International audience

PR | IFPRI3; ISI; CRP3.2; 4 Transforming Agricultural and Rural Economies | Development Strategies and Governance (DSG); Transformation Strategies | CGIAR Research Program on Maize (MAIZE)

The present study aims to determine the associations of multiple plasma metal levels and plasma microRNAs (miRNAs) with diabetes risk, and further explore the mediating effects of plasma miRNAs on the associations of plasma metal with diabetes risk. We detected plasma levels of 23 metals by inductively coupled plasma mass spectrometry (ICP-MS) among 94 newly diagnosed and untreated diabetic cases and 94 healthy controls. The plasma miRNAs were examined by microRNA Array screening and Taqman real-time PCR validation among the same study population. The multivariate logistic regression models were employed to explore the associations of plasma metal and miRNAs levels with diabetes risk. Generalized linear regression models were utilized to investigate the relationships between plasma metal and plasma miRNAs, and mediation analysis was used to assess the mediating effects of plasma miRNAs on the relationships between plasma metals and diabetes risk. Plasma aluminum (Al), titanium (Ti), copper (Cu), zinc (Zn), selenium (Se), rubidium (Rb), strontium (Sr), barium (Ba), and Thallium (Tl) levels were correlated with elevated diabetic risk while molybdenum (Mo) with decreased diabetic risk (P < 0.05 after FDR multiple correction). MiR-122–5p and miR-3141 were positively associated with diabetes risk (all P < 0.05). Ti, Cu, and Zn were positively correlated with miR-122–5p (P = 0.001, 0.028 and 0.004 respectively). Ti, Cu, and Se were positively correlated with miR-3141 (P = 0.003, 0.015, and 0.031 respectively). In addition, Zn was positively correlated with miR-193b-3p (P = 0.002). Ti was negatively correlated with miR-26b-3p (P = 0.016), while Mo and miR-26b-3p were positively correlated (P = 0.042). In the mediation analysis, miR-122–5p mediated 48.0% of the association between Ti and diabetes risk. The biological mechanisms of the association are needed to be explored in further studies.

While the health impacts of larger particulate matter, such as PM₁₀ and PM₂.₅, have been studied extensively, research regarding ultrafine particles (UFPs or PM₀.₁) and particle surface area concentration (PSC) is lacking. This case-crossover study assessed the associations between exposure to PSC and UFP number concentration (UFPnc) and hospital admissions for cardiovascular diseases (CVDs) in New York State (NYS), 2013–2018. We used a time-stratified case-crossover design to compare the PSC and UFPnc levels between hospitalization days and control days (similar days without admissions) for each CVD case. We utilized NYS hospital discharge data to identify all CVD cases who resided in NYS. UFP simulation data from GEOS-Chem-APM, a state-of-the-art chemical transport model, was used to define PSC and UFPnc. Using a multi-pollutant model and conditional logistic regression, we assessed excess risk (ER)% per inter-quartile change of PSC and UFPnc after controlling for meteorological factors, co-pollutants, and time-varying variables. We found immediate and lasting associations between PSC and overall CVDs (lag0–lag0-6: ERs% (95% CI%) ranges: 0.4 (0.1,0.7) - 0.9 (0.7–1.2), and delayed and prolonged ERs%: 0.1–0.3 (95% CIs: 0.1–0.5) between UFPnc and CVDs (lag0-3–lag0-6). Exposure to larger PSC was associated with immediate ER increases in stroke, hypertension, and ischemic heart diseases (1.1%, 0.7%, 0.8%, respectively, all p < 0.05). The adverse effects of PSC on CVDs were highest among children (5–17 years old), in the fall and winter, and during cold temperatures. In conclusion, we found an immediate, lasting effects of PSC on overall CVDs and a delayed, prolonged impact of UFPnc. PSC was a more sensitive indicator than UFPnc. The PSC effects were higher among certain CVD subtypes, in children, in certain seasons, and during cold days. Further studies are needed to validate our findings and evaluate the long-term effects.

The endocrine-disrupting effects of human exposure to polybrominated diphenyl ethers (PBDEs) have been studied, but their associations with menstrual status were not clearly clarified. This study was to evaluate the associations between PBDE levels in adipose tissues and the menstrual cycle and menstrual bleeding duration alteration. A total of 298 female cases undergoing surgery were recruited from two hospitals in Shantou, China. Demographic, clinical, and pathological information were collected, and adipose tissues were obtained during mammary or abdominal surgery. Gas chromatography with mass spectrometry was used to analyze 14 PBDE congeners in the adipose samples. The associations between PBDE levels and menstrual cycle (MC) and menstrual duration (MD) were analyzed by logistic regression models, estimating odds ratios (ORs) and 95% confidence intervals (CIs). BDE-153 had the highest level in the adipose samples, followed by BDE-209, BDE-183 and BDE-47. Compared with referents, median levels of BDE-47, -71, −153, −183 were higher in women with MC > 30 days (all P < 0.05); BDE-47, -153, −183, −209 were also higher in women with MD > 5.5 days (all P < 0.05). After adjusted for age and parity, BDE-47, -71, −153, −183 were associated with prolonged MC (OR = 1.20, 1.15, 1.12, 1.11, respectively, all P < 0.05) in the logistic regression models; and BDE-47, -153, −183, −209 were associated with the prolonged MD (OR = 1.13, 1.09, 1.10, 1.11, respectively, all P < 0.05). Several individual PBDE congeners in female adipose were found associated with prolonged menstrual cycle and menstrual duration. PBDEs may influence reproductive health of women by altering menstrual status.

Liver disease has become a growing health burden, and little is known about the impairment of liver function caused by exposure to organophosphate esters (OPEs) in adolescents aged 12–19 years in the United States. To investigate the relationship between urinary metabolites of OPEs including diphenyl phosphate (DPHP), bis(1,3-dichloro-2-propyl) phosphate (BDCPP), bis(1-chloroethyl) phosphate (BCPP), bis(2-chloroethyl) phosphate (BCEP), and dibutyl phosphate (DBUP) and liver function in US adolescents aged 12–19 years. Liver function tests (LFTs) include aspartate aminotransferase (AST), albumin (ALB), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), total bilirubin (TBIL), total protein (TP), and AST/ALT. Meanwhile, potential confounding and interaction effects were assessed. The study sample included 592 adolescents aged 12–19 from two consecutive NHANES cycles (2011–2012, 2013–2014). A composite statistical strategy combining traditional linear regression with advanced multi-pollutant models quantile based g-computation (QGC) and eXtreme Gradient Boosting (XGBoost) regression was used to analyze the joint effects of multiple OPEs on liver function indicators, and to describe the interaction between different OPEs in detail. 592 adolescent participants were 15 (14–17) years old, with similar numbers of males and females (304 vs. 288). The analysis results showed that (1) in the linear regression model, individual DPHP, BCEP exposure and ALP changes, BCEP and AST/ALT changes were positively associated. DPHP, BDCPP were negatively associated with TP changes. (2) The combined effects of various OPEs on ALB, ALT, ALP, GGT, TBIL, TP, and AST/ALT were statistically significant. (3) There is no potential interaction between different OPEs. Several OPEs and their combinations are closely related to the 8 LFT indicators. In addition, data suggest that exposure to OPEs in adolescents may be associated with liver damage. Due to limited evidence in the literature and potential limitations of the current study, our findings require more studies to confirm.

As reservoirs for pollutants transported via the Yangtze and Yellow Rivers, the Bohai Sea (BS) and Yellow Sea (YS) play an important role in transporting microplastics (MPs) to the Pacific Ocean. The fate, sources and mass budget of MPs in the BS and the YS were investigated by Pearson correlation, principal component analysis-multilinear regression analysis (PCA-MRLA) and a mass balance model to sedimentary MPs data. Average MP abundances were 137 and 119 items kg⁻¹ in the Bohai and Yellow Seas, respectively. MPs <1000 μm exhibited similar distribution patterns to total organic carbon and fine-grained sediments, while MPs >1000 μm were confined in the BS and exhibited a strong positive correlation with chlorophyll-a and polyethylene terephthalate, suggesting that larger MPs might deposit faster due to biofouling or when comprised of high density polymers. PCA-MLRA analysis indicated land-based inputs (packing materials, textile material and daily commodities) were dominant in the BS, while maritime activities (fishing and mariculture) were the main source of MPs in the YS. The mass balance model revealed that the total MP input and output to the BS and the YS was 3396.92 t yr⁻¹ and 3814.81 t yr⁻¹, respectively. The major input pathway of MPs to the BS and the YS were river discharge and air deposition, respectively. Notably, 94% of MPs in the BS and the YS were deposited to sediments. This study revealed that BS and YS sediments play an important role in preventing MPs from being further transported to the Pacific Ocean, thus more attention should be paid to local ecological risk assessment.

The inherent oxidation potential (OP) of atmospheric particulate matter has been shown to be an important metric in assessing the biological activity of inhaled particulate matter and is associated with the composition of PM₂.₅. The current study examined the chemical composition of 388 personal PM₂.₅ samples collected from students and guards living in urban and suburban areas of Beijing, and assessed the ability to predict OP from the calculated metrics of carcinogenic risk, represented by ELCR (excess lifetime cancer risk), non-carcinogenic risk represented by HI (hazard index), and the composition and sources of the particulate matter using multiple linear regression methods. The correlations between calculated ELCR and HI and the measured OP were 0.37 and 0.7, respectively. HI was a better predictor of OP than ELCR. The prediction models based on pollutants (Model_1) and pollution sources (Model_2) were constructed by multiple linear regression method, and Pearson correlation coefficients between the predicted results of Model_1 and Model_2 with the measured volume normalized OP are 0.81 and 0.80, showing good prediction ability. Previous investigations in Europe and North America have developed location-specific relationships between the chemical composition of particulate matter and OP using regression methods. We also examined the ability of relationships between OP and composition, sources, developed in Europe and North America, to predict the OP of particulate matter in Beijing from the composition and sources determined in Beijing. The relationships developed in Europe and North America provided good predictive ability in Beijing and it suggests that these relationships can be used to predict OP from the chemical composition measured in other regions of the world.

Currently, studies on the association between per-/polyfluoroalkyl substances (PFAS) concentrations and the renal function of residents, especially teenagers, living near fluorochemical industrial plants, are relatively rare, and not all these studies suggested associations. In this cross-sectional study, 775 local teenagers (11–15 years old) were included, and serum concentrations of 18 PFAS were measured. Perfluorooctanoic acid (PFOA) was found to be the dominant PFAS with a concentration of 22.3–3310 ng/mL (mean = 191 ng/mL), accounting for 71.5–99.1% of ΣPFAS. Statistical analyses demonstrated that internal exposure of perfluoroalkyl carboxylic acids (PFCA, C8–C10) was related to the plant. In addition, the prevalence rate of chronic kidney disease (CKD) (35.0%) in the participants was relatively high. A significantly positive association was observed between the increase in PFOA concentration and increasing risk of CKD (OR = 1.741; 95% CI: 1.004, 3.088; p = 0.048) by adjusting for gender, age, body mass index (BMI), and household income. Similar positive correlation was also observed in PFHpA with CKD (OR = 1.628, 95% CI: 1.031, 2.572; p = 0.037). However, no significant correlation was observed for concentrations of other PFAS and CKD (p > 0.05). Furthermore, linear regression analyses demonstrated that none of the PFAS concentrations were significantly correlated with estimated glomerular filtration rate (eGFR) or urine albumin/urine creatinine ratio (ACR) (p > 0.05). However, a significantly negative correlation was observed between PFOA concentration and abnormal ACR (β = −0.141, 95% CI: −0.283, 0.001; p = 0.048) after stratifying by CKD. Sensitivity analyses further confirmed these results. This cross-sectional study is the first, to our knowledge, to investigate the association between PFAS concentrations and renal function in teenagers living near a Chinese industrial plant. Further prospective and metabonomic studies are needed to interpret the results and clarify the biological mechanisms underlying this association.

In recent years, the naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil Cd content and bioavailability among different parent materials, the previous land classification scheme based on total soil Cd content as the classification standard, has certain shortcomings. This study aims to explore the factors influencing soil Cd bioavailability in typical karst areas of Guilin and to suggest a scientific and effective farmland use management plan based on the prediction model. A total of 9393 and 8883 topsoil samples were collected from karst and non-karst areas, respectively. Meanwhile, 149 and 145 rice samples were collected together with rhizosphere soil in karst and non-karst areas, respectively. The results showed that the higher CaO level in the karst area was a key factor leading to elevated soil pH value. Although Cd was highly enriched in karst soils, the higher pH value and adsorption of Mn oxidation inhibited Cd mobility in soils. Conversely, the Cd content in non-karst soils was lower, whereas the Cd level in rice grains was higher. To select the optimal prediction model based on the correlation between Cd bioaccumulation factors and geochemical parameters of soil, artificial neural network (ANN) and linear regression prediction models were established in this study. The ANN prediction model was more accurate than the traditional linear regression model according to the evaluation parameters of the test set. Furthermore, a new land classification scheme based on an ANN prediction model and soil Cd concentration is proposed in this study, making full use of the spatial resources of farmland to ensure safe rice consumption.