A quantitative analysis of statistical power identifies obesity end points for improved in vivo preclinical study design

ABSTRACT The design of well-powered _in vivo_ preclinical studies is a key element in building the knowledge of disease physiology for the purpose of identifying and effectively testing

potential antiobesity drug targets. However, as a result of the complexity of the obese phenotype, there is limited understanding of the variability within and between study animals of

macroscopic end points such as food intake and body composition. This, combined with limitations inherent in the measurement of certain end points, presents challenges to study design that

can have significant consequences for an antiobesity program. Here, we analyze a large, longitudinal study of mouse food intake and body composition during diet perturbation to quantify the

variability and interaction of the key metabolic end points. To demonstrate how conclusions can change as a function of study size, we show that a simulated preclinical study properly

powered for one end point may lead to false conclusions based on secondary end points. We then propose the guidelines for end point selection and study size estimation under different

conditions to facilitate proper power calculation for a more successful _in vivo_ study design. Access through your institution Buy or subscribe This is a preview of subscription content,

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TESTS IN MICE Article Open access 25 November 2024 DIETARY RESTRICTION IMPACTS HEALTH AND LIFESPAN OF GENETICALLY DIVERSE MICE Article Open access 09 October 2024 AN UNBIASED RANKING OF

MURINE DIETARY MODELS BASED ON THEIR PROXIMITY TO HUMAN METABOLIC DYSFUNCTION-ASSOCIATED STEATOTIC LIVER DISEASE (MASLD) Article Open access 12 June 2024 REFERENCES * Finucane MM, Stevens

GA, Cowan MJ, Danaei G, Lin JK, Paciorek CJ _et al_. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and

epidemiological studies with 960 country-years and 9·1 million participants. _Lancet_ 2011; 377: 557–567. Article  Google Scholar  * Dietrich MO, Horvath TL . Limitations in anti-obesity

drug development: the critical role of hunger-promoting neurons. _Nat Rev Drug Discov_ 2012; 11: 675–691. Article  CAS  Google Scholar  * Tsang R, Colley L, Lynd LD . Inadequate statistical

power to detect clinically significant differences in adverse event rates in randomized controlled trials. _J Clin Epidemiol_ 2009; 62: 609–616. Article  Google Scholar  * Ravussin Y, Gutman

R, LeDuc CA, Leibel RL . Estimating energy expenditure in mice using an energy balance technique. _Int J Obes_ 2013; 37: 399–403. Article  CAS  Google Scholar  * Morton GJ, Thatcher BS,

Reidelberger RD, Ogimoto K, Wolden-Hanson T, Baskin DG _et al_. Peripheral oxytocin suppresses food intake and causes weight loss in diet-induced obese rats. _Am J Physiol Endocrinol Metab_

2012; 302: E134–E144. Article  CAS  Google Scholar  * Wagner JD, Zhang L, Kavanagh K, Ward GM, Chin JE, Hadcock JR _et al_. A selective cannabinoid-1 receptor antagonist, PF-95453, reduces

body weight and body fat to a greater extent than pair-fed controls in obese monkeys. _J Pharmacol Exp Ther_ 2010; 335: 103–113. Article  CAS  Google Scholar  * Ravussin Y, LeDuc CA,

Watanabe K, Mueller BR, Skowronski A, Rosenbaum M _et al_. Effects of chronic leptin infusion on subsequent body weight and composition in mice: can body weight set point be reset? _Mol

Metab_ 2014; 3: 432–440. Article  CAS  Google Scholar  * Guo J, Hall KD . Estimating the continuous-time dynamics of energy and fat metabolism in mice. _PLoS Comput Biol_ 2009; 5: e1000511.

Article  Google Scholar  * Guo J, Hall KD . Predicting changes of body weight, body fat, energy expenditure and metabolic fuel selection in C57BL/6 mice. _PLoS One_ 2011; 6: e15961. Article

  CAS  Google Scholar  * Lin B, Koibuchi N, Hasegawa Y, Sueta D, Toyama K, Uekawa K _et al_. Glycemic control with empagliflozin, a novel selective SGLT2 inhibitor, ameliorates

cardiovascular injury and cognitive dysfunction in obese and type 2 diabetic mice. _Cardiovasc Diabetol_ 2014; 13: 2215–2215. Google Scholar  * Doyon C, Denis RG, Baraboi ED, Samson P,

Lalonde J, Deshaies Y _et al_. Effects of rimonabant (SR141716) on fasting-induced hypothalamic-pituitary-adrenal axis and neuronal activation in lean and obese zucker rats. _Diabetes_ 2006;

55: 3403–3410. Article  CAS  Google Scholar  * Guo J, Jou W, Gavrilova O, Hall KD . Persistent diet-induced obesity in male C57BL/6 mice resulting from temporary obesigenic diets. _PLoS

One_ 2009; 4: e5370–e5379. Article  Google Scholar  * Stevens J, Truesdale KP, McClain JE, Cai J . The definition of weight maintenance. _Int J Obes_ 2006; 30: 391–399. Article  CAS  Google

Scholar  * Wang JL, Chiou JM, Mueller HG . Review of functional data analysis. _Annu Rev Stat Appl_ 2015; 3: 257–295. Article  Google Scholar  * Senn SS . _Statistical Issues in Drug

Development_. John Wiley & Sons: Chichester, UK, 2008. Google Scholar  Download references ACKNOWLEDGEMENTS Pfizer Inc. supported the research of JS, WCT and CJM. Data used in this

research were from a study sponsored by the Intramural Research Program of the NIH, NIDDK. AUTHOR INFORMATION Author notes * W C Thompson Present address: 3Current address: SAS Institute,

Cary, NC, USA., AUTHORS AND AFFILIATIONS * Internal Medicine Research Unit, Pfizer Inc., Cambridge, MA, USA J Selimkhanov, W C Thompson & C J Musante * Laboratory of Biological Modeling,

National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA J Guo & K D Hall Authors * J Selimkhanov View author publications You can also search for this author

inPubMed Google Scholar * W C Thompson View author publications You can also search for this author inPubMed Google Scholar * J Guo View author publications You can also search for this

author inPubMed Google Scholar * K D Hall View author publications You can also search for this author inPubMed Google Scholar * C J Musante View author publications You can also search for

this author inPubMed Google Scholar CORRESPONDING AUTHOR Correspondence to C J Musante. ETHICS DECLARATIONS COMPETING INTERESTS JS, WCT and CJM were employees of Pfizer Inc. during this

study. The remaining authors declare no conflict of interest. ADDITIONAL INFORMATION Supplementary Information accompanies this paper on International Journal of Obesity website

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION (PDF 482 KB) RIGHTS AND PERMISSIONS Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Selimkhanov, J., Thompson, W., Guo, J.

_et al._ A quantitative analysis of statistical power identifies obesity end points for improved _in vivo_ preclinical study design. _Int J Obes_ 41, 1306–1309 (2017).

https://doi.org/10.1038/ijo.2017.93 Download citation * Received: 21 September 2016 * Revised: 15 February 2017 * Accepted: 18 February 2017 * Published: 10 April 2017 * Issue Date: August

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