Vitamin B12 Assessment in Sickle Cell Disease: A Systematic Evaluation of Diagnostic Validity and Misclassification

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Conventional biomarkers are not validated for the altered physiology of SCD, where haemolysis, inflammation, and increased erythropoietic demand distort standard assay interpretation. Methods We conducted a systematic review of observational studies (2000–2026) assessing biochemical B12 status in SCD, following PRISMA 2020 guidelines and registered in PROSPERO (CRD420251087800). PubMed, African Journals Online (AJOL), and Google Scholar were searched, with citation tracking and dual independent screening. Diagnostic methodology was evaluated across four domains: biomarker strategy (circulating vs functional), analytical platform, diagnostic thresholds, and confounder integration. A diagnostic validity framework was applied to classify methodological robustness and misclassification risk. Results Thirteen studies were included (62% high-income, 38% low- and middle-income settings). Most used single-marker approaches (circulating B12 alone) and immunoassays, with limited use of functional markers or LC–MS/MS. Diagnostic thresholds were inconsistently defined, and no study adjusted for key confounders. Reported prevalence (0–70%) tracked methodological design: single-marker, non-adjusted studies reported low prevalence (0–7.1%), while multi-marker approaches reported higher estimates (6.9–53%). Misclassification risk was high (54–62%), and greater in low-resource settings (≥ 60% vs 40–50%). Conclusion B12 deficiency estimates in SCD are largely method-dependent rather than biologically determined, limiting comparability and clinical reliability. We propose a context-integrated, multi-marker framework to reduce misclassification and improve diagnostic validity, supporting more accurate and equitable care. Hematology Vitamin B12 sickle cell disease Diagnostic bias Global health equity HIC LMIC Figures Figure 1 Figure 2 Introduction Sickle cell disease (SCD), the most common monogenic disorder globally, is a substantial yet under-recognised driver of nutritional vulnerability, particularly in sub-Saharan Africa (SSA) ( 1 , 2 ). It results from a single missense mutation (rs334) in the β-globin gene, producing haemoglobin S (HbS). Under hypoxic or inflammatory stress, HbS polymerises, deforming erythrocytes into rigid cells that undergo premature haemolysis and obstruct the microvasculature, leading to vaso-occlusive crises (VOCs), severe anaemia, acute chest syndrome, stroke, and progressive multi-organ injury ( 1 ). Disease severity varies by genotype, with HbSS and HbSβ⁰-thalassaemia associated with more severe phenotypes, and HbSC and HbSβ⁺-thalassaemia with intermediate expression ( 3 , 4 ). Across genotypes, chronic haemolysis, inflammation, oxidative stress (OS), and nitric oxide depletion impose sustained metabolic demand, increasing resting energy expenditure and erythropoiesis ( 5 , 6 ). These processes elevate micronutrient requirements, particularly vitamin B12 (B12), through increased utilisation, impaired absorption, renal dysfunction, and chronic inflammation ( 7 – 9 ). This burden coincides with a rapidly rising global prevalence: between 2000 and 2021, SCD increased by over 40%, with SSA accounting for nearly 80% of affected newborns ( 2 ). Despite this, diagnostic capacity, laboratory infrastructure, and access to specialised care remain constrained, reinforcing the need for accurate and scalable nutritional assessment. B12 deficiency in SCD remains poorly characterised and may impair erythropoiesis, elevate homocysteine (Hcy), and exacerbate OS, thereby worsening anaemia and vascular dysfunction ( 10 – 12 ). Reported prevalence ranges from 7% to 70% ( 13 – 24 ), a disparity more consistent with methodological inconsistency than true biological variation. Most studies rely on serum or plasma total B12, a non-validated biomarker in SCD that is highly cut-off dependent and influenced by inflammation, liver dysfunction, renal impairment, and altered binding proteins ( 25 , 26 ), limiting its ability to reflect intracellular status. Functional biomarkers, including methylmalonic acid (MMA) and holotranscobalamin (holoTC), may better capture cellular B12 activity ( 27 ), but their application is constrained by cost, availability, and standardisation challenges in low- and middle-income settings. Critically, most studies do not account for key confounders influencing B12 metabolism ( 28 ). Genetic determinants of B12 metabolism, including methylenetetrahydrofolate reductase (MTHFR), transcobalamin II (TCN2), and fucosyltransferase 2 (FUT2), which regulate homocysteine metabolism, cellular uptake, and absorption ( 29 , 30 ), have not been evaluated in SCD populations. This omission overlooks substantial heritability (~ 59%) ( 29 , 31 ) and increases the risk of systematic misclassification of functional deficiency. When biomarkers developed in physiologically dissimilar populations are applied without adjustment, diagnostic accuracy becomes structurally compromised rather than biologically informative. To date, no systematic review has evaluated how circulating B12 is measured in SCD or assessed the validity of the biomarkers, thresholds, and analytical approaches used. This represents a critical gap in both nutritional science and clinical practice. In this study, we systematically evaluate B12 diagnostic methodology in SCD, examining biomarker selection, analytical platforms, diagnostic thresholds, and confounder integration. We introduce the Context-Integrated Biomarker Validity Framework (CIBVF) to systematically assess diagnostic reliability in SCD. By shifting the focus from prevalence to validity, this work reframes variability not as noise, but as a signal of methodological inconsistency. This provides a foundation for more reliable clinical interpretation and improved standardisation in future research. Materials and Methods Search Strategy, Sources, and Selection Process A comprehensive literature search was conducted in PubMed, African Journals Online (AJOL), and Google Scholar from January 2000 to March 2026. Embase and Web of Science were not searched due to institutional access constraints; this was mitigated by the high coverage of PubMed for haematology and clinical nutrition literature and substantial database overlap. To maximise retrieval, we supplemented database searches with Scopus indexing, forward and backward citation tracking of included studies, and screening of the first 200 relevance-ranked results in Google Scholar. No additional eligible studies were identified through these strategies. The review was prospectively registered with PROSPERO (CRD420251087800) prior to study selection and data extraction and conducted in accordance with PRISMA 2020 guidelines (32). Search strategies combined controlled vocabulary and free-text terms related to B12 (cobalamin), MMA, holoTC, Hcy, SCD, haemoglobin S, and deficiency states (Supplementary Table 1). No language restrictions were applied. Titles and abstracts were independently screened by two reviewers, followed by full-text assessment using predefined eligibility criteria. Discrepancies were resolved by consensus or third-reviewer adjudication. The selection process is presented in the PRISMA flow diagram (Figure 1). Eligibility Criteria Studies were eligible if they met all of the following criteria: (1) peer-reviewed observational human studies (cross-sectional, case–control, or cohort); (2) assessment of biochemical B12 status using serum or plasma B12 and/or functional biomarkers (MMA, holoTC, or Hcy); (3) inclusion of individuals with confirmed SCD of any genotype, age group, or clinical setting; and (4) sufficient methodological and results detail to enable data extraction and critical appraisal. Studies were excluded if they were non–peer-reviewed (e.g., abstracts, conference proceedings, protocols, reviews), conducted in animal or in vitro models, involved non-SCD populations, or lacked extractable biochemical or methodological data. Where multiple reports described the same cohort, the most complete dataset was included. Conceptual Framework for Validity Assessment To move beyond descriptive prevalence, we developed a structured framework to evaluate the methodological validity of B12 assessment in SCD, reflecting the multidimensional determinants of biomarker interpretation in a disease characterised by haemolysis, inflammation, and altered metabolic turnover. Three domains were defined a priori: (1) Biomarker Strategy Studies were classified based on diagnostic approach: Single: total B12 only Dual: total B12 plus one functional biomarker (MMA, Hcy, or holoTC) Functional dominant: primary reliance on functional biomarkers or combined definitions This classification reflects increasing proximity to intracellular B12 activity. (2) Analytical Platform Assay methodology was categorised as immunoassay-based, liquid chromatography–tandem mass spectrometry (LC–MS/MS), or not reported, capturing variability in sensitivity, specificity, and susceptibility to interference. (3) Confounder measurement Confounders were selected based on established effects on B12 metabolism and biomarker interpretation. Data Extraction and Risk of Bias Assessment Data extraction followed a predefined framework capturing study characteristics (author, year, country, design, sample size), participant variables (age, sex, genotype, disease state, transfusion status), and biomarker parameters (B12, MMA, Hcy, holoTC). Additional variables included assay methodology, pre-analytical conditions (e.g., fasting), diagnostic thresholds, and definitions of deficiency. Confounders extracted included renal and liver function, inflammation, haemolysis, medication exposure (e.g., hydroxyurea, supplementation, metformin, proton pump inhibitors), and dietary assessment. Missing variables were recorded without imputation. AI-assisted tools were used for preliminary organisation; all data were manually verified against source publications. Final extraction and interpretation were conducted by T.A., F.I., and A.R., with discrepancies resolved by consensus. Methodological quality and risk of bias were assessed using the Newcastle–Ottawa Scale (NOS), evaluating selection, comparability, and outcome domains. The comparability domain was adapted to incorporate SCD-relevant confounders. Failure to account for key confounders informed downgrading and interpretation of reported prevalence estimates. Data Synthesis and Certainty of Evidence A structured narrative synthesis was undertaken, stratified by biomarker strategy, analytical platform, and confounder adjustment score. Studies were grouped to distinguish circulating B12-only approaches from those incorporating functional or combined biomarkers, with findings interpreted in the context of confounder control and analytical validity. Meta-analysis was not performed due to irreducible heterogeneity in biomarker selection, assay platforms, diagnostic thresholds, populations, and pre-analytical conditions. Variability in reported prevalence (0%–70%) was therefore interpreted as methodological divergence rather than equivalent biological phenomena. Certainty of evidence was qualitatively assessed as low to moderate, reflecting the predominance of observational designs, inconsistent biomarker strategies, and variable confounder control. Standard grading frameworks were not applied, as they do not account for biomarker validity in complex disease contexts. Findings were interpreted with emphasis on methodological structure rather than pooled estimates. RESULTS Study Selection The database search identified 316 records (PubMed n = 101; Google Scholar n = 138; AJOL n = 77). After removal of duplicates (n = 122) and clearly ineligible records (n = 86), 108 records underwent title and abstract screening. Sixty-seven were excluded based on predefined criteria. Forty-one full-text articles were assessed, of which 28 were excluded (reviews n = 9; animal studies n = 4; case reports n = 3; conference abstracts n = 1; non-SCD populations n = 8; other methodological exclusions). Thirteen studies met all inclusion criteria and were included in the final synthesis. The selection process is summarised in Figure 1 Study Characteristics and Population Context The 13 included studies comprised cross-sectional (16, 17, 20, 23, 24), case–control (13, 22, 28), longitudinal (19), prospective (14), pilot (21), secondary analysis (18), and observational designs (15). Studies were conducted across both high-income settings (USA, Canada, France, Saudi Arabia ) and low- and middle-income countries (Nigeria, Tanzania, Sudan, India). Sample sizes ranged from 11 to 820 participants. Populations included paediatric, adult, and mixed cohorts, with HbSS as the predominant genotype and occasional inclusion of HbSC and HbSβ-thalassaemia variants. Disease state was variably defined: steady-state cohorts (15, 19), VOC-restricted cohorts (14, 18), and mixed or unspecified states (13, 16, 23). Transfusion status and clinical context variables were inconsistently reported across studies. Biomarker Strategy Five studies (38.5%) relied exclusively on total circulating B12 (13, 19, 20, 24, 28). Three studies (23.1%) incorporated B12 alongside Hcy (15, 16, 23), while a further three studies (23.1%) utilised a combined biomarker panel including B12, MMA, and Hcy (14, 17, 22). Only one study employed a functional-dominant approach using MMA and Hcy without reliance on circulating B12 (18). This shows that fewer than half of included studies incorporated MMA, the most specific functional indicator of intracellular B12 activity, while HC was assessed in only a single study across the entire evidence base. The majority of studies relied on biomarker strategies with limited capacity to distinguish true intracellular deficiency from alterations driven by inflammation, haemolysis, or binding protein dynamics characteristic of SCD. Pre-analytical Conditions, Analytical platforms , and Diagnostic Threshold Pre-analytical conditions were variably reported. Sample matrices included serum, plasma, and urine (18), with fasting status largely unreported. Sampling occurred across steady-state and VOC conditions, further contributing to heterogeneity. Analytical platform selection was heavily skewed toward immunoassay-based methods. Nine of thirteen studies (69.2%) quantified circulating B12 using immunoassays, including ELISA and chemiluminescent-based techniques (13, 14, 16, 17, 19, 20, 21, 23, 24). One study utilised an automated analyser (15), and one employed high-performance liquid chromatography (HPLC) (22). One study did not measure circulating B12, instead measured functional biomarkers (18), while another did not report the analytical platform used (28). Notably, no study measured circulating B12 using liquid chromatography–tandem mass spectrometry (LC–MS/MS). Where LC–MS/MS was used, it was restricted to the quantification of MMA rather than B12 itself (Table 1). Reporting of diagnostic thresholds was inconsistent across the evidence base. Among HIC studies (n=8), three (37.5%) reported explicit thresholds for circulating B12 (17, 21, 24), while two (25%) defined thresholds for MMA (14, 18). In LMIC studies (n=5), two (40%) reported thresholds for B12 (13, 28), and one (20%) reported a threshold for Hcy (23). However, three LMIC studies (60%) did not report a clearly defined diagnostic threshold for B12 (15, 22, 23) (Table 1). Most studies lacked explicit diagnostic criteria, relying instead on inconsistent laboratory reference ranges that preclude meaningful deficiency classification. Definitions of deficiency included absolute (B12 only), functional (MMA ± Hcy), and combined approaches. Prevalence reporting and Outcome Associations Seven of thirteen studies (53.8%) reported a prevalence estimate for B12 deficiency. Of these, six were conducted in high-income settings, with reported prevalence ranging from 0% to 70%: 0% (24), 3% (20), 6.9% (17), 13% (14), 53% (18), and 70% (19). Only one LMIC study reported prevalence, at 7.1% (13). The wide distribution of prevalence estimates was observed primarily in studies from high-income settings, where reporting was more frequent but methodologically heterogeneous. In LMIC settings, limited reporting precluded meaningful comparison (Table 1). Associations with clinical outcomes were limited. High Hcy was linked to microvasculopathy (16) and increased concentrations during VOC compared to steady state (23). No consistent associations were identified for anaemia severity, stroke, VOCs, or neurocognitive outcomes. Treatment Exposure and Clinical Context Participant-level modifiers relevant to B12 metabolism were inconsistently reported. Daily folic acid supplementation was documented in 10/13 studies (76.9%), and hydroxyurea use in 6/13 (46.2%). Transfusion status was not reported in 7/13 studies (53.8%). Among those that addressed transfusion, four studies excluded recently transfused participants, one allowed transfusion exposure without restriction (19), and one permitted inclusion ≥1 month post-transfusion (16). Disease state at sampling was also variably defined. Five studies (38.5%) did not report whether participants were in steady state or VOC. Four studies (30.8%) included only steady-state participants (15, 16, 19, 21), two (15.4%) were conducted in hospitalised VOC populations (14, 18), and two (15.4%) included a mixture of both states (22, 23) (Table 2). Confounder Assessment Integration of disease-relevant confounders varied substantially by setting. Among studies conducted in high-income settings (n=8), renal function was assessed in 5/8 (62.5%) studies (16, 17, 18, 19, 21), haemolysis markers in 4/8 (50%) (18, 19, 20, 24), and liver function in 2/8 (25%) (18, 19). Gastrointestinal investigations, including assessment of potential malabsorption, were performed in 2/8 (25%) studies (17, 19). Despite this, no study incorporated all major confounders concurrently. In contrast, studies conducted in LMIC settings (n=5) demonstrated minimal confounder integration. Only one study assessed both renal function and haemolysis (1/5) (28), and one study evaluated inflammatory status (1/5) (15). The majority of LMIC studies (3/5; 60%) did not report assessment of any key physiological confounders. Risk of Bias Risk-of-bias assessment using the adapted NOS identified common limitations, including small sample sizes, single-centre designs, incomplete reporting of analytical methods and thresholds, and inadequate adjustment for confounders. These factors were most pronounced in studies relying on B12 alone and contributed to reduced confidence in prevalence estimates (Table 3). Table 1: Study Characteristics, Vitamin B12 Assessment & Biomarker Validity Author, Year, Country Design & Sample Size Age group/ Genotype B12 Biomarkers B12 Assessment Type Assay Methods Diagnostic Thresholds Prevalence Ajayi 2013, USA Cross-sectional; n=86 Adults; HbSS, HbSC, HbSβthal B12, MMA, Hcy Combined ELISA B12 260nmol/L 6.9% Hatabah 2025, USA Secondary analysis; n=94 Children; HbSS, HbSC, HbSβthal MMA, holoTC, Hcy Functional LC-MS/MS MMA ≥592nmol/L 53% Olaniyi 2014, Nigeria Case-control; n=90 Adults; HbSS B12, MMA, Hcy Functional HPLC NR NR Williams 2021, Canada Cross-sectional; n=11 Children; HbSS, HbSC, HbSβthal B12 Absolute Chemiluminescent Immunoassay <150 pmol/L 0% Kisali 2023, Tanzania Observational; n=820 Children/adolescents & adults; HbSS, HbSC, HbSβ0thal B12, Hcy Absolute Autoanalyzer NR NR Desprairies 2020, France Prospective; n=39 Children; HbSS, HbSC,HbSβ0thal B12, MMA, Hcy Functional B12 (ELISA), MMA/Hcy (LC-MS/MS) NR 13% Kamal 2021, Saudi Arabia Longitudinal; n=62 Adults; HbSS B12 Absolute Immunoassay NR 70% Ahmed 2016, Sudan Case-control; n=160 Children; HbSS B12 Absolute ELISA <148 pmol/L 7.1% Narayan 2019, India Case control; n=70 Teenagers/Adults; genotype (NR) B12 Absolute NR <118 pmol/L NR Samarron 2020, USA Cross-sectional; n=38 Children & adults; HbSS/HbSC B12, Hcy No definition Immunoassay, HPLC NR NR Orolu 2022, Nigeria Cross-sectional; n=110 Adults; HbSS B12, Hcy No definition ELISA Hcy 5-<15µmol/L NR Lowenthal 2000, USA Pilot; n=55 Adults; HbSS/HbSC B12, Hcy No definition B12 (Radioimmunoassay), Hcy (HPLC) NR NR Kennedy 2000, USA Cross-sectional; n=70 Children/adolescents; HbSS B12 Absolute Radio-immunoassay <148pmol/L 3% B12 = total serum/plasma vitamin B12; MMA = methylmalonic acid; Hcy = homocysteine; holoTC = holotranscobalamin; ELISA = enzyme-linked immunosorbent assay; LC-MS/MS = liquid chromatography–tandem mass spectrometry; HPLC = high-performance liquid chromatography; NR = not reported; VOC = vaso-occlusive crisis; HbSS, HbSC, HbSβthal = sickle cell genotypes. Functional assessment defined as inclusion of ≥1 metabolic biomarker (MMA, Hcy, or holoTC) alongside or independent of total B12; Diagnostic thresholds varied across studies and were not standardized; Prevalence estimates reflect study-specific definitions and may not be comparable; Biomarker validity considerations include study-reported and biologically plausible confounders. *Prevalence reported despite unspecified diagnostic threshold Table 2. Clinical Context and Exposure Characteristics of Included Studies Study Disease State Transfusion Status Medication Exposure Dietary Assessment Clinical Confounders Key Limitation Key Finding Ajayi et al., 2013 NR NR HU (75%); Folic acid (all) NR Renal function (creatinine); gastrointestinal ( H. pylori ) Small sample Functional deficiency exceeds absolute Hatabah et al., 2025 VOC (hospitalized) NR HU (72%); folic acid (92.5%), parenteral opiods NR Renal (creatinine, eGFR); liver (ALT, AST); haemolysis (bilirubin, reticulocytes) No confounder adjustment Very high deficiency burden Olaniyi et al., 2014 VOC and steady state NR Folic acid (5 mg daily); B-complex supplementation NR Inflammation (reported conceptually); no objective measures No thresholds Metabolic disruption in VOC Williams et al., 2021 NR NR HU (55%); folic acid (1 mg daily) NR Reticulocyte count (limited hemolysis data) Very small sample size No deficiency detected Kisali et al., 2024 Steady state No transfusion within 30 days Folic acid (5 mg daily), chloroquine NR Inflammation (CRP normal) No cut-offs Large cohort, unclear deficiency Desprairies et al., 2020 VOC (hospitalized) Recent transfusion excluded (≤3 months) HU (59%), universal nitrous oxide exposure NR NR Small sample size Subclinical disturbance Kamal et al., 2021 Steady state Some participants received blood transfusion HU; multivitamins (B, D, zinc) 48-hour dietary recall Renal (creatinine, BUN); liver function; gastrointestinal investigations (H. pylori, celiac, inflammatory bowel disease); hemolysis markers (bilirubin, LDH) Poor definition High deficiency burden Ahmed et al., 2016 NR NR NR NR NR No confounder control Low prevalence Narayan et al., 2019 NR NR Folic acid (5 mg daily) NR Renal (creatinine); hemolysis markers (bilirubin, reticulocytes) No prevalence data Stratified B12 levels only Samarron et al., 2020 Steady state Allowed; ≥1 month post-transfusion HU(57%); folic acid (variable adherence), multivitamin supplementation. NR Renal (creatinine); adjusted for age and sex No B12 interpretation Hcy linked to microvasculopathy Orolu et al., 2022 VOC, hyper-hemolytic crisis, and steady state NR NR NR None reported (exclusions applied for confounding conditions) No B12 definition Hcy elevated in VOC Lowenthal et al., 2000 Steady state Recent transfusion assessed (≤6 weeks) Folic acid (1 mg daily) NR Renal (creatinine) No thresholds No clear deficiency pattern Kennedy et al., 2000 NR Chronic transfusion excluded Folic acid (1 mg daily) 24-hour dietary recall CBC, reticulocytes No functional markers Low deficiency Hb= Haemogobin, HU = hydroxyurea; VOC = vaso-occlusive crisis; NR = not reported; CRP = C-reactive protein; ALT = alanine aminotransferase ; AST = aspartate aminotransferase; LDH = lactate dehydrogenase ; BUN = blood urea nitrogen; eGFR = estimated glomerular filtration rate; CBC = complete blood count. Disease state classified as VOC, steady state, or not reported; Transfusion exposure reflects timing relative to sampling where available; Medication exposure includes disease-modifying and nutritional interventions; Clinical confounders represent variables assessed or acknowledged in each study; Absence of confounder adjustment reflects either non-measurement or non-integration into analysis Table 3: Newcastle–Ottawa Scale (NOS) Risk of Bias Assessment of Included Studies Study Selection (0–4) Comparability (0–2) Outcome/Exposure (0–3) Total (0–9) Quality Ajayi et al., 2013 3 1 3 7 High Hatabah et al., 2025 3 0 2 7 High Olaniyi et al., 2014 3 0 2 5 Moderate Williams et al., 2021 2 0 2 4 Low Orolu et al., 2022 3 1 1 5 Moderate Kisali et al., 2024 3 0 1 4 Low Desprairies et al., 2020 3 0 2 5 Moderate Kamal et al., 2021 3 2 2 7 High Ahmed et al., 2016 3 0 3 6 Moderate Narayan et al., 2019 2 0 1 3 Low Samarron et al., 2020 2 1 2 5 Moderate Lowenthal et al., 2000 2 0 1 3 Low Kennedy et al., 2000 3 1 2 6 Moderate NOS = Newcastle–Ottawa Scale; Selection (0–4), Comparability (0–2), Outcome/Exposure (0–3) domains scored per NOS criteria; Total score range: 0–9. High quality: 7–9; Moderate quality: 5–6; Low quality: ≤4. **NOS assessment adapted for observational biomarker studies Comparability domain reflects adjustment for key confounders (e.g., renal function, inflammation, disease state) DISCUSSION Principal Findings This systematic review shows that the wide variability in reported B12 deficiency in SCD (0–70%), primarily reflects methodological heterogeneity in biomarker strategy, analytical platform, confounder adjustment, and diagnostic thresholds. Notably, 38.5% (5/13) of studies relied solely on circulating B12 (Table 1 ), which lacks specificity for intracellular deficiency in SCD and may miss functional deficiency under inflammatory conditions ( 25 , 33 , 34 ), representing the minimum proportion at high risk of misclassification. Fewer than half incorporated MMA, and only one applied a functional-dominant approach ( 18 ). This uneven biomarker distribution corresponded with a clear prevalence gradient, with the widest range observed when relying solely on circulating B12 and a narrower range when functional markers were used. These combined methodological constraints suggest that the majority of prevalence estimates may not reflect true intracellular B12 status. Biomarker strategy, diagnostic thresholds, and analytical platform constraints The biomarker strategies employed across studies varied substantially, with important implications for diagnostic validity. The inclusion of Hcy ( 15 , 16 , 23 ) reflects a partial shift toward functional assessment but remains limited by low specificity and multiple non–B12 influences ( 35 , 36 ). MMA more directly reflects B12-dependent metabolism, and is less affected by folate status ( 37 ), yet was measured in fewer than half of the included studies and prioritised as a primary marker in only one ( 18 ). Holotranscobalamin, a marker of bioavailable B12, was assessed in a single study. Prevalence gradients across biomarker configurations support a hierarchical model of diagnostic validity, whereby greater physiological specificity reduces variability. However, residual heterogeneity in multi-marker studies indicates that biomarker selection alone is insufficient. A key additional limitation is inconsistent diagnostic definition: fewer than half of studies specified explicit thresholds, and reporting of both cut-offs and prevalence was incomplete. Without harmonised criteria, prevalence measures definitional variability, not deficiency. Analytical platform choice further limits diagnostic validity. The predominance of immunoassays (69.2%) increases susceptibility to interference from haemolysis, inflammation, and altered binding proteins ( 38 ). No study measured circulating B12 using LC–MS/MS, the reference method; its use was confined to MMA in a subset. This mismatch between biomarker selection and analytical specificity constrains interpretation, reducing diagnostic precision and increasing the risk of both over- and underestimation of deficiency. Mechanisms and Consequences of Misclassification SCD biology systematically biases B12 biomarkers. Inflammation and hepatic processes alter B12-binding proteins, increasing circulating B12 despite functional deficiency when used in isolation ( 39 , 40 ). Concurrently, increased erythropoietic demand heightens metabolic flux and OS, raising Hcy and MMA independent of true deficiency ( 41 ). Renal dysfunction complicates interpretation by reducing MMA clearance, leading to MMA accumulation unrelated to B12 status, particularly without renal adjustment ( 16 , 17 , 18 , 28 ). Treatment and iatrogenic factors compound biological biases: nitrous oxide exposure likely leads to functional B12 inactivation, without reducing circulating concentrations ( 14 ); folate supplementation masks deficiency ( 42 ); hydroxyurea, proton pump inhibitors, and metformin further disrupt B12 metabolism/absorption ( 42 – 44 ). Despite their relevance, these modifiers are rarely integrated into diagnostic interpretation. Critically, no study integrates these modifiers. Biomarkers thus reflect physiological interference rather than true intracellular B12 status. Global Diagnostic Disparities in Methodological Rigour A clear disparity in methodological rigour was observed between HIC and LMIC settings. Across biomarker strategy, analytical platform, confounder integration, and prevalence reporting, LMIC studies consistently demonstrated more limited diagnostic depth. Functional biomarkers were less frequently used in LMIC settings (20%) compared with HIC studies, where MMA was incorporated in 37.5% and measured using LC–MS/MS in a subset. Holotranscobalamin was assessed in only one study across the dataset, conducted in a HIC. In contrast, ≥ 60% of LMIC studies relied on circulating B12 alone, with no use of LC–MS/MS and no assessment of bioavailable B12. This disparity extended to confounder integration. HIC studies partially incorporated renal function (62.5%), haemolysis (50%), liver function (25%), and gastrointestinal investigation (25%), yet none achieved comprehensive adjustment. In LMIC settings, ≥ 60% of studies did not assess any key physiological confounders, with only isolated measurements reported (Table 2 ). Inconsistent reporting of transfusion status (> 50% unreported) and disease state (38.5% undefined) introduced additional variability, further limiting interpretability. These combined constraints translated into a higher estimated risk of misclassification, with 54–62% of studies classified as high risk. This implies that at least one-third, and potentially over half, of reported classifications may not reflect true intracellular B12 status. Risk was disproportionately concentrated in LMIC settings (≥ 60%) compared with 40–50% in HIC settings. While misclassification was evident across all settings, its structure differed: in HIC, it primarily reflects incomplete biomarker integration despite access to advanced platforms; in LMIC, it is driven by compounded constraints, including single-marker reliance, undefined thresholds, and minimal confounder assessment. These findings indicate that limitations in B12 assessment are pervasive but unevenly distributed, reflecting differences in diagnostic context rather than true population variation. Diagnostic validity, rather than geography or resource availability, emerges as the principal determinant of reported B12 deficiency in SCD. Proposed Context-Integrated Diagnostic Framework We propose a stepwise, context-integrated approach to B12 assessment in SCD. Circulating B12 may serve as an initial screening marker, consistent with the 2024 NICE guideline for vitamin B₁₂ deficiency in individuals aged ≥ 16 years ( 45 ), but its limited sensitivity and specificity in SCD must be explicitly recognised. Functional assessment, preferably using MMA, should be incorporated where feasible to reflect intracellular metabolic activity, with analytical platform selection influencing interpretive reliability ( 45 ). Diagnostic interpretation should be contextualised through adjustment for key physiological modifiers, including renal function, haemolysis, inflammation, transfusion exposure, and disease state. Classification should therefore rely on concordance between biomarkers and clinical context rather than isolated thresholds (Fig. 2), aligning with current recommendations for context-dependent B12 interpretation ( 45 ). This framework is designed to be scalable across settings. In resource-limited contexts, circulating B12 may support initial screening but should not be used in isolation for diagnostic decision-making. Targeted incorporation of functional markers within referral centres or high-risk populations may enhance detection. Standardising pre-analytical conditions, defining explicit thresholds, and integrating basic confounder assessment (e.g., renal function, inflammation, transfusion status) represent high-yield strategies that do not require advanced infrastructure. Strengthening regional laboratory networks and implementing stepwise, context-adapted diagnostic pathways may provide a pragmatic route to improving diagnostic accuracy. Implications for Interpretation of the Evidence Base These findings indicate that the current literature does not yield a stable or directly comparable estimate of B12 deficiency in SCD. Reported prevalence is structurally determined by methodological configuration rather than reflecting true biological variation. Consequently, apparent differences across studies or regions should be interpreted cautiously, as they are likely driven by measurement strategy rather than population-level differences. The inconsistent association between B12 status and clinical outcomes may similarly reflect diagnostic misclassification rather than absence of effect. In this context, studies are not measuring deficiency per se, but the methodological frameworks used to define it. This fragmentation deviates from established diagnostic principles in haematology and nutrition. Contemporary guidance, including National Institute for Health and Care Excellence NG239, advocates a staged, multi-marker approach, using serum B12 for initial assessment and functional markers such as MMA or Hcy to resolve diagnostic uncertainty ( 45 ). The proposed Clinical Diagnostic Guidance framework extends this principle by operationalising guideline-based recommendations into a structured, context-integrated system that standardises biomarker selection, strengthens analytical validity, and embeds physiological interpretation. This approach provides a scalable pathway to improve diagnostic consistency and restore comparability across heterogeneous settings. Strengths and Limitations This review systematically evaluated the methodological determinants of B12 assessment in SCD using a predefined validity framework, allowing structured comparison across studies. By integrating biomarker strategy, analytical platform, and confounder assessment, the analysis provides a multidimensional perspective on sources of heterogeneity. However, several limitations should be acknowledged. The number of included studies was modest, and reporting was incomplete across multiple domains, including assay methods, thresholds, and clinical context. The analysis was based on study-level data, limiting the ability to directly quantify the magnitude or direction of misclassification. As such, conclusions regarding diagnostic validity are inferential and based on established principles of biomarker physiology and assay performance rather than direct validation within individual studies. Conclusion The variability in reported B12 deficiency in SCD is closely linked to methodological heterogeneity across the evidence base. Differences in biomarker strategy, analytical platform, confounder integration, and diagnostic thresholds collectively constrain the interpretability and comparability of existing studies. These findings underscore the need for more standardised, physiologically informed approaches to B12 assessment in SCD to enable more reliable estimation of deficiency and its clinical relevance. Declarations Author contributions statement T.A. conceptualised the study, led manuscript development, and provided overarching intellectual leadership. F.I., T.A., and A.R. executed rigorous data extraction and synthesis with precision. All authors delivered critical revisions, ensuring scientific excellence, and unanimously approved the final submission. Funding Information This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interest statement The authors do not have any conflict of interest to disclose for the submitted work. Declaration of generative AI and AI assisted technologies in the manuscript preparation process During manuscript preparation, the author(s) leveraged Perplexity AI for high-efficiency summarization, condensation, and grammar optimisation, alongside AI-assisted preliminary data identification and organisation. All AI-generated outputs underwent rigorous author review, editing, and validation, with full accountability retained for the final published content. References Inusa BPD, Hsu LL, Kohli N, Patel A, Ominu-Evbota K, Anie KA, et al. Sickle Cell Disease Genetics, Pathophysiology, Clinical Presentation and Treatment. Int J Neonatal Screen. 2019;5(2):20. doi:10.3390/ijns5020020. Thomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V, et al. 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J Clin Sci. 2022;19(3):80. doi:10.4103/jcls.jcls_33_22. Williams BA, Mayer C, McCartney H, Devlin AM, Lamers Y, Vercauteren SM, et al. Detectable Unmetabolized Folic Acid and Elevated Folate Concentrations in Folic Acid-Supplemented Canadian Children With Sickle Cell Disease. Front Nutr. 2021;8. doi:10.3389/fnut.2021.642306. Hannibal L, Lysne V, Bjørke-Monsen AL, et al. Biomarkers and Algorithms for the Diagnosis of Vitamin B12 Deficiency. Front Mol Biosci. 2016;3:27. doi:10.3389/fmolb.2016.00027 Carmel R. Biomarkers of cobalamin (vitamin B-12) status in the epidemiologic setting: a critical overview of context, applications, and performance characteristics of cobalamin, methylmalonic acid, and holotranscobalamin II. Am J Clin Nutr. 2011 Jul;94(1):348S-358S. doi: 10.3945/ajcn.111.013441. Wolffenbuttel BHR, Wouters H, De Jong WHA, Huls G, Van der Klauw MM. Association of vitamin B12, methylmalonic acid, and functional parameters. Neth J Med. 2020;78(1):10–24. Narayan S, Jha B, Kumar S, Sirkar S. Study of vitamin B12 levels in patients of sickle cell disease in a tertiary care hospital, Jharkhand. 2019. [Preprint]. Surendran S, Adaikalakoteswari A, Saravanan P, Shatwaan IA, Lovegrove JA, Vimaleswaran KS. An update on vitamin B12-related gene polymorphisms and B12 status. Genes Nutr. 2018;13(1):2. Velkova A, Diaz JEL, Pangilinan F, Molloy AM, Mills JL, Shane B, et al. The FUT2 secretor variant p.Trp154Ter influences serum vitamin B12 concentration via holo-haptocorrin, but not holo-transcobalamin, and is associated with haptocorrin glycosylation. Hum Mol Genet. 2017;26(24):4975-88. doi:10.1093/hmg/ddx369. Allin KH, Friedrich N, Pietzner M, Grarup N, Thuesen BH, et al. Genetic determinants of serum vitamin B12 and their relation to body mass index. Eur J Epidemiol. 2017;32(2):125-34. doi:10.1007/s10654-016-0215-x Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71. Dastidar R, Sikder K. Diagnostic reliability of serum active B12 (holo-transcobalamin) in true evaluation of vitamin B12 deficiency: relevance in current perspective. BMC Res Notes. 2022;15(1):329. doi:10.1186/s13104-022-06224-8. Thain A, Hart K, Ahmadi KR. Addressing the gaps in the Vitamin B12 deficiency 2024 NICE Guidelines: highlighting the need for better recognition, diagnosis, and management of pernicious anaemia. Eur J Clin Nutr. 2025;79(7):607-610. doi: 10.1038/s41430-025-01583-4 Lee SM, Oh J, Chun MR, Lee SY. Methylmalonic acid and homocysteine as indicators of vitamin B12 deficiency in patients with gastric cancer after gastrectomy. Nutrients. 2019;11(2):450. doi: 10.3390/nu11020450 Langan RC, Goodbred AJ. Vitamin B12 deficiency: recognition and management. Am Fam Physician. 2017;96(6):384-389. Bailey RL, Carmel R, Green R, Pfeiffer CM, Cogswell ME, Osterloh JD, et al. Monitoring of vitamin B-12 nutritional status in the United States by using plasma methylmalonic acid and serum vitamin B-12. Am J Clin Nutr. 2011;94(2):552-61. doi: 10.3945/ajcn.111.015222 Marandola M, Napoli G, Leggeri S, Lombardi C, Urbani A, Baroni S. Total vitamin B12 and holotranscobalamin: current evidence, limitations, and clinical utility [Preprint]. Preprints. 2026. doi: 10.20944/preprints202603.0826.v1 Young MF, Guo J, Williams A, Whitfield KC, Nasrin S, Kancherla V, et al. Interpretation of vitamin B-12 and folate concentrations in population-based surveys does not require adjustment for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2020;111(4):919-26. doi:10.1093/ajcn/nqz303. Sugihara T, Koda M, Okamoto T, Miyoshi K, Matono T, Oyama K, et al. Falsely elevated serum vitamin B12 levels were associated with the severity and prognosis of chronic viral liver disease. Yonago Acta Med. 2017;60(1):31-9. Riphagen IJ, Minović I, Groothof D, Post A, Eggersdorfer ML, Kootstra-Ros JE, de Borst MH, Navis G, Muskiet FAJ, Kema IP, et al. Methylmalonic acid, vitamin B12, renal function, and risk of all-cause mortality in the general population: results from the prospective Lifelines-MINUTHE study. BMC Med. 2020;18(1):380. doi: 10.1186/s12916-020-01853-x Mills JL, Molloy AM, Reynolds EH. Do the benefits of folic acid fortification outweigh the risk of masking vitamin B12 deficiency? BMJ. 2018;360:k724. doi: 10.1136/bmj.k724. Erratum in: BMJ. 2018;360:k1334. doi: 10.1136/bmj.k1334 Agrawal RK, Patel RK, Shah V, Nainiwal L, Trivedi B. Hydroxyurea in sickle cell disease: drug review. Indian J Hematol Blood Transfus. 2014;30(2):91-6. doi:10.1007/s12288-013-0261-4. Miller JW. Proton pump inhibitors, H2-receptor antagonists, metformin, and vitamin B-12 deficiency: clinical implications. Adv Nutr. 2018;9(4):511S-8S. doi:10.1093/advances/nmy023. National Institute for Health and Care Excellence. Vitamin B12 deficiency in over 16s: diagnosis and management. NICE guideline NG239. London: National Institute for Health and Care Excellence; 2024. Available from: https://www.nice.org.uk/guidance/ng239 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryfile.docx Database-Specific Search Strategies for Vitamin B12 and Sickle Cell Disease Studies (January 2000–March 2026) PRISMACHECKLISTreal.docx PRISMA CHECKLIST Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Interpretation is modified by key confounders, renal dysfunction, inflammation, hemolysis, VOC, transfusion, and medication exposure, which may have bidirectional bias. The framework integrates biomarker selection with clinical context to reduce misclassification and improve diagnostic accuracy.\u003c/p\u003e","description":"","filename":"Figure2.ContextintegratedFramework.png","url":"https://assets-eu.researchsquare.com/files/rs-9611368/v1/07462b21a195ed83eddd54ad.png"},{"id":108809563,"identity":"546f5ab8-9cc9-40ca-8f56-0b76bfcc46a6","added_by":"auto","created_at":"2026-05-08 15:53:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1296552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9611368/v1/d0e88367-31e1-4580-954e-e38ddb48561e.pdf"},{"id":108805075,"identity":"2707f73a-a955-4353-aff0-f0611a0a0cc4","added_by":"auto","created_at":"2026-05-08 15:24:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12349,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDatabase-Specific Search Strategies for Vitamin B12 and Sickle Cell Disease Studies (January 2000–March 2026)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-9611368/v1/87caae4799cd327b7ffec06f.docx"},{"id":108638303,"identity":"e7d34759-cd08-406b-95c4-091b3bc65fa6","added_by":"auto","created_at":"2026-05-06 18:41:03","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19385,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA CHECKLIST\u003c/p\u003e","description":"","filename":"PRISMACHECKLISTreal.docx","url":"https://assets-eu.researchsquare.com/files/rs-9611368/v1/05ffcc7d5c76c9897620320a.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eVitamin B12 Assessment in Sickle Cell Disease: A Systematic Evaluation of Diagnostic Validity and Misclassification\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSickle cell disease (SCD), the most common monogenic disorder globally, is a substantial yet under-recognised driver of nutritional vulnerability, particularly in sub-Saharan Africa (SSA) (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It results from a single missense mutation (rs334) in the β-globin gene, producing haemoglobin S (HbS). Under hypoxic or inflammatory stress, HbS polymerises, deforming erythrocytes into rigid cells that undergo premature haemolysis and obstruct the microvasculature, leading to vaso-occlusive crises (VOCs), severe anaemia, acute chest syndrome, stroke, and progressive multi-organ injury (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Disease severity varies by genotype, with HbSS and HbSβ⁰-thalassaemia associated with more severe phenotypes, and HbSC and HbSβ⁺-thalassaemia with intermediate expression (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAcross genotypes, chronic haemolysis, inflammation, oxidative stress (OS), and nitric oxide depletion impose sustained metabolic demand, increasing resting energy expenditure and erythropoiesis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). These processes elevate micronutrient requirements, particularly vitamin B12 (B12), through increased utilisation, impaired absorption, renal dysfunction, and chronic inflammation (\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This burden coincides with a rapidly rising global prevalence: between 2000 and 2021, SCD increased by over 40%, with SSA accounting for nearly 80% of affected newborns (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite this, diagnostic capacity, laboratory infrastructure, and access to specialised care remain constrained, reinforcing the need for accurate and scalable nutritional assessment.\u003c/p\u003e \u003cp\u003eB12 deficiency in SCD remains poorly characterised and may impair erythropoiesis, elevate homocysteine (Hcy), and exacerbate OS, thereby worsening anaemia and vascular dysfunction (\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Reported prevalence ranges from 7% to 70% (\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), a disparity more consistent with methodological inconsistency than true biological variation. Most studies rely on serum or plasma total B12, a non-validated biomarker in SCD that is highly cut-off dependent and influenced by inflammation, liver dysfunction, renal impairment, and altered binding proteins (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e), limiting its ability to reflect intracellular status. Functional biomarkers, including methylmalonic acid (MMA) and holotranscobalamin (holoTC), may better capture cellular B12 activity (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), but their application is constrained by cost, availability, and standardisation challenges in low- and middle-income settings. Critically, most studies do not account for key confounders influencing B12 metabolism (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenetic determinants of B12 metabolism, including methylenetetrahydrofolate reductase (MTHFR), transcobalamin II (TCN2), and fucosyltransferase 2 (FUT2), which regulate homocysteine metabolism, cellular uptake, and absorption (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), have not been evaluated in SCD populations. This omission overlooks substantial heritability (~\u0026thinsp;59%) (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) and increases the risk of systematic misclassification of functional deficiency. When biomarkers developed in physiologically dissimilar populations are applied without adjustment, diagnostic accuracy becomes structurally compromised rather than biologically informative.\u003c/p\u003e \u003cp\u003eTo date, no systematic review has evaluated how circulating B12 is measured in SCD or assessed the validity of the biomarkers, thresholds, and analytical approaches used. This represents a critical gap in both nutritional science and clinical practice. In this study, we systematically evaluate B12 diagnostic methodology in SCD, examining biomarker selection, analytical platforms, diagnostic thresholds, and confounder integration. We introduce the Context-Integrated Biomarker Validity Framework (CIBVF) to systematically assess diagnostic reliability in SCD. By shifting the focus from prevalence to validity, this work reframes variability not as noise, but as a signal of methodological inconsistency. This provides a foundation for more reliable clinical interpretation and improved standardisation in future research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eSearch Strategy, Sources, and Selection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive literature search was conducted in PubMed, African Journals Online (AJOL), and Google Scholar from January 2000 to March 2026. Embase and Web of Science were not searched due to institutional access constraints; this was mitigated by the high coverage of PubMed for haematology and clinical nutrition literature and substantial database overlap. To maximise retrieval, we supplemented database searches with Scopus indexing, forward and backward citation tracking of included studies, and screening of the first 200 relevance-ranked results in Google Scholar. No additional eligible studies were identified through these strategies.\u003c/p\u003e\n\u003cp\u003eThe review was prospectively registered with PROSPERO (CRD420251087800) prior to study selection and data extraction and conducted in accordance with PRISMA 2020 guidelines (32). Search strategies combined controlled vocabulary and free-text terms related to B12 (cobalamin), MMA, holoTC, Hcy, SCD, haemoglobin S, and deficiency states (Supplementary Table 1). No language restrictions were applied. Titles and abstracts were independently screened by two reviewers, followed by full-text assessment using predefined eligibility criteria. Discrepancies were resolved by consensus or third-reviewer adjudication. The selection process is presented in the PRISMA flow diagram (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudies were eligible if they met all of the following criteria: (1) peer-reviewed observational human studies (cross-sectional, case\u0026ndash;control, or cohort); (2) assessment of biochemical B12 status using serum or plasma B12 and/or functional biomarkers (MMA, holoTC, or Hcy); (3) inclusion of individuals with confirmed SCD of any genotype, age group, or clinical setting; and (4) sufficient methodological and results detail to enable data extraction and critical appraisal. Studies were excluded if they were non\u0026ndash;peer-reviewed (e.g., abstracts, conference proceedings, protocols, reviews), conducted in animal or in vitro models, involved non-SCD populations, or lacked extractable biochemical or methodological data. Where multiple reports described the same cohort, the most complete dataset was included.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptual Framework for Validity Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo move beyond descriptive prevalence, we developed a structured framework to evaluate the methodological validity of B12 assessment in SCD, reflecting the multidimensional determinants of biomarker interpretation in a disease characterised by haemolysis, inflammation, and altered metabolic turnover.\u003c/p\u003e\n\u003cp\u003eThree domains were defined a priori:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(1) Biomarker Strategy\u003c/strong\u003e\u003cbr\u003e\u0026nbsp; Studies were classified based on diagnostic approach:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSingle: total B12 only\u003c/li\u003e\n \u003cli\u003eDual: total B12 plus one functional biomarker (MMA, Hcy, or holoTC)\u003c/li\u003e\n \u003cli\u003eFunctional dominant: primary reliance on functional biomarkers or combined definitions\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis classification reflects increasing proximity to intracellular B12 activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(2) Analytical Platform\u003cbr\u003e\u003c/strong\u003eAssay methodology was categorised as immunoassay-based, liquid chromatography\u0026ndash;tandem mass spectrometry (LC\u0026ndash;MS/MS), or not reported, capturing variability in sensitivity, specificity, and susceptibility to interference.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(3) Confounder measurement\u003cbr\u003e\u003c/strong\u003eConfounders were selected based on established effects on B12 metabolism and biomarker interpretation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction and Risk of Bias Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData extraction followed a predefined framework capturing study characteristics (author, year, country, design, sample size), participant variables (age, sex, genotype, disease state, transfusion status), and biomarker parameters (B12, MMA, Hcy, holoTC). Additional variables included assay methodology, pre-analytical conditions (e.g., fasting), diagnostic thresholds, and definitions of deficiency. Confounders extracted included renal and liver function, inflammation, haemolysis, medication exposure (e.g., hydroxyurea, supplementation, metformin, proton pump inhibitors), and dietary assessment. Missing variables were recorded without imputation.\u003c/p\u003e\n\u003cp\u003eAI-assisted tools were used for preliminary organisation; all data were manually verified against source publications. Final extraction and interpretation were conducted by T.A., F.I., and A.R., with discrepancies resolved by consensus. Methodological quality and risk of bias were assessed using the Newcastle\u0026ndash;Ottawa Scale (NOS), evaluating selection, comparability, and outcome domains. The comparability domain was adapted to incorporate SCD-relevant confounders. Failure to account for key confounders informed downgrading and interpretation of reported prevalence estimates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cstrong\u003eData Synthesis and Certainty of Evidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA structured narrative synthesis was undertaken, stratified by biomarker strategy, analytical platform, and confounder adjustment score. Studies were grouped to distinguish circulating B12-only approaches from those incorporating functional or combined biomarkers, with findings interpreted in the context of confounder control and analytical validity. Meta-analysis was not performed due to irreducible heterogeneity in biomarker selection, assay platforms, diagnostic thresholds, populations, and pre-analytical conditions. Variability in reported prevalence (0%\u0026ndash;70%) was therefore interpreted as methodological divergence rather than equivalent biological phenomena.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCertainty of evidence was qualitatively assessed as low to moderate, reflecting the predominance of observational designs, inconsistent biomarker strategies, and variable confounder control. Standard grading frameworks were not applied, as they do not account for biomarker validity in complex disease contexts. Findings were interpreted with emphasis on methodological structure rather than pooled estimates.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003ch3\u003e\u003cstrong\u003eStudy Selection\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe database search identified 316 records (PubMed n = 101; Google Scholar n = 138; AJOL n = 77). After removal of duplicates (n = 122) and clearly ineligible records (n = 86), 108 records underwent title and abstract screening. Sixty-seven were excluded based on predefined criteria. Forty-one full-text articles were assessed, of which 28 were excluded (reviews n = 9; animal studies n = 4; case reports n = 3; conference abstracts n = 1; non-SCD populations n = 8; other methodological exclusions). Thirteen studies met all inclusion criteria and were included in the final synthesis. The selection process is summarised in Figure 1\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eStudy Characteristics and Population Context\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eThe 13 included studies comprised cross-sectional (16, 17, 20, 23, 24), case\u0026ndash;control (13, 22, 28), longitudinal (19), prospective (14), pilot (21), secondary analysis (18), and observational designs (15). Studies were conducted across both high-income settings (USA, Canada, France, Saudi Arabia ) and low- and middle-income countries (Nigeria, Tanzania, Sudan, India). Sample sizes ranged from 11 to 820 participants. Populations included paediatric, adult, and mixed cohorts, with HbSS as the predominant genotype and occasional inclusion of HbSC and HbS\u0026beta;-thalassaemia variants. Disease state was variably defined: steady-state cohorts (15, 19), VOC-restricted cohorts (14, 18), and mixed or unspecified states (13, 16, 23). Transfusion status and clinical context variables were inconsistently reported across studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomarker Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive studies (38.5%) relied exclusively on total circulating B12 (13, 19, 20, 24, 28). Three studies (23.1%) incorporated B12 alongside Hcy (15, 16, 23), while a further three studies (23.1%) utilised a combined biomarker panel including B12, MMA, and Hcy (14, 17, 22). Only one study employed a functional-dominant approach using MMA and Hcy without reliance on circulating B12 (18). This shows that fewer than half of included studies incorporated MMA, the most specific functional indicator of intracellular B12 activity, while HC was assessed in only a single study across the entire evidence base. The majority of studies relied on biomarker strategies with limited capacity to distinguish true intracellular deficiency from alterations driven by inflammation, haemolysis, or binding protein dynamics characteristic of SCD.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003ePre-analytical Conditions, Analytical platforms , and Diagnostic Threshold\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003ePre-analytical conditions were variably reported. Sample matrices included serum, plasma, and urine (18), with fasting status largely unreported. Sampling occurred across steady-state and VOC conditions, further contributing to heterogeneity.\u0026nbsp;Analytical platform selection was heavily skewed toward immunoassay-based methods. Nine of thirteen studies (69.2%) quantified circulating B12 using immunoassays, including ELISA and chemiluminescent-based techniques (13, 14, 16, 17, 19, 20, 21, 23, 24). One study utilised an automated analyser (15), and one employed high-performance liquid chromatography (HPLC) (22). One study did not measure circulating B12, instead measured functional biomarkers (18), while another did not report the analytical platform used (28). Notably, no study measured circulating B12 using liquid chromatography\u0026ndash;tandem mass spectrometry (LC\u0026ndash;MS/MS). Where LC\u0026ndash;MS/MS was used, it was restricted to the quantification of MMA rather than B12 itself (Table 1).\u003c/p\u003e\n\u003cp\u003eReporting of diagnostic thresholds was inconsistent across the evidence base. Among HIC studies (n=8), three (37.5%) reported explicit thresholds for circulating B12 (17, 21, 24), while two (25%) defined thresholds for MMA (14, 18). In LMIC studies (n=5), two (40%) reported thresholds for B12 (13, 28), and one (20%) reported a threshold for Hcy (23). However, three LMIC studies (60%) did not report a clearly defined diagnostic threshold for B12 (15, 22, 23) (Table 1). Most studies lacked explicit diagnostic criteria, relying instead on inconsistent laboratory reference ranges that preclude meaningful deficiency classification. Definitions of deficiency included absolute (B12 only), functional (MMA \u0026plusmn; Hcy), and combined approaches.\u0026nbsp;\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003ePrevalence reporting and Outcome Associations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eSeven of thirteen studies (53.8%) reported a prevalence estimate for B12 deficiency. Of these, six were conducted in high-income settings, with reported prevalence ranging from 0% to 70%: 0% (24), 3% (20), 6.9% (17), 13% (14), 53% (18), and 70% (19). Only one LMIC study reported prevalence, at 7.1% (13). The wide distribution of prevalence estimates was observed primarily in studies from high-income settings, where reporting was more frequent but methodologically heterogeneous. In LMIC settings, limited reporting precluded meaningful comparison (Table 1). Associations with clinical outcomes were limited. High Hcy was linked to microvasculopathy (16) and increased concentrations during VOC compared to steady state (23). No consistent associations were identified for anaemia severity, stroke, VOCs, or neurocognitive outcomes.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eTreatment Exposure and Clinical Context\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eParticipant-level modifiers relevant to B12 metabolism were inconsistently reported. Daily folic acid supplementation was documented in 10/13 studies (76.9%), and hydroxyurea use in 6/13 (46.2%). Transfusion status was not reported in 7/13 studies (53.8%). Among those that addressed transfusion, four studies excluded recently transfused participants, one allowed transfusion exposure without restriction (19), and one permitted inclusion \u0026ge;1 month post-transfusion (16). Disease state at sampling was also variably defined. Five studies (38.5%) did not report whether participants were in steady state or VOC. Four studies (30.8%) included only steady-state participants (15, 16, 19, 21), two (15.4%) were conducted in hospitalised VOC populations (14, 18), and two (15.4%) included a mixture of both states (22, 23) (Table 2).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eConfounder Assessment\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eIntegration of disease-relevant confounders varied substantially by setting. Among studies conducted in high-income settings (n=8), renal function was assessed in 5/8 (62.5%) studies (16, 17, 18, 19, 21), haemolysis markers in 4/8 (50%) (18, 19, 20, 24), and liver function in 2/8 (25%) (18, 19). Gastrointestinal investigations, including assessment of potential malabsorption, were performed in 2/8 (25%) studies (17, 19). Despite this, no study incorporated all major confounders concurrently. In contrast, studies conducted in LMIC settings (n=5) demonstrated minimal confounder integration. Only one study assessed both renal function and haemolysis (1/5) (28), and one study evaluated inflammatory status (1/5) (15). The majority of LMIC studies (3/5; 60%) did not report assessment of any key physiological confounders.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eRisk of Bias\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eRisk-of-bias assessment using the adapted NOS identified common limitations, including small sample sizes, single-centre designs, incomplete reporting of analytical methods and thresholds, and inadequate adjustment for confounders. These factors were most pronounced in studies relying on B12 alone and contributed to reduced confidence in prevalence estimates (Table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1: Study Characteristics, Vitamin B12 Assessment \u0026amp; Biomarker Validity\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"964\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAuthor, Year, Country\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDesign \u0026amp; Sample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group/ Genotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB12 Biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB12 Assessment Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAssay Methods\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Thresholds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrevalence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eAjayi 2013, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCross-sectional; n=86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdults; HbSS, HbSC, HbS\u0026beta;thal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, MMA, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCombined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eB12 \u0026lt;148pmol/L + MMA \u0026gt;260nmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eHatabah 2025, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSecondary analysis; n=94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren; \u0026nbsp;HbSS, HbSC, HbS\u0026beta;thal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMMA, holoTC, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFunctional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLC-MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMMA \u0026ge;592nmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOlaniyi 2014, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCase-control; n=90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdults; HbSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, MMA, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFunctional \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eHPLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eWilliams 2021, Canada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCross-sectional; n=11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren; \u0026nbsp;HbSS, HbSC, HbS\u0026beta;thal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChemiluminescent Immunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;150 pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eKisali 2023, Tanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eObservational; n=820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren/adolescents \u0026amp; adults; HbSS, HbSC, HbS\u0026beta;0thal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAutoanalyzer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eDesprairies 2020, France\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eProspective; n=39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren; HbSS, HbSC,HbS\u0026beta;0thal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, MMA, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eFunctional\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eB12 (ELISA), MMA/Hcy (LC-MS/MS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e13%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eKamal 2021, Saudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eLongitudinal; n=62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdults; HbSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eImmunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eAhmed 2016, Sudan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCase-control; n=160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren; HbSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;148 pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eNarayan 2019, India\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCase control; n=70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eTeenagers/Adults; genotype (NR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;118 pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eSamarron 2020, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCross-sectional; n=38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren \u0026amp; adults; HbSS/HbSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eImmunoassay, HPLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eOrolu 2022, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCross-sectional; n=110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdults; HbSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eELISA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHcy 5-\u0026lt;15\u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eLowenthal 2000, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003ePilot; n=55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAdults; HbSS/HbSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12, Hcy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eB12 (Radioimmunoassay), Hcy (HPLC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003eKennedy 2000, USA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eCross-sectional; n=70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eChildren/adolescents; HbSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eB12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eAbsolute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eRadio-immunoassay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026lt;148pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eB12 = total serum/plasma vitamin B12; MMA = methylmalonic acid; Hcy = homocysteine; holoTC = holotranscobalamin; ELISA = enzyme-linked immunosorbent assay; LC-MS/MS = liquid chromatography\u0026ndash;tandem mass spectrometry; HPLC = high-performance liquid chromatography; NR = not reported; VOC = vaso-occlusive crisis; HbSS, HbSC, HbS\u0026beta;thal = sickle cell genotypes. Functional assessment defined as inclusion of \u0026ge;1 metabolic biomarker (MMA, Hcy, or holoTC) alongside or independent of total B12; Diagnostic thresholds varied across studies and were not standardized; Prevalence estimates reflect study-specific definitions and may not be comparable; Biomarker validity considerations include study-reported and biologically plausible confounders. *Prevalence reported despite unspecified diagnostic threshold\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Clinical Context and Exposure Characteristics of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"982\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease State\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransfusion Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedication Exposure\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDietary Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical Confounders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Limitation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Finding\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAjayi et al., 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU (75%); Folic acid (all)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal function (creatinine); gastrointestinal (\u003cem\u003eH. pylori\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSmall sample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eFunctional deficiency exceeds absolute\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eHatabah et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eVOC (hospitalized)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU (72%); folic acid (92.5%), parenteral opiods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal (creatinine, eGFR); liver (ALT, AST); haemolysis (bilirubin, reticulocytes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo confounder adjustment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eVery high deficiency burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOlaniyi et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eVOC and steady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFolic acid (5 mg daily); B-complex supplementation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eInflammation (reported conceptually); no objective measures\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eMetabolic disruption in VOC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eWilliams et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU (55%); folic acid (1 mg daily)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eReticulocyte count (limited hemolysis data)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eVery small sample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNo deficiency detected\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eKisali et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSteady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNo transfusion within 30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFolic acid (5 mg daily), chloroquine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eInflammation (CRP normal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo cut-offs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eLarge cohort, unclear deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eDesprairies et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eVOC (hospitalized)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eRecent transfusion excluded (\u0026le;3 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU (59%), universal nitrous oxide exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eSmall sample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eSubclinical disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eKamal et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSteady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eSome participants received blood transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU; \u0026nbsp;multivitamins (B, D, zinc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e48-hour dietary recall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal (creatinine, BUN); liver function; gastrointestinal investigations (H. pylori, celiac, inflammatory bowel disease); hemolysis markers (bilirubin, LDH)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003ePoor definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHigh deficiency burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAhmed et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo confounder control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eLow prevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eNarayan et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFolic acid \u0026nbsp;(5 mg daily)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal (creatinine); hemolysis markers (bilirubin, reticulocytes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo prevalence data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eStratified B12 levels only\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSamarron et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSteady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eAllowed; \u0026ge;1 month post-transfusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHU(57%); folic acid (variable adherence), multivitamin supplementation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal (creatinine); adjusted for age and sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo B12 interpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHcy linked to microvasculopathy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eOrolu et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eVOC, hyper-hemolytic crisis, and steady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eNone reported (exclusions applied for confounding conditions)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo B12 definition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eHcy elevated in VOC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eLowenthal et al., 2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eSteady state\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eRecent transfusion assessed (\u0026le;6 weeks)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFolic acid (1 mg daily)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eRenal (creatinine)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo thresholds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNo clear deficiency pattern\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 88px;\"\u003e\n \u003cp\u003eKennedy et al., 2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eChronic transfusion excluded\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFolic acid (1 mg daily)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e24-hour dietary recall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eCBC, reticulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 127px;\"\u003e\n \u003cp\u003eNo functional markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eLow deficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHb= Haemogobin, HU = hydroxyurea; VOC = vaso-occlusive crisis; NR = not reported; CRP = C-reactive protein; ALT = alanine aminotransferase ; AST = aspartate aminotransferase; LDH = lactate dehydrogenase ; BUN = blood urea nitrogen; eGFR = estimated glomerular filtration rate; CBC = complete blood count. Disease state classified as VOC, steady state, or not reported; Transfusion exposure reflects timing relative to sampling where available; Medication exposure includes disease-modifying and nutritional interventions; Clinical confounders represent variables assessed or acknowledged in each study; Absence of confounder adjustment reflects either non-measurement or non-integration into analysis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Newcastle\u0026ndash;Ottawa Scale (NOS) Risk of Bias Assessment of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"625\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelection (0\u0026ndash;4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComparability (0\u0026ndash;2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome/Exposure (0\u0026ndash;3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal (0\u0026ndash;9)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAjayi et al., 2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eHatabah et al., 2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOlaniyi et al., 2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eWilliams et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eOrolu et al., 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eKisali et al., 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eDesprairies et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eKamal et al., 2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eAhmed et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eNarayan et al., 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eSamarron et al., 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eLowenthal et al., 2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 132px;\"\u003e\n \u003cp\u003eKennedy et al., 2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 122px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNOS = Newcastle\u0026ndash;Ottawa Scale; Selection (0\u0026ndash;4), Comparability (0\u0026ndash;2), Outcome/Exposure (0\u0026ndash;3) domains scored per NOS criteria; Total score range: 0\u0026ndash;9. High quality: 7\u0026ndash;9; Moderate quality: 5\u0026ndash;6; Low quality: \u0026le;4. **NOS assessment adapted for observational biomarker studies Comparability domain reflects adjustment for key confounders (e.g., renal function, inflammation, disease state)\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings\u003c/h2\u003e \u003cp\u003eThis systematic review shows that the wide variability in reported B12 deficiency in SCD (0\u0026ndash;70%), primarily reflects methodological heterogeneity in biomarker strategy, analytical platform, confounder adjustment, and diagnostic thresholds. Notably, 38.5% (5/13) of studies relied solely on circulating B12 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which lacks specificity for intracellular deficiency in SCD and may miss functional deficiency under inflammatory conditions (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), representing the minimum proportion at high risk of misclassification. Fewer than half incorporated MMA, and only one applied a functional-dominant approach (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). This uneven biomarker distribution corresponded with a clear prevalence gradient, with the widest range observed when relying solely on circulating B12 and a narrower range when functional markers were used. These combined methodological constraints suggest that the majority of prevalence estimates may not reflect true intracellular B12 status.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBiomarker strategy, diagnostic thresholds, and analytical platform constraints\u003c/h2\u003e \u003cp\u003eThe biomarker strategies employed across studies varied substantially, with important implications for diagnostic validity. The inclusion of Hcy (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) reflects a partial shift toward functional assessment but remains limited by low specificity and multiple non\u0026ndash;B12 influences (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). MMA more directly reflects B12-dependent metabolism, and is less affected by folate status (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), yet was measured in fewer than half of the included studies and prioritised as a primary marker in only one (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Holotranscobalamin, a marker of bioavailable B12, was assessed in a single study. Prevalence gradients across biomarker configurations support a hierarchical model of diagnostic validity, whereby greater physiological specificity reduces variability. However, residual heterogeneity in multi-marker studies indicates that biomarker selection alone is insufficient. A key additional limitation is inconsistent diagnostic definition: fewer than half of studies specified explicit thresholds, and reporting of both cut-offs and prevalence was incomplete. Without harmonised criteria, prevalence measures definitional variability, not deficiency.\u003c/p\u003e \u003cp\u003eAnalytical platform choice further limits diagnostic validity. The predominance of immunoassays (69.2%) increases susceptibility to interference from haemolysis, inflammation, and altered binding proteins (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). No study measured circulating B12 using LC\u0026ndash;MS/MS, the reference method; its use was confined to MMA in a subset. This mismatch between biomarker selection and analytical specificity constrains interpretation, reducing diagnostic precision and increasing the risk of both over- and underestimation of deficiency.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMechanisms and Consequences of Misclassification\u003c/h2\u003e \u003cp\u003eSCD biology systematically biases B12 biomarkers. Inflammation and hepatic processes alter B12-binding proteins, increasing circulating B12 despite functional deficiency when used in isolation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Concurrently, increased erythropoietic demand heightens metabolic flux and OS, raising Hcy and MMA independent of true deficiency (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Renal dysfunction complicates interpretation by reducing MMA clearance, leading to MMA accumulation unrelated to B12 status, particularly without renal adjustment (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Treatment and iatrogenic factors compound biological biases: nitrous oxide exposure likely leads to functional B12 inactivation, without reducing circulating concentrations (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e); folate supplementation masks deficiency (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e); hydroxyurea, proton pump inhibitors, and metformin further disrupt B12 metabolism/absorption (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). Despite their relevance, these modifiers are rarely integrated into diagnostic interpretation. Critically, no study integrates these modifiers. Biomarkers thus reflect physiological interference rather than true intracellular B12 status.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGlobal Diagnostic Disparities in Methodological Rigour\u003c/h2\u003e \u003cp\u003eA clear disparity in methodological rigour was observed between HIC and LMIC settings. Across biomarker strategy, analytical platform, confounder integration, and prevalence reporting, LMIC studies consistently demonstrated more limited diagnostic depth. Functional biomarkers were less frequently used in LMIC settings (20%) compared with HIC studies, where MMA was incorporated in 37.5% and measured using LC\u0026ndash;MS/MS in a subset. Holotranscobalamin was assessed in only one study across the dataset, conducted in a HIC. In contrast, \u0026ge;\u0026thinsp;60% of LMIC studies relied on circulating B12 alone, with no use of LC\u0026ndash;MS/MS and no assessment of bioavailable B12. This disparity extended to confounder integration. HIC studies partially incorporated renal function (62.5%), haemolysis (50%), liver function (25%), and gastrointestinal investigation (25%), yet none achieved comprehensive adjustment. In LMIC settings, \u0026ge;\u0026thinsp;60% of studies did not assess any key physiological confounders, with only isolated measurements reported (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Inconsistent reporting of transfusion status (\u0026gt;\u0026thinsp;50% unreported) and disease state (38.5% undefined) introduced additional variability, further limiting interpretability.\u003c/p\u003e \u003cp\u003eThese combined constraints translated into a higher estimated risk of misclassification, with 54\u0026ndash;62% of studies classified as high risk. This implies that at least one-third, and potentially over half, of reported classifications may not reflect true intracellular B12 status. Risk was disproportionately concentrated in LMIC settings (\u0026ge;\u0026thinsp;60%) compared with 40\u0026ndash;50% in HIC settings. While misclassification was evident across all settings, its structure differed: in HIC, it primarily reflects incomplete biomarker integration despite access to advanced platforms; in LMIC, it is driven by compounded constraints, including single-marker reliance, undefined thresholds, and minimal confounder assessment. These findings indicate that limitations in B12 assessment are pervasive but unevenly distributed, reflecting differences in diagnostic context rather than true population variation. Diagnostic validity, rather than geography or resource availability, emerges as the principal determinant of reported B12 deficiency in SCD.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eProposed Context-Integrated Diagnostic Framework\u003c/h2\u003e \u003cp\u003eWe propose a stepwise, context-integrated approach to B12 assessment in SCD. Circulating B12 may serve as an initial screening marker, consistent with the 2024 NICE guideline for vitamin B₁₂ deficiency in individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;16 years (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), but its limited sensitivity and specificity in SCD must be explicitly recognised. Functional assessment, preferably using MMA, should be incorporated where feasible to reflect intracellular metabolic activity, with analytical platform selection influencing interpretive reliability (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Diagnostic interpretation should be contextualised through adjustment for key physiological modifiers, including renal function, haemolysis, inflammation, transfusion exposure, and disease state. Classification should therefore rely on concordance between biomarkers and clinical context rather than isolated thresholds (Fig.\u0026nbsp;2), aligning with current recommendations for context-dependent B12 interpretation (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis framework is designed to be scalable across settings. In resource-limited contexts, circulating B12 may support initial screening but should not be used in isolation for diagnostic decision-making. Targeted incorporation of functional markers within referral centres or high-risk populations may enhance detection. Standardising pre-analytical conditions, defining explicit thresholds, and integrating basic confounder assessment (e.g., renal function, inflammation, transfusion status) represent high-yield strategies that do not require advanced infrastructure. Strengthening regional laboratory networks and implementing stepwise, context-adapted diagnostic pathways may provide a pragmatic route to improving diagnostic accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eImplications for Interpretation of the Evidence Base\u003c/h2\u003e \u003cp\u003eThese findings indicate that the current literature does not yield a stable or directly comparable estimate of B12 deficiency in SCD. Reported prevalence is structurally determined by methodological configuration rather than reflecting true biological variation. Consequently, apparent differences across studies or regions should be interpreted cautiously, as they are likely driven by measurement strategy rather than population-level differences. The inconsistent association between B12 status and clinical outcomes may similarly reflect diagnostic misclassification rather than absence of effect. In this context, studies are not measuring deficiency per se, but the methodological frameworks used to define it.\u003c/p\u003e \u003cp\u003eThis fragmentation deviates from established diagnostic principles in haematology and nutrition. Contemporary guidance, including National Institute for Health and Care Excellence NG239, advocates a staged, multi-marker approach, using serum B12 for initial assessment and functional markers such as MMA or Hcy to resolve diagnostic uncertainty (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). The proposed Clinical Diagnostic Guidance framework extends this principle by operationalising guideline-based recommendations into a structured, context-integrated system that standardises biomarker selection, strengthens analytical validity, and embeds physiological interpretation. This approach provides a scalable pathway to improve diagnostic consistency and restore comparability across heterogeneous settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eThis review systematically evaluated the methodological determinants of B12 assessment in SCD using a predefined validity framework, allowing structured comparison across studies. By integrating biomarker strategy, analytical platform, and confounder assessment, the analysis provides a multidimensional perspective on sources of heterogeneity. However, several limitations should be acknowledged. The number of included studies was modest, and reporting was incomplete across multiple domains, including assay methods, thresholds, and clinical context. The analysis was based on study-level data, limiting the ability to directly quantify the magnitude or direction of misclassification. As such, conclusions regarding diagnostic validity are inferential and based on established principles of biomarker physiology and assay performance rather than direct validation within individual studies.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe variability in reported B12 deficiency in SCD is closely linked to methodological heterogeneity across the evidence base. Differences in biomarker strategy, analytical platform, confounder integration, and diagnostic thresholds collectively constrain the interpretability and comparability of existing studies. These findings underscore the need for more standardised, physiologically informed approaches to B12 assessment in SCD to enable more reliable estimation of deficiency and its clinical relevance.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.A. conceptualised the study, led manuscript development, and provided overarching intellectual leadership. F.I., T.A., and A.R. executed rigorous data extraction and synthesis with precision. All authors delivered critical revisions, ensuring scientific excellence, and unanimously approved the final submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors do not have any conflict of interest to disclose for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring manuscript preparation, the author(s) leveraged Perplexity AI for high-efficiency summarization, condensation, and grammar optimisation, alongside AI-assisted preliminary data identification and organisation. All AI-generated outputs underwent rigorous author review, editing, and validation, with full accountability retained for the final published content.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eInusa BPD, Hsu LL, Kohli N, Patel A, Ominu-Evbota K, Anie KA, et al. Sickle Cell Disease Genetics, Pathophysiology, Clinical Presentation and Treatment. Int J Neonatal Screen. 2019;5(2):20. doi:10.3390/ijns5020020.\u003c/li\u003e\n\u003cli\u003eThomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V, et al. Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000\u0026ndash;2021: A systematic analysis from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(8):e585\u0026ndash;e599. doi:10.1016/S2352-3026(23)00118-7.\u003c/li\u003e\n\u003cli\u003eNelson M, Noisette L, Pugh N, Gordeuk V, Hsu L, Wun T, et al. 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Int J Lab Hematol. 2013;35(1):31\u0026ndash;7. doi:10.1111/j.1751-553X.2012.01457.x.\u003c/li\u003e\n\u003cli\u003eHatabah D, Krieger R, Brown LA, Harris F, Korman R, Reyes L, et al. Cobalamin Deficiency in Children and Adolescents with Sickle Cell Disease. Nutrients. 2025;17(3):3. doi:10.3390/nu17030597.\u003c/li\u003e\n\u003cli\u003eKamal S, Naghib MM, Al Zahrani J, Hassan H, Moawad K, Arrahman O. Influence of Nutrition on Disease Severity and Health-related Quality of Life in Adults with Sickle Cell Disease: A Prospective Study. Mediterr J Hematol Infect Dis. 2021;13(1):e2021007. doi:10.4084/MJHID.2021.007.\u003c/li\u003e\n\u003cli\u003eKennedy TS, Fung EB, Kawchak DA, Zemel BS, Ohene-Frempong K, Stallings VA. Red Blood Cell Folate and Serum Vitamin B12 Status in Children With Sickle Cell Disease. J Pediatr Hematol Oncol. 2001;23(3):165\u0026ndash;9. doi:10.1097/00043426-200103000-00009.\u003c/li\u003e\n\u003cli\u003eLowenthal EA, Mayo MS, Cornwell PE, Thornley-Brown D. Homocysteine Elevation in Sickle Cell Disease. J Am Coll Nutr. 2000;19(5):608\u0026ndash;12. doi:10.1080/07315724.2000.10718958.\u003c/li\u003e\n\u003cli\u003eOlaniyi JA, Akinlade KS, Atere AD, Arinola OG. Plasma homocysteine, methyl-malonic acid, vitamin B12 and folate levels in adult Nigerian sickle cell anaemia patients. Ann Afr Med. 2014;13(4):214-8. doi:10.4103/1596-3519.142285.\u003c/li\u003e\n\u003cli\u003eOrolu AK, Adeyemo TA, Akanmu AS. Elevated homocysteine and crises state in patients with sickle cell anemia: A comparative study. J Clin Sci. 2022;19(3):80. doi:10.4103/jcls.jcls_33_22.\u003c/li\u003e\n\u003cli\u003eWilliams BA, Mayer C, McCartney H, Devlin AM, Lamers Y, Vercauteren SM, et al. Detectable Unmetabolized Folic Acid and Elevated Folate Concentrations in Folic Acid-Supplemented Canadian Children With Sickle Cell Disease. Front Nutr. 2021;8. doi:10.3389/fnut.2021.642306.\u003c/li\u003e\n\u003cli\u003eHannibal L, Lysne V, Bj\u0026oslash;rke-Monsen AL, et al. Biomarkers and Algorithms for the Diagnosis of Vitamin B12 Deficiency. Front Mol Biosci. 2016;3:27. doi:10.3389/fmolb.2016.00027\u003c/li\u003e\n\u003cli\u003eCarmel R. Biomarkers of cobalamin (vitamin B-12) status in the epidemiologic setting: a critical overview of context, applications, and performance characteristics of cobalamin, methylmalonic acid, and holotranscobalamin II. Am J Clin Nutr. 2011 Jul;94(1):348S-358S. doi: 10.3945/ajcn.111.013441. \u003c/li\u003e\n\u003cli\u003eWolffenbuttel BHR, Wouters H, De Jong WHA, Huls G, Van der Klauw MM. Association of vitamin B12, methylmalonic acid, and functional parameters. Neth J Med. 2020;78(1):10\u0026ndash;24. \u003c/li\u003e\n\u003cli\u003eNarayan S, Jha B, Kumar S, Sirkar S. Study of vitamin B12 levels in patients of sickle cell disease in a tertiary care hospital, Jharkhand. 2019. [Preprint].\u003c/li\u003e\n\u003cli\u003eSurendran S, Adaikalakoteswari A, Saravanan P, Shatwaan IA, Lovegrove JA, Vimaleswaran KS. An update on vitamin B12-related gene polymorphisms and B12 status. Genes Nutr. 2018;13(1):2.\u003c/li\u003e\n\u003cli\u003eVelkova A, Diaz JEL, Pangilinan F, Molloy AM, Mills JL, Shane B, et al. The FUT2 secretor variant p.Trp154Ter influences serum vitamin B12 concentration via holo-haptocorrin, but not holo-transcobalamin, and is associated with haptocorrin glycosylation. Hum Mol Genet. 2017;26(24):4975-88. doi:10.1093/hmg/ddx369.\u003c/li\u003e\n\u003cli\u003eAllin KH, Friedrich N, Pietzner M, Grarup N, Thuesen BH, et al. Genetic determinants of serum vitamin B12 and their relation to body mass index. Eur J Epidemiol. 2017;32(2):125-34. doi:10.1007/s10654-016-0215-x\u003c/li\u003e\n\u003cli\u003ePage MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi:10.1136/bmj.n71.\u003c/li\u003e\n\u003cli\u003eDastidar R, Sikder K. Diagnostic reliability of serum active B12 (holo-transcobalamin) in true evaluation of vitamin B12 deficiency: relevance in current perspective. BMC Res Notes. 2022;15(1):329. doi:10.1186/s13104-022-06224-8.\u003c/li\u003e\n\u003c/ol\u003e\n\n\u003col start=\"34\" type=\"1\"\u003e\n\u003cli\u003eThain A, Hart K, Ahmadi KR. Addressing the gaps in the Vitamin B12 deficiency 2024 NICE Guidelines: highlighting the need for better recognition, diagnosis, and management of pernicious anaemia. Eur J Clin Nutr. 2025;79(7):607-610. doi: 10.1038/s41430-025-01583-4\u003c/li\u003e\n\u003cli\u003eLee SM, Oh J, Chun MR, Lee SY. Methylmalonic acid and homocysteine as indicators of vitamin B12 deficiency in patients with gastric cancer after gastrectomy. Nutrients. 2019;11(2):450. doi: 10.3390/nu11020450\u003c/li\u003e\n\u003cli\u003eLangan RC, Goodbred AJ. Vitamin B12 deficiency: recognition and management. Am Fam Physician. 2017;96(6):384-389.\u003c/li\u003e\n\u003cli\u003eBailey RL, Carmel R, Green R, Pfeiffer CM, Cogswell ME, Osterloh JD, et al. Monitoring of vitamin B-12 nutritional status in the United States by using plasma methylmalonic acid and serum vitamin B-12. Am J Clin Nutr. 2011;94(2):552-61. doi: 10.3945/ajcn.111.015222\u003c/li\u003e\n\u003cli\u003eMarandola M, Napoli G, Leggeri S, Lombardi C, Urbani A, Baroni S. Total vitamin B12 and holotranscobalamin: current evidence, limitations, and clinical utility [Preprint]. Preprints. 2026. doi: 10.20944/preprints202603.0826.v1\u003c/li\u003e\n\u003cli\u003eYoung MF, Guo J, Williams A, Whitfield KC, Nasrin S, Kancherla V, et al. Interpretation of vitamin B-12 and folate concentrations in population-based surveys does not require adjustment for inflammation: Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia (BRINDA) project. Am J Clin Nutr. 2020;111(4):919-26. doi:10.1093/ajcn/nqz303.\u003c/li\u003e\n\u003cli\u003eSugihara T, Koda M, Okamoto T, Miyoshi K, Matono T, Oyama K, et al. Falsely elevated serum vitamin B12 levels were associated with the severity and prognosis of chronic viral liver disease. Yonago Acta Med. 2017;60(1):31-9.\u003c/li\u003e\n\u003cli\u003eRiphagen IJ, Minović I, Groothof D, Post A, Eggersdorfer ML, Kootstra-Ros JE, de Borst MH, Navis G, Muskiet FAJ, Kema IP, et al. Methylmalonic acid, vitamin B12, renal function, and risk of all-cause mortality in the general population: results from the prospective Lifelines-MINUTHE study. BMC Med. 2020;18(1):380. doi: 10.1186/s12916-020-01853-x\u003c/li\u003e\n\u003cli\u003eMills JL, Molloy AM, Reynolds EH. Do the benefits of folic acid fortification outweigh the risk of masking vitamin B12 deficiency? BMJ. 2018;360:k724. doi: 10.1136/bmj.k724. Erratum in: BMJ. 2018;360:k1334. doi: 10.1136/bmj.k1334\u003c/li\u003e\n\u003cli\u003eAgrawal RK, Patel RK, Shah V, Nainiwal L, Trivedi B. Hydroxyurea in sickle cell disease: drug review. Indian J Hematol Blood Transfus. 2014;30(2):91-6. doi:10.1007/s12288-013-0261-4.\u003c/li\u003e\n\u003cli\u003eMiller JW. Proton pump inhibitors, H2-receptor antagonists, metformin, and vitamin B-12 deficiency: clinical implications. Adv Nutr. 2018;9(4):511S-8S. doi:10.1093/advances/nmy023.\u003c/li\u003e\n\u003cli\u003eNational Institute for Health and Care Excellence. Vitamin B12 deficiency in over 16s: diagnosis and management. NICE guideline NG239. London: National Institute for Health and Care Excellence; 2024. Available from: https://www.nice.org.uk/guidance/ng239\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Biotechnology Development Agency","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vitamin B12, sickle cell disease, Diagnostic bias, Global health equity, HIC, LMIC","lastPublishedDoi":"10.21203/rs.3.rs-9611368/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9611368/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eReported prevalence of vitamin B12 deficiency in sickle cell disease (SCD) ranges from 0\u0026ndash;70%, suggesting that current estimates may reflect systematic measurement error rather than true biological variation. Conventional biomarkers are not validated for the altered physiology of SCD, where haemolysis, inflammation, and increased erythropoietic demand distort standard assay interpretation.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a systematic review of observational studies (2000\u0026ndash;2026) assessing biochemical B12 status in SCD, following PRISMA 2020 guidelines and registered in PROSPERO (CRD420251087800). PubMed, African Journals Online (AJOL), and Google Scholar were searched, with citation tracking and dual independent screening. Diagnostic methodology was evaluated across four domains: biomarker strategy (circulating vs functional), analytical platform, diagnostic thresholds, and confounder integration. A diagnostic validity framework was applied to classify methodological robustness and misclassification risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThirteen studies were included (62% high-income, 38% low- and middle-income settings). Most used single-marker approaches (circulating B12 alone) and immunoassays, with limited use of functional markers or LC\u0026ndash;MS/MS. Diagnostic thresholds were inconsistently defined, and no study adjusted for key confounders. Reported prevalence (0\u0026ndash;70%) tracked methodological design: single-marker, non-adjusted studies reported low prevalence (0\u0026ndash;7.1%), while multi-marker approaches reported higher estimates (6.9\u0026ndash;53%). Misclassification risk was high (54\u0026ndash;62%), and greater in low-resource settings (\u0026ge;\u0026thinsp;60% vs 40\u0026ndash;50%).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eB12 deficiency estimates in SCD are largely method-dependent rather than biologically determined, limiting comparability and clinical reliability. We propose a context-integrated, multi-marker framework to reduce misclassification and improve diagnostic validity, supporting more accurate and equitable care.\u003c/p\u003e","manuscriptTitle":"Vitamin B12 Assessment in Sickle Cell Disease: A Systematic Evaluation of Diagnostic Validity and Misclassification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-06 18:40:51","doi":"10.21203/rs.3.rs-9611368/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"18591f93-99ee-4056-ac24-ef021d76c1ce","owner":[],"postedDate":"May 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":67510584,"name":"Hematology"}],"tags":[],"updatedAt":"2026-05-06T18:40:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-06 18:40:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9611368","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9611368","identity":"rs-9611368","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}