Identification of atopic dermatitis-associated diseases based on the National Health and Nutrition Examination Survey (NHANES) 2013-2018

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Massive cohort studies revealed that AD was associated with allergic diseases, inflammatory diseases, autoimmune diseases, cardiovascular diseases, and mental disorders. Objective We comprehensively and systematically analyzed the correlation between AD and diseases to identify AD-associated diseases (ADADs). Methods We involved 17924 individuals from the National Health and Nutrition Examination Survey (NHANES) (2013–2018) dataset, and analyzed the correlation between AD and 422 diseases classified by International Classification of Diseases-10 (ICD-10) using four logistic regression models. Results We found that AD is significantly associated with 33 diseases: (1) allergic diseases, including urticaria, allergic rhinitis, allergy, asthma, other seasonal allergic rhinitis; (2) inflammatory diseases, including noninfective gastroenteritis and colitis, acute atopic conjunctivitis, osteoarthritis, and unspecified chronic bronchitis; (3) mental disorders with impairment (MDI), including comorbid mental disorders, schizophrenia and sleep disorder; (4) malignant tumors, including malignant neoplasm of prostate, malignant (primary) neoplasm and malignant neoplasm of breast; (5) other symptoms and diseases, other symptoms and diseases, such as wheezing, pruritus and gout. Notably, non-infective gastroenteritis and colitis showed the strongest correlation (OR: 38.39, 95% CI: 3.08-478.01) among the 33 ADADs. Conclusion We identified 33 ADADs based on the NHANES (2013–2018) dataset, which provide new insights into understanding the development of these ADADs associated with AD. Biological sciences/Immunology/Adaptive immunity Health sciences/Diseases/Skin diseases Atopic dermatitis AD-associated diseases NHANES ICD-10 allergic diseases Figures Figure 1 Introduction Atopic dermatitis (AD) is one of the most common chronic relapsing inflammatory skin diseases, characterized by persistent itching of the skin 1 . Within the past decades, the incidence of AD increased steadily in both urbanized and developing countries 2 , 3 . Currently, AD impact 15–20% of children and up to 10% adults worldwide, causing significant economic burden and reduced life quality to patients and their family 4 – 6 . It is noteworthy that AD primarily affects pediatric demographic and leading to life-long recurrent course, wherein approximately 24% of children suffer from AD, and the prevalence rate increases from 15–38% between children aged 1 and children aged 4–5 7 . Many adults suffering AD experienced childhood onset of the disease, and this long-term persistent itching of skin can interfere their occupations, sleeping quality and even intimate relationships, causing life-long burdens both physically and mentally 7 , 8 . More importantly, growing evidence suggested robust correlations between AD and various allergic diseases, such as allergic asthma (AA), allergic rhinitis (AR), and food allergy (FA) 4 , 9 , 10 . The progressing onset of allergic diseases follow a time-based-order, as AD and FA in infancy can evolve into AS and AR in childhood, this evolution from skin to gastrointestinal and respiratory tract was defined as “atopic march" 11 , 12 . Elevated comorbidities were also noticed between AD and autoimmune diseases, cardiovascular diseases (CVD), MDI and other disorders: recent meta-analysis showed increased possibility of comorbidities between AD and multiple autoimmune diseases, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) 13 – 15 . Increased risk of CVD including ischemic stroke, hypertension and myocardial infarction (MI) were observed in AD patients 16 – 19 . Patients suffering mental disorders, particularly depression, anxiety, sleep disorders and suicidal ideation also show higher prevalence of AD than normal 20 . To identify the risk of AD and conduct interventions to improve health-related outcomes, we proposed that the patients suffered from AD and a certain disease that is demonstrated to be associated with AD should be diagnosed as AD associated disease (ADAD). Herein, we comprehensively and systematically analysis the correlation between AD and 422 diseases using the National Health and Nutrition Examination Survey (NHANES) (2013–2018) dataset to provide a useful guideline for clinics to diagnose ADADs. Methods Study population We utilized the data extracted from the National Health and Nutrition Examination Survey dataset of the United States (NHANES) spanning from 2013 to 2018 ( https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/labmethods.aspx?BeginYear=201 ) 21 . 12,226 individuals were excluded from the study due to missing or incomplete data on body mass index (BMI), education level, AD records, and uncertain or incomplete information. Additionally, 24 individuals who lacked ICD-10 diagnosis information were also rule out in this study. A total of 17,924 subjects remained for the final analysis. The entire study was performed according to the guidelines approved by the Research Ethics Review Board of the National Center for Health Statistics (Protocol #2011-17, #2018-01). Moreover, written informed consent was duly obtained from all participants involved in this project. Population Characteristics The sample population age, BMI, sex, race and education were examined. Race characteristics include Mexican American, Non-Hispanic Black, Non-Hispanic White and others. Education characteristics include college graduate or above, college or above, high school or equivalent and less than high school (Table 1 ). Table 1 basic information of the sample population variables Without AD group AD group P value Age BMI Sex Female Male Race Mexican American Non-Hispanic Black Non-Hispanic White Others Education College graduate or above College or above High School or equivalent Less than high school 46.97 ± 0.29 29.38 ± 0.14 9254(51.82%) 8596(48.18%) 2689(9.21%) 3835(11.45%) 6481(63.61%) 4845(15.73%) 4174(30.00%) 5211(30.98%) 5933(31.16%) 2532(7.86%) 47.88 ± 2.90 29.50 ± 1.06 43(51.93%) 31(48.07%) 8(5.52%) 19(10.05%) 27(69.18%) 20(15.24%) 19(41.43%) 23(20.41%) 22(32.97%) 10(5.19%) 0.76 0.90 0.99 0.62 0.18 The sample population age, BMI, sex, race and education were examined. Race characteristics include Mexican American, Non-Hispanic Black, Non-Hispanic White and others. Education characteristics include college graduate or above, college or above, high school or equivalent and less than high school. Clinical markers measurement In the NHANES MEC, samples of whole blood undergo meticulous analysis. The NHANES Laboratory Procedures Manual (LPM) thoroughly expounds on the collection and processing of specimens. As a fundamental gauge of inflammation, we utilized the platelet count (PLT), neutrophil count (NEU), monocyte count (MO), and lymphocyte count (LYM). To gain a more comprehensive understanding of the correlation between inflammation-related indices and sex hormones, we derived the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic immune response index (SIRI). The measurements of PLT, NEU, and LYM were taken at a concentration of 1000 cells/uL. SIRI was computed by (NEU × Mo) / LYM. SII was determined by dividing NEU by LYM. MLR was obtained by the ratio of MO to LYM. The detection limits remained consistent for all analytes in the dataset, and none of the results fell below the limits of detection. The Collaborative Laboratory Services (Ottumwa, IA) used a Beckman Synchron LX20 analyzer to measure biochemistry profile, including levels of ALT, AST, ALP and GGT. For NHANES cycles prior to 2015, serum CRP levels were determined by latex-enhanced nephelometry on a Behring Nephelometer, with a lower limit of detection (LLOD) of 0.2 mg/L. For NHANES cycles 2015 and 2017, CRP levels were assayed on Beckman Coulter Synchron analyzers, with LLODs of 0.11 mg/L (for 2015) and 0.15 mg/L (for 2017). Soluble klotho levels (pg/ml) were analyzed with a commercially available ELISA kit produced by IBL International, Japan during the period 2019–2020. All analyses were performed at the University of Washington research laboratories. A description of the laboratory methodology can be found at https://wwwn.cdc.gov/Nchs/Nhanes/ . Model categories The association between AD and each disease was assessed through four multivariable logistic regression models: one unadjusted and three adjusted models. Model 1 did not include any covariate adjustments. In Model 2, adjustments were made for age, gender, and race. Model 3 accounted for age, gender, race, education level, and poverty to income ratio. Finally, Model 4 incorporated adjustments for age, gender, race, education level, poverty to income ratio, AD status, body mass index, and bone mineral density. Statistical analyses We have performed all statistical analysis with the R software (Version 4.2.2) according to the CDC guidelines ( https://wwwn.cdc.gov/nchs/nhanes/tutorials ). Sample weights were taken into account in all of the estimates to produce representative data of the civilian noninstitutionalized US population. Population characteristics of the sample of individuals were summarized and compared using ANOVA with Bonferroni adjusted p-values for multiple comparisons for continuous variables, and Pearson Chi-square test for the categorical variables. Results Baseline information of study participants A total of 17,924 participants (mean age 53.90 ± 0.35, 46% males) were included in this study. A total of 47.88 ± 2.9 patients with AD (51.93% female, 48.07% male) were compared to 46.97 ± 0.29 individuals without AD (51.82% female, 48.18% male). The weighted distribution of the characteristics according to AD was shown in Table 1 . A two-sided P -value < 0.05 was considered a significant association between AD and ADADs. The average age and body mass index in these two groups did not have a significant difference. Males and Non-Hispanic Whites were more likely to have a higher rate of AD. Laboratory examination of AD patients We conducted a study examining the disparities in clinical indexes between AD and non-AD in Table 2 . 15 clinical parameters were analyzed, the majorities of clinical-related indexes were no significantly different compared to the non-AD population, only white blood cell count (WBC), lymphocyte count (LYM) and SII are slightly elevated in the AD population. Interestingly, there were notable increases in metabolic indicators, especially Gamma-Glutamyl Transferase (GGT) (AD: 41.35 ± 4.43 compared to non-AD: 27.72 ± 0.34, p = 0.004) and Fe (AD: 316.53 ± 83.82 compared to non-AD: 126.10 ± 2.99, p = 0.03) are significantly enhanced in the AD population. Table 2 Variables of clinical markers Variables Without AD group AD group P value WBC NEU LYM MO PLT NLR MLR SII SIRI ALP TBIL ALB GGT Fe α-klotho 7.41 ± 0.04 4.38 ± 0.03 2.19 ± 0.02 0.59 ± 0.00 239.60 ± 1.22 2.19 ± 0.02 0.29 ± 0.00 525.42 ± 4.51 0.01 ± 0.00 70.19 ± 0.42 0.56 ± 0.01 4.24 ± 0.01 27.72 ± 0.34 126.10 ± 2.99 827.91 ± 6.51 7.52 ± 0.28 4.34 ± 0.22 2.27 ± 0.13 0.59 ± 0.03 244.28 ± 8.02 2.12 ± 0.17 0.28 ± 0.01 550.85 ± 67.11 0.01 ± 0.00 67.19 ± 3.26 0.54 ± 0.03 4.17 ± 0.07 41.35 ± 4.43 316.53 ± 83.82 832.16 ± 137.89 0.70 0.86 0.56 0.98 0.55 0.67 0.17 0.70 0.15 0.37 0.65 0.35 0.00 0.03 0.98 White blood cell count (WBC), Neutrophil (NEU), Lymphocyte count (LYM), Monocyte (MO), Platelet (PLT), Neutrophil-lymphocyte-ratio (NLR), Monocyte-lymphocyte-ratio (MLR), Systemic immune-inflammation index (SII), System inflammation response index(SIRI), Alkaline phosphatase (ALP), Total bilirubin (TBIL), Albumin(ALB), γ-Glutamyl Transferase(GGT) The association analysis of AD with 422 diseases Within this investigation, we delved into the association between AD and various diseases. We examined a comprehensive collection of 422 prevalent illnesses categorized by the ICD-10 classification system and calculated the disparities in disease incidence between AD and non-AD. In the Table 3, we identified 33 diseases that exhibited a significant positive correlation with AD using model 1. Figure 1 represented the correlation between 33 diseases and AD using the length of rays. In analysis, we observed that AD comorbid allergy diseases, such as urticaria (L50, OR: 49.67, 95% CI: 4.90-503.62), acute atopic conjunctivitis (H10.1, OR: 31.26, 95% CI: 4.44-219.93), rash and other nonspecific skin eruption (R21, OR: 21.47, 95% CI: 5.96–77.34), allergic rhinitis (J30.9), allergy (T78.4) and asthma(J45). Moreover, comorbid mental disorders (F99, OR:11.94, 95% CI: 1.46–97.68) were increased in prevalence in AD patients, including schizophrenia (F20) and sleep disorder (G47.9). Interestingly, we found evidence for statistically significant interaction between atopic dermatitis and inflammatory diseases, including non-infective diseases and infective diseases (K52.9). Among non-infective diseases, noninfective gastroenteritis and colitis (K52.9, OR:38.39, 95% CI: 3.08-478.01) has strongest association with AD, secondly are osteoarthritis (M19.9) and chronic bronchitis (J42). Among infective diseases include HIV disease (B20), dermatophytosis (B35.9) and local infection of the skin and subcutaneous tissue (L08.9). In addition, we found AD was associated with malignant diseases, particularly malignant neoplasm of prostate (C61, OR:38.52, 95% CI: 5.32-278.74). Interestingly, some metabolic diseases, such as gout (M10.9, OR:12.11, 95% CI: 2.08–70.65), overweight and obesity (E66, OR:9, 95% CI: 1.10-73.94), hyperuricemia without signs of inflammatory arthritis and prevent high cholesterol (E79) and renal disease, such as dependence on renal dialysis (Z99.2) and other specified urinary incontinence (N39.4) are significantly associated with atopic dermatitis. What’s more, AD is also associated with other diseases, including other chest pain (R07.89), wheezing (R06.2), disorder of ear (H93.9), abnormal sputum (R09.3), nasal congestion (R09.81) and enlarged prostate (N40). Discussion Genetic predisposition, epidermal dysfunction, skin microbiome abnormalities and immune dysregulation jointly contribute to the development of AD. Th2 inflammation mediates the most typical symptom of AD, atopic pruritus, and causing consistent itching to the patients 22 . Epidermis destructing factors such as skin damage, infections and inflammation can activate keratinocytes and exaggerate production of proinflammatory factors, such as thymic stromal lymphopoietin (TSLP), IL-25 and IL-33, adding to recruitment of immune cells especially Th2 cells. Th2 cells, eosinophils, neutrophils and mast cells therefore release pro-inflammatory cytokines and peptides to stimulate pruritoceptive pathways, in which IL-31 expressed by Th2 cells acts as the most significant pruritus mediator. IL-31 can bind to IL-31A receptor and activate transient receptor potential vanilloid 1 (TRPV1) and transient receptor potential ankyrin 1 (TRPA1) to awake sensory neurons and exacerbate pruritus. Excessive production of Th2 lymphocytes lead to elevation in IL-4, IL-5 and IL-13 levels to recruit and activate immune cells and trigger B cells to generate allergen-specific IgE, adding to defections in epidermal barrier and stimulate type 2 inflammatory responses 23 . Immunological aberrations are believed to be significant in triggering disruptions of the epidermal barrier, microbial dysbiosis and disorders of immune responses in AD. Our analysis indicated that innate and adaptive immune system were activated in AD patients. We observed infiltration of inflammatory cells and noticed that GGT, an enzyme in antioxidant system (AOS), was significantly increased in AD. Oxidative stress (OS) causes direct damage to the cell membrane and DNA to establish skin barrier defection, as well as activating NF-κB pathway to stimulate IL-6, IL-8, IL-9 and IL-33 releasing 24 , 25 . These factors jointly contribute to dermal inflammation, elevated histamine releasing and itching, which in turn determine a vicious cycle that exaggerate OS and add to epidermal barrier disruption. Considering the fact GGT level was notably increased in AD patients, it may act as a potential clinical diagnosis and prognostic indicators for OS in AD. In our analysis, no differences among the clinical characteristics of age, sex and race were reflected on the morbidity of AD. In association analysis, we found that AD individuals show higher risks among a series of allergy diseases, including urticaria, allergic rhinitis, allergy, asthma and other seasonal allergic rhinitis. These findings show consistence with previous researches that the early onset of AD increases co-occurrence of other allergic diseases and establish the pathogenesis of atopic march 26 , 27 . AD also show correlations with various inflammatory diseases, including noninfective gastroenteritis and colitis, acute atopic conjunctivitis, osteoarthritis, and unspecified chronic bronchitis, in which noninfective gastroenteritis and colitis show the most robust correlation in our analysis. The possible mechanisms of AD increasing comorbidity of these diseases are complex. First, one possible pathway is the inflammatory pathway: The imbalance of Th2 to Th1 cytokines, such as IL-4, IL-5, IL-9 and IL-13, observed in AD can create alterations in the cell mediated immune responses 28 . These inflammatory cytokines circulate to the different tissues through related anatomical pathways and triggering inflammation. Of note, inflammatory bowel disease (IBD) is caused by an abnormal adaptive immune response. In particular, ulcerative colitis (UC) has been rather related to a non-conventional Th2 response 29 . Therefore, Th2 inflammatory cytokines created by AD enters into bowels and contribute to a vicious inflammatory cycle, finally exacerbating colitis. The potential mechanisms of AD increasing atopic conjunctivitis and osteoarthritis maybe consistent with this. Second, some studies proposed the hypothesis of the interplay of intestinal dysbacteria in AD patients. Li et al has reported that differential abundance of genera in the skin, oral and gut 30 . Accordingly, we speculate that abnormal oral microbiota through oral-gut axis or intestinal dysbacteria probably impact the risk of colitis. Additionally, our analysis also emphasized correlation between AD and MDI, including comorbid mental disorders, schizophrenia and sleep disorder. Corresponding to our findings, previous studies showed that elevated prevalence of MDI and relatively severe phenotype were noticed in children suffering AD, including mental health disturbances including anxiety, depression, ADHD, conduct disorder, and autism 31 , 32 . Although the underlying mechanisms between AD and MDI remain unclear, correlation between diseases severity, eczema area and wake experiences after sleep onset were notice in AD children, highlighting lower sleep efficiency and sleep disorder. Sleep disturbance along with consistent itching may add to daily anxiety and depression and drive the association between neuropsychiatric disorders and AD 32 . Therefore, the concept of ADAD may offer therapeutic guidance to take AD into consideration when faced with mental disorders of unknown origins, especially in children with allergic history. Previous research reached contrary results about the correlations between AD and multiple cancers due to substantial heterogeneity and severe bias between studies 33 , 34 . However, our analysis found correlation between AD and malignant tumor, including malignant neoplasm of prostate, malignant (primary) neoplasm and malignant neoplasm of breast. Wherein the effect of AD in the pathogenesis of malignant tumor are likely multifactorial. Other than genetic predispositions and systemic inflammation, another pathway could be psychosocial disease 20 . Besides, our result also found correlations between AD and other diseases, including dermatophytosis, dependence on renal dialysis, other chest pain, wheezing, rash and other nonspecific skin eruption, pruritus, gout, nasal congestion, hyperuricemia without signs of inflammatory arthritis and tophaceous disease, unspecified disorder of ear, abnormal sputum, prevent high cholesterol, overweight and obesity, local infection of the skin and subcutaneous tissue, enlarged prostate, other specified urinary incontinence, and other acute postprocedural pain, emphasizing the overall affection of AD. Previous studies have proved that allergic diseases mediated type 2 immunity induced atherosclerosis, which affects arteries of different organs such as the heart and the kidney 35 , 36 , which the underlying cause of metabolic diseases and renal disease. Of note, we should diagnose the patient suffered from AD and a certain disease that is demonstrated to be associated with AD as AD associated disease (ADAD). Compared with previous studies, our report first explored the association between AD and related diseases via analyzing the NHANES database and successfully revealed a cluster of 33 diseases correlating to increased risk of AD. However, limitations still exist as our research examined no obvious correlation among the prevalence of cardiovascular disease, autoimmune disease and AD due to insufficiency in sample size. Besides, as a cross-sectional studies, our findings may have uncovered correlations but could not disentangle noncausal or causal associations, thus there might still be bidirectional correlations between AD and other diseases. Recommendations should be made with caution when guiding clinical practice. Conclusion Our data revealed a cluster of 33 diseases correlating to increased risk of AD and proposed the concept of AD-associated disease (ADAD). Given the influence of AD, the concept of ADADs may add to early prediction, diagnosis and treatment of AD among ADADs, providing clinical guidance in comorbidities analysis and advanced treatment. Further replication in larger samples is needed to validate our findings, and experimental studies are needed to explore the underlying mechanisms. Abbreviations AD: Atopic dermatitis; ADAD: AD-associated disease; NHANES: National Health and Nutrition Examination Survey; BMI: body mass index; ICD-10: International Classification of Diseases-10; MDI: mental disorders with impairment; HIV: human immunodeficiency virus; OR: odds ratio; AA: allergic asthma; AR: allergic rhinitis; FA: food allergy; CVD: cardiovascular diseases; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; LPM: Laboratory Procedures Manual; PLT: platelet count; NEU: neutrophil count; MO: monocyte count; LYM: lymphocyte count; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic immune response index; LLOD: lower limit of detection; WBC: white blood cell count; LYM: lymphocyte count; GGT: Gamma-Glutamyl Transferase; TSLP: thymic stromal lymphopoietin; TRPV1: transient receptor potential vanilloid 1; TRPA1: transient receptor potential ankyrin 1; IBD: inflammatory bowel disease; UC: ulcerative colitis Declarations Acknowledgments We would like to thank Zhang Jing (Shanghai Tongren Hospital) for his work on the NHANES database, which makes it easier for us to explore. Thanks to those who contributed to NHANES data, including all anonymous participants in the study. Authors’ contributions X.C. analyzed the data; Y. L. and ZY. S wrote the manuscript; All authors help to revise this manuscript and approved it to publish. Funding This work was supported by the Significant Science and Technology Project of Beijing Life Science Academy [grant number 2024500CB0030, 2023000CA0040]; the National Natural Science Foundation of China [grant number 81603119]; the Natural Science Foundation of Beijing Municipality [grant number 7174316]; the Peking University Medicine Seed Fund for Interdisciplinary Research supported by “the Fundamental Research Funds for the Central Universities” [grant number No. BMU2022MX017, No. BMU2022MX003]. Availability of data and materials The survey data are publicly available on the NHANES website for all researchers worldwide (www.cdc.gov/nchs/nhanes/). 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Inflammatory skin diseases and the risk of chronic kidney disease: population-based case-control and cohort analyses. Br. J. Dermatol. 185 (4), 772–780 (2021). Tables Table 3 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table3.docx Cite Share Download PDF Status: Published Journal Publication published 21 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 19 Feb, 2025 Reviews received at journal 19 Feb, 2025 Reviewers agreed at journal 10 Feb, 2025 Reviews received at journal 04 Nov, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers agreed at journal 30 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 08 Oct, 2024 Editor invited by journal 05 Sep, 2024 Submission checks completed at journal 03 Sep, 2024 First submitted to journal 02 Sep, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5015254","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":361837801,"identity":"0457d523-b9f8-433b-bbbb-3b3ccd839604","order_by":0,"name":"Yuan Liu","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":361837804,"identity":"6bb6c08b-72d1-4f9c-80b3-76872058b62a","order_by":1,"name":"Xi Chen","email":"","orcid":"","institution":"Beijing Jishuitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Chen","suffix":""},{"id":361837806,"identity":"f267c700-0419-46d9-88de-7a02a0abbd41","order_by":2,"name":"Ziyue Su","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ziyue","middleName":"","lastName":"Su","suffix":""},{"id":361837808,"identity":"7147a141-ec17-42af-b73f-dfd808c14adc","order_by":3,"name":"Yiting Wang","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yiting","middleName":"","lastName":"Wang","suffix":""},{"id":361837809,"identity":"96ca4989-0762-403c-9653-1ede851414d9","order_by":4,"name":"Yintong Xue","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yintong","middleName":"","lastName":"Xue","suffix":""},{"id":361837811,"identity":"2335d1c4-97d2-4d02-acdf-996e83c2eca7","order_by":5,"name":"Yan Li","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Li","suffix":""},{"id":361837812,"identity":"e46b6155-3095-4bfe-8da6-e6e8250e9993","order_by":6,"name":"Xiang Gao","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Gao","suffix":""},{"id":361837813,"identity":"396cc5f6-4be5-4a37-8143-7ae09b66902e","order_by":7,"name":"Lijun Wang","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lijun","middleName":"","lastName":"Wang","suffix":""},{"id":361837814,"identity":"1d34de31-475b-4f68-a586-678492d57025","order_by":8,"name":"Jie Hao","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Hao","suffix":""},{"id":361837815,"identity":"6b957750-d898-498b-be7b-4a10028eaa91","order_by":9,"name":"Yuedan Wang","email":"","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuedan","middleName":"","lastName":"Wang","suffix":""},{"id":361837816,"identity":"886f80f6-4ded-4902-9c11-a49d96dc7c05","order_by":10,"name":"Ming Chu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYBAC9gYwZZPACGawEaGF5wCYSiNdy+EECJcoLeynEx/z/DmfxzztjAHDh7LDDPyzGwho4cndbMzbdruYcXaOAeOMc4cZJO4cwK/FniF3mzRvw+3ERqAWZt62wwwGEgkEbOF/u02a5885iJa/RGmRANrCw3YAooWROC1vNxvObUsG+iWt4GDPuXQeiRsEHZa78cGbP3Z5hrOTNz74UWYtxz+DgBY4MGxgYDgAMoNI9UAgT7zSUTAKRsEoGGkAACuHQnl3ZrmvAAAAAElFTkSuQmCC","orcid":"","institution":"Peking University School of Basic Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Ming","middleName":"","lastName":"Chu","suffix":""}],"badges":[],"createdAt":"2024-09-02 04:03:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5015254/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5015254/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98551-6","type":"published","date":"2025-04-21T15:58:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65918077,"identity":"52574d63-5fa7-45b8-99c7-51ff27e440fd","added_by":"auto","created_at":"2024-10-04 11:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between AD and 33 diseases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur data revealed a cluster of 33 diseases correlating to increased risk of AD. Correlating diseases were separated into 5 groups, including allergy diseases, inflammatory diseases, MDI, malignant tumors, and other diseases. Length of the line represented the ORs. \u0026nbsp;This figure was created using https://www.biorender.com.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5015254/v1/13ed277cfa4b6b04c054703b.png"},{"id":81569866,"identity":"d4cfd622-f388-4af6-a537-4c24c6550dbb","added_by":"auto","created_at":"2025-04-28 16:12:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":913531,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5015254/v1/89d8d5a3-b89d-460f-8370-fbe7f61fdc53.pdf"},{"id":65918076,"identity":"0d813193-39eb-4c30-a798-72799ca766b1","added_by":"auto","created_at":"2024-10-04 11:09:05","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22363,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5015254/v1/22f3fe00fa6ebe0231e41010.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of atopic dermatitis-associated diseases based on the National Health and Nutrition Examination Survey (NHANES) 2013-2018","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAtopic dermatitis (AD) is one of the most common chronic relapsing inflammatory skin diseases, characterized by persistent itching of the skin \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Within the past decades, the incidence of AD increased steadily in both urbanized and developing countries \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Currently, AD impact 15\u0026ndash;20% of children and up to 10% adults worldwide, causing significant economic burden and reduced life quality to patients and their family \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. It is noteworthy that AD primarily affects pediatric demographic and leading to life-long recurrent course, wherein approximately 24% of children suffer from AD, and the prevalence rate increases from 15\u0026ndash;38% between children aged 1 and children aged 4\u0026ndash;5 \u003csup\u003e7\u003c/sup\u003e. Many adults suffering AD experienced childhood onset of the disease, and this long-term persistent itching of skin can interfere their occupations, sleeping quality and even intimate relationships, causing life-long burdens both physically and mentally \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMore importantly, growing evidence suggested robust correlations between AD and various allergic diseases, such as allergic asthma (AA), allergic rhinitis (AR), and food allergy (FA) \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The progressing onset of allergic diseases follow a time-based-order, as AD and FA in infancy can evolve into AS and AR in childhood, this evolution from skin to gastrointestinal and respiratory tract was defined as \u0026ldquo;atopic march\" \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Elevated comorbidities were also noticed between AD and autoimmune diseases, cardiovascular diseases (CVD), MDI and other disorders: recent meta-analysis showed increased possibility of comorbidities between AD and multiple autoimmune diseases, including rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) \u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Increased risk of CVD including ischemic stroke, hypertension and myocardial infarction (MI) were observed in AD patients \u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Patients suffering mental disorders, particularly depression, anxiety, sleep disorders and suicidal ideation also show higher prevalence of AD than normal \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo identify the risk of AD and conduct interventions to improve health-related outcomes, we proposed that the patients suffered from AD and a certain disease that is demonstrated to be associated with AD should be diagnosed as AD associated disease (ADAD). Herein, we comprehensively and systematically analysis the correlation between AD and 422 diseases using the National Health and Nutrition Examination Survey (NHANES) (2013\u0026ndash;2018) dataset to provide a useful guideline for clinics to diagnose ADADs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eWe utilized the data extracted from the National Health and Nutrition Examination Survey dataset of the United States (NHANES) spanning from 2013 to 2018 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/labmethods.aspx?BeginYear=201\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/labmethods.aspx?BeginYear=201\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. 12,226 individuals were excluded from the study due to missing or incomplete data on body mass index (BMI), education level, AD records, and uncertain or incomplete information. Additionally, 24 individuals who lacked ICD-10 diagnosis information were also rule out in this study. A total of 17,924 subjects remained for the final analysis. The entire study was performed according to the guidelines approved by the Research Ethics Review Board of the National Center for Health Statistics (Protocol #2011-17, #2018-01). Moreover, written informed consent was duly obtained from all participants involved in this project.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Characteristics\u003c/h2\u003e \u003cp\u003eThe sample population age, BMI, sex, race and education were examined. Race characteristics include Mexican American, Non-Hispanic Black, Non-Hispanic White and others. Education characteristics include college graduate or above, college or above, high school or equivalent and less than high school (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebasic information of the sample population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eWithout AD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003eBMI\u003c/p\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eRace\u003c/p\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003cp\u003eOthers\u003c/p\u003e \u003cp\u003eEducation\u003c/p\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003cp\u003eHigh School or equivalent\u003c/p\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e46.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003cp\u003e29.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003cp\u003e9254(51.82%)\u003c/p\u003e \u003cp\u003e8596(48.18%)\u003c/p\u003e \u003cp\u003e2689(9.21%)\u003c/p\u003e \u003cp\u003e3835(11.45%)\u003c/p\u003e \u003cp\u003e6481(63.61%)\u003c/p\u003e \u003cp\u003e4845(15.73%)\u003c/p\u003e \u003cp\u003e4174(30.00%)\u003c/p\u003e \u003cp\u003e5211(30.98%)\u003c/p\u003e \u003cp\u003e5933(31.16%)\u003c/p\u003e \u003cp\u003e2532(7.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e47.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.90\u003c/p\u003e \u003cp\u003e29.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e \u003cp\u003e43(51.93%)\u003c/p\u003e \u003cp\u003e31(48.07%)\u003c/p\u003e \u003cp\u003e8(5.52%)\u003c/p\u003e \u003cp\u003e19(10.05%)\u003c/p\u003e \u003cp\u003e27(69.18%)\u003c/p\u003e \u003cp\u003e20(15.24%)\u003c/p\u003e \u003cp\u003e19(41.43%)\u003c/p\u003e \u003cp\u003e23(20.41%)\u003c/p\u003e \u003cp\u003e22(32.97%)\u003c/p\u003e \u003cp\u003e10(5.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003cp\u003e0.90\u003c/p\u003e \u003cp\u003e0.99\u003c/p\u003e \u003cp\u003e0.62\u003c/p\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eThe sample population age, BMI, sex, race and education were examined. Race characteristics include Mexican American, Non-Hispanic Black, Non-Hispanic White and others. Education characteristics include college graduate or above, college or above, high school or equivalent and less than high school.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003eClinical markers measurement\u003c/h2\u003e \u003cp\u003eIn the NHANES MEC, samples of whole blood undergo meticulous analysis. The NHANES Laboratory Procedures Manual (LPM) thoroughly expounds on the collection and processing of specimens. As a fundamental gauge of inflammation, we utilized the platelet count (PLT), neutrophil count (NEU), monocyte count (MO), and lymphocyte count (LYM). To gain a more comprehensive understanding of the correlation between inflammation-related indices and sex hormones, we derived the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), and systemic immune response index (SIRI). The measurements of PLT, NEU, and LYM were taken at a concentration of 1000 cells/uL. SIRI was computed by (NEU \u0026times; Mo) / LYM. SII was determined by dividing NEU by LYM. MLR was obtained by the ratio of MO to LYM. The detection limits remained consistent for all analytes in the dataset, and none of the results fell below the limits of detection.\u003c/p\u003e \u003cp\u003eThe Collaborative Laboratory Services (Ottumwa, IA) used a Beckman Synchron LX20 analyzer to measure biochemistry profile, including levels of ALT, AST, ALP and GGT. For NHANES cycles prior to 2015, serum CRP levels were determined by latex-enhanced nephelometry on a Behring Nephelometer, with a lower limit of detection (LLOD) of 0.2 mg/L. For NHANES cycles 2015 and 2017, CRP levels were assayed on Beckman Coulter Synchron analyzers, with LLODs of 0.11 mg/L (for 2015) and 0.15 mg/L (for 2017).\u003c/p\u003e \u003cp\u003eSoluble klotho levels (pg/ml) were analyzed with a commercially available ELISA kit produced by IBL International, Japan during the period 2019\u0026ndash;2020. All analyses were performed at the University of Washington research laboratories. A description of the laboratory methodology can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/Nchs/Nhanes/\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/Nchs/Nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eModel categories\u003c/h2\u003e \u003cp\u003eThe association between AD and each disease was assessed through four multivariable logistic regression models: one unadjusted and three adjusted models. Model 1 did not include any covariate adjustments. In Model 2, adjustments were made for age, gender, and race. Model 3 accounted for age, gender, race, education level, and poverty to income ratio. Finally, Model 4 incorporated adjustments for age, gender, race, education level, poverty to income ratio, AD status, body mass index, and bone mineral density.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eWe have performed all statistical analysis with the R software (Version 4.2.2) according to the CDC guidelines (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes/tutorials\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes/tutorials\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Sample weights were taken into account in all of the estimates to produce representative data of the civilian noninstitutionalized US population. Population characteristics of the sample of individuals were summarized and compared using ANOVA with Bonferroni adjusted p-values for multiple comparisons for continuous variables, and Pearson Chi-square test for the categorical variables.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline information of study participants\u003c/h2\u003e \u003cp\u003eA total of 17,924 participants (mean age 53.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35, 46% males) were included in this study. A total of 47.88\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 patients with AD (51.93% female, 48.07% male) were compared to 46.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 individuals without AD (51.82% female, 48.18% male). The weighted distribution of the characteristics according to AD was shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A two-sided \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered a significant association between AD and ADADs. The average age and body mass index in these two groups did not have a significant difference. Males and Non-Hispanic Whites were more likely to have a higher rate of AD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eLaboratory examination of AD patients\u003c/h2\u003e \u003cp\u003eWe conducted a study examining the disparities in clinical indexes between AD and non-AD in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. 15 clinical parameters were analyzed, the majorities of clinical-related indexes were no significantly different compared to the non-AD population, only white blood cell count (WBC), lymphocyte count (LYM) and SII are slightly elevated in the AD population. Interestingly, there were notable increases in metabolic indicators, especially Gamma-Glutamyl Transferase (GGT) (AD: 41.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43 compared to non-AD: 27.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34, p\u0026thinsp;=\u0026thinsp;0.004) and Fe (AD: 316.53\u0026thinsp;\u0026plusmn;\u0026thinsp;83.82 compared to non-AD: 126.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99, p\u0026thinsp;=\u0026thinsp;0.03) are significantly enhanced in the AD population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariables of clinical markers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWithout AD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eAD group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC\u003c/p\u003e \u003cp\u003eNEU\u003c/p\u003e \u003cp\u003eLYM\u003c/p\u003e \u003cp\u003eMO\u003c/p\u003e \u003cp\u003ePLT\u003c/p\u003e \u003cp\u003eNLR\u003c/p\u003e \u003cp\u003eMLR\u003c/p\u003e \u003cp\u003eSII\u003c/p\u003e \u003cp\u003eSIRI\u003c/p\u003e \u003cp\u003eALP\u003c/p\u003e \u003cp\u003eTBIL\u003c/p\u003e \u003cp\u003eALB\u003c/p\u003e \u003cp\u003eGGT\u003c/p\u003e \u003cp\u003eFe\u003c/p\u003e \u003cp\u003eα-klotho\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003cp\u003e4.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003cp\u003e239.60\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003cp\u003e0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003cp\u003e525.42\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003cp\u003e70.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003cp\u003e0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e27.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003cp\u003e126.10\u0026thinsp;\u0026plusmn;\u0026thinsp;2.99\u003c/p\u003e \u003cp\u003e827.91\u0026thinsp;\u0026plusmn;\u0026thinsp;6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003cp\u003e2.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003cp\u003e0.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e244.28\u0026thinsp;\u0026plusmn;\u0026thinsp;8.02\u003c/p\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003cp\u003e550.85\u0026thinsp;\u0026plusmn;\u0026thinsp;67.11\u003c/p\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003cp\u003e67.19\u0026thinsp;\u0026plusmn;\u0026thinsp;3.26\u003c/p\u003e \u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003cp\u003e41.35\u0026thinsp;\u0026plusmn;\u0026thinsp;4.43\u003c/p\u003e \u003cp\u003e316.53\u0026thinsp;\u0026plusmn;\u0026thinsp;83.82\u003c/p\u003e \u003cp\u003e832.16\u0026thinsp;\u0026plusmn;\u0026thinsp;137.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e0.86\u003c/p\u003e \u003cp\u003e0.56\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003cp\u003e0.55\u003c/p\u003e \u003cp\u003e0.67\u003c/p\u003e \u003cp\u003e0.17\u003c/p\u003e \u003cp\u003e0.70\u003c/p\u003e \u003cp\u003e0.15\u003c/p\u003e \u003cp\u003e0.37\u003c/p\u003e \u003cp\u003e0.65\u003c/p\u003e \u003cp\u003e0.35\u003c/p\u003e \u003cp\u003e0.00\u003c/p\u003e \u003cp\u003e0.03\u003c/p\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eWhite blood cell count (WBC), Neutrophil (NEU), Lymphocyte count (LYM), Monocyte (MO), Platelet (PLT), Neutrophil-lymphocyte-ratio (NLR), Monocyte-lymphocyte-ratio (MLR), Systemic immune-inflammation index (SII), System inflammation response index(SIRI), Alkaline phosphatase (ALP), Total bilirubin (TBIL), Albumin(ALB), γ-Glutamyl Transferase(GGT)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe association analysis of AD with 422 diseases\u003c/h2\u003e \u003cp\u003eWithin this investigation, we delved into the association between AD and various diseases. We examined a comprehensive collection of 422 prevalent illnesses categorized by the ICD-10 classification system and calculated the disparities in disease incidence between AD and non-AD. In the Table\u0026nbsp;3, we identified 33 diseases that exhibited a significant positive correlation with AD using model 1. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e represented the correlation between 33 diseases and AD using the length of rays. In analysis, we observed that AD comorbid allergy diseases, such as urticaria (L50, OR: 49.67, 95% CI: 4.90-503.62), acute atopic conjunctivitis (H10.1, OR: 31.26, 95% CI: 4.44-219.93), rash and other nonspecific skin eruption (R21, OR: 21.47, 95% CI: 5.96\u0026ndash;77.34), allergic rhinitis (J30.9), allergy (T78.4) and asthma(J45). Moreover, comorbid mental disorders (F99, OR:11.94, 95% CI: 1.46\u0026ndash;97.68) were increased in prevalence in AD patients, including schizophrenia (F20) and sleep disorder (G47.9). Interestingly, we found evidence for statistically significant interaction between atopic dermatitis and inflammatory diseases, including non-infective diseases and infective diseases (K52.9). Among non-infective diseases, noninfective gastroenteritis and colitis (K52.9, OR:38.39, 95% CI: 3.08-478.01) has strongest association with AD, secondly are osteoarthritis (M19.9) and chronic bronchitis (J42). Among infective diseases include HIV disease (B20), dermatophytosis (B35.9) and local infection of the skin and subcutaneous tissue (L08.9). In addition, we found AD was associated with malignant diseases, particularly malignant neoplasm of prostate (C61, OR:38.52, 95% CI: 5.32-278.74). Interestingly, some metabolic diseases, such as gout (M10.9, OR:12.11, 95% CI: 2.08\u0026ndash;70.65), overweight and obesity (E66, OR:9, 95% CI: 1.10-73.94), hyperuricemia without signs of inflammatory arthritis and prevent high cholesterol (E79) and renal disease, such as dependence on renal dialysis (Z99.2) and other specified urinary incontinence (N39.4) are significantly associated with atopic dermatitis. What\u0026rsquo;s more, AD is also associated with other diseases, including other chest pain (R07.89), wheezing (R06.2), disorder of ear (H93.9), abnormal sputum (R09.3), nasal congestion (R09.81) and enlarged prostate (N40).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGenetic predisposition, epidermal dysfunction, skin microbiome abnormalities and immune dysregulation jointly contribute to the development of AD. Th2 inflammation mediates the most typical symptom of AD, atopic pruritus, and causing consistent itching to the patients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Epidermis destructing factors such as skin damage, infections and inflammation can activate keratinocytes and exaggerate production of proinflammatory factors, such as thymic stromal lymphopoietin (TSLP), IL-25 and IL-33, adding to recruitment of immune cells especially Th2 cells. Th2 cells, eosinophils, neutrophils and mast cells therefore release pro-inflammatory cytokines and peptides to stimulate pruritoceptive pathways, in which IL-31 expressed by Th2 cells acts as the most significant pruritus mediator. IL-31 can bind to IL-31A receptor and activate transient receptor potential vanilloid 1 (TRPV1) and transient receptor potential ankyrin 1 (TRPA1) to awake sensory neurons and exacerbate pruritus. Excessive production of Th2 lymphocytes lead to elevation in IL-4, IL-5 and IL-13 levels to recruit and activate immune cells and trigger B cells to generate allergen-specific IgE, adding to defections in epidermal barrier and stimulate type 2 inflammatory responses \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eImmunological aberrations are believed to be significant in triggering disruptions of the epidermal barrier, microbial dysbiosis and disorders of immune responses in AD. Our analysis indicated that innate and adaptive immune system were activated in AD patients. We observed infiltration of inflammatory cells and noticed that GGT, an enzyme in antioxidant system (AOS), was significantly increased in AD. Oxidative stress (OS) causes direct damage to the cell membrane and DNA to establish skin barrier defection, as well as activating NF-κB pathway to stimulate IL-6, IL-8, IL-9 and IL-33 releasing \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. These factors jointly contribute to dermal inflammation, elevated histamine releasing and itching, which in turn determine a vicious cycle that exaggerate OS and add to epidermal barrier disruption. Considering the fact GGT level was notably increased in AD patients, it may act as a potential clinical diagnosis and prognostic indicators for OS in AD.\u003c/p\u003e \u003cp\u003eIn our analysis, no differences among the clinical characteristics of age, sex and race were reflected on the morbidity of AD. In association analysis, we found that AD individuals show higher risks among a series of allergy diseases, including urticaria, allergic rhinitis, allergy, asthma and other seasonal allergic rhinitis. These findings show consistence with previous researches that the early onset of AD increases co-occurrence of other allergic diseases and establish the pathogenesis of atopic march\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAD also show correlations with various inflammatory diseases, including noninfective gastroenteritis and colitis, acute atopic conjunctivitis, osteoarthritis, and unspecified chronic bronchitis, in which noninfective gastroenteritis and colitis show the most robust correlation in our analysis. The possible mechanisms of AD increasing comorbidity of these diseases are complex. First, one possible pathway is the inflammatory pathway: The imbalance of Th2 to Th1 cytokines, such as IL-4, IL-5, IL-9 and IL-13, observed in AD can create alterations in the cell mediated immune responses \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. These inflammatory cytokines circulate to the different tissues through related anatomical pathways and triggering inflammation. Of note, inflammatory bowel disease (IBD) is caused by an abnormal adaptive immune response. In particular, ulcerative colitis (UC) has been rather related to a non-conventional Th2 response \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Therefore, Th2 inflammatory cytokines created by AD enters into bowels and contribute to a vicious inflammatory cycle, finally exacerbating colitis. The potential mechanisms of AD increasing atopic conjunctivitis and osteoarthritis maybe consistent with this. Second, some studies proposed the hypothesis of the interplay of intestinal dysbacteria in AD patients. Li et al has reported that differential abundance of genera in the skin, oral and gut \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Accordingly, we speculate that abnormal oral microbiota through oral-gut axis or intestinal dysbacteria probably impact the risk of colitis.\u003c/p\u003e \u003cp\u003eAdditionally, our analysis also emphasized correlation between AD and MDI, including comorbid mental disorders, schizophrenia and sleep disorder. Corresponding to our findings, previous studies showed that elevated prevalence of MDI and relatively severe phenotype were noticed in children suffering AD, including mental health disturbances including anxiety, depression, ADHD, conduct disorder, and autism \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Although the underlying mechanisms between AD and MDI remain unclear, correlation between diseases severity, eczema area and wake experiences after sleep onset were notice in AD children, highlighting lower sleep efficiency and sleep disorder. Sleep disturbance along with consistent itching may add to daily anxiety and depression and drive the association between neuropsychiatric disorders and AD \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Therefore, the concept of ADAD may offer therapeutic guidance to take AD into consideration when faced with mental disorders of unknown origins, especially in children with allergic history.\u003c/p\u003e \u003cp\u003ePrevious research reached contrary results about the correlations between AD and multiple cancers due to substantial heterogeneity and severe bias between studies \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, our analysis found correlation between AD and malignant tumor, including malignant neoplasm of prostate, malignant (primary) neoplasm and malignant neoplasm of breast. Wherein the effect of AD in the pathogenesis of malignant tumor are likely multifactorial. Other than genetic predispositions and systemic inflammation, another pathway could be psychosocial disease \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Besides, our result also found correlations between AD and other diseases, including dermatophytosis, dependence on renal dialysis, other chest pain, wheezing, rash and other nonspecific skin eruption, pruritus, gout, nasal congestion, hyperuricemia without signs of inflammatory arthritis and tophaceous disease, unspecified disorder of ear, abnormal sputum, prevent high cholesterol, overweight and obesity, local infection of the skin and subcutaneous tissue, enlarged prostate, other specified urinary incontinence, and other acute postprocedural pain, emphasizing the overall affection of AD. Previous studies have proved that allergic diseases mediated type 2 immunity induced atherosclerosis, which affects arteries of different organs such as the heart and the kidney \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, which the underlying cause of metabolic diseases and renal disease.\u003c/p\u003e \u003cp\u003eOf note, we should diagnose the patient suffered from AD and a certain disease that is demonstrated to be associated with AD as AD associated disease (ADAD). Compared with previous studies, our report first explored the association between AD and related diseases via analyzing the NHANES database and successfully revealed a cluster of 33 diseases correlating to increased risk of AD. However, limitations still exist as our research examined no obvious correlation among the prevalence of cardiovascular disease, autoimmune disease and AD due to insufficiency in sample size. Besides, as a cross-sectional studies, our findings may have uncovered correlations but could not disentangle noncausal or causal associations, thus there might still be bidirectional correlations between AD and other diseases. Recommendations should be made with caution when guiding clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur data revealed a cluster of 33 diseases correlating to increased risk of AD and proposed the concept of AD-associated disease (ADAD). Given the influence of AD, the concept of ADADs may add to early prediction, diagnosis and treatment of AD among ADADs, providing clinical guidance in comorbidities analysis and advanced treatment. Further replication in larger samples is needed to validate our findings, and experimental studies are needed to explore the underlying mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAD: Atopic dermatitis; ADAD: AD-associated disease; NHANES: National Health and Nutrition Examination Survey; BMI: body mass index; ICD-10: International Classification of Diseases-10; MDI: mental disorders with impairment; HIV: human immunodeficiency virus; OR: odds ratio; AA: allergic asthma; AR: allergic rhinitis; FA: food allergy; CVD: cardiovascular diseases; RA: rheumatoid arthritis; SLE: systemic lupus erythematosus; LPM: Laboratory Procedures Manual; PLT: platelet count; NEU: neutrophil count; MO: monocyte count; LYM: lymphocyte count; NLR: neutrophil-to-lymphocyte ratio; MLR: monocyte-to-lymphocyte ratio; SII: systemic immune-inflammation index; SIRI: systemic immune response index; LLOD: lower limit of detection; WBC: white blood cell count; LYM: lymphocyte count; GGT: Gamma-Glutamyl Transferase; TSLP: thymic stromal lymphopoietin; TRPV1: transient receptor potential vanilloid 1; TRPA1: transient receptor potential ankyrin 1; IBD: inflammatory bowel disease; UC: ulcerative colitis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank Zhang Jing (Shanghai Tongren Hospital) for his work on the NHANES database, which makes it easier for us to explore. Thanks to those who contributed to NHANES data, including all anonymous participants in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.C. analyzed the data; Y. L. and ZY. S wrote the manuscript; All authors help to revise this manuscript and approved it to publish.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Significant Science and Technology Project of Beijing Life Science Academy [grant number 2024500CB0030, 2023000CA0040]; the National Natural Science Foundation of China [grant number 81603119]; the Natural Science Foundation of Beijing Municipality [grant number 7174316]; the Peking University Medicine Seed Fund for Interdisciplinary Research supported by \u0026ldquo;the Fundamental Research Funds for the Central Universities\u0026rdquo; [grant number No. BMU2022MX017, No. BMU2022MX003].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey data are publicly available on the NHANES website for all researchers worldwide (www.cdc.gov/nchs/nhanes/).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ethics review board of the National Center for Health Statistics approved all NHANES protocols and written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBoothe, D., Tarbox, W. 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C. et al. \u003cem\u003eThe uni-directional association of atopic dermatitis and rheumatoid arthritis: a systematic review and meta-analysis\u003c/em\u003e (Arch Dermatol Res, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePonvilawan, B. et al. Association of atopic dermatitis with an increased risk of systemic lupus erythematosus: A systematic review and meta-analysis. \u003cem\u003eJ. Postgrad. Med.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e (3), 139\u0026ndash;145 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunner, P. M. et al. The atopic dermatitis blood signature is characterized by increases in inflammatory and cardiovascular risk proteins. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1), 8707 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, J. et al. Investigating the association of atopic dermatitis with ischemic stroke and coronary heart disease: A mendelian randomization study. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 956850 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYousaf, M. et al. Association between atopic dermatitis and hypertension: a systematic review and meta-analysis. \u003cem\u003eBr. J. Dermatol.\u003c/em\u003e \u003cb\u003e186\u003c/b\u003e (2), 227\u0026ndash;235 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrucker, A. M. et al. Atopic dermatitis is not independently associated with nonfatal myocardial infarction or stroke among US women. \u003cem\u003eAllergy\u003c/em\u003e. \u003cb\u003e71\u003c/b\u003e (10), 1496\u0026ndash;1500 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKage, P., Simon, J. C. \u0026amp; Treudler, R. Atopic dermatitis and psychosocial comorbidities. \u003cem\u003eJ. Dtsch. Dermatol. Ges\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e (2), 93\u0026ndash;102 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSommers, T. et al. Prevalence of Chronic Constipation and Chronic Diarrhea in Diabetic Individuals in the United States. \u003cem\u003eAm. J. Gastroenterol.\u003c/em\u003e \u003cb\u003e114\u003c/b\u003e (1), 135\u0026ndash;142 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. Update on the Pathogenesis and Therapy of Atopic Dermatitis. \u003cem\u003eClin. Rev. Allergy Immunol.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e (3), 324\u0026ndash;338 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSroka-Tomaszewska, J. \u0026amp; Trzeciak, M. \u003cem\u003eMolecular Mechanisms of Atopic Dermatitis Pathogenesis\u003c/em\u003e. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e, \u003cb\u003e22\u003c/b\u003e(8). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertino, L. et al. \u003cem\u003eOxidative Stress and Atopic Dermatitis\u003c/em\u003e. \u003cem\u003eAntioxid. (Basel)\u003c/em\u003e, \u003cb\u003e9\u003c/b\u003e(3). (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorgia, F. et al. \u003cem\u003eOxidative Stress and Phototherapy in Atopic Dermatitis: Mechanisms, Role, and Future Perspectives\u003c/em\u003e. \u003cem\u003eBiomolecules\u003c/em\u003e, \u003cb\u003e12\u003c/b\u003e(12). (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrunner, P. M. et al. Increasing Comorbidities Suggest that Atopic Dermatitis Is a Systemic Disorder. \u003cem\u003eJ. Invest. Dermatology\u003c/em\u003e. \u003cb\u003e137\u003c/b\u003e (1), 18\u0026ndash;25 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaller, A. S. et al. The atopic march and atopic multimorbidity: Many trajectories, many pathways. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e143\u003c/b\u003e (1), 46\u0026ndash;55 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDubin, C., Duca, E. D. \u0026amp; Guttman-Yassky, E. The IL-4, IL-13 and IL-31 pathways in atopic dermatitis. \u003cem\u003eExpert Rev. Clin. Immunol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (8), 835\u0026ndash;852 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFuss, I. J. et al. Disparate CD4\u0026thinsp;+\u0026thinsp;lamina propria (LP) lymphokine secretion profiles in inflammatory bowel disease. Crohn's disease LP cells manifest increased secretion of IFN-gamma, whereas ulcerative colitis LP cells manifest increased secretion of IL-5. \u003cem\u003eJ. Immunol.\u003c/em\u003e \u003cb\u003e157\u003c/b\u003e (3), 1261\u0026ndash;1270 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, W. et al. Inverse Association Between the Skin and Oral Microbiota in Atopic Dermatitis. \u003cem\u003eJ. Invest. Dermatol.\u003c/em\u003e \u003cb\u003e139\u003c/b\u003e (8), 1779\u0026ndash;1787e12 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWan, J. et al. Mental health impairment among children with atopic dermatitis: A United States population-based cross-sectional study of the 2013\u0026ndash;2017 National Health Interview Survey. \u003cem\u003eJ. Am. Acad. Dermatol.\u003c/em\u003e \u003cb\u003e82\u003c/b\u003e (6), 1368\u0026ndash;1375 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFishbein, A. B. et al. Sleep disturbance in children with moderate/severe atopic dermatitis: A case-control study. \u003cem\u003eJ. Am. Acad. Dermatol.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e (2), 336\u0026ndash;341 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, L. et al. Noncutaneous and Cutaneous Cancer Risk in Patients With Atopic Dermatitis: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Dermatol.\u003c/em\u003e \u003cb\u003e156\u003c/b\u003e (2), 158\u0026ndash;171 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHalling-Overgaard, A. S. et al. Atopic dermatitis and cancer in solid organs: a systematic review and meta-analysis. \u003cem\u003eJ. Eur. Acad. Dermatol. Venereol.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e (2), e81\u0026ndash;e82 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandez-Gallego, N. et al. The impact of type 2 immunity and allergic diseases in atherosclerosis. \u003cem\u003eAllergy\u003c/em\u003e. \u003cb\u003e77\u003c/b\u003e (11), 3249\u0026ndash;3266 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchonmann, Y. et al. Inflammatory skin diseases and the risk of chronic kidney disease: population-based case-control and cohort analyses. \u003cem\u003eBr. J. Dermatol.\u003c/em\u003e \u003cb\u003e185\u003c/b\u003e (4), 772\u0026ndash;780 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Atopic dermatitis, AD-associated diseases, NHANES, ICD-10, allergic diseases","lastPublishedDoi":"10.21203/rs.3.rs-5015254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5015254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAtopic dermatitis (AD) is the most common chronic inflammatory skin disease. Massive cohort studies revealed that AD was associated with allergic diseases, inflammatory diseases, autoimmune diseases, cardiovascular diseases, and mental disorders.\u003c/p\u003e\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe comprehensively and systematically analyzed the correlation between AD and diseases to identify AD-associated diseases (ADADs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe involved 17924 individuals from the National Health and Nutrition Examination Survey (NHANES) (2013\u0026ndash;2018) dataset, and analyzed the correlation between AD and 422 diseases classified by International Classification of Diseases-10 (ICD-10) using four logistic regression models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe found that AD is significantly associated with 33 diseases: (1) allergic diseases, including urticaria, allergic rhinitis, allergy, asthma, other seasonal allergic rhinitis; (2) inflammatory diseases, including noninfective gastroenteritis and colitis, acute atopic conjunctivitis, osteoarthritis, and unspecified chronic bronchitis; (3) mental disorders with impairment (MDI), including comorbid mental disorders, schizophrenia and sleep disorder; (4) malignant tumors, including malignant neoplasm of prostate, malignant (primary) neoplasm and malignant neoplasm of breast; (5) other symptoms and diseases, other symptoms and diseases, such as wheezing, pruritus and gout. Notably, non-infective gastroenteritis and colitis showed the strongest correlation (OR: 38.39, 95% CI: 3.08-478.01) among the 33 ADADs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eWe identified 33 ADADs based on the NHANES (2013\u0026ndash;2018) dataset, which provide new insights into understanding the development of these ADADs associated with AD.\u003c/p\u003e","manuscriptTitle":"Identification of atopic dermatitis-associated diseases based on the National Health and Nutrition Examination Survey (NHANES) 2013-2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-04 11:08:54","doi":"10.21203/rs.3.rs-5015254/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-20T04:53:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-19T16:49:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228340176428376933427921510675153764905","date":"2025-02-10T16:11:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-04T09:34:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16873826382662586735844773253981655639","date":"2024-10-30T14:47:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"217721029034395943834861304658463342901","date":"2024-10-30T13:59:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-08T14:36:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-08T14:35:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-05T13:38:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-03T10:42:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-09-02T04:02:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85a8c9d9-1ad4-4813-88c3-3366a22723df","owner":[],"postedDate":"October 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38494424,"name":"Biological sciences/Immunology/Adaptive immunity"},{"id":38494426,"name":"Health sciences/Diseases/Skin diseases"}],"tags":[],"updatedAt":"2025-04-28T16:06:27+00:00","versionOfRecord":{"articleIdentity":"rs-5015254","link":"https://doi.org/10.1038/s41598-025-98551-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-21 15:58:06","publishedOnDateReadable":"April 21st, 2025"},"versionCreatedAt":"2024-10-04 11:08:54","video":"","vorDoi":"10.1038/s41598-025-98551-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98551-6","workflowStages":[]},"version":"v1","identity":"rs-5015254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5015254","identity":"rs-5015254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}