S1059 The Incidence of COVID-19 in Patients With Metabolic Syndrome and Non-Alcoholic Steatohepatitis: A Population-Based Study
Bibliographic record
Abstract
INTRODUCTION: The novel coronavirus disease (COVID-19) emerged from China in 2019 and rapidly spread worldwide. Patients with metabolic comorbid conditions are more susceptible to COVID-19. Non-alcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome, with non-alcoholic steatohepatitis (NASH) representing a severe form of NAFLD. The aim of this study is to determine the relationship between metabolic syndrome components and the risk of COVID-19. METHODS: We reviewed data from a large commercial database (Explorys IBM) that aggregates electronic health records from 26 large nationwide healthcare systems. We identified adult with the diagnosis of metabolic syndrome and its components from 1999 to 2019. Patients with the diagnosis of COVID-19 from December 2019 to May 2020 were identified. Comorbidities known to be associated with COVID-19 and metabolic syndrome such as obesity, diabetes mellitus, dyslipidemia, smoking, gender, race, and hypertension were collected. Univariable and multivariable analyses were performed to investigate whether metabolic syndrome or its individual components are independently associated with the risk of COVID-19. RESULTS: Out of 61.4 million patients, 8,885 (0.01%) had documented COVID-19 infection. Using univariate analysis, patients with metabolic syndrome had a higher cumulative incidence of COVID-19 than those without (0.10% vs 0.01%, OR 7.00 [6.11–8.01). Patients had increased risk if they were diagnosed with hypertension (0.04% vs 0.01%, OR 5.81 [5.57–6.07]), diabetes (0.05% vs 0.01%, OR 4.88 [4.67–5.10]), dyslipidemia (0.04% vs 0.01%, OR 4.22 [4.05–4.40]) or obesity (0.06% vs 0.01%, OR 6.13 [5.86–6.40]). African Americans had a higher risk of COVID-19 (0.07% vs 0.01%, OR 9.20 [8.82–9.59]), but the risk was lower if they were male (0.02% vs 0.01%, OR 0.85 [0.81–0.89]). The overall risk of COVID-19 was the highest in those diagnosed with NASH (0.20% vs 0.01%, OR 14.10 [11.63–17.10]). The adjusted odds (aOR) of having COVID-19 was higher if patients were African American (OR 7.45 [7.14–7.77]), hypertensive (aOR 2.53 [2.40–2.68]), obese (aOR 2.20 [2.10 2.32]), diabetic (aOR 1.41 [1.33–1.48]) had dyslipidemia (OR 1.70 [1.56–1.74]) or NASH (OR 4.93 [4.05–6.00]).The adjusted odds of having COVID-19 was also lower in males compared to females (aOR 0.88 [0.84–0.92]). CONCLUSION: The incidence of COVID-19 in patients with metabolic syndrome is high. Among all comorbid metabolic conditions, NASH has the strongest association with COVID-19.Table 1.: Baseline Characteristics of Study PopulationTable 2.: Multivariable model with COVID-19 being the dependent variable. Abbreviation: OR: odds ratio; CI: confidence intervalFigure 1.: Forest plot showing adjusted odds ratio of having COVID-19. The dots represent the odds ratio and the horizontal line represents the 95% confidence interval.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".