Differential Association of Viral Dynamics With Disease Severity Depending on Patients’ Age Group in COVID-19

Abstract

Despite a clear association of patient’s age with COVID-19 severity, there has been conflicting data on the association of viral load with disease severity. Here, we investigated the association of viral load dynamics with patient’s age and severity of COVID-19 using a set of respiratory specimens longitudinally collected (mean: 4.8 times/patient) from 64 patients with broad distribution of clinical severity and age during acute phase. Higher viral burden was positively associated with inflammatory responses, as assessed by IL-6, C-reactive protein, and lactate dehydrogenase levels in patients’ plasma collected on the same day, primarily in the younger cohort (≤59 years old) and in mild cases of all ages, whereas these were barely detectable in elderly patients (≥60 years old) with critical disease. In addition, viral load dynamics in elderly patients were not significantly different between mild and critical cases, even though more enhanced inflammation was consistently observed in the elderly group when compared to the younger group during the acute phase of infection. The positive correlation of viral load with disease severity in younger patients may explain the increased therapeutic responsiveness to current antiviral drugs and neutralizing antibody therapies in younger patients compared to elderly patients. More careful intervention against aging-associated inflammation might be required to mitigate severe disease progression and reduce fatality in COVID-19 patients more than 60 years old.

Viral load at hospitalization is an independent predictor of severe COVID‐19

“…13 The results can also vary depending on host factors, for example one study reported a positive correlation of viral load with disease severity in younger age groups (<60 years), but not in older ones. 23 However, a more consistent association between higher viral load and disease severity is found in cohorts of hospitalized patients 13,15,16,24 such as ours, although not always in multivariate analysis. 18 This was also observed during the previous SARS-CoV epidemic, where Chu et al showed that high viral load at hospital admittance was independently associated with more severe forms of SARS.…”

“…Comparison between these studies is difficult as they vary to a large extent with regards to cohort composition, study design, sampling time‐points after onset of symptoms and sample selection 13 . The results can also vary depending on host factors, for example one study reported a positive correlation of viral load with disease severity in younger age groups (<60 years), but not in older ones 23 . However, a more consistent association between higher viral load and disease severity is found in cohorts of hospitalized patients 13,15,16,24 such as ours, although not always in multivariate analysis 18 .…”

Severity of infection with the SARS-CoV-2 B.1.1.7 lineage among hospitalized COVID-19 patients in Belgium

“…One hypothesis to explain these observations is that the B.1.1.7 variant causes a higher viral load [ 40 ] as compared to previously circulating variants, but that the positive correlation between viral load and disease severity is only observed in younger patients. Indeed, it has been shown that respiratory viral loads were generally correlated with inflammatory responses in younger patients, but less correlated with those in older patients [ 73 ].…”

Perspectives of (/memorandum for) systems thinking on COVID‐19 pandemic and pathology

“… 102 , 103 , 104 This model also explains, why the prognosis of COVID‐19 is worse in males, where TMPRSS2 expression is higher, 105 and why biomarkers of the inflammatory response show a higher dispersion in critical cases of COVID‐19. 106 , 107 …”

“…The modelling strategy of templates and anchors can be subdivided into distinct although often overlapping conceptual steps. The following steps of systemic conceptualisation of COVID‐19 as an epistemic object appear to be useful 106 , 120 : assemble a transdisciplinary group of experts; define the boundary structure of the system; collect observations and data, ideally within informational contexts; establish the organisational levels of analysis; for the overall template model, and for each potential anchor, identify the key drivers and their networks of interrelationships; perform analyses at each layer, some of which may be detailed and others quite coarse, due to the availability or lack of data; assess interactions of anchor models at the same level; use input–output relationships of all anchor models of a given level to provide information for the model(s) at next higher level; assess reasonableness of the integration of information at the next higher level; validate the model, as far as possible, by testing the effects of low‐level inputs on the overall responses of the template model; replace fixed parameters with ranges of possible values and explore consequences, for instance, through Monte‐Carlo simulations; test and validate the model with new data, especially from real‐world situations (see Section 1.3 ). …”