AI and DNA Predict Mental Health Problems 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, showing how the combination of artificial intelligence (AI) and genomics can produce DNA biomarkers that identify mental health problems nearly 17 Years after childhood exposure predict trauma.

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

Over 970 blood samples were used from over 480 participants who submitted more than 670 samples before age 21, along with a subset of over 300 participants who submitted a sample in adulthood.

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

Neuropsychiatric disorders and cancer have been linked to changes in DNA methylation. According to van den Oord, there are 28 million places in the human genome where methylation can occur.

“We know where all the SNPs (single nucleotide polymorphisms) are,” said van den Oord. “We take the human reference genome from the human genome project and look for CG sites, and then we plug in all the SNPs.”

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

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DNA can be modified by environmental factors, an epigenetic change that can alter gene expression. DNA methylation, the addition of methyl groups to DNA bases, is an epigenetic modification. Because methylation often occurs at CpG or CG sites, the researchers determined the regions in the human genome where these sites exist. In particular, 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 a majority of humans, researchers began by identifying CpG sites in the human reference genome from the Human Genome Project.

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

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Scientists calculated methylation risk scores using artificial intelligence (AI) machine learning. In AI, Elastic Net linear regression is a method that combines lasso (least absolute shrinkage and selection operator) and ridge regression methods.

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

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

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