“Is there a simple number that can be used to identify which of the COVID-19 infected patients will NOT develop complications and will NOT require additional support?<\/em><\/strong>“<\/p>\n\n\n\n A recently conducted survey <\/a>shows that this is a burning issue in today’s pandemic haunting everyone.<\/p>\n\n\n\n It\u2019s clear that COVID-19 infection can\u2019t be really stopped. One wave after the other have been raging across the world and will continue. It\u2019s really terrifying. But we also know that majority recover easily. Only a few require hospitalization. Despite this fact, each wave of this pandemic overloads the existing healthcare systems with hospitals running out of beds.<\/p>\n\n\n\n At present there is really no way to predict right at the start which patient infected with COVID-19 will develop complications. If such prediction is made possible, we shall be able to focus our resources on only those who really need, and decisively intervene at a very early stage- while other patients might just relax at their homes. Such early intervention focused on the ones who will eventually develop complications on COVID-19 infection will significantly reduce hospitalization rate and mortality. But is there a way to accurately predict which patient will deteriorate and will require such early intervention?<\/strong> <\/p>\n\n\n\n This is exactly what we will try to answer. Let\u2019s see if we succeed. <\/p>\n\n\n\n <\/p>\n\n\n\n This post is the fourth and the last part of the four-parts series<\/strong> that intends to eventually show how we can easily predict outcomes of COVID-19 infected patient at a very early stage. <\/p>\n\n\n\n (If you haven\u2019t read the first three parts, I will strongly recommend doing so. For the first part, click HERE<\/a><\/strong>. For the second part, click HERE<\/a><\/strong><\/em>. For the third part, click HERE<\/a><\/strong><\/em>)<\/p>\n\n\n\n In the first part<\/strong> <\/a>of this series we focused on understanding why it is more important to predict who will die in COVID-19 rather than just focusing on prevention and treatment of all. We also explored how vaccines and anti- viral drugs might not help in completely wiping off this pandemic. In the second part<\/a><\/strong>, we explored the basics of immunology behind \u201cCOVID-19 induced cytokine storm\u201d, which is real of cause of death in majority. In the third part<\/a><\/strong> we thoroughly investigated how a healthy immune system prevents cytokine storm and understood the defects in immune system of few people that make them unlucky enough to suffer from this horrific complication of COVID-19 infection.<\/p>\n\n\n\n In this post, we will finally try to arrive at a simple scientific way to predict \u201cWho can relax at home and not panic in COVID-19?\u201d<\/em><\/strong><\/strong><\/p>\n\n\n\n Armed with the basic knowledge you gathered in Part 1<\/a><\/strong>, Part 2<\/a><\/strong> and Part 3<\/a><\/strong>, this post should be extremely simple to follow. Anyways, I will be using very simple language such that every reader, irrespective of his\/her background will easily understand. Hopefully after that, if you see a patient infected with COVID-19 you might have a way to predict if he\/she will deteriorate and require hospitalization.<\/p>\n\n\n\n We can now appreciate what’s going on around- why few unlucky people succumb to Covid-19 infection. We have conclusively identified in the earlier parts of this blog series that lack of enough number of \u201ctrained T cells”<\/strong> is the true cause<\/span> behind the killer cytokine storm that kills a patient of COVID-19.<\/p>\n\n\n\n Based on this available knowledge, the logical next step is to correctly identify these individuals around us who suffer from this scarcity of mature T cells and focus all efforts to prevent Covid-19 in such individuals. Further, if we can identify such individuals, we will know who will need critical care in case infection happens. This will help channelize resources in the right direction and prevent deaths. <\/p>\n\n\n\n But how can we identify who has critically low number of mature trained T cells? <\/em><\/strong><\/p>\n\n\n\n To be fair, almost 4 decades of research has gone into understanding this immunologically critical state, which is popularly known as a state of immunosenescence<\/strong>. Immunosenescence<\/span> refers to aging of immune system<\/span>. This \u201cimmune- aging\u201d might happen naturally as we age or might result from chronic state of inflammation such as uncontrolled diabetes mellitus (experts also this as \u201cinflammaging<\/strong><\/em>\u201d) [1<\/a>] [2<\/a>]. Hundreds of studies have now established patterns to identify this state of \u201ccritical\u201d immunosenescence which can help us identify these \u201cat risk\u201d individuals with critically low number of trained T cells [3<\/a>]. <\/p>\n\n\n\n To understand and make sense of these patterns we will have to dissect the family of T cells a bit and understand them.<\/p>\n\n\n\n <\/p>\n\n\n\n There are many types of T cells inside our body. Each type has a specific biological signature and well-defined functions. And I won\u2019t dare to bore you with details of each type. What\u2019s important for us to know is that of these many types, there are two predominant ones. One is marked by signature called CD4<\/strong> and the other one is marked by CD8<\/strong>. If the signature is present you mark it as + or else, you will mark it as -. So, CD4+ T cells would mean T cells bearing signature of CD4. Similarly, CD8- T cells would mean T cells NOT bearing signature of CD8. Hope the nomenclature is clear.<\/p>\n\n\n\n CD4+ T cells<\/mark> are also known as helper T cells.<\/mark> CD8+ T cell<\/mark>s are also known as cytotoxic T cells<\/mark>. While understanding the details will require a great deal of training in immunobiology, what you can generalize is that CD8+ T cells are designed to attack and destroy cells that pose as threats. For example-\u00a0tumour\u00a0cells, cells\u00a0harbouring\u00a0viruses etc. They, along with components of innate immune system, directly kill cells<\/mark> bearing pathogens within them. CD4+ T cells<\/mark> are designed to target pathogens that roam outside the cells<\/mark>. So, a bacterium or a virus that has been released from a cell will be targeted by CD4+ T cells. CD4+ T cells primarily do not kill self- cells\u00a0harbouring\u00a0viruses<\/mark>- that\u2019s the role of CD8+ T cells [4<\/a>].\u00a0<\/p>\n\n\n\n <\/p>\n\n\n\n (To be honest, this is a very simplified version of the story. In reality, components of immune system are interdependent, and their individual roles can\u2019t be delineated just like that. For example, the Th1 component of CD4+ T cells do contribute to direct killing of \u201cself- cells\u201d bearing pathogens. However, for the sake of simplicity, you can consider that killing \u201cself- cells\u201d bearing threats directly is generally done by CD8+ T cells [5<\/a>].<\/em>)<\/p>\n\n\n\n Now, in a healthy immune system with enough \u201ctrained\u201d T cells, CD4+ T cells are more numerous than CD8+ T cells. And that\u2019s simply because in thymus the training is all about being tolerant to self-cells<\/strong>. Since CD8+ T cells are technically meant to destroy self-cells, many fail to clear the tests posed by Thymus. Eventually, we end up having higher number of trained CD4+ T cells compared to trained CD8+ T cells. <\/p>\n\n\n\n But if Thymus fails to replenish the growing need to supply new T cells, maybe due to aging or maybe due to chronic immune stimulation or maybe both, the body ends up having a surplus of untrained T cells<\/strong>. In absence of right training, the focus becomes only tackling the threats. These threats are generally from \u201cself-cells\u201d (eg. deranged cells due to onslaught of product of sugar in case of diabetes or cells harboring Covid-19 virus), which must be tackled by CD8+ T cells. So, in an aging immune system one will have more of CD8+ T cells. <\/span><\/strong><\/p>\n\n\n\n (For GEEKS: these CD8+ T cells will also be mostly CD28-. This means they get activated without requirement of any co-stimulation. Other readers may ignore this comment!)<\/span><\/em><\/p>\n\n\n\n It is this reversal of CD4:CD8 T cell ratio that has been consistently noted, amongst many other markers of \u201cdangerous\u201d immunosenescence\u201d. This has been famously called as immune risk profile (IRP)<\/strong> and is defined as CD4\/CD8 ratio < 1<\/strong> [6<\/a>]. If we consider all the points we have discussed till now, and correlate them with this concept of IRP we should be able predict a few outcomes. For example: <\/p>\n\n\n\n The first 3 points have already been proven convincingly in several studies. Let me give examples of the classical studies done by Swedish scientists [7<\/a>]. The first study is known as OCTO [8<\/a>]. And the second study is known as NONA [6<\/a>]. Both the studies aimed to understand immune system of people whose age crossed 84 years. Swedish scientists did not restrict their research only to extremely old individuals. Their aim was to eventually evaluate changes in immune system across adult life span. HEXA study and Kristianstad Survey focused on younger populations [9<\/a>]. Altogether, the studies evaluated individuals with ages spanning from 20 to 94 years.<\/p>\n\n\n\n All these studies conclusively showed that IRP predicted survival and age [9<\/a>] [6<\/a>] [8<\/a>]. Sensitivity of CD4\/CD8 inversion in predicting mortality is very high<\/span>. Interestingly these predictions using CD4\/CD8 ratio were not related with health condition of individuals. What it means is if one has IRP (i.e. CD4\/CD8 <1) the chances of survival is very low even if that individual looks physically fit, while if someone doesn\u2019t have IRP (i.e. if CD4\/CD8 ratio is NOT <1), the chances of survival is very high although the individual might be suffering from a disease. <\/em><\/strong>IRP has also been convincingly proven to be correlated with mortality.<\/p>\n\n\n\n The findings were not limited only to Sweden. The scientists from Cambridge University, UK also came with very similar findings in their 2003 study [10<\/a>]. CD4\/CD8 ratio predicted survival and mortality in their patient population as well- when the ratio went <1, survival went down.<\/em><\/strong><\/p>\n\n\n\n Swedish studies also show that CMV infection correlated strongly with IRP. From whatever we have discussed till now in this series of four posts, a rapidly proliferating long standing viral infection such as CMV can easily drive IRP in any individual by inducing rapid proliferation of CD+ T cells<\/p>\n\n\n\n We can now conclude scientifically that with age CD4\/CD8 ratio keeps falling. In fact, if you somehow manage to prevent this ratio from falling, most probably you will live quite long. People with survival to the age of 100 years have shown a stable high CD4\/CD8 ratio. But, for most, this ratio keeps falling with age.<\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n Now try to link this knowledge about IRP with whatever we have learnt till now about the cause of COVID-19 led cytokine storm. I can simply hope that you will naturally come to the direct conclusion that I have reached- that IRP can be used to predict who might progress into cytokine storm if infected with COVID-19.<\/p>\n\n\n\n IRP indicates immunosenescence (aging of immune system) of critical level. Individuals with IRP (CD4\/CD8 ration <1) have immature immune system. <\/span>This immature immune system, when faced with the wrath of COVID-19, can easily turn into psychopathic killers killing not only the virus but also all the surrounding \u201cself- cells\u201d leading to the deadly cytokine storm.<\/p>\n\n\n\n Experts in this field have already recognized immunosenescence as predictor of cytokine storm in COVID-19. But, I do understand that this needs to be proven via clinical studies. I hope I have built a strong case to put forth CD4\/CD8 ratio < 1 as the marker to identify individuals who might be at high risk of complications if infected with Covid-19.<\/strong><\/p>\n\n\n\n CD4\/CD8 ratio is a fairly common test<\/span>. It is common done to predict survival in HIV+ patients. It has been long known that as CD4\/CD8 ratio falls in HIV positive individuals, chances of death rise furiously. It easily available in all laboratories. In India the test cost ranges from INR 400 to INR 1200 depending on the lab. If we know an individual\u2019s CD4\/CD8 ratio, it seems we can predict whether he\/ she will need extra care in case he\/she gets infected with COVID-19.<\/p>\n\n\n\n Best thing is (if proven), its just one test, easily available and can be used as a predictive marker at a very early stage of infection. Infact, if you don\u2019t have IRP (I.e. your CD4\/CD8 ratio is >1), you really won\u2019t have to fear getting infected with COVID-19. If you do have IRP, you are the one who should avoid venturing outside, take every precaution to prevent any infection, and be marked for hospitalization if infected with COVID-19.<\/span><\/em><\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n Hence we really need to test this predictive role of IRP via good clinical studies.<\/p>\n\n\n\n The issue that we are trying to resolve is a cause of great concern globally. Early identification of patients with COVID-19 who are at high risk of mortality is of vital importance. Lot of researchers across the world are trying to solve this. As hospitals around the world are faced with an influx of patients with Covid-19, clinicians agree that there is an urgent need for a pragmatic risk stratification tool that will allow the early identification of those patients who are at the highest risk of death, to guide management and optimize resource allocation.<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n More than 20 such different predictive tools have been developed and validated till date. Few are useful. Few are just academic exercises. I won\u2019t go through all of them but let me quickly cover a few important ones which do have potential use.<\/p>\n\n\n\n If you have read this section closely you will agree with following observations regarding the predictive tools we have discussed in this section:<\/p>\n\n\n\n We need a predictive tool which can be used in all patients. We need a tool which can be used to predict outcomes even if before anyone is infected COVID-19. <\/strong>The tool has to be simple- maybe just 1 single test<\/strong>. And we need a tool whose prediction is binary- it should answer either YES or NO<\/strong> to the question- will this individual progress into severe stages on infection with COVID 19?<\/p>\n\n\n\n IRP (CD4\/CD8 ratio < 1) seems to be the answer.<\/span><\/strong><\/p>\n\n\n\n Covid-19 will not be the last biggest pandemic. Many more will come- bigger ones. Humanity will continue to prevail. Most will survive such pandemics. But few will succumb. This \u201cfew\u201d succumbing will number in millions. Hence, we must have something which will say who will face this eventuality. Because, without this information, a similar number or maybe more will die because of economic ramifications of stringent measures such as lockdowns. <\/p>\n\n\n\n CD4\/CD8 ratio < 1, also known as IRP, might prove to be simple test to predict which of us might require critical support if infected with COVID-19. Similarly, if someone\u2019s CD4\/CD8 ratio is >1, he\/she may be least likely to develop any complications.<\/span><\/strong><\/p>\n\n\n\n Heath authorities must quickly identify the importance of this simple marker of complications in Covid-19 and must design studies to verify predictive role of IRP. If IRP stands the test of clinical studies, we will end up saving millions of lives- lives that might get lost not only due to Covid-19 but any other viral infection that kills via cytokine storm. <\/p>\n\n\n\n IRP (if proven) will simplify the current complicated life in COVID-19. If I don\u2019t have IRP (i.e. my CD4\/CD8 ratio is >1), I won\u2019t care about infection with COVID-19. I know I will recover easily even if I get infected. If I do have IRP (i.e. my CD4\/CD8 ratio is <1), I will be super careful. I will avoid venturing outside, take every precaution to prevent any infection, and be prepared for hospitalization if infected with COVID-19.<\/span><\/strong><\/p>\n\n\n\n Health authorities can group populations into two- those with IRP and those without IRP. Based on this data, resource requirements can be predicted and planned. The current situation of unnecessary fear and panic can be calmed down. Doctors will then know who will really need hospitalization and who should be send home to relax.<\/p>\n\n\n\n I would end this long 4- posts series by repeating once again that use of IRP as a predictor of complications in COVID-19 needs to be tested and proved before actual clinical usage<\/strong>. But this should be done as a priority<\/span>. It can solve the current crisis of needy patients standing in long queues and not getting beds in hospitals.<\/p>\n\n\n\n [1] S. Roy, \u201cImmunosenescence in rheumatoid arthritis\u202f: Use of CD28 negative T cells to predict treatment response,\u201d Indian J. Rheumatol.<\/em>, vol. 9, pp. 62-68, 2014.<\/a><\/p>\n\n\n\n [2] M. T. Ventura, M. Casciaro, S. Gangemi, and R. Buquicchio, \u201cImmunosenescence in aging: Between immune cells depletion and cytokines up-regulation,\u201d Clinical and Molecular Allergy<\/em>, vol. 15. BioMed Central Ltd., p. 21, 2017, doi: 10.1186\/s12948-017-0077-0.<\/a><\/p>\n\n\n\n [3] J. Feehan, N. Tripodi, and V. Apostolopoulos, \u201cThe twilight of the immune system: The impact of immunosenescence in aging,\u201d Maturitas<\/em>, vol. 147. Elsevier Ireland Ltd, pp. 7-13, May 01, 2021, doi: 10.1016\/j.maturitas.2021.02.006.<\/a><\/p>\n\n\n\n [4] P. C. L. Beverley and M. K. Maini, \u201cDifferences in the regulation of CD4 and CD8 T-cell clones during immune responses,\u201d Philos. Trans. R. Soc. B Biol. Sci.<\/em>, vol. 355, no. 1395, pp. 401-406, Mar. 2000, doi: 10.1098\/rstb.2000.0580.<\/a><\/p>\n\n\n\n [5] B. M. Hall, \u201cT cells: Soldiers and spies-the surveillance and control of effector T cells by regulatory T cells,\u201d Clin. J. Am. Soc. Nephrol.<\/em>, vol. 10, no. 11, pp. 2050-2064, Nov. 2015, doi: 10.2215\/CJN.06620714.<\/a><\/p>\n\n\n\n [6] J. Strindhall et al.<\/em>, \u201cNo Immune Risk Profile among individuals who reach 100 years of age: Findings from the Swedish NONA immune longitudinal study,\u201d Exp. Gerontol.<\/em>, vol. 42, no. 8, pp. 753-761, Aug. 2007, doi: 10.1016\/j.exger.2007.05.001.<\/a><\/p>\n\n\n\n [7] A. Wikby, B. Johansson, and F. Ferguson, \u201cThe OCTO and NONA immune longitudinal studies: a review of 11 years studies of Swedish very old humans,\u201d Adv. Cell Aging Gerontol.<\/em>, vol. 13, pp. 1-16, 2002, doi: 10.1016\/S1566-3124(02)13001-X.<\/a><\/p>\n\n\n\n [8] J. Olsson, A. Wikby, B. Johansson, S. L\u00f6fgren, B. O. Nilsson, and F. G. Ferguson, \u201cAge-related change in peripheral blood T-lymphocyte subpopulations and cytomegalovirus infection in the very old: The Swedish longitudinal OCTO immune study,\u201d Mech. Ageing Dev.<\/em>, vol. 121, no. 1-3, pp. 187-201, Jan. 2001, doi: 10.1016\/S0047-6374(00)00210-4.<\/a><\/p>\n\n\n\n [9] J. Strindhall et al.<\/em>, \u201cThe inverted CD4\/CD8 ratio and associated parameters in 66-year-old individuals: The Swedish HEXA immune study,\u201d Age (Omaha).<\/em>, vol. 35, no. 3, pp. 985-991, Jun. 2013, doi: 10.1007\/s11357-012-9400-3.<\/a><\/p>\n\n\n\n [10] F. A. Huppert, E. M. Pinto, K. Morgan, and C. Brayne, \u201cSurvival in a population sample is predicted by proportions of lymphocyte subsets,\u201d in Mechanisms of Ageing and Development<\/em>, Apr. 2003, vol. 124, no. 4, pp. 449-451, doi: 10.1016\/S0047-6374(03)00021-6.<\/a><\/p>\n\n\n\n [11] J. Gong et al.<\/em>, \u201cY (covid-19): A multicenter study using the risk nomogram in Wuhan and Guangdong, China,\u201d Clin. Infect. Dis.<\/em>, vol. 71, no. 15, pp. 833-840, Aug. 2020, doi: 10.1093\/cid\/ciaa443.<\/a><\/p>\n\n\n\n![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-deadly-1024x576.png\")
<\/figure>\n\n\n\nWhat will we learn in this post?<\/h2>\n\n\n\n
Who will die? Solving the confusion <\/h2>\n\n\n\n
Immunosenescence: the main culprit- Can it be measured?<\/h3>\n\n\n\n
![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-T-cells-1024x576.png\")
<\/figure>\n\n\n\n![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-T-cells-action-1024x576.png\")
<\/figure>\n\n\n\nHow to Predict: Who can relax at home and not panic in COVID-19?<\/em><\/strong><\/h3>\n\n\n\n
![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-IRP-1-1024x576.png\")
<\/figure>\n\n\n\n![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-IRP-1024x576.png\")
<\/figure>\n\n\n\nAre there any prediction tools currently in use?<\/h2>\n\n\n\n
![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-predict-1024x576.png\")
<\/figure>\n\n\n\nFinal comments<\/h2>\n\n\n\n
![\"Who](\"https:\/\/wisethalamus.com\/wp-content\/uploads\/2021\/05\/Who-can-relax-at-home-and-not-panic-in-COVID-19-IRP-2-1024x576.png\")
<\/figure>\n\n\n\nReferences<\/h2>\n\n\n\n