Transferrin Receptor and Sociodemographic Factors: A Multimodal Model for Heavy Drinking Risk Stratification in Women

Transferrin Receptor and Sociodemographic Factors: A Multimodal Model for Heavy Drinking Risk Stratification in Women | 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 Transferrin Receptor and Sociodemographic Factors: A Multimodal Model for Heavy Drinking Risk Stratification in Women Hong Guo, Lu Dai, Jian Zhan, Xia Guo, Youchao Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8731999/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 This study aimed to develop and validate a clinically actionable nomogram-based prediction model integrating sociodemographic factors and biological biomarkers to identify heavy alcohol consumption among adult women in the United States. Methods Data were extracted from the 2021 to 2023 National Health and Nutrition Examination Survey (NHANES), including 994 eligible adult women (18 + years, past 12-month alcohol use, and complete data on key variables). The Boruta algorithm (random forest-based) was used for feature selection to identify critical predictors of heavy drinking. A logistic regression model was constructed, and performance was evaluated via discrimination(Receiver operating characteristic, ROC), calibration (calibration curve, Hosmer-Lemeshow test), and clinical utility (decision curve analysis, DCA). Internal validation was performed using 1000 bootstrap resamples to ensure robustness. Results Eight key predictors were selected: transferrin receptor, ferritin, high-sensitivity C-reactive protein (HS-CRP), alpha-1-acid glycoprotein, income-to-poverty ratio, education level, country of birth, and age. Multivariate logistic regression revealed significant associations with heavy drinking: transferrin receptor (OR = 1.19, 95% CI = 1.10–1.29, p < 0.001), ferritin (OR = 1.00, 95% CI = 1.00–1.01, p = 0.003), alpha-1-acid glycoprotein (OR = 2.98, 95% CI = 1.36–6.51, p = 0.006), lower education levels (e.g., high school graduates: OR = 3.51, p < 0.001 vs. college graduates), non-U.S. birth (OR = 2.44, p < 0.001), and younger age (OR = 0.96, p < 0.001). The model showed good discrimination with a corrected AUC of 0.710, excellent calibration (alignment between predicted and observed probabilities), and significant net clinical benefit via DCA. Conclusion The nomogram-based model effectively identifies adult women at risk of heavy alcohol consumption by combining biological markers of iron dysregulation with key sociodemographic risk factors. This model provides a powerful tool for enhancing early detection and facilitating personalized interventions. Neurology Psychiatry Excessive Alcohol Use Nomogram Model Transferrin Receptor Ferritin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION Excessive alcohol use by adult women in the U.S. continues to be a major public health issue, with unique effects on their physical and mental health. Recent estimates indicate that approximately 12% of adult women engage in heavy drinking, defined as consuming more than seven drinks per week or binge drinking on multiple occasions[ 1 ]. The consequences of heavy alcohol use are particularly severe for women, including increased risks of liver disease, cardiovascular complications, and mental health disorders such as depression and anxiety[ 2 ]. Despite extensive public health campaigns and policy interventions, a substantial proportion of the global population continues to engage in hazardous drinking patterns, necessitating further research on the predictive factors and risk stratification[ 3 ]. Compelling evidence underscores that women are more likely than men to drink in response to psychological distress, a pattern often termed "drinking to cope"[ 4 ]. This phenomenon is frequently rooted in trauma and gender-specific stressors including interpersonal violence, caregiving burdens, and societal pressures. Furthermore, the bidirectional relationship between alcohol use and internalizing psychiatric disorders was significantly stronger among women. Women with Alcohol Use Disorder (AUD) exhibit higher rates of comorbid anxiety and depression; conversely, women with these disorders are at a heightened risk of developing AUD[ 5 ]. A systematic review has confirmed that negative affect is a robust and reliable trigger for alcohol consumption, specifically among women, reinforcing the centrality of emotional dysregulation in hazardous drinking patterns for this population[ 6 ]. While these internalizing factors are paramount, they operate within a broader sociodemographic context that further shapes risk profiles. Demographic characteristics such as age, race, and socioeconomic status have been consistently linked to heavy alcohol consumption. Younger adults, particularly those aged 18–34 years, exhibit higher rates of daily heavy drinking than do older adults [ 1 ]. Racial and ethnic disparities also play a critical role, with certain groups demonstrating elevated risks owing to cultural, genetic, and socioeconomic factors[ 7 ]. National differences further complicate this landscape, with variations in alcohol policies, taxation, and cultural norms influencing consumption patterns[ 8 ]. Additionally, socioeconomic factors such as income inequality and poverty status have been linked to increased alcohol misuse, as financial stress may exacerbate coping-related drinking behaviors[ 9 ]. Marital status and household composition further influence drinking behavior among women. Unmarried or divorced women exhibit higher rates of heavy drinking than their married counterparts, possibly because of social isolation or lack of familial support[ 10 ]. Pregnancy is a crucial factor because alcohol intake during this period can severely affect fetal development. However, some women continue to drink even when they are aware of the potential dangers[ 11 ]. The household structure, including the number of dependents, may also influence drinking patterns, with larger families potentially mitigating excessive consumption due to caregiving responsibilities[ 12 ]. In addition to demographic and socioeconomic factors, biological markers have also emerged as promising predictors of high alcohol consumption. For example, elevated levels of alpha-1-acid glycoprotein have been associated with chronic alcohol use, reflecting systemic inflammation and immune response alterations[ 13 ]. Similarly, HS-CRP, a marker of low-grade inflammation, has been linked to increased alcohol intake, suggesting its potential role in identifying individuals at risk of alcohol-related harm[ 14 ]. Iron metabolism biomarkers, including ferritin and transferrin receptor levels, have also been implicated in alcohol-related disorders, because chronic drinking disrupts iron homeostasis, leading to hepatic iron overload and oxidative damage[ 15 ]. Ferritin levels, indicative of iron storage, are often elevated in alcohol-related liver diseases, whereas transferrin receptor levels reflect altered iron metabolism due to chronic ethanol exposure[ 16 ]. A recent cross-sectional study involving over 3,000 Korean adults reinforced this association, demonstrating that even low-risk alcohol consumption was linked to significantly higher serum ferritin concentrations, a finding that holds true within the female subcohort [ 17 ]. Although direct research linking the transferrin receptor specifically as a biomarker for alcohol intake in women is less extensive than that for ferritin, its role as a key regulator in the iron cycle makes it a mechanistically plausible and valuable component of a multi-marker panel[ 18 ]. These biomarkers offer valuable insights into the metabolic and inflammatory consequences of heavy drinking, and enhance predictive models for clinical and public health interventions. Despite these advances, few studies have integrated sociodemographic factors and iron metabolism biomarkers into a unified predictive framework for women, limiting the clinical utility of existing models. Given the multifactorial nature of heavy alcohol use among women, predictive models that integrate sociodemographic and biomarker data may enhance early identification and intervention. In clinical practice, nomograms further enhance clinical utility by translating regression results into visual, individualized risk estimates, facilitating actionable decision making in real-world settings[ 19 , 20 ]. By leveraging statistical approaches, logistic regression, and nomogram modeling, this study aimed to develop a clinically actionable predictive model for heavy alcohol use among adult women in the U.S., incorporating both traditional sociodemographic variables and novel biomarkers. 2. MATERIALS AND METHODS 2.1 Patient Data The information comes from the National Health and Nutrition Examination Survey(NHANES), a set of cross-sectional surveys that offer a national snapshot of the health and nutritional status of individuals in the United States. The data collection process included three main steps: conducting home interviews, performing physical examinations at mobile centers, and executing laboratory tests. For more information, please refer to the official website( https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ). The Institutional Review Board of the Centers for Disease Control and Prevention provided ethical approval for the NHANES, and each participant provided written informed consent[ 21 ]. This study examined data from the National Health and NHANES gathered between August 2021 and August 2023. Participants were deemed eligible if they fulfilled the following criteria: (1) female adults aged 18 years or older; (2) self-reported alcohol consumption within the past 12 months; and (3) provided complete data on essential variables such as age, race/ethnicity, educational level, marital status, pregnancy status, household size, ratio of family income to poverty, alpha-1-acid glycoprotein, HS-CRP, ferritin, and transferrin receptor. The exclusion criteria were as follows: (1) male sex, (2) age < 18 years, (3) incomplete data on alcohol consumption or critical biomarkers (ferritin or TfR), and (4) lifetime abstention from alcohol. Following the application of these inclusion and exclusion criteria, 994 participants were included in the final analysis (Fig. 1 ). Based on the National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria for risky drinking, female adults were categorized as heavy drinkers if they consumed more than three drinks per day[ 22 ]. 2.2 Predictors Data Collection Demographic and clinical data served as predictors: (1) demographic factors: age, race/ethnicity, educational level, marital status, pregnancy status, household size, and ratio of family income to poverty, and (2) biomarkers:alpha-1 acid glycoprotein (g/L), HS-CRP (mg/L), ferritin (ng/L), and transferrin receptor (mg/L). The outcome variable was the average daily intake of alcoholic drinks over the past 12 months, classified as low-risk (≤ 3 drinks/day) or heavy (> 3 drinks/day) according to the NIAAA criteria for risky drinking[ 22 ]. 2.3 Statistical Analysis The baseline demographic characteristics were stratified by outcome (average number of alcoholic drinks per day over the past 12 months). Mean ± standard deviation was used for normally distributed continuous variables, whereas median (interquartile range) was used for non-normally distributed data. For categorical variables, the chi-square test or Fisher's exact test was applied, whereas the Welch two-sample t-test or rank-sum test was used for continuous variables[ 23 , 24 ]. Subsequently, Boruta feature selection was implemented via a random forest classifier to identify the key predictors for heavy drinkers. This algorithm assesses predictor importance by comparing scores with randomly generated shadow features, retaining only those deemed significant through iterative testing[ 25 ]. Model performance was then evaluated across three domains: discrimination, assessed via receiver operating characteristic (ROC) curve and AUC; calibration, determined using calibration curves and the Hosmer-Lemeshow test, with a p-value > 0.05, suggesting a good fit. Clinical utility was assessed using decision curve analysis (DCA) to evaluate the net benefit across threshold probabilities[ 24 ]. Finally, a corrected AUC was computed through internal validation using 1000 iterations of bootstrap resampling to ensure robustness. For statistical significance, a two-tailed p-value < 0.05 was used. Analyses were performed using the R software (version 4.2.2) and MSTATA software ( www.mstata.com ). 3. RESULTS 3.1 Patient Characteristics Analysis of baseline demographics and clinical features demonstrated notable links with heavy drinking status(Table 1 ). Compared to low-risk drinkers (mean age: 35 ± 9 years), heavy drinkers were significantly younger (32 ± 9 years; p < 0.001). Racial composition differed markedly between the groups (p < 0.001), with heavy drinkers showing a lower proportion of Non-Hispanic White individuals (40.5% vs. 56.7%) but a higher representation of Other Hispanic ethnicities (22.0% vs. 9.9%). Country of birth was also significantly associated, as heavy drinkers were less frequently U.S.-born (74.6% vs. 83.8%; P = 0.002). However, active duty served by the US armed forces showed no significant group difference (p = 0.394). Education level exhibited a strong association: heavy drinkers had substantially lower rates of college graduation or higher education (22.4% vs. 50.6%). Although marital status was not significantly correlated (p = 0.113), heavy drinkers included more never-married participants (43.9% vs. 36.0%, respectively). Pregnancy status did not differ between the groups (p = 0.235). Regarding household characteristics, heavy drinkers resided in larger households (mean: 3.35 ± 1.57 vs. 3.07 ± 1.50; p = 0.022) and had reduced ratio of family income to poverty (2.20 ± 1.45 vs. 2.91 ± 1.59; p < 0.001). Several biomarkers were significantly elevated in heavy drinkers, including alpha-1-acid glycoprotein (0.85 ± 0.24 g/L vs. 0.77 ± 0.25 g/L; p < 0.001) and transferrin receptor levels (4.15 ± 3.41 mg/L vs. 3.29 ± 1.39 mg/L; p < 0.001). In contrast, HS C-reactive protein (p = 0.159) and ferritin levels (p = 0.256) were not significantly different. Collectively, these findings indicate that heavy drinking correlates with distinct demographic profiles, socioeconomic factors, and specific biomarker alterations, suggesting that alcohol consumption influences health outcomes. Table 1 Patient demographics and baseline characteristics Characteristic Avg alcoholic drinks/day/past 12 mos-group p-value low‑risk drinkers N = 789 Heavy drinkers N = 205 Age, Mean ± SD 35 ± 9 32 ± 9 < 0.001 1 Race, n (%) < 0.001 2 Mexican American 70 (8.9%) 24 (11.7%) Other Hispanic 78 (9.9%) 45 (22.0%) Non-Hispanic White 447 (56.7%) 83 (40.5%) Non-Hispanic Black 95 (12.0%) 31 (15.1%) Other Race 99 (12.5%) 22 (10.7%) Country of birth, n (%) 0.002 2 Born in 50 US states or Washington 661 (83.8%) 153 (74.6%) Others 128 (16.2%) 52 (25.4%) Served active duty in US Armed Forces, n (%) 0.394 3 Yes 17 (2.2%) 2 (1.0%) No 772 (97.8%) 203 (99.0%) Education level, n (%) College graduate or above 399 (50.6%) 46 (22.4%) Some college or AA degree 254 (32.2%) 93 (45.4%) High school graduate/GED or equivalent 90 (11.4%) 50 (24.4%) 9-11th grade 37 (4.7%) 14 (6.8%) Less than 9th grade 9 (1.1%) 2 (1.0%) Marital status, n (%) 0.113 2 Married/Living with partner 379 (48.0%) 87 (42.4%) Widowed/Divorced/Separated 126 (16.0%) 28 (13.7%) Never married 284 (36.0%) 90 (43.9%) Pregnancy status, n (%) 0.235 3 Yes 22 (2.8%) 7 (3.4%) No 759 (96.2%) 193 (94.1%) Cannot ascertain 8 (1.0%) 5 (2.4%) Total number of people in the Household, Mean ± SD 3.07 ± 1.50 3.35 ± 1.57 0.022 1 Ratio of family income to poverty, Mean ± SD 2.91 ± 1.59 2.20 ± 1.45 < 0.001 1 alpha-1-acid glycoprotein (g/L), Mean ± SD 0.77 ± 0.25 0.85 ± 0.24 < 0.001 1 HS-CRP (mg/L), Mean ± SD 4.6 ± 9.0 5.5 ± 8.1 0.159 1 Ferritin (ng/mL), Mean ± SD 63 ± 66 70 ± 83 0.256 1 Transferrin receptor (mg/L), Mean ± SD 3.29 ± 1.39 4.15 ± 3.41 < 0.001 1 1 Welch Two Sample t-test 2 Pearson's Chi-squared test 3 Fisher's exact test 3.2 Predictive Model We initially considered 13 candidate predictors in the original model, including demographic factors (Age, Race, Country of birth, education level, and marital status), socioeconomic indicators (total household members and ratio of family income to poverty), military service history (served as an active duty in the US Armed Forces), physiological status (pregnancy status), and biomarkers [alpha-1-acid glycoprotein (g/L), HS-CRP (mg/L), ferritin (ng/mL), and transferrin receptor (mg/L] ). Utilizing the Boruta algorithm, a random-forest-based feature-selection method, we identified statistically relevant predictors by iteratively comparing the importance of each variable with randomly shuffled shadow features[ 26 ]. Specifically, this approach employs a random forest classifier to compute variable importance scores through multiple iterations, preserving only those features that demonstrate significantly higher importance than their synthetic counterparts[ 27 ]. Consequently, the model was narrowed down to eight key predictors that substantially contributed to outcome prediction, as determined by their consistent importance across iterations[ 28 ](Fig. 2 and Fig. 3 ). Figure 2 shows boxplots with green for important attributes, yellow for tentative attributes, red for non-important attributes, and blue for shadow attributes. Variable names are listed on the vertical axis, and Z-values are listed on the horizontal axis. The logistic model, which incorporated eight independent predictors (transferrin receptor (mg/L), ferritin (ng/mL), HS-CRP (mg/L), alpha-1-acid glycoprotein (g/L), ratio of family income to poverty, education level, country of birth, and age), was developed into a nomogram for ease of use, as demonstrated in Fig. 4 and available online( https://guohongdp.shinyapps.io/dynnomapp/ )(Fig. 4 ). Multivariate logistic regression analysis revealed several significant predictors of the outcome events (Table 2 ). Transferrin receptor (mg/L) demonstrated a significant positive association (OR 1.19, 95% CI 1.10–1.29, p < 0.001), as did ferritin (ng/mL) (OR 1.00, 95% CI 1.00–1.01, p = 0.003) and alpha-1-acid glycoprotein (g/L) (OR 2.98, 95% CI 1.36–6.51, p = 0.006). In contrast, HS C-Reactive Protein (mg/L) (OR 0.99, 95% CI 0.97–1.01, p = 0.511) and the ratio of family income to poverty (OR 0.94, 95% CI 0.83–1.07, p = 0.364) showed no significant associations. For education level, using college graduates or above as the reference group, significant increases in risk were observed for 9–11th grade (OR 2.17, 95% CI 1.00–4.71, p = 0.049), high school graduates/GED or equivalent (OR 3.51, 95% CI 2.05–6.02, p < 0.001), and some college or AA degree (OR 2.51, 95% CI 1.59–3.94, p < 0.001), while less than 9th grade showed no significant difference (OR 1.24, 95% CI 0.24–6.38, p = 0.797). Participants born outside the 50 US states or Washington had significantly higher odds than those born within (OR 2.44, 95% CI 1.61–3.70, p < 0.001). Age was inversely associated with outcomes (OR 0.96, 95% CI 0.94–0.98, p < 0.001). The curve (AUC) of the model is shown in the following Fig. 5 . Table 2 Results of Multivariate Logistic regression for Training Cohort Characteristic N Event N OR 95% CI p-value Transferrin receptor (mg/L) 994 205 1.19 1.10, 1.29 < 0.001 Ferritin(ng/mL) 994 205 1.00 1.00, 1.01 0.003 HS-CRP (mg/L) 994 205 0.99 0.97, 1.01 0.511 alpha-1-acid glycoprotein (g/L) 994 205 2.98 1.36, 6.51 0.006 Ratio of family income to poverty 994 205 0.94 0.83, 1.07 0.364 Education level College graduate or above 445 46 — — Less than 9th grade 11 2 1.24 0.24, 6.38 0.797 9-11th grade 51 14 2.17 1.00, 4.71 0.049 High school graduate/GED or equivalent 140 50 3.51 2.05, 6.02 < 0.001 Some college or AA degree 347 93 2.51 1.59, 3.94 < 0.001 Country of birth Born in 50 US states or Washington 814 153 — — Others 180 52 2.44 1.61, 3.70 < 0.001 Age 994 205 0.96 0.94, 0.98 < 0.001 Abbreviations: CI = Confidence Interval, OR = Odds Ratio, HS-CRP = high-sensitivity C-reactive protein 3.3 Internal validation Internal validation used non-parametric bootstrapping with 1000 resamples to evaluate the robustness and discriminative ability of the model. The corrected Harrell's C-Index was 0.710. 3.4 Calibration Analysis The calibration plot of the nomogram is shown in Fig. 6 , demonstrating good agreement between the observed and predicted average alcohol drinks/day/past 12 months. The calibration curve aligned well with a perfect line, suggesting that the predicted probabilities matched the actual results. 3.5 Decision Curve Analysis The DCA curve linked to the nomogram is shown in Fig. 7 . According to this study, the nomogram delivered considerable net advantages for clinical application, as evidenced by its DCA curve. 4. DISCUSSION The present study developed and validated a clinical prediction model incorporating both sociodemographic factors and biochemical markers to identify predictors of heavy drinking among adult women in the US. Multivariate logistic regression analysis revealed multiple significant predictive factors, including transferrin receptor, ferritin, and AGP concentrations, along with educational level, place of birth, and age, which contributed to the understanding of the complex interplay of factors influencing alcohol consumption in this population. Regarding sociodemographic predictors, the results largely align with the existing literature, while offering novel observations. The inverse correlation between age and heavy drinking concurs with national surveys, indicating reduced alcohol consumption in older populations[ 29 ]. Similarly, the observed educational attainment patterns, where lower education levels correlated with higher odds of heavy drinking, corroborate the findings from national surveys that education is a protective factor against problematic drinking[ 30 ]. Notably, women born outside the 50 U.S. states exhibited approximately 2.5-fold higher odds of heavy drinking, a result warranting further investigation, as it contrasts with some studies reporting lower consumption among immigrant groups, which may be related to acculturation processes or cohort-specific migration dynamics[ 31 ]. The biochemical markers included in the model offer new insights into the potential physiological mechanisms underlying heavy alcohol consumption. The strong association with AGP, an acute-phase protein, suggests a potential role for inflammation in heavy drinking patterns, which is consistent with emerging research on alcohol-induced inflammatory responses[ 32 ]. Similarly, the association with transferrin receptor levels may indicate alterations in iron metabolism among heavy drinkers, based on the findings of Yehia et al.[ 33 ] regarding the effects of alcohol on iron-regulatory proteins. These biomarker associations correspond to the established pathophysiological mechanisms underlying the impact of alcohol on iron regulation and inflammatory responses[ 34 ]. Specifically, increased transferrin receptor and ferritin levels likely indicate alcohol-induced disruption of iron homeostasis, which is consistent with the documented dysregulation of iron metabolism during chronic alcohol use[ 34 ]. Notably, the effect of alcohol on iron metabolism exhibited significant sex-specific differences. While premenopausal women generally have lower iron stores than men due to menstrual blood loss, they may be more susceptible to alcohol-induced oxidative damage from modest iron elevations[ 33 ]. Chronic alcohol use can exacerbate inflammatory responses, and in females, this may lead to an exaggerated interplay between iron-driven oxidative stress and inflammation, potentially through mechanisms such as ferroptosis (iron-dependent cell death), which has been implicated in alcohol-related liver injury [ 33 ]. The model demonstrated moderate discriminative performance, with an AUC of 0.738 and a corrected C-index of 0.710. This predictive accuracy is comparable to that of other clinical prediction tools used in substance use research, although it falls below the threshold typically considered excellent (AUC > 0.8). Despite this, the model retains potential utility for population-level screening and monitoring of alcohol-related health consequences. Collectively, these findings advance the risk stratification literature for alcohol misuse in women, a demographic historically underrepresented in research compared with men[ 31 , 35 ]. These findings corroborate and extend prior research on predictors of heavy alcohol consumption. These results suggest several potential therapeutic targets. This pronounced inverse association with educational attainment suggests that tailored prevention initiatives for women with lower education levels may yield positive outcomes. Incorporating biochemical markers, such as phosphatidylethanol (PEth), which demonstrates high sensitivity and specificity for recent alcohol intake[ 36 ], offers a foundation for developing objective screening tools in clinical practice. However, establishing causality and determining whether these markers reflect predispositions to or consequences of heavy drinking necessitates further investigation. The moderate performance of the model suggests that it could serve as a basis for risk stratification in primary care settings, thereby identifying women who would benefit from brief alcohol interventions[ 37 ]. Interpreting these results requires attention to methodological limitations. The cross-sectional design inherently constrains causal deductions, making it difficult to discern whether the observed associations represent risk factors or sequelae of heavy drinking. Reliance on self-reported alcohol consumption, while the current standard in population research[ 38 ], introduces potential measurement errors. Although biomarkers, such as PEth, show a strong correlation with intake[ 36 ], self-reporting remains primary for population-level assessment. Future research should prioritize three critical avenues to advance our understanding. First, prospective validation across diverse, independent cohorts, particularly encompassing the racial/ethnic heterogeneity of the U.S. population[ 39 ], is essential to establish generalizability. Second, longitudinal studies are crucial to elucidate the temporal dynamics between biomarker levels and evolving drinking patterns. Third, examining potential effect modifiers such as menopausal status or psychiatric comorbidities could refine the model's clinical utility. Additionally, elucidating the biological mechanisms linking the identified biomarkers to drinking behavior may reveal novel therapeutic targets[ 40 ]. 5. CONCLUSION This study presents a validated and clinically promising nomogram for identifying heavy alcohol consumption among adult women in the U.S. By synergistically combining biological markers of iron dysregulation and inflammation with key sociodemographic risk factors, this model provides a powerful tool to enhance early detection and facilitate personalized intervention. This lays the groundwork for a more proactive and stratified approach to addressing the significant public health challenge of high-risk drinking in this population. Abbreviations AUC Area under the receiver operating characteristic curve HS-CRP High-sensitivity C-reactive protein AUD Alcohol Use Disorder NHANES National Health and Nutrition Examination Survey NIAAA National Institute on Alcohol Abuse and Alcoholism ROC Receiver operating characteristic DCA Decision curve analysis Declarations All authors contributed to the article and agreed to the submitted version. Author Contributions Data Acquisition and Analysis: Executed by Hong Guo and Lu Dai; Figure and Table Drafting: Completed by Hong Guo and Lu Dai; Original Manuscript Composition: Authored by Hong Guo and Lu Dai; Manuscript Review and Editing: Conducted by Hong Guo, Lu Dai, Xia Guo and Youchao Zeng; Manuscript Revision and Refinement: Undertaken by Hong Guo, Lu Dai, Youchao Zeng, and Jian Zhan. Data Availability This study's data is available on the National Health and Nutrition Examination Survey website, which can be accessed at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. These data are publicly available. Conflict of interest The authors declare no conflicts of interest. Consent for publication Not applicable. Funding This study received No funding. Clinical trial number Not applicable. Ethics approval The NHANES is performed by the Centers for Disease Control and Prevention (CDC) in collaboration with the National Center for Health Statistics (NCHS). The NHANES study protocol underwent review and approval by the NCHS Research Ethics Review Committee. Written informed consent was obtained from all the participants. Acknowledgements We acknowledge the assistance of Deepseek-R1 in improving the readability and refining the language of this manuscript. We have assumed full responsibility for the content and conclusions of this study. 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Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2008, 26 (8):1364-1370.https://doi.org/10.1200/jco.2007.12.9791 Terry AL, Chiappa MM, McAllister J et al : Plan and Operations of the National Health and Nutrition Examination Survey, August 2021-August 2023 . Vital and health statistics Ser 1, Programs and collection procedures 2024(66):1-21. Hagman BT, Falk D, Litten R et al : Defining Recovery From Alcohol Use Disorder: Development of an NIAAA Research Definition . The American journal of psychiatry 2022, 179 (11):807-813.https://doi.org/10.1176/appi.ajp.21090963 Zhang M, Chen R, Yang Y et al : Machine learning analysis of lab tests to predict bariatric readmissions . Sci Rep 2024, 14 (1):16845.https://doi.org/10.1038/s41598-024-67710-6 Flentje A, Barger BT, Capriotti MR et al : Screening gender minority people for harmful alcohol use . PloS one 2020, 15 (4):e0231022.https://doi.org/10.1371/journal.pone.0231022 Cai Q, He B, Zhang P et al : Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods . Journal of translational medicine 2020, 18 (1):463.https://doi.org/10.1186/s12967-020-02635-y Shuryak I, Nemzow L, Bacon BA et al : Machine learning approach for quantitative biodosimetry of partial-body or total-body radiation exposures by combining radiation-responsive biomarkers . Sci Rep 2023, 13 (1):949.https://doi.org/10.1038/s41598-023-28130-0 Huang L, Chen H, Liang Z: Enhancing the convenience of frailty index assessment for elderly Chinese people with machine learning methods . Sci Rep 2024, 14 (1):23227.https://doi.org/10.1038/s41598-024-74194-x Shuryak I, Brenner DJ, Blattnig SR et al : Modeling space radiation induced cognitive dysfunction using targeted and non-targeted effects . Sci Rep 2021, 11 (1):8845.https://doi.org/10.1038/s41598-021-88486-z Callinan S, Livingston M, Dietze P et al : Age-based differences in quantity and frequency of consumption when screening for harmful alcohol use . Addiction (Abingdon, England) 2022, 117 (9):2431-2437.https://doi.org/10.1111/add.15904 Kim-Vences SIH, Zoorob RJ, Hirth JM: Lower Educational Attainment Widens Racial/Ethnic Disparities in Alcohol Use Disorder . Journal of studies on alcohol and drugs 2025.https://doi.org/10.15288/jsad.24-00377 Vladimirov D, Niemelä S, Auvinen J et al : Changes in alcohol use in relation to sociodemographic factors in early midlife . Scandinavian journal of public health 2016, 44 (3):249-257.https://doi.org/10.1177/1403494815622088 de Carvalho Ribeiro M, Iracheta-Vellve A, Babuta M et al : Alcohol-induced extracellular ASC specks perpetuate liver inflammation and damage in alcohol-associated hepatitis even after alcohol cessation . Hepatology (Baltimore, Md) 2023, 78 (1):225-242.https://doi.org/10.1097/hep.0000000000000298 Yehia A, Sousa RAL, Abulseoud OA: Sex difference in the association between blood alcohol concentration and serum ferritin . Frontiers in psychiatry 2023, 14 :1230406.https://doi.org/10.3389/fpsyt.2023.1230406 Harrison-Findik DD, Schafer D, Klein E et al : Alcohol metabolism-mediated oxidative stress down-regulates hepcidin transcription and leads to increased duodenal iron transporter expression . Journal of biological chemistry 2006, 281 (32):22974-22982. Cousins G, Galvin R, Flood M et al : Potential for alcohol and drug interactions in older adults: evidence from the Irish longitudinal study on ageing . BMC geriatrics 2014, 14 :57.https://doi.org/10.1186/1471-2318-14-57 Jain J, Evans JL, Briceño A et al : Comparison of phosphatidylethanol results to self-reported alcohol consumption among young injection drug users . Alcohol and alcoholism (Oxford, Oxfordshire) 2014, 49 (5):520-524.https://doi.org/10.1093/alcalc/agu037 Chi FW, Parthasarathy S, Palzes VA et al : Alcohol brief intervention, specialty treatment and drinking outcomes at 12 months: Results from a systematic alcohol screening and brief intervention initiative in adult primary care . Drug and alcohol dependence 2022, 235 :109458.https://doi.org/10.1016/j.drugalcdep.2022.109458 Bilal U, McCaul ME, Crane HM et al : Predictors of Longitudinal Trajectories of Alcohol Consumption in People with HIV . Alcoholism, clinical and experimental research 2018, 42 (3):561-570.https://doi.org/10.1111/acer.13583 Witbrodt J, Mulia N, Zemore SE et al : Racial/ethnic disparities in alcohol-related problems: differences by gender and level of heavy drinking . Alcoholism, clinical and experimental research 2014, 38 (6):1662-1670.https://doi.org/10.1111/acer.12398 Shukla S, Hsu CL: Alcohol Use Disorder and the Gut-Brain Axis: A Narrative Review of the Role of Gut Microbiota and Implications for Treatment . Microorganisms 2025, 13 (1).https://doi.org/10.3390/microorganisms13010067 Additional Declarations The authors declare potential competing interests as follows: 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. We do this by developing innovative software and high quality services for the global research community. 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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-8731999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582475595,"identity":"466c5220-3636-4495-bade-a8e247c8a89f","order_by":0,"name":"Hong Guo","email":"","orcid":"","institution":"Department of Neurology, The Second Affiliated Hospital of ZunYi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Guo","suffix":""},{"id":582475596,"identity":"966f2f9d-542b-430c-9961-2a7495a78c60","order_by":1,"name":"Lu Dai","email":"","orcid":"","institution":"Department of Medical Laboratory, The Second Affiliated Hospital of ZunYi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Dai","suffix":""},{"id":582475597,"identity":"7306b04c-9196-47ff-97f7-160efdbdcebd","order_by":2,"name":"Jian Zhan","email":"","orcid":"","institution":"Department of Neurology, The Second Affiliated Hospital of ZunYi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhan","suffix":""},{"id":582475598,"identity":"e9cf264b-ac43-4093-98c6-aaa3ae39d060","order_by":3,"name":"Xia Guo","email":"","orcid":"","institution":"Department of Neurology, The Second Affiliated Hospital of ZunYi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Guo","suffix":""},{"id":582475599,"identity":"d5e9bd03-f304-4785-9174-6c970c606417","order_by":4,"name":"Youchao Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACeWb+57//VNjw8BOtxbC9h0GC50yajGQD0XrOnGGQ4G07bGNwgFgdjDNyDxhInEnjMT6evIHhR8U2wlrYJfISEgyAfjE786yAsefMbWJsSTA4kAC0xexGjgEzYxsRWhhuJBg2HGw7zGM8g2gtZ84YMzYCtRhIEKvFsL0tjZkB6DAJoF8OEuUXeWbmY8wMFTb2/O3JGx/8qCDGYQiQQHzUILSQqmMUjIJRMApGCAAA2+c9nV7km/8AAAAASUVORK5CYII=","orcid":"","institution":"Department of Neurology, The Second Affiliated Hospital of ZunYi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Youchao","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2026-01-29 13:12:19","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8731999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8731999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101493157,"identity":"808033cd-4b17-4fd4-a3ed-de25270c8afd","added_by":"auto","created_at":"2026-01-30 11:28:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71914,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/2127fc32ac21af47fe9b7ff6.jpeg"},{"id":101752235,"identity":"2958c7df-02b4-4f8f-a742-8826cea25ed9","added_by":"auto","created_at":"2026-02-03 10:26:14","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57642,"visible":true,"origin":"","legend":"\u003cp\u003eRanking of clinical variables for predicting perioperative complication by Boruta algorithm.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/026624513f421e568dbdd48e.jpeg"},{"id":101493156,"identity":"de7da10c-106e-400b-87d4-12ed2ba83fb5","added_by":"auto","created_at":"2026-01-30 11:28:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":111591,"visible":true,"origin":"","legend":"\u003cp\u003eHistory graph of each decision of accepting or rejecting by Random Forest in Boruta algorithm.The accepted attributes (green) have distinctly higher importance than the other attributes.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/73ca765684d7b089da69e1be.jpeg"},{"id":101493161,"identity":"fbf0fded-19bb-48e2-9ba7-a5f54a2c63d7","added_by":"auto","created_at":"2026-01-30 11:28:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60187,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/b9e4ffdd1d23c549b554bdb6.jpeg"},{"id":101751742,"identity":"95f7f600-a104-4587-a891-5fb3adca6bb1","added_by":"auto","created_at":"2026-02-03 10:23:04","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":39086,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve of the Prediction Model.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/1ef103f4fcf4e8b3bd249362.jpeg"},{"id":101880768,"identity":"1beaa353-896c-4f6c-acc3-f262bd928b2f","added_by":"auto","created_at":"2026-02-04 15:06:06","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34892,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram prediction mode for the training cohort.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/44ce39a75a153184e07b2731.jpeg"},{"id":101493160,"identity":"702e5c35-0385-49b7-ba2b-66ea03d81ad0","added_by":"auto","created_at":"2026-01-30 11:28:27","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":33335,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram of the training cohort.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/6084ae24410375dfacc0b48d.jpeg"},{"id":101943062,"identity":"8e1af9b1-62cd-46fe-90b6-ff9c7e3ccb50","added_by":"auto","created_at":"2026-02-05 09:40:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3227318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8731999/v1/f009e014-f0a2-4fcd-8472-2ab86498ae30.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: ","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTransferrin Receptor and Sociodemographic Factors: A Multimodal Model for Heavy Drinking Risk Stratification in Women\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eExcessive alcohol use by adult women in the U.S. continues to be a major public health issue, with unique effects on their physical and mental health. Recent estimates indicate that approximately 12% of adult women engage in heavy drinking, defined as consuming more than seven drinks per week or binge drinking on multiple occasions[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The consequences of heavy alcohol use are particularly severe for women, including increased risks of liver disease, cardiovascular complications, and mental health disorders such as depression and anxiety[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite extensive public health campaigns and policy interventions, a substantial proportion of the global population continues to engage in hazardous drinking patterns, necessitating further research on the predictive factors and risk stratification[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompelling evidence underscores that women are more likely than men to drink in response to psychological distress, a pattern often termed \"drinking to cope\"[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This phenomenon is frequently rooted in trauma and gender-specific stressors including interpersonal violence, caregiving burdens, and societal pressures. Furthermore, the bidirectional relationship between alcohol use and internalizing psychiatric disorders was significantly stronger among women. Women with Alcohol Use Disorder (AUD) exhibit higher rates of comorbid anxiety and depression; conversely, women with these disorders are at a heightened risk of developing AUD[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A systematic review has confirmed that negative affect is a robust and reliable trigger for alcohol consumption, specifically among women, reinforcing the centrality of emotional dysregulation in hazardous drinking patterns for this population[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While these internalizing factors are paramount, they operate within a broader sociodemographic context that further shapes risk profiles.\u003c/p\u003e \u003cp\u003eDemographic characteristics such as age, race, and socioeconomic status have been consistently linked to heavy alcohol consumption. Younger adults, particularly those aged 18\u0026ndash;34 years, exhibit higher rates of daily heavy drinking than do older adults [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Racial and ethnic disparities also play a critical role, with certain groups demonstrating elevated risks owing to cultural, genetic, and socioeconomic factors[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. National differences further complicate this landscape, with variations in alcohol policies, taxation, and cultural norms influencing consumption patterns[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Additionally, socioeconomic factors such as income inequality and poverty status have been linked to increased alcohol misuse, as financial stress may exacerbate coping-related drinking behaviors[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Marital status and household composition further influence drinking behavior among women. Unmarried or divorced women exhibit higher rates of heavy drinking than their married counterparts, possibly because of social isolation or lack of familial support[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pregnancy is a crucial factor because alcohol intake during this period can severely affect fetal development. However, some women continue to drink even when they are aware of the potential dangers[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The household structure, including the number of dependents, may also influence drinking patterns, with larger families potentially mitigating excessive consumption due to caregiving responsibilities[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to demographic and socioeconomic factors, biological markers have also emerged as promising predictors of high alcohol consumption. For example, elevated levels of alpha-1-acid glycoprotein have been associated with chronic alcohol use, reflecting systemic inflammation and immune response alterations[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Similarly, HS-CRP, a marker of low-grade inflammation, has been linked to increased alcohol intake, suggesting its potential role in identifying individuals at risk of alcohol-related harm[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Iron metabolism biomarkers, including ferritin and transferrin receptor levels, have also been implicated in alcohol-related disorders, because chronic drinking disrupts iron homeostasis, leading to hepatic iron overload and oxidative damage[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Ferritin levels, indicative of iron storage, are often elevated in alcohol-related liver diseases, whereas transferrin receptor levels reflect altered iron metabolism due to chronic ethanol exposure[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A recent cross-sectional study involving over 3,000 Korean adults reinforced this association, demonstrating that even low-risk alcohol consumption was linked to significantly higher serum ferritin concentrations, a finding that holds true within the female subcohort [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although direct research linking the transferrin receptor specifically as a biomarker for alcohol intake in women is less extensive than that for ferritin, its role as a key regulator in the iron cycle makes it a mechanistically plausible and valuable component of a multi-marker panel[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These biomarkers offer valuable insights into the metabolic and inflammatory consequences of heavy drinking, and enhance predictive models for clinical and public health interventions. Despite these advances, few studies have integrated sociodemographic factors and iron metabolism biomarkers into a unified predictive framework for women, limiting the clinical utility of existing models.\u003c/p\u003e \u003cp\u003eGiven the multifactorial nature of heavy alcohol use among women, predictive models that integrate sociodemographic and biomarker data may enhance early identification and intervention. In clinical practice, nomograms further enhance clinical utility by translating regression results into visual, individualized risk estimates, facilitating actionable decision making in real-world settings[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By leveraging statistical approaches, logistic regression, and nomogram modeling, this study aimed to develop a clinically actionable predictive model for heavy alcohol use among adult women in the U.S., incorporating both traditional sociodemographic variables and novel biomarkers.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Patient Data\u003c/h2\u003e\n \u003cp\u003eThe information comes from the National Health and Nutrition Examination Survey(NHANES), a set of cross-sectional surveys that offer a national snapshot of the health and nutritional status of individuals in the United States. The data collection process included three main steps: conducting home interviews, performing physical examinations at mobile centers, and executing laboratory tests. For more information, please refer to the official website( \u003cspan\u003e\u003cspan\u003ehttps://wwwn.cdc.gov/nchs/nhanes/Default.aspx\u003c/span\u003e\u003c/span\u003e ). The Institutional Review Board of the Centers for Disease Control and Prevention provided ethical approval for the NHANES, and each participant provided written informed consent[\u003cspan\u003e21\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThis study examined data from the National Health and NHANES gathered between August 2021 and August 2023. Participants were deemed eligible if they fulfilled the following criteria: (1) female adults aged 18 years or older; (2) self-reported alcohol consumption within the past 12 months; and (3) provided complete data on essential variables such as age, race/ethnicity, educational level, marital status, pregnancy status, household size, ratio of family income to poverty, alpha-1-acid glycoprotein, HS-CRP, ferritin, and transferrin receptor. The exclusion criteria were as follows: (1) male sex, (2) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years, (3) incomplete data on alcohol consumption or critical biomarkers (ferritin or TfR), and (4) lifetime abstention from alcohol. Following the application of these inclusion and exclusion criteria, 994 participants were included in the final analysis (Fig.\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e). Based on the National Institute on Alcohol Abuse and Alcoholism (NIAAA) criteria for risky drinking, female adults were categorized as heavy drinkers if they consumed more than three drinks per day[\u003cspan\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Predictors Data Collection\u003c/h2\u003e\n \u003cp\u003eDemographic and clinical data served as predictors: (1) demographic factors: age, race/ethnicity, educational level, marital status, pregnancy status, household size, and ratio of family income to poverty, and (2) biomarkers:alpha-1 acid glycoprotein (g/L), HS-CRP (mg/L), ferritin (ng/L), and transferrin receptor (mg/L). The outcome variable was the average daily intake of alcoholic drinks over the past 12 months, classified as low-risk (\u0026le;\u0026thinsp;3 drinks/day) or heavy (\u0026gt;\u0026thinsp;3 drinks/day) according to the NIAAA criteria for risky drinking[\u003cspan\u003e22\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe baseline demographic characteristics were stratified by outcome (average number of alcoholic drinks per day over the past 12 months). Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used for normally distributed continuous variables, whereas median (interquartile range) was used for non-normally distributed data. For categorical variables, the chi-square test or Fisher\u0026apos;s exact test was applied, whereas the Welch two-sample t-test or rank-sum test was used for continuous variables[\u003cspan\u003e23\u003c/span\u003e, \u003cspan\u003e24\u003c/span\u003e]. Subsequently, Boruta feature selection was implemented via a random forest classifier to identify the key predictors for heavy drinkers. This algorithm assesses predictor importance by comparing scores with randomly generated shadow features, retaining only those deemed significant through iterative testing[\u003cspan\u003e25\u003c/span\u003e]. Model performance was then evaluated across three domains: discrimination, assessed via receiver operating characteristic (ROC) curve and AUC; calibration, determined using calibration curves and the Hosmer-Lemeshow test, with a p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05, suggesting a good fit. Clinical utility was assessed using decision curve analysis (DCA) to evaluate the net benefit across threshold probabilities[\u003cspan\u003e24\u003c/span\u003e]. Finally, a corrected AUC was computed through internal validation using 1000 iterations of bootstrap resampling to ensure robustness. For statistical significance, a two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used. Analyses were performed using the R software (version 4.2.2) and MSTATA software ( \u003cspan\u003e\u003cspan\u003ewww.mstata.com\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eAnalysis of baseline demographics and clinical features demonstrated notable links with heavy drinking status(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Compared to low-risk drinkers (mean age: 35\u0026thinsp;\u0026plusmn;\u0026thinsp;9 years), heavy drinkers were significantly younger (32\u0026thinsp;\u0026plusmn;\u0026thinsp;9 years; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Racial composition differed markedly between the groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with heavy drinkers showing a lower proportion of Non-Hispanic White individuals (40.5% vs. 56.7%) but a higher representation of Other Hispanic ethnicities (22.0% vs. 9.9%). Country of birth was also significantly associated, as heavy drinkers were less frequently U.S.-born (74.6% vs. 83.8%; P\u0026thinsp;=\u0026thinsp;0.002). However, active duty served by the US armed forces showed no significant group difference (p\u0026thinsp;=\u0026thinsp;0.394).\u003c/p\u003e \u003cp\u003eEducation level exhibited a strong association: heavy drinkers had substantially lower rates of college graduation or higher education (22.4% vs. 50.6%). Although marital status was not significantly correlated (p\u0026thinsp;=\u0026thinsp;0.113), heavy drinkers included more never-married participants (43.9% vs. 36.0%, respectively). Pregnancy status did not differ between the groups (p\u0026thinsp;=\u0026thinsp;0.235). Regarding household characteristics, heavy drinkers resided in larger households (mean: 3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57 vs. 3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50; p\u0026thinsp;=\u0026thinsp;0.022) and had reduced ratio of family income to poverty (2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45 vs. 2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eSeveral biomarkers were significantly elevated in heavy drinkers, including alpha-1-acid glycoprotein (0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24 g/L vs. 0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25 g/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and transferrin receptor levels (4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41 mg/L vs. 3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39 mg/L; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, HS C-reactive protein (p\u0026thinsp;=\u0026thinsp;0.159) and ferritin levels (p\u0026thinsp;=\u0026thinsp;0.256) were not significantly different. Collectively, these findings indicate that heavy drinking correlates with distinct demographic profiles, socioeconomic factors, and specific biomarker alterations, suggesting that alcohol consumption influences health outcomes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAvg alcoholic drinks/day/past 12 mos-group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003elow‑risk drinkers\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;789\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeavy drinkers\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;205\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (8.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e447 (56.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (12.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (15.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry of birth, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in 50 US states or Washington\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e661 (83.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (74.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (25.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eServed active duty in US Armed Forces, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.394\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e772 (97.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e203 (99.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e399 (50.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254 (32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (24.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (4.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried/Living with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e379 (48.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e87 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed/Divorced/Separated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126 (16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (13.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284 (36.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePregnancy status, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e759 (96.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCannot ascertain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal number of people in the Household, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.07\u0026thinsp;\u0026plusmn;\u0026thinsp;1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.35\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatio of family income to poverty, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.91\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ealpha-1-acid glycoprotein (g/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHS-CRP (mg/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFerritin (ng/mL), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63\u0026thinsp;\u0026plusmn;\u0026thinsp;66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70\u0026thinsp;\u0026plusmn;\u0026thinsp;83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransferrin receptor (mg/L), Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.15\u0026thinsp;\u0026plusmn;\u0026thinsp;3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e1\u003c/sup\u003eWelch Two Sample t-test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePearson's Chi-squared test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e3\u003c/sup\u003eFisher's exact test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Predictive Model\u003c/h2\u003e \u003cp\u003eWe initially considered 13 candidate predictors in the original model, including demographic factors (Age, Race, Country of birth, education level, and marital status), socioeconomic indicators (total household members and ratio of family income to poverty), military service history (served as an active duty in the US Armed Forces), physiological status (pregnancy status), and biomarkers [alpha-1-acid glycoprotein (g/L), HS-CRP (mg/L), ferritin (ng/mL), and transferrin receptor (mg/L] ). Utilizing the Boruta algorithm, a random-forest-based feature-selection method, we identified statistically relevant predictors by iteratively comparing the importance of each variable with randomly shuffled shadow features[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Specifically, this approach employs a random forest classifier to compute variable importance scores through multiple iterations, preserving only those features that demonstrate significantly higher importance than their synthetic counterparts[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Consequently, the model was narrowed down to eight key predictors that substantially contributed to outcome prediction, as determined by their consistent importance across iterations[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e](Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows boxplots with green for important attributes, yellow for tentative attributes, red for non-important attributes, and blue for shadow attributes. Variable names are listed on the vertical axis, and Z-values are listed on the horizontal axis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe logistic model, which incorporated eight independent predictors (transferrin receptor (mg/L), ferritin (ng/mL), HS-CRP (mg/L), alpha-1-acid glycoprotein (g/L), ratio of family income to poverty, education level, country of birth, and age), was developed into a nomogram for ease of use, as demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eand available online( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guohongdp.shinyapps.io/dynnomapp/\u003c/span\u003e\u003cspan address=\"https://guohongdp.shinyapps.io/dynnomapp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e )(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMultivariate logistic regression analysis revealed several significant predictors of the outcome events (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Transferrin receptor (mg/L) demonstrated a significant positive association (OR 1.19, 95% CI 1.10\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did ferritin (ng/mL) (OR 1.00, 95% CI 1.00\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.003) and alpha-1-acid glycoprotein (g/L) (OR 2.98, 95% CI 1.36\u0026ndash;6.51, p\u0026thinsp;=\u0026thinsp;0.006). In contrast, HS C-Reactive Protein (mg/L) (OR 0.99, 95% CI 0.97\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.511) and the ratio of family income to poverty (OR 0.94, 95% CI 0.83\u0026ndash;1.07, p\u0026thinsp;=\u0026thinsp;0.364) showed no significant associations. For education level, using college graduates or above as the reference group, significant increases in risk were observed for 9\u0026ndash;11th grade (OR 2.17, 95% CI 1.00\u0026ndash;4.71, p\u0026thinsp;=\u0026thinsp;0.049), high school graduates/GED or equivalent (OR 3.51, 95% CI 2.05\u0026ndash;6.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and some college or AA degree (OR 2.51, 95% CI 1.59\u0026ndash;3.94, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while less than 9th grade showed no significant difference (OR 1.24, 95% CI 0.24\u0026ndash;6.38, p\u0026thinsp;=\u0026thinsp;0.797). Participants born outside the 50 US states or Washington had significantly higher odds than those born within (OR 2.44, 95% CI 1.61\u0026ndash;3.70, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age was inversely associated with outcomes (OR 0.96, 95% CI 0.94\u0026ndash;0.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The curve (AUC) of the model is shown in the following Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Multivariate Logistic regression for Training Cohort\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEvent N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransferrin receptor (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10, 1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFerritin(ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHS-CRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.97, 1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ealpha-1-acid glycoprotein (g/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36, 6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRatio of family income to poverty\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.83, 1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 9th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24, 6.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9-11th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00, 4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school graduate/GED or equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.05, 6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.59, 3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCountry of birth\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBorn in 50 US states or Washington\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61, 3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.94, 0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, OR\u0026thinsp;=\u0026thinsp;Odds Ratio, HS-CRP\u0026thinsp;=\u0026thinsp;high-sensitivity C-reactive protein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Internal validation\u003c/h2\u003e \u003cp\u003eInternal validation used non-parametric bootstrapping with 1000 resamples to evaluate the robustness and discriminative ability of the model. The corrected Harrell's C-Index was 0.710.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Calibration Analysis\u003c/h2\u003e \u003cp\u003eThe calibration plot of the nomogram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, demonstrating good agreement between the observed and predicted average alcohol drinks/day/past 12 months. The calibration curve aligned well with a perfect line, suggesting that the predicted probabilities matched the actual results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Decision Curve Analysis\u003c/h2\u003e \u003cp\u003eThe DCA curve linked to the nomogram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. According to this study, the nomogram delivered considerable net advantages for clinical application, as evidenced by its DCA curve.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe present study developed and validated a clinical prediction model incorporating both sociodemographic factors and biochemical markers to identify predictors of heavy drinking among adult women in the US. Multivariate logistic regression analysis revealed multiple significant predictive factors, including transferrin receptor, ferritin, and AGP concentrations, along with educational level, place of birth, and age, which contributed to the understanding of the complex interplay of factors influencing alcohol consumption in this population. Regarding sociodemographic predictors, the results largely align with the existing literature, while offering novel observations. The inverse correlation between age and heavy drinking concurs with national surveys, indicating reduced alcohol consumption in older populations[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Similarly, the observed educational attainment patterns, where lower education levels correlated with higher odds of heavy drinking, corroborate the findings from national surveys that education is a protective factor against problematic drinking[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Notably, women born outside the 50 U.S. states exhibited approximately 2.5-fold higher odds of heavy drinking, a result warranting further investigation, as it contrasts with some studies reporting lower consumption among immigrant groups, which may be related to acculturation processes or cohort-specific migration dynamics[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe biochemical markers included in the model offer new insights into the potential physiological mechanisms underlying heavy alcohol consumption. The strong association with AGP, an acute-phase protein, suggests a potential role for inflammation in heavy drinking patterns, which is consistent with emerging research on alcohol-induced inflammatory responses[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similarly, the association with transferrin receptor levels may indicate alterations in iron metabolism among heavy drinkers, based on the findings of Yehia et al.[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] regarding the effects of alcohol on iron-regulatory proteins. These biomarker associations correspond to the established pathophysiological mechanisms underlying the impact of alcohol on iron regulation and inflammatory responses[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Specifically, increased transferrin receptor and ferritin levels likely indicate alcohol-induced disruption of iron homeostasis, which is consistent with the documented dysregulation of iron metabolism during chronic alcohol use[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNotably, the effect of alcohol on iron metabolism exhibited significant sex-specific differences. While premenopausal women generally have lower iron stores than men due to menstrual blood loss, they may be more susceptible to alcohol-induced oxidative damage from modest iron elevations[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Chronic alcohol use can exacerbate inflammatory responses, and in females, this may lead to an exaggerated interplay between iron-driven oxidative stress and inflammation, potentially through mechanisms such as ferroptosis (iron-dependent cell death), which has been implicated in alcohol-related liver injury [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The model demonstrated moderate discriminative performance, with an AUC of 0.738 and a corrected C-index of 0.710. This predictive accuracy is comparable to that of other clinical prediction tools used in substance use research, although it falls below the threshold typically considered excellent (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.8). Despite this, the model retains potential utility for population-level screening and monitoring of alcohol-related health consequences. Collectively, these findings advance the risk stratification literature for alcohol misuse in women, a demographic historically underrepresented in research compared with men[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings corroborate and extend prior research on predictors of heavy alcohol consumption. These results suggest several potential therapeutic targets. This pronounced inverse association with educational attainment suggests that tailored prevention initiatives for women with lower education levels may yield positive outcomes. Incorporating biochemical markers, such as phosphatidylethanol (PEth), which demonstrates high sensitivity and specificity for recent alcohol intake[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], offers a foundation for developing objective screening tools in clinical practice. However, establishing causality and determining whether these markers reflect predispositions to or consequences of heavy drinking necessitates further investigation. The moderate performance of the model suggests that it could serve as a basis for risk stratification in primary care settings, thereby identifying women who would benefit from brief alcohol interventions[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterpreting these results requires attention to methodological limitations. The cross-sectional design inherently constrains causal deductions, making it difficult to discern whether the observed associations represent risk factors or sequelae of heavy drinking. Reliance on self-reported alcohol consumption, while the current standard in population research[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], introduces potential measurement errors. Although biomarkers, such as PEth, show a strong correlation with intake[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], self-reporting remains primary for population-level assessment. Future research should prioritize three critical avenues to advance our understanding. First, prospective validation across diverse, independent cohorts, particularly encompassing the racial/ethnic heterogeneity of the U.S. population[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], is essential to establish generalizability. Second, longitudinal studies are crucial to elucidate the temporal dynamics between biomarker levels and evolving drinking patterns. Third, examining potential effect modifiers such as menopausal status or psychiatric comorbidities could refine the model's clinical utility. Additionally, elucidating the biological mechanisms linking the identified biomarkers to drinking behavior may reveal novel therapeutic targets[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study presents a validated and clinically promising nomogram for identifying heavy alcohol consumption among adult women in the U.S. By synergistically combining biological markers of iron dysregulation and inflammation with key sociodemographic risk factors, this model provides a powerful tool to enhance early detection and facilitate personalized intervention. This lays the groundwork for a more proactive and stratified approach to addressing the significant public health challenge of high-risk drinking in this population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAUC \u0026nbsp; \u0026nbsp; \u0026nbsp; Area under the receiver operating characteristic curve\u003c/p\u003e\n\u003cp\u003eHS-CRP \u0026nbsp; \u0026nbsp;High-sensitivity C-reactive protein\u003c/p\u003e\n\u003cp\u003eAUD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alcohol Use Disorder\u003c/p\u003e\n\u003cp\u003eNHANES \u0026nbsp; National Health and Nutrition Examination Survey\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNIAAA \u0026nbsp; \u0026nbsp; National Institute on Alcohol Abuse and Alcoholism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; \u0026nbsp; \u0026nbsp; Receiver operating characteristic \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA \u0026nbsp; \u0026nbsp; \u0026nbsp; Decision curve analysis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAll authors contributed to the article and agreed to the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData Acquisition and Analysis: Executed by Hong Guo and Lu Dai; Figure and Table Drafting: Completed by Hong Guo and Lu Dai; Original Manuscript Composition: Authored by Hong Guo and Lu Dai; Manuscript Review and Editing: Conducted by Hong Guo, Lu Dai,\u0026nbsp;Xia Guo\u0026nbsp;and Youchao Zeng; Manuscript Revision and Refinement: Undertaken by Hong Guo, Lu Dai, Youchao Zeng, and Jian Zhan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study\u0026apos;s data is available on the National Health and Nutrition Examination Survey website, which can be accessed at: https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. These data are publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThis study received No funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NHANES is performed by the Centers for Disease Control and Prevention (CDC) in collaboration with the National Center for Health Statistics (NCHS). The NHANES study protocol underwent review and approval by the NCHS Research Ethics Review Committee. Written informed consent was obtained from all\u0026nbsp;the participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the assistance of Deepseek-R1 in improving the readability and refining the language of this manuscript. We have assumed full responsibility for the content and conclusions of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGrant BF, Chou SP, Saha TD\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003ePrevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001-2002 to 2012-2013: Results From the National Epidemiologic Survey on Alcohol and Related Conditions\u003c/strong\u003e. \u003cem\u003eJAMA Psychiatry \u003c/em\u003e2017, \u003cstrong\u003e74\u003c/strong\u003e(9):911-923.https://doi.org/10.1001/jamapsychiatry.2017.2161\u003c/li\u003e\n\u003cli\u003eWhite AM, Castle IP, Hingson RW\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eUsing Death Certificates to Explore Changes in Alcohol-Related Mortality in the United States, 1999 to 2017\u003c/strong\u003e. \u003cem\u003eAlcoholism, clinical and experimental research \u003c/em\u003e2020, 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A Narrative Review of the Role of Gut Microbiota and Implications for Treatment\u003c/strong\u003e. \u003cem\u003eMicroorganisms \u003c/em\u003e2025, \u003cstrong\u003e13\u003c/strong\u003e(1).https://doi.org/10.3390/microorganisms13010067\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Not applicable","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":"Excessive Alcohol Use, Nomogram Model, Transferrin Receptor, Ferritin","lastPublishedDoi":"10.21203/rs.3.rs-8731999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8731999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to develop and validate a clinically actionable nomogram-based prediction model integrating sociodemographic factors and biological biomarkers to identify heavy alcohol consumption among adult women in the United States.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were extracted from the 2021 to 2023 National Health and Nutrition Examination Survey (NHANES), including 994 eligible adult women (18\u0026thinsp;+\u0026thinsp;years, past 12-month alcohol use, and complete data on key variables). The Boruta algorithm (random forest-based) was used for feature selection to identify critical predictors of heavy drinking. A logistic regression model was constructed, and performance was evaluated via discrimination(Receiver operating characteristic, ROC), calibration (calibration curve, Hosmer-Lemeshow test), and clinical utility (decision curve analysis, DCA). Internal validation was performed using 1000 bootstrap resamples to ensure robustness.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eEight key predictors were selected: transferrin receptor, ferritin, high-sensitivity C-reactive protein (HS-CRP), alpha-1-acid glycoprotein, income-to-poverty ratio, education level, country of birth, and age. Multivariate logistic regression revealed significant associations with heavy drinking: transferrin receptor (OR\u0026thinsp;=\u0026thinsp;1.19, 95% CI\u0026thinsp;=\u0026thinsp;1.10\u0026ndash;1.29, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ferritin (OR\u0026thinsp;=\u0026thinsp;1.00, 95% CI\u0026thinsp;=\u0026thinsp;1.00\u0026ndash;1.01, p\u0026thinsp;=\u0026thinsp;0.003), alpha-1-acid glycoprotein (OR\u0026thinsp;=\u0026thinsp;2.98, 95% CI\u0026thinsp;=\u0026thinsp;1.36\u0026ndash;6.51, p\u0026thinsp;=\u0026thinsp;0.006), lower education levels (e.g., high school graduates: OR\u0026thinsp;=\u0026thinsp;3.51, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 vs. college graduates), non-U.S. birth (OR\u0026thinsp;=\u0026thinsp;2.44, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and younger age (OR\u0026thinsp;=\u0026thinsp;0.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The model showed good discrimination with a corrected AUC of 0.710, excellent calibration (alignment between predicted and observed probabilities), and significant net clinical benefit via DCA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe nomogram-based model effectively identifies adult women at risk of heavy alcohol consumption by combining biological markers of iron dysregulation with key sociodemographic risk factors. This model provides a powerful tool for enhancing early detection and facilitating personalized interventions.\u003c/p\u003e","manuscriptTitle":"Transferrin Receptor and Sociodemographic Factors: A Multimodal Model for Heavy Drinking Risk Stratification in Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-30 11:28:16","doi":"10.21203/rs.3.rs-8731999/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":"3d3992e1-6992-4d6e-a6c6-9f5627775403","owner":[],"postedDate":"January 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61972023,"name":"Neurology"},{"id":61972024,"name":"Psychiatry"}],"tags":[],"updatedAt":"2026-01-30T11:28:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-30 11:28:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8731999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8731999","identity":"rs-8731999","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}