Abstract
Introduction
Chronic kidney disease (CKD) is a risk factor for acquiring severe COVID-19, but underlying mechanisms are unknown. We aimed to study the risk associated with CKD for severe COVID-19 outcomes in relation to body mass index (BMI) and diabetes because they are common risk factors for both CKD and severe COVID-19.
Methods
This nationwide case-control study with data from mandatory national registries included 4684 patients (cases) admitted to the intensive care units (ICUs) requiring mechanical ventilation and 46,840 population-based controls matched by age, sex, and district of residency. Logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) for associations between severe COVID-19 and exposures with adjustment for confounders, in subgroups by BMI, and matched by type 2 diabetes.
Results
The median age was 64 years, and 27.7% were female. CKD was observed in 5.4% of the cases and 1.5% of the controls, whereas 1.9% and 0.3% had end-stage CKD, respectively. CKD was associated with severe COVID-19 (OR, 2.20 [95% CI, 1.85–2.62]), continuous renal replacement therapy (CRRT) in ICU (OR, 7.36 [95% CI, 5.39–10.05]), and death any time after ICU admission (OR, 2.51 [95% CI, 1.96–3.22]). The risk associated with CKD for severe COVID-19 did not differ significantly by weight but was higher in those without diabetes (OR, 2.76 [95% CI, 2.15–3.55]) than in those with diabetes (OR, 1.88 [95% CI, 1.37–2.59]).
Conclusion
CKD, especially end-stage CKD, is an important risk factor for severe COVID-19 and death after ICU admission also in patients with normal BMI and without type 2 diabetes.
Keywords: cardiometabolic risk factors, chronic kidney disease, COVID-19, mechanical ventilation, obesity
Graphical abstract
Observational studies indicate that several cardiometabolic conditions, especially obesity,1 are associated with increased risk for severe COVID-19.2, 3, 4 Given the strong association of obesity with severe COVID-19 and the close association of obesity with CKD both direct and indirect via obesity-related cardiometabolic risk factors,5,6 it is not surprising that CKD has been identified as the most prevalent severe COVID-19 risk factor worldwide.7 Up to 850 million people are estimated to have CKD worldwide,8 and in 2016, it was at the 13th place on the list of causes of death worldwide.9 Although studies have consistently showed an increased risk for severe COVID-19 associated with CKD,10,11 mechanisms remain poorly understood. It is still unknown whether CKD is a risk factor for severe COVID-19 in different BMI groups, and if so, whether the panorama of underlying conditions leading to CKD is different in normal weight, overweight, and obese patients with CKD and severe COVID-19. There is also a need to further study CKD as a risk factor for the most severe COVID-19 outcomes such as admission to ICU, acute kidney injury in need of CRRT, and all-cause mortality after admission to the ICU.
We hypothesized that CKD is a significant risk factor for severe COVID-19, acting independent of BMI and diabetes. In this study, severe COVID-19 is defined as admission to the ICU with need of mechanical ventilation. To date, no study has investigated the association between CKD and severe COVID-19 at different levels of BMI and with or without diabetes. Therefore, we aimed to compare CKD and CKD risk factors among patients with severe COVID-19 requiring mechanical ventilation in a nationwide case-control study overall and specifically in different BMI groups and subgroups with or without diabetes.
Methods
Study Design and Ethics
We performed a nationwide case-control study based on data from the Swedish Intensive Care Registry (SIR) on patients (cases) with severe COVID-19 treated with mechanical ventilation in the ICU between March 1, 2020, and June 8, 2021. For each case, 10 controls were randomly selected from the Swedish Population Register and matched by age, sex, and district of residence (corresponding to part of municipality). Details regarding the sampling of control subjects are available in the Supplementary Methods. The study database was merged with multiple mandatory Swedish national registries at Statistics Sweden and the National Board of Health and Welfare, using each individual’s unique personal identification number. The data used in the study were already collected and pseudonymized, which involved minimal violation of the integrity of personally identifiable information. The initial results from this study covering the first wave of the pandemic have been reported previously.3
National Registries and Data Collection
Eligible patients with severe COVID-19 were identified in SIR, a national register with about 95% coverage of all ICU admissions in Sweden.12,13 Primary and secondary diagnoses from previous hospital admissions and outpatient visits coded according to the International Classification of Diseases version 10 were collected from the National Patient Register (NPR). The longitudinal integrated database for health insurance and labor market studies is managed by Statistics Sweden and includes annual measurements of several socioeconomic and sociodemographic variables, including income, education, and country of birth. The national registers used in the study are further described in Supplementary Methods.
Definition of Exposures
The primary exposure of interest was a history of CKD. Secondarily, we studied stages of CKD and important causes of CKD associated with the metabolic syndrome (type 2 diabetes, hypertension, and obesity) as well as type 1 diabetes and inflammatory diseases (vasculopathies). History of CKD was based on the International Classification of Diseases version 10 diagnosis (N18) in the NPR within up to 15 years preceding admission. Stage of CKD was based on having one or more of the following diagnoses in the NPR: N18.1, N18.2, N18.3, N18.4, or N18.5 corresponding to CKD stages 1 to 5. If several codes were present for the same individual, the most severe stage was used and if none of these codes were present, it was classified as unknown stage of CKD. Furthermore, the time from first diagnosis of CKD in NPR to index date was calculated. In addition, the following conditions were identified based on reported diagnosis in NPR or prescribed drugs within the preceding 12 months: hypertension (I10 or prescription of antihypertensive drugs), hyperlipidemia (E78 or prescription of lipid-lowering drugs), diabetes mellitus type 2 (E11 or prescription of antidiabetic drugs), diabetes mellitus type 1 (E10) and no prescription of oral antidiabetic drugs, obesity (E66), heart failure (I50.1, I50.9), atrial fibrillation (I48), venous thromboembolism (I26, I80), asthma (J45), chronic obstructive pulmonary disease (J44), CKD (N18), malignancy (C, D40–48), rheumatoid arthritis (M05, M06), systemic inflammatory disease (M30–M36), and inflammatory bowel disease (K50, K51). A history of cardiovascular disease was defined as a record of either myocardial infarction (MI) (I21, I22), ischemic heart disease (I25), ischemic stroke (I63), or peripheral vascular disease (I70–I73) in the NPR (Supplementary Table S1).
Definition of Covariables and Variables for Subgroup Analyses
To adjust for SES in the analyses, information on level of education, region of birth, and disposable income were retrieved for all cases and control subjects. Level of education was categorized as ≤9 years, 10 to 12 years, and >12 years based on the highest educational level attained during the year before admission. Region of birth was categorized as a country of birth within the 15-nation European Union (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and UK) and/or the Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden) or having a country of birth outside this region. Disposable income (average disposable income per household consumption unit) that accounts for household size and composition was obtained in the year preceding the admission. Income levels were categorized by quintiles into calendar year–specific fifths (lowest referent). Because the median income in women was lower than in men, fifths were also stratified by gender.
Outcomes
The primary outcome, severe COVID-19, was defined as ICU admission because of COVID-19 with a laboratory-confirmed SARS-CoV-2 infection registered in SIR, with at least 1 episode of invasive mechanical ventilation during the ICU stay. All eligible patients during the study period between March 1, 2020, and June 8, 2021, were included as cases in the study. Secondary outcomes were acute kidney injury in need of CRRT defined as at least 1 episode of CRRT during the ICU stay and all-cause mortality after admission to the ICU during the total follow-up until August 31, 2021.
Statistical Methods
Categorical variables are reported as frequencies and percentages, whereas continuous variables are reported as median and interquartile range. Missing data are reported in Supplementary Table S2. ORs and 95% CIs for the association between the different exposures and the outcome were calculated by means of logistic regression adjusted for age, sex, and sociodemographic and socioeconomic variables (marital status, region of birth, educational level, and household disposable income). For all exposures, additional adjustments were made for all the following medical conditions: CKD, diabetes type 1, diabetes type 2, hypertension, hyperlipidemia, obesity, and cardiovascular disease that were used as covariates in a fully adjusted regression model to analyze both total effects unconfounded of sociodemographic variables and direct effects in accordance to our perception of causal relationships as illustrated by the directed acyclic graphs in Supplementary Figure S1. This was done in a complete case analysis with no imputation of missing data. Standard errors were calculated using the robust sandwich estimator, and the significance level was set at an alpha of 0.05.
In addition, to further study the impact of CKD on the outcome in relation to obesity and diabetes mellitus, the following subgroup analyses were performed with a case-control design: all cases with a BMI reported in SIR at admission (3 subgroups with normal weight [<25 kg/m2], overweight [25–30 kg/m2], and obese [>30 kg/m2]) and history of any diabetes mellitus in cases (yes or no). Given that information on diabetes was available for all control subjects and the large number of control subjects, control subjects were matched for history of diabetes in the diabetes subgroup analysis. Unmatched cases were not included.
For a formal test of a significant difference among the ORs for different subgroups, likelihood ratio tests were conducted between a model with and without an interaction term between the indicator variable for the subgroup and the risk factor. For these tests, the robust sandwich estimator was not used in the underlying logistic regression models.
To adjust for BMI in a sensitivity analysis, BMI categorical values (3 levels, same as subgroups) were imputed 3 times in population-based control subjects based on normal reference values in the Swedish population.14 For each imputation, a random sample was drawn from a multinomial distribution with 3 events. The event probabilities depended on the observed frequencies of BMI category reference values for the corresponding age (18–29, 30–44, 45–64, and 65–84 years), gender, and level of education category of the respective control subject. Because there were no reference values given for individuals 85 years and older, BMI was not imputed for control subjects of that age group. Thereafter, imputed BMI values were adjusted for in sensitivity analyses to investigate whether adjusting for BMI would change the conclusions. In a second sensitivity analysis, a similar analysis was performed for the periods before and after the start of vaccination in Sweden.
The analyses were repeated for the secondary outcomes CRRT and all-cause mortality. Statistical analyses were performed using Stata version 16.1 (StataCorp, College Station, TX).
Results
During the study period between March 1, 2020, and June 8, 2021, a total of 7423 patients were admitted to an ICU in Sweden because of COVID-19, of whom 4684 received mechanical ventilation. For each patient who received mechanical ventilation, 10 matched control subjects were randomly selected from the Swedish population matched for age, sex, and location, rendering a total of 46,840 control subjects. A total of 1956 patients admitted to ICU had a record of BMI in SIR and for each of these cases, the 10 matched control subjects were used for subgroup analyses in different BMI groups. The study population selection procedure and exclusions are described in Supplementary Figure S2 and presented in Table 1.
Table 1.
Baseline characteristics of the study population
| Characteristics | COVID-19 |
Control subjects |
P value |
|---|---|---|---|
| n = 4684 | n = 46,840 | ||
| Age (yr) | 64 (55–72) | 64 (55–72) | 1.00 |
| Gender | 1.00 | ||
| Male | 3388 (72.3%) | 33,880 (72.3%) | |
| Female | 1296 (27.7%) | 12,960 (27.7%) | |
| Education (yr) | <0.001 | ||
| <9 | 1306 (28.9%) | 9738 (21.1%) | |
| 9–12 | 2043 (45.2%) | 21,032 (45.6%) | |
| >12 | 1172 (25.9%) | 15,305 (33.2%) | |
| Marital status | <0.001 | ||
| Unmarried | 2091 (44.7%) | 22,142 (47.3%) | |
| Married | 2584 (55.3%) | 24,658 (52.7%) | |
| Region of birth | <0.001 | ||
| Non-EU15 | 1593 (34.1%) | 8924 (19.1%) | |
| EU15a and/or Nordics | 3072 (65.9%) | 37,910 (80.9%) | |
| Disposable income | <0.001 | ||
| Quintile 1 | 1357 (29.1%) | 9428 (20.2%) | |
| Quintile 2 | 1001 (21.5%) | 9081 (19.5%) | |
| Quintile 3 | 852 (18.3%) | 9278 (19.9%) | |
| Quintile 4 | 763 (16.4%) | 9411 (20.2%) | |
| Quintile 5 | 686 (14.7%) | 9480 (20.3%) | |
| Type 1 diabetes | 20 (0.4%) | 194 (0.4%) | 0.90 |
| Type 2 diabetes | 1277 (27.3%) | 5925 (12.6%) | <0.001 |
| Obesity | 786 (16.8%) | 1,655 (3.5%) | <0.001 |
| Hypertension | 2690 (57.4%) | 20,084 (42.9%) | <0.001 |
| Hyperlipidemia | 938 (20.0%) | 7586 (16.2%) | <0.001 |
| Chronic kidney disease | 251 (5.4%) | 708 (1.5%) | <0.001 |
| Cardiovascular disease | 675 (14.4%) | 4656 (9.9%) | <0.001 |
| Myocardial infarction | 342 (7.3%) | 2608 (5.6%) | <0.001 |
| Ischemic stroke | 184 (3.9%) | 1299 (2.8%) | <0.001 |
| Intracerebral hemorrhage | 45 (1.0%) | 227 (0.5%) | <0.001 |
| Peripheral arterial disease | 205 (4.4%) | 1217 (2.6%) | <0.001 |
| Heart failure | 309 (6.6%) | 1502 (3.2%) | <0.001 |
| Atrial fibrillation | 500 (10.7%) | 2999 (6.4%) | <0.001 |
| Deep vein thrombosis | 168 (3.6%) | 1021 (2.2%) | <0.001 |
| Pulmonary embolism | 95 (2.0%) | 550 (1.2%) | <0.001 |
| COPD | 234 (5.0%) | 1187 (2.5%) | <0.001 |
| Asthma | 955 (20.4%) | 5024 (10.7%) | <0.001 |
| Malignancy | 936 (20.0%) | 8696 (18.6%) | 0.018 |
| Rheumatoid arthritis | 115 (2.5%) | 539 (1.2%) | <0.001 |
| Systemic inflammatory disease | 142 (3.0%) | 653 (1.4%) | <0.001 |
| Pharmacologic treatments | |||
| ACE inhibitors | 779 (16.6) | 6276 (13.4) | <0.001 |
| ARBs | 1088 (23.2) | 8072 (17.2) | <0.001 |
| Calcium channel blockers | 1146 (24.5) | 7814 (16.7) | <0.001 |
| Beta-blockers | 1193 (25.5) | 8660 (18.5) | <0.001 |
| Diuretics | 342 (7.3) | 2661 (5.7) | <0.001 |
| Any antidiabetics | 1100 (23.5) | 5398 (11.5) | <0.001 |
| Statins | 1333 (28.5) | 10,639 (22.7) | <0.001 |
ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; COPD, chronic obstructive pulmonary disease; EU, European Union.
Data are presented as median (interquartile range) for continuous measures and n (%) for categorical measures. Characteristics of patients with COVID-19 requiring mechanical ventilation and control subjects are presented.
EU15 comprises Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and United Kingdom. The Nordic countries include Denmark, Finland, Iceland, Norway, and Sweden.
Risk Factors for Severe COVID-19 and Outcomes After Admission to the ICU
CKD, type 2 diabetes, hypertension, and obesity in the population were all significantly associated with experiencing a severe course of COVID-19 requiring mechanical ventilation (Table 2 and Figure 1). A total of 251 (5.4%) cases had a previous diagnosis of CKD compared with 708 (1.5%) of the control subjects. The OR for receiving mechanical ventilation in patients with CKD was 3.47 (95% CI, 2.97–4.06) when adjusted for age, sex, and SES. When additionally adjusted for comorbidities, the OR was 2.20 (95% CI, 1.85–2.62).
Table 2.
Odds ratios for severe COVID-19 outcomes (mechanical ventilation, CRRT, and death after intensive care unit admission) associated with history of chronic kidney disease and cardiometabolic risk factors
| Severe COVID-19 outcomes, history of CKD and cardiometabolic risk factors | Adjusted for age and sex and SES |
Multiadjusted modela |
||||
|---|---|---|---|---|---|---|
| OR | 95% CI | P value | OR | 95% CI | P value | |
| Mechanical ventilation | ||||||
| CKD | 3.47 | 2.97–4.06 | 0.000 | 2.20 | 1.85–2.62 | 0.000 |
| Type 1 diabetes | 1.15 | 0.71–1.86 | 0.563 | 1.17 | 0.72–1.88 | 0.526 |
| Type 2 diabetes | 2.34 | 2.16–2.52 | 0.000 | 1.70 | 1.55–1.86 | 0.000 |
| Hypertension | 2.02 | 1.88–2.18 | 0.000 | 1.57 | 1.44–1.70 | 0.000 |
| Obesity | 5.35 | 4.86–5.90 | 0.000 | 4.02 | 3.62–4.56 | 0.000 |
| CVD | 1.46 | 1.33–1.60 | 0.000 | 0.91 | 0.81–1.02 | 0.102 |
| CRRT | ||||||
| CKD | 12.83 | 9.73–16.91 | 0.000 | 7.36 | 5.39–10.05 | 0.000 |
| Type 1 diabetes | 2.55 | 1.10–5.94 | 0.030 | 2.16 | 1.01–4.63 | 0.048 |
| Type 2 diabetes | 3.08 | 2.58–3.68 | 0.000 | 1.76 | 1.42–2.19 | 0.000 |
| Hypertension | 2.86 | 2.38–3.44 | 0.000 | 1.74 | 1.41–2.15 | 0.000 |
| Obesity | 6.76 | 5.42–8.42 | 0.000 | 4.17 | 3.22–5.40 | 0.000 |
| CVD | 1.67 | 1.34–2.07 | 0.000 | 0.91 | 0.69–1.21 | 0.534 |
| Death any time after admission ICU | ||||||
| CKD | 3.89 | 3.10–4.87 | 0.000 | 2.51 | 1.96–3.22 | 0.000 |
| Type 1 diabetes | 0.97 | 0.39–2.41 | 0.955 | 1.05 | 0.44–2.51 | 0.906 |
| Type 2 diabetes | 2.24 | 1.98–2.53 | 0.000 | 1.65 | 1.42–1.90 | 0.000 |
| Hypertension | 1.86 | 1.64–2.10 | 0.000 | 1.36 | 1.18–1.56 | 0.000 |
| Obesity | 5.33 | 4.56–6.23 | 0.000 | 3.70 | 3.10–4.43 | 0.000 |
| CVD | 1.68 | 1.47–1.93 | 0.000 | 1.14 | 0.96–1.34 | 0.143 |
CI, confidence interval; CKD, chronic kidney disease; CRRT, continuous renal replacement therapy; CVD, cardiovascular disease; ICU, intensive care unit; OR, odds ratio; SES, socioeconomic status.
Results are based on complete case analysis with no missing data on covariates (4479 cases and 46,012 control subjects).
The model adjusted for age, sex, educational level, income, marital status, region of birth, and the following conditions: CKD, diabetes type 1, diabetes type 2, hypertension, hyperlipidemia, obesity, and CVD.
Figure 1.
Risk for COVID-19 requiring mechanical ventilation associated with history of chronic kidney disease and cardiometabolic risk factors. Odds ratios adjusted for sex, age, and SES. CI, confidence interval; CKD, chronic kidney disease; SES, socioeconomic status.
Risk factors significantly associated with receiving CRRT in the ICU because of severe COVID-19 were CKD, type 2 diabetes, hypertension, and obesity. CKD had the strongest association, with an OR of 12.8 (95% CI, 9.73–16.9) for receiving CRRT when adjusted for age, sex, and SES. In the multiadjusted model, the OR was 7.36 (95% CI, 5.39–10.05).
During a median of 85 days (23−362 days) of follow-up of cases after ICU admission, risk factors that were significantly associated with death any time after admission were CKD, type 2 diabetes, hypertension, and obesity, where obesity and CKD had the strongest association with mortality. OR for death any time after admission to ICU in patients with CKD was 3.89 (95% CI, 3.10–4.87), whereas the OR in the multiadjusted model was 2.51 (95% CI, 1.96–3.22).
Patient Characteristics in Different BMI Groups
The characteristics of patients with severe COVID-19 in 3 different BMI groups (normal weight [<25 kg/m2], overweight [25–30 kg/m2], and obese [>30 kg/m2]) are shown in Table 3. Age, sex, hypertension, type 2 diabetes, asthma, and malignancy differed between the groups, but the prevalence of CKD was similar (6.1%, 6.3%, and 5.1% for normal weight, overweight, and obese groups, respectively). Patients with lower BMI were older with a mean age ranging from 69 years in normal weight, to 66 years in overweight, and to 60 years in the obese group. Male sex was overrepresented in all BMI groups but less so in the obese group. Hypertension was more common among patients with higher BMI with a prevalence rate ranging from 52.1% in the normal weight group to 63.2% in the obese group, whereas type 2 diabetes ranged from 24.2% in the normal weight group to 32.2% in the obese group. Conversely, a history of malignancy was more common among patients with lower BMI ranging from 26.7% in the normal weight group to 21.9% in the overweight group and to 16.4% in the obese group.
Table 3.
Characteristics of patients with COVID-19 requiring mechanical ventilation by BMI group
| Characteristics | <25 kg/m2 |
25–30 kg/m2 |
>30 kg/m2 |
P value |
|---|---|---|---|---|
| n = 359 | n = 712 | n = 885 | ||
| Age (yr) | 69 (60–75) | 66 (58–72) | 60 (51–69) | <0.001 |
| Gender | <0.001 | |||
| Male | 264 (73.5%) | 554 (77.8%) | 575 (65.0%) | |
| Female | 95 (26.5%) | 158 (22.2%) | 310 (35.0%) | |
| Education (yr) | 0.65 | |||
| ≤9 | 100 (29.1%) | 196 (28.2%) | 225 (26.3%) | |
| 9–12 | 148 (43.0%) | 316 (45.4%) | 408 (47.7%) | |
| ≥12 | 96 (27.9%) | 184 (26.4%) | 222 (26.0%) | |
| Marital status | 0.22 | |||
| Unmarried | 157 (43.7%) | 316 (44.6%) | 427 (48.2%) | |
| Married | 202 (56.3%) | 392 (55.4%) | 458 (51.8%) | |
| Region of birth | 0.42 | |||
| Non-EU15 | 124 (35.0%) | 249 (35.2%) | 285 (32.3%) | |
| EU15a and/or Nordics | 230 (65.0%) | 458 (64.8%) | 597 (67.7%) | |
| Disposable income | 0.26 | |||
| Quintile 1 | 107 (29.8%) | 205 (29.0%) | 252 (28.6%) | |
| Quintile 2 | 85 (23.7%) | 159 (22.5%) | 187 (21.2%) | |
| Quintile 3 | 61 (17.0%) | 130 (18.4%) | 167 (18.9%) | |
| Quintile 4 | 48 (13.4%) | 103 (14.6%) | 163 (18.5%) | |
| Quintile 5 | 58 (16.2%) | 109 (15.4%) | 113 (12.8%) | |
| Type 1 diabetes | 3 (0.8%) | 2 (0.3%) | 4 (0.5%) | 0.45 |
| Type 2 diabetes | 87 (24.2%) | 188 (26.4%) | 285 (32.2%) | 0.005 |
| Obesity | 4 (1.1%) | 46 (6.5%) | 317 (35.8%) | <0.001 |
| Hypertension | 187 (52.1%) | 400 (56.2%) | 559 (63.2%) | <0.001 |
| Hyperlipidemia | 59 (16.4%) | 130 (18.3%) | 193 (21.8%) | 0.054 |
| Chronic kidney disease | 22 (6.1%) | 45 (6.3%) | 45 (5.1%) | 0.54 |
| Cardiovascular disease | 65 (18.1%) | 120 (16.9%) | 104 (11.8%) | 0.002 |
| Myocardial infarction | 28 (7.8%) | 58 (8.1%) | 57 (6.4%) | 0.40 |
| Ischemic stroke | 25 (7.0%) | 41 (5.8%) | 27 (3.1%) | 0.004 |
| Intracerebral hemorrhage | 5 (1.4%) | 6 (0.8%) | 9 (1.0%) | 0.70 |
| Peripheral arterial disease | 20 (5.6%) | 39 (5.5%) | 34 (3.8%) | 0.23 |
| Heart failure | 18 (5.0%) | 46 (6.5%) | 59 (6.7%) | 0.54 |
| Atrial fibrillation | 32 (8.9%) | 83 (11.7%) | 91 (10.3%) | 0.37 |
| Deep vein thrombosis | 13 (3.6%) | 26 (3.7%) | 31 (3.5%) | 0.99 |
| Pulmonary embolism | 4 (1.1%) | 20 (2.8%) | 20 (2.3%) | 0.21 |
| COPD | 19 (5.3%) | 37 (5.2%) | 27 (3.1%) | 0.059 |
| Asthma | 55 (15.3%) | 140 (19.7%) | 227 (25.6%) | <0.001 |
| Malignancy | 96 (26.7%) | 156 (21.9%) | 145 (16.4%) | <0.001 |
| Rheumatoid arthritis | 5 (1.4%) | 21 (2.9%) | 24 (2.7%) | 0.29 |
| Systemic inflammatory disease | 12 (3.3%) | 23 (3.2%) | 29 (3.3%) | 1.00 |
BMI, body mass index; COPD, chronic obstructive pulmonary disease; EU, European Union.
Data are presented as median (interquartile range) for continuous measures and n (%) for categorical measures.
EU15 comprises Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden, and United Kingdom. The Nordic countries include Denmark, Finland, Iceland, Norway, and Sweden.
The characteristics of the patients with CKD and severe COVID-19 in the normal weight, overweight, and obese groups are shown in Table 4. Among patients with CKD and severe COVID-19, type 2 diabetes was more common in the overweight and obese groups than in the normal weight group, whereas type 1 diabetes was more common in individuals with CKD and severe COVID-19 in the normal weight group. Furthermore, systemic inflammatory disease was more common in the overweight group, whereas hypertension and heart failure did not differ between the groups.
Table 4.
Characteristics of patients with CKD with COVID-19 requiring mechanical ventilation by BMI group
| Characteristics | <25 kg/m2 |
25–30 kg/m2 |
>30 kg/m2 |
P value | Controls |
|---|---|---|---|---|---|
| n = 22 | n = 45 | n = 45 | n = 316 | ||
| Age (yr) | 69 (58–76) | 69 (54–73) | 66 (59–72) | 0.65 | 72 (66–77) |
| Gender (male) | 20 (91%) | 38 (84%) | 33 (73%) | 0.17 | 263 (86%) |
| Type 1 diabetes | 2 (9%) | 0 (0%) | 0 (0%) | 0.016 | 4 (1%) |
| Type 2 diabetes | 4 (18%) | 24 (53%) | 24 (53%) | 0.012 | 141 (46%) |
| Obesity | 0 (0%) | 9 (20%) | 20 (44%) | <0.001 | 44 (14%) |
| Hypertension | 19 (86%) | 44 (98%) | 42 (93%) | 0.19 | 286 (94%) |
| Hyperlipidemia | 7 (32%) | 16 (36%) | 21 (47%) | 0.41 | 100 (33%) |
| Cardiovascular disease | 7 (32%) | 17 (38%) | 9 (20%) | 0.17 | 103 (34%) |
| Myocardial infarction | 4 (18%) | 8 (18%) | 6 (13%) | 0.81 | 62 (20%) |
| Ischemic stroke | 4 (18%) | 6 (13%) | 1 (2%) | 0.071 | 32 (10%) |
| Peripheral arterial disease | 5 (23%) | 7 (16%) | 6 (13%) | 0.61 | 46 (15%) |
| Heart failure | 5 (23%) | 8 (18%) | 10 (22%) | 0.84 | 79 (26%) |
| Atrial fibrillation | 4 (18%) | 7 (16%) | 8 (18%) | 0.95 | 76 (25%) |
| COPD | 2 (9%) | 2 (4%) | 2 (4%) | 0.69 | 24 (8%) |
| Asthma | 8 (36%) | 1 (2%) | 10 (22%) | 0.001 | 52 (17%) |
| Malignancy | 9 (41%) | 17 (38%) | 10 (22%) | 0.18 | 146 (48%) |
| Rheumatoid arthritis | 1 (5%) | 2 (4%) | 0 (0%) | 0.36 | 5 (2%) |
| Systemic inflammatory disease | 2 (9%) | 1 (2%) | 7 (16%) | 0.085 | 20 (7%) |
| PAN | 0 (0%) | 0 (0%) | 1 (2%) | 0.10 | 1 (0%) |
| Vasculopathies | 1 (1%) | 4 (3%) | 3 (6%) | 0.22 | 8 (3%) |
| SLE | 0 (0%) | 2 (1%) | 2 (4%) | 0.15 | 4 (1%) |
| Polymyalgia rheumatica | 1 (1%) | 5 (4%) | 3 (6%) | 0.22 | 9 (3%) |
BMI, body mass index; CKD, chronic kidney disease; COPD, chronic obstructive pulmonary disease; PAN, polyartheritis nodosa; SLE, systemic lupus erythematosus.
Control subjects (n = 316) with CKD added as reference.
CKD Stages and Risk for Severe COVID-19
The distribution of CKD stages in all cases and controls with CKD is presented in Table 5 together with their pharmacologic treatment. The distribution differed significantly. Stage 5 CKD was the most common stage in patients with severe COVID-19 and CKD, whereas stage 3 was the most common stage in control subjects with CKD. Thus, not only was CKD more common in severe COVID-19, but CKD, when present, was also more severe. Time since first CKD diagnosis did not differ between cases and controls. Neither did treatment with angiotensin-converting enzyme inhibitors nor angiotensin receptor blockers differ between cases and controls with CKD.
Table 5.
Stages of CKD and pharmacologic treatment in cases and control subjects with CKD
| CKD-stages and pharmacologic treatments | COVID-19 (n = 251) | Control subjects (n = 708) | P value |
|---|---|---|---|
| CKD-stages | <0.001 | ||
| Unknown | 63 (25.1%) | 181 (25.6%) | |
| 1 | 6 (2.4%) | 15 (2.1%) | |
| 2 | 2 (0.8%) | 43 (6.1%) | |
| 3 | 43 (17.1%) | 197 (27.8%) | |
| 4 | 42 (16.7%) | 133 (18.8%) | |
| 5 | 95 (36.8%) | 139 (19.6%) | |
| Time since first CKD diagnosis (yr) | 4.3 (1.3–8.1) | 4.2 (2.0–7.9) | 0.21 |
| Pharmacologic treatments | |||
| ACE inhibitor | 53 (21.1%) | 162 (22.9%) | 0.56 |
| ARB | 102 (40.6%) | 286 (40.4%) | 0.95 |
| Calcium channel blockers | 140 (55.8%) | 357 (50.4%) | 0.14 |
| Beta-blockers | 152 (60.6%) | 444 (62.7%) | 0.55 |
| Diuretics | 29 (11.6%) | 111 (15.7%) | 0.110 |
ACE, angiotensin-converting enzyme; ARB, angiotensin II receptor blocker; CKD, chronic kidney disease.
Data are presented as median (25th–75th percentile) for continuous measures and n (%) for categorical measures.
Comparison of Case Versus Controls by Different BMI Groups and by Diabetes
The 3 COVID-19 BMI groups and their corresponding control subjects are shown in Supplementary Table S3A−C. Among patients with information on BMI at admission, the OR for receiving mechanical ventilation owing to severe COVID-19 in patients with CKD was 3.58 (95% CI, 2.79–4.58) when adjusted for age, sex, BMI, and SES. Therefore, the risk associated with CKD was similar as in the main analysis. When additionally adjusted for comorbidities, the OR was 2.23 (95% CI, 1.71–2.92). Furthermore, this risk associated with CKD was apparent in a sensitivity analysis restricted to the period before (OR, 1.78 [95% CI, 1.16–2.73]) and after (OR, 2.66 [95% CI, 1.87–3.79]) the start of vaccination in Sweden.
When comparing patients with BMI <25 kg/m2 to controls using multivariable logistic regression analyses accounting for age, sex, SES, and comorbidities, we found that CKD had the strongest association with severe COVID-19 (OR, 3.16 [95% CI, 1.76–5.64]). Type 2 diabetes was also associated with severe COVID-19 (OR, 1.77 [95% CI, 1.27–2.44]). The relative risk for severe COVID-19 associated with CKD was slightly lower in the overweight group (OR, 2.29 [95% CI, 1.56–3.38]) and in the obese group (OR, 1.85 [95% CI, 1.16–2.94]) but the ORs did not differ significantly when the interaction was tested (Table 6).
Table 6.
Risk for severe COVID-19 associated with CKD and cardiometabolic risk factors by subgroups of BMI and by history of type 2 diabetes
| BMI and diabetes subgroups | CKD | Type 2 diabetes | Hypertension | Obesity |
|---|---|---|---|---|
| BMI | ||||
| <25 kg/m2 | 3.16 (1.76–5.64) | 1.77 (1.27–2.44) | 0.93 (0.68–1.28) | |
| 25–30 kg/m2 | 2.29 (1.56–3.38) | 1.83 (1.45–2.30) | 1.44 (1.17–1.78) | |
| >30 kg/m2 | 1.85 (1.16–2.94) | 2.00 (1.60–2.50) | 2.74 (2.23–3.36) | |
| P value for interaction | NS | |||
| Type 2 diabetes | ||||
| Yes | 1.88 (1.37–2.59) | NA | 1.25 (0.98–1.61) | 2.43 (1.94–3.03) |
| No | 2.76 (2.15–3.55) | NA | 1.53 (1.39–1.68) | 5.35 (4.66–6.14) |
| P value for interaction | 0.0029 |
BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular disease; NA, not applicable; NS, not significant.
All odds ratios were adjusted for age, sex, educational level, income, marital status, region of birth, and the following conditions: CKD, type 1 diabetes, type 2 diabetes, hypertension, hyperlipidemia, obesity, and CVD. Control subjects in type 2 diabetes subgroups were matched for age, sex, and history of type 2 diabetes. P values denote likelihood ratio tests between a model with and one without an interaction term between the indicator variable for the subgroup and the risk factor added to fully adjusted model. Odds ratios for COVID-19 receiving mechanical ventilation in normal weight, overweight, and obese groups and in patients with or without type 2 diabetes compared with matched control subjects are presented.
Thereafter, we studied the effect of CKD on severe COVID-19 in those with type 2 diabetes and those without diabetes, separately, using subgroups matched for type 2 diabetes and multivariable logistic regression analyses accounting for age, sex, SES, and comorbidities. We found that CKD was associated with severe COVID-19 (OR, 1.88 [95% CI, 1.37–2.59]) in patients with type 2 diabetes, but when we compared patients without diabetes to controls without diabetes, we found that the association between CKD and severe COVID-19 (OR 2.76 [95% CI, 2.15–3.55]) was stronger (P value for interaction = 0.0029) (Table 6 and Figure 2). The characteristics of patients with severe COVID-19 and control subjects matched for type 2 diabetes are presented in Supplementary Table S4.
Figure 2.
Risk for COVID-19 requiring mechanical ventilation associated with history of chronic kidney disease in the population with and without type 2 diabetes. Odds ratios adjusted for sex, age, SES, and comorbidities. CI, confidence interval; SES, socioeconomic status.
Discussion
In the present nationwide case-control study, assessing the risk for severe COVID-19, we found that CKD was strongly and independently associated with the most severe adverse outcomes after COVID-19 infection such as need for mechanical ventilation during ICU stay and death at any time after ICU admission. In particular, end-stage CKD was overrepresented in severe COVID-19. The relative risk associated with CKD for severe COVID-19 did not differ significantly in the normal weight, overweight, and obese subgroups but was higher in those without diabetes than in those with diabetes. Our findings therefore support CKD as an important and independent risk factor for severe COVID-19 that needs further attention.
Next to obesity, CKD had the strongest association with mechanical ventilation, death at any time after ICU admission, and the strongest association with CRRT compared with other metabolic risk factors. These findings are in line with earlier studies such as a previous study by Flythe et al.15 of more than 4200 critically ill patients with COVID-19 admitted to an ICU in the United States, which found that CKD was associated with higher in-hospital mortality rates early in the pandemic. Another early study by Cheng et al.16 found that patients with elevated baseline serum creatinine were more likely to be admitted to the ICU with mechanical ventilation.
Obesity together with cardiometabolic risk factors related to obesity, in particular type 2 diabetes and hypertension, have been the most prominent risk factors for a severe course of COVID-19.3 Our findings regarding obesity to be the strongest risk factor for severe COVID-19 in need of mechanical ventilation and death any time after admission to the ICU correspond to previous studies. A recent Swedish study showed that high BMI was associated with increased risk of the composite outcome death and prolonged length of stay in the ICU in patients with COVID-19.17 Given that obesity, type 2 diabetes, and hypertension are the most important risk factors in the population for CKD, it has not been unexpected that CKD has been overrepresented among patients with severe COVID-19.8 Although CKD has been the most prevalent risk factor for severe COVID-19,7 the role of CKD is unclear.
Even though some of the effect of obesity on the development of CKD is mediated via diabetes, there is also evidence of a direct effect of obesity on CKD.5,6,18 Furthermore, abdominal obesity and CKD share a genetic influence.19 Therefore, a reasonable explanation for CKD as a COVID-19 risk factor in previous studies may be an underlying common mechanism related to CKD, obesity, and the cardiometabolic risk factors. Microcirculatory dysfunction has been suggested, possibly making these patients more vulnerable to COVID-19. One other explanation of why CKD may be associated with adverse outcomes of COVID-19 could be that patients with CKD have a higher risk for adverse outcomes in pulmonary infections in general.20 Our findings shed new light on this association. We found that the prevalence of CKD in patients with severe COVID-19 was similar to patients in the normal weight group compared with those in the overweight and obese groups. This was observed even though common risk factors for CKD associated with obesity, in particular hypertension and type 2 diabetes, were more common in patients with severe COVID-19 and overweight and/or obesity. Furthermore, in cases with both CKD and severe COVID-19, the pattern of comorbidities related to CKD differed in different BMI groups, with more type 2 diabetes in the obese group with CKD and more type 1 diabetes in the nonobese group with CKD. Therefore, irrespective of the underlying pathology contributing to CKD, it posed a similar risk.
The current study found that the most severe stages of CKD were the most overrepresented in severe COVID-19. It is possible that the activation of the renin-angiotensin system in CKD may be the factor that explains the vulnerability to COVID-19, thereby making patients with CKD prone to a more severe course of the disease. In general, there is an activation of systemic renin-angiotensin system in CKD.21 There is also a change in the intrarenal generation of angiotensin II with less of protective angiotensin 1 to 7 and toward more generation of angiotensin II.22 This renin-angiotensin system dysregulation in CKD is mediated by increased chymase-mediated angiotensin II23 and less neprilysin-generated angiotensin 1 to 7.24 Angiotensin-converting enzyme 2, involved in the renin-angiotensin system was identified early as a receptor for the spike glycoprotein of SARS-CoV-2.25,26 However, no increased angiotensin-converting enzyme 2 expression in the kidney has been verified in CKD.27,28 A recent study by Garreta et al.29 observed that high oscillatory glucose regimes induced expression of angiotensin-converting enzyme 2 at both mRNA and protein levels in kidney organoids and increased viral load of SARS-CoV-2 at the mRNA and protein levels in the diabetic kidney organoids, which may explain a greater vulnerability to COVID-19 in these patients. Partly, in contrast to these findings, we observed a higher risk for severe COVID-19 in patients with CKD both with and without type 2 diabetes and with a slightly higher relative risk associated with CKD in those without diabetes. Therefore, it is important that future studies on mechanistic evidence also consider the vulnerability to severe COVID-19 in nondiabetic kidney disease.
The strengths of our study lie in the study design with nationwide sampling of cases and control subjects and the rigorous control of confounders including important matching of age, sex, living area, and adjustment for detailed data on SES and region of birth, which are important risk factors.30 However, there are limitations to this study. First, previous history of CKD, including stages of CKD, and obesity were only presented as a diagnosis in national health care registers. This probably underestimates the prevalence of milder stages of CKD in the current study, with a higher sensitivity for more severe degrees of CKD.31 However, this limitation applies for both control subjects and patients with severe COVID-19 and, thus, should not affect the relative risk. Second, we did not have reference BMI in the control group. However, we had access to population-based BMI and were able to impute BMI values in the control subjects. SIR also had missing data regarding BMI, but still, our data comprise one of the largest materials we know of patients in the ICU with registered BMI. Third, the study duration included different phases of the COVID-19 pandemic where treatment recommendations, vaccination status in risk groups, and clinical practice rapidly changed, probably affecting mortality, the need for mechanical ventilation, and CRRT. However, the risks associated with CKD were apparent in sensitivity analyses that were restricted to periods both before and after the start of vaccination programs in Sweden.
Overall, our findings show that CKD, especially end-stage CKD, is a risk factor for the most severe COVID-19 outcomes in patients with normal BMI and in patients with or without type 2 diabetes and affects independently of other cardiovascular risk factors, including SES. Thus, CKD is an important risk factor, irrespective of the underlying pathology leading to CKD, but further research regarding mechanisms and the impact of milder stages of CKD is needed.
Disclosure
All the authors declared no competing interests.
Acknowledgements
The research was funded with grants from Region Stockholm (ALF).
Footnotes
Supplementary Methods–Register information and selection of control subjects.
Figure S1. Directed acyclic graph.
Figure S2. Study flowchart.
Table S1. Hypertension definitions.
Table S2. Missing data in the study population.
Table S3A–C. Characteristics of the study population by BMI group.
Table S4. Characteristics of the study population by type 2 diabetes.
Supplementary Material
Supplementary Methods–Register information and selection of control subjects.
Figure S1. Directed acyclic graph.
Figure S2. Study flowchart.
Table S1. Hypertension definitions.
Table S2. Missing data in the study population.
Table S3A–C. Characteristics of the study population by BMI group.
Table S4. Characteristics of the study population by type 2 diabetes.
References
- 1.Gao M., Piernas C., Astbury N.M., et al. Associations between body-mass index and COVID-19 severity in 6.9 million people in England: a prospective, community-based, cohort study. Lancet Diabetes Endocrinol. 2021;9:350–359. doi: 10.1016/s2213-8587(21)00089-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Kolkailah A.A., Riggs K., Navar A.M., Khera A. COVID-19 and cardiometabolic health: lessons gleaned from the pandemic and insights for the next wave. Curr Atheroscler Rep. 2022;24:607–617. doi: 10.1007/s11883-022-01033-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Svensson P., Hofmann R., Häbel H., Jernberg T., Nordberg P. Association between cardiometabolic disease and severe COVID-19: a nationwide case-control study of patients requiring invasive mechanical ventilation. BMJ Open. 2021;11 doi: 10.1136/bmjopen-2020-044486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.CDC COVID-19 science brief: evidence used to update the list of underlying medical conditions associated with higher risk for severe COVID-19. National Center for Immunization and Respiratory Diseases, Division of Viral Diseases. https://www.ncbi.nlm.nih.gov/books/NBK570436/#_NBK570436_pubdet_ [PubMed]
- 5.Kovesdy C.P., Furth S.L., Zoccali C. Obesity and kidney disease: hidden consequences of the epidemic. Kidney Int. 2017;91:260–262. doi: 10.1016/j.kint.2016.10.019. [DOI] [PubMed] [Google Scholar]
- 6.Xu H., Kuja-Halkola R., Chen X., Magnusson P.K.E., Svensson P., Carrero J.J. Higher body mass index is associated with incident diabetes and chronic kidney disease independent of genetic confounding. Kidney Int. 2019;95:1225–1233. doi: 10.1016/j.kint.2018.12.019. [DOI] [PubMed] [Google Scholar]
- 7.Clark A., Jit M., Warren-Gash C., et al. Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study. Lancet Glob Health. 2020;8:e1003–e1017. doi: 10.1016/S2214-109X(20)30264-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Council E.E., Group E.W. Chronic kidney disease is a key risk factor for severe COVID-19: a call to action by the ERA-EDTA. Nephrol Dial Transplant. 2021;36:87–94. doi: 10.1093/ndt/gfaa314. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Jager K.J., Kovesdy C., Langham R., Rosenberg M., Jha V., Zoccali C. A single number for advocacy and communication-worldwide more than 850 million individuals have kidney diseases. Nephrol Dial Transplant. 2019;34:1803–1805. doi: 10.1093/ndt/gfz174. [DOI] [PubMed] [Google Scholar]
- 10.Jdiaa S.S., Mansour R., El Alayli A., et al. COVID-19 and chronic kidney disease: an updated overview of reviews. J Nephrol. 2022;35:69–85. doi: 10.1007/s40620-021-01206-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Pakhchanian H., Raiker R., Mukherjee A., Khan A., Singh S., Chatterjee A. Outcomes of COVID-19 in CKD patients: a multicenter electronic medical record cohort study. Clin J Am Soc Nephrol. 2021;16:785–786. doi: 10.2215/cjn.13820820. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Parenmark F., Walther S.M. Intensive care unit to unit capacity transfers are associated with increased mortality: an observational cohort study on patient transfers in the Swedish Intensive Care Register. Ann Intensive Care. 2022;12:31. doi: 10.1186/s13613-022-01003-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.National Quality Registry for Intensive Care. Nationella Kvalitetsregister. http://kvalitetsregister.se/englishpages/findaregistry/registerarkivenglish/nationalqualityregistryforintensivecaresir.2175.html
- 14.Public health reporting The Public Health Agency of Sweden. https://www.folkhalsomyndigheten.se/the-public-health-agency-of-sweden/public-health-reporting/
- 15.Flythe J.E., Assimon M.M., Tugman M.J., et al. Characteristics and outcomes of individuals with pre-existing kidney disease and COVID-19 admitted to intensive care units in the United States. Am J Kidney Dis. 2021;77:190–203.e1. doi: 10.1053/j.ajkd.2020.09.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cheng Y., Luo R., Wang K., et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020;97:829–838. doi: 10.1016/j.kint.2020.03.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Sjögren L., Stenberg E., Thuccani M., et al. Impact of obesity on intensive care outcomes in patients with COVID-19 in Sweden-a cohort study. PLoS One. 2021;16 doi: 10.1371/journal.pone.0257891. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kjaergaard A.D., Teumer A., Witte D.R., et al. Obesity and kidney function: a two-sample Mendelian randomization study. Clin Chem. 2021;68:461–472. doi: 10.1093/clinchem/hvab249. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chen X., Bhuiyan I., Kuja-Halkola R., Magnusson P.K.E., Svensson P. Genetic and environmental influences on the correlations between traits of metabolic syndrome and CKD. Clin J Am Soc Nephrol. 2019;14:1590–1596. doi: 10.2215/CJN.11971018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Sarnak M.J., Jaber B.L. Pulmonary infectious mortality among patients with end-stage renal disease. Chest. 2001;120:1883–1887. doi: 10.1378/chest.120.6.1883. [DOI] [PubMed] [Google Scholar]
- 21.Nangaku M., Fujita T. Activation of the renin-angiotensin system and chronic hypoxia of the kidney. Hypertens Res. 2008;31:175–184. doi: 10.1291/hypres.31.175. [DOI] [PubMed] [Google Scholar]
- 22.Laffer C.L., Elijovich F., Sahinoz M., Pitzer A., Kirabo A. New insights into the renin-angiotensin system in chronic kidney disease. Circ Res. 2020;127:607–609. doi: 10.1161/CIRCRESAHA.120.317624. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Vibhushan S., Bratti M., Montero-Hernández J.E., et al. Mast cell chymase and kidney disease. Int J Mol Sci. 2020;22:302. doi: 10.3390/ijms22010302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Kaltenecker C.C., Domenig O., Kopecky C., et al. Critical role of neprilysin in kidney angiotensin metabolism. Circ Res. 2020;127:593–606. doi: 10.1161/CIRCRESAHA.119.316151. [DOI] [PubMed] [Google Scholar]
- 25.Wrapp D., Wang N., Corbett K.S., et al. Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation. Science. 2020;367:1260–1263. doi: 10.1126/science.abb2507. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Zhou P., Yang X.L., Wang X.G., et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020;579:270–273. doi: 10.1038/s41586-020-2012-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Milne S., Yang C.X., Timens W., Bossé Y., Sin D.D. SARS-CoV-2 receptor ACE2 gene expression and RAAS inhibitors. Lancet Respir Med. 2020;8:e50–e51. doi: 10.1016/S2213-2600(20)30224-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Maksimowski N., Williams V.R., Scholey J.W. Kidney ACE2 expression: implications for chronic kidney disease. PLoS One. 2020;15 doi: 10.1371/journal.pone.0241534. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Garreta E., Prado P., Stanifer M.L., et al. A diabetic milieu increases ACE2 expression and cellular susceptibility to SARS-CoV-2 infections in human kidney organoids and patient cells. Cell Metab. 2022;34:857–873.e9. doi: 10.1016/j.cmet.2022.04.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Nordberg P., Jonsson M., Hollenberg J., et al. Immigrant background and socioeconomic status are associated with severe COVID-19 requiring intensive care. Sci Rep. 2022;12 doi: 10.1038/s41598-022-15884-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Friberg L., Gasparini A., Carrero J.J. A scheme based on ICD-10 diagnoses and drug prescriptions to stage chronic kidney disease severity in healthcare administrative records. Clin Kidney J. 2018;11:254–258. doi: 10.1093/ckj/sfx085. [DOI] [PMC free article] [PubMed] [Google Scholar]
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