Closing the Evidence Gaps in Trimester-Specific Pregnancy Reference Intervals for Biochemical Markers of Health and Disease
Bibliographic record
Abstract
BACKGROUND: Pregnancy induces a series of physiological adaptations, making accurate interpretation of clinical laboratory tests essential for maternal and fetal health. However, significant knowledge gaps persist in understanding how gestation affects circulating blood biochemistry, leading to potential clinical misinterpretations and underscoring the need for updated reference intervals in contemporary obstetric populations. METHODS: A cohort of approximately 135 healthy pregnant individuals was recruited with informed consent. Participants were excluded based on the use of prescribed medications, multiple gestations, age above 40 years, a history of chronic illness, or presence of acute illness during pregnancy. Blood samples were collected at 4 time points: 0 to 13 weeks, 14 to 27 weeks, 28 to 42 weeks, and 1 to 3 months postpartum. A total of 24 biochemical markers were analyzed. Statistically significant differences across gestational and early postpartum periods were assessed, and trimester-specific reference intervals were established. RESULTS: Of the 24 biochemical markers assessed, 17 exhibited statistically significant variations across gestation and the early postpartum period. Notably, albumin, total protein, creatinine, urea, uric acid, free thyroxine (FT4), and anemia markers showed significant decreases in late gestation compared to postpartum. Conversely, alkaline phosphatase, cholesterol, and triglycerides demonstrated substantial increases during gestation, followed by a marked decrease in the postpartum period. CONCLUSION: This study provides comprehensive biochemical profiling of healthy pregnant individuals in Canada across gestation and the postpartum period. The findings highlight substantial alterations in circulating blood biochemistry during pregnancy, emphasizing the need for trimester-specific reference intervals to ensure accurate clinical test interpretation. Without such gestational age-specific benchmarks, maternal-fetal care standards remain suboptimal.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".