Inferring skin-brain-skin connections from infodemiology data using dynamic Bayesian networks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
04 May 2024
04 May 2024
Historique:
received:
20
11
2023
accepted:
29
04
2024
medline:
5
5
2024
pubmed:
5
5
2024
entrez:
4
5
2024
Statut:
epublish
Résumé
The relationship between skin diseases and mental illnesses has been extensively studied using cross-sectional epidemiological data. Typically, such data can only measure association (rather than causation) and include only a subset of the diseases we may be interested in. In this paper, we complement the evidence from such analyses by learning an overarching causal network model over twelve health conditions from the Google Search Trends Symptoms public data set. We learned the causal network model using a dynamic Bayesian network, which can represent both cyclic and acyclic causal relationships, is easy to interpret and accounts for the spatio-temporal trends in the data in a probabilistically rigorous way. The causal network confirms a large number of cyclic relationships between the selected health conditions and the interplay between skin and mental diseases. For acne, we observe a cyclic relationship with anxiety and attention deficit hyperactivity disorder (ADHD) and an indirect relationship with depression through sleep disorders. For dermatitis, we observe directed links to anxiety, depression and sleep disorders and a cyclic relationship with ADHD. We also observe a link between dermatitis and ADHD and a cyclic relationship between acne and ADHD. Furthermore, the network includes several direct connections between sleep disorders and other health conditions, highlighting the impact of the former on the overall health and well-being of the patient. The average
Identifiants
pubmed: 38704447
doi: 10.1038/s41598-024-60937-3
pii: 10.1038/s41598-024-60937-3
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10266Informations de copyright
© 2024. The Author(s).
Références
Lee, S. H., Jeong, S. K. & Ah, S. K. An update of the defensive Barrier function of skin. Yonsei Med. J. 47, 293–306 (2006).
pubmed: 16807977
pmcid: 2688147
doi: 10.3349/ymj.2006.47.3.293
Zhou, L. et al. The influence of benzoyl peroxide on skin microbiota and the epidermal barrier for acne vulgaris. Dermatol. Ther. 35, e15288 (2022).
pubmed: 34962033
doi: 10.1111/dth.15288
Rocha, M. A. & Bagatin, E. Skin barrier and microbiome in acne. Arch. Dermatol. Res. 310, 181–185 (2018).
pubmed: 29147769
doi: 10.1007/s00403-017-1795-3
Evans, A. The skin and the stress connection. Dermatol. World (2020).
Roberts, W. Air pollution and skin disorders. Int. J. Women’s Dermatol. 7, 91–97 (2021).
doi: 10.1016/j.ijwd.2020.11.001
Marshall, M. The hidden links between mental disorders. Nature 581, 19–21 (2020).
pubmed: 32372044
doi: 10.1038/d41586-020-00922-8
Uhlenhake, E., Yentzer, B. A. & Feldman, S. R. Acne vulgaris and depression: A retrospective examination. J. Cosmet. Dermatol. 9, 59–63 (2010).
pubmed: 20367674
doi: 10.1111/j.1473-2165.2010.00478.x
Purvis, D., Robinson, E. & Merry, S. Acne, anxiety, depression and suicide in teenagers: A cross-sectional survey of New Zealand secondary school students. J. Pediatr. Child Health 42, 793–796 (2006).
doi: 10.1111/j.1440-1754.2006.00979.x
Samuels, D. V., Rosenthal, R., Lin, R., Chaudhari, S. & Natsuaki, M. N. Acne vulgaris and risk of depression and anxiety: A meta-analytic review. J. Am. Acad. Dermatol. 83, 532–541 (2020).
pubmed: 32088269
doi: 10.1016/j.jaad.2020.02.040
Møller Rønnstad, A. T. et al. Association of atopic dermatitis with depression, anxiety, and suicidal ideation in children and adults: A systematic review and meta-analysis. J. Am. Acad. Dermatol. 79, 448–456 (2018).
Patel, K. R., Immaneni, S., Singam, V., Rastogi, S. & Silverberg, J. I. Association between atopical dermatitis, depression and suicidal ideation: A systematic review and meta-analysis. J. Am. Acad. Dermatol. 80, 402–410 (2019).
pubmed: 30365995
doi: 10.1016/j.jaad.2018.08.063
Yaghmaie, P., Koudelka, C. W. & Simpson, E. L. Mental health comorbidity in patients with atopic dermatitis. J. Allergy Clin. Immunol. 131, 428–433 (2013).
pubmed: 23245818
doi: 10.1016/j.jaci.2012.10.041
Chen, M. et al. Is atopy in early childhood a risk factor for Adhd and Asd? A longitudinal study. J. Psychosomat. Res. 77, 316–321 (2014).
doi: 10.1016/j.jpsychores.2014.06.006
Barankin, B. & DeKoven, J. Psychosocial effect of common skin diseases. Can. Fam. Phys. 48, 712–716 (2002).
Hong, J., Koo, B. & Koo, J. The psychosocial and occupational impact of chronic skin disease. Dermatol. Ther. 21, 54–59 (2008).
pubmed: 18318886
doi: 10.1111/j.1529-8019.2008.00170.x
Yew, Y. W. et al. Psychosocial impact of skin diseases: A population-based study. PLoS One 15, e0244765 (2020).
pubmed: 33382864
pmcid: 7775076
doi: 10.1371/journal.pone.0244765
Lavery, M. J., Stull, C., Kinney, M. O. & Yosipovich, G. Nocturnal pruritus: The battle for a peaceful night’s sleep. Int. J. Mol. Sci. 17, 425 (2016).
pubmed: 27011178
pmcid: 4813276
doi: 10.3390/ijms17030425
Hawro, T. et al. Pruritus and sleep disturbances in patients with psoriasis. Arch. Dermatol. Res. 312, 103–111 (2020).
pubmed: 31616971
doi: 10.1007/s00403-019-01998-7
Chamlin, S. L. et al. The price of pruritus: Sleep disturbance and cosleeping in atopic dermatitis. Arch. Pediatr. Adolesc. Med. 159, 745–750 (2005).
pubmed: 16061782
doi: 10.1001/archpedi.159.8.745
Dahl, R. E., Bernhisel-Broadbent, J., Scanlon-Holdford, S., Sampson, H. A. & Lupo, M. Sleep disturbances in children with atopic dermatitis. Arch. Pediatr. Adolesc. Med. 149, 856–860 (1995).
pubmed: 7633537
doi: 10.1001/archpedi.1995.02170210030005
Mouzas, O., Angelopoulos, N., Papaliagka, M. & Tsogas, P. Increased frequency of self-reported parasomnias in patients suffering from vitiligo. Eur. J. Dermatol. 18, 165–168 (2008).
pubmed: 18424376
Kaaz, K., Szepietowski, J. C. & Matusiak, łL. Influence of itch and pain on sleep quality in patients with hidradenitis suppurativa. Acta Dermato-Venereol. 98, 757–761 (2018).
Gupta, M., Simpson, F. & Gupta, A. K. Psoriasis and sleep disorders: A systematic review. Sleep Med. Rev. 29, 63–75 (2016).
pubmed: 26624228
doi: 10.1016/j.smrv.2015.09.003
Chang, Y. S. & Chiang, B. L. Sleep disorders and atopic dermatitis: A 2-way street?. J. Allergy Clin. Immunol. 142, 1033–1040 (2018).
pubmed: 30144472
doi: 10.1016/j.jaci.2018.08.005
Myers, B. et al. Sleep, immunological memory, and inflammatory skin disease. Dermatology 237, 1035–1038 (2021).
pubmed: 32966973
doi: 10.1159/000510082
Shah, M., Sachdeva, M., Alavi, A., Shi, V. Y. & Hsiao, J. L. Optimizing care for atopic dermatitis patients during the Covid-19 pandemic. J. Am. Acad. Dermatol. 83, E165–E167 (2020).
pubmed: 32405123
pmcid: 7217788
doi: 10.1016/j.jaad.2020.05.027
Snast, I. et al. Psychological stress and psoriasis: A systematic review and meta-analysis. Br. J. Dermatol. 178, 1044–1055 (2018).
pubmed: 29124739
doi: 10.1111/bjd.16116
Arck, P. C., Slominski, A., Theoharides, T. C., Peters, E. M. J. & Paus, R. Neuroimmunology of stress: Skin takes center stage. J. Investig. Dermatol. 126 (2006).
Galli, S. J. & Tsai, M. Mast cells in allergy and infection: Versatile effector and regulatory cells in innate and acquired immunity. Eur. J. Immunol. 40, 1843–1851 (2010).
pubmed: 20583030
pmcid: 3581154
doi: 10.1002/eji.201040559
Choe, S. J. et al. Psychological stress deteriorates skin barrier function by activating 11β-hydroxysteroid dehydrogenase 1 and the HPA axis. Sci. Rep. 8, 6334 (2018).
pubmed: 29679067
pmcid: 5910426
doi: 10.1038/s41598-018-24653-z
Papadopoulos, L., Bor, R. C. L. & Hawk, J. L. Impact of life events on the onset of vitiligo in adults: Preliminary evidence for a psychological dimension in aetiology. Clin. Exp. Dermatol. 23, 243–246 (1998).
pubmed: 10233617
doi: 10.1046/j.1365-2230.1998.00384.x
Schmid-Ott, G. et al. Immunological effects of stress in psoriasis. Br. J. Dermatol. 160, 782–785 (2009).
pubmed: 19210504
doi: 10.1111/j.1365-2133.2008.09013.x
Misery, L. et al. Stress and seborrheic dermatitis. Annales De Dermatologie Et De Venereologie 134, 833–837 (2007).
pubmed: 18033062
doi: 10.1016/S0151-9638(07)92826-4
Rahman, S. M., Abduelmula, A. & Jafferany, M. Psychopathological symptoms in dermatology: A basic approach towards psychocutaneous disorders. Int. J. Dermatol. 62, 346–356 (2023).
pubmed: 35816285
doi: 10.1111/ijd.16344
Google. Covid-19 Open Data. https://github.com/GoogleCloudPlatform/covid-19-open-data .
Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009).
pubmed: 19020500
doi: 10.1038/nature07634
Van Riel, N., Auwerx, K., Debbaut, P., Van Hees, S. & Schoenmakers, B. The effect of Dr google on doctor-patient encounters in primary care: A quantitative, observational, cross-sectional study. BJGP Open 1, bjgpopen17X100833 (2017).
Oberlo. Most Visited Websites. https://www.oberlo.com/statistics/most-visited-websites .
Lampos, V. et al. Tracking Covid-19 using online search. NPJ Digit. Med. 4, 17 (2021).
pubmed: 33558607
pmcid: 7870878
doi: 10.1038/s41746-021-00384-w
Lu, T. & Reis, B. Y. Internet search patterns reveal clinical course of Covid-19 disease progression and pandemic spread across 32 countries. NPJ Digit. Med. 4, 22 (2021).
pubmed: 33574582
pmcid: 7878474
doi: 10.1038/s41746-021-00396-6
Nuti, S. V. et al. The use of google trends in health care research: A systematic review. PLoS One 9, e109583 (2014).
pubmed: 25337815
pmcid: 4215636
doi: 10.1371/journal.pone.0109583
Cervellin, G., Comelli, I. & Lippi, G. Is google trends a reliable tool for digital epidemiology? Insights from different clinical settings. J. Epidemiol. Glob. Health 7, 185–189 (2017).
pubmed: 28756828
pmcid: 7320449
doi: 10.1016/j.jegh.2017.06.001
Vaswani, A. et al. Attention is all you need. Adv. Neural Inf. Process. Syst. 30, 6000–6010 (2017).
Yin, W., Hay, J. & Roth, D. Benchmarking zero-shot text classification: Datasets, evaluation and entailment approach. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 3912–3921 (2019).
Ye, H., Hu, H., Zhan, D. & Sha, F. Few-shot learning via embedding adaptation with set-to-set functions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8088–8817 (2020).
Scutari, M. & Denis, J. Bayesian networks with examples in R, 2nd edn (Chapman & Hall/CRC, 2021).
Bressler, S. L. & Seth, A. K. Wiener–Granger causality: A well established methodology. Neuroimage 58, 323–329 (2011).
pubmed: 20202481
doi: 10.1016/j.neuroimage.2010.02.059
Pearl, J. Causality, 2nd edn (Cambridge University Press, 2009).
Yaneva, M. & Darlenski, R. The link between atopic dermatitis and asthma-immunological imbalance and beyond. Asthma Res. Pract. 7, 6 (2021).
doi: 10.1186/s40733-021-00082-0
Katon, W. L. R., Lozano, P. & McCauley, E. The relationship between asthma and anxiety disorders. Psychosomat. Med. 66, 349–355 (2004).
Gariepy, G., Nikta, D. & Schmitz, N. The association between obesity and anxiety disorders in the population: A systematic review and meta-analysis. Int. J. Obes. 34, 407–419 (2010).
doi: 10.1038/ijo.2009.252
Ali, Z., Ulrik, C. S., Agner, T. & Thomsen, S. F. Is atopic dermatitis associated with obesity? A systematic review of observational studies. J. Eur. Acad. Dermatol. Venereol. 32, 1246–1255 (2018).
pubmed: 29444366
doi: 10.1111/jdv.14879
Jensen, P. & Skov, L. Psoriasis and obesity. Dermatology 232, 633–639 (2016).
pubmed: 28226326
doi: 10.1159/000455840
Velurajah, R., Brunckhorst, O., Waqar, M., McMullen, I. & Ahmed, K. Erectile dysfunction in patients with anxiety disorders: A systematic review. Int. J. Impot. Res. 34, 177–186 (2022).
pubmed: 33603242
doi: 10.1038/s41443-020-00405-4
Cho, J. W. & Duffy, J. F. Sleep, sleep disorders, and sexual dysfunction. World J. Men’s Health 37, 261–275 (2019).
doi: 10.5534/wjmh.180045
Abbas, M., Morland, T. B., Hall, E. S. & El-Manzalawy, Y. Associations between google search trends for symptoms and COVID-19 confirmed and death cases in the United States. Int. J. Environ. Res. Public Health 18, 4560 (2021).
pubmed: 33923094
pmcid: 8123439
doi: 10.3390/ijerph18094560
Rochford, B., Pendse, S., Kumar, N. & De Choudhury, M. Leveraging symptom search data to understand disparities in us mental health care: Demographic analysis of search engine trace data. JMIR Ment. Health 10, e43253 (2023).
pubmed: 36716082
pmcid: 9926343
doi: 10.2196/43253
McDonald, D. J. et al. Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?. Proc. Natl. Acad. Sci. 118, e2111453118 (2021).
pubmed: 34903655
pmcid: 8713796
doi: 10.1073/pnas.2111453118
Morgenstern, H. Ecologic studies in epidemiology: Concepts, principles, and methods. Annu. Rev. Public Health 16, 61–81 (1995).
pubmed: 7639884
doi: 10.1146/annurev.pu.16.050195.000425
Greenland, S. & Robins, J. Invited commentary: Ecologic studies-biases, misconceptions, and counterexamples. Am. J. Epidemiol. 139, 747–760 (1994).
pubmed: 8178788
doi: 10.1093/oxfordjournals.aje.a117069
Tsai, C.-J. et al. Asthma in patients with attention-deficit/hyperactivity disorder: A nationwide population-based study. Ann. Clin. Psychiatry 26, 254–260 (2014).
pubmed: 25401712
Gong, T. et al. Parental Socioeconomic status, childhood asthma and medication use-a population-based study. PLoS One 9, e106579 (2014).
pubmed: 25188036
pmcid: 4154738
doi: 10.1371/journal.pone.0106579
Zhu, Z. et al. Shared genetics of asthma and mental health disorders: A large-scale genome-wide cross-trait analysis. Eur. Respir. J. 54, 1901507 (2019).
pubmed: 31619474
doi: 10.1183/13993003.01507-2019
Fluegge, K. & Fluegge, K. Attention-deficit/hyperactivity disorder and comorbid asthma. Chest J. 153, 1279–1280 (2018).
doi: 10.1016/j.chest.2018.01.052
Russell, A. E., Ford, T. & Russell, G. Socioeconomic associations with ADHD: Findings from a mediation analysis. PLoS One 10, e0128248 (2015).
pubmed: 26030626
pmcid: 4451079
doi: 10.1371/journal.pone.0128248
Busby, J. et al. Impact of socioeconomic status on adult patients with asthma: A population-based cohort study from UK primary care. J. Asthma Allergy 14, 1375–1388 (2021).
pubmed: 34785911
pmcid: 8591110
doi: 10.2147/JAA.S326213
Schrom, K. P. et al. Acne severity and sleep quality in adults. Clocks Sleep 1, 510–516 (2019).
pubmed: 33089183
pmcid: 7445853
doi: 10.3390/clockssleep1040039
Connolly, D., Vu, H. L., Mariwalla, K. & Saedi, N. Acne scarring-pathogenesis, evaluation, and treatment options. J. Clin. Aesthet. Dermatol. 10, 12–23 (2017).
pubmed: 29344322
pmcid: 5749614
Robson, M. C., Steed, D. L. & Franz, M. G. Wound healing: Biologic features and approaches to maximize healing trajectories. Curr. Probl. Surg. 38, 72–140 (2001).
pubmed: 11452260
doi: 10.1067/msg.2001.111167
Farrukh, O. & Goutos, I. Scar Symptoms: Pruritus and pain. In Textbook on Scar Management: State of the Art Management and Emerging Technologies, 87–101 (Springer, 2020).
R Core Team. R: A Language and Environment for Statistical Computing (2022).
Pinheiro, J. C. & Bates, D. M. Mixed-Effects Models in S and S-Plus (Springer, 2000).
Bates, D. M., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
doi: 10.18637/jss.v067.i01
Moritz., S. & Bartz-Beielstein, T. ImputeTS: Time series missing value imputation in R. R J. 9, 207–218 (2017).
Scutari, M. Learning Bayesian networks with the bnlearn R package. J. Stat. Softw. 35, 1–22 (2010).
doi: 10.18637/jss.v035.i03
Bavadekar, S. et al. Google COVID-19 Search Trends Symptoms Dataset: Anonymization Process Description (1.0) (2020). https://arxiv.org/abs/2009.01265 .
Google. Covid-19 search trends symptoms dataset (2021). https://storage.googleapis.com/gcp-public-data-symptom-search/COVID-19%20Search%20Trends%20symptoms%20dataset%20documentation%20.pdf .
Center, P. R. Internet, Broadband Fact Sheet (2024). https://www.pewresearch.org/internet/fact-sheet/internet-broadband/ .
Liew, B. X. W. et al. Probing the mechanisms underpinning recovery in post-surgical patients with cervical radiculopathy using Bayesian networks. Eur. J. Pain 24, 909–920 (2020).
pubmed: 31985097
doi: 10.1002/ejp.1537
Scher, J. U. et al. Decreased bacterial diversity characterizes the altered gut microbiota in patients with psoriatic arthritis, resembling dysbiosis in inflammatory bowel disease. Arthritis Rheumatol. 67, 128–139 (2015).
pubmed: 25319745
pmcid: 4280348
doi: 10.1002/art.38892
McNally, R. J., Mair, P., Mugno, B. L. & Riemann, B. C. Co-morbid obsessive-compulsive disorder and depression: A Bayesian network approach. Psychol. Med. 47, 1204–1214 (2017).
pubmed: 28052778
doi: 10.1017/S0033291716003287
Lütkepohl, H. New Introduction to Multiple Time Series Analysis (Springer, 2005).
Russel, S. J. & Norvig, P. Artificial Intelligence: A Modern Approach, 3rd edn. (Prentice Hall, 2009)
Schwarz, G. Estimating the dimension of a model. Ann. Stat. 6, 461–464 (1978).
doi: 10.1214/aos/1176344136
Scutari, M. & Nagarajan, R. On identifying significant edges in graphical models of molecular networks. Artif. Intell. Med. 57, 207–217 (2013).
pubmed: 23395009
pmcid: 4070079
doi: 10.1016/j.artmed.2012.12.006