Research Article
Austin Diabetes Res. 2022; 7(1): 1026.
Hyperglycemia as a Sequel to Covid-19
Flores HA*, Orozco GM, García DL, López ME, Escobedo BL, Ramírez G
¹Department of Education, Mexican Institute of Social Security UMF32, University of Monterrey, Mexico
*Corresponding author: Flores HA, Department of Education, Mexican Institute of Social Security UMF32, University of Monterrey, Baltic Sea#1950 Fracc. Bernardo Reyes, Monterrey, Nuevo Leon, Mexico
Received: September 02, 2022; Accepted: October 18, 2022; Published: October 25, 2022
Abstract
Background: SARS-CoV-2 has generated a severe economic, social and health crisis throughout the world. Multiple experimental and clinical studies have suggested that in addition to generating a pulmonary clinical presentation, it can also cause lesions in different systems. The present study seeks to associate that SARS-CoV-2 infection can generate a hyperglycemicstate in patients without a previous diagnosis of Diabetes Mellitus, as well as to find the prevalence of this complication in specific groups of the population.
Overall Objective: To determine the prevalence of hyperglycemia in the subjects who studied COVID-19 of the Family Medicine unit No 32.
Material and Methods: An observational, descriptive, cross-sectional and retrospective study was developed with a non-probabilistic sampling of consecutive cases in the population of patients with COVID-19 of the Family Medicine Unit No. 32 of the Mexican Institute of Social Security with an age range of 18 - 65 years. Obtaining a sample of 152 participants who met the inclusion criteria.
Results: A prevalence of 30.26% was found in the participants who presented post-COVID hyperglycemic figures.
Conclusion: In the present study we can conclude that there was an alteration in post-COVID serum glucose levels without the use of steroids, which is why future follow-up is suggested for patients who suffered from COVID-19 to monitor the possible onset of pre-diabetes or diabetes.
Keywords: Beta coronavirus; Coronavirus infections; Hyperglycemia
Introduction
The current COVID-19 pandemic is caused by a mutant strain of corona virus. Starting in China at the end of December 2019, in the province of Hubei, Wuhan; where a group of 27 cases of pneumonia of unknown etiology, with seven seriously ill patients, was reported [1]. The severity of illness has ranged from a mild self-limiting flulike illness to fulminant pneumonia, respiratory failure, and death. The population that is generally vulnerable to this corona virus is older adults and patients with certain diseases, including high blood pressure and Diabetes Mellitus, are more vulnerable to SARS-CoV-2 [2].
During the course of the pandemic, multiple experimental and clinical studies have been carried out that have suggested that SARSCoV- 2, apart from generating a pulmonary clinical presentation, can also cause lesions in multiple systems [3].
Hyperglycemia is frequently reported in COVID-19 [4], occurring in 50% of patients hospitalized for COVID-19, when the prevalence of pre-existing Diabetes Mellitus in the same population is approximately 7-10% [5]. A recent study explored the physiological model for SARS-CoV- 2 resulting in hyperglycemia and acute diabetes [6], which may change the course of the disease in patients with COVID-19 as a risk factor for severe disease [7].
There are two hypotheses for which hyperglycemia is particularly acute. The first is that acute hyperglycemia is accompanied by a large increase in inflammatory mediators caused by interleukin-6 [8], triggering an inflammatory and stress response with increased cortisol, sympathetic activity, and growth hormone. This will induce a state of insulin resistance leading to hyperglycemia [9].
The second is related to its union with an Angiotensin Converting Enzyme (ACE2) in the type 2 pneumocyte, through its glycoprotein S [8]. The S glycoprotein of the virus activates ACE 2 as a receptor for entry into the pancreas [10].
The SARS-CoV-2 corona virus penetrates the pancreatic islets and damages the beta cells, which would produce a deficiency in insulin secretion. This would lead to an aggravation in people with previous Diabetes and induce acute hyperglycemia even in people without Diabetes Mellitus [11].
The Spanish Society of Internal Medicine conducted a study of 11,312 patients, classifying them into three groups, according to blood glucose levels, at admission: <140 mg/dl, 140-180 mg/dl and >180 mg/dl. Hospital mortality rates were 15.7% for patients with blood glucose <140 mg/dl, 33.7% for those with 140-180 mg/dl, and 41.1% for patients with >180 mg/dl. Blood glucose [12].
In a review of studies published in the United States, SARSCoV- 2 was shown to cause higher fasting plasma glucose levels and pancreatic beta-cell damage was incriminated as a cause of the development of “Acute Diabetes” [10].
Multiple studies have sought a relationship between hyperglycemia levels and the severity of the infection, finding that patients with COVID-19 without Diabetes Mellitus had a relatively higher increase in blood glucose, regardless of the severity of the infection, compared to with COVID-19 patients with Diabetes Mellitus [13].
In studies of glycemic characteristics of 1,122 hospitalized patients with COVID-19, he reported that 257 patients had uncontrolled hyperglycemia, acute diabetes could represent a complication of a viral infection in pancreatic islet cells [14].
In a cohort of 184 patients, 88 patients with Diabetes Mellitus (47.8%) and 96 with uncontrolled hyperglycemia (52.2%). Patients with uncontrolled hyperglycemia had a longer duration from admission to death (8.4 vs 6.0 days, p < 0.001), of the 184 patients who died or were discharged, 40 of 96 patients with Uncontrolled hyperglycemia (41.7%) died compared to 13 of 88 patients with Diabetes Mellitus (14.8%, p < 0.001) [15].
In China, it was carried out in a population of 166 patients divided into three groups depending on their glycemic figures and previous history of diabetes. This study suggests that COVID-19 patients with secondary hyperglycemia constitute another population with a poor prognosis. Adding a significantly higher hospital mortality rate for patients with new hyperglycemia, who also had worse outcomes than patients with a previous history of diabetes and subjects with normoglycemia [16].
Material and Methods
An observational, descriptive, retrospective cross-sectional study was developed in a population of patients with COVID-19 from the Family Medicine Unit No.32 of the Mexican Institute of Social Security with an age range of 18-65 years.
The population was made up of patients who were assigned to the Family Medicine Unit No.32, and who met the inclusion criteria.
Inclusion criteria
Be attached to the Family Medicine Unit No. 32 Age 18-65 years.
Participants without previous diagnosis of Type 1 or 2 Diabetes Mellitus prior to COVID-19 infection.
Positive SARS-CoV-2 test result Have pre- and post-infection glucose laboratory results.
Exclusion criteria
Not being pregnant during the infection Participant Death Steroid use. Hospitalized Participants
Elimination criteria
Incomplete files.
The study considered all patients who met the aforementioned selection criteria, in the established place and time, through a nonprobabilistic sampling of consecutive cases, obtaining a sample of 152 participants who met the inclusion criteria.
A review of the electronic files of the Family Medicine Unit No.32 was carried out.
In them, information was obtained regarding the beneficiaries assigned to the unit, the social security number, the pathological history, if it was managed on an outpatient basis and the result of the confirmatory test of these, from participants who met the selection criteria. in a period of time from July 2020 to March June 2021.
Subsequently, the SILAB system of the unit was used, where the laboratory results of the selected participants were searched and a comparison of the glycemic figures before and after the COVID-19 infection was made.
Results
An observational, descriptive, cross-sectional and retrospective study was carried out with a non-probabilistic sampling of consecutive cases, obtaining a sample of 152 participants who met the inclusion criteria during the years 2020-2021, with a composition of 25% (N =38) men and 75% (N=114) women (Table 1).
Sex
F
%
CI 95%
female
114
75.00%
67.34%
81.66%
Male
38
25.00%
18.34%
32.66%
epi source info V. 7.2 F: frequency IC: Confidence interval.
Table 1: Frequency table of the qualitative variable of sex.
Age was distributed from 18 to 65 years with a mean of 41.86 years and a SD of 11.85. Glycemic Variation in Normoglycemic Participants they present a mean of 0.52 mg/dl, and a SD of 9.93 and an average of glycemic variation with a mean of 0.01% and a SD of 0.11. Number of characteristic symptoms of COVID-19 that the participant presented at the time of diagnosis presented a mean of 6.69 symptoms and SD 2.92 (Table 2).
Obs
Half
Median
Fashion
Var
OF
Total
Age
152
41.8684
43.5
Four. Five
140.6448
11.8594
6364
No SYMPTOMS
152
6.6974
6
6
8.5303
2.9207
1018
Pre -Covid figure
152
90.9421
92.5
98.2
60.0629
7.75
13823.2
Post-Covid Figure
152
96.3118
93.35
83
193.7316
13.9188
14639.4
Variation Figure
152
5.3697
5.55
9
186.0464
13.6399
816.2
Variation Average
152
0.063
0.0603
- 0.2381
0.0223
0.1495
9.5734
Time In Weeks Between Dx And Post -Covid Result
152
19.1684
17.65
23.1
131.35
11.4608
2913.6
Pre -Covid figure
( Normoglycemic )106
89.1434
90
76
65.7731
8.1101
9449.2
Post-Covid Figure
( Normoglycemic )106
89.7057
91
83
47.5819
6,898
9508.8
Variation Figure
( Normoglycemic )106
0.5623
-0.1
9
98.7997
9.9398
59.6
Variation Average
( Normoglycemic )106
0.0141
-0.0011
-0.2381
0.0139
0.1178
1.4946
epi source info V. 7.2 Var: Variable, SD: Standard Deviation
Table 2: Table of measures of central tendency of quantitative variables.
In the study, it was observed that 30.26% (N=46) resulted in hyperglycemia after SARS-CoV-2 infection and 69.74% (N=106) with a 95% CI (61.77-76.92) remained normoglycemic (Table 3).
hyperglycemia
46
30.26%
23.08%
38.23%
Normoglycemia
106
69.74%
61.77%
76.92%
epi source info V. 7.2 F: frequency IC: Confidence interval
Table 3: Frequency table of the qualitative variable of final glycemic status.
An association was made between the qualitative variables of gender and the final glycemic status of the participants, where we assessed the susceptibility between belonging to one gender and presenting or not hyperglycemia. We can observe that hyperglycemia occurred more in men with 42.11% (N=16) compared to women 26.32% (N=30); we can corroborate it with the OR of 2.03 (CI: 0.9456 - 4.3855) and a p 0.03, presenting a X2 corrected to 0.10 and the exact Fisher test of 0.10 (Table 4).
Table 4: Association between qualitative variables of sex vs final glycemic status.
An association of the variables works status and final glycemic status was made; employment status was divided into three categories: employed 73.68% (N=112), unemployed 25% (N=38), and retired 1.32% (N=2).
We observed that the participants who currently have a job are the ones that make up the largest number of total cases of hyperglycemia in the sample with 69.57% (N=32). However, when compared with the other two categories, the employees are the ones with the least tendency to hyperglycemia; In the category of retirees, although it presents a small population of 2 members, all its participants presented hyperglycemia. In unemployed people it has a probability of generating hyperglycemia with 31.58% (N=12). We obtained a X² of 4.79, a p of 0.09 and a Fisher exact test of 0.13 (Table 5).
Table 5: Association between qualitative variables of occupation vs final glycemic status.
When analyzing the variables of labor status associated with the gender variable of the participants, it was observed that in the case of men, the group of employees were the ones that presented a lower probability of suffering from hyperglycemia due to labor status with 35.71%, the group of retirees I present a greater probability of suffering from hyperglycemia when presenting in 100% (N=2) of the cases of the sample, with the group of unemployed they presented a probability of 50% in presenting both hyperglycemic figures .
In the case of women, it was found that both groups have a similar probability of suffering from hyperglycemia, with the group of unemployed women being more likely with 26.67% (N=8) than the group of employees with 26.19% (N=22) the cases.
In the study, the participants were grouped by age groups regardless of sex or employment status (N=152), in 8 age ranges. We observed that group 5 was more susceptible to contracting SARSCoV- 2 since this age group represents 22.37% (N=34) of cases in the sample and this same age group was the one that showed a higher prevalence for present hyperglycemia with 26.06% (N=12) of the sample. Group 1 did not present any case of hyperglycemia and group 4 was the one with the highest probability of hyperglycemia by age group with 50% (N=10) of the cases (Table 6).
Table 6: Association between the qualitative variables of age groups vs final glycemic status.
With the same age groups, a table of association of age group with glycemic variation was made in normoglycemic participants (N=106), where 49.06% (N=52) presented an increase in glycemia in relation to their result. Prior to infection and the other 50.94% (N=54) decreased. We observed that group 8 was the one that presented a greater upward glycemic variation in Post-COVID glycaemia since 100% (N=8) of it presented glycemic elevations. Group 1 was the age group that presented the greatest downward glycemic variation with 80% (N=8) of the members. There is a p of 0.06 and an exact Fisher’s test of 0.045 with statistical significance (Table 7).
Table 7: Association between the qualitative variables of age groups vs glycemic variation in normoglycemic participants.
The number of symptoms suffered was associated with the final glycemic status of the participants, observing that the participants who presented 7 symptoms had a higher probability of hyperglycemia with 21.74% (N=10), followed by the participants who presented 5 and 8 symptoms with 13.04%. Obtaining a p of 0.02 with statistical significance (Table 8).
Table 8: Association between the variables of number of symptoms vs final glycemic status.
Finally, a grouping of the variable time elapsed in weeks between the time of diagnosis was made, having a total of 4 groups and the Post-COVID blood glucose figure, dividing it into two groups according to blood glucose, the first of a blood glucose <99.9mg/ dl and a blood glucose >100mg/dl, to assess whether the time elapsed since the diagnosis and the Post-COVID figure play a role in the level of blood glucose that we can obtain.
We can observe that after week 33 there is a decrease in cases of hyperglycemia up to 91.30% (N=42) (Table 9).
Table 9: Association between qualitative variables groups of time elapsed since diagnosis and post -covid figure.
Discussion
Currently, at the time this work was carried out, no studies were found where outpatients were analyzed, focusing on hospitalized patients. A high percentage of participants were found to have hyperglycemia as a complication, being 30.26% of the total study, disagreeing with Sánchez (2020), who informs us in his study that hyperglycemia occurs in 50% of patients hospitalized for COVID-19 and that 10% of them had Diabetes Mellitus prior to infection. It is important to mention that in our population, no participant was hospitalized, this being a risk factor for the appearance of hyperglycemia, nor did they have a previous diagnosis of Diabetes Mellitus.
In our results, we observed that the participants showed an increase in Post-COVID plasma glucose levels, comparing them with their plasma glucose results obtained prior to the infection, where it occurred in 64.47% of the cases. This coincides with what was investigated by Ilias (2021) where they found that patients with COVID-19 without Diabetes Mellitus had a relatively greater increase in blood glucose, regardless of the severity of the infection. Unlike the Ilias study, in which the blood sample was taken at the time of admission to the hospital and monitored during its evolution, our study used the results of laboratories that were in the database before and after infection.
At the time that an association was made regarding the time elapsed from diagnosis and Post-COVID glucose measurement, we can observe that after week 33 there is a decrease in cases of hyperglycemia up to 91.30%, which suggests that with Over time, glycemic levels normalize, in agreement with the work of Thaweerat (2020), where he raises the idea of transient hyperglycemia, however, since the participants were not followed up, it could not be corroborated. Despite the fact that in our study the data were not significant, it would be pertinent to carry out a closer follow-up in the participants who presented hyperglycemia in order to corroborate its existence.
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