text:
Journal club of one: "Genome-wide enhancer maps link risk variants to disease genes"
Publicerat i english, genetik	av mrtnj
(Here is a a paper from a recent journal club.)
Nasser et al. (2021) present a way to prioritise potential noncoding causative variants. This is part of solving the fine mapping problem, i.e. how to find the underlying causative variants from genetic mapping studies. They do it by connecting regulatory elements to genes and overlapping those regulatory elements with variants from statistical fine-mapping. Intuitively, it might not seem like connecting regulatory elements to genes should be that hard, but it is a tricky problem. Enhancers — because that is the regulatory element most of this is about; silencers and insulators get less attention — do not carry any sequence that tells us what gene they will act on. Instead, this needs to be measured or predicted somehow. These authors go the measurement route, combining chromatin sequencing with chromosome conformation capture.
This figure from the supplementary materials show what the method is about:
Additional figure 1 from Nasser et al. (2021) showing an overview of the workflow and an example of two sets of candidate variants derived from-fine mapping, each with variants that overlap enhancers connected to IL10.
They use chromatin sequence data (such as ATAC-seq, histone ChIP-seq or DNAse-seq) in combination with HiC chromosome conformation capture data to identify enhancers and connect them to genes (this was developed earlier in Fulco et al. 2019). The "activity-by-contact model" means to multiply the contact frequency (from HiC) between promoter and enhancer with the enhancer activity (read counts from chromatin sequencing), standardised by the total contact–activity product with all elements within a window. Fulco et al. (2019) previously found that this conceptually simple model did well at connecting enhancers to genes (as evaluated with a CRISPR inhibition assay called CRISPRi-FlowFISH, which we're not going into now, but it's pretty ingenious).
In order to use this for fine-mapping, they calculated activity-by-contact maps for every gene combined with every open chromatin element within 5 Mbp for 131 samples from ENCODE and other sources. The HiC data were averaged of contacts in ten cell types, transformed to be follow a power-law distribution. That is, they did not do the HiC analysis per cell type, but use the same average HiC contact matrix combined with chromatin data from different cell types. Thus, the specificity comes from the promoters and enhancers that are called as active in each sample — I assume this is because the HiC data comes from a different (and smaller) set of cell types than the chromatin sequencing. Element–gene pairs that reached above a threshold were considered connected, for a total of about six million connections, involving 23,000 genes and 270,000 enhancers. On average, a gene had 2.8 enhancers and an enhancer connected to 2.7 genes.
They picked putative causative variants by overlapping the variant sets with these activity-by-contact maps and selecting the highest scoring enhancer gene pair.They used fine-mapping results from multiple previous studies. These variants were estimated with different methods, but they are all some flavour of fine-mapping by variable selection. Statistical fine mapping estimate sets of variants, called credible sets, that have high posterior probability of being the causative variant. They included only completely noncoding credible sets, i.e. those that did not include a coding sequence or splice variant. They applied this to 72 traits in humans, generating predictions for ~ 5000 noncoding credible sets of variants.
Variants for fine-mapping were enriched in connected enhancers more than in open chromatin in general, in cell types that are relevant to the traits. In particular, inflammatory bowel disease variants were enriched in enhancers in 65 samples, including immune cell types and gut cells. The strongest enrichment was in activated dendritic cells.
They used a set of genes previously known to be involved in inflammatory bowel disease, assuming that they were the true causative genes for their respective noncoding credible sets, and then compared their activity-by-contact based prioritisation of the causative gene to simply picking the closest gene. Picking the closest gene was right in 30 out of 37 sets. Picking the gene with the highest activity-by-contact score was right in 17 cases out of 18 sets that overlapped an activity-by-contact enhancer. Thus, this method had higher precision but worse recall. They also tested a number of eQTL-based, enrichment and enhancer–gene mapping methods, that did worse.
What it tells us about causative variants
Most of the putative causative variants, picked based on maximising activity-by-contact, were close to the proposed gene (median 13 kbp) and most involved the closest gene (77%). They were often found their putative causative variants to be located in enhancers that were only active in a few cell- or tissue types (median 4), compared to the promoters of the target genes, that were active in a broader set (median 120). For example, in inflammatory bowel disease, there were several examples where the putatively causal enhancer was only active in a particular immune cell or a stimulated state of an immune cell.
What I like about this model is that it is so different to many of the integrative and machine learning methods we see in genomics. It uses two kinds of data, and relatively simple computations. There is no machine learning. There is no sequence evolution or conservation analysis. Instead, measure two relevant quantities, standardise and preprocess them a bit, and then multiply them together.
If the success of the activity-by-contact model for prioritising causal enhancers generalises beyond the 18 causative genes investigated in the original paper, this is an argument for simple biology-based heuristics over machine learning models. It also suggest that, in the absence of contact data, one might do well by prioritising variant in enhancers that are highly active in relevant cell types, and picking the closest gene as the proposed causative gene.
However, the dataset needs to cover the relevant cell types, and possibly cells that are in the relevant stimulated states, meaning that it provides a motivation for rich conditional atlas-style datasets of chromatin and chromosome conformation.
I am, personally, a little bit sad that expression QTL methods seem to doing poorly. On the other hand, it makes some sense that eQTL might not have the genomic resolution to connect enhancers to genes.
Finally, if the relatively simple activity-by-contact model or the ridiculously simple method of "picking the closest gene" beats machine learning models using the same data types and more, it suggests that the machine learning methods might not be solving theright problem. After all, they are not trained directly to prioritise variants for complex traits — because there are too few known causative variants for complex traits.
Fulco, C. P., Nasser, J., Jones, T. R., Munson, G., Bergman, D. T., Subramanian, V., … & Engreitz, J. M. (2019). Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nature genetics.
Nasser, J., Bergman, D. T., Fulco, C. P., Guckelberger, P., Doughty, B. R., Patwardhan, T. A., … & Engreitz, J. M. (2021). Genome-wide enhancer maps link risk variants to disease genes. Nature.
fine mapping, functional genomics, gwas, journal club of one
« Researchers in ecology and evolution don't use Platt's strong inference, and that's okay
Reflektioner om högskolepedagogik: undervisning online »


Do you have questions about delivery, returns, payments or specific product queries? Find your answers here.
We are sorry that your order is not with you yet. First thing to do is to have a look again at your order dispatch confirmation email which confirms the shipping method of your order. Please allow the fully scheduled time for your parcel to arrive.
Click here to see all our delivery information.
You can also try and track your order. Your dispatch confirmation email will show a "click here" link for UKMail or DPD parcels to allow you to track your individual parcel.
Which scooter should I buy?
We have lots of information to help you choose the right child or adult scooter. It's very important to us that you are totally happy with your chosen product so we have tried to include as much information as we can in the links below.
Book an appointment with a Micro Expert today and we'll call you back at a time that's convenient for you.
How to shop for replacement parts?
We are the only company who stock the full range of replacement parts for your Micro Scooter, meaning you can replace and revamp any worn part providing excellent value for money!
How do I actually do the repairs/replacements?
When your item is delivered it will come with a Delivery Note, on the back of this is the RETURNS FORM.
My items have arrived damaged - What should I do?
Please get in touch with us! info@micro-scooters.co.uk we'd like to resolve this with you as soon as we can. All you will need to include is your order confirmation as proof of purchase and if you can a photograph of the damage. We will be in touch within 24 hours to advise you of the next steps.
My order is missing items - What should I do?
Check inside the scooter box for the missing items as we often pack smaller items inside – this is a better way of keeping things together.
If your order contained a scooter and helmet then please wait a couple of days for the helmet as these are sent in a different dispatch.
How do I find out about your discount codes?
The perfect gift! Give the recipient chance to choose their gift from our fantastic selection of scooters and accessories.
Tweet us or send us a message on Instagram or Facebook on the links below.


text:
The right voice for the Alamo City.
About The Tower
County Commissioner Candidate Falsifies Campaign Contribution Reports
by Victoria Ydens
Christine Hortick, a candidate for County Commissioner in Bexar County, has raised over $7,000 in political contributions to her campaign. According to members of her opponent Trish DeBerry's Finance Committee, at least 17% of that money comes from falsified donation claims.
The DeBerry Campaign first noticed the discrepancies in Hortick's campaign contribution reports when Daniel Ortiz, a member of DeBerry's Finance Committee, was notified that he had erroneously been listed on the report. "This is extremely upsetting," said Ortiz. "I am shocked to find my name on Ms. Hortick's campaign report. I have thrown my full support behind Trish [DeBerry] and, until this morning, had very little information about her opponent. I should be removed." In addition to Ortiz, Brad Carson and Debra Guerrero were also listed as financial supporters of Hortick's campaign, despite both stating that they have never supported, nor even met Hortick. Hortick claims to have received $1,250 in total from these three individuals. It begs the question: how many other campaign contributions have been falsified on Hortick's report?
It is a felony to falsify governmental records. Hortick, or at least members of her campaign, allegedly committed a felony in falsifying this information to further her campaign's success. When asked about Hortick's campaign contribution reports, DeBerry said: "a complaint should be filed with the Texas Ethics Commission." DeBerry also said that a complaint should be leveled against Hortick with the Bar Association because, as a practicing attorney with her own private practice, she should be held accountable for such a discrepancy happening under her nose.
"The fact that it was riddled with errors makes me wonder if she even reviewed the report. It's sloppy and unprofessional"
-Trish DeBerry
The campaign contribution and expense reports made 30 days before the election are some of the most important documents to a candidate's campaign. "The fact that it was riddled with errors makes me wonder if she even reviewed the report. It's sloppy and unprofessional," DeBerry said. "This tells voters that my opponent is not to be trusted. If she can't manage $7,000 in campaign contributions, how is she going to handle a $2B Bexar County budget?"
NewsBexar County, Campaign Contributions, Christine Hortick, County Commissioner, Fraud, Local ElectionsVictoria YdensLeave a comment
Socialism: What Would Jesus Do?
Saint Ignatius of Antioch: A Model for Our Times
The Tower is run solely by Trinity students. The views expressed here are the views of the author alone, and do not represent the views of The Tower, the Collegiate Network, or of Trinity University.
The Tower is a project of the Collegiate Network at Trinity University in San Antonio. The Tower serves the San Antonio area, and the Trinity University community especially.
The Tower is completely committed to independence. We will never take direction from Trinity or any other outside organization. Our goal is to serve the students of Trinity and the people of San Antonio with the best news stories and the right ideas. The Tower has no paid personnel—we are all taking the time out of our schedules to work on this project because we believe it will better the communities we care so much about. If you read something on the Tower, you can trust that no one told us what to publish or not publish, and that we are ready to stand behind what we've written.
We chose to name our publication for the distinctive Murchison Tower on Trinity's campus, for the Tower of the Americas and for the idea of intellectual pursuit that the image of a tower evokes. With the right ideas, we believe the right perspective will come across in everything we publish. If you understand that ideas have consequences and that factual and independent reporting matters, then the Tower, the right voice for the Alamo City, is the publication for you.
We thank the Collegiate Network, the Intercollegiate Studies Institute and the many Trinity alumni, community members and former staff members for their continued support.
The Tower is not affiliated with Trinity University and the views of writers reflect their own only, and should not be taken as the views of Trinity University or of The Tower.

redpajama_set_name: RedPajamaCommonCrawl


Oprah's such a powerful influence and icon but she was also gracious and awed enough to recognize and to celebrate Charice's pursuit of her own stardom and dreams. David Foster, a legendary music producer himself who also mentored the towering Celine Dion and Whitney Houston gave a heart warming remark of seeing in Charice a talent like he first saw Celine when she's 18.
Diane Warren, actually wrote the song 'A Note To God' and now getting it to line up along her hit makers "Because You Loved Me," "Un-Break My Heart," and "How Do I Live." Diane and David talked about Charice singing this song early on. However, it did not just fit with her but she was captivated that Charice exceeded their expectations.
Charice really hit it powerfully, sang it beautifully but we can't deny that she just let it flow from her heart. Tears have flown from her eyes and she's just happy that she can call it her song. Oprah and the audience did share her tears of joy even before she went on finale. Moved and awed, they all just stood up, clapped and all of them just like a prayer, sang a note to God with her in their hearts.
It's good to note too that Oprah did gave her an advice before they finished the interview. She said to Charice, 'keep your feet on the ground even though you're wearing better shoes.' A very powerful advice too, isn't it.
This one's cute! So Charice is not playing any Dorothy in a 'Wizard of Oz' or with any of the 'Looney Tunes.' She's gonna play as a contestant to compete against Alvin And The Chipmunks in it's sequel movie.
It'll gonna be fun for sure and pretty cuddly if she can hold one of those animated characters for real. If you've watched the chipmunks, then I guess you have some good memories attached to it. And with Charice on it in it's comeback, that'll be more than entertaining!
Charice Pempengco is really a singing phenomenon and a born 'Diva' idolized worldwide that right now she just flew heading Los Angeles to shoot a Hollywood movie.


text:
Health Care & Life Science
Law & Goverment
Podcast Charting her course
East Ventura County
You are here: Home > Latest news > Current Article
Top 10 'wish list' for leadership
Latest news Topic
Guest commentary Author
By Guest commentary Friday, January 8th, 2016
Many Americans would agree that our nation is more divided than ever and could use a new crop of leaders. Here's my New Year "wish list" for better leadership and a new focus in areas ranging from politics to sports to business to universities to the military.
1. A cessation of inflammatory, hurtful and incendiary political rhetoric that fuels our worst fears and further fractures our already divided nation.
2. A new mayor for the Windy City. Chicago needs a leader who is less brash, brusque and bruising and, most importantly, is proactive and can build trust among all its citizens.
3. A new group of executives in America's corporations who "don't smile up and frown down the organization" — in other words who don't blame and belittle subordinates while pandering to the C-suite, who fail to acknowledge their own mistakes and who have difficulty accepting accountability when things don't go as planned.
4. A reminder that colleges and universities need to redouble their efforts in eliminating campus racism and preventing sexual assaults while also teaching courses in the First Amendment and not bucking to student demands for "trigger warnings" and "safe spaces" or other politically correct nonsense.
5. A course for new NFL referees that demonstrates how excessive "flag throwing" totally disrupts the flow and momentum of professional football games.
6. A course for NFL lineman and defensive backs on "technique" designed to reduce the appallingly large number of penalties imposed for false starts, holding, interference and other infractions. Most games are frankly becoming unwatchable.
7. A way to reduce the alarming number of commanding officers in the United States military who are being relieved from command for a variety of leadership flaws.
8. A new general manager of my lifelong favorite professional team, the San Francisco 49ers.
9. A new president who is seasoned, can work effectively with Congress, will avoid making impractical promises, can unite all Americans, who genuinely values and holds the military and its leaders in high regard, and who will ensure that his or her administration is diverse and held accountable for executing policy.
10. A new way to reach out, engage and influence the climate change doubters who continue to defy sound scientific knowledge. Climate change is real and cannot be ignored.
• Ritch K. Eich was president of Eich Associated, a former leadership and management consulting firm in Thousand Oaks.
Friday, September 2nd, 2022
Ventura County's Kolibri Energy strikes formula for growth in Oklahoma oil fields
Thousand Oaks real estate firm manager arrested on charges of stealing $1M from business
Tuesday, June 21st, 2022
Real Estate: Developer breaks ground on housing at CSUCI
Charting Her Course Podcast
© 2023 Pacific Coast Business Times
Refunds/Cancellations
Created by NDIC


text:
Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study
Bertrand Cariou ORCID: orcid.org/0000-0002-1580-80401,
Samy Hadjadj ORCID: orcid.org/0000-0001-7110-69941,
Matthieu Wargny ORCID: orcid.org/0000-0001-6027-94861,2,
Matthieu Pichelin ORCID: orcid.org/0000-0002-6822-61321,
Abdallah Al-Salameh ORCID: orcid.org/0000-0002-7951-99263,
Ingrid Allix4,
Coralie Amadou ORCID: orcid.org/0000-0002-0581-75925,
Gwénaëlle Arnault6,
Florence Baudoux7,
Bernard Bauduceau8,9,
Sophie Borot ORCID: orcid.org/0000-0001-6240-825310,
Muriel Bourgeon-Ghittori11,
Olivier Bourron ORCID: orcid.org/0000-0001-7348-716112,
David Boutoille ORCID: orcid.org/0000-0001-9722-202913,
France Cazenave-Roblot ORCID: orcid.org/0000-0003-4325-023X14,15,
Claude Chaumeil16,
Emmanuel Cosson ORCID: orcid.org/0000-0002-8785-338517,18,
Sandrine Coudol2,
Patrice Darmon ORCID: orcid.org/0000-0003-1726-229619,
Emmanuel Disse ORCID: orcid.org/0000-0002-4045-050320,
Amélie Ducet-Boiffard21,
Bénédicte Gaborit ORCID: orcid.org/0000-0002-4180-158X22,
Michael Joubert ORCID: orcid.org/0000-0002-8731-735523,
Véronique Kerlan24,
Bruno Laviolle ORCID: orcid.org/0000-0002-9541-670825,
Lucien Marchand ORCID: orcid.org/0000-0001-9101-500226,
Laurent Meyer ORCID: orcid.org/0000-0003-3687-552227,
Louis Potier ORCID: orcid.org/0000-0001-6268-736028,
Gaëtan Prevost29,
Jean-Pierre Riveline ORCID: orcid.org/0000-0001-7991-074130,31,32,
René Robert ORCID: orcid.org/0000-0001-5989-540933,
Pierre-Jean Saulnier ORCID: orcid.org/0000-0003-1862-425234,
Ariane Sultan ORCID: orcid.org/0000-0002-9540-822235,
Jean-François Thébaut ORCID: orcid.org/0000-0003-4926-994316,
Charles Thivolet ORCID: orcid.org/0000-0002-0696-974X36,37,
Blandine Tramunt38,
Camille Vatier ORCID: orcid.org/0000-0001-8342-159639,40,
Ronan Roussel ORCID: orcid.org/0000-0003-2292-836328,
Jean-François Gautier ORCID: orcid.org/0000-0001-6458-200130,32 &
Pierre Gourdy ORCID: orcid.org/0000-0002-5362-381338
for the CORONADO investigators
Diabetologia volume 63, pages 1500–1515 (2020)Cite this article
A Correction to this article was published on 02 July 2020
Aims/hypothesis
Coronavirus disease-2019 (COVID-19) is a life-threatening infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Diabetes has rapidly emerged as a major comorbidity for COVID-19 severity. However, the phenotypic characteristics of diabetes in COVID-19 patients are unknown.
We conducted a nationwide multicentre observational study in people with diabetes hospitalised for COVID-19 in 53 French centres in the period 10–31 March 2020. The primary outcome combined tracheal intubation for mechanical ventilation and/or death within 7 days of admission. Age- and sex-adjusted multivariable logistic regressions were performed to assess the prognostic value of clinical and biological features with the endpoint. ORs are reported for a 1 SD increase after standardisation.
The current analysis focused on 1317 participants: 64.9% men, mean age 69.8 ± 13.0 years, median BMI 28.4 (25th–75th percentile: 25.0–32.7) kg/m2; with a predominance of type 2 diabetes (88.5%). Microvascular and macrovascular diabetic complications were found in 46.8% and 40.8% of cases, respectively. The primary outcome was encountered in 29.0% (95% CI 26.6, 31.5) of participants, while 10.6% (9.0, 12.4) died and 18.0% (16.0, 20.2) were discharged on day 7. In univariate analysis, characteristics prior to admission significantly associated with the primary outcome were sex, BMI and previous treatment with renin–angiotensin–aldosterone system (RAAS) blockers, but not age, type of diabetes, HbA1c, diabetic complications or glucose-lowering therapies. In multivariable analyses with covariates prior to admission, only BMI remained positively associated with the primary outcome (OR 1.28 [1.10, 1.47]). On admission, dyspnoea (OR 2.10 [1.31, 3.35]), as well as lymphocyte count (OR 0.67 [0.50, 0.88]), C-reactive protein (OR 1.93 [1.43, 2.59]) and AST (OR 2.23 [1.70, 2.93]) levels were independent predictors of the primary outcome. Finally, age (OR 2.48 [1.74, 3.53]), treated obstructive sleep apnoea (OR 2.80 [1.46, 5.38]), and microvascular (OR 2.14 [1.16, 3.94]) and macrovascular complications (OR 2.54 [1.44, 4.50]) were independently associated with the risk of death on day 7.
Conclusions/interpretations
In people with diabetes hospitalised for COVID-19, BMI, but not long-term glucose control, was positively and independently associated with tracheal intubation and/or death within 7 days.
clinicaltrials.gov NCT04324736.
Avoid the most common mistakes and prepare your manuscript for journal editors.
Since the first case in China in December 2019, the epidemic of coronavirus disease-2019 (COVID-19), a disease caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) virus, rapidly spread worldwide and was declared a pandemic by the World Health Organization on 11 March 2020 [1, 2].
It is well known that people with diabetes have increased infection risk, especially for influenza and pneumonia [3, 4]. Moreover, diabetes was previously reported as a major risk factor for mortality in people infected with the 2009 H1N1 pandemic influenza and, more recently, with the Middle East respiratory syndrome-related coronavirus (MERS-CoV) [5, 6]. Epidemiological studies have quickly and consistently pointed out diabetes as one of the major comorbidities associated with COVID-19 and affecting its severity.
The prevalence of diabetes in patients with COVID-19 was first reported to range from 5% to 20% in Chinese studies, increasing with the severity of the disease [7]. More recently, Grasselli et al have reported a diabetes prevalence of 17% in patients admitted to intensive care units (ICUs) for severe COVID-19 infection in Lombardy, Italy [8]. Furthermore, the COVID-19-Associated Hospitalisation Surveillance Network (COVID-NET) reported a diabetes prevalence of 28.3% in hospitalised patients in the USA [9].
More importantly, all studies published so far have reported a two- to threefold higher prevalence of diabetes in patients in ICUs compared with those with less severe disease and an increased mortality in people with diabetes [10,11,12,13,14]. For instance, in a retrospective study from Wuhan, diabetes was present in 19% of 191 COVID-19 inpatients but its prevalence raised to 31% in deceased people compared with 14% in those who survived [12]. A recent meta-analysis further demonstrated that diabetes was associated with a more than doubled risk for ICU admission and a more than tripled risk for death [14].
In this context, patients with diabetes have been listed as people at higher risk for severe illness from COVID-19 by several health authorities and learned medical societies [15]. However, precise data regarding diabetes characteristics in hospitalised people with COVID-19 are still lacking. Moreover, the relationship between diabetes-related phenotypes and the severity of COVID-19 remains unknown. CORONADO (Coronavirus SARS-CoV-2 and Diabetes Outcomes) is a nationwide multicentre observational study that aims to identify the clinical and biological features associated with disease severity and mortality risk in people with diabetes hospitalised for COVID-19.
Study oversight
The CORONADO study was launched in all French hospitals volunteering to share data on hospitalised COVID-19 patients with diabetes. The study was sponsored by CHU (centre hospitalier universitaire) Nantes, designed in accordance with the declaration of Helsinki and conducted in accordance with French legislation with approval obtained from the local ethics committee (Institutional Review Board/Institutional Ethics Committee – GNEDS [groupe nantais d'éthique dans le domaine de la santé]; Ref. CORONADOV2), the CEREES (comité d'expertise pour les recherches, les études et les évaluations dans le domaine de la santé; n° INDS [institut national des données de santé]:1544730) and the CNIL (commission nationale de l'informatique et des libertés; DR-2020-155/920129). In light of the purely non-interventional design of this observational study and the emergency situation related to the COVID-19 pandemic, the CNIL and the GNEDS have repealed the systematic collection of written informed consent. They recommended that we collect an 'oral non-opposition to participate' as far as possible (in particular by publishing study information via posters in the hospitals). Living patients who were unable to give consent on admission all received information about their inclusion in the CORONADO study before discharge, and therefore had a clear and free choice to object to the use of their clinical data. Any patient declining to participate in the study or expressing his or her opposition to data collection from hospital information systems, even after hospital discharge, was excluded from the study.
Study design and participants
The aim of the CORONADO study was to describe the phenotypic characteristics and prognosis of individuals admitted to hospital with COVID-19 between 10 March and 10 April 2020. Inclusion criteria were (1) hospitalisation in a dedicated COVID-19 unit with COVID-19 diagnosis confirmed biologically (by SARS-CoV-2 PCR test) and/or clinically/radiologically (i.e. as ground-glass opacity and/or crazy paving on chest computed tomography [CT] scan); (2) personal history of diabetes or newly diagnosed diabetes on admission (i.e. HbA1c ≥48 mmol/mol [6.5%] during hospitalisation).
Owing to the rapid recruitment rate and in order to make clinically relevant findings available as quickly as possible, the scientific committee, on 5 April 2020, suggested a premature database lock on 18 April 2020 for participants admitted in the period 10–31 March 2020, and continuation of recruitment with no further modification. A first set of analyses was performed in 1317 participants selected according to the following criteria: (1) meeting the eligibility criteria; (2) available information on the main outcome, recorded on day 7 following admission; (3) available data on age and sex (see flowchart in Fig. 1).
Study flowchart. aTwo patients ruled out for not meeting inclusion criteria were in two categories
Patient follow-up and clinical outcomes
The composite primary endpoint combined tracheal intubation for mechanical ventilation and death within 7 days of admission. Secondary outcomes included death on day 7, tracheal intubation on day 7, admission to ICUs and discharge on day 7. Participants discharged before day 7 were systematically contacted to check for the non-occurrence of these events on day 7.
Data collection was performed by clinical research associates and physicians in participating centres. 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 needed, contact the patient's general and/or specialist practitioners, regular pharmacist or biomedical laboratory. Collected data included clinical data (age, sex, ethnicity, 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. two most recent HbA1c levels determined before admission), microvascular and macrovascular complications and comorbidities. 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. Microvascular complications were defined as severe diabetic retinopathy (proliferative retinopathy and/or laser photocoagulation and/or clinically significant macular oedema requiring laser and/or intra-vitreal injections) and/or diabetic kidney disease (proteinuria [AER ≥300 mg/24 h; urinary albumin/creatinine ratio ≥300 mg/g; urinary albumin/creatinine ratio ≥30 mg/mmol creatinine; proteinuria ≥500 mg/24 h] and/or eGFR equal to or lower than 60 ml min−1 [1.73 m]−2, using the Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] formula) and/or history of diabetic foot ulcer. Macrovascular complications were defined as ischaemic heart disease (acute coronary syndrome and/or coronary artery revascularisation) and/or cerebrovascular disease (stroke and/or transient ischaemic attack) and/or peripheral artery disease (amputation owing to ischaemic disease and/or lower limb artery revascularisation). In addition, COVID-19-related clinical, radiological and biological characteristics were collected at admission as well as their clinical evolution during the hospital stay.
Quantitative data were expressed as mean ± SD or median [25th –75th percentile]. Categorical variables were given as number (percentage) of participants. As prespecified in the protocol, the main objective of the study was descriptive. We calculated that a population size of 300 participants with an attrition of 20% for missing data, and a percentage of 16% of our main outcome, would give us a 95% CI equal to 11.7–21.1% using the Clopper–Pearson estimate.
Univariate logistic regression models were used to calculate OR associated with primary outcome or death on day 7. Natural log transformation was consistently considered in cases of skewed distribution, which applied to BMI and biological features.
Age- and sex-adjusted ORs for the primary outcome were plotted for BMI, HbA1c and admission plasma glucose using degree 2 fractional polynomial approaches [16].
Multivariable logistic regression models were used to separately assess the association of the primary outcome and death on day 7 with clinical and biological features. A standardisation process was also applied using z scores for the purpose of direct comparison. In our initial statistical analysis plan, four covariates were systematically forced in the models: age, sex, BMI and HbA1c. However, since HbA1c did not contribute to the risk of either the primary outcome or death on day 7, and owing to a significant number of missing data for HbA1c and BMI, our multivariable models ultimately took only age and sex into account. Other variables were considered only if associated with the main outcome in univariate analysis (threshold: two-sided p value ≤0.10) and selected in the final model after a stepwise backward/forward selection process. In the event of obvious collinearity (such as alanine aminotransferase [ALT] with aspartate aminotransferase [AST], or white cell count with lymphocyte count), only the variable associated with the smaller p value was considered for multivariable analysis. In the final model, interactions were checked between all pairs of covariates.
We built two distinct multivariable models both separately for the main outcome and for the risk of death: (1) the first included covariates related to patient history prior to admission (chronic diabetes complications and other comorbidities) and routine medications; (2) the second included covariates related to medical presentation on admission, such as COVID-19 symptoms and biological determinations. This corresponds to the situation of a physician in an emergency room or department, assessing the prognosis of his/her patient.
All statistical tests were two-sided with a type 1 error set at 5%. All analyses were performed on available data, without imputation, and using statistical software R version 3.6.2 (https://cran.r-project.org/bin/windows/base/old/3.6.2/).
Population and clinical outcomes
The present analysis focused on 1317 participants with diabetes and confirmed COVID-19 admitted to 53 French hospitals during the period 10–31 March 2020.
A total of 382 patients (29.0%; 95% CI 26.6, 31.5) met the primary outcome. Overall, 410 patients (31.1%; 95% CI 28.6, 33.7) were admitted to ICUs within 7 days of hospital admission, including 267 individuals who required tracheal intubation for mechanical ventilation (20.3%; 95% CI 18.1, 22.5). One hundred and forty deaths (10.6%; 95% CI 9.0, 12.4) were recorded on day 7. In contrast, 237 participants (18.0%; 95% CI 16.0, 20.2) were discharged on day 7 (see flowchart in Fig. 1).
Demographic and diabetes-related characteristics
The clinical characteristics of the whole population are shown in Table 1. Mean (± SD) age was 69.8 ± 13.0 years and 64.9% were men. The classification of diabetes cases mainly included type 2 diabetes (88.5%), and less frequently type 1 diabetes (3.0%) or other aetiologies (5.4%). In addition, 3.1% of the participants were newly diagnosed with diabetes on admission (HbA1c ≥48 mmol/mol [6.5%]). The median BMI was 28.4 (25th–75th percentile 25.0–32.7) kg/m2. The mean HbA1c value was 65 ± 21 mmol/mol (8.1 ± 1.9%). A medical history of hypertension and dyslipidaemia were found in 77.2% and 51.0% of the participants, respectively. Microvascular and macrovascular complications were reported in 46.8% and 40.8% of individuals, respectively. Regarding routine glucose-lowering medications, 38.3% of the participants were on insulin therapy while 56.6% received metformin and 21.6% dipeptidyl peptidase 4 (DPP-4) inhibitors. Moreover, treatment with renin–angiotensin–aldosterone system (RAAS) blockers (ACE inhibitors and/or angiotensin II receptor blockers [ARBs] and/or mineralocorticoid-receptor antagonists [MRAs]) and statins was used by 57.1% and 47.6% of the participants, respectively.
Table 1 Clinical characteristics prior to admission of CORONADO participants, according to primary outcome (tracheal intubation and/or death within 7 days of admission), and death on day 7
Characteristics of COVID-19 on admission
Characteristics of COVID-19 on admission are provided in Table 2. The median duration of symptoms before admission was 5 days (25th–75th percentile, 2–8 days). As expected, the most common signs were fever (77.9%), cough (68.7%), fatigue (62.4%), dyspnoea (61.8%) and digestive disorders (34.5%). SARS-CoV-2 PCR testing was performed in 1268 participants, with a positive result in 96.8%. Thoracic CT imaging demonstrated typical ground-glass opacity and/or crazy paving in 818 individuals (90.0%). Biological findings were consistent with obvious infection as illustrated by a median C-reactive protein (CRP) at 77.8 (38.4–132.7) mg/l. Median plasma glucose at admission was 9.20 (6.80–12.62) mmol/l.
Table 2 COVID-19-related clinical, radiological and biological characteristics on admission in CORONADO participants, according to primary outcome (tracheal intubation and/or death within 7 days of admission), and death on day 7
Of interest, diabetes-related disorders were reported in 11.1% of the participants on admission with 132 episodes of severe hyperglycaemia, including 40 of ketosis, of which 19 were ketoacidosis, as well as 14 hypoglycaemic events, while severe anorexia was reported in 83 participants (6.3%).
Factors prior to admission associated with study outcomes
In univariate analysis considering the primary outcome, male sex was more frequent (69.1% vs 63.2%, p = 0.0420) and BMI was significantly higher (median 29.1 [25.9–33.6] vs 28.1 [24.8–32.0] kg/m2, p = 0.0009) in patients who met the primary outcome compared with the others, as was the use of RAAS blockers (61.5% vs 55.3%, p = 0.0386) (Table 1 and electronic supplementary material [ESM] Table 1).
Furthermore, several characteristics prior to admission were associated with the risk of death on day 7 including age, hypertension, micro- and macrovascular diabetic complications and comorbidities such as heart failure or treated obstructive sleep apnoea (OSA). Among prior medications, metformin use was lower in people who died. In contrast, insulin therapy, RAAS blockers, β-blockers, loop diuretics and MRAs were found to be associated with death on day 7 (Table 1 and ESM Table 1).
When using age- and sex-adjusted nonlinear models, BMI was significantly and positively associated with the primary outcome (p = 0.0001) but not with death on day 7 (p = 0.1488) (Fig. 2). In contrast, HbA1c level was neither associated with the primary outcome nor with death on day 7.
Sex- and age-adjusted ORs for the main outcome and for death, using logistic regression models with degree 2 multiple fractional polynomials. (a, b) OR for BMI for the primary outcome (a; p = 0.0001) and for death (b; p = 0.1488) on day 7 (reference value 20 kg/m2; n = 1117). (c, d) OR for HbA1c for the primary outcome (c; p = 0.2897) and for death (d; p = 0.9129) on day 7 (reference value 42 mmol/mol; n = 846). (e, f) OR for admission plasma glucose for the primary outcome (e; p = 0.0001) and for death (f; p = 0.0059) on day 7 (reference value 5.55 mmol/l; n = 940). The thick black line gives the OR compared with the reference point, the thin grey lines are the 95% CI, and the red dotted red line (OR = 1) corresponds to a similar risk-level as the reference point
Multivariable analyses were then conducted with characteristics prior to admission. BMI remained associated with the primary outcome in a model where sex and age were forced into models. When comorbidities and routine treatment were entered in an adjusted model with stepwise selection, BMI was the only independent factor associated with the primary outcome, with an adjusted OR of 1.28 (95% CI 1.10, 1.47) (Table 3). Finally, age, history of microvascular or macrovascular complications, and treated OSA were found to be independently associated with the risk of death on day 7 (Table 4). A sensitivity analysis conducted only in patients with a positive SARS-CoV-2 PCR test found similar results for both the primary outcome and death (data not shown).
Table 3 Multivariable analysis of the primary outcome in CORONADO participants: covariables prior to admission
Table 4 Multivariable analysis of the risk of death on day 7 in CORONADO participants: covariables prior to admission
Factors on admission associated with study outcomes
Regarding COVID-19 symptoms on admission, dyspnoea was positively associated with the primary outcome and with death on day 7, whereas cephalalgia, upper respiratory tract symptoms (rhinitis and/or pharyngeal symptoms), and ageusia/anosmia, as well as time between symptom onset and admission, were negatively associated with death on day 7 (Table 2 and ESM Table 2). Several biological parameters reflecting the severity of the infection were also associated with both primary outcome and death on day 7, such as CRP, creatine phosphokinase (CPK) and lymphopaenia. Reduced kidney function, assessed by admission eGFR, and increased plasma AST level, was associated with both outcomes (Table 2 and ESM Table 2). In age- and sex-adjusted nonlinear models, admission plasma glucose was significantly and positively associated with the primary outcome (p = 0.0001) and with death on day 7 (p = 0.0059) (Fig. 2).
The prognostic value of characteristics on admission was finally investigated in multivariable models (Table 5). Among clinical symptoms, dyspnoea was the only predictor of the primary outcome. Regarding biological parameters, lymphopaenia on admission was independently associated with the primary outcome, as were increased AST and CRP concentrations. Dyspnoea and plasma levels of both AST and CRP were also independently associated with the risk of death on day 7, as well as decrease in platelet count and eGFR (Table 6). Once again, similar results were obtained for both the primary outcome and death on day 7 when considering only the patients with a positive PCR test (data not shown).
Table 5 Multivariable analysis of the primary outcome in CORONADO participants: covariables on admission
Table 6 Multivariable analysis of the risk of death on day 7 in CORONADO participants: covariables on admission
CORONADO is the first study specifically dedicated to people with diabetes infected with SARS-CoV-2 and admitted to hospital. CORONADO was designed to address three main goals: (1) assess the phenotypic characteristics of patients with diabetes hospitalised for COVID-19; (2) estimate the prevalence of the primary outcome, which combines death and tracheal intubation for mechanical ventilation within the first 7 days following admission; (3) identify in this specific population certain prognostic factors associated with early severity of COVID-19. When considering variables prior to admission, our results support no independent association between a severe course of COVID-19 and age, sex, long-term glucose control, chronic complications, hypertension or usual medications, including RAAS blockers and DPP-4 inhibitors. Only BMI turned out to be independently associated with the primary outcome. When considering variables on admission, dyspnoea, lymphopaenia, and increased AST and CRP levels were independent prognostic factors for severe course of COVID-19.
To our knowledge, CORONADO is the first study that provides precise information regarding the characteristics of diabetes in the severe forms of COVID-19. The study population roughly resembles the French population of people living with diabetes, except for HbA1c, which was clearly higher in our study (65 mmol/mol [8.1%]) compared with the nationwide ENTRED-2 survey participants older than 65 years (54 mmol/mol [7.1%]) [17]. Of note, there was no overrepresentation of declared type 1 diabetes (only 3.0% of participants) in people with diabetes hospitalised for COVID-19.
The primary outcome occurred in 29.0% of CORONADO participants. While the design of the present study did not enable comparison of the severity of COVID-19 in people with or without diabetes, 20.3% of the study population required tracheal intubation for mechanical ventilation with a mortality rate of 10.6% as early as 7 days after admission. The severity of the prognosis of COVID-19 observed in people with diabetes in the present study is in accordance with previous epidemiological studies [10,11,12,13, 18, 19], and meta-analyses [14, 20]. An important issue is the choice of our primary endpoint, which combines death (an unequivocal outcome) with tracheal intubation for mechanical ventilation. It should be emphasised that the latter outcome can result from different factors, which were impossible to standardise in all centres, such as (1) clinical deterioration, (2) refusal to be intubated, or (3) futility (i.e. a medical decision not to intubate), leading to potentially fewer patients actually intubated compared with those meeting intubation criteria.
Regarding the clinical characteristics of COVID-19 in CORONADO participants, there was a high prevalence of fever and respiratory symptoms (cough, dyspnoea) and, to a lesser extent, digestive disorders. In addition to symptoms directly related to COVID-19, people with diabetes can also require management of acute metabolic disorders. In particular, physicians should be warned not only of the risk of ketoacidosis but also of hypoglycaemia, probably favoured by COVID-19-induced anorexia without concomitant adaptation of glucose-lowering drugs.
With the aim of providing clinicians with criteria to evaluate the risk of severe COVID-19 on an individual level in people with diabetes, we performed multivariable analyses to identify pre-admission and on-admission prognostic factors. Since some preclinical studies previously highlighted potential mechanistic links between glucose control, immune response and MERS-CoV infection [21], we were particularly interested in studying the relationship between long-term glucose control and COVID-19 prognosis. In fact, we failed to find any association between HbA1c (even with the highest values, >75 mmol/mol [9.0%]) and either the primary outcome or death on day 7. On the basis of this result and in order to increase the sample size for our analyses, we decided not to force HbA1c in the multivariate models.
An interesting finding is the association of BMI with study outcomes. Indeed, in our study, BMI was positively and independently associated with the primary outcome, which is largely driven by tracheal intubation. Interestingly, a recent report on COVID-19 patients in ICU showed an association between BMI and the requirement for mechanical ventilation, irrespective of diabetic status [22]. However, such an association with BMI was no longer statistically significant when considering death on day 7. It should also be noted that the increased risk for the primary outcome appears to be less pronounced in patients with morbid obesity (grade 3, BMI ≥40 kg/m2) compared with those who were overweight or with grade 1–2 obesity, a situation previously described as the 'obesity paradox' in ICUs [23]. Additional studies are clearly warranted to decipher the link between obesity, metabolic complications and COVID-19 severity with specific attention to fat mass distribution, insulin resistance and inflammatory/immune profiles.
While hypertension was previously reported as the most prevalent comorbidity in the general population with severe COVID-19 [2, 9, 12], it was not independently associated with the severity of the disease in the study. In addition, RAAS blockers (ACE inhibitors, ARBs and MRAs) were not independently associated with the main outcome, supporting the recent recommendation not to discontinue RAAS blockade [24]. Moreover, we found no association between glucose-lowering drugs, including DPP-4 inhibitors, that have been suggested to potentially interfere with coronavirus infection and COVID-19 prognosis [21, 25].
Our complementary multivariable approach was suitable for the identification of characteristics on admission associated with COVID-19 prognosis, of particular relevance for the management of people with diabetes in the setting of an emergency room. Notably, we found an age- and sex-independent association between increased admission plasma glucose levels and the severity of COVID-19, as previously reported in critically ill patients [26]. However, we speculate that this observation is rather the consequence of the severity of the infection than a causal primary factor.
Another important result concerns the identification of the prognostic factors of early death in people with diabetes and COVID-19. Compared with the primary outcome, which reflects aggressive management in ICUs with tracheal intubation, death on day 7 was more prevalent in elderly participants with an OR >14 for people older than 75 years, compared with younger individuals. In addition, these individuals also very frequently exhibited complications of diabetes (microvascular and macrovascular complications, mainly coronary heart disease) as well as pulmonary diseases (such as OSA). As expected, they were also more frequently on insulin therapy and taking multiple drugs (such as diuretics). Conversely, metformin use was associated with a reduced risk of early death, probably reflecting a less advanced stage of diabetes with fewer comorbidities (such as severe chronic kidney disease) that contraindicate its use. In multivariable analyses, age, diabetic complications and treated OSA remained significantly and independently associated with death on day 7. In addition, dyspnoea, reduced eGFR and platelet count, and increased AST and CRP on admission were independent markers of early death.
The discrepancy between the primary combined outcome (mainly driven by tracheal intubation) and death on day 7 could be explained by the fact that there were medical decisions not to pursue aggressive therapy in this frail population. In contrast, our data can be considered reassuring for the majority of people living with type 1 diabetes. Indeed, there was no death in participants with type 1 diabetes younger than 65 years. Additional data collection is currently ongoing to provide a precise picture of the rare individuals with type 1 diabetes hospitalised for COVID-19.
Some limitations must be acknowledged in the current analysis. We focused on hospitalised COVID-19 cases and our results cannot be generalised to all people with COVID-19 and diabetes, especially those with a less severe form of the disease. A secondary limitation is the size of our study population and the large proportion (i.e. 35.7%) of patients without available HbA1c. This is in accordance with the observation that only 55% of the people with diabetes had had three or more HbA1c determinations in the previous year according to French national registry data [27]. Finally, the present report focuses only on short-term prognosis (i.e. 7 days after admission) and one cannot exclude the possibility that diabetes characteristics prior to admission could be associated with severe COVID-19 outcomes in the longer term. However, strengths must be acknowledged such as the originality of the medical question leading to the CORONADO initiative and the inclusion of participants on a national basis. In addition, a large majority (>93%) of COVID-19 cases were confirmed with a positive PCR test, with few cases diagnosed from medical and/or radiological observations only. We also structured data collection in order to obtain a precise and standardised recording of phenotypic characteristics of the diabetic study population.
In conclusion, the CORONADO study refined the phenotypes of COVID-19 individuals with diabetes admitted to hospital and showed that chronic glycaemic control did not impact the immediate severity of COVID-19. Elderly populations with long-term diabetes with advanced diabetic complications and/or treated OSA were particularly at risk of early death, and might require specific management to avoid contamination with SARS-CoV-2. BMI also appears as an independent prognostic factor for COVID-19 severity in the population living with diabetes, requiring hospital admission.
A data-sharing statement is available in the ESM.
The link in reference 15 has been corrected.
A Correction to this paper has been published: https://doi.org/10.1007/s00125-020-05207-3
ARB:
Angiotensin II receptor blocker
CKD-EPI:
Chronic Kidney Disease Epidemiology Collaboration
CORONADO:
Coronavirus SARS-CoV-2 and Diabetes Outcomes
Coronavirus disease-2019
CPK:
Creatine phosphokinase
CRP:
DPP-4:
Dipeptidyl peptidase 4
ICU:
MERS-CoV:
Middle East respiratory syndrome-related coronavirus
MRA:
Mineralocorticoid-receptor antagonist
OSA:
Obstructive sleep apnoea
RAAS:
Renin–angiotensin–aldosterone system
SARS-CoV-2:
Severe acute respiratory syndrome coronavirus-2
Huang C, Wang Y, Li X et al (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395(10223):497–506. https://doi.org/10.1016/S0140-6736(20)30183-5
Guan WJ, Ni ZY, Hu Y et al (2020) Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 382(18):1708–1720. https://doi.org/10.1056/NEJMoa2002032
Shah BR, Hux JE (2003) Quantifying the risk of infectious diseases for people with diabetes. Diabetes Care 26(2):510–513. https://doi.org/10.2337/diacare.26.2.510
Muller LM, Gorter KJ, Hak E et al (2005) Increased risk of common infections in patients with type 1 and type 2 diabetes mellitus. Clin Infect Dis 41(3):281–288. https://doi.org/10.1086/431587
Yang JK, Feng Y, Yuan MY (2006) Plasma glucose levels and diabetes are independent predictors for mortality and morbidity in patients with SARS. Diabet Med 23(6):623–628. https://doi.org/10.1111/j.1464-5491.2006.01861.x
Alqahtani FY, Aleanizy FS, Ali El Hadi Mohamed R et al (2018) Prevalence of comorbidities in cases of Middle East respiratory syndrome coronavirus: a retrospective study. Epidemiol Infect 147:1–5. https://doi.org/10.1017/S0950268818002923
Yang J, Zheng Y, Gou X et al (2020) Prevalence of comorbidities in the novel Wuhan coronavirus (COVID-19) infection: a systematic review and meta-analysis. Int J Infect Dis 94:91–95. https://doi.org/10.1016/j.ijid.2020.03.01
Grasselli G, Zangrillo A, Zanella A et al (2020) Baseline characteristics and outcome of 1591 patients infected with SARS-CoV-2 admitted to ICUs of the Lombardy region, Italy. JAMA 323(16):1674–1581. https://doi.org/10.1001/jama.2020.5394
Garg S, Kim L, Whitaker M et al (2020) Hospitalization rates and characteristics of patients hospitalized with laboratory-confirmed coronavirus disease 2019 – COVID-NET, 14 States, March 1–30, 2020. MMWR Morb Mortal Wkly Rep 69(15):458–464. https://doi.org/10.15585/mmwr.mm6915e3
Onder G, Rezza G, Brusaferro S (2020) Case-fatality rate and characteristics of patients dying in relation to COVID-19 in Italy. JAMA. https://doi.org/10.1001/jama.2020.4683
Bhatraju PK, Ghassemieh BJ, Nichols M et al (2020) Covid-19 in critically ill patients in the Seattle region – case series. N Engl J Med. https://doi.org/10.1056/NEJMoa2004500
Zhou F, Yu T, Du R et al (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 395(10229):1054–1062. https://doi.org/10.1016/S0140-6736(20)305
Wu C, Chen X, Cai Y et al (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Intern Med. https://doi.org/10.1001/jamainternmed.2020.0994
Roncon L, Zuin M, Rigatelli G, Zuliani G (2020) Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome. J Clin Virol 127:104354. https://doi.org/10.1016/j.jcv.2020.104354
Centers for Disease Control and Prevention (2020) Coronavirus Disease 2019 (COVID-19): groups at higher risk for severe illness. Available from https://aimvein.com/blogs/news/covid-19-measure-for-high-risk-groups. Accessed 21 April 2020
Sauerbrei W, Meier-Hirmer C, Benner A, Royston P (2006) Multivariable regression model building by using fractional polynomials: description of SAS, STATA and R programs. Comput Stat Data Anal 50(12):3464–3485. https://doi.org/10.1016/j.csda.2005.07.015
Pornet C, Bourdel-Marchasson I, Lecomte P et al (2011) Trends in the quality of care for elderly people with type 2 diabetes: the need for improvements in safety and quality (the 2001 and 2007 ENTRED Surveys). Diabetes Metab 37(2):152–161. https://doi.org/10.1016/j.diabet.2011.02.001
Yang X, Yu Y, Xu J et al (2020) Clinical course and outcomes of critically ill patients with SARS-CoV2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 8(5):475–481. https://doi.org/10.1016/S2213-2600(20)30079-5
CDC COVID-19 response team (2020) Preliminary estimates of the prevalence of selected underlying health conditions among patients with coronavirus disease 2019 – United States, February 12–March 28, 2020. MMWR Morb Mortal Wkly Rep 69(13):382–386. https://doi.org/10.15585/mmwr.mm6913
Article PubMed Central Google Scholar
Huang I, Anthonius M, Pranata R (2020) Diabetes mellitus is associated with increased mortality and severity of disease in COVID-19 pneumonia – a systematic review, meta-analysis, and meta-regression. Diabetes Metab Syndr 14(4):395–403. https://doi.org/10.1016/j.dsx.2020.04.018
Kulcsar J, Coleman CM, Beck SE, Frieman MD (2019) Comorbid diabetes results in immune dysregulation and enhanced disease severity following MERS-CoV infection. JCI Insight 4(20):e131774. https://doi.org/10.1172/jci.insight.13177
Simonnet A, Chetboun M, Poissy J et al (2020) High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity (Silver Spring). https://doi.org/10.1002/oby.22831
Schetz M, De Jong A, Deane AM et al (2019) Obesity in the critically ill: a narrative review. Intensive Care Med 45(6):757–769. https://doi.org/10.1007/s00134-019-05594-1
Vaduganathan M, Vardeny O, Michel T, McMurray JJV, Pfeffer MA, Solomon SD (2020) Renin–angiotensin–aldosterone system inhibitors in patients with COVID-19. N Engl J Med 382(17):1653–1659. https://doi.org/10.1056/NEJMsr2005760
Drucker DJ (2020) Coronavirus infection and type 2 diabetes shared-pathways with therapeutic implications. Endocr Rev 41(3):bnaa011. https://doi.org/10.1210/endrev/bnaa011
Van den Berghe G, Wouters P, Weekers F et al (2001) Intensive insulin therapy in critically ill patients. N Engl J Med 345(19):1359–1367. https://doi.org/10.1056/NEJMoa011300
Fosse-Edorh S, Mandereau-Bruno L, Piffaretti C (2018). Le poids du diabète en France en 2016 Synthèse épidémiologique. Santé publique France:8p; available from https://www.santepubliquefrance.fr/maladies-et-traumatismes/diabete/documents/rapport-synthese/le-poids-du-diabete-en-france-en-2016.-synthese-epidemiologique. Accessed 21 April 2020 [Article in French]
We thank the sponsor (DRCI [délégation à la recherche clinique et à l'innovation] CHU Nantes), the Clinical Project Manager (M. Saignes, CHU Nantes, Nantes, France) and assistant (J. Saunier, CHU Nantes, Nantes, France), Clinical Research Associates (S. El Andaloussi [CHU Nantes, Nantes, France], J. Martin-Gauthier [CHU Nantes, Nantes, France], E. Rebouilleau, [CHU Nantes, Nantes, France]) and data managers (B. Guyomarch [CHU Nantes, Nantes, France], T. Roman [CHU Nantes, Nantes, France]). We are grateful to P. Tucker (Trets, France) for editing the manuscript. We acknowledge all medical staff involved in the diagnosis and treatment of patients with COVID-19 in participating centres. We thank all general practitioners, specialists, pharmacists and biological laboratories responsible for hospitalised patients for providing additional medical information to investigators. We thank the Société Francophone du Diabète (SFD) and Société Française d'Endocrinologie (SFE) for disseminating the study design and organisation, and the Fédération Française des Diabétiques (FFD) for participating in the study organisation.
Authors' relationships and activities
BC reports grants and personal fees from Amgen, personal fees from AstraZeneca, personal fees from Akcea, personal fees from Genfit, personal fees from Gilead, personal fees from Eli Lilly, personal fees from Novo Nordisk, personal fees from MSD, grants and personal fees from Sanofi, and grants and personal fees from Regeneron. SH reports personal fees and non-financial support from AstraZeneca, grants and personal fees from Bayer, personal fees from Boehringer Ingelheim, grants from Dinno Santé, personal fees from Eli Lilly, non-financial support from LVL, personal fees and non-financial support from MSD, personal fees from Novartis, grants from Pierre Fabre Santé, personal fees and non-financial support from Sanofi, personal fees and non-financial support from Servier, and personal fees from Valbiotis. MP reports personal fees and non-financial support from Novo Nordisk, non-financial support from Sanofi, and non-financial support from Amgen. SB reports personal fees from Novo Nordisk, personal fees from Sanofi, personal fees from Eli Lilly, personal fees from Medtronic, and personal fees from Abbott. PD reports personal fees from Novo Nordisk, personal fees from Sanofi, personal fees from Eli Lilly, personal fees from MSD, personal fees from Novartis, personal fees from Abbott, personal fees from AstraZeneca, personal fees from Boehringer Ingelheim, and personal fees from Mundipharma. BG reports personal fees from Eli Lilly, personal fees from Novo Nordisk, personal fees from Mellitus Care, personal fees from Isis Diabetes, and grants from Boehringer Ingelheim. MJ reports personal fees and non-financial support from Sanofi, personal fees and non-financial support from Eli Lilly, personal fees and non-financial support from Novo Nordisk, grants and personal fees from Boehringer Ingelheim, grants, personal fees and non-financial support from Medtronic, personal fees and non-financial support from Abbott, personal fees and non-financial support from BMS, personal fees and non-financial support from MSD, and grants, personal fees and non-financial support from AstraZeneca. LP reports personal fees and non-financial support from Sanofi, personal fees and non-financial support from Eli Lilly, personal fees and non-financial support from Novo Nordisk, and personal fees and non-financial support from MSD. RR reports grants, personal fees and non-financial support from Sanofi, grants, personal fees and non-financial support from Novo Nordisk, personal fees and non-financial support from Eli Lilly, personal fees from Mundipharma, personal fees from Janssen, personal fees from Servier, grants and personal fees from AstraZeneca, personal fees from MSD, personal fees from Medtronic, personal fees from Abbott, grants from Diabnext, and personal fees from Applied Therapeutics. JFG reports personal fees and non-financial support from Eli Lilly, personal fees and non-financial support from Novo Nordisk, personal fees and non-financial support from Gilead, and personal fees and non-financial support from AstraZeneca. PG reports personal fees from Abbott, personal fees from Amgen, personal fees from AstraZeneca, personal fees from Boehringer Ingelheim, personal fees from Eli Lilly, personal fees from MSD, personal fees from Mundipharma, grants and personal fees from Novo Nordisk, personal fees from Sanofi, and personal fees from Servier. All other authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.
This study received the following funding: the Fondation Francophone de Recherche sur le Diabète (FFRD), supported by Novo Nordisk, MSD, Abbott, AstraZeneca, Lilly and FFD (Fédération Française des Diabétiques) – CORONADO initiative emergency grant; Société Francophone du Diabète (SFD) – CORONADO initiative emergency grant; Air Liquide Health Care international. All research facilities are acknowledged for providing research associates and research technicians for clinical investigations pro bono. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. The corresponding authors (BC and SH) had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Bertrand Cariou, Samy Hadjadj and Matthieu Wargny contributed equally to this article.
Département d'Endocrinologie, Diabétologie et Nutrition, l'institut du thorax, Inserm, CNRS, UNIV Nantes, CHU Nantes, Hôpital Guillaume et René Laennec, 44093, Nantes Cedex 01, France
Bertrand Cariou, Samy Hadjadj, Matthieu Wargny & Matthieu Pichelin
CIC-EC 1413, Clinique des Données, CHU Nantes, Nantes, France
Matthieu Wargny & Sandrine Coudol
Département d'Endocrinologie, Diabétologie et Nutrition, CHU Amiens, PeriToxUMR_I 01, Université de Picardie, Amiens, France
Abdallah Al-Salameh
Département d'Endocrinologie, Diabétologie, Nutrition, CHU de Angers, Angers, France
Ingrid Allix
Département de Diabétologie, Centre Hospitalier Sud Francilien, Corbeil Essonne, France
Coralie Amadou
Département d'Endocrinologie, Diabétologie et Maladies Métaboliques, Centre Hospitalier Bretagne Atlantique, Vannes, France
Gwénaëlle Arnault
Clinique d'Endocrinologique Marc-Linquette, Hôpital Claude-Huriez, CHRU de Lille, Lille, France
Florence Baudoux
Département de Diabétologie, H.I.A. Begin, Saint Mandé, France
Bernard Bauduceau
Fondation Francophone pour la Recherche sur le Diabète (FFRD), Paris, France
Département d'Endocrinologie, Diabétologie et Nutrition, CHU de Besançon, Besançon, France
Sophie Borot
Département d'Endocrinologie, Diabétologie et Nutrition, Assistance Publique Hôpitaux de Paris, Université Paris Saclay, Hôpital Antoine Béclère, Clamart, Hôpital Bicêtre, Le Kremlin Bicêtre, France
Muriel Bourgeon-Ghittori
Sorbonne Université, Assistance Publique Hôpitaux de Paris, Département de Diabétologie, CHU La Pitié Salpêtrière-Charles Foix, Inserm, UMR_S 1138, Centre de Recherche des Cordeliers, Paris 06, Institute of Cardiometabolism and Nutrition ICAN, Paris, France
Olivier Bourron
Département des Maladies Infectieuses et Tropicales, CHU Nantes, Nantes, France
David Boutoille
Département des Maladies Infectieuses et Tropicales, CHU de Poitiers, INSERM U1070, Poitiers, France
France Cazenave-Roblot
Société de Pathologie Infectieuse de langue Française (SPILF), Paris, France
Fédération Française des Diabétiques (FFD), Paris, France
Claude Chaumeil & Jean-François Thébaut
Assistance Publique Hôpitaux de Paris, Hôpital Avicenne, Université Paris 13, Sorbonne Paris Cité, Département d'Endocrinologie, Diabétologie et Nutrition, CRNH-IdF, CINFO, Bobigny, France
Emmanuel Cosson
Université Paris 13, Sorbonne Paris Cité, UMR U557 Inserm / U11125 INRAE / CNAM / Université Paris13, Unité de Recherche Epidémiologique Nutritionnelle, Bobigny, France
Département d'Endocrinologie et de Diabétologie, Hôpital de la Conception, Assistance Publique Hôpitaux de Marseille, Marseille, France
Département d'Endocrinologie, Diabétologie et Nutrition, Hospices Civils de Lyon, CarMeN Laboratory, Inserm 1060, Lyon, France, Université Claude Bernard Lyon 1, Lyon, France
Emmanuel Disse
Département d'Endocrinologie et de Diabétologie, Centre Hospitalier Départemental de Vendée, La Roche sur Yon, France
Amélie Ducet-Boiffard
Département d'Endocrinologie et de Diabétologie, Hôpital Nord, Assistance Publique Hôpitaux de Marseille, Marseille, France
Bénédicte Gaborit
Département de Diabétologie, CHU de Caen, Caen, France
Michael Joubert
Département d'Endocrinologie, CHU de Brest, EA 3878 GETBO, Brest, France
Véronique Kerlan
Université de Rennes, CHU Rennes, Inserm, CIC 1414 (Centre d'Investigation Clinique de Rennes), Rennes, France
Bruno Laviolle
Département d'Endocrinologie et de Diabétologie, Centre Hospitalier St. Joseph - St. Luc, Lyon, France
Lucien Marchand
Département d'Endocrinologie, Diabétologie et Nutrition, Hôpitaux Universitaires de Strasbourg, Strasbourg, France
Laurent Meyer
Département d'Endocrinologie, Diabétologie et Nutrition, Hôpital Bichat, Assistance Publique Hôpitaux de Paris, Centre de Recherche des Cordeliers, Inserm, U-1138, Université de Paris, Paris, France
Louis Potier & Ronan Roussel
Département d'Endocrinologie, Diabétologie et Maladies Métaboliques, CHU de Rouen, Université de Rouen, Rouen, France
Gaëtan Prevost
Département Diabète et Endocrinologie, Hôpital Lariboisière, Assistance Publique Hôpitaux de Paris, Paris, France
Jean-Pierre Riveline & Jean-François Gautier
Paris Diderot–Paris VII Université, Paris, France
Jean-Pierre Riveline
Inserm UMRS 1138, Université Paris Diderot–Paris VII, Sorbonne Paris Cité, Paris, France
Université de Poitiers, CIC Inserm 1402, Poitiers, Médecine Intensive Réanimation, Poitiers, France
René Robert
Centre d'Investigation Clinique CIC 1402, Université de Poitiers, Inserm, CHU de Poitiers, Poitiers, France
Pierre-Jean Saulnier
Département d'Endocrinologie, Diabète, Nutrition et CIC Inserm 1411, CHU de Montpellier, Montpellier, France
Ariane Sultan
Centre du Diabète DIAB-eCARE, Hospices Civils de Lyon et Laboratoire CarMeN, Inserm, INRA, INSA, Université Claude Bernard Lyon 1, Lyon, France
Charles Thivolet
Société Francophone du Diabète (SFD), Paris, France
Département d'Endocrinologie, Diabétologie et Nutrition, CHU Toulouse, Institut des Maladies Métaboliques et Cardiovasculaires, UMR1048 Inserm/UPS, Université de Toulouse, Toulouse, France
Blandine Tramunt & Pierre Gourdy
Assistance Publique Hôpitaux de Paris, Saint-Antoine Hospital, Reference Center of Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Department of Endocrinology, Paris, France
Camille Vatier
Sorbonne University, Inserm UMRS 938, Saint-Antoine Research Center, Paris, France
Bertrand Cariou
Samy Hadjadj
Matthieu Wargny
Matthieu Pichelin
Claude Chaumeil
Sandrine Coudol
Louis Potier
Jean-François Thébaut
Blandine Tramunt
Ronan Roussel
Jean-François Gautier
Pierre Gourdy
BC, SH and MW had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. BC, SH and MW contributed to the work equally and should be regarded as co-first authors. Concept and design: BC, JFG, PG, SH, MP, RRoussel, MW. Acquisition, analysis, or interpretation of data: BB, DB, BC, FC-R, CC, J-FG, PG, SH, VK, BL, MP, RRobert, RRoussel, J-FT, CT, MW on behalf of the scientific committee of the study (the list of scientific committee is available in the ESM). Statistical analysis: MW, SC, PJS. Patient recruitment: AA-S, IA, CA, GA, FB, SB, MB-G, OB, BC, EC, PD, ED, AD-B, BG, J-FG, PG, SH, MJ, VK, LMarchand, LMeyer, LP, GP, J-PR, RRoussel, AS, CT, BT, CV. Fundraising: BC, PG, SH, MP and BB. Drafting the manuscript: BC, PG, SH, MP, MW. Critical revision of the manuscript for important intellectual content: all co-authors. All authors have approved the final version of the manuscript.
Correspondence to Bertrand Cariou or Samy Hadjadj.
A complete list of the CORONADO trial investigators is provided in the Electronic supplementary material (ESM).
ESM 1
(PDF 405 kb)
Cariou, B., Hadjadj, S., Wargny, M. et al. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study. Diabetologia 63, 1500–1515 (2020). https://doi.org/10.1007/s00125-020-05180-x
Issue Date: August 2020
DOI: https://doi.org/10.1007/s00125-020-05180-x


Hair is one of the most important beauty element of a woman and it can either make or break your look for the day! Alas! We all face many hair problems on a daily basis whether it is a bad hair day, no time for washing hair, dandruff, oily hair, or just dull lifeless hair that's hard to manage. When it comes to 'good hair day' they might come rarely, but we have the most amazing solution for you!
You don't have to worry anymore because today we have brought a complete kit of the solution to your hair problems, which will make your hair look gorgeous – every single day! Too good to be true? You might believe us after reading this article.
Divatress is a magical solution for women with thinning hair, balding scalps, alopecia, and those who want to protect what hair they do have now. It's also one of the best places for the solution of daily hair problems.
Divatress is a very popular online platform which provides all the solutions for daily hair problems. It has a wide range of hair wigs, ranging from different lengths and colors and also has many hair care products for a better maintenance and care. It is hard to cover all the styles in words because they are so amazing and are professionally designed by the popular stylists. The variety of wigs is so wide that it will not cause any difficulty to you to select the one which you require for a particular occasion. From a glamsham party looks to the daily sophisticated office look, the range is so vast that it is difficult to even explore.
Out of all these hair solutions which they provide, my personal favorite is curly crochet hair. What's so special about Divatress curly hair? The special thing is that curly crochet hair is not easy to design, it requires so much effort and time to bring perfection in this style. You will not only succeed in creating this style in a single go, because it takes a lot of hard work and practice to bring perfection to them, but Divatress helps you in saving that time and pressure by providing you a wide collection of curly crochet hair for different occasions.
However, there are so many other styles also, which you must explore for a wonderful experience. So, girls go and check it now! Divatress has the largest online selection of wigs that I think you'll absolutely love!
How Does Foreign Exchange Trading Work?
How to Manage Multiple Properties Efficiently?
Why is it High Time to Learn Analytics?
What are the Advantages of Short-Term Loans With No Credit Check?