The mathematical sciences including engineering, statistics, computer science, physics, econometrics, psychometrics, epidemiology, and mathematics qua mathematics are increasingly being applied to advance our understanding of the causes, consequences, and alleviation of obesity. These applications do not merely involve routine well-established approaches easily implemented in widely available commercial software. Rather, they increasingly involve computationally demanding tasks, use and in some cases development of novel analytic methods and software, new derivations, computer simulations, and unprecedented interdigitation of two or more existing techniques. Such advances at the interface of the mathematical sciences and obesity research require bilateral training and exposure for investigators in both disciplines. Yet, no ongoing resource exists to provide such training and role models of scientists who regularly can and do traverse these disciplines are in short supply at most institutions. The proposed annual five day short course on the mathematical sciences in obesity research features some of the world's finest scientists working in this domain to fill this unmet need by providing nine topic driven modules designed to bridge the disciplines. Each module will begin with a mathematical method applied in obesity research. This introduction will be followed by a lecture on a completed application of the method. Directly after the completed application, participants will be engaged in a guided interactive session performing calculations using software or analysis pertaining to the module topic. Finally, the module closes with a lecture on remaining open questions. In recognition of the challenges in successfully managing an interdisciplinary career, each day of the short course will contain a two hour long interactive working session guided by established senior researchers in building the next step. Participants may choose to use this interactive session to begin develop an abstract for submission at national level obesity conferences or plan an application for a small interdisciplinary group of researchers to advance their ideas at the National Institute of Mathematical and Biological Synthesis in Knoxville, TN. Based on the degree of completion, ten participants will be selected to present their work on the last day of the short course. The NIH and the scientific community at large has voiced the need for more training at the interface of the mathematical science and key biomedical domains, and we request the opportunity to be part of the solution.

Public Health Relevance

Obesity affects more than one-third of the US population generating a need for novel interdisciplinary strategies to resolve growing obesity related negative health outcomes. Advanced mathematical methods play a critical role on numerous fronts in obesity research from evaluating the effect of population wide obesity prevention policy measures to monitoring patients during lifestyle interventions. The proposed course will develop connections between mathematical scientists and obesity researchers to address research challenges with novel approaches.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Education Projects (R25)
Project #
5R25DK099080-03
Application #
8853277
Study Section
Kidney, Urologic and Hematologic Diseases D Subcommittee (DDK)
Program Officer
Saslowsky, David E
Project Start
2013-07-01
Project End
2016-06-30
Budget Start
2015-07-01
Budget End
2016-06-30
Support Year
3
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Alabama Birmingham
Department
Type
Schools of Public Health
DUNS #
063690705
City
Birmingham
State
AL
Country
United States
Zip Code
35294
Ejima, Keisuke; Thomas, Diana M; Allison, David B (2018) A Mathematical Model for Predicting Obesity Transmission with Both Genetic and Nongenetic Heredity. Obesity (Silver Spring) 26:927-933
Kahathuduwa, Chanaka N; Thomas, Diana M; Siu, Cynthia et al. (2018) Unaccounted for regression to the mean renders conclusion of article titled ""Uric acid lowering in relation to HbA1c reductions with the SGLT2 inhibitor tofogliflozin"" unsubstantiated. Diabetes Obes Metab 20:2039-2040
Dickinson, Stephanie L; Brown, Andrew W; Mehta, Tapan et al. (2018) Incorrect analyses were used in ""Different enteral nutrition formulas have no effect on glucose homeostasis but on diet-induced thermogenesis in critically ill medical patients: a randomized controlled trial"" and corrected analyses are requested. Eur J Clin Nutr :
McComb, Bryan; Frazier-Wood, Alexis C; Dawson, John et al. (2018) Drawing conclusions from within-group comparisons and selected subsets of data leads to unsubstantiated conclusions: Letter regarding Malakellis et al. Aust N Z J Public Health 42:214
Hannon, Bridget A; Thomas, Diana M; Siu, Cynthia et al. (2018) The claim that effectiveness has been demonstrated in the Parenting, Eating and Activity for Child Health (PEACH) childhood obesity intervention is unsubstantiated by the data. Br J Nutr 120:958-959
Brown, Andrew W; Kaiser, Kathryn A; Allison, David B (2018) Issues with data and analyses: Errors, underlying themes, and potential solutions. Proc Natl Acad Sci U S A 115:2563-2570
Ejima, K; Pavela, G; Li, P et al. (2018) Generalized lambda distribution for flexibly testing differences beyond the mean in the distribution of a dependent variable such as body mass index. Int J Obes (Lond) 42:930-933
Allison, David B (2018) The Conclusions Are Unsupported by the Data, Are Based on Invalid Analyses, Are Incorrect, and Should be Corrected: Letter Regarding ""Sleep Quality and Body Composition Variations in Obese Male Adults after 14 weeks of Yoga Intervention: A Randomized Con Int J Yoga 11:83-84
Dawson, J A; Brown, A W; Allison, D B (2018) The stated conclusions are contradicted by the data, based on inappropriate statistics, and should be corrected: comment on 'intervention for childhood obesity based on parents only or parents and child compared with follow-up alone'. Pediatr Obes 13:656-657
Speakman, J R; Loos, R J F; O'Rahilly, S et al. (2018) GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity. Int J Obes (Lond) 42:1524-1531

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