The associations of apolipoprotein E and angiotensin-converting enzyme polymorphisms and cognitive function in Type 1 diabetes based on an 18-year follow-up of the DCCT cohort

Increased incidence of neurodegenerative diseases in Finnish individuals with type 1 diabetes

INTRODUCTION

Diabetes is linked to neurodegenerative diseases (NDs), but data in type 1 diabetes are scarce. Our aim was to assess the standardized incidence ratios (SIRs) of different NDs in type 1 diabetes, and to evaluate the impact of diabetic vascular complications and age at diabetes onset.

RESEARCH DESIGN AND METHODS

In this observational cohort study, we included 4261 individuals with type 1 diabetes from the Finnish Diabetic Nephropathy study, and 11 653 matched population-based controls without diabetes. NDs were identified from registers until the end of 2017. Diabetic complications were assessed at the baseline study visit. SIRs were calculated from diabetes onset, except for impact of complications that was calculated from baseline study visit.

RESULTS

The SIRs for NDs were increased in type 1 diabetes: any dementia 2.24 (95% CI 1.79 to 2.77), Alzheimer's disease 2.13 (95% CI 1.55 to 2.87), vascular dementia 3.40 (95% CI 2.08 to 5.6), other dementias 1.70 (95% CI 1.22 to 2.31), and Parkinson's disease 1.61 (95% CI 1.04 to 2.37). SIR showed a twofold increased incidence already in those without albuminuria (1.99 (1.44-2.68)), but further increased in presence of diabetic complications: kidney disease increased SIR for Alzheimer's disease, while cardiovascular disease increased SIR for both Alzheimer's disease and other dementias. Diabetes onset <15 years, compared with ≥15 years, increased SIR of Alzheimer's disease, 3.89 (2.21-6.35) vs 1.73 (1.16-2.48), p<0.05, but not the other dementias.

CONCLUSIONS

ND incidence is increased 1.7-3.4-fold in type 1 diabetes. The presence of diabetic kidney disease and cardiovascular disease further increased the incidence of dementia.

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.

Diagnostic, Prognostic, and Mechanistic Biomarkers of Diabetes Mellitus-Associated Cognitive Decline

Cognitive dysfunctions such as mild cognitive impairment (MCI), Alzheimer's disease (AD), and other forms of dementia are recognized as common comorbidities of type 2 diabetes mellitus (T2DM). Currently, there are no disease-modifying therapies or definitive clinical diagnostic and prognostic tools for dementia, and the mechanisms underpinning the link between T2DM and cognitive dysfunction remain equivocal. Some of the suggested pathophysiological mechanisms underlying cognitive decline in diabetes patients include hyperglycemia, insulin resistance and altered insulin signaling, neuroinflammation, cerebral microvascular injury, and buildup of cerebral amyloid and tau proteins. Given the skyrocketing global rates of diabetes and neurodegenerative disorders, there is an urgent need to discover novel biomarkers relevant to the co-morbidity of both conditions to guide future diagnostic approaches. This review aims to provide a comprehensive background of the potential risk factors, the identified biomarkers of diabetes-related cognitive decrements, and the underlying processes of diabetes-associated cognitive dysfunction. Aging, poor glycemic control, hypoglycemia and hyperglycemic episodes, depression, and vascular complications are associated with increased risk of dementia. Conclusive research studies that have attempted to find specific biomarkers are limited. However, the most frequent considerations in such investigations are related to C reactive protein, tau protein, brain-derived neurotrophic factor, advanced glycation end products, glycosylated hemoglobin, and adipokines.

Neural networks in the predictive diagnosis of cognitive impairment in type 1 and type 2 diabetes mellitus

Background. Cognitive dysfunction, including mild cognitive impairment and dementia, is increasingly recognized as a serious complication of diabetes mellitus (DM) that affects patient well-being and disease management. Magnetic resonance imaging (MRI)-studies have shown varying degrees of cortical atrophy, cerebral infarcts, and deep white matter lesions. To explain the relationship between DM and cognitive decline, several hypotheses have been proposed, based on the variability of glycemia leading to morphometric changes in the brain. The ability to predict cognitive decline even before its clinical development will allow the early prevention of this pathology, as well as to predict the course of the existing pathology and to adjust medication regimens. Aim. To create a computer neural network model for predicting the development of cognitive impairment in DM on the basis of brain neuroimaging techniques. Materials and methods. The study was performed in accordance with the standards of good clinical practice; the protocol was approved by the Ethics Committee. The study included 85 patients with type 1 diabetes and 95 patients with type 2 diabetes, who were divided into a group of patients with normal cognitive function and a group with cognitive impairment. The patient groups were comparable in age and duration of disease. Cognitive impairment was screened using the Montreal Cognitive Assessment Scale. Data for glycemic variability were obtained using continuous glucose monitoring (iPro2, Libre). A standard MRI scan of the brain was performed axially, sagittally, and coronally on a Signa Creator E, GE Healthcare, 1.5 Tesla, China. For MRI data processing we used Free Surfer program (USA) for analysis and visualization of structural and functional neuroimaging data from cross-sectional or longitudinal studies, and for segmentation we used Recon-all batch program directly. All statistical analyses and data processing were performed using Statistica Statsofi software (version 10) on Windows 7/XP Pro operating systems. The IBM WATSON cognitive system was used to build a neural network model. Results. As a result of the study, cognitive impairment in DM type 1was predominantly of mild degree 36.9% (n=24) and moderate degree 30.76% (n=20), and in DM type 2 mild degree 37% (n=30), moderate degree 49.4% (n=40) and severe degree 13.6% (n=11). Cognitive functions in DM type 1 were impaired in memory and attention, whereas in DM type 2 they were also impaired in tasks of visual-constructive skills, fluency, and abstraction (p0.001). The analysis revealed differences in glycemic variability indices in patients with type 1 and type 2 DM and cognitive impairment. Standard MRI of the brain recorded the presence of white and gray matter changes (gliosis and leukoareosis). General and regional cerebral atrophy is characteristic of type 1 and type 2 DM, which is associated with dysglycemia. When building neural network models for type 1 diabetes, the parameters of decreased volumes of the brain regions determine the development of cognitive impairment by 93.5%, whereas additionally, the coefficients of glycemic variability by 98.5%. The same peculiarity was revealed in type 2 DM 95.3% and 97.9%, respectively. Conclusion. In DM type 1 and type 2 with cognitive impairment, elevated coefficients of glycemic variability are more frequently recorded. This publication describes laboratory and instrumental parameters as potential diagnostic options for effective management of DM and prevention of cognitive impairment. Neural network models using glycemic variability coefficients and MR morphometry allow for predictive diagnosis of cognitive disorders in both types of diabetes.

Cognitive Functioning and Hippocampal Connectivity in Patients With Longstanding Type 1 Diabetes and Apolipoprotein E ε4

OBJECTIVE

While the apolipoprotein E ε4 allele (ApoE-ε4) is related to cognitive and brain decline in the general population, its effect on the brain in type 1 diabetes mellitus (T1DM) remains unclear. Therefore, the aim was to determine the interaction between ApoE-ε4 and T1DM on cognitive performance and hippocampal structure and connectivity as the brain area most vulnerable to ApoE-ε4 effects in adult patients with T1DM.

RESEARCH DESIGN AND METHODS

Blood sampling was performed in 104 patients with T1DM and 49 control subjects for ApoE genotyping, neuropsychology, and neuroimaging to determine hippocampal volume and resting-state connectivity. The interaction between T1DM status and ApoE-ε4 presence was investigated and adjusted for age and mean systolic blood pressure.

RESULTS

ApoE genotyping could not be performed for three patients with T1DM. Significant interaction effects, indicating a differential effect of ApoE-ε4 between both groups, were found for overall cognitive functioning and for the subdomains of information processing speed and attention. Additionally, interaction effects were present for right hippocampal connectivity with the right posterior cingulate and supramarginal gyri. Subsequent group analysis showed that patients with T1DM with ApoE-ε4 performed worse on these cognitive domains with increased connectivity, relative to their counterparts without ApoE-ε4. In contrast, no cognitive effects, but decreased connectivity, were observed in control subjects with ApoE-ε4. In patients with T1DM, higher right hippocampus connectivity with the posterior cingulate gyrus was related to poorer overall cognitive functioning.

CONCLUSIONS

The results may suggest that ApoE-ε4 presence leaves our patients with T1DM more susceptible to cognitive decrements at a younger age, possibly through vascular pathways, warranting further longitudinal studies.

A systematic review of non-genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis

Type 1 diabetes (T1D) results from autoimmune destruction of the pancreatic βcells. Although all T1D patients require daily administration of exogenous insulin, their insulin requirement to achieve good glycaemic control may vary significantly. Glycated haemoglobin (HbA1c) level represents a stable indicator of glycaemic control and is a reliable predictor of long-term complications of T1D. The purpose of this article is to systematically review the role of non-genetic predictors and genetic factors of HbA1c level in T1D patients after the first year of T1D, to exclude the honeymoon period. A total of 1974 articles published since January 2011 were identified and 78 were finally included in the analysis of non-genetic predictors. For genetic factors, a total of 277 articles were identified and 14 were included. The most significantly associated factors with HbA1c level are demographic (age, ethnicity, and socioeconomic status), personal (family characteristics, parental care, psychological traits...) and features related to T1D (duration of T1D, adherence to treatment …). Only a few studies have searched for genetic factors influencing HbA1c level, most of which focused on candidate genes using classical genetic statistical methods, with generally limited power and incomplete adjustment for confounding factors and multiple testing. Our review shows the complexity of explaining HbA1c level variations, which involves numerous correlated predictors. Overall, our review underlines the lack of studies investigating jointly genetic and non-genetic factors and their interactions to better understand factors influencing glycaemic control for T1D patients.

Biomarkers for cognitive decline in patients with diabetes mellitus: evidence from clinical studies

Diabetes mellitus is considered as an important factor for cognitive decline and dementia in recent years. However, cognitive impairment in diabetic patients is often underestimated and kept undiagnosed, leading to thousands of diabetic patients suffering from worsening memory. Available reviews in this field were limited and not comprehensive enough. Thus, the present review aimed to summarize all available clinical studies on diabetic patients with cognitive decline, and to find valuable biomarkers that might be applied as diagnostic and therapeutic targets of cognitive impairment in diabetes. The biomarkers or risk factors of cognitive decline in diabetic patients could be classified into the following three aspects: serum molecules or relevant complications, functional or metabolic changes by neuroimaging tools, and genetic variants. Specifically, factors related to poor glucose metabolism, insulin resistance, inflammation, comorbid depression, micro-/macrovascular complications, adipokines, neurotrophic molecules and Tau protein presented significant changes in diabetic patients with cognitive decline. Besides, neuroimaging platform could provide more clues on the structural, functional and metabolic changes during the cognitive decline progression of diabetic patients. Genetic factors related to cognitive decline showed inconsistency based on the limited studies. Future studies might apply above biomarkers as diagnostic and treatment targets in a large population, and regulation of these parameters might shed light on a more valuable, sensitive and specific strategy for the diagnosis and treatment of cognitive decline in diabetic patients.

Cognitive disorders in diabetic patients

This chapter gives an overview of the literature on cognitive dysfunction in adults with type 1 or type 2 diabetes. First, methods to evaluate cognitive functioning and the pattern and severity of cognitive dysfunction in relation to diabetes will be discussed. The reader will note that diabetes is associated with worse cognitive functioning and an increased dementia risk. Next, diabetes-associated abnormalities on brain MRI, including reductions in brain volume - i.e., cerebral atrophy - and vascular lesions, will be addressed. At the group level there are clear relations between these imaging abnormalities and cognitive dysfunction, but at the level of the individual patient these relations are often less clear. Subsequently, risk factors for cognitive performance will be discussed. Evidently, these risk factors are related to diabetes type and the age of the patients involved. For type 1 diabetes, an early age at diabetes onset is the most consistent risk factor, whereas in type 2 diabetes, vascular risk factors and vascular comorbidities are consistent indicators of increased risk. The final section of the chapter addresses possible preventive and treatment measures and implications for daily care.

Influence of Alzheimer's disease genes on cognitive decline: the Guangzhou Biobank Cohort Study

Cognitive decline is a reduction in cognitive ability usually associated with aging, and those with more extreme cognitive decline either have or are at risk of progressing to mild cognitive impairment and dementia including Alzheimer's disease (AD). We hypothesized that genetic variants predisposing to AD should be predictive of cognitive decline in elderly individuals. We selected 1325 subjects with extreme cognitive decline and 1083 well-matched control subjects from the Guangzhou Biobank Cohort Study in which more than 30,000 southern Chinese older people have been recruited and followed up. Thirty single-nucleotide polymorphisms in 29 AD-associated genes were genotyped. No statistically significant allelic associations with cognitive decline were found by individual variant analysis. At the level of genotypic association, we confirmed that the APOE ε4 homozygote significantly accelerated cognitive decline and found that carriers of the ACE rs1800764_C allele were more likely to show cognitive decline than noncarriers, particularly in those without college education. However, these effects do not survive after multiple testing corrections, and together they only explain 1.7% of the phenotypic variance in cognitive score change. This study suggests that AD risk variants and/or genes are not powerful predictors of cognitive decline in our Chinese sample.

The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: summary and future directions

OBJECTIVE The Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study continues to address knowledge gaps in our understanding of type 1 diabetes and the effects of intensive therapy on its long-term complications. RESEARCH DESIGN AND METHODS During the DCCT (1982-1993), a controlled clinical trial of 1,441 subjects with type 1 diabetes, and the EDIC (1994-present), an observational study of the DCCT cohort, core data collection has included medical history questionnaires, surveillance health exams, and frequent laboratory and other evaluations for microvascular and macrovascular disease. Numerous collaborations have expanded the outcome data with more detailed investigations of cardiovascular disease, cognitive function, neuropathy, genetics, and potential biological pathways involved in the development of complications. RESULTS The longitudinal follow-up of the DCCT/EDIC cohort provides the opportunity to continue monitoring the durability of intensive treatment as well as to address lingering questions in type 1 diabetes research. Future planned analyses will address the onset and progression of microvascular triopathy, evidence-based screening for retinopathy and nephropathy, effects of glycemic variability and nonglycemic risk factors on outcomes, long-term impact of intensive therapy on cognitive decline, and health economics. Three new proposed investigations include an examination of residual C-peptide secretion and its impact, prevalence of hearing impairment, and evaluation of gastrointestinal dysfunction. CONCLUSIONS With the comprehensive data collection and the remarkable participant retention over 30 years, the DCCT/EDIC continues as an irreplaceable resource for understanding type 1 diabetes and its long-term complications.