A Precision Screening Framework for Thalassemia in a High-Mobility Population: Integrating 3D Phenotypic Mapping and Genotype-Specific Thresholds

A Precision Screening Framework for Thalassemia in a High-Mobility Population: Integrating 3D Phenotypic Mapping and Genotype-Specific Thresholds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Precision Screening Framework for Thalassemia in a High-Mobility Population: Integrating 3D Phenotypic Mapping and Genotype-Specific Thresholds Hou Qian, Weifeng Li, Yike Wu, Weihong Qin, Weihua Zhao, Dayong Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9213369/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective In regions characterized by high hemoglobinopathy prevalence and significant population mobility, defining population-specific diagnostic thresholds is critical to overcoming "phenotypic masking" in complex thalassemia genotypes. This study evaluates the diagnostic utility of hematological indices and HbA 2 through a novel 3D topographical framework to refine screening protocols for the South China population. Methods A retrospective analysis was conducted on 1,672 genetically confirmed thalassemia carriers 1,090 α -thalassemia, 522 β -thalassemia, 60 αβ -compound) and 5,051 healthy controls from January 2018 to June 2025. We utilized Receiver Operating Characteristic (ROC) curves and 3D spatial clustering analysis of Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Hemoglobin A 2 ( HbA 2 ) to define optimal cut-off values and diagnostic performance. Results The molecular landscape was dominated by the -- SEA /αα (54.5%) and β CD41–42 /β N (30.5%) genotypes, reflecting a distinct regional mutational spectrum. While HbA 2 functioned as a near-perfect biomarker for β -thalassemia (AUC 0.984) and αβ -compound states (AUC 0.964), it lacked discriminatory power for silent α -carriers (AUC 0.563). Notably, αβ -coinheritances demonstrated "phenotypic masking," where concurrent α -globin defects partially "normalized" erythrocyte indices compared to pure β -thalassemia (22.00 ± 2.11 vs. 20.60 ± 1.96 pg). Our 3D topographical model revealed significant spatial overlap between controls and silent α -carriers, representing a critical diagnostic "blind spot". Conclusion To mitigate the risk of occult carrier transmission, we propose a hierarchical precision framework: indices HbA 2 > 3.75% for primary β -thalassemia screening. MCH < 28.5 pg combined with HbA 2 < 2.77% as a trigger for reflex α -molecular testing to capture silent carriers. This multidimensional approach addresses the limitations of traditional single-parameter thresholds and provides a robust scientific basis for thalassemia prevention in high-prevalence urban environments. αβ-thalassemia Phenotypic Masking 3D Topographical Analysis Reflex Testing HbA2 Figures Figure 1 Figure 2 Figure 3 Introduction Thalassemia represents a profound global public health challenge, with a carrier burden exceeding 350 million individuals 1 , 2 . While Mediterranean regions like Italy and Greece have established robust prevention pathways through the Italian Society for Thalassemia and Hemoglobinopathies (SITE), these frameworks are optimized for β -globin-dominant populations 3 , 4 . In the "Thalassemia Belt" of South China, particularly Guangdong, the diagnostic landscape is significantly more complex, with carrier frequencies exceeding 21% in localized hotspots 5 , 6 . Unlike the Mediterranean spectrum, South China is characterized by a high prevalence of α -globin defects, particularly the -- SEA deletion 7 . This genetic diversity introduces two critical diagnostic vulnerabilities: Silent α -Carrier Escape: Traditional screening thresholds often fail to capture silent α -carriers, who carry a 25% risk of conceiving offspring with Hb H disease when paired with a minor α -thalassemia partner. Phenotypic Masking: The co-inheritance of α - and β -thalassemia (accounting for 3.6% of our cohort) creates a "diagnostic paradox". The concurrent reduction in both globin chains partially restores intracellular balance, "normalizing" hematological indices and potentially concealing underlying β -globin defects during routine β -thalassemia screening 8 . The established clinical cascade relies on Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Hemoglobin A 2 ( HbA 2 ). However, as critics have noted, HbA 2 remains a non-standardized parameter across global laboratories, and reliance on single-parameter cut-offs can be misleading in regions with diverse mutational pressures 9 . This necessitates the calibration of diagnostic thresholds to local populations and the development of multi-parametric models to resolve spatial overlaps between healthy and pathological phenotypes. To address these "blind spots," this study systematically correlates genotypic distributions with hematological phenotypes in a large Shenzhen-based cohort (n = 6,723). We introduce a 3D topographical model integrating MCV, MCH, and HbA 2 to visualize and quantify the phenotypic space of various thalassemia subtypes 10 . Our objective is to establish refined, population-specific screening thresholds and a reflex molecular testing framework that optimizes clinical counseling and prevents occult carrier transmission in high-prevalence urban environments. Method and materials Study Population and Ethical Considerations This retrospective study was approved by the Ethics Committee of Shenzhen Second People’s Hospital (Approval No: 2025-765-01PJ) and conducted in accordance with the principles of the Declaration of Helsinki. Between January 2018 and June 2025, we enrolled a cohort of 1,672 genetically confirmed thalassemia carriers (1,090 α -thalassemia, 522 β -thalassemia, and 60 αβ -compound heterozygotes) and 5,051 healthy controls. Control subjects were verified as negative for both α - and β -globin mutations via molecular testing. Inclusion criteria for the study group were: (1) age 18–60 years; (2) availability of comprehensive clinical, hematological, and genomic data; and (3) definitive diagnosis supported by laboratory findings and pedigree analysis. Exclusion criteria included: (1) concomitant iron-deficiency anemia (to eliminate interference with microcytic indices); (2) β -thalassemia major or intermedia; and (3) significant systemic organ dysfunction or malignancy. No statistically significant demographic differences were observed between the study and control cohorts ( P > 0.05), ensuring inter-group comparability. Hematological and Phenotypic Analysis Peripheral blood (2–3 mL) was collected in EDTA-K 2 anticoagulant tubes. Hematological indices, specifically Mean Corpuscular Volume (MCV) and Mean Corpuscular Hemoglobin (MCH), were determined using a Sysmex XN-1000 automated analyzer (Sysmex Co., Kobe, Japan) with manufacturer-certified reagents. To address concerns regarding the standardization of Hemoglobin A 2 ( HbA 2 ), quantification was performed via capillary electrophoresis on the Helena V8 system (Helena Biosciences, USA). The system underwent daily multi-level calibration and rigorous internal quality control using manufacturer-certified reagents to ensure analytical precision and inter-laboratory comparability, mitigating the impact of non-standardized parameters on diagnostic thresholds. Molecular Genotyping Genomic DNA was isolated from peripheral blood leukocytes using an automated nucleic acid extraction platform (Kaishuo Biological Co., Xiamen, China). α -Thalassemia Analysis : Common deletional variants (- α 3.7 , - α 4.2 , -- SEA , -- THAI ) were identified using Gap-Polymerase Chain Reaction (Gap-PCR). Non-deletional mutations, including Hb Constant Spring (Hb CS), Hb Quong Sze (Hb QS), and Hb Westmead, were characterized by Reverse Dot Blot PCR (RDB-PCR). β -Thalassemia Analysis : A panel of 19 β -globin gene mutations (including − 28, -29, CD17, CD26, CD41-42, and IVS-II-654, among others) was screened using reverse dot blot PCR (RDB-PCR). All assays were performed using dedicated thalassemia detection kits (Yilifang Biological Products Co., Ltd., Shenzhen, China) and a Bio-Rad C-1000 thermal cycler. The comprehensive list of the 19 mutations and their corresponding HGVS nomenclature is provided in Supplementary Table S1 . 3D Topographical and Statistical Analysis To resolve the diagnostic challenge of "phenotypic masking" in complex genotypes, we employed a 3D topographical spatial analysis. This multidimensional framework integrated MCV, MCH, and HbA 2 into a unified diagnostic space to visualize the clustering of different thalassemia phenotypes. ROC curve analysis was performed using MedCalc® (v20.217) and Python (v3.13) to define optimal cut-off values. Statistical significance was established at a two-tailed P < 0.0001. Data were analyzed using MedCalc® Statistical Software (v20.217) and Python (v3.13). Normality was assessed via the D'Agostino-Pearson test. Given the non-normal distribution of HbA2 , MCV, and MCH ( P < 0.0001), continuous variables are presented as medians with interquartile ranges (IQR). Inter-group comparisons were performed using the Kruskal-Wallis test followed by Conover’s post-hoc analysis for pairwise differences. Receiver Operating Characteristic (ROC) curves were constructed to determine optimal diagnostic thresholds, sensitivity, and specificity. Statistical significance was established at a two-tailed P < 0.0001. Results Molecular Landscape and Allelic Heterogeneity Systematic molecular profiling of 1,672 thalassemia carriers identified 19 distinct variants, reflecting a highly heterogeneous mutational spectrum (Table 1 ). Rare genotypes, including non-deletional α -variants and complex αβ -globin coinheritances accounting for < 1%, were collectively categorized as 'Other' (comprehensive breakdown provide in Supplementary Table S2 ). Table 1 Genotype Distribution of 1672 Thalassemia Carriers Diagnostic Category and Genotype HGVS Nomenclature / Variant Description No. of Cases (n) Frequency (%) α- Thalassemia 1090 65.19 Silent (-α/αα) -α 3.7 /αα NG_000006.1: g.34164_37967del3804 281 16.81 -α 4.2 /αα NG_000006.1: g.30131_34352del4222 90 5.38 α CS α/αα HBA2 : c.427T > C; p.Ter143Glnext*31 37 2.21 Minor ( -- /αα or - α/-α) -- SEA /αα NG_000006.1:g.26264_45564del19301 594 35.53 Hb H Disease -α 3.7 /-- SEA Compound heterozygosity ( α + / α 0 ) 17 1.02 Other d 71 4.25 β - Thalassemia 522 31.22 β CD41–42 /β N HBB : c.124_127delTTCT; 159 9.51 β IVS−Ⅱ−654 /β N HBB : c.316-197C > T 151 9.03 β CD17 /β N HBB : c.52 A > T 82 4.90 β −28 /β N HBB : c.-78 A > G 73 4.37 Other d 57 3.41 αβ - Compound 60 3.59 -- SEA /αα; β IVS−Ⅱ−654 /β N 10 0.60 Other d 50 2.99 Total 1672 100.00 Note: N = 1672. Genotypes are described according to the Human Genome Variation Society (HGVS) nomenclature. α -thalassemia deletions ( - α 3.7 , - α 4.2 , -- SEA ) are referenced to NG_000006.1. Gene names ( HBA2 , HBB ) are italicized per standard nomenclature. d Other: Includes rare non-deletional α-globin variants, β-point mutations, and complex αβ-globin configurations with an individual frequency of <1%. A comprehensive breakdown of the mutational spectra for all categories is provided in Supplementary Table S2 . Abbreviations: CS, Constant Spring; QS, Quong Sze; WS, Westmead; Hb, Hemoglobin; n, number of cases. α -Thalassemia Spectrum : Among 1,090 α -thalassemia carriers, deletional variants were the primary molecular drivers. The -- SEA /αα genotype was most prevalent (54.5% of α -thalassemia cases), followed by -α 3.7 /αα (25.8%) and -α 4.2 /αα (8.3%) (Fig. 1 A). Non-deletional variants, although less frequent, were dominated by α CS α/αα (3.4%) and α WS α/αα (2.6%) alleles, which typically correlate with more pronounced clinical phenotypes. β -Thalassemia Profile : Analysis of 522 β -thalassemia subjects identified a highly concentrated mutational spectrum. Four recurrent mutations accounted for 89.1% of all β -globin defects: β CD41–42 /β N (30.5%) and β IVS−Ⅱ−654(A>G) /β N (28.9%) were the predominant alleles, followed by β Codons17(A>T) /β N (15.7%) and β Codons−28(A>G) /β N (14.0%) (Fig. 1 B). Complex αβ -Coinheritance Concurrent α - and β -globin mutations were identified in 3.6% of the cohort (n = 60). The most frequent configuration was -- SEA /αα co-inherited with β IVS−Ⅱ−654 /β N (16.7%; Fig. 1 C). Quantifying Phenotypic Masking in αβ -Compound States Our results demonstrate a significant "phenotypic masking" effect in αβ -compound heterozygotes (n = 60). While HbA 2 levels remained diagnostically elevated (5.37 ± 0.63%), the co-inheritance of α -globin defects partially normalized red cell indices compared to pure β -thalassemia carriers. Specifically, MCH levels were significantly higher in the αβ -compound group than in the β -thalassemia group (22.00 ± 2.11 vs. 20.60 ± 1.96 pg, P < 0.0001). This partial restoration of α/β globin chain balance suggests that relying solely on microcytic thresholds established in other populations may lead to the underdiagnosis of underlying α -globin defects during routine β -carrier screening. Phenotypic Clustering and 3D Topographical Analysis To elucidate genotype-phenotype correlations, we mapped the cohort within a three-dimensional (3D) diagnostic space defined by MCV, MCH, and HbA 2 (Fig. 2 ). Statistical analysis confirmed profound hematological divergence across genotypes ( P < 0.0001; Table 2 ). β -thalassemia and αβ -compound carriers formed a distinct, high-density cluster characterized by significantly elevated HbA 2 levels compared to healthy controls (2.54 ± 0.42; P < 0.0001). Table 2 Comparison of hematological indices across various thalassemia genotypes. Genotype Group n HbA 2 (%) MCV (fL) MCH (pg) Healthy controls 5051 2.54 ± 0.42 87.89 ± 6.86 29.14 ± 2.94 Silent α -thalassemia 450 2.48 ± 0.34 81.76 ± 5.18 α 26.51 ± 2.03 α Minor α -thalassemia 609 2.38 ± 0.29 68.33 ± 3.64 α 21.84 ± 1.25 α Hb H disease 31 1.65 ± 0.61 64.87 ± 8.89 αc 18.82 ± 1.95 αc β -thalassemia 522 5.20 ± 0.63 b 64.94 ± 5.25 α 20.60 ± 1.96 α αβ -Compound 60 5.37 ± 0.63 b 68.99 ± 5.84 α 22.00 ± 2.11 α Note : Data expressed as Mean ± SD. Statistical analysis was performed using MedCalc® Statistical Software version 20.217 (MedCalc Software Ltd, Ostend, Belgium) ; α P < 0.0001 vs. Healthy Controls: Indicates profound microcytosis and hypochromia, consistent with quantitative defects in globin chain synthesis . b P < 0.0001 vs. Healthy Controls: Denotes diagnostic elevation of HbA2 levels, a hallmark of β -thalassemia and αβ -compound phenotypes . c P < 0.0001 vs. α -thalassemia minor: Reflects the significantly exacerbated erythrocyte index depressions characteristic of the clinically severe Hb H disease phenotype . Abbreviations : HbA2 , h emoglobin A 2 ; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin. Statistical Methodology: Normality was assessed via the D'Agostino-Pearson test. Inter-group significance was determined using the Kruskal-Wallis test followed by Conover’s post-hoc analysis for all pairwise comparisons to account for non-parametric distributions. Within the α -globin spectrum, a clear gradient of microcytic hypochromic severity was observed. While a 27 pg MCH threshold effectively sequestered most pathological genotypes, "silent" α -carriers showed substantial phenotypic overlap with healthy controls, representing a critical diagnostic "blind spot" for conventional screening. Comparative Diagnostic Performance and High-Risk Genotypes ROC curve analysis was performed to quantify the diagnostic utility of hematological markers (Figures S1 - S3 , Fig. 3 , Table 3 ). HbA 2 demonstrated near-perfect diagnostic accuracy for classical β -thalassemia (AUC 0.984; 95% CI: 0.980–0.987) and αβ -compound states ( AUC 0.964; 95% CI: 0.959–0.968), validating it as the primary screening metric for β -globin defects. Table 3 Diagnostic performance, optimal cut-off values, and 95% confidence intervals of HbA 2 , MCV, and MCH across thalassemia genotypes. Diagnosis Group Marker Cut-off AUC (95%CI) Sensitivity Specificity Silent α -thalassemia (n = 450) HbA 2 ≤ 2.77 0.563 (0.551–0.575) 88.0 27.8 MCV ≤ 86.3 0.659 (0.648–0.670) 85.8 57.8 MCH ≤ 28.5 0.683 (0.671–0.694) 89.1 59.8 Minor α - thalassemia (n = 609) HbA 2 ≤ 2.62 0.684 (0.673–0.695) 90.0 38.8 MCV ≤ 75.4 0.903 (0.895–0.910) 97.5 85.1 MCH ≤ 24.1 0.894 (0.887–0.902) 99.0 84.0 Hb H disease (n = 31) HbA 2 ≤ 2.00 0.850 (0.842–0.859) 74.2 94.6 MCV ≤ 79.6 0.904 (0.897–0.911) 93.5 73.1 MCH ≤ 22.0 0.954 (0.948–0.959) 100 83.1 β -thalassemia (n = 522) HbA 2 > 3.75 0.984 (0.980–0.987) 97.9 98.5 MCV ≤ 76.2 0.953 (0.948–0.958) 96.0 83.0 MCH ≤ 24.4 0.937 (0.931–0.943) 95.4 82.2 αβ -Compound (n = 60) HbA 2 > 3.9 0.964 (0.959–0.968) 98.3 91.9 MCV ≤ 79.6 0.859 (0.851–0.867) 96.7 73.4 MCH ≤ 25.0 0.842 (0.833–0.851) 95.0 75.5 Note : Bold values indicate the highest AUC within each diagnostic group. AUC, area under the curve; CI, confidence interval. For the most clinically severe phenotype, Hb H disease, MCH demonstrated exceptional diagnostic accuracy with an AUC of 0.954 and 100% sensitivity at a cut-off of ≤ 22.0 pg. This robust sensitivity ensures that high-risk genotypes are effectively captured, directly addressing the necessity for reliable screening markers to prevent severe birth defects in high-prevalence regions. For minor α -thalassemia, MCV provided the highest diagnostic yield (AUC 0.903; 95% CI: 0.895–0.910). Discussion and Conclusion Divergence from Mediterranean Models: Regional Genetic Specificity While established guidelines from Mediterranean regions, such as those provided by the Italian Society for Thalassemia and Hemoglobinopathies (SITE) 3 , 4 , 11 , offer a foundational framework for β -thalassemia management, they frequently underrepresent the unique mutational pressures found in the South China "Thalassemia Belt" 7 . Our cohort's molecular landscape is dominated by the -- SEA deletion (54.5% of α -cases) and β CD41–42 (30.5% of β -cases), reflecting a distinct regional signature. Unlike Mediterranean populations where β -thalassemia is the primary concern, the high prevalence of large-fragment α -globin deletions in Shenzhen creates a high risk for Hb H disease and Hb Bart’s hydrops fetalis. Consequently, applying generic Mediterranean cut-offs to this population is not only suboptimal but potentially misses high-risk carriers 8 . Quantifying "Phenotypic Masking": Resolving the Diagnostic Paradox A significant contribution of this study is the rigorous quantification of "phenotypic masking" in αβ -compound heterozygotes. It is widely recognized in clinical hematology that red cell indices alone may be insufficient for a definitive risk assessment of complex thalassemia genotypes 8 . Our data supports this: αβ -compound carriers exhibit significantly higher MCH levels compared to pure β -thalassemia carriers (22.00 ± 2.11 vs. 20.60 ± 1.96 pg). This partial restoration of globin chain balance effectively "cloaks" the underlying α -defect 12 . By integrating HbA 2 (which remains elevated at 5.37 ± 0.63) with these "normalized" indices, our 3D topographical model provides a visual and statistical mechanism to identify these complex genotypes that would otherwise bypass traditional 2D screening filters. HbA2 Reliability and "Precision Trigger" Strategy The non-standardization of HbA 2 across different analytical platforms is a recognized challenge in hemoglobinopathy screening 8 . However, within the standardized environment of the Helena V8 capillary electrophoresis system, our results demonstrate that HbA 2 remains the gold standard for β -globin defects (AUC 0.984) 9 . We propose that our established cut-offs (e.g., HbA 2 >3.75% for β -thalassemia) should be viewed not as universal reference intervals, but as "Precision Triggers" tailored to the local genetic background 8 , 9 . This approach mitigates the risk of "misleading" results by ensuring that molecular testing is triggered even in cases with borderline microcytosis. Overcoming the "Blind Spot" of Silent α-Thalassemia Silent α -thalassemia carriers (- α/αα ) remain the most elusive targets in primary screening, with MCV and MCH AUCs falling below 0.70 8 . The extensive density overlap between these carriers and healthy controls in our 3D model underscores the systemic vulnerability of index-only screening 10 , 13 . To address this, we advocate for a lowered threshold for molecular intervention (MCH < 28.5 pg) in high-prevalence urban centers. This reflex testing strategy is essential for preventing the conception of children with Hb H disease, particularly in high-mobility populations like Shenzhen where pedigree analysis may be incomplete 7 . Conclusion: Toward a Precision Screening Framework In conclusion, this study establishes a precision screening framework tailored to the unique mutational landscape of South China, moving beyond the limitations of generic international guidelines. This reflex testing strategy is essential for preventing the conception of children with Hb H disease, particularly in high-mobility populations like Shenzhen where pedigree analysis may be incomplete. Our proposed regional protocol utilizes: HbA 2 > 3.75% serves as the primary trigger for β -thalassemia. MCH < 28.5 pg combined with HbA 2 < 2.77% as a mandatory threshold for reflex α -molecular testing to capture silent carriers. This multidimensional approach provides a rigorous scientific basis for primary medical institutions to minimize occult carrier transmission and ensure the safety of prenatal counseling in high-mobility urban environments. Abbreviations CS Constant Spring QS Quong Sze WS Westmead Hb Hemoglobin n number of cases. Declarations Funding This work was supported by the Clinical Research Special Project of the Medical and Health Technology Development Research Center, National Health Commission (Grant No. WKZX2024DN0181), the Guangdong Science and Technology Foundation (Grant No. 2023B0101200003), and the Shenzhen Science and Technology Foundation (Grant No. KJZD20230923115359001). Ethics approval and consent to participate The study protocol was in line with the Declaration of Helsinki (as revised in Brazil 2013). This study was approved by the Ethics Committee of Shenzhen Second People's Hospital (Approval No: 2025-765-01PJ) and all participants were informed and signed a written informed consent. Consent for publication Not applicable Competing interests The authors declare that they have no competing interests Availability of data and materials Data is provided within the supplementary information files. Acknowledgements not applicable. Authors' contributions D.G. designed and supervised the overall study, interpreted data, and performed final revision of the manuscript. H.Q. conceptualized the project, performed experiments, and drafted the initial manuscript. W.L. and Y.W. collected and analyzed clinical samples. All authors reviewed and approved the final version of the manuscript. References Weatherall DJ (2010) The inherited diseases of hemoglobin are an emerging global health burden. 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(2025) Chetta M et al (2025) The Masked Thalassemia: A Rare Case of a Patient with Normal HbA2 Levels, beta-Thalassemia Pathogenic Variant (CD39 C > T), and a Novel delta-Globin Gene Deletion. Appl Clin Genet 18:233–241. 10.2147/TACG.S544633 Xie S, Liu S, Gao Y, Tang J, Cao L (2022) Application of Hemostatic Devices in Laparoscopic Hepatectomy. J Vis Exp. 10.3791/63368 Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS1S3legends.docx TableS1S2.docx rawdate.xlsx cofeforfigure1.docx codeforfigure2.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9213369","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616051675,"identity":"3c5fd78a-244e-49f9-818f-c2222cc72941","order_by":0,"name":"Hou Qian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACfv7mAwc+VNjw2Lc3EKlFcsaxxIczzqTJGPAcIFKLwYEcY2PetsM2BhIJxLrswBkzCR62wzzmko833mCosYkmqIOxua1MQoInncdydlqxBcOxtNwGQlqYGQ5vkzCQsOZhuJ1jJsHYcJiwFjaGBDOJBANmHoabZ4jUwsOQYmxwIMGZx+AGD5FaJCSAgdxwII1HsgfolwRi/GJ/vvnA4b//bOz52Q9vvPGhxoawFmRAQtQgtJCqYxSMglEwCkYGAAB740Al30EZogAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Laboratory Medicine Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University)","correspondingAuthor":true,"prefix":"","firstName":"Hou","middleName":"","lastName":"Qian","suffix":""},{"id":616051676,"identity":"c9dc84ce-ba88-4104-a0d5-c0852e74b837","order_by":1,"name":"Weifeng Li","email":"","orcid":"","institution":"Department of Laboratory Medicine Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"prefix":"","firstName":"Weifeng","middleName":"","lastName":"Li","suffix":""},{"id":616051677,"identity":"6e6347b2-1d3a-48ab-aa86-28cad240bcd8","order_by":2,"name":"Yike Wu","email":"","orcid":"","institution":"Department of Laboratory Medicine Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"prefix":"","firstName":"Yike","middleName":"","lastName":"Wu","suffix":""},{"id":616051678,"identity":"4a1124b7-968a-4fb6-b95f-733f8ef093fa","order_by":3,"name":"Weihong Qin","email":"","orcid":"","institution":"Department of Laboratory Medicine Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Qin","suffix":""},{"id":616051679,"identity":"4f943067-e233-4263-9d18-74afcf5bbbbf","order_by":4,"name":"Weihua Zhao","email":"","orcid":"","institution":"Prenatal Diagnosis Center, Shenzhen Second People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weihua","middleName":"","lastName":"Zhao","suffix":""},{"id":616051680,"identity":"c35d2992-f948-4625-bfab-f5dcf3b7d92d","order_by":5,"name":"Dayong Gu","email":"","orcid":"","institution":"Department of Laboratory Medicine Shenzhen Second People's Hospital (The First Affiliated Hospital of Shenzhen University)","correspondingAuthor":false,"prefix":"","firstName":"Dayong","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2026-03-24 14:39:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9213369/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9213369/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106403093,"identity":"25d04457-5a4c-443b-af53-dd38b93a15f5","added_by":"auto","created_at":"2026-04-08 09:13:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4988064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMolecular spectrum of thalassemia carriers. \u003c/strong\u003eA: Genotype distribution of \u003cem\u003eα\u003c/em\u003e-thalassemia; B: Mutation spectrum of \u003cem\u003eβ\u003c/em\u003e-thalassemia; C: Genotype combinations of \u003cem\u003eαβ\u003c/em\u003e-compound thalassemia.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/283f22a2bf0cd7d5a6b003c1.png"},{"id":106252791,"identity":"3daf94cf-989e-4da7-b6f3-190e1a5eb941","added_by":"auto","created_at":"2026-04-06 17:47:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5062329,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMultiparametric 3D and 2D Spatial Distribution of Hematological Indices Across Thalassemia Genotypes.\u003c/strong\u003e The integration of Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Hemoglobin \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) demonstrates the distinct phenotypic landscape of controls (n=5,051) versus five thalassemia subgroups. (1): Global 3D Topographical View (Left): Illustrates the robust spatial separation of \u003cem\u003eβ\u003c/em\u003e-thalassemia (purple) and \u003cem\u003eαβ\u003c/em\u003e-compound heterozygotes (brown), driven primarily by \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e elevation. The \u003cem\u003eα\u003c/em\u003e-thalassemia spectrum (green/orange/red) demonstrates a progressive downward trajectory in the MCV-MCH plane. (2): Panel A (MCV vs. MCH 2D Projection): Highlights the varying degrees of microcytosis and hypochromia. The dashed red lines (MCV 80 fL, MCH 27 pg) represent conventional screening thresholds, showing significant control overlap with silent α-carriers (n= 450). (3): Panel B (MCV vs. \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e 2D Projection): Demonstrates the absolute diagnostic power of \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e in segregating β-globin defects. The red dashed line denotes the 3.5% \u003cem\u003eβ\u003c/em\u003e-thalassemia screening cutoff. (4): Panel C (MCH vs. \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e with KDE Density Contours): Kernel Density Estimation (KDE) contours reveal the high-density centers of each phenotype. Note the extensive density overlap between controls and silent α-carriers, contrasting with the discrete, isolated cluster of \u003cem\u003eβ\u003c/em\u003e-thalassemia (n=522) and the extreme deviation of \u003cem\u003eHb H\u003c/em\u003e disease (n=31).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/25ac581abbb298f875980819.png"},{"id":106252793,"identity":"539154c8-fc57-4654-bc85-267e8dfb651f","added_by":"auto","created_at":"2026-04-06 17:47:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4976794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative diagnostic efficacy of MCV, MCH, and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eHbA\u003c/strong\u003e\u003c/em\u003e\u003csub\u003e\u003cem\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/em\u003e\u003c/sub\u003e\u003cstrong\u003e across thalassemia genotypes.\u003c/strong\u003e Overlaid ROC curves comparing the discriminatory power of the three hematological markers for each clinical subtype. The curves represent MCV (blue solid line), MCH (green dashed line), and \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (orange dotted line). (A) Silent \u003cem\u003eα\u003c/em\u003e-thalassemia: All markers showed suboptimal performance (\u003cem\u003eAUC \u003c/em\u003e\u0026lt; 0.70). (B): Minor \u003cem\u003eα\u003c/em\u003e-thalassemia: MCV and MCH significantly outperformed \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e. (C) Hb H disease: MCH demonstrated the highest diagnostic accuracy (\u003cem\u003eAUC\u003c/em\u003e = 0.954), followed closely by MCV. (D)\u003cem\u003e β\u003c/em\u003e-thalassemia: \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e proved to be the superior marker (\u003cem\u003eAUC\u003c/em\u003e = 0.984). (E) \u003cem\u003eαβ\u003c/em\u003e-compound thalassemia: \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e maintained high diagnostic efficiency, outperforming red cell indices. The diagonal line represents the line of no discrimination (\u003cem\u003eAUC\u003c/em\u003e = 0.5).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/d6100377c8fb5e5e16fb79b4.png"},{"id":109534152,"identity":"b78ee8d2-3e3c-49d0-b6c4-f67d8d9a4246","added_by":"auto","created_at":"2026-05-19 08:42:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8954670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/bcff6801-786f-4087-9da2-4e505c1785f0.pdf"},{"id":106252788,"identity":"58688c02-2cb2-430c-a3f2-3f7349d835d6","added_by":"auto","created_at":"2026-04-06 17:47:38","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":971824,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/c9f2bc52c8f2f476186cede8.tif"},{"id":106404053,"identity":"92e79b01-d931-4711-a1cd-dd19ed9ed12f","added_by":"auto","created_at":"2026-04-08 09:15:24","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1117916,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/50139c8e0e5f522c9b9b1452.tif"},{"id":106403366,"identity":"ece72e07-c8ee-4a04-b553-e9b0ec30e2ef","added_by":"auto","created_at":"2026-04-08 09:14:10","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1187688,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/d92ca21d5d7082f645249bae.tif"},{"id":106403110,"identity":"6f2af435-b9a1-43e1-a85b-586921bd55c2","added_by":"auto","created_at":"2026-04-08 09:13:35","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17201,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1S3legends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/cef590777f20f4d8e261b70b.docx"},{"id":106252796,"identity":"962221dd-8c77-4e94-b8f1-de4a7d070e47","added_by":"auto","created_at":"2026-04-06 17:47:39","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":30153,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/017781b8af4e7cfb84574789.docx"},{"id":106403719,"identity":"74009c65-cecc-45e3-976a-b1fe90904926","added_by":"auto","created_at":"2026-04-08 09:14:52","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":466579,"visible":true,"origin":"","legend":"","description":"","filename":"rawdate.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/b79a4e9a4d566b1a2fad5333.xlsx"},{"id":106403651,"identity":"bb340dca-e08f-4cb9-b88e-d024ae6894a5","added_by":"auto","created_at":"2026-04-08 09:14:41","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":19716,"visible":true,"origin":"","legend":"","description":"","filename":"cofeforfigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/1293cd71f531a03323f26e41.docx"},{"id":106403539,"identity":"03dee432-76ce-424f-8b6f-e0a20cdb4ee1","added_by":"auto","created_at":"2026-04-08 09:14:28","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":19838,"visible":true,"origin":"","legend":"","description":"","filename":"codeforfigure2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9213369/v1/e58cf2cfecc347675174178a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Precision Screening Framework for Thalassemia in a High-Mobility Population: Integrating 3D Phenotypic Mapping and Genotype-Specific Thresholds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThalassemia represents a profound global public health challenge, with a carrier burden exceeding 350\u0026nbsp;million individuals\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. While Mediterranean regions like Italy and Greece have established robust prevention pathways through the Italian Society for Thalassemia and Hemoglobinopathies (SITE), these frameworks are optimized for \u003cem\u003eβ\u003c/em\u003e-globin-dominant populations\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In the \"Thalassemia Belt\" of South China, particularly Guangdong, the diagnostic landscape is significantly more complex, with carrier frequencies exceeding 21% in localized hotspots\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eUnlike the Mediterranean spectrum, South China is characterized by a high prevalence of \u003cem\u003eα\u003c/em\u003e-globin defects, particularly the \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e deletion\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. This genetic diversity introduces two critical diagnostic vulnerabilities: Silent \u003cem\u003eα\u003c/em\u003e-Carrier Escape: Traditional screening thresholds often fail to capture silent \u003cem\u003eα\u003c/em\u003e-carriers, who carry a 25% risk of conceiving offspring with Hb H disease when paired with a minor \u003cem\u003eα\u003c/em\u003e-thalassemia partner. Phenotypic Masking: The co-inheritance of \u003cem\u003eα\u003c/em\u003e- and \u003cem\u003eβ\u003c/em\u003e-thalassemia (accounting for 3.6% of our cohort) creates a \"diagnostic paradox\". The concurrent reduction in both globin chains partially restores intracellular balance, \"normalizing\" hematological indices and potentially concealing underlying \u003cem\u003eβ\u003c/em\u003e-globin defects during routine \u003cem\u003eβ\u003c/em\u003e-thalassemia screening\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe established clinical cascade relies on Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Hemoglobin \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e). However, as critics have noted, \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e remains a non-standardized parameter across global laboratories, and reliance on single-parameter cut-offs can be misleading in regions with diverse mutational pressures\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This necessitates the calibration of diagnostic thresholds to local populations and the development of multi-parametric models to resolve spatial overlaps between healthy and pathological phenotypes.\u003c/p\u003e \u003cp\u003eTo address these \"blind spots,\" this study systematically correlates genotypic distributions with hematological phenotypes in a large Shenzhen-based cohort (n\u0026thinsp;=\u0026thinsp;6,723). We introduce a 3D topographical model integrating MCV, MCH, and \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e to visualize and quantify the phenotypic space of various thalassemia subtypes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Our objective is to establish refined, population-specific screening thresholds and a reflex molecular testing framework that optimizes clinical counseling and prevents occult carrier transmission in high-prevalence urban environments.\u003c/p\u003e"},{"header":"Method and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population and Ethical Considerations\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the Ethics Committee of Shenzhen Second People\u0026rsquo;s Hospital (Approval No: 2025-765-01PJ) and conducted in accordance with the principles of the Declaration of Helsinki. Between January 2018 and June 2025, we enrolled a cohort of 1,672 genetically confirmed thalassemia carriers (1,090 \u003cem\u003eα\u003c/em\u003e-thalassemia, 522 \u003cem\u003eβ\u003c/em\u003e-thalassemia, and 60 \u003cem\u003eαβ\u003c/em\u003e-compound heterozygotes) and 5,051 healthy controls. Control subjects were verified as negative for both \u003cem\u003eα\u003c/em\u003e- and \u003cem\u003eβ\u003c/em\u003e-globin mutations via molecular testing.\u003c/p\u003e \u003cp\u003eInclusion criteria for the study group were: (1) age 18\u0026ndash;60 years; (2) availability of comprehensive clinical, hematological, and genomic data; and (3) definitive diagnosis supported by laboratory findings and pedigree analysis. Exclusion criteria included: (1) concomitant iron-deficiency anemia (to eliminate interference with microcytic indices); (2) \u003cem\u003eβ\u003c/em\u003e-thalassemia major or intermedia; and (3) significant systemic organ dysfunction or malignancy. No statistically significant demographic differences were observed between the study and control cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), ensuring inter-group comparability.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHematological and Phenotypic Analysis\u003c/h3\u003e\n\u003cp\u003ePeripheral blood (2\u0026ndash;3 mL) was collected in \u003cem\u003eEDTA-K\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e anticoagulant tubes. Hematological indices, specifically Mean Corpuscular Volume (MCV) and Mean Corpuscular Hemoglobin (MCH), were determined using a Sysmex XN-1000 automated analyzer (Sysmex Co., Kobe, Japan) with manufacturer-certified reagents.\u003c/p\u003e \u003cp\u003eTo address concerns regarding the standardization of Hemoglobin \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e), quantification was performed via capillary electrophoresis on the Helena V8 system (Helena Biosciences, USA). The system underwent daily multi-level calibration and rigorous internal quality control using manufacturer-certified reagents to ensure analytical precision and inter-laboratory comparability, mitigating the impact of non-standardized parameters on diagnostic thresholds.\u003c/p\u003e\n\u003ch3\u003eMolecular Genotyping\u003c/h3\u003e\n\u003cp\u003eGenomic DNA was isolated from peripheral blood leukocytes using an automated nucleic acid extraction platform (Kaishuo Biological Co., Xiamen, China).\u003c/p\u003e \u003cp\u003e \u003cb\u003eα\u003c/b\u003e \u003cb\u003e-Thalassemia Analysis\u003c/b\u003e: Common deletional variants (-\u003cem\u003eα\u003c/em\u003e\u003csup\u003e3.7\u003c/sup\u003e, -\u003cem\u003eα\u003c/em\u003e\u003csup\u003e4.2\u003c/sup\u003e, \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e, \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eTHAI\u003c/em\u003e\u003c/sup\u003e) were identified using Gap-Polymerase Chain Reaction (Gap-PCR). Non-deletional mutations, including Hb Constant Spring (Hb CS), Hb Quong Sze (Hb QS), and Hb Westmead, were characterized by Reverse Dot Blot PCR (RDB-PCR).\u003c/p\u003e \u003cp\u003e \u003cb\u003eβ\u003c/b\u003e \u003cb\u003e-Thalassemia Analysis\u003c/b\u003e: A panel of 19 \u003cem\u003eβ\u003c/em\u003e-globin gene mutations (including\u0026thinsp;\u0026minus;\u0026thinsp;28, -29, CD17, CD26, CD41-42, and IVS-II-654, among others) was screened using reverse dot blot PCR (RDB-PCR). All assays were performed using dedicated thalassemia detection kits (Yilifang Biological Products Co., Ltd., Shenzhen, China) and a Bio-Rad C-1000 thermal cycler. The comprehensive list of the 19 mutations and their corresponding HGVS nomenclature is provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e3D Topographical and Statistical Analysis\u003c/strong\u003e \u003cp\u003eTo resolve the diagnostic challenge of \"phenotypic masking\" in complex genotypes, we employed a 3D topographical spatial analysis. This multidimensional framework integrated MCV, MCH, and \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e into a unified diagnostic space to visualize the clustering of different thalassemia phenotypes. ROC curve analysis was performed using MedCalc\u0026reg; (v20.217) and Python (v3.13) to define optimal cut-off values. Statistical significance was established at a two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eData were analyzed using MedCalc\u0026reg; Statistical Software (v20.217) and Python (v3.13). Normality was assessed via the D'Agostino-Pearson test. Given the non-normal distribution of \u003cem\u003eHbA2\u003c/em\u003e, MCV, and MCH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), continuous variables are presented as medians with interquartile ranges (IQR). Inter-group comparisons were performed using the Kruskal-Wallis test followed by Conover\u0026rsquo;s post-hoc analysis for pairwise differences. Receiver Operating Characteristic (ROC) curves were constructed to determine optimal diagnostic thresholds, sensitivity, and specificity. Statistical significance was established at a two-tailed \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular Landscape and Allelic Heterogeneity\u003c/h2\u003e\n \u003cp\u003eSystematic molecular profiling of 1,672 thalassemia carriers identified 19 distinct variants, reflecting a highly heterogeneous mutational spectrum (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Rare genotypes, including non-deletional \u003cem\u003e\u0026alpha;\u003c/em\u003e-variants and complex \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-globin coinheritances accounting for \u0026lt;\u0026thinsp;1%, were collectively categorized as \u0026apos;Other\u0026apos; (comprehensive breakdown provide in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eGenotype Distribution of 1672 Thalassemia Carriers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiagnostic Category and Genotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eHGVS Nomenclature / Variant Description\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eNo. of Cases (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eFrequency (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;-\u003c/em\u003eThalassemia\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e65.19\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eSilent (-\u0026alpha;/\u0026alpha;\u0026alpha;)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e-\u0026alpha;\u003c/em\u003e\u003csup\u003e3.7\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNG_000006.1: g.34164_37967del3804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e16.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e-\u0026alpha;\u003c/em\u003e\u003csup\u003e4.2\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNG_000006.1: g.30131_34352del4222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e5.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCS\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026alpha;/\u0026alpha;\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHBA2\u003c/em\u003e: c.427T\u0026thinsp;\u0026gt;\u0026thinsp;C; p.Ter143Glnext*31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eMinor (\u003c/em\u003e--\u003cem\u003e/\u0026alpha;\u0026alpha; or\u003c/em\u003e-\u003cem\u003e\u0026alpha;/-\u0026alpha;)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003csup\u003eSEA\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eNG_000006.1:g.26264_45564del19301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e35.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eHb H Disease\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e-\u0026alpha;\u003c/em\u003e\u003csup\u003e3.7\u003c/sup\u003e\u003cem\u003e/--\u003c/em\u003e\u003csup\u003eSEA\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eCompound heterozygosity (\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e+\u003c/sup\u003e/\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eThalassemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e522\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e31.22\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCD41\u0026ndash;42\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHBB\u003c/em\u003e: c.124_127delTTCT;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e9.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eIVS\u0026minus;Ⅱ\u0026minus;654\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHBB\u003c/em\u003e: c.316-197C\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCD17\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHBB\u003c/em\u003e: c.52\u003cem\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;T\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u0026minus;28\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHBB\u003c/em\u003e: c.-78\u003cem\u003eA\u0026thinsp;\u0026gt;\u0026thinsp;G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eCompound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.59\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e--\u003c/em\u003e\u003csup\u003eSEA\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;; \u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eIVS\u0026minus;Ⅱ\u0026minus;654\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003eOther\u003c/em\u003e\u003csup\u003e\u003cem\u003ed\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cstrong\u003e1672\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote:\u003c/em\u003e\u003c/strong\u003e \u003cem\u003eN\u003c/em\u003e = 1672. Genotypes are described according to the Human Genome Variation Society (HGVS) nomenclature. \u003cstrong\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/strong\u003e-thalassemia deletions (\u003cem\u003e-\u003c/em\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e3.7\u003c/sup\u003e, \u003cem\u003e-\u003c/em\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e4.2\u003c/sup\u003e, \u003cem\u003e--\u003c/em\u003e\u003csup\u003eSEA\u003c/sup\u003e) are referenced to NG_000006.1. Gene names (\u003cem\u003eHBA2\u003c/em\u003e, \u003cem\u003eHBB\u003c/em\u003e) are italicized per standard nomenclature.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Other:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eIncludes rare non-deletional \u0026alpha;-globin variants, \u0026beta;-point mutations, and complex \u0026alpha;\u0026beta;-globin configurations with an individual frequency of \u0026lt;1%. A comprehensive breakdown of the mutational spectra for all categories is provided\u003cem\u003e\u0026nbsp;in \u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e\u003c/strong\u003e CS, Constant Spring; QS, Quong Sze; WS, Westmead; Hb, Hemoglobin; n, number of cases.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026alpha;\u003c/strong\u003e \u003cstrong\u003e-Thalassemia Spectrum\u003c/strong\u003e: Among 1,090 \u003cem\u003e\u0026alpha;\u003c/em\u003e-thalassemia carriers, deletional variants were the primary molecular drivers. The \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e genotype was most prevalent (54.5% of \u003cem\u003e\u0026alpha;\u003c/em\u003e-thalassemia cases), followed by \u003cem\u003e-\u0026alpha;\u003c/em\u003e\u003csup\u003e3.7\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e (25.8%) and \u003cem\u003e-\u0026alpha;\u003c/em\u003e\u003csup\u003e4.2\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e (8.3%) (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Non-deletional variants, although less frequent, were dominated by \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCS\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026alpha;/\u0026alpha;\u0026alpha;\u003c/em\u003e (3.4%) and \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003csup\u003e\u003cem\u003eWS\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u0026alpha;/\u0026alpha;\u0026alpha;\u003c/em\u003e (2.6%) alleles, which typically correlate with more pronounced clinical phenotypes.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e \u003cstrong\u003e-Thalassemia Profile\u003c/strong\u003e: Analysis of 522 \u003cem\u003e\u0026beta;\u003c/em\u003e-thalassemia subjects identified a highly concentrated mutational spectrum. Four recurrent mutations accounted for 89.1% of all \u003cem\u003e\u0026beta;\u003c/em\u003e-globin defects: \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCD41\u0026ndash;42\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (30.5%) and \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eIVS\u0026minus;Ⅱ\u0026minus;654(A\u0026gt;G)\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (28.9%) were the predominant alleles, followed by \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCodons17(A\u0026gt;T)\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (15.7%) and \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eCodons\u0026minus;28(A\u0026gt;G)\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (14.0%) (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eComplex \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-Coinheritance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eConcurrent \u003cem\u003e\u0026alpha;\u003c/em\u003e- and \u003cem\u003e\u0026beta;\u003c/em\u003e-globin mutations were identified in 3.6% of the cohort (n\u0026thinsp;=\u0026thinsp;60). The most frequent configuration was \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026alpha;\u0026alpha;\u003c/em\u003e co-inherited with \u003cem\u003e\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eIVS\u0026minus;Ⅱ\u0026minus;654\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/\u0026beta;\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (16.7%; Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eQuantifying Phenotypic Masking in \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-Compound States\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOur results demonstrate a significant \u0026quot;phenotypic masking\u0026quot; effect in \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-compound heterozygotes (n\u0026thinsp;=\u0026thinsp;60). While \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e levels remained diagnostically elevated (5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63%), the co-inheritance of \u003cem\u003e\u0026alpha;\u003c/em\u003e-globin defects partially normalized red cell indices compared to pure \u003cem\u003e\u0026beta;\u003c/em\u003e-thalassemia carriers. Specifically, MCH levels were significantly higher in the \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-compound group than in the \u003cem\u003e\u0026beta;\u003c/em\u003e-thalassemia group (22.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 vs. 20.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 pg, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This partial restoration of \u003cem\u003e\u0026alpha;/\u0026beta;\u003c/em\u003e globin chain balance suggests that relying solely on microcytic thresholds established in other populations may lead to the underdiagnosis of underlying \u003cem\u003e\u0026alpha;\u003c/em\u003e-globin defects during routine \u003cem\u003e\u0026beta;\u003c/em\u003e-carrier screening.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003ePhenotypic Clustering and 3D Topographical Analysis\u003c/h2\u003e\n \u003cp\u003eTo elucidate genotype-phenotype correlations, we mapped the cohort within a three-dimensional (3D) diagnostic space defined by MCV, MCH, and \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (Fig. \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Statistical analysis confirmed profound hematological divergence across genotypes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003e\u0026beta;\u003c/em\u003e-thalassemia and \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-compound carriers formed a distinct, high-density cluster characterized by significantly elevated \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e levels compared to healthy controls (2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of hematological indices across various thalassemia genotypes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGenotype Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003eMCV (fL)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eMCH (pg)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealthy controls\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e5051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e87.89\u0026thinsp;\u0026plusmn;\u0026thinsp;6.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e29.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003cstrong\u003e-thalassemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e81.76\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e26.51\u0026thinsp;\u0026plusmn;\u0026thinsp;2.03\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor\u003c/strong\u003e \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003cstrong\u003e-thalassemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e2.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e68.33\u0026thinsp;\u0026plusmn;\u0026thinsp;3.64\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e21.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb H\u003c/strong\u003e \u003cstrong\u003edisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e64.87\u0026thinsp;\u0026plusmn;\u0026thinsp;8.89\u003csup\u003e\u003cem\u003e\u0026alpha;c\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e18.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003csup\u003e\u003cem\u003e\u0026alpha;c\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cstrong\u003e-thalassemia\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e64.94\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e20.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e\u003cstrong\u003e-Compound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003csup\u003e\u003cem\u003eb\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e68.99\u0026thinsp;\u0026plusmn;\u0026thinsp;5.84\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e22.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11\u003csup\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Data expressed as Mean \u0026plusmn; SD.\u0026nbsp;\u003c/em\u003e\u003cem\u003eStatistical analysis was performed using MedCalc\u0026reg; Statistical Software version 20.217 (MedCalc Software Ltd, Ostend, Belgium)\u003c/em\u003e\u003cem\u003e;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003csup\u003e\u0026alpha;\u003c/sup\u003eP \u0026lt; 0.0001\u0026nbsp;\u003c/em\u003e\u003cem\u003evs. Healthy Controls: Indicates profound microcytosis and hypochromia, consistent with quantitative defects in globin chain synthesis\u003c/em\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eP \u0026lt; 0.0001\u0026nbsp;\u003c/em\u003e\u003cem\u003evs. Healthy Controls: Denotes diagnostic elevation of\u0026nbsp;\u003c/em\u003e\u003cem\u003eHbA2\u003c/em\u003e\u003cem\u003e\u0026nbsp;levels, a hallmark of\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cem\u003e-thalassemia and\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e\u003cem\u003e-compound phenotypes\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003e\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/em\u003e\u003cem\u003eP \u0026lt; 0.0001\u0026nbsp;\u003c/em\u003e\u003cem\u003evs.\u0026nbsp;\u003c/em\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e\u003cem\u003e-thalassemia minor: Reflects the significantly exacerbated erythrocyte index depressions characteristic of the clinically severe Hb H disease phenotype\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAbbreviations\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e\u003cem\u003eHbA2\u003csub\u003e,\u0026nbsp;\u003c/sub\u003eh\u003c/em\u003e\u003cem\u003eemoglobin\u0026nbsp;\u003c/em\u003e\u003cem\u003eA\u003csub\u003e2\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e; MCV, mean corpuscular volume; MCH, mean corpuscular hemoglobin.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Methodology:\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e\u0026nbsp;Normality was assessed via the D\u0026apos;Agostino-Pearson test. Inter-group significance was determined using the Kruskal-Wallis test followed by Conover\u0026rsquo;s post-hoc analysis for all pairwise comparisons to account for non-parametric distributions.\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eWithin the \u003cem\u003e\u0026alpha;\u003c/em\u003e-globin spectrum, a clear gradient of microcytic hypochromic severity was observed. While a 27 pg MCH threshold effectively sequestered most pathological genotypes, \u0026quot;silent\u0026quot; \u003cem\u003e\u0026alpha;\u003c/em\u003e-carriers showed substantial phenotypic overlap with healthy controls, representing a critical diagnostic \u0026quot;blind spot\u0026quot; for conventional screening.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eComparative Diagnostic Performance and High-Risk Genotypes\u003c/h3\u003e\n\u003cp\u003eROC curve analysis was performed to quantify the diagnostic utility of hematological markers (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e-\u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e, Fig. \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e demonstrated near-perfect diagnostic accuracy for classical \u003cem\u003e\u0026beta;\u003c/em\u003e-thalassemia (AUC 0.984; 95% CI: 0.980\u0026ndash;0.987) and \u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e-compound states (\u003cem\u003eAUC\u003c/em\u003e 0.964; 95% CI: 0.959\u0026ndash;0.968), validating it as the primary screening metric for\u0026nbsp;\u003cem\u003e\u0026beta;\u003c/em\u003e-globin defects.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic performance, optimal cut-off values, and 95% confidence intervals of \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e, MCV, and MCH across thalassemia genotypes.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiagnosis Group\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMarker\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eCut-off\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cem\u003eAUC\u003c/em\u003e (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c6\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e \u003cem\u003e\u0026alpha;\u003c/em\u003e\u003cstrong\u003e-thalassemia (n\u0026thinsp;=\u0026thinsp;450)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.563 (0.551\u0026ndash;0.575)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;86.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.659 (0.648\u0026ndash;0.670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e85.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e57.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.683\u003c/strong\u003e (0.671\u0026ndash;0.694)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e89.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e59.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinor\u003c/strong\u003e\u003cem\u003e\u0026alpha;\u003c/em\u003e-\u003cstrong\u003ethalassemia (n\u0026thinsp;=\u0026thinsp;609)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.684 (0.673\u0026ndash;0.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;75.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.903\u003c/strong\u003e (0.895\u0026ndash;0.910)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e85.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.894 (0.887\u0026ndash;0.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e99.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eHb H\u003c/strong\u003e \u003cstrong\u003edisease (n\u0026thinsp;=\u0026thinsp;31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.850 (0.842\u0026ndash;0.859)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e74.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e94.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.904 (0.897\u0026ndash;0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e73.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.954\u003c/strong\u003e (0.948\u0026ndash;0.959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e83.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003cstrong\u003e-thalassemia (n\u0026thinsp;=\u0026thinsp;522)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.984\u003c/strong\u003e (0.980\u0026ndash;0.987)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e97.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;76.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.953 (0.948\u0026ndash;0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;24.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.937 (0.931\u0026ndash;0.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e82.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026alpha;\u0026beta;\u003c/em\u003e\u003cstrong\u003e-Compound (n\u0026thinsp;=\u0026thinsp;60)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.964\u003c/strong\u003e (0.959\u0026ndash;0.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e98.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e91.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;79.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.859 (0.851\u0026ndash;0.867)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e96.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\n \u003cp\u003e0.842 (0.833\u0026ndash;0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c5\"\u003e\n \u003cp\u003e95.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\n \u003cp\u003e75.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNote\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Bold values indicate the highest AUC within each diagnostic group. AUC, area under the curve; CI, confidence interval.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor the most clinically severe phenotype, Hb H disease, MCH demonstrated exceptional diagnostic accuracy with an AUC of 0.954 and 100% sensitivity at a cut-off of \u0026le;\u0026thinsp;22.0 pg. This robust sensitivity ensures that high-risk genotypes are effectively captured, directly addressing the necessity for reliable screening markers to prevent severe birth defects in high-prevalence regions. For minor \u003cem\u003e\u0026alpha;\u003c/em\u003e-thalassemia, MCV provided the highest diagnostic yield (AUC 0.903; 95% CI: 0.895\u0026ndash;0.910).\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDivergence from Mediterranean Models: Regional Genetic Specificity\u003c/h2\u003e \u003cp\u003eWhile established guidelines from Mediterranean regions, such as those provided by the Italian Society for Thalassemia and Hemoglobinopathies (SITE)\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, offer a foundational framework for \u003cem\u003eβ\u003c/em\u003e-thalassemia management, they frequently underrepresent the unique mutational pressures found in the South China \"Thalassemia Belt\"\u003csup\u003e7\u003c/sup\u003e. Our cohort's molecular landscape is dominated by the \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e deletion (54.5% of \u003cem\u003eα\u003c/em\u003e-cases) and \u003cem\u003eβ\u003c/em\u003e\u003csup\u003e\u003cem\u003eCD41\u0026ndash;42\u003c/em\u003e\u003c/sup\u003e (30.5% of \u003cem\u003eβ\u003c/em\u003e-cases), reflecting a distinct regional signature. Unlike Mediterranean populations where \u003cem\u003eβ\u003c/em\u003e-thalassemia is the primary concern, the high prevalence of large-fragment \u003cem\u003eα\u003c/em\u003e-globin deletions in Shenzhen creates a high risk for Hb H disease and Hb Bart\u0026rsquo;s hydrops fetalis. Consequently, applying generic Mediterranean cut-offs to this population is not only suboptimal but potentially misses high-risk carriers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQuantifying \"Phenotypic Masking\": Resolving the Diagnostic Paradox\u003c/h2\u003e \u003cp\u003eA significant contribution of this study is the rigorous quantification of \"phenotypic masking\" in \u003cem\u003eαβ\u003c/em\u003e-compound heterozygotes. It is widely recognized in clinical hematology that red cell indices alone may be insufficient for a definitive risk assessment of complex thalassemia genotypes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Our data supports this: \u003cem\u003eαβ\u003c/em\u003e-compound carriers exhibit significantly higher MCH levels compared to pure \u003cem\u003eβ\u003c/em\u003e-thalassemia carriers (22.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 vs. 20.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 pg). This partial restoration of globin chain balance effectively \"cloaks\" the underlying \u003cem\u003eα\u003c/em\u003e-defect\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. By integrating \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (which remains elevated at 5.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63) with these \"normalized\" indices, our 3D topographical model provides a visual and statistical mechanism to identify these complex genotypes that would otherwise bypass traditional 2D screening filters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHbA2 Reliability and \"Precision Trigger\" Strategy\u003c/h2\u003e \u003cp\u003eThe non-standardization of \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e across different analytical platforms is a recognized challenge in hemoglobinopathy screening\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, within the standardized environment of the Helena V8 capillary electrophoresis system, our results demonstrate that \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e remains the gold standard for \u003cem\u003eβ\u003c/em\u003e-globin defects (AUC 0.984)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. We propose that our established cut-offs (e.g., \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026gt;3.75% for \u003cem\u003eβ\u003c/em\u003e-thalassemia) should be viewed not as universal reference intervals, but as \"Precision Triggers\" tailored to the local genetic background\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This approach mitigates the risk of \"misleading\" results by ensuring that molecular testing is triggered even in cases with borderline microcytosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eOvercoming the \"Blind Spot\" of Silent α-Thalassemia\u003c/h2\u003e \u003cp\u003eSilent \u003cem\u003eα\u003c/em\u003e-thalassemia carriers (-\u003cem\u003eα/αα\u003c/em\u003e) remain the most elusive targets in primary screening, with MCV and MCH AUCs falling below 0.70\u003csup\u003e8\u003c/sup\u003e. The extensive density overlap between these carriers and healthy controls in our 3D model underscores the systemic vulnerability of index-only screening\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. To address this, we advocate for a lowered threshold for molecular intervention (MCH\u0026thinsp;\u0026lt;\u0026thinsp;28.5 pg) in high-prevalence urban centers. This reflex testing strategy is essential for preventing the conception of children with Hb H disease, particularly in high-mobility populations like Shenzhen where pedigree analysis may be incomplete\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eConclusion: Toward a Precision Screening Framework\u003c/h2\u003e \u003cp\u003e In conclusion, this study establishes a precision screening framework tailored to the unique mutational landscape of South China, moving beyond the limitations of generic international guidelines. This reflex testing strategy is essential for preventing the conception of children with Hb H disease, particularly in high-mobility populations like Shenzhen where pedigree analysis may be incomplete.\u003c/p\u003e \u003cp\u003eOur proposed regional protocol utilizes: \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;3.75% serves as the primary trigger for \u003cem\u003eβ\u003c/em\u003e-thalassemia. MCH\u0026thinsp;\u0026lt;\u0026thinsp;28.5 pg combined with \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.77% as a mandatory threshold for reflex \u003cem\u003eα\u003c/em\u003e-molecular testing to capture silent carriers.\u003c/p\u003e \u003cp\u003eThis multidimensional approach provides a rigorous scientific basis for primary medical institutions to minimize occult carrier transmission and ensure the safety of prenatal counseling in high-mobility urban environments.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConstant Spring\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuong Sze\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWestmead\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003en\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003enumber of cases.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Clinical Research Special Project of the Medical and Health Technology Development Research Center, National Health Commission (Grant No. WKZX2024DN0181), the Guangdong Science and Technology Foundation (Grant No. 2023B0101200003), and the Shenzhen Science and Technology Foundation (Grant No. KJZD20230923115359001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was in line with the Declaration of Helsinki (as revised in Brazil 2013). This study was approved by the Ethics Committee of Shenzhen Second People\u0026apos;s Hospital (Approval No:\u0026nbsp;2025-765-01PJ) and all participants were informed and signed a written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD.G.\u003c/strong\u003e designed and supervised the overall study, interpreted data, and performed final revision of the manuscript. \u003cstrong\u003eH.Q.\u003c/strong\u003e conceptualized the project, performed experiments, and drafted the initial manuscript. \u003cstrong\u003eW.L.\u003c/strong\u003e and \u003cstrong\u003eY.W.\u003c/strong\u003e collected and analyzed clinical samples. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWeatherall DJ (2010) The inherited diseases of hemoglobin are an emerging global health burden. Blood 115:4331\u0026ndash;4336. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2010-01-251348\u003c/span\u003e\u003cspan address=\"10.1182/blood-2010-01-251348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaher AT, Musallam KM, Cappellini M (2021) D. beta-Thalassemias. 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Sci Rep 15:7483. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-91908-x\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-91908-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang J et al (2023) Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nat Biomed Eng 7:797\u0026ndash;810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41551-022-00991-2\u003c/span\u003e\u003cspan address=\"10.1038/s41551-022-00991-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ein \u003cem\u003eGuidelines for the Management of Transfusion-Dependent beta-Thalassaemia (TDT)\u003c/em\u003e (eds A. T. Taher et al.) (2025)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChetta M et al (2025) The Masked Thalassemia: A Rare Case of a Patient with Normal HbA2 Levels, beta-Thalassemia Pathogenic Variant (CD39 C\u0026thinsp;\u0026gt;\u0026thinsp;T), and a Novel delta-Globin Gene Deletion. Appl Clin Genet 18:233\u0026ndash;241. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/TACG.S544633\u003c/span\u003e\u003cspan address=\"10.2147/TACG.S544633\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie S, Liu S, Gao Y, Tang J, Cao L (2022) Application of Hemostatic Devices in Laparoscopic Hepatectomy. J Vis Exp. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3791/63368\u003c/span\u003e\u003cspan address=\"10.3791/63368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"αβ-thalassemia, Phenotypic Masking, 3D Topographical Analysis, Reflex Testing, HbA2","lastPublishedDoi":"10.21203/rs.3.rs-9213369/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9213369/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eIn regions characterized by high hemoglobinopathy prevalence and significant population mobility, defining population-specific diagnostic thresholds is critical to overcoming \"phenotypic masking\" in complex thalassemia genotypes. This study evaluates the diagnostic utility of hematological indices and \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e through a novel 3D topographical framework to refine screening protocols for the South China population.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA retrospective analysis was conducted on 1,672 genetically confirmed thalassemia carriers 1,090 \u003cem\u003eα\u003c/em\u003e-thalassemia, 522 \u003cem\u003eβ\u003c/em\u003e-thalassemia, 60 \u003cem\u003eαβ\u003c/em\u003e-compound) and 5,051 healthy controls from January 2018 to June 2025. We utilized Receiver Operating Characteristic (ROC) curves and 3D spatial clustering analysis of Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), and Hemoglobin \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e) to define optimal cut-off values and diagnostic performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe molecular landscape was dominated by the \u003cem\u003e--\u003c/em\u003e\u003csup\u003e\u003cem\u003eSEA\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/αα\u003c/em\u003e (54.5%) and \u003cem\u003eβ\u003c/em\u003e\u003csup\u003e\u003cem\u003eCD41\u0026ndash;42\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/β\u003c/em\u003e\u003csup\u003e\u003cem\u003eN\u003c/em\u003e\u003c/sup\u003e (30.5%) genotypes, reflecting a distinct regional mutational spectrum. While \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e functioned as a near-perfect biomarker for \u003cem\u003eβ\u003c/em\u003e-thalassemia (AUC 0.984) and \u003cem\u003eαβ\u003c/em\u003e-compound states (AUC 0.964), it lacked discriminatory power for silent \u003cem\u003eα\u003c/em\u003e-carriers (AUC 0.563). Notably, \u003cem\u003eαβ\u003c/em\u003e-coinheritances demonstrated \"phenotypic masking,\" where concurrent \u003cem\u003eα\u003c/em\u003e-globin defects partially \"normalized\" erythrocyte indices compared to pure \u003cem\u003eβ\u003c/em\u003e-thalassemia (22.00\u0026thinsp;\u0026plusmn;\u0026thinsp;2.11 vs. 20.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 pg). Our 3D topographical model revealed significant spatial overlap between controls and silent \u003cem\u003eα\u003c/em\u003e-carriers, representing a critical diagnostic \"blind spot\".\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eTo mitigate the risk of occult carrier transmission, we propose a hierarchical precision framework: indices \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cb\u003e\u0026gt;\u003c/b\u003e\u0026thinsp;3.75% for primary \u003cem\u003eβ\u003c/em\u003e-thalassemia screening. MCH\u0026thinsp;\u0026lt;\u0026thinsp;28.5 pg combined with \u003cem\u003eHbA\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2.77% as a trigger for reflex \u003cem\u003eα\u003c/em\u003e-molecular testing to capture silent carriers. This multidimensional approach addresses the limitations of traditional single-parameter thresholds and provides a robust scientific basis for thalassemia prevention in high-prevalence urban environments.\u003c/p\u003e","manuscriptTitle":"A Precision Screening Framework for Thalassemia in a High-Mobility Population: Integrating 3D Phenotypic Mapping and Genotype-Specific Thresholds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-06 17:47:33","doi":"10.21203/rs.3.rs-9213369/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":"4c6d42a3-80f2-4e75-b05f-df61dbaeee93","owner":[],"postedDate":"April 6th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-19T08:29:45+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"13626807971662045271168694633184944408","date":"2026-05-19T02:50:05+00:00","index":21,"fulltext":""},{"type":"reviewerAgreed","content":"330999384855738935365336184485242720186","date":"2026-05-09T05:28:58+00:00","index":17,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-19T08:42:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-06 17:47:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9213369","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9213369","identity":"rs-9213369","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}