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Relationship between blood levels of visfatin and glycolipid metabolism and mild cognitive impairment in patients with type 2 diabetes

2017· article· en· W3032160380 on OpenAlexaboutno aff
Yan Ma, Lei Wu

Bibliographic record

VenueZhongguo yishi zazhi · 2017
Typearticle
Languageen
FieldNeuroscience
TopicNeurological Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineInternal medicineInsulin resistanceDiabetic retinopathyEndocrinologyType 2 Diabetes MellitusDiabetes mellitusType 2 diabetesMontreal Cognitive AssessmentNicotinamide phosphoribosyltransferaseLogistic regressionInsulinCognitive impairmentDiseaseBiologyBiochemistryNAD+ kinase

Abstract

fetched live from OpenAlex

Objective To explore the relationship between blood levels of visfatin and glycolipid metabolism and mild cognitive impairment (MCI) in patients with type 2 diabetes (T2DM). Methods From February 2016 to January 2017, a total of 91 patients with T2DM were recruited as investigation objects. Cognitive function was measured by Montreal Cognitive Assessment (MoCA), and fasting serum was collected to determine the relevant laboratory indexes. Results In the 91 patients, 50 cases developed MCI. Compared to non-MCI group, MCI group had significant difference in age, total cholesterol, insulin, insulin resistance index, visfatin, MoCA score and diabetic retinopathy (P<0.05). Pearson correlation analysis showed that the MoCA score was negatively correlated with visfatin and insulin resistance index (P<0.05). Further logistic regression analysis showed that age, diabetic retinopathy, insulin resistance index and visfatin were independent risk factors for MCI in T2DM patients. Conclusions MCI in T2DM patients increases with the increasing of elder, diabetic retinopathy, insulin resistance index, and visfatin. Key words: Nicotinamide phosphoribosyltransferase/ME; Glycolipids/ME; Diabetes mellitus, type 2/CO/ME; Cognition disorders/CO/ME

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)0.001
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 score0.009
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.047
GPT teacher head0.274
Teacher spread0.227 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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