Large Changes in Brain Volume Observed in an Asymptomatic Young Child With Type 1 Diabetes

•  Subject was a white male child with type 1 diabetes in a large research study.

•  Initial diagnosis was made at age 5.6 years, with age-appropriate cognitive development and no other known medical conditions.

•  MRI scans at ages 6.5 and 8.0 years showed a 13.5% decrease in total gray matter volume and a 165% increase in ventricular volume. These changes were partially reversed at age 11.8 years.

•  This observation is among the largest quantitative brain volume changes ever reported in the literature.

•  Blood glucose level was 63 mg/dL before the scan at age 6.5 years, 324 mg/dL before the scan at age 8.0 years, and 169 mg/dL before the scan at age 11.8 years.

•  HbA_1c levels were 8.8% (73 mmol/mol), 10.5% (91 mmol/mol), and 11.4% (101 mmol/mol) at the scan time points.

This case was recorded during the Diabetes Research in Children Network (DirecNet) longitudinal study (1), in which children (N = 214) had MRI scans at three time points over 5 years. During the analyses, we noted that one subject with diabetes had unusually large brain volume changes (13.5% decrease of gray matter volume and 165% increase in ventricular volume) from age 6.5 to age 8.0 years.

The patient was diagnosed with type 1 diabetes at age 5.6 years when he presented without diabetic ketoacidosis. Neuroradiological reviews of the MRI scans at each time point reported no evidence of gross pathological abnormalities or diffusion restrictions. The patient was on multiple daily insulin injections at the time of his first MRI scan and on infusion pump therapy at the later scans. He was healthy besides type 1 diabetes, prepubertal, with full-term birth, normal weight development, normal thyroid function, and no evidence of neurological deficits. Moreover, he had age-appropriate vocabulary and stable cognitive test scores over the 5-year period. His medical history included one hypoglycemic event with disorientation at age 6.3 years (10 weeks before the first MRI scan) and one hospitalization for diabetic ketoacidosis at age 11.5 years (15 weeks before the third MRI scan). Continuous glucose monitoring (CGM) data collected for a total of 39 days showed a mean glucose (± SD) level of 235 ± 88 mg/dL and numerous large, rapid changes in sensor glucose, with increases as large as 200 mg/dL and decreases as large as 334 mg/dL within 1 h. The CGM data were not collected immediately before or during any MRI scan.

High-resolution MRI structural images (1 × 1 × 1 mm) and diffusion-weighted images (2 × 2 × 2 mm) were collected using identical image protocols on a Siemens 3T Tim Trio MR system and were analyzed using FreeSurfer imaging software (2). The structural images of the patient showed small ventricles and very thin sulcal spaces at age 6.5 years compared with large ventricles and wide sulcal spaces at age 8.0 years (Fig. 1). There was a 13.5% reduction in total gray matter volume, a 12.6% reduction in cortical thickness, and a 7.7% reduction in total brain volume when comparing these scans and, conversely, a 165% increase in volume of the lateral ventricles (Fig. 2). Average diffusivity of white matter did not decrease as expected from age 6.5 years to age 8.0 years, suggesting unusually low diffusivity at age 6.5 years (Fig. 2D). Total gray matter volume and mean cortical thickness increased by 6% and 4%, respectively, from age 8.0 to age 11.8 years.

Participants with type 1 diabetes were required to have blood glucose readings between 70 and 300 mg/dL within 60 min prior to all scan sessions, and treatment was provided if glucose was out of range. At age 6.5 years, blood glucose was 63 mg/dL on arrival at the MRI. The subject took 15 g of oral carbohydrates, raising his blood glucose to 83 mg/dL before scanning was initiated. At age 8.0 years, blood glucose was 324 mg/dL on arrival, and his glucose was lowered to 299 mg/dL after drinking water. Blood glucose was 169 mg/dL for the scan at age 11.8 years, and no treatment was given (Fig. 2F).

Compared with the cohort, the case study subject had the lowest glucose value prior to any scan, the greatest range of blood glucose values across consecutive scans, and by far the largest change in brain volume across time points. Other subjects in the DirecNet study showed a subtle correlation between brain volume and blood glucose, with brain volume variation considerably smaller than the case study subject (2).

This case is notable for the unusually large gray matter volume and cortical thickness at time 1 relative to the other time points. We speculate that these characteristics were an osmotic response to a sudden large drop in blood glucose shortly before the time 1 scan. For example, if glucose dropped from 300 mg/dL to 63 mg/dL within 1 h, similar to other large sudden glucose drops observed in his CGM data, then plasma osmolality is estimated to have dropped from 297 mOsmol/kg to 282 mOsmol/kg (3). (Unfortunately, actual measurements were not available.) This osmotic shift would induce water-based cellular swelling, decreased ventricular volume (4), and decreased diffusivity (5) until counteracted by cell volume regulation mechanisms (6). The second MRI at age 8.0 years had the smallest gray matter volume and largest ventricle volume. These volume characteristics are consistent with cell shrinkage (7) from both chronic hyperglycemia and from the acute hyperglycemic condition (324 mg/dL) before the scan. Thus, opposite osmotic stresses were present at the times of the first two scans. One possibility is that this particular child had an abnormally large volume response to osmotic stresses due to an impaired cell volume regulation system. However, the exact cause of the large volume change is unknown.

The gray matter volume difference reported here is an order of magnitude larger than the 1% changes observed from the osmotic effects of mild dehydration or steroid therapy (7,8). Recent longitudinal studies have shown large reversible gray matter volume changes, including a 2.3% increase following recovery from anorexia nervosa (9) and a 6% decrease from 2 months of ultramarathon running (10). Thus, this case study provides rare longitudinal observations of an unusually large reversible change in brain volume. Moreover, it serves as a reminder that diabetes may impose large osmotic stresses on the brain from extreme levels and variability of glycemic conditions and from associated dehydration.

Acknowledgments. The authors thank the participant and his family as well as the DirecNet study team for their close collaboration, sharing of the data, and advice on the manuscript. MRI scans for the subject were conducted at the University of California, San Francisco (San Francisco, CA) and El Camino Hospital (Mountain View, CA). Neuroradiological reviews were provided by El Camino Hospital. MRI data analysis was provided by the DirecNet imaging core located at the Stanford University School of Medicine, Stanford, CA. CGM data analysis was provided by the Jaeb Center for Health Research, Tampa, FL. Cognitive testing was provided by the DirecNet cognitive core located at Nemours Children’s Health System, Jacksonville, FL.

Funding. This research was supported by the National Institutes of Health Eunice Kennedy Shriver National Institute of Child Health and Human Development (grants DIRECNET HD-41906, DIRECNET HD-41908, DIRECNET HD-41915, DIRECNET HD-41918, DIRECNET HD-56526, and R01-HD-078463).

Duality of Interest. B.A.B. is a consultant for Dexcom and has received research support from and conducted research studies for Dexcom and Medtronic. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. P.K.M., T.A., A.L.R., and B.A.B. researched data, contributed to discussion, wrote the manuscript, and reviewed and edited the manuscript. The DirecNet consortium conducted the studies, researched data, and reviewed and edited the manuscript. P.K.M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.