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Record W4389248647 · doi:10.1182/blood-2023-174774

Evaluating Sex-Based Hemoglobin Reference Intervals Using Strict Definitions of Health

2023· article· en· W4389248647 on OpenAlexaff
Lauren E. Merz, Alexander Chaitoff, Angela C. Weyand, Michael Fralick, Michelle Sholzberg

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineHemoglobinTransferrin saturationFerritinCohortAnemiaInternal medicinePopulationNational Health and Nutrition Examination SurveyIron deficiencyGastroenterologyEndocrinologyPhysiologyEnvironmental health

Abstract

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Introduction: Hemoglobin reference intervals (RIs) are developed by laboratory assay manufacturers, and laboratories establish a RI using 120 “normal” samples when first implementing a new assay. Many institutions report different hemoglobin RIs for females and males with wide variation between institutions. Re-evaluation of hemoglobin RIs is warranted to ensure that optimal health for all patients is accurately represented. Using National Health and Nutrition Examination Survey (NHANES) data, we evaluated the impact of excluding adults with chronic disease, iron deficiency (ID), inflammation, and red blood cell (RBC) disorders on hemoglobin lower limit of normal (LLN). Methods: Adults > 20 years in the NHANES from 2001-2006 and 2017-2020 were included. Demographic characteristics, self-reported medical history and medication use, and laboratory parameters were extracted. We generated hemoglobin distributions weighted to represent the population of the USA. From these distributions, 2.5%, 50%, and 97.5% values were individually and compositely evaluated when excluding chronic disease (defined as the healthy cohort), ID, inflammation, and RBC disorders. The healthy cohort excluded patients with a medical history of anemia, malignancy, current pregnancy, smoking, or liver, lung, kidney, or heart disease. Iron deficiency was defined as ferritin < 50 ng/mL, transferrin saturation (Tsat)<20%, or total iron binding capacity (TIBC) > 400 μg/dL. Inflammation was defined as ferritin>300 ng/mL, hs-CRP>1 mg/L, albumin<3.5 g/dL, white blood cells>10 K/uL, or TIBC<200 μg/dL. RBC disorders were defined as history of blood transfusion, MCV <80 or >100, Tsat>55%, MCHC >36, Mentzer index<13, or hydroxyurea or iron chelator use. Finally, we conducted several sensitivity analyses that assessed degree of ID at different levels of ferritin ranging from < 15 ng/mL to < 100 ng/mL. All analyses were conducted in R using the survey package. Results: There were 10,338 individuals in the total cohort, 5757 in the healthy cohort, and 1609 in the cohort with all exclusions applied. Among females, the hemoglobin LLN was 10.7g/dL in the total cohort, 10.6g/dL in the healthy cohort, 10.4g/dL in the cohort excluding inflammation, 11.7g/dL in the cohort excluding RBC disorders, 12.2g/dL in the cohort excluding ID, and 12.3g/dL in the cohort with all exclusions applied. Among males, the hemoglobin LLN was 12.5g/dL in the total cohort, 12.9g/dL in the healthy cohort, 12.9g/dL in the cohort excluding inflammation, 13.2g/dL in the cohort excluding RBC disorders, 13.0g/dL in the cohort excluding ID, and 13.3g/dL in the cohort with all exclusions applied. The difference between male and female hemoglobin LLN narrowed from 2.3g/dL (12.9g/dL vs 10.6g/dL) in the healthy cohort to 1g/dL (13.3g/dL vs 12.3g/dL) in the cohort with all exclusions applied. For males, the largest difference in hemoglobin LLN was seen when RBC disorders were excluded (12.9g/dL to 13.2g/dL; 0.3g/dL difference), and for females the greatest difference was seen when ID was excluded (10.6g/dL to 12.2g/dL; 1.6g/dL difference). Hemoglobin LLN by menopausal status for females and by age for males is shown in Figure 1. Pre-menopausal females had a significant increase in hemoglobin LLN when ID alone was excluded (10.3g/dL to 12.3g/dL), but this degree of change was not observed in healthy post-menopausal females or males. Degree of ID by ferritin level by sex and menopausal status or age is shown in Figure 2. Using a threshold of ferritin <30 ng/mL, 38.9% of pre-menopausal females were iron deficient compared with 10.3% of post-menopausal females and 3.5% of males. Discussion: Applying strict criteria for health and specifically excluding inflammation, RBC disorders, and ID resulted in higher hemoglobin LLN for both sexes and narrowed the difference in LLN between males and females. Pre-menopausal females had the most significant change in hemoglobin LLN from the healthy cohort to the cohort with all exclusions applied, and this was predominately driven by a very high prevalence of ID. ID is a pandemic that disproportionally impacts females of reproductive age. These findings emphasize the burden of ID and its systemic consequences, including normalization of anemia. It is imperative that institutions rigorously and thoughtfully define their hemoglobin RIs by ensuring that strict definitions of health are applied with an emphasis on excluding those with ID.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.104
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.104
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.004
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.200
GPT teacher head0.388
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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