Correlation between PPARG Pro12Ala Polymorphism and Therapeutic Responses to Thiazolidinediones in Patients with Type 2 Diabetes: A Meta-Analysis
Bibliographic record
Abstract
Background: Thiazolidinediones (TZDs) are a type of oral drug that are utilized for the treatment of type 2 diabetes mellitus (T2DM). They function by acting as agonists for a nuclear transcription factor known as peroxisome proliferator-activated receptor-gamma (PPAR-γ). TZDs, such as pioglitazone and rosiglitazone, help enhance the regulation of metabolism in individuals with T2DM by improving their sensitivity to insulin. Previous studies have suggested a relationship between the therapeutic efficacy of TZDs and the PPARG Pro12Ala polymorphism (C > G, rs1801282). However, the small sample sizes of these studies may limit their applicability in clinical settings. To address this limitation, we conducted a meta-analysis assessing the influence of the PPARG Pro12Ala polymorphism on the responsiveness of TZDs. Method: We registered our study protocol with PROSPERO, number CRD42022354577. We conducted a comprehensive search of the PubMed, Web of Science, and Embase databases, including studies published up to August 2022. We examined studies investigating the association between the PPARG Pro12Ala polymorphism and metabolic parameters such as hemoglobin A1C (HbA1C), fasting plasma glucose (FPG), triglyceride (TG), low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and total cholesterol (TC). The mean difference (MD) and 95% confidence intervals (CIs) between pre- and post-drug administration were evaluated. The quality of the studies included in the meta-analysis was assessed by using the Newcastle–Ottawa Scale (NOS) tool for cohort studies. Heterogeneity across studies was assessed by using the I2 value. An I2 value greater than 50% indicated substantial heterogeneity, and a random-effects model was used for meta-analysis. If the I2 value was below 50%, a fixed-effects model was employed instead. Both Begg’s rank correlation test and Egger’s regression test were performed to detect publication bias, using R Studio software. Results: Our meta-analysis incorporated 6 studies with 777 patients for blood glucose levels and 5 studies with 747 patients for lipid levels. The included studies were published between 2003 and 2016, with the majority involving Asian populations. Five of the six studies utilized pioglitazone, while the remaining study employed rosiglitazone. The quality scores, as assessed with the NOS, ranged from 8 to 9. Patients carrying the G allele exhibited a significantly greater reduction in HbA1C (MD = −0.3; 95% CI = −0.55 to −0.05; p = 0.02) and FPG (MD = −10.91; 95% CI = −19.82 to −2.01; p = 0.02) levels compared to those with the CC genotype. Furthermore, individuals with the G allele experienced a significantly larger decrease in TG levels than those with the CC genotype (MD = −26.88; 95% CI = −41.30 to −12.46; p = 0.0003). No statistically significant differences were observed in LDL (MD = 6.69; 95% CI = −0.90 to 14.29; p = 0.08), HDL (MD = 0.31; 95% CI = −1.62 to 2.23; p = 0.75), and TC (MD = 6.4; 95% CI = −0.05 to 12.84; p = 0.05) levels. No evidence of publication bias was detected based on Begg’s test and Egger’s test results. Conclusions: This meta-analysis reveals that patients with the Ala12 variant in the PPARG Pro12Ala polymorphism are more likely to exhibit positive responses to TZD treatment in terms of HbA1C, FPG, and TG levels compared to those with the Pro12/Pro12 genotype. These findings suggest that genotyping the PPARG Pro12Ala in diabetic patients may be advantageous for devising personalized treatment strategies, particularly for identifying individuals who are likely to respond favorably to TZDs.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.052 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".