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Sebum modifications in COVID-19 sufferers, for future testing and prognosis

The COVID-19 pandemic began in the last month of 2019 and spread rapidly across the world. To date, over a million people have been killed, along with tens of millions of confirmed infections. It is particularly dangerous because of its rapid and widespread infectiousness with high mortality at the same time. A new study published in September 2020 on the preprint server medRxiv * now shows that sebum lipids are changing to COVID-19. This could help develop an inexpensive and uncomplicated method of non-invasive diagnosis.

Virus test using PCR

The SARS-CoV-2 virus enters the human body via the angiotensin converting enzyme 2 receptor (ACE2) on the target host cells. Most of the symptoms are related to the airways. A significant minority of cases develop severe and hyperinflammatory features of the lungs leading to acute distress syndrome (ARDS), which is often associated with a cytokine storm.

Thus, the clinical manifestations of COVID-19 reflect both the direct damage caused by the viral infection and the host’s immune response. Mass testing is needed to contain the virus and take the pressure off health services. Typically, upper respiratory tract samples are taken and tested for viral RNA using polymerase chain reaction (PCR).

However, this approach has a significant false negative rate due to many factors and offers no predictive value. Therefore, research is underway to determine how the virus affects the host rather than the virus itself as an additional test method.

Box plots of the diagnostic measured values ​​from plasma: COVID-19 negative versus positive

COVID-19 and sebum lipidoma

The current study focuses on the effect of COVID-19 on sebum lipidoma. Previous studies have shown that patients with COVID-19 may have disturbed lipidomas, as shown by an examination of blood plasma and lipids from the nasopharyngeal swab.

The researchers in the current study suggest that these people’s skin is likely to have a dysregulated lipidoma as dogs can be trained to track down COVID-19 patients. So they say, “Lipidomics therefore offers a promising way to better understand and possibly diagnose and predict COVID-19.”

The study focuses on examining sebum, the fatty fluid excreted by the sebum glands in the skin, using liquid chromatography-mass spectrometry. A sebum sample is painless and easy to obtain. The study of sebum lipids has been shown to provide characteristic features in diseases such as Parkinson’s disease. Therefore, the researchers tried to identify the sebum lipid patterns characteristic of COVID-19. This could be developed in the future as a non-invasive sampling method for diagnostic tests in the future.

The COVID-19 International Mass Spectrometry (MS) Coalition was formed by a number of British institutions in May 2020 to derive information about the virus at the molecular level from infected people. This would help understand how the virus affects metabolic pathways to improve the diagnosis and treatment of COVID-19. The current study is part of the work of this coalition.

Study group

The study included 67 participants, 30 with and 37 without clinical symptoms of COVID-19. All in the first group also had a positive RT-PCR test. The male to female ratio was slightly higher in the first group, possibly because the infection is more severe in men.

The first group had a lower prevalence of underlying chronic diseases, but diagnostic markers like CRP were higher. However, the lymphocyte counts were lower, while bilateral changes on the chest x-ray were more likely in this group. The need for oxygen was also higher and the risk of requiring CPAP was higher. They had higher chances of disease progression and lower chances of survival.

Analysis by lipid class

The researchers found that COVID-19 patients had lower levels of triglycerides, diglycerides, and monoglycerides in the serum samples. This indicates a dysregulation of the skin lipids. The differences between the levels of these lipids between the first and second groups were similar to CRP levels and therefore equally valuable as indicators of the presence of COVID-19.

Previous work has been consistent with the existence of abnormal plasma lipid levels, but there is no evidence as to whether lipid levels are rising or falling. Mild COVID-19, for example, may be associated with elevated plasma triglyceride (TAG) levels, but this may decrease with more severe illness. However, most of the lipid in the skin is synthesized locally and not supplied by the blood.

Population-level clustering analyzes

Population-level clustering (PCA) analyzes showed no clustering of serum lipid concentrations by type, while OPLS-DA showed insignificant and non-predictive separation of COVID-19 and non-COVID-19 individuals. When confounders such as age, CRP, and lymphocyte count were adjusted, they found that the predictive or diagnostic value barely improved.

In itself, low lymphocyte counts correlated better with the diagnosis of COVID-19. Age was less important, indicating that it does not significantly affect skin lipids.

Classification according to comorbidity

The model performed better in four subgroups when separate comorbidities and drugs classified the sample.

This included patients with a chronic disease that was treated with medication, namely high cholesterol, type 2 diabetes mellitus (T2DM) and IHD, and patients on statins. These subgroups had better predictive power and an improved separation of COVID-19 positives from negatives. The reasons could be the increased homogeneity of these subgroups with regard to confounding factors.

Reasons for an increased predictive power

Classifying patients according to comorbidity treatment results in a more even distribution in each subgroup. For example, patients being treated for high blood pressure or T2DM are likely to be the same age, and even more patients with high cholesterol. Gender subgroups also provide adequate separation of cases from negatives.

The researchers posit that predictive power will increase even further as larger numbers of patients and controls are used to allow for better stratification.

Another reason may be that the use of medication alters the effects of the confounders and reduces patients to a more consistent baseline, which is useful for assessing the disorder resulting from the influence of COVID-19 on the lipidoma. For example, statins are classically used to treat high cholesterol and diabetes, as well as ischemic heart disease, as a preventive measure for better long-term results. Analysis of the statin group reveals good predictive power and separation.

Noting that this is a pilot study, the researchers say that the risk of overfitting the model can be reduced by using more data to train the model and then validating the model against multiple sets of data.

Implications and Future Directions

The non-COVID-19 patients in this study were sampled in May, June or July, and therefore had a lower incidence of other respiratory diseases caused by seasonal respiratory viruses. This could have led to a possible lack of confounding factors, as the latter could also cause lipid metabolism to register a change that could potentially prevent the identification of characteristic COVID-19 traits.

In the UK, such viruses circulate in autumn and winter and this needs to be taken into account in future research. In addition, sebum samples taken from COVID-19 patients over time will help identify the timeframe in which sebum lipids will return to normal after COVID-19 and the predictive power of these changes that would dictate their use in clinical trials or mass tests determine.

The study concludes that “COVID-19 infection leads to dyslipidemia in the stratum corneum.” Sebum lipidomics can help identify COVID-positive and -negative patients with greater certainty when grouped by comorbidity. The ease with which sebum samples are obtained, transported, and stored makes this a promising approach to sebum sampling for the diagnosis and prognosis of COVID-19.

* Important NOTE

medRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or treated as established information.

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