The prevalent trajectory of early menopause associated with PM2.5 exposure across 1956-2018 extrapolated from LightGBM algorithm.

BACKGROUND: Fine particulate matter (PM2.5) is noxious to female reproductive development and facilitates the occurrence of subsequent diseases. Early menopause is initiative factor of female aging. But due to the lack of historical exposure of PM2.5, we could not gain insight into the linkage between ambient PM2.5 exposure and early menopause. METHODS: We conducted a community-based retrospective cross-sectional study and pooled 1173 postmenopausal women. The machine learning algorithm of LightGBM was processed to derive the historical concentrations of PM2.5 based on aerography of 1956-2022. The quantile g-computation and binary logistic regression were employed to estimate the mixed and single associations between PM2.5 and early menopause. RESULTS: The visibility topped the most important feature for derivations of historical PM2.5 concentrations. The R2 of 10-fold cross-validation and predictive capability during processing were all above 0.8. The prevalence of early menopause was 7.3 %. Each 10 µg/m3 PM2.5 increased the prevalence of early menopause during prior 2 years exposure (OR: 1.49, 95 %CI: 1.03-2.16) and spring and autumn (OR: 1.28, 95 %CI: 1.07-1.54). After adjusting the reverse effects of temperature, the prior 2 years exposure of PM2.5 remained positively associated with early menopause in the fourth quantile vs the first quantile (OR: 3.36, 95 %CI: 1.53-7.36) in the spring and autumn. The higher BMI (OR: 1.40, 95 %CI: 1.14-1.72), waistline (OR: 1.42, 95 %CI: 1.09-1.85) and unfavourable dietary habits of less meat (OR: 1.72, 95 %CI: 1.11-2.68), more fried food (OR: 2.39, 95 %CI: 1.15-4.99) elevated the prevalence of early menopause. CONCLUSIONS: The accurate environmental exposure assessment of historical PM2.5 vigorously promoted the researches on the relationship between PM2.5 and early menopause. It sounds the alarm on female infertility menace associated with particulate matter especially during the turbulent 2 years before menopause.

Effects of long-term exposure to air pollutant mixture on blood pressure in typical areas of North China.

BACKGROUND: Studies about the combined effects of gaseous air pollutants and particulate matters are still rare. OBJECTIVES: This study was performed based on baseline survey of the Diverse Life-Course Cohort in the Beijing-Tianjin-Hebei (BTH) Region of North China to evaluate the association of long-term air pollutants with blood pressure and the combined effect of the air pollutants mixture among 32821 natural han population aged 20 years or above. METHODS: Three-year average exposure to air pollutants (PM10, PM2.5, PM1, O3, SO2, NO2, and CO) and PM2.5 components [black carbon (BC), ammonium (NH4+), nitrate (NO3-), sulfate (SO42-), and organic matter (OM)] of residential areas were calculated based on well-validated models. Generalized linear mixed models (GLMMs) were used to estimate the associations of air pollutants exposure with the systolic blood pressure (SBP), diastolic blood pressure (DBP), Mean arterial pressure (MAP), pulse pressure (PP) and prevalent hypertension. Quantile g-Computation and Bayesian Kernel Machine Regression (BKMR) were employed to assess the combined effect of the air pollutant mixture. RESULTS: We found that long-term exposures of O3, PM2.5, and PM2.5 components were stably and strongly associated with elevated SBP, DBP, and MAP and prevalent hypertension. O3 increased SBP, DBP, and MAP at a similar extent, but with greater effects; while, PM2.5 and PM2.5 components had a greater impact on SBP than DBP, which increased PP simultaneously. In multi-pollutant models, the combined effects of the air pollutant mixture on blood pressure and prevalent hypertension was predominantly influenced by O3, PM2.5, and O3, OM in different models, respectively. For example, O3, PM2.5 contributed 57.25 %, 39.22 % of the positive combined effect of the air pollutant mixture on SBP; and O3, OM positively contributed 70.00 %, 30.00 % on prevalent hypertension, respectively. There were interactions between O3, CO, SO2 and PM2.5 components on hbp, SBP and PP. CONCLUSIONS: The results showed positive associations of air pollutant mixtures with blood pressure, where O3 and PM2.5 (especially OM) might be primary contributors. There were interactions between gaseous air pollutants and PM2.5 components on blood pressure and prevalent hypertension.

A Bayesian nonparametric approach for causal mediation with a post-treatment confounder.

We propose a new Bayesian nonparametric method for estimating the causal effects of mediation in the presence of a post-treatment confounder. The methodology is motivated by the Rural Lifestyle Intervention Treatment Effectiveness Trial (Rural LITE) for which there is interest in estimating causal mediation effects but is complicated by the presence of a post-treatment confounder. We specify an enriched Dirichlet process mixture (EDPM) to model the joint distribution of the observed data (outcome, mediator, post-treatment confounder, treatment, and baseline confounders). For identifiability, we use the extended version of the standard sequential ignorability (SI) as introduced in Hong et al. along with a Gaussian copula model assumption. The observed data model and causal identification assumptions enable us to estimate and identify the causal effects of mediation, that is, the natural direct effects (NDE) and natural indirect effects (NIE). Our method enables easy computation of NIE and NDE for a subset of confounding variables and addresses missing data through data augmentation under the assumption of ignorable missingness. We conduct simulation studies to assess the performance of our proposed method. Furthermore, we apply this approach to evaluate the causal mediation effect in the Rural LITE trial, finding that there was not strong evidence for the potential mediator.

Sex-specific associations of per- and polyfluoroalkyl substances with brain-derived neurotrophic factors (BDNF) in cord serum.

BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) is perceived as an emerging environmental endocrine disruptor, which have been linked to children neurodevelopment. However, the potential mechanisms are not clear. Brain-derived neurotrophic factor (BDNF) is a vital protein in neurodevelopment, and the associations between PFAS exposure and BDNF require exploration. OBJECTIVE: We aimed to explore the relationships between PFAS exposure and the levels of BDNF in cord serum. METHODS: A total of 1,189 mother-infant dyads from the Sheyang Mini Birth Cohort Study (SMBCS) were enrolled. The levels of 12 PFAS and BDNF were measured in cord serum. We utilized generalized linear models (GLMs), quantile-based g-computation (QGC) models, and Bayesian Kernel Machine Regression (BKMR) models to explore the relationships between single and mixed PFAS exposure and BDNF concentration. Additionally, the potential sex differences were explored by sex-stratified analysis. RESULTS: Median concentrations of the included 10 PFAS ranged from 0.04 to 3.97 µg/L. In the single chemical models, four PFAS congeners, namely perfluorononanoic acid (PFNA), perfluorooctane sulfonic acid (PFOS), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), were negatively associated with BDNF levels in cord serum among females only (ß: -0.116 to -0.062, p < 0.05). In the BKMR models of total mother-infant dyads and female fetuses, the significant negative relationships between PFAS mixtures and BDNF were observed, and PFUnDA was identified as an important contributor (Posterior inclusion probability, PIP = 0.8584 for the total subjects; PIP = 0.8488 for the females). PFOS was another important driver based on the mixture approaches. CONCLUSIONS: We found that PFNA, PFOS, PFDA, and PFUnDA were associated with decreased BDNF concentration in the females, although the causal inference might be limited. PFAS mixtures were also negatively linked with BDNF levels in the total mother-infant pairs and female fetuses. The adverse effect of PFAS exposure on fetal BDNF levels might be sex-specific.

Association between fat-soluble vitamins and metabolic syndromes in US adults: a cross-section study from NHANES database.

BACKGROUND: Previous studies have shown significant associations between individual fat-soluble vitamins (FSVs) and metabolic syndromes (MetS). However, evidence on the multiple FSVs co-exposure and MetS odds is limited. Given that individuals are typically exposed to different levels of FSVs simultaneously, and FSVs can interact with each other. It's necessary to explore the association between multiple FSVs co-exposure and MetS odds. This study aims to address this gap in general U.S. adults aged ≥ 20 years. METHODS: We conducted a cross-sectional study utilizing data from the National Health and Nutrition Examination Surveys (NHANESs) 2003-2006 and 2017-2018. Three FSV, including vitamin A (VA), vitamin E (VE), and vitamin D (VD), and MetS diagnosed according to the ATP III guidelines were selected as exposure and outcome, respectively. Multivariable-adjusted logistic model was used to explore the associations of individual FSV exposure with MetS odds and MetS components. Restricted cubic splines were performed to explore the dose-response relationships among them. The quantile g-computation method was adopted to explore the associations of multiple FSVs co-exposure with MetS odds and MetS components. RESULTS: The presented study included a total of 13,975 individuals, with 2400 (17.17%) were diagnosed with MetS. After adjusting for various confounders, a positive linear pattern was observed for serum VA and VE and MetS associations. Serum VD was found to be negatively associated with MetS in a linear dose-response way. For each component of MetS, higher serum VA and VE were associated with higher triglyceride and high-density lipoprotein; higher serum VD was negatively associated with triglyceride, blood pressure, and fasting plasma glucose. MetS odds increased by 15% and 13%, respectively, in response to one quartile increase in FSVs co-exposure index (qgcomp) in the conditional model (OR = 1.15, 95%CI: 1.06, 1.24) and the marginal structural model (OR = 1.13, 95%CI: 1.06, 1.20). Besides, co-exposure to VA, VE, and VD was positively associated with triglyceride, high-density lipoprotein, and blood pressure levels. CONCLUSION: Findings in the present study revealed that high serum VA and VE levels were associated with elevated MetS odds, while serum VD was inversely associated with MetS odds. FSVs co-exposure was positively associated with MetS odds.

Association of ambient air pollutant mixtures with IVF/ICSI-ET clinical pregnancy rates during critical exposure periods.

STUDY QUESTION: Does exposure to a mixture of ambient air pollutants during specific exposure periods influence clinical pregnancy rates in women undergoing IVF/ICSI-embryo transfer (ET) cycles? SUMMARY ANSWER: The specific exposure period from ET to the serum hCG test was identified as a critical exposure window as exposure to sulfur dioxide (SO2) or a combination of air pollutants was associated with a decreased likelihood of clinical pregnancy. WHAT IS KNOWN ALREADY: Exposure to a single pollutant may impact pregnancy outcomes in women undergoing ART. However, in daily life, individuals often encounter mixed pollution, and limited research exists on the effects of mixed air pollutants and the specific exposure periods. STUDY DESIGN SIZE DURATION: This retrospective cohort study involved infertile patients who underwent their initial IVF/ICSI-ET cycle at an assisted reproduction center between January 2020 and January 2023. Exclusions were applied for patients meeting specific criteria, such as no fresh ET, incomplete clinical and address information, residency outside the 17 cities in the Sichuan Basin, age over 45 years, use of donor semen, thin endometrium (<8 mm) and infertility factors unrelated to tubal or ovulation issues. In total, 5208 individuals were included in the study. PARTICIPANTS/MATERIALS SETTING METHODS: Daily average levels of six air pollutants (fine particulate matter (PM2.5), inhalable particulate matter (PM10), SO2, nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were acquired from air quality monitoring stations. The cumulative average levels of various pollutants were determined using the inverse distance weighting (IDW) method across four distinct exposure periods (Period 1: 90 days before oocyte retrieval; Period 2: oocyte retrieval to ET; Period 3: ET to serum hCG test; Period 4: 90 days before oocyte retrieval to serum hCG test). Single-pollutant logistic regression, two-pollutant logistic regression, Quantile g-computation (QG-C) regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate the influence of pollutants on clinical pregnancy rates. Stratified analyses were executed to discern potentially vulnerable populations. MAIN RESULTS AND THE ROLE OF CHANCE: The clinical pregnancy rate for participants during the study period was 54.53%. Single-pollutant logistic models indicated that for PM2.5 during specific exposure Period 1 (adjusted odds ratio [aOR] = 0.83, 95% CI: 0.70-0.99) and specific exposure Period 4 (aOR = 0.83, 95% CI: 0.69-0.98), and SO2 in specific exposure Period 3 (aOR = 0.92, 95% CI: 0.86-0.99), each interquartile range (IQR) increment exhibited an association with a decreased probability of clinical pregnancy. Consistent results were observed with dual air pollution models. In the multi-pollution analysis, QG-C indicated a 12% reduction in clinical pregnancy rates per IQR increment of mixed pollutants during specific exposure Period 3 (aOR = 0.89, 95% CI: 0.79-0.99). Among these pollutants, SO2 (33.40%) and NO2 (33.40%) contributed the most to the negative effects. The results from BKMR and QG-C were consistent. Stratified analysis revealed increased susceptibility to ambient air pollution among individuals who underwent transfer of two embryos, those with BMI ≥ 24 kg/m2 and those under 35 years old. LIMITATIONS REASONS FOR CAUTION: Caution was advised in interpreting the results due to the retrospective nature of the study, which was prone to selection bias from non-random sampling. Smoking and alcohol, known confounding factors in IVF/ICSI-ET, were not accounted for. Only successful cycles that reached the hCG test were included, excluding a few patients who did not reach the ET stage. While IDW was used to estimate pollutant concentrations at residential addresses, data on participants' work locations and activity patterns were not collected, potentially affecting the accuracy of exposure prediction. WIDER IMPLICATIONS OF THE FINDINGS: Exposure to a mixture of pollutants, spanning from ET to the serum hCG test (Period 3), appeared to be correlated with a diminished probability of achieving clinical pregnancy. This association suggested a potential impact of mixed pollutants on the interaction between embryos and the endometrium, as well as embryo implantation during this critical stage, potentially contributing to clinical pregnancy failure. This underscored the importance of providing women undergoing ART with comprehensive information to comprehend the potential environmental influences and motivating them to adopt suitable protective measures when feasible, thereby mitigating potential adverse effects of contaminants on reproductive health. STUDY FUNDING/COMPETING INTERESTS: This work received support from the National Key Research and Development Program of China (No. 2023YFC2705900), the National Natural Science Foundation of China (Nos. 82171664, 81971391, 82171668), the Natural Science Foundation of Chongqing Municipality of China (Nos. CSTB2022NSCQ-LZX0062, CSTB2023TIAD-KPX0052) and the Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (No. 2021KFKT013). The authors report no conflicts of interest. TRIAL REGISTRATION NUMBER: N/A.

Causal inference using multivariate generalized linear mixed-effects models.

Dynamic prediction of causal effects under different treatment regimens is an essential problem in precision medicine. It is challenging because the actual mechanisms of treatment assignment and effects are unknown in observational studies. We propose a multivariate generalized linear mixed-effects model and a Bayesian g-computation algorithm to calculate the posterior distribution of subgroup-specific intervention benefits of dynamic treatment regimes. Unmeasured time-invariant factors are included as subject-specific random effects in the assumed joint distribution of outcomes, time-varying confounders, and treatment assignments. We identify a sequential ignorability assumption conditional on treatment assignment heterogeneity, that is, analogous to balancing the latent treatment preference due to unmeasured time-invariant factors. We present a simulation study to assess the proposed method's performance. The method is applied to observational clinical data to investigate the efficacy of continuously using mycophenolate in different subgroups of scleroderma patients.

Effect of combined exposure to phthalates and polycyclic aromatic hydrocarbons during early pregnancy on gestational age and neonatal size: A prospective cohort study.

Many studies have indicated that individual exposure to phthalates (PAEs) or polycyclic aromatic hydrocarbons (PAHs) affects pregnancy outcomes. However, combined exposure to PAEs and PAHs presents a more realistic situation, and research on the combined effects of PAEs and PAHs on gestational age and newborn size is still limited. This study aimed to assess the effects of combined exposure to PAEs and PAHs on neonatal gestational age and birth size. Levels of 9 PAE and 10 PAH metabolites were measured from the urine samples of 1030 women during early pregnancy from the Zunyi Birth Cohort in China. Various statistical models, including linear regression, restricted cubic spline, Bayesian kernel machine regression, and quantile g-computation, were used to study the individual effects, dose-response relationships, and combined effects, respectively. The results of this prospective study revealed that each ten-fold increase in the concentration of monoethyl phthalate (MEP), 2-hydroxynaphthalene (2-OHNap), 2-hydroxyphenanthrene (2-OHPhe), and 1-hydroxypyrene (1-OHPyr) decreased gestational age by 1.033 days (95 % CI: -1.748, -0.319), 0.647 days (95 % CI: -1.076, -0.219), 0.845 days (95 % CI: -1.430, -0.260), and 0.888 days (95 % CI: -1.398, -0.378), respectively. Moreover, when the concentrations of MEP, 2-OHNap, 2-OHPhe, and 1-OHPyr exceeded 0.528, 0.039, 0.012, and 0.002 µg/g Cr, respectively, gestational age decreased in a dose-response manner. Upon analyzing the selected PAE and PAH metabolites as a mixture, we found that they were significantly negatively associated with gestational age, birth weight, and the ponderal index, with 1-OHPyr being the most important contributor. These findings highlight the adverse effects of single and combined exposure to PAEs and PAHs on gestational age. Therefore, future longitudinal cohort studies with larger sample sizes should be conducted across different geographic regions and ethnic groups to confirm the impact of combined exposure to PAEs and PAHs on birth outcomes.

History-restricted marginal structural model and latent class growth analysis of treatment trajectories for a time-dependent outcome.

In previous work, we introduced a framework that combines latent class growth analysis (LCGA) with marginal structural models (LCGA-MSM). LCGA-MSM first summarizes the numerous time-varying treatment patterns into a few trajectory groups and then allows for a population-level causal interpretation of the group differences. However, the LCGA-MSM framework is not suitable when the outcome is time-dependent. In this study, we propose combining a nonparametric history-restricted marginal structural model (HRMSM) with LCGA. HRMSMs can be seen as an application of standard MSMs on multiple time intervals. To the best of our knowledge, we also present the first application of HRMSMs with a time-to-event outcome. It was previously noted that HRMSMs could pose interpretation problems in survival analysis when either targeting a hazard ratio or a survival curve. We propose a causal parameter that bypasses these interpretation challenges. We consider three different estimators of the parameters: inverse probability of treatment weighting (IPTW), g-computation, and a pooled longitudinal targeted maximum likelihood estimator (pooled LTMLE). We conduct simulation studies to measure the performance of the proposed LCGA-HRMSM. For all scenarios, we obtain unbiased estimates when using either g-computation or pooled LTMLE. IPTW produced estimates with slightly larger bias in some scenarios. Overall, all approaches have good coverage of the 95 % confidence interval. We applied our approach to a population of older Quebecers composed of 57,211 statin initiators and found that a greater adherence to statins was associated with a lower combined risk of cardiovascular disease or all-cause mortality.

Associations of the intake of individual and multiple fatty acids with depressive symptoms among adults in NHANES 2007-2018.

BACKGROUND: Previous studies have mainly focused on the effects of individual fatty acids on depressive symptoms, while the combined effect of fatty acids on the risk of depressive symptoms has not yet been extensively reported. This study evaluate the associations between individual and multiple fatty acids with depressive symptoms in U.S. adults. METHODS: Data sets were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007-2018 cycles. Both males and females aged above 18 years with complete information about dietary fatty acids intake, depression symptoms, and covariates were included. Weighted linear regression models were conducted to evaluate the relationships between individual fatty acid intake and depressive symptoms, and restricted cubic spline (RCS) models were utilized to explore the corresponding dose-response relationships. Additionally, we implemented the weighted quantile sum (WQS) regression and quantile g-computation (QGC) models to estimate the mixed effects of 19 fatty acids and identify the predominant types. RESULTS: After multivariable adjustments, an increase of one unit in Linoleic acid (LA), Alpha-Linolenic Acid (ALA), Arachidonic acid (AA), Docosapentaenoic acid(DPA), Docosahexaenoic acid(DHA), was associated with a decrease in depressive scores by -0.021 (95 % CI: -0.039,-0.003, p = 0.021),-0.028 (95 % CI: -0.045,-0.011, p = 0.002),-0.026 (95 % CI: -0.044,-0.008, p = 0.005), -0.026 (95 % CI: -0.042,-0.009, p = 0.003), and - 0.022 (95 % CI: -0.041,-0.003, p = 0.022), respectively. However, a per unit increase in Hexanoic acid and Octanoic acid was associated with an increase in depressive scores of 0.020 (95 % CI: 0.002,0.038, p = 0.029) and 0.026 (95 % CI: 0.004,0.048, p = 0.020), respectively. Meanwhile, significant dose-response relationships were supported by the RCS models. As for the mixed effects, both WQS and QGC models demonstrated that the mixture of polyunsaturated fatty acids (PUFAs) was inversely related to depressive symptoms, and ALA and DPA were the most critical contributors. DHA was negatively correlated with depressive symptoms in WQS analysis, but positively correlated with depressive symptoms in QGC analysis. LIMITATIONS: The cross-sectional design limits our ability to establish causality, and 24-hour dietary recall can lead to potential inaccuracies reflecting participants' true eating habits. CONCLUSION: Our study suggests that the single effects of each PUFA were inversely associated with depressive symptoms, except for octadecatetraenoic acid. Moreover, higher combined intake of dietary PUFAs is inversely associated with depressive symptoms in U.S. adults. Among the mixed effects of PUFAs, ALA and DPA may play predominant roles. However, DHA mixed with other fatty acids may have different effects on depressive symptoms, and further study is needed.

Metabolic signatures of population exposure to metal mixtures: A metabolome-wide association study.

Numerous studies have explored the health impacts of individual metal exposures, yet the effects of metal mixtures on human endogenous metabolism remain largely unexplored. We aimed to assess the serum metabolic signatures of people exposed to metal mixtures. Serum and urine samples were collected from 186 workers at a steel factory in Anhui, China, in September 2019. Inductively coupled plasma mass spectrometry was used to analyze the concentrations of 23 metal elements. The serum metabolome was determined by liquid chromatography-mass spectrometry (LC-MS). A metabolome-wide association study (MWAS) was performed across the metal exposures and metabolism using quantile g-computation modeling. Pathway enrichment analysis was performed using MetaboAnalyst. We identified 226 metabolites associated with metal mixtures, primarily involving lipid metabolism (glycerophospholipids, sphingolipids), amino acid metabolism (arginine and proline, alanine, aspartate and glutamate metabolism) and caffeine metabolic pathways. Exposure to metal mixtures is mainly associated with alterations in lipid metabolism and amino acid metabolism, particularly in the glycerophospholipid and arginine and proline metabolism pathways.

Associations between individual and mixed urinary metal exposure and dyslipidemia among Chinese adults: Data from the China Multi-Ethnic Cohort Study.

The prevalence of dyslipidemia is increasing, and it has become a significant global public health concern. Some studies have demonstrated contradictory relationships between urinary metals and dyslipidemia, and the combined effects of mixed urinary metal exposure on dyslipidemia remain ambiguous. In this study, we examined how individual and combined urinary metal exposure are associated with the occurrence of dyslipidemia. According to the data from the 2018-2019 baseline survey database of the China Multi-Ethnic Cohort (CMEC) Study, a population of 9348 individuals was studied. Inductively coupled plasmamass spectrometry (ICP-MS) was used to measure 21 urinary metal concentrations in the collected adult urinary samples. The associations between urinary metals and dyslipidemia were analyzed by logistic regression, weighted quantile sum regression (WQS), and quantile-based g-computation (qgcomp), controlled for potential confounders to examine single and combined effects. Dyslipidemia was detected in 3231 individuals, which represented approximately 34.6 % of the total population. According to the single-exposure model, Al and Na were inversely associated with the risk of dyslipidemia (OR = 0.95, 95 % CI: 0.93, 0.98; OR = 0.89, 95 % CI: 0.83, 0.95, respectively), whereas Zn, Ca, and P were positively associated (OR = 1.69, 95 % CI: 1.42, 2.01; OR = 1.12, 95 % CI: 1.06, 1.18; OR = 1.21, 95 % CI: 1.09, 1.34, respectively). Moreover, Zn and P were significantly positively associated even after adjusting for these metals, whereas Al and Cr were negatively associated with the risk of dyslipidemia. The results of the WQS and qgcomp analyses showed that urinary metal mixtures were positively associated with the risk of dyslipidemia (OR = 1.26, 95 % CI: 1.15, 1.38; OR = 1.09, 95 % CI: 1.01, 1.19). This positive association was primarily driven by Zn, P, and Ca. In the sensitivity analyses with collinearity diagnosis, interaction, and stratified analysis, the results remained, confirming the reliability of the study findings. In this study, the individual and combined effects of urinary Zn, P, and Ca on dyslipidemia were determined, which provided novel insights into the link between exposure to metals and dyslipidemia.

Associations of sugary beverage consumption with chronic obstructive pulmonary disease, asthma, and asthma-chronic obstructive pulmonary disease overlap syndrome: a prospective cohort study.

BACKGROUND: The associations between specific types of sugary beverages and major chronic respiratory diseases remain relatively unexplored. OBJECTIVES: This study aimed to investigate the associations of sugar-sweetened beverages (SSBs), artificially sweetened beverages (ASBs), and natural juices (NJs) with chronic obstructive pulmonary disease (COPD), asthma, and asthma-chronic obstructive pulmonary disease overlap syndrome (ACOS). METHODS: This prospective cohort study included 210,339 participants from the UK Biobank. Sugary beverage intake was measured in units (glasses/cans/cartons/250 mL) through 24-h dietary questionnaires. Logistic regression and Cox proportional hazards models were used to analyze the prevalence and incidence, respectively. Quantile G-computation was used to estimate the joint associations and relative contributions of the 3 types of sugary beverages. RESULTS: Over a median follow-up of 11.6 y, 3491 participants developed COPD, 4645 asthma, and 523 ACOS. In prevalence analysis, certain categories of SSB and NJ consumption were associated with increased asthma prevalence, while high ASB consumption (>2 units/d) was linked to higher risks of all 3 outcomes. In incidence analysis, high SSB consumption (>2 units/d) was associated with incident COPD (hazard ratio [HR]: 1.53; 95% confidence interval [CI]: 1.19, 1.98) and asthma (HR: 1.22; 95% CI: 0.98, 1.52). Dose‒response relationships were observed for ASB consumption with all 3 outcomes (continuous HR: 1.98; 95% CI: 1.36, 2.87, for COPD; continuous HR: 1.65; 95% CI: 1.24, 2.20, for asthma; and continuous HR: 2.84; 95% CI: 1.20, 6.72, for ACOS). Moderate NJ consumption (>0-1 unit/d) was inversely associated with COPD (HR: 0.89; 95% CI: 0.82, 0.97), particularly grapefruit and orange juice. Joint exposure to these beverages (per unit increase) was associated with COPD (HR: 1.15; 95% CI: 1.02, 1.29) and asthma (HR: 1.16; 95% CI: 1.06, 1.27), with ASBs having greater positive weights than SSBs. CONCLUSIONS: Consumption of SSBs and ASBs was associated with increased risks of COPD, asthma, and potentially ACOS, whereas moderate NJ consumption was associated with reduced risk of COPD, depending on the juice type.

The joint impact of PM2.5 constituents on the risk of cerebrovascular diseases hospitalization: A large community-based cohort study.

Air pollution poses significant health risks to urban areas, with limited focus on the chronic association of PM2.5 and its constituents on cerebrovascular diseases (CERs), especially regarding the joint associations. This study explores the individual and joint associations between PM2.5 constituents and CER hospitalization risks through a cohort analysis of 36,271 adults in the Pearl River Delta, South China, from 2015 to 2020. Cox proportional hazards regression and quantile-based g-computation models were used to quantify the individual and joint associations of annual mean concentrations of PM2.5 constituents with hospitalization for CERs. 1151 participants were hospitalized due to CERs during the five-year follow-up period. Joint associations analyses identified that one quartile increase in co-exposure may result in hazard ratios of 1.530 (1.441-1.623), 1.840 (1.710-1.980), and 1.609 (1.491-1.737) for CERs, total, and ischemic stroke hospitalization, respectively. The adverse effect was primarily driven by organic matter and chlorine. Men, those with a history of tobacco or alcohol use or with low residential greenness, were more susceptible to CERs hospitalization following PM2.5 constituents co-exposure. Upcoming strategies should focus on monitoring and regulating PM2.5 constituents, encouraging healthy lifestyles, and enhancing urban greenery.

Semiparametric g-computation for survival outcomes with time-fixed exposures: An illustration.

PURPOSE: Generalized (g-) computation is a useful tool for causal inference in epidemiology. However, in settings when the outcome is a survival time subject to right censoring, the standard pooled logistic regression approach to g-computation requires arbitrary discretization of time, parametric modeling of the baseline hazard function, and the need to expand one's dataset. We illustrate a semiparametric Breslow estimator for g-computation with time-fixed treatments and survival outcomes that is not subject to these limitations. METHODS: We compare performance of the Breslow g-computation estimator to the pooled logistic g-computation estimator in simulations and illustrate both approaches to estimate the effect of a 3-drug vs 2-drug antiretroviral therapy regimen among people with HIV. RESULTS: In simulations, both approaches performed well at the end of follow-up. The pooled logistic approach was biased at times between the endpoints of the discrete time intervals used, while the Breslow approach was not. In the example, both approaches estimated a 1-year risk difference of about 6 % in favor of the 3-drug regimen, but the shape of the survival curves differed. CONCLUSIONS: The Breslow g-computation estimator of counterfactual risk functions does not rely on strong parametric assumptions about the time-to-event distribution or onerous dataset expansions.

Effects of exposure to multiple metallic elements in the first trimester of pregnancy on the risk of preterm birth.

Exposure to certain heavy metals has been demonstrated to be associated with a higher risk of preterm birth (PTB). However, studies focused on the effects of other metal mixtures were limited. A nested case‒control study enrolling 94 PTB cases and 282 controls was conducted. Metallic elements were detected in maternal plasma collected in the first trimester using inductively coupled plasma‒mass spectrometry. The effect of maternal exposure on the risk of PTB was investigated using logistic regression, least absolute shrinkage and selection operator, restricted cubic spline (RCS), quantile g computation (QGC) and Bayesian kernel machine regression (BKMR). Vanadium (V) and arsenic (As) were positively associated with PTB risk in the logistic model, and V remains positively associated in the multi-exposure logistic model. QGC analysis determined V (69.42%) and nickel (Ni) (70.30%) as the maximum positive and negative contributors to the PTB risk, respectively. BKMR models further demonstrated a positive relationship between the exposure levels of the mixtures and PTB risk, and V was identified as the most important independent variable among the elements. RCS analysis showed an inverted U-shape effect of V and gestational age, and plasma V more than 2.18 µg/L was considered a risk factor for shortened gestation length. Exposure to metallic elements mixtures consisting of V, As, cobalt, Ni, chromium and manganese in the first trimester was associated with an increased risk of PTB, and V was considered the most important factor in the mixtures in promoting the incidence of PTB.

Association between long-term exposure to fine particulate matter and its chemical constituents and premature death in individuals living with HIV/AIDS.

Long-term exposure to fine particulate matter (PM2.5) is associated with an increased total mortality. However, the association of PM2.5 with mortality in people living with human immunodeficiency virus/acquired immunodeficiency syndrome (HIV/AIDS, PLWHA) and the relationship between its constituents and adverse outcomes remain unknown. In this cohort study, 28,140 PLWHA were recruited from the HIV/AIDS Comprehensive Response Information Management System of the Hubei Provincial Centre for Disease Control and Prevention in China between 2001 and 2020. The annual PM2.5 chemical composition data, including sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), black carbon (BC), and organic matter (OM), was extracted from the Tracking Air Pollution (TAP) dataset in China. A Cox proportional hazard model with time-varying exposure and time-to-event quantile-based generalized (g) computation was used to assess the associations between PM2.5 chemical constituents, and mortality in PLWHA. A multivariate Cox proportional hazard model estimated an excess hazard ratio (eHR) of 0.32% [95% confidence interval (CI): (0.01%, 0.64%)] for AIDS-related death (ARD), associated with 1 µg/m3 rise in PM2.5 exposure. An increase of 1 µg/m3 in NH4+ was associated with 5.13% [95% CI: (2.89%, 7.43%)] and 2.97% [95% CI: (1.52%, 4.44%)] increase in the risk of ARD and all-cause deaths (ACD), respectively. When estimated using survival-based quantile g-computation, the eHR for ARD with a joint change in a decile increase in all five components was 6.10% [95% CI: 3.77%, 8.48%)]. Long-term exposure to PM2.5 chemical composition, particularly NH4+ increased the risk of death in PLWHA. This study provides epidemiological evidence that SO42- and NH4+ increased the risk of ARD and that NH4+ increased the risk of ACD in PLWHA. Multi-constituent analyses further suggested that NH4+ may be a key component in increasing the risk of premature death in patients with HIV/AIDS. Individuals aged ≥65 with HIV/AIDS are more vulnerable to SO42-, and consequent ACD.

Association between PM2.5 constituents and cardiometabolic risk factors: Exploring individual and combined effects, and mediating inflammation.

BACKGROUND: The individual and combined effects of PM2.5 constituents on cardiometabolic risk factors are sparsely investigated. Besides, the key cardiometabolic risk factor that PM2.5 constituents targeted and the biological mechanisms remain unclear. METHOD: A multistage, stratified cluster sampling survey was conducted in two typically air-polluted Chinese cities. The PM2.5 and its constituents including sulfate, nitrate, ammonium, organic matter, and black carbon were predicted using a machine learning model. Twenty biomarkers in three category were simultaneously adopted as cardiometabolic risk factors. We explored the individual and mixture association of long-term PM2.5 constituents with these markers using generalized additive model and quantile-based g-computation, respectively. To minimize potential confounding effects, we accounted for covariates including demographic, lifestyle, meteorological, temporal trends, and disease-related information. We further used ROC curve and mediation analysis to identify the key subclinical indicators and explore whether inflammatory mediators mediate such association, respectively. RESULT: PM2.5 constituents was positively correlated with HOMA-B, TC, TG, LDL-C and LCI, and negatively correlated with PP and RC. Further, PM2.5 constituent mixture was positive associated with DBP, MAP, HbA1c, HOMA-B, AC, CRI-1 and CRI-2, and negative associated with PP and HDL-C. The ROC analysis further reveals that multiple cardiometabolic risk factors can collectively discriminate exposure to PM2.5 constituents (AUC>0.9), among which PP and CRI-2 as individual indicators exhibit better identifiable performance for nitrate and ammonium (AUC>0.75). We also found that multiple blood lipid indicators may be affected by PM2.5 and its constituents, possibly mediated through complement C3 or hsCRP. CONCLUSION: Our study suggested associations of individual and combined PM2.5 constituents exposure with cardiometabolic risk factors. PP and CRI-2 were the targeted markers of long-term exposure to nitrate and ammonium. Inflammation may serve as a mediating factor between PM2.5 constituents and dyslipidemia, which enhance current understanding of potential pathways for PM2.5-induced preclinical cardiovascular responses.

Associations of single and multiple vitamin exposure with childhood eczema: data from the national health and nutrition examination survey.

Aim: The association between vitamins and eczema has garnered attention, yet few studies have evaluated the effects of co-exposure to multiple vitamins on this condition. This study aims to assess the association of vitamin mixtures with eczema in children. Methods: This cross-sectional study analyzed data from 2,244 children aged 6-17 years from the National Health and Nutrition Examination Surveys. Eczema served as the primary outcome. Six serum vitamins, namely, vitamins A, B6, B12, C, D, and E, were the main variables. Weighted multivariate logistic regression was adopted to analyze the association between each serum vitamin and eczema. Odds ratios (OR) with a 95% confidence interval (CI) were calculated. Bayesian kernel machine regression (BKMR) analysis and the quantile g-computation (qgcomp) model were used to evaluate the association of co-exposure to multiple vitamins with eczema. Results: In total, 10.83% of children (n = 243) developed eczema. After adjusting for confounding factors, we observed that compared with the reference group (vitamin B12 with second quartile), the OR for eczema was 0.604 (95% CI: 0.373-0.978, P = 0.041) for the first quartile of vitamin B12. Both BKMR analysis and the qgcomp model consistently showed that co-exposure to the six vitamins was positively correlated with the risk of eczema, with vitamin B6 contributing most to the overall effect. In BKMR analyses, we observed an interaction between vitamins B6 and B12 concerning eczema risk. Conclusion: Co-exposure to vitamins A, C, B6, B12, D, and E was found to be associated with an increased risk of eczema in children, with vitamin B6 as the greatest positive contributor driving the overall effect.