key: cord-0718271-413hbfzx authors: Hayek, Samah; Ben‐Shlomo, Yatir; Balicer, Ran; Byrne, Katherine; Katz, Mark; Kepten, Eldad; Raz, Itamar; Roitman, Eytan; Zychma, Marcin; Barda, Noam title: Preinfection glycaemic control and disease severity among patients with type 2 diabetes and COVID‐19: A retrospective, cohort study date: 2021-04-16 journal: Diabetes Obes Metab DOI: 10.1111/dom.14393 sha: 93069c97d46095c561ac2807bfa58cad476825f7 doc_id: 718271 cord_uid: 413hbfzx nan . 9 The primary exposure of interest was the most recent HbA1c value measured in the 6 months before the index date. Demographic and clinical variables such as disease diagnosis and medication use (Table 1) were extracted from EHRs up to 5 years prior to the index date. Full lists of the extracted diagnosis and treatment variables and definitions are provided in Table S3 (T2D and other conditions) and Table S4 (antidiabetic medications) . Missing data were imputed once using the R package MICE, 10 with the complete dataset used for the study. The characteristics of the study population were described using summary statistics, and the two-sample t-test and the χ 2 test were used to compare characteristics between patients with severe and non-severe COVID-19. To assess the association between HbA1c as a continuous variable and COVID-19 severity, we used a generalized additive model. 11 A thin-plate spline was used for the primary exposure to allow a non-linear relationship, which was postulated based on clinical reasoning. 12 We present the non-linear relationship on the linear predictor (log) scale with the standard error. The model was adjusted for age, sex, BMI, ethnicity, socioeconomic status, smoking, co-morbidities, diabetes complications and antidiabetic medications. To obtain 'dose-response' estimates (with 95% confidence intervals [CIs]) for the relative risk (RR) when reducing HbA1c from a reference value of 8.0% to different target values from 7.8% to 6.0%, we performed 1000 bootstrap iterations. Each iteration consisted of the following: (a) sampling (with replacement) from the dataset; (b) fitting a generalized additive model with a Poisson distribution and a log link function; and (c) obtaining and storing the differences in the estimated (log) risk (i.e. the risk on the linear predictor scale) between the reference HbA1c and each of the possible target values. We obtained 95% CIs using the percentile method for the RR associated with each HbA1c percentage reduction from the reference level to each of the HbA1c target levels (7.8%-6.0%). To facilitate comparison with the existing literature, we stratified HbA1c into five categories (≤6.0%, 6.1%-7.0%, 7.1%-8.0%, 8.1%-10.0% and >10.0%) and presented the association between HbA1c as a categorical exposure and disease severity, expressed as RRs with 95% CIs. A generalized linear model with a Poisson outcome distribution and a log link function was used. The model was adjusted for the same variables as the continuous model, with an HbA1c of 6.0% or less used as the reference category. Furthermore, as part of our sensitivity analysis, a subgroup analysis of patients with an HbA1c value available in the 3 months before the index date was performed. This study was approved by the Institutional Review Board of CHS and was exempt from the requirement for informed consent. In total, 102,514 CHS members received a diagnosis of PCRconfirmed COVID-19 during 22 February-25 September 2020. Of this population, 5869 patients were eligible for enrolment ( Figure S2 ). Baseline demographic and clinical characteristics are shown in Table 1 . Mean (standard deviation [SD]) HbA1c was 7.24% (1.55%); the distribution density of HbA1c is shown in Figure S3 . Most patients experienced non-severe COVID-19 (n = 4855; 82.7%). There were significant differences between the demographic and clinical characteristics of the two groups ( (Table S5) . Results from the generalized additive model showed a positive, significant, sigmoidal, non-linear association between preinfection HbA1c and the risk of developing severe COVID-19 ( Figure 1 ). The strongest positive association was observed between HbA1c values of 6% and 12%. There was a gradual dose-response relationship between HbA1c level and risk: a difference in HbA1c from 8.0% to 6.0% was associated with a 29.0% decreased risk of developing severe COVID-19 (RR 0.71, 95% CI: 0.52-0.87; Table 2 ). The smallest HbA1c difference examined, from 8.0% to 7.8%, was associated with a statistically significant 4% lower risk of severe COVID-19 (RR 0.96; 95% CI: The sensitivity analysis using categorized HbA1c as the primary exposure confirmed the association between HbA1c and severe COVID-19 ( Figure S4 ). Compared with patients who had an HbA1c of 6.0% or less, those with an HbA1c of 8% or higher had an increased T A B L E 1 Baseline characteristics of patients with type 2 diabetes (T2D) and a diagnosis of coronavirus disease-2019 (COVID-19), for the full cohort and by disease severity Note: The estimates and confidence intervals (CIs) were derived using the bootstrap percentile method, with 1000 iterations. In each resample, the generalized additive model was refit using a log link function and a Poisson outcome distribution, and the change in risk going from the baseline to the target HbA1c was noted. F I G U R E 1 Results from a generalized additive model for the association between preinfection HbA1c level and the risk of developing severe coronavirus disease-2019 (COVID-19) . The coefficient of HbA1c is shown. The exposure was modelled using a thin-plate spline in a generalized additive model. In the top panel, which shows the full range of HbA1c values, a sigmoidal shape is evident, showing the slope tapering at HbA1c values of less than 5% and higher than 10%. In the bottom panel, which shows a magnified view of the central part of the data (HbA1c values of 5.8%-9.3%), a consistently positive slope is seen, illustrating the dose-response effect detailed in the text. The ribbon around the line shows the standard error. The 'rug' at the bottom shows the actual distribution of HbA1c values in the sample. The model was adjusted for age, sex, body mass index, ethnicity, socioeconomic status, smoking, hypertension, cardiovascular disease, hyperlipidaemia, malignancy, chronic kidney disease, peripheral artery disease, pulmonary diseases, diabetes duration, diabetic neuropathy, diabetic retinopathy, diabetic nephropathy and antidiabetic medications (glucose-like peptide-1 agonists, sodium-glucose co-transporter-2 inhibitors, metformin, dipeptidyl peptidase-4 inhibitors, insulin, thiazolidinediones, sulphonylureas, statins and renin-angiotensin system inhibitors) irrespective of co-morbidities, current medications or history of poor glycaemic control. The results of our sensitivity analysis modelling HbA1c as a categorical exposure support and extend the findings of previous analyses. In a study of more than 17 million adults in the UK, an HbA1c of less than 7.5% was associated with an approximately 30% increased risk of COVID-19-related death compared with no T2D, but an HbA1c of 7.5% or higher was associated with a nearly twofold increase in risk (hazard ratio 1.95; 95% CI: 1.83-2.08). 7 Research published prior to the COVID-19 pandemic provides a possible explanation for the relationship between HbA1c and severe COVID-19. These studies showed that hyperglycaemia can lead to impaired immune defences, [14] [15] [16] cytokine storms and elevated lactate levels, which are associated with COVID-19 severity in patients with diabetes. 17 Additionally, COVID-19 infections increase the production of mitochondrial reactive oxygen species, which induces hypoxiainducible factor-1α stabilization and consequently promotes glycolysis. 16 Thus, people with diabetes may have a higher risk of serious infections compared with the general population. 18 Furthermore, hyperglycaemia is common in patients who are critically ill owing to stress-induced insulin resistance and enhanced glucose production. Hence, strict control of blood glucose levels is considered essential. 19 Moreover, diabetes medications, such as sodium-glucose co- identify patients most at risk of developing severe COVID-19; furthermore, it suggests a clear and achievable strategy for reducing this risk. Indeed, given the probability that COVID-19 will remain a significant concern for several months, clinicians must strive to optimize glycaemic control in patients with T2D to reduce the risk of progression of COVID-19. tion, analysis and interpretation of data. All authors participated in preparing the manuscript, with the support of medical writing services. MK, IR and ER provided clinical consultation. All authors read and approved the submitted version of the manuscript. The peer review history for this article is available at https://publons. com/publon/10.1111/dom.14393. Due to data privacy regulations, the individual-level raw data of this study cannot be shared. 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The authors acknowledge the medical writing assistance of Caroline Samah Hayek, DrPH, Clalit Research Institute, Toval 40, Ramat Gan, Israel.Email: samahha@clalit.org.il Novo Nordisk International Operations funded the study and participated in the conception of the design. Hayek https://orcid.org/0000-0002-3300-1769Noam Barda https://orcid.org/0000-0002-3400-235X