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Journal of Research in Applied and Basic Medical Sciences 2024, 10(1): 47-53 |
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Balasundararaj D, Rajaragupathy S, Krishnan H, Parkavi S K, Shaji L N. Lipid profile as a predictor of severity in COVID-19. Journal of Research in Applied and Basic Medical Sciences 2024; 10 (1) :47-53
URL: http://ijrabms.umsu.ac.ir/article-1-279-en.html
URL: http://ijrabms.umsu.ac.ir/article-1-279-en.html
Dhanalakshmi Balasundararaj * 
, Sujatha Rajaragupathy , Haritha Krishnan , Karthika Parkavi S , Nijin Shaji L
, Sujatha Rajaragupathy , Haritha Krishnan , Karthika Parkavi S , Nijin Shaji L
Assistant Professor, Department of Biochemistry, PSG Institute of Medical Sciences & Research, Peelamedu, Coimbatore-641 004, Tamil Nadu, India , dr.dhanalakshmi27@gmail.com
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Table 1. Descriptive statistics of categorical variables
Table 2. Comparison of lipid profile with ICU admission, mortality and mechanical ventilation
Abbreviation: MW – Mann Whitney U test, * indicates statistical significance
Table 3. Correlation of lipid profile with CT score, CORADS score, and duration of hospital stay
Abbreviation: SP – Spearman’s rank correlation test, * indicates statistical significance
Table 4. Correlation of lipid profile with biochemical markers of COVID-19
Discussion
It has been found that lipids and lipoproteins as part of the innate immune system play a vital role in host defense against viral infections (8). On the other hand, it is established that the viruses target the cellular metabolism by reprogramming the lipid synthesis, signaling, and metabolism, thereby creating an environment ideal for replication (6,9). Viral infections cause a variety of alterations in lipid metabolism like decrease in serum HDL-c, TC, and LDL-c, and increase in TGL (10). Hence this study was designed to evaluate the lipid profile of COVID-19 patients and their correlation with disease severity, duration of hospital stays, and inflammatory markers.
In the present study, it has been found that TG was elevated in patients who required mechanical ventilation and ICU admission, but LDL-c was decreased significantly in mechanically ventilated and non-survivor patients. TC was also found to be decreased in the non-survivor patients. These findings were substantiated by the studies by Sun et al., (11), Masanaet al., (12) and Rezaei et al., (13) where there was a downward trend in LDL-c and TC in non-survivor COVID-19 groups. Similarly, a Saudi Arabian study reported high TG levels and low LDL-c levels in the patients with severe COVID-19 infection (14).
Meanwhile, Spearman’s rank correlation showed an inverse correlation between TC and LDL-c with duration of hospital stay. Lower levels of TC and LDL-c levels were related to increased severity; the higher the severity the longer the hospital stay, recovery, and infection spread. This was in line with a study by Rezaei et al. (13). On contrary, Sun et al., opined no significant difference in hospitalization considering mild or severe COVID cases (11). Our investigation of CT score established an inverse correlation with TC, LDL-c and HDL-c, but there is no direct studies to link CT score association with lipid profile distribution. However, there are studies that positively associate CT score to inflammatory markers like CRP, D-dimer, and IL-6, which alter the lipid metabolism in COVID-19 patients (15).
The markers of inflammation, LDH, CRP, ferritin, and IL6 have negative correlations with TC, HDL-c and LDL-c. Similar findings were noticed in majority of the studies, which explained that the severity was determined by increased levels of the pro-inflammatory cytokines CRP and IL-6 and their inverse relationship to HDL-c and LDL-c (16). A study by Masana et al. also observed high levels of ferritin in mild and severe COVID-19 patients (12). A meta-analysis of biomarkers has shown inverse association of lipid profile with elevated LDH and CRP (17).
The changes in TG and LDL-c could be attributed to the inflammatory cytokines released during infection, which leads to decreased lipoprotein lipase synthesis in adipose tissue and muscles. As a sequel there is a decrease in the clearance of triglyceride rich lipoproteins and decrease in LDL-c levels. Apart from this, the increase in hepatic VLDL synthesis also contributes to elevated TGL (10). While the mechanism by which the HDL-c is decreased is uncertain, some of the proposed hypotheses include decrease in Cholesteryl Ester Transfer Protein (CETP) activity, decreased synthesis of Apo A1, and increase in the clearance of HDL-c (1).
The findings of our study suggest that derangement in lipid profile must prompt us to look beyond cardiovascular health, since it indicates the degree of inflammation in infectious disease like COVID-19 probably due to its underlying role in pathophysiology. Hence, lipid profile can be used as a biomarker to predict the prognosis of a patient in COVID-19.
Nevertheless, the present study does have a smaller sample size as lipid profile was a lesser requested investigation in patients with COVID 19. Also, the study cohorts were not categorized for mild/severe/acute symptoms, co-morbidities, or underlying therapy affecting the severity of the disease.
Conclusion
During the COVID-19 pandemic, the cardiovascular complications due to thromboembolic disturbances could be attributed to increase in TGL, LDL-c and decrease in HDL-c, as these alterations are pro-atherogenic in nature. From the present study, it is evident that the lipid profile is linked to both severity and mortality in COVID-19 patients. Because lipid profile analysis is inexpensive and widely available in all laboratories, it may be useful in determining the severity and prognosis of COVID-19 in resource-constrained locations. There are studies which suggest that these alterations in lipoprotein metabolism persist in COVID-19 patients even after they have recovered from the infection. These findings advocate the importance of carrying out longitudinal studies in COVID-19 patients to evaluate the duration of dysregulation in lipoprotein metabolism and their effects on the long-term sequel of COVID-19 infection.
Acknowledgements
We express our gratitude to all the patients involved in the study.
Conflict of interests
The authors declare that they have no conflicts of interest
Funding/Support
The authors did not receive any financial support from any funding agency in the public, commercial, or not-for-profit sectors.
Data Availability
The raw data supporting the conclusions of this article are available from the authors upon reasonable request.
Ethical Statement
Ethical clearance was obtained from the Institutional Human Ethics Committee Institutional Ethics Board reference no: PSG/IHEC/2021/Appr/Exp/209
Full-Text: (493 Views)
Introduction
The coronavirus disease (COVID-19), a potentially fatal condition, was discovered towards the end of 2019 (1). It is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Although WHO declared an end to the COVID global health emergency, the virus has not been completely eradicated, necessitating further research. During the pandemic, the clinical presentation of the disease varied from asymptomatic to catastrophic. In more extreme cases, it led to pneumonia, requiring respiratory support and even death. Apart from respiratory illness, the other causes for mortality include venous thromboembolism, coagulopathy, multi-organ dysfunction and shock (1). Old age, male gender, obesity, cardiovascular diseases, hypertension, and diabetes mellitus were some of the key factors responsible for COVID-19 mortality (2). Even though several studies have elaborated on various pathophysiological aspects of COVID-19, there is not enough data on how the metabolic pathways are affected.
SARS-CoV-2 is an RNA virus with a higher mutation rate and frequency of recombination. In contrast to DNA viruses, RNA viruses control host metabolism through post-transcriptional regulation, to keep up with the replication (3). SARS-CoV-2 requires four major structural proteins namely spike, envelope, membrane, and nucleocapsid to complete the virion assembly and host cell infection (4).
Lipids, especially cholesterol enriched lipid rafts play a crucial role in activation, incorporation, and cell-to-cell infection spread of SARS-CoV-2 virus (5). The spike protein of SARS-CoV-2 virus binds with the cholesterol of HDL-c and when the scavenger receptor binding protein B1 (SRB1) uptakes HDL-c, it enters the host cell. The cytosolic phospholipase A2 also plays a role in the assembly of SARS-CoV 2 replication organelles (6).
The above examples emphasize the importance of lipids in the life cycle of the virus, leading us to theorize that harnessing the pathways of lipid metabolism for therapeutic intervention might yield prolific results. The most challenging task in any such antiviral agent development is that it requires a profound knowledge of lipid metabolism alteration by SARS COV-2 infection. Hence, the present descriptive study was carried out on COVID-19 patients to observe if there was an association between lipid profile with inflammatory markers, disease severity, and duration of hospital stay.
Materials & Methods
This retrospective observational study was conducted in a tertiary care hospital in Coimbatore, India, after the approval from the Institutional Human Ethics Committee. The study included subjects admitted between March-2020 and July-2021 with RT-PCR positive COVID-19 tests as per WHO’s interim guidance (7). By convenient sampling, the study involved 320 participants of either gender in whom lipid profile testing was done.
For all subjects, demographic data comprising of age, gender, clinical history including CT score, CO-RADS scores, ICU admission, mechanical ventilation requirement, duration of hospital stay, and mortality were sourced from patients’ medical records. The Laboratory Information System (LIS) was used to obtain lipid profile data including total cholesterol (TC), triglycerides (TGL), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c). In addition, data of inflammatory markers such as lactate dehydrogenase (LDH), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and interleukin-6 (IL-6) were also obtained. The laboratory parameters were analyzed in Roche Cobas auto-analyzers using dedicated kits and reagents.
Data was analyzed using R software version 4.1.2 and MS Excel. Categorical variables were stated as a frequency table. Mann Whitney U test was applied to compare distributions of lipid profile with indicators of disease severity like ICU admission, mechanical ventilation, and mortality. Spearman’s correlation test analyzed the correlation of CT score, CO-RADS score, duration of hospital stay, and inflammatory markers of COVID-19 with lipid profile. A p-value <0.05 was considered statistically significant.
Results
In the present study, among 320 subjects with age ranging from 18 to 91 years, the mean age was 55.14 ± 14.84 years. Among the study participants, 216 (67.5%) were male and 104 (32.5%) were female with a female-
to-male ratio of 1: 2.08 (Table 1). While 197 (61.56%) subjects needed ICU admission, 26 (8.13%) required mechanical ventilation, and 41 (12.81%) were non-survivor patients (Table 1).
The coronavirus disease (COVID-19), a potentially fatal condition, was discovered towards the end of 2019 (1). It is caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Although WHO declared an end to the COVID global health emergency, the virus has not been completely eradicated, necessitating further research. During the pandemic, the clinical presentation of the disease varied from asymptomatic to catastrophic. In more extreme cases, it led to pneumonia, requiring respiratory support and even death. Apart from respiratory illness, the other causes for mortality include venous thromboembolism, coagulopathy, multi-organ dysfunction and shock (1). Old age, male gender, obesity, cardiovascular diseases, hypertension, and diabetes mellitus were some of the key factors responsible for COVID-19 mortality (2). Even though several studies have elaborated on various pathophysiological aspects of COVID-19, there is not enough data on how the metabolic pathways are affected.
SARS-CoV-2 is an RNA virus with a higher mutation rate and frequency of recombination. In contrast to DNA viruses, RNA viruses control host metabolism through post-transcriptional regulation, to keep up with the replication (3). SARS-CoV-2 requires four major structural proteins namely spike, envelope, membrane, and nucleocapsid to complete the virion assembly and host cell infection (4).
Lipids, especially cholesterol enriched lipid rafts play a crucial role in activation, incorporation, and cell-to-cell infection spread of SARS-CoV-2 virus (5). The spike protein of SARS-CoV-2 virus binds with the cholesterol of HDL-c and when the scavenger receptor binding protein B1 (SRB1) uptakes HDL-c, it enters the host cell. The cytosolic phospholipase A2 also plays a role in the assembly of SARS-CoV 2 replication organelles (6).
The above examples emphasize the importance of lipids in the life cycle of the virus, leading us to theorize that harnessing the pathways of lipid metabolism for therapeutic intervention might yield prolific results. The most challenging task in any such antiviral agent development is that it requires a profound knowledge of lipid metabolism alteration by SARS COV-2 infection. Hence, the present descriptive study was carried out on COVID-19 patients to observe if there was an association between lipid profile with inflammatory markers, disease severity, and duration of hospital stay.
Materials & Methods
This retrospective observational study was conducted in a tertiary care hospital in Coimbatore, India, after the approval from the Institutional Human Ethics Committee. The study included subjects admitted between March-2020 and July-2021 with RT-PCR positive COVID-19 tests as per WHO’s interim guidance (7). By convenient sampling, the study involved 320 participants of either gender in whom lipid profile testing was done.
For all subjects, demographic data comprising of age, gender, clinical history including CT score, CO-RADS scores, ICU admission, mechanical ventilation requirement, duration of hospital stay, and mortality were sourced from patients’ medical records. The Laboratory Information System (LIS) was used to obtain lipid profile data including total cholesterol (TC), triglycerides (TGL), high-density lipoprotein cholesterol (HDL-c), and low-density lipoprotein cholesterol (LDL-c). In addition, data of inflammatory markers such as lactate dehydrogenase (LDH), C-reactive protein (CRP), ferritin, procalcitonin (PCT), and interleukin-6 (IL-6) were also obtained. The laboratory parameters were analyzed in Roche Cobas auto-analyzers using dedicated kits and reagents.
Data was analyzed using R software version 4.1.2 and MS Excel. Categorical variables were stated as a frequency table. Mann Whitney U test was applied to compare distributions of lipid profile with indicators of disease severity like ICU admission, mechanical ventilation, and mortality. Spearman’s correlation test analyzed the correlation of CT score, CO-RADS score, duration of hospital stay, and inflammatory markers of COVID-19 with lipid profile. A p-value <0.05 was considered statistically significant.
Results
In the present study, among 320 subjects with age ranging from 18 to 91 years, the mean age was 55.14 ± 14.84 years. Among the study participants, 216 (67.5%) were male and 104 (32.5%) were female with a female-
to-male ratio of 1: 2.08 (Table 1). While 197 (61.56%) subjects needed ICU admission, 26 (8.13%) required mechanical ventilation, and 41 (12.81%) were non-survivor patients (Table 1).
Table 1. Descriptive statistics of categorical variables
| Variables | Number of Subjects (%) | |
| Age (years) | <20 | 5 (1.5%) |
| 21-40 | 51 (15.9%) | |
| 41-60 | 139 (43.4%) | |
| 61-80 | 118 (36.8%) | |
| 81-100 | 7 (2.1%) | |
| Mean ± SD (Median) |
55.14 ± 14.84 56 |
|
| Gender | Female | 104 (32.5%) |
| Male | 216 (67.5%) | |
| ICU Admission | No | 123 (38.4%) |
| Yes | 197 (61.5%) | |
| Mortality | No | 279 (87.1%) |
| Yes | 41 (12.8%) | |
| Mechanical ventilation | No | 294 (91.8%) |
| Yes | 26 (8.1%) | |
Among the lipid profile parameters, TG showed a significant association among patients requiring ICU admission (p = 0.0359) and mechanical ventilation (p = 0.0085). In case of non-survivor patients, there was a significant difference in distribution of TC (p = 0.0181) and LDL-c (p = 0.0237). Furthermore, a significant association of LDL-c was observed in patients requiring mechanical ventilation (p = 0.0211). There was no significant difference in the distribution of HDL-c among patients who required ICU admission, mechanical ventilation, or non-survivor patients (Table 2).
Table 2. Comparison of lipid profile with ICU admission, mortality and mechanical ventilation
| Lipid profile | Variables | ||||||||
| ICU admission | Mortality | Mechanical Ventilation | |||||||
| YES | No | p-value | YES | NO | p-value | YES | NO | p-value | |
| TC mg/dL (Ref: <200) Mean ± SD Median |
135.05 ± 40.59 130 |
139.79 ± 36.55 135 |
0.2345MW | 126.02 ± 49.92 115 |
138.46 ± 37.09 135 |
0.0181MW* | 133.12 ± 42.63 128 |
137.2 ± 38.83 132.5 |
0.4468MW |
| HDL-c mg/dL (Ref: 40-60) Mean ± SD Median |
30.42 ± 9.92 29 |
32.3 ± 9.03 31 |
0.118MW | 30.76 ± 13 28 |
31.2 ± 9.04 30 |
0.7632MW | 32.38 ± 11.39 32 |
31.03 ± 9.46 30 |
0.4989MW |
| LDL-c mg/dL (Ref: <100) Mean ± SD Median |
81.28 ± 38.08 79 |
88.25 ± 34.01 84 |
0.0670MW | 74.2 ± 43.48 63 |
85.4 ± 35.42 82 |
0.0237MW* | 69.77 ± 38.07 66 |
85.22 ± 36.34 82 |
0.0211MW* |
| TG mg/dL (Ref: <150) Mean ± SD Median |
142.25 ± 84.48 120 |
137.52 ± 129.09 110 |
0.0359MW* | 142.83 ± 105.54 109 |
140.08 ± 103.67 117 |
0.5764MW | 171.62 ± 91.24 152.5 |
137.68 ± 104.47 112 |
0.0085MW* |
From the Spearman’s rank correlation, no significant correlation was found between the lipid profile and CO-RADS score. However, there was a significant inverse correlation of TC, HDL-c and LDL-c with CT score and also TC and LDL-c with duration of hospital stay (Table 3). Where the inflammatory markers were considered, a significant inverse correlation of TC, HDL-C and LDL-C with LDH, CRP, ferritin and IL6 was found. However, there was no significant correlation of lipid profile with PCT (Table 4).
Table 3. Correlation of lipid profile with CT score, CORADS score, and duration of hospital stay
| Lipid profile | CT score | CO-RADS score | Duration of hospital stay | |||
| Correlation Coefficient | p-value SP | Correlation Coefficient | p-value SP | Correlation Coefficient | p-value SP | |
| TC | -0.2536 | < 0.001* | -0.1051 | 0.1137 | -0.2102 | < 0.001* |
| HDL –c | -0.1765 | 0.0138* | -0.0785 | 0.2377 | -0.1011 | 0.071 |
| LDL-c | -0.3137 | < 0.001* | -0.0934 | 0.1597 | -0.2626 | < 0.001* |
| TG | 0.0476 | 0.5099 | 0.0546 | 0.4121 | 0.0789 | 0.1591 |
Table 4. Correlation of lipid profile with biochemical markers of COVID-19
| Biochemical markers of COVID-19 | Lipid profile | ||||
| TC | HDL-c | LDL-c | TG | ||
| LDH | Correlation Coefficient | -0.1845 | -0.1870 | -0.2403 | 0.0366 |
| p-value SP | 0.0016* | 0.0014* | < 0.001* | 0.534 | |
| CRP | Correlation Coefficient | -0.2919 | -0.1361 | -0.3654 | -0.0083 |
| p-value SP | < 0.001* | 0.0318* | < 0.001* | 0.8962 | |
| Ferritin | Correlation Coefficient | -0.1949 | -0.2253 | -0.2911 | 0.0776 |
| p-value SP | < 0.001* | < 0.001* | < 0.001* | 0.1825 | |
| PCT | Correlation Coefficient | -0.1015 | -0.0568 | -0.155 | 0.0678 |
| p-value SP | 0.3053 | 0.5668 | 0.1162 | 0.4941 | |
| IL6 | Correlation Coefficient | -0.2703 | -0.1721 | -0.2847 | 0.0252 |
| p-value SP | < 0.001* | 0.0027* | < 0.001* | 0.6632 | |
Abbreviation: SP – Spearman’s rank correlation test, * indicates statistical significance
It has been found that lipids and lipoproteins as part of the innate immune system play a vital role in host defense against viral infections (8). On the other hand, it is established that the viruses target the cellular metabolism by reprogramming the lipid synthesis, signaling, and metabolism, thereby creating an environment ideal for replication (6,9). Viral infections cause a variety of alterations in lipid metabolism like decrease in serum HDL-c, TC, and LDL-c, and increase in TGL (10). Hence this study was designed to evaluate the lipid profile of COVID-19 patients and their correlation with disease severity, duration of hospital stays, and inflammatory markers.
In the present study, it has been found that TG was elevated in patients who required mechanical ventilation and ICU admission, but LDL-c was decreased significantly in mechanically ventilated and non-survivor patients. TC was also found to be decreased in the non-survivor patients. These findings were substantiated by the studies by Sun et al., (11), Masanaet al., (12) and Rezaei et al., (13) where there was a downward trend in LDL-c and TC in non-survivor COVID-19 groups. Similarly, a Saudi Arabian study reported high TG levels and low LDL-c levels in the patients with severe COVID-19 infection (14).
Meanwhile, Spearman’s rank correlation showed an inverse correlation between TC and LDL-c with duration of hospital stay. Lower levels of TC and LDL-c levels were related to increased severity; the higher the severity the longer the hospital stay, recovery, and infection spread. This was in line with a study by Rezaei et al. (13). On contrary, Sun et al., opined no significant difference in hospitalization considering mild or severe COVID cases (11). Our investigation of CT score established an inverse correlation with TC, LDL-c and HDL-c, but there is no direct studies to link CT score association with lipid profile distribution. However, there are studies that positively associate CT score to inflammatory markers like CRP, D-dimer, and IL-6, which alter the lipid metabolism in COVID-19 patients (15).
The markers of inflammation, LDH, CRP, ferritin, and IL6 have negative correlations with TC, HDL-c and LDL-c. Similar findings were noticed in majority of the studies, which explained that the severity was determined by increased levels of the pro-inflammatory cytokines CRP and IL-6 and their inverse relationship to HDL-c and LDL-c (16). A study by Masana et al. also observed high levels of ferritin in mild and severe COVID-19 patients (12). A meta-analysis of biomarkers has shown inverse association of lipid profile with elevated LDH and CRP (17).
The changes in TG and LDL-c could be attributed to the inflammatory cytokines released during infection, which leads to decreased lipoprotein lipase synthesis in adipose tissue and muscles. As a sequel there is a decrease in the clearance of triglyceride rich lipoproteins and decrease in LDL-c levels. Apart from this, the increase in hepatic VLDL synthesis also contributes to elevated TGL (10). While the mechanism by which the HDL-c is decreased is uncertain, some of the proposed hypotheses include decrease in Cholesteryl Ester Transfer Protein (CETP) activity, decreased synthesis of Apo A1, and increase in the clearance of HDL-c (1).
The findings of our study suggest that derangement in lipid profile must prompt us to look beyond cardiovascular health, since it indicates the degree of inflammation in infectious disease like COVID-19 probably due to its underlying role in pathophysiology. Hence, lipid profile can be used as a biomarker to predict the prognosis of a patient in COVID-19.
Nevertheless, the present study does have a smaller sample size as lipid profile was a lesser requested investigation in patients with COVID 19. Also, the study cohorts were not categorized for mild/severe/acute symptoms, co-morbidities, or underlying therapy affecting the severity of the disease.
Conclusion
During the COVID-19 pandemic, the cardiovascular complications due to thromboembolic disturbances could be attributed to increase in TGL, LDL-c and decrease in HDL-c, as these alterations are pro-atherogenic in nature. From the present study, it is evident that the lipid profile is linked to both severity and mortality in COVID-19 patients. Because lipid profile analysis is inexpensive and widely available in all laboratories, it may be useful in determining the severity and prognosis of COVID-19 in resource-constrained locations. There are studies which suggest that these alterations in lipoprotein metabolism persist in COVID-19 patients even after they have recovered from the infection. These findings advocate the importance of carrying out longitudinal studies in COVID-19 patients to evaluate the duration of dysregulation in lipoprotein metabolism and their effects on the long-term sequel of COVID-19 infection.
Acknowledgements
We express our gratitude to all the patients involved in the study.
Conflict of interests
The authors declare that they have no conflicts of interest
Funding/Support
The authors did not receive any financial support from any funding agency in the public, commercial, or not-for-profit sectors.
Data Availability
The raw data supporting the conclusions of this article are available from the authors upon reasonable request.
Ethical Statement
Ethical clearance was obtained from the Institutional Human Ethics Committee Institutional Ethics Board reference no: PSG/IHEC/2021/Appr/Exp/209
Type of Study: orginal article |
Subject:
General
References
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