Since December 2019, the new coronavirus, Severe Acute Respiratory Syndrome-CoronaVirus-2 (SARS-CoV-2), which causes coronavirus disease-2019 (COVID- 19) , has spread throughout the world, leading the World Health Organization (WHO) to issue a pandemic alert on 11 March 2020. In addition to the urgent need to develop efficient therapeutic strategies, identifying those who are most at risk of severe disease has quickly emerged as a major factor in facing this worldwide challenge.
From the first epidemiological reports, obesity and diabetes have both been identified as co-morbidities frequently associated with severe forms of COVID-19, 1,2 in accordance with previous reports from epidemic outbreaks due to the H1N1 influenza in 2009 or MERS-CoV in 2012. [3] [4] [5] For instance, the prevalence of obesity and diabetes, respectively, reached 41.7% and 33.8% among 5700 patients admitted for COVID-19 in 12 hospitals in the New York City area. 6 In patients admitted to intensive care units (ICUs) in the UK, 73.3% were reported to be overweight or obese. 7 Moreover, a French study recently reported that obesity is more prevalent in patients with critical COVID-19 compared with those with either non-critical forms of the disease or those admitted to ICUs for diagnoses other than In addition to its high prevalence in patients with COVID-19, obesity has also been identified as an independent risk factor for the severity of the disease. 9 A recent study in the United States showed that people with a body mass index (BMI) of 35 kg/m 2 or higher displayed a 3.5-fold increased risk of COVID-19-related death compared with those with a BMI of 25-34 kg/m 2 . 10 Severe COVID-19 was recently reported to affect younger people in US populations with a high prevalence of obesity. 11 Similarly, diabetes was recently recognized as an independent predictor of poor prognosis in patients with COVID-19 with a greater than 2-fold risk of ICU admission and a higher than 3-fold risk of death. 12 Obesity and diabetes, especially type 2 diabetes (T2D), are two commonly associated conditions that are supported by epidemiological as well as genetic studies. Their combination could therefore confer a particularly high risk of severe COVID-19. Accordingly, in an interim analysis of the CORONAvirus-SARS-CoV-2 and Diabetes Outcomes (CORONADO) study, we recently showed that BMI was positively and independently associated with severe COVID-19-related outcomes (i.e. intubation for mechanical ventilation [IMV] or death within 7 days of hospital admission) in patients with diabetes hospitalized for COVID- 19. 13 In the present analysis of the entire CORONADO population, we aimed at further deciphering the relationship between obesity and COVID-19 severity in patients with T2D hospitalized for this infectious disease. To this end, clinical characteristics and COVID-19related outcomes were assessed according to patient BMI status, ranging from normal weight to class II/III obesity. Finally, as the elderly are particularly prone to severe forms of the disease, we specifically addressed the question of the influence of age on the relationship between BMI and COVID-19 prognosis. Focusing on the relationship between obesity and COVID-19 severity in T2D, the present analysis excluded individuals with type 1 diabetes, other types of diabetes and those with newly diagnosed diabetes upon admission (n = 406), as well underweight patients (BMI < 18.5 kg/m 2 ) to avoid interference caused by concomitant severe comorbidities (n = 40) ( Figure 1 ). Finally, 1965 CORONADO patients with a medical history of T2D and available data for age, sex and BMI were included in the analysis. Subjects were categorized by four subgroups according to the WHO classification: (a) normal weight (18.5-24.9 kg/m 2 ), (b) overweight (25-29.9 kg/m 2 ), (c) class I obesity (30-34.9 kg/m 2 ) and (d) class II/III obesity (≥35 kg/m 2 ). Subjects in the normal weight range were considered to be the reference group.
Data collection was performed by clinical research associates and physicians in each participating centre. They were instructed to systematically review the medical files of all COVID-19 inpatients, select those with diabetes, extract data from their medical files and, if necessary, contact the patient's general and/or specialist practitioners, regular pharmacist or biomedical laboratory. Collected data included clinical characteristics (age, sex, ethnicity and BMI), classification of diabetes as noted in the medical file by the physician in charge of the patient, duration of diabetes, recent glycaemic control (i.e. the two most recent HbA1c levels determined before admission), microvascular and macrovascular complications and co-morbidities. Full details of data collection are provided in the supporting information. Classification of diabetes, body weight and height (and/or BMI value) were collected as noted in the medical file by the physician in charge of the patient. HbA1c considered in the analysis was determined locally in the 7 days following admission or, if not available, was the result of a routine determination in the previous 6 months. Diabetic microvascular and macrovascular complications, as well as co-morbidities and routine treatment, were noted as reported in the medical file. Moreover, COVID-19-related clinical, radiological and biological characteristics were collected upon admission as well as the clinical evolution during hospital stays.
The composite primary endpoint combined IMV and/or death by day 7 (D7). Secondary outcomes included death, IMV and hospital discharge, all considered by D7. Patients discharged before D7 were systematically contacted to check for the non-occurrence of these events by D7.
The study population was distributed according to the BMI categories.
Quantitative variables were expressed as mean ± standard deviation (SD) or median [25th-75th percentile] and categorical variables as number (%) of patients. The statistical association between categorical variables was tested using Fisher's exact test. The statistical association between binary and quantitative variables was tested using unpaired ttest (Mann-Whitney U-test in case of skewed distribution) and, for variables with more than two categories, we used ANOVA (Kruskal-Wallis in case of skewed distribution). If necessary, confidence intervals for proportions were calculated using the Clopper-Pearson estimate.
Unconditional logistic regression models were used to calculate the OR associated with the different outcomes by D7. For quantitative variables, in case of skewed distribution, natural-log transformation was considered, and if applied, OR were expressed for an increase of 1 SD of the given variable. Multiple logistic regression analyses were performed focusing on the OR associated with BMI, considering covariates identified either as clinically relevant and/or significantly associated with obesity status in univariable analysis.
All statistical tests were two-sided with a type 1 error set at 5%.
All analyses were performed on available data, without imputation, using statistical software R version 4.0.0. The patients' clinical characteristics before admission (i.e. medical history and routine treatment) and upon admission (clinical symptoms, radiological and biological findings) are detailed in Table 1 and   Table S1 , respectively. In contrast to a strong male predominance in the normal weight group, women were more represented in patients 
To better understand the influence of age on the relationship between BMI status and COVID-19 prognosis, we studied the distribution of COVID-19-related outcomes according to BMI status in three age subgroups (<65, 65-74 and ≥75 years) ( Figure 2 ). As shown in Figure 2 , an increased rate of early death as well as a decreased rate of IMV and discharge by D7 was observed in patients aged 75 years or older compared with younger people, irrespective of BMI status.
Given the specificity of BMI distribution in the elderly (Table 1 ) and
considering the shift in the occurrence of clinical outcomes above 75 years (Figure 2 ), we then separately assessed the influence of overweight and obesity on clinical characteristics and COVID-19-related outcomes in patients with T2D younger than and older than 75 years of age. As shown in Table 3 , the primary outcome was significantly associated with obesity in individuals younger than 75 years (P = .0077), whereas such an association was no longer found in the older patients (P = .1507).
To assess the effect of age on the clinical consequences of obesity, we compared the clinical features of patients with and without obesity (BMI ≥30 vs. <30 kg/m 2 ) separately within populations aged younger than and older than 75 years (Tables S2 and S3 ). Briefly, diabetes complications were more frequent in the elderly, irrespective of obesity. COPD and OSA were more prevalent in obese groups regardless of age, while an increase in NAFLD prevalence with obesity was only observed in individuals younger than 75 years. In insulin users, daily insulin dose was significantly higher in obese patients younger than 75 years whereas no difference was observed according to obesity status in those older than 75 years, suggesting less marked insulin resistance in older than in younger obese patients. Upon admission, an increased frequency of COVID-19 symptoms, such as cough and dyspnoea, was observed with obesity in older people, but obesity was not concomitant with higher biological inflammation, whatever the age.
The interim analysis of the CORONADO study recently identified BMI as an independent predictor of poor early prognosis in patients with diabetes hospitalized for COVID-19. 13 In the present study, we took advantage of the whole CORONADO population with T2D to specifically address the relationship between BMI classes and severity of COVID-19, taking into account the influence of age. The main conclusion is that obesity is significantly and increasingly associated with the severity of COVID-19 in this population. Indeed, the primary outcome (IMV and/or death by D7) was gradually associated with overweight and obesity in patients with T2D. The second key message is that the relationship between obesity status and the primary outcome was no longer found in elderly patients with T2D aged 75 years or older.
Our present results therefore confirm in inpatients with T2D the close relationship between obesity and disease severity previously reported in COVID-19 patients with and without diabetes. In France, two monocentre studies of COVID-19 patients admitted to ICUs underlined a positive and significant association between the need for IMV and severe obesity (BMI ≥35 kg/m 2 ). 14, 15 In line with these stud- Ref.
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Ref. The primary outcome is defined as IMV and/or death by D7. Therefore, by design, some patients met the two events, and the sum of both groups is greater than the number of primary outcomes. Multiple logistic regression model unadjusted and adjusted for sex, age, tobacco use, microvascular complications, macrovascular complications, hypertension, non-alcoholic fatty liver disease, chronic obstructive pulmonary disease, treated obstructive sleep apnea and routine treatment with insulin and GLP1-RA.
a P-value: test for homogeneity for BMI as a nominal variable. Bold values denote statistical significance at the P <.05 level.
6916 patients with COVID-19, Tartof et al. described a gradually positive association between BMI and risk of death, even after adjustment for obesity-related co-morbidities. 16 The strength of the CORONADO study relies on a large and well-phenotyped population of COVID-19 patients with T2D admitted to medical units or ICUs. Hence, our data further indicate that an association between IMV and BMI already appeared from overweight, while no association was found for death occurring within 7 days.
The combined prevalence of overweight and obesity reached 77.8% in our study population, in agreement with the French epidemiological ENTRED-2 study, in which 80% of people living with T2D
were overweight or obese. 17 As expected, the prevalence of obesity decreased with advancing age, as previously reported in the French population of elderly people with T2D. 18 Sex also impacted BMI distribution, with a clear male predominance in the normal weight group, whereas women were more represented in patients with obesity, especially in those with a BMI of 35 kg/m 2 or higher. Sex-dependent BMI distribution and a similarly higher prevalence of obesity in women than in men have already been described in the general population, such as in people with T2D. 19 Moreover, women have been reported to exhibit higher BMI values than men upon diagnosis of T2D. 20 Age and sex are therefore potential confounding factors when assessing the relationship between obesity and severe COVID-19
outcomes.
Considering the lower prevalence of elderly people among participants with obesity as well as the association with severe COVID-19 in this population, we further investigated the relationship between BMI status and COVID-19 prognosis according to age. Because our analyses revealed a shift in the occurrence of IMV and death by D7 above the age of 75 years, we decided to compare patients aged younger and older than 75 years, a cut-off used by numerous studies to capture an elderly population. 21, 22 To balance the size of subgroups, we In a very different context to the COVID-19 pandemic, obesity has been reported to be deleterious in younger people (aged <65 years) with T2D but associated with better survival in older individuals (aged ≥65 years). 25 Accordingly, although obesity is a wellrecognized risk factor for numerous cardiovascular and respiratory diseases in epidemiological studies, some protective role has already been linked with obesity status in the literature, a phenomenon known as the 'obesity paradox'. 26 Indeed, several studies have reported that patients with elevated BMI are characterized by lower all-cause and cardiovascular mortality than people in the normal weight range. 27, 28 Importantly, the obesity paradox has mainly been reported in the elderly, 26 where excess weight is associated with preserved lean mass, and can limit the risk of undernutrition and frailty.
Nevertheless, the concept of the obesity paradox has recently been challenged by the Fremantle diabetes study, which highlights the limitation of solely using BMI for a precise phenotyping of obesity. 29 Unfortunately, the specific COVID-19 context and the retrospective design of our study did not enable us to include measurements of waist and hip circumferences.
The observational design of the CORONADO study also did not allow us to identify clear mechanistic explanations for this differential impact of BMI according to age. This difference might be related to a power issue, even if the number of primary outcomes was substantial in elderly patients. However, we tried to identify some differences in Adipose tissue repartition should also be questioned, because visceral obesity is known to correlate with metabolic disorders, lowgrade inflammation and higher mortality rates. 32 Ageing has already been reported to induce significant changes in body composition and adipose tissue distribution. Unfortunately, in the emergency context of COVID-19, we were unable to collect additional anthropometric and imaging variables to further characterize fat mass distribution.
Among the multiple pathways by which obesity may worsen COVID-19 prognosis, the deleterious role of adipose tissue as a reservoir for more extensive viral spread and inflammatory response amplification has recently been suggested. 33 36 Thus, we strongly believe that COVID-19 severity is adequately captured by our primary composite endpoint by D7 in the vast majority of patients. Nevertheless, the short follow-up period is a limitation and caution is necessary when interpreting the results, especially in terms of causal relationships. Finally, the results reported in this paper originated from a post hoc analysis conducted on observatory data from a retrospective cohort study and they must therefore be confirmed by observations from large cohorts.
In conclusion, overweight and obesity are associated with poor early prognosis in patients with T2D hospitalized for COVID-19, supporting the need for reinforced prevention measures in these populations. The deleterious impact of obesity on COVID-19 prognosis appears to be much less pronounced in the elderly and advanced age by itself must be considered as a major risk for severe outcomes in all people with T2D, irrespective of BMI status. 
The peer review history for this article is available at https://publons. com/publon/10.1111/dom.14228.
No sharing of participant data is allowed by our regulatory authorities.
So far, French regulations have not validated deidentified data or avatar for data sharing. Our statement might be modified in case French law changes. Whether additional, related documents will be available (eg, study protocol, statistical analysis plan, informed consent form)
We will be happy to share study protocol, SAP and information document. • When these data will be available (beginning and end date, or "with publication", as applicable) Study protocol, SAP and information document will be made available with publication. Data dictionary will be made available Summer 2020 (JULY 15th). • Where the data will be made available (including complete URLs or email addresses if relevant); The CORONADO website is not active yet but we will give access to the scientific committee through our website, as soon as it is launched. Direct requests can be directed to PI (bertrand.
cariou@univ-nantes.fr) or Chairman of the scientific committee (samy.
hadjadj@univ-nantes.fr) • By what access criteria data will be shared (including with whom, for what types of analyses, by what mechanism -eg, with or without investigator support, after approval of a proposal, with a signed data access agreement -or any additional restrictions). Our data-base is open for any collaborative work with priority to academic partnership. Any proposal for collaboration requires examination by the scientific committee and the sponsor (CHU Nantes). A structured application proposal for collaboration will be available on request.