Mental Health Care Study: Effective depressive disorder treatment in the Precision Medicine
Received: 04-Nov-2022, Manuscript No. ejbi-22- 82987; Editor assigned: 07-Nov-2022, Pre QC No. ejbi-22- 82987; Reviewed: 21-Nov-2022 QC No. ejbi-22- 82987; Revised: 23-Nov-2022, Manuscript No. ejbi-22- 82987; Published: 30-Nov-2022, DOI: 10.24105/ejbi.2022.18.11.126-127
Citation: Matts AV (2022). Mental Health Care Study: Effective Depressive Disorder Treatment in the Precision Medicine. EJBI. 18(10):126-127.
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Abstract
The “typical patient” who requires standard therapies does not exist, according to contemporary research, which highlights the necessity for more specialised methods of managing unique clinical profiles as opposed to general diagnosis. In this regard, precision psychiatry has emerged, focusing on improving clinical subgroup identification, personalised evidence-based intervention suggestions, evaluation of the efficacy of various interventions, and identification of risk and protective factors for remission, relapse, and vulnerability. Numerous potential causes of depression have been postulated, and a number of elements, such as neural circuitry, biotypes, biopsychosocial indicators, genetics, and metabolomics, have been found to explain individual variations in pathology and response to therapy. Despite the precision approach’s potential to improve diagnosis and treatment choices, many obstacles are impeding its clinical adoption.In addition to ethical issues like safeguarding the confidentiality and security of patient data and preserving health equity, these include the clinical diversity of mental disorders, the complexities and costs of multiomics data, and the requirement for specialised training in precision health for medical practitioners.
Introduction
Precision medicine, also referred to as "personalised medicine," is a cutting-edge strategy for customising illness prevention and treatment that considers variations in people's genes, environments, and lifestyles. With the use of precision medicine, medical professionals may offer and plan customised care for their patients based on the unique genes, proteins, and other components of each patient's body. This method is often referred to as tailored care or personalised medicine. Examples of precision medicine include the use of targeted treatments to treat particular cancer cell types, such as breast cancer cells that are HER2-positive, or the use of tumour marker tests to assist in the diagnosis of cancer. Also known as customised medicine, long with evidence-based treatments, research has extensively looked at a variety of risk and protective factors in relation to psychiatric diseases with new developments and insights [1].
The "typical patient" who requires standard therapies does not exist, according to contemporary research, which highlights the necessity for more specialised methods of managing unique clinical profiles as opposed to general diagnosis. In this regard, precision psychiatry has emerged, focusing on improving clinical subgroup identification, personalised evidence-based intervention suggestions, evaluation of the efficacy of various interventions, and identification of risk and protective factors for remission, relapse, and vulnerability. Literature demonstrates that current developments in precision psychiatry are more data-driven, indicating the importance of and ongoing need for such research as well as its importance [2].
There are several potential causes of depression, and a number of elements have been discovered, such as neural circuitry, biotypes, biopsychosocial indicators, genetics, and metabolomics, which have been demonstrated to explain individual differences in pathology and response to therapy. The clinical use of the precision method is being hampered by significant obstacles, despite the possibility that it would improve diagnosis and treatment choices. Along with ethical issues like preserving health equity and protecting patient data privacy and security, these include the clinical diversity of psychiatric disorders, the technical challenges and high costs of multiomics data, and the requirement for specialised training in precision health for healthcare professionals [3].
It may be possible to identify those who would benefit from specific therapies by using technology enabled solutions to identify mental problems. Before we can stratify therapy suggestions based on mental diseases, there are several knowledge gaps to fill and obstacles to overcome. Overall, this research suggests that treating mental problems as a component of precision medicine may be very beneficial fornormal diabetes care and may even result in better diabetes outcomes.
Research has shown that internet-based therapies are beneficial in helping people manage their depression, as well as the effects of these interventions on particular symptoms. For instance, the well-known Deprexis programme has shown success in terms of clinical improvements and low dropout rates, and it has also shown success in reducing seven specific symptoms of depression in mild and moderate cases. According to these findings, automated self-help programmes [4].
By offering forecasts of each patient's therapy response, functional neuroimaging is another promising method for assisting treatment decisions. It can be used to predict response to both pharmacological and psychotherapy therapies by revealing the pretreatment activation level in some particular brain circuitries. Antidepressants perform better than psychotherapy in treating depression in patients with higher pretreatment ventral and pregenual anterior cingulate cortex (ACC) activation levels. Functional neuroimaging may best serve as a therapy decision-making tool for various subtypes of depression in light of such findings. Similar to this, a recent systematic review of the use of neuroimaging and behavioural predictors of treatment efficacy found that low baseline responsiveness in limbic regions of the brain and increased medial and dorsal prefrontal responses to emotional stimuli were the most consistently significant predictors of response to treatment [5].
Conclusion
This review has concluded that a number of mental illnesses are linked to an increased risk of type 2 diabetes and its consequences. However, there is some evidence that suggests that treating a number of mental problems may help prevent diabetes and improve diabetes outcomes. Individuals who may benefit from specific treatments may be identified using technologically advanced methods of diagnosing mental problems. Prior to stratifying treatment recommendations based on mental diseases, there are still significant knowledge gaps and a number of obstacles to overcome. Overall, this narrative review suggests that addressing mental problems as a component of precision medicine may be very beneficial for normal diabetes management and may even lead to better diabetes outcomes.
References
- Seidel EM, Satterthwaite TD, Eickhoff SB, Schneider F, Gur RC, Wolf DH, et al. Neural correlates of depressive realism An fMRI study on causal attribution in depression. J Affec Dis. 2012;138(3):268-276.
- Eshel N, Roiser JP. Reward and punishment processing in depression. Biol Psychia. 2010;68(2):118-124.
- Williams LM. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry. 2016;3(5):472-480.
- Bousman CA, Hopwood M. Commercial pharmacogenetic-based decision-support tools in psychiatry. Lancet Psychiatry. 2016;3(6):585-590.
- Dinan TG. Inflammatory markers in depression. Curr Opi Psychiatry. 2009;22(1):32-36.
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