# Identifying lifelong factors that impact brain health and outcomes in type 1 diabetes: The Cognition and Longitudinal Assessments of Risk Factors over 30 Years (CLARiFY) Diabetes Complications Study

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $509,996

## Abstract

ABSTRACT
Type 1 diabetes (T1D) is associated with brain and cognition changes as well as well-documented longer-term
micro- and macrovascular complications. The impact of brain changes and cognitive deficits on functional out-
comes, however, is less clear, and most current data relates to T1D youth. Thus, T1D is associated with signif-
icant health and functional morbidity, and in addition to personal costs, community and public health burden is
significant and likely increasing in line with the rising global T1D prevalence. Much current information about
T1D outcomes is derived from cross-sectional or longitudinal studies with short follow-up periods. These have
provided rich data about the short-term T1D impact on individuals, but have an inability to discern causal asso-
ciations and longer-term effects. Longitudinal studies examining within-individual changes in brain and cogni-
tion over time are required to better define specific risk and resilience factors that influence long-term out-
comes. The Royal Children’s Hospital (RCH) diabetes cohort study is the only prospective study of individuals
with T1D from childhood diagnosis through adulthood, with four previous waves of data collection. At baseline,
participants with T1D did not differ from healthy controls on IQ, specific cognitive skills, or emotional well-being.
Subsequent waves of data collection documented structural and functional brain changes, cognitive decre-
ments, and poorer functional outcome in T1D compared to healthy controls into early adulthood. We now pro-
pose a further follow-up of this cohort, the CLARiFY study (The Cognition and Longitudinal Assessments of
Risk Factors over 30 Years (CLARiFY) Diabetes Complications Study), to document brain, cognition, and func-
tional outcomes in mid-adult life ~30 years after T1D onset. We will use longitudinal data from the current study
and from previous waves of data collection to identify which glycemic insults are most detrimental to the
brain/cognition and at which age/stage of neurodevelopment. The following specific hypotheses will be tested:
1) in cross-sectional analyses, T1D subjects will have lower brain volumes, lower cognitive scores, and poorer
functional outcomes at middle adulthood than non-T1D subjects, and in longitudinal analyses, T1D subjects
will exhibit a greater decline in cognitive performance that associates with greater MRI differences at middle
adulthood, and greater change in MRI brain volumes from 12 years to 30 years post-diagnosis; and 2) hyper-
glycemia in childhood (<18 years) will be the strongest dysglycemic determinant of brain health in mid-adult
life. Further, the association between pre-pubertal hyperglycemia and brain outcomes will be most pronounced
in those with (a) T1D onset at <6 years of age; (b) severe hypoglycemia and/or DKA episodes after early expo-
sure to hyperglycemia, and (c) evidence of extra-cerebral microvascular and/or macrovascular disease in mid-
adult life. An empirical...

## Key facts

- **NIH application ID:** 10274067
- **Project number:** 1R01DK129320-01
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Richard Beare
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $509,996
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10274067

## Citation

> US National Institutes of Health, RePORTER application 10274067, Identifying lifelong factors that impact brain health and outcomes in type 1 diabetes: The Cognition and Longitudinal Assessments of Risk Factors over 30 Years (CLARiFY) Diabetes Complications Study (1R01DK129320-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10274067. Licensed CC0.

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