ArticlePloS one2024
Machine learning assessment of vildagliptin and linagliptin effectiveness in type 2 diabetes: Predictors of glycemic control.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveDifferential effects of linagliptin and vildagliptin may help us personalize treatment for Type 2 Diabetes Mellitus (T2DM). The current study compares the effect of these drugs on glycated hemoglobin (HbA1c) in an artificial neural network (ANN) model.
methodsPatients with T2DM who received either vildagliptin or linagliptin, with predefined exclusion criteria, qualified for the study. Two input variable datasets were constructed: with or without imputation for missing values. The primary outcome was HbA1c readings between 3 to 12 months or the reduction in HbA1c levels.
resultsThe cohort comprised 191 individuals (92 vildagliptin and 99 linagliptin). Linagliptin group had significantly higher disease burden. For imputed dataset, HbA1c was lower with linagliptin at 3 to 12 months (7.442 ± 0.408 vs. 7.626 ± 0.408, P < 0.001). However, there was a small yet significant difference in HbA1c reduction favoring vildagliptin over linagliptin (-1.123 ± 0.033 vs. -1.111 ± 0.043, P < 0.001). LDL level, uric acid, and the drug group were identified as predictors for HbA1c levels. In the non-imputed dataset HbA1c at 3 to 12 months was lower with linagliptin (median ± IQR: 7.489 ± 0.467 vs. 7.634 ± 0.467, P-value < 0.001). However, both linagliptin and vildagliptin exhibited similar reductions in HbA1c levels (both median ± IQR of -1.07 ± 0.02). Predictors for HbA1c levels included eGFR level and the drug group.
conclusionLinagliptin effectively lowers HbA1c levels more than vildagliptin including in patients with comorbidities. DPP4-I choice is a constant predictor of HbA1c in all models.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.