{"id":4720,"date":"2023-03-28T01:51:17","date_gmt":"2023-03-27T23:51:17","guid":{"rendered":"https:\/\/www.jphres.org\/?p=4720"},"modified":"2023-04-02T14:11:23","modified_gmt":"2023-04-02T12:11:23","slug":"artificial-neural-network-to-predict-the-effect-of-obesity-on-the-risk-of-tuberculosis-infection","status":"publish","type":"page","link":"https:\/\/www.jphres.us.com\/index.php\/jphres\/article\/view\/1985\/","title":{"rendered":"Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection"},"content":{"rendered":"<div class=\"item doi\"><span class=\"value\"><a href=\"https:\/\/doi.org\/10.4081\/jphr.2021.1985\">https:\/\/doi.org\/10.4081\/jphr.2021.1985<\/a><\/span><\/div>\n<ul class=\"item authors\">\n<li><span class=\"name\"><strong>Alaa Badawi<\/strong><br \/>\n<\/span><span class=\"affiliation\">Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada.<\/span><span class=\"orcid\"><br \/>\n<a href=\"https:\/\/orcid.org\/0000-0002-9115-0025\" target=\"_blank\" rel=\"noopener\">https:\/\/orcid.org\/0000-0002-9115-0025<\/a><\/span><\/li>\n<li><span class=\"name\"><strong>Christina J. Liu<\/strong><br \/>\n<\/span><span class=\"affiliation\">Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Canada.<\/span><span class=\"orcid\"><br \/>\n<a href=\"https:\/\/orcid.org\/0000-0001-9375-1361\" target=\"_blank\" rel=\"noopener\">https:\/\/orcid.org\/0000-0001-9375-1361<\/a><\/span><\/li>\n<li><span class=\"name\"><strong>Anas A. Rehim<\/strong><br \/>\n<\/span><span class=\"affiliation\">Ontario Tech University, Oshawa, Canada.<\/span><\/li>\n<li><span class=\"name\"><strong>Alind Gupta<\/strong><br \/>\n<\/span><span class=\"affiliation\">Cytel Inc., Toronto, Canada.<\/span><\/li>\n<\/ul>\n<div class=\"item abstract\">\n<h3 class=\"label\">ABSTRACT<\/h3>\n<p><em>Background:<\/em>\u00a0Body weight has been implicated as a risk factor for latent tuberculosis infection (LTBI) and the active disease.<br \/>\n<em><br \/>\nDesign and Methods:<\/em>\u00a0This study aimed to develop artificial neural network (ANN) models for predicting LTBI from body weight and other host-related disease risk factors. We used datasets from participants of the US-National Health and Nutrition Examination Survey (NHANES; 2012; n=5,156; 514 with LTBI and 4,642 controls) to develop three ANNs employing body mass index (BMI, Network I), BMI and HbA1C (as a proxy for diabetes; Network II) and BMI, HbA1C and education (as a proxy for socioeconomic status; Network III). The models were trained on n=1018 age- and sex-matched subjects equally distributed between the control and LTBI groups. The endpoint was the prediction of LTBI.<br \/>\n<em><br \/>\nResults<\/em><strong><em>:<\/em><\/strong>\u00a0When data was adjusted for age, sex, diabetes and level of education, odds ratio (OR) and 95% confidence intervals (CI) for risk of LTBI with increased BMI was 0.85 (95%CI: 0.77 \u2013 0.96, p=0.01). The three ANNs had a predictive accuracy varied from 75 to 80% with sensitivities ranged from 85% to 94% and specificities of approximately 70%. Areas under the receiver operating characteristic curve (AUC) were between 0.82 and 0.87. Optimal ANN performance was noted using BMI as a risk indicator.<br \/>\n<em><br \/>\nConclusion:\u00a0<\/em>Body weight can be employed in developing artificial intelligence-based tool to predict LTBI. This can be useful in precise decision making in clinical and public health practices aiming to curb the burden of tuberculosis,<em>\u00a0e.g<\/em>., in the management and monitoring of the tuberculosis prevention programs and to evaluate the impact of healthy weight on tuberculosis risk and burden.<\/p>\n<h3 class=\"label\">REFERENCES<\/h3>\n<div class=\"value\">\n<p>GBD Tuberculosis Collaborators. Global, regional, and national burden of tuberculosis, 1990-2016: results from the Global Burden of Diseases, Injuries, and Risk Factors 2016 Study. Lancet Infect Dis 2018;18:1329-49. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S1473-3099(18)30625-X\">https:\/\/doi.org\/10.1016\/S1473-3099(18)30625-X<\/a><\/p>\n<p>GBD Tuberculosis Collaborators. The global burden of tuberculosis: results from the Global Burden of Disease Study 2015. Lancet Infect Dis 2018;18:261\u201384. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S1473-3099(17)30703-X\">https:\/\/doi.org\/10.1016\/S1473-3099(17)30703-X<\/a><\/p>\n<p>Kahwati LC, Feltner C, Halpern M, et al. Screening for latent tuberculosis infection in adults: An evidence review for the U.S. Preventive Services Task Force. Agency for Healthcare Research and Quality (US). Evidence Syntheses, No. 142; 2016. Accessed: 18 July 2020. Available from:\u00a0<a href=\"https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK385124\/\">https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK385124\/<\/a><\/p>\n<p>Getahun H, Matteelli A, Chaisson RE, et al. Latent Mycobacterium tuberculosis infection. N Engl J Med 2015; 372:2127\u201335. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1056\/NEJMra1405427\">https:\/\/doi.org\/10.1056\/NEJMra1405427<\/a><\/p>\n<p>Uplekar M, Weil D, L\u00f6nnroth K, et al. WHO\u2019s new End TB Strategy. Lancet 2015;385:1799-801. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S0140-6736(15)60570-0\">https:\/\/doi.org\/10.1016\/S0140-6736(15)60570-0<\/a><\/p>\n<p>Dye C, Glaziou P, Floyd K, et al. Prospects for tuberculosis elimination. Annu Rev Public Health 2013;34:271-86. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1146\/annurev-publhealth-031912-114431\">https:\/\/doi.org\/10.1146\/annurev-publhealth-031912-114431<\/a><\/p>\n<p>Tverdal A. Body mass index and incidence of tuberculosis. Eur J Respir Dis 1986;69:355-62.<\/p>\n<p>Cegielski JP, McMurray DN. The relationship between malnutrition and tuberculosis: Evidence from studies in humans and experimental animals. Int J Tuberc Lung Dis 2014;8:286-98.<\/p>\n<p>L\u00f6nnroth K, Williams BG, Cegielski P, et al. A consistent log-linear relationship be\u00actween tuberculosis incidence and body mass index. Int J Epidemiol 2010;39:149-55. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1093\/ije\/dyp308\">https:\/\/doi.org\/10.1093\/ije\/dyp308<\/a><\/p>\n<p>Badawi A, Gregg B, Vasileva D. Systematic analysis for the relationship between obesity and tuberculosis. Pub Health 2020;186:246-56. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.puhe.2020.06.054\">https:\/\/doi.org\/10.1016\/j.puhe.2020.06.054<\/a><\/p>\n<p>Roth J, Sahota N, Patel P, et al. Obesity paradox, obesity orthodox, and the metabolic syndrome: An approach to unity. Mol Med 2016; 2:873-85. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2119\/molmed.2016.00211\">https:\/\/doi.org\/10.2119\/molmed.2016.00211<\/a><\/p>\n<p>Nuttall FQ. Body mass index: Obesity, BMI, and health: A critical review. Nutr Today 2015;50:117-28. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1097\/NT.0000000000000092\">https:\/\/doi.org\/10.1097\/NT.0000000000000092<\/a><\/p>\n<p>Critchley JA, Restrepo BI, Ronacher K, et al. Defining a research agenda to address the converging epidemics of tuberculosis and diabetes: Part 1: Epidemiology and clinical management. Chest 2017;152:165-73. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.chest.2017.04.155\">https:\/\/doi.org\/10.1016\/j.chest.2017.04.155<\/a><\/p>\n<p>L\u00f6nnroth K, Roglic G, Harries AD. Improving tuberculosis prevention and care through addressing the global diabetes epidemic: From evidence to policy and practice. Lancet Diabetes Endocrinol 2014;2:730-9. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S2213-8587(14)70109-3\">https:\/\/doi.org\/10.1016\/S2213-8587(14)70109-3<\/a><\/p>\n<p>Riza AL, Pearson F, Ugarte-Gil C, et al. Clinical management of concurrent diabetes and tuberculosis and the implications for patient services. Lancet Diabetes Endocrinol 2014;2:740-53. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S2213-8587(14)70110-X\">https:\/\/doi.org\/10.1016\/S2213-8587(14)70110-X<\/a><\/p>\n<p>Badawi A, Sayegh S, Sallam M, et al. The global relationship between the prevalence of diabetes mellitus and incidence of tuberculosis: 2000-2012. Glob J Health Sci 2014;7:183-91. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.5539\/gjhs.v7n2p183\">https:\/\/doi.org\/10.5539\/gjhs.v7n2p183<\/a><\/p>\n<p>Cubilla-Batista I, Ruiz N, Sambrano D, et al. Overweight, obesity, and older age favor latent tuberculosis infection among household contacts in low tuberculosis-incidence settings within Panama. Am J Trop Med Hyg 2019;100:1141-4. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.4269\/ajtmh.18-0927\">https:\/\/doi.org\/10.4269\/ajtmh.18-0927<\/a><\/p>\n<p>Zhang H, Li X, Xin H, et al. Association of body mass index with the tuberculosis infection: A population-based study among 17796 adults in rural China. Sci Rep 2017;7:41933. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/srep41933\">https:\/\/doi.org\/10.1038\/srep41933<\/a><\/p>\n<p>WHO, European Respiratory Society. Digital health for the End TB Strategy: An agenda for action (WHO\/HTM\/TB\/2015.21). 2015. Accessed: 19 June 2020. Available from:\u00a0https:\/\/www.who.int\/tb\/areas-of-work\/digital-health\/Digital_health_EndTBstrategy.pdf<\/p>\n<p>Doshi R, Falzon D, Thomas BV, et al. Tuberculosis control, and the where and why of artificial intelligence. ERJ Open Res 2017;3:56. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1183\/23120541.00056-2017\">https:\/\/doi.org\/10.1183\/23120541.00056-2017<\/a><\/p>\n<p>Luger GF. Artificial intelligence: Structures and strategies for complex problem solving. 5th ed. Essex: Pearson Education Ltd.; 2005.<\/p>\n<p>Cui S, Tseng HH, Pakela J, et al. Introduction to machine and deep learning for medical physicists. Med Phys 2020;47:e127\u201047. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1002\/mp.14140\">https:\/\/doi.org\/10.1002\/mp.14140<\/a><\/p>\n<p>Rajpurkar P, Irvin J, Ball RL, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med 2018;15:e1002686. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1371\/journal.pmed.1002686\">https:\/\/doi.org\/10.1371\/journal.pmed.1002686<\/a><\/p>\n<p>Rajkomar A, Oren E, Chen K, et al. Scalable and accurate deep learning with electronic health records. NPJ Digit Med 2018;1:18. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41746-018-0029-1\">https:\/\/doi.org\/10.1038\/s41746-018-0029-1<\/a><\/p>\n<p>Khan MT, Kaushik AC, Ji L, et al. Artificial neural networks for prediction of tuberculosis disease. Front Microbiol 2019;10:395. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.3389\/fmicb.2019.00395\">https:\/\/doi.org\/10.3389\/fmicb.2019.00395<\/a><\/p>\n<p>Er O, Temurtas F, Tanrikulu AC. Tuberculosis disease diagnosis using artificial neural networks. J Med Syst 2010;34:299\u2010302. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/s10916-008-9241-x\">https:\/\/doi.org\/10.1007\/s10916-008-9241-x<\/a><\/p>\n<p>Lakhani P, Sundaram B. Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284:574-82. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1148\/radiol.2017162326\">https:\/\/doi.org\/10.1148\/radiol.2017162326<\/a><\/p>\n<p>Mohadjer LMJ, Montaquila J, Waksberg J, et al. National Health and Nutrition Examination Survey III: Weighting and examination methodology. Hyattsville, MD; 1996. Available from:\u00a0<a href=\"https:\/\/ceb.nlm.nih.gov\/proj\/dxpnet\/nhanes\/docs\/doc\/nhanes_analysis\/wgt_exec.pdf\">https:\/\/ceb.nlm.nih.gov\/proj\/dxpnet\/nhanes\/docs\/doc\/nhanes_analysis\/wgt_exec.pdf<\/a><\/p>\n<p>Barron MM, Shaw KM, Bullard KM, et al. Diabetes is associated with increased prevalence of latent tuberculosis infection: Findings from the National Health and Nutrition Examination Survey, 2011-2012. Diabetes Res Clin Pract 2018;139:366\u201079. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.diabres.2018.03.022\">https:\/\/doi.org\/10.1016\/j.diabres.2018.03.022<\/a><\/p>\n<p>Curtin LR, Mohadjer LK, Dohrmann SM, et al. National Health and Nutrition Examination Survey: sample design, 2007-2010. Vital Health Stat2 2013;160:1\u201023.<\/p>\n<p>Johnson CL, Dohrmann SM, Burt VL, Mohadjer LK. National health and nutrition examination survey: sample design, 2011-2014. Vital Health Stat2 2014;162:1\u201033.<\/p>\n<p>Mazurek GH, Jereb J, Vernon A, et al. Updated guidelines for using Interferon Gamma Release Assays to detect Mycobacterium tuberculosis infection &#8211; United States, 2010. MMWR Recomm Rep 2010;59:1\u201025.<\/p>\n<p>Miramontes R, Hill AN, Yelk Woodruff RS, et al. Tuberculosis infection in the United States: Prevalence estimates from the National Health and Nutrition Examination Survey, 2011-2012. PLoS One 2015;10:e0140881. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0140881\">https:\/\/doi.org\/10.1371\/journal.pone.0140881<\/a><\/p>\n<p>American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diab Care 2010;33:S62-9. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2337\/dc10-S062\">https:\/\/doi.org\/10.2337\/dc10-S062<\/a><\/p>\n<p>Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: An American Heart Association\/National Heart, Lung, and Blood Institute scientific statement. Circulation 2005;112:2735-52. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.105.169404\">https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.105.169404<\/a><\/p>\n<p>Brenner DR, Arora P, Garcia-Bailo B, et al. Plasma vitamin D levels and risk of metabolic syndrome in Canadians. Clin Invest Med 2011;34:E377. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.25011\/cim.v34i6.15899\">https:\/\/doi.org\/10.25011\/cim.v34i6.15899<\/a><\/p>\n<p>Setayeshgar S, Whiting SJ, Vatanparast H. Prevalence of 10-year risk of cardiovascular diseases and associated risks in Canadian adults: The contribution of cardiometabolic risk assessment introduction. Int J Hyper 2013;2013:276564. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1155\/2013\/276564\">https:\/\/doi.org\/10.1155\/2013\/276564<\/a><\/p>\n<p>Matthews DR, Hosker JP, Rudenski AS, et al. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412\u20109. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/BF00280883\">https:\/\/doi.org\/10.1007\/BF00280883<\/a><\/p>\n<p>Badawi A, Sayegh S, Sadoun E, et al. Relationship between insulin resistance and plasma vitamin D in adults. Diabetes Metab Syndr Obes 2014;7:297\u2010303. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2147\/DMSO.S60569\">https:\/\/doi.org\/10.2147\/DMSO.S60569<\/a><\/p>\n<p>CDC. National Health and Nutrition Examination Survey: 2011\u20132012 data documentation, codebook, and frequencies. Atlanta: National Center for Health Statistics; 2013.<\/p>\n<p>Badawi A, Di Giuseppe G, Arora P. Cardiovascular disease risk in patients with hepatitis C infection: Results from two general population health surveys in Canada and the United States (2007-2017). PLoS One 2018;13:e0208839. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0208839\">https:\/\/doi.org\/10.1371\/journal.pone.0208839<\/a><\/p>\n<p>Raschka S and Mirjalili V. Python Machine Learning, 2nd Edition. Birmingham: Packt Publishing Ltd.; 2017.<\/p>\n<p>Chollet F. Xception: Deep learning with depthwise separable convolutions. arXiv 2017. 1610.02357v3. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1109\/CVPR.2017.195\">https:\/\/doi.org\/10.1109\/CVPR.2017.195<\/a><\/p>\n<p>Valueva MV, Nagornov NN, Lyakhov PA, et al. Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Math Comp Simulation 2020;177:232-43. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.matcom.2020.04.031\">https:\/\/doi.org\/10.1016\/j.matcom.2020.04.031<\/a><\/p>\n<p>Dupond S. A thorough review on the current advance of neural network structures. Ann Rev Control 2019;14:200-30.<\/p>\n<p>Han SH, Kim KW, Kim S, et al. Artificial neural network: Understanding the basic concepts without mathematics. Dement Neurocogn Disord 2018;17:83-9. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.12779\/dnd.2018.17.3.83\">https:\/\/doi.org\/10.12779\/dnd.2018.17.3.83<\/a><\/p>\n<p>He K, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. arXiv 2015;1502.01852. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1109\/ICCV.2015.123\">https:\/\/doi.org\/10.1109\/ICCV.2015.123<\/a><\/p>\n<p>Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv 2015;1502.03167.<\/p>\n<p>Santurkar S, Tsipras D, Ilyas A, et al. How does batch normalization help optimization? arXiv 2019;1805.11604v5.<\/p>\n<p>Nwankpa C, Ijomah W, Gachagan A, et al. Activation functions: Comparison of trends in practice and research for deep learning. arXiv 2018;1811.03378.<\/p>\n<p>Goodfellow I, Bengio Y, Courville A. Deep Learning. Chapter 6: Deep feedforward Networks. MIT Press; 2016. pp. 164-223.<\/p>\n<p>Riley RD, Ahmed I, Derbay TPA, et al. Summarising and validating test accuracy results across multiple studies for use in clinical practice. Stat Med 2015;34:2081-103. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1002\/sim.6471\">https:\/\/doi.org\/10.1002\/sim.6471<\/a><\/p>\n<p>Trevethan R. Sensitivity, specificity, and predictive values: Foundations, pliabilities, and pitfalls in research and practice. Front Publ Health 2017;5:307. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.3389\/fpubh.2017.00307\">https:\/\/doi.org\/10.3389\/fpubh.2017.00307<\/a><\/p>\n<p>Denisko D, Hoffman MM. Classification and interaction in random forests. Proc Natl Acad Sci USA 2018; 15:1690-2. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1073\/pnas.1800256115\">https:\/\/doi.org\/10.1073\/pnas.1800256115<\/a><\/p>\n<p>Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995;20:273-97. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1007\/BF00994018\">https:\/\/doi.org\/10.1007\/BF00994018<\/a><\/p>\n<p>Tolles J, Meurer WJ. Logistic regression: Relating patient characteristics to outcomes. JAMA 2016;316:533\u20134. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1001\/jama.2016.7653\">https:\/\/doi.org\/10.1001\/jama.2016.7653<\/a><\/p>\n<p>Yu B, Kumbier K. Artificial intelligence and statistics. Front Inf Technol Electronic Eng 2018;19:6-9. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1631\/FITEE.1700813\">https:\/\/doi.org\/10.1631\/FITEE.1700813<\/a><\/p>\n<p>Cirillo D, Catuara-Solarz S, Morey C, et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit Med 2020;3:81. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41746-020-0288-5\">https:\/\/doi.org\/10.1038\/s41746-020-0288-5<\/a><\/p>\n<p>Zhang X, Jia H, Liu F, et al. Prevalence and risk factors for latent tuberculosis infection among health care workers in China: A cross-sectional study. PLoS One 2013;8:e66412. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1371\/journal.pone.0066412\">https:\/\/doi.org\/10.1371\/journal.pone.0066412<\/a><\/p>\n<p>Sarivalasis A, Zellweger JP, Faouzi M, et al. Factors associated with latent tuberculosis among asylum seekers in Switzerland: a cross-sectional study in Vaud County. BMC Infect Dis 2012;12:285. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1186\/1471-2334-12-285\">https:\/\/doi.org\/10.1186\/1471-2334-12-285<\/a><\/p>\n<p>Lule SA, Mawa PA, Nkurunungi G, et al. Factors associated with tuberculosis infection, and with anti-mycobacterial immune responses, among five-year old BCG-immunised at birth in Entebbe, Uganda. Vaccine 2015;33:796\u2010804. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.vaccine.2014.12.015\">https:\/\/doi.org\/10.1016\/j.vaccine.2014.12.015<\/a><\/p>\n<p>Mart\u00ednez-Aguilar G, Serrano CJ, Casta\u00f1eda-Delgado JE, et al. Associated risk factors for latent tuberculosis infection in subjects with diabetes. Arch Med Res 2015;46:221\u20107. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.arcmed.2015.03.009\">https:\/\/doi.org\/10.1016\/j.arcmed.2015.03.009<\/a><\/p>\n<p>Kizza FN, List J, Nkwata AK, et al. Prevalence of latent tuberculosis infection and associated risk factors in an urban African setting. BMC Infect Dis 2015;15:165. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1186\/s12879-015-0904-1\">https:\/\/doi.org\/10.1186\/s12879-015-0904-1<\/a><\/p>\n<p>Leung CC, Lam TH, Chan WM, et al. Lower risk of tuberculosis in obesity. Arch Intern Med 2007;167:1297\u2010304. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1001\/archinte.167.12.1297\">https:\/\/doi.org\/10.1001\/archinte.167.12.1297<\/a><\/p>\n<p>Prospective Studies Collaboration, Whitlock G, Lewington S, et al. Body-mass index and cause-specific mortality in 900,000 adults: collaborative analyses of 57 prospective studies. Lancet 2009;373:1083\u201096. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S0140-6736(09)60318-4\">https:\/\/doi.org\/10.1016\/S0140-6736(09)60318-4<\/a><\/p>\n<p>Pednekar MS, Hakama M, Hebert JR, et al. Association of body mass index with all-cause and cause-specific mortality: Findings from a prospective cohort study in Mumbai (Bombay), India. Int J Epidemiol 2008;37:524\u201035. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1093\/ije\/dyn001\">https:\/\/doi.org\/10.1093\/ije\/dyn001<\/a><\/p>\n<p>Qin ZZ, Sander MS, Rai B et al. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep 2019;9:15000. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41598-019-51503-3\">https:\/\/doi.org\/10.1038\/s41598-019-51503-3<\/a><\/p>\n<p>Da Costa LA, Arora P, Garc\u00eda-Bailo B, et al. The association between obesity, cardiometabolic disease biomarkers, and innate immunity-related inflammation in Canadian adults. Diabetes Metab Syndr Obes 2012;5:347\u201055. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2147\/DMSO.S35115\">https:\/\/doi.org\/10.2147\/DMSO.S35115<\/a><\/p>\n<p>Park HS, Park JY, Yu R. Relationship of obesity and visceral adiposity with serum concentrations of CRP, TNF-alpha and IL-6. Diabetes Res Clin Pract 2005;69:29\u201035. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/j.diabres.2004.11.007\">https:\/\/doi.org\/10.1016\/j.diabres.2004.11.007<\/a><\/p>\n<p>Karlsson EA, Beck MA. The burden of obesity on infectious disease. Exp Biol Med (Maywood) 2010;235:1412\u201024. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1258\/ebm.2010.010227\">https:\/\/doi.org\/10.1258\/ebm.2010.010227<\/a><\/p>\n<p>Lamas O, Marti A, Mart\u00ednez JA. Obesity and immunocompetence. Eur J Clin Nutr 2002;56:S42\u20105. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/sj.ejcn.1601484\">https:\/\/doi.org\/10.1038\/sj.ejcn.1601484<\/a><\/p>\n<p>Philips L, Visser J, Nel D, et al. The association between tuberculosis and the development of insulin resistance in adults with pulmonary tuberculosis in the Western sub-district of the Cape Metropole region, South Africa: A combined cross-sectional, cohort study. BMC Infect Dis 2017;17:570. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1186\/s12879-017-2657-5\">https:\/\/doi.org\/10.1186\/s12879-017-2657-5<\/a><\/p>\n<p>Thomson S. Achievement at school and socioeconomic background &#8211; an educational perspective. npj Science Learn 2018;3:5. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1038\/s41539-018-0022-0\">https:\/\/doi.org\/10.1038\/s41539-018-0022-0<\/a><\/p>\n<p>Olson NA, Davidow AL, Winston CA, et al. A national study of socioeconomic status and tuberculosis rates by country of birth, United States, 1996-2005. BMC Publ Health 2012;12:365. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1186\/1471-2458-12-365\">https:\/\/doi.org\/10.1186\/1471-2458-12-365<\/a><\/p>\n<p>Cantwell MF, McKenna MT, McCray E, et al. Tuberculosis and race\/ethnicity in the United States: Impact of socioeconomic status. Am J Respir Crit Care Med 1998;157:1016-20. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1164\/ajrccm.157.4.9704036\">https:\/\/doi.org\/10.1164\/ajrccm.157.4.9704036<\/a><\/p>\n<p>Barr RG, Diez-Roux AV, Knirsch CA, et al. Neighborhood poverty and the resurgence of tuberculosis in New York City, 1984\u20131992. Am J Publ Health 2001;91:1487-93. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2105\/AJPH.91.9.1487\">https:\/\/doi.org\/10.2105\/AJPH.91.9.1487<\/a><\/p>\n<p>Oliveira AL. Biotechnology, Big data and artificial intelligence. Biotechnol J 2019;14:1800613. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1002\/biot.201800613\">https:\/\/doi.org\/10.1002\/biot.201800613<\/a><\/p>\n<p>Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff 2018;37:694-701. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1377\/hlthaff.2017.1624\">https:\/\/doi.org\/10.1377\/hlthaff.2017.1624<\/a><\/p>\n<p>Fan W, Bifet A. Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 2014;16:1-5.<\/p>\n<p>Caruana R, Lawrence S, Giles L. Overfitting in neural nets: backpropagation, conjugate gradient, and early stopping. In: Proc 13th Int Conf Neural Info Process Sys 2000; MIT Press, Cambridge, MA, USA. pp. 381-7. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1109\/IJCNN.2000.857823\">https:\/\/doi.org\/10.1109\/IJCNN.2000.857823<\/a><\/p>\n<p>Badawi A, Drebot M, Ogden NH. Convergence of chronic and infectious diseases: a new direction in public health policy. Can J Publ Health 2019;110:523\u20104. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.17269\/s41997-019-00228-x\">https:\/\/doi.org\/10.17269\/s41997-019-00228-x<\/a><\/p>\n<p>Barajas A, Ochoa S, Obiols JE et al. Gender differences in individuals at high-risk of psychosis: A comprehensive literature review. Sci World J 2015;2015:430735. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1155\/2015\/430735\">https:\/\/doi.org\/10.1155\/2015\/430735<\/a><\/p>\n<p>Obermeyer Z, Powers B, Vogeli C, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science 2019;366:447-53. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1126\/science.aax2342\">https:\/\/doi.org\/10.1126\/science.aax2342<\/a><\/p>\n<p>James WP, Ferro-Luzzi A, Waterlow JC. Definition of chronic energy deficiency in adults. Report of a working party of the International Dietary Energy Consultative Group. Eur J Clin Nutr 1988;42:969\u201081.<\/p>\n<p>Moreira-Teixeira L, Mayer-Barber K, Sher A, et al. Type I interferons in tuberculosis: Foe and occasionally friend. J Exp Med 2018;215:1273\u201085. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1084\/jem.20180325\">https:\/\/doi.org\/10.1084\/jem.20180325<\/a><\/p>\n<p>Procaccini C, Lourenco EV, Matarese G, et al. Leptin signaling: A key pathway in immune responses. Curr Signal Transduct Ther 2009;4:22\u201030. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.2174\/157436209787048711\">https:\/\/doi.org\/10.2174\/157436209787048711<\/a><\/p>\n<p>Odone A, Houben RM, White RG, et al. The effect of diabetes and undernutrition trends on reaching 2035 global tuberculosis targets. Lancet Diabet Endocrinol 2014;2:754\u201064. DOI:\u00a0<a href=\"https:\/\/doi.org\/10.1016\/S2213-8587(14)70164-0\">https:\/\/doi.org\/10.1016\/S2213-8587(14)70164-0<\/a><\/p>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>https:\/\/doi.org\/10.4081\/jphr.2021.1985 Alaa Badawi Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada. https:\/\/orcid.org\/0000-0002-9115-0025 Christina J. Liu Department of Pharmacology and Toxicology, Faculty of Medicine, University of Toronto, Canada. https:\/\/orcid.org\/0000-0001-9375-1361 Anas A. Rehim Ontario Tech University, Oshawa, Canada. Alind Gupta Cytel Inc., Toronto, &#8230; <a title=\"Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection\" class=\"read-more\" href=\"https:\/\/www.jphres.us.com\/index.php\/jphres\/article\/view\/1985\/\" aria-label=\"More on Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection\">Read more<\/a><\/p>\n","protected":false},"author":3,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-4720","page","type-page","status-publish"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection - Journal of Public Health Research<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.jphres.us.com\/index.php\/jphres\/article\/view\/1985\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Artificial neural network to predict the effect of obesity on the risk of tuberculosis infection - Journal of Public Health Research\" \/>\n<meta property=\"og:description\" content=\"https:\/\/doi.org\/10.4081\/jphr.2021.1985 Alaa Badawi Public Health Risk Sciences Division, Public Health Agency of Canada, Toronto; Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Canada. https:\/\/orcid.org\/0000-0002-9115-0025 Christina J. 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