tRNA-derived small RNAs in human cancers: roles, mechanisms, and clinical application.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) are a new type of non-coding RNAs (ncRNAs) produced by the specific cleavage of precursor or mature tRNAs. tsRNAs are involved in various basic biological processes such as epigenetic, transcriptional, post-transcriptional, and translation regulation, thereby affecting the occurrence and development of various human diseases, including cancers. Recent studies have shown that tsRNAs play an important role in tumorigenesis by regulating biological behaviors such as malignant proliferation, invasion and metastasis, angiogenesis, immune response, tumor resistance, and tumor metabolism reprogramming. These may be new potential targets for tumor treatment. Furthermore, tsRNAs can exist abundantly and stably in various bodily fluids (e.g., blood, serum, and urine) in the form of free or encapsulated extracellular vesicles, thereby affecting intercellular communication in the tumor microenvironment (TME). Meanwhile, their abnormal expression is closely related to the clinicopathological features of tumor patients, such as tumor staging, lymph node metastasis, and poor prognosis of tumor patients; thus, tsRNAs can be served as a novel type of liquid biopsy biomarker. This review summarizes the discovery, production, and expression of tsRNAs and analyzes their molecular mechanisms in tumor development and potential applications in tumor therapy, which may provide new strategies for early diagnosis and targeted therapy of tumors.

Joint inference for competing risks data using multiple endpoints.

Competing risks data are commonly encountered in randomized clinical trials and observational studies. This paper considers the situation where the ending statuses of competing events have different clinical interpretations and/or are of simultaneous interest. In clinical trials, often more than one competing event has meaningful clinical interpretations even though the trial effects of different events could be different or even opposite to each other. In this paper, we develop estimation procedures and inferential properties for the joint use of multiple cumulative incidence functions (CIFs). Additionally, by incorporating longitudinal marker information, we develop estimation and inference procedures for weighted CIFs and related metrics. The proposed methods are applied to a COVID-19 in-patient treatment clinical trial, where the outcomes of COVID-19 hospitalization are either death or discharge from the hospital, two competing events with completely different clinical implications.

Simultaneous hypothesis testing for multiple competing risks in comparative clinical trials.

Competing risks data are commonly encountered in randomized clinical trials or observational studies. Ignoring competing risks in survival analysis leads to biased risk estimates and improper conclusions. Often, one of the competing events is of primary interest and the rest competing events are handled as nuisances. These approaches can be inadequate when multiple competing events have important clinical interpretations and thus of equal interest. For example, in COVID-19 in-patient treatment trials, the outcomes of COVID-19 related hospitalization are either death or discharge from hospital, which have completely different clinical implications and are of equal interest, especially during the pandemic. In this paper we develop nonparametric estimation and simultaneous inferential methods for multiple cumulative incidence functions (CIFs) and corresponding restricted mean times. Based on Monte Carlo simulations and a data analysis of COVID-19 in-patient treatment clinical trial, we demonstrate that the proposed method provides global insights of the treatment effects across multiple endpoints.

Longitudinal genome-wide association studies of milk production traits in Holstein cattle using whole-genome sequence data imputed from medium-density chip data.

Longitudinal traits, such as milk production traits in dairy cattle, are featured by having phenotypic values at multiple time points, which change dynamically over time. In this study, we first imputed SNP chip (50-100K) data to whole-genome sequence (WGS) data in a Chinese Holstein population consisting of 6,470 cows. The imputation accuracies were 0.88 to 0.97 on average after quality control. We then performed longitudinal GWAS in this population based on a random regression test-day model using the imputed WGS data. The longitudinal GWAS revealed 16, 39, and 75 quantitative trait locus regions associated with milk yield, fat percentage, and protein percentage, respectively. We estimated the 95% confidence intervals (CI) for these quantitative trait locus regions using the logP drop method and identified 581 genes involved in these CI. Further, we focused on the CI that covered or overlapped with only 1 gene or the CI that contained an extremely significant top SNP. Twenty-eight candidate genes were identified in these CI. Most of them have been reported in the literature to be associated with milk production traits, such as DGAT1, HSF1, MGST1, GHR, ABCG2, ADCK5, and CSN1S1. Among the unreported novel genes, some also showed good potential as candidate genes, such as CCSER1, CUX2, SNTB1, RGS7, OSR2, and STK3, and are worth being further investigated. Our study provided not only new insights into the candidate genes for milk production traits, but also a general framework for longitudinal GWAS based on random regression test-day model using WGS data.

Antidepressant-like effects of geniposide in chronic unpredictable mild stress-induced mice by regulating the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 networks.

Evidence exists suggesting the anti-depressive activities of geniposide (GP), a major compound in Gardenia jasminoides Ellis. Accordingly, the present study attempts to explore the anti-depressive mechanism of GP in chronic unpredictable mild stress (CUMS)-induced depression-like behaviors of mice. CUMS-induced mice were given GP daily and subjected to behavioral tests to observe the effect of GP on the depression-like behaviors. It was noted that GP administration reduced depression-like behaviors in CUMS mice. Transcriptome sequencing was conducted in three control and three CUMS mice. Differentially expressed circRNAs, lncRNAs and mRNAs were then screened by bioinformatics analyses. Intersection analysis of the transcriptome sequencing results with the bioinformatics analysis results was followed to identify the candidate targets. We found that Gata2 alleviated depression-like behaviors via the metabolism- and synapse-related pathways. Gata2 was a target of miR-25-3p, which had binding sites to circ_0008405 and Oip5os1. circ_0008405 and Oip5os1 competitively bound to miR-25-3p to release the expression of Gata2. GP administration ameliorated depression-like behaviors in CUMS mice through regulation of the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 crosstalk networks. Taken together, GP may exert a potential antidepressant-like effect on CUMS mice, which is ascribed to regulation of the circ_0008405/miR-25-3p/Gata2 and Oip5os1/miR-25-3p/Gata2 crosstalk networks.

Increased suicidal erythrocyte death in patients with hepatitis B-related acute-on-chronic liver failure.

Anemia is a common complication of hepatitis B-related acute-on-chronic liver failure (HB-ACLF). Eryptosis, a suicidal erythrocyte death characterized by phosphatidylserine (PS) externalization and red blood cell-derived microparticle (RMP) generation, decreases erythrocyte lifespan. Herein, we investigated whether enhanced eryptosis is involved in the anemia pathophysiology associated with HB-ACLF. PS exposure, cell volume, cytosolic Ca2+, and reactive oxygen species (ROS) production were determined using flow cytometry. RMPs were extracted using a polyethylene glycol (PEG)-based method. We found that hemoglobin (Hb) and hematocrit (Hct) were significantly lower in patients with HB-ACLF than in healthy controls (HC), patients with chronic hepatitis B (CHB), and patients with cirrhosis. The direct antiglobulin test positive rate was 75.9% in patients with HB-ACLF while its intensity was associated with anemia. The ratio of abnormal erythrocytes was higher in patients with HB-ACLF than in HC, CHB, and cirrhosis. The percentage of PS-exposed erythrocytes was higher in patients with HB-ACLF (2.07 ± 0.11%) compared with HC (0.37 ± 0.05%), CHB (0.38 ± 0.03%), and cirrhosis (0.38 ± 0.04%). The cytosolic Ca2+ and ROS abundance were also higher in patients with HB-ACLF compared with HC, patients with CHB, and patients with cirrhosis, and were inversely correlated with the anemia in patients with HB-ACLF. PS exposure of erythrocytes collected from HC was significantly pronounced following incubation in plasma from patients with HB-ACLF compared with incubation in plasma from HC. The protein concentration and RMPs size significantly increased in patients with HB-ACLF compared with HC. Thus, the anemia in patients with HB-ACLF is associated with increased eryptosis, which is partially triggered by increased cytosolic Ca2+ and oxidative stress.NEW & NOTEWORTHY Acute chronic liver failure (ACLF) is a critical syndrome characterized by multiple organ failures and high short-term mortality. A common complication of HB-ACLF is anemia, however, the mechanism of anemia in HB-ACLF remains to be elucidated. We confirm that the accelerated eryptosis is involved in the pathophysiology of anemia associated with HB-ACLF, which progressively aggravates the clinical outcome. Our study illustrates the mechanism regarding the anemia pathogenesis of HB-ACLF, which may be utilized further toward therapeutic ends.

Obtaining optimal cutoff values for tree classifiers using multiple biomarkers.

In biomedical practices, multiple biomarkers are often combined using a prespecified classification rule with tree structure for diagnostic decisions. The classification structure and cutoff point at each node of a tree are usually chosen on an ad hoc basis, depending on decision makers' experience. There is a lack of analytical approaches that lead to optimal prediction performance, and that guide the choice of optimal cutoff points in a pre-specified classification tree. In this paper, we propose to search for and estimate the optimal decision rule through an approach of rank correlation maximization. The proposed method is flexible, theoretically sound, and computationally feasible when many biomarkers are available for classification or prediction. Using the proposed approach, for a prespecified tree-structured classification rule, we can guide the choice of optimal cutoff points at tree nodes and estimate optimal prediction performance from multiple biomarkers combined.

Association of Lifestyle Activities with Functional Brain Connectivity and Relationship to Cognitive Decline among Older Adults.

This study examines the relationship of engagement in different lifestyle activities to connectivity in large-scale functional brain networks, and whether network connectivity modifies cognitive decline, independent of brain amyloid levels. Participants (N = 153, mean age = 69 years, including N = 126 with amyloid imaging) were cognitively normal when they completed resting-state functional magnetic resonance imaging, a lifestyle activity questionnaire, and cognitive testing. They were followed with annual cognitive tests up to 5 years (mean = 3.3 years). Linear regressions showed positive relationships between cognitive activity engagement and connectivity within the dorsal attention network, and between physical activity levels and connectivity within the default-mode, limbic, and frontoparietal control networks, and global within-network connectivity. Additionally, higher cognitive and physical activity levels were independently associated with higher network modularity, a measure of functional network specialization. These associations were largely independent of APOE4 genotype, amyloid burden, global brain atrophy, vascular risk, and level of cognitive reserve. Moreover, higher connectivity in the dorsal attention, default-mode, and limbic networks, and greater global connectivity and modularity were associated with reduced cognitive decline, independent of APOE4 genotype and amyloid burden. These findings suggest that changes in functional brain connectivity may be one mechanism by which lifestyle activity engagement reduces cognitive decline.

Development of Severe COVID-19 Adaptive Risk Predictor (SCARP), a Calculator to Predict Severe Disease or Death in Hospitalized Patients With COVID-19.

BACKGROUND: Predicting the clinical trajectory of individual patients hospitalized with coronavirus disease 2019 (COVID-19) is challenging but necessary to inform clinical care. The majority of COVID-19 prognostic tools use only data present upon admission and do not incorporate changes occurring after admission. OBJECTIVE: To develop the Severe COVID-19 Adaptive Risk Predictor (SCARP) (https://rsconnect.biostat.jhsph.edu/covid_trajectory/), a novel tool that can provide dynamic risk predictions for progression from moderate disease to severe illness or death in patients with COVID-19 at any time within the first 14 days of their hospitalization. DESIGN: Retrospective observational cohort study. SETTINGS: Five hospitals in Maryland and Washington, D.C. PATIENTS: Patients who were hospitalized between 5 March and 4 December 2020 with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) confirmed by nucleic acid test and symptomatic disease. MEASUREMENTS: A clinical registry for patients hospitalized with COVID-19 was the primary data source; data included demographic characteristics, admission source, comorbid conditions, time-varying vital signs, laboratory measurements, and clinical severity. Random forest for survival, longitudinal, and multivariate (RF-SLAM) data analysis was applied to predict the 1-day and 7-day risks for progression to severe disease or death for any given day during the first 14 days of hospitalization. RESULTS: Among 3163 patients admitted with moderate COVID-19, 228 (7%) became severely ill or died in the next 24 hours; an additional 355 (11%) became severely ill or died in the next 7 days. The area under the receiver-operating characteristic curve (AUC) for 1-day risk predictions for progression to severe disease or death was 0.89 (95% CI, 0.88 to 0.90) and 0.89 (CI, 0.87 to 0.91) during the first and second weeks of hospitalization, respectively. The AUC for 7-day risk predictions for progression to severe disease or death was 0.83 (CI, 0.83 to 0.84) and 0.87 (CI, 0.86 to 0.89) during the first and second weeks of hospitalization, respectively. LIMITATION: The SCARP tool was developed by using data from a single health system. CONCLUSION: Using the predictive power of RF-SLAM and longitudinal data from more than 3000 patients hospitalized with COVID-19, an interactive tool was developed that rapidly and accurately provides the probability of an individual patient's progression to severe illness or death on the basis of readily available clinical information. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

Patient Trajectories Among Persons Hospitalized for COVID-19 : A Cohort Study.

BACKGROUND: Risk factors for progression of coronavirus disease 2019 (COVID-19) to severe disease or death are underexplored in U.S. cohorts. OBJECTIVE: To determine the factors on hospital admission that are predictive of severe disease or death from COVID-19. DESIGN: Retrospective cohort analysis. SETTING: Five hospitals in the Maryland and Washington, DC, area. PATIENTS: 832 consecutive COVID-19 admissions from 4 March to 24 April 2020, with follow-up through 27 June 2020. MEASUREMENTS: Patient trajectories and outcomes, categorized by using the World Health Organization COVID-19 disease severity scale. Primary outcomes were death and a composite of severe disease or death. RESULTS: Median patient age was 64 years (range, 1 to 108 years); 47% were women, 40% were Black, 16% were Latinx, and 21% were nursing home residents. Among all patients, 131 (16%) died and 694 (83%) were discharged (523 [63%] had mild to moderate disease and 171 [20%] had severe disease). Of deaths, 66 (50%) were nursing home residents. Of 787 patients admitted with mild to moderate disease, 302 (38%) progressed to severe disease or death: 181 (60%) by day 2 and 238 (79%) by day 4. Patients had markedly different probabilities of disease progression on the basis of age, nursing home residence, comorbid conditions, obesity, respiratory symptoms, respiratory rate, fever, absolute lymphocyte count, hypoalbuminemia, troponin level, and C-reactive protein level and the interactions among these factors. Using only factors present on admission, a model to predict in-hospital disease progression had an area under the curve of 0.85, 0.79, and 0.79 at days 2, 4, and 7, respectively. LIMITATION: The study was done in a single health care system. CONCLUSION: A combination of demographic and clinical variables is strongly associated with severe COVID-19 disease or death and their early onset. The COVID-19 Inpatient Risk Calculator (CIRC), using factors present on admission, can inform clinical and resource allocation decisions. PRIMARY FUNDING SOURCE: Hopkins inHealth and COVID-19 Administrative Supplement for the HHS Region 3 Treatment Center from the Office of the Assistant Secretary for Preparedness and Response.

How I do it? Full endoscopic lumbar rhizotomy for chronic facet joint pain due to failed back surgery syndrome.

BACKGROUND: Failed back surgery syndrome (FBSS) is a general term for persistent postoperative back pain with or without accompanying radicular pain. FBSS may present as chronic facet joint pain. METHODS: We introduced full endoscopic lumbar rhizotomy for patients suffering from facet joint pain due to FBSS. Facet joint block was introduced into the facet joint to determine whether pain improved after the injection. CONCLUSION: With full endoscopic lumbar rhizotomy, the surgeon can identify the regions involved more clearly and directly. Although it is an invasive procedure, it provides a more effective and safe treatment for patients with FBSS-related facet joint pain.

Assessment of the performance of different imputation methods for low-coverage sequencing in Holstein cattle.

Low-coverage sequencing (LCS) followed by imputation has been proposed as a cost-effective genotyping approach for obtaining genotypes of whole-genome variants. Imputation performance is essential for the effectiveness of this approach. Several imputation methods have been proposed and successfully applied in genomic studies in human and other species. However, there are few reports on the performance of these methods in livestock. Here, we evaluated a variety of imputation methods, including Beagle v4.1, GeneImp v1.3, GLIMPSE v1.1.0, QUILT v1.0.0, Reveel, and STITCH v1.6.5, with varying sequencing depth, sample size, and reference panel size using LCS data of Holstein cattle. We found that all of these methods, except Reveel, performed well in most cases with an imputation accuracy over 0.9; on the whole, GLIMPSE, QUILT, and STITCH performed better than the other methods. For species with no reference panel available, STITCH followed by Beagle would be an optimal strategy, whereas for species with reference panel available, QUILT would be the method of choice. Overall, this study illustrated the promising potential of LCS for genomic analysis in livestock.

Bias correction via outcome reassignment for cross-sectional data with binary disease outcome.

Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies to identify the relationship between covariates and disease outcome. A cross-sectional population is defined as a population of living individuals at the sampling or observational time. It is generally understood that binary disease outcome from cross-sectional data contains less information than longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. Wang and Yang (2021) presented the complexity and bias in cross-sectional data with binary disease outcome with detailed analytical explorations into the data structure. As the distribution of the cross-sectional binary outcome is quite different from the population risk distribution, bias can arise when using cross-sectional data analysis to draw inference for population risk. In this paper we argue that the commonly adopted age-specific risk probability is biased for the estimation of population risk and propose an outcome reassignment approach which reassigns a portion of the observed binary outcome, 0 or 1, to the other disease category. A sign test and a semiparametric pseudo-likelihood method are developed for analyzing cross-sectional data using the OR approach. Simulations and an analysis based on Alzheimer's Disease data are presented to illustrate the proposed methods.

Analyzing wearable device data using marked point processes.

This paper introduces two sets of measures as exploratory tools to study physical activity patterns: active-to-sedentary/sedentary-to-active rate function (ASRF/SARF) and active/sedentary rate function (ARF/SRF). These two sets of measures are complementary to each other and can be effectively used together to understand physical activity patterns. The specific features are illustrated by an analysis of wearable device data from National Health and Nutrition Examination Survey (NHANES). A two-level semiparametric regression model for ARF and the associated activity magnitude is developed under a unified framework using the marked point process formulation. The inactive and active states measured by accelerometers are treated as a 0-1 point process, and the activity magnitude measured at each active state is defined as a marked variable. The commonly encountered missing data problem due to device nonwear is referred to as "window censoring," which is handled by a proper estimation approach that adopts techniques from recurrent event data. Large sample properties of the estimator and comparison between two regression models as measurement frequency increases are studied. Simulation and NHANES data analysis results are presented. The statistical inference and analysis results suggest that ASRF/SARF and ARF/SRF provide useful analytical tools to practitioners for future research on wearable device data.

Complexity and bias in cross-sectional data with binary disease outcome in observational studies.

A cross sectional population is defined as a population of living individuals at the sampling or observational time. Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies for identifying how covariates correlate with disease occurrence. It is generally understood that cross-sectional binary outcome is not as informative as longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. As the progression of a disease typically involves both time and disease status, we consider how the binary disease outcome from the cross-sectional population is connected to birth-illness-death process in the target population. We argue that the distribution of cross-sectional binary outcome is different from the risk distribution from the target population and that bias would typically arise when using cross-sectional data to draw inference for population risk. In general, the cross-sectional risk probability is determined jointly by the population risk probability and the ratio of duration of diseased state to the duration of disease-free state. Through explicit formulas we conclude that bias can almost never be avoided from cross-sectional data. We present age-specific risk probability (ARP) and argue that models based on ARP offers a compromised but still biased approach to understand the population risk. An analysis based on Alzheimer's disease data is presented to illustrate the ARP model and possible critiques for the analysis results.

Fracture asperity evolution during the transition from stick slip to stable sliding.

Fracture asperities interlock or break during stick slip and ride over each other during stable sliding. The evolution of fracture asperities during the transition between stick slip and stable sliding has attracted less attention, but is important to predict fracture behaviour. Here, we conduct a series of direct shear experiments on simulated fractures in homogeneous polycarbonate to examine the evolution of fracture asperities in the transition stage. Our results show that the transition stage occurs between the stick slip and stable sliding stages during the progressive reduction in normal stress on the smooth and rough fractures. Both the fractures exhibit the alternative occurrence of small and large shear stress drops followed by the deterministic chaos in the transition stage. Our data indicate that the asperity radius of curvature correlates linearly with the dimensionless contact area under a given normal stress. For the rough fracture, a bifurcation of acoustic energy release appears when the dimensionless contact area decreases in the transition stage. The evolution of fracture asperities is stress-dependent and velocity-dependent. This article is part of the theme issue 'Fracture dynamics of solid materials: from particles to the globe'.

Low hemoglobin levels are associated with direct antiglobulin test positivity in patients with acute-on-chronic liver failure.

BACKGROUND: Multiple factors contribute to anemia in patients with Hepatitis B virus (HBV)related acute-on-chronic liver failure (ACLF); however, the mechanism is unclear. The purpose of this study was to evaluate the clinical significance of the direct antiglobulin test (DAT) in patients with HBV related ACLF. METHODS: DAT was used to detect immunoglobulins and/or complement proteins on the surface of erythrocytes. RESULTS: We recruited 78 HBV-associated ACLF patients, 30 chronic hepatitis B(CHB)patients and 40 healthy people between October 2015 and May 2016. In HBV related ACLF patients, the hemoglobin concentration, number of erythrocytes, and hematocrit value were significantly lower, while the erythrocyte distribution width was significantly higher, compared to patients with CHB and healthy controls (HCs) (P < 0.001). The rates of DAT positivity in HBV related ACLF patients, CHB patients, and HCs were 62.8 %, 13.3 %, and 0%, respectively. DAT-positive ACLF patients exhibited lower Hb levels, older average age, as well as higher total bilirubin, alanine aminotransferase, and complement component 3 levels compared to DAT-negative patients. CONCLUSIONS: HBV related ACLF patients showed significant alterations in erythrocyte parameters, possibly reflecting disease development and severity. The high presence of erythrocyte autoantibodies suggested that immunologic clearance of erythrocytes contributed to multifactorial anemia in HBV related ACLF patients.

Semiparametric modelling and estimation of covariate-adjusted dependence between bivariate recurrent events.

A time-dependent measure, termed the rate ratio, was proposed to assess the local dependence between two types of recurrent event processes in one-sample settings. However, the one-sample work does not consider modeling the dependence by covariates such as subject characteristics and treatments received. The focus of this paper is to understand how and in what magnitude the covariates influence the dependence strength for bivariate recurrent events. We propose the covariate-adjusted rate ratio, a measure of covariate-adjusted dependence. We propose a semiparametric regression model for jointly modeling the frequency and dependence of bivariate recurrent events: the first level is a proportional rates model for the marginal rates and the second level is a proportional rate ratio model for the dependence structure. We develop a pseudo-partial likelihood to estimate the parameters in the proportional rate ratio model. We establish the asymptotic properties of the estimators and evaluate the finite sample performance via simulation studies. We illustrate the proposed models and methods using a soft tissue sarcoma study that examines the effects of initial treatments on the marginal frequencies of local/distant sarcoma recurrence and the dependence structure between the two types of cancer recurrence.

ROC-guided survival trees and ensembles.

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.

In utero exposure to mercury and childhood overweight or obesity: counteracting effect of maternal folate status.

BACKGROUND: Low-dose mercury (Hg) exposure has been associated with cardiovascular diseases, diabetes, and obesity in adults, but it is unknown the metabolic consequence of in utero Hg exposure. This study aimed to investigate the association between in utero Hg exposure and child overweight or obesity (OWO) and to explore if adequate maternal folate can mitigate Hg toxicity. METHODS: This prospective study included 1442 mother-child pairs recruited at birth and followed up to age 15 years. Maternal Hg in red blood cells and plasma folate levels were measured in samples collected 1-3 days after delivery (a proxy for third trimester exposure). Adequate folate was defined as plasma folate ≥ 20.4 nmol/L. Childhood OWO was defined as body mass index ≥ 85% percentile for age and sex. RESULTS: The median (interquartile range) of maternal Hg levels were 2.11 (1.04-3.70) µg/L. Geometric mean (95% CI) of maternal folate levels were 31.1 (30.1-32.1) nmol/L. Maternal Hg levels were positively associated with child OWO from age 2-15 years, independent of maternal pre-pregnancy OWO, diabetes, and other covariates. The relative risk (RR = 1.24, 95% CI 1.05-1.47) of child OWO associated with the highest quartile of Hg exposure was 24% higher than those with the lowest quartile. Maternal pre-pregnancy OWO and/or diabetes additively enhanced Hg toxicity. The highest risk of child OWO was found among children of OWO and diabetic mothers in the top Hg quartile (RR = 2.06; 95% CI 1.56-2.71) compared to their counterparts. Furthermore, adequate maternal folate status mitigated Hg toxicity. Given top quartile Hg exposure, adequate maternal folate was associated with a 34% reduction in child OWO risk (RR = 0.66, 95% CI 0.51-0.85) as compared with insufficient maternal folate. There was a suggestive interaction between maternal Hg and folate levels on child OWO risk (p for interaction = 0.086). CONCLUSIONS: In this US urban, multi-ethnic population, elevated in utero Hg exposure was associated with a higher risk of OWO in childhood, and such risk was enhanced by maternal OWO and/or diabetes and reduced by adequate maternal folate. These findings underscore the need to screen for Hg and to optimize maternal folate status, especially among mothers with OWO and/or diabetes.