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Autoantibodies to GAD and IA‐2 in Saudi Arabian diabetic patients

2005· article· en· W2047713271 on OpenAlexaff
L. H. Damanhouri, James A. Dromey, M. R. Christie, H. Nasrat, M. Ardawi, R A Robins, Ian Todd

Bibliographic record

VenueDiabetic Medicine · 2005
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiabetes and associated disorders
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsMedicineAutoantibodyInternal medicineGestational diabetesType 1 diabetesDiabetes mellitusDiseaseIsletAntibodyGastroenterologyInsulinImmunologyEndocrinologyPregnancyGestation

Abstract

fetched live from OpenAlex

AIMS: To determine the prevalence of autoantibodies in sera of Saudi diabetic patients including Type 1 and Type 2 diabetes mellitus (DM) and gestational diabetes mellitus (GDM) living in Jeddah, Saudi Arabia. Apart from data on the prevalence of islet-cell antibodies in patients in Ryhadh (Al-Attas et al. Frequency of islet cell antibodies in adult newly diagnosed diabetic patients. Ann Saudi Med 1990; 10: 369-373) immunological markers of autoimmune diabetes have not been explored in Saudi Arabians. METHODS: Autoantibodies to GAD65 (GADA) and IA-2 (IA-2A) were determined using radio-immunoprecipitation assays. RESULTS: In Type 1 DM patients, 54% were GADA+ and 27% were IA-2A+. A greater negative effect of disease duration was noted for IA-2A than for GADA positivity. Autoantibodies were more prevalent with younger age of onset. GADA were slightly more common in female Type 1 DM patients. In Type 2 DM, 8/99 patients were GADA+, and three of these patients with shorter disease duration were also IA-2A+. GADA, and particularly IA-2A, were associated with a younger age of onset of Type 2 DM and all the autoantibody-positive Type 2 DM patients were insulin-treated. GADA were detected in 2.2% of GDM patients, but none of these patients possessed IA-2A. CONCLUSIONS: The prevalence and associations of autoantibodies in Saudi diabetic patients are very similar to those reported for diabetic patients in other ethnic groups.

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 score0.082
Threshold uncertainty score0.654

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 head0.005
GPT teacher head0.242
Teacher spread0.237 · 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

Citations35
Published2005
Admission routes1
Has abstractyes

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