A time-restricted feeding intervention in children and adolescents with obesity: The TRansForm study protocol

Paula Molina-Giraldo, Serafin Murillo, Laura Meis, Oscar Sans, Montse Amat-Bou, Marina Llobet, Josep C Jimenez-Chillaron, Marta Ramon-Krauel, Carles Lerin, Paula Molina-Giraldo, Serafin Murillo, Laura Meis, Oscar Sans, Montse Amat-Bou, Marina Llobet, Josep C Jimenez-Chillaron, Marta Ramon-Krauel, Carles Lerin

Abstract

Obesity during childhood is of special concern as adiposity is typically tracked into adult life and it constitutes a major risk factor for future obesity and associated metabolic disorders. Recent studies indicate that time-restricted feeding (TRF) interventions may provide a promising strategy for obesity treatment. However, TRF interventions have only been tested in adult subjects. This study aims to determine both short- and long-term effects of a TRF intervention in children and adolescents with obesity. We will also investigate potential mechanisms mediating the response to the intervention, including the circadian rhythm and the gut microbiota composition. We have designed a randomized-controlled parallel-group clinical study in which children and adolescents (age range 8-18 year-old) with obesity will be subjected to time-restricted eating or no time restrictions for 2 months. Follow-up visits will allow for long-term effect assessments. We will measure anthropometric (BMI, body composition) and metabolic parameters (glucose and lipid metabolism), indicators of the circadian rhythm, and gut microbiota composition will be analyzed. This study will (1) determine safety and effectiveness of the TRF intervention in children and adolescents; (2) assess its long-term effects; and (3) evaluate potential mechanisms involved in the response to the intervention.

Clinical trial registration: [www.ClinicalTrials.gov], identifier [NCT05174871].

Keywords: actigraphy; childhood obesity; circadian rhythm; gut microbiota; time-restricted feeding (TRF).

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Molina-Giraldo, Murillo, Meis, Sans, Amat-Bou, Llobet, Jimenez-Chillaron, Ramon-Krauel and Lerin.

Figures

FIGURE 1
FIGURE 1
Summary of the study hypothesis.
FIGURE 2
FIGURE 2
Schematic representation of study design.

References

    1. Risk Factor NCD. Collaboration. Lancet. (2016) 387:1377–96. 10.1016/S0140-6736(16)30054-X
    1. Freedman DS, Dietz WH, Srinivasan SR, Berenson GS. The relation of overweight to cardiovascular risk factors among children and adolescents: The bogalusa heart study. Pediatrics. (1999) 103:1175–82.
    1. The NS, Suchindran C, North KE, Popkin BM, Gordon-Larsen P. Association of adolescent obesity with risk of severe obesity in adulthood. JAMA. (2010) 304:2042–7. 10.1001/jama.2010.1635
    1. Lawlor DA, Benfield L, Logue J, Tilling K, Howe LD, Fraser A, et al. Association between general and central adiposity in childhood, and change in these, with cardiovascular risk factors in adolescence: Prospective cohort study. BMJ. (2010) 341:c6224. 10.1136/bmj.c6224
    1. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC. Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med. (2010) 362:485–93. 10.1056/NEJMoa0904130
    1. Juonala M, Magnussen CG, Berenson GS, Venn A, Burns TL, Sabin MA, et al. Childhood adiposity, adult adiposity, and cardiovascular risk factors. N Engl J Med. (2011) 365:1876–85. 10.1056/NEJMoa1010112
    1. Bjerregaard LG, Baker JL. Change in overweight from childhood to early adulthood and risk of type 2 diabetes. N Engl J Med. (2018) 378:2537–8. 10.1056/NEJMc1805984
    1. Regmi P, Heilbronn LK. Time-restricted eating: Benefits, mechanisms, and challenges in translation. iScience. (2020) 23:101161. 10.1016/j.isci.2020.101161
    1. Sutton EF, Beyl R, Early KS, Cefalu WT, Ravussin E, Peterson CM. Early time-restricted feeding improves insulin sensitivity, blood pressure, and oxidative stress even without weight loss in men with prediabetes. Cell Metab. (2018) 27:1212.e–21.e. 10.1016/j.cmet.2018.04.010
    1. Leal-Witt MJ, Llobet M, Samino S, Castellano P, Cuadras D, Jimenez-Chillaron JC, et al. Lifestyle intervention decreases urine trimethylamine N-Oxide levels in prepubertal children with obesity. Obesity. (2018) 26:1603–10. 10.1002/oby.22271
    1. Leal-Witt MJ, Ramon-Krauel M, Samino S, Llobet M, Cuadras D, Jimenez-Chillaron JC, et al. Untargeted metabolomics identifies a plasma sphingolipid-related signature associated with lifestyle intervention in prepubertal children with obesity. Int J Obes. (2018) 42:72–8. 10.1038/ijo.2017.201
    1. President and Fellows of Harvard College,. Healthy Eating Plate. (2022). Available online at: (accessed August 19, 2022).
    1. Bruni O, Ottaviano S, Guidetti V, Romoli M, Innocenzi M, Cortesi F, et al. The sleep disturbance scale for children (SDSC). Construction and validation of an instrument to evaluate sleep disturbances in childhood and adolescence. J Sleep Res. (1996) 5:251–61. 10.1111/j.1365-2869.1996.00251.x
    1. Chervin RD, Hedger K, Dillon JE, Pituch KJ. Pediatric sleep questionnaire (PSQ): Validity and reliability of scales for sleep-disordered breathing, snoring, sleepiness, and behavioral problems. Sleep Med. (2000) 1:21–32. 10.1016/s1389-9457(99)00009-x
    1. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. (2019) 95:103208. 10.1016/j.jbi.2019.103208
    1. Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. (2009) 42:377–81. 10.1016/j.jbi.2008.08.010
    1. Jebeile H, Gow ML, Lister NB, Mosalman Haghighi M, Ayer J, Cowell CT, et al. Intermittent energy restriction is a feasible, effective, and acceptable intervention to treat adolescents with obesity. J Nutr. (2019) 149:1189–97. 10.1093/jn/nxz049
    1. Vidmar AP, Naguib M, Raymond JK, Salvy SJ, Hegedus E, Wee CP, et al. Time-limited eating and continuous glucose monitoring in adolescents with obesity: A pilot study. Nutrients. (2021) 13:3697. 10.3390/nu13113697
    1. Dibner C, Schibler U, Albrecht U. The mammalian circadian timing system: Organization and coordination of central and peripheral clocks. Annu Rev Physiol. (2010) 72:517–49. 10.1146/annurev-physiol-021909-135821
    1. Feng D, Lazar MA. Clocks, metabolism, and the epigenome. Mol Cell. (2012) 47:158–67. 10.1016/j.molcel.2012.06.026
    1. Longo VD, Panda S. Fasting, circadian rhythms, and time-restricted feeding in healthy lifespan. Cell Metab. (2016) 23:1048–59. 10.1016/j.cmet.2016.06.001
    1. Koronowski KB, Kinouchi K, Welz PS, Smith JG, Zinna VM, Shi J, et al. Defining the independence of the liver circadian clock. Cell. (2019) 177:1448.e–62.e. 10.1016/j.cell.2019.04.025
    1. Eckel-Mahan KL, Patel VR, de Mateo S, Orozco-Solis R, Ceglia NJ, Sahar S, et al. Reprogramming of the circadian clock by nutritional challenge. Cell. (2013) 155:1464–78. 10.1016/j.cell.2013.11.034
    1. Rudic RD, McNamara P, Curtis AM, Boston RC, Panda S, Hogenesch JB, et al. BMAL1 and CLOCK, two essential components of the circadian clock, are involved in glucose homeostasis. PLoS Biol. (2004) 2:e377. 10.1371/journal.pbio.0020377
    1. Turek FW, Joshu C, Kohsaka A, Lin E, Ivanova G, McDearmon E, et al. Obesity and metabolic syndrome in circadian clock mutant mice. Science. (2005) 308:1043–5. 10.1126/science.1108750
    1. Shimba S, Ogawa T, Hitosugi S, Ichihashi Y, Nakadaira Y, Kobayashi M, et al. Deficient of a clock gene, brain and muscle Arnt-like protein-1 (BMAL1), induces dyslipidemia and ectopic fat formation. PLoS One. (2011) 6:e25231. 10.1371/journal.pone.0025231
    1. Zhang EE, Liu Y, Dentin R, Pongsawakul PY, Liu AC, Hirota T, et al. Cryptochrome mediates circadian regulation of cAMP signaling and hepatic gluconeogenesis. Nat Med. (2010) 16:1152–6. 10.1038/nm.2214
    1. Feng D, Liu T, Sun Z, Bugge A, Mullican SE, Alenghat T, et al. A circadian rhythm orchestrated by histone deacetylase 3 controls hepatic lipid metabolism. Science. (2011) 331:1315–9. 10.1126/science.1198125
    1. Spiegel K, Tasali E, Leproult R, Van Cauter E. Effects of poor and short sleep on glucose metabolism and obesity risk. Nat Rev Endocrinol. (2009) 5:253–61. 10.1038/nrendo.2009.23
    1. Kawakami N, Takatsuka N, Shimizu H. Sleep disturbance and onset of type 2 diabetes. Diabetes Care. (2004) 27:282–3. 10.2337/diacare.27.1.282
    1. Scheer FA, Hilton MF, Mantzoros CS, Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. Proc Natl Acad Sci U.S.A. (2009) 106:4453–8. 10.1073/pnas.0808180106
    1. Garaulet M, Ordovás JM, Madrid JA. The chronobiology, etiology and pathophysiology of obesity. Int J Obes. (2010) 34:1667–83. 10.1038/ijo.2010.118
    1. Garaulet M, Corbalán-Tutau MD, Madrid JA, Baraza JC, Parnell LD, Lee YC, et al. PERIOD2 variants are associated with abdominal obesity, psycho-behavioral factors, and attrition in the dietary treatment of obesity. J Am Diet Assoc. (2010) 110:917–21. 10.1016/j.jada.2010.03.017
    1. Maury E, Ramsey KM, Bass J. Circadian rhythms and metabolic syndrome: From experimental genetics to human disease. Circ Res. (2010) 106:447–62. 10.1161/CIRCRESAHA.109.208355
    1. Woon PY, Kaisaki PJ, Bragança J, Bihoreau MT, Levy JC, Farrall M, et al. Aryl hydrocarbon receptor nuclear translocator-like (BMAL1) is associated with susceptibility to hypertension and type 2 diabetes. Proc Natl Acad Sci U.S.A. (2007) 104:14412–7. 10.1073/pnas.0703247104
    1. Sookoian S, Gemma C, Gianotti TF, Burgueño A, Castaño G, Pirola CJ. Genetic variants of clock transcription factor are associated with individual susceptibility to obesity. Am J Clin Nutr. (2008) 87:1606–15. 10.1093/ajcn/87.6.1606
    1. Reitmeier S, Kiessling S, Clavel T, List M, Almeida EL, Ghosh TS, et al. Arrhythmic gut microbiome signatures predict risk of type 2 diabetes. Cell Host Microbe. (2020) 28:258.e–72.e. 10.1016/j.chom.2020.06.004
    1. Ding L, Xiao XH. Gut microbiota: Closely tied to the regulation of circadian clock in the development of type 2 diabetes mellitus. Chin Med J. (2020) 133:817–25. 10.1097/cm9.0000000000000702
    1. Zhou L, Kang L, Xiao X, Jia L, Zhang Q, Deng M. “Gut microbiota-circadian clock axis” in deciphering the mechanism linking early-life nutritional environment and abnormal glucose metabolism. Int J Endocrinol. (2019) 2019:5893028. 10.1155/2019/5893028
    1. Teichman EM, O’Riordan KJ, Gahan CGM, Dinan TG, Cryan JF. When rhythms meet the blues: Circadian interactions with the microbiota-gut-brain axis. Cell Metab. (2020) 31:448–71. 10.1016/j.cmet.2020.02.008
    1. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, et al. A core gut microbiome in obese and lean twins. Nature. (2009) 457:480–4. 10.1038/nature07540
    1. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. (2012) 490:55–60. 10.1038/nature11450
    1. Ridaura VK, Faith JJ, Rey FE, Cheng J, Duncan AE, Kau AL, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. (2013) 341:1241214. 10.1126/science.1241214

Source: PubMed

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