The Retinal Nerve Fiber Layer Thickness Is Associated with Systemic Neurodegeneration in Long-Term Type 1 Diabetes

Purpose

To determine whether the retinal nerve fiber layer thickness can be used as an indicator for systemic neurodegeneration in diabetes.

Methods

We used existing data from 38 adults with type 1 diabetes and established polyneuropathy. Retinal nerve fiber layer thickness values of four scanned quadrants (superior, inferior, temporal, and nasal) and the central foveal thickness were extracted directly from optical coherence tomography. Nerve conduction velocities were recorded using standardized neurophysiologic testing of the tibial and peroneal motor nerves and the radial and median sensory nerves, 24-hour electrocardiographic recordings were used to retrieve time- and frequency-derived measures of heart rate variability, and a pain catastrophizing scale was used to assess cognitive distortion.

Results

When adjusted for hemoglobin A1c, the regional thickness of the retinal nerve fiber layers was (1) positively associated with peripheral nerve conduction velocities of the sensory and motor nerves (all P < 0.036), (2) negatively associated with time and frequency domains of heart rate variability (all P < 0.033), and (3) negatively associated to catastrophic thinking (all P < 0.038).

Conclusions

Thickness of the retinal nerve fiber layer was a robust indicator for clinically meaningful measures of peripheral and autonomic neuropathy and even for cognitive comorbidity.

Translational Relevance

The findings indicate that the thickness of the retinal nerve fiber layer should be studied in adolescents and people with prediabetes to determine whether it is useful to predict the presence and severity of systemic neurodegeneration.

DIgestive COmplications in DIabetes - the DICODI population study

BACKGROUND

Diabetes type 1 and type 2 may develop gastrointestinal complications e.g., gastroparesis and gastroenteropathy. Concomitant celiac disease and pancreatic exocrine insufficiency occur with high prevalence in diabetes and with symptomatic overlap. Consequently, it is a challenge to disentangle symptoms of these conditions and separate them from functional dyspepsia. We aim to develop a clinical decision-support tool to differentiate the underlying disease in a plethora of gastrointestinal symptoms.

METHODS

An internet-based computerized survey will collect basic characteristics (diabetes type, age, gender, duration, HbA1c, treatment) and patient reported outcomes by validated questionnaires focusing on (1) gastroparesis using Gastroparesis Cardinal Symptom Index; (2) gastroenteropathy using Gastrointestinal Symptom Rating Scale; (3) celiac disease using Celiac Symptom Index and (4) pancreatic exocrine insufficiency with Pancreatic Exocrine Insufficiency Questionnaire. Logistic regression and multiple regression analyses will identify risk factors and gastrointestinal complications. Cluster analyses and machine learning will classify different symptoms and co-existing presentations, into a likely diagnosis. We seek biomarkers for autonomic neuropathy by characterizing development of retinopathy using the Visual Function Questionnaire-25 and peripheral neuropathy by the Michigan neuropathy questionnaire. Participants are re-examined yearly for disease progression over time.

RESULTS

From focus group studies gastrointestinal symptoms are of major concern in diabetes. Potentially, estimates of symptom prevalence, risk factor identification and classifications of gastrointestinal complications can be unraveled for feedback to health care providers.

CONCLUSION

The web-based DICODI project will open up possibilities to detect gastrointestinal complications of diabetes in a societal setting, benefitting people living with diabetes, health care professionals, and society.