Yudiyang Ma

Background: Exposure to metals in the external environment and system circulation has been linked to cerebrovascular health. However, population‐based epidemiological evidence on the association between multiple metals and cerebral microbleeds (CMBs) is lacking. Methods: We conducted a cross‐sectional study using data from the META‐KLS (Multi‐modality Medical Imaging Study Based on Kailuan Study) to investigate the association between plasma metals and CMBs. CMBs were determined through brain magnetic resonance imaging as having ≥1 CMB lesion. Fasting plasma concentrations of 14 metals were measured. A total of 1206 adults were included in this study, with a median (interquartile range) age of 54.4 (19.2) years; 630 (52.2%) were female, and 291 (24.1%) were identified as CMBs cases. Binary logistic regression models and the quartile g‐computation model were used to assess single‐metal and multi‐metal associations with CMBs, respectively. Results: After fully covariate adjustment, each interquartile range increment in plasma calcium, magnesium, lead, and vanadium was associated with higher CMBs risk. The odds ratios (ORs) and 95% CIs were 1.36 (1.12–1.64) for calcium, 1.34 (1.10–1.63) for magnesium, 1.38 (1.13–1.69) for lead, and 1.25 (1.06–1.47) for V. In the quartile g‐computation model, lead, magnesium, and calcium exhibited significant positive effects with weights of 28.6%, 8.0%, and 3.6%, respectively. Conclusions: Elevated levels of plasma calcium, magnesium, lead, and vanadium in the body may increase the risk of CMBs. Thus, limiting exposure and timely monitoring of these metals may help prevent CMBs.

BACKGROUND: Evidence about the impact of multiple metal exposure on brain neuroimaging metrics remains limited. We aim to investigate the effects of single and mixed metal exposure on white matter hyperintensities (WMHs). METHODS: This cross-sectional study included 1183 subjects without stroke history from the META-KLS (Multi-modality Medical Imaging Study Based on Kailuan Study), which is an existing prospective cohort in Tangshan, China. Plasma metal levels, including Mg, Ca, V, Mn, Co, Ni, Cu, Zn, As, Se, Rb, Cs, Tl, Pb, and Cd, were measured using an inductively coupled plasma mass spectrometer. Ordinal and binary logistic regression models were used to examine the effects of metal exposure on the WMH burden, deep white matter hyperintensity, and periventricular white matter hyperintensity. All metal concentrations were naturally log-transformed to reduce skewness and were analyzed as both continuous and tertile forms. Weighted quantile sum regression, quantile-based g-computation model, and Bayesian Kernel Machine Regression were used in the metal mixture analysis. RESULTS: Compared with the first tertile, the adjusted odds ratios and 95% CIs for the WMH burden in the third tertile were 1.57 (1.05–2.34) for As, 2.01 (1.28–3.18) for Cu, 1.68 (1.14–2.50) for V, 1.61 (1.07–2.44) for Cs, and 1.56 (1.04–2.34) for Tl (all P for trend<0.05). Additionally, Pb, Se, and Mg showed significant positive associations with WMH burden exclusively in continuous analysis, with odds ratios of 1.27 (1.02–1.56) for Pb, 1.32 (1.07–1.61) for Se, and 1.27 (1.04–1.55) for Mg for per interquartile range increase in Ln-transformed metal concentrations. The weighted quantile sum index revealed a significant positive correlation with WMH burden risk (each interquartile range increment in the weighted quantile sum index was associated with 60% higher odds for WMH burden [95% CI, 1.09–2.34]). The primary contributors to the weighted quantile sum index were As (39.4%), followed by Pb (12.5%) and Cu (11.3%). The bivariate exposure-response relationships suggested potential interactions between As and Cu, as well as As and Co. CONCLUSIONS: There were positive associations between individual exposures to As, Pb, Cu, V, Se, Cs, Tl, and Mg, and mixed metal exposure with WMH burden among the Chinese population, strengthening the evidence of detrimental effects of specific metals on brain health.

Background: First cardiometabolic disorders (FCMDs) and dementia pose a significant burden on aging populations. Investigating the association between atmospheric pollutants and the progression of FCMDs and dementia is crucial. Methods: A prospective analysis was conducted on 413 149 participants from the UK Biobank. Atmospheric pollutant concentrations were calculated using land‐use regression models. We used a multistate model and calculated the polygenic risk score for dementia to analyze the associations of pollutants on the relevant trajectories from baseline to FCMD, subsequent dementia, and death. Results: Over 13 years, 65 336 patients with FCMD were identified, with 2584 developing dementia and 10 664 dying. Each interquartile range increase in pollutant concentrations showed the hazard ratios (HRs) of particulate matter with aerodynamic diameter ≤2.5 μm (HR, 1.03 [95% CI, 1.02–1.05]); nitrogen dioxide (HR, 1.04 [95% CI, 1.03–1.05]), and nitrogen oxides (HR, 1.03 [95% CI, 1.02–1.04]) for transitioning from baseline to FCMD; particulate matter with aerodynamic diameter ≤2.5 μm (HR, 1.11 [95% CI, 1.05–1.16]; nitrogen dioxide: HR, 1.12 [95% CI, 1.06–1.18]; and nitrogen oxides (HR, 1.07 [95% CI, 1.03–1.12]) for transitioning from FCMD to dementia, and the association between pollutants and this trajectory remained consistent after adjusting for genetic susceptibility to dementia (particulate matter with aerodynamic diameter ≤2.5 μm: HR, 1.06 [95% CI, 1.04–1.08]; nitrogen dioxide: HR, 1.05 ([95% CI, 1.03–1.07); and nitrogen oxides: HR, 1.05 [95% CI, 1.03–1.06]) for transitioning from baseline to death; and particulate matter with aerodynamic diameter ≤2.5 μm: HR, 1.03 ([95% CI, 1.00–1.05); nitrogen dioxide: HR, 1.05 ([95% CI, 1.02–1.08); nitrogen oxides: HR, 1.04 ([95% CI, 1.01–1.06]) for transitioning from subsequent FCMD to death. Particulate matter with aerodynamic diameter ≤10 μm was only significantly associated with the transition from baseline to FCMD. Conclusions: Our study provides the first evidence that atmospheric pollutants are associated with the progression of FCMD and subsequent dementia.

Early-life tobacco exposure serves as a non-negligible risk factor for aging-related diseases. To understand the underlying mechanisms, we explored the associations of early-life tobacco exposure with accelerated biological aging and further assessed the joint effects of tobacco exposure and genetic susceptibility. Compared with those without in utero exposure, participants with in utero tobacco exposure had an increase in Klemera-Doubal biological age (KDM-BA) and PhenoAge acceleration of 0.26 and 0.49 years, respectively, but a decrease in telomere length of 5.34% among 276,259 participants. We also found significant dose-response associations between the age of smoking initiation and accelerated biological aging. Furthermore, the joint effects revealed that high–polygenic risk score participants with in utero exposure and smoking initiation in childhood had the highest accelerated biological aging. There were interactions between early-life tobacco exposure and age, sex, deprivation, and diet on KDM-BA and PhenoAge acceleration. These findings highlight the importance of reducing early-life tobacco exposure to improve healthy aging.

Background: Benzene affects human health through environmental exposure in addition to occupational contact. However, few studies have examined the associations between long-term exposure to low concentrations of ambient benzene and mortality risks in nonoccupational settings. Methods: This prospective cohort study consists of 393,042 participants without stroke, myocardial infarction, or cancer at baseline from the UK Biobank. Annual average concentrations of benzene for each year during follow-up were measured using air dispersion models. The main outcomes were all-cause mortality and mortality from specific causes. Cox proportional-hazards models with time-varying exposure measurements were used to estimate the hazard ratios and 95% confidence intervals (CIs) for mortality risks. Restricted cubic spline models were used to estimate exposure–response relationships. Measurements and Main Results: With each interquartile range increase in the average annual concentration of benzene, the adjusted hazard ratios of mortality risk from all causes, cardiovascular disease, cancer, and respiratory disease were 1.26 (95% CI, 1.24–1.27), 1.24 (95% CI, 1.21–1.28), 1.27 (95% CI, 1.25–1.29), and 1.25 (95% CI, 1.20–1.30), respectively. The monotonically increasing exposure–response curves showed no threshold and plateau within the observed concentration range. Furthermore, the effect of benzene exposure on mortality persisted across different subgroups and was somewhat stronger in younger and White people (P for interaction < 0.05). Conclusions: Long-term exposure to low concentrations of ambient benzene significantly increases mortality risk in the general population. Ambient benzene represents a potential threat to public health, and further investigations are needed to support timely pollution regulation and health protection.

BACKGROUND: The extent to which genetic susceptibility modifies the associations between air pollutants and the risk of incident stroke is still unclear. This study was designed to investigate the separate and joint associations of long-term exposure to air pollutants and genetic susceptibility on stroke risk. METHODS: The participants of this study were recruited by the UK Biobank between 2006 and 2010. These participants were followed up from the enrollment until the occurrence of stroke events or censoring of data. Hazard ratios (HRs) and 95% CIs for stroke events associated with long-term exposure to air pollutants were estimated by fitting both crude and adjusted Cox proportional hazards models. Additionally, the polygenic risk score was calculated to estimate whether the polygenic risk score modifies the associations between exposure to air pollutants and incident stroke. RESULTS: A total of 502 480 subjects were included in this study. After exclusion, 452 196 participants were taken into the final analysis. During a median follow-up time of 11.7 years, 11 334 stroke events were observed, with a mean age of 61.60 years, and men accounted for 56.2% of the total cases. Long-term exposures to particulate matter with an aerodynamic diameter smaller than 2.5 µm (adjusted HR, 1.70 [95% CI, 1.43–2.03]) or particulate matter with an aerodynamic diameter smaller than 10 µm (adjusted HR, 1.50 [95% CI, 1.36–1.66]), nitrogen dioxide (adjusted HR, 1.10 [95% CI, 1.07–1.12]), and nitrogen oxide (adjusted HR, 1.04 [95% CI, 1.02–1.05]) were pronouncedly associated with increased risk of stroke. Meanwhile, participants with high genetic risk and exposure to high air pollutants had ≈45% (31%, 61%; particulate matter with an aerodynamic diameter smaller than 2.5 µm), 48% (33%, 65%; particulate matter with an aerodynamic diameter smaller than 10 µm), 51% (35%, 69%; nitrogen dioxide), and 39% (25%, 55%; nitrogen oxide) higher risk of stroke compared with those with low genetic risk and exposure to low air pollutants, respectively. Of note, we observed additive and multiplicative interactions between genetic susceptibility and air pollutants on stroke events. CONCLUSIONS: Chronic exposure to air pollutants was associated with an increased risk of stroke, especially in populations at high genetic risk.

To date, no study has explored the extent to which genetic susceptibility modifies the effects of air pollutants on the risk of atrial fibrillation (AF). This study was designed to investigate the separate and joint effects of long-term exposure to air pollutants and genetic susceptibility on the risk of AF events. This study included 401,251 participants without AF at baseline from UK Biobank. We constructed a polygenic risk score and categorized it into three categories. Cox proportional hazards models were fitted to assess the separate and joint effects of long-term exposure to air pollutants and genetics on the risk of AF. Additionally, we further evaluated the effect modification of genetic susceptibility. The hazard ratios and corresponding 95% confidence intervals of incident AF for per interquartile range increase in particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM _2.5 ) or 10 µm (PM ₁₀ ), nitrogen dioxide (NO ₂ ), and nitrogen oxide (NO _x ) were 1.044 (1.025, 1.063), 1.063 (1.044, 1.083), 1.061 (1.042, 1.081), and 1.039 (1.023, 1.055), respectively. For the combined effects, participants exposed to high air pollutants levels and high genetic risk had approximately 149.2% (PM _2.5 ), 181.7% (PM ₁₀ ), 170.2% (NO ₂ ), and 157.2% (NO _x ) higher risk of AF compared to those with low air pollutants levels and low genetic risk, respectively. Moreover, the significant additive interactions between PM ₁₀ and NO ₂ and genetic risk on AF risk were observed, with around 16.4% and 35.1% of AF risk could be attributable to the interactive effects. In conclusion, long-term exposure to air pollutants increases the risk of AF, particularly among individuals with high genetic susceptibility.

Background: Micromobility sharing platforms have involved skyrocketing numbers of users in multiple countries since 2010. However, few studies have examined the overall impact of the growing micromobility market on road injuries.Method: We use road injury data from the Global Burden of Disease Study database to examine the effect of age, period, and cohort on micromobility injury-related deaths and incidence. We compared four countries that vary in demographic background and road infrastructure. By comparing the countries, we analyzed the relationship between the trends in road injuries and these factors.Results: We found an overall upward trend in micromobility injuries. A higher risk of micromobility-related injuries was witnessed in China and the US in 2015-2019, and people older than 45 showed a growing micromobility-related mortality and incidence rate in China, India, and the US. Cohorts after 1960 showed higher micromobility injury incidence risks in China and India, but the population born after 1990 in India showed a slightly lower risk compared to those before it.Conclusions: The boosted usage of micromobility devices explains these increasing trends. Road infrastructure and separated traffic ease the collisions from micromobility devices. The overall situation calls for improvement in legislation as well as road infrastructure.