Organization, W.H. Media Centre. http://who.int/mediacentre/factsheets/fs312/en/. Accessed 27 Sept 2016

Donath MY, Schumann DM, Faulenbach M, Ellingsgaard H, Perren A, Ehses JA. Islet inflammation in type 2 diabetes. Diabetes Care. 2008;31(Supplement 2):161–4.

Article
CAS
Google Scholar

Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat Rev Immunol. 2011;11(2):98–107.

Article
CAS
PubMed
Google Scholar

Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol. 2011;29(1):415–45.

Article
CAS
PubMed
Google Scholar

Akash MSH, Rehman K, Chen S. Role of inflammatory mechanisms in pathogenesis of type 2 diabetes mellitus. J Cell Biochem. 2013;114(3):525–31.

Article
CAS
PubMed
Google Scholar

Hotamisligil GS. Inflammation and metabolic disorders. Nature. 2006;444(7121):860–7.

Article
CAS
PubMed
Google Scholar

Hotamisligil GS, Erbay E. Nutrient sensing and inflammation in metabolic diseases. Nat Rev Immunol. 2008;8(12):923.

Article
CAS
PubMed
PubMed Central
Google Scholar

Donath MY, Dalmas É, Sauter NS, BÉni-Schnetzler M. Inflammation in obesity and diabetes: islet dysfunction and therapeutic opportunity. Cell Metab. 2013;17(6):860–72.

Article
CAS
PubMed
Google Scholar

Castiglione F, Tieri P, De Graaf A, Franceschi C, Liò P, Van Ommen B, Mazzà C, Tuchel A, Bernaschi M, Samson C, Colombo T, Castellani GC, Capri M, Garagnani P, Salvioli S, Nguyen VA, Bobeldijk-Pastorova I, Krishnan S, Cappozzo A, Sacchetti M, Morettini M, Ernst M. The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc. 2013;2(2):44.

Article
Google Scholar

Sacks J, Welch WJ, Mitchell TJ, Wynn HP. Design and analysis of computer experiments. Stat Sci. 1989;4(4):409–23.

Article
Google Scholar

Currin C, Mitchell T, Morris M, Ylvisaker D. Bayesian prediction of deterministic functions, with applications to the design and analysis of computer experiments. J Am Stat Assoc. 1991;86(416):953–63.

Article
Google Scholar

Meert K, Rijckaert M. Intelligent modelling in the chemical process industry with neural networks: a case study. Comput Chem Eng. 1998;22:587–93.

Article
Google Scholar

Banerjee S, Gelfand AE, Finley AO, Sang H. Gaussian predictive process models for large spatial data sets. J R Stat Soc Ser B (Stat Methodol). 2008;70(4):825–48.

Article
Google Scholar

Reichert P, White G, Bayarri MJ, Pitman EB. Mechanism-based emulation of dynamic simulation models: concept and application in hydrology. Comput Stat Data Anal. 2011;55(4):1638–55.

Article
Google Scholar

Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Comput Chem Eng. 2018;108:250–67.

Article
CAS
Google Scholar

Babic A, Bodemar G, Mathiesen U, Ahlfeldt H, Franzen L, Wigertz O. Machine learning to support diagnostics in the domain of asymptomatic liver disease. Medinfo. MEDINFO. 1995;8:809–13.

PubMed
Google Scholar

Ellis RJ, Wang Z, Genes N, Ma’ayan A. Predicting opioid dependence from electronic health records with machine learning. BioData Min. 2019;12(1):3.

Article
PubMed
PubMed Central
Google Scholar

Engchuan W, Dimopoulos AC, Tyrovolas S, Caballero FF, Sanchez-Niubo A, Arndt H, Ayuso-Mateos JL, Haro JM, Chatterji S, Panagiotakos DB. Sociodemographic indicators of health status using a machine learning approach and data from the English longitudinal study of aging (ELSA). Med Sci Monit Int Med J Exp Clin Res. 2019;25:1994.

Google Scholar

Fernandes R, GL RD. A new approach to predict user mobility using semantic analysis and machine learning. J Med Syst. 2017;41(12):188.

Article
PubMed
Google Scholar

Fritz BA, Chen Y, Murray-Torres TM, Gregory S, Ben Abdallah A, Kronzer A, McKinnon SL, Budelier T, Helsten DL, Wildes TS, Sharma A, Avidan MS. Using machine learning techniques to develop forecasting algorithms for postoperative complications: protocol for a retrospective study. BMJ Open. 2018;8(4):e020124.

Article
PubMed
PubMed Central
Google Scholar

Fuscà E, Bolzon A, Buratin A, Ruffolo M, Berchialla P, Gregori D, Perissinotto E, Baldi I. Measuring caloric intake at the population level (notion): protocol for an experimental study. JMIR Res Protoc. 2019;8(3):12116.

Article
Google Scholar

Kang J, Rancati T, Lee S, Oh JH, Kerns SL, Scott JG, Schwartz R, Kim S, Rosenstein BS. Machine learning and radiogenomics: lessons learned and future directions. Front Oncol. 2018;8:228.

Article
PubMed
PubMed Central
Google Scholar

Lacson RC, Baker B, Suresh H, Andriole K, Szolovits P, Lacson J. Eduardo: use of machine-learning algorithms to determine features of systolic blood pressure variability that predict poor outcomes in hypertensive patients. Clin Kidney J. 2018;12(2):206–12.

Article
PubMed
PubMed Central
Google Scholar

Belizario GO, Junior RGB, Salvini R, Lafer B, da Silva Dias R. Predominant polarity classification and associated clinical variables in bipolar disorder: a machine learning approach. J Affect Disord. 2019;245:279–82.

Article
PubMed
Google Scholar

Kurasawa H, Hayashi K, Fujino A, Takasugi K, Haga T, Waki K, Noguchi T, Ohe K. Machine-learning-based prediction of a missed scheduled clinical appointment by patients with diabetes. J Diabetes Sci Technol. 2016;10(3):730–6.

Article
PubMed
Google Scholar

Casanova R, Saldana S, Simpson SL, Lacy ME, Subauste AR, Blackshear C, Wagenknecht L, Bertoni AG. Prediction of incident diabetes in the jackson heart study using high-dimensional machine learning. PLoS ONE. 2016;11(10):e0163942.

Article
PubMed
PubMed Central
CAS
Google Scholar

Alghamdi M, Al-Mallah M, Keteyian S, Brawner C, Ehrman J, Sakr S. Predicting diabetes mellitus using smote and ensemble machine learning approach: the henry ford exercise testing (fit) project. PLoS ONE. 2017;12(7):e0179805.

Article
PubMed
PubMed Central
CAS
Google Scholar

Choi BG, Rha S-W, Kim SW, Kang JH, Park JY, Noh Y-K. Machine learning for the prediction of new-onset diabetes mellitus during 5-year follow-up in non-diabetic patients with cardiovascular risks. Yonsei Med J. 2019;60(2):191–9.

Article
CAS
PubMed
PubMed Central
Google Scholar

Cinar A. Multivariable adaptive artificial pancreas system in type 1 diabetes. Curr Diabetes Rep. 2017;17(10):88.

Article
CAS
Google Scholar

Basu S, Raghavan S, Wexler DJ, Berkowitz SA. Characteristics associated with decreased or increased mortality risk from glycemic therapy among patients with type 2 diabetes and high cardiovascular risk: Machine learning analysis of the accord trial. Diabetes Care. 2018;41(3):604–12.

Article
CAS
PubMed
Google Scholar

Farran B, AlWotayan R, Alkandari H, Al-Abdulrazzaq D, Channanath A, Thanaraj TA. Use of non-invasive parameters and machine-learning algorithms for predicting future risk of type 2 diabetes: a retrospective cohort study of health data from kuwait. Front Endocrinol. 2019;10:624.

Article
Google Scholar

Klonoff DC, Gutierrez A, Fleming A, Kerr D. Real-world evidence should be used in regulatory decisions about new pharmaceutical and medical device products for diabetes. Los Angeles: SAGE Publications; 2019.

Book
Google Scholar

Alimadadi A, Aryal S, Manandhar I, Munroe PB, Joe B, Cheng X. Artificial intelligence and machine learning to fight covid-19. Physiol Genom. 2020;52(4):200–2.

Article
CAS
Google Scholar

Tárnok A. Machine learning, covid-19 (2019-ncov), and multi-omics. Cytometry Part A. 2020;97(3):215–6.

Article
CAS
Google Scholar

Castiglione F, Diaz V, Gaggioli A, Liò P, Mazzà C, Merelli E, Meskers CGM, Pappalardo F, von Ammon R. Physio-environmental sensing and live modeling. Interact J Med Res. 2013;2(1):3.

Article
Google Scholar

Yoram V, Csete M, Bartels J, Chang S, An G. Translational systems biology of inflammation. PLoS Comput Biol. 2008;4(4):1–6.

Google Scholar

Palumbo MC, Morettini M, Tieri P, Diele F, Sacchetti M, Castiglione F. Personalizing physical exercise in a computational model of fuel homeostasis. PLoS Comput Biol. 2018;14(4):e1006073.

Article
PubMed
PubMed Central
CAS
Google Scholar

Palumbo M, Morettini M, Tieri P, de Graaf A, Krishnan S, Castiglione F. Modeling meal consumption and physical exercise for fuel homeostasis (2020) (in preparation)

Kim J, Saidel GM, Cabrera ME. Multi-scale computational model of fuel homeostasis during exercise: effect of hormonal control. Ann Biomed Eng. 2007;35(1):69–90.

Article
PubMed
Google Scholar

Saunders PT, Koeslag JH, Wessels JA. Integral rein control in physiology. J Theore Biol. 1998;194(2):163–73.

Article
CAS
Google Scholar

Roy A, Parker RS. Dynamic modeling of exercise effects on plasma glucose and insulin levels. IFAC Proc Vol. 2006;39(2):509–14.

Article
Google Scholar

Kildegaard J, Christensen TF, Johansen MD, Randløv J, Hejlesen OK. Modeling the effect of blood glucose and physical exercise on plasma adrenaline in people with type 1 diabetes. Diabetes Technol Therapeut. 2007;9(6):501–8.

Article
CAS
Google Scholar

Dalla Man C, Camilleri M, Cobelli C. A system model of oral glucose absorption: validation on gold standard data. IEEE Trans Biomed Eng. 2006;53(12):2472–8.

Article
PubMed
Google Scholar

Elashoff JD, Reedy TJ, Meyer JH. Analysis of gastric emptying data. Gastroenterology. 1982;83(6):1306–12.

Article
CAS
PubMed
Google Scholar

Palumbo M, Morettini M, Tieri P, de Graaf A, Liò P, Diele F, Castiglione F. An integrated multi-scale model for the simulation and prediction of metabolic and inflammatory processes in the onset and progress of type 2 diabetes (in preparation) (2020)

Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51(2):241–7.

Article
CAS
PubMed
Google Scholar

Westerterp KR, Donkers JHHLM, Fredrix EWHM, Oekhoudt P. Energy intake, physical activity and body weight: a simulation model. Br J Nutr. 1995;73(3):337–47.

Article
CAS
PubMed
Google Scholar

Prana V, Tieri P, Palumbo MC, Mancini E, Castiglione F. Modeling the effect of high calorie diet on the interplay between adipose tissue, inflammation, and diabetes. Comput Math Methods Med 2019;2019

Morettini M, Palumbo MC, Sacchetti M, Castiglione F, Mazza C. A system model of the effects of exercise on plasma interleukin-6 dynamics in healthy individuals: role of skeletal muscle and adipose tissue. PLoS ONE. 2017;12(7):e0181224.

Article
PubMed
PubMed Central
CAS
Google Scholar

Bernaschi M, Castiglione F. Design and implementation of an immune system simulator. Comput Biol Med. 2001;31(5):303–31.

Article
CAS
PubMed
Google Scholar

Castiglione F, Duca K, Jarrah A, Laubenbacher R, Hochberg D, Thorley-Lawson D. Simulating Epstein-Barr virus infection with C-ImmSim. Bioinformatics. 2007;23(11):1371–7.

Article
CAS
PubMed
Google Scholar

Pappalardo F, Lollini P-L, Castiglione F, Motta S. Modeling and simulation of cancer immunoprevention vaccine. Bioinformatics. 2005;21(12):2891–7.

Article
CAS
PubMed
Google Scholar

Mancini E, Quax R, De Luca A, Fidler S, Stohr W, Sloot PM. A study on the dynamics of temporary hiv treatment to assess the controversial outcomes of clinical trials: an in-silico approach. PLoS ONE. 2018;13(7):e0200892.

Article
PubMed
PubMed Central
Google Scholar

Baldazzi V, Paci P, Bernaschi M, Castiglione F. Modeling lymphocyte homing and encounters in lymph nodes. BMC Bioinform. 2009;10(1):387.

Article
Google Scholar

Castiglione F, Tieri P, Palma A, Jarrah AS. Statistical ensemble of gene regulatory networks of macrophage differentiation. BMC Bioinform. 2016;17(19):506.

Article
CAS
Google Scholar

Madonia A, Melchiorri C, Bonamano S, Marcelli M, Bulfon C, Castiglione F, Galeotti M, Volpatti D, Mosca F, Tiscar P-G, Romano N. Computational modeling of immune system of the fish for a more effective vaccination in aquaculture. Bioinformatics. 2017;33(19):3065–71.

Article
CAS
PubMed
Google Scholar

Melanson EL, Keadle SK, Donnelly JE, Braun B, King NA. Resistance to exercise-induced weight loss: compensatory behavioral adaptations. Med Sci Sports Exerc. 2013;45(8):1600.

Article
PubMed
PubMed Central
Google Scholar

Westerterp KR. Diet induced thermogenesis. Nutr Metab. 2004;1(1):5.

Article
CAS
Google Scholar

Atwater WO, Bryant AP. The chemical composition of American food materials, vol. 28. Washington: US Government Printing Office; 1906.

Google Scholar

Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.

Article
Google Scholar

Friedman J, Hastie T, Tibshirani R. The elements of statistical learning, vol. 1. New York: Springer; 2001.

Google Scholar

Ishwaran H. Variable importance in binary regression trees and forests. Electron J Stat. 2007;1:519–37.

Article
Google Scholar

Franceschi C, Garagnani P, Parini P, Giuliani C, Santoro A. Inflammaging: a new immune-metabolic viewpoint for age-related diseases. Nat Rev Endocrinol. 2018;14(10):576–90.

Article
CAS
PubMed
Google Scholar

Ishwaran H, Kogalur UB, Gorodeski EZ, Minn AJ, Lauer MS. High-dimensional variable selection for survival data. J Am Stat Assoc. 2010;105(489):205–17.

Article
CAS
Google Scholar

Ishwaran H, Kogalur UB, Chen X, Minn AJ. Random survival forests for high-dimensional data. Stat Anal Data Min ASA Data Sci J. 2011;4(1):115–32.

Article
Google Scholar

Ashrafzadeh S, Hamdy O. Patient-driven diabetes care of the future in the technology era. Cell Metab. 2019;29(3):564–75.

Article
CAS
PubMed
Google Scholar

Basch E, Schrag D. The evolving uses of “real-world” data. JAMA. 2019;321:1359–60.

Article
PubMed
Google Scholar

Stolfi P, Valentini I, Palumbo MC, Tieri P, Grignolio A, Castiglione F. Potential predictors of type-2 diabetes risk: machine learning, synthetic data and wearable health devices. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM), pp 2214–2221 (2019)