Prospective Evaluation of Fetal Haemoglobin Induction in Maternal Erythrocytes: A Preliminary Analysis of a Cohort of 345 Parturients
Bibliographic record
Abstract
Abstract Introduction. The mechanisms of haemoglobin (Hb) subtype switch remain largely unknown even though it has a therapeutic potential (Bauer et al., 2012). In fact, reactivation of fetal haemoglobin (HbF) production in adult erythrocytes (F-cells) is protective in many Hb disorders. Pregnancy may represent the only physiological condition where HbF production increases transiently (Ibrahim et al, 2009; Yamada et al., 2012). Moreover, flow cytometry (FC), by intracellular staining using anti-HbF antibodies, has improved the evaluation of maternal F-cells (Corcoran et al., 2014; Chen et al., 2000). In this study, the objectives are: 1) to document HbF expression prospectively peri- and post-partum and confirm its maternal origin; 2) to assess potential causes of HbF modulation throughout pregnancy (i.e. β-hCG, EPO) and; 3) to identify gene differences between HbF-positive and HbF-negative pregnant women. Methods. In this observational, single institution, prospective study, 879 pregnant women were screened. A total of 345 participants were included in the study: the first consecutive 176 women with negative total HbF expression (<1%), and the first consecutive 169 women with positive total HbF expression (≥1%). Hb studies were conducted with high performance liquid chromatography (HPLC), and FC was performed if total HbF expression was ≥1%. Other assessments included DNA sampling, complete blood counts, β-hCG and EPO levels, and qualitative data (i.e. to account for other factors such as tobacco, progesterone use, etc.). Analyses were conducted at each trimester except DNA sampling, which was done during the first visit only. HbF-positive women had an additional post-partum visit to assess changes of HbF expression and account for hereditary persistence of HbF. Descriptive statistics and Pearson correlations were planned for this analysis. Results. At the time of analysis, 93 HbF-positive and 147 HbF-negative women had completed follow-up. Results revealed that, among all pregnant women screened, 22.07% (n = 194) had positive HbF expression. Of this percentage, 90.72% (n = 176) filled the inclusion criteria. Of all women with positive HbF expression during 1 or more of the first 3 visits, 72.04% showed a post-partum decrease of HbF below 1%. Furthermore, FC analyses in HbF-positive women confirmed the maternal origin of HbF in all cases and revealed an average of 13.28% (sd = 5.38%) F-cells when total HbF was ≥ 1%. Only modest correlations could be found so far between HbF levels and β-hCG (ρ = 0.25) or EPO (ρ = 0.07). Comparative DNA analyses of HbF-positive and HbF-negative women are currently underway and will be presented at the meeting. Conclusions. To our knowledge, this represents the largest prospective study evaluating HbF expression throughout pregnancy, as well as the first to evaluate post-partum HbF dynamics and the genetic background of HbF-negative and HbF-positive pregnant women. Preliminary data shows that: 1) a significant percentage of pregnant women have a transient elevation of HbF which cannot be explained by fetomaternal hemorrhage nor hereditary persistence of HbF; 2) the proportion of F-cells in women remains constant peri- and post-partum when HbF ≥ 1%; 3) a large proportion of maternal erythrocytes express HbF at low levels and; 4) HbF modulation during pregnancy is only modestly correlated to β-hCG and EPO. These data may lead to the study of a new paradigm of Hb subtype switch as well as HbF-induction therapeutics for the benefit of Hb disorders. Disclosures No relevant conflicts of interest to declare.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".