Increased Metabolic Burden Among Blacks: A Putative Mechanism for Disparate COVID-19 Outcomes.
Jean-Louis GirardinArlener D TurnerPeng JinMengling LiuCarla Boutin-FosterSamy I McFarlaneAzizi SeixasPublished in: Diabetes, metabolic syndrome and obesity : targets and therapy (2020)
Mounting evidence shows a disproportionate COVID-19 burden among Blacks. Early findings indicate pre-existing metabolic burden (eg, obesity, hypertension and diabetes) as key drivers of COVID-19 severity. Since Blacks exhibit higher prevalence of metabolic burden, we examined the influence of metabolic syndrome on disparate COVID-19 burden. We analyzed data from a NIH-funded study to characterize metabolic burden among Blacks in New York (Metabolic Syndrome Outcome Study). Patients (n=1035) were recruited from outpatient clinics, where clinical and self-report data were obtained. The vast majority of the sample was overweight/obese (90%); diagnosed with hypertension (93%); dyslipidemia (72%); diabetes (61%); and nearly half of them were at risk for sleep apnea (48%). Older Blacks (age≥65 years) were characterized by higher levels of metabolic burden and co-morbidities (eg, heart disease, cancer). In multivariate-adjusted regression analyses, age was a significant (p≤.001) independent predictor of hypertension (OR=1.06; 95% CI: 1.04-1.09), diabetes (OR=1.03; 95% CI: 1.02-1.04), and dyslipidemia (OR=0.98; 95% CI: 0.97-0.99), but not obesity. Our study demonstrates an overwhelmingly high prevalence of the metabolic risk factors related to COVID-19 among Blacks in New York, highlighting disparate metabolic burden among Blacks as a possible mechanism conferring the greater burden of COVID-19 infection and mortality represented in published data.
Keyphrases
- metabolic syndrome
- coronavirus disease
- sars cov
- type diabetes
- risk factors
- blood pressure
- weight loss
- cardiovascular disease
- insulin resistance
- sleep apnea
- weight gain
- electronic health record
- physical activity
- bariatric surgery
- glycemic control
- primary care
- squamous cell carcinoma
- adipose tissue
- uric acid
- machine learning
- big data
- body mass index
- coronary artery disease
- chronic kidney disease
- prognostic factors
- newly diagnosed
- artificial intelligence
- cardiovascular risk factors
- obese patients
- lymph node metastasis