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Record W2517295226 · doi:10.5152/npa.2016.11319

Serum Leptin Levels and Cognition in Parkinson’s Disease Patients

2016· article· en· W2517295226 on OpenAlexaboutno aff
Gülay Kenangıl, Betül Özdilek

Bibliographic record

VenueNöro Psikiyatri Arşivi · 2016
Typearticle
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsnot available
Fundersnot available
KeywordsMontreal Cognitive AssessmentLeptinMedicineInternal medicineMorningWaistBody mass indexRating scaleParkinson's diseaseCognitionAdipokineGastroenterologyEndocrinologyObesityDiseasePhysical therapyCognitive impairmentPsychologyPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: To investigate the relationship between serum leptin levels and cognition in Parkinson's disease (PD) patients. METHODS: Thirty patients with idiopathic PD and 30 healthy controls were enrolled. At baseline, all patients had their standing height, weight, and waist circumference measurements taken using a standard scale. Their body mass index was then calculated. A fasting blood of 5 ml was obtained from each patient in the morning. ELISA was used to analyze leptin concentrations. The severity of PD was evaluated using the Hoehn and Yahr scale, and the clinical status of patients was evaluated using the Unified Parkinson's Disease Rating Scale. The cognitive status of whole patients was evaluated using a validated form of the Montreal Cognitive Assessment Scale in Turkey (MoCA-TR). RESULTS: The mean ages of the patients and controls were 59.37±9.22 and 58.50±9.85 years, while the mean leptin levels were 4.13±3.61 and 3.12±2.43 ng/mL, respectively. Leptin levels did not differ between PD patients and the controls. PD patients had significantly lower MoCA-TR scores than the controls (p=0.028). MoCA-TR scores were not correlated to leptin levels in PD patients. CONCLUSION: In this study, we could not find a relationship between blood leptin levels of PD patients and cognition as assessed by MoCA-TR. Larger longitudinal studies are needed.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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 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

Citations6
Published2016
Admission routes1
Has abstractyes

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