Metabolic, Anthropometric, and Type 2 Diabetes Mellitus Related Risk Factors in Normal and Pre-Diabetic Adults
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) is a major global health problem. The present study examines the relationship between the metabolic, anthropometric and Finnish risk score (FINDRISC) among normal and pre-diabetic adults. Subjects (n = 1319, aged above 18 years) from the Qatari population were classified into two groups based on their hemoglobin A1c (HbA1c) measurements (non-diabetic A1c<5.6% and pre-diabetic 5.6% ≤ A1c ≤ 6.4%) were examined for their anthropometric (height, weight and waist circumference), metabolic [fat, fat free mass (FFM), muscle mass (MM), total body water (TBW), bone mass, degree of obesity, basal metabolic rate (BMR), body mass index (BMI), metabolic age, visceral fat rating, systolic blood pressure (SBP), diastolic blood pressure (DBP), total cholesterol (Total-C), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides (TG), fasting / random plasma glucose (FPG / RPG), HbA1c and vitamin D (VitD)] and FINDRISC. Means and frequencies were determined in aggregate and by subgroups for all variables and correlations between categorical variables were tested to estimate the association between the anthropometric and metabolic risk factors with the FINDRISC. A percentage of 74.8% (n = 987) of the study population aged below 45 years old and their overall BMI was 28.8±5.2kg/m2 (overweight). Pre-diabetic subgroup have shown a statistically higher FINDRISC compared to their non-diabetic counterparts (11.2±4.1 vs. 9.8±4, p<0.001). The FINDRISC was significantly and directly correlated with the BMI, HbA1c and FPG. However, HbA1c was correlated directly with BMI, SBP, DBP, FPG / RPG and indirectly with the levels of HDL. This study demonstrates an apparent relationship between the HbA1c and FINDRISC score. Pursuing further research on this association may permit using HbA1c with the FINDRISC in predicting the risk of T2DM to be a better tool rather than using the current FPG/RPG, OGTT methods.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".