimating equation model was conducted to assess the effect of PM2.5 and PM1 on blood pressure. The present study included 22,821 pregnant women (mean age, 29.1 years) from 2013 to 2017. PM2.5 and PM1 were significantly associated with an increased risk of de novo HDP during the first trimester (OR = 1.070, 95% CI: 1.013–1.130; OR = 1.264, 95% CI: 1.058–1.511 for per 10 μg/m3) and early second trimester (OR = 1.045, 95% CI: 1.003–1.088; OR = 1.170, 95% CI: 1.002–1.366 for per 10 μg/m3). Significant trends of increased de novo HDP risk was also observed with the increment of PM (all P for trend <0.05). The stratified analyses demonstrated that the associations between exposure to fine PM and the risk of HDP were more pronounced among the pregnant women with maternal age above 35 and low maternal education level (all OR >1.047). Each 10 μg/m3 increase of PM1 and PM2.5 before diagnosis of de novo HDP elevated 0.204 (95% CI: 0.098–0.310) and 0.058 (95%CI: 0.033–0.083) mmHg of systolic blood pressure. Exposure to PM2.5 and PM1 during the first and early second trimester were positively associated with the risk of de novo HDP. The fine PM before diagnosis of de novo HDP elevated the systolic blood pressure.

BACKGROUND:Meningitis can cause devastating epidemics and is susceptible to climate change. It is unclear how temperature variability, an indicator of climate change, is associated with meningitis incidence. METHODS:We used global meningitis incidence data along with meteorological and demographic data over 1990-2019 to identify the association between temperature variability and meningitis. We also employed future (2020-2100) climate data to predict meningitis incidence under different emission levels (SSPs: Shared Socioeconomic Pathways). RESULTS:We found that the mean temperature variability increased by almost 3 folds in the past 30 years. The largest changes occurred in Australasia, Tropical Latin America, and Central Sub-Saharan Africa. With a logarithmic unit increase in temperature variability, the overall global meningitis risk increases by 4.8 %. Australasia, Central Sub-Saharan Africa, and High-income North America are the most at-risk regions. Higher statistical differences were identified in males, children, and the elderly population. Compared to high-emission (SSP585) scenario, we predicted a median reduction of 85.8 % in meningitis incidence globally under the low-emission (SSP126) climate change scenario by 2100. CONCLUSION:Our study provides evidence for temperature variability being in association with meningitis incidence, which suggests that global actions are urgently needed to address climate change and to prevent meningitis occurrence.

Background: Meningitis can cause devastating epidemics and is susceptible to climate change. It is unclear how temperature variability, an indicator of climate change, is associated with meningitis incidence.Methods: We used global meningitis incidence data along with meteorological and demographic data from 1990-2019 to identify the association between temperature variability and meningitis. We also use future (2020-2100) climate data to predict the meningitis incidence under different shared socioeconomic pathways (SSPs).Findings: We found that the mean temperature variability increased by almost 3-fold in the past 30 years. The largest changes occurred in Australasia, Tropical Latin America, and Central Sub-Saharan Africa. With a one logarithmic unit increase in temperature variability, the overall global meningitis risk increases by 4.8%. Australasia, central sub-Saharan Africa, and high income North America are the most at-risk regions. The associations were stronger in males, children, and the elderly population. By 2100, compared to SSP585, we predicted an 85.8% reduction in meningitis occurrence globally under the SSP126 climate change scenario.Interpretation: Our study provides evidence for temperature variability in association with increased meningitis incidents globally. Our findings suggest that actions are needed to address climate change and prevent meningitis occurrence.Funding Information: This research received no specific funding.Declaration of Interests: No conflict of interest.

The relationship between first-trimester GWG ( T 1 GWG) and risk of hypertensive disorders of pregnancy (HDP) remained uncertain. This study aimed to investigate the association between T 1 GWG and risk of de novo HDP. Meanwhile, we explored the mediated effect and constructed an early GWG category to evaluate the predictive capacity for HDP. T 1 GWG was defined as the weight difference between 13 ± 1 gestational weeks and pre-conception. HDP group was defined as having diagnosis of de novo HDP, including gestational hypertension or de novo pre-eclampsia (PE) during the current pregnancy. Early GWG category was constructed according to the risk of HDP within each pre-pregnancy body mass index (BMI) group. Cox regression model was utilized to check the association between the T 1 GWG and HDP. Serial mediation model was adopted to evaluate the potential mediators including mean arterial pressure (MAP) at 13th and 20th week. The logistic regression model with bootstrap was performed to assess the predictive capacity of Early GWG category and MAP for the risk of HDP. A total of 17,901 pregnant women (mean age, 29.0 years) were recruited from 2013 to 2017 at the Tongzhou Maternal and Child Health Hospital in Beijing, China. Compared to women in Class 1 of early GWG category, women in the Class 2, 3, 4 have increased risks of HDP by 1.42, 4.27, and 4.62 times, respectively (hazard ratio [ HR ] = 2.42, 95% CI : 2.11–2.77; HR = 5.27, 95% CI : 4.05–6.86; HR = 5.62, 95% CI : 4.05–7.79). The MAP measured at 13th and 20th week totally mediated 33.1 and 26.7% of association between T 1 GWG GWG and HDP in total participants and overweight/obesity pregnancies, respectively. The area under receiver operator characteristic curve for predictive model utilizing early GWG category and MAP measured at 13th and 20th week for the risk of HDP is 0.760 (95% CI : 0.739–0.777). The T 1 GWG was associated with de novo HDP, which was partially mediated by MAP measured at 13th and 20th week. Early GWG category showed a better predictive capacity for the risk of HDP compared to the National Academy of Medicine criteria for T 1 GWG.

Effectively identifying high-risk patients with de novo hypertensive disorder of pregnancy (HDP) is required to enable timely intervention and to reduce adverse maternal and perinatal outcomes. Electronic medical record of pregnant women with de novo HDP were extracted from a birth cohort in Beijing, China. The adverse outcomes included maternal and fetal morbidities, mortality, or any other adverse complications. A multitude of machine learning statistical methods were employed to develop two prediction models, one for maternal complications and the other for perinatal deteriorations. The maternal model using the random forest algorithm produced an AUC of 0.984 (95% CI (0.978, 0.991)). The strongest predictors variables selected by the model were platelet count, fetal head/abdominal circumference ratio, and gestational age at the diagnosis of de novo HDP; The perinatal model using the boosted tree algorithm yielded an AUC of 0.925 (95% CI (0.907, 0.945]). The strongest predictor variables chosen were gestational age at the diagnosis of de novo HDP, fetal femur length, and fetal head/abdominal circumference ratio. These prediction models can help identify de novo HDP patients at increased risk of complications who might need intense maternal or perinatal care.