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Record W2982302881 · doi:10.1177/0004867419882497

Diet quality, dietary inflammatory index and body mass index as predictors of response to adjunctive <i>N</i> -acetylcysteine and mitochondrial agents in adults with bipolar disorder: A sub-study of a randomised placebo-controlled trial

2019· article· en· W2982302881 on OpenAlexaff
Melanie M. Ashton, Olivia Dean, Wolfgang Marx, Mohammadreza Mohebbi, Michael Berk, Gin S. Malhi, Chee H. Ng, Sue Cotton, Seetal Dodd, Jerome Sarris, Malcolm Hopwood, Keshav Faye-Chauhan, Yesul Kim, Sarah Dash, Felice N. Jacka, Nitin Shivappa, James R. Hébert, Alyna Turner

Bibliographic record

VenueAustralian & New Zealand Journal of Psychiatry · 2019
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsOntario Tech University
FundersNational Health and Medical Research CouncilCRC for Mental HealthStanley Medical Research Institute
KeywordsMedicineBody mass indexDepression (economics)Internal medicinePlaceboAdjunctive treatmentPhysical therapyMass indexRandomized controlled trial

Abstract

fetched live from OpenAlex

Aims: We aimed to explore the relationships between diet quality, dietary inflammatory potential or body mass index and outcomes of a clinical trial of nutraceutical treatment for bipolar depression. Methods: This is a sub-study of a randomised controlled trial of participants with bipolar depression who provided dietary intake data ( n = 133). Participants received 16 weeks adjunctive treatment of either placebo or N-acetylcysteine-alone or a combination of mitochondrial-enhancing nutraceuticals including N-acetylcysteine (combination treatment). Participants were followed up 4 weeks post-treatment discontinuation (Week 20). Diet was assessed by the Cancer Council Victoria Dietary Questionnaire for Epidemiological Studies, Version 2, converted into an Australian Recommended Food Score to measure diet quality, and energy-adjusted dietary inflammatory index score to measure inflammatory potential of diet. Body mass index was also measured. Generalised estimating equation models were used to assess whether diet quality, energy-adjusted dietary inflammatory index score and/or body mass index were predictors of response to significant outcomes of the primary trial: depression symptoms, clinician-rated improvement and functioning measures. Results: In participants taking combination treatment compared to placebo, change in depression scores was not predicted by Australian Recommended Food Score, dietary inflammatory index or body mass index scores. However, participants with better diet quality (Australian Recommended Food Score) reported reduced general depression and bipolar depression symptoms ( p = 0.01 and p = 0.03, respectively) and greater clinician-rated improvement ( p = 0.02) irrespective of treatment and time. Participants who had a more anti-inflammatory dietary inflammatory index had less impairment in functioning ( p = 0.01). Combination treatment may attenuate the adverse effects of pro-inflammatory diet ( p = 0.03) on functioning. Participants with lower body mass index who received combination treatment ( p = 0.02) or N-acetylcysteine ( p = 0.02) showed greater clinician-rated improvement. Conclusion: These data support a possible association between diet (quality and inflammatory potential), body mass index and response to treatment for bipolar depression in the context of a nutraceutical trial. The results should be interpreted cautiously because of limitations, including numerous null findings, modest sample size and being secondary analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.271
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2019
Admission routes1
Has abstractyes

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