key: cord-0887220-3b1plr8s authors: Sathish, Thirunavukkarasu; Kapoor, Nitin; Cao, Yingting; Tapp, Robyn J.; Zimmet, Paul title: Proportion of newly diagnosed diabetes in COVID‐19 patients: a systematic review and meta‐analysis date: 2020-11-27 journal: Diabetes Obes Metab DOI: 10.1111/dom.14269 sha: 144369ea03cbc12ec48f72f8051100f018a79d5b doc_id: 887220 cord_uid: 3b1plr8s nan previously undiagnosed diabetes (FPG ≥7.0 mmol/l or RBG ≥11.1 mmol/l and HbA1c ≥6.5% or HbA1c ≥6.5% only). 18 We used the search terms "new-onset diabetes", "newly diagnosed diabetes", "incident diabetes", "transient hyperglycemia", and "secondary hyperglycemia" in conjunction with "COVID-19" (see Supplement file for search strategies). No language restrictions were applied. We also checked the reference list of relevant articles to identify additional eligible studies. If the study cohorts overlapped (i.e., patients from the same hospital with similar time periods of data collection), then the study with the largest sample size was selected. Data on first author name, country, study design, hospital name, study period, age, sex, total number of patients, number of patients with newly diagnosed diabetes, definition of newly diagnosed diabetes, time of diagnosis, and type of diabetes were extracted independently by the same two authors (TS and YC) using a data extraction form that was adapted from the Cochrane Collaboration. 19 We did not contact the authors of the included studies to obtain missing data due to time constraints. The National Institutes of Health Quality Assessment Tool for observational studies was used to assess the quality of the included studies. 20 Disagreements in study selection, data extraction, and quality assessment were resolved by consensus between the two authors (TS and YC) or by discussion with a third author (RJT). We pooled the proportion of newly diagnosed diabetes across studies with the DerSimonian and Laird random-effects model. 21 The variances of the proportions were stabilized with the Freeman-Turkey Double Arcsine Transformation method. 22 95% confidence interval (CI) for the proportion in individual studies was calculated using the Accepted Article exact method. Heterogeneity between the studies was assessed using the Cochran's Q Test (p<0.01 for heterogeneity) and Higgins I 2 statistic (low: <25%, moderate: 25-50%, and high: >50%). 23 To investigate the sources of between-study heterogeneity, we did a subgroup analysis by the country of origin of studies and univariate random effects meta-regression models 19 were fitted for mean age (median age was used if mean was not reported), sex (proportion of males), and sample size of studies. We did not assess for publication bias, as there were fewer than ten studies in this meta-analysis. 19, 24 Analyses were performed using Stata software version 16.1 (StataCorp, College Station, TX, USA). A total of 148 studies were retrieved during the search, of which 100 studies were duplicates, and 30 were case reports, case series, letters, commentaries, or review articles. After full-text review, a further 10 studies were excluded, including three overlapping cohorts, three studies that were conducted only among patients with diabetes, and four that did not satisfy the criteria of newly diagnosed diabetes. A total of eight studies were included in the final analysis 4,25-31 (see S Figure 2 for the PRISMA flow diagram). Table 1 presents the characteristics of the included studies. All eight studies were retrospective cohort studies, including four from China, 4,29-31 two from Italy, 25, 26 and the remaining two from the USA. 27, 28 All studies were conducted during the first six months of the pandemic (i.e., between January and May 2020). The mean or median age of patients in these studies varied from 47 to 64.9 years. All studies (except two with no Accepted Article data on sex) 29,30 had more males than females, with the proportion of males ranging from 53.3 to 80%. Data on new-onset diabetes were available in two studies 4,31 and three studies (or cohorts) had previously undiagnosed diabetes cases. 4, 28, 30 In six studies (or cohorts), 4,25-29 HbA1c was not performed for all participants, so it was not possible to differentiate between new-onset and previously undiagnosed diabetes. In the majority of studies (n=5), 25, 26, 28, 30, 31 the exact time of detection of newly diagnosed diabetes was not reported, whereas, in three studies, 4,27,29 the diagnosis was made within 24 hours to three days after hospital admission. Only one study reported on the type of diabetes (i.e., type 2 diabetes). 27 The quality of studies was either fair or good, with most (n=6, 75%) studies were of good quality. With a total of 3711 COVID-19 patients with 492 cases of newly diagnosed diabetes from eight studies, the randomeffects meta-analysis estimated a pooled proportion of 14.4% (95% CI: 5.9-25.8%) with a high degree of heterogeneity (I 2 : 98.6%, p<0.001). The pooled proportion was nonsignificantly lower in China than in other countries (13.4% vs. 15.4%, p=0.87, See S Figure 3 ). Meta-regression models found no significant association between the pooled proportion and mean study age (p=0.84), the proportion of males (p=0.89), and sample size (p=0.81) (see S Table 2 ). While newly diagnosed diabetes in COVID-19 patients could be due to the stress response associated with severe illness or treatment with glucocorticoids, the diabetogenic effect of COVID-19 should also be considered. 3 This is supported by reports showing exceptionally high insulin requirement in severely or critically ill COVID-19 patients with diabetes. These appear disproportionate when compared with critical Accepted Article illness caused by other conditions. 5, 7 Further, it has been noted that diabetic ketoacidosis and hyperosmolar hyperglycemic state are unusually common in COVID-19 patients with diabetes. 1, 2, 5, 9, 14 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19, by attaching to angiotensin-converting enzyme-2 (ACE2) receptors in beta cells of the pancreas, could cause acute impairment in insulin secretion. 3 Indeed, an organoid study has shown that SARS-CoV-2 can enter and damage the pancreatic beta cells. 32 SARS-CoV-2 may also injure the beta cells by triggering a plethora of proinflammatory cytokines (e.g., Interleukin-6) or by enhancing autoimmunity in genetically predisposed people. 3 In addition to defective insulin secretion, COVID-19 patients also present with a high degree of insulin resistance, particularly those with severe illness. 5 It is not known whether this is due to insulin receptor defects in the key metabolic organs associated with glucose metabolism or interference with the insulin receptor signaling by the virus. ACE2 receptors are expressed in the liver, adipose tissue, and skeletal muscle, and binding of SARS-CoV-2 to these receptors may impair responses to insulin. 3 The insulin receptor signaling could be impaired by the proinflammatory cytokines induced by SARS-CoV-2 or by enhanced actions of angiotensin II, resulting from the downregulation of ACE2 after the virus entry into the cells. 3, 5, 9 Similar mechanisms with other viral infections, such as Hepatitis C, leading to type 2 diabetes have been described before. 33 This is the first systematic review and meta-analysis to study the extent of newly diagnosed diabetes in COVID-19 patients. We used robust and standard methods, the search was comprehensive (includes grey literature too), literature search, study Accepted Article screening, selection, data extraction, and quality assessment were performed independently by two researchers, and the quality of most studies was good. Finally, we removed overlapping cohorts in our analysis, which was not commonly performed in many other systematic reviews and meta-analyses conducted in COVID-19 patients. However, our study has some limitations. The true proportion is unknown as all studies were hospital-based, and the patients were mostly severely or critically ill. Further, of the eight studies, 50% were from China, and the rest were from only two other countries, which limits the generalizability of the findings. Finally, the subgroup and meta-regression analyses lack sufficient power to detect associations between variables, as they are limited to the use of study-level data, and the number of studies was small. 19, 24 These limitations clearly emphasize the need for more studies with larger samples, including those conducted in community settings where mild cases are treated, from several regions of the world. In conclusion, this meta-analysis of eight studies with more than 3700 patients shows a pooled proportion of 14.4% for newly diagnosed diabetes in hospitalized COVID-19 patients. Recent reports have shown that newly diagnosed diabetes may confer a greater risk for poor prognosis of COVID-19 than no diabetes or pre-existing diabetes. 4,13 Therefore, COVID-19 patients with newly diagnosed diabetes should be managed early and appropriately and closely monitored for the emergence of full-blown diabetes and other cardiometabolic disorders in the long term. In this regard, the establishment of the CoviDiab Registry (covidiab.e-dendrite.com) 2 is timely and should provide valuable insights into issues regarding COVID-related diabetes. We are now Accepted Article seeing a classic example of a lethal intersection between a communicable and noncommunicable disease! This article is protected by copyright. All rights reserved. Accepted Article COVID-19, ketoacidosis and new-onset diabetes: Are there possible cause and effect relationships among them? 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All rights reserved. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.