Plasma Nervonic Acid Is a Potential Biomarker for Major Depressive Disorder: A Pilot Study

Kageyama, Yuki; Kasahara, Takaoki; Nakamura, Takemichi; Hattori, Kotaro; Deguchi, Yasuhiko; Tani, Munehide; Kuroda, Kenji; Yoshida, Sumiko; Goto, Yu‐ichi; Inoue, Koki; Kato, Takeshi

doi:10.1093/ijnp/pyx089

Cited by 51 publications

(38 citation statements)

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“…Instead of utilizing a single metabolite as a risk factor to predict depression, prediction models utilizing metabolomic data have recently been proposed to improve the accuracy of the prediction of depressive symptoms 4,5 . However, these initial trials to identify specific metabolite markers have not been well replicated 2,3,6 . As major limitations underlying current attempts to detect the risk metabolites for depression, issues regarding statistics and sample size remain to be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the statistical issues, another major limitation is that most previous studies have had limited sample sizes to perform machine learning 3,4,6 . Although machine learning is an effective approach for analyzing metabolomic data, it usually requires larger sample sizes than traditional statistics because of the much greater number of degrees of freedom that ought to be covered 17 .…”

Section: Introductionmentioning

confidence: 99%

“…Although machine learning is an effective approach for analyzing metabolomic data, it usually requires larger sample sizes than traditional statistics because of the much greater number of degrees of freedom that ought to be covered 17 . However, the sample sizes of the prior metabolomic studies to predict depressive symptoms, utilizing either traditional statistics or machine learning, included up to a couple of hundred samples 3,4,6 .…”

Section: Introductionmentioning

confidence: 99%

“…In this study, we applied the novel nonparametric prediction model utilizing HSIC Lasso and SVM/KR to the prediction of depressive symptoms based on metabolomic data from 897 plasma samples, which is more than double the number of samples in the largest previous studies 3,4,6 . The samples were collected by the Tohoku University Tohoku Medical Megabank Organization to survey the health condition of residents of prefectures that were primarily affected by the Great East Japan Earthquake and Tsunami 18 .…”

Section: Introductionmentioning

confidence: 99%

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Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

et al. 2020

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To solve major limitations in algorithms for the metabolite-based prediction of psychiatric phenotypes, a novel prediction model for depressive symptoms based on nonlinear feature selection machine learning, the Hilbert-Schmidt independence criterion least absolute shrinkage and selection operator (HSIC Lasso) algorithm, was developed and applied to a metabolomic dataset with the largest sample size to date. In total, 897 population-based subjects were recruited from the communities affected by the Great East Japan Earthquake; 306 metabolite features (37 metabolites identified by nuclear magnetic resonance measurements and 269 characterized metabolites based on the intensities from mass spectrometry) were utilized to build prediction models for depressive symptoms as evaluated by the Center for Epidemiologic Studies-Depression Scale (CES-D). The nested fivefold cross-validation was used for developing and evaluating the prediction models. The HSIC Lasso-based prediction model showed better predictive power than the other prediction models, including Lasso, support vector machine, partial least squares, random forest, and neural network. L-leucine, 3-hydroxyisobutyrate, and gamma-linolenyl carnitine frequently contributed to the prediction. We have demonstrated that the HSIC Lasso-based prediction model integrating nonlinear feature selection showed improved predictive power for depressive symptoms based on metabolome data as well as on risk metabolites based on nonlinear statistics in the Japanese population. Further studies should use HSIC Lasso-based prediction models with different ethnicities to investigate the generality of each risk metabolite for predicting depressive symptoms.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

et al. 2020

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“…It is not entirely clear to what extent daydreaming or mind-wandering from a present task is deliberate, i.e., characterized by conscious control and awareness. Although straying from task-relevant to task irrelevant thought may represent a failure of executive control ( 9 ), it has also been claimed that once mind-wandering has been initiated, executive control is needed to ensure the continuity of a self-generated internal “train of thought” ( 10 ). Yet another conceptualization for mind-wandering is the breakdown of meta-awareness ( 11 ), including a reduction in one's ability to regulate consciousness back to a goal-directed state.…”

Section: Introductionmentioning

confidence: 99%

Trapped in a Daydream: Daily Elevations in Maladaptive Daydreaming Are Associated With Daily Psychopathological Symptoms

Soffer‐Dudek

Somer

2018

Front. Psychiatry

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Background: Maladaptive Daydreaming (MD) characterizes individuals who engage in vivid, fanciful daydreaming for hours on end, neglecting real-life relationships and responsibilities, resulting in clinical distress and functional impairment. Sufferers have embraced the term MD in cyber-communities devoted to this problem because it seemed to uniquely fit their experience and since existing diagnostic labels and their therapies seemed inadequate. However, scientific research in the field has been scarce, relying on cross-sectional or case study designs. Existing knowledge on MD suggests the involvement of dissociative and obsessive-compulsive symptoms, as well as positive reinforcement comparable to processes in addiction disorders. The present study aimed to rigorously explore factors that accompany MD employing a longitudinal daily-diary design, hypothesizing that temporal increases in MD will associate concurrently with, and will temporally precede, other symptoms and emotional changes. In addition, we aimed to explore which symptoms may act as precursors to increases in MD, in order to identify possible mechanisms bringing about daydreaming in these individuals.Methods: In a sample of 77 self-diagnosed individuals with MD we assessed relevant daily symptoms for 14 days, including MD, depression, general anxiety, social anxiety, obsessive-compulsive symptoms, and dissociation, as well as positive and negative emotion.Results: Increases in MD were strongly related to concurrent increases in all other symptoms and negative emotion, and to decreased positive emotion. Obsessive-compulsive symptoms, dissociation, and negative emotion also temporally followed MD. Obsessive-compulsive symptoms were the only consistent temporal antecedent of MD.Conclusions: MD and obsessive-compulsive symptoms coincided in what seems to be a vicious cycle; understanding possible shared mechanisms between these symptoms may inform our understanding of the etiology of MD. For example, Serotonin levels may possibly be involved in the development or maintenance of this condition. The findings may also provide clues as to potentially beneficial interventions for treating MD. For example, perhaps utilizing response prevention techniques may be useful for curbing or intercepting unwanted daydreaming. Future studies on MD should address its compulsory nature.

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Nervonic acid level in cerebrospinal fluid is a candidate biomarker for depressive and manic symptoms: A pilot study

et al. 2021

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Objective Our previous metabolomics study showed that the plasma nervonic acid levels were higher in patients with major depressive disorder (MDD) than those in healthy controls and patients with bipolar disorder (BD). To examine whether the nervonic acid levels differ in the central nervous system, we investigated the levels in the cerebrospinal fluid (CSF) of patients with MDD, BD, and healthy controls. Methods Nervonic acid levels in CSF were measured by gas chromatography time‐of‐flight mass spectrometry. The participants included 30 patients with MDD, 30 patients with BD, and 30 healthy controls. Results In contrast to our previous study, no significant differences were found in the nervonic acid level in the CSF among the patients with MDD, BD, and the healthy controls. Though no significant state‐dependent changes were found among the three groups, we did observe a significant negative correlation between the nervonic acid levels and depressive symptoms in the depressive state of patients with MDD and BD (r = −0.38, p = .046). Further, a significant positive correlation was found between the nervonic acid levels and manic symptoms in the manic state of patients with BD (r = 0.79, p = .031). Conclusion The nervonic acid levels in the CSF did not differ among the patients with MDD, BD, and the healthy controls; however, a significant negative correlation with depressive symptoms and a positive correlation with manic symptoms was observed. Thus, the nervonic acid levels in the CSF may be a candidate biomarker for mood symptoms.

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Plasma Nervonic Acid Is a Potential Biomarker for Major Depressive Disorder: A Pilot Study

Cited by 51 publications

References 48 publications

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Improved metabolomic data-based prediction of depressive symptoms using nonlinear machine learning with feature selection

Trapped in a Daydream: Daily Elevations in Maladaptive Daydreaming Are Associated With Daily Psychopathological Symptoms

Nervonic acid level in cerebrospinal fluid is a candidate biomarker for depressive and manic symptoms: A pilot study

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