Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes

Plasma Biomarkers of Brain Injury and Their Association With Brain MRI and Cognition in Type 1 Diabetes

OBJECTIVE

To evaluate associations between plasma biomarkers of brain injury and MRI and cognitive measures in participants with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study.

RESEARCH DESIGN AND METHODS

Plasma amyloid-β-40, amyloid-β-42, neurofilament light chain (NfL), phosphorylated Tau-181 (pTau-181), and glial fibrillary acidic protein (GFAP) were measured in 373 adults who participated in the DCCT/EDIC study. MRI assessments included total brain and white matter hyperintensity volumes, white matter mean fractional anisotropy, and indices of Alzheimer disease (AD)-like atrophy and predicted brain age. Cognitive measures included memory and psychomotor and mental efficiency tests and assessments of cognitive impairment.

RESULTS

Participants were 60 (range 44-74) years old with 38 (30-51) years' T1D duration. Higher NfL was associated with an increase in predicted brain age (0.51 years per 20% increase in NfL; P < 0.001) and a 19.5% increase in the odds of impaired cognition (P < 0.01). Higher NfL and pTau-181 were associated with lower psychomotor and mental efficiency (P < 0.001) but not poorer memory. Amyloid-β measures were not associated with study measures. A 1% increase in mean HbA1c was associated with a 14.6% higher NfL and 12.8% higher pTau-181 (P < 0.0001).

CONCLUSIONS

In this aging T1D cohort, biomarkers of brain injury did not demonstrate an AD-like profile. NfL emerged as a biomarker of interest in T1D because of its association with higher HbA1c, accelerated brain aging on MRI, and cognitive dysfunction. Our study suggests that early neurodegeneration in adults with T1D is likely due to non-AD/nonamyloid mechanisms.

Automated Insulin Delivery Systems in Pediatric Type 1 Diabetes: A Narrative Review

This narrative review assesses the use of automated insulin delivery (AID) systems in managing persons with type 1 diabetes (PWD) in the pediatric population. It outlines current research, the differences between various AID systems currently on the market and the challenges faced, and discusses potential opportunities for further advancements within this field. Furthermore, the narrative review includes various expert opinions on how different AID systems can be used in the event of challenges with rapidly changing insulin requirements. These include examples, such as during illness with increased or decreased insulin requirements and during physical activity of different intensities or durations. Case descriptions give examples of scenarios with added user-initiated actions depending on the type of AID system used. The authors also discuss how another AID system could have been used in these situations.

Predicting brain age using partition modeling strategy and atlas-based attentional enhancement in the Chinese population

As a biomarker of human brain health during development, brain age is estimated based on subtle differences in brain structure from those under typical developmental. Magnetic resonance imaging (MRI) is a routine diagnostic method in neuroimaging. Brain age prediction based on MRI has been widely studied. However, few studies based on Chinese population have been reported. This study aimed to construct a brain age predictive model for the Chinese population across its lifespan. We developed a partition prediction method based on transfer learning and atlas attention enhancement. The participants were separated into four age groups, and a deep learning model was trained for each group to identify the brain regions most critical for brain age prediction. The Atlas attention-enhancement method was also used to help the models focus only on critical brain regions. The proposed method was validated using 354 participants from domestic datasets. For prediction performance in the testing sets, the mean absolute error was 2.218 ± 1.801 years, and the Pearson correlation coefficient (r) was 0.969, exceeding previous results for wide-range brain age prediction. In conclusion, the proposed method could provide brain age estimation to assist in assessing the status of brain health.

Accelerated Aging and the Life Course of Individuals Born Preterm

Individuals born preterm have shorter lifespans and elevated rates of chronic illness that contribute to mortality risk when compared to individuals born at term. Emerging evidence suggests that individuals born preterm or of low birthweight also exhibit physiologic and cellular biomarkers of accelerated aging. It is unclear whether, and to what extent, accelerated aging contributes to a higher risk of chronic illness and mortality among individuals born preterm. Here, we review accelerated aging phenotypes in adults born preterm and biological pathways that appear to contribute to accelerated aging. We highlight biomarkers of accelerated aging and various resiliency factors, including both pharmacologic and non-pharmacologic factors, that might buffer the propensity for accelerated aging among individuals born preterm.