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Record W3214486785 · doi:10.3389/fendo.2021.739257

Elevated Fasting Plasma Glucose Is Associated With an Increased Risk of MCI: A Community-Based Cross-Sectional Study

2021· article· en· W3214486785 on OpenAlexaboutno aff
Wei Li, Ling Yue, Lin Sun, Shifu Xiao

Bibliographic record

VenueFrontiers in Endocrinology · 2021
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersShanghai Jiao Tong UniversityScience and Technology Commission of Shanghai MunicipalityChinese Academy of SciencesShanghai Clinical Research CenterNational Natural Science Foundation of China
KeywordsMedicineMontreal Cognitive AssessmentDementiaLogistic regressionInternal medicineRisk factorCohortCross-sectional studyCognitive declineCognitionCognitive impairmentCohort studyGerontologyPsychiatryDiseasePathology

Abstract

fetched live from OpenAlex

Background: Mild cognitive impairment (MCI) is a transitional state between normal elderly people and dementia, with a higher risk of dementia transition. The primary purpose of the current study was to investigate whether routine blood and blood biochemical markers could be used to predict the onset of MCI. Methods: Data was obtained from the cohort study on brain health of the elderly in Shanghai. A total of 1015 community elders were included in the current study. Based on clinical evaluation and the scores of Montreal Cognitive Assessment (MoCA), these participants were divided into the MCI (n=444) and cognitively normal groups (n=571). Then we tested their fasting blood routine and blood biochemical indexes, and collected their general demographic data by using a standard questionnaire. Results: By using binary logistic regression analysis and the ROC curve, we found that elevated fasting plasma glucose (p=0.025, OR=1.118, OR=1.014-1.233) was a risk factor for MCI. Conclusions: Elevated fasting blood glucose may be a risk factor for mild cognitive impairment, but the above conclusions need to be verified by longitudinal studies.

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)0.001
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 score0.031
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.323
Teacher spread0.294 · 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

Citations13
Published2021
Admission routes1
Has abstractyes

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