Vitamin D and Suicidal Ideation: A Predictive Model Based on NHANES Database

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Methods We analyzed data from the National Health and Nutrition Examination Survey (NHANES), restricting the sample to individuals free of severe physical or psychiatric conditions. Suicidal ideation served as the primary outcome measure, with prognostic variables including sex, age, race, military service background, educational attainment, marital status, household size, income-to-poverty ratio, and vitamin D biomarkers (VD2 and VD3). Multivariate logistic regression was used to identify significant prognostic factors using a nomogram developed for risk visualization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with internal validation conducted via bootstrap resampling. Results Multivariable analysis revealed that younger age (odds ratio [OR] 0.99, p = 0.003), never having been married (OR 2.07, p < 0.001), and being widowed, divorced, or separated (OR 1.81, p < 0.001) correlated with increased odds of suicidal ideation. Additionally, a lower income-to-poverty ratio (OR 0.83, p < 0.001) and decreased VD3 concentration (OR 0.99, p < 0.001) emerged as predictive factors. The model showed moderate discriminative ability with an AUC of 0.721 (95% CI: 0.690–0.751), and internal validation via bootstrap resampling yielded a corrected C-index of 0.714. Conclusion Key demographic, socioeconomic, and nutritional factors, specifically marital status, economic hardship, and vitamin D status, represent significant prognostic indicators for suicidal ideation in the general U.S. adult population. The resulting nomogram offers a practical instrument for personalized risk evaluation and underscores potential targets for public health prevention initiatives. Suicidal ideation Vitamin D Logistic regression Nomogram NHANES Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Suicidal behaviors encompassing suicidal ideation (SI), planning, and attempts represent a formidable and persistent public health crisis on a global scale [1]. In the United States, suicide remains a leading cause of mortality, and the prevalence of suicidal ideation far exceeds that of completed suicide, signaling a vast and often hidden burden of severe psychological distress within the population. The presence of SI is a critical precursor to overt suicidal acts and serves as a crucial intervention point for preventative strategies. Consequently, accurate and early identification of individuals at a heightened risk of developing SI is a paramount objective in clinical practice and public health research. This imperative has driven an extensive search for reliable and clinically accessible risk factors to improve the precision of suicide risk assessment protocols, which have historically struggled with low predictive accuracy [2]. Historically, research has focused on a wide array of demographic and socioeconomic variables as potential indicators of suicide risk. Factors such as age, sex, race/ethnicity, marital status, educational attainment, and employment status have been consistently investigated [3]. A comprehensive meta-analysis examining the predictive utility of these variables confirmed that many were statistically significant risk factors for suicidal thoughts and behaviors [3]. However, the same body of evidence reveals a critical limitation: the effect sizes associated with individual demographic factors are typically small, rendering their clinical utility for individual risk prediction weak and often insignificant when used in isolation [3]. This suggests that, while these factors contribute to a foundational understanding of risk at the population level, they lack the necessary predictive power for robust clinical decision-making and highlight the necessity of integrating them with other, more dynamic risk markers [3]. In response to the limited predictive capacity of conventional risk factors, the field has increasingly turned its attention toward identifying objective biological markers that may underpin the pathophysiology of suicidality. Among many candidates, serum vitamin D levels have emerged as a promising, modifiable, and readily measurable biomarker. Vitamin D, a neurosteroid hormone, is integral to central nervous system function, and its receptors are widely distributed throughout the brain regions implicated in mood regulation and executive function, such as the prefrontal cortex and hippocampus [4, 5]. A growing corpus of observational research has documented a consistent association between hypovitaminosis D and a range of adverse psychiatric outcomes, including depression and suicidal behaviors [6, 7]. Recent systematic reviews and meta-analyses have bolstered this line of inquiry. For instance, a major 2023 meta-analysis concluded that low vitamin D levels are significantly associated with an increased probability of suicidal behaviors, with subgroup analyses confirming this relationship for both suicide attempts and suicidal ideation [4]. The analysis reported that individuals with SI had significantly lower vitamin D levels than the controls [4]. Nevertheless, the literature is not entirely uniform; some studies have yielded null findings, and inconsistencies persist, potentially attributable to methodological heterogeneity, including differences in study populations, definitions of vitamin D deficiency, and inadequate control for confounding variables such as season, diet, or pre-existing psychiatric conditions [8–10]. Therefore, the challenge lies not only in confirming the association between individual risk factors and SI, but also in integrating them into a cohesive, multidimensional predictive model that possesses both statistical robustness and clinical applicability. Although complex machine learning and genomic models are being explored [11] there remains a pressing need for more straightforward tools that clinicians can use for personalized risk stratification at the point of care. Nomograms have proven to be valuable instruments in this regard, offering a graphical representation of a statistical model that can calculate the probability of an outcome for an individual patient by combining various prognostic variables [12, 13]. While nomograms have been developed for SI in specific populations (e.g., adolescents with depression and cancer patients) and have demonstrated acceptable to excellent discriminative ability, with Area Under the Receiver Operating Characteristic Curve (AUC) values typically ranging from 0.70 to over 0.90 [12–14] there is a notable gap in the literature. Specifically, few studies have developed such a model by combining demographic, socioeconomic, and biochemical markers within a large, nationally representative sample of the general U.S. population, particularly one screened to be free of severe confounding physical and psychiatric disorders. The development of such a model would provide a more nuanced understanding of risk in a non-clinical general population context. Given these considerations, this study aims to address this critical gap. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), a robust and representative dataset of the U.S. population, our primary objective was to identify a parsimonious set of demographic, socioeconomic, and vitamin D biomarkers that independently predict suicidal ideation in a cohort of generally healthy adults. Our secondary objective was to use these findings to construct and internally validate a novel nomogram, providing a practical and quantitative tool for estimating the risk of suicidal ideation. We hypothesized that a multifactorial model incorporating younger age, non-married status, economic strain, and lower serum vitamin D3 concentrations would significantly predict suicidal ideation and that the resulting nomogram would demonstrate moderate-to-good predictive accuracy, thereby offering a valuable resource for public health screening and targeted prevention initiatives. 2. MATERIALS AND METHODS 2.1 Study Population and Data Source This cross-sectional analysis was based on data from the National Health and Nutrition Examination Survey (NHANES), a program designed to evaluate the health and nutritional status of U.S. adults and children via a complex, stratified, multistage probability sampling framework. Administered by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC), NHANES integrates in-person interviews, standardized physical examinations at mobile centers, and laboratory testing of biological samples [15]. The study protocol was approved by the NCHS Research Ethics Review Board and all participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki. De-identified data were obtained from the NHANES and Nutrition Examination Survey public database. The present analysis is based on a consolidated dataset from the most recent continuous NHANES cycles, spanning August 2021 to August 2023. This initial period included 11,933 individuals. To align with the study objectives, we applied a sequence of exclusion criteria. First, we excluded 3,189 participants younger than 18 years of age, as assessment tools and risk factor profiles for suicidal ideation can differ significantly in pediatric populations. Second, 2,987 participants with incomplete or missing data on the Patient Health Questionnaire-9 (PHQ-9), which is essential for our outcome variable, were excluded. Third, we excluded 668 participants who lacked laboratory measurements of serum 25-hydroxyvitamin D. Following the application of these criteria, the final analytical cohort for this study comprised 5,089 eligible adult participants. A detailed flowchart of the participant selection process is shown in Fig. 1 . 2.2 Ascertainment of Suicidal Ideation (Outcome Variable) Suicidal ideation was identified via the Depression Screener Questionnaire (DPQ) module in the NHANES, operationally comparable to the validated 9-item PHQ-9 [16]. Specifically, item 9 (coded as DPQ090) was used, which queries: “Over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?” [17, 18]. Responses were recorded on a 4-point Likert scale: 0 (“Not at all”), 1 (“Several days”), 2 (“More than half the days”), or 3 (“Nearly every day”) [19]. A dichotomous outcome variable (DPQ090-group) was generated: participants with a score of 0 were categorized as “No Suicidal Ideation,” while those with scores ≥ 1 (endorsing any frequency of such thoughts) were classified as “Suicidal Ideation” [18]. This approach aligns with standard psychiatric epidemiological methods, as any positive response to this item is clinically significant for suicide risk assessment [20]. 2.3 Assessment of Vitamin D and Covariates Vitamin D Biomarkers Serum levels of vitamin D biomarkers (total 25-hydroxyvitamin D [25(OH)D], 25-hydroxyvitamin D2 [25(OH)D2], and 25-hydroxyvitamin D3 [25(OH)D3]) were quantified at the CDC’s Division of Laboratory Sciences using standardized liquid chromatography-tandem mass spectrometry (LC-MS/MS) [21, 22], a gold-standard method that avoids cross-reactivity issues of older immunoassays [23, 24]. Accuracy was ensured via calibration with the National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 972a [25, 26]. For the analysis, 25(OH)D, 25(OH)D2, and 25(OH)D3 were treated as continuous variables. Demographic and Socioeconomic Covariates A comprehensive set of potential confounding variables was extracted from the NHANES interviews and examination data. These variables were selected based on their established or theoretical associations with vitamin D status and mental health outcomes. Including: 1) Demographic Variables were included: age, sex, race/ethnicity, country of birth, veteran/military Status; 2) Socioeconomic Variables: Education Level, Marital Status, Family Size, Family Income-to-Poverty Ratio (PIR). 2.4 Statistical Analysis Baseline demographic features were stratified according to the presence or absence of suicidal ideation (DPQ090.group). Normally distributed continuous data are expressed as mean ± standard deviation, whereas non-normally distributed variables are presented as medians (interquartile ranges). Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test, as appropriate. For continuous variables, intergroup differences were assessed using Welch’s t-test (for normal distributions) or the Mann-Whitney U test (for non-normal distributions). To identify independent predictors and develop a nomogram for predicting suicidal ideation, least absolute shrinkage and selection operator (LASSO) logistic regression was employed to refine the variable set [27]. Model performance was evaluated using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) quantifying discriminative ability (AUC range: 0.5 = no discrimination, 1.0 = perfect discrimination). Internal validation was conducted via bootstrap resampling to calculate the corrected concordance index (C-index) [28]. Calibration curves were generated to visualize the agreement between the predicted probabilities and observed outcomes, and decision curve analysis (DCA) was performed to assess the clinical net benefit of the prediction model [27]. Statistical significance was defined as a two-tailed p-value of < 0.05. All analyses were conducted using the R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) and MSTATA software ( www.mstata.com ). 3. RESULTS 3.1 Patient Characteristics Table 1 presents the baseline characteristics of the study population stratified according to suicidal ideation status. The weighted sample sizes were 149,712,668 for the no suicidal ideation group and 8,199,695 for the suicidal ideation groups, respectively. Significant differences were observed between the groups for several variables. Individuals with suicidal ideation were significantly younger (median age: 36 vs. 49 years, p < 0.001) and had lower family income-to-poverty ratios (median 2.18 vs. 3.26, p < 0.001). The suicidal ideation group had a significantly higher proportion of never-married individuals (42.9% vs. 20.9%) and a lower proportion of married/living with partners (35.5% vs. 61.3%, p < 0.001). Educational attainment showed significant differences, with lower levels of college graduation (23.8% vs. 38.5%) and higher representation in lower educational categories, among those with suicidal ideation (p < 0.001). The VID and VD3 variables were significantly lower in the suicidal ideation group (p < 0.001). No significant differences were found for sex (p = 0.413), race (p = 0.124), country of birth (p = 0.599), armed service history (p = 0.065), family size (p = 0.574), and VD2 (p = 0.087). Table 1 Weighted Patient demographics and baseline characteristics Characteristic DPQ090-group p-value No idea of suicide Weighted N = 149,712,668 Unweighted n = 4,819 1 Idea of suicide Weighted N = 8,199,695 Unweighted n = 270 1 Gender 0.413 2 Male 49.6% 46.5% Female 50.4% 53.5% Age 49 (34, 63) 36 (24, 54) < 0.001 3 Race 0.124 2 Mexican American 6.8% 9.6% Non-Hispanic White 63.5% 56.2% Other Hispanic 9.1% 13.4% Non-Hispanic Black 9.7% 9.1% Other Race - Including Multi-Racial 11.0% 11.7% Country 0.599 2 Others 17.2% 15.8% Born in 50 US states or Washington 82.8% 84.2% Served Armed 0.065 2 No 91.4% 95.0% Yes 8.6% 5.0% Education level < 0.001 2 College graduate or above 38.5% 23.8% Some college or AA degree 30.7% 33.0% High school graduate/GED or equivalent 23.0% 28.8% 9-11th grade 5.3% 11.0% Less than 9th grade 2.6% 3.5% Marital status < 0.001 2 Married/Living with partner 61.3% 35.5% Never married 20.9% 42.9% Widowed/Divorced/Separated 17.8% 21.6% Total number of family 2.00 (2.00, 4.00) 3.00 (2.00, 4.00) 0.574 3 Family income to poverty ratio 3.26 (1.91, 5.00) 2.18 (1.10, 3.29) < 0.001 3 VID 77 (56, 99) 59 (41, 77) < 0.001 3 VD2 1.57 (1.57, 1.57) 1.57 (1.57, 2.50) 0.087 3 VD3 73 (51, 96) 55 (36, 72) < 0.001 3 1 %; Median (Q1, Q3) 2 Pearson's X^2: Rao & Scott adjustment 3 Design-based Kruskal Wallis test 3.2 Predictive Model The candidate predictors, gender, age, race, country, served-arm, education level, marital status, total number of families, family income to poverty ratio, VID, VD2, and VD3, were included in the original model using LASSO regression analysis performed in the training cohort. The coefficients are listed in Table 2 , and the coefficient profile is plotted in Fig. 2 A. A cross-validated error plot of the LASSO regression model is also shown in the Fig. 2 B. The most regularized and parsimonious model, with a cross-validated error within one standard error of the minimum (λ = 0.018195743309169), included four variables (Fig. 3 ). Figure 2 A. 10-fold cross-validation was applied to select the most suitable feature using the Lasso regression model. (λ = 0.018195743309169). Table 2 The coefficients of Lasso regression analysis Variable Coefficient (Intercept) -2.527404554 Gender Female 0.000000000 Age -0.001573960 Race Non-Hispanic White 0.000000000 Other Hispanic 0.000000000 Non-Hispanic Black 0.000000000 Other Race - Including Multi-Racial 0.000000000 Country Born in 50 US states or Washington 0.000000000 Served Armed Yes 0.000000000 Education level Some college or AA degree 0.000000000 High school graduate/GED or equivalent 0.000000000 9-11th grade 0.000000000 Less than 9th grade 0.000000000 Marital status Never married 0.248119199 Widowed/Divorced/Separated 0.000000000 Total number of family 0.000000000 Family income to poverty ratio -0.049605200 VID 0.000000000 VD2 0.000000000 VD3 -0.002606154 As shown in Table 3 and Fig. 4 , the ROC analysis of the above-mentioned variables yielded AUC values greater than 0.5. Receiver operating characteristic (ROC) curve analysis demonstrated the predictive performance of individual variables for the outcome DPQ090.group, with vitamin D (VD3) showing the highest discriminative ability (AUC = 0.663; 95% CI, 0.631–0.695), followed by marital status (AUC = 0.644, 95% CI: 0.612–0.676), family income-to-poverty ratio (AUC = 0.639, 95% CI: 0.606–0.671), and age (AUC = 0.631, 95% CI: 0.594–0.669). All variables exhibited modest predictive utility, with overlapping confidence intervals indicating a comparable performance. Table 3 AUC Values and 95% Confidence Intervals for Variables Variable AUC Value AUC 95% Confidence Interval Age 0.631 (0.594–0.669) Marital status 0.644 (0.612–0.676) Family income to poverty ratio 0.639 (0.606–0.671) VD3 0.663 (0.631–0.695) AUC calculated using the model predictions; confidence intervals are estimated using DeLong's method. The final logistic model included four independent predictors (Age, Marital status, family income to poverty ratio, and VD3) and was developed as a simple-to-use nomogram, as illustrated in Fig. 5 and available online ( https://guohongdp.shinyapps.io/predict/ ). Multivariable logistic regression analysis revealed that age was significantly associated with outcome (OR 0.99, 95% CI 0.98–1.00, p = 0.003). Compared to married or cohabitating individuals, never-married participants had significantly higher odds of the outcome (OR 2.07, 95% CI 1.49–2.88, p < 0.001), as did those who were widowed, divorced, or separated (OR 1.81, 95% CI 1.28–2.55, p < 0.001). A lower family-income to poverty ratio was associated with reduced odds of the outcome (OR 0.83, 95% CI 0.76–0.90, p < 0.001). Similarly, VD3 levels were inversely associated with outcome (OR 0.99, 95% CI 0.98–0.99; p < 0.001) (Table 4 ). The area under the curve (AUC) of the model is shown in the following Fig. 6 . Table 4 Results of Multivariate Logistic regression for Training Cohort Characteristic N Event N OR 95% CI p-value Age 5,089 270 0.99 0.98, 1.00 0.003 Marital status Married/Living with partner 2,674 81 — — Never married 1,190 120 2.07 1.49, 2.88 < 0.001 Widowed/Divorced/Separated 1,225 69 1.81 1.28, 2.55 < 0.001 Family income to poverty ratio 5,089 270 0.83 0.76, 0.90 < 0.001 VD3 5,089 270 0.99 0.98, 0.99 < 0.001 Abbreviations: CI = Confidence Interval, OR = Odds Ratio 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 C-index was 0.714. 3.4 Calibration Analysis The calibration plot of the nomogram is shown in Fig. 7 , demonstrating good agreement between the observed and predicted DPQ090-group. The calibration curve closely followed the ideal line, indicating that the predicted probabilities were consistent with the actual outcomes. 3.5 Decision Curve Analysis Figure 8 shows the DCA curve related to the nomogram. This study shows that the nomogram offers substantial net benefits for clinical application through its DCA curve. 4. DISCUSSION This study, leveraging a large and nationally representative sample from the U.S. population, presents a multifactorial predictive model for suicidal ideation, integrating demographic, socioeconomic, and biochemical markers. Our principal finding was the identification of four robust and independent predictors through LASSO regression: younger age, unmarried status, lower family income-to-poverty ratio, and diminished serum 25-hydroxyvitamin D3 (VD3) concentrations. Among these variables, VD3 demonstrated the highest individual discriminative ability for identifying individuals with suicidal ideation, as evidenced by its superior Area Under the Curve (AUC). The development of a nomogram incorporating these four factors provides a novel, user-friendly tool for quantifying individual risk and translating complex statistical findings into a practical instrument for potential clinical screening and public health surveillance. The parsimonious nature of this model, which focuses on readily ascertainable variables, underscores its potential utility in diverse healthcare settings. The identified associations between suicidal ideation and sociodemographic factors of younger age, unmarried status, and economic hardship are largely congruent with a substantial body of existing literature. Younger individuals, particularly during adolescence and young adulthood, often face unique developmental, social, and psychological stressors that increase their risk of mental health crises [29]. Similarly, marital status, as a proxy for social support and integration, has been consistently linked to suicide-related outcomes, with unmarried, divorced, and widowed individuals frequently exhibiting higher risk profiles [30]. Furthermore, our finding that a lower family income-to-poverty ratio is a potent predictor aligns with the well-established socioeconomic gradient of mental health. Financial strain acts as a significant psychosocial stressor, exacerbating feelings of hopelessness and contributing to the development of depressive symptoms and suicidal thoughts [31]. Confirmation of these known risk factors within our model enhances its face validity and reinforces the understanding that suicidal ideation is a multifaceted phenomenon deeply embedded in an individual's social and economic context. The most significant biochemical finding of our investigation was the independent predictive role of low VD3 levels in suicidal ideation. This result contributes compelling evidence to a growing but sometimes inconsistent body of research. While some earlier studies reported null findings [4, 9] a surge in recent research from 2020 onwards has increasingly solidified this connection. For instance, large-scale cohort studies and meta-analyses have demonstrated a significant association between vitamin D deficiency and an elevated risk of both suicidal ideation and suicide attempts [4, 9, 32]. Some Studies have provided robust epidemiological data supporting this link in large populations [32, 33]. Our analysis, conducted on a vast, representative U.S. cohort with direct biochemical measurements, strengthens this association and crucially positions VD3 not just as a correlate but as a key predictive biomarker, outperforming several established sociodemographic risk factors in its individual predictive capacity. The neurobiological plausibility of the link between hypovitaminosis D and suicidal ideation is supported by several mechanistic pathways identified in recent neuropsychiatric studies. Vitamin D is a potent neurosteroid, and its receptors (VDR) are widely expressed throughout the brain in areas critical for mood and emotional regulation, including the prefrontal cortex and hippocampus [4, 34]. Mechanistically, vitamin D is known to modulate neuroinflammatory processes by suppressing pro-inflammatory cytokines, which are increasingly implicated in the pathophysiology of depression and suicidality [35, 36]. Moreover, preclinical and clinical evidence suggests that vitamin D directly regulates the synthesis of key neurotransmitters, particularly serotonin, by controlling the expression of tryptophan hydroxylase 2 (TPH2), the rate-limiting enzyme in serotonin production in the central nervous system [37, 38]. Therefore, vitamin D deficiency could plausibly disrupt serotonergic homeostasis and promote a pro-inflammatory state, thereby creating a neurobiological environment conducive to the development of depressive symptoms and suicidal thoughts. This study has notable strengths, including its use of the NHANES dataset, which ensures a large, diverse, and nationally representative sample, thus enhancing the generalizability of our findings. The application of LASSO regression for parsimonious variable selection and the subsequent development and validation of a nomogram [12, 14] represent a methodologically rigorous approach to risk prediction. However, the limitations of this study must be acknowledged. The cross-sectional design precludes any inference of causality; it is impossible to determine whether low vitamin D is a cause of suicidal ideation or a consequence of behaviors associated with it (e.g., poor nutrition and reduced sun exposure due to social withdrawal). Furthermore, the predictive model did not include clinical variables, such as a formal diagnosis of major depressive disorder, substance use disorders, or prior suicide attempts, which are known to be powerful predictors. Future research should prioritize longitudinal cohort studies to elucidate the temporal dynamics between changes in vitamin D status and onset of suicidal ideation. Ultimately, randomized controlled trials investigating vitamin D supplementation as a primary or adjunctive intervention are warranted to establish causality and to evaluate its potential as a safe, accessible, and cost-effective strategy for suicide prevention. 5. CONCLUSION This study identified several demographic, socioeconomic, and biological factors associated with suicidal ideation in a representative U.S. sample. These findings underscore the multifactorial nature of suicide risk and highlight potential avenues for improved risk identification and future mechanistic research. While the prediction model demonstrates moderate accuracy, substantial work remains to enhance our ability to identify at-risk individuals and develop effective prevention strategies. Abbreviations SI Suicidal Ideation NHANES National Health and Nutrition Examination Survey PHQ-9 Patient Health Questionnaire-9 DPQ Depression Screener Questionnaire OR Odds Ratio AUC Area Under the Receiver Operating Characteristic Curve CI Confidence Interval LC-MS/MS Liquid Chromatography-Tandem Mass Spectrometry NIST National Institute of Standards and Technology SRM Standard Reference Material LASSO Least Absolute Shrinkage and Selection Operator ROC Receiver Operating Characteristic C-index Concordance Index VID Total 25-hydroxyvitamin D VD2 25-hydroxyvitamin D2 VD3 25-hydroxyvitamin D3 VDR Vitamin D Receptor TPH2 Tryptophan Hydroxylase 2 PIR Income-to-Poverty Ratio GED General Educational Development CDC Centers for Disease Control and Prevention NCHS National Center for Health Statistics Declarations Written informed consent was obtained from all individual participants included in the NHANES database prior to data collection. De-identified data for this secondary analysis were accessed from the NHANES public repository, which is exempt from additional consent requirements. All authors contributed to the article and agreed to the submitted version. Acknowledgements I am appreciative of my consistent self, along with the editors and reviewers! We acknowledge Deepseek-R1's help in improving the manuscript's readability and language. The responsibility for the study's content and conclusions lies entirely with us. Author Contributions Hong Guo and Lu Dai performed the tasks of acquiring and analyzing data, drafting figures and tables, and composing the original manuscript. Hong Guo and Lu Dai were responsible for reviewing and editing the manuscript, while revisions and refinements were carried out by Hong Guo, Lu Dai, and Jian Zhan. All authors have read and approved the final manuscript. Funding No funding was received for this research. Clinical trial number Not applicable. Data Availability Data available for this study can be found on the National Health and Nutrition Examination Survey website ( https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ). All the data used were publicly available. Ethics approval This research was carried out following the Declaration of Helsinki. Approval for the NHANES study protocol was granted by the NCHS Research Ethics Review Committee, with written informed consent collected from every participant. Consent for publication Not applicable. Conflict of interest The authors declare no competing interest. References Nock MK, Borges G, Bromet EJ, Cha CB, Kessler RC, Lee S: Suicide and suicidal behavior . Epidemiologic reviews 2008, 30 (1):133-154. 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Canadian journal of psychiatry Revue canadienne de psychiatrie 2022, 67 (4):259-267. Wang N, Yan X, Imm K, Xu T, Li S, Gawronska J, Wang R, Smith L, Yang L, Cao C: Racial and ethnic disparities in prevalence and correlates of depressive symptoms and suicidal ideation among adults in the United States, 2017-2020 pre-pandemic . J Affect Disord 2024, 345 :272-283. Uebelacker LA, German NM, Gaudiano BA, Miller IW: Patient health questionnaire depression scale as a suicide screening instrument in depressed primary care patients: a cross-sectional study . The primary care companion for CNS disorders 2011, 13 (1). Schleicher RL, Sternberg MR, Lacher DA, Sempos CT, Looker AC, Durazo-Arvizu RA, Yetley EA, Chaudhary-Webb M, Maw KL, Pfeiffer CM et al : The vitamin D status of the US population from 1988 to 2010 using standardized serum concentrations of 25-hydroxyvitamin D shows recent modest increases . The American journal of clinical nutrition 2016, 104 (2):454-461. Subramanian A, Burrowes HB, Rumph JT, Wilkerson J, Jackson CL, Jukic AMZ: Vitamin D Levels in the United States: Temporal Trends (2011-2018) and Contemporary Associations with Sociodemographic Characteristics (2017-2018) . Nutrients 2024, 16 (19). Orces C: The Association between Body Mass Index and Vitamin D Supplement Use among Adults in the United States . Cureus 2019, 11 (9):e5721. Zelzer S, Goessler W, Herrmann M: Measurement of vitamin D metabolites by mass spectrometry, an analytical challenge . Journal of laboratory and precision medicine 2018, 3 (-):99-99. de la Hunty A, Wallace AM, Gibson S, Viljakainen H, Lamberg-Allardt C, Ashwell M: UK Food Standards Agency Workshop Consensus Report: the choice of method for measuring 25-hydroxyvitamin D to estimate vitamin D status for the UK National Diet and Nutrition Survey . The British journal of nutrition 2010, 104 (4):612-619. Phinney KW, Bedner M, Tai SS, Vamathevan VV, Sander LC, Sharpless KE, Wise SA, Yen JH, Schleicher RL, Chaudhary-Webb M et al : Development and certification of a standard reference material for vitamin D metabolites in human serum . Analytical chemistry 2012, 84 (2):956-962. Liang S, Li D, Liu X, Jiang I, Zhang J, Liu J, Sha S: Development and validation of a prediction nomogram for non-suicidal self-injury in female patients with mood disorder . Frontiers in psychiatry 2023, 14 :1130335. Liang S, Liu X, Li D, Zhang J, Zhao G, Yu H, Zhao X, Sha S: Development and validation of a nomogram to predict suicidal behavior in female patients with mood disorder . Frontiers in psychiatry 2023, 14 :1212579. Heo J, Lee J, Cho H, Cho J, Kang D: Relationship between qualitative and quantitative loneliness and suicidal ideation by occupational classification in the working-age population: a nationally-representative survey . BMC public health 2024, 24 (1):2708. Corcoran P, Nagar A: Suicide and marital status in Northern Ireland . Social psychiatry and psychiatric epidemiology 2010, 45 (8):795-800. Lindström M, Rosvall M: Economic stress in childhood and suicide thoughts and suicide attempts: a population-based study among adults . Public health 2018, 163 :42-45. Lavigne JE, Gibbons JB: The association between vitamin D serum levels, supplementation, and suicide attempts and intentional self-harm . PloS one 2023, 18 (2):e0279166. PuSUroGLu M, BaltacioGLu M, Helvaci ÇElIK F, BahcecI B, Hocaoglu C: SIZOFRENI HASTALARINDA SERUM D VITAMINI SEVIYELERI ILE INTIHAR DÜSÜNCESI ARASINDAKI ILISKI . Ankara Egitim ve Arastirma Hastanesi Tip Dergisi 2022, - (-):-. Al-Sabah R, Al-Taiar A, Shaban L, Albatineh AN, Sharaf Alddin R, Durgampudi PK: Vitamin D level in relation to depression symptoms during adolescence . Child and adolescent psychiatry and mental health 2022, 16 (1):53. Yagci I, Avci S: Biochemical predictors in presentations to the emergency department after a suicide attemp . Bratislavske lekarske listy 2021, 122 (3):224-229. Tae H, Chae JH: Association Between Riboflavin Intake and Suicidal Ideation: A Nationwide Study in Korea . Nutrients 2025, 17 (3). Somoza-Moncada MM, Turrubiates-Hernández FJ, Muñoz-Valle JF, Gutiérrez-Brito JA, Díaz-Pérez SA, Aguayo-Arelis A, Hernández-Bello J: Vitamin D in Depression: A Potential Bioactive Agent to Reduce Suicide and Suicide Attempt Risk . Nutrients 2023, 15 (7). Fenercioglu AK: The Anti-Inflammatory Roles of Vitamin D for Improving Human Health . Current issues in molecular biology 2024, 46 (12):13514-13525. Additional Declarations No competing interests reported. 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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-7631764","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521138648,"identity":"a88b31ec-cbc7-41a8-89d8-582063b3edd2","order_by":0,"name":"Hong Guo","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Guo","suffix":""},{"id":521138649,"identity":"9f5d0b0c-7d1f-4042-8be5-e0345272caba","order_by":1,"name":"Lu Dai","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Dai","suffix":""},{"id":521138650,"identity":"3e389602-9202-47dc-9a73-f3151f247c6f","order_by":2,"name":"Jian Zhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYDACCTBpw8PP3kC0FmYQmSYj2XOANC2HbQxuOBCpQz66//Br3rbzPAw3GBg/fMwhQovhncNs1jxnbvMwzm5glpy5jRgtM5LZjHkqbvMwyxxgY+YlXovBOR42iQQitchLJDM/5qk4wMNDtBYDiWQzxjlnknkkeA42E+cX+RmJjz+8bbOztz/efPDDR6JsOcDAJsUDZjI2EKEeZEsDA/PHH8SpHQWjYBSMgpEKAAFrMspMjRMcAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Zunyi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhan","suffix":""}],"badges":[],"createdAt":"2025-09-16 14:53:23","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7631764/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7631764/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":92381095,"identity":"4ebdbbfd-85c1-4942-a0e2-7257ff7bbd4f","added_by":"auto","created_at":"2025-09-29 06:14:34","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65714,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of participant inclusion.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/b87816b37eb46083e530ced3.jpeg"},{"id":92381100,"identity":"120e3f36-9381-4598-8984-c7434f0394e2","added_by":"auto","created_at":"2025-09-29 06:14:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72962,"visible":true,"origin":"","legend":"\u003cp\u003eA. 10-fold cross-validation was applied to select the most suitable feature using the Lasso regression model. (λ = 0.018195743309169).\u003c/p\u003e\n\u003cp\u003eB. Plot of the Lasso regression coefficient profiles (λ = 0.018195743309169).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/2275e0697eaf253526a33328.png"},{"id":92381101,"identity":"3a197435-6f9e-40fb-861c-f9820fd08c56","added_by":"auto","created_at":"2025-09-29 06:14:35","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36226,"visible":true,"origin":"","legend":"\u003cp\u003eLasso-Selected Predictors and Corresponding Coefficients. Only variables with non-zero coefficients after Lasso selection are shown.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/d17596ed63904890a216814f.jpeg"},{"id":92381092,"identity":"41cb4a1a-a5d5-4e33-9ef7-60bbe76058f0","added_by":"auto","created_at":"2025-09-29 06:14:32","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":70900,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of ROC Curves for Individual Predictive Variables. Receiver operating characteristic (ROC) curves for individual predictors assessed in univariate logistic regression.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/97a438492a4b53064369ee18.jpeg"},{"id":92381080,"identity":"47c3a281-64b2-44c9-ad1f-89050c104f3c","added_by":"auto","created_at":"2025-09-29 06:14:30","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46565,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model.\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/61fde9855d0fb312e140947a.jpeg"},{"id":92381096,"identity":"7acfc7b5-4e45-4acf-8c76-67c407d482dd","added_by":"auto","created_at":"2025-09-29 06:14:34","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":71995,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curve of the Prediction Model.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/88b619a6b8d507120d3c380c.jpeg"},{"id":92381079,"identity":"eda6ae1d-e739-4a53-bd3b-ebea6a5c29f0","added_by":"auto","created_at":"2025-09-29 06:14:30","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":19461,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve of the nomogram prediction mode for the training cohort.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/fd4693047c53ad0b28024483.jpeg"},{"id":92381507,"identity":"6f88151f-35c3-4837-92e4-3593ec0192ab","added_by":"auto","created_at":"2025-09-29 06:22:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":55267,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis of the nomogram of the training coho\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/7f37d62b9405ce81a42671a6.jpeg"},{"id":92381512,"identity":"e9c10fa3-3910-48a8-8d6c-222759f4abab","added_by":"auto","created_at":"2025-09-29 06:22:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3231308,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7631764/v1/86e044ce-9d39-4625-b332-3327ce55534f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vitamin D and Suicidal Ideation: A Predictive Model Based on NHANES Database","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSuicidal behaviors encompassing suicidal ideation (SI), planning, and attempts represent a formidable and persistent public health crisis on a global scale [1]. In the United States, suicide remains a leading cause of mortality, and the prevalence of suicidal ideation far exceeds that of completed suicide, signaling a vast and often hidden burden of severe psychological distress within the population. The presence of SI is a critical precursor to overt suicidal acts and serves as a crucial intervention point for preventative strategies. Consequently, accurate and early identification of individuals at a heightened risk of developing SI is a paramount objective in clinical practice and public health research. This imperative has driven an extensive search for reliable and clinically accessible risk factors to improve the precision of suicide risk assessment protocols, which have historically struggled with low predictive accuracy [2].\u003c/p\u003e\u003cp\u003eHistorically, research has focused on a wide array of demographic and socioeconomic variables as potential indicators of suicide risk. Factors such as age, sex, race/ethnicity, marital status, educational attainment, and employment status have been consistently investigated [3]. A comprehensive meta-analysis examining the predictive utility of these variables confirmed that many were statistically significant risk factors for suicidal thoughts and behaviors [3]. However, the same body of evidence reveals a critical limitation: the effect sizes associated with individual demographic factors are typically small, rendering their clinical utility for individual risk prediction weak and often insignificant when used in isolation [3]. This suggests that, while these factors contribute to a foundational understanding of risk at the population level, they lack the necessary predictive power for robust clinical decision-making and highlight the necessity of integrating them with other, more dynamic risk markers [3].\u003c/p\u003e\u003cp\u003eIn response to the limited predictive capacity of conventional risk factors, the field has increasingly turned its attention toward identifying objective biological markers that may underpin the pathophysiology of suicidality. Among many candidates, serum vitamin D levels have emerged as a promising, modifiable, and readily measurable biomarker. Vitamin D, a neurosteroid hormone, is integral to central nervous system function, and its receptors are widely distributed throughout the brain regions implicated in mood regulation and executive function, such as the prefrontal cortex and hippocampus [4, 5]. A growing corpus of observational research has documented a consistent association between hypovitaminosis D and a range of adverse psychiatric outcomes, including depression and suicidal behaviors [6, 7]. Recent systematic reviews and meta-analyses have bolstered this line of inquiry. For instance, a major 2023 meta-analysis concluded that low vitamin D levels are significantly associated with an increased probability of suicidal behaviors, with subgroup analyses confirming this relationship for both suicide attempts and suicidal ideation [4]. The analysis reported that individuals with SI had significantly lower vitamin D levels than the controls [4]. Nevertheless, the literature is not entirely uniform; some studies have yielded null findings, and inconsistencies persist, potentially attributable to methodological heterogeneity, including differences in study populations, definitions of vitamin D deficiency, and inadequate control for confounding variables such as season, diet, or pre-existing psychiatric conditions [8\u0026ndash;10].\u003c/p\u003e\u003cp\u003eTherefore, the challenge lies not only in confirming the association between individual risk factors and SI, but also in integrating them into a cohesive, multidimensional predictive model that possesses both statistical robustness and clinical applicability. Although complex machine learning and genomic models are being explored [11] there remains a pressing need for more straightforward tools that clinicians can use for personalized risk stratification at the point of care. Nomograms have proven to be valuable instruments in this regard, offering a graphical representation of a statistical model that can calculate the probability of an outcome for an individual patient by combining various prognostic variables [12, 13]. While nomograms have been developed for SI in specific populations (e.g., adolescents with depression and cancer patients) and have demonstrated acceptable to excellent discriminative ability, with Area Under the Receiver Operating Characteristic Curve (AUC) values typically ranging from 0.70 to over 0.90 [12\u0026ndash;14] there is a notable gap in the literature. Specifically, few studies have developed such a model by combining demographic, socioeconomic, \u003cem\u003eand\u003c/em\u003e biochemical markers within a large, nationally representative sample of the general U.S. population, particularly one screened to be free of severe confounding physical and psychiatric disorders. The development of such a model would provide a more nuanced understanding of risk in a non-clinical general population context.\u003c/p\u003e\u003cp\u003eGiven these considerations, this study aims to address this critical gap. Utilizing data from the National Health and Nutrition Examination Survey (NHANES), a robust and representative dataset of the U.S. population, our primary objective was to identify a parsimonious set of demographic, socioeconomic, and vitamin D biomarkers that independently predict suicidal ideation in a cohort of generally healthy adults. Our secondary objective was to use these findings to construct and internally validate a novel nomogram, providing a practical and quantitative tool for estimating the risk of suicidal ideation. We hypothesized that a multifactorial model incorporating younger age, non-married status, economic strain, and lower serum vitamin D3 concentrations would significantly predict suicidal ideation and that the resulting nomogram would demonstrate moderate-to-good predictive accuracy, thereby offering a valuable resource for public health screening and targeted prevention initiatives.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study Population and Data Source\u003c/h2\u003e\u003cp\u003eThis cross-sectional analysis was based on data from the National Health and Nutrition Examination Survey (NHANES), a program designed to evaluate the health and nutritional status of U.S. adults and children via a complex, stratified, multistage probability sampling framework. Administered by the National Center for Health Statistics (NCHS) at the Centers for Disease Control and Prevention (CDC), NHANES integrates in-person interviews, standardized physical examinations at mobile centers, and laboratory testing of biological samples [15]. The study protocol was approved by the NCHS Research Ethics Review Board and all participants provided written informed consent. This study was conducted in accordance with the Declaration of Helsinki. De-identified data were obtained from the NHANES and Nutrition Examination Survey public database.\u003c/p\u003e\u003cp\u003eThe present analysis is based on a consolidated dataset from the most recent continuous NHANES cycles, spanning August 2021 to August 2023. This initial period included 11,933 individuals. To align with the study objectives, we applied a sequence of exclusion criteria. First, we excluded 3,189 participants younger than 18 years of age, as assessment tools and risk factor profiles for suicidal ideation can differ significantly in pediatric populations. Second, 2,987 participants with incomplete or missing data on the Patient Health Questionnaire-9 (PHQ-9), which is essential for our outcome variable, were excluded. Third, we excluded 668 participants who lacked laboratory measurements of serum 25-hydroxyvitamin D. Following the application of these criteria, the final analytical cohort for this study comprised 5,089 eligible adult participants. A detailed flowchart of the participant selection process is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.2 Ascertainment of Suicidal Ideation (Outcome Variable)\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eSuicidal ideation was identified via the Depression Screener Questionnaire (DPQ) module in the NHANES, operationally comparable to the validated 9-item PHQ-9 [16]. Specifically, item 9 (coded as DPQ090) was used, which queries: \u0026ldquo;Over the last 2 weeks, how often have you been bothered by thoughts that you would be better off dead or of hurting yourself in some way?\u0026rdquo; [17, 18]. Responses were recorded on a 4-point Likert scale: 0 (\u0026ldquo;Not at all\u0026rdquo;), 1 (\u0026ldquo;Several days\u0026rdquo;), 2 (\u0026ldquo;More than half the days\u0026rdquo;), or 3 (\u0026ldquo;Nearly every day\u0026rdquo;) [19]. A dichotomous outcome variable (DPQ090-group) was generated: participants with a score of 0 were categorized as \u0026ldquo;No Suicidal Ideation,\u0026rdquo; while those with scores\u0026thinsp;\u0026ge;\u0026thinsp;1 (endorsing any frequency of such thoughts) were classified as \u0026ldquo;Suicidal Ideation\u0026rdquo; [18]. This approach aligns with standard psychiatric epidemiological methods, as any positive response to this item is clinically significant for suicide risk assessment [20].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Assessment of Vitamin D and Covariates\u003c/h2\u003e\u003cp\u003e\u003cb\u003eVitamin D Biomarkers\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSerum levels of vitamin D biomarkers (total 25-hydroxyvitamin D [25(OH)D], 25-hydroxyvitamin D2 [25(OH)D2], and 25-hydroxyvitamin D3 [25(OH)D3]) were quantified at the CDC\u0026rsquo;s Division of Laboratory Sciences using standardized liquid chromatography-tandem mass spectrometry (LC-MS/MS) [21, 22], a gold-standard method that avoids cross-reactivity issues of older immunoassays [23, 24]. Accuracy was ensured via calibration with the National Institute of Standards and Technology (NIST) Standard Reference Material (SRM) 972a [25, 26]. For the analysis, 25(OH)D, 25(OH)D2, and 25(OH)D3 were treated as continuous variables.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographic and Socioeconomic Covariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA comprehensive set of potential confounding variables was extracted from the NHANES interviews and examination data. These variables were selected based on their established or theoretical associations with vitamin D status and mental health outcomes. Including: 1) Demographic Variables were included: age, sex, race/ethnicity, country of birth, veteran/military Status; 2) Socioeconomic Variables: Education Level, Marital Status, Family Size, Family Income-to-Poverty Ratio (PIR).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\u003cp\u003eBaseline demographic features were stratified according to the presence or absence of suicidal ideation (DPQ090.group). Normally distributed continuous data are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, whereas non-normally distributed variables are presented as medians (interquartile ranges). Categorical variables were compared using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. For continuous variables, intergroup differences were assessed using Welch\u0026rsquo;s t-test (for normal distributions) or the Mann-Whitney U test (for non-normal distributions). To identify independent predictors and develop a nomogram for predicting suicidal ideation, least absolute shrinkage and selection operator (LASSO) logistic regression was employed to refine the variable set [27]. Model performance was evaluated using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) quantifying discriminative ability (AUC range: 0.5\u0026thinsp;=\u0026thinsp;no discrimination, 1.0\u0026thinsp;=\u0026thinsp;perfect discrimination). Internal validation was conducted via bootstrap resampling to calculate the corrected concordance index (C-index) [28]. Calibration curves were generated to visualize the agreement between the predicted probabilities and observed outcomes, and decision curve analysis (DCA) was performed to assess the clinical net benefit of the prediction model [27]. Statistical significance was defined as a two-tailed p-value of \u0026lt;\u0026thinsp;0.05. All analyses were conducted using the R software (version 4.2.2; R Foundation for Statistical Computing, Vienna, Austria) and MSTATA software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.mstata.com\" target=\"_blank\"\u003ewww.mstata.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.mstata.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the baseline characteristics of the study population stratified according to suicidal ideation status. The weighted sample sizes were 149,712,668 for the no suicidal ideation group and 8,199,695 for the suicidal ideation groups, respectively. Significant differences were observed between the groups for several variables. Individuals with suicidal ideation were significantly younger (median age: 36 vs. 49 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and had lower family income-to-poverty ratios (median 2.18 vs. 3.26, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The suicidal ideation group had a significantly higher proportion of never-married individuals (42.9% vs. 20.9%) and a lower proportion of married/living with partners (35.5% vs. 61.3%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Educational attainment showed significant differences, with lower levels of college graduation (23.8% vs. 38.5%) and higher representation in lower educational categories, among those with suicidal ideation (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The VID and VD3 variables were significantly lower in the suicidal ideation group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant differences were found for sex (p\u0026thinsp;=\u0026thinsp;0.413), race (p\u0026thinsp;=\u0026thinsp;0.124), country of birth (p\u0026thinsp;=\u0026thinsp;0.599), armed service history (p\u0026thinsp;=\u0026thinsp;0.065), family size (p\u0026thinsp;=\u0026thinsp;0.574), and VD2 (p\u0026thinsp;=\u0026thinsp;0.087).\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\u003eWeighted Patient 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\u003eDPQ090-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\u003eNo idea of suicide\u003c/p\u003e\u003cp\u003eWeighted N\u0026thinsp;=\u0026thinsp;149,712,668\u003c/p\u003e\u003cp\u003eUnweighted n\u0026thinsp;=\u0026thinsp;4,819\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIdea of suicide\u003c/p\u003e\u003cp\u003eWeighted N\u0026thinsp;=\u0026thinsp;8,199,695\u003c/p\u003e\u003cp\u003eUnweighted n\u0026thinsp;=\u0026thinsp;270\u003csup\u003e1\u003c/sup\u003e\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\u003eGender\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\u003e0.413\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.5%\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\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.5%\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\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (34, 63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36 (24, 54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\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\u003e0.124\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\u003e6.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.6%\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\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56.2%\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\u003e9.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4%\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\u003eNon-Hispanic Black\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.1%\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 - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.7%\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\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\u003e0.599\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\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\u003e17.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.8%\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\u003eBorn in 50 US states or Washington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e82.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.2%\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 Armed\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\u003e0.065\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\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\u003e91.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95.0%\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0%\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\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\u003eCollege graduate or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e38.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8%\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\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.0%\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\u003eHigh school graduate/GED or equivalent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.8%\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\u003e9-11th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0%\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\u003eLess than 9th grade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.5%\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\u003e\u003cb\u003eMarital status\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\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.5%\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\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.9%\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\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21.6%\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\u003e\u003cb\u003eTotal number of family\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.00 (2.00, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (2.00, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.574\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily income to poverty ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.26 (1.91, 5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.18 (1.10, 3.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVID\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e77 (56, 99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59 (41, 77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57 (1.57, 1.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.57 (1.57, 2.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73 (51, 96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 (36, 72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e3\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\u003e%; Median (Q1, Q3)\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 X^2: Rao \u0026amp; Scott adjustment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDesign-based Kruskal Wallis test\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Predictive Model\u003c/h2\u003e\u003cp\u003eThe candidate predictors, gender, age, race, country, served-arm, education level, marital status, total number of families, family income to poverty ratio, VID, VD2, and VD3, were included in the original model using LASSO regression analysis performed in the training cohort. The coefficients are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the coefficient profile is plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. A cross-validated error plot of the LASSO regression model is also shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. The most regularized and parsimonious model, with a cross-validated error within one standard error of the minimum (λ\u0026thinsp;=\u0026thinsp;0.018195743309169), included four variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA. 10-fold cross-validation was applied to select the most suitable feature using the Lasso regression model. (λ\u0026thinsp;=\u0026thinsp;0.018195743309169).\u003c/p\u003e\u003cp\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\u003eThe coefficients of Lasso regression analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\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\u003e(Intercept)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2.527404554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\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\u003e-0.001573960\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNon-Hispanic White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\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\u003e0.000000000\u003c/p\u003e\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\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther Race - Including Multi-Racial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCountry\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBorn in 50 US states or Washington\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eServed Armed\u003c/b\u003e\u003c/p\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\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\u003cp\u003eSome college or AA degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\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\u003e0.000000000\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\u003e0.000000000\u003c/p\u003e\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\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.248119199\u003c/p\u003e\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\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal number of family\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily income to poverty ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.049605200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVID\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000000000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVD3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.002606154\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the ROC analysis of the above-mentioned variables yielded AUC values greater than 0.5. Receiver operating characteristic (ROC) curve analysis demonstrated the predictive performance of individual variables for the outcome DPQ090.group, with vitamin D (VD3) showing the highest discriminative ability (AUC\u0026thinsp;=\u0026thinsp;0.663; 95% CI, 0.631\u0026ndash;0.695), followed by marital status (AUC\u0026thinsp;=\u0026thinsp;0.644, 95% CI: 0.612\u0026ndash;0.676), family income-to-poverty ratio (AUC\u0026thinsp;=\u0026thinsp;0.639, 95% CI: 0.606\u0026ndash;0.671), and age (AUC\u0026thinsp;=\u0026thinsp;0.631, 95% CI: 0.594\u0026ndash;0.669). All variables exhibited modest predictive utility, with overlapping confidence intervals indicating a comparable performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAUC Values and 95% Confidence Intervals for Variables\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAUC Value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAUC 95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.594\u0026ndash;0.669)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.612\u0026ndash;0.676)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily income to poverty ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.606\u0026ndash;0.671)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVD3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.631\u0026ndash;0.695)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eAUC calculated using the model predictions; confidence intervals are estimated using DeLong's method.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe final logistic model included four independent predictors (Age, Marital status, family income to poverty ratio, and VD3) and was developed as a simple-to-use nomogram, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and available online ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guohongdp.shinyapps.io/predict/\u003c/span\u003e\u003cspan address=\"https://guohongdp.shinyapps.io/predict/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eMultivariable logistic regression analysis revealed that age was significantly associated with outcome (OR 0.99, 95% CI 0.98\u0026ndash;1.00, p\u0026thinsp;=\u0026thinsp;0.003). Compared to married or cohabitating individuals, never-married participants had significantly higher odds of the outcome (OR 2.07, 95% CI 1.49\u0026ndash;2.88, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did those who were widowed, divorced, or separated (OR 1.81, 95% CI 1.28\u0026ndash;2.55, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A lower family-income to poverty ratio was associated with reduced odds of the outcome (OR 0.83, 95% CI 0.76\u0026ndash;0.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, VD3 levels were inversely associated with outcome (OR 0.99, 95% CI 0.98\u0026ndash;0.99; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The area under the curve (AUC) of the model is shown in the following Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\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\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\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\u003e0.98, 1.00\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\u003eMarital status\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\u003eMarried/Living with partner\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,674\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e81\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\u003eNever married\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.49, 2.88\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\u003eWidowed/Divorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.28, 2.55\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\u003eFamily income to poverty ratio\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.76, 0.90\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\u003eVD3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270\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\u003e0.98, 0.99\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\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, OR\u0026thinsp;=\u0026thinsp;Odds Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" 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 C-index was 0.714.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" 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=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, demonstrating good agreement between the observed and predicted DPQ090-group. The calibration curve closely followed the ideal line, indicating that the predicted probabilities were consistent with the actual outcomes.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Decision Curve Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the DCA curve related to the nomogram. This study shows that the nomogram offers substantial net benefits for clinical application through its DCA curve.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThis study, leveraging a large and nationally representative sample from the U.S. population, presents a multifactorial predictive model for suicidal ideation, integrating demographic, socioeconomic, and biochemical markers. Our principal finding was the identification of four robust and independent predictors through LASSO regression: younger age, unmarried status, lower family income-to-poverty ratio, and diminished serum 25-hydroxyvitamin D3 (VD3) concentrations. Among these variables, VD3 demonstrated the highest individual discriminative ability for identifying individuals with suicidal ideation, as evidenced by its superior Area Under the Curve (AUC). The development of a nomogram incorporating these four factors provides a novel, user-friendly tool for quantifying individual risk and translating complex statistical findings into a practical instrument for potential clinical screening and public health surveillance. The parsimonious nature of this model, which focuses on readily ascertainable variables, underscores its potential utility in diverse healthcare settings.\u003c/p\u003e\u003cp\u003eThe identified associations between suicidal ideation and sociodemographic factors of younger age, unmarried status, and economic hardship are largely congruent with a substantial body of existing literature. Younger individuals, particularly during adolescence and young adulthood, often face unique developmental, social, and psychological stressors that increase their risk of mental health crises [29]. Similarly, marital status, as a proxy for social support and integration, has been consistently linked to suicide-related outcomes, with unmarried, divorced, and widowed individuals frequently exhibiting higher risk profiles [30]. Furthermore, our finding that a lower family income-to-poverty ratio is a potent predictor aligns with the well-established socioeconomic gradient of mental health. Financial strain acts as a significant psychosocial stressor, exacerbating feelings of hopelessness and contributing to the development of depressive symptoms and suicidal thoughts [31]. Confirmation of these known risk factors within our model enhances its face validity and reinforces the understanding that suicidal ideation is a multifaceted phenomenon deeply embedded in an individual's social and economic context.\u003c/p\u003e\u003cp\u003eThe most significant biochemical finding of our investigation was the independent predictive role of low VD3 levels in suicidal ideation. This result contributes compelling evidence to a growing but sometimes inconsistent body of research. While some earlier studies reported null findings [4, 9] a surge in recent research from 2020 onwards has increasingly solidified this connection. For instance, large-scale cohort studies and meta-analyses have demonstrated a significant association between vitamin D deficiency and an elevated risk of both suicidal ideation and suicide attempts [4, 9, 32]. Some Studies have provided robust epidemiological data supporting this link in large populations [32, 33]. Our analysis, conducted on a vast, representative U.S. cohort with direct biochemical measurements, strengthens this association and crucially positions VD3 not just as a correlate but as a key predictive biomarker, outperforming several established sociodemographic risk factors in its individual predictive capacity.\u003c/p\u003e\u003cp\u003eThe neurobiological plausibility of the link between hypovitaminosis D and suicidal ideation is supported by several mechanistic pathways identified in recent neuropsychiatric studies. Vitamin D is a potent neurosteroid, and its receptors (VDR) are widely expressed throughout the brain in areas critical for mood and emotional regulation, including the prefrontal cortex and hippocampus [4, 34]. Mechanistically, vitamin D is known to modulate neuroinflammatory processes by suppressing pro-inflammatory cytokines, which are increasingly implicated in the pathophysiology of depression and suicidality [35, 36]. Moreover, preclinical and clinical evidence suggests that vitamin D directly regulates the synthesis of key neurotransmitters, particularly serotonin, by controlling the expression of tryptophan hydroxylase 2 (TPH2), the rate-limiting enzyme in serotonin production in the central nervous system [37, 38]. Therefore, vitamin D deficiency could plausibly disrupt serotonergic homeostasis and promote a pro-inflammatory state, thereby creating a neurobiological environment conducive to the development of depressive symptoms and suicidal thoughts.\u003c/p\u003e\u003cp\u003eThis study has notable strengths, including its use of the NHANES dataset, which ensures a large, diverse, and nationally representative sample, thus enhancing the generalizability of our findings. The application of LASSO regression for parsimonious variable selection and the subsequent development and validation of a nomogram [12, 14] represent a methodologically rigorous approach to risk prediction. However, the limitations of this study must be acknowledged. The cross-sectional design precludes any inference of causality; it is impossible to determine whether low vitamin D is a cause of suicidal ideation or a consequence of behaviors associated with it (e.g., poor nutrition and reduced sun exposure due to social withdrawal). Furthermore, the predictive model did not include clinical variables, such as a formal diagnosis of major depressive disorder, substance use disorders, or prior suicide attempts, which are known to be powerful predictors. Future research should prioritize longitudinal cohort studies to elucidate the temporal dynamics between changes in vitamin D status and onset of suicidal ideation. Ultimately, randomized controlled trials investigating vitamin D supplementation as a primary or adjunctive intervention are warranted to establish causality and to evaluate its potential as a safe, accessible, and cost-effective strategy for suicide prevention.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study identified several demographic, socioeconomic, and biological factors associated with suicidal ideation in a representative U.S. sample. These findings underscore the multifactorial nature of suicide risk and highlight potential avenues for improved risk identification and future mechanistic research. While the prediction model demonstrates moderate accuracy, substantial work remains to enhance our ability to identify at-risk individuals and develop effective prevention strategies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eSI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eSuicidal Ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePHQ-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003ePatient Health Questionnaire-9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eDPQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eDepression Screener Questionnaire\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eArea Under the Receiver Operating Characteristic Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eLC-MS/MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eLiquid Chromatography-Tandem Mass Spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eNational Institute of Standards and Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eSRM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eStandard Reference Material\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eLeast Absolute Shrinkage and Selection Operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eROC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eC-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eConcordance Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eVID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eTotal 25-hydroxyvitamin D\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eVD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003e25-hydroxyvitamin D2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eVD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003e25-hydroxyvitamin D3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eVDR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eVitamin D Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eTPH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eTryptophan Hydroxylase 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eIncome-to-Poverty Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eGED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eGeneral Educational Development\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eCDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eCenters for Disease Control and Prevention\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003eNCHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 413px;\"\u003e\n \u003cp\u003eNational Center for Health Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eWritten informed consent was obtained from all individual participants included in the NHANES database prior to data collection. De-identified data for this secondary analysis were accessed from the NHANES public repository, which is exempt from additional consent requirements. All authors contributed to the article and agreed to the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI am appreciative of my consistent self, along with the editors and reviewers! We acknowledge Deepseek-R1\u0026apos;s help in improving the manuscript\u0026apos;s readability and language. The responsibility for the study\u0026apos;s content and conclusions lies entirely with us.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHong Guo and Lu Dai performed the tasks of acquiring and analyzing data, drafting figures and tables, and composing the original manuscript. Hong Guo and Lu Dai were responsible for reviewing and editing the manuscript, while revisions and refinements were carried out by Hong Guo, Lu Dai, and Jian Zhan. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e No funding was received for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available for this study can be found on the National Health and Nutrition Examination Survey website ( https://wwwn.cdc.gov/nchs/nhanes/Default.aspx ). All the data used were publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was carried out following the Declaration of Helsinki. Approval for the NHANES study protocol was granted by the NCHS Research Ethics Review Committee, with written informed consent collected from every participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eNock MK, Borges G, Bromet EJ, Cha CB, Kessler RC, Lee S: \u003cstrong\u003eSuicide and suicidal behavior\u003c/strong\u003e. \u003cem\u003eEpidemiologic reviews\u0026nbsp;\u003c/em\u003e2008, \u003cstrong\u003e30\u003c/strong\u003e(1):133-154.\u003c/li\u003e\n \u003cli\u003eBokor J, Sutori S, Torok D, Gal Z, Eszlari N, Gyorik D, Baksa D, Petschner P, Serafini G, Pompili M\u003cem\u003e\u0026nbsp;et al\u003c/em\u003e: \u003cstrong\u003eInflamed Mind: Multiple Genetic Variants of IL6 Influence Suicide Risk 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Guti\u0026eacute;rrez-Brito JA, D\u0026iacute;az-P\u0026eacute;rez SA, Aguayo-Arelis A, Hern\u0026aacute;ndez-Bello J: \u003cstrong\u003eVitamin D in Depression: A Potential Bioactive Agent to Reduce Suicide and Suicide Attempt Risk\u003c/strong\u003e. \u003cem\u003eNutrients\u0026nbsp;\u003c/em\u003e2023, \u003cstrong\u003e15\u003c/strong\u003e(7).\u003c/li\u003e\n \u003cli\u003eFenercioglu AK: \u003cstrong\u003eThe Anti-Inflammatory Roles of Vitamin D for Improving Human Health\u003c/strong\u003e. \u003cem\u003eCurrent issues in molecular biology\u0026nbsp;\u003c/em\u003e2024, \u003cstrong\u003e46\u003c/strong\u003e(12):13514-13525.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Suicidal ideation, Vitamin D, Logistic regression, Nomogram, NHANES","lastPublishedDoi":"10.21203/rs.3.rs-7631764/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7631764/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study sought to determine the demographic, socioeconomic, and biochemical prognostic factors for suicidal ideation in a nationally representative cohort of healthy adults in the United States and to construct a clinically feasible predictive model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe analyzed data from the National Health and Nutrition Examination Survey (NHANES), restricting the sample to individuals free of severe physical or psychiatric conditions. Suicidal ideation served as the primary outcome measure, with prognostic variables including sex, age, race, military service background, educational attainment, marital status, household size, income-to-poverty ratio, and vitamin D biomarkers (VD2 and VD3). Multivariate logistic regression was used to identify significant prognostic factors using a nomogram developed for risk visualization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) with internal validation conducted via bootstrap resampling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eMultivariable analysis revealed that younger age (odds ratio [OR] 0.99, p\u0026thinsp;=\u0026thinsp;0.003), never having been married (OR 2.07, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and being widowed, divorced, or separated (OR 1.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) correlated with increased odds of suicidal ideation. Additionally, a lower income-to-poverty ratio (OR 0.83, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and decreased VD3 concentration (OR 0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) emerged as predictive factors. The model showed moderate discriminative ability with an AUC of 0.721 (95% CI: 0.690\u0026ndash;0.751), and internal validation via bootstrap resampling yielded a corrected C-index of 0.714.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eKey demographic, socioeconomic, and nutritional factors, specifically marital status, economic hardship, and vitamin D status, represent significant prognostic indicators for suicidal ideation in the general U.S. adult population. The resulting nomogram offers a practical instrument for personalized risk evaluation and underscores potential targets for public health prevention initiatives.\u003c/p\u003e","manuscriptTitle":"Vitamin D and Suicidal Ideation: A Predictive Model Based on NHANES Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 06:13:47","doi":"10.21203/rs.3.rs-7631764/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":"b503ae89-43f4-42f5-a65d-d6ca21f2d838","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T06:13:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 06:13:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7631764","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7631764","identity":"rs-7631764","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}