Abstract
Background
Diabetes mellitus (DM) is associated with higher risk of developing infectious disease and COVID-19 is not the exception. There is a need to generate more data on clinical characteristics and risks of COVID19 patients presenting with DM. In this retrospective study we aimed to report on demographic features, clinical data, and outcomes of COVID-19 patients with DM in comparison with age- and sex-matched patients without DM.
Methods
This was a retrospective study that relied on the nationwide data on all COVID-19 patients who were diagnosed from 14 March to 18 April, 2020. Overall, there were 31 cases with DM for which we randomly matched 4 patients without DM by age and sex.
Results
COVID-19 patients with associated DM had less beneficial outcomes and more severe disease course both at hospital admission and final diagnosis, as compared with the age and sex-matched non-DM patients. Diabetics were more predisposed to impaired breathing (29.0 % versus 4.9 % in controls), nausea/vomiting (6.5 % versus 0 % in controls) and weakness/lethargy (45.2 % versus 26.0 % in controls). Finally, 48.4 % of diabetics showed the signs of pneumonia on CT scans versus 20.3 % of non-diabetics (p = 0.001), and 32.3 % of DM patients were admitted to intensive care units as compared with just 5.7 % of non-DM patients (p<0.001).
Conclusion
There is a need to envisage early status monitoring and supportive care in this vulnerable category of patients to enable better prognosis.
Keywords: COVID-19, Diabetes mellitus, Kazakhstan, Outcomes, Case-control study
COVID-19, diabetes mellitus, Kazakhstan, outcomes, case-control study
1. Introduction
Nowadays the world is in the grips of the novel coronavirus (CoV) named SARS-CoV-2 and the infection it causes named COVID-19 [1]. As currently reported by the World Health Organization (WHO), more than 40 million cases were identified globally, with the death toll already exceeding 1 million [2]. The beginning of the epidemic can be traced back to November 2019 and it has originated from Wuhan, China [3]. This highly contagious CoV reached nearly every nation of the world and was deemed a pandemic by the mid of March 2020 [4].
At present there is neither approved vaccine nor specific treatment for COVID-19. The infection spreads between humans by means of a close contact from predominantly symptomatic individuals, but asymptomatic carriers can also transmit it although to a lesser extent [5]. The incubation period is estimated to be between 2 to 11 days [6]. Most individuals have mild symptoms, reported in about 87.4 % of Kazakhstani patients, which include cough, sore throat, fever, myalgia, and mild pneumonia at chest CT. The rate of moderate disease equaled 11.3 %, while 1.3 % showed signs of severe disease that was manifested by severe pneumonia, dyspnoea, respiratory rate >30/min, and a very low blood saturation of oxygen (≤93%). q [7]. Mild infections were observed to clear-up in a week, whereas severe cases commonly lasted for weeks and those patients experiencing acute respiratory and/or multiple organ failure may develop lethal outcome [6].
In China, lethal outcome was observed in 2.3 % of confirmed COVID-19 cases but it was more predominant in senile patients (8.0 % out of 70–79 year old patients and 14.8 % out of patients aged 80 years or more) or critically ill patients (49.0 %). Higher lethality was also seen in patients affected by cardiovascular disease (10.5 %), diabetes mellitus (7.3 %), chronic pulmonary disease (6.3 %), arterial hypertension (6.0 %) and malignant neoplasms (5.6 %) [8]. Still, fatality from COVID-19 was higher in Italy and constituted 7.2 % [9]. This fact was explained by a higher share of individuals aged 70 years or more [10]. Nevertheless, a number of countries including Russian Federation, Germany and South Korea reported on a lower lethality among COVID-19 patients [11]. These data should be treated with caution as there is inter-country variability in case definition and registration of fatality causes.
It is a well-known fact that diabetes mellitus (DM) is associated with higher risk of developing infectious disease and pneumonia is not the exception [12]. Besides, DM is a frequent finding in COVID-19 patients, accounting for as many as 10 % of all cases, according to some reports [13]. Moreover, underlying DM was found in 20–30% of nonsurviving COVID-19 patients [14, 15].
The incidence of diabetes mellitus in the Republic of Kazakhstan is growing every year. According to the data of the large epidemiological study NOMAD, which was conducted among both urban and rural Kazakhstani population (aged 20–79 years), 8.2 % of individuals were diagnosed with type 2 DM, and in 38.2 % of individuals pre-diabetes was identified. The prevalence of type 2 DM varied from 8.3 % (95 % Confidence Interval: 7.1–9.4 %) to 9.3 % (95 % Confidence Interval: 8.5–10.0 %) [16].
Thus, there is a need to generate more data on clinical characteristics and risks of COVID19 patients presenting with DM. In this retrospective study we aimed to report on demographic features, clinical data, and outcomes of hospitalized COVID-19 patients with DM in comparison with age- and sex-matched patients without DM.
2. Materials and methods
2.1. Data sources
This was a retrospective study that relied on the nationwide data on all COVID-19 patients who were hospitalized to the government-designated hospitals in Kazakhstan from 14 March to 18 April, 2020. Within this period, the Ministry of Health took approach to hospitalize all identified COVID-19 cases, regardless of their severity and relied solely on reverse-transcriptase PCR (RT-PCR) to confirm the diagnosis. Overall, there were 1961 hospitalized patients, of which we retrieved medical records on 31 cases with DM (1.58 %).
On the next step we applied the propensity score matching method for non-random matching of cases and controls that was aimed at creation of matched case-control pairs, which would be as similar to each other as possible. For each COVID-19 patient with DM we matched four COVID-19 patients without DM by age (±2 years) and sex, following the strategy that was used in the earlier study [17]. In situations when more than 4 matches were available for each DM patient, we randomly selected out of those available.
2.2. Data extraction
Ministry of Health, Kazakhstan provided access to anonymized electronic medical records of COVID-19 patients solely to Semey Medical University, Kazakhstan. All medical records were reviewed by a team of experienced physicians – faculty members of Semey Medical University. Any incomplete medical records were omitted. The data extracted included patient's age, sex, disease history, history of DM and other underlying comorbidities (arterial hypertension, chronic cardiac failure, ischemic heart disease, chronic kidney disease, and cerebrovascular disease), vital signs at admission and discharge (heart rate, breath rate, median systolic and diastolic blood pressure, SpO2 and body temperature), laboratory parameters (blood glucose level, white blood cell count, C-reactive protein, blood biochemistry, etc.), description of chest computed tomographic (CT) scans and chest X-ray scans, severity of COVID-19 at admission and final diagnosis, treatment provided to patients in intensive care unit (ICU): supplemental oxygen, noninvasive mechanical ventilation, invasive mechanical ventilation; and presence or absence of lethal outcome. Besides, we retrieved the data on total length of hospital stay, coexisting risk behaviors (smoking and alcohol consumption) and COVID-19-associated symptoms (sore throat, chest pain, headache, diarrhea, nasal congestion, muscle pain, impaired breathing, vomiting/nausea, weakness/lethargy, smell and taste change, etc.).
2.3. Definitions used
COVID-19 was diagnosed in strict adherence to the WHO interim guidance [18]. Throat swabs with subsequent RT-PCR were used for confirmation of COVID-19 case. The diagnosis of DM was made in accordance with the guidelines of American Diabetes Association [19]. We identified pregnant women with gestational diabetes and patients with glucocorticoid-induced hyperglycemia and excluded them from the analyses. Our primary outcome was the presence of severe disease or in-hospital mortality in COVID-19 patients with DM, as well as associated risk factors. The secondary study outcomes were clinical characteristics, co-existing pathologies, laboratory findings, and treatment strategies in comparison between COVID-19 patients with and without DM.
2.4. Ethical statement
Before the study beginning, we obtained the permission from Ethical Committee of Semey Medical University, Semey, Republic of Kazakhstan. Because only anonymized medical records were available to the study team, the ethical committee waived informed consent.
2.5. Statistical analyses
Categorical variables were described as frequencies and proportions, while continuous variables were described as medians and interquartile ranges. We used the Pearson x2-test and Mann–Whitney U-test, as appropriate. The level of significance of all statistical tests was preset at P < 0.05. The statistical tests were performed with the help of IBM SPSS Statistics 20 software.
3. Results
Although the average length of hospital stay was almost the same in diabetics and non-diabetics (15 and 14 days, respectively), there was a significant difference in disease severity at hospital admission and final diagnosis. Such, at the stage of hospital admission the proportion of diabetics at severe or critical condition constituted 22.6 % as compared with 3.2 % of non-diabetics. Accordingly, at the time of final diagnosis 41.9 % of diabetics were recognized as having severe or critical state in contrast with 4.0 % of non-diabetics. Moreover, DM as comorbidity to COVID-19 was significantly associated with in-hospital lethality (Table 1).
Table 1.
General characteristics of population under study, (N = 155).
| Variables | Diabetes Mellitus status |
Test of difference |
|||||
|---|---|---|---|---|---|---|---|
| Controls (-) |
Cases (+) |
||||||
| N | % | N | % | χ2 | p-value | ||
| Gender | Female | 54 | 43.5 | 18 | 58.1 | 2.101 | 0.147 |
| Male | 70 | 56.5 | 13 | 41.9 | |||
| Age (median; 25th and 75th percentile)∗ | 34 (26; 47) | 34 (26; 47) | -0.123 | 0.902 | |||
| Age group | 18–39 | 71 | 57.3 | 18 | 58.1 | 0.007 | 0.996 |
| 40–59 | 45 | 36.3 | 11 | 35.5 | |||
| 60 > | 8 | 6.5 | 2 | 6.5 | |||
| Ethnicity | Russian | 8 | 6.5 | 1 | 3.2 | 1.849 | 0.397 |
| Kazakh | 94 | 75.8 | 27 | 87.1 | |||
| Other | 22 | 17.7 | 3 | 9.7 | |||
| Number of bed days (median; 25th and 75th percentile)∗ | 14 (6; 17) | 15 (4; 17) | -0.378 | 0.706 | |||
| Initial severity at hospital admission | Moderate | 120 | 96.8 | 24 | 77.4 | 14.091 | 0.000 |
| Severe or critical | 4 | 3.2 | 7 | 22.6 | |||
| Severity degree at final diagnosis | Moderate | 119 | 96.0 | 18 | 58.1 | 34.712 | 0.000 |
| Severe or critical | 5 | 4.0 | 13 | 41.9 | |||
| Outcome associated with COVID-19 | Alive | 124 | 100.0 | 29 | 93.5 | 8.105 | 0.004 |
| Died | 0 | 0.0 | 2 | 6.5 | |||
Test of difference: Mann–Whitney U-test.
Table 2 presents the behavioral risk factors and presence or absence of DM. There were significantly more smokers and alcohol consumers among diabetics, which was rather surprising. However, it was not unexpected that a significantly higher proportion of diabetics had stroke or myocardial infarction in past history. Likewise, DM was more commonly associated with other comorbidities: chronic cardiac failure, arterial hypertension, ischemic heart disease, and kidney disease.
Table 2.
Behavior risk factors and comorbidity, (N = 155).
| Variables | Diabetes Mellitus status |
Test of difference |
|||||
|---|---|---|---|---|---|---|---|
| Controls (-) |
Cases (+) |
||||||
| n | % | n | % | χ2 | p-value | ||
| Smoking | No | 111 | 90.2 | 22 | 71.0 | 7.812 | 0.005 |
| Yes | 12 | 9.8 | 9 | 29.0 | |||
| Alcohol consumption | No | 120 | 97.6 | 27 | 87.1 | 6.249 | 0.012 |
| Yes | 3 | 2.4 | 4 | 12.9 | |||
| Past stroke | No | 123 | 99.2 | 29 | 93.5 | 4.164 | 0.041 |
| Yes | 1 | 0.8 | 2 | 6.5 | |||
| Past myocardial infarction | No | 124 | 100.0 | 29 | 93.5 | 8.105 | 0.004 |
| Yes | 0 | 0.0 | 2 | 6.5 | |||
| Chronic Cardiac Failure | No | 123 | 99.2 | 29 | 93.5 | 4.164 | 0.041 |
| Yes | 1 | 0.8 | 2 | 6.5 | |||
| Arterial hypertension | No | 118 | 95.2 | 17 | 54.8 | 35.880 | 0.000 |
| Yes | 6 | 4.8 | 14 | 45.2 | |||
| Ischemic Heart Disease | No | 124 | 100.0 | 30 | 96.8 | 4.026 | 0.045 |
| Yes | 0 | 0.0 | 1 | 3.2 | |||
| Kidney disease | No | 120 | 96.8 | 19 | 61.3 | 33.732 | 0.000 |
| Yes | 4 | 3.2 | 12 | 38.7 | |||
As for the symptoms at hospitalization stage, the median systolic blood pressure and breath rate were significantly higher in diabetics as compared with non-diabetics. Also, diabetics were more predisposed to impaired breathing (29.0 % versus 4.9 % in controls), nausea/vomiting (6.5 % versus 0 % in controls) and weakness/lethargy (45.2 % versus 26.0 % in controls). Such symptoms as taste and smell change, cough, hemoptysis, and dyspnea were more frequently reported for patients with DM. Finally, 48.4 % of diabetics showed the signs of pneumonia on CT scans versus 20.3 % of non-diabetics (p = 0.001) – Table 3.
Table 3.
Symptoms at the stage of hospitalization, (N = 155).
| Variables | Diabetes Mellitus status |
Test of difference |
|||||
|---|---|---|---|---|---|---|---|
| Controls (-) |
Cases (+) |
||||||
| n | % | n | % | χ2 | p-value | ||
| Heart Rate (median; 25th and 75th percentile)∗ | 78 | 72; 82 | 80 | 78; 85 | -1.920 | 0.055 | |
| SAP (median; 25th and 75th percentile)∗ | 110 | 110; 120 | 120.0 | 110; 140 | -2.666 | 0.008 | |
| DAP (median; 25th and 75th percentile)∗ | 80 | 70; 80 | 80 | 70; 90 | -2.060 | 0.039 | |
| Breath Rate (median; 25th and 75th percentile)∗ | 18 | 17; 19 | 26 | 18; 28 | -5.192 | 0.000 | |
| SpO2 (median; 25th and 75th percentile)∗ | 98 | 96; 98 | 96 | 94; 98 | -1.461 | 0.144 | |
| Body temperature (median; 25th and 75th percentile)∗ | 36.6 | 36.10; 37.00 | 37.00 | 36.1; 37.6 | -0.668 | 0.504 | |
| Sore throat | No | 94 | 76.4 | 24 | 77.4 | 0.014 | 0.907 |
| Yes | 29 | 23.6 | 7 | 22.6 | |||
| Chest pain | No | 108 | 87.8 | 25 | 80.6 | 1.078 | 0.299 |
| Yes | 15 | 12.2 | 6 | 19.4 | |||
| Headache | No | 93 | 75.6 | 26 | 83.9 | 0.962 | 0.327 |
| Yes' | 30 | 24.4 | 5 | 16.1 | |||
| Diarrhea | No | 120 | 97.6 | 30 | 96.8 | 0.061 | 0.806 |
| Yes | 3 | 2.4 | 1 | 3.2 | |||
| Nasal congestion | No | 110 | 89.4 | 25 | 80.6 | 1.767 | 0.184 |
| Yes | 13 | 10.6 | 6 | 19.4 | |||
| Muscle pain | No | 116 | 94.3 | 28 | 90.3 | 0.648 | 0.421 |
| Yes | 7 | 5.7 | 3 | 9.7 | |||
| Impaired breathing | No | 117 | 95.1 | 22 | 71.0 | 16.431 | 0.000 |
| Yes | 6 | 4.9 | 9 | 29.0 | |||
| Vomiting/nausea | No | 123 | 100.0 | 29 | 93.5 | 8.040 | 0.005 |
| Yes | 0 | 0.0 | 2 | 6.5 | |||
| Weakness/lethargy | No | 91 | 74.0 | 17 | 54.8 | 4.332 | 0.037 |
| Yes | 32 | 26.0 | 14 | 45.2 | |||
| Convulsions | No | 119 | 96.7 | 30 | 96.8 | 0.000 | 0.994 |
| Yes | 4 | 3.3 | 1 | 3.2 | |||
| Fatigue | No | 109 | 88.6 | 29 | 93.5 | 0.647 | 0.421 |
| Yes | 14 | 11.4 | 2 | 6.5 | |||
| Smell change | No | 118 | 95.9 | 23 | 74.2 | 15.143 | 0.000 |
| Yes | 5 | 4.1 | 8 | 25.8 | |||
| Taste change | No | 117 | 95.1 | 25 | 80.6 | 7.222 | 0.007 |
| Yes | 6 | 4.9 | 6 | 19.4 | |||
| Cough | No | 107 | 87.0 | 16 | 51.6 | 19.276 | 0.000 |
| Yes | 16 | 13.0 | 15 | 48.4 | |||
| Hemoptysis | No | 114 | 95.8 | 24 | 77.4 | 11.287 | 0.001 |
| Yes | 5 | 4.2 | 7 | 22.6 | |||
| Dyspnea | No | 110 | 90.9 | 16 | 51.6 | 26.875 | 0.000 |
| Yes | 11 | 9.1 | 15 | 48.4 | |||
| Lung damage on chest X-ray | No | 100 | 81.3 | 22 | 71.0 | 1.606 | 0.205 |
| Yes | 23 | 18.7 | 9 | 29.0 | |||
| Lung damage on CT-scan | No | 98 | 79.7 | 16 | 51.6 | 10.140 | 0.001 |
| Yes | 25 | 20.3 | 15 | 48.4 | |||
Test of difference: Mann–Whitney U-test.
Considering the laboratory data at hospitalization stage, there was significant difference in concentrations of monocytes, urea, C-reactive protein, and blood glucose between the study groups (Table 4). According to Table 5, admission to ICU was the only severity-associated factor, which reached the level of statistical significance when compared between the two study groups.
Table 4.
Lab data at the stage of hospitalization, (N = 155).
| Variables | Diabetes Mellitus status |
Test of difference∗ |
||||||
|---|---|---|---|---|---|---|---|---|
| Controls (-) |
Cases (+) |
|||||||
| Median | 25th percentile | 75th percentile | Median | 25th percentile | 75th percentile | Z | p-value | |
| Hemoglobin | 140.00 | 129.00 | 151.00 | 128.00 | 121.00 | 146.00 | -1.483 | 0.138 |
| Erythrocytes | 4.61 | 4.10 | 5.00 | 4.55 | 4.00 | 5.00 | -0.576 | 0.565 |
| Hematocrit | 39.00 | 36.00 | 43.00 | 39.00 | 36.00 | 44.50 | -0.203 | 0.839 |
| Platelets | 241.00 | 201.50 | 281.50 | 228.50 | 186.50 | 265.00 | -1.289 | 0.198 |
| Leukocytes | 6.30 | 5.30 | 8.00 | 6.00 | 5.30 | 8.00 | -0.633 | 0.527 |
| Stab cells | 3.00 | 2.00 | 5.00 | 2.00 | 1.00 | 3.00 | -1.763 | 0.078 |
| Segmented cells | 59.00 | 50.00 | 65.00 | 64.95 | 56.50 | 67.00 | -1.276 | 0.202 |
| Monocytes | 6.00 | 4.00 | 8.00 | 4.00 | 1.00 | 6.00 | -2.434 | 0.015 |
| Lymphocytes | 30.00 | 24.00 | 37.00 | 25.50 | 13.00 | 32.50 | -1.918 | 0.055 |
| Eosinophils | 1.00 | 1.00 | 2.50 | 1.00 | 0.00 | 2.00 | -0.863 | 0.388 |
| ‡ECR | 10.00 | 5.00 | 17.00 | 15.00 | 5.00 | 25.00 | -1.165 | 0.244 |
| #APTT | 30.50 | 26.15 | 37.50 | 28.90 | 24.80 | 36.00 | -1.211 | 0.226 |
| Prothrombin time | 12.00 | 11.50 | 14.00 | 12.50 | 11.70 | 18.00 | -1.204 | 0.229 |
| Prothrombin index | 85.80 | 80.20 | 100.00 | 82.50 | 73.50 | 90.50 | -1.324 | 0.186 |
| Prothrombin ratio | 1.30 | 1.00 | 12.80 | 1.00 | 0.00 | 1.34 | -1.289 | 0.197 |
| International normalized ratio | 1.00 | 1.00 | 1.12 | 1.00 | 1.00 | 1.13 | -0.029 | 0.977 |
| Fibrinogen | 3.00 | 2.37 | 3.80 | 3.00 | 1.68 | 6.00 | -0.011 | 0.991 |
| Total protein | 71.94 | 67.00 | 77.00 | 68.35 | 65.00 | 73.80 | -1.580 | 0.114 |
| Albumen | 39.00 | 34.60 | 41.00 | 37.00 | 33.50 | 45.00 | -0.146 | 0.884 |
| ALT | 19.00 | 9.00 | 36.00 | 22.20 | 9.00 | 33.00 | -0.315 | 0.753 |
| AST | 16.30 | 11.55 | 22.95 | 18.00 | 13.00 | 33.00 | -0.650 | 0.516 |
| Total bilirubin | 12.00 | 8.94 | 16.20 | 11.00 | 8.00 | 14.00 | -0.816 | 0.415 |
| Direct bilirubin | 3.00 | 2.00 | 3.85 | 2.00 | 0.00 | 2.50 | -2.205 | 0.207 |
| Glucose on admission | 5.00 | 4.50 | 5.70 | 6.00 | 5.00 | 11.00 | -3.257 | 0.001 |
| Glucose in ∞ICU | 8.35 | 5.10 | 10.05 | 22.40 | 16.80 | 28.00 | -1.852 | 0.064 |
| Urea | 4.00 | 2.80 | 4.90 | 5.46 | 4.00 | 6.00 | -3.316 | 0.001 |
| Creatinine | 69.30 | 56.00 | 82.00 | 66.00 | 54.00 | 98.00 | -0.404 | 0.686 |
| C-reactive protein | 0.00 | 0.00 | 1.00 | 10.50 | 0.30 | 12.00 | -2.378 | 0.017 |
| Alpha-amylase | 0.00 | 0.00 | 39.00 | 26.14 | 6.00 | 39.00 | -1.333 | 0.182 |
‡ECR – erythrocytes sedimentation rate.
#APTT – activated partial thromboplastin time.
∗Test of difference: Mann–Whitney U-test.
∞ICU – intensive care unit.
Table 5.
Severity associated factors, (N = 155).
| Variables | Diabetes Mellitus status |
Test of difference |
|||||
|---|---|---|---|---|---|---|---|
| Controls (-) |
Cases (+) |
||||||
| n | % | n | % | χ2 | p-value | ||
| Admission to ICU | No | 116 | 94.3 | 21 | 67.7 | 17.795 | 0.000 |
| Yes | 7 | 5.7 | 10 | 32.3 | |||
| Intubation | No | 4 | 57.1 | 1 | 14.3 | 2.800 | 0.094 |
| Yes | 3 | 42.9 | 6 | 85.7 | |||
| Oxygen therapy without mechanical ventilation | No | 3 | 42.9 | 0 | 0.0 | 2.357 | 0.125 |
| Yes | 4 | 57.1 | 4 | 100.0 | |||
| Number of days in ∞ICU (median; 25h and 75h percentile)∗ | 8 | 4; 9 | 5 | 4; 6 | -1.242 | 0.214 | |
| Number of days on mechanical ventilation (median; 25h and 75h percentile)∗ | 4 | 1; 6 | 5 | 4; 7 | -1.150 | 0.250 | |
∞ICU – intensive care unit.
∗Test of difference: Mann–Whitney U-test.
Based on analysis of outcomes and severity associated factors in groups of patients with different glucose levels, lung damage on CT-scan and admission to ICU were more prevalent in patients with serum glucose levels exceeding 7 mmol/L (Figure 1).
Figure 1.
Outcomes and severity-associated factors in patients with different glucose levels, % (N = 155).
4. Discussion
Recent research has demonstrated that DM serves as a risk factor for severe COVID-19 illness [20]. This was also true for influenza A (H1N1) pandemic, which demonstrated the three-fold increase in hospitalization rate and admission to ICU among DM patients [21]. In Lombardy region of Italy, out of 1591 patients admitted to ICUs due to COVID-19, 17 % presented with DM [22]. In the USA, DM was identified in 6 % of non-hospitalized, in 24 % of hospitalized, and in 32 % of COVID-19 patients admitted to ICU [23]. Another study from the USA showed that the proportion of DM among patients hospitalized due to COVID-19 constituted 31.8 %, which was substantially higher than that among non-hospitalized patients with mild disease course (5.4 %) [24]. A study conducted in Russian Federation on a large cohort of patients presenting with acute respiratory distress syndrome secondary to COVID-19 revealed the underlying DM in 25 % of cases [25].
However, the worse outcomes for COVID-19 on the background of DM could be explained by the presence of other comorbidities, which have the potential to deteriorate the disease course. The sample of such comorbidities could be made of cardiovascular disease and obesity that frequently accompany DM. According to the findings of retrospective case series composed of 3615 COVID-19 patients who presented to a large academic hospital system in New York City, 21 % had the body mass index (BMI) within the range of 30–34 kg/m2, while 16 % of them had BMI ≥35 kg/m2. In patients aged <60 years, obesity and severe obesity were associated with 1.8-fold and 3.6-fold increased risk for admission to ICU, respectively. Meanwhile, no association between obesity and admission to ICU was found in COVID-19 patients aged 60 years and more [26]. In UK, out of 3883 critically ill COVID-19 patients the proportion of individuals having BMI >30 kg/m2 was significantly higher as compared with the controls – individuals who were admitted to ICU due to viral pneumonia within 2017–2019. Moreover, increase in BMI was associated with growing fatality as compared with the controls [27].
As for cardiovascular disease, a nationwide analysis from China demonstrated that arterial hypertension was the commonest comorbidity (16.9%) in COVID-19 patients [28]. This was confirmed by the findings from Russian Federation, according to which cardiovascular disease was the most prevalent comorbidity (61.4 %) in a cohort of severely and critically ill COVID-19 patients, although its prevalence did not exceed that in the general population [25]. An Italian study reported on higher mortality among patients with cardiac disease compared with the others (36% vs. 15%) and on increased rate of thromboembolic events during the hospitalization [29]. Besides, SARS-CoV2 is capable to directly affect cardiomyocytes leading to acute cardiac injury that is observed in 8–12 % of all COVID-19 patients and is manifested by a significant elevation of cardiac troponins. Apart from pre-existing cardiovascular disease, the appearance of acute cardiac injury contributes to worsened clinical outcomes of COVID-19 patients [30].
In fact, DM, obesity and cardiovascular disease are inter-related and thus, their effects on adverse clinical course of COVID-19 should not be considered separately. Many patients with DM also present with some type of cardiovascular disease and obesity, which could potentiate the harmful effects of each other [31]. Poorly controlled blood glucose levels, metabolic changes in DM and obesity coupled by arterial hypertension form both macrovascular and microvascular abnormalities creating a vicious cycle and further promoting adverse cardiovascular events, like thromboembolism [32]. Such, COVID-19 patients with DM and other underlying comorbidities, in particular obesity and arterial hypertension, deserve special attention and care.
Advanced age is another predictor of unfavorable outcomes in COVID-19 patients. To some degree, this could be attributed to the age-dependent decrease in immune response, which is further affected by coexisting DM [33]. Critically ill COVID-19 patients often have lymphopenia, which, aside from senile age and DM, could be explained by the destruction of lymphocytes by SARS-CoV-2, similar to what happened in case of SARS-CoV [34]. Still, this hypothesis requires further investigation. Association between lymphopenia and DM could be explained by the impairment of both innate and adaptive immunity that is frequently observed in DM [35]. Besides, vitamin D deficiency was recently hypothesized to be the independent predictor of poorer clinical outcome in COVID-19 patients. Vitamin D is needed for the production of antipathogenic molecules by epithelium of respiratory tract and also for regulation of inflammatory response to viral infection [36]. Kazakhstan is a Vitamin D deficient area [37] and this might be potentiated by the recent nationwide quarantine, when people were insistently advised to stay home [38]. Considering that in Kazakhstan the prevalence of pre-diabetes reaches 38.2 % and type 2 DM is up to 9.3 % in nationwide study a large group of people under the risk of developing unfavorable outcomes and complications of COVID-19 [16].
This study has several limitations. Firstly, we relied on retrospective electronic patient records, which had to be transferred manually to a unified database. To overcome this drawback, we double checked all entries into resulting database with the help of independent researcher, who inspected it and performed statistical tests for validation of data accuracy. Secondly, we did not possess the actual CTs but had to rely on their description. Thirdly, short duration of study due to which the sample size was relatively small that might influence the observations made. Fourthly, we did not have enough data to calculate BMI and to evaluate the role played by obesity as a contributing factor. Fifthly, we selected all patients on the basis of pre-diagnosed DM. Some patients who were not recognized as having DM could be excluded from the analyses. The concentration of HbA1c was not captured which could help interpret the diabetic control status of the individual which could affect the outcome. Finally, we used a matched design, and for this we could not asses the roles of sex and age in the outcomes of COVID-19 patients with DM. A prospective study would yield a more reliable and detailed picture of the effect of DM on COVID19 patients and their outcome.
5. Conclusion
In agreement with the earlier international reports, COVID-19 patients with associated DM had less beneficial outcomes and more severe disease course both at hospital admission and final diagnosis, as compared with the age and sex-matched non-DM patients. DM was associated with not just increased lethality from COVID-19, but also with higher rates of coexisting cardiovascular pathology and kidney disease. Besides, clinical symptoms of COVID-19 and laboratory blood parameters were less favorable in DM patients in contrast with their non-DM counterparts. Obviously, there is a need to envisage early status monitoring and supportive care in this vulnerable category of patients to enable better prognosis.
Declarations
Author contribution statement
Azhar Dyusupova: Conceived and designed the experiments; Wrote the paper.
Raida Faizova: Conceived and designed the experiments.
Oksana Yurkovskaya and Tatiana Belyaeva: Performed the experiments.
Tatiana Terekhova, Amina Khismetova and Dmitry Bokov: Analyzed and interpreted the data.
Natalya Glushkova: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Antonio Sarria-Santamera: Analyzed and interpreted the data; Wrote the paper.
Alexandr Ivankov: Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data availability statement
Data will be made available on request.
Declaration of interests statement
The authors declare no conflict of interest.
Additional information
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References
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Associated Data
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Data Availability Statement
Data will be made available on request.