MicroRNAs serve as prediction and treatment-response biomarkers of attention-deficit/hyperactivity disorder and promote the differentiation of neuronal cells by repressing the apoptosis pathway

MicroRNAs in Umbilical Cord Blood and Development in Full-Term Newborns: A Prospective Study

Background

Exploring the epigenetic regulations, such as microRNA, in newborns holds significant promise for enhancing our ability to address and potentially prevent early-life developmental delays.

Objectives

Hence, this research seeks to investigate if the expression of miRNA in the umbilical cord blood of infants can forecast their developmental outcomes as they grow older.

Design and method

We enrolled 143 full-term newborns, delivered either via cesarean section (CS) or through natural spontaneous delivery (NSD). We then analyzed the profiles of specific miRNAs (miR-486-5p, miR-126-5p, miR-140-3p, miR-151a-3p, miR-142-5p, and miR-30e-5p) in the umbilical cord blood of these infants. Subsequently, we performed follow-up assessments using Bayley-III scores when the cohort reached 1 year of age. Furthermore, we conducted pathway-enrichment analyses on the target genes associated with these examined miRNAs.

Results

When comparing newborns delivered via cesarean section (CS) to those born via natural spontaneous delivery (NSD), we observed notable differences. Specifically, newborns through NSD displayed significantly higher ΔCt values for miR-486-5p, alongside lower ΔCt values for miR-126-5p and miR-151a-3p in their cord blood. At 1 year of age, cognitive development was significantly linked to the ΔCt values of miR-140-3p and miR-142-5p, while language development showed a significant association with the ΔCt values of miR-140-3p. Moreover, our pathway enrichment analyses revealed that the target genes of these miRNAs were consistently involved in the pathways related to neurons, such as axon guidance and the neurotrophin signaling pathway.

Conclusion

In summary, this study represents a pioneering effort in elucidating the potential connections between miRNA levels in cord blood and the health indicators and neurodevelopment of newborns at 1 year of age. Our findings underscore the significance of miRNA levels at birth in influencing mechanisms related to neurodevelopment.

Individualized prediction models in ADHD: a systematic review and meta-regression

There have been increasing efforts to develop prediction models supporting personalised detection, prediction, or treatment of ADHD. We overviewed the current status of prediction science in ADHD by: (1) systematically reviewing and appraising available prediction models; (2) quantitatively assessing factors impacting the performance of published models. We did a PRISMA/CHARMS/TRIPOD-compliant systematic review (PROSPERO: CRD42023387502), searching, until 20/12/2023, studies reporting internally and/or externally validated diagnostic/prognostic/treatment-response prediction models in ADHD. Using meta-regressions, we explored the impact of factors affecting the area under the curve (AUC) of the models. We assessed the study risk of bias with the Prediction Model Risk of Bias Assessment Tool (PROBAST). From 7764 identified records, 100 prediction models were included (88% diagnostic, 5% prognostic, and 7% treatment-response). Of these, 96% and 7% were internally and externally validated, respectively. None was implemented in clinical practice. Only 8% of the models were deemed at low risk of bias; 67% were considered at high risk of bias. Clinical, neuroimaging, and cognitive predictors were used in 35%, 31%, and 27% of the studies, respectively. The performance of ADHD prediction models was increased in those models including, compared to those models not including, clinical predictors (β = 6.54, p = 0.007). Type of validation, age range, type of model, number of predictors, study quality, and other type of predictors did not alter the AUC. Several prediction models have been developed to support the diagnosis of ADHD. However, efforts to predict outcomes or treatment response have been limited, and none of the available models is ready for implementation into clinical practice. The use of clinical predictors, which may be combined with other type of predictors, seems to improve the performance of the models. A new generation of research should address these gaps by conducting high quality, replicable, and externally validated models, followed by implementation research.

Expression Patterns of miRNAs in Egyptian Children with ADHD: Clinical Study with Correlation Analysis

ADHD has huge knowledge gaps concerning its etiology. MicroRNAs (miRNAs) provide promising diagnostic biomarkers of human pathophysiology and may be a novel therapeutic option. The aim was to investigate the levels of miR-34c-3p, miR-155, miR-138-1, miR-296-5p, and plasma brain-derived neurotrophic factor (BDNF) in a group of children with ADHD compared to neurotypicals and to explore correlations between these measures and some clinical data. The participants were children with ADHD in Group I (N = 41; age: 8.2 ± 2) and neurotypical ones in Group II (N = 40; age: 8.6 ± 2.5). Group I was subjected to clinical examination, the Stanford Binet intelligence scale-5, the preschool language scale, and Conner's parent rating scale-R. Measuring the expression levels of the miRNAs was performed by qRT-PCR for all participants. The BDNF level was measured by ELISA. The lowest scores on the IQ subtest were knowledge and working memory. No discrepancies were noticed between the receptive and expressive language ages. The highest scores on the Conner's scale were those for cognitive problems. Participants with ADHD exhibited higher plasma BDNF levels compared to controls (p = 0.0003). Expression patterns of only miR-34c-3p and miR-138-1 were downregulated with significant statistical differences (p˂0.01). However, expression levels of miR-296-5p showed negative correlation with the total scores of IQ (p = 0.03). MiR-34c-3p, miR-138-1, while BDNF showed good diagnostic potential. The downregulated levels of miR-34c-3p and miR-138-1, together with high BDNF levels, are suggested to be involved in the etiology of ADHD in Egyptian children. Gender differences influenced the expression patterns of miRNAs only in children with ADHD.

MicroRNAs as potential biomarkers for diagnosis of attention deficit hyperactivity disorder

Inappropriate levels of hyperactivity, impulsivity, and inattention characterize attention deficit hyperactivity disorder, a common childhood-onset neuropsychiatric disorder. The cognitive function and learning ability of children with attention deficit hyperactivity disorder are affected, and these symptoms may persist to adulthood if they are not treated. The diagnosis of attention deficit hyperactivity disorder is only based on symptoms and objective tests for attention deficit hyperactivity disorder are missing. Treatments for attention deficit hyperactivity disorder in children include medications, behavior therapy, counseling, and education services which can relieve many of the symptoms of attention deficit hyperactivity disorder but cannot cure it. There is a need for a molecular biomarker to distinguish attention deficit hyperactivity disorder from healthy subjects and other neurological conditions, which would allow for an earlier and more accurate diagnosis and appropriate treatment to be initiated. Abnormal expression of microRNAs is connected to brain development and disease and could provide novel biomarkers for the diagnosis and prognosis of attention deficit hyperactivity disorder. The recent studies reviewed had performed microRNA profiling in whole blood, white blood cells, blood plasma, and blood serum of children with attention deficit hyperactivity disorder. A large number of microRNAs were dysregulated when compared to healthy controls and with some overlap between individual studies. From the studies that had included a validation set of patients and controls, potential candidate biomarkers for attention deficit hyperactivity disorder in children could be miR-140-3p, let-7g-5p, -30e-5p, -223-3p, -142-5p, -486-5p, -151a-3p, -151a-5p, and -126-5p in total white blood cells, and miR-4516, -6090, -4763-3p, -4281, -4466, -101-3p, -130a-3p, -138-5p, -195-5p, and -106b-5p in blood serum. Further studies are warranted with children and adults with attention deficit hyperactivity disorder, and consideration should be given to utilizing rat models of attention deficit hyperactivity disorder. Animal studies could be used to confirm microRNA findings in human patients and to test the effects of targeting specific microRNAs on disease progression and behavior.

Unraveling the Role of miR-200b-3p in Attention-Deficit/Hyperactivity Disorder (ADHD) and Its Therapeutic Potential in Spontaneously Hypertensive Rats (SHR)

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children with unknown etiology. Impaired learning ability was commonly reported in ADHD patients and has been associated with dopamine uptake in the striatum of an animal model. Another evidence also indicated that micro-RNA (miR)-200b-3p is associated with learning ability in various animal models. However, the association between miR-200b-3p and ADHD-related symptoms remains unclear. Therefore, the current study investigated the role of miR-200b-3p in ADHD-related symptoms such as inattention and striatal inflammatory cytokines. To verify the influence of miR-200b-3p in ADHD-related symptoms, striatal stereotaxic injection of miR-200b-3p antagomir (AT) was performed on spontaneously hypertensive rats (SHR). The antioxidant activity and expressions of miR-200b-3p, slit guidance ligand 2 (Slit2), and inflammatory cytokines in the striatum of SHR were measured using quantitative real-time polymerase chain reaction (RT-qPCR), immunohistochemistry (IHC), immunoblotting, and enzyme-linked immunosorbent assay (ELISA). The spontaneous alternation of SHR was tested using a three-arm Y-shaped maze. The administration of miR-200b-3p AT or taurine significantly decreased striatal tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6 in SHR, along with increased super-oxide dismutase (SOD) and glutathione peroxidase (GSH-Px) activities and significantly higher spontaneous alternation. In this paper, we show that miR-200b-3p AT and taurine alleviates ADHD-related symptoms in SHR. These findings provide insights into ADHD's molecular basis and suggest miR-200b-3p as a potential therapeutic target. Concurrently, this study also suggests broad implications for treating neurodevelopmental disorders affecting learning activity such as ADHD.

Mutant PIK3CA is a targetable driver alteration in histiocytic neoplasms

Langerhans cell histiocytosis (LCH) is an inflammatory myeloid neoplasm characterized by the accumulation of clonal mononuclear phagocyte system cells expressing CD1a and CD207. In the past decade, molecular profiling of LCH as well as other histiocytic neoplasms demonstrated that these diseases are driven by MAPK activating alterations, with somatic BRAFV600E mutations in >50% of patients with LCH, and clinical inhibition of MAPK signaling has demonstrated remarkable clinical efficacy. At the same time, activating alterations in kinase-encoding genes, such as PIK3CA, ALK, RET, and CSF1R, which can activate mitogenic pathways independent from the MAPK pathway, have been reported in a subset of histiocytic neoplasms with anecdotal evidence of successful targeted treatment of histiocytoses harboring driver alterations in RET, ALK, and CSF1R. However, evidence supporting the biological consequences of expression of PIK3CA mutations in hematopoietic cells has been lacking, and whether targeted inhibition of PI3K is clinically efficacious in histiocytic neoplasms is unknown. Here, we provide evidence that activating mutations in PIK3CA can drive histiocytic neoplasms in vivo using a conditional knockin mouse expressing mutant PIK3CAH1047R in monocyte/dendritic cell progenitors. In parallel, we demonstrate successful treatment of PIK3CA-mutated, multisystemic LCH using alpelisib, an inhibitor of the alpha catalytic subunit of PI3K. Alpelisib demonstrated a tolerable safety profile at a dose of 750 mg per week and clinical and metabolic complete remission in a patient with PIK3CA-mutated LCH. These data demonstrate PIK3CA as a targetable noncanonical driver of LCH and underscore the importance of mutational analysis-based personalized treatment in histiocytic neoplasms.

Blood miRNA levels associated with ADHD traits in children across six European birth cohorts

BACKGROUND

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent and highly heritable neurodevelopmental disorder of major societal concern. Diagnosis can be challenging and there are large knowledge gaps regarding its etiology, though studies suggest an interplay of genetic and environmental factors involving epigenetic mechanisms. MicroRNAs (miRNAs) show promise as biomarkers of human pathology and novel therapies, and here we aimed to identify blood miRNAs associated with traits of ADHD as possible biomarker candidates and further explore their biological relevance.

METHODS

Our study population consisted of 1126 children (aged 5-12 years, 46% female) from the Human Early Life Exposome study, a study spanning six ongoing population-based European birth cohorts. Expression profiles of miRNAs in whole blood samples were quantified by microarray and tested for association with ADHD-related measures of behavior and neuropsychological functions from questionnaires (Conner's Rating Scale and Child Behavior Checklist) and computer-based tests (the N-back task and Attention Network Test).

RESULTS

We identified 29 miRNAs significantly associated (false discovery rate < .05) with the Conner's questionnaire-rated trait hyperactivity, 15 of which have been linked to ADHD in previous studies. Investigation into their biological relevance revealed involvement in several pathways related to neurodevelopment and function, as well as being linked with other neurodevelopmental or psychiatric disorders known to overlap with ADHD both in symptomology, genetic risk, and co-occurrence, such as autism spectrum disorder or schizophrenia. An additional three miRNAs were significantly associated with Conner's-rated inattention. No associations were found with questionnaire-rated total ADHD index or with computer-based tests.

CONCLUSIONS

The large overlap of our hyperactivity-associated miRNAs with previous studies on ADHD is intriguing and warrant further investigation. Though this study should be considered explorative and preliminary, these findings contribute towards identifying a set of miRNAs for use as blood-based biomarkers to aid in earlier and easier ADHD diagnosis.

Machine learning in attention-deficit/hyperactivity disorder: new approaches toward understanding the neural mechanisms

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder in children and has a high chance of persisting in adulthood. The development of individualized, efficient, and reliable treatment strategies is limited by the lack of understanding of the underlying neural mechanisms. Diverging and inconsistent findings from existing studies suggest that ADHD may be simultaneously associated with multivariate factors across cognitive, genetic, and biological domains. Machine learning algorithms are more capable of detecting complex interactions between multiple variables than conventional statistical methods. Here we present a narrative review of the existing machine learning studies that have contributed to understanding mechanisms underlying ADHD with a focus on behavioral and neurocognitive problems, neurobiological measures including genetic data, structural magnetic resonance imaging (MRI), task-based and resting-state functional MRI (fMRI), electroencephalogram, and functional near-infrared spectroscopy, and prevention and treatment strategies. Implications of machine learning models in ADHD research are discussed. Although increasing evidence suggests that machine learning has potential in studying ADHD, extra precautions are still required when designing machine learning strategies considering the limitations of interpretability and generalization.

Alterations in the Glycan Composition of Serum Glycoproteins in Attention-Deficit Hyperactivity Disorder

Changes in protein glycosylation are associated with most biological processes, and the importance of glycomic analysis in the research of disorders is constantly increasing, including in the neurodevelopmental field. We glycoprofiled sera in 10 children with attention-deficit hyperactivity disorder (ADHD) and 10 matching healthy controls for 3 types of samples: whole serum, sera after depletion of abundant proteins (albumin and IgG), and isolated IgG. The analytical methods used were a lectin-based glycoprotein microarray enabling high-throughput glycan analysis and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) as a standard method for the identification of glycan structures. For microarray analysis, the samples printed on microarray slides were incubated with biotinylated lectins and detected using the fluorescent conjugate of streptavidin by a microarray scanner. In the ADHD patient samples, we found increased antennary fucosylation, decreased di-/triantennary N-glycans with bisecting N-acetylglucosamine (GlcNAc), and decreased α2-3 sialylation. The results obtained by both independent methods were consistent. The study's sample size and design do not allow far-reaching conclusions to be drawn. In any case, there is a strong demand for a better and more comprehensive diagnosis of ADHD, and the obtained results emphasize that the presented approach brings new horizons to studying functional associations of glycan alterations in ADHD.

Can Epigenetics Predict Drug Efficiency in Mental Disorders?

Psychiatric disorders affect millions of individuals and their families worldwide, and the costs to society are substantial and are expected to rise due to a lack of effective treatments. Personalized medicine-customized treatment tailored to the individual-offers a solution. Although most mental diseases are influenced by genetic and environmental factors, finding genetic biomarkers that predict treatment efficacy has been challenging. This review highlights the potential of epigenetics as a tool for predicting treatment efficacy and personalizing medicine for psychiatric disorders. We examine previous studies that have attempted to predict treatment efficacy through epigenetics, provide an experimental model, and note the potential challenges at each stage. While the field is still in its infancy, epigenetics holds promise as a predictive tool by examining individual patients' epigenetic profiles in conjunction with other indicators. However, further research is needed, including additional studies, replication, validation, and application beyond clinical settings.

Can biomarkers be used to diagnose attention deficit hyperactivity disorder?

Currently, the diagnosis of attention deficit hyperactivity disorder (ADHD) is solely based on behavioral tests prescribed by the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5). However, biomarkers can be more objective and accurate for diagnosis and evaluating treatment efficacy. Thus, this review aimed to identify potential biomarkers for ADHD. Search terms “ADHD,” and “biomarker” combined with one of “protein,” “blood/serum,” “gene,” and “neuro” were used to identify human and animal studies in PubMed, Ovid Medline, and Web of Science. Only papers in English were included. Potential biomarkers were categorized into radiographic, molecular, physiologic, or histologic markers. The radiographic analysis can identify specific activity changes in several brain regions in individuals with ADHD. Several molecular biomarkers in peripheral blood cells and some physiologic biomarkers were found in a small number of participants. There were no published histologic biomarkers for ADHD. Overall, most associations between ADHD and potential biomarkers were properly controlled. In conclusion, a series of biomarkers in the literature are promising as objective parameters to more accurately diagnose ADHD, especially in those with comorbidities that prevent the use of DSM-5. However, more research is needed to confirm the reliability of the biomarkers in larger cohort studies.

Predicting childhood and adolescent attention-deficit/hyperactivity disorder onset: a nationwide deep learning approach

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder with a high degree of psychiatric and physical comorbidity, which complicates its diagnosis in childhood and adolescence. We analyzed registry data from 238,696 persons born and living in Sweden between 1995 and 1999. Several machine learning techniques were used to assess the ability of registry data to inform the diagnosis of ADHD in childhood and adolescence: logistic regression, random Forest, gradient boosting, XGBoost, penalized logistic regression, deep neural network (DNN), and ensemble models. The best fitting model was the DNN, achieving an area under the receiver operating characteristic curve of 0.75, 95% CI (0.74-0.76) and balanced accuracy of 0.69. At the 0.45 probability threshold, sensitivity was 71.66% and specificity was 65.0%. There was an overall agreement in the feature importance among all models (τ > .5). The top 5 features contributing to classification were having a parent with criminal convictions, male sex, having a relative with ADHD, number of academic subjects failed, and speech/learning disabilities. A DNN model predicting childhood and adolescent ADHD trained exclusively on Swedish register data achieved good discrimination. If replicated and validated in an external sample, and proven to be cost-effective, this model could be used to alert clinicians to individuals who ought to be screened for ADHD and to aid clinicians' decision-making with the goal of decreasing misdiagnoses. Further research is needed to validate results in different populations and to incorporate new predictors.