Anatomy of mental disordersNovember 15, 2019
Mental health was defined by World Health Organization as “a state of well being in which every individual realizes his/her own potential, can cope with the normal stress of life, can work productively and fruitfully and is able to make a contribution to her/his community”.
As of 2004, 13% of the global disease burden was due to mental disorders, of which 4.3 percentage points were contributed by depression. Depression may yet become the highest contributor to the mental disease burden by 2030.
China and India have seen large changes in their social and economic aspects in the past 2-3 decades. How these changes have affected the mental health of 2.6 billion people is an important question, the answer to which can have a defining impact on future generations. In the case of India, economic growth has been associated with an increase in life expectancy from 58 years to 66 years, as well as a shift in the disease burden from communicable to non-communicable diseases (NCDs). With the significant increase in population and the changing age structure, the prevalence of all mental, neurological and substance-abuse-related diseases have increased substantially. For example, 48.5 million people were suffering from major depression disorder and 36.8 million from anxiety at any point in 2013 in India. One major disorder observed among Indian men was alcohol dependence, which, at 1.7%, was higher than that in China (1.4%). The age-standardised prevalence of schizophrenia in India (0·2%; 95% UI 0·2–0·2) has not changed much, but is still higher than that of several other mental illnesses.
The WHO devised and adopted a comprehensive mental health action plan (MHAP) 2013-2020 to reduce stigma, increase services and the effective use of resources to promote mental well-being. The four key objectives were “strengthening effective leadership, governance, providing comprehensive mental health services, and promoting information systems for the prevention of mental illness”. Even though some progress has been made, several challenges are yet to be met. India was one of the first countries to adopt a mental health programme (MHP), in 1982. However, due to several failures in implementing the programme, no major outcomes were seen. In National Mental Health Survey (NMHS-2016), conducted across 12 states, 150 million Indians needed active interventions, of which the majority were of 40 to 49 years of age, whereas the prevalence of substance abuse was higher in the 50-59 year category. India spends 1.3% of its total health budget on mental health, which is grossly low in comparison to several other nations. The one major approach which has not yet been tried is early-stage detection and robust education of people to tackle these diseases. One of the best ways of early detection is to develop genetic risk prediction models by doing genome wide association studies (GWAS).
Understanding psychiatric disorders at the genetic level can improve both disease classification and management. All mental diseases, including major depression disorder (MDD), anxiety disorders (AD), schizophrenia (SCZ), bipolar disorder, autism and attention deficit hyperactivity disorder (ADHD) are genetically complex. The polygenic nature of these phenotypes is assumed to be due to the interactions of several single nucleotide polymorphisms (SNPs) and large chromosomal rearrangements. GWAS data from Psychiatric Genomics Consortium (PGC), the largest scientific network in psychiatry, have demonstrated the same. The present classification of psychiatric diseases is based on a collection of symptoms and clinical phenotypes which can certainly take advantage of integrating molecular classification such as the Research Domain Criteria (RDoC) initiative by NIMH (National Institute of Mental Health – US). RDoC links the clinical phenotype with underlying biological structure and genetic predispositions, which brings hope to the development of biological-based diagnostics.
Present molecular diagnostics are limited to predicting response or adverse effects of drugs using CYP450 testing, conforming or supporting the selection of therapy (for example, in Fragile X syndrome, phenylketonuria, Down syndrome and 22q11 deletion syndrome). This suggests that there is a lot more research needed to discover the variants associated with a higher risk of psychiatric disorders. With the advent of next-generation sequencing (NGS), global efforts towards identifying genes and mutations associated with brain function are underway. NGS has already revealed important genetic underpinnings and knowledge related to various psychiatric diseases. NGS-based tests are under development for neurodevelopmental disorders, early-onset of neuropsychiatric conditions, patients without family history but with relevant diseases (search for de novo variants) and the members of a family with a history of mental illnesses.
A recent study by Periyasamy et al (JAMA Psychiatry 2019), on Indian subjects affected with SCZ, compared the frequency of alleles or variants from a panel of SNPs with control subjects using GWAS. The results suggested
that A allele of rs10866912 is a risk factor of SCZ with a moderate odds ratio of 1.27 (p=4.35×10-8). This is a first-of-its-kind GWAS study conducted in the Indian population for psychiatric disorders where 3,092 individuals were studied over 2 decades. Apart from genome sequencing, in silico analysis suggested that variant rs10866912 is under natural selection and very unlikely to be functionally neutral. This SNP was also found to be strongly associated with nicotinate phosphoribosyltransferase (NAPTR1) gene located at chromosome 8q24.3, which is a rate limiting enzyme catalyzing the conversion of nicotinamide adenine dinucleotide, an important coenzyme in several metabolic pathways. NAPRT1 was shown to be widely expressed in the human brain. An altered NAPRT1, along with other risk factors, could play an important role in the disease. These kinds of studies give great scope for identifying disease-specific variants of genes, which can help in early diagnosis and in improving clinical outcomes.