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From Complexity to Simplicity in Psychiatry2024-04-29

“Simplicity is the ultimate sophistication.”

—Leonardo da Vinci

To understand, treat and prevent mental health issues, it is clear that a simpler, more quantitative, and more comprehensive bio-socio-psychological approach is needed, that leads directly to precise and personalized treatments.

Simplicity comes from conceptualizing patients as being tri-dimensional, in a mental landscape (Mindscape)1. The three dimensions are anxiety, mood, and cognition. Quantitative measuring on each dimension is possible. The scores can come from biological measures, social determinants checklists, psychological measures, separately or as a combination of all of them. Everybody at a certain moment in time is a point in this 3D Mindscape, with (x, y, z) coordinates of anxiety, mood and cognition, and over time they are a cloud.

We have started to put together the pieces of this puzzle. We can measure people biologically with blood biomarkers for anxiety2, mood3 and cognition (psychosis4, memory disorders5). We can measure people with a checklist of social determinants6. And we can measure people psychologically with self-rating visual-analog scales for anxiety2, mood3, and psychosis4.

Lastly, externally driven challenges like physical trauma (pain7) or psychological trauma (stress8), along with key negative outcomes like suicidality9, and key positive outcomes like active longevity10, should also be assessed and improved in a bio-socio-psychological way. The silver lining is that knowing about your mental health risks is not a life sentence anymore, things can be readily improved, and the reward for that is not just a normal life, but a longer one.

Live. Happier. Longer.

Alexander B. Niculescu, MD, PhD Founder + CEO| MindX Sciences

Footnotes

  1. Niculescu, A. B., 3rd, Schork, N. J. & Salomon, D. R. Mindscape: a convergent perspective on life, mind, consciousness and happiness. J Affect Disord 123, 1-8, doi:S0165-0327(09)00283-3 pii 10.1016/j.jad.2009.06.022. ↩

  2. Roseberry, K. et al. Towards precision medicine for anxiety disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs. Mol Psychiatry 28, 2894-2912, doi:10.1038/s41380-023-01998-0 (2023). ↩ ↩2

  3. Le-Niculescu, H. et al. Precision medicine for mood disorders: objective assessment, risk prediction, pharmacogenomics, and repurposed drugs. Mol Psychiatry 26, 2776-2804, doi:10.1038/s41380-021-01061-w (2021). ↩ ↩2

  4. Hill, M. D. et al. Precision medicine for psychotic disorders: objective assessment, risk prediction, and pharmacogenomics. Mol Psychiatr, doi:10.1038/s41380-024-02433-8 (2024). ↩ ↩2

  5. Niculescu, A. B. et al. Blood biomarkers for memory: toward early detection of risk for Alzheimer disease, pharmacogenomics, and repurposed drugs. Mol Psychiatr 25, 1651-1672, doi:10.1038/s41380-019-0602-2 (2020). ↩

  6. Cheng, M. et al. Polyphenic risk score shows robust predictive ability for long-term future suicidality. Discov Ment Health 2, 13, doi:10.1007/s44192-022-00016-z (2022). ↩

  7. Niculescu, A. B. et al. Towards precision medicine for pain: diagnostic biomarkers and repurposed drugs. Mol Psychiatry 24, 501-522, doi:10.1038/s41380-018-0345-5 (2019). ↩

  8. Le-Niculescu, H. et al. Towards precision medicine for stress disorders: diagnostic biomarkers and targeted drugs. Mol Psychiatry, doi:10.1038/s41380-019-0370-z (2019). ↩

  9. Niculescu, A. B. et al. Precision medicine for suicidality: from universality to subtypes and personalization. Mol Psychiatry 22, 1250-1273, doi:10.1038/mp.2017.128 (2017). ↩

  10. Rangaraju, S. et al. Mood, stress and longevity: convergence on ANK3. Mol Psychiatry 21, 1037-1049, doi:10.1038/mp.2016.65 (2016). ↩