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Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy

Abstract

Children of individuals with bipolar disorder (bipolar offspring) are at increased risk for developing mood disorders, but strategies to predict mood episodes are unavailable. In this study, we used support vector machine (SVM) to characterize the potential of proton magnetic resonance spectroscopy (1H-MRS) in predicting the first mood episode in youth bipolar offspring. From a longitudinal neuroimaging study, 19 at-risk youth who developed their first mood episode (converters), and 19 without mood episodes during follow-up (non-converters) were selected and matched for age, sex and follow-up time. Baseline 1H-MRS data were obtained from anterior cingulate cortex (ACC) and bilateral ventrolateral prefrontal cortex (VLPFC). Glutamate (Glu), myo-inositol (mI), choline (Cho), N-acetyl aspartate (NAA), and phosphocreatine plus creatine (PCr + Cr) levels were calculated. SVM with a linear kernel was adopted to classify converters and non-converters based on their baseline metabolites. SVM allowed the significant classification of converters and non-converters across all regions for Cho (accuracy = 76.0%), but not for other metabolites. Considering all metabolites within each region, SVM allowed the significant classification of converters and non-converters for left VLPFC (accuracy = 76.5%), but not for right VLPFC or ACC. The combined mI, PCr + Cr, and Cho from left VLPFC achieved the highest accuracy differentiating converters from non-converters (79.0%). Our findings from this exploratory study suggested that 1H-MRS levels of mI, Cho, and PCr + Cr from left VLPFC might be useful to predict the development of first mood episode in youth bipolar offspring using machine learning. Future studies that prospectively examine and validate these metabolites as predictors of mood episodes in high-risk individuals are necessary.

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Acknowledgements

This study was supported by National Institute of Mental Health (NIMH) Grants (Grant Nos. P50 MH077138 (Strakowski), 5R01MH080973 (DelBello)), National Natural Science Foundation of China (Grants Nos. 81671664, 81621003), the Postdoctoral Interdisciplinary Research Project of Sichuan University (Grant No. 0040204153248), Miaozi Project in Science and Technology Innovation Program of Sichuan Province and 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (ZYYC08001, ZYJC18020).

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Correspondence to Su Lui.

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Conflict of interest

Dr. Nery’s spouse is an employee of Eli Lilly and Co. Dr. Patino has received research support through the American Academy of Child and Adolescent Psychiatry through the Junior Investigator Award program. Dr. Adler has received research support from Johnson and Johnson, Merck, Forest, Otsuka, Purdue, Takeda, Pfizer, Shire, Sunovion, and SyneuRx. He is a consultant to Sunovion. Dr. Strawn has received research support from Edgemont, Forest Research Institute/Allergan, Lundbeck, Shire, Neuronetics and the National Institute of Mental Health (NIMH and NIEHS). He receives royalties from Springer Publishing and UpToDate and has received material support from Assurex. Dr. Strakowski has recently received research grants from Janssen, Otsuka, the Michael and Susan Dell Foundation and the NIMH; he serves as DSMB chair for Sunovion. Dr. DelBello has received research support from Amarex, Johnson and Johnson, Pfizer, Otsuka, Shire, Sunovion, Supernus and Lundbeck. She is a consultant to Akili, CMEology, Johnson and Johnson, Lundbeck, Neuronetics, Pfizer, Sunovion, Supernus, and Takeda. The remaining authors declare no conflicts of interests.

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Zhang, W., Nery, F.G., Tallman, M.J. et al. Individual prediction of symptomatic converters in youth offspring of bipolar parents using proton magnetic resonance spectroscopy. Eur Child Adolesc Psychiatry (2020). https://doi.org/10.1007/s00787-020-01483-x

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Keywords

  • Bipolar disorder
  • Offspring
  • Magnetic resonance spectroscopy
  • Support vector machine
  • Mood episode