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Introduction
Coronavirus disease 2019 (COVID-19), a highly in- fectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly throughout the world and remains a major threat to global public health (1). The clinical spec- trum of patients with COVID-19 infection varies widely, from asymptomatic to subjects with mild, moderate, and severe symptoms (2). In patients with severe pneumonia, SARS-CoV-2 infection can lead to severe acute respiratory distress syndrome (ARDS), which rapidly progresses to multi-organ dysfunction syndrome (MODS) 1,2. Similarly, many patients with mild symptoms can suddenly progress from mild to severe ARDS, septic shock, or multi-organ dysfunction
3. These rapid changes in disease progression call for identifying markers for early diagnosis of potentially severe or critical cases. Such tools would allow for timely clinical decisions and appropriate and imme- diate treatment.

The neutrophil-to-lymphocyte ratio (NLR) is the ratio of the absolute number of neutrophils to the absolute number of lymphocytes. It is a new marker of subclin- ical inflammation with good predictive capacity for the progression and clinical outcome of various dis- eases, such as solid tumors 4, cardiovascular disease 5, chronic obstructive pulmonary disease 6, pancreati- tis 7, renal disease 8,9, and diagnosis of colon cancer 10. In addition, several studies have recently reported that the NLR can differentiate between mild/moder- ate and severe/critical groups and predict the likeli- hood of death in patients with COVID-19 infection. Indeed, several studies show that elevated NLR is as- sociated with increased mortality in COVID-19 pa- tients 11–16. This evidence is highly relevant, as the NLR is an easily accessible parameter that can be ob- tained from a complete blood count (CBC), which is always on hand in first-contact care units. The lym- phocyte to C-reactive protein ratio (LCR) is another interesting biomarker of disease progression 17. LCR has been used as a suggestive indicator of inflamma- tion in cancer 18, and has also been proven useful in predicting adverse outcomes in COVID-19 19. Com- pared to NLR, LCR predictability is less specific in mild and moderate cases, but it has adequate predictive ranges for mortality and worsening clinical status in hospitalized patients 20.

Although several authors have shown that increased NLR 3,21–25 and decreased LCR 19,26–28 are useful for predicting disease progression in COVID-19, so far, there is no information about the predictive proper- ties of NLR and LCR in COVID-19 in human popula- tions living at high altitude (between 2,500-5,000 m). Our research group and others have reported that the incidence and mortality of COVID-19 are signifi- cantly lower in populations at high altitudes com- pared to populations at sea level 29–31. At high altitude, the lower barometric pressure (about one-third compared to sea level) is the most important factor in this effect 32. To cope with these conditions, high-alti- tude dwellers have developed physiological, cellular, and molecular adaptations that effectively prevent the imbalance between oxygen supply and demand 29.

These modifications include increased ventilation, increased oxygen-carrying cells in the blood, in- creased vasodilation, and augmented  angiogenesis 33. These physiological features are associated with lower incidence and mortality of COVID-19 disease at high altitude and suggest that the inflammatory re- sponse induced by the SARS-CoV-2 virus is less im- portant (or better tolerated) in high-altitude resi- dents. This observation raises the question of whether clinical biomarkers, such as the NLR and LCR, used to predict the progression and clinical outcome of COVID-19 in patients at sea level have the same pre- dictive value at high altitude. Accordingly, we investigated whether NLR and LCR can be used as predictors of severity in high-altitude permanent residents with a confirmed COVID-19 in- fection. To do so, we evaluated the serum NLR and LCR levels as a function of progressing COVID-19 se- verity. Our results show that NLR and LCR can be used reliably to predict the severity of COVID-19 disease in high-altitude patients.

Methods
ETHICS DECLARATION
The present study complies with the basic principles for research in human beings established by the Declaration of the World Assembly of the Helsinki Treaty, Nuremberg and Finland. The study was carried out per the General Health Law of the Republic of Peru on research for health.

STUDY DESIGN
We conducted an observational, descriptive, and cross-sectional study from August 2020 to March 2021 at San Pablo Clinic in Huaraz, Peru, located at 3,050 meters above sea level (masl).

PATIENTS AND CLINICAL PARAMETERS
A total of 368 patients were included, 249 men and 119 women. For each patient, sex and age were an- notated, and the values of C-reactive protein (CRP), arterial partial pressure of oxygen (PaO2), arterial partial pressure of CO2 (PaCO2), lactate in blood, total leukocytes, number of lymphocytes, number of neutrophils, and number of band neutrophils were recorded at admission to the hospital. The neutrophil/ lymphocyte (NLR) and the Lymphocyte/C-reactive protein (LCR) ratios were also calculated.
Inclusion/exclusion criteria
Patients were included in the study if they were: 1) 18 years old or older, 2) high-altitude permanent residents (no history of migration from lower lands during the last year), 3) admitted to the ICU due to diagnosis of acute hypoxemic respiratory insuffi- ciency caused by COVID-19-related pneumonia. Pa- tients were excluded if they were diagnosed with non-COVID-19 related pneumonia, uncontrolled en- docrine metabolic disease, active hepatitis C virus in- fection, HIV infection, pregnancy, or taking vitamins and/or food complexes. Patients data were eliminated if informed consent was withdrawn, if medical history was incomplete, or if biological samples were improperly handled.

Statistical analysis
Statistical analyses, except for ROC curves analysis, were performed using GraphPad Prism version 9.1 for Windows, GraphPad Software (San Diego, Cali- fornia USA, www.graphpad.com) or IBM SPSS Statis- tics for Windows (Version 26.0. Armonk, NY: IBM Corp). Test significances were set to p<0.05. Values are presented as medians and interquartile ranges (IQR).

Univariate analysis
Data were anonymized before processing. The dif- ference between survivors and deceased patients within each clinical parameter was evaluated using multiple Student’s t-tests for independent samples. The difference in NLR and LCR between survivors and deceased patients admitted or not to the ICU was tested via two-way ANOVA, where survival and ICU requirement were the factors. LSD Fishers post- hoc analyses were applied to the results.

Multivariate analysis
We built two separate multivariate models, one in- cluding patients admitted or not to ICU, and a second one with only patients admitted to ICU.
First, we investigated the most important factors in predicting the probability of survival with a multiple logistic regression. For this, we used a subset of pa- tients (30+ years old) admitted or not to ICU to de- termine the effect of age, sex, band neutrophils, total leukocytes, C-reactive protein, lactate, PaO2, PaCO2, NLR, LCR, and admission to ICU on survival probabil- ity. PRISM’s built-in tests were used to check for mul- ticollinearity and correlation between independent variables.
Second, we repeated this analysis to determine the best predictor of ICU admission using a subset of pa- tients (30+ years old) not admitted to ICU. The best predictors were selected according to the highest val- ues of area under the curve (AUC). Values below 0.6 were considered poorly discriminative, while those above 0.75 were considered good. The optimal cut- off values for NLR and LCR as predictors of death and ICU admission were calculated from ROC curves analyses using the Youden index method in MedCalc Statistical Software version 20.114 (MedCalc Soft- ware bv, Ostend, Belgium; https://www.med- calc.org; 2020). For the analysis of fatal outcomes at 60+ days of follow-up after admission to the hospi- tal, survival analysis was performed using the Kaplan-Meier method.

Results
NLR VALUES ARE HIGHER IN DECEASED HIGH-AL- TITUDE COVID-19 PATIENTS ADMITTED TO IN- TENSIVE CARE UNITS.
Of 368 patients, 319 survived, representing 84.3% of the male population and 91.6% of the female population. Survivors had a median age of 46 years (interquartile range [IQR]: 36 to 59 years), with a median hospital stay of 9 days (IQR: 6-13 days), of which 34 patients were admitted to the ICU (10.7%) (Table 1). 

Table 1. Description of the study population

We found a significant effect of the NLR on sur- vival in patients admitted to the ICU but not in those not admitted to the ICU (Two-way ANOVA Fsurvival (1, 364) = 5.30, p=0.022; FICU requirement (1, 364) = 4.71, p=0.031). NLR values were highly heterogeneous in patients who did not require ICU admission; conse- quently, no significant differences were found be- tween survivors and deceased patients (Fishers LS  test pNo_ICU = 0.281). However, among patients ad- mitted to the ICU, those who eventually died had sig- nificantly higher NLR values than survivors (pICU = 0.004). Thus, these results suggest that NLR values may indicate the probability of mortality in ICU-ad- mitted patients. (Fig 1A). On the other hand, LCR was not significantly different in patients admitted or not to the ICU (Fig 1B).

Figure 1. A. NLR values are higher in deceased COVID-19 patients in high-altitude admitted to ICU. B. LCR values are highly heterogeneous in survivors and deceased patients. The vertical axis indicates log10 values. nSurvivors_No_ICU = 285; nDeceased_No_ICU = 4; nSurvivors_ICU = 34; nDeceased_ICU = 45.**: p<0.01 
NLR AND LCR ARE PREDICTORS OF DEATH IN HIGH-ALTITUDE COVID-19 PATIENTS
2mine predictors of mortality in high-altitude COVID- 19 patients (Table 2). Our results show that older age, male sex, high band neutrophil count, low PaO2, high NLR, low LCR, and admission to the ICU are sig- nificant predictors of mortality. However, only NLR, LCR, and admission to ICU have good discriminative capacity based on the AUC values (>0.75). ICU ad- mission is the most important predictor of mortality (Table 2).

Table 2. NLR and LCR are predictors of death in high-altitude COVID-19 patients

Table 3. NLR and LCR are predictors of ICU requirement in high-altitude COVID-19 patients

NLR AND LCR ARE GOOD PREDICTORS OF IN- TENSIVE CARE UNIT REQUIREMENT IN HIGH-AL- TITUDE COVID-19 PATIENTS
Since patients admitted to ICU are clearly at higher risk of death, we investigated which clinicalparame- ters can be used as predictors of requiring ICU admission. We found that male sex, high total leukocytes, low PaO2, high NLR, and low LCR can significantly predict ICU requirement. Moreover, NLR and LCR are the only parameters with good discriminative power (Table 3).

Table 4. ROC curve analysis for NLR and LCR in high-altitude COVID-19 patients

ROC CURVE ANALYSIS FOR NLR IN HIGH-ALTI- TUDE COVID-19 PATIENTS
Finally, we determined the cut-off values for NLR and LCR as predictors of ICU requirement and death. ROC curves showed that NLR values higher than 4.47 indicate a high risk of requiring ICU admission, while values greater than 5.06 predict a high risk of death. On the other hand, for LCR  0.2 was determined as the cut-off value for both the odds of requiring ICU and death (Table 4). Consistently, patients with NLR greater than 5.06 had a lower probability of survival after 20 days of hospitalization. A similar trend was observed in patients with LCR equal to or lower than
0.2 (Figure 2).

Figure 2. High-altitude COVID-19 patients with NLR > 5.06 AND LCR ≤ 0.2 have lower probabilities of survival. 
Discussion
In this work, we investigated the potential predictive capacity of NLR and LCR for the severity of SARS- CoV-2 infection in patients permanently residing at high altitude (3,050 masl, Huaraz, Peru). The main findings are that: (i) patients with NLR values lower than 4.47 do not require ICU admission, (ii) patients with NLR values between 4.47 and 5.06 will require ICU but are at low risk of death, (iii) patients with NLR greater than 5.06 are at high risk of death, and (iv) patients with LCR less than or equal to 0.2 are at high risk of death.

COVID-19 presents a wide range of symptoms, with most cases being mild and self-limited 34. However, the disease can have fatal consequences when it pro- gresses to severe pneumonia, leading to acute respir- atory distress syndrome (ARDS) and multiple organ failure 35. Inflammation plays an important role in the development and progression of COVID-19 as the hemostatic system acts in concert with inflammation. Following the inflammatory response, various media- tors activate the hemostatic system through endothe- lial dysfunction, platelet activation, and coagulation, promoting thrombosis, also known as thromboinflam- mation 36–38. The inflammasome acquires special rel- evance in this process since stimulation promotes in- nate and adaptive immune responses 36–38. This results in a generalized inflammatory response with the re- lease of proinflammatory cytokines and interleukins 39,40. It is known that people infected with COVID-19 have a dysregulated immune system that can cause an abnormal immune response 41. For patients who become critically ill, timely identification and inter- vention are necessary to reduce hospital stays and mortality. Circulating inflammatory biomarkers can be used to assess disease severity and potentially predict disease progression. One of these biomarkers is the NLR, which can be easily obtained from a sim- ple blood test by dividing the number of neutrophils by the number of lymphocytes. Traditionally, the NLR has been used as a predictor of morbidity and mor- tality in critically ill patients, including cancer 42, heart disease 43, and sepsis 44. More recently, the NLR was also shown to predict the prognosis of patients with COVID-1945,46

The LCR, another circulating biomarker, has also been investigated as a predictor of COVID-19 severity. Historically, LCR has been used as a prognostic marker for various types of cancer, including colon and gastric carcinomas 47,48. The rationale behind this is that the LCR serves as a good surrogate for the complex host-tumor immunological interactions that result in a systemic inflammatory process, which is be- lieved to contribute to the pathogenesis and progres- sion of these carcinomas 49. Since COVID-19 also pre- cipitates a systemic inflammatory response, LCR has also been reported as a good prognostic marker for this disease 19.Many mechanisms have been postu- lated regarding the response of neutrophils and lym- phocytes to coronavirus infection. Neutrophils activate the immune system and release reactive oxygen spe- cies that can induce damage to cellular DNA and re- lease virus from cells, which is then targeted by anti- bodies. In addition, neutrophils trigger the production of various cytokines and effector molecules. On the other hand, although the viral infection itself triggers a predominantly lymphocytic response, systemic in- flammation, especially elevated interleukin 6, para- doxically lowers the lymphocyte count and resulting cellular immunity. Both factors result in an elevated NLR and LCR 45,50,51. Therefore, elevated NLR and re- duced LCR levels predict inflammation severity.

High and very high-altitude environments are charac- terized by barometric hypoxia. At the onset of the COVID-19 pandemic, this condition predicted a dev- astating number of fatalities in these regions. Surpris- ingly, high-altitude inhabitants, particularly in coun- tries in the Americas and the Tibetan region 29,52 , were found to have lower infection rates and/or fewer severe COVID-19 symptoms compared to low- land inhabitants 29,52. This epidemiological finding suggests that chronic exposure to hypobaric hypoxia in such high-altitude environments elicits physiological adaptations that may provide some protection against SARS-CoV-2 infection and symptoms. Indeed, these results suggest that the exaggerated immune response (cytokine storm) in low-land is diminished in high-altitude patients.

The results clearly show that NLR values are higher in deceased high-altitude COVID-19 patients who were admitted to the ICU. Indeed, there is a significant ef- fect of NLR on survival in patients admitted to the ICU, suggesting that NLR values may indicate the proba- bility of mortality in patients admitted to the ICU. In- deed, in the ICU-admitted group, patients who even- tually died showed significantly higher NLR values than survivors (pICU = 0.004). On the contrary, this ef- fect is absent in patients not admitted to the ICU, in whom NLR values showed great heterogeneity. Fur- thermore, we found that older age, male sex, high neutrophil band, low PaO2, high NLR, low LCR and ICU admission are significant predictors of mortality. Of these, ICU admission is clearly the most important risk factor. Furthermore, we investigated whether older age, male sex, high total leukocytes, low PaO2, high NLR and low LCR can significantly predict the need for ICU admission. Of all these parameters, NLR and LCR have the best discriminative power. Finally, ROC analysis showed that patients with NLR higher than 5.06 and LCR lower than 0.2 had lower odds of survival after 20 days of hospitalization. However, there are some limitations to our study. Our data was collected from a single hospital, and the results need to be validated in other high-altitude medical centers. Moreover, multicenter studies with larger cohorts are required to validate the reproducibility of the opti- mal cut-off point and the prognostic value for NLR and LCR found in this work.

In conclusion, although our results suggest that both NLR and LCR have good predictive power of mortal- ity, only NLR is sensitive enough to identify patients requiring ICU admission at low risk of death. The cut- off values calculated for LCR are not useful in distin- guishing the probabilities of ICU need and death. Since NLR and LCR are simple and readily available indicators, our results are of high clinical value in high- altitude hospitals.

Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or non- profit sectors. Christian Arias-Reyes received a doc- toral scholarship from the Fonds de Recherche de Québec - Santé (FRQ-S). Jorge Soliz is funded by the Canadian Institutes of Health Research (CIHR).

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