A Pilot randomized trial to examine effects of a hybrid closed-loop insulin delivery system on neurodevelopmental and cognitive outcomes in adolescents with type 1 diabetes
Allan L Reiss, Booil Jo, Ana Maria Arbelaez, Eva Tsalikian, Bruce Buckingham, Stuart A Weinzimer, Larry A Fox, Allison Cato, Neil H White, Michael Tansey, Tandy Aye, William Tamborlane, Kimberly Englert, John Lum, Paul Mazaika, Lara Foland-Ross, Matthew Marzelli, Nelly Mauras, Diabetes Research in Children Network (DirecNet) Consortium, Gabby Tong, Hanyang Shen, Zetan Li, Ryan Kingman, Lucy Levandoski, Julie Coffey, Rachel Bisbee, Amy Stephen, Kate Weyman, Keisha Bird, Kimberly Ponthieux, Juan Marrero, Allan L Reiss, Booil Jo, Ana Maria Arbelaez, Eva Tsalikian, Bruce Buckingham, Stuart A Weinzimer, Larry A Fox, Allison Cato, Neil H White, Michael Tansey, Tandy Aye, William Tamborlane, Kimberly Englert, John Lum, Paul Mazaika, Lara Foland-Ross, Matthew Marzelli, Nelly Mauras, Diabetes Research in Children Network (DirecNet) Consortium, Gabby Tong, Hanyang Shen, Zetan Li, Ryan Kingman, Lucy Levandoski, Julie Coffey, Rachel Bisbee, Amy Stephen, Kate Weyman, Keisha Bird, Kimberly Ponthieux, Juan Marrero
Abstract
Type 1 diabetes (T1D) is associated with lower scores on tests of cognitive and neuropsychological function and alterations in brain structure and function in children. This proof-of-concept pilot study (ClinicalTrials.gov Identifier NCT03428932) examined whether MRI-derived indices of brain development and function and standardized IQ scores in adolescents with T1D could be improved with better diabetes control using a hybrid closed-loop insulin delivery system. Eligibility criteria for participation in the study included age between 14 and 17 years and a diagnosis of T1D before 8 years of age. Randomization to either a hybrid closed-loop or standard diabetes care group was performed after pre-qualification, consent, enrollment, and collection of medical background information. Of 46 participants assessed for eligibility, 44 met criteria and were randomized. Two randomized participants failed to complete baseline assessments and were excluded from final analyses. Participant data were collected across five academic medical centers in the United States. Research staff scoring the cognitive assessments as well as those processing imaging data were blinded to group status though participants and their families were not. Forty-two adolescents, 21 per group, underwent cognitive assessment and multi-modal brain imaging before and after the six month study duration. HbA1c and sensor glucose downloads were obtained quarterly. Primary outcomes included metrics of gray matter (total and regional volumes, cortical surface area and thickness), white matter volume, and fractional anisotropy. Estimated power to detect the predicted treatment effect was 0.83 with two-tailed, α = 0.05. Adolescents in the hybrid closed-loop group showed significantly greater improvement in several primary outcomes indicative of neurotypical development during adolescence compared to the standard care group including cortical surface area, regional gray volumes, and fractional anisotropy. The two groups were not significantly different on total gray and white matter volumes or cortical thickness. The hybrid closed loop group also showed higher Perceptual Reasoning Index IQ scores and functional brain activity more indicative of neurotypical development relative to the standard care group (both secondary outcomes). No adverse effects associated with study participation were observed. These results suggest that alterations to the developing brain in T1D might be preventable or reversible with rigorous glucose control. Long term research in this area is needed.
Conflict of interest statement
N.M. had institutional device supply agreements from Medtronic for CGM and LifeScan for test strips for the study, research support from Novo Nordisk. B.B. had consultant agreements with Medtronic Diabetes, Novo Nordisk, Dexcom, ConvaTec, Lilly, and Tolerion; has provided expert testimony for Dexcom; and has research grants with Insulet, Tandem, Medtronic, and Beta Bionics. E.T. had institutional research grants with AstraZeneca, Boehringer Ingelheim, Novo Nordisk, Grifols Therapeutics, Takeda, and Amgen. S.A.W. had consultant agreements with Zealand; institutional grant support from Abbott and Medtronic; and speaker honoraria from Abbott, Dexcom, and Insulet. L.A.F. had a device supply agreement with Dexcom. A.M.A. had a device supply agreement with Dexcom. M.T. had a data safety monitoring board agreement with Daiichi Sankyo. W.T. had consultant agreements with AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, and Sanofi and data safety monitoring board agreements with Eisai, MannKind, and Tolerion. K.E. had a consultant agreement with PicoLife Technology. J.L. received consulting fees, paid to his institution, from Animas Corporation, Bigfoot Biomedical, Tandem Diabetes Care, and Eli Lilly and Company. No other potential competing interests relevant to this article were reported.
© 2022. The Author(s).
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References
- Bober E, Buyukgebiz A. Hypoglycemia and its effects on the brain in children with type 1 diabetes mellitus. Pediatr. Endocrinol. Rev. 2005;2:378–382.
- Urakami T. Severe hypoglycemia: is it still a threat for children and adolescents with type 1 diabetes? Front Endocrinol. 2020;11:609. doi: 10.3389/fendo.2020.00609.
- Ahmet A, Dagenais S, Barrowman NJ, Collins CJ, Lawson ML. Prevalence of nocturnal hypoglycemia in pediatric type 1 diabetes: a pilot study using continuous glucose monitoring. J. Pediatr. 2011;159:297–302.e291. doi: 10.1016/j.jpeds.2011.01.064.
- Shivaprasad, C. et al. Continuous glucose monitoring for the detection of hypoglycemia in patients with diabetes of the exocrine pancreas. J. Diabetes Sci. Technol. 15, 1313–1319 (2020).
- Jaser SS, Jordan LC. Brain health in children with type 1 diabetes: risk and protective factors. Curr. Diab Rep. 2021;21:12. doi: 10.1007/s11892-021-01380-w.
- Cato A, Hershey T. Cognition and type 1 diabetes in children and adolescents. Diabetes Spectr. 2016;29:197–202. doi: 10.2337/ds16-0036.
- Fritsch SL, Overton MW, Robbins DR. The interface of child mental health and juvenile diabetes mellitus. Psychiatr. Clin. North Am. 2015;38:59–76. doi: 10.1016/j.psc.2014.11.007.
- Buchberger B, et al. Symptoms of depression and anxiety in youth with type 1 diabetes: a systematic review and meta-analysis. Psychoneuroendocrinology. 2016;70:70–84. doi: 10.1016/j.psyneuen.2016.04.019.
- Northam EA, et al. Neuropsychological profiles of children with type 1 diabetes 6 years after disease onset. Diabetes Care. 2001;24:1541–1546. doi: 10.2337/diacare.24.9.1541.
- Perantie DC, et al. Effects of prior hypoglycemia and hyperglycemia on cognition in children with type 1 diabetes mellitus. Pediatr. Diabetes. 2008;9:87–95. doi: 10.1111/j.1399-5448.2007.00274.x.
- Foland-Ross LC, et al. Executive task-based brain function in children with type 1 diabetes: an observational study. PLoS Med. 2019;16:e1002979. doi: 10.1371/journal.pmed.1002979.
- Foland-Ross LC, et al. Brain function differences in children with type 1 diabetes: a functional MRI study of working memory. Diabetes. 2020;69:1770–1778. doi: 10.2337/db20-0123.
- Fox LA, et al. Persistence of abnormalities in white matter in children with type 1 diabetes. Diabetologia. 2018;61:1538–1547. doi: 10.1007/s00125-018-4610-6.
- Hosseini SM, et al. Altered Integration of structural covariance networks in young children with type 1 diabetes. Hum. Brain Mapp. 2016;37:4034–4046. doi: 10.1002/hbm.23293.
- Marzelli MJ, et al. Neuroanatomical correlates of dysglycemia in young children with type 1 diabetes. Diabetes. 2014;63:343–353. doi: 10.2337/db13-0179.
- Mazaika PK, et al. Variations in brain volume and growth in young children with type 1 diabetes. Diabetes. 2016;65:476–485. doi: 10.2337/db15-1242.
- Saggar M, et al. Compensatory hyperconnectivity in developing brains of young children with type 1 diabetes. Diabetes. 2017;66:754–762. doi: 10.2337/db16-0414.
- Mauras N, et al. Impact of type 1 diabetes in the developing brain in children: a longitudinal study. Diabetes Care. 2021;44:983–992. doi: 10.2337/dc20-2125.
- Peters BD, et al. White matter development in adolescence: diffusion tensor imaging and meta-analytic results. Schizophr. Bull. 2012;38:1308–1317. doi: 10.1093/schbul/sbs054.
- Goddings AL, et al. The influence of puberty on subcortical brain development. Neuroimage. 2014;88:242–251. doi: 10.1016/j.neuroimage.2013.09.073.
- Wierenga L, et al. Typical development of basal ganglia, hippocampus, amygdala and cerebellum from age 7 to 24. Neuroimage. 2014;96:67–72. doi: 10.1016/j.neuroimage.2014.03.072.
- Dennison M, et al. Mapping subcortical brain maturation during adolescence: evidence of hemisphere- and sex-specific longitudinal changes. Dev. Sci. 2013;16:772–791. doi: 10.1111/desc.12057.
- Foulkes L, Blakemore SJ. Studying individual differences in human adolescent brain development. Nat. Neurosci. 2018;21:315–323. doi: 10.1038/s41593-018-0078-4.
- Fischl B. FreeSurfer. Neuroimage. 2012;62:774–781. doi: 10.1016/j.neuroimage.2012.01.021.
- Hershey T, et al. Hippocampal volumes in youth with type 1 diabetes. Diabetes. 2010;59:236–241. doi: 10.2337/db09-1117.
- Lo W, O’Donnell M, Tancredi D, Orgain M, Glaser N. Diabetic ketoacidosis in juvenile rats is associated with reactive gliosis and activation of microglia in the hippocampus. Pediatr. Diabetes. 2016;17:127–139. doi: 10.1111/pedi.12251.
- Driscoll, M. E., Bollu, P. C. & Tadi, P. Neuroanatomy, nucleus caudate. In StatPearls (Treasure Island, FL : StatPearls Publishing LLC, 2021). .
- Grahn JA, Parkinson JA, Owen AM. The cognitive functions of the caudate nucleus. Prog. Neurobiol. 2008;86:141–155. doi: 10.1016/j.pneurobio.2008.09.004.
- Yendiki A, et al. Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinform. 2011;5:23. doi: 10.3389/fninf.2011.00023.
- Walhovd KB, et al. Maturation of cortico-subcortical structural networks-segregation and overlap of medial temporal and fronto-striatal systems in development. Cereb. Cortex. 2015;25:1835–1841. doi: 10.1093/cercor/bht424.
- Rubia K. Functional brain imaging across development. Eur. Child Adolesc. Psychiatry. 2013;22:719–731. doi: 10.1007/s00787-012-0291-8.
- Rubia K, Hyde Z, Halari R, Giampietro V, Smith A. Effects of age and sex on developmental neural networks of visual-spatial attention allocation. Neuroimage. 2010;51:817–827. doi: 10.1016/j.neuroimage.2010.02.058.
- Watkins MW, Smith LG. Long-term stability of the Wechsler Intelligence Scale for Children–Fourth Edition. Psychol. Assess. 2013;25:477–483. doi: 10.1037/a0031653.
- Yu H, McCoach DB, Gottfried AW, Gottfried AE. Stability of intelligence from infancy through adolescence: an autoregressive latent variable model. Intelligence. 2018;69:8–15. doi: 10.1016/j.intell.2018.03.011.
- Irby SM, Floyd RG. Test review: Wechsler abbreviated scale of intelligence, second edition. Can. J. Sch. Psychol. 2013;28:295–299. doi: 10.1177/0829573513493982.
- Foland-Ross, L. C. et al. Longitudinal assessment of hippocampus structure in children with type 1 diabetes. Pediatr. Diabetes19, 1116–1123 (2018).
- Roberto CA, et al. Brain tissue volume changes following weight gain in adults with anorexia nervosa. Int. J. Eat. Disord. 2011;44:406–411. doi: 10.1002/eat.20840.
- Gazdzinski S, Durazzo TC, Mon A, Yeh PH, Meyerhoff DJ. Cerebral white matter recovery in abstinent alcoholics–a multimodality magnetic resonance study. Brain. 2010;133:1043–1053. doi: 10.1093/brain/awp343.
- van Eijk J, et al. Rapid partial regeneration of brain volume during the first 14 days of abstinence from alcohol. Alcohol Clin. Exp. Res. 2013;37:67–74. doi: 10.1111/j.1530-0277.2012.01853.x.
- Ibrahim I, et al. Fractional anisotropy and mean diffusivity in the corpus callosum of patients with multiple sclerosis: the effect of physiotherapy. Neuroradiology. 2011;53:917–926. doi: 10.1007/s00234-011-0879-6.
- Piatkowska-Chmiel, I., Herbet, M., Gawronska-Grzywacz, M., Ostrowska-Lesko, M. & Dudka, J. The role of molecular and inflammatory indicators in the assessment of cognitive dysfunction in a mouse model of diabetes. Int. J. Mol. Sci. 22, 3878 (2021).
- Sharma S, Brown CE. Microvascular basis of cognitive impairment in type 1 diabetes. Pharm. Ther. 2022;229:107929. doi: 10.1016/j.pharmthera.2021.107929.
- Harris PA, et al. The REDCap consortium: Building an international community of software platform partners. J. Biomed. Inf. 2019;95:103208. doi: 10.1016/j.jbi.2019.103208.
- Harris PA, et al. Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support. J. Biomed. Inf. 2009;42:377–381. doi: 10.1016/j.jbi.2008.08.010.
- Barnea-Goraly N, et al. High success rates of sedation-free brain MRI scanning in young children using simple subject preparation protocols with and without a commercial mock scanner–the Diabetes Research in Children Network (DirecNet) experience. Pediatr. Radiol. 2014;44:181–186. doi: 10.1007/s00247-013-2798-7.
- Mauras N, et al. Longitudinal assessment of neuroanatomical and cognitive differences in young children with type 1 diabetes: association with hyperglycemia. Diabetes. 2015;64:1770–1779. doi: 10.2337/db14-1445.
- Talairach, J. & Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging (Thieme Medical Publishers, 1988).
- Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage. 2007;38:95–113. doi: 10.1016/j.neuroimage.2007.07.007.
- Ashburner J, Friston KJ. Unified segmentation. Neuroimage. 2005;26:839–851. doi: 10.1016/j.neuroimage.2005.02.018.
- Reuter M, Fischl B. Avoiding asymmetry-induced bias in longitudinal image processing. Neuroimage. 2011;57:19–21. doi: 10.1016/j.neuroimage.2011.02.076.
- Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent registration: a robust approach. Neuroimage. 2010;53:1181–1196. doi: 10.1016/j.neuroimage.2010.07.020.
- Hagler DJ, Jr., Saygin AP, Sereno MI. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage. 2006;33:1093–1103. doi: 10.1016/j.neuroimage.2006.07.036.
- Oguz I, et al. DTIPrep: quality control of diffusion-weighted images. Front. Neuroinform. 2014;8:4. doi: 10.3389/fninf.2014.00004.
- Behrens TE, et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson Med. 2003;50:1077–1088. doi: 10.1002/mrm.10609.
- Guillaume B, et al. Fast and accurate modelling of longitudinal and repeated measures neuroimaging data. Neuroimage. 2014;94:287–302. doi: 10.1016/j.neuroimage.2014.03.029.
- Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE. Permutation inference for the general linear model. Neuroimage. 2014;92:381–397. doi: 10.1016/j.neuroimage.2014.01.060.
- Muthén, L. K. & Muthén, B. O. In Mplus User’s Guide. Vol. Eighth (ed. Muthén, M.) (Los Angeles, CA: Muthén & Muthén, 2017).
- Little, R. J. A. & Rubin, D. B. Statistical Analysis with Missing Data (Wiley, 2002).
- Kraemer HC, Kiernan M, Essex M, Kupfer DJ. How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychol. 2008;27:S101–S108. doi: 10.1037/0278-6133.27.2(Suppl.).S101.
- Kraemer HC, Wilson GT, Fairburn CG, Agras WS. Mediators and moderators of treatment effects in randomized clinical trials. Arch. Gen. Psychiatry. 2002;59:877–883. doi: 10.1001/archpsyc.59.10.877.
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