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

Stefański, Adrian; Wolf, Jacek; Harazny, Joanna; Miszkowska-Nagórna, Eliza; Wolnik, Bogumił; Murawska, Joanna; Schmieder, Roland E.

doi:10.1016/j.mvr.2023.104499

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…For example, using SLDF, Stefanski et al found that new-onset diabetes is associated with an increase in retinal capillary flow, whereas structural changes (as shown via increased WLR) are seen later (>10 years duration of diabetes). 34 In addition, using SLDF, Jumar et al found that early stages of type 2 diabetes did not exhibit hypertrophic remodeling (no increased wall thickness) in the retinal arterioles, while those with diabetes for more than 5 years did have these changes. 35 In our study, we add to the scientific literature by showing that in healthy subjects and subjects with DM no DR, hypertension was a strong predictor of WLR, independent of age or diabetes.…”

Section: Discussionmentioning

confidence: 99%

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

Huang,

Fawzi

2024

Trans. Vis. Sci. Tech.

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Section: Discussionmentioning

confidence: 99%

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

Huang,

Fawzi

2024

Trans. Vis. Sci. Tech.

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

Hoffmann,

Poschenrieder,

Incudini

et al. 2024

Nucleic Acids Research

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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 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.

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

Hoffmann,

Poschenrieder,

Incudini

et al. 2023

Preprint

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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-basedepistasisdetection vialocal 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.

show abstract

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

Cited by 4 publications

References 27 publications

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

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

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

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

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