The Frequency of Abnormally High Liver Enzymes and their Connection to Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Objective: The purpose of this research is to measure liver enzyme activity in Pakistani people with type 2 diabetes. Study Design: Cross sectional Place and Duration: The study was conducted in department of Pathology Women Medical& Dental College Abbottabad from January 2022 to June 2022. Methods: 400 people were enlisted for the study (200 in the healthy control group I, and another 200 with type 2 diabetes). Alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were assessed, as was body mass index (BMI). We also checked total protein (TP), albumin, and fasting blood glucose (FBG), and we tested glycosylated haemoglobin (HbA1c). Averages and standard deviations were used to describe quantitative data, whereas frequencies were used to characterize qualitative information. Tests for associations, significance, and correlations between groups were conducted using the Pearson/Spearman correlation test, the unpaired t-test, and the Chi-squared test. Statistical significance was defined as a P-value less than.05. Each piece of information was put via SPSS 24.0 for statistical analysis. Results: Among 200 patients of Type 2 DM females were higher in numbers than males. Type 2 DM patients had a mean age of 48.8 years (SD 11.12), whereas the healthy population had a mean age of 40.6 years (SD 11.3) (P .0001). AST activity in Type 2 DM was found to be similar to that of healthy individuals (P =.060). Type 2 DM is associated with elevated levels of ALT, total bilirubin, and alkaline phosphatase when compared to healthy controls (P .0001). Hepatic enzyme levels were abnormal in 60% of those with type 2 diabetes compared to 30% of those without the disease. Conclusion: Liver function tests (LFTs) were shown to be significantly abnormal in the diabetic community as a whole, demonstrating widespread co-existing derangements. It is possible that early diagnosis and therapy might benefit from a thorough workup in such patients. Furthermore, reducing liver-related morbidity and mortality in Type 2 DM would benefit from early diagnosis and therapy of aberrant liver parameters. Keywords: Liver Hepatic pathology, Diabetes mellitus type 2, Liver function tests
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 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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".