Advances and perspectives in environmental health research in China

Environmental health research aims to identify environmental conditions suitable for the healthy living and reproduction of human beings.Through the interdisciplinary research bridging environmental sciences and health/medical sciences,the impacts of physical,chemical,and biological environmental factors on human health are investigated.This includes identifying environmental factors detrimental to human health,evaluating human exposure characteristics to environmental factors,clarifying causal relationships between environmental exposure and health effects,analyzing the underlying biochemical mechanisms,linking environmental factors to the onset and progression of diseases,establishing exposure-response relationships,and determining effect thresholds.Ultimately,the results of environmental health research can serve as a scientific basis for formulating environmental management strategies and guiding prevention and intervention measures at both the public and individual levels.This paper summarizes the recent advances and future perspectives of environmental health research in China,as reported by a group of Chinese scientists who recently attended a workshop in Hainan,China.While it is not intended to provide a comprehensive review of this expansive field,it offers a glimpse into the significant progress made in understanding the health impacts of environmental factors over the past decade.Looking ahead,it is imperative not only to sustain efforts in studying the health effects of traditional environmental pollution,but also to prioritize research on the health impacts of emerging pollutants and climate change.

Trimester-specific urinary metabolome alterations associated with gestational diabetes mellitus:A study in different pregnancy stages

Gestational diabetes mellitus(GDM),a frequently-occurring disease during pregnancy,may cause some adverse healthy outcome of both mother and offspring.However,the knowledge about metabolite alterations during the pathogenesis and development process is limited.Here,a large longitudinal nontargeted metabolomics study of 195 pregnant women(64 women with subsequently developed GDM and131 healthy controls)was conducted.Each participant provided urine samples at three timepoints during early,middle and late pregnancy,respectively.The metabolic profiles of 585 urine samples(195×3)were measured by using ultra-high performance liquid chromatography coupled with Orbitrap high-resolution mass spectrometry.Among the 56 identified metabolites,the levels of eight metabolites increased and three ones decreased in the first trimester,the concentration of one metabolite increased and those of 20 decreased in the second trimester,as well as the levels of five metabolites increased and two decreased in the third trimester.After false discovery rate correction,the levels of valine and 5-acetamidovalerate in GDM group significantly increased in the first trimester,the levels of 1-methylguanine and 1,3-dihydro-(2 H)-indol-2-one significantly decreased in the second trimester and three metabolites(threonine,OH-octanedioyl-carnitine and pimelylcarnitine)increased and N-acetyltryptophan decreased in the third trimester,respectively.Six metabolites,such as pantothenic acid and threonine,had significant interaction effects between gestational stage(different trimester)and group(GDM or control).The differential metabolites were involved in“tryptophan metabolism”,“purine metabolism”,“valine,leucine and isoleucine degradation”and other pathways.The findings may provide insights into further pathogenesis study of GDM.

Assessment of estrogen disrupting potency in animal foodstuffs of China by combined biological and chemical analyses

Food has been documented as one of major routes for human exposure to environmental estrogens（EEs）, but information on the occurrence of EEs in animal foodstuffs is still scarce.This study analyzed estrogenic activity in 16 types of animal foodstuffs（n = 142） collected from four cities（Wuhan, Guangzhou, Wenzhou and Yantai） of China by combined yeast estrogen screen（YES） bioassay and chemical analysis. By bioassay, all samples＇ extracts were found to induce estrogenic activities and the bioassay-derived 17β-estradiol equivalent（EEQbio） ranged from 8.29 to 118.32 ng/g. In addition, the samples were analyzed by liquid chromatography coupled to tandem mass spectrometry for further chemical analysis. 17β-Estradiol was found in all samples in this survey at levels of 0.44 to 15.04 ng/g.All samples had 33.1% detection rate of 17α-ethinylestradiol（EE2）, and the maximum concentration was 2.80 ng/g. Bisphenol A and 4-nonylphenols were detected in 83.8% and83.1% of samples, with concentrations up to 12.56 ng/g and 35.76 ng/g, respectively.However, the concentrations of estrone, diethylstilbestrol and 4-t-octylphenol were found to be below the limit of detection. A comparison of EEQbiomeasured from the YES assay and EEQchemcalculated from chemical analysis showed good correlation（R2= 0.84）. Based on the results, the YES assay can be used as a rapid pre-screening method for monitoring the levels of estrogenic activity in large numbers of animal foodstuffs, and chemical analysis used in combination can be used for the identification of specific EEs.

Simultaneous determination of bisphenols, benzophenones and parabens in human urine by using UHPLC-TQMS

Bisphenols, benzophenones and parabens may alter one or more functions of endocrine system and consequently cause adverse health effects on human. A rapid and sensitive analytical method for the simultaneous determination of six bisphenols, five benzophenones and seven parabens in human urine by using ultra-high performance liquid chromatography coupled with triple quadrupole mass spectrometry was developed and applied for human urine sample analysis. The eighteen targeted compounds were separated within 11 min. Heated-electrospray ionization was operated with multiple reaction monitoring in negative-ion mode. The limit of detection ranged from 0.01 ng/mL to 0.5 ng/mL. Seven isotope-labeled internal standards were used in the quantitative analysis. After the method was validated for sensitivity, linearity, precision, accuracy and matrix effect, it was applied for the analysis of human urine samples to evaluate the profiles of human exposure. Eight of the targeted analytes were detected in more than 50% urine samples.

Cold chain and severe acute respiratory syndrome coronavirus 2 transmission:a review for challenges and coping strategies

Since June 2020,the re-emergence of coronavirus disease 2019(COVID-19)epidemics in parts of China was linked to the cold chain,which attracted extensive attention and heated discussions from the public.According to the typical characteristics of these epidemics,we speculated a possible route of transmission from cold chain to human.A series of factors in the supply chain contributed to the epidemics if the cold chain were contaminated by severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),such as temperature,humidity,personal hygiene/protection,and disinfection.The workers who worked in the cold chain at the receiving end faced a higher risk of being infected when they were not well protected.Facing the difficult situation,China put forward targeted and powerful countermeasures to block the cold chain-related risk.However,in the context of the unstable pandemic situation globally,the risk of the cold chain needs to be recognized and evaluated seriously.Hence,in this review,we reviewed the cold chain-related epidemics in China,analyzed the possible mechanisms,introduced the Chinese experience,and suggested coping strategies for the global epidemic prevention and control.

Association of altered serum acylcarnitine levels in early pregnancy and risk of gestational diabetes mellitus

Gestational diabetes mellitus(GDM)is a high-prevalence disease and diagnosed in middle pregnancy.Acylcarnitines are a series of fatty acid esters of carnitine and play important roles in fatty acid and carbohydrate metabolism.However,the role of acylcarnitine on the development of GDM remains unclear.This case-control study involving 214 study participants(107 GDM cases and 107 matched controls)was conducted in a cohort,in China,from 2013 to 2015.The levels of carnitine and 36 acylcarnitines in serum samples collected at the early stage of pregnancy were determined by using ultra-high performance liquid chromatography coupled with tandem mass spectrometry.The associations of the levels of the 37 targeted compounds with GDM risk were investigated by using binary conditional logistic regression models.Alterations in acylcarnitine levels were observed 9–17 weeks before GDM diagnosis.The increases in levels of propionyl-carnitine,malonyl-carnitine,isovaleryl-carnitine,palmitoyl-carnitine and linoleoyl-carnitine were associated with GDM risk with odds ratios(ORs)per standard deviation(SD)increment greater than 1(p<0.05),after adjustment for potential confounding factors(pre-pregnancy body mass index and parity).On the contrary,the increases of decanoyl-carnitine,decenoyl-carnitine,tetradecenoyl-carnitine,tetradecandienoylcarnitine levels were associated with the reduced risk for GDM(ORs per SD<1,p<0.05).To our knowledge,the present study is the largest case-control study to investigate the association between early-pregnancy acylcarnitine levels in serum and GDM risk.The findings add to the evidence for the association between acylcarnitine levels and GDM risk.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort

Endocrine-disrupting chemicals(EDCs)are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy.However,the adverse effects of EDCs in the body of pregnant women were underestimated.The interaction between dynamic concentration of EDCs and endogenous hormones(EHs)on gestational age and delivery time remains unclear.To define a temporal interaction between the EDCs and EHs during pregnancy,comprehensive,unbiased,and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women.We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women.The optimal combination of EHs and EDCs can identify when labor occurs(time to delivery within two and four weeks,AUROC of 0.82).Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time.This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’mixture effect on pregnancy with potential clinical utilities.

Machine Learning for Investigation on Endocrine-Disrupting Chemicals with Gestational Age and Delivery Time in a Longitudinal Cohort

Endocrine-disrupting chemicals(EDCs)are widespread environmental chemicals that are often considered as risk factors with weak activity on the hormone-dependent process of pregnancy.However,the adverse effects of EDCs in the body of pregnant women were underestimated.The interaction between dynamic concentration of EDCs and endogenous hormones(EHs)on gestational age and delivery time remains unclear.To define a temporal interaction between the EDCs and EHs during pregnancy,comprehensive,unbiased,and quantitative analyses of 33 EDCs and 14 EHs were performed for a longitudinal cohort with 2317 pregnant women.We developed a machine learning model with the dynamic concentration information of EDCs and EHs to predict gestational age with high accuracy in the longitudinal cohort of pregnant women.The optimal combination of EHs and EDCs can identify when labor occurs(time to delivery within two and four weeks,AUROC of 0.82).Our results revealed that the bisphenols and phthalates are more potent than partial EHs for gestational age or delivery time.This study represents the use of machine learning methods for quantitative analysis of pregnancy-related EDCs and EHs for understanding the EDCs’mixture effect on pregnancy with potential clinical utilities.

Prenatal metal mixture exposure and birth weight: A two-stage analysis in two prospective cohort studies

The understanding of the impact of prenatal exposure to metal mixtures on birth weight is limited.We aimed to identify metal mixture components associated with birth weight and to determine additional pairwise interactions between metals showing such associations.Concentrations of 18 metals were measured using inductively coupled plasma mass spectrometry in urine samples collected in the 3rd trimester from a prenatal cohort(discovery;n=1849)and the Healthy Baby Cohort(replication;n=7255)in Wuhan,China.In the discovery set,we used two penalized regression models,i.e.,elastic net regression for main effects and a lasso for hierarchical interactions,to identify important mixture components associated with birth weight,which were then replicated.We observed that 8 of the 18 measured metals were retained by elastic net regression,with five metals(vanadium,manganese,iron,cesium,and barium)showing negative associations with Z-scores for birth weight and three metals(cobalt,zinc,and strontium)showing positive associations.In replication set,associations remained significant for vanadium(β=-0.035;95%confidence interval[CI],-0.059 to0.010),cobalt(β=-0.073;95%CI,0.049 to 0.097),and zinc(β=-0.040;95%CI,0.016 to 0.065)after Bonferroni correction.We additionally identified and replicated a single pairwise interaction between iron and copper exposure on birth weight(P<0.001).Using a two-stage analysis,we identified and replicated individual metals and additional pairwise interactions-associated birth weight.The approach could be used in other studies estimating the effect of complex mixtures on human health.

Epidemiological and Clinical Characteristics of COVID-19 in Adolescents and Young Adults

BACKGROUND Adolescents and young adults might play a key role in the worldwide spread of Coronavirus Disease 2019(COVID-19)because they are more likely to be involved in overseas study,business,work,and travel.However,the epidemiological and clinical characteristics remain unknown.