A Cross-Sectional Study on Prevalence and Pattern of Dyslipidemia and Its Associated Factors among Patients with Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background and Aim: Diabetes mellitus type 2 (T2DM) and dyslipidemia are growing health issues. Multidimensional dyslipidemia is a characteristic of diabetes. Diabetes mellitus and dyslipidemia are comorbid conditions associated with an increased risk of cardiovascular disease. The aim of the present study was to determine the prevalence and pattern of dyslipidemia in T2DM patients. Material and Methods: This cross-sectional study was conducted on 426 type 2 diabetes mellitus patients in the Department of Family Medicine and General Surgery, Hayatabad Medical Complex, Peshawar for the duration from May 2022 to October 2022. Prior to study conduction, ethical approval was taken from institute research and ethical committee. Patient’s demographic details, laboratory findings, medications, and clinical features were recorded. Dyslipidemia was analyzed as categorical variables described as frequency and percentages. Laboratory findings included Low-density lipoprotein cholesterol (LDL-C), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and total cholesterol (TC). SPSS version 26 was used for data analysis. Results: Of the total 426 T2DM patients, there were 224 (52.6%) male and 202 (47.4%) females. The incidence of dyslipidemia was 92.6% among T2DM patients. Based on dyslipidemia patterns, the incidence of low HDL-C, hypertriglyceridemia, and high LDL-C were 72.4%, 62.6%, and 68.8% respectively. Diabetic dyslipidemia patterns were significantly associated with gender, hypertension, and obesity. Hypercholesterolemia and high LDL-C were significantly associated with poor glycemic control and duration of T2DM. Smoking and reduced glycemic control was related with hypertriglyceridemia. Conclusion: It has been found that the incidence of dyslipidemia among T2DM patients was 92.6%. Patients with type 2 diabetes are most likely to have low HDL-C and high triglycerides. It is strongly recommended to provide educational programs emphasizing the significance of adopting a healthy routine. Keywords: Dyslipidemia, Pattern, Type 2 diabetes mellitus
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".