Objective

Retinopathy impacts over one-third of those with diabetes mellitus and is associated with impaired cognitive performance and cerebrovascular lesions in middle-aged adults with type 1 diabetes. However, the association between diabetic retinopathy (DR) and risk of dementia in type 1 diabetes is unknown. We investigated the association between DR and incident dementia in a large, elderly population with type 1 diabetes.

Methods

A cohort of 3742 patients with type 1 diabetes aged 50 years and above was followed from January 1, 1996 to September 30, 2015 for incident dementia. DR diagnoses were identified from electronic medical records. Age as timescale Cox proportional hazard models evaluated associations between time-updated DR and dementia risk. Models were adjusted for demographics, severe glycemic events, glycosylated hemoglobin, and vascular comorbidities.

Results

Among 3742 patients with type 1 diabetes (47% female, 21% nonwhite), 182 (5%) were diagnosed with dementia during a mean follow-up of 6.2 years. No significant association was found between DR and incident dementia in the main analyses [adjusted Hazard Ratio=1.12; 95% confidence interval, 0.82-1.54), nor among subgroup restricted to those aged 60 years and above or 70 years and above.

Conclusions

DR was not associated with risk of dementia, suggesting that pathophysiological processes underlying dementia may be different in type 1 versus type 2 diabetes.

Background

Although patients with type 2 diabetes are twice as likely to develop dementia as those without this disease, prediction of who has the highest future risk is difficult. We therefore created and validated a practical summary risk score that can be used to provide an estimate of the 10 year dementia risk for individuals with type 2 diabetes.

Methods

Using data from two longitudinal cohorts of patients with type 2 diabetes (aged ≥60 years) with 10 years of follow-up, we created (n=29,961) and validated (n=2413) the risk score. We built our prediction model by evaluating 45 candidate predictors using Cox proportional hazard models and developed a point system for the risk score based on the size of the predictor's β coefficient. Model prediction was tested by discrimination and calibration methods. Dementia risk per sum score was calculated with Kaplan-Meier estimates.

Findings

Microvascular disease, diabetic foot, cerebrovascular disease, cardiovascular disease, acute metabolic events, depression, age, and education were most strongly predictive of dementia and constituted the risk score (C statistic 0·736 for creation cohort and 0·746 for validation cohort). The dementia risk was 5·3% (95% CI 4·2-6·3) for the lowest score (-1) and 73·3% (64·8-81·8) for the highest (12-19) sum scores.

Interpretation

To the best of our knowledge, this is the first risk score for the prediction of 10 year dementia risk in patients with type 2 diabetes mellitus. The risk score can be used to increase vigilance for cognitive deterioration and for selection of high-risk patients for participation in clinical trials.

Funding

Kaiser Permanente Community Benefit, National Institute of Health, Utrecht University, ZonMw, and Fulbright.

Introduction

White matter hyperintensities (WMH) are associated with key dementia etiologies, in particular arteriolosclerosis and amyloid pathology. We aimed to identify WMH locations associated with vascular risk or cerebral amyloid-β_1-42 (Aβ42)-positive status.

Methods

Individual patient data (n = 3,132; mean age 71.5 ± 9 years; 49.3% female) from 11 memory clinic cohorts were harmonized. WMH volumes in 28 regions were related to a vascular risk compound score (VRCS) and Aß42 status (based on cerebrospinal fluid or amyloid positron emission tomography), correcting for age, sex, study site, and total WMH volume.

Results

VRCS was associated with WMH in anterior/superior corona radiata (B = 0.034/0.038, p < 0.001), external capsule (B = 0.052, p < 0.001), and middle cerebellar peduncle (B = 0.067, p < 0.001), and Aß42-positive status with WMH in posterior thalamic radiation (B = 0.097, p < 0.001) and splenium (B = 0.103, p < 0.001).

Discussion

Vascular risk factors and Aß42 pathology have distinct signature WMH patterns. This regional vulnerability may incite future studies into how arteriolosclerosis and Aß42 pathology affect the brain's white matter.

Highlights

Key dementia etiologies may be associated with specific patterns of white matter hyperintensities (WMH). We related WMH locations to vascular risk and cerebral Aβ42 status in 11 memory clinic cohorts. Aβ42 positive status was associated with posterior WMH in splenium and posterior thalamic radiation. Vascular risk was associated with anterior and infratentorial WMH. Amyloid pathology and vascular risk have distinct signature WMH patterns.

Introduction

The spatial distribution of white matter hyperintensities (WMH) on MRI is often considered in the diagnostic evaluation of patients with cognitive problems. In some patients, clinicians may classify WMH patterns as "unusual", but this is largely based on expert opinion, because detailed quantitative information about WMH distribution frequencies in a memory clinic setting is lacking. Here we report voxel wise 3D WMH distribution frequencies in a large multicenter dataset and also aimed to identify individuals with unusual WMH patterns.

Methods

Individual participant data (N = 3525, including 777 participants with subjective cognitive decline, 1389 participants with mild cognitive impairment and 1359 patients with dementia) from eleven memory clinic cohorts, recruited through the Meta VCI Map Consortium, were used. WMH segmentations were provided by participating centers or performed in Utrecht and registered to the Montreal Neurological Institute (MNI)-152 brain template for spatial normalization. To determine WMH distribution frequencies, we calculated WMH probability maps at voxel level. To identify individuals with unusual WMH patterns, region-of-interest (ROI) based WMH probability maps, rule-based scores, and a machine learning method (Local Outlier Factor (LOF)), were implemented.

Results

WMH occurred in 82% of voxels from the white matter template with large variation between subjects. Only a small proportion of the white matter (1.7%), mainly in the periventricular areas, was affected by WMH in at least 20% of participants. A large portion of the total white matter was affected infrequently. Nevertheless, 93.8% of individual participants had lesions in voxels that were affected in less than 2% of the population, mainly located in subcortical areas. Only the machine learning method effectively identified individuals with unusual patterns, in particular subjects with asymmetric WMH distribution or with WMH at relatively rarely affected locations despite common locations not being affected.

Discussion

Aggregating data from several memory clinic cohorts, we provide a detailed 3D map of WMH lesion distribution frequencies, that informs on common as well as rare localizations. The use of data-driven analysis with LOF can be used to identify unusual patterns, which might serve as an alert that rare causes of WMH should be considered.

White matter hyperintensities of presumed vascular origin (WMH) are associated with cognitive impairment and are a key imaging marker in evaluating brain health. However, WMH volume alone does not fully account for the extent of cognitive deficits and the mechanisms linking WMH to these deficits remain unclear. Lesion network mapping (LNM) enables us to infer if brain networks are connected to lesions and could be a promising technique for enhancing our understanding of the role of WMH in cognitive disorders. Our study employed LNM to test the following hypotheses: (i) LNM-informed markers surpass WMH volumes in predicting cognitive performance; and (ii) WMH contributing to cognitive impairment map to specific brain networks. We analysed cross-sectional data of 3485 patients from 10 memory clinic cohorts within the Meta VCI Map Consortium, using harmonized test results in four cognitive domains and WMH segmentations. WMH segmentations were registered to a standard space and mapped onto existing normative structural and functional brain connectome data. We employed LNM to quantify WMH connectivity to 480 atlas-based grey and white matter regions of interest (ROI), resulting in ROI-level structural and functional LNM scores. We compared the capacity of total and regional WMH volumes and LNM scores in predicting cognitive function using ridge regression models in a nested cross-validation. LNM scores predicted performance in three cognitive domains (attention/executive function, information processing speed, and verbal memory) significantly better than WMH volumes. LNM scores did not improve prediction for language functions. ROI-level analysis revealed that higher LNM scores, representing greater connectivity to WMH, in grey and white matter regions of the dorsal and ventral attention networks were associated with lower cognitive performance. Measures of WMH-related brain network connectivity significantly improve the prediction of current cognitive performance in memory clinic patients compared to WMH volume as a traditional imaging marker of cerebrovascular disease. This highlights the crucial role of network integrity, particularly in attention-related brain regions, improving our understanding of vascular contributions to cognitive impairment. Moving forward, refining WMH information with connectivity data could contribute to patient-tailored therapeutic interventions and facilitate the identification of subgroups at risk of cognitive disorders.