Non-obese population with the rs7903146 T allele exhibits higher sugar and more dyslipidaemia in subjects with Type-2-diabetes
Bibliographic record
Abstract
ABSTRACT Type 2 diabetes (T2D), the most prevalent type of diabetes has been associated with Transcription-Factor-7-Like-2 gene Single Nucleotide Polymorphisms (SNPs), rs12255372 and rs7903146 as risk factors, thought to be modulated by obesity status. In sub-Saharan Africa, the onset of T2D in the non-obese is rarely suspected. This study looks into the genetics and the biochemical parameters in non-obese population, with and without T2D and living in Jos, Nigeria. A total of 68 subjects, 40 diabetic patients and 28 healthy control group, all with closely matched age, height, nutrition, family history, Body Mass Index and socioeconomic status, recruited from within the same population were studied. SNPs Genotyping were performed using Polymerase Chain Reaction and Sangers Sequencing. Lipid profiles, Fasting Blood Sugar and C-peptide levels were measured and analysed alongside with demographic data from questionnaire. Odd-ratio at 95% confidence interval at a conventional level of alpha, <0.05 and Product Moment Correlation Coefficient Analysis were used to analyse the data in both groups. The entire population showed the GG genotype for the rs12255372. However, different genotype combination, CC, CT and TT were observed with the rs7903146. Though no significant association was observed between the genotypes and the odd of T2D, healthy subjects with the T allele showed a higher level of two hours postprandial plasma glucose level than those with CC genotype. Patients with T allele shows a more abnormal level of diabetes metabolic syndrome indicators such as Fasting Blood Sugar; two hours postprandial plasma glucose level; C-peptide; Low Density Lipoprotein, High Density Lipoprotein and Total Cholesterol. The study suggests that lower sugar metabolism and more dyslipidaemia are observed in subject with T allele. Hence, this could constitute poorer prognosis and a risk factor for non-obese population, particularly with high carbohydrate intake.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".