A SYSTEMATIC REVIEW OF ENVIRONMENTAL TOXINS AND HORMONAL IMBALANCE
Bibliographic record
Abstract
Background: Environmental toxins, particularly endocrine-disrupting chemicals (EDCs), have been increasingly implicated in the dysregulation of hormonal systems, contributing to a growing burden of endocrine disorders. Despite mounting concern, the literature remains fragmented with inconsistencies in exposure definitions and outcome measures, highlighting the need for a consolidated evidence base to clarify these associations. Objective: This systematic review aimed to evaluate and synthesize current evidence on the relationship between environmental toxin exposure and hormonal imbalance in human populations, with a focus on mechanisms, health outcomes, and clinical relevance. Methods; A systematic review was conducted in accordance with PRISMA guidelines. Four databases (PubMed, Scopus, Web of Science, and Cochrane Library) were searched for articles published between 2018 and 2024 using a combination of terms related to “environmental toxins,” “endocrine disruptors,” and “hormonal imbalance.” Studies were included if they examined human populations and reported hormonal or endocrine-related outcomes. Risk of bias was assessed using the Newcastle-Ottawa Scale and the Cochrane Risk of Bias Tool, and data were synthesized qualitatively due to methodological heterogeneity. Results: Eight studies met the inclusion criteria. The findings consistently demonstrated that exposure to environmental toxins such as heavy metals, pesticides, and industrial chemicals is associated with epigenetic changes, reproductive hormone disruption, and increased risk of conditions such as PCOS and hormone-sensitive cancers. Notably, synergistic effects from multiple contaminants were also identified. While evidence was moderate to strong across studies, variability in design and exposure measurement limited the ability to conduct meta-analysis. Conclusion: This review supports a clear link between environmental toxin exposure and hormonal imbalance, underscoring the need for clinical awareness and public health policies that minimize exposure risks. Future research should prioritize longitudinal studies with standardized exposure metrics to better establish causality and guide regulatory action.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".