Bibliographic record
Abstract
This issue features five independent studies examining aspects of iron status in blood donors from around the developed world. Each adds to the inevitable conclusion that the strongest predictors of iron deficiency (as measured by a ferritin concentration below some cutoff level) are sex and menopausal status (variables that could only be addressed by avoiding recruitment from premenopausal women), donation frequency, and time since last donation, which are direct results of our recruitment strategies. For example, the Danish study of Rigas and colleagues1 used multiple variable regression analysis of donation frequency, physiologic factors (sex and menstruation status), lifestyle (including alcohol and smoking status), and iron consumption (vitamin or iron supplements and dietary intake). The study included a questionnaire with detailed inquiries about smoking, alcohol, and coffee consumption as well as nutritional supplements and dietary history in more than 14,000 participants with completed questionnaires and measured values for both hemoglobin (Hb) and ferritin. Similar to observations of the US REDS II RISE study, the strongest predictors of a low ferritin (defined as <15 ng/mL ferritin for both sexes) were sex, menopausal status, donation frequency, and time since last donation. Dietary and iron supplementation factors were much weaker predictors confirming the impression that simply recommending additional meat intake in frequent blood donors is unlikely to achieve higher ferritin levels. This observation was independently confirmed by a poster presentation by Steele and coworkers that documented minimal awareness that repeated blood donation leads to iron deficiency. Many repeat donors already take iron-containing supplements but most confused the Hb screening test with a measure of their iron status.2 Among the most frequent blood donors (nine or more donations in 3 years) iron deficiency was found in approximately one in 10 males, four in 10 premenopausal females, and two in 10 postmenopausal female donors, which were approximately 20-fold higher frequencies than for infrequent male and postmenopausal female donors (not more than three whole blood donations in 3 years) and twofold higher for premenopausal females. In short, iron deficiency is quite uncommon in infrequent male and postmenopausal female blood donors but dramatically more frequent among regular donors. Indeed, the authors observed that “there were almost no iron-deficient men unless they had been bled at least seven times in a 3-year period.” While iron deficiency among premenopausal females is common, its frequency and magnitude are significantly exacerbated by frequent donation. Iron deficiency occurred despite the practice of providing iron supplements to premenopausal female Danish donors, one-third of whom confirmed receiving the iron supplements.1 While meat intake had a modest positive correlation with ferritin levels, the correlation coefficient was low. Relatively few of the participants were vegetarian of whom one-third had low ferritin levels. The Australian study by Booth and colleagues3 focuses on dietary intake of 184 premenopausal women, 165 regular blood donors, and 19 first-time donors. The study used a validated dietary history tool with an extensive list of iron-containing foods updated for availability in the Australian market. Despite an almost 25% higher mean daily dietary iron intake by regular blood donors, the frequency of depletion of iron stores (ferritin ≤ 15 ng/mL) was 50% as opposed to 24% among new donors. The median serum ferritin was 73% higher in the new donor group, despite no difference in Hb levels. They also observed a donation frequency–dependent negative correlation with ferritin level. Of note, in Australia, the minimum Hb level for females is 12 g/dL, and current practice for those not meeting this cutoff level is deferral from blood donation for 6 months to allow for regeneration of iron stores. The study by Baart and coworkers uses multivariate logistic regression to validate a predictive model for Hb deferral for both sexes.4 They use a model developed by Sanquin, using the EU-specific donor cutoffs of 13.5 g/dL for males and 12.5 g/dL for females. The Irish national transfusion service uses the lower cutoffs of 13 and 12 g/dL for males and females, respectively. The variables include sex, age, seasonality, Hb at previous visit, difference in Hb since previous visit, time since previous visit, total number of donations in the previous 2 years, and deferral at the prior visit. It is my understanding that the intent of this model is to have a tool to better predict which donation attempts might end in low Hb deferral with the goal of decreasing deferrals, both for operational efficiency and to prevent donor attrition from repeated deferrals. The article uses various statistical manipulations to improve the predictive value of the models but even the idealized model is only half way between a coin flip and a perfect predictor. While not practical on a real-time basis such models do allow insight into public policies to assist in reducing deferral rates. Perhaps most relevant to the US audience is that using a deferral Hb cutoff of 12 and 13 g/dL for females and males, respectively, resulted in deferral rates of 2.4 and 8.4% as opposed to US REDS II data of approximately 1.7% (male) and 17.7% (female) using a cutoff of 12.5 for both sexes.5 Since multiple studies document that even a single deferral results in lower return rates it is clear that the lower cutoff for females defers dramatically fewer females while the higher cutoff for males has far less impact on donor availability. The study of Canadian blood donors by Goldman and colleagues6 compared iron deficiency among 550 successful donors versus 50 donors deferred for low Hb (<12.5 g/dL for both males and females). Ninety percent of eligible donors agreed to enroll in the study and completed an interview about effects of donation on health and had ferritin levels measured. Results confirmed the dramatically higher rate of low ferritin levels in regular blood donors. Furthermore, they noted that among donors qualified by repeating an initial borderline low Hb level, approximately 90% had ferritin levels below 25 ng/mL. They also confirmed the poor correlation (r = 0.24) between Hb level and ferritin level among those passing the minimum Hb level. Finally, they confirmed that iron deficiency among infrequent male donors is rare but much more prevalent among frequent donors including even those passing the Hb screen. The interview confirmed that the vast majority of donors are not aware of the correlation between blood donation with low iron stores, nor do most donors share their blood donation history with their primary health care provider. So much for defining the source and prevalence of the problem; the next step is what to do about it. Again, the Australian study of Marks and coworkers7 has confirmed the logical conclusion that giving iron-depleted donors iron improves their iron status. The Australians have a 12 g/dL eligibility for females but the minimum interdonation interval is already 12 weeks. They have already implemented a donor iron strategy of measuring ferritin levels on those not passing the minimum Hb or having a 2 g/dL Hb decrease between donations. Females with less than 15 ng/mL and males with less than 30 ng/mL are considered iron deficient and may be deferred for an extended period or referred for additional medical evaluation. The study randomized 282 premenopausal (age 18-45 years) female whole blood donors to a double-blind study of an 8-week postdonation course of carbonyl iron (45 mg of elemental iron) daily versus placebo. Ferritin levels were measured at baseline and after 12 weeks, which is the first possible return point. Hb and eligibility upon return were measured. Of note 50% of the donors were noted to be iron deficient upon enrollment, which confirms that simply lengthening the donor interval to 12 weeks will not solve the donor iron depletion dilemma. There was 85% self-reported compliance with the therapy in both therapeutic and placebo arms with a similar number in each group dropping out due to side effects, documenting the utility of having a blinded placebo control arm to evaluate drug side effects! The only side effect that was significantly increased in the treatment arm was, not surprisingly, “darkened bowel motions.” The ferritin levels and Hb levels of the treated arm were higher and subsequent deferral rate lower for the treated arms, but in fact the carbonyl iron only replaced that which was removed and did not replenish stores; these conclusions were reached since the pre- and posttreatment ferritin levels were statistically unchanged in the treated arm, whereas the placebo arm had both decreasing ferritin and Hb levels. This study documents that a low-dose but sustained 8-week therapy can replace the donated iron with minimal side effects. Might inducing iron deficiency have greater clinical significance for a subset of donors? Basic science studies in pregnant mice document that making mice iron deficient during critical periods of brain development yield pups with diminished neuronal connections and those neuronal connections are not developed as well, even if the pups are iron replaced after birth.8 This deficiency has measurable effects on objective measures of learning in the offspring. The implication of these studies is that we should be cautious about depleting iron stores in those individuals likely to become pregnant after blood donation. An adequate blood supply is determined by both supply and demand. Ongoing efforts in blood management have, for the time being, diminished demand and have assured an adequate supply until at least the demands of our aging population overshadow the changes in transfusion practice that are diminishing usage. Rational changes in donor eligibility (such as decreasing the Hb cutoff in females to 12 g/dL and increasing to 13 g/dL in males) and helping prevent iron deficiency in regular donors by lengthening the donation interval and providing supplemental iron will help ensure future supply. In short, we the blood collection agencies are biting the hand that feeds us by depleting our greatest asset, our committed donor base, and it has always been our clinical and ethical duty to care for that resource to the best of our abilities. These steps include not pushing donors to donate more frequently that their iron stores can sustain and availing ourselves of strategies such as direct iron replacement in regimens that improve iron status with minimal side effects. Alternative strategies to identify iron deficiency among donors at time of donation, such as measuring zinc protoporphyrin levels, are currently under way, which may simplify targeting donors most in need of iron replacement. The author reports no conflicts of interest or funding sources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".