Journal of Antivirals & Antiretrovirals
Open Access

Comorbidities of COVID-19 Patients with Low Cycle Threshold (Ct) Value of Nucleocapsid (N) Gene: An Application to Cluster-Based Logistic Model

Sujan Rudra¹, Shuva Das^2*, Md Ehsanul Hoque², Abul Kalam², Mohammad Arifur Rahman², Swagata Nandy Shizuka² and Tazrina Rahman^{2 *}Correspondence: Shuva Das, Department of Microbiology, Chittagong Medical College, Chattogram, Bangladesh, Email:

Author info »

Background

In December 2019, SARS-CoV-2 presented for the reason of Coronavirus Disease 19 (COVID-19) pandemic [1,2]. The International Committee on Taxonomy of Viruses (ICTV) declared this virus as the novel coronavirus in January 2020. ICTV named this virus because of their 9-12 nm long surface spikes that resemble a corona (equal to the crown in Latin). Coronavirus is an enveloped virus. They are single-stranded and belong to a positive- sense RNA genome. It is the 7^th coronavirus and 3^rd zoonotic Human Coronavirus (HCoV) of the century. It is posing threats to international health too.

It has now affected almost all countries and has led to over 25 million confirmed cases. Globally 870497 deaths have occurred till August 2020. There are also asymptomatic or mildly symptomatic cases, but enormous patients progress to severe COVID-19 with high mortality rates [1-4]. By July 2020, over 600,000 deaths recorded worldwide. Presently seven CoVs (including SARS- CoV-2) can cause human respiratory diseases to date, only Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and SARS-CoV-2 have caused a large outbreak with high mortality. They can harm almost all systems: respiratory, gastrointestinal, cardiovascular, and nervous systems with variable severity of our body [5].

High viral loads in severe COVID-19 show higher viral replication and pathology, which results in multi-organ failure [6,7]. But it is not always true, as some studies found asymptomatic individuals can have high viral loads [8]. In this research, we explored the association of low Ct value of viral N gene with the comorbidities in both home and hospital cluster. We also tried to find the clinical outcomes of the corresponding patient in this research.

Methods

The molecular biology laboratory of Chittagong Medical College conducted this cross-sectional study from May 2020 to August 2020. Reverse Transcriptase-Polymerase Chain Reaction was done in the QuantStudio 5 Real-Time PCR instrument (96-Well 0.2 ml Block) by Sansure Biotech Novel Coronavirus Nucleic Acid Diagnostic Kit.

We included rRT-PCR confirmed COVID-19 patients who had three days of symptoms. Interviewers collected the following parameters of the corresponding patients over the phone: presence of symptoms, days of illness, contact with the symptomatic patient, types of personal protective equipment used, traveling history, oxygen requirement, hospital admission history, ICU support, co-morbidities, pH, HCO₃, Oxygen saturation, Arterial CO₂, Arterial O₂, other baseline investigations, and treatment sheet.

The WHO website has provided several Real-time RT-PCR protocols for detecting SARS-CoV-2 in different countries [9]. Three fluorescent signals from FAM, ROX, and CY5 channels detected the viral copy DNA (cDNA). We selected the nucleocapsid protein gene N and the RNA-dependent RNA polymerase gene (RdRP) [also reported as Open Reading Frame 1ab (ORF1ab)] [10]. Internal control helped to check for biases of the total procedure. We used a positive control such as plasmids containing the complete SARS-CoV- 2 N gene in our study [11].

Here, the Ct values of the targeted genes express the RNA virus. We listed 300 patients from 436 COVID-19 patients in our study. We got relevant history properly from those 300 patients. We filled up the questionnaires by clinical characteristics and treatment of the patients. We took written informed consent from the patients or by next-of-kin. This study got approval from local regulatory authorities.

We used descriptive statistics, logistics regression, and cluster-based logistics regression to explain the N gene-related factors.

Results

We classified patients' conditions into three categories: ICU support with non-invasive procedures, ICU support with mechanical ventilation, and those who need none of these. The patients who had a low Ct value (15.00-30.00) of the nucleocapsid gene needed more ICU support (13%) than the patients who had a high Ct value (30.01-40.00). With the ORF1ab gene, it was different. Here, patients with a high Ct value used 16.3% ICU (both Non-invasive and Mechanical ventilation) facilities than the patients with low Ct value. In our study, elderly patients (≥ 60 years age) belonging low (15.00-30.00) Ct value of N gene needed the highest (34.6%) ICU support. Children did not require any intensive care support. Only 40-59 years of age group patients needed mechanical ventilation (8.3%).

From Figure 1, we found the highest number (79) of cases who were suffering from hypertension (63.7%) had low Ct value (15.00-30.00) of N gene, and those patients needed more intensive care support for deteriorating their health conditions than the patients of high Ct value (30.01-40.00). Diabetic patients were in the second (48.4%) position. The patients who had low risk were suffering from CVD (2.4%) with a low Ct value of the N gene. From those comorbid conditions, hypertension and type 2 DM are significant with N gene values by chi-square test.

Figure 1: Percentage distribution of the patient’s comorbid conditions.

Table 1 represented that home treatment cured the patients who were not suffering from type 2 DM (29%) and hypertension (25%), though they had a low Ct value of the N gene. But diabetic and hypertensive patients needed more (11.00% and 12.00%, respectively) hospital treatment for those who had a low Ct value of the N gene. Having a high Ct value of the N gene, non-diabetic and normotensive patients cured more in-home treatment (25.00% and 25.70%, respectively).

Table 1: N gene range with cluster-based comorbid conditions.

Building logistics regression and cluster-based logistic regression, we defined the N gene range (low and high) as a dependent variable and significant comorbidities as independent variables. In the logistics regression model for general data, type 2 diabetic patients had a 1.324 times more low Ct value of the N gene. Similarly, in the home cluster, it was 1.192 times higher with CI (0.365, 3.888). But in the hospital cluster, the scenario was reversed (0.605), which was not significant. Hypertensive patients possessed 1.871 times significantly (p=0.042) more low Ct value of the N gene in the full dataset. Similarly, it was 1.375 times more in the hospital cluster. But, the home cluster represented that hypertensive patients had significantly (0.179 times with p-value 0.003) low Ct value of N gene (Table 2).

Table 2: Cluster-based logistic model: Effect of comorbidities on low Ct value.

From Table 3, 23.00% of patients needed ICU, where 20.00% used non-invasive methods, and 3.00% went for mechanical ventilation. Besides, 11.00% of hospitalized patients needed ICU, who had a low Ct value of the N gene. On the alternative, 8.00% of hospitalized patients needed ICU, who had a high Ct value of the N gene.

Table 3: ICU support according to Ct value range of N gene.

Building logistics regression and cluster-based logistic regression, we defined the patients' condition into two categories: Who needed ICU support and those who did not need any ICU support rolled as a dependent variable. Table 4 represented that ICU patients were 0.17 times lower than those who had a low Ct value (15.00-30.00) of viral N gene in the rRT-PCR method than the high Ct value (30.01-40.00). But, hospital clusters in the cluster-based logistic regression method, ICU admissions were significantly 2.480 times (CI 0.882, 5.026) higher than patients having a low Ct value of the N gene.

Table 4: Cluster-based logistic model: Effect of N gene on ICU support.

Discussion

We found that patients with low Ct values were more likely to have Hypertension (n=79), Type 2 DM (n=60), COPD (n=33), Bronchial Asthma (n=15), CKD (n=9). Decreased oxygenation in the heart and kidney or increased infection may be responsible for this. Another study revealed that SARS-CoV-2 directly infects both the heart and kidney [12]. Renal and cardiovascular complications are common in severe COVID-19 [13,14]. Further studies can evaluate the relationships between the lower level of Ct value of the N gene and disease severity.

Maximum patients of COVID-19 had mild symptoms, but 18% to 33% of patient’s required invasive procedures in ICU, and 20% of patients died in the hospital in different studies [15-20]. Previous studies reflect that the low Ct value corresponds with the high viral loads. Our study highlights the low level of the Ct value of the N gene detecting SARS-CoV-2 can deteriorate patients' condition.

Viral loads express COVID-19 severity as a prognostic biomarker, but there is no harmony across the studies [21-24]. In our analysis, more patients in ICU had low Ct of N gene. Magleby et al. also found a similar result [21]. Ct values help to identify patients who are in a risky zone and who need more intensive care. It may also help to select antiviral agents [25].

There were some limitations in our study. First, we analyzed only the Ct value of a single time sample, so we could not assess viral load dynamics overtime. Second, our study population size was small, and the study period was short.

Conclusion

We pointed the hazardous conditions of the patients depend on the Ct value range of the N gene. Even it is worst when there are comorbidities (Type 2 DM and Hypertension). This information is useful for selecting patients for COVID-19 who require precise management and follow-up at home or hospital. These findings also highlight the critical role of viral load. It also suggests that clinicians should follow the Ct values in identifying patients at high-risk conditions.

Declarations

Ethics approval and consent to participate

The study protocol was assessed and approved by the medical ethics committee of the Chittagong Medical College and Hospital, Chattogram.

Consent for publication: Not applicable.

Availability of data and materials: Upon reasonable request.

Competing interests: The authors declare no conflict of interest.

Funding: Not applicable.

Author contributions

Sujan Rudra: Data curation; Statistical analysis.

Shuva Das: Conceptualization, roles/writing-original draft.

Md. Ehsanul Hoque: Supervision.

Abul Kalam: Writing, review and editing.

Mohammad Arifur Rahman: Validation, visualization.

Swagata Nandy Shizuka: Investigation.

Tazrina Rahman: Methodology.

All the authors have accepted equal responsibility for the entire content of this submitted manuscript and approved submission.

Acknowledgements

We would like to thanks the editors and referees whose constructive criticism led us to develop the presentation and maintain the quality of the paper. We would also like to thanks the Molecular Biology Laboratory of Chittagong Medical College, Chattogram, to give their almost all opportunity for conducting the research.

References

1. Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J, et al. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: A study of a family cluster. Lancet. 2020;395(10223):514-523.
2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.  Lancet. 2020;395(10223):497-506.
3. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet. 2020;395(10223):507-513.
4. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of Coronavirus disease 2019 in China. N Eng J Med. 2020;382(18):1708-1720.
5. Madjid M, Safavi-Naeini P, Solomon SD, Vardeny O. Potential effects of coronaviruses on the cardiovascular system: A review. JAMA Cardiol. 2020;5(7):831-840.
6. Mason RJ. Pathogenesis of COVID-19 from a cell biology perspective. Eur Respir J. 2020;55(4):2000607.
7. Jose RJ, Manuel A. COVID-19 cytokine storm: The interplay between inflammation and coagulation. Lancet Respir Med. 2020;8(6):e46-e47.
8. World Health Organization. Coronavirus disease (COVID-19) technical guidance: Laboratory testing for 2019-nCoV in humans.2020.
9. Laboratory testing for 2019 Novel Coronavirus (2019-nCoV) in Suspected Human Cases. Interim guidance. 2020.
10. Wu Y, Xu X, Chen Z, Duan J, Hashimoto K, Yang L, et al. Nervous system involvement after infection with COVID-19 and other coronaviruses. Brain Behav Immun. 2020;87:18-22.
11. Puelles VG, Lütgehetmann M, Lindenmeyer MT, Sperhake JP, Wong MN, Allweiss L, et al. Multiorgan and renal tropism of SARS-CoV-2. N Engl J Med. 2020;383(6):590-592.
12. Shi S, Qin M, Shen B, Cai Y, Liu T, Yang F, et al. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan, China. JAMA Cardiol. 2020;5(7):802-810.
13. Pei G, Zhang Z, Peng J, Liu L, Zhang C, Yu C, et al. Renal involvement and early prognosis in patients with COVID-19 pneumonia. J Am Soc Nephrol. 2020;31(6):1157-1165.
14. Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA. 2020;323(13):1239-1242.
15. Richardson S, Hirsch JS, Narasimhan M, Crawford JM, McGinn T, Davidson KW, et al. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area. JAMA. 2020;323(20):2052-2059.
16. Gold JA. Characteristics and clinical outcomes of adult patients hospitalized with COVID-19-Georgia, March 2020. MMWR. Morb Mortal wkly Rep. 2020;69(18):545-550.
17. Goyal P, Choi JJ, Pinheiro LC, Schenck EJ, Chen R, Jabri A, et al. Clinical characteristics of Covid-19 in New York city. N Engl J Med. 2020;382(24):2372-2374.
18. Cummings MJ, Baldwin MR, Abrams D, Jacobson SD, Meyer BJ, Balough EM, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: A prospective cohort study. Lancet. 2020;395(10239):1763-1770.
19. Argenziano MG, Bruce SL, Slater CL, Tiao JR, Baldwin MR, Barr RG, et al. Characterization and clinical course of 1000 patients with coronavirus disease 2019 in New York: Retrospective case series. BMJ. 2020;369.
20. Magleby R, Westblade LF, Trzebucki A, Simon MS, Rajan M, Park J, et al. Impact of SARS-CoV-2 viral load on risk of intubation and mortality among hospitalized patients with coronavirus disease 2019. Clin Infect Dis. 2020;ciaa851.
21. Amirian ES. Potential fecal transmission of SARS-CoV-2: Current evidence and implications for public health. Int J Infect Dis. 2020;95:363-370.
22. Argyropoulos KV, Serrano A, Hu J, Black M, Feng X, Shen G, et al. Association of initial viral load in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients with outcome and symptoms. Am J Pathol. 2020;190(9):1881-1887.
23. Arons MM, Hatfield KM, Reddy SC, Kimball A, James A, Jacobs JR, et al. Presymptomatic SARS-CoV-2 infections and transmission in a skilled nursing facility. N Eng J Med. 2020.
24. Ison MG, Wolfe C, Boucher HW. Emergency use authorization of Remdesivir: The need for a transparent distribution process. JAMA. 2020;323(23):2365-2366.