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Circulating MicroRNAs in Cord Blood to Predict Attention-Deficit/Hyperactivity Disorder Diagnosis
Background
There are large knowledge gaps in the etiology of attention-deficit/hyperactivity disorder (ADHD), and although it is a prevalent and highly heritable neurodevelopmental disorder, diagnosis can be challenging. We aimed to assess the association of circulating blood plasma microRNAs (miRNAs) at birth with ADHD for use as biomarker candidates and build an miRNA-based prediction model.
Methods
Our study population consisted of 206 children with ADHD (33.0% female), 207 control children (33.8% female), and their parents from the MoBa (Norwegian Mother, Father, and Child Cohort Study). Expression levels of 51 selected miRNAs in plasma from children’s cord blood at birth and from both parents during early pregnancy were quantified by quantitative polymerase chain reaction and tested for association with children’s ADHD diagnosis and ADHD symptom scores based on ratings by parents.
Results
Seven miRNAs were differentially expressed at birth in children with ADHD and control children (false discovery rate < .05), and 31 had a statistically significant linear relationship with parent-rated ADHD symptom score at 8 years. A 19-miRNA ADHD prediction model achieved good discrimination in the test population (area under the receiver operating curve = 0.959, accuracy = 0.893). Functional analysis for the 19-miRNA prediction set revealed involvement in several highly relevant pathways, e.g., dopaminergic synapse, circadian rhythm, and axon guidance. We also found that parental miRNA expression levels significantly associated with children’s ADHD diagnoses and/or ADHD symptoms scores.
Conclusions
We showed that expression levels of circulating miRNAs at birth may be used to predict increased risk of ADHD diagnosis, and our 19-miRNA set should be included in future efforts to develop a biomarker panel.
2025
Building-related symptoms in school environment: Predictability using machine learning approach
Building-related symptoms (BRS) are commonly experienced by students in schools and are potentially affecting academic performance and health. Even though indoor environment quality (IEQ) measurements indicated fair conditions, students still perceived discomfort that led to symptoms, highlighting the necessity of collecting user-feedback about IEQ-complaints. This study aimed to predict and understand the prevalence of BRS (headache, tiredness, cough, dry eyes-hands) experienced by students in classrooms using machine-learning (ML) approach based on measurement data, building factors, and prevalence of IEQ-complaints. We collected measurement data (from indoor and outdoor climate), building factors, and user-feedback by students via online-platform across three sampled classrooms each campaign during three consecutive school semesters. Significant input variables for ML were pre-selected using statistical tests. ML models were evaluated based on accuracy metrics and SHAP analysis for input interpretation. Models using measurement data alone performed poorly (testing R² <50 %) to predict prevalence of BRS, whereas adding building factors and prevalence of IEQ-complaints increased accuracy (R² up to 95 %) of prediction of BRS with lower RMSE. In addition, interpretation from SHAP analysis showed IEQ-complaints especially related with indoor air quality (e.g., heavy air, dust & dirt, and dry air) as significant contributors for predicting prevalence of BRS. We conclude that the framework of combining objective measurements with occupant-reported complaints can be reliable, interpretable predictions of symptom prevalence. This study is limited by single-school setting, health confounders, and symptoms verification. Future research may contribute to exploring wider set of input variables, applicability, and variation of complaints preference.
2025
2025
NILU og Akvaplan-niva har et felles overvåkingsprosjekt rundt Hammerfest LNG på Melkøya. I perioden 2025 – 2028 skal overvåkingen omfatte bly (Pb), kvikksølv (Hg) og PAH i nedbør, vegetasjon, jord, ferskvann, ferskvannsfisk og ferskvannssediment, samt kvikksølv i luft. For de fleste komponenter og prøvemedier er verdiene lave.
NILU
2025
2025
2025
Congener group profiles of MCCPs in environmental samples from Norwegian monitoring programmes, 2025
2025
2025
2025
2025
2025
The report provides the annual update of the European air quality concentration maps and population and vegetation exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, peak season average of maximum daily 8-hour means, SOMO35, SOMO10), NO2 (annual average) and benzo(a)pyrene (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2023. The report contains also maps of Phytotoxic ozone dose (PODY) for selected crops (wheat, potato and tomato) and trees (spruce and beech) and NOx annual average map for the same year 2023. The trends in exposure estimates in the period 2005-2023 are summarized. The analysis for 2023 is based on the interpolation of the annual statistics of the 2023 observational data reported by the EEA member and cooperating countries and other voluntary reporting countries and stored in the Air Quality e-reporting database, complemented, when needed, with measurements from additional sources. The mapping method is the Regression – Interpolation – Merging Mapping (RIMM). It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The report presents the mapping results and gives an uncertainty analysis of the interpolated maps. It also presents concentration change in 2023 in comparison to the 5-year average 2018-2022 using the difference maps and exposure estimates.
European Topic Centre on Human Health and the Environment (ETC HE)
2025