New approach can predict autism diagnosis earlier in life

NIEHS-funded researchers developed an approach to predict autism spectrum disorder (ASD) diagnosis earlier than current techniques. The tool, which uses information from an infant’s hair samples and machine-learning techniques, can enable more timely interventions for children with ASD.

Toxic metals and deficiencies of nutritional elements have been linked with increased likelihood of ASD, so the team analyzed patterns of metal metabolism over time in infants' hair strands. Then, they looked for features that differed between children with and without ASD and identified key patterns in altered metal metabolism, or biomarkers, that could be used to predict ASD via machine learning. Their study population included children from three different studies — from Japan, Sweden, and the U.S.

They identified 567 individual features in hair that significantly differed between samples from children with and without ASD and a broad pattern of dysregulated metal metabolism. Their predictive algorithm detected ASD risk as early as 1 month of age with high sensitivity, specificity, and accuracy.

According to the authors, the biomarkers can be detected in hair samples to predict ASD diagnosis at a young age, allowing for earlier interventions and therapies for children.

Citation: Austin C, Curtin P, Arora M, Reichenberg A, Curtin A, Iwai-Shimada M, Wright RO, Wright RJ, Remnelius KL, Isaksson J, Bölte S, Nakayama SF. 2022. Elemental dynamics in hair accurately predict future autism spectrum disorder diagnosis: an international multi-center study. J Clin Med 11(23):7154.

Body weight and arsenic exposure interact to worsen type 2 diabetes indicators in mice

NIEHS-funded researchers uncovered complex interactions between exposure to arsenic, body weight and composition, and indicators of type 2 diabetes in Diversity Outbred (DO) mice. DO mice better capture the genetic diversity of human populations, which may help explain differences in susceptibility to arsenic-induced disease.

Researchers examined the interaction between arsenic exposure and body composition in a group of 75 male DO mice exposed to inorganic arsenic in drinking water for 26 weeks. Then they used magnetic resonance imaging to determine body composition and measured inorganic arsenic and its metabolites, which are substances that form when the body breaks arsenic down, in the liver and urine. They also looked at indicators of diabetes: blood glucose and plasma insulin after fasting and following a glucose challenge, as well as measures of insulin resistance and insulin-producing cell function.

Although the team found little variation in the mice’s ability to metabolize arsenic, they found significant variation in body weight and composition that explained differences in diabetes indicators. Mice with higher body weight or body fat percentage had higher fasting and glucose challenge plasma insulin, and worse measures of insulin resistance and insulin-producing cell function.

According to the authors, this study is the first to use genetically diverse DO mice to reveal significant interactive effects between body composition and arsenic exposure that related to higher type 2 diabetes indicators. More research is needed to better understand and quantify these complex associations in larger populations, the team noted.

Citation: Xenakis JG, Douillet C, Bell TA, Hock P, Farrington J, Liu T, Murphy CEY, Saraswatula A, Shaw GD, Nativio G, Shi Q, Venkatratnam A, Zou F, Fry RC, Stýblo M, Pardo-Manuel de Villena F. 2022. An interaction of inorganic arsenic exposure with body weight and composition on type 2 diabetes indicators in Diversity Outbred mice. Mamm Genome 33(4):575–589.

Diet may protect against metabolic effects of PFOS

Dietary fiber may protect against metabolic and liver diseases related to perfluorooctoane sulfonate (PFOS) exposure, according to an NIEHS-funded study in mice.

PFOS can alter the collection of bacteria in the gut — or the microbiome — and liver metabolism, potentially increasing the risk for cardiometabolic disorders such as hypertension, obesity, and diabetes. Research shows that diets high in soluble fiber can decrease metabolic disease risk by attaching to cholesterol particles and removing them from the body. To investigate whether soluble fibers could protect against PFOS-induced metabolic changes specifically, the team used multi-omics analysis to compare PFOS-exposed or unexposed mice fed diets with or without soluble fiber. Multi-omics combines information about genes, metabolism, and lipids to enable a more complete understanding of molecular changes that may contribute to disease.

PFOS-exposed mice had altered metabolism and expression of genes related to fat metabolism and higher levels of fatty molecules in their livers compared to unexposed mice. The soluble fiber diet protected against these effects. Mice fed soluble fiber had less PFOS in their plasma and in their livers compared to those fed a standard diet. The authors noted that soluble fiber also appeared to protect against PFOS-related differences in gut microbe communities, revealing a potential mechanistic link between diet, microbes, and liver metabolism.

According to the team, these findings suggest that mice fed soluble fiber were less susceptible to PFOS-induced metabolic outcomes, which may be useful for designing more effective intervention strategies to reduce disease risk in PFOS-exposed populations.

Citation: Deng P, Durham J, Liu J, Zhang X, Wang C, Li D, Gwag T, Ma M, Hennig B. 2022. Metabolomic, lipidomic, transcriptomic, and metagenomic analyses in mice exposed to PFOS and fed soluble and insoluble dietary fibers. Environ Health Perspect 130(11):117003.

High-throughput approach screens chemicals that may harm the placenta

NIEHS-funded researchers developed a new screening method that evaluates the ability of chemicals to interfere with normal placenta function. The placenta provides nutrients and oxygen to keep the developing fetus healthy during pregnancy. Abnormalities in placental function have been associated with adverse pregnancy outcomes, including preeclampsia, reduced fetal growth, and fetal death.

The scientists created an assay that captures the movement of extravillous trophoblasts (EVTs) — specialized cells that attach the placenta to the uterus and enable access to nutrients for the embryo throughout pregnancy — in response to chemical exposures. To develop the assay, they cultured human EVTs and measured their response to chemicals known to increase or decrease EVT migration. Next, they tested a panel of environmental chemicals, including cadmium, four bisphenols, four mycoestrogens, and three flame retardants at various concentrations to screen for their ability to alter EVT migration.

The team reported that cadmium treatment reduced EVT migration in a dose-dependent manner. Compared to controls, the four bisphenols reduced EVT migration up to 15% and three of the mycoestrogens decreased migration up to 17% with similar changes across concentrations. The three flame retardants significantly increased EVT migration up to 20% compared to controls. According to the team, this real-time method to track EVT migration can be scaled up to hundreds of chemicals, offering promise to screen contaminants as placental toxicants.

Citation: Meakin C, Kim C, Lampert T, Aleksunes LM. 2022. High-throughput screening of toxicants that modulate extravillous trophoblast migration. Toxicol Lett 375:1-7.