Longitudinal associations between ambient PM2.5 exposure and lipid levels in two Indian cities.

Background: Exposure to ambient PM2.5 is known to affect lipid metabolism through systemic inflammation and oxidative stress. Evidence from developing countries, such as India with high levels of ambient PM2.5 and distinct lipid profiles, is sparse. Methods: Longitudinal nonlinear mixed-effects analysis was conducted on >10,000 participants of Centre for cArdiometabolic Risk Reduction in South Asia (CARRS) cohort in Chennai and Delhi, India. We examined associations between 1-month and 1-year average ambient PM2.5 exposure derived from the spatiotemporal model and lipid levels (total cholesterol [TC], triglycerides [TRIG], high-density lipoprotein cholesterol [HDL-C], and low-density lipoprotein cholesterol [LDL-C]) measured longitudinally, adjusting for residential and neighborhood-level confounders. Results: The mean annual exposure in Chennai and Delhi was 40 and 102 µg/m3 respectively. Elevated ambient PM2.5 levels were associated with an increase in LDL-C and TC at levels up to 100 µg/m3 in both cities and beyond 125 µg/m3 in Delhi. TRIG levels in Chennai increased until 40 µg/m3 for both short- and long-term exposures, then stabilized or declined, while in Delhi, there was a consistent rise with increasing annual exposures. HDL-C showed an increase in both cities against monthly average exposure. HDL-C decreased slightly in Chennai with an increase in long-term exposure, whereas it decreased beyond 130 µg/m3 in Delhi. Conclusion: These findings demonstrate diverse associations between a wide range of ambient PM2.5 and lipid levels in an understudied South Asian population. Further research is needed to establish causality and develop targeted interventions to mitigate the impact of air pollution on lipid metabolism and cardiovascular health.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 µg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 µg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 µg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 µg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

Impact of heatwaves on all-cause mortality in India: A comprehensive multi-city study.

BACKGROUND: Heatwaves are expected to increase with climate change, posing a significant threat to population health. In India, with the world's largest population, heatwaves occur annually but have not been comprehensively studied. Accordingly, we evaluated the association between heatwaves and all-cause mortality and quantifying the attributable mortality fraction in India. METHODS: We obtained all-cause mortality counts for ten cities in India (2008-2019) and estimated daily mean temperatures from satellite data. Our main extreme heatwave was defined as two-consecutive days with an intensity above the 97th annual percentile. We estimated city-specific heatwave associations through generalised additive Poisson regression models, and meta-analysed the associations. We reported effects as the percentage change in daily mortality, with 95% confidence intervals (CI), comparing heatwave vs non-heatwave days. We further evaluated heatwaves using different percentiles (95th, 97th, 99th) for one, two, three and five-consecutive days. We also evaluated the influence of heatwave duration, intensity and timing in the summer season on heatwave mortality, and estimated the number of heatwave-related deaths. FINDINGS: Among âˆ¼ 3.6 million deaths, we observed that temperatures above 97th percentile for 2-consecutive days was associated with a 14.7 % (95 %CI, 10.3; 19.3) increase in daily mortality. Alternative heatwave definitions with higher percentiles and longer duration resulted in stronger relative risks. Furthermore, we observed stronger associations between heatwaves and mortality with higher heatwave intensity. We estimated that around 1116 deaths annually (95 %CI, 861; 1361) were attributed to heatwaves. Shorter and less intense definitions of heatwaves resulted in a higher estimated burden of heatwave-related deaths. CONCLUSIONS: We found strong evidence of heatwave impacts on daily mortality. Longer and more intense heatwaves were linked to an increased mortality risk, however, resulted in a lower burden of heatwave-related deaths. Both definitions and the burden associated with each heatwave definition should be incorporated into planning and decision-making processes for policymakers.

PM2.5 exposure, glycemic markers and incidence of type 2 diabetes in two large Indian cities.

INTRODUCTION: Exposure to fine particulate matter has been associated with several cardiovascular and cardiometabolic diseases. However, such evidence mostly originates from low-pollution settings or cross-sectional studies, thus necessitating evidence from regions with high air pollution levels, such as India, where the burden of non-communicable diseases is high. RESEARCH DESIGN AND METHODS: We studied the associations between ambient PM2.5 levels and fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c) and incident type 2 diabetes mellitus (T2DM) among 12 064 participants in an adult cohort from urban Chennai and Delhi, India. A meta-analytic approach was used to combine estimates, obtained from mixed-effects models and proportional hazards models, from the two cities. RESULTS: We observed that 10 µg/m3 differences in monthly average exposure to PM2.5 was associated with a 0.40 mg/dL increase in FPG (95% CI 0.22 to 0.58) and 0.021 unit increase in HbA1c (95% CI 0.009 to 0.032). Further, 10 µg/m3 differences in annual average PM2.5 was associated with 1.22 (95% CI 1.09 to 1.36) times increased risk of incident T2DM, with non-linear exposure response. CONCLUSIONS: We observed evidence of temporal association between PM2.5 exposure, and higher FPG and incident T2DM in two urban environments in India, thus highlighting the potential for population-based mitigation policies to reduce the growing burden of diabetes.

Vitamin B6 Levels and Impaired Folate Status but Not Vitamin B12 Associated with Low Birth Weight: Results from the MAASTHI Birth Cohort in South India.

Vitamins B12 and B6 and folate are known to have implications for pregnancy outcomes. We aimed to describe B6, B12, and folate status in pregnancy and investigate their associations with low birth weight and preterm delivery in mothers recruited from public hospitals in urban Bengaluru. Pregnant women between 18 and 45 years were included in the MAASTHI prospective cohort study. Each participant's age, socioeconomic status, and anthropometry were recorded during baseline and followed up after delivery. Blood samples were collected between the 24th and 32nd weeks of gestation and stored at -80° for analysis. B6, B12, folate, homocysteine, and methylmalonic acid (MMA) levels were analyzed in the stored samples. We found low plasma vitamin B12, folate, and B6 levels in 48.5%, 42.0%, and 10.4% of the women (n = 230), respectively. Elevated MMA and homocysteine were observed among 73.6% and 6.1% of the women, respectively. We found B6 levels were significantly associated with birth weight (ß(SE) -0.002(0.0), p = 0.001) after adjusting for age, parity, adiposity, gestational diabetes, and socioeconomic status of the mother. Those with impaired folate deficiency were twice at risk (AOR 1.95 (1.29, 3.07), p = 0.002) of low birth weight. Vitamin B6 levels and impaired folate status were associated with low birth weight in the MAASTHI birth cohort.

The effect of cumulative early life adversities, and their differential mediation through hair cortisol levels, on childhood growth and cognition: Three-year follow-up of a birth cohort in rural India.

Background: Early adversities negatively impact children's growth and development, putatively mediated by chronic physiological stress resulting from these adverse experiences. We aimed to estimate the associations between prospectively measured cumulative early adversities with growth and cognition outcomes in rural Indian preschool children and to explore if hair cortisol concentration (HCC), a measure of chronic physiological stress, mediated the above association. Methods: Participants were recruited from the SPRING cRCT in rural Haryana, India. Adversities experienced through pregnancy and the first year of life were measured in 1304 children at 12-months. HCC was measured at 12-months in 845 of them. Outcome measures were height-for-age-z-score (HAZ), weight-for-age-z-score (WAZ) and cognition, measured in 1124 children followed up at 3-years. Cognition was measured using a validated tablet-based gamified tool named DEEP. Results: Cumulative adversities at 12-months were inversely associated with all outcomes measures at 3-years. Each unit increase in adversity score led to a decrease of 0·08 units [95% confidence interval (CI):-0·11,-0·06] in DEEP-z-score; 0·12 units [-0·14,-0·09] in HAZ and 0·11 units [-0·13,-0·09] in WAZ. 12-month HCC was inversely associated with DEEP-z-score (-0·09 [-0·16,-0·01]) and HAZ (-0·12 [-0·20,-0·04]), but the association with WAZ was not significant (p = 0·142). HCC marginally mediated the association between cumulative adversities and HAZ (proportion mediated = 0·06, p = 0·014). No evidence of mediation was found for the cognition outcome. Conclusions: Cumulative early adversities and HCC measured at 12-months have persistent negative effects on child growth and cognition at 3-years. The association between adversities and these two child outcomes were differentially mediated by HCC, with no evidence of mediation observed for the cognitive outcome. Future studies should focus on other stress biomarkers, and alternate pathways such as the immune, inflammation and cellular ageing pathways, to unpack key mechanisms underlying the established relationship between early adversities and poor child outcomes.

Estimated glomerular filtration rate trajectories in south Asians: Findings from the cardiometabolic risk reduction in south Asia study.

Background: Few longitudinal data characterize kidney function decline among South Asians, one of the world's largest population groups. We aimed to identify estimated glomerular filtration rate (eGFR) trajectories in a population-based cohort from India and assess predictors of rapid kidney function decline. Methods: We used 6-year longitudinal data from participants of a population-representative study from Delhi and Chennai, India who had at least two serum creatinine measures and baseline CKD-EPI eGFR> 60 ml/min/1.73m2 (n=7779). We used latent class trajectory modeling to identify patterns of kidney function trajectory (CKD-EPI eGFR) over time. In models accounting for age, sex, education, and city, we tested the association between 15 hypothesized risk factors and rapid kidney function decline. Findings: Baseline mean eGFR was 108 (SD 16); median eGFR was 110 [IQR: 99-119] ml/min/1.73m2. Latent class trajectory modeling and functional characterization identified three distinct patterns of eGFR: class-1 (no decline; 58%) annual eGFR change 0.2 [0.1, 0.3]; class-2 (slow decline; 40%) annual eGFR change -0.2 [-0.4, -0.1], and class-3 (rapid decline; 2%) annual eGFR change -2.7 [-3.4, -2.0] ml/min/1.73m2. Albuminuria (>30 mg/g) was associated with rapid eGFR decline (OR for class-3 vs class-1: 5.1 [95% CI: 3.2; 7.9]; class-3 vs. class-2: 4.3 [95% CI:2.7; 6.6]). Other risk factors including self-reported diabetes, cardiovascular disease, peripheral arterial disease, and metabolic biomarkers such as HbA1c and systolic blood pressure were associated with rapid eGFR decline phenotype but potential 'non-traditional' risk factors such as manual labor or household water sources were not. Interpretation: Although mean and median eGFRs in our population-based cohort were higher than those reported in European cohorts, we found that a sizeable number of adults residing in urban India are experiencing rapid kidney function decline. Early and aggressive risk modification among persons with albuminuria could improve kidney health among South Asians. Funding: The CARRS study has been funded with federal funds from the National Heart, Lung, and Blood Institute, National Institutes of Health, under Contract No. HHSN2682009900026C and P01HL154996. Dr. Anand was supported by NIDDK K23DK101826 and R01DK127138.

Daily nonaccidental mortality associated with short-term PM2.5 exposures in Delhi, India.

Ambient particulate matter of aerodynamic diameter less than 2.5 microns PM2.5) levels in Delhi routinely exceed World Health Organization (WHO) guidelines and Indian National Ambient Air Quality Standards (NAAQS) for acceptable levels of daily exposure. Only a handful of studies have examined the short-term mortality effects of PM in India, with none from Delhi examining the contribution of PM2.5. OBJECTIVES: We aimed to analyze the association between short-term PM2.5 exposures and daily nonaccidental mortality in Delhi, India. METHODS: Using generalized additive Poisson regression models, we examined the association between daily PM2.5 exposures and nonaccidental mortality between June 2010 and December 2016. Daily exposures to PM2.5 were estimated using an ensemble averaging technique developed by our research group, and mortality data were obtained from the Municipal Corporations of Delhi and the New Delhi Municipal Council. RESULTS: Median exposures to PM2.5 were 91.1 µg/m3 (interquartile range = 68.9, 126.2), with minimum and maximum exposures of 21.4 µg/m3 and 276.7 µg/m3, respectively. Total nonaccidental deaths recorded in Delhi during the study period were 700,512. Each 25 µg/m3 increment in exposure was associated with a 0.8% (95% confidence intervals [CI] = 0.3, 1.3%) increase in daily nonaccidental mortality in the study population and a 1.5% (95% CI = 0.8, 2.2%) increase in mortality among those with 60 years of age or over. The exposure-response relationship was nonlinear in nature, with relative risk rising rapidly before tapering off above 125 µg/m3. Meeting WHO guidelines for acceptable levels of exposure over the study period would have likely averted 17,526 (95% CI = 6,837, 25,589) premature deaths, with older and male populations disproportionately affected. DISCUSSION: This study provides robust evidence of the impact of short-term exposure to PM2.5 on nonaccidental mortality with important considerations for various stakeholders including policymakers and physicians. Most importantly, we find that reducing exposures significantly below current levels would substantially decrease the mortality burden associated with PM2.5.

Maternal exposure to a human based mixture of persistent organic pollutants (POPs) affect gene expression related to brain function in mice offspring hippocampus.

Male and female mice pups were exposed to a low and high dose of a human relevant mixture of persistent organic pollutants (POPs) during pregnancy and lactation. Most compounds detected in the dams were found in offspring brains. The mice offspring exhibited changed expression of hippocampal genes involved in cognitive function (Adora2a, Auts2, Crlf1, Chrnb2, Gdnf, Gnal, Kcnh3), neuroinflammation (Cd47, Il1a), circadian rhythm (Per1, Clock), redox signalling (Hmox2) and aryl hydrocarbon receptor activation (Cyp1b1). A few genes were differentially expressed in males versus females. Mostly, similar patterns of gene expression changes were observed between the low and high dose groups. Effects on learning and memory function measured in the Barnes maze (not moving, escape latency) were found in the high dose group when combined with moderate stress exposure (air flow from a fan). Mediation analysis indicated adaptation to the effects of exposure since gene expression compensated for learning disabilities (escape latency, walking distance and time spent not moving in the maze). Additionally, random forest analysis indicated that Kcnh3, Gnal, and Crlf1 were the most important genes for escape latency, while Hip1, Gnal and the low exposure level were the most important explanatory factors for passive behaviour (not moving). Altogether, this study showed transfer of POPs to the offspring brains after maternal exposure, modulating the expression level of genes involved in brain function.

Building capacity for air pollution epidemiology in India.

Air pollution represents a major public health threat in India affecting 19% of the world's population at extreme levels. Despite this, research in India lags behind in large part due to a lack of comprehensive air pollution exposure assessment that can be used in conjunction with health data to investigate health effects. Our vision is to provide a consortium to rapidly expand the evidence base of the multiple effects of ambient air pollution. We intend to leapfrog current limitations of exposure assessment by developing a machine-learned satellite-informed spatiotemporal model to estimate daily levels of ambient fine particulate matter measuring less than 2.5 µm (PM2.5) at a fine spatial scale across all of India. To catalyze health effects research on an unprecedented scale, we will make the output from this model publicly available. In addition, we will also apply these PM2.5 estimates to study the health outcomes of greatest public health importance in India, including cardiovascular diseases, chronic obstructive pulmonary disease, pregnancy (and birth) outcomes, and cognitive development and/or decline. Thus, our efforts will directly generate actionable new evidence on the myriad effects of air pollution on health that can inform policy decisions, while providing a comprehensive and publicly available resource for future studies on both exposure and health effects. In this commentary, we discuss the motivation, rationale, and vision for our consortium and a path forward for reducing the enormous burden of disease from air pollution in India.

Maternal fibre and gluten intake during pregnancy and risk of childhood celiac disease: the MoBa study.

Maternal diet can influence the developing immune system of the offspring. We hypothesized that maternal fibre and gluten intake during pregnancy were associated with the risk of celiac disease in the child. In the Norwegian Mother, Father and Child Cohort Study (MoBa, n = 85,898) higher maternal fibre intake (median 29.5 g/day) was associated with a lower risk of celiac disease in the offspring (adjusted relative risk 0.90, 95% CI 0.83 to 0.98 per 10 g/d increase). Gluten intake during pregnancy (median 13.0 g/d) was associated with a higher risk of childhood CD (adjusted relative risk = 1.21, 95% CI 1.02 to 1.43 per 10 g/d increase). These results were largely unaffected by adjustment for the child's gluten intake at 18 months. In an independent study of 149 mother/child dyads, maternal fibre intake did not predict concentrations of total or sub-types of short-chain fatty acids in repeated infant stool samples, or fecal microbiome diversity in the mother or child. Our results suggest that high fibre and low gluten intake during pregnancy could be protective factors for celiac disease, although the mechanism is unknown.

Exposure to Particulate Matter Is Associated With Elevated Blood Pressure and Incident Hypertension in Urban India.

Ambient air pollution, specifically particulate matter of diameter <2.5 µm, is reportedly associated with cardiovascular disease risk. However, evidence linking particulate matter of diameter <2.5 µm and blood pressure (BP) is largely from cross-sectional studies and from settings with lower concentrations of particulate matter of diameter <2.5 µm, with exposures not accounting for myriad time-varying and other factors such as built environment. This study aimed to study the association between long- and short-term ambient particulate matter of diameter <2.5 µm exposure from a hybrid spatiotemporal model at 1-km×1-km spatial resolution with longitudinally measured systolic and diastolic BP and incident hypertension in 5342 participants from urban Delhi, India, within an ongoing representative urban adult cohort study. Median annual and monthly exposure at baseline was 92.1 µg/m3 (interquartile range, 87.6-95.7) and 82.4 µg/m3 (interquartile range, 68.4-107.0), respectively. We observed higher average systolic BP (1.77 mm Hg [95% CI, 0.97-2.56] and 3.33 mm Hg [95% CI, 1.12-5.52]) per interquartile range differences in monthly and annual exposures, respectively, after adjusting for covariates. Additionally, interquartile range differences in long-term exposures of 1, 1.5, and 2 years increased the risk of incident hypertension by 1.53× (95% CI, 1.19-1.96), 1.59× (95% CI, 1.31-1.92), and 1.16× (95% CI, 0.95-1.43), respectively. Observed effects were larger in individuals with higher waist-hip ratios. Our data strongly support a temporal association between high levels of ambient air pollution, higher systolic BP, and incident hypertension. Given that high BP is an important risk factor of cardiovascular disease, reducing ambient air pollution is likely to have meaningful clinical and public health benefits.

Ensemble averaging based assessment of spatiotemporal variations in ambient PM2.5 concentrations over Delhi, India, during 2010-2016.

Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 µg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 µg/m3 with prediction bias ranging between 4.6-11.2 µg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Leveraging Existing Cohorts to Study Health Effects of Air Pollution on Cardiometabolic Disorders: India Global Environmental and Occupational Health Hub.

Air pollution is a growing public health concern in developing countries and poses a huge epidemiological burden. Despite the growing awareness of ill effects of air pollution, the evidence linking air pollution and health effects is sparse. This requires environmental exposure scientist and public health researchers to work more cohesively to generate evidence on health impacts of air pollution in developing countries for policy advocacy. In the Global Environmental and Occupational Health (GEOHealth) Program, we aim to build exposure assessment model to estimate ambient air pollution exposure at a very fine resolution which can be linked with health outcomes leveraging well-phenotyped cohorts which have information on geolocation of households of study participants. We aim to address how air pollution interacts with meteorological and weather parameters and other aspects of the urban environment, occupational classification, and socioeconomic status, to affect cardiometabolic risk factors and disease outcomes. This will help us generate evidence for cardiovascular health impacts of ambient air pollution in India needed for necessary policy advocacy. The other exploratory aims are to explore mediatory role of the epigenetic mechanisms (DNA methylation) and vitamin D exposure in determining the association between air pollution exposure and cardiovascular health outcomes. Other components of the GEOHealth program include building capacity and strengthening the skills of public health researchers in India through variety of training programs and international collaborations. This will help generate research capacity to address environmental and occupational health research questions in India. The expertise that we bring together in GEOHealth hub are public health, clinical epidemiology, environmental exposure science, statistical modeling, and policy advocacy.

Association between socioeconomic position and cardiovascular disease risk factors in rural north India: The Solan Surveillance Study.

BACKGROUND: Although most Indians live in rural settings, data on cardiovascular disease risk factors in these groups are limited. We describe the association between socioeconomic position and cardiovascular disease risk factors in a large rural population in north India. METHODS: We performed representative, community-based sampling from 2013 to 2014 of Solan district in Himachal Pradesh. We used education, occupation, household income, and household assets as indicators of socioeconomic position. We used tobacco use, alcohol use, low physical activity, obesity, hypertension, and diabetes as risk factors for cardiovascular disease. We performed hierarchical multivariable logistic regression, adjusting for age, sex and clustering of the health sub-centers, to evaluate the cross-sectional association of socioeconomic position indicators and cardiovascular disease risk factors. RESULTS: Among 38,457 participants, mean (SD) age was 42.7 (15.9) years, and 57% were women. The odds of tobacco use was lowest in participants with graduate school and above education (adjusted OR 0.11, 95% CI 0.09, 0.13), household income >15,000 INR (adjusted OR 0.35, 95% CI 0.29, 0.43), and highest quartile of assets (adjusted OR 0.28, 95% CI 0.24, 0.34) compared with other groups but not occupation (skilled worker adjusted OR 0.93, 95% CI 0.74, 1.16). Alcohol use was lower among individuals in the higher quartile of income (adjusted OR 0.75, 95% CI 0.64, 0.88) and assets (adjusted OR 0.70, 95% CI 0.59, 0.82). The odds of obesity was highest in participants with graduate school and above education (adjusted OR 2.33, 95% CI 1.85, 2.94), household income > 15,000 Indian rupees (adjusted OR 1.89, 95% CI 1.63, 2.19), and highest quartile of household assets (adjusted OR 2.87, 95% CI 2.39, 3.45). The odds of prevalent hypertension and diabetes were also generally higher among individuals with higher socioeconomic position. CONCLUSIONS: Individuals with lower socioeconomic position in Himachal Pradesh were more likely to have abnormal behavioral risk factors, and individuals with higher socioeconomic position were more likely to have abnormal clinical risk factors.

Environmental toxicants in breast milk of Norwegian mothers and gut bacteria composition and metabolites in their infants at 1 month.

BACKGROUND: Early disruption of the microbial community may influence life-long health. Environmental toxicants can contaminate breast milk and the developing infant gut microbiome is directly exposed. We investigated whether environmental toxicants in breastmilk affect the composition and function of the infant gut microbiome at 1 month. We measured environmental toxicants in breastmilk, fecal short-chain fatty acids (SCFAs), and gut microbial composition from 16S rRNA gene amplicon sequencing using samples from 267 mother-child pairs in the Norwegian Microbiota Cohort (NoMIC). We tested 28 chemical exposures: polychlorinated biphenyls (PCBs), polybrominated flame retardants (PBDEs), per- and polyfluoroalkyl substances (PFASs), and organochlorine pesticides. We assessed chemical exposure and alpha diversity/SCFAs using elastic net regression modeling and generalized linear models, adjusting for confounders, and variation in beta diversity (UniFrac), taxa abundance (ANCOM), and predicted metagenomes (PiCRUSt) in low, medium, and high exposed groups. RESULTS: PBDE-28 and the surfactant perfluorooctanesulfonic acid (PFOS) were associated with less microbiome diversity. Some sub-OTUs of Lactobacillus, an important genus in early life, were lower in abundance in samples from infants with relative "high" (> 80th percentile) vs. "low" (< 20th percentile) toxicant exposure in this cohort. Moreover, breast milk toxicants were associated with microbiome functionality, explaining up to 34% of variance in acetic and propionic SCFAs, essential signaling molecules. Per one standard deviation of exposure, PBDE-28 was associated with less propionic acid (- 24% [95% CI - 35% to - 14%] relative to the mean), and PCB-209 with less acetic acid (- 15% [95% CI - 29% to - 0.4%]). Conversely, PFOA and dioxin-like PCB-167 were associated with 61% (95% CI 35% to 87%) and 22% (95% CI 8% to 35%) more propionic and acetic acid, respectively. CONCLUSIONS: Environmental toxicant exposure may influence infant gut microbial function during a critical developmental window. Future studies are needed to replicate these novel findings and investigate whether this has any impact on child health.

Prenatal iron exposure and childhood type 1 diabetes.

Iron overload due to environmental or genetic causes have been associated diabetes. We hypothesized that prenatal iron exposure is associated with higher risk of childhood type 1 diabetes. In the Norwegian Mother and Child cohort study (n = 94,209 pregnancies, n = 373 developed type 1 diabetes) the incidence of type 1 diabetes was higher in children exposed to maternal iron supplementation than unexposed (36.8/100,000/year compared to 28.6/100,000/year, adjusted hazard ratio 1.33, 95%CI: 1.06-1.67). Cord plasma biomarkers of high iron status were non-significantly associated with higher risk of type 1 diabetes (ferritin OR = 1.05 [95%CI: 0.99-1.13] per 50 mg/L increase; soluble transferrin receptor: OR = 0.91 [95%CI: 0.81-1.01] per 0.5 mg/L increase). Maternal but not fetal HFE genotypes causing high/intermediate iron stores were associated with offspring diabetes (odds ratio: 1.45, 95%CI: 1.04, 2.02). Maternal anaemia or non-iron dietary supplements did not significantly predict type 1 diabetes. Perinatal iron exposures were not associated with cord blood DNA genome-wide methylation, but fetal HFE genotype was associated with differential fetal methylation near HFE. Maternal cytokines in mid-pregnancy of the pro-inflammatory M1 pathway differed by maternal iron supplements and HFE genotype. Our results suggest that exposure to iron during pregnancy may be a risk factor for type 1 diabetes in the offspring.

Preterm infants have distinct microbiomes not explained by mode of delivery, breastfeeding duration or antibiotic exposure.

Background: Preterm infants have low gut microbial diversity and few anaerobes. It is unclear whether the low diversity pertains to prematurity itself or is due to differences in delivery mode, feeding mode or exposure to antibiotics. Methods: The Norwegian Microbiota Study (NoMIC) was established to examine the colonization of the infant gut and health outcomes. 16S rRNA gene Illumina amplicon-sequenced samples from 519 children (160 preterms), collected at 10 days, 4 months and 1 year postnatally, were used to calculate alpha diversity. Short-chain fatty acids (SCFA) were analysed with gas chromatography and quantified using flame ionization detection. We regressed alpha diversity on gestational age, taking into account possible confounding and mediating factors, such as breastfeeding and antibiotics. Taxonomic differences were tested using Analysis of Composition of Microbiomes (ANCOM) and SCFA profile (as a functional indicator of the microbiota) was tested by Wilcoxon rank-sum. Results: Preterm infants had 0.45 Shannon units lower bacterial diversity at 10 days postnatally compared with infants born at term (95% confidence interval: -0.60, -0.32). Breastfeeding status and antibiotic exposure were not significant mediators of the gestational age-diversity association, although time spent in the neonatal intensive care unit was. Vaginally born, exclusively breastfed preterm infantss not exposed to antibiotics at 10 days postnatally had fewer Firmicutes and more Proteobacteria than children born at term and an SCFA profile indicating lower saccharolytic fermentation. Conclusions: Preterm infants had distinct gut microbiome composition and function in the early postnatal period, not explained by factors more common in preterms, such as shorter breastfeeding duration, more antibiotics or caesarean delivery.

A human exposure based mixture of persistent organic pollutants affects the stress response in female mice and their offspring.

Persistent organic pollutants (POPs) are found in the food chain of both humans and animals and exert a wide spectrum of potentially adverse effects. The present experiment aimed to investigate whether a defined mixture of 29 POPs, based on the dietary intake of Scandinavians, could affect the stress response in female mice exposed through ingestion, and in their offspring. Female mice 129:C57BL/6F0 hybrids were exposed from weaning, throughout pregnancy, and up until necropsy, to either 5000 × or 100 000 × the estimated daily intake for Scandinavians. The offspring were fed a reference diet containing no POPs. Both the mothers and their offspring were tested for basal and stress responsive corticosterone levels, and in an open field test to measure locomotor activity and anxiety-like behaviours. We found mothers to have elevated basal corticosterone levels, as well as a prolonged stress response following POP exposure. In the offspring, there was no effect of POPs on the stress response in females, but the exposed males had an over-sensitised stress response. There was no effect on behaviour in either the mothers or the offspring. In conclusion, we found a human relevant POP mixture can lead to subtle dysregulation of the hypothalamus-pituitary-adrenal axis in mice. As HPA axis dysregulation is commonly associated with neurological disorders, further studies should explore the relevance of this outcome for humans.

Global forecast of antimicrobial resistance in invasive isolates of Escherichia coli and Klebsiella pneumoniae.

OBJECTIVES: To project future antimicrobial resistance (AMR) in Escherichia coli and Klebsiella pneumoniae. METHODS: Mixed linear models were constructed from a sample of countries with AMR data in the ResistanceMap database. Inverse probability weighting methods were used to account for countries without AMR data. RESULTS: The estimated prevalence of AMR in 2015 was 64.5% (95% confidence interval (CI) 42-87%) for third-generation cephalosporin-resistant (3GCR) Escherichia coli, 5.8% (95% CI 1.8-9.7%) for carbapenem-resistant (CR) E. coli, 66.9% (95% CI 47.1-86.8%) for 3GCR Klebsiella pneumoniae, and 23.4% (95% CI 7.4-39.4%) for CR K. pneumoniae. The projected AMR prevalence in 2030 was 77% (95% CI 55-99.1%) for 3GCR E. coli, 11.8% (95% CI 3.7-19.9%) for CR E. coli, 58.2% (95% CI 50.2-66.1%) for 3GCR K. pneumoniae, and 52.8% (95% CI 16.3-89.3%) for CR K. pneumoniae. CONCLUSIONS: The models suggest that third-generation cephalosporins and carbapenems could be ineffective against a sizeable proportion of infections by E. coli and K. pneumoniae in most parts of the world by 2030, supporting both the need to enhance stewardship efforts and to prioritize research and development of new antibiotics for resistant Enterobacteriaceae.