Impact of type 1 diabetes and its duration on wall-to-lumen ratio and blood flow in retinal arterioles

Hypertension Likely Drives Arteriolar Wall Thickening in Preclinical Diabetic Retinopathy While Diabetes Drives Wall Thickness in Clinical Retinopathy

Purpose

Both hypertension and diabetes are known to increase the wall-to-lumen ratio (WLR) of retinal arterioles, but the differential effects are unknown. Here, we study the timing and relative impact of hypertension versus diabetes on the WLR in diabetic retinopathy (DR) to address this unresolved question.

Methods

This prospective cross-sectional study compared the retinal arteriolar WLR in 17 healthy eyes, 15 with diabetes but no apparent DR (DM no DR), and 8 with diabetic macular edema (DME) and either nonproliferative or proliferative DR. We imaged each arteriole using adaptive optics scanning laser ophthalmoscopy and measured the WLR using ImageJ. Multiple linear regression (MLR) was performed to estimate the effects of hypertension, diabetes, and age on the WLR.

Results

Both subjects with DM no DR and subjects with DME had significantly higher WLR than healthy subjects (0.36 ± 0.08 and 0.42 ± 0.08 vs. 0.29 ± 0.07, 1-way ANOVA P = 0.0009). MLR in healthy subjects and subjects with DM no DR showed hypertension had the strongest effect (regression coefficient = 0.08, P = 0.009), whereas age and diabetes were not significantly correlated with WLR. MLR in all three groups together (healthy, DM no DR, and DME) showed diabetes had the strongest effect (regression coefficient = 0.05, P = 0.02), whereas age and hypertension were not significantly correlated with WLR.

Conclusions

Hypertension may be an early driver of retinal arteriolar wall thickening in preclinical DR, independent of age or diabetes, whereas changes specific to DR may drive wall thickening in DME and later DR stages.

Translational Relevance

We offer a framework for understanding the relative contributions of hypertension and diabetes on the vascular wall, and emphasize the importance of hypertension control early in diabetes even before DR onset.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)1-3. Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL is the first application that demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.