Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk

Sood, Tushar; Perrot, Nicolas; Chong, Michael; Mohammadi-Shemirani, Pedrum; Mushtaha, Maha; Leong, Darryl P.; Rangarajan, Sumathy; Hess, Sibylle; Yusuf, Salim; Gerstein, Hertzel C.; Paré, Guillaume; Pigeyre, Marie

doi:10.1001/jamanetworkopen.2023.25914

Cited by 4 publications

(5 citation statements)

References 47 publications

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“…Additionally, another review and meta-analysis conducted in the first year of the pandemic showed that obesity increased the risk of hospitalization and mortality among COVID-19 patients [ 14 ]. Furthermore, an international genetic study that included over 200,000 subjects found that diabetes was one of the factors that increased the risk of COVID-19-related death [ 8 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have shown that even short-term exposure to air pollution increases the susceptibility to SARS-CoV-2 infection [ 6 , 7 ]. More recently, a genetic association study that employed the mendelian randomization to explore the relationship between cardiometabolic risk biomarkers and severe SARS-CoV-2 infection has incriminated obesity and diabetes as contributors in the occurrence of severe COVID-19 outcomes such as intensive care unit admissions and deaths [ 8 ]. However, studies that explored the association between COVID-19 severe outcomes (disease case fatality or disability-adjusted life years, DALYs) at regional level and/or air pollution are scarcely found in the medical literature.…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study

Ngatu,

Tonduangu,

Munyeshyaka

et al. 2024

Environ. Health Prev. Med.

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Background Air pollution and a number of metabolic disorders have been reported to increase the risk of severe COVID-19 outcomes. This study explored the association between severe COVID-19 outcomes, metabolic disorders and environmental air pollutants, at regional level, across 38 countries. Methods We conducted an ecological study using COVID-19 data related to countries of the Organization for Economic Cooperation and Development (OECD), with an estimated population of 1.4 billion. They were divided into 3 regions: 1. Europe & Middle east; 2. Americas (north, central & south America); 3. East-Asia & West Pacific. The outcome variables were: COVID-19 case-fatality rate (CFR) and disability-adjusted life years (DALYs) at regional level. Freely accessible datasets related to regional DALYs, demographics and other environmental pollutants were obtained from OECD, WHO and the World in Data websites. Generalized linear model (GLM) was performed to determine the regional determinants of COVID-19 CFR and DALYs using the aggregate epidemiologic data (Dec. 2019–Dec. 2021). Results Overall cumulative deaths were 65,000 per million, for mean CFR and DALYs of 1.31 (1.2)% and 17.35 (2.3) years, respectively. Globally, GLM analysis with adjustment for elderly population rate, showed that COVID-19 CFR was positively associated with atmospheric PM 2.5 level (beta = 0.64(0.0), 95%CI: 0.06–1.35; p < 0.05), diabetes prevalence (beta = 0.26(0.1), 95%CI: 0.12–0.41; p < 0.001). For COVID-19 DALYs, positive associations were observed with atmospheric NOx level (beta = 0.06(0.0), 95%CI: 0.02–0.82; p < 0.05) and diabetes prevalence (beta = 0.32(0.2), 95%CI: 0.04–0.69; p < 0.05). At regional level, adjusted GLM analysis showed that COVID-19 CFR was associated with atmospheric PM 2.5 level in the Americas and East-Asia & Western Pacific region; it was associated with diabetes prevalence for countries of Europe & Middle east and East-Asia & Western Pacific region. Furthermore, COVID-19 DALYs were positively associated with atmospheric PM 2.5 and diabetes prevalence for countries of the Americas only. Conclusion These findings confirm that diabetes and air pollution increase the risk of disability and fatality due to COVID-19, with disparities in terms of their impact. They suggest that efficient preventive and management programs for diabetes and air pollution countermeasures would have curtailed severe COVID-19 outcome rates.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study

Ngatu,

Tonduangu,

Munyeshyaka

et al. 2024

Environ. Health Prev. Med.

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“…For the outcome that exhibited a significant correlation with exposure in TSMR, we performed another TSMR for another independent GWAS. Then, meta-analysis across different GWAS cohorts was performed using fixed-effects analysis, under the assumption that 1 true effect size applied across all GWAS considered, as in previous studies[ 45 ].…”

Section: Methodsmentioning

confidence: 99%

Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases

Wen,

Zhang,

Zhang

et al. 2024

J Transl Med

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Background Epidemiological evidence links a close correlation between long-term exposure to air pollutants and autoimmune diseases, while the causality remained unknown. Methods Two-sample Mendelian randomization (TSMR) was used to investigate the role of PM10, PM2.5, NO2, and NOX (N = 423,796–456,380) in 15 autoimmune diseases (N = 14,890–314,995) using data from large European GWASs including UKB, FINNGEN, IMSGC, and IPSCSG. Multivariable Mendelian randomization (MVMR) was conducted to investigate the direct effect of each air pollutant and the mediating role of common factors, including body mass index (BMI), alcohol consumption, smoking status, and household income. Transcriptome-wide association studies (TWAS), two-step MR, and colocalization analyses were performed to explore underlying mechanisms between air pollution and autoimmune diseases. Results In TSMR, after correction of multiple testing, hypothyroidism was causally associated with higher exposure to NO2 [odds ratio (OR): 1.37, p = 9.08 × 10–4] and NOX [OR: 1.34, p = 2.86 × 10–3], ulcerative colitis (UC) was causally associated with higher exposure to NOX [OR: 2.24, p = 1.23 × 10–2] and PM2.5 [OR: 2.60, p = 5.96 × 10–3], rheumatoid arthritis was causally associated with higher exposure to NOX [OR: 1.72, p = 1.50 × 10–2], systemic lupus erythematosus was causally associated with higher exposure to NOX [OR: 4.92, p = 6.89 × 10–3], celiac disease was causally associated with lower exposure to NOX [OR: 0.14, p = 6.74 × 10–4] and PM2.5 [OR: 0.17, p = 3.18 × 10–3]. The risky effects of PM2.5 on UC remained significant in MVMR analyses after adjusting for other air pollutants. MVMR revealed several common mediators between air pollutants and autoimmune diseases. Transcriptional analysis identified specific gene transcripts and pathways interconnecting air pollutants and autoimmune diseases. Two-step MR revealed that POR, HSPA1B, and BRD2 might mediate from air pollutants to autoimmune diseases. POR pQTL (rs59882870, PPH4=1.00) strongly colocalized with autoimmune diseases. Conclusion This research underscores the necessity of rigorous air pollutant surveillance within public health studies to curb the prevalence of autoimmune diseases. Graphical abstract (Built by the Biorender)

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“…Thus, genic IVs can be identified by searching through databases or reports that assess the relationship between genetic factors and the observed variable in question. Previous genome-wide association studies (GWASs) are especially useful in this regard [ 146 , 147 ], as they are hypothesis-free scans that depict the correlation between millions of SNPs and the observed variables and clinical outcomes.…”

Section: Evaluating the Consequences Of Controlling Confounders On Th...mentioning

confidence: 99%

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Qiao,

Chen,

Qian

et al. 2024

J Transl Med

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Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

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Biomarkers Associated With Severe COVID-19 Among Populations With High Cardiometabolic Risk

Cited by 4 publications

References 47 publications

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study

COVID-19 fatality and DALYs, and associated metabolic disorders and ambient air pollutants in pre-Omicron era of the pandemic: an international comparative study

Large-scale genome-wide association studies reveal the genetic causal etiology between air pollutants and autoimmune diseases

Clinical data mining: challenges, opportunities, and recommendations for translational applications

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