Assessment of determining factors for severity of NeoCOVIDiabetes in India: A pan India multicentric retrospective study

Abstract

Background and aims

There is a bidirectional relationship between COVID-19 and diabetes. The primary objective of this study was to estimate the prevalence of patients newly detected to have diabetes (NDD) who recovered from COVID-19 in India whilst comparing NDD with patients without diabetes (ND) and those who have known to have diabetes (KD) in terms of glycemic status pre- and post-COVID with disease severity.

Materials & methodology

There were 2212 participants enrolled from 15 sites, with 1630 active participants after the respective execution of selection criteria. Data collection was done using a specialized Case Record Form (CRF). Planned statistical analysis and descriptive statistics were concluded for significance between patient groups on various parameters.

Result

The differences in age between the study groups were statistically significant. The average blood glucose at COVID-19 onset was significantly higher in KD than in NDD. Significantly more proportion of NDD (83%) had been hospitalized for COVID management when compared to KD (45%) and ND (55%). The NDD group received higher doses of steroids than the other two groups. On average, patients in the NDD group who received at least one vaccination (one dose or two doses) had a higher High-Resolution Computed Tomography (HRCT) score. Patients who had not been vaccinated in ND and KD groups experienced a higher HRCT score.

Conclusion

Prospective metabolism studies in post-acute COVID-19 will be required to understand the etiology, prognosis, and treatment opportunities.

1. Introduction

The virus responsible for the COVID-19 pandemic, Severe Acute Respiratory Syndrome Coronavirus 2 (SARCoV- 2), was identified initially in Wuhan, China, in 2019. It spreads primarily via droplet transmissions, leading to variable symptoms that range from asymptomatic or mild respiratory illness to severe multiorgan failure and death in infected individuals [1,2]. As of May 11, 2022, the World Health Organization (WHO) reported more than 516 million confirmed SARS-CoV-2 infections and more than six million deaths globally [3]. As per the Ministry of Health and Family Welfare (MOHFW), 42.57 million cases were reported to be cured/discharged in India, and the reported deaths tallied 524,000 [4].

Diabetes is a common chronic metabolic disease and one of the significant causes of morbidity and mortality, which leads to considerable health and financial burden worldwide. Patients living with diabetes have an increased risk of developing severe macroscopic and microscopic complications along with an added risk of Severe Acute Respiratory Syndrome (SARS) and multiorgan failure [5]. Individuals with Type 1 Diabetes Mellitus (T1DM) or Type 2 Diabetes Mellitus (T2DM) often have comorbidities such as hypertension, obesity, and cardiovascular disease, all of which have also been implicated in increased susceptibility to and mortality from COVID-19 infection [1,6]. There is a bidirectional relationship between COVID-19 and diabetes [7]. On one hand, diabetes mellitus is associated with an increased risk of severe COVID-19. Whereas new-onset diabetes and severe metabolic complications of pre-existing diabetes in patients with COVID-19 have been observed [8]. One proposed mechanism is that SARS-CoV-2 binds to the angiotensin-converting enzyme-2 (ACE-2) receptors expressed on adipose tissue, lungs, small intestine, kidneys, and pancreas. After endocytosis of the virus, downregulation of ACE-2 occurs. This suppression leads to overexpression of angiotensin II, which may impede insulin secretion. Similarly, it has been suggested that the direct entry of SARS-CoV-2 into the pancreatic islet cells damages the beta cells [9].

The second wave of COVID-19 has left long-term effects in recovered patients, referred to as long COVID. The most significant impact, however, has been on the elevated blood glucose levels both in patients living with diabetes and no diabetes require a long-term follow-up and management of what has been termed as "Covidiabetes" [10].

The phenomenon of new-onset diabetes following admission to the hospital has been observed previously with other viral infections and acute illnesses [11]. It has been observed that COVID-19 is associated with hyperglycemia in people with and without known diabetes [12]. Several recently published studies have reported that new-onset diabetes and insulin resistance are associated with COVID-19, [13]; however, it is still unclear whether these metabolic changes are transient or if they indicate a greater propensity to develop diabetes in the future [14].

1.1. Objectives of the study

This study aims to understand the relationship between COVID-19 and newly detected diabetes rather than new-onset diabetes. The distinction between new-onset and NDD is more academic as it cannot be elucidated easily in a real-world setting, further undermined by the rush of the COVID-19 pandemic. Under the guidance of the American College of Physicians – India Chapter, a Real-World retrospective analysis of the experience of the severity of COVID and post-COVID symptoms was planned to gain practical insights from practicing physicians of the country, with a strong focus on post-COVID hyperglycemic states and diabetes detection.

The primary objective of this study was to estimate the prevalence of NDD in patients who recovered from COVID in India. The secondary objectives of this study involved the comparison of patients newly detected to have diabetes (NDD) with patients without diabetes (ND) and those known to have diabetes (KD) in terms of glycemic status pre-and post-COVID. Along with the disease severity (HRCT score & surrogate measures - hospitalization, oxygen requirement, steroid administration & dosage and inflammatory markers).

2. Materials and methodology

A total of 15 sites participated in the study by invitation, and investigators in these sites had a large data repository of COVID-19 patients that they had managed during the first and second waves. All site investigators were explained the study aims and objectives, inclusion and exclusion criteria and were trained for the electronic data entry process by the investigator. These 15 centers were based in 15 different geographical areas of India, to ensure that the collected data would represent the country. The study was initiated after ethical clearance from all sites, and additional institutional review board clearance was sought in the institutional sites.

All Indian patients above 18 years of age who had reported to the study sites for the management of COVID-19 were eligible to be included in the study. A positive report of COVID-19 through a Reverse Transcriptase - Polymerase Chain Reaction (RT-PCR) test in the past was mandatory to confirm the diagnosis. In situations where complete data was unavailable at the site, the remaining data for these patients were captured when they reported to the center for regular care with their records.

The study was explained to the patients, and patients who agreed to participate shared their anonymous data and signed the informed consent enrolled in a consecutive sequence at each site. Patients who did not agree to sign the informed consent were excluded from the study.

The first wave of COVID-19 in India started in March 2020 and continued for 5–6 months, and the second wave started in April 2021 and continued for 2–3 months [15]. This study was conducted in June 2021, and the expected sample size was achieved by November 2021 (6 months).

Three groups were considered for this study:

• 1.People known to have diabetes (KD): patients with pre-existing diabetes before they were diagnosed with COVID.
• 2.People without diabetes (ND): patients who didn't have diabetes before and remained without it even after the treatment of COVID-19.
• 3.People newly detected to have diabetes (NDD): patients with detected hyperglycemia for the first time during or after the treatment of COVID-19. They were under diabetes management at the time of the survey.

A specialized Case Record Form (CRF) was created to capture the data after consultation with the investigators. This form had the following sections:

• 1.Demographic details
• 2.Status of COVID infection
• 3.Medical history before COVID
• 4.
  Diabetes-related history

  • 1.
    Highest blood glucose during and post-COVID
  • 2.
    Glycated hemoglobin (HbA1c)
  • 3.
    Medications used for the management of the Hyperglycemic state
  • 4.
    Current use of medications for the management of diabetes
• 5.
  COVID related history

  • 1.
    Symptoms
  • 2.
    Vaccination status
  • 3.
    Need for hospitalization
  • 4.
    Date of diagnosis
  • 5.
    Management of COVID
  • 6.
    Details about steroids (Generic steroid name, strength, and duration)
  • 7.
    Post-COVID symptoms
• 6.
  Laboratory measurements before and after COVID

  • 1.
    D-dimer
  • 2.
    C-Reactive Protein (CRP)
  • 3.
    Interleukin 6 (IL-6)
  • 4.
    Neutrophil
  • 5.
    Lymphocyte
  • 6.
    Serum glutamic-oxaloacetic transaminase (SGOT)
  • 7.
    Serum glutamic pyruvic transaminase (SGPT)
  • 8.
    High-Resolution Computed Tomography (HRCT) severity score

Medeva, a cloud-based EHR, was used to capture retrospective data for this study. Once the structure of the CRF was established and validated by capturing a few patients’ retrospective data, the form was finalized and added as a study form in the Medeva EHR.

The use of corticosteroids was an integral part of the management of COVID-19. The most common steroids used were hydrocortisone, prednisone, methylprednisolone, prednisolone, triamcinolone, betamethasone, dexamethasone and deflazacort. The dosage of all these was analyzed after adjustment to respective prednisolone equivalents [16,17]. The patients were segregated into three groups - no steroids, low steroids [less than or equal to the median dose (50 mg)] and high steroids [greater than the median dose (50 mg)].

Considering an incidence of 14.4% of people newly detected to have diabetes amongst COVID-19 patients, with an absolute precision of 2% [18], the required sample size at a 5% significance level was 1184.

The data from different centers were pooled and explored using Python 3.6 for initial understanding and overview. Multiple visualizations were created to understand the data structure and quality better. The planned statistical analysis, detailed below, was performed in SPSS (Statistical Package for Social Sciences) version 18. The data were also analyzed for descriptive statistics like mean, mode and SD and compared for significance between patient groups on various parameters.

Two-way ANOVA (Analysis of Variance) was used to test the relationship between diabetes status and steroid usage (No Steroids, Low Steroids, and High Steroids). One-way ANOVA was used to test if the severity of COVID (in terms of HRCT) was statistically different in the three groups. Power and level of significance were considered as 0.2 and 0.05, respectively. When the p-value was <0.05, we concluded that the difference was significant at a 5% significance level.

3. Results

From the 15 sites, the total study sample size collected was 2212 patients who were eligible for the study. The count was then reduced to 1986 since the status of diabetes for 226 was unknown. The final sample was then concluded to be 1630 after 356 patients were excluded on the missing blood glucose values pre and post-COVID.

3.1. Total sample and study group profiles

The average age of the study population was 50.4 years ranging from 19 to 95 years. The gender distribution was 62% (n = 1012) males and 38% (n = 618) females (Refer: Table 1 ).

Table 1.

Descriptive details of the Total patient sample & three study groups.

Characteristics	Overall	Diabetic groups
		Known Diabetes	Newly detected Diabetes	Non Diabetes
n =	1630	958 (58.8%)	224 (13.7%)	448 (27.5%)
Average Age (years)	50.4 (50–51)	52.7 (52–54)	50.8 (49–53)	45.1 (44–47)
Age Groups (years)
18–24 years	31 (1.9%)	3 (0.3%)	8 (3.6%)	20 (4.5%)
25–34 years	228 (14%)	80 (8.4%)	44 (19.6%)	104 (23.2%)
35–44 years	331 (20.3%)	191 (19.9%)	30 (13.4%)	110 (24.6%)
45–54 years	395 (24.2%)	244 (25.5%)	48 (21.4%)	103 (23%)
55–64 years	331 (20.3%)	236 (24.6%)	36 (16.1%)	59 (13.2%)
65+ years	314 (19.3%)	204 (21.3%)	58 (25.9%)	52 (11.6%)
Gender
Male	1012 (62.1%)	609 (63.6%)	134 (59.8%)	269 (60%)
Female	618 (37.9%)	349 (36.4%)	90 (40.2%)	179 (40%)
Blood pressure groupings
Normal (SBP<130 & DBP<80)	284 (17.4%)	118 (12.3%)	52 (23.2%)	114 (25.4%)
High BP Stage1 (SBP ≥ 130 & ≤ 139) & (DBP ≥ 80 & ≤ 89)	543 (33.3%)	341 (35.6%)	69 (30.8%)	133 (29.7%)
High BP Stage2 (SBP ≥ 140 & DBP ≥ 80)	419 (25.7%)	268 (28%)	66 (29.5%)	85 (19%)
No Data	384 (23.6%)	231 (24.1%)	37 (16.5%)	116 (25.9%)
Number of Comorbidities
No Known Comorbidities	350(21.5%)	0(0%)	77(34.4%)	273(60.9%)
1 Comorbidity	567(34.8%)	312(32.6%)	103(46%)	152(33.9%)
2 Comorbidities	337(20.7%)	284(29.7%)	33(14.7%)	20(4.5%)
3 Comorbidities	172(10.6%)	160(16.7%)	9(4%)	3(0.7%)
4+ Comorbidities	204(12.5%)	202(21.1%)	2(0.9%)	0(0%)
History of Known Comorbidities (Pre-COVID)
Diabetes type 2	926 (56.8%)	926 (96.7%)	0 (0%)	0 (0%)
Hypertension	564 (34.6%)	450 (47%)	66 (29.5%)	48 (10.7%)
Dyslipidemia	274 (16.8%)	237 (24.7%)	27 (12.1%)	10 (2.2%)
Obesity	218 (13.4%)	205 (21.4%)	12 (5.4%)	1 (0.2%)
Hypothyroid	128 (7.9%)	88 (9.2%)	11 (4.9%)	29 (6.5%)
Heart disease	106 (6.5%)	84 (8.8%)	10 (4.5%)	12 (2.7%)
Liver disease	80 (4.9%)	60 (6.3%)	6 (2.7%)	14 (3.1%)
Kidney disease	76 (4.7%)	65 (6.8%)	7 (3.1%)	4 (0.9%)
Arthritis	64 (3.9%)	51 (5.3%)	10 (4.5%)	3 (0.7%)
Lung disease	59 (3.6%)	38 (4%)	15 (6.7%)	6 (1.3%)
Diabetes type 1	34 (2.1%)	34 (3.6%)	0 (0%)	0 (0%)
Autoimmune disorders	19 (1.2%)	17 (1.8%)	2 (0.9%)	0 (0%)
Cancer	2 (0.1%)	0 (0%)	2 (0.9%)	0 (0%)
Others	130 (8%)	36 (3.8%)	26 (11.6%)	68 (15.2%)
No known comorbidities	350 (21.5%)	0 (0%)	77 (34.4%)	273 (60.9%)

-Counts and percentages are shown for all the categorical measures.

-95% Confidence intervals are shown in brackes for all average values.

-Blood Pressure groupings are defined as per the guidelines; Both Elevated and Normal blood pressure are grouped together and considered as Normal.

-Comorbidities are medical conditions that the participant had been diagnosed with and on treatment or not on treatment for the same.

Common comorbidities identified were hypertension, dyslipidemia, obesity, hypothyroidism and heart disease. 21% (n = 350) of the population had no known comorbidity, 35% (n = 567) had only one comorbidity, while the remaining patients (44%, n = 713) had more than one comorbidity. 59% of the population had a history of diabetes mellitus before COVID (57% (n = 926) with Type 2 DM and 2% (n = 34) with Type 1 DM). The prevalence of other comorbidities like arthritis, asthma, autoimmune diseases, cancer, kidney disease, liver disease, and lung disease was less than 5% each. All patients in the study had suffered from COVID-19 at least once in the past (Refer: Table 1).

Three study groups were identified, KD (known to have diabetes, n = 958), ND (without diabetes, n = 448) and NDD (newly detected to have diabetes, n = 224). KD group was 58.8%, ND was 27.5%, and NDD was 13.7%, respectively, of the total sample size. Patients in ND with a mean age of 45.1 years (43.8–46.5 years) were younger than KD with a mean age of 52.7 years (51.9–53.5 years) and NDD with a mean age of 50.8 years (48.6–52.9 years). The differences in age between the study groups were statistically significant, with a p-value <0.001. The gender distribution was consistent for all three study groups, with around 60% of the patients being male.

The average highest blood glucose at COVID-19 onset was significantly higher in KD with a mean value of 310 mg/dL (301.9–317.1 mg/dL) as compared to 240 mg/dL (226.8–253.9 mg/dL) in NDD; and 145 mg/dL (140.3–149.1 mg/dL) in ND. (Refer Table 2; See Fig. 1 )

Table 2.

Study groups Vs COVID Severity/Vaccination Status/Glycemic levels.

Characteristics	Diabetic groups			ANOVA		Post-hoc analysis
	KD	NDD	ND	F/chi-square	P	KD vs NDD	KD Vs ND	NDD Vs ND
Average HRCT Severity Score	11.1 (10.6–11.5)	15.8 (15–17)	11 (10–12)	57.30	0.00	0.00	0.83	0.00
n =	555	164	333
HRCT Severity Score
Hrct_mild(0–7)	121 (21.8%)	14 (8.5%)	93 (27.9%)	96.68	0.00
Hrct_moderate(8–17)	350 (63.1%)	72 (43.9%)	186 (55.9%)
Hrct_severe(18–25)	84 (15.1%)	78 (47.6%)	54 (16.2%)
Hospitalization for COVID care
n =	954	224	445
Got hospitalized (%)	433 (45.4%)	185 (82.6%)	244 (54.8%)	101.54	0.00
Average # of days of hospitalization	8.7 (8–9)	9.5 (9–10)	8.1 (7–9)	3.96	0.02	0.09	0.12	0.01
n =	414	180	237
Oxygen support
n =	860	200	398
Oxygen support needed (%)	367 (42.7%)	148 (74.0%)	174 (43.7%)	66.63	0.00
Average # of days Oxygen support needed	6.7 (6–7)	6.8 (6–7)	5.3 (5–6)	6.26	0.00	0.88	0.00	0.00
Medications given for COVID care
Antibiotics	905 (94.5%)	207 (92.4%)	429 (95.8%)	17.74	0.00
Anti virals	675 (70.5%)	184 (82.1%)	321 (71.7%)	70.74	0.00
Steroids	713 (74.4%)	191 (85.3%)	332 (74.1%)	52.91	0.00
Steroids
n =	685	185	314
Average of the Maximum strength of Steroids given	67.5 (61–74)	86.6 (70–103)	62.4 (55–70)	5.00	0.01	0.01	0.38	0.00
n =	549	177	311
Average duration of Steriods (days)	9.5 (9–10)	8.8 (8–9)	10 (9–11)	3.05	0.05	0.13	0.16	0.01
Vaccine
n =	585	142	249
Covaxin	128 (21.9%)	22 (15.5%)	50 (20.1%)	5.74	0.22
Covishield	452 (77.3%)	118 (83.1%)	199 (79.9%)
Sputnik V	5 (0.9%)	2 (1.4%)	0 (0%)
COVID & Vaccination
n =	247	78	256
COVID before 2 doses	230 (93%)	73 (94%)	245 (96%)	1.98	0.74
COVID in between 2 doses	9 (4%)	3 (4%)	7 (3%)
COVID after 2 doses	8 (3%)	2 (3%)	4 (2%)
Glycemic measures
Random Blood Glucose (at COVID onset)	309.5 (302–317)	240.4 (227–254)	144.7 (140–149)	395.71	0.00	0.00	0.00	0.00
Random Blood Glucose (post COVID recovery)	234.6 (230–240)	223.4 (212–235)	134.9 (132–138)	301.71	0.00	0.04	0.00	0.00
Average HbA1c	8.1 (8.0–8.2)	6.7 (6.4–6.9)	5.8 (5.7–5.8)	307.09	0.00	0.00	0.00	0.00
n =	649	134	309

∗∗F/chi-square - chi-square test done for categorical measure and F-statistic for continuous measure.

∗∗95% Confidence intervals are shown in brackes for all average values.

∗∗Counts and percentages are shown for all the categorical measures.

∗∗Tuckey's Post-Hoc analysis is done to test the significance between two groups and the group p-values are shown in the tables.

∗∗For the measures where n-value is different from the overall sample (due to unanswered responses), n-values are shown separately in a row.

Fig. 1.

(A). Box plot for Random Blood Glucose (RBG) (at onset and post COVID), HbA1c and HRCT score (B) HRCT scores plotted by study groups and 3 groups of HbA1c

∗∗Box plots displays outliers and visualizes the differences between groups. ∗∗The horizontal line in the middle of the box is the median value and the lower and upper boundaries indicate the 25th and 75th percentiles. ∗∗Values falling outside the Whiskers (End points of line) are Outliers. ∗∗RBS values were available for the total sample whereas, HbA1C and HRCT values were there for >60% sample.

The difference in average highest blood glucose between COVID-19 onset and post-COVID recovery was 75 mg/dL (310 - 235 mg/dL) in KD, 17 mg/dL (240 - 223 mg/dL) in NDD and 10 mg/dL (145 - 135 mg/dL) in ND. This drop in blood glucose level is significantly higher in KD compared to NDD and ND (p-value = 0.00). (Refer Table 2)

3.2. Study groups & COVID-19 severity

The severity of COVID was measured using the HRCT score and the need for additional hospitalization and auxiliary & palliative care. CT scores are helpful in the stratification of patients' risk and prediction of short-term outcomes in patients with COVID-19 pneumonia. There is a high correlation between the extent of CT damage with various parameters of disease, including clinical staging and laboratory parameters [19].

The average HRCT score for NDD was higher (15.8/25) than that of KD (11.1/25) and ND (11/25). The proportion of severe disease (HRCT>18) was more among NDD (48%) as compared to KD (15%) and ND (16%). (Refer Table 2)

Significantly more proportion of the NDD (83%) group has been hospitalized for COVID management when compared to KD (45%) and ND (55%) (p-value = 0.00). Similarly, the proportion of oxygen support requirement was also high among NDD (74%) as compared to KD (43%) and ND (44%) (p-value = 0.00). On analyzing the steroid dosage for the management of COVID in the NDD (n = 185), it was observed that these were the population who received higher doses of steroids (p-value <0.05). All steroid doses were adjusted and standardized to prednisolone 5 mg. The average maximum doses of steroids used in the management of COVID for NDD was 86.6 mg/day (SD 113.5), KD was 67.5 mg/day (SD 83.3), and ND was 62.4 mg/day (SD 69.0), prednisolone equivalents.

3.3. Exploration of causes of severe experience of covid in NDD

The NDD group received higher doses of steroids than the other two groups (Table 2 ). Within the NDD group, those who received a Lower steroid dose (<median) had a higher HRCT score (17.2/25) as compared to the No steroid (13.2/25) and High steroid groups (14.9/25) (p value = 0.006). (See Fig. 3 )

Fig. 3.

HRCT scored plotted by Study groups, Steroid groups and HbA1c groups

∗∗Box plots displays outliers and visualizes the differences between groups, ∗∗The horizontal line in the middle of the box is the median value and the lower and upper boundaries indicate the 25th and 75th percentiles, ∗∗Values falling outside the Whiskers (End points of line) are Outliers, ∗∗HRCT values were there for >60% of the total sample.

Also, amongst all patients who did not receive steroids for the management of COVID, NDD had a statistically higher HRCT score (13.2/25) compared to KD (7.1/25) and ND (10.6/25).

On average, patients in the NDD group who had received at least one vaccination (one dose or two doses) had a higher HRCT score. This contrasts with the trend observed in the other two groups. Patients who had not been vaccinated in ND and KD groups experienced a higher HRCT score, which also aligns with the hypothesis that vaccination provides some protection against COVID infection or reduces its profound effects. The reason why the trend is opposite in NDD will have to be explored further.

The difference in the severity of HRCT within the three groups is possibly due to the strain of prevailing Coronavirus during a particular wave. When we break up the three groups by the wave in which they developed COVID, we can see the same trend in all groups - patients who developed COVID between the first and second wave experienced a more severe HRCT score.

Lastly, we can also see the three groups' breakup by the vaccine they used. There is no consistent trend to comment upon, but amongst all patients who received Covishield, NDD had the highest HRCT score compared to KD and ND. (See Fig. 2)

Fig. 2.

HRCT scored plotted by Study groups and Steroid groups/Vaccination Status/Wave/Vaccine

∗∗Box plots displays outliers and visualizes the differences between groups. ∗∗The horizontal line in the middle of the box is the median value and the lower and upper boundaries indicate the 25th and 75th percentiles. ∗∗Values falling outside the Whiskers (End points of line) are Outliers ∗∗HRCT values were there for >60% of the total sample.

4. Discussion

This study adds to the knowledge available on newly detected diabetes post-COVID. The average age of the population in our study was 50.4 years, concordant with data by Sarkar A et al. [15] We reported a male skew in the sample (62% male vs 38% female), similar to Kushwaha S. et al. [20] One such study from Wuhan of hospitalized, elderly COVID-19 patients reported that 21.6% had a history of diabetes. Based on the first glucose measurement upon admission, 20.8% were newly diagnosed with diabetes (fasting admission glucose ≥7.0 mmol/L and/or HbA1c ≥ 6.5%). In comparison, in a study by Khunti et al. [21], 28.4% were diagnosed with dysglycemia (fasting glucose 5.6–6.9 mmol/L and/or HbA1c 5.7–6.4%). In our study, 59% had a pre-existing condition of diabetes pre-COVID, 14% were patients with newly detected diabetes, post-COVID, and 27% did not have diabetes, pre- and post-COVID. COVID-19 in patients with diabetes (including newly diagnosed diabetes) could inflict marked hyperglycemia through several complex but interrelated factors. These include, but are not limited to, the inflammatory response triggered by the virus and subsequent release of counterregulatory hormones, activation of the renin-angiotensinogen system, and destruction of pancreatic β cells by the virus itself or by the cytokines triggered by the virus [22]. The average HbA1c in the newly detected diabetes group was 6.7% post-COVID, concurrent with the study by Sathish T et al., where the mean HbA1c was ≥6.5%. This can also suggest that they likely had previously undiagnosed diabetes [23].

Our study here is a unique cross-sectional analysis of the Indian population studying the demographics of the people affected by COVID-19. On the analysis of the complete data pool, three distinct groups were established -people known to have diabetes 59% – who were on treatment pre-COVID; people newly detected to have diabetes 14% first time detected hyperglycemia during COVID and remained under treatment for diabetes post-COVID too (average duration of post-COVID follow up of 147 days). The last identifiable group was people without diabetes pre-and post-COVID, which comprised 27% of the cohort.

According to a study by Li et al. [24], patients with newly diagnosed diabetes had the highest percentage of being admitted to the ICU and requiring IMV (Invasive Mechanical Ventilation, followed by patients with known diabetes (4.1%; 9.2%). In the present study, patients getting hospitalized & duration of stay was highest in newly diagnosed diabetes (82.6% and 9.5). Rangankar V et al. [25], showed that lung involvement of patients with diabetes was higher, while in our study, the proportion of patients with severe HRTC in patients with known diabetes was lower (15.1%). HRCT score in newly diagnosed diabetes was 47.6%, higher than that of the KD [26].

Of 1630 individuals admitted with COVID-19, 594 (31.2%) were KD; 77 (13.0%) were NDD. There are multiple possibilities in this cohort ‘newly detected diabetes’ group. It could be that it is pre-existing diabetes, but undiagnosed (that got detected during COVID routine care) or new-onset diabetes, which could be a corticosteroid-induced response or COVID-19-caused pancreatic ß cell destruction or a post-viral infection associated with diabetes [21].

Although stated by Sosale et al. [26], the risk of severity among people with known diabetes increases rapidly due to the chronic use of steroids. On the contrary, our study showed that the NDD group received higher doses of steroids than the other two groups. Amongst all patients who did not receive steroids for the management of COVID, NDD had a statistically higher HRCT score (13.2/25) compared to KD (7.1/25) and ND (10.6/25). This relationship, therefore, questions the prevailing hypothesis that increased application of steroids causes diabetes-like disease spectrums in patients recovering from COVID. This analysis requires more research, but these findings require reconsidering steroid usage as the leading cause of COVID-induced diabetes.

Due to the limitation and unavailability of the previous evidence of the hyperglycemia state of the patients in our cohort, the authors chose to use the term “ Newly detected” instead of “New-onset.” [27].

The phenomenon of hyperglycemia following admission to the hospital has been observed previously with other viral infections and acute illnesses. The precise mechanisms for this in people with COVID-19 are not known. However, it is likely that several complexes and interrelated processes are involved, including previously undiagnosed diabetes, stress hyperglycemia, steroid-induced hyperglycemia, and direct or indirect effects of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) on the β-cell. Further to the above factors, the etiology responsible for hyperglycemia can impair glucose disposal and insulin secretion [28].

Emerging evidence shows that new-onset diabetes is also observed in the post-acute COVID-19 phase, the so-called long COVID. As seen in England, amongst a population of 47,780 discharged COVID-19 elderly patients, the rate of new-onset diabetes was 29 (95% CI, 26–32) per 1000 person-years over a mean follow-up of 4.6 months [29]. Similar to the US, where among 113 COVID-19 patients with a mean age of >65 years, new-onset diabetes was the sixth most common post-acute clinical sequelae over a median follow-up of 2.9 months [30].

4.1. Points of clinical relevance

As part of this study, patients newly detected to have diabetes (NDD) was compared with patients with no diabetes (ND) and known to have diabetes (KD) in terms of blood glucose levels before and after COVID. Within the NDD group, those who received a lower steroid dose (<median) had a higher HRCT score than the No steroid and High steroid groups. Furthermore, the NDD group received higher doses of steroids than the other two groups. Hence, highlighting that the cause of the severity of the disease was not solely steroid induced opens up another potential avenue for future research.

On average, NDD patients with at least one vaccination scored higher on HRCT. In contrast, neither of the other two groups showed a similar trend. ND and KD patients without vaccination had a higher HRCT score, which also supports the hypothesis that vaccination reduces the severity of COVID infection. We need to investigate why the trend in NDD is opposite since vaccination isn't the only possible explanation, and the severity of the disease in the group could have influenced the results more significantly.

The different severity of HRCT within the three groups is likely due to the strain of Coronavirus prevalent during a particular wave. The same trend can be seen in all three groups if we separate the patients based on the wave in which they developed COVID.

4.2. Limitations

The study was conducted in the middle of the pandemic, which led to several discrepancies during the execution of the study due to the lockdowns and restrictions. The process of collecting data was delayed, with difficulty in taking follow-ups. Many patients became apprehensive about coming to the hospital for follow-up after recovering from COVID. The follow ups were mainly done through phone calls and electronic medical record (EMR) software. There are, however, still many unanswered questions for future research. First, it is unclear whether pre-existing diabetes becomes apparent during COVID-19 as a consequence of immunological activation or stress-induced hyperglycemia or whether some patients have the propensity to develop new-onset diabetes. Second, it needs to be investigated if post-COVID diabetes could be reversed after full recovery. Third, the management strategies of new-onset diabetes post-COVID should be evaluated. Diabetes ketoacidosis has been observed in some individuals without known diabetes months after COVID-19. Thus, serological testing for diabetes-associated autoantibodies and C-peptides may be indicated in individuals even without known risk factors for diabetes. Finally, it should be investigated whether the risk of hyperglycemia in individuals with COVID-19 is a continuum depending on risk factors such as injury of beta cells, an exaggerated pro-inflammatory response, or merely because of changes in health behavior during the attempts to manage the pandemic [14,31,32].

5. Conclusion

Given the increased mortality in people with newly detected diabetes, hospital protocols should include efforts to recognize and manage acute hyperglycemia, including DKA, in people admitted to the hospital. Whether this diabetes is likely to remain lifelong or not yet needs to be known, as the long-term follow-up of these patient is limited. Prospective studies of metabolism in post-acute COVID-19 will be required to understand the etiology, prognosis, and treatment opportunities.

Appendix A. Supplementary data

The following is the supplementary data related to this article: