key: cord-0751704-ierrff0p
authors: Ouedraogo, Elise; Allard, Lucie; Bihan, Hélène; Goupil de Bouille, Jeanne; Giroux-Leprieur, Bénédicte; Sutton, Angela; Baudry, Camille; Josse, Constant; Didier, Morgane; Deutsch, David; Rezgani, Imen; Bouchaud, Olivier; Cosson, Emmanuel
title: The association of metabolic syndrome and COVID-19 deterioration
date: 2021-08-25
journal: Nutr Metab Cardiovasc Dis
DOI: 10.1016/j.numecd.2021.08.036
sha: 23c6630b79a8f44a9cb98620a9bc07095b17f1fd
doc_id: 751704
cord_uid: ierrff0p

Background and aims To evaluate the prevalence and prognostic value of metabolic syndrome (MetS) in patients admitted for Coronavirus Disease 2019 (COVID-19). Methods and results In this monocentric cohort retrospective study, we consecutively included all adult patients admitted to COVID 19 units between April 9 and May 29, 2020, and between February 1 and March 26, 2021. MetS was defined when at least three of the following components were met: android obesity, high HbA1c, hypertension, hypertriglyceridemia and low HDL-cholesterol. COVID-19 deterioration was defined as the need for nasal oxygen flow ≥ 6 l/min within 28 days after admission. We included 155 patients (55.5% men, mean age 61.7 years old, mean body mass index 29.8 kg/m2). Fifty-six patients (36.1%) had COVID-19 deterioration. MetS was present in 126 patients (81.3%) and was associated with COVID-19 deterioration (no MetS vs MetS: 13.7% and 41.2%, respectively, p<0.01). Logistic regression taking into account MetS, age, gender, ethnicity, period of inclusion and Charlson Index showed that COVID-19 deterioration was 5.3 times more likely in MetS patients (95% confidence interval 1.3-20.2) than non-MetS patients. Conclusions Over 81.3% of patients hospitalized in COVID-19 units had MetS. This syndrome appears to be an independent risk factor of COVID-19 deterioration.

Coronavirus Disease 2019 (COVID-19) first appeared as a contagious respiratory viral emerging pathology in Hubei region in December 2019. In the subsequent 3 months, it grew into a pandemic and has not been stopped yet even a global vaccine policy. By July 2021, more than 181 million people had been infected, with over 3,9 million deaths (1) . Severe forms of COVID-19 occur in 15% of patients (2) . Many risk factors of severe infection have been identified including male gender (3), advanced age (4) and cardiovascular risk factors (5) such as hyperglycemia (5) , hypertension (6) and lipid disorders (7) . Furthermore, obesity seems to be predictive of COVID-19 deterioration: patients with grade 2 obesity are seven times more likely to be admitted to an intensive care unit (ICU) than patients with lean body weight (8) . In one study, the prevalence of mechanical ventilation in ICU was 81% for patients with obesity compared with 41% for patients with lean body weight (9) .

There are different phenotypes of obesity. Android obesity might be a key prognostic factor of severe COVID-19 through inflammatory mechanisms (10) .

Metabolic syndrome (MetS) is defined by the coexistence of between 3 and 5 of the following components: android obesity, high glucose level, hypertension, hypertriglyceridaemia and low levels of HDL cholesterol (11) . In this context, we hypothesized that MetS diagnosis (as per this definition but using HbA1c to define hyperglycaemia) is associated with deterioration of patients admitted to non-ICUs with COVID-19, and that the higher the number of components, the more frequent the deterioration of COVID-19 infection. The poor prognosis of metabolic syndrome, defined by obesity instead of high waist circumference and diabetes instead of fasting hyperglycemia, was recently described (12, 13) . The aim of our study was to evaluate in patients admitted to four COVID-19 hospital departments (excluding ICU), (i) the prevalence of MetS and (ii) whether MetS is associated with COVID-19 deterioration.

J o u r n a l P r e -p r o o f

We conducted a retrospective observational study in Avicenne Hospital, Bobigny, France. All consecutive patients admitted between April 9 and May 29, 2020 and between February 1 and March 26, 2021, in one of the hospital's four in-patient's departments receiving COVID-19infected patients directly from the emergency room, were included. COVID-19 was confirmed by nasal polymerase chain reaction (PCR). Non-inclusion criteria were (i) pregnancy and (ii) ineligibility to be subsequently transferred to an ICU in case of disease deterioration.

In Avicenne hospital, all patients (COVID or not) are informed at admission that their medical records could be used for research, unless they indicate their opposition. For the present study, no patient indicated any opposition. Approval for the use of patient data was provided by the local ethics committee (approval number: CLEA-2020-121). Data were analyzed anonymously.

Data were extracted from patients' medical files and collected in a secure health database.

General data included routine prescribed treatments, health insurance coverage, current tobacco consumption and ethnicity.

Medical history data including diabetes, hypertension and dyslipidemia were self-reported or inferred from blood glucose, blood pressure or lipid-lowering agents, respectively. Cancer, chronic bronchopneumopathy and Human Immunodeficiency Virus (HIV) infection were self-reported or recorded from patients' medical files. Additionally, we collected data for any comorbid conditions to calculate the Charlson Index (14) and any history of sleep apnea, J o u r n a l P r e -p r o o f nonalcoholic steatosis hepatitis. Obesity was defined by body mass index (BMI) ≥ 30 kg/m². Weight and height were measured within 24 hours following emergency unit admission.

Chest computed tomography scan data included the extent of typical COVID-19 lesions was classified into two groups: minimal to moderate (<25%) and extended to critical (≥25%).

Treatment received during hospitalization was collected.

Biomarkers data included (i) metabolic biomarkers as fasting plasma glucose, HDL cholesterol, triglycerides, HbA1c (ii) inflammation biomarkers, C reactive protein (CRP) and fibrinogen. All these biomarkers were routinely measured on plasma from fasting individuals.

A Cobas 6000 analyzer (Roche diagnostics) was used for all biomarkers except fibrinogen (chronometry -Thrombin Dade), where a Siemens analyzer was used. SARS-CoV-2 RT-PCR was performed on naso-pharyngeal samples collected in virus transport medium, using the SARS-CoV-2 Amp Kit on the automated device Alinity strictly following the recommendations from the manufacturer.

We adapted the National cholesterol education program adult treatment panel III to define MetS (11) . Because fasting glycaemia values can be higher during infection, we decided to define MetS replacing the component 'fasting glycaemia  6 mmol/L' with 'HbA1c  5.7%' which reflects hyperglycaemia independently of stress. More specifically, patients meeting at least three of the following five MetS components (11) were categorized as having the syndrome: (i) android obesity (i.e., waist circumference measured with a plastic anthropometric tape placed at half distance between the costal edge and the iliac crest, in the standing position, exceeding 88 cm for women and 102 cm for men), (ii) HbA1c  5.7% or pharmacologic treatment or already diagnosed diabetes, (iii) medical history of hypertension or evidence of a repeated measurements of high blood pressures leading to introduction of antihypertensive medication during hospitalization, (iv) triglycerides level  1.6 mmol/L or J o u r n a l P r e -p r o o f fibrate usual medication, and (v) HDL-cholesterol level < 1 mmol/L in males or < 1.6 mmol/L in females or routine prescribed fibrate medication. We tallied the number of MetS components in patients for whom the status for all five components was known.

We made a sensitivity analysis defining metabolic syndrome using fasting hyperglycemia instead of Hb1ac: MetSFPG (hereafter called).

COVID-19 deterioration was defined as the need for nasal oxygen flow at or above 6 liters per minute during hospitalization within 28 days after admission. We also considered noninvasive ventilation requirement as a secondary outcome.

International standards of care were followed according to a local guideline which was updated as necessary on a daily basis.

For power calculation, we estimated that 40% of the inpatients in the hospital's four COVID-19 departments (i.e., excluding the emergency unit and ICU) would be diagnosed with MetS.

We presumed that 30% of the MetS group would have COVID-19 deterioration vs 10% of the no-MetS group. Accordingly, we calculated that a minimum study population of 120 patients would be needed to show a significant difference in the rate of COVID-19 deterioration between the two groups (alpha risk of 5% and power of 80%).

Baseline continuous variables were expressed as mean ± standard deviation. Categorical variables were expressed as frequencies (percentages). Missing data were not replaced. The Kruskal-Wallis test was used to compare continuous variables, while Pearson's chi-squared (X 2 ) test or the Fisher's exact test were used (depending on the number of patients within each J o u r n a l P r e -p r o o f group) for categorical variables. The correlation between the number of MetS components and COVID-19 deterioration was assessed thanks to exact Cochran-Armitage test for trend.

The associations between MetS and COVID-19 deterioration (model 1), and between MetSFPG (sensitivity analysis) and COVID-19 deterioration (model 2) were computed using logistic regression, taking into account the following variables: age, gender, ethnicity, period of inclusion and Charlson Index. All tests were two-sided and used a significance level p-value of 0.05. Analyses were conducted with SAS 9.4 software.

A total of 223 patients were admitted to the four hospital units after being transferred from the emergency department. Twenty-two of these were excluded because they were not eligible to be subsequently transferred to ICU in case of deterioration. A further 17 were excluded because any positive COVID-19 PCR was available. Finally, we could not characterize a possible MetS for 29 patients. This was because, all patients combined, we did not have enough information for all MetS components. More specifically, a patient could not be classified (i.e., MetS or no-MetS) if s/he did not have data for at least 3 components. Therefore, 155 patients (i.e., the study population) were included (Flow chart in Additional 

MetS was associated with COVID-19 deterioration (no MetS vs MetS, 13.8% vs 41.3% respectively, p<0.01, Figure 1 ) and with non-invasive ventilation ( (16) . The few available data on fasting hyperglycaemia suggest a prevalence of between 50% and 62% (17, 18) . Hypertension was much more prevalent in our study (60.0%) than in the literature (approximately 17%) (19) . We did not find any other J o u r n a l P r e -p r o o f study exploring the prevalence of hypertriglyceridaemia. One previous study reported that approximately 20% of its study population had low HDL cholesterol (20) .

Our results suggest that MetS was independently associated with COVID-19 deterioration. In two different studies, it was associated with mortality, invasive mechanical ventilation, and ICU transfer for COVID-19 (12, 13) . In the study from Iran having included only patients admitted in ICU, the patients with MetS as compared to those without had a 3.3-fold increased risk of death (15) .

In our study and as in another one (15), we found an association between a higher number of MetS components and more frequent COVID-19 deterioration. It would suggest that interassociation between all five MetS components, and their association with the disease, should be taken into account in order to identify patients most at risk of COVID-19 deterioration.

Elsewhere, plasma glucose levels and obesity at hospital admission have been associated with poor prognosis in COVID-19 patients with diabetes (21) . Interestingly, another study highlighted that COVID-19 prognosis associated with type 1 diabetes was better than that for type 2 diabetes after adjustment for age (22) , probably because individuals with type 1 diabetes rarely meet the criteria for MetS, especially android obesity.

COVID-19 deterioration is characterized by late and sudden respiratory deterioration (day 7 to 10) driven by a cytokine storm, resulting in lung infiltration and hyperactivation of innate immune cells (23) . Visceral fat, which characterizes MetS, is a well-known trigger of low grade systemic and chronic inflammation (24, 25) . This permissive inflammatory environment increases the COVID-19 cytokine inflammatory storm and induces acute respiratory distress syndrome (24) . Furthermore, obesity is associated with leptin resistance (26) . It impacts Tcells and natural killer cell differentiation, alters T CD8 and regulatory T-cells' number and function, inducing immunodepression (27) .

One limitation of our work is that the study population was not representative of all COVID-19 infected patients as we only investigated patients admitted for infection. Therefore, our results only apply to patients whose symptoms were too severe for them to be released homeeither because of respiratory symptoms or severe comorbidities -but not severe enough to warrant being transferred to ICU or directly intubated. We had considered that waist circumference would have been difficult to measure in patients in ICU. The study is small but we recruited 155 patients when 120 were necessary according to power calculation. The retrospective nature of our study exposed to risk for bias due to its and potential for missing or misclassified data. The strengths of our study include a multiethnic cohort likely to be translatable to different populations, despite single center lacking generalizability, and a pragmatic guidance-based approach. Finally, we defined MetS with its classical definition, considering especially waist circumference and triglycerides/ HDL-cholesterol measurements.

But we used HbA1c instead of fasting plasma glucose, as the latter may increase due to stress (28) . However, we found similar results for MetS and MetSFPG.

Approximately 80% of patients admitted to Avicenne Hospital, Bobigny, France for COVID- The five components of metabolic syndrome are (i) large waist circumference: waist circumference exceeding 88 cm for women and 102 cm for men, (ii) high HbA1c level ( 5.7%) or fasting hyperglycemia (fasting glycaemia  6.0 mmol/)L or pharmacologic treatment or known diabetes, (iii) high blood pressure: medical history of hypertension or antihypertensive medication introduced during hospitalization, (iv) high triglycerides level: triglycerides level  1.6 mmol/L or routine prescribed fibrate medication, and (v) low HDLcholesterol level: HDL-cholesterol level < 1 mmol/L in males or < 1.6 mmol/L in females or routine prescribed fibrate medication.

J o u r n a l P r e -p r o o f 

Chiffres clés et évolution de la covid 19 en France et dans le monde

Hypothesis to explain the severe form of COVID-19 in Northern Italy

Association between male sex and outcomes of Coronavirus Disease 2019 (Covid-19) -a Danish nationwide, register-based study

Sex-and Gender-specific Observations and Implications for COVID-19

Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia -A systematic review, meta-analysis, and meta-regression

COVID-19 and hypertension

Declined serum high density lipoprotein cholesterol is associated with the severity of COVID-19 infection

Obesity as a predictor for a poor prognosis of COVID-19: A systematic review

High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity (Silver Spring)

COVID-19 and the role of chronic inflammation in patients with obesity

A comprehensive definition for metabolic syndrome

Metabolic Syndrome and COVID-19 Mortality Among Adult Black Patients in New Orleans. Diabetes Care

Metabolic syndrome and clinical outcomes in patients infected with COVID-19: Does age, sex, and race of the patient with metabolic syndrome matter?

A new method of classifying prognostic comorbidity in longitudinal studies: development and validation

The impact of metabolic syndrome on morbidity and mortality among intensive care unit admitted COVID-19 patients

Diabetes in COVID-19: Prevalence, pathophysiology, prognosis and practical considerations

Fasting blood glucose predicts the occurrence of critical illness in COVID-19 patients: A multicenter retrospective cohort study

Elevation of blood glucose level predicts worse outcomes in hospitalized patients with COVID-19: a retrospective cohort study

Prevalence of Diabetes and Hypertension and Their Associated Risks for Poor Outcomes in Covid-19 Patients

Low high-density lipoprotein level is correlated with the severity of COVID-19 patients: an observational study

Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study

Diabetes in People Hospitalized for COVID-19: New Insights From the CORONADO Study. Diabetes Care

Cytokine storm and leukocyte changes in mild versus severe SARS-CoV-2 infection: Review of 3939 COVID-19 patients in China and emerging pathogenesis and therapy concepts

Obesity, and Metabolic Inflammation Create the Perfect Storm for COVID-19

Mechanisms of obesity-induced inflammation and insulin resistance: insights into the emerging role of nutritional strategies

Modifications of immunity in obesity: The impact on the risk of infection

Subcutaneous and visceral adipose tissue: structural and functional differences

From the insight of glucose metabolism disorder: Oxygen therapy and blood glucose monitoring are crucial for quarantined COVID-19 patients

Pre-existing obesity and certain cardiovascular risk factors are predictive of poor health outcomes in persons with coronavirus disease

Metabolic syndrome, which is defined as meeting at least 3 of the syndrome's 5 components (android obesity and four metabolic risk markers)

Contributors:EO, LA, OB and EC conceived and designed this study and had full access to all of the study data. They take responsibility for the integrity of the data and the accuracy of the data analyses. JM, CB and HB helped design the study and searched existing literature. EO, LA, IR and EC collected the data and EO, LA and EC drafted the paper. LA, EO, EC and CJ performed the analyses. All authors critically revised the manuscript for important intellectual content and gave final approval for the version to be published. All authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy and integrity of any part of the work are appropriately investigated and resolved.

Our thanks to LVL Medical who partly funded this study. They were not involved in the collection, analyses or interpretation of data. Neither were they involved in writing of the manuscript or the decision to submit it for publication.We also thank thank Rachida Mazouzi, Flory Mfutila Kaykay, Phuc Thu Trang Nguyen, Dr Fatma Kort and Dr Julien Caliez, for their help in collecting data. Furthermore, we thank SANOÏA-Real World Digital CRO for digital data collection and data management services, and Jude Sweeney (Milan, Italy) for the English editing and revision of the manuscript.J o u r n a l P r e -p r o o f