Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications in the medium and long term.

We aimed to evaluate the correlation of plasma levels of thiobarbituric acid reactive substances (TBARS) and reduced thiols with morbidity, mortality and immune response during and after SARS-CoV-2 infection. This was an observational study that included inpatients with SARS-CoV-2 infection older than 65 years. The individuals were followed up to the twelfth month post-discharge. Plasma levels of TBARS and reduced thiols were quantified as a measure of lipid and protein oxidation, respectively. Fatal and non-fatal events were evaluated during admission and at the third, sixth and twelfth month post-discharge. Differences in oxidative stress markers between the groups of interest, time to a negative RT-qPCR and time to significant anti-SARS-CoV-2 IgM titers were assessed. We included 61 patients (57% women) with a mean age of 83 years old. After multivariate analysis, we found differences in TBARS and reduced thiol levels between the comparison groups in fatal and non-fatal events during hospital admission. TBARS levels were also correlated with fatal events at the 6th and 12th months post-discharge. One year after hospital discharge, other predictors rather than oxidative stress markers were relevant in the models. The median time to reach significant anti-SARS-CoV-2 IgM titers was lower in patients with low levels of reduced thiols. Assessment of some parameters related to oxidative stress may help identify groups of patients with a higher risk of morbidity, mortality and delayed immune response during and after SARS-CoV-2 infection.

Introduction

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was identified as the cause of a cluster of pneumonia cases in Wuhan (China) at the end of 2019. Symptoms appeared in six of ten patients and about 15% of individuals had severe disease defined as the presence of dyspnea, hypoxemia and major lung involvement in imaging tests [1, 2].

The literature suggests that an exacerbated inflammatory response in predisposed individuals could be one of the main causes of prognostic differences between patients with SARS-CoV-2 infection [3]. Some studies have pointed to the existence of crosstalk between oxidative stress and inflammation that involves an upregulation of some transcription factors such as Nrf2, NF-KB, and the NLRP3 inflammasome, enhancing pro-inflammatory status [4, 5]. However, non-inflammatory cellular pathways related to redox imbalance and their role in SARS-CoV-2 infection severity and prognosis are not yet fully understood.

Patients with high levels of some inflammatory markers, such as C-reactive protein (CRP), ferritin, fibrinogen and some pro-inflammatory cytokines, such as interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-α) are at an increased risk of severe complications [6]. However, the role of redox imbalance in the prognosis of SARS-CoV-2 infection in the short and even medium to long term remains poorly understood since most research on SARS-CoV-2 and oxidative stress are reviews of clinical, translational and base studies related to SARS-CoV-2 virulence and pathogenicity or to several diseases that could share some pathogenic mechanisms [7, 8].

The instability and high reactivity of reactive oxygen species (ROS) implies the need for quantification of secondary but more stable organic products derived from their oxidative action. Some of the most quantified oxidative stress markers are thiobarbituric acid reactive substances (TBARS) as a measure of lipid peroxidation and reduced thiols as a measure of protein oxidation. The assessment of TBARS and reduced thiols as a prognostic tool has been widely supported in some diseases and it would not be negligible that some oxidative stress markers could also provide prognostic information in SARS-CoV-2 infection [9-11].

We aimed to quantify plasma TBARS and reduced thiol levels in patients with SARS-CoV-2 infection and to assess whether there is a correlation between these oxidative stress markers and some prognostic variables related to morbidity, mortality and immunity response during and after SARS-CoV-2 infection.

Material and methods

Study design and framework

This was an observational study conducted in a SARS-CoV-2 inpatient unit belonging to the Department of Internal Medicine of the University Hospital of Santiago de Compostela (Galicia, Spain) from November/2020 to January/2021. Patients were recruited at hospital admission and followed up during the next 12 months post-discharge. The events of interest were quantified during admission and at the third, sixth and twelfth months post-discharge.

Participants

We identified patients older than 65 years with confirmed SARS-CoV-2 infection by microbiological criteria who met admission criteria (presence of risk factors or severe disease characterized as the appearance of dyspnea, hypoxemia (O2 saturation lower than 94%) or pulmonary involvement greater than 50%). Patients were randomly selected from the complete cohort of individuals admitted to the SARS-CoV-2 inpatient unit. Patients with a Barthel index of lower than 20 points and those without a record of a laboratory test within the first 7 days since admission were excluded [12].

Clinical variables

All patients were assessed for demographic characteristics (age and sex), cardiovascular (CV) risk factors (CVRFs) including smoking status (non-smokers versus current or former smokers), alcohol intake (no consumption versus consumption of any amount), body weight (normal weight versus obesity according to body mass index [BMI]), diabetes mellitus (DM), hyperlipidemia (HLP) and arterial hypertension (AHT). AHT and HLP were defined according to ESC Clinical Practice Guidelines [13, 14]. DM was considered according to the American Diabetes Association guidelines (ADA) [15]. Patients were also investigated for the presence of concomitant chronic cardiovascular, respiratory, and renal diseases [16-18].

The presence of cognitive impairment was confirmed with data collected from the patient’s clinical history and in those cases without previous information, we performed the Pfeiffer test [19]. A Barthel index score of lower than or equal to 35 points was considered severe physical dependence [12].

We considered as drug-related variables long-term treatment with renin-angiotensin-aldosterone-system (RAAS) blockers and statins and acute management with antibiotics (azithromycin or others such as B-lactams), systemic steroids and the need for supplemental oxygen therapy.

Laboratory variables

All patients underwent a blood test between day five and seven of hospital admission. Blood samples were obtained at 08:00 AM following overnight fasting. The collected laboratory parameters were complete blood count, including platelet (PTC) and white blood cell (WBC) count, biochemistry parameters, including serum glucose, creatinine, albumin, triglycerides (TG), ferritin and lactate dehydrogenase, as well as some variables related to coagulation, highlighting fibrinogen levels [20-22].

The diagnosis of SARS-CoV-2 infection was made using upper respiratory tract samples that were collected in compliance with the established protocols [23]. We employed the real-time reverse transcriptase-polymerase chain reaction (RT-qPCR) for SARS-CoV-2 detection and the results were qualitatively reported as negative, positive, or indeterminate. To assess IgM antibodies against the receptor-binding domain (RBD) of the spike protein, we performed enzyme-linked immunosorbent assay (ELISA) and the results were provided quantitatively [24, 25].

Assessment of oxidative stress markers

Blood samples were collected in tubes with EDTA, centrifuged in less than 1 h after extraction at 1000 G and 4°C for 10 min. The plasma fraction was deposited at -80°C for less than 1 month until analysis [26].

The assessment of TBARS is a well-established method for screening and monitoring lipid peroxidation. The most relevant and quantified final products of lipid peroxidation are malondialdehyde (MDA) and 4-hydroxynonenal (4-HNE). Thiobarbituric acid (TBA) forms an adduct with MDA under high temperature (90–100°C) and acidic conditions yielding a violaceus pigment that can be measured spectrophotometrically at 530–540 nm. The absorbance is directly proportional to the level of plasma lipid peroxides. We followed the protocol of Ohkawa, et al. and TBARS levels were given in micromolar (μmol/L) [27]. According to the literature, physiological concentrations of plasma TBARS range between 0.26 and 3.94 μmol/L [28].

The assessment of reduced thiols is a well-systematized technique for the quantification of protein oxidation. Ellman’s technique uses 5,5-dithio-bis-(2-nitrobenzoic acid) as a reagent to form a compound with the sulfhydryl groups of some amino acid residues in proteins, yielding a colorful pigment that can be measured spectrophotometrically at 412 nm and whose absorbance is proportional to reduced thiol levels in plasma proteins. Concentrations were given in millimolar (mmol/L). According to the literature, physiological levels of plasma reduced thiols range between 0.4 and 0.6 mmol/L [10, 29].

All samples were analyzed in duplicate and a standard calibration for each protocol was performed to obtain a linear model with a coefficient of determination (R2) greater than 98%.

Outcomes

Fatal events were referred to medical circumstances that led to the patient’s death. We grouped them into the follow categories: I) In-hospital fatal events; II) 3rd month post-discharge fatal events; III) 6th month post-discharge fatal events; and IV) 12th month post-discharge fatal events. Non-fatal events were grouped into the following categories: I) In-hospital non-fatal events, which referred to the need for transfer during admission to a critical respiratory care unit; and II) post-discharge non-fatal events, which referred to the need for readmission to hospital for respiratory or cardiovascular disease within the 3rd, 6th, or 12th month post-discharge.

We considered the time during admission to reach a certain threshold of anti-SARS-CoV-2 IgM titers as a possible indicator of immune response efficiency and the selected cut-off was the median anti-SARS-CoV-2 IgM titers of the sample. The time during admission for a negative RT-qPCR was considered a measure of virus clearance efficiency.

Calculation of sample size and treatment of variables

For an unknown population size, considering a variance of 0.5 and 0.05 with a threshold for the mean difference to be detected of 0.5 and 0.05 units for TBARS and reduced thiols, respectively, the sample size calculated to estimate mean differences between the groups with a 95% confidence interval was a minimum of n = 30 patients [30]. Variables were collected according to data provided by the regional digital health records (IANUS) belonging to the Galician (Spain) Health Service (SERGAS). Most of the clinical variables were coded as qualitative ones and laboratory parameters, including TBARS and reduce thiols, were collected as continuous quantitative variables. Outcomes were coded as dichotomous qualitative variables.

Ethical approval

The Research Ethics Committee of Santiago de Compostela-Lugo approved this study (reference number 2020/578). All procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1975. Written informed consent was obtained from all patients for being included in the study.

Statistical analysis

Statistical analysis was performed using SPSS 22.0 statistical software (SPSS Inc, Chicago, IL). First, we performed a descriptive analysis in which the frequencies of qualitative variables were expressed as number (n) and percentage (%). The Kolmogorov-Smirnov test was used to determine whether quantitative continuous variables were normally distributed. For those variables with a non-normal distribution, we performed a logarithmic transformation. Normally distributed variables were expressed as mean (m) and standard deviation (± SD), normally distributed variables after transformation were back-transformed and expressed as mean with 95% confidence interval (95%CI) and non-normally distributed ones were expressed as median and interquartile range (IQR). A missing value analysis was carried out on those variables with more than 5% missing values. We performed a comparative analysis between the groups of patients according to the outcome variables. The chi-square test was used to compare categorical variables, while quantitative variables were compared using the Student’s t-test or Mann–Whitney U test as appropriate.

We performed a binary logistic regression analysis for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge using a non-automatic analysis procedure. Variables that showed clinical or statistical relevant differences (P-value < 0.1) in the univariate analysis were included. The validity of the model was evaluated using the Omnibus test in which a P-value of lower than 0.05 was considered necessary to assume that the current model was better than the null model. We provided a model summary with the deviance (-2 log-likelihood ratio test (-2LL)), coefficient of determination (R2) and the overall accuracy score. The parameters of those variables in the model were the non-standardized Beta coefficients (B), P-value of the Wald test and 95%CI for the coefficients. The variables with a P-value of lower than 0.1 (P< 0.150) were kept in the model. A P-value of lower than 0.05 (P< 0.05) was considered for statistical significance.

We developed a survival analysis model using the Kaplan–Meier estimator for time to reach negative RT-qPCR and time to achieve significant anti-SARS-CoV-2 IgM titers during admission based on plasma levels of TBARS and reduced thiols and using as cut-off points the physiological limits of TBARS (4.0 μmol/L) and reduced thiol (0.40 mmol/L) levels. We compared the evolution of the variables of interest as a function of time between patients with lower or equal levels and higher levels of the oxidative stress markers. The relevance of the results was evaluated using the Log-Rank test and a P-value of lower than 0.05 (P< 0.05) was considered for statistical significance.

Results

We included 61 patients (57% women) with a mean age of 83 years, and among them, 42 (69%), 31 (51%) and 15 (25%) had AHT, HLP and DM, respectively. A total of 27 (44%) suffered from chronic heart failure (HF). Approximately one of four individuals had tobacco or alcohol abuse. The mean levels of plasma TBARS and reduced thiols were 2.91 (2.06–4.13) μmol/L and 0.47 (0.40–0.55) mmol/L respectively. The median anti-SARS-CoV-2 IgM titers were 27 (40) U/mL. All the results are shown in Table 1.

Table 1

Clinical and laboratory features of the comparison groups attending to mortality.

Variables	Total sample	In-hospital fatal events		Post-discharge fatal events
				3rd month		6th month		12th month
	n = 61	No n = 57	Yes n = 4	No n = 54	Yes n = 7	No n = 52	Yes n = 9	No n = 47	Yes n = 13
Age (years)†	83 ± 7	83 ± 8	81 ± 5	83 ± 8	80 ± 4	83 ± 7	78 ± 4**	83 ± 7	81 ± 6
Sex (women)‡	35 (57)	33 (58)	2 (50)	31 (57)	4 (57)	30 (58)	5 (56)	28 (60)	6 (46)
Obesity‡	23 (38)	23 (40)	0 (0)	23 (43)	0 (0)**	23 (44)	0 (0)**	22 (47)	1 (8)**
Alcohol intake‡	15 (25)	14 (25)	1 (25)	14 (26)	1 (14)	13 (25)	2 (22)	12 (26)	3 (23)
Current/Former smokers‡	16 (26)	15 (26)	1 (25)	14 (26)	2 (29)	13 (25)	3 (33)	10 (21)	6 (46)*
AHT‡	42 (69)	38 (67)	4 (100)	35 (65)	7 (100)*	34 (65)	8 (89)	30 (64)	11 (85)
HLP‡	31 (51)	30 (53)	1 (25)	28 (52)	3 (43)	27 (52)	4 (44)	24 (51)	7 (54)
DM‡	15 (25)	15 (26)	0 (0)	15 (28)	0 (0)	13 (25)	2 (22)	13 (28)	2 (15)
HF‡	27 (44)	26 (46)	1 (25)	23 (43)	4 (57)	22 (42)	5 (56)	18 (38)	8 (62)
Cognitive impairment‡	28 (46)	25 (44)	3 (75)	24 (44)	4 (57)	23 (44)	5 (56)	22 (47)	6 (46)
Barthel index (≤ 35)‡	9 (15)	8 (14)	1 (25)	8 (15)	1 (14)	8 (15)	1 (11)	7 (15)	2 (15)
RAAS blockers‡	22 (36)	22 (39)	0 (0)	20 (37)	2 (29)	20 (39)	2 (22)	19 (40)	3 (23)
Statins‡	22 (36)	21 (37)	1 (25)	20 (37)	2 (29)	19 (37)	3 (33)	16 (34)	6 (46)
Azithromycin‡	22 (36)	19 (33)	3 (75)	17 (32)	5 (71)	17 (33)	5 (56)	13 (28)	9 (69)**
Systemic steroids‡	45 (74)	41 (72)	4 (100)	38 (70)	7 (100)	36 (69)	9 (100)*	32 (68)	13 (100)**
Oxygen therapy‡	23 (38)	20 (35)	3 (75)	19 (35)	4 (57)	18 (35)	5 (56)	17 (36)	6 (46)
FPG (mg/dL)¶	95 (35)	95 (33)	127 (249)	95 (30)	119(43)	95 (27)	119 (46)	95 (29)	117 (44)
Creatinine (mg/dL)††	0.9 (0.6–1.3)	0.9 (0.6–1.3)	0.7 (0.6–0.8)	0.9 (0.6–1.3)	0.9 (0.6–1.3)	0.9 (0.6–1.3)	0.9 (0.7–1.2)	0.9 (0.6–1.3)	0.9 (0.7–1.2)
Albumin (g/dL)††	3.8 (3.4–4.2)	3.8 (3.3–4.3)	3.6 (3.4–3.9)	3.8 (3.4–4.2)	3.7 (3.4–4.0)	3.8 (3.4–4.3)	3.7 (3.3–4.1)	3.9 (3.5–4.3)	3.5 (3.1–4.0)**
TG (mg/dL)††	109 (64–186)	107 (63–184)	138 (90–212)	109 (64–185)	118 (66–209)	108 (63–185)	119 (73–199)	108 (62–186)	120 (73–195)
LDH (IU/L)††	401 (286–563)	393 (281–550)	545 (426–697)*	394 (281–554)	466 (341–637)	396 (281–559)	434 (317–593)	3901 (282–542)	454 (313–660)
Ferritin (ng/mL)††	200 (60–666)	185 (56–613)	605 (323–1132)*	186 (55–634)	345 (140–855)	180 (55–590)	374 (120–1166)*	176 (53–590)	313 (98–996)
Fibrinogen (mg/dL)¶	472 (222)	466 (224)	495 (222)	472 (217)	391 (309)	472 (217)	423 (268)	472 (217)	438 (199)
PTC (103/μL)††	189 (127–281)	192 (129–284)	160 (103–251)	190 (128–284)	183 (123–271)	192 (128–287)	178 (124–255)	190 (127–283)	175 (128–241)
Neutrophils (103/μL)¶	3.4 (4.1)	3.3 (3.6)	8.7 (7.6)	3.2 (3.6)	7.1 (6.1)	3.2 (3.4)	7.1 (6.3)	3.1 (3.2)	6.4 (5.0)
Lymphocytes (103/μL) ††	1.2 (0.7–2.3)	1.3 (0.7–2.4)	0.7 (0.4–1.2)*	1.3 (0.7–2.4)	0.9 (0.5–1.7)	1.3 (0.7–2.4)	0.9 (0.5–1.7)	1.3 (0.7–2.5)	1.0 (0.6–1.6)*

AHT–Arterial hypertension. HLP–Hyperlipidemia. DM–Diabetes mellitus. HF–Heart failure. RAAS–Renin-angiotensin-aldosterone-system. FPG–Fasting plasma glucose. TG–Triglycerides. LDH–Lactate dehydrogenase. PTC–Platelet count. Results expressed as † refer to mean ± standard deviation. Results expressed as ‡ refer to number (percentage). Results expressed as †† refer to mean (95%CI) after back-transformation. Results expressed as ¶ refer to median (Interquartile range).

* Indicated comparison with patients without in-hospital, 3rd, 6th, or 12th month post-discharge fatal events (P < 0.10).

** Indicated comparison with patients without in-hospital, 3rd, 6th, or 12th month post-discharge fatal events (P < 0.05).

Fatal events

Results of the univariate analysis are summarized in Table 1. We did not find differences in age or sex between the comparison groups except at the 6th month post-discharge in which patients with fatal events were younger (P = 0.016). Tobacco abuse was more frequent in patients with fatal events after the 3rd month post-discharge. We found a higher frequency of AHT in the worst prognosis groups bordering statistical significance at the 3rd month post-discharge (P = 0.088). The presence of obesity was generally lower in patients with a fatal event with relevant results at the 3rd (P = 0.038), 6th (P = 0.011) and 12th (P = 0.011) months post-discharge. Except during admission, the presence of HF was higher in the groups with the worst prognosis.

For all groups, there was a higher frequency of azithromycin and systemic steroids use reaching statistical significance at the 12th month post-discharge (P = 0.009 and P = 0.026, respectively). Patients with a fatal event had higher levels of FPG than the comparison groups and individuals with a worse prognosis tended to have higher TG, LDH and ferritin levels. We saw a tendency for lower PTC and lymphocyte count with higher levels of neutrophils in patients with fatal events.

Patients who had a fatal event showed higher TBARS levels than controls measured in μmol/L and expressed as mean and 95%CI. These differences reached relevant results in the following groups: In-hospital (no: 2.84 (2.03–3.97), yes: 4.20 (2.93–6.01); P = 0.029), 3rd (no: 2.83 (2.00–3.98), yes: 3.69 (2.71–5.04); P = 0.054) and 6th (no: 2.81 (2.02–3.92), yes: 3.57 (2.42–5.26); P = 0.058) months post-discharge fatal events. The levels of reduced thiols measured in mmol/L and expressed as mean and 95%CI were lower in patients who suffered a fatal event. Such differences reached relevant results in the following groups: 3rd (no: 0.47 (0.40–0.56), yes: 0.42 (0.37–0.46); P = 0.059), 6th (no: 0.48 (0.40–0.56), yes: 0.41 (0.36–0.48); P = 0.026) and 12th (no: 0.48 (0.41–0.56), yes: 0.43 (0.36–0.51); P = 0.034) month post-discharge fatal events. The results are shown in Fig 1A and 1B.

Fig 1

Differences between plasma levels of TBARS and reduced thiols in the groups of interest.

(A) TBARS and fatal events.(B) Reduced thiols and fatal events. (C) TBARS and non-fatal events. (D) Reduced thiols and non-fatal events. The results are shown as mean ± standard deviation. TBARS–Tiobarbituric acid reactive substances. * Refers to P-value < 0.10. ** Refers to P-value < 0.05.

Non-fatal events

The results of the comparison groups are summarized in Table 2. We did not find differences in age or sex between the comparison groups. Tobacco abuse was generally more frequent in patients with a non-fatal event and we found a higher percentage of AHT in the poor prognosis groups bordering on statistical significance at the 3rd (P = 0.064) month post-discharge. The presence of HF was higher in the worst prognosis groups.

Table 2

Clinical and laboratory features of the comparison groups attending to non-fatal events.

Variables	In-hospital non-fatal events		Post-discharge non-fatal events
			3rd month		6th month		12th month
	No	Yes	No	Yes	No	Yes	No	Yes
	n = 58	n = 3	n = 44	n = 17	n = 38	n = 23	n = 28	n = 33
Age (years)†	82 ± 7	84 ± 6	83 ± 8	83 ± 5	83 ± 8	82 ± 6	82 ± 7	83 ± 8
Gender (women)‡	33 (57)	2 (67)	26 (59)	9 (53)	23 (61)	12 (52)	18 (64)	17 (52)
Obesity‡	23 (40)	0 (0)	17 (39)	6 (35)	15 (40)	8 (35)	10 (36)	13 (39)
Alcohol intake‡	15 (26)	0 (0)	10 (23)	5 (29)	8 (21)	7 (30)	5 (18)	10 (30)
Current/Former smokers‡	15 (26)	1 (33)	10 (23)	6 (35)	7 (18)	9 (39)	5 (18)	11 (33)
AHT‡	39 (67)	3 (100)	27 (61)	15 (88)*	24 (63)	18 (78)	18 (64)	24 (73)
HLP‡	29 (50)	2 (67)	21 (48)	10 (59)	18 (47)	13 (57)	13 (46)	18 (55)
DM‡	15 (26)	0 (0)	10 (23)	5 (29)	8 (21)	7 (30)	6 (21)	9 (27)
HF‡	25 (43)	2 (67)	17 (39)	10 (59)	14 (37)	13 (57)	9 (32)	18 (55)
Cognitive impairment‡	27 (47)	1 (33)	21 (48)	7 (41)	19 (50)	9 (39)	14 (50)	14 (42)
Barthel index (≤ 35)‡	9 (16)	0(0)	8 (18)	1 (6)	7 (18)	2 (9)	7 (25)	2 (6)*
RAAS blockers‡	20 (35)	2 (67)	16 (36)	6 (35)	16 (42)	6 (26)	10 (36)	12 (36)
Statins‡	20 (35)	2 (67)	15 (34)	7 (41)	12 (32)	10 (44)	9 (32)	13 (39)
Azithromycin‡	19 (33)	3 (100)**	14 (32)	8 (47)	11 (29)	11 (48)	8 (29)	14 (42)
Systemic steroids‡	42 (72)	3 (100)	31 (71)	14 (82)	26 (68)	19 (83)	20 (71)	25 (76)
Oxygen therapy‡	21 (36)	2 (67)	15 (34)	8 (47)	14 (37)	9 (39)	9 (32)	14 (42)
FPG (mg/dL)¶	95 (35)	117 (-)	94 (25)	115 (57)*	94 (26)	106 (45)	95 (28)	96 (43)
Creatinine (mg/dL)††	0.9 (0.6–1.3)	0.8 (0.7–1.0)	0.9 (0.6–1.3)	0.9 (0.6–1.4)	0.9 (0.7–1.3)	0.9 (0.6–1.3)	0.9 (0.6–1.2)	0.9 (0.7–1.3)
Albumin (g/dL)††	3.8 (3.4–4.2)	3.4 (3.0–3.9)	3.8 (3.4–4.3)	3.7 (3.4–4.1)	3.8 (3.4–4.3)	3.7 (3.4–4.2)	3.8 (3.3–4.3)	3.8 (3.4–4.2)
TG (mg/dL)††	107 (63–182)	179 (144–222)	120 (64–188)	110 (66–185)	110 (63–193)	110 (68–178)	117 (82–167)	104 (55–197)
LDH (IU/L)††	397 (282–559)	506 (423–605)	387 (283–530)	443 (300–653)	376 (281–502)	449 (305–661)**	385 (291–510)	417 (285–610)
Ferritin (ng/mL)††	193 (57–653)	387 (194–775)	179 (53–607)	266 (85–834)	186 (54–648)	225 (72–705)	239 (66–869)	172 (56–528)
Fibrinogen (mg/dL)¶	472 (216)	374 (–)	465 (228)	466 (217)	473 (227)	449 (219)	465 (222)	466 (224)
PTC (103/μL)††	189 (126–282)	205 (147–285)	181 (128–257)	213 (131–346)	179 (124–259)	208 (135–320)	169 (119–241)	209 (139–319)**
Neutrophils (103/μL)¶	3.2 (4.1)	6.3 (–)	3.2 (3.4)	4.7 (5.4)	3.1 (4.0)	4.4 (4.6)	3.1 (4.6)	3.7 (4.3)
Lymphocytes(103/μL) ††	1.3 (0.7–2.4)	0.7 (0.6–1.0)	1.3 (0.8–2.2)	1.1 (0.5–2.6)	1.3 (0.8–2.3)	1.1 (0.5–2.3)	1.4 (0.8–2.3)	1.2 (0.6–2.3)

AHT–Arterial hypertension. HLP–Hyperlipidemia. DM–Diabetes mellitus. HF–Heart failure. RAAS–Renin-angiotensin-aldosterone-system. FPG–Fasting plasma glucose. TG–Triglycerides. LDH–Lactate dehydrogenase. PTC–Platelet count. Results expressed as † refer to mean ± standard deviation. Results expressed as ‡ refer to number (percentage). Results expressed as †† refer to mean (95%CI) after back-transformation. Results expressed as ¶ refer to median (Interquartile range).

* Indicated comparison with patients without in-hospital, 3rd, 6th, or 12th month post-discharge non-fatal events (P< 0.10).

** Indicated comparison with patients without in-hospital, 3th, 6th, or 12th month post-discharge fatal events (P < 0.05).

For all groups, there was a higher frequency of acute use of azithromycin that reached statistical significance during hospital admission (P = 0.043) and the frequency of systemic steroid use was also higher in patients with a non-fatal event. We found clinical differences in TG levels between the comparison groups during admission. LDH levels and PTC were higher in patients with non-fatal events, reaching statistical significance at the 6th (P = 0.044) and 12th (0.039) month post-discharge respectively. Except for the 12th month post-discharge, higher ferritin levels showed a clinical correlation with non-fatal events.

Patients who had a non-fatal event presented higher TBARS levels than controls measured in μmol/L and expressed as mean and 95%CI. Such differences reached relevant results in the following groups: 3rd (no: 2.89 (2.03–4.13), yes: 3.31 (2.93–3.73); P = 0.074) and 6th (no: 2.77 (1.96–3.91), yes: 3.31 (2.37–4.62); P = 0.037) months post-discharge non-fatal events. The levels of reduced thiols measured in mmol/L and expressed as mean and 95%CI were lower in patients who suffered non-fatal events. Such differences reached relevant results in the following groups: In-hospital (no: 0.47 (0.40–0.56), yes: 0.37 (0.32–0.43); P = 0.019) non-fatal events. The results are extended in Fig 1C and 1D.

Multivariate analysis

We performed a multivariate analysis for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge using binary logistic regression. We considered some clinical and laboratory variables that could influence TBARS and reduced thiol levels or be relevant to the outcomes as follows: age, sex, obesity, toxic habits (tobacco abuse and alcohol intake), CVR variables (AHT, DM and HLP), HF, Barthel index, chronic treatments (statins and RAAS blockers), acute treatments (Azithromycin and systemic steroids) and levels of some analytical parameters (FPG, creatinine, albumin, ferritin, fibrinogen and LDH). Table 3 shows the model summaries and the parameters of those variables that were kept in the model for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge.

Table 3

Multiple linear regression models for fatal and non-fatal events.

Variables	B	P-value	Odds ratio	95%CI
				Inferior	Superior
In-hospital
Fatal events. P-value (Omnibus test) = 0.006, -2LL = 17.206, R2 (Nagelkerke) = 0.477, Total accuracy: 96.7%.
TBARS (μmol/L)	1.489	0.023	4.433	1.228	16.004
Neutrophils (103/μL)	0.463	0.035	1.589	1.033	2.445
PTC (103/μL)	-0.017	0.114	0.983	0.963	1.004
Non-fatal events. P-value (Omnibus test) = 0.010, -2LL = 14.674, R2 (Nagelkerke) = 0.434, Total accuracy: 95.1%.
Reduced Thiols (mmol/L)	-35.460	0.033	0.001	0.000	0.055
Fibrinogen (mg/dL)	-0.006	0.157	0.994	0.985	1.002
3rd month
Fatal events. P-value (Omnibus test) = 0.011, -2LL = 32.337, R2 (Nagelkerke) = 0.327, Total accuracy: 86.9%.
Azitromicine (yes)	2.324	0.038	10.215	1.133	92.121
TBARS (μmol/L)	0.946	0.030	2.575	1.097	6.044
Fibrinogen (mg/dL)	-0.007	0.080	0.993	0.985	1.001
Non-fatal events. P-value (Omnibus test) = 0.021, -2LL = 64.466, R2 (Nagelkerke) = 0.171, Total accuracy: 72.1%.
AHT (yes)	1.628	0.053	5.094	0.978	26.539
TBARS (μmol/L)	0.478	0.085	1.613	0.937	2.778
6th month
Fatal events. P-value (Omnibus test) = 0.013, -2LL = 40.312, R2 (Nagelkerke) = 0.285, Total accuracy: 83.6%.
Ferritin (ng/mL)	0.002	0.032	1.002	1.000	1.003
TBARS (μmol/L)	0.746	0.028	1.886	1.003	3.545
Fibrinogen (mg/dL)	-0.006	0.078	0.994	0.988	1.001
Non-fatal events. P-value (Omnibus test) = 0.018, -2LL = 72.853, R2 (Nagelkerke) = 0.167, Total accuracy: 68.9%.
LDH (IU/L)	0.003	0.065	1.003	1.000	1.007
TBARS (μmol/L)	0.472	0.076	1.603	0.951	2.703
12th month
Fatal events. P-value (Omnibus test)< 0.001, -2LL = 43.762, R2 (Nagelkerke) = 0.418, Total accuracy: 85%.
Obesity (yes)	-2.969	0.018	0.051	0.004	0.605
Tobacco abuse (yes)	1.949	0.030	7.022	1.212	40.671
Azitromicine use (yes)	1.747	0.026	5.735	1.230	26.741
Non-fatal events. P-value (Omnibus test) = 0.002, -2LL = 65.011, R2 (Nagelkerke) = 0.360, Total accuracy: 67.2%.
HF (yes)	1266	0.057	3.548	0.962	13.083
Barthel index (≤ 35)	-1.689	0.067	0.185	0.030	1.129
PTC (103/μL)	0.011	0.020	1.011	1.002	1.021
TBARS (μmol/L)	0.572	0.086	1.772	0.922	3.405
Ferritin (ng/mL)	-0.002	0.104	0.998	0.997	1.000

TBARS—Thiobarbituric acid reactive substances. PTC—Platelet count. AHT—Arterial hypertension. LDH—Lactate dehydrogenase. HF—Heart failure. -2LL—-2 Log-Likelihood.

TBARS levels were correlated with mortality during admission (B = 1.489, P = 0.023, OR = 4.433, 95%CI: 1.228‒16.004) and at the 3rd (B = 0.946, P = 0.030, OR = 2.575, 95%CI: 1.097‒6.044) and 6th (B = 0.746, P = 0.028, OR = 1.886, 95%CI: 1.003‒3.545) month post-discharge. The results for reduced thiols did not reach statistical significance.

Non-fatal events were strongly correlated with reduced thiol levels only during admission (B = -35.460, P = 0.033, OR = 0.001, 95%CI: 0.000‒0.055). We found a clinical correlation between TBARS levels and non-fatal events at the 3rd and 6th months post-discharge.

One year after hospital discharge, the variables within the model for fatal events were the presence of obesity, tobacco abuse and use of azithromycin during hospital admission. Non-fatal events were correlated with the presence of HF, Barthel index of equal to or lower than 35 points, PTC, TBARS and ferritin levels during hospital admission.

Correlation between oxidative stress markers and time to a negative RT-qPCR

Fig 2 represents a survival analysis that shows on the ordinate axis the percentage of patients with a positive RT-qPCR for SARS-CoV-2 and on the abscissa axis the time to evolution in days since the hospital admission. At the beginning, the percentage of patients with a positive RT-qPCR for SARS-CoV-2 was 100% and progressively decreased with the evolution of the disease. The black curve in Fig 2A refers to patients with reduced thiol levels equal to or less than 0.40 mmol/L, while the gray curve refers to individuals with reduced thiol levels higher than 0.40 mmol/L. The black curve in Fig 2B refers to patients with TBARS levels higher than 4.0 μmol/L, while the gray curve refers to individuals with TBARS levels equal to or less than 4.0 μmol/L. The Log-Rank test showed that there were no differences in the median of days to achieving a negative RT-qPCR for SARS-CoV-2 based on different levels of TBARS or reduced thiols.

Fig 2

Survival analysis.

Correlation between oxidative stress markers and time to a negative RT-qPCR. (A) Correlation of plasma levels of reduced thiols with time to a negative RT-qPCR. Log Rank (Mantel-Cox), P-value = 0.802; (B) Correlation of plasma levels of TBARS with time to a negative RT-qPCR. Log Rank (Mantel-Cox), P-value = 0.396. TBARS–Tiobarbituric acid reactive substances. % Positive RT-qPCR–Percentage of patients with a positive Real-time reverse transcriptase-polymerase chain reaction for SARS-CoV-2.

Correlation between oxidative stress markers and time to significant anti-SARS-CoV-2 IgM titers

Fig 3 represents a survival analysis that shows on the ordinate axis the percentage of patients with anti-SARS-CoV-2 IgM levels below the median (27 U/mL) and on the abscissa axis the time of evolution in days since the hospital admission. At the beginning, the percentage of patients with non-significant titers was 100% and progressively decreased with the evolution of the disease. The black curve in Fig 3A refers to patients with reduced thiol levels equal to or less than 0.40 mmol/L, while the gray curve refers to individuals with reduced thiol levels higher than 0.40 mmol/L. The black curves in Fig 3B refer to patients with TBARS levels higher than 4.0 μmol/L, while the gray curves refer to individuals with TBARS levels equal to or less than 4.0 μmol/L. We found that the median time to achieving anti-SARS-CoV-2 IgM titers of higher than 27 U/mL was 22 and 14 days for patients with reduced thiol levels greater than 0.40 mmol/L and lower than or equal to 0.40 mmol/L, respectively (P-value = 0.006).

Fig 3

Correlation between oxidative stress markers and time to significant anti-SARS-CoV-2 IgM titers.

(A) Correlation of plasma levels of reduced thiols with time to significant anti-SARS-CoV-2 IgM titers. Log Rank (Mantel-Cox), P-value = 0.006. (B) Correlation of plasma levels of TBARS with time to significant anti-SARS-CoV-2 IgM titers. Log Rank (Mantel-Cox), P-value = 0.841. TBARS–Tiobarbituric acid reactive substances. % SARS-CoV-2 IgM ≤ 27 U/mL–Percentage of patients with anti-SARS-CoV-2 IgM titers equal to or lower than 27 U/mL.

Discussion

The results are summarized as follows: 1) We found differences in the levels of oxidative stress markers for fatal and non-fatal events during admission and at the 3rd, 6th and 12th month post-discharge that reached clinical relevance. 2) Differences in TBARS and reduced thiol levels showed a correlation with the presence of fatal and non-fatal events during admission, respectively. 3) We found a correlation between TBARS levels and fatal events at the 3rd and 6th month post-discharge. 4) One year after hospital discharge, other predictors more related to concurrent cardiovascular risk factors and chronic diseases rather than oxidative stress markers were relevant in the models. 5) The median time to reach significant anti-SARS-CoV-2 IgM titers was lower in patients with low levels of reduced thiols.

The role of multiple clinical and laboratory markers as indicators of severity in SARS-CoV-2 infection has been widely reported [31]. Oxidative stress is a global process in which all organic molecules are involved and lipids, glucids, proteins and nucleic acids are the main acceptors of unpaired electrons from unstable ROS [32]. Several studies have established the possible role of some oxidative stress markers in the short-term prognosis of SARS-CoV-2 infection and we also found that the presence of abnormal levels of reduced thiols and TBARS was correlated with more severe disease during admission [33, 34].

Decreased plasma levels of reduced thiols and abnormalities of the thiol/disulphyde homeostasis were the most studied redox features in SARS-CoV-2 infection and represent a direct measure of protein oxidation [9, 35]. The assessment of TBARS is a non-specific but highly sensitive measure of lipid peroxidation that has been established as a global estimation of the sample oxidation level. Free radicals can attack polyunsaturated fatty acids (PUFAs) leading to the formation of lipid peroxides that can interact with other fatty acids, propagating the process [36, 37].

We found that the differences in reduced thiol levels for fatal and non-fatal events in the medium and long term were not as consistent as those during acute SARS-CoV-2 infection. However, TBARS levels were higher for fatal events both during admission and mid-term with results reaching statistical significance. Given the complexity of redox balance and the large number of involved factors, the differences observed between the two markers could have several readings.

The thiol pool is a major extracellular buffer for excess plasma ROS and represents the first line of defense to curb the impact of an acute redox imbalance [38]. The depletion of reduced thiols faced with a redox imbalance could be abrupt since the extracellular concentration of reduced thiols is substantially lower than the intracellular levels [9]. However, in the presence of reducing power, the glutathione pathway efficiently replenishes sulfhydryl groups at a rate that attempts to compensate for their consumption to reestablish the balance between the production of ROS and the reduced thiol pool [39].

An increased ROS production enhances the excessive formation of TBARS at a greater rate than the capacity of plasma antioxidant systems to neutralize them and this mismatch between production and clearance leads to their accumulation during acute processes. The natural evolution of TBARS levels consists of an initial increase in plasma concentrations that return to normal levels within weeks or months [40, 41]. However, there is a tendency toward persistently elevated plasma TBARS levels in some clinical processes and many of these clinical situations have in common the indefinite persistence of low-grade inflammation [42].

The results were quite unspecific as the number of factors that could enhance medium-long term abnormalities in oxidative stress markers increases progressively with the time after SARS-CoV-2 infection [43]. However, differences in the levels of some redox markers in the medium term would add to other clinical and analytical abnormalities extended over time that have been observed after SARS-CoV-2 infection. In this line, the presence of remnant and underlying inflammation in predisposed individuals could be behind syndromes such as the long or persistent COVID [44].

We observed differences in PTC, ferritin and LDH levels between some groups of patients, especially in the mid and long term outcomes. Although in some cases the results did not correspond to differences in TBARS or reduced thiols between the groups, these inflammatory markers might suggest the persistence of endothelial dysfunction, platelet activation and redox imbalance after SARS-CoV-2 infection as in other processes already explored [20, 45]. As time since SARS-CoV-2 infection progresses, other variables became relevant in the prognostic differences between the groups. The greatest impact on morbidity and mortality in elderly individuals with multiple CVRFs and comorbidities is consistent with the presence of some chronic diseases with intercurrent exacerbations [46].

More controversial could be the correlation found between the time of adaptative immune response development and the levels of plasma reduced thiols during admission. A decrease in reduced thiols has already been involved in increased viral virulence due to a greater ability to enter the cell via the ACEI receptor [47]. The higher rate of SARS-CoV-2 entry into the cell due to and abnormal thiol/disulphite balance may be a determinant for an earlier and more intense immune response, which in turn has been associated with a severe inflammatory response and clinical pictures of worse prognosis, although further studies are needed [33].

Factors influencing the time to a negative RT-qPCR have been extensively studied and stronger inflammatory responses were associated with better viral clearance rates [24]. However, there is insufficient evidence to suspect that redox imbalance may be involved and we found no differences in SARS-CoV-2 clearance time between the comparison groups based on TBARS or reduced thiol levels.

The role of redox imbalance in the prognosis of SARS-CoV-2 infection in the short, medium and long term remains underexplored. The relationship between unfavorable redox status and the presence of persistent, underlying low-grade inflammation may partly explain some fatal and non-fatal events following SARS-CoV-2 infection as in other clinical processes. The role of SARS-CoV-2 infection in the inflammatory response has already been addressed but factors related to the adaptive immune response appear complex and less known. Redox imbalance might be one more variable in the efficiency of the specific immune response. However, more scientific evidence is needed.

Limitations and strengths

This was a prospective single-center study of real clinical practice conducted in white elderly patients with SARS-CoV-2 infection from the northwest region of Spain (Galicia). Our results should be interpreted with caution when applying them to other population, race or ethnicity.

The selection of a small sample from the total number of patients admitted to the unit was a limitation that could influence the validity and accuracy of the results. Clinical and laboratory parameters, including redox markers, were measured at one point without monitoring. Although we attempted to identify the factors that could be involved in the redox status and prognosis of SARS-CoV-2 infection, some relevant variables may not have been explored. The existence of differences between the comparison groups could also be a limitation to the results of the survival analysis. Furthermore, some results should be viewed only as a clinical trend due to the limited number of events in some categories.

The assessment of TBARS and reduced thiols are only two of the multiple oxidative stress markers available to evaluate plasma oxidation and may therefore reflect a partial view of the true redox status. Furthermore, decreased reduced thiols and increased TBARS levels have low specificity for protein oxidation and lipid peroxidation, respectively. However, these procedures are quite sensible and offer a global estimation of plasma redox imbalance. The use of azithromycin and other acute treatments was higher in patients with a worse prognosis. However, A poor clinical evolution may justify a greater use of these therapies. The lower frequency of obesity in patients with worse outcomes coincided with a clinical profile of protein-calorie malnutrition in these patients on a case-by-case review which could partly explain the correlation with a worse prognosis.

8 Jun 2022

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Reviewer #1: In the manuscript entitled "Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications. An observational study", the authors presented an attempt to evaluate the possible correlation between several biochemical and physiological parameters and SARS-CoV-2 infection in elderly patients. I have major and minor concerns regarding the manuscript in its current form. Here are SOME of my major concerns:

1- The collected data is rich and numerous. However, the collected data definitely needs deeper analyses.

2. The interpretation of the data is not always clearly supported by the results.

3. Not all data is discussed and analyzed.

4. I am not an expert on statistics, but I feel that the statistical analysis provided in the manuscript needs double-checking given the complexity of the correlation among different tested parameters.

5. The organization of the manuscript, especially in the RESULTS section, is very weak. The sub-sections are literally "2 words" each!

6. The figures are very hard to read and analyze.

7. The writing of the manuscript needs major revision. The English language and the structuring of the sentences is very poor.

Reviewer #2: This is an interesting paper exploring a very intriguing correlation, ie the one between Oxidative Stress and COVID-19 infection

The only issue with this paper is that as compared to those used in many other papers, the biomarker of oxidative stress used here are not among the ones that are currently employed and are only two. Indeed, the majority of papers dealing with plasmatic biomarkers of oxidative stress use a number of different biomarkers e.g. 8-iso-prostaglandin F2α (8-iso-PGF2α), advanced oxidative protein products (AOPPs), protein carbonyl (PCO), glutathione peroxidase-3 (GPX-3), paraoxonase-1 (PON1). Other papers also analyze superoxide dismutase (SOD), Malondialdehyde (MDA), Glutathione reduced (GSH), and oxidated (GSSG).

In my opinion, this manuscript requires the assessment of, at least, another biomarker among those mentioned above in order to make a more solid correlation between Oxidative Stress and COVID-19 infection.

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25 Aug 2022

Dear Editor and Reviewers,

First, we would like to thank you for giving us the opportunity to share our research with you and your editorial group and for your time and dedication in the evaluation of the manuscript. Your recommendations, suggestions and advice were very much considered and undoubtedly contributed to improving the study.

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1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

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We ensure that our manuscript met PLOS ONE's style requirements, including those for file naming, and followed the style templates.

2. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

We have provided the required details regarding participant consent in the ethics statement in the Methods section and online submission information.

Section: Material and Methods. Subsection: Ethical approval: Paragraph Nº 1: “””. . .The Research Ethics Committee of Santiago de Compostela-Lugo approved this study (reference number 2020/578). All procedures were in accordance with the ethical standards of the responsible committee on human experimentation and with the Helsinki Declaration of 1975. Written informed consent was obtained from all patients for being included in the study. . .”””

3. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

The data was not a core part of the research being presented in the study, so we agreed to remove the phrase that referred to these data in the results section.

Section: Results. Paragraph Nº 1: “””. . . We included 61 patients (57% women) with a mean age of 83 years, and among them, 42 (69%), 31 (51%) and 15 (25%) had AHT, HLP and DM, respectively. A total of 27 (44%) suffered from chronic heart failure (HF). Approximately one of four individuals had tobacco or alcohol abuse. . .”””

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Data relevant to the results will be fully anonymized and incorporated into a repository recommended by your publisher.

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The anonymized database will be available from Open Science Framework (DOI: 10.17605/OSF.IO/R3S8U)...”””

To the attention of the Reviewer 1:

After carefully reading the article again and supported by your perspective, the authors agreed that the manuscript needed a profound restructuring of the statistical methodology and an inevitable redrafting of the results and discussion sections. Additionally, we have discussed the main limitations of the methodology and results in the corresponding subsection. Given the magnitude of the amendments, the response to all your concerns will be accompanied by representative text fragments of the complete modifications. Without further ado, we proceed to respond to your concerns.

1. The collected data is rich and numerous. However, the collected data definitely needs deeper analysis.

Given the unusual and worrying pandemic situation and the absence of scientific evidence in SARS-CoV-2 infection at that time, our objective was to carefully collect as much information as possible. We aimed to perform a descriptive analysis showing the most representative picture of the elderly patients admitted with SARS-CoV-2 infection to an Internal Medicine Department. However, we understand that total events at the 12th month was an ambiguous and non-specific variable that only yielded confusion, so we restructured the univariate and multivariate analysis by decomposing total events into fatal and non-fatal events during admission and at the 3rd 6th, and 12th months post-discharge.

Section: Material and Methods. Subsection: Outcomes. Paragraph Nº 1: """. . .Fatal events were referred to medical circumstances that led to the patient's death. We grouped them into the following categories: I) In-hospital fatal events; II) 3rd month post-discharge fatal events; III) 6th month post-discharge fatal events; and IV) 12th month post-discharge fatal events. Non-fatal events were grouped into the following categories: I) in-hospital non-fatal events, which referred to the need for transfer during admission to a critical respiratory care unit; and II) post-discharge non-fatal events, which referred to the need for readmission to hospital for respiratory or cardiovascular disease within the 3rd, 6th, or 12th month post-discharge. . ."""

We acknowledge that some results of the univariate analysis were not evaluated in depth since the total number of variables collected far exceeded the number of variables that it would be advisable to combine for a good performance in the multivariate analysis [1]. To increase the analysis dept, we decided to limit the number of included clinical and laboratory variables to those that could have the greatest explanatory capacity or influence on the results.

The small number of observations in some categories also limited the inclusion of some variables in the binary logistic regression for deeper analysis. As we explained in the text, it is possible that some variables evaluated in the univariate analysis were excluded from the multivariate analysis. However, we tried to ensure that those variables collected that could influence the redox balance and the outcomes were included.

Section: Discussion. Subsection: Limitations and strengths. Paragraph Nº 2: """...The selection of a small sample from the total number of patients admitted to the unit was a limitation that could influence the validity and accuracy of the results… Although we attempted to identify the factors that could be involved in the redox status and prognosis of SARS-CoV-2 infection, some relevant variables may not have been explored…"""

2. The interpretation of the data is not always clearly supported by the results.

We apologize if at any time we were too enthusiastic or categorical in the interpretation of the results. Given the limitations of the multivariate analysis, we understand that some comments in the discussion may not have been adequately supported by the results. With the redrafting of the manuscript, we intended to offer the results with greater resolution, avoiding the concept of total events at the 12th month and focusing on fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge. The new organization of the results also entailed rebuilding of the discussion section to deal with each outcome in a more concrete and detailed way.

3. Not all data is discussed and analyzed.

We found a mismatch between the outcomes that were evaluated in the univariate and multivariate analysis so that fatal and non-fatal events during admission and at the 3rd, 6th, and 12th months post-discharge were not seen in depth, while the 12th month total events were not explicitly evaluated in the univariate analysis. As we explained in previous points, we avoided general outcomes and divided this variable into eight different variables (fatal and non-fatal events during admission and at the 3rd, 6th, and 12th months post discharge). Additionally, the results of the univariate analysis were examined in depth using a multivariate procedure. The discussion focused on the results that reached clinical relevance or statistical significance in the multivariate analysis.

4. I am not an expert on statistics, but I feel that the statistical analysis provided in the manuscript needs double-checking given the complexity of the correlation among different tested parameters.

Thanks to your comments, we realized that the statistical methodology subsection needed a thorough evaluation and the implementation of changes in both format and content, so we worked on a deep remodeling of the subsection. Two researchers conducted the statistical procedures independently, achieving concordance in the results. The medical records were reviewed to reduce missing values to below 5% and the remaining data were imputed as the sample median or mean depending on the distribution. After a new in-depth exploratory analysis of the quantitative variables, most of the analytical variables failed to meet some assumption of normality. Since parametric tests provide more powerful results than non-parametric tests, we first attempted to perform a logarithmic transformation of these non-normally distributed variables. The univariate results of the log-normally distributed variables were them back-transformed and expressed as mean and 95%CI [2]. The main methodological amendments are detailed as follows:

A) Detailed description of the methodology related to the descriptive and univariate analysis.

Section: Material and Methods. Subsection: Statistical analysis. Paragraph nº 1: """. . . Statistical analysis was performed using SPSS 22.0 statistical software (SPSS Inc, Chicago, IL). First, we performed a descriptive analysis in which the frequencies of qualitative variables were expressed as number (n) and percentage (%). The Kolmogorov-Smirnov test was used to determine whether quantitative continuous variables were normally distributed. For those variables with a non-normal distribution, we performed a logarithmic transformation. Normally distributed variables were expressed as mean (m) and standard deviation (± SD), normally distributed variables after transformation were back-transformed and expressed as mean with 95% confidence interval (95%CI) and non-normally distributed ones were expressed as median and interquartile range (IQR). A missing value analysis was carried out on those variables with more than 5% missing values. We performed a comparative analysis between the groups of patients according to the outcome variables. The chi-square test was used to compare categorical variables, while quantitative variables were compared using the Student’s t-test or Mann–Whitney U test as appropriate. . ."""

B) Re-foundation of the multivariate analysis using binary logistic regression for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge and including in the models the variables that could influence redox markers and outcomes. We removed the results corresponding to total events, including the ROC curve model due to redundancy, unspecificity and ambiguity.

Section: Material and Methods. Subsection: Statistical analysis. Paragraph no. 2: """. . . We performed a binary logistic regression analysis for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge using a non-automatic analysis procedure. Variables that showed clinical or statistical relevant differences (P-value < 0.1) in the univariate analysis were included. The validity of the model was evaluated using the Omnibus test in which a P-value of lower than 0.05 was considered necessary to assume that the current model was better than the null model. We provided a model summary with the deviance (-2 log-likelihood ratio test (-2LL)), coefficient of determination (R2) and the overall accuracy score. The parameters of those variables in the model were the non-standardized Beta coefficients (B), P-value of the Wald test and 95%CI for the coefficients. The variables with a P-value of lower than 0.1 (P< 0.150) were kept in the model. A P-value of lower than 0.05 (P< 0.05) was considered for statistical significance. . ."

C) Review of the survival analysis with emphasis on a better description of the statistical methodology and its application to our data.

Section: Material and Methods. Subsection: Statistical analysis. Paragraph nº 3: """. . .We developed a survival analysis model using the Kaplan–Meier estimator for time to reach negative RT-qPCR and time to achieve significant anti-SARS-CoV-2 IgM titers during admission based on plasma levels of TBARS and reduced thiols and using as cut-off points the physiological limits of TBARS (4.0 µmol/L) and reduced thiol (0.40 mmol/L) levels. We compared the evolution of the variables of interest as a function of time between patients with lower or equal levels and higher levels of the oxidative stress markers. The relevance of the results was evaluated using the Log-Rank test and a P-value of lower than 0.05 (P< 0.05) was considered for statistical significance. . ."""

5. The organization of the manuscript, especially in the RESULTS section, is very weak. The sub-sections are literally "2 words" each!

The results section was redrafted on the basis of the profound modifications in the statistical methodology as follows:

A) As we provided in the text, the subsections corresponding to the univariate analysis for fatal and non-fatal events described in depth the differences between the comparison groups considering the methodological modifications.

Section: Results. Subsection: Fatal events. Paragraph No. 3: """. . . Patients who had a fatal event showed higher TBARS levels than controls measured in μmol/L and expressed as mean and 95%CI. These differences reached relevant results in the following groups: In-hospital (no: 2.84 (2.03–3.97), yes: 4.20 (2.93–6.01); P= 0.029), 3rd (no: 2.83 (2.00–3.98), yes: 3.69 (2.71–5.04); P= 0.054) and 6th (no: 2.81 (2.02–3.92), yes: 3.57 (2.42–5.26); P= 0.058) months post-discharge fatal events. The levels of reduced thiols measured in mmol/L and expressed as mean and 95%CI were lower in patients who suffered a fatal event. Such differences reached relevant results in the following groups: 3rd (no: 0.47 (0.40–0.56), yes: 0.42 (0.37–0.46); P= 0.059), 6th (no: 0.48 (0.40–0.56), yes: 0.41 (0.36–0.48); P= 0.026) and 12th(no: 0.48 (0.41–0.56), yes: 0.43 (0.36–0.51); P= 0.034) month post-discharge fatal events. The results are shown in Fig 1A and 1B.. . ."""

Section: Results. Subsection: Non-fatal events. Paragraph No. 3: """. . .Patients who had a non-fatal event presented higher TBARS levels than controls measured in μmol/L and expressed as mean and 95%CI. Such differences reached relevant results in the following groups: 3rd (no: 2.89 (2.03–4.13), yes: 3.31 (2.93–3.73); P= 0.074) and 6th (no: 2.77 (1.96–3.91), yes: 3.31 (2.37–4.62); P= 0.037) months post-discharge non-fatal events. The levels of reduced thiols measured in mmol/L and expressed as mean and 95%CI were lower in patients who suffered non-fatal events. Such differences reached relevant results in the following groups: In-hospital (no: 0.47 (0.40–0.56), yes: 0.37 (0.32–0.43); P= 0.019) non-fatal events. The results are extended in Fig 1C and 1D.…"""

B) In accordance with the methodological modifications, the tables for the results of the univariate analysis were completely redrawn. In order to reduce confusion in the presentation of the results, we eliminated the supplementary tables and completed the main tables with the most relevant clinical and analytical variables.

C) For the results of the multivariate analysis, we include a brief description of the binary logistic regression models focusing on the variables included, model reliability, summary statistics and parameters of the relevant variables. We also summarized the results in Table 3.

Section: Results. Subsection: Multivariate analysis. Paragraph 1: """...We performed a multivariate analysis for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge using binary logistic regression. We considered some clinical and laboratory variables that could influence TBARS and reduced thiol levels or be relevant to the outcomes as follows: age, sex, obesity, toxic habits (tobacco abuse and alcohol intake), CVR variables (AHT, DM and HLP), HF, Barthel index, chronic treatments (statins and RAAS blockers), acute treatments (Azithromycin and systemic steroids) and levels of some analytical parameters (FPG, creatinine, albumin, ferritin, fibrinogen and LDH). Table 3 shows the model summaries and the parameters of those variables that were kept in the model for fatal and non-fatal events during admission and at the 3rd, 6th and 12th months post-discharge…"""

D) For the survival analysis, we made a detailed description of the evolution of the variable studied over time based on the levels of the redox markers.

Section: Results. Subsection: Correlation between oxidative stress markers and time to a negative RT-qPCR. Paragraph Nº 1: """… Fig 2 represents a survival analysis that shows on the ordinate axis the percentage of patients with a positive RT-qPCR for SARS-CoV-2 and on the abscissa axis the time to evolution in days since the hospital admission. At the beginning, the percentage of patients with a positive RT-qPCR for SARS-CoV-2 was 100% and progressively decreased with the evolution of the disease. The black curve in Fig 2A refers to patients with reduced thiol levels equal to or less than 0.40 mmol/L, while the gray curve refers to individuals with reduced thiol levels higher than 0.40 mmol/L. The black curve in Fig 2B refers to patients with TBARS levels higher than 4.0 µmol/L, while the gray curve refers to individuals with TBARS levels equal to or less than 4.0 µmol/L. The Log-Rank test showed that there were no differences in the median of days to achieving a negative RT-qPCR for SARS-CoV-2 based on different levels of TBARS or reduced thiols…"""

Section: Results. Subsection:Correlation between oxidative stress markers and time to significant anti-SARS-CoV-2 IgM titers. Paragraph Nº 1: """… Fig 3 represents a survival analysis that shows on the ordinate axis the percentage of patients with anti-SARS-CoV-2 IgM levels below the median (27 U/mL) and on the abscissa axis the time of evolution in days since the hospital admission. At the beginning, the percentage of patients with non-significant titers was 100% and progressively decreased with the evolution of the disease. The black curve in Fig 3A refers to patients with reduced thiol levels equal to or less than 0.40 mmol/L, while the gray curve refers to individuals with reduced thiol levels higher than 0.40 mmol/L. The black curves in Fig 3B refer to patients with TBARS levels higher than 4.0 umol/L, while the gray curves refer to individuals with TBARS levels equal to or less than 4.0 umol/L. We found that the median time to achieving anti-SARS-CoV-2 IgM titers of higher than 27 U/mL was 22 and 14 days for patients with reduced thiol levels greater than 0.40 mmol/L and lower than or equal to 0.40 mmol/L, respectively…"""

6. The figures are very hard to read and analyze.

We made changes in the format of the figures to improve readability and interpretation, including font modification, change to the required file format and dimension setting.

A) Fig 1: We increased the font size, removed the images corresponding to total events and corrected some mistakes in the labels since they did not coincide with the results shown in the text.

B) Fig 2: Based on the previous points, we decided to disregard the ROC curve model.

C) Fig 3 was renamed Fig 2. We incorporated a concise caption and a legend describing in detail the variables evaluated. The information in the figure is completed with that of the text in the corresponding subsection.

Section: Results. Subsection: Correlation between oxidative stress markers and time to a negative RT-qPCR. Fig 2: “””. . . % positive RT-qPCR– Percentage of patients with a positive Real-time reverse transcriptase-polymerase chain reaction for SARS-CoV-2. . .”””

D) Fig 4 was renamed Fig 3. We incorporated a concise caption and a legend describing in detail the variables evaluated. The information in the figure is completed with that of the text in the corresponding subsection.

Section: Results. Subsection: Correlation between oxidative stress markers and time to significant anti-SARS-CoV-2 IgM titers. Fig 3: “””...% SARS-CoV-2 IgM ≤ 27 U/mL–Percentage of patients with anti-SARS-CoV-2 IgM titers equal to or lower than 27 U/mL...”””

7. The writing of the manuscript needs major revision. The English language and the structuring of the sentences is very poor.

After your advice, we subscribed to a premium version of a prestigious next-gen grammar correction and language enhancement writing assistant designed for academic and technical writing named Trinka AI with the subscription ID sub_1LRZjGBOAkLRyixTEeCTbnhM (https://www.trinka.ai/es/). Many of the specific structural modifications implemented in the manuscript in the different sections corresponded to the recommendations of the writing assistant.

Closing remarks

Dear Reviewer 1.

We endeavored to adhere to your concerns in making all the relevant changes, which have meant a real revolution in the design of the manuscript. However, we firmly believe that the new version is better than the previous one and we thank you very much for your collaboration. We would only like to emphasize that the essence of the article and therefore an aspect that differentiates it from other studies on oxidative stress and prognosis in SARS-CoV-2 infection is the insight it provides in the medium and long term on a possible role of redox imbalance in the prognosis of patients beyond the acute stage of SARS-CoV-2 infection.

Thank you very much in the name of the group.

Kind regards;

Nestor Vazquez Agra

To the attention of the Reviewer 2:

Thank you for your contribution to this manuscript. Without further ado, we proceed to address your concerns.

1. The only issue with this paper is that as compared to those used in many other papers, the biomarker of oxidative stress used here are not among the ones that are currently employed and are only two. Indeed, the majority of papers dealing with plasmatic biomarkers of oxidative stress use a number of different biomarkers e.g. 8-iso-prostaglandin F2α (8-iso-PGF2α), advanced oxidative protein products (AOPPs), protein carbonyl (PCO), glutathione peroxidase-3 (GPX-3), paraoxonase-1 (PON1). Other papers also analyze superoxide dismutase (SOD), Malondialdehyde (MDA), Glutathione reduced (GSH), and oxidated (GSSG).

In my opinion, this manuscript requires the assessment of, at least, another biomarker among those mentioned above in order to make a more solid correlation between Oxidative Stress and COVID-19 infection.

When assessing oxidative stress, the quantification of reactive oxygen species (ROS) is very laborious and inaccurate due to their instability and short half-life. However, the reaction of free radicals with organic molecules generates organic products derived from the oxidation of carbohydrates, lipids, proteins and nucleic acids that are more stable and allow a more accurate assessment of oxidative stress [3].

Malondialdehyde (MDA), 8-iso-prostaglandin F2a (8-iso-PGF2a) and 4-hydroxynonenal (4-HNE) among others are by-products of lipid peroxidation and the assessment of TBARS is a measure of MDA levels. Although most MDA comes from lipid peroxidation of polyunsaturated fatty acids (PUFAs), it can also be the end product of oxidation of other biomolecules oxidation such as proteins. Therefore, as mentioned in the text, the weakness of these markers is their lack of specificity. However, it provides an estimation of the level of lipid peroxidation and an overall view of the level of oxidation of the sample [4].

The assessment of reduced (GSH) and oxidized (GSSG) Glutathione, thiol/disulphide balance and reduced thiols are estimators of the plasma pool of sulfhydryl groups, which are one of the main lines of defense against oxidative stress. Although the major source of thiols are plasma proteins and specifically albumin, there are free thiols and thiols forming part of other biomolecules such as glutathione or carbohydrates. The measurement of reduced plasma thiols in a non-proteinized sample is a measure of the level of protein oxidation. However, as we discussed for TBARS, the decrease in reduced thiols is the common pathway of several biomolecuolas oxidation. Thus, it provides an estimation of the level of protein oxidation and an overall view of the level of oxidation of the sample [5].

The evaluation of TBARS and reduced thiols during SARS-CoV-2 infection and their possible impact in the short, medium or even long term seemed very attractive to us as it had not yet been addressed and provided us with a global and very sensitive view of the levels of plasma oxidation in patients with SARS-CoV-2 infection. However, the measurement of other biomolecules derived from oxidative stress or the implementation of enzymatic method to evaluate the redox status would have provided greater value to the results and strength to the conclusions. Additionally, the use of more specific techniques, such as those based on chromatography, would have added specificity and precision to the estimates.

The limited number of techniques and their lack of specificity were limitations that we noted in the corresponding section.

Section: Discussion. Subsection: Limitations and Strengths. Paragraph Nº 3: """. . .The assessment of TBARS and reduced thiols are only two of the multiple oxidative stress markers available to assess plasma oxidation and may therefore reflect a partial view of the true redox status. Furthermore, decreased reduced thiols and increased TBARS levels have low specificity for protein oxidation and lipid peroxidation, respectively. However, these procedures are quite sensible and offer a global estimation of plasma redox imbalance. . ."""

Closing remarks

Dear Reviewer 2.

As you well commented, most studies on oxidative stress and SARS-CoV-2 employ a greater number and variety of redox markers, thus providing more robust results on short-term prognosis during SARS-CoV-2 infection. However, our main objective was to globally evaluate the impact of a prooxidant internal milieu during SARS-CoV-2 infection on prognosis also in the medium and long term. We are aware of these limitations and it is our duty to highlight them.

Thank you very much in the name of the group.

Kind regards;

Nestor Vazquez Agra

References

1. Stoltzfus JC. Logistic regression: a brief primer. Acad Emerg Med. 2011;18: 1099–1104. doi:10.1111/j.1553-2712.2011.01185.x

2. West RM. Best practice in statistics: The use of log transformation. Ann Clin Biochem. 2022;59: 162–165. doi:10.1177/00045632211050531

3. Jakubczyk K, Dec K, Kałduńska J, Kawczuga D, Kochman J, Janda K. Reactive oxygen species - sources, functions, oxidative damage. Pol Merkur Lekarski. 2020;48: 124–127.

4. Tsikas D. Assessment of lipid peroxidation by measuring malondialdehyde (MDA) and relatives in biological samples: Analytical and biological challenges. Anal Biochem. 2017;524: 13–30. doi:10.1016/j.ab.2016.10.021

5. Turell L, Radi R, Alvarez B. The thiol pool in human plasma: the central contribution of albumin to redox processes. Free Radic Biol Med. 2013;65: 244–253. doi:10.1016/j.freeradbiomed.2013.05.050

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19 Sep 2022

Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications in the medium and long term

PONE-D-22-13272R1

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Reviewer #1: All my comments have been addressed. The revised manuscript is a much enhanced version compared to the original one. However, I must stress again that I am not an expert on statistics, and I cannot make any judgement on the accuracy of the statistical analyses provided in this manuscript. In addition, I still believe that the manuscript should be re-checked for any linguistic (typographical and grammatical) mistakes. For example, "adaptative immune response" should read "adaptive immune response", "However, A poor clinical evolution" should read "However, a poor clinical evolution", "due to and abnormal thiol/disulphite balance" should read "due to an abnormal thiol/disulphite balance", ...., etc.

Reviewer #2: The authors have addressed all my concerns. Thanks !...............................................

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27 Sep 2022

PONE-D-22-13272R1

Assessment of oxidative stress markers in elderly patients with SARS-CoV-2 infection and potential prognostic implications in the medium and long term

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