Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study

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Hardoy, Jacob F. Giffin, Maria T. Paulino, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8904473/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Background Iron deficiency anemia (IDA) has been increasingly associated with neuropsychiatric conditions, including major depressive disorder (MDD). However, demographic variation in the relationship between IDA and MDD remains underexplored in large clinical populations. Methods We conducted a retrospective cohort study using de-identified electronic health records from 336,973 adult patients at the University of California, Irvine Medical Center (2017–2024). IDA was identified using ICD-10-CM codes, and MDD was classified as single-episode or recurrent. Multivariate logistic regression adjusted for age, sex, race, and ethnicity was used to assess demographic variation in MDD diagnoses among patients with IDA. Results Among 8,593 patients with IDA, single-episode MDD was present in 23.2% and recurrent MDD in 7.2%. Female patients demonstrated significantly higher odds of single-episode MDD (OR 1.52, 95% CI 1.35–1.72) and recurrent MDD (OR 1.84, 95% CI 1.50–2.28) compared with males. Adults ≥ 55 years demonstrated increased odds of single-episode MDD (OR 1.62, 95% CI 1.27–2.08 and OR 1.40, 95% CI 1.12–1.76, respectively), with no significant association observed for recurrent MDD. Asian individuals had significantly lower odds of single-episode (OR 0.44, 95% CI 0.37–0.52) and recurrent MDD (OR 0.39, 95% CI 0.28–0.52) than White patients. Conclusion This large cohort study identified significant demographic variation in MDD incidence among patients with IDA. These findings highlight the importance of demographic risk stratification in patients with IDA and support targeted screening for depressive disorders in medically vulnerable populations. Iron deficiency anemia major depressive disorder demographic disparities Figures Figure 1 Figure 2 Introduction Anemia is a multifactorial, global health concern in which the body lacks the red blood cells or hemoglobin to adequately supply oxygen. 1 In particular, women and children younger than five years old are significantly more affected by anemia. 1 – 3 A cross-sectional survey of the United States population conducted between 2021 and 2023 observed a significantly higher prevalence of anemia in females (13.0% v. 5.5%) compared to males. 1 Existing demographic differences in the prevalence of anemia remain an area of interest given the potential negative health outcomes and preventability of the condition. Iron deficiency anemia (IDA) is the most common anemia, affecting 1.2 billion people across the world. 1 , 3 The World Health Organization defines IDA as anemia, diagnosed via low hemoglobin concentration, with biochemical evidence of an iron deficiency via low serum ferritin. 4 Iron is known to be a critical contributor to many metabolic processes, including but not limited to immune response, cellular transport, and DNA synthesis. 5 , 6 In the nervous system, iron is necessary for proper development via neurotransmitter synthesis, myelination of neurons, and cellular respiration to support biochemical processes that preserve essential cell functions and neural activity. 7 , 8 Iron deficiency in pregnancy or childhood has been observed to produce deficits in language comprehension, speech production, memory, and motor skills. 8 , 9 Further studies have identified a potential association between iron deficiency and neurodegenerative diseases like Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and multiple sclerosis. 10 – 12 Given that iron deficiency has been implicated in many neurodegenerative diseases, it is plausible that a similar association may be observed in psychiatric conditions. The very same biological pathways that support brain development are also involved in mood regulation and cognitive-emotional processing. 13 , 14 Studies following children previously treated with IDA demonstrated poor performance in cognitive, socioemotional, and motor testing. 15 , 16 As a potential consequence of these deficits, children and adolescents with IDA were also identified to be at higher risk of psychiatric disorders compared to those without any history of IDA. 17 This further supports the idea of IDA not just as a hematologic condition, but also one with robust neurodevelopmental and psychiatric outcomes. Early development of IDA in children and adolescents may also influence psychiatric vulnerability later in life. The literature remains largely lacking and often conflicting in regards to the role of iron deficiency in adult psychiatric illness. A nationwide database analysis study conducted in 2020 found that adult patients with IDA had an 52% increased adjusted risk of psychiatric conditions such as anxiety disorders, depression, and sleep disorders, compared to patients without IDA. 18 However, this data lacked demographic stratification to help identify at-risk adult populations, making it unclear if the risk of psychiatric conditions is uniform across sex, race, age, and other demographic variables. A major highlight of this study was the lower risk of psychiatric disorders observed in patients with IDA who had been given iron supplementation. 18 The sentiment of iron supplementation as a therapeutic intervention is not new. A growing body of evidence has shown that iron therapy can provide tangible benefits to cognition, mood, and energy even in patients without anemia. 19 , 20 Specifically, patients with MDD have been shown to respond favorably to iron therapy. Children and adolescents treated with iron supplementation showed improved scores on depression scales after just 12 weeks. 21 In a population of adult patients with MDD and iron deficiency, over half of the patients experienced a reduction or even complete elimination of psychiatric symptoms. 22 These studies provide promising support for the possibility of a modifiable link between IDA and MDD. However, little is known about how demographic factors influence the odds of MDD in patients with IDA. Understanding these factors would allow for better identification of at-risk patient populations who would derive the most clinical benefit from iron supplementation. Therefore, this study seeks to identify potential racial, ethnic, and sex-based differences in odds of major depressive disorder among patients with iron deficiency anemia. Identifying demographic variation in MDD risk among patients with IDA may improve early detection of depression and inform targeted, potentially modifiable interventions in clinical practice. Methods Data Sources This retrospective study utilized the Epic electronic health record (EHR) database from the University of California, Irvine (UCI) Medical Center. Data was accessed in a de-identified manner through the institution’s Honest Broker service, which ensures secure provision of clinical information for research. The International Classification of Disease, 10th Revision Clinical Modification (ICD-10-CM) codes were used to identify diagnoses within the database. This study was approved by the Institutional Review Board of UCI Medical Center in Orange, California (IRB No: 4289). Study Population The study period spanned from January 1, 2017, to January 1, 2024, aligning with the implementation of the Epic electronic health record system at UCI Health. Patients aged 18 years or older as of January 1, 2017 who had at least one outpatient visit within the UCI Health system and a diagnosis of iron deficiency anemia (IDA) were included. IDA was defined by a new diagnosis coded as ICD-10-CM D50., excluding IDA due to blood loss (D50.0). Demographic variables including age, sex at birth, and ethnicity were collected. Major depressive disorder (MDD) was identified using ICD-10-CM codes for single episode (F32.*) or recurrent episodes (F33.*). MDD was classified as a single episode if the patient had only one documented depressive episode in their lifetime, and as recurrent if the patient had two or more distinct episodes separated by a period of full or near-full remission, consistent with DSM-5 criteria. Statistical Analysis This analysis assessed the presence of MDD diagnoses among patients with IDA during the study period and was not designed to establish temporal or causal relationships between IDA onset and depression. Multivariable logistic regression was used to examine whether age bracket, sex, race, and ethnicity were associated with the odds of developing either single episode or recurrent MDD. Adjusted odds ratios (aOR) with 95% confidence intervals (CIs) were reported. Baseline demographic and clinical characteristics were summarized using descriptive statistics. Statistical significance was defined as p < 0.05. All analyses were performed in RStudio v4.3.2 (Boston, MA). Results Among the population of 336,973 patients that fulfilled the inclusion criteria, there were 8,593 cases of IDA identified. The odds of single and recurrent MDD diagnosis in patients with a diagnosis of IDA was stratified on the basis of sex, race, ethnicity, age, and single or recurrent MDD diagnosis. Among patients with IDA, 69.8% were female, 59.8% were white, and 67.4% were not Hispanic or Latino. The median age at visit was 53 years old (IQR: 37–71). Table 1 presents a summary of the full patient demographic characteristics. The identified reference groups were determined to be patients who were male, ages 18–24, White, and not Hispanic or Latino. The population was identified for odds of MDD. Single episode MDD was present in 23.2% of patients. Recurrent MDD was present in 7.2% of patients. When compared to the reference group, females had a higher odds of single episode MDD (Odds Ratio (OR) 1.52, 95% CI [1.35–1.72], p < 0.001) and recurrent MDD (OR 1.84, 95% CI [1.50–2.28], p 0.05 for all). However, patients ages 55–64 had significantly greater odds of single episode MDD (OR 1.62, 95% CI [1.27–2.08], p < 0.001) but not recurrent MDD (OR 1.03, 95% CI [0.70–1.54], p = 0.88). Similarly, patients ages 65 + had increased odds of single episode MDD (OR 1.40, 95% CI [1.12–1.76], p = 0.003), though there was no significant association found for recurrent MDD (OR 1.09, 95% CI [0.78–1.57], p = 0.62). Relative to White patients, Asian individuals had significantly decreased odds of single episode (OR 0.44, 95% CI [0.37–0.52], p < 0.001) and recurrent MDD (OR 0.39, 95% CI [0.28–0.52], p < 0.001). Patients who identified as “Other Race” also exhibited significantly lower odds of single episode MDD (OR 0.67, 95% CI [0.57–0.77], p < 0.001) and recurrent MDD (OR 0.49, 95% CI [0.37–0.65], p < 0.001). Patients of unknown race were found to have the lowest odds of single episode MDD (OR 0.22, 95% CI [0.11–0.38], p < 0.001) and recurrent MDD (OR 0.12, 95% CI [0.02–0.39], p = 0.004) compared to white patients. Patients who identified as “Multirace” or Native Hawaiian or Pacific Islander did not demonstrate significantly different odds in either single episode or recurrent MDD. Black or African patients showed a slightly higher odds of single MDD but not of significance. American Indian or Alaska Native individuals demonstrated a non-significant trend toward higher odds of single episode MDD (OR 2.41, 95% CI [0.97–5.80], p = 0.051), but no significant difference was observed for recurrent MDD. Hispanic or Latino patients had significantly lower odds or recurrent MDD (OR 0.74, 95% CI [0.61–0.91], p = 0.004) compared to patients who were not Hispanic or Latino, but no significant difference was observed for single episode MDD. Patients of unknown ethnicity demonstrated significantly lower odds of both single episode (OR 0.36, 95% CI [0.20–0.59], p < 0.001) and recurrent MDD (OR 0.31, 95% CI [0.10–0.75], p = 0.024). A summary of these associations are presented in Table 2 . Discussion In this large, real-world cohort, iron deficiency anemia identified a population with a high burden of major depressive disorder, with meaningful variation across demographic subgroups. Nearly one-quarter of patients with IDA were diagnosed with single-episode MDD, and over 7% with recurrent MDD, underscoring the relevance of psychiatric risk assessment in patients with chronic medical conditions. Given the critical role of iron in neurotransmitter synthesis, energy metabolism, and brain function, the overall observed trend of elevated risk for MDD in patients with IDA, consistent across all subgroups, is consistent with emerging evidence that iron deficiency has neurobiological effects that may contribute to depression. Dysregulation of hippocampal glucocorticoid receptor signaling has been proposed as a potential mechanism, as cerebral iron deficiency in animal models leads to neuronal injury, reduced neurogenesis, impaired HPA axis negative feedback, and exacerbation of depressive-like behaviors. 23 This provides a mechanistic framework for understanding the elevated risk of both single and recurrent MDD in patients with IDA. While these mechanistic models provide biological plausibility, the present findings primarily support IDA as a clinically relevant risk marker rather than a confirmed causal factor for depression. Sex Differences Female patients with IDA demonstrated a substantially higher risk of both single and recurrent MDD compared to male patients with IDA. These sex differences are consistent with prior literature demonstrating an association between IDA and depressive symptoms in women of reproductive age. At baseline, female patients are known to have a greater prevalence of iron deficiency due to blood loss during menstruation or pregnancy. The observed increase in both single and recurrent MDD demonstrate that iron may play a role in both the initial odds of a depressive episode along with continued persistent depression or relapse of depression. However, causal inference cannot be established due to the observational nature of this study. Such differences may be due to a combination of biological, psychosocial, and healthcare systems-level influences. Age Differences Older adults (≥ 55 years) were observed to have an increased risk of single MDD but no significant difference in risk for recurrent MDD. This finding may demonstrate an elevated vulnerability to the first odds of depression although the mechanisms remain unclear. Prior studies have theorized that functional decline, medical comorbidities, and metabolic and neurological dysfunction due to inflammatory processes in aging may contribute to the risk of late-onset depression in older adults. 24 – 27 The lack of association with recurrent MDD in older adults suggests that late-onset odds of a first MDD diagnosis may represent a distinct phenotype of depression associated with age-related neurodegenerative or vascular changes that may not be present with recurrent depression. 28 Younger and middle-aged adults were not observed to have elevated risk of single or recurrent MDD compared to the reference group of patients aged 18–24 years old. Thus, age-related risks associated with IDA may appear more prominently only later in life. Race and Ethnicity Differences Asian patients with IDA demonstrated substantially lower risk of both single and recurrent MDD compared to other racial and ethnic groups. While these findings may reflect biological mechanisms, they may also be influenced by disparities in diagnosis or access to mental health services. Past studies have demonstrated an a four to ten times greater risk of lifetime depression in Western countries compared to Asia, with this discrepancy largely associated with cultural differences including emotional regulation strategies and stigma towards mental health disorders. 29 , 30 Asian Americans are also less likely to express specific somatic and psychological symptoms of depression than European Americans even after matching for depression severities, thereby potentially contributing to disparities in diagnostic threshold for depression. 31 Patients who identified as Hispanic or Latino demonstrated lower odds of recurrent MDD but no significant difference in odds of single MDD. Thus, differences may reflect disparities in access to mental health treatment or desire for continued follow-up as prior studies have demonstrated a lower rate of perceived need for treatment despite an increased odds of unmet needs. 32 These findings should be interpreted in the context of known underdiagnosis and differential help-seeking behaviors across racial and ethnic groups, which may contribute to observed differences in documented MDD prevalence. Still, such findings highlight the need for increased accessibility of culturally competent mental health services. Notably, Black or African American and American Indian or Alaska Native patients did not demonstrate statistically significant differences in MDD incidence compared to White patients, although the latter group showed a trend toward higher odds of single-episode MDD. These results should be interpreted cautiously given smaller subgroup sizes and the potential for residual confounding. Strengths and Limitations One of the primary strengths of our study is the ability to represent a large portion of California’s Orange County, a portion of which had been previously unstudied for psychiatric illness in the context of IDA. UCI Health’s facilities extend throughout the county and UCI Health serves a large population due to the lack of a county psychiatric hospital in the region. Such a large dataset of over 345,000 patients allowed robust estimation of psychiatric outcomes, improved statistical power to detect associations, and enhanced generalizability to a diverse, real-world population. Some important limitations for this study include the lifestyle information that is not sufficiently documented for each patient or patient visit in the health record such as sleep quality/quantity, dietary habits, or exercise frequency, which may be confounding variables. Furthermore, in identifying cases of IDA, the use of ICD-10 codes do not necessarily represent a laboratory-confirmed diagnosis of IDA but rather a clinical diagnosis. In conjunction with the exclusion of the D50.0 code to avoid cases of acute blood loss, there is a potential risk of missing clinically significant cases due to physician coding errors in which an acute blood loss was attributed to the diagnosis despite an underlying IDA. However, this risk is outweighed by the benefits of being able to more clearly isolate cases of IDA caused by nutritional deficiencies or chronic conditions. Still, the findings should be interpreted with caution. Additionally, as with many studies, the true prevalence of MDD was most likely underestimated as individuals who did not seek psychiatric care are not included in the EHR. Residual confounding due to unmeasured socioeconomic factors, health literacy, and access to mental health services may also influence observed associations. Our results indicate a robust statistical association between iron deficiency anemia and the presence of major depressive disorders, consistent across most demographic subgroups. The extremely low p-values observed in chi-square analyses suggest that this relationship is unlikely to be due to chance alone. However, given our large sample size, statistical significance may reflect even modest absolute differences, and effect sizes should be interpreted with caution. Clinical Implications This cohort study identified significant demographic variation in the likelihood of MDD diagnosis among patients with IDA. These findings underscore the importance of considering demographic context in clinical psychiatric evaluation and support future investigation into the mechanisms underlying these observed differences. Improved understanding of demographic-specific risk patterns may also inform targeted screening, prevention, and treatment strategies, thereby enhancing population-level mental health outcomes. Further study is required to investigate the relationship between the severity of IDA and the severity of MDD and/or presence of psychotic features as well as to better control for confounding factors such as lifestyle factors or family history of mental illness. Additional study may also focus on the changes in MDD prevalence and/or severity with sufficient treatment of IDA. Conclusion In this large, real-world cohort, IDA identifies a medically vulnerable population with a substantial burden of both single-episode and recurrent MDD, with important demographic variation. Nearly 25% of patients with IDA experienced a single depressive episode and over 7% of recurrent MDD, highlighting the clinical significance of psychiatric comorbidity in this population. Older age and female sex were associated with higher odds of MDD among patients with IDA, while Asian race was associated with lower odds. Although causal inference cannot be established due to the observational design, these findings are plausible given iron’s role in neurochemical and neuroendocrine function. Taken together, the results support the clinical relevance of iron deficiency in psychiatric assessment and underscore the potential value of integrated screening for depressive symptoms in patients with IDA. Future longitudinal and interventional studies are needed to clarify temporal relationships and determine whether treatment of IDA mitigates depression risk or recurrence. Declarations Human Ethics and Consent to Participate Not applicable IRB Approved by the UCI Institutional Review Board (#4289) Informed Consent : Requirement for informed consent waived by IRB due to retrospective nature of study and de-identified data Funding sources/conflicts of interest: None to declare Author Contribution C.Y.: Conceptualization, Writing – Original Draft, Writing – Review & Editing, Visualization, Formal AnalysisC.H.: Conceptualization, Methodology, Investigation, Writing – Original Draft, Formal Analysis, Writing – Review & EditingM.P.: Methodology, Software, Formal Analysis, Data Curation, Writing – Original Draft, Writing – Review & EditingE.P.: Methodology, Investigation, Writing – Original Draft, Writing – Review & Editing, Visualization. J.G.: Conceptualization, Investigation, Writing – Review & EditingA.A: Conceptualization, Methodology, Formal Analysis, Project Administration, Writing – Review & Editing. R.B.: Conceptualization, Investigation, Supervision, Project Administration, Writing – Review & Editing. A.N.A: Conceptualization, Investigation, Methodology, Supervision, Project Administration, Writing – Review & Editing. Acknowledgements: None Data Availability Data may be accessed at:Hardoy, Christian (2026), “Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study”, Mendeley Data, V1, https://doi.org/10.17632/www4mpy6sb.1 Data Set Hardoy, Christian (2026), “Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study”, Mendeley Data, V1, doi: 10.17632/www4mpy6sb.1 References Williams A, Ansai N, Ahluwalia N, Nguyen D. Anemia Prevalence: United States, August 2021- August 2023. National Center for Health Statistics (U.S.); 2024. 10.15620/cdc/168890 . Liu Y, Ren W, Wang S, Xiang M, Zhang S, Zhang F. Global burden of anemia and cause among children under five years 1990–2019: findings from the global burden of disease study 2019. Front Nutr. 2024;11:1474664. GBD 2021 Anaemia Collaborators. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990–2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(9):e713–34. Archived. Iron deficiency anaemia: assessment, prevention and control. Accessed September 27, 2025. https://www.who.int/publications/m/item/iron-children-6to23--archived-iron-deficiency-anaemia-assessment-prevention-and-control?utm_source=chatgpt.com Li X, Finberg KE. Iron deficiency anemia. Adv Exp Med Biol. 2025;1480:163–78. Dallman PR. Iron deficiency and the immune response. Am J Clin Nutr. 1987;46(2):329–34. Kulaszyńska M, Kwiatkowski S, Skonieczna-Żydecka K. The iron metabolism with a specific focus on the functioning of the nervous system. Biomedicines. 2024;12(3):595. McCann S, Perapoch Amadó M, Moore SE. The role of iron in brain development: A systematic review. Nutrients. 2020;12(7):2001. Georgieff MK. Long-term brain and behavioral consequences of early iron deficiency: Nutrition Reviews©, Vol. 69, No. s1. Nutr Rev . 2011;69 Suppl 1(Suppl 1):S43-S48. Tian Y, Tian Y, Yuan Z, et al. Iron metabolism in aging and age-related diseases. Int J Mol Sci. 2022;23(7):3612. Rouault TA. Iron on the brain. Nat Genet. 2001;28(4):299–300. Walker FO. Huntington’s disease. Lancet. 2007;369(9557):218–28. Wu Q, Ren Q, Meng J, Gao WJ, Chang YZ. Brain iron homeostasis and mental disorders. Antioxid (Basel). 2023;12(11):1997. Berthou C, Iliou JP, Barba D. Iron, neuro-bioavailability and depression. EJHaem. 2022;3(1):263–75. Lozoff B, Jimenez E, Hagen J, Mollen E, Wolf AW. Poorer behavioral and developmental outcome more than 10 years after treatment for iron deficiency in infancy. Pediatrics. 2000;105(4):E51. Lozoff B, Jimenez E, Wolf AW. Long-term developmental outcome of infants with iron deficiency. N Engl J Med. 1991;325(10):687–94. Chen MH, Su TP, Chen YS, et al. Association between psychiatric disorders and iron deficiency anemia among children and adolescents: a nationwide population-based study. BMC Psychiatry. 2013;13(1):161. Lee HS, Chao HH, Huang WT, Chen SCC, Yang HY. Psychiatric disorders risk in patients with iron deficiency anemia and association with iron supplementation medications: a nationwide database analysis. BMC Psychiatry. 2020;20(1):216. Houston BL, Hurrie D, Graham J, et al. Efficacy of iron therapy on fatigue and work capacity in non-anemic iron deficient adults: A systematic review of randomized controlled trials. Blood. 2017;130(Supplement 1):3497–3497. Falkingham M, Abdelhamid A, Curtis P, Fairweather-Tait S, Dye L, Hooper L. The effects of oral iron supplementation on cognition in older children and adults: a systematic review and meta-analysis. Nutr J. 2010;9(1):4. Mikami K, Akama F, Kimoto K, et al. Iron supplementation for hypoferritinemia-related psychological symptoms in children and adolescents. J Nippon Med Sch. 2022;89(2):203–11. Kassir A. Carence en fer: une perspective diagnostique et thérapeutique en psychiatrie. Encephale. 2017;43(1):85–9. Zhang H, He L, Li S, et al. Cerebral iron deficiency may induce depression through downregulation of the hippocampal glucocorticoid-glucocorticoid receptor signaling pathway. J Affect Disord. 2023;332:125–35. Agustini B, Lotfaliany M, Woods RL, et al. Patterns of association between depressive symptoms and chronic medical morbidities in older adults. J Am Geriatr Soc. 2020;68(8):1834–41. Wu Q, Feng J, Pan CW. Risk factors for depression in the elderly: An umbrella review of published meta-analyses and systematic reviews. J Affect Disord. 2022;307:37–45. Jellinger KA. Pathomechanisms of vascular depression in older adults. Int J Mol Sci. 2021;23(1):308. Biological Factors Influencing Depression in Later Life: Role of Aging Processes and Treatment Implications. Translational Psychiatry. 2023. Szymkowicz SM . Taylor WD. Depression in the elderly. N Engl J Med. 2014;371(13):1228–36. Vaus D, Hornsey J, Kuppens MJ, Bastian P. Exploring the East-West Divide in Prevalence of Affective Disorder: A Case for Cultural Differences in Coping With Negative Emotion. Pers Soc Psychol Rev. 2018;22(3):285–304. Chang SM, Hahm BJ, Lee JY, et al. Cross-national difference in the prevalence of depression caused by the diagnostic threshold. J Affect Disord. 2008;106(1–2):159–67. Kim JM, López SR. The expression of depression in Asian Americans and European Americans. J Abnorm Psychol. 2014;123(4):754–63. Kwong K, Ahuvia IL, Schleider JL. Help-seeking at the intersection of race and age: Perceived need and treatment access for depression in the United States. J Affect Disord. 2025;386(119428):119428. Tables Table 1 Demographics of IDA Population Demographics IDA (N = 8593) Sex Female 6002 (69.8%) Male 2591 (30.2%) Race American Indian/Alaska Native 21 ( 0.2%) Asian 1256 (14.6%) Black or African American 326 ( 3.8%) Multirace 241 ( 2.8%) Native Hawaiian/Pacific Islander 50 ( 0.6%) Other Race 1360 (15.8%) Unknown 200 ( 2.3%) White 5139 (59.8%) Ethnicity Hispanic or Latino 2607 (30.3) Not Hispanic or Latino 5793 (67.4) Unknown 193 ( 2.2) Age Age at visit, median (IQR) 53.00 [37.00, 71.00] 18–24 581 ( 6.8) 25–34 1202 (14.0) 35–44 1379 (16.0) 45–54 1265 (14.7) 55–64 1097 (12.8) 65+ 3069 (35.7) MDD Diagnosis Single Episode MDD 1993 (23.2) Recurrent MDD 618 ( 7.2) Table 2 Single Episode MDD Recurrent MDD Group OR CI (95%) P value OR CI (95%) P value Male Reference - - Reference - - Female 1.518 (1.345–1.715) 1.51e-11 1.843 (1.500-2.278) 9.28e-09 Age 18–24 Reference - - Reference - - Age 25–34 1.146 (0.899–1.467) 0.275 0.894 (0.610–1.326) 0.569 Age 35–44 1.117 (0.880–1.426) 0.367 0.920 (0.634–1.355) 0.667 Age 45–54 1.089 (0.854–1.396) 0.495 0.973 (0.668–1.438) 0.889 Age 55–64 1.619 (1.267–2.080) 1.37E-04 1.032 (0.699–1.542) 0.875 Age 65+ 1.401 (1.121–1.761) 0.003 1.094 (0.778–1.57) 0.616 White Reference - - Reference - - Black or African 1.14 (0.881–1.463) 0.312 0.678 (0.421–1.039) 0.091 Asian 0.438 (0.366–0.523) 1.00E-19 0.387 (0.282–0.521) 1.12E-09 Native Hawaiian or Pacific Islander 0.967 (0.492–1.785) 0.919 1.249 (0.473–2.743) 0.614 American Indian or Alaska Native 2.406 (0.966–5.801) 0.051 1.247 (0.197–4.393) 0.769 Multirace 0.869 (0.635–1.171) 0.367 0.672 (0.370–1.126) 0.159 Other Race 0.665 (0.571–0.773) 1.27E-04 0.492 (0.368–0.647) 7.69E-07 Race Unknown 0.219 (0.114–0.380) 5.47E-07 0.124 (0.020–0.393) 0.0036 Not Hispanic or Latino Reference - - Reference - - Hispanic or Latino 1.013 (0.898–1.144) 0.828 0.741 (0.605–0.905) 0.0035 Ethnicity Unknown 0.358 (0.203–0.590) 1.42E-04 0.313 (0.095–0.753) 0.0236 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 15 May, 2026 Reviews received at journal 03 May, 2026 Reviews received at journal 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor invited by journal 04 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Submission checks completed at journal 02 Mar, 2026 First submitted to journal 17 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Hardoy","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"R.","lastName":"Hardoy","suffix":""},{"id":594236903,"identity":"01a9cd68-16ff-4d45-b5c2-5fb7de4129b5","order_by":2,"name":"Jacob F. Giffin","email":"","orcid":"","institution":"Brooke Army Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"F.","lastName":"Giffin","suffix":""},{"id":594236905,"identity":"21a9df60-f83a-4a59-a112-d1124aff08a0","order_by":3,"name":"Maria T. Paulino","email":"","orcid":"","institution":"University of California, Irvine School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"T.","lastName":"Paulino","suffix":""},{"id":594236907,"identity":"ba3f998c-d5fc-4b3b-b8ab-fa41ecd77d28","order_by":4,"name":"Ellee P. Vikram","email":"","orcid":"","institution":"University of California, Irvine School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ellee","middleName":"P.","lastName":"Vikram","suffix":""},{"id":594236908,"identity":"b6151047-8ace-4cca-a6d7-75254aab407b","order_by":5,"name":"Anna Aledia","email":"","orcid":"","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Aledia","suffix":""},{"id":594236910,"identity":"e27bffaa-7622-4876-869c-8ace003a38e9","order_by":6,"name":"Rimal Bera","email":"","orcid":"","institution":"University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Rimal","middleName":"","lastName":"Bera","suffix":""},{"id":594236914,"identity":"8801940b-6ff8-44f2-bc83-ea2951e23028","order_by":7,"name":"Alpesh N. Amin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYLCCBAYbBgZmIIOHBC1ppGphYDgMoYjSwj+7+emGhzvOJ/a3MzA+eNtGhBaJO8fMbiSeuZ044zADs+FcYrQw3EgAamm7nbiBmYFNmpcYLfI30r8BtZwDaWH/TZQWgxs5IFsOgG1hJkqL4Y2cMqCWZOMZhxmbJeecI0KL3I30bTd/ttnJ9vcfPvjhTRkRWpAAYwNp6kfBKBgFo2AU4AYAzQQ4OEBIV8gAAAAASUVORK5CYII=","orcid":"","institution":"University of California, Irvine","correspondingAuthor":true,"prefix":"","firstName":"Alpesh","middleName":"N.","lastName":"Amin","suffix":""}],"badges":[],"createdAt":"2026-02-17 21:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8904473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8904473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103566921,"identity":"6a52e49e-20e9-4919-b223-b00df33ffb66","added_by":"auto","created_at":"2026-02-27 07:27:10","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":287197,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds of Single Episode MDD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8904473/v1/1111d54fb0463d7dcc54ea86.jpeg"},{"id":103566906,"identity":"e88c5b49-b9ff-4c60-9aa2-01a2acc124d6","added_by":"auto","created_at":"2026-02-27 07:27:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21990,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOdds of Recurrent MDD\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8904473/v1/eff8bc8f50c2ce63a16cabd3.png"},{"id":103566990,"identity":"ff60d164-81d4-4b4a-b8c6-d72e67e154dc","added_by":"auto","created_at":"2026-02-27 07:27:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1153226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8904473/v1/820e7a1d-4cd3-4136-bd7d-171fb4630dfb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAnemia is a multifactorial, global health concern in which the body lacks the red blood cells or hemoglobin to adequately supply oxygen.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e In particular, women and children younger than five years old are significantly more affected by anemia.\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e A cross-sectional survey of the United States population conducted between 2021 and 2023 observed a significantly higher prevalence of anemia in females (13.0% v. 5.5%) compared to males.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Existing demographic differences in the prevalence of anemia remain an area of interest given the potential negative health outcomes and preventability of the condition. Iron deficiency anemia (IDA) is the most common anemia, affecting 1.2\u0026nbsp;billion people across the world.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e The World Health Organization defines IDA as anemia, diagnosed via low hemoglobin concentration, with biochemical evidence of an iron deficiency via low serum ferritin.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Iron is known to be a critical contributor to many metabolic processes, including but not limited to immune response, cellular transport, and DNA synthesis.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn the nervous system, iron is necessary for proper development via neurotransmitter synthesis, myelination of neurons, and cellular respiration to support biochemical processes that preserve essential cell functions and neural activity.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Iron deficiency in pregnancy or childhood has been observed to produce deficits in language comprehension, speech production, memory, and motor skills.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Further studies have identified a potential association between iron deficiency and neurodegenerative diseases like Alzheimer\u0026rsquo;s disease, Parkinson\u0026rsquo;s disease, Huntington\u0026rsquo;s disease, and multiple sclerosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eGiven that iron deficiency has been implicated in many neurodegenerative diseases, it is plausible that a similar association may be observed in psychiatric conditions. The very same biological pathways that support brain development are also involved in mood regulation and cognitive-emotional processing.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Studies following children previously treated with IDA demonstrated poor performance in cognitive, socioemotional, and motor testing.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e As a potential consequence of these deficits, children and adolescents with IDA were also identified to be at higher risk of psychiatric disorders compared to those without any history of IDA.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e This further supports the idea of IDA not just as a hematologic condition, but also one with robust neurodevelopmental and psychiatric outcomes. Early development of IDA in children and adolescents may also influence psychiatric vulnerability later in life.\u003c/p\u003e \u003cp\u003eThe literature remains largely lacking and often conflicting in regards to the role of iron deficiency in adult psychiatric illness. A nationwide database analysis study conducted in 2020 found that adult patients with IDA had an 52% increased adjusted risk of psychiatric conditions such as anxiety disorders, depression, and sleep disorders, compared to patients without IDA.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e However, this data lacked demographic stratification to help identify at-risk adult populations, making it unclear if the risk of psychiatric conditions is uniform across sex, race, age, and other demographic variables. A major highlight of this study was the lower risk of psychiatric disorders observed in patients with IDA who had been given iron supplementation.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The sentiment of iron supplementation as a therapeutic intervention is not new. A growing body of evidence has shown that iron therapy can provide tangible benefits to cognition, mood, and energy even in patients without anemia.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Specifically, patients with MDD have been shown to respond favorably to iron therapy. Children and adolescents treated with iron supplementation showed improved scores on depression scales after just 12 weeks.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e In a population of adult patients with MDD and iron deficiency, over half of the patients experienced a reduction or even complete elimination of psychiatric symptoms.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e These studies provide promising support for the possibility of a modifiable link between IDA and MDD. However, little is known about how demographic factors influence the odds of MDD in patients with IDA. Understanding these factors would allow for better identification of at-risk patient populations who would derive the most clinical benefit from iron supplementation. Therefore, this study seeks to identify potential racial, ethnic, and sex-based differences in odds of major depressive disorder among patients with iron deficiency anemia. Identifying demographic variation in MDD risk among patients with IDA may improve early detection of depression and inform targeted, potentially modifiable interventions in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Sources\u003c/h2\u003e \u003cp\u003eThis retrospective study utilized the Epic electronic health record (EHR) database from the University of California, Irvine (UCI) Medical Center. Data was accessed in a de-identified manner through the institution\u0026rsquo;s Honest Broker service, which ensures secure provision of clinical information for research. The International Classification of Disease, 10th Revision Clinical Modification (ICD-10-CM) codes were used to identify diagnoses within the database. This study was approved by the Institutional Review Board of UCI Medical Center in Orange, California (IRB No: 4289).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Population\u003c/h3\u003e\n\u003cp\u003eThe study period spanned from January 1, 2017, to January 1, 2024, aligning with the implementation of the Epic electronic health record system at UCI Health. Patients aged 18 years or older as of January 1, 2017 who had at least one outpatient visit within the UCI Health system and a diagnosis of iron deficiency anemia (IDA) were included. IDA was defined by a new diagnosis coded as ICD-10-CM D50., excluding IDA due to blood loss (D50.0). Demographic variables including age, sex at birth, and ethnicity were collected. Major depressive disorder (MDD) was identified using ICD-10-CM codes for single episode (F32.*) or recurrent episodes (F33.*). MDD was classified as a single episode if the patient had only one documented depressive episode in their lifetime, and as recurrent if the patient had two or more distinct episodes separated by a period of full or near-full remission, consistent with DSM-5 criteria.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThis analysis assessed the presence of MDD diagnoses among patients with IDA during the study period and was not designed to establish temporal or causal relationships between IDA onset and depression. Multivariable logistic regression was used to examine whether age bracket, sex, race, and ethnicity were associated with the odds of developing either single episode or recurrent MDD. Adjusted odds ratios (aOR) with 95% confidence intervals (CIs) were reported. Baseline demographic and clinical characteristics were summarized using descriptive statistics. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed in RStudio v4.3.2 (Boston, MA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the population of 336,973 patients that fulfilled the inclusion criteria, there were 8,593 cases of IDA identified. The odds of single and recurrent MDD diagnosis in patients with a diagnosis of IDA was stratified on the basis of sex, race, ethnicity, age, and single or recurrent MDD diagnosis. Among patients with IDA, 69.8% were female, 59.8% were white, and 67.4% were not Hispanic or Latino. The median age at visit was 53 years old (IQR: 37\u0026ndash;71). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a summary of the full patient demographic characteristics.\u003c/p\u003e \u003cp\u003eThe identified reference groups were determined to be patients who were male, ages 18\u0026ndash;24, White, and not Hispanic or Latino. The population was identified for odds of MDD. Single episode MDD was present in 23.2% of patients. Recurrent MDD was present in 7.2% of patients.\u003c/p\u003e \u003cp\u003eWhen compared to the reference group, females had a higher odds of single episode MDD (Odds Ratio (OR) 1.52, 95% CI [1.35\u0026ndash;1.72], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent MDD (OR 1.84, 95% CI [1.50\u0026ndash;2.28], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than males. Compared to patients ages 18\u0026ndash;24, patients ages 25\u0026ndash;34, 35\u0026ndash;44, and 45\u0026ndash;54 did not indicate significantly different odds of either single episode or recurrent MDD (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). However, patients ages 55\u0026ndash;64 had significantly greater odds of single episode MDD (OR 1.62, 95% CI [1.27\u0026ndash;2.08], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but not recurrent MDD (OR 1.03, 95% CI [0.70\u0026ndash;1.54], p\u0026thinsp;=\u0026thinsp;0.88). Similarly, patients ages 65\u0026thinsp;+\u0026thinsp;had increased odds of single episode MDD (OR 1.40, 95% CI [1.12\u0026ndash;1.76], p\u0026thinsp;=\u0026thinsp;0.003), though there was no significant association found for recurrent MDD (OR 1.09, 95% CI [0.78\u0026ndash;1.57], p\u0026thinsp;=\u0026thinsp;0.62).\u003c/p\u003e \u003cp\u003eRelative to White patients, Asian individuals had significantly decreased odds of single episode (OR 0.44, 95% CI [0.37\u0026ndash;0.52], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent MDD (OR 0.39, 95% CI [0.28\u0026ndash;0.52], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients who identified as \u0026ldquo;Other Race\u0026rdquo; also exhibited significantly lower odds of single episode MDD (OR 0.67, 95% CI [0.57\u0026ndash;0.77], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent MDD (OR 0.49, 95% CI [0.37\u0026ndash;0.65], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients of unknown race were found to have the lowest odds of single episode MDD (OR 0.22, 95% CI [0.11\u0026ndash;0.38], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent MDD (OR 0.12, 95% CI [0.02\u0026ndash;0.39], p\u0026thinsp;=\u0026thinsp;0.004) compared to white patients. Patients who identified as \u0026ldquo;Multirace\u0026rdquo; or Native Hawaiian or Pacific Islander did not demonstrate significantly different odds in either single episode or recurrent MDD. Black or African patients showed a slightly higher odds of single MDD but not of significance. American Indian or Alaska Native individuals demonstrated a non-significant trend toward higher odds of single episode MDD (OR 2.41, 95% CI [0.97\u0026ndash;5.80], p\u0026thinsp;=\u0026thinsp;0.051), but no significant difference was observed for recurrent MDD.\u003c/p\u003e \u003cp\u003eHispanic or Latino patients had significantly lower odds or recurrent MDD (OR 0.74, 95% CI [0.61\u0026ndash;0.91], p\u0026thinsp;=\u0026thinsp;0.004) compared to patients who were not Hispanic or Latino, but no significant difference was observed for single episode MDD. Patients of unknown ethnicity demonstrated significantly lower odds of both single episode (OR 0.36, 95% CI [0.20\u0026ndash;0.59], p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and recurrent MDD (OR 0.31, 95% CI [0.10\u0026ndash;0.75], p\u0026thinsp;=\u0026thinsp;0.024). A summary of these associations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, real-world cohort, iron deficiency anemia identified a population with a high burden of major depressive disorder, with meaningful variation across demographic subgroups. Nearly one-quarter of patients with IDA were diagnosed with single-episode MDD, and over 7% with recurrent MDD, underscoring the relevance of psychiatric risk assessment in patients with chronic medical conditions.\u003c/p\u003e \u003cp\u003eGiven the critical role of iron in neurotransmitter synthesis, energy metabolism, and brain function, the overall observed trend of elevated risk for MDD in patients with IDA, consistent across all subgroups, is consistent with emerging evidence that iron deficiency has neurobiological effects that may contribute to depression. Dysregulation of hippocampal glucocorticoid receptor signaling has been proposed as a potential mechanism, as cerebral iron deficiency in animal models leads to neuronal injury, reduced neurogenesis, impaired HPA axis negative feedback, and exacerbation of depressive-like behaviors.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e This provides a mechanistic framework for understanding the elevated risk of both single and recurrent MDD in patients with IDA. While these mechanistic models provide biological plausibility, the present findings primarily support IDA as a clinically relevant risk marker rather than a confirmed causal factor for depression.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSex Differences\u003c/h2\u003e \u003cp\u003eFemale patients with IDA demonstrated a substantially higher risk of both single and recurrent MDD compared to male patients with IDA. These sex differences are consistent with prior literature demonstrating an association between IDA and depressive symptoms in women of reproductive age. At baseline, female patients are known to have a greater prevalence of iron deficiency due to blood loss during menstruation or pregnancy. The observed increase in both single and recurrent MDD demonstrate that iron may play a role in both the initial odds of a depressive episode along with continued persistent depression or relapse of depression. However, causal inference cannot be established due to the observational nature of this study. Such differences may be due to a combination of biological, psychosocial, and healthcare systems-level influences.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAge Differences\u003c/h3\u003e\n\u003cp\u003eOlder adults (\u0026ge;\u0026thinsp;55 years) were observed to have an increased risk of single MDD but no significant difference in risk for recurrent MDD. This finding may demonstrate an elevated vulnerability to the first odds of depression although the mechanisms remain unclear. Prior studies have theorized that functional decline, medical comorbidities, and metabolic and neurological dysfunction due to inflammatory processes in aging may contribute to the risk of late-onset depression in older adults.\u003csup\u003e\u003cspan additionalcitationids=\"CR25 CR26\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e The lack of association with recurrent MDD in older adults suggests that late-onset odds of a first MDD diagnosis may represent a distinct phenotype of depression associated with age-related neurodegenerative or vascular changes that may not be present with recurrent depression.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e Younger and middle-aged adults were not observed to have elevated risk of single or recurrent MDD compared to the reference group of patients aged 18\u0026ndash;24 years old. Thus, age-related risks associated with IDA may appear more prominently only later in life.\u003c/p\u003e\n\u003ch3\u003eRace and Ethnicity Differences\u003c/h3\u003e\n\u003cp\u003eAsian patients with IDA demonstrated substantially lower risk of both single and recurrent MDD compared to other racial and ethnic groups. While these findings may reflect biological mechanisms, they may also be influenced by disparities in diagnosis or access to mental health services. Past studies have demonstrated an a four to ten times greater risk of lifetime depression in Western countries compared to Asia, with this discrepancy largely associated with cultural differences including emotional regulation strategies and stigma towards mental health disorders.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e Asian Americans are also less likely to express specific somatic and psychological symptoms of depression than European Americans even after matching for depression severities, thereby potentially contributing to disparities in diagnostic threshold for depression.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePatients who identified as Hispanic or Latino demonstrated lower odds of recurrent MDD but no significant difference in odds of single MDD. Thus, differences may reflect disparities in access to mental health treatment or desire for continued follow-up as prior studies have demonstrated a lower rate of perceived need for treatment despite an increased odds of unmet needs.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e These findings should be interpreted in the context of known underdiagnosis and differential help-seeking behaviors across racial and ethnic groups, which may contribute to observed differences in documented MDD prevalence. Still, such findings highlight the need for increased accessibility of culturally competent mental health services.\u003c/p\u003e \u003cp\u003eNotably, Black or African American and American Indian or Alaska Native patients did not demonstrate statistically significant differences in MDD incidence compared to White patients, although the latter group showed a trend toward higher odds of single-episode MDD. These results should be interpreted cautiously given smaller subgroup sizes and the potential for residual confounding.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eOne of the primary strengths of our study is the ability to represent a large portion of California\u0026rsquo;s Orange County, a portion of which had been previously unstudied for psychiatric illness in the context of IDA. UCI Health\u0026rsquo;s facilities extend throughout the county and UCI Health serves a large population due to the lack of a county psychiatric hospital in the region. Such a large dataset of over 345,000 patients allowed robust estimation of psychiatric outcomes, improved statistical power to detect associations, and enhanced generalizability to a diverse, real-world population. Some important limitations for this study include the lifestyle information that is not sufficiently documented for each patient or patient visit in the health record such as sleep quality/quantity, dietary habits, or exercise frequency, which may be confounding variables.\u003c/p\u003e \u003cp\u003eFurthermore, in identifying cases of IDA, the use of ICD-10 codes do not necessarily represent a laboratory-confirmed diagnosis of IDA but rather a clinical diagnosis. In conjunction with the exclusion of the D50.0 code to avoid cases of acute blood loss, there is a potential risk of missing clinically significant cases due to physician coding errors in which an acute blood loss was attributed to the diagnosis despite an underlying IDA. However, this risk is outweighed by the benefits of being able to more clearly isolate cases of IDA caused by nutritional deficiencies or chronic conditions. Still, the findings should be interpreted with caution. Additionally, as with many studies, the true prevalence of MDD was most likely underestimated as individuals who did not seek psychiatric care are not included in the EHR. Residual confounding due to unmeasured socioeconomic factors, health literacy, and access to mental health services may also influence observed associations.\u003c/p\u003e \u003cp\u003eOur results indicate a robust statistical association between iron deficiency anemia and the presence of major depressive disorders, consistent across most demographic subgroups. The extremely low p-values observed in chi-square analyses suggest that this relationship is unlikely to be due to chance alone. However, given our large sample size, statistical significance may reflect even modest absolute differences, and effect sizes should be interpreted with caution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eThis cohort study identified significant demographic variation in the likelihood of MDD diagnosis among patients with IDA. These findings underscore the importance of considering demographic context in clinical psychiatric evaluation and support future investigation into the mechanisms underlying these observed differences. Improved understanding of demographic-specific risk patterns may also inform targeted screening, prevention, and treatment strategies, thereby enhancing population-level mental health outcomes. Further study is required to investigate the relationship between the severity of IDA and the severity of MDD and/or presence of psychotic features as well as to better control for confounding factors such as lifestyle factors or family history of mental illness. Additional study may also focus on the changes in MDD prevalence and/or severity with sufficient treatment of IDA.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn this large, real-world cohort, IDA identifies a medically vulnerable population with a substantial burden of both single-episode and recurrent MDD, with important demographic variation. Nearly 25% of patients with IDA experienced a single depressive episode and over 7% of recurrent MDD, highlighting the clinical significance of psychiatric comorbidity in this population. Older age and female sex were associated with higher odds of MDD among patients with IDA, while Asian race was associated with lower odds. Although causal inference cannot be established due to the observational design, these findings are plausible given iron\u0026rsquo;s role in neurochemical and neuroendocrine function. Taken together, the results support the clinical relevance of iron deficiency in psychiatric assessment and underscore the potential value of integrated screening for depressive symptoms in patients with IDA. Future longitudinal and interventional studies are needed to clarify temporal relationships and determine whether treatment of IDA mitigates depression risk or recurrence.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eHuman Ethics and Consent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003eIRB\u0026nbsp;Approved by the UCI Institutional Review Board (#4289)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e \u003cstrong\u003eInformed Consent\u003c/strong\u003e:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRequirement for informed consent waived by IRB due to retrospective nature of study and de-identified data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources/conflicts of interest:\u003c/strong\u003e None to declare\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eC.Y.: Conceptualization, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization, Formal AnalysisC.H.: Conceptualization, Methodology, Investigation, Writing \u0026ndash; Original Draft, Formal Analysis, Writing \u0026ndash; Review \u0026amp; EditingM.P.: Methodology, Software, Formal Analysis, Data Curation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; EditingE.P.: Methodology, Investigation, Writing \u0026ndash; Original Draft, Writing \u0026ndash; Review \u0026amp; Editing, Visualization. J.G.: Conceptualization, Investigation, Writing \u0026ndash; Review \u0026amp; EditingA.A: Conceptualization, Methodology, Formal Analysis, Project Administration, Writing \u0026ndash; Review \u0026amp; Editing. R.B.: Conceptualization, Investigation, Supervision, Project Administration, Writing \u0026ndash; Review \u0026amp; Editing. A.N.A: Conceptualization, Investigation, Methodology, Supervision, Project Administration, Writing \u0026ndash; Review \u0026amp; Editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements:\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData may be accessed at:Hardoy, Christian (2026), \u0026ldquo;Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study\u0026rdquo;, Mendeley Data, V1, https://doi.org/10.17632/www4mpy6sb.1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Set\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHardoy, Christian (2026), \u0026ldquo;Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study\u0026rdquo;, Mendeley Data, V1, doi: 10.17632/www4mpy6sb.1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWilliams A, Ansai N, Ahluwalia N, Nguyen D. Anemia Prevalence: United States, August 2021- August 2023. National Center for Health Statistics (U.S.); 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15620/cdc/168890\u003c/span\u003e\u003cspan address=\"10.15620/cdc/168890\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Ren W, Wang S, Xiang M, Zhang S, Zhang F. Global burden of anemia and cause among children under five years 1990\u0026ndash;2019: findings from the global burden of disease study 2019. Front Nutr. 2024;11:1474664.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGBD 2021 Anaemia Collaborators. Prevalence, years lived with disability, and trends in anaemia burden by severity and cause, 1990\u0026ndash;2021: findings from the Global Burden of Disease Study 2021. Lancet Haematol. 2023;10(9):e713\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArchived. Iron deficiency anaemia: assessment, prevention and control. Accessed September 27, 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/publications/m/item/iron-children-6to23--archived-iron-deficiency-anaemia-assessment-prevention-and-control?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.who.int/publications/m/item/iron-children-6to23--archived-iron-deficiency-anaemia-assessment-prevention-and-control?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Finberg KE. Iron deficiency anemia. Adv Exp Med Biol. 2025;1480:163\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDallman PR. Iron deficiency and the immune response. Am J Clin Nutr. 1987;46(2):329\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKulaszyńska M, Kwiatkowski S, Skonieczna-Żydecka K. The iron metabolism with a specific focus on the functioning of the nervous system. Biomedicines. 2024;12(3):595.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCann S, Perapoch Amad\u0026oacute; M, Moore SE. The role of iron in brain development: A systematic review. Nutrients. 2020;12(7):2001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgieff MK. Long-term brain and behavioral consequences of early iron deficiency: Nutrition Reviews\u0026copy;, Vol. 69, No. s1. \u003cem\u003eNutr Rev\u003c/em\u003e. 2011;69 Suppl 1(Suppl 1):S43-S48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian Y, Tian Y, Yuan Z, et al. Iron metabolism in aging and age-related diseases. Int J Mol Sci. 2022;23(7):3612.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRouault TA. Iron on the brain. Nat Genet. 2001;28(4):299\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalker FO. Huntington\u0026rsquo;s disease. Lancet. 2007;369(9557):218\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Ren Q, Meng J, Gao WJ, Chang YZ. Brain iron homeostasis and mental disorders. Antioxid (Basel). 2023;12(11):1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerthou C, Iliou JP, Barba D. Iron, neuro-bioavailability and depression. EJHaem. 2022;3(1):263\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLozoff B, Jimenez E, Hagen J, Mollen E, Wolf AW. Poorer behavioral and developmental outcome more than 10 years after treatment for iron deficiency in infancy. Pediatrics. 2000;105(4):E51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLozoff B, Jimenez E, Wolf AW. Long-term developmental outcome of infants with iron deficiency. N Engl J Med. 1991;325(10):687\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen MH, Su TP, Chen YS, et al. Association between psychiatric disorders and iron deficiency anemia among children and adolescents: a nationwide population-based study. BMC Psychiatry. 2013;13(1):161.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee HS, Chao HH, Huang WT, Chen SCC, Yang HY. Psychiatric disorders risk in patients with iron deficiency anemia and association with iron supplementation medications: a nationwide database analysis. BMC Psychiatry. 2020;20(1):216.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHouston BL, Hurrie D, Graham J, et al. Efficacy of iron therapy on fatigue and work capacity in non-anemic iron deficient adults: A systematic review of randomized controlled trials. Blood. 2017;130(Supplement 1):3497\u0026ndash;3497.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalkingham M, Abdelhamid A, Curtis P, Fairweather-Tait S, Dye L, Hooper L. The effects of oral iron supplementation on cognition in older children and adults: a systematic review and meta-analysis. Nutr J. 2010;9(1):4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMikami K, Akama F, Kimoto K, et al. Iron supplementation for hypoferritinemia-related psychological symptoms in children and adolescents. J Nippon Med Sch. 2022;89(2):203\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKassir A. Carence en fer: une perspective diagnostique et th\u0026eacute;rapeutique en psychiatrie. Encephale. 2017;43(1):85\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, He L, Li S, et al. Cerebral iron deficiency may induce depression through downregulation of the hippocampal glucocorticoid-glucocorticoid receptor signaling pathway. J Affect Disord. 2023;332:125\u0026ndash;35.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgustini B, Lotfaliany M, Woods RL, et al. Patterns of association between depressive symptoms and chronic medical morbidities in older adults. J Am Geriatr Soc. 2020;68(8):1834\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Q, Feng J, Pan CW. Risk factors for depression in the elderly: An umbrella review of published meta-analyses and systematic reviews. J Affect Disord. 2022;307:37\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJellinger KA. Pathomechanisms of vascular depression in older adults. Int J Mol Sci. 2021;23(1):308.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eBiological Factors Influencing Depression in Later Life: Role of Aging Processes and Treatment Implications. Translational Psychiatry. 2023. Szymkowicz SM\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor WD. Depression in the elderly. N Engl J Med. 2014;371(13):1228\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaus D, Hornsey J, Kuppens MJ, Bastian P. Exploring the East-West Divide in Prevalence of Affective Disorder: A Case for Cultural Differences in Coping With Negative Emotion. Pers Soc Psychol Rev. 2018;22(3):285\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang SM, Hahm BJ, Lee JY, et al. Cross-national difference in the prevalence of depression caused by the diagnostic threshold. J Affect Disord. 2008;106(1\u0026ndash;2):159\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim JM, L\u0026oacute;pez SR. The expression of depression in Asian Americans and European Americans. J Abnorm Psychol. 2014;123(4):754\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwong K, Ahuvia IL, Schleider JL. Help-seeking at the intersection of race and age: Perceived need and treatment access for depression in the United States. J Affect Disord. 2025;386(119428):119428.\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eDemographics of IDA Population\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIDA (N\u0026thinsp;=\u0026thinsp;8593)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6002 (69.8%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2591 (30.2%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian/Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21 ( 0.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1256 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e326 ( 3.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultirace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e241 ( 2.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian/Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50 ( 0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1360 (15.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e200 ( 2.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5139 (59.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2607 (30.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Hispanic or Latino\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5793 (67.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e193 ( 2.2)\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\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at visit, median (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53.00 [37.00, 71.00]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e581 ( 6.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1202 (14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1379 (16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1265 (14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1097 (12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3069 (35.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMDD Diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle Episode MDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1993 (23.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecurrent MDD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e618 ( 7.2)\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 \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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSingle Episode MDD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRecurrent MDD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCI (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.345\u0026ndash;1.715)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.51e-11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.500-2.278)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e9.28e-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge 18\u0026ndash;24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 25\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.899\u0026ndash;1.467)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.610\u0026ndash;1.326)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 35\u0026ndash;44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.880\u0026ndash;1.426)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.634\u0026ndash;1.355)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 45\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.854\u0026ndash;1.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.668\u0026ndash;1.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 55\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.267\u0026ndash;2.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.37E-04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.699\u0026ndash;1.542)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.121\u0026ndash;1.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.778\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eReference\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.881\u0026ndash;1.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.421\u0026ndash;1.039)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.366\u0026ndash;0.523)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.00E-19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.282\u0026ndash;0.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1.12E-09\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNative Hawaiian or Pacific Islander\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.492\u0026ndash;1.785)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.473\u0026ndash;2.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian or Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.966\u0026ndash;5.801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.197\u0026ndash;4.393)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd 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Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Iron deficiency anemia, major depressive disorder, demographic disparities","lastPublishedDoi":"10.21203/rs.3.rs-8904473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8904473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIron deficiency anemia (IDA) has been increasingly associated with neuropsychiatric conditions, including major depressive disorder (MDD). However, demographic variation in the relationship between IDA and MDD remains underexplored in large clinical populations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using de-identified electronic health records from 336,973 adult patients at the University of California, Irvine Medical Center (2017\u0026ndash;2024). IDA was identified using ICD-10-CM codes, and MDD was classified as single-episode or recurrent. Multivariate logistic regression adjusted for age, sex, race, and ethnicity was used to assess demographic variation in MDD diagnoses among patients with IDA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 8,593 patients with IDA, single-episode MDD was present in 23.2% and recurrent MDD in 7.2%. Female patients demonstrated significantly higher odds of single-episode MDD (OR 1.52, 95% CI 1.35\u0026ndash;1.72) and recurrent MDD (OR 1.84, 95% CI 1.50\u0026ndash;2.28) compared with males. Adults\u0026thinsp;\u0026ge;\u0026thinsp;55 years demonstrated increased odds of single-episode MDD (OR 1.62, 95% CI 1.27\u0026ndash;2.08 and OR 1.40, 95% CI 1.12\u0026ndash;1.76, respectively), with no significant association observed for recurrent MDD. Asian individuals had significantly lower odds of single-episode (OR 0.44, 95% CI 0.37\u0026ndash;0.52) and recurrent MDD (OR 0.39, 95% CI 0.28\u0026ndash;0.52) than White patients.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis large cohort study identified significant demographic variation in MDD incidence among patients with IDA. These findings highlight the importance of demographic risk stratification in patients with IDA and support targeted screening for depressive disorders in medically vulnerable populations.\u003c/p\u003e","manuscriptTitle":"Demographic Predictors of Major Depressive Disorder Among Patients with Iron Deficiency Anemia: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-27 07:23:55","doi":"10.21203/rs.3.rs-8904473/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-15T12:23:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T02:04:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T01:18:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T10:25:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-18T05:37:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"39627996373130378575059831279925463076","date":"2026-04-13T05:27:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250102406361708476287937774327291008621","date":"2026-04-12T01:10:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"235393944020087266390439285477321325334","date":"2026-04-10T20:24:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"288275588353456886001570925481526532533","date":"2026-04-10T16:00:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T14:03:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-04T17:13:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T14:34:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-02T14:23:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-02-17T21:45:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb3f70a1-07bd-4116-9912-559b80978a81","owner":[],"postedDate":"February 27th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-15T12:23:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T02:04:58+00:00","index":73,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T01:18:31+00:00","index":72,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T12:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-27 07:23:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8904473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8904473","identity":"rs-8904473","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}