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Record W3034901998 · doi:10.1111/jdi.13323

Inverse correlation between serum high‐molecular‐weight adiponectin and proinsulin level in a Japanese population: The Dynamics of Lifestyle and Neighborhood Community on Health Study

2020· article· en· W3034901998 on OpenAlexaff
Akinobu Nakamura, Hideaki Miyoshi, Shigekazu Ukawa, Koshi Nakamura, Takafumi Nakagawa, Yasuo Terauchi, Akiko Tamakoshi, Tatsuya Atsumi

Bibliographic record

VenueJournal of Diabetes Investigation · 2020
Typearticle
Languageen
FieldMedicine
TopicAdipokines, Inflammation, and Metabolic Diseases
Canadian institutionsCentre for Family Medicine
FundersJapan Society for the Promotion of Science
KeywordsProinsulinMedicineInternal medicineBody mass indexAdiponectinEndocrinologyConfidence intervalPopulationInsulin resistanceDiabetes mellitusInsulinCorrelation

Abstract

fetched live from OpenAlex

Abstract Serum high‐molecular‐weight adiponectin (HMWA) has a positive correlation with insulin secretion in the Japanese population. To validate this correlation, we investigated the correlation between serum HMWA and proinsulin, a marker of β‐cell dysfunction, in this population. A total of 488 participants (53.9% women) aged 35–79 years not taking oral hypoglycemic agents and/or insulin were enrolled. HMWA was significantly and inversely correlated with proinsulin adjusted for age and sex (partial regression coefficient β = −0.37; 95% confidence interval −0.46 to −0.28). When the participants were divided into two groups by median values of body mass index (23.2 kg/m 2 ), serum insulin (4.3 µU/mL) or homeostasis model assessment of insulin resistance (1.0), similar inverse correlations were observed adjusted for age and sex in both groups. Our results showed that the HMWA level was inversely correlated with the proinsulin level in a general Japanese population.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.269
Teacher spread0.238 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

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