abstract: # Objective
  .
  To characterise the clinical features of patients admitted to hospital with coronavirus disease 2019 (covid-19) in the United Kingdom during the growth phase of the first wave of this outbreak who were enrolled in the International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study, and to explore risk factors associated with mortality in hospital.
  .
  # Design
  .
  Prospective observational cohort study with rapid data gathering and near real time analysis.
  .
  # Setting
  .
  208 acute care hospitals in England, Wales, and Scotland between 6 February and 19 April 2020. A case report form developed by ISARIC and WHO was used to collect clinical data. A minimal follow-up time of two weeks (to 3 May 2020) allowed most patients to complete their hospital admission.
  .
  # Participants
  .
  20 133 hospital inpatients with covid-19.
  .
  # Main outcome measures
  .
  Admission to critical care (high dependency unit or intensive care unit) and mortality in hospital.
  .
  # Results
  .
  The median age of patients admitted to hospital with covid-19, or with a diagnosis of covid-19 made in hospital, was 73 years (interquartile range 58-82, range 0-104). More men were admitted than women (men 60%, n=12 068; women 40%, n=8065). The median duration of symptoms before admission was 4 days (interquartile range 1-8). The commonest comorbidities were chronic cardiac disease (31%, 5469/17 702), uncomplicated diabetes (21%, 3650/17 599), non-asthmatic chronic pulmonary disease (18%, 3128/17 634), and chronic kidney disease (16%, 2830/17 506); 23% (4161/18 525) had no reported major comorbidity. Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the reporting date. 17% (3001/18 183) required admission to high dependency or intensive care units; of these, 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the reporting date. Of those receiving mechanical ventilation, 17% (276/1658) were discharged alive, 37% (618/1658) died, and 46% (764/1658) remained in hospital. Increasing age, male sex, and comorbidities including chronic cardiac disease, non-asthmatic chronic pulmonary disease, chronic kidney disease, liver disease and obesity were associated with higher mortality in hospital.
  .
  # Conclusions
  .
  ISARIC WHO CCP-UK is a large prospective cohort study of patients in hospital with covid-19. The study continues to enrol at the time of this report. In study participants, mortality was high, independent risk factors were increasing age, male sex, and chronic comorbidity, including obesity. This study has shown the importance of pandemic preparedness and the need to maintain readiness to launch research studies in response to outbreaks.
  .
  # Study registration
  .
  ISRCTN66726260.
author: Annemarie B Docherty; Ewen M Harrison; Christopher A Green; Hayley E Hardwick; Riinu Pius; Lisa Norman; Karl A Holden; Jonathan M Read; Frank Dondelinger; Gail Carson; Laura Merson; James Lee; Daniel Plotkin; Louise Sigfrid; Sophie Halpin; Clare Jackson; Carrol Gamble; Peter W Horby; Jonathan S Nguyen-Van-Tam; Antonia Ho; Clark D Russell; Jake Dunning; Peter JM Openshaw; J Kenneth Baillie; Malcolm G SempleCorrespondence to: M G Semple <firstname.lastname@example.com> (or [@TweedieChap](https://twitter.com/tweediechap) on Twitter)
date: 2020
references:
title: Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study

# Introduction

The outbreak of disease caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was declared a pandemic by the World Health Organization on 11 March 2020.1 The WHO situation report dated 30 April 2020 stated 3 090 445 people had confirmed coronavirus disease 2019 (covid-19) and 217 769 people had died across the world.2

In the wake of the influenza A H1N1 pandemic (2009) and the emergence of Middle East respiratory syndrome coronavirus (2012), it was recognised that the effectiveness of a response to a future pandemic threat would critically depend on the speed and focus of that response. The United Kingdom set up and maintained a "sleeping" prepandemic suite of protocols, documents, and agreements in preparation for future outbreaks. The International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) WHO Clinical Characterisation Protocol UK (CCP-UK) study was a core component of this portfolio.3 Further details about ISARIC WHO CCP-UK can be found at <https://isaric4c.net> and in the online supplement.

In response to the emergence of SARS-CoV-2 and its pandemic potential, the ISARIC WHO CCP-UK study was activated on 17 January 2020, in time to enrol the first wave of patients with covid-19 admitted to hospitals in England and Wales. The first confirmed patient with covid-19 in the UK was reported on 31 January 2020.

Hospital admission rates for patients with covid-19 have been difficult to estimate because rates depend on the prevalence of community testing and admission criteria, which vary between countries. However, an estimated one in 10 to one in five adults have illnesses of sufficient severity to warrant hospital admission.4 Patients have mostly been admitted with severe acute respiratory infection or severe acute respiratory syndrome according to the previous WHO case definitions.5 6 The provision of intensive care also varies between countries. Studies first from China, and more recently from Europe and the United States, have found rates of admission to intensive care range from 5% to 32%.7 8 Old age, chronic major comorbidity, and male sex have consistently been associated with increased mortality.9 10 11 12

In this first report of the ISARIC WHO CCP-UK study, we characterise the clinical features of patients admitted to hospital with covid-19 in England, Scotland, and Wales during the growth phase of the first wave of this outbreak, up to 19 April 2020. Future reports will include Northern Ireland. We describe all patient outcomes as known on 3 May 2020 and explore risk factors associated with mortality in hospital.

# Methods

## Study design and setting

The ISARIC WHO CCP-UK (National Institute for Health Research Clinical Research Network Central Portfolio Management System ID: 14152) study is an ongoing prospective cohort study in 208 acute care hospitals in England, Scotland, and Wales. The protocol (supplementary material 2), revision history, case report form (version 9.2; supplementary material 3), information leaflets, consent forms and details of the Independent Data and Material Access Committee are available online.21

## Participants

Inclusion criteria were people of all ages who were admitted to one of 208 acute care hospitals in England, Scotland, and Wales with proven or high likelihood of infection with a pathogen of public health interest, defined as SARS-CoV-2 for this event by Public Health England. Reverse transcriptase polymerase chain reaction was the only mode of testing available during the period of study. The decision to test was at the discretion of the clinician attending the patient, and not defined by protocol. The enrolment criterion "high likelihood of infection" reflects that a preparedness protocol cannot assume that a diagnostic test will be available for an emergent pathogen. Site training emphasises that only patients who tested positive for covid-19 were eligible for enrolment.

National guidance was provided by Public Health England and other UK public health agencies that advised who to test based on clinical case definitions for possible covid-19 (online supplement). We also included patients who had been admitted for a separate condition but had tested positive for covid-19 during their hospital stay. We collected additional biological samples for research purposes when consent was given (please see online supplement for details of consent procedures and biological samples). These samples are currently undergoing analysis and we will present the results when they become available. Patients were only enrolled during their index admission. We used three tiers in the ISARIC WHO CCP-UK protocol. Patients in tier 0 had clinical information from their routine health records uploaded into the case report form. Consent was not required for collection of depersonalised routine healthcare data for research in England and Wales. A waiver for consent was given by the Public Benefit and Privacy Panel in Scotland. Tier 1 and 2 of the protocol involve additional biological sampling for research purposes for which consent by, or assent for, participants was obtained.

## Data collection

We collected baseline demographic data on a paper case report form (version 9.2; supplementary material 2) that was developed by ISARIC and WHO for use in outbreak investigations. Data were uploaded from admission, and usually before hospital episodes were complete, to a REDCap database (Research Electronic Data Capture, Vanderbilt University, US, hosted by University of Oxford, UK). We aimed to record measures of illness severity and routine blood test results at a minimum of four time points: day of hospital admission (day 1), day 3, day 6, day 9, and day of any admission to critical care. We recorded relevant treatments that patients received in hospital, level of care (ward based, high dependency unit, or intensive care unit), complications, and details of discharge or death while in hospital. Further information about these variables can be found in the online supplement.

## Outcomes

The main outcomes were critical care admission (high dependency unit or intensive care unit) and mortality in hospital or palliative discharge. We chose a priori to restrict analysis of outcomes to patients who were admitted more than two weeks before data extraction (3 May 2020) to enable most patients to finish their hospital admission.

## Bias

Research nurses relied on local covid-19 test reports to enrol patients. Capacity to enrol was limited by staff resources at times of high covid-19 activity. Otherwise we are unable to comment on the potential selection bias of our cohort. We are in the process of linking to routine administrative healthcare data and will be able to make comparisons at that point.

## Missing data

The nature of the study means that a large amount of data were missing, particularly during the later parts of the growth curve of the UK outbreak. Because this paper is mainly descriptive, we have not performed any imputation for missing data, and describe the data as they stand. To reduce the impact of missing data on outcome analyses, we restricted these analyses to patients who had been admitted for at least two weeks before data extraction.

## Statistical analyses

Continuous data are summarised as median (interquartile range) and categorical data as frequency (percentage). For univariate comparisons, the Mann-Whitney U test or Kruskal-Wallis test were used. We compared categorical data by using the χ^2^ test.

We used several approaches to model survival. Discharge from hospital was considered an absorbing state, meaning that once discharged, patients were considered no longer at risk of death. Patients who were discharged were not censored and held within the risk set, therefore accounting for the competing risk of discharge on death. We checked this approach by using a formal Fine and Gray competing risks approach. Hierarchical Cox proportional hazards approaches included geographical region (clinical commissioning group or health board) as a random intercept. We used a parsimonious criterion based model building approach based on several principles: clinically relevant explanatory variables were identified a priori for exploration; population stratification was incorporated; interactions were checked at first order level; final model selection was informed by log likelihood tests and the concordance statistic, with appropriate assumptions checked including the distribution of residuals and requirement for proportional hazards. We set statistical significance at 5%. All tests were two sided. We analysed data by using R (R Core Team version 3.6.3, Vienna, Austria), with packages including tidyverse, finalfit, survival, cmprsk, and coxme.

## Patient and public involvement

This was an urgent public health research study in response to a Public Health Emergency of International Concern. Patients or the public were not involved in the design, conduct, or reporting of this rapid response research.

# Results

On behalf of ISARIC WHO CCP-UK, 2468 research nurses, administrators, and medical students enrolled 20 133 patients who were admitted with covid-19 to 208 hospitals in England, Scotland, and Wales between 6 February and 14:00 on 19 April 2020 (table 1 and fig E1). This figure represents 34% of the 59 215 covid-19 admissions in these countries. The median time from onset of symptoms of covid-19 in the community to presentation at hospital was 4 days (interquartile range 1-8; n=16 221).

Baseline characteristics of 20 133 patients with coronavirus disease 2019 stratified by sex. Data are numbers (percentages) unless stated otherwise

| Characteristics | Male | Female | All |
|----|----|----|----|
| Total No (%) | 12 068 (59.9) | 8065 (40.1) | 20 133 |
| Age at admission (n=20 133) |  |  |  |
|  Median (interquartile range) | 72.0 (58.0-81.0) | 74.0 (58.0-84.0) | 72.9 (58.0-82.0) |
| Age (n=20 133) |  |  |  |
|  \<18 | 180 (1.5) | 130 (1.6) | 310 (1.5) |
|  18-39 | 534 (4.4) | 533 (6.6) | 1067 (5.3) |
|  40-50 | 888 (7.4) | 530 (6.6) | 1418 (7.0) |
|  50-59 | 1728 (14.3) | 980 (12.2) | 2708 (13.5) |
|  60-69 | 2115 (17.5) | 1181 (14.6) | 3296 (16.4) |
|  70-79 | 2972 (24.6) | 1720 (21.3) | 4692 (23.3) |
|  ≥80 | 3651 (30.3) | 2991 (37.1) | 6642 (33.0) |
| Any comorbidity (n=18 525) |  |  |  |
|  No | 2591 (23.4) | 1570 (21.1) | 4161 (22.5) |
|  Yes | 8492 (76.6) | 5872 (78.9) | 14 364 (77.5) |
| Chronic cardiac disease (n=17 702) |  |  |  |
|  No | 7086 (66.8) | 5147 (72.6) | 12 233 (69.1) |
|  Yes | 3527 (33.2) | 1942 (27.4) | 5469 (30.9) |
| Chronic pulmonary disease, not asthma (n=17 634) |  |  |  |
|  No | 8616 (81.7) | 5890 (83.1) | 14 506 (82.3) |
|  Yes | 1931 (18.3) | 1197 (16.9) | 3128 (17.7) |
| Asthma (n=17 535) |  |  |  |
|  No | 9274 (88.6) | 5721 (80.9) | 14 995 (85.5) |
|  Yes | 1192 (11.4) | 1348 (19.1) | 2540 (14.5) |
| Smoker (n=14 184) |  |  |  |
|  Never smoked | 5030 (58.8) | 3938 (69.9) | 8968 (63.2) |
|  Former smoker | 2972 (34.8) | 1392 (24.7) | 4364 (30.8) |
|  Yes | 549 (6.4) | 303 (5.4) | 852 (6.0) |
| Chronic kidney disease (n=17 506) |  |  |  |
|  No | 8792 (84.0) | 5884 (83.5) | 14 676 (83.8) |
|  Yes | 1671 (16.0) | 1159 (16.5) | 2830 (16.2) |
| Diabetes without complications (n=17 599) |  |  |  |
|  No | 8254 (78.3) | 5695 (80.7) | 13 949 (79.3) |
|  Yes | 2290 (21.7) | 1360 (19.3) | 3650 (20.7) |
| Diabetes with complications (n=17 516) |  |  |  |
|  No | 9628 (91.8) | 6589 (93.8) | 16 217 (92.6) |
|  Yes | 860 (8.2) | 439 (6.2) | 1299 (7.4) |
| Obesity (n=16 081) |  |  |  |
|  No | 8725 (90.6) | 5671 (87.8) | 14 396 (89.5) |
|  Yes | 900 (9.4) | 785 (12.2) | 1685 (10.5) |
| Chronic neurological disorder (n=17 382) |  |  |  |
|  No | 9222 (88.6) | 6189 (88.7) | 15 411 (88.7) |
|  Yes | 1181 (11.4) | 790 (11.3) | 1971 (11.3) |
| Dementia (n=17 459) |  |  |  |
|  No | 9211 (88.2) | 5888 (83.9) | 15 099 (86.5) |
|  Yes | 1232 (11.8) | 1128 (16.1) | 2360 (13.5) |
| Malignancy (n=17 354) |  |  |  |
|  No | 9251 (89.2) | 6360 (91.0) | 15 611 (90.0) |
|  Yes | 1117 (10.8) | 626 (9.0) | 1743 (10.0) |
| Moderate or severe liver disease (n=17 360) |  |  |  |
|  No | 10 181 (98.0) | 6869 (98.5) | 17 050 (98.2) |
|  Yes | 204 (2.0) | 106 (1.5) | 310 (1.8) |
| Mild liver disease (n=17 331) |  |  |  |
|  No | 10 195 (98.3) | 6855 (98.5) | 17 050 (98.4) |
|  Yes | 174 (1.7) | 107 (1.5) | 281 (1.6) |
| Chronic haematological disease (n=17 328) |  |  |  |
|  No | 9951 (96.0) | 6684 (96.0) | 16 635 (96.0) |
|  Yes | 415 (4.0) | 278 (4.0) | 693 (4.0) |
| Rheumatological disorder (n=17 289) |  |  |  |
|  No | 9562 (92.4) | 6031 (86.9) | 15 593 (90.2) |
|  Yes | 787 (7.6) | 909 (13.1) | 1696 (9.8) |
| Malnutrition (n=16 695) |  |  |  |
|  No | 9768 (97.8) | 6531 (97.4) | 16 299 (97.6) |
|  Yes | 222 (2.2) | 174 (2.6) | 396 (2.4) |
| Previous immunosuppressant drug treatment (n=18 009) |  |  |  |
|  Yes | 876 (8.1) | 791 (11.0) | 1667 (9.3) |
|  No | 9339 (86.6) | 6032 (83.5) | 15 371 (85.4) |
|  Not applicable | 573 (5.3) | 398 (5.5) | 971 (5.4) |
| Previous anti-infective treatment (n=18 017) |  |  |  |
|  No | 1940 (18.0) | 1311 (18.2) | 3251 (18.0) |
|  Yes | 8285 (76.8) | 5520 (76.4) | 13 805 (76.6) |
|  Not applicable | 569 (5.3) | 392 (5.4) | 961 (5.3) |
| AIDS/HIV (n=17 251) |  |  |  |
|  No | 10 259 (99.5) | 6909 (99.6) | 17 168 (99.5) |
|  Yes | 55 (0.5) | 28 (0.4) | 83 (0.5) |

## Age and sex

The median age of patients was 73 years (interquartile range 58-82, range 0-104; fig 1); 310 patients (1.5%) were less than 18 years old and 194 (1.0%) were less than 5 years old. More men (59.9%, n=12 068) than women (40.1%, n=8065) were admitted to hospital with covid-19. One hundred women (10%) of reproductive age (n=1033) were recorded as being pregnant.

## Symptoms

The most common symptoms were cough (68.9%, 12 896/18 730), fever (71.6%, 12 499/17 452), and shortness of breath (71.2%, 12 107/16 999; fig 2, top left panel), though these data reflect the case definition. Only 4.5% (855/19 178) of patients reported no symptoms on admission. We found a high degree of overlap between the three most common symptoms (fig 2, lower left panel).

Clusters of symptoms on admission were apparent (fig E2). The most common symptom cluster encompassed the respiratory system: cough, sputum, shortness of breath, and fever. We also observed three other clusters: one encompassing musculoskeletal symptoms (myalgia, joint pain, headache, and fatigue); a cluster of enteric symptoms (abdominal pain, vomiting, and diarrhoea); and less commonly, a mucocutaneous cluster. Twenty nine per cent (5384/18 605) of all patients complained of enteric symptoms on admission, mostly in association with respiratory symptoms; however, 4% of all patients described enteric symptoms alone.

## Comorbidities

Figure 2 (top right panel) and table 1 show major comorbidities recorded on admission. The most common major comorbidities were chronic cardiac disease (30.9%, 5469/17 702), diabetes without complications (20.7%, 3650/17 599), chronic pulmonary disease excluding asthma (17.7%, 3128/17 634), chronic kidney disease (16.2%, 2830/17 506), and asthma (14.5%, 2540/17 535). Of 18 525 patients, 22.5% (4161) had no documented major comorbidity. There was little overlap between the three most common comorbidities (fig 2, lower right panel).

Six per cent (852/14 184) of patients were current smokers, 30.8% (4364) were previous smokers, and 63.2% (8968) had never smoked. Figure E3 shows the pattern of major comorbidity stratified by age.

## Level of care

A high proportion of patients required admission to high dependency or intensive care units (17%, 3001/18 183; fig 3), and 55% (9244/16 849) received high flow oxygen at some point during their admission. Sixteen per cent of patients (2670/16 805) were treated with non-invasive ventilation, while 10% (1658/16 866) received invasive ventilation.

## Patient outcomes

Overall, 41% (8199/20 133) of patients were discharged alive, 26% (5165/20 133) died, and 34% (6769/20 133) continued to receive care at the date of reporting (fig 4). The median age of patients who died in hospital from covid-19 in the study was 80 years, and only 11% (559/4880) of these patients had no documented major comorbidity.

For patients who received only ward care, 47% (7203/15 297) were discharged alive, 26% (3954/15 297) died, and 27% (4140/15 297) remained in hospital at the date of reporting. As expected, outcomes were worse for those who needed higher levels of care.

Of patients admitted to critical care (high dependency unit or intensive care unit), 28% (826/3001) were discharged alive, 32% (958/3001) died, and 41% (1217/3001) continued to receive care at the date of reporting. Although the patients who received mechanical ventilation were younger than the overall cohort (61 years, interquartile range 52-69), only 17% (276/1658) had been discharged alive by 19 April 2020, 37% (618/1658) had died, and 46% (764/1658) continued to receive care.

Length of stay increased with age for patients discharged alive (fig E4). For patients who died, we found no association between age and time to death, with around 80% dying before day 14 of hospital admission.

## Association of pre-existing patient characteristics and survival

The online supplement (table E4) describes univariable and multivariable associations with mortality. Figure 5 shows variables that remained significant in the multivariable model. Increasing age was a strong predictor of mortality in hospital after adjusting for major comorbidity (reference age \<50 years): 50-59 years, hazard ratio 2.63 (95% confidence interval 2.06 to 3.35, P\<0.001); 60-69 years, 4.99 (3.99 to 6.25, P\<0.001); 70-79 years, 8.51 (6.85 to 10.57, P\<0.001); ≥80 years, 11.09 (8.93 to 13.77, P\<0.001). Female sex was associated with lower mortality (0.81, 0.75 to 0.86, P\<0.001). Chronic cardiac disease, chronic non-asthmatic pulmonary disease, chronic kidney disease, obesity, chronic neurological disorder (such as stroke), dementia, malignancy, and liver disease were also associated with increased hospital mortality. An interactive infographic is available at <https://isaric4c.net/info>. This information must not be used as a predictive tool in practice or to inform individual treatment decisions.

# Discussion

## Principal findings

Patients with covid-19 usually presented with fever, cough, and shortness of breath, and met the WHO case definitions for severe acute respiratory infection or severe acute respiratory syndrome. The most common previous major comorbidities were chronic cardiac disease, diabetes, and chronic non-asthmatic pulmonary disease. Seventeen per cent of patients were admitted to critical care (high dependency unit or intensive care unit). Mortality in hospital was at least 26%, with 34% of the cohort still in hospital at the time of analysis; these proportions increased with escalating level of care. Factors associated with mortality in hospital were increasing age, male sex, and major comorbidities (cardiac disease, non-asthmatic pulmonary disease, kidney disease, liver disease, malignancy, obesity, and dementia).

The data presented in this study describe patients admitted to hospital during the growth phase of the SARS-CoV-2 pandemic in the UK. The first 101 patients were enrolled in the early phase of the outbreak as part of a high consequence infectious disease containment strategy that ended on 10 March 2020. These patients and others who were identified through screening in hospital, or who contracted covid-19 after admission (hospital acquired infection), are included in the 855 patients who were admitted without covid-19 symptoms. The impact these patients have had on the overall cohort characteristics has diminished as numbers have increased, and we believe it is important to keep these patients in the study. Other patients in our cohort without covid-19 symptoms are those who were diagnosed with the disease at the discretion of the clinician looking after them while staying in hospital for other reasons.

The pattern of disease we describe broadly reflects the pattern reported globally.7 Patients in our study had a higher median age and higher rates of chronic obstructive pulmonary disease and asthma than patients in China8 and the US.11 12 The prevalence of obesity in our study (11%) was considerably lower than the overall UK prevalence (29%).13 This proportion could reflect the relatively elderly male population admitted to hospital and misclassification or under reporting by admitting physicians. Our patients presented with a relatively short time interval between onset of symptoms and admission to hospital, which might also be a function of the older and vulnerable patient population.

The current case definition of cough and fever, if strictly applied, would miss 7% of our inpatients. A smaller proportion, 4% of patients, presented with enteric symptoms only. This figure could be an underestimate because these patients fall outside standard criteria for testing. This enteric presentation risks misclassification of patients, and assignment to non-covid-19 care areas, which could pose a nosocomial transmission risk. Severe SARS-CoV-2 infections are rare in people younger than 18 years, comprising only 1.5% of those admitted to hospital. Only 1.0% of those in our study were younger than 5 years. The J shaped age distribution is starkly different to the U shaped age distribution seen in seasonal influenza and the W shaped distribution observed in the 2009 influenza pandemic.14 The reason why SARS-CoV-19 has mostly spared children is not clear, but we speculate this could be because angiotensin converting enzyme 2 receptors are expressed differently in younger lungs.

Other studies have not widely reported that obesity as recognised by clinical staff is associated with mortality in hospital after adjustment for other comorbidities, age, and sex. Obesity was recognised as a risk factor in the 2009 influenza A H1N1 pandemic, but not for the 2012 Middle East respiratory syndrome coronavirus.15 16

The proportion of pregnant women in our cohort was small (10%), similar to the estimated proportion of pregnant women in the community.15 Pregnancy was not associated with mortality, in apparent contrast to influenza.17

## Comparison with other studies

The proportion of patients admitted to critical care in our study was similar to that reported in Italy (17%),18 19 and New York (14.2%),11 12 but higher than China.8 At the time of enrolment, the Intensive Care Society had issued guidance to its members that there would be no rationing of critical care admission until all capacity in the country had been exhausted. As far as we are aware, critical care capacity was not exceeded in the UK during the period of the study. We do not believe that any equipment shortages existed during this period that might have prompted more aggressive futility discussions.

Mortality in our cohort was high in patients admitted to general wards who were not admitted to critical care, which suggests that advanced care planning occurred. We were unable to capture treatment limiting decisions about level of care. The high median age of patients who died in the cohort (80 years) could partly explain the high mortality rate. Mortality rates were extremely high for patients who received invasive mechanical ventilation in the intensive care unit compared with the 2009 influenza A H1N1 pandemic, for which mortality in intensive care was 31%.15 Our data were in line with the initial ICNARC (intensive care national audit and research centre) audit reports, which represent intensive care units in England, Wales, Scotland, and Northern Ireland.20

Outcome analyses only included patients who were admitted before 19 April to allow most patients to complete their hospital admission. However, an inherent reporting bias exists because the sickest of patients, particularly those admitted to intensive care, have the longest hospital stays; mortality rates in hospital could therefore increase. These mortality rates were considerably higher than the 24% mortality rate in hospital seen in patients in intensive care units in Italy19 and the US.11 12 The lower rate in the US could in part be explained by differences in healthcare systems and the proportion of intensive care unit beds to hospital beds between the two countries. In Italy, a lower proportion of patients received mechanical ventilation, and most of their patients (72%) remained in hospital at the time of the analysis.19

The finding of independent associations of advancing age, male sex, chronic respiratory (non-asthmatic) disease, chronic cardiac disease, and chronic neurological disease with mortality in hospital is in line with early international reports.9 10 However, although age adjusted mortality rates are high in elderly patients, most of these patients were admitted to hospital with symptoms of covid-19 and would not have been in hospital otherwise. Enhanced severity in male patients was seen across all ages.

## Strengths and limitations of study

ISARIC WHO CCP-UK stood ready to conduct large scale studies of pandemic outbreaks for eight years, enabling us to enrol 34% of all patients with covid-19 admitted to 208 acute care hospitals across England, Wales and Scotland in the early phase of the pandemic.

Our study has some limitations. We do not currently have data on the inpatients that were not enrolled, or people managed in community settings, such as usual domestic residences and older people's care homes. We are unable to comment on community risk factors that drive hospital admission except by inference from expected representation at admission. We will be linking to routine administrative healthcare datasets which will enable us to assess the presence of any selection bias.

A large amount of data were missing and we suggest there are two main reasons for this. Firstly, enrolment occurred in the nonlinear growth phase of the outbreak, and outcomes for recent admissions have not been reported yet; these admissions account for 18% of the total number of patients enrolled. Secondly, the research network was dealing with unprecedented numbers of patients at a time when many were seconded to clinical practice or themselves off sick. This study is ongoing, and further data are being added to case report forms.

We suggest it is possible that the sickest patients were enrolled in our study, and this could partly explain our high mortality rates in hospital. Some of the sickest patients in the study had the longest lengths of hospital stay and we do not have outcome data for all of these patients yet.

## Conclusions and policy implications

This large and rapidly conducted study of patients admitted to hospital in England, Wales, and Scotland with covid-19 shows the importance of putting plans in place for the study of epidemic and pandemic threats, and the need to maintain these plans. Our study identifies sectors of the population that are at greatest risk of a poor outcome, and reports the use of healthcare resources. Most patients with covid-19 experience mild disease. However, in our cohort, of those who were admitted to hospital two weeks before data extraction, less than half have been discharged alive and a quarter have died. The remainder continued to receive care at the date of reporting. Seventeen percent of patients admitted to hospital required critical care. Factors associated with mortality in hospital were increasing age, male sex, obesity, and major comorbidities.

ISARIC Coronavirus Clinical Characterisation Consortium21 investigators have submitted regular reports to the UK Government's New and Emerging Respiratory Virus Threats Advisory Group (NERVTAG)22 and the Scientific Advisory Group for Emergencies (SAGE).23 Patient level data have been shared and independently analysed by the Scientific Pandemic Influenza Group on Modelling (SPI-M)24 and other investigators. Aggregated data have been shared with WHO in the ISARIC covid-19 report.

Studies such as this cannot be developed, approved, and opened from the start of a pandemic in time to inform case management and public health policy. Our study has shown the importance of forward planning and investment in preparedness studies. Over the next few months we will issue reports in *The BMJ* on specific topics and analyses that are key to understanding the impact of covid-19 and focus on improving patient outcomes.

### What is already known on this topic

1.  Observational studies in China have reported risk factors associated with severe covid-19 that requires hospital admission

2.  Studies describing the features and outcomes of patients with severe covid-19 who have been admitted to hospital in Europe are lacking

3.  Older male adults, people with diabetes, hypertension, cardiovascular disease, or chronic respiratory disease are at greater risk of severe covid-19 that requires hospital admission and higher levels of care, and are at higher risk of death

### What this study adds

1.  This rapid prospective investigation of patients with covid-19 admitted to hospital in England, Wales, and Scotland showed that obesity, chronic kidney disease, and liver disease were also associated with increased hospital mortality

2.  Obesity is a major additional risk factor that was not highlighted in data from China

3.  Severe covid-19 leads to a prolonged hospital stay and a high mortality rate; over a quarter of inpatients in this study had died at the time of reporting, and nearly a third remained in hospital

The study protocol is available at <http://isaric4c.net/protocols>; study registry <https://www.isrctn.com/ISRCTN66726260>. This work uses data provided by patients and collected by the NHS as part of their care and support \#DataSavesLives. We are extremely grateful to the 2648 frontline NHS clinical and research staff and volunteer medical students who collected these data in challenging circumstances; and the generosity of the patients and their families for their individual contributions in these difficult times. We also acknowledge the support of Jeremy J Farrar, Nahoko Shindo, Devika Dixit, Nipunie Rajapakse, Piero Olliaro, Lyndsey Castle, Martha Buckley, Debbie Malden, Katherine Newell, Kwame O'Neill, Emmanuelle Denis, Claire Petersen, Scott Mullaney, Sue MacFarlane, Chris Jones, Nicole Maziere, Katie Bullock, Emily Cass, William Reynolds, Milton Ashworth, Ben Catterall, Louise Cooper, Terry Foster, Paul Matthew Ridley, Anthony Evans, Catherine Hartley, Chris Dunn, D Sales, Diane Latawiec, Erwan Trochu, Eve Wilcock, Innocent Gerald Asiimwe, Isabel Garcia-Dorival, J Eunice Zhang, Jack Pilgrim, Jane A Armstrong, Jordan J Clark, Jordan Thomas, Katharine King, Katie Neville, Alexandra Ahmed, Krishanthi S Subramaniam, Lauren Lett, Laurence McEvoy, Libby van Tonder, Lucia Alicia Livoti, Nahida S Miah, Rebecca K Shears, Rebecca Louise Jensen, Rebekah Penrice-Randal, Robyn Kiy, Samantha Leanne Barlow, Shadia Khandaker, Soeren Metelmann, Tessa Prince, Trevor R Jones, Benjamin Brennan, Agnieska Szemiel, Siddharth Bakshi, Daniella Lefteri, Maria Mancini, Julien Martinez, Angela Elliott, Joyce Mitchell, John McLauchlan, Aislynn Taggart, Oslem Dincarslan, Annette Lake, Claire Petersen, Scott Mullaney, and Graham Cooke.

ISARIC Coronavirus Clinical Characterisation Consortium (ISARIC4C): Consortium lead investigator: J Kenneth Baillie; chief investigator: Malcolm G Semple; co-lead investigator: Peter JM Openshaw; ISARIC clinical coordinator: Gail Carson; co-investigators: Beatrice Alex, Benjamin Bach, Wendy S Barclay, Debby Bogaert, Meera Chand, Graham S Cooke, Annemarie B Docherty, Jake Dunning, Ana da Silva Filipe, Tom Fletcher, Christopher A Green, Julian A Hiscox, Antonia Ying Wai Ho, Peter W Horby, Samreen Ijaz, Saye Khoo, Paul Klenerman, Andrew Law, Wei Shen Lim, Alexander J Mentzer, Laura Merson, Alison M Meynert, Mahdad Noursadeghi, Shona C Moore, Massimo Palmarini, William A Paxton, Georgios Pollakis, Nicholas Price, Andrew Rambaut, David L Robertson, Clark D Russell, Vanessa Sancho-Shimizu, Janet T Scott, Tom Solomon, Shiranee Sriskandan, David Stuart, Charlotte Summers, Richard S Tedder, Emma C Thomson, Ryan S Thwaites, Lance CW Turtle, Maria Zambon; project managers: Hayley E Hardwick, Chloe Donohue, Jane Ewins, Wilna Oosthuyzen, Fiona Griffiths; data analysts: Lisa Norman, Riinu Pius, Tom M Drake, Cameron J Fairfield, Stephen Knight, Kenneth A Mclean, Derek Murphy, Catherine A Shaw; data and information system managers: Jo Dalton, Michelle Girvan, Egle Saviciute, Stephanie Roberts, Janet Harrison, Laura Marsh, Marie Connor; data integration and presentation: Gary Leeming, Andrew Law, Ross Hendry; material management: William Greenhalf, Victoria Shaw, Sarah McDonald; local principal investigators: Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Ana Alegria, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Nicola Best, Pieter Bothma, David Brealey, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, David Chadwick, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Graham Cooke, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B Hawcutt, Antonia Ho, Maria Hobrok, Luke Hodgson, Anita Holme, Anil Hormis, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Mariyam Mirfenderesky, Kavya Mohandas, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Megan Nagel, Thapas Nagarajan, Mark Nelson, Igor Otahal, Mark Pais, Selva Panchatsharam, Hassan Paraiso, Brij Patel, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Jagtur Singh Pooni, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Brendan Ryan, Taranprit Saluja, Aarti Shah, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jeremy Sizer, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Andrew Ustianowski, Nick Vallotton, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Sarah Wilson, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G Wooton, Andrew Workman, Bryan Yates, Peter Young.

Extra material supplied by authors

Web appendix: Supplementary material 1—online supplement

Web appendix: Supplementary material 2—protocol

Web appendix: Supplementary material 3—case report form


abstract: 1.  IVIG, combined with moderate-dose of corticosteroids, might improve patient outcomes.
  .
  2.  The use of corticosteroids might accelerate recovery from COVID-19.
  .
  3.  No controlled clinical trials exist on the use of corticosteroids for COVID-19.
  .
  4.  IL6 correlates with severity, criticality, viral load, and prognosis of COVID-19.
  .
  5.  Tocilizumab, an anti-IL6, can confer benefit in patients with COVID-19 and high IL6.
author: Amene Saghazadeh; Nima Rezaei⁎Corresponding author at: Children's Medical Center Hospital, Dr. Qarib St, Keshavarz Blvd, Tehran 14194, Iran.
date: 2020-05-08
references:
title: Towards treatment planning of COVID-19: Rationale and hypothesis for the use of multiple immunosuppressive agents: Anti-antibodies, immunoglobulins, and corticosteroids

# Introduction

Several generations have been exposed to COVID-19 under different conditions of life at varying locations around the world. As long as the COVID-19 continues to spread, its power of genome modification would probably be increased. What concerns us more is that the 2019-nCoV, by the process of modification of genome structure, might become more and more fitted to humans to profoundly affect those who have already escaped – children and young people without a pre-existing condition. No one can tell how much time it takes to reach a new level of perfection, and that the development of vaccines for active immunization is a long process.

Moreover, most of the best available anti-viral agents are not helpful in the treatment of COVID-19. A randomized controlled trial of 199 hospitalized adults with confirmed COVID-19 demonstrated no actual difference between patients who received lopinavir-ritonavir and patients who received standard care alone in clinical improvement, death rate, and positive virus test rate at day 28 \[1\]. Here we review intermediate indicators of the pathogenesis of COVID-19 that have the potential of being considered for the treatment of COVID-19.

# SARS-CoV2 cell entry is not complex

Fig. 1 is a schematic illustration of the minimum proteins required to mediate the SARS-CoV2 cell entry. The trimeric, transmembrane spike (S) glycoprotein of the virus, SARS-2-S, includes two main functional subunits: S1 and S2. The former, in turn, consists of the four core domains, S1~A~, S1~B~, S1~C~, and S1~D~, that contribute to the attachment to the surface receptor of target cells. Then, the latter coordinates the fusion of viral and cellular membranes. Receptor binding activates proteases that can carry out proteolytic cleavage of the S protein. In coronaviruses, cleavage occurs at two sites: the S1/S2 junction and at the S2′, a region close to the viral fusion peptide \[2\]. Proteolytic cleavage of the S protein causes conformational changes so that they cannot revert to the original structure and profound enough to prime the S2 subunit for the fusion of viral and cellular membranes.

# Certain characteristics of SARS-CoV2

## SARS-CoV2 has acquired an S glycoprotein that highly underwent genetic variation and glycosylation

### Polybasic cleavage site

As evidenced by sequence analysis, there is a residue insertion formed of four amino acids (12 nucleotides) at the boundary between S1 and S2 subunits of the SARS-CoV 2 S. It defines a polybasic furin cleavage site of RRAR for the human SARS-CoV 2 that was absent in human SARS-CoV, bat SARS-like CoVs, and pangolin SARS-like CoV while might be present in other species \[3\]. After the introduction of mutation to the residue insertion and furin cleavage site, the S1/S2 cleavage of the SARS-CoV 2 S did not longer take place. However, the SARS-CoV 2 S entry raised for VeroE6 cells and remained high in BHK cells that express human ACE. Therefore, it seems that SARS-CoV2 transmissibility does not depend on the S1/S2 cleavage.

A polybasic cleavage site explains a virus that is highly-pathogenic for humans while it is low-pathogenic for other species. For example, using reverse genetic tools, an avian paramyxovirus type 7 (APMV-7) was developed by mutating the fusion (F) protein cleavage site \[4\]. The constructed APMV-7 showed furin cleavage and increased replication and syncytium formation in cell cultures. However, chicken exposed to the virus did not exhibit infection.

### Glycosylation

Glycosylation and its related products, i.e., glycans, introduce changes to the viral envelope that make the virus fitted for interaction with the host cell membrane \[5\]. Generally, glycans are oligosaccharides linked to the dense decoration of the spike glycoprotein. In particular, these oligosaccharides have shown to influence the folding of the S protein and proteolytic process so that they facilitate the virus cell entry. Moreover, a virus with the glycosylated glycoprotein gains an extra feature for an escape from the immune responses. Therefore, glycans are a good target for vaccine design.

Two main types of glycans are N-linked and O-linked glycans. Both are released from glycoproteins. Whereas enzymes fulfill the construction of N-glycans, chemical methods perform the release of O-glycans. N-glycans are linked to the amino acid asparagine (Asn) residues (Asn-any amino acids except for proline- Ser or Thr) utilizing an N-glycosidic bond, mostly N-acetylglucosamine. O-glycans are attached to the amino acid serine (Ser) and threonine (Thr) residues by the addition of an N-acetyl galactosamine (GalNAc). For example, N-glycans exist in Hendra virus, SARS-CoV, influenza virus, hepatitis virus, HIV-1, and West Nile virus \[6\], and O-glycans have occurred in the Ebola virus.

The 2019-nCoV S protein includes 13 and 9N-linked glycosylation sequons in the S1 and S2 subunit, respectively \[3\]. All of these have previously occurred in the SARS-CoV S glycoprotein, except for four-linked glycosylation sequons in the S1. Also, due to the existence of an amino acid proline in the polybasic cleavage site, which makes the inserted sequence PRRA, there are three O-linked glycans introduced to the 2019-nCoV RBD residues S673, T678, and S686 \[7\].

### SARS-CoV2 receptor binding domain contains six amino acids providing favorable positions for binding to human ACE2

When compared to SARS-CoV S~B~, the motif binding the human ACE2 to the SARS-CoV 2 S~B~ showed a relatively higher binding affinity for human ACE2 as indicated in smaller equilibrium dissociation constant (2.9 nM vs. 7.7 nM) \[3\]. Structural analysis has demonstrated that fourteen positions critically take part in the receptor-binding domain of the SARS-CoV S~B~ that contain eight conserved (T402, Y436, Y440, N473, Y475, T486, G488, and Y491) positions and six semi-conserved (R426 to N448, Y442 to L464, L472 to F495, N479 to Q502, Y484 to Q507, and T487 to N510) substitutions with respect to the SARS-CoV 2 S~B~.

### SARS-CoV2 receptor binding domain contains cyclic regions that can make interaction with cell-surface GRP78

Pep42 is a cyclic oligopeptide that, with its hydrophobic character, can selectively interact with cell surface glucose‐regulated protein 78 (GRP78), a member of 70 kDa heat shock proteins. GRP78, also known as BiP or HSPA5, under endoplasmic reticulum stress, can be translocated from the endoplasmic reticulum to the membrane and helps to maintain cellular integrity under physiologic and pathological stress. It critically contributes to various functions ranging from protein folding, transportation, and degradation to cell-signaling, proliferation, survival, apoptosis, inflammation, and immunity. The expression of GRP78 decreases with age.

On the 2019-nCoV spike protein, 13 disulfide bonds are corresponding to 13 different cyclic regions thought to be similar to the cyclic form of Pep42 \[8\]. Among these, four regions I-IV take place in the outer surface of a putative receptor-binding domain (RBD) on the viral spike., including C361, C379, C391, and C480. These regions share sequence similarity with Pep42, ranging from 15.38% to 46.15%. However, only one of them, i.e., region IV (GRAVY = 0.08), is a hydrophobic region, like Pep42 (GRAVY = 1.1). Structural models evaluate the energy contribution for region IV as a part of region III to the GRP78 to be about (−9.8 out of −14.0 kcal/mol), and the docking platform proposes region IV as the best region binding to GRP78. The region IV can be linked to the substrate-binding domain β (SBDB) of GRP78 using five H-bonds (through P479, N481, E484, and N487) and four hydrophobic interactions (through T478, E484, and F486).

# Passive immunization

There are two main ways to induce protection against infections: active and passive immunization. Active immunization comes to exist when the body's own immune system can produce antibodies actively following exposure to a viral antigen. On the contrary, there is a passive working mode of the immune system that would appear following the transfer of antibodies that act directly to neutralize viral infectivity.

## Serum therapy

### Convalescent plasma from patients recovered from COVID-19

#### Hypothesis: Plasma from patients recovered from COVID-19 contain antibodies against 2019-nCoV

A look at the history of viral outbreaks offers convalescent plasma as the only remedy to avoid further fatalities. The most recent examples include the pandemic of influenza A H1N1 (H1N1pdm09) in virus infection 2009, the Western African Ebola virus epidemic in 2014, and the outbreak of MERS-CoV in 2015 \[9\]. Meta-analysis studies have shown a reduced risk of mortality in patients with SARS-CoV and influenza receiving convalescent plasma \[10\].

#### Rationale: Convalescent plasma from patients recovered from COVID-19 can treat patients with severe COVID-19

A pilot study \[11\] recently has investigated the safety and effect of convalescent plasma that contain antibody levels higher than 1:640 combined with regular anti-viral agents and standard supportive care on clinical outcomes of ten patients with severe COVID-19. The study showed clinical improvement of all the ten patients accompanied by an increase in lymphocyte count and a decrease in CRP. Following transfusion of convalescent plasma, all the seven patients who were SARS-CoV2 RNA positive before transfusion of convalescent plasma turned SARS-CoV2 negative. There were no control groups receiving convalescent plasma alone or standard therapy without convalescent therapy to evaluate the main effect of convalescent plasma.

### Serum from bats with SARS-like CoV

#### Hypothesis: whole-genome sequencing and phylogenetic analysis shed light on the origin of the 2019-nCoV virus – It has been probably introduced from bats to men

Genome sequencing of a fecal bat sample, Rp3, could detect an isolate of coronaviruses, which was almost identical to the causative agent of the SARS-CoV outbreak of 2002–2003. Hence, it attained the name SARS-like coronavirus isolate Rp3 (SL-CoV Rp3) \[12\]. The bronchoalveolar lavage fluid (BALF) of a patient with SARS contained the 2019-nCoV. RNA sequencing could reveal about 90% similarity in nucleotides of the novel coronavirus and of SARS-like coronavirus that had previously related to bats \[13\], \[14\]. In particular, the S protein of the 2019-nCoV has a high sequence identity of 80–98% with the S protein of bat SARS-like CoVs, such as SARSr-CoV ZXC21 S, ZC45 S, and RaTG13 \[3\]. Moreover, in phylogenomic trees, branches for the 2019-nCoV are of greater length than those for the 2003 SARS-CoV, and therefore more favorable to bats.

#### Rationale: Bat serum is not able to efficiently neutralize SARS-CoV

The range of bats, belonging to the genus *Rhinolophus* (horseshoe bats) and the family *Rhinolophidae*, produce the SARS-CoV antibody \[12\]. Polymerase chain reaction (PCR) will confirm the presence of SARS-CoV nucleocapsid (N) and polymerase (P) proteins in fecal samples if an individual bat being seropositive for SARS-CoV \[12\]. There is a significant degree of resemblance of greater than 90% in the nucleotide sequence of the viral genomes between SL-CoV Rp3 \[12\] and the Tor2 strain of SARS-CoV – which was isolated in Toronto \[15\]. The differences in the genome sequences of SARS-CoV in the two species occur merely in the S gene – which encodes the S1 domain of the coronavirus spike protein and contains regions with high mutation rates \[12\]. The coronaviruses commonly possess five open reading frames (ORF) that correlate with the production of the replicase polyprotein (P), the spike (S), envelope (E), and membrane (M) glycoproteins and the nucleocapsid (N) protein. The human SARS-CoV Tor2 and bat SL-CoV Rp3 strains remain more than 90% identical at the proteins P, E, M, and N. the protein S consists of two main domains: 1) the S1 domain conveys the role of receptor binding and 2) the S2 domain assumes the role of the fusion of viral and host-cell membranes. In particular, the human SARS-CoV Tor2 strain shows a noticeable degree of difference in the S1 domain from the bat SL-CoV Rp3 strain. This diversity would suffice to produce functional differences between the species, and is an apparent reason why bat sera having high levels of cross-reactive antibodies not acted efficiently to neutralize SARS-CoV.

### Serum from convalescent SARS patients

#### Hypothesis: SARS-CoV and SARS-CoV2 are ideally similar in the structure and the cell entry receptor and protease

SARS-CoV and SARS-CoV2 share absolutely the same cleavage junctions, almost the same sequence (96%) of their main protease, a high degree (76%) of similarity in the amino acid sequence of their S protein, a similar S2′ cleavage site, a similar spectrum of cells they can enter, and the similarity of the most residues essential for binding ACE2 \[16\], \[17\], \[18\]. Also, both of them utilize the same domain of S1~B~ to interact with the ACE2 receptor. However, they differ in proteolytic processing to some degree. Study \[16\] of the human embryonic kidney (HEK) cell line, 293 T, has shown that a signal for the S2 subunit is present in cells inoculated with SARS-2-S, but not in cells inoculated with SARS-S.

Two main proteases for both SARS-S and SARS-2-S are endosomal cysteine proteases cathepsin B and L (CatB/L) and the transmembrane protease, serine 2TMPRSS2 \[16\]. In 293 T cells lacking 2TMPRSS2, blocking CatB/L activity through increasing the endosomal pH by ammonium chloride could significantly limit the entry of both SARS-S and SARS-2-S. In TMPRSS2 + Caco-2 cells, the effect of ammonium chloride existed to a lesser extent. A combination of camostat mesylate, a blocker of TMPRSS2, and E-64d, an inhibitor of CatB/L, yielded the complete inhibition of SARS-2-S entry in TMPRSS2 + Caco-2 cells. In both the human lung cancer cell line Calu-3 and the primary human lung cells, there was a reduction of the entry of both SARS-S and SARS-2-S by camostat mesylate, indicating that SARS-S and SARS-2-S partially require TMPRSS2 for a lung infection.

#### Rational: Serum from convalescent SARS patients is able to neutralize SARS-CoV2 efficiently

Antiserum that contains antibodies against human ACE2 could hinder the entry of both SARS-S and SARS-2-S pseudotypes while not affected the entry of VSV-G and MERS-S pseudotypes. It supports the notion that SARS-S and SARS-2-S utilize the same primary entry receptor, i.e., ACE2, which is different from the primary receptors VSV-G and MERS-S engage for cell entry that is LDLR and DPP4, respectively.

Sera from three convalescent SARS patients reduced the SARS-S entry and, to e lesser degree, the SARS-2-S entry. The patient serum effect was in a dose-dependent manner \[16\].

### Serum from rabbits immunized with SARS

#### Hypotheses: Serum from rabbits immunized with SARS is more effective than serum from convalescent SARS patients

Paraoxonases (PON) are mammalian enzymes associated with anti-oxidant and anti-inflammatory effects \[19\]. Rabbit PON differs from human PON in terms of more activity and more stability under the circumstances \[20\].

#### Rational: Serum from rabbits immunized with the S1 subunit of SARS-S is able to neutralize SARS-CoV2 very efficiently

Sera from rabbits immunized with the S1 subunit of SARS-S could effectively reduce the entry of both SARS-S and SARS-2-S \[16\]. When compared with patient serum, rabbit serum revealed to us higher efficiency in inhibition of SARS-2-S entry at the same concentration.

## Intravenous immunoglobulins (IVIG)

### Hypothesis: IVIG contains a large pool of human antibodies

IVIG is an immunomodulatory treatment currently useful for a variety of human diseases that share an idiopathic origin, ranging from autoimmune disorders to primary antibody deficiencies. Also, IVIG has shown promising results in case of severe (such as sepsis, Parvovirus B19 infection, West Nile virus encephalitis, HIV, Clostridium difficile infections, Mycobacterium avium, Mycobacterium tuberculosis, and Nocardia infections) and recurrent infections in primary antibodies deficiencies \[21\].

Most patients develop antibodies against the NP and RBD of 2019-nCoV during the second week after infection onset \[22\]. Analysis of serum samples collected 14 or more days after symptom onset revealed detection of IgG and IgM antibodies against NP in 94% and 88% and RBD in 100% and 94% among patients with COVID19. Studies consistently show that increased immunoglobulin levels accompany the transition from early to late course of COVID19. It poses the possibility that IVIG therapy might help to accelerate recovery from COVID-19.

### Rationale: IVIG might help to improve the outcome of patients with COVID-19

The study \[23\] included ten patients with COVID-19 who demonstrated worsening symptoms, e.g., decreased lymphocyte count and decreased PaO2/FIO2 ratio and oxygen saturation, following treatment with a short-term moderate-dose corticosteroid (methylprednisolone 80 mg/d) plus immunoglobulin (10 g/d). After switching to the double dose of 1600 mg/d methylprednisolone plus 20 g/d immunoglobulin, all of the patients improved in the clinical, laboratory, and paraclinical outcomes.

Passive immunization protects against disease, and so it should be administered as early as possible when the patient is diagnosed. Studies show that the viral RNA of 2019-nCoV reaches its peak during the first week and then gradually decreases and that IgG and IgM begin to rise from the 10th day so that most patients have anti-viral antibodies by the 14th day.

# Recombinant type I IFN

## Hypothesis: IFNs play a role in anti-viral immunity

Type I IFNs play a primary role in the inhibition of viral replication through the coordination of anti-viral immune responses and modulation of inflammatory responses \[24\].

## Rational: Recombinant type I IFN can effectively inhibit SARS-CoV2 replication

In 24–48 h after infection, SARS-CoV2 replication reaches titer that causes the cytopathic effect in Vero E6 cells, which express ACE2 and thus are susceptible to SARS-CoV2 infection \[25\]. It is similar to the viral replication kinetics for SARS-CoV \[25\]. Pretreatment with recombinant type I IFN could effectively inhibit SARS-CoV2 replication at both 24 and 48 h after infection \[25\]. For SARS-CoV, such an effect was present at 24 h but absent at 48 h after infection \[25\]. The difference between SARS-CoV2 and SARS-CoV in response to type I IFN treatment may be due to differences in structural proteins of these viruses, including NSP3, ORF3b, and ORF6 \[25\].

# Development of a human monoclonal antibody (mAb)

## Targeting the S protein

### Hypothesis: A mAb against the binding domain of the virus can inhibit SARS-CoV2 infection

Recently, hybridoma supernatants containing antibody repertoires from immunized transgenic mice that express the human immunoglobulin heavy and light chains and rat origin immunoglobulin constant regions were used to detect antibodies that can cross neutralize SARS-S and SARS-2-S \[26\]. One chimeric 47D11 H2L2 antibody displayed such a cross-neutralizing activity, decreased syncytia formation induced by SARS-S and SARS2-S, and could protect VeroE6 cells against SARS-S and SARS2-S pseudotyped virus \[26\]. It may lie in its similar affinities for interacting with the same domain of the S1 subunit, i.e., S1~B~, of each SARS-S and SARS-2-S.

### Rational: The chimeric 47D11 H2L2 might not be effective in human lung cells as it is *in vitro*

47D11 carried a higher affinity for interacting with the S2 subunit of SARS-S than that of SARS-2-S. It is important that for both SARS-S and SARS-2-S, the binding of the 47D11 antibody to the target – the S1~B~ domain – does not block the binding of S1~B~ and S2 to ACE2 receptor \[26\]. By contrast, neutralizing antibodies that specifically target SARS-S could compete with S1~B~ and S2 for binding to ACE2.

## Targeting pro-inflammatory cytokines

### Hypothesis: A mAb against IL6 can attenuate hyper inflammation

Tocilizumab, also known as atlizumab, is a humanized anti-human IL6 receptor antibody approved by FDA for several inflammatory and autoimmune diseases severe, such as cytokine release syndrome, rheumatoid arthritis, giant cell arteritis, polyarticular juvenile idiopathic arthritis, and systematic juvenile idiopathic arthritis. It is safe and effective for both adults and children two years of age and older.

### Rationale: Tocilizumab can treat lung injury in patients with critical and severe COVID-19

In the study \[27\], 21 patients with COVID-19 whose condition was severe or critical received one or two doses of Tocilizumab plus standard therapy. Patients who had a mean IL6 level of more than 100 pg/ml before tocilizumab treatment showed improvement in clinical symptoms and peripheral oxygen saturation and normalization for lymphocyte proportion and CRP levels. Also, lung lesion opacity was absorbed in 90% of patients. Neither serious adverse effects nor deaths occurred with tocilizumab treatment.

There are ongoing clinical trials for tocilizumab treatment in patients with moderate and severe COVID-19. Currently, the use of Tocilizumab is recommended for patients with COVID-19 who have warning signs of hyper inflammation, as can be measured by IL6, ferritin, platelet counts, inflammatory markers, and H score \[28\].

# Corticosteroids

## Hypothesis: Corticosteroids can modulate inflammation

Corticosteroids are commonly used for modulation of a variety of inflammatory conditions. In addition to a daily regimen, they can be used in the form of pulse therapy to treat flares of autoimmune diseases. However, caution in the use of corticosteroids is needed due to the potential serious side effects associated with corticosteroid drugs and that corticosteroids generally suppress the immune system. The latter means that corticosteroids modulate hyper inflammation and, on the other hand, inhibit immune responses that are vital for the host defense against the virus \[29\].

## Rationale: Corticosteroids might help accelerate recovery from COVID-19

The study \[30\] investigated the effect of inhaled corticosteroids ciclesonide, cortisone, prednisolone, dexamethasone, and fluticasone on the replication of the MERS-CoV. Among the four compounds, the only ciclesonide was capable of inhibiting viral replication. Also, ciclesonide induced a significant inhibition of viral replication of other human coronaviruses, such as HCoV-229E and SARS-CoV, and another positive-strand RNA virus, rubella virus, while not affect the viral replication of negative-strand RNA viruses, e.g., influenza and respiratory syncytial virus. For the MERS-CoV, a nonstructural protein 15 (NSP15) appeared to act as the target of ciclesonide. An amino acid substitution in the NSP15 conferred resistance of the mutated MERS-CoV to ciclesonide. Mometasone could help deal effectively with the mutated MERS-CoV. For the SARS-CoV2, all three ciclesonide, mometasone, and lopinavir were able to inhibit viral replication to a similar degree. Interestingly, their effect was more noticeable than serine protease inhibitors, e.g., nafamostat and camostat in cells that Vero cells that express TMPRSS2. It indicates the tendency of the SARS-CoV2 to enter the cell through the cathepsin/endosomal pathway rather than through the TMPRSS2/cell surface pathway.

The study \[31\] included 46 patients with severe COVID-19, of these 26 patients received methylprednisolone (1–2 mg/kg/d for 5–7 days), and 20 patients received standard therapy without methylprednisolone. The first group achieved faster improvement in clinical symptoms (fever and peripheral oxygen saturation) and lung lesions detected by CT imaging. However, two deaths occurred in the first group and one death in the second group. Moreover, the two groups did not differ in laboratory parameters, including WBC, lymphocyte count, monocyte count, and cytokines (IL-2, IL-4, IL-6, and IL-10) six days after treatment.

There is a report of the patient with COVID-19 treated with methylprednisolone since day 8 of the disease course. However, his situation worsened and developed respiratory failure and died on day 14 \[32\].

# Eggs for increasing ACE2 and copper

Egg ovotransferrin contains an angiotensin-converting enzyme (ACE) inhibitory peptide, known as IRW, that has shown to decrease blood pressure in hypertensive rats \[33\]. Through the up-regulation of ACE2, E-cadherin, ABCB-1, and IRF-8, IRW can decrease RAS activity, hyperplasia, and vascular inflammation and aid differential regulation of leukocytes. On the other hand, by the down-regulation of pro-inflammatory molecules, e.g., ICAM-1 and VCAM-1, IRW can reduce the recruitment of leukocytes and vascular inflammation.

Copper might help the immune system to combat viral infections. A High-content of copper exists in eggs and eggshells of domestic birds. In particular, the eggshell of pigeon (4 $\pm$ 0.29 μg/g) and the egg of quail (4.67 $\pm$ 1.08 μg/g) contains high concentrations of copper \[34\].

# Conclusion

The novel coronavirus, SARS-CoV2, can cause a potentially fatal disease, COVID-19, in humans. The infection of human cells by SARS-CoV2 includes two sequential steps: attachment of the virus to the surface receptor of target cells and the fusion of viral and host membranes. The former requires at least a receptor-binding domain on the SARS-CoV2 Spike protein that can interact with a cell surface receptor, for example, ACE2, expressed on human cells. The latter requires at least the host protease(s) to mediate proteolytic cleavage of the SARS-CoV2 Spike protein into S1 and S2 subunits and consequently promote the fusion of viral and host membranes. Also, SARS-CoV2 possesses a polybasic cleavage site that can explain the high pathogenicity of SARS-CoV2, N-glycans and O-glycans that make the dense decoration of SARS-CoV2 S protein, and cyclic regions that can interact with cell-surface GRP78. Essential elements that process SARS-CoV2 cell entry and specific characteristics that allow SARS-CoV2 to escape the immune system have the potential as targets for COVID-19 therapy.

Lack of specific treatments for COVID-19 and the very time-consuming process of vaccine development lead us to trust traditional notions of immunization using passive transfer of humoral immunity. Passive immunization can be done using plasma therapy and IVIG therapy. Plasma from patients recovered from COVID-19 that contains antibodies against SARS-CoV2 has shown promising results in patients with severe COVID-19. Also, SARS-CoV and SARS-CoV2 are ideally similar in the structure and the cell entry receptor and protease. Studies show that serum from convalescent SARS patients and serum from rabbits immunized with SARS are both able to neutralize SARS-CoV2 efficiently. However, serum from bats immunized with SARS-lice coronavirus SL-CoV Rp3 could not exert such an effect. It is due to a noticeable degree of difference in the S1 domain in the S1 domain between the bat SL-CoV Rp3 strain and SARS-CoV2. A short-term moderate dose of IVIG combined with moderate-dose of corticosteroids might improve patient outcomes. Studies show that the viral RNA of SARS-CoV2 reaches its peak during the first week and then gradually decreases and that IgG and IgM begin to rise from the 10th day so that most patients have anti-viral antibodies by the 14th day. Passive immunization protects against disease, and so it should be administered as early as possible when the patient is diagnosed.

Evidence links COVID-19 to variable degrees of inflammation. Corticosteroids offer a potent anti-inflammatory option. However, they do not dictate precise actions and might cause suppression of anti-viral immune responses as well. Studies show that the use of corticosteroids might accelerate recovery from COVID-19. There are no controlled clinical trials that show whether the use of corticosteroids can reduce COVID-19-related death. Moreover, the pro-inflammatory cytokine IL6 is the best-documented cytokine in COVID-19 correlated with severity, criticality, viral load, and prognosis of patients with COVID-19. Tocilizumab, a monoclonal antibody against IL6, could confer clinical benefit in patients with high IL6 levels.

# References


author: Gunther Eysenbach
date: 1999
references:
title: Welcome to the Journal of Medical Internet Research

Welcome to the "Journal of Medical Internet Research" - JMIR, the first international scientific peer-reviewed journal on all aspects of research, information and communication in healthcare using Internet and Intranet-related technologies.

Why does the world need the JMIR? The Internet - and more specifically, the World-Wide-Web - has an impact on many areas of medicine - broadly we can divide them into "clinical information and telemedicine", "medical education and information exchange" and "consumer health informatics":

-   First, Internet protocols are used for clinical information and communication. In the future, Internet technology will be the platform for many telemedical applications.

-   Second, the Internet revolutionizes the gathering, access and dissemination of non-clinical information in medicine: Bibliographic and factual databases are now world-wide accessible via graphical user interfaces, epidemiological and public health information can be gathered using the Internet, and increasingly the Internet is used for interactive medical education applications.

-   Third, the Internet plays an important role for consumer health education, health promotion and teleprevention. (As an aside, it should be emphasized that "health education" on the Internet goes beyond the traditional model of health education, where a medical professional teaches the patient: On the Internet, much "health education" is done "consumer-to-consumer" by means of patient self support groups organizing in cyberspace. These patient-to-patient interchanges are becoming an important part of healthcare and are redefining the traditional model of preventive medicine and health promotion).

All these aspects of "cybermedicine" have implications for consumer empowerment and evidence-based medicine: The Internet (or Intranets) enables health professionals to access clinical data just in time, it allows health professionals to access the evidence on the efficacy of available interventions, and finally, it empowers consumers to actively take part in the decision making process.

Clearly, the medical use of the Internet presents enormous opportunities and challenges. The need for research and rapid publication of the findings is obvious. Research in this area should go beyond mere development and provision of technical solutions; it should also address social and human factors, and evaluate the impact of the Internet on society and health care, and public health.

JMIR wishes to publish papers that help physicians and consumers to maximize the use of the Internet. We invite researchers to evaluate the effectiveness and efficiency of Internet communications in health care. We encourage publishers of Internet health information to apply rigorous research methods (such as randomization of users) to evaluate different methods and determinants of communication effectiveness. We invite researchers to compare the effectiveness of communication and information on the Internet with other (traditional) methods of communication. We call for papers that describe and evaluate the effects of the Internet on the patient-physician relationship and the impact on public health. We invite papers that describe the use of the Internet for evidence-based medicine, for example work that demonstrates the development and dissemination of clinical guidelines using the Internet. We wish to receive papers on ethical and legal problems, as well as cross-border and cross-cultural issues, affecting medicine on the Internet, and papers describing possible solutions to the problem of equity of information access. We would like to receive systematic studies examining the quality of medical information available in various online venues. We encourage thought regarding methods of evaluation, quality assessment and improvement of Internet information. We would like to receive proposals for standards in the field of medical publishing on the Internet, including self-regulation issues, policies and guidelines to provide reliable healthcare information. We encourage researchers to experiment with online questionnaires and other data collection experiments such as medical surveys and psychological tests. We would like to publish innovative approaches to use the Internet for healthcare research, examples might include clinical studies, drug reaction reporting and surveillance systems. We would like to publish comments and papers on electronic medical publishing and the use of the Internet for traditional scholarly publishing. We welcome descriptions of websites with innovative content or form, but authors should always make attempts to evaluate the impact of their work, for example trying to determine basic information about user demographics and traffic, where appropriate.

As publishers of a journal *about* the Internet, we are also dedicated to using and experimenting with the Internet as a medium itself. Obviously, we are utilizing the Internet for communication with authors (which is done exclusively by email), and communication with external reviewers, but we also intend to experiment with some novel methods of peer-review. We further invite authors to experiment with innovative methods to communicate their findings, for example submitting HypER-papers (Hypertext Enriched Research Papers) \[1\] or by the inclusion of animated figures (animated gifs), audio and video into their documents, or by attaching original data which could be downloaded and possibly dynamically analyzed using JAVA applets.

New Internet standards and tools are developing at a breathtaking pace, and many Internet trends have a half-life of less than 6 months. We think that traditional paper journals are simply too slow for the fast-moving field of Internet-technologies. One of our editors reported that an article submitted to a leading medical informatics journal was only published 2 years later - obviously an unacceptable (and unnecessary) delay. Thus, we are trying to publish fast - usually our peer-review time is 1-2 weeks, and we publish all e-papers as soon as they have been accepted (though the printed version may be published later). We will follow a dual publishing strategy - full articles will be published on the Internet, all abstracts and short important articles will be published as a printed version, mainly for archiving and indexing purposes. Our peer-review process will be rigorous and constructive, helping authors to improve their manuscripts and guaranteeing a high-quality journal. We eagerly look forward to your contributions.

Gunther Eysenbach MD

*Editor,*

*Journal of Medical Internet Research*


author: Arnold von Eckardstein; Jerzy-Roch NoferCorresponding author: Arnold von Eckardstein, <email@example.com>.
date: 2013-08
references:
title: Frail HDLs and Stiff Arteries in Type 2 Diabetes in Juveniles

Lowering of LDL cholesterol plasma levels with statins reduces coronary heart disease (CHD) event rates by up to 50% (1), implying a residual cardiovascular risk of the same magnitude despite treatment. Moreover, statins increase the risk of type 2 diabetes mellitus (T2DM), especially in patients showing components of the metabolic syndrome (2). Hence, there is considerable need for novel therapeutic regimens improving CHD prevention without increasing the risk of T2DM.

HDLs are an interesting target for this objective. Most observational studies and meta-analyses thereof demonstrated the inverse relationship of HDL cholesterol (HDL-C) levels with the CHD risk (3) as well as T2DM and its vascular complications (4,5). HDL particles exert various potentially antiatherogenic (6–8) and antidiabetogenic activities (4). Atherosclerotic lesions were decreased or even reversed in animals by transgenic overexpression or application of exogenous apolipoprotein (apo) A-I, which constitutes the most abundant protein of HDL (6). Animal experiments also provided evidence that HDL improves the function and survival of pancreatic β-cells and glucose uptake into muscle, liver, and adipose tissue (4). In humans, artificially reconstituted HDL particles reduced coronary plaque volume (9,10) and improved glycemia (11). In contrast to these promising results, addition of fenofibrate, niacin, torcetrapib, or dalcetrapib to statins failed to reduce cardiovascular risk beyond that provided by statin treatment alone despite increasing HDL-C (12–15). Moreover, alterations in HDL-C, either associated with mutations in the human genome or provoked in genetic mouse models, did not consistently translate into opposite changes of cardiovascular risk and atherosclerotic plaque load, respectively (16,17).

Because of these controversial data, the suitability of HDL as a therapeutic target has been increasingly questioned. However, it is important to emphasize that interventional trials and Mendelian randomization studies targeted HDL-C, which neither exerts nor reflects any of the potentially antiatherogenic activities of HDL (6). In a prototypic HDL particle, two to five molecules of apoA-I and ∼100 molecules of phosphatidylcholine form an amphipathic shell in which several molecules of unesterified cholesterol are imbedded and it surrounds a core of cholesterol esters (18). Molar differences in the content of these major protein and lipid constituents produce considerable heterogeneity of HDL in shape, size, density, and charge (Fig. 1). The macroheterogeneity of HDL is further compounded by quantitatively minor proteins, lipids, or microRNAs (19–21), many of which contribute to the potentially antiatherogenic and antidiabetogenic properties of HDL. Additional HDL microheterogeneity is a consequence of various inflammatory diseases, including T2DM or CHD, which lead to the loss of or structural modification of typical HDL constituents or the acquisition of atypical HDL constituents (22). Several alterations of HDL structure and composition have been associated with the loss of potentially vasoprotective functions, such as stimulation of cholesterol efflux (7) and endothelium-dependent vasodilation (8), independently of plasma HDL-C levels. Importantly, the plasma concentrations of many microcomponents of HDL amount to only ≤1 μmol/L. Hence, they are three to four orders of magnitude lower than those of HDL-C (usually \>1 mmol/L) and one to two orders of magnitude lower than those of apoA-I (50 μmol/L) or HDL particles (10–20 μmol/L). Accordingly, these microcomponents are nonrandomly distributed among HDL subclasses and are not recovered by measurements of HDL-C, apoA-I, or HDL subclasses.

Many laboratories worldwide currently are searching for functional biomarkers of HDL, which are more closely related to atherosclerosis and cardiovascular outcomes than HDL-C. One promising approach in this direction has been undertaken by Gordon et al. (23), who investigated the association of HDL subclasses and their proteomes with the presence of T2DM and obesity in adolescents and with pulse wave velocity (PWV), a noninvasive measure of vascular stiffness and hence a surrogate of atherosclerosis. Among 12 HDL subfractions identified by the authors, large HDL particles showed greatest differences between T2DM patients and nondiabetic controls. Compared with those in healthy controls, these subfractions were deprived of several proteins, including apoA-I, apoA-II, apoE, apoM, and paraoxonase 1 (PON1) in T2DM patients. These results in humans closely correspond to recent findings by Kothapalli et al. (24) showing increased arterial stiffness in apoE-deficient mice and favorable effects on arterial elasticity exerted by apoE-containing HDL particles. In addition to changes in protein composition, the phospholipid content of large HDL subfractions showed a significant inverse correlation with PWV. In agreement with protective functionality, large HDL particles were enriched in the sphingosine-1-phosphate–binding lipocalin apoM (25) and the antioxidative enzyme PON1 (26) in nondiabetic subjects. In contrast, cholesterol concentrations in smaller HDL particles showed positive correlations with PWV, suggesting adverse effects on vascular health. Of note, HDL-C did not show any significant correlation with PWV.

The study has several strengths. By investigating young patients with diabetes and controls, Gordon et al. (23) minimized the effect of confounders complicating data interpretation in diabetic adults. By using gel filtration rather than ultracentrifugation, the authors eliminated artifacts arising from protein and lipid displacement by shear forces or high ion concentrations. It is noteworthy that the authors retrieved several proteins, including apoM and PON1, in large HDL particles that were previously assigned to small HDL particles by ultracentrifugation (27). The risk of recording false-positive results after using the comprehensive proteomic approach was limited by the stringent selection of those proteins for statistical analyses, which were identified by previous proteomic examination of HDL.

General limitations of these explorative studies are the cross-sectional design and the small number of patients. As acknowledged by the authors, statistical association does not imply causality. In this respect, it will be interesting to test the effect of large HDL fractions on endothelial functions, which were previously found to be modulated by PON1 or apoM (8,25,26). The expansion to prospective studies will require methodological advancements permitting analyses of hundreds or even thousands of samples. To this end, a refined proteomic and lipidomic examination of large HDL particles might help to identify proteins or lipids that can be specifically targeted by high-throughput technologies such as immunoassays or single-reaction monitoring mass spectrometry. It also should be emphasized that the enzymatic assay used by the authors, which quantifies choline rather than phospholipids, neither discriminates between phosphatidylcholines, lysophoshatidylcholines, plasmalogens, and sphingomyelins nor records noncholine phospholipids such as phosphatidylethanolamines, phosphatidylserines, and sphingosine-1-phosphate. Another limitation of the study is that the mass spectrometry approach measured only relative concentrations. Interestingly, however, this semiquantitative approach unraveled reduced peptide signals in HDL from T2DM patients. It is possible that posttranslational protein modifications altered the mass of peptides and thereby prevented their recording by the assigned molecular mass. This explanation is congruent with previous findings showing enhanced glycation, nitration, chlorination, sulfoxidation, or carbamylation in HDL from diabetic subjects (22). Such modifications may offer new opportunities for use as biomarkers (7,8,22).

In conclusion, the study by Gordon et al. (23) provides new insights into the molecular heterogeneity of HDL and its association with T2DM and atherosclerosis. Apart from reproducing and extending these findings in larger observational studies, it will be important to resolve the structure, function, and metabolism of large HDL fractions. Further structure–function studies may help to select molecules or modifications within HDL, which can be used as biomarkers for identification, treatment stratification, and monitoring of patients at increased risk for cardiovascular diseases or diabetes mellitus.

## ACKNOWLEDGMENTS

A.v.E. is supported by grants from the Swiss National Science Foundation (3100A0-116404/1, 3100A0-130836/1), the FP7 Programme of the European Commission (RESOLVE, 305707), and the Zurich Center of Integrative Human Physiology, and has received honoraria for lectures and advisory activities from Hoffmann-La Roche, Merck Sharpe & Dohme, and AstraZeneca. J.-R.N. is supported by grants from the German Foundation for Pathobiochemistry and Molecular Diagnostics, the Italian Ministry for Education, University, and Research (IDEAS RBID08777T), Novartis Germany, Actelion Pharmaceuticals Germany, and Siemens Healthcare Diagnostics. No other potential conflicts of interest relevant to this article were reported.

# REFERENCES