AI and DNA predict mental health issues years after trauma

AI and DNA predict mental health issues years after trauma

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The Center for Biomarker Research and Precision Medicine at Virginia Commonwealth University announced a new study published in Molecular Psychiatry that demonstrates how the combination of artificial intelligence (AI) and genomics can produce DNA biomarkers that predict problems mental health nearly 17 years after exposure to childhood trauma.

Childhood trauma was assessed from events that meet the DSM criteria for post-traumatic stress disorder in the Child and Adolescent Psychiatric Assessment (CAPA) and the Childhood Psychiatric Assessment. Young Adult (YAPA) from hundreds of children ages 9 to 13 who participated in the 30-year study. initiated by Duke University and the North Carolina Department of Health and Human Services, called the Great Smoky Mountain Study (GSMS). Bloodstain samples and clinical data were collected at each wave.

More than 970 bloodstain samples were used on more than 480 participants who provided more than 670 samples before reaching age 21, as well as a subset of more than 300 participants who provided a adult sample.

“We predict from DNA methylation the outcome in adults,” said study lead author Edwin van den Oord, PhD, a Dutch psychiatric geneticist, professor and director of the Center for Biomarker Research. and Precision Medicine from Virginia Commonwealth University. “We found a wide range of outcomes like adult depression, anxiety, alcohol abuse, nicotine addiction, poverty, social problems and medical problems.”

Neuropsychiatric diseases and cancer have been linked to changes in DNA methylation. In the human genome, there are 28 million sites where methylation can occur according to van den Oord.

“We know where all the SNPs (single nucleotide polymorphisms) are,” van den Oord said. “We take the human reference genome from the Human Genome Project and search for the CG sites, then we grab all the SNPs.”

Genetics is the branch of biology that studies genes, genetic variation, and heredity in living organisms. DNA, deoxyribonucleic acid, is the hereditary material in humans and most organisms where information is stored as a code made up of four chemical bases: adenine (A), guanine (G), cytosine (C) and thymine (T).

DNA can be modified by environmental factors, an epigenetic change, which can alter gene expression. DNA methylation, the process of adding methyl groups to DNA bases, is an epigenetic modification. Since methylation frequently occurs at CpG sites, or CG sites, the researchers determined the areas of the human genome where these sites exist. Specifically, they identified regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide.

To determine all possible sites that can be methylated in the majority of people, the researchers started by identifying CpG sites in the human reference genome from the Human Genome Project.

“We fragment DNA and turn it into small pieces like 100 base pairs and then sequence it,” van den Oord said. “And now we know the sequence of all these little fragments. And then we have to align it to the reference genome. If something aligns with a location that has a CpG, we calculate for that site how much methylation is occurring.

Scientists calculated methylation risk scores using artificial intelligence (AI) machine learning. In AI, Elastic Net linear regression is a method that combines the Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge regression methods.

The predictive ability of the methylation risk scores generated by the AI ​​algorithm was “higher than that of reported trauma and could not be explained by reported trauma, correlations with demographic variables, or continuity of health conditions. predicted from childhood to adulthood”.

According to the researchers, methylation risk scores predicted a wide range of adverse outcomes and have the potential to serve as a clinical biomarker to assess health risks related to trauma exposure.

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