Exploring Enamel Hypoplasia and Metabolic Impacts on Dental Structures in Chronic Kidney Disease: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: The loss of mineral homeostasis in patients with chronic kidney disease causes significant dental problems, including enamel hypoplasia and increased tooth damage. The current clinical treatments show an insufficient ability to resolve these metabolic dental complications. This review aimed to determine the prevalence, severity, and associated metabolic risk factors of enamel hypoplasia and tooth wear in patients with chronic kidney disease (CKD), and to evaluate the effectiveness of current interventions. Methods: The present systematic review and meta-analysis were carried out in line with PRISMA 2020 requirements. Thorough research was conducted as far as 2025. The inclusion criteria included human subjects with CKD who were reporting measurable values of enamel hypoplasia, tooth wear, or biomarkers in saliva. Studies were dismissed on the basis of the animal model, the in vitro studies, and unoriginal data. Eight articles were used. Risk of bias was evaluated by the Newcastle-Ottawa tool, and Meta-analyses were being done under the RevMan 5.4.1 utilizing the random-effects models. Dichotomous outcome variables were pooled into odds ratios (ORs) and 95% confidence intervals (CIs) and continuous outcomes standardized mean differences (SMDs). The measure of heterogeneity was represented by the I2 statistic and was visually illustrated by means of forest plots. Results: In the eight studies identified, the findings were mixed: enamel hypoplasia or mineralization defect, together called CKD-related enamel hypoplasia, had raised DMFT (Decayed, Missing, Filled teeth) index and Developmental Defects of Enamel (DDE) scores in some studies, but not in others. The random-effects pooled analysis proved significant in the difference between the groups, and the compiled odds ratio (OR) was 2.88 (95% CI:1.69 4.91; p < 0.001). Among subgroups of patients with advanced CKD (stage 4 and 5), the effect size was much larger as the OR was 6.05 (95% CI: 2.0118.20; p < 0.001). The result of heterogeneity was about medium (I2 = 65%) in all the studies, but there was no heterogeneity in the subgroup CKD 4 5 (I2 = 0%). Discussion: CKD produces significant, measurable impacts on dental structures through metabolic pathways. The existing evidence demonstrates this connection but heterogeneous data underlines the necessity for standardizing and conducting multi-site research to enhance appropriate preventive and intervention strategies for these high-risk patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".