Risk Factors for COVID-19: Diabetes, Hypertension, and Obesity

Buscemi, Silvio; Corleo, Davide; Randazzo, Claudia

doi:10.1007/978-3-030-85113-2_7

Cited by 16 publications

(19 citation statements)

References 72 publications

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“…In addition, the molecules forming the networks of human proteins related to SARS-CoV-2 were found to be significantly overrepresented in the gene networks of the diabetes complications (CVD, diabetic neuropathy, diabetic nephropathy, and diabetic retinopathy), as well as in the insulin resistance and beta-cell dysfunction networks. These findings are consistent with clinical data on more severe courses and poorer outcomes of COVID-19 in subjects with diabetes [2,3] and give further support to notion of parallels between COVID-19 and diabetes pathology [8].…”

Section: Discussionsupporting

confidence: 89%

“…During the outbreak of the epidemic, individuals with diabetes turned out to be one of the most vulnerable cohorts. Though there is no strong evidence that diabetes predisposes to infection with SARS-CoV-2, patients with diabetes demonstrated more severe COVID-19 and higher intensive care unit admission and mortality rates [2,3]. Hyperglycemia has been repeatedly recognized as a risk factor for poor outcomes from COVID-19 in patients with pre-existing diabetes [4,5].…”

Section: Introductionmentioning

confidence: 99%

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Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

Saik

Климонтов

2022

IJMS

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People with diabetes are more likely to have severe COVID-19 compared to the general population. Moreover, diabetes and COVID-19 demonstrate a certain parallelism in the mechanisms and organ damage. In this work, we applied bioinformatics analysis of associative molecular networks to identify key molecules and pathophysiological processes that determine SARS-CoV-2-induced disorders in patients with diabetes. Using text-mining-based approaches and ANDSystem as a bioinformatics tool, we reconstructed and matched networks related to hyperglycemia, diabetic complications, insulin resistance, and beta cell dysfunction with networks of SARS-CoV-2-targeted proteins. The latter included SARS-CoV-2 entry receptors (ACE2 and DPP4), SARS-CoV-2 entry associated proteases (TMPRSS2, CTSB, and CTSL), and 332 human intracellular proteins interacting with SARS-CoV-2. A number of genes/proteins targeted by SARS-CoV-2 (ACE2, BRD2, COMT, CTSB, CTSL, DNMT1, DPP4, ERP44, F2RL1, GDF15, GPX1, HDAC2, HMOX1, HYOU1, IDE, LOX, NUTF2, PCNT, PLAT, RAB10, RHOA, SCARB1, and SELENOS) were found in the networks of vascular diabetic complications and insulin resistance. According to the Gene Ontology enrichment analysis, the defined molecules are involved in the response to hypoxia, reactive oxygen species metabolism, immune and inflammatory response, regulation of angiogenesis, platelet degranulation, and other processes. The results expand the understanding of the molecular basis of diabetes and COVID-19 comorbidity.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

Saik

Климонтов

2022

IJMS

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“…Along the same line, we found that comorbidities were overall less frequent in the northern region when compared to the rest of Italy. Clearly, since comorbidities increase the risk of death and severe COVID-19 [3] , [29] , comorbidities are likely another explanation for worse clinical outcomes in the south compared to the north of Italy. Moreover, the main comorbidities that constitute risk factors for severe COVID-19 are diabetes, cardiovascular diseases and chronic lung diseases, cancer, chronic kidney diseases, liver diseases, obesity, sickle cell disease, dementia and other neurological conditions, disabilities, mental health conditions, physical inactivity, and immunocompromising conditions [30] , [31] , [32] , [33] , [34] , [35] .…”

Section: Discussionmentioning

confidence: 99%

“…[1] Several studies showed that pre-existing comorbidities (diabetes mellitus and hyperglycemia, obesity, arterial hypertension, kidney disease, cancer, and liver disease) and elderly age were the main risk factors for severe disease evolution and death. [2] , [3] , [4] , [5] However, infection and death rates were not homogeneous. Available national data at the beginning of pandemic showed that infection cases and death rates were approximately 7 to 12 times higher in the northern region than in the southern region.…”

Section: Introductionmentioning

confidence: 99%

The three facets of the SARS-CoV-2 pandemic during the first two waves in the northern, central, and southern Italy

Buscemi¹,

Davoli²,

Trecarichi³

et al. 2023

Journal of Infection and Public Health

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“…Both T2D and hypertension are risk factors associated with stroke and other serious and life-threatening events [8]. In fact, during the recent COVID-19 pandemic, outcomes of patients seem to be negatively affected by the presence of T2D, hypertension and obesity [9].…”

Section: Introductionmentioning

confidence: 99%

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

Pineda

Vernekar

Grau³

et al. 2022

Preprint

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IntroductionA major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.MethodsWe collected genotyped data from a novel DTC platform where participants upload their genotype data files, and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case-control setting using the BASIL algorithm.ResultsWe collected data on N=4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D; and N=4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC=0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers.DiscussionDTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.ConclusionsThe genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.

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Risk Factors for COVID-19: Diabetes, Hypertension, and Obesity

Cited by 16 publications

References 72 publications

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

Gene Networks of Hyperglycemia, Diabetic Complications, and Human Proteins Targeted by SARS-CoV-2: What Is the Molecular Basis for Comorbidity?

The three facets of the SARS-CoV-2 pandemic during the first two waves in the northern, central, and southern Italy

Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research

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