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Record W3133264773 · doi:10.2147/ndt.s295026

Exploring the Impact of Modifiable Factors on Serum BDNF in Psychiatric Patients and Community Controls

2021· article· en· W3133264773 on OpenAlexaff
Galen Chan, Tea Rosic, Stanislav Pasyk, Mahshid Dehghan, Zainab Samaan

Bibliographic record

VenueNeuropsychiatric Disease and Treatment · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsImpactPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsMedicineConfidence intervalDepression (economics)Body mass indexPsychiatryBrain-derived neurotrophic factorInternal medicineSchizophrenia (object-oriented programming)Cross-sectional studyCase-control studyNeurotrophic factors

Abstract

fetched live from OpenAlex

Background: Brain-derived neurotrophic factor (BDNF) has been a focus of psychiatric research for the past two decades. BDNF has been shown to impact neural function and development. Studies have investigated serum BDNF as a biomarker for psychiatric disorders such as depression and schizophrenia. In some studies, investigators attempt to control for variables such as smoking status, exercise, or diet. However, the relationship between these factors and BDNF is not clearly established. Furthermore, some studies have questioned whether a difference in the impact of BDNF exists between psychiatric and healthy populations. Purpose: We aim to examine the association between serum BDNF levels and modifiable risk factors such as body mass index (BMI), smoking, exercise levels, and diet. Subsequently, we aim to examine whether the relationship between these risk factors and serum BDNF is different between psychiatric and control populations. Patients and Methods: We use cross-sectional data from an age- and sex-matched case–control study of participants with psychiatric inpatients and community controls without psychiatric diagnoses. Participants completed comprehensive assessments at study enrolment including sociodemographic information, smoking status, exercise, diet, and BMI. Serum BDNF levels were collected from participants. Linear regression analysis was performed to determine the association between modifiable factors and serum BDNF level. Results: A significant association was found between sedentary activity level and lower serum BDNF levels (Beta coefficient = – 2.49, 95% confidence interval [CI] – 4.70, – 0.28, p = 0.028). Subgroup analysis demonstrated that this association held for psychiatric inpatients but not for community controls; it also held in females (Beta coefficient = – 3.18, 95% CI – 6.29, – 0.07, p = 0.045) but not in males (Beta coefficient = – 1.42, 95% CI – 4.61, 1.78, p = 0.383). Antidepressant use had a significantly different association between male (Beta coefficient = 3.20, 95% CI 0.51, 5.88, p = 0.020) and female subgroups (Beta coefficient = – 3.10, 95% CI – 5.75, – 0.46, p = 0.022). No significant association was found between other factors and serum BDNF. Conclusion: Sedentary activity level may lead to lower serum BDNF levels in individuals with psychiatric diagnoses. Our findings support the notion that physical activity can provide a positive impact as part of treatment for psychiatric illness. Keywords: BDNF, psychiatric disorders, physical activity, diet, behavioral risk factors, depression

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.079
GPT teacher head0.287
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2021
Admission routes1
Has abstractyes

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