Clinical-functional vulnerability, but not oxidative stress markers, is associated with self-perceived post-COVID sequelae in community-dwelling older adults: a six-month longitudinal study

Abstract

Oxidative stress has been proposed as a central mechanism in post-COVID condition pathophysiology; however, its relationship with subjective symptom burden in older adults remains uncertain. This longitudinal study investigated whether oxidative stress markers are associated with self-perceived postCOVID sequelae in community-dwelling older adults over six months. Fifty-five individuals with confirmed SARS-CoV-2 infection were included, and 49 had complete laboratory data. Hematological, biochemical, and oxidative stress markersincluding thiobarbituric acid reactive substances (TBARS), protein carbonylation, and DCFH-DA derived reactive oxygen species (ROS)were assessed at baseline and six months. Self-perceived sequelae were measured using a Likert scale, and clinical-functional vulnerability was evaluated with the IVCF-20. Over time, TBARS levels increased and protein carbonylation decreased, while ROS levels remained stable. Some biochemical markers showed significant variation, although mean values remained within reference ranges. No significant associations were observed between oxidative stress markers and self-perceived post-COVID impact. In contrast, greater clinical-functional vulnerability, particularly memory-related domains, was significantly associated with increased perceived burden. Most oxidative changes occurred independently of classical risk factors such as age, sex, diabetes, and obesity. These findings suggest that subjective post-COVID impact in older adults is more strongly related to cognitive and functional vulnerability than to persistent systemic oxidative imbalance.

Key words
Age-related outcomes; long-term impact; oxidative damage; SARS-CoV-2; self-reported symptoms

INTRODUCTION

The COVID-19 pandemic, caused by SARS-CoV-2, has had profound global health consequences (Coronaviridae Study Group of the International Committee on Taxonomy of Viruses 2020, Lu et al. 2020). Although the acute phase has subsided, a considerable proportion of individuals continue to experience persistent symptoms, a condition referred to as post-COVID-19 condition (PCC). PCC is characterized by heterogeneous multisystem manifestations that may include respiratory, cardiovascular, neurological, and functional impairments, significantly affecting quality of life (Huang et al. 2021, SeyedAlinaghi et al. 2023). Cognitive dysfunction—often described as “brain fog”—is among the most frequently reported sequelae and encompasses memory deficits, attention disturbances, and executive dysfunction (Pettemeridou et al. 2025).

Emerging evidence suggests that oxidative stress and persistent immune activation may contribute to the pathophysiology of PCC. SARS-CoV-2 infection has been associated with mitochondrial dysfunction, excessive production of reactive oxygen species (ROS), endothelial injury, and sustained low-grade inflammation (Szögi et al. 2025, Mironova et al. 2026). These mechanisms are closely interconnected, as mitochondrial ROS generation can amplify inflammatory signaling and neuroinflammatory cascades. Importantly, even non-hospitalized individuals may exhibit systemic oxidative stress that persists beyond the acute phase and has been linked to post-COVID manifestations (Vlaming-van et al. 2024).

Older adults represent a particularly vulnerable group due to immunosenescence, higher prevalence of comorbidities, and reduced physiological reserve (Dessie & Zewotir 2021). However, oxidative stress is also intrinsically associated with aging and chronic conditions such as diabetes, making it challenging to disentangle infection-related redox alterations from age-related baseline changes.

Given these considerations, we conducted a six-month longitudinal study to investigate whether systemic oxidative stress markers—lipid peroxidation, protein carbonylation, and global ROS levels—are associated with self-perceived post-COVID sequelae in community-dwelling older adults, while accounting for established risk factors and clinical-functional vulnerability.

MATERIALS AND METHODS

Study design and ethical issues

This is a prospective longitudinal study of community-dwelling older adults, with a six-month follow-up, conducted in the city of Manaus, in the state of Amazonas (Brazil). Manaus recorded a high number of COVID-19 cases and can be considered one of the cities with the highest infection and mortality rates in the world in 2020, the first year of the pandemic (He et al. 2023). The study investigated the potential association of oxidative markers with the self-perception of post-COVID sequelae determined through a Likert scale constructed for this purpose. Potential socioeconomic, cultural, anthropometric, and health factors, including medication use, were evaluated, as well as hematological and biochemical parameters that influenced both the self-perception of post-COVID sequelae and the levels of oxidative markers.

For its execution, the study was divided into four main stages (Figure 1): (1) selection of volunteer older adults volunteers and informed consent signed; (2) Collection of anthropometric and physiological markers (weight, height, waist circumference, blood pressure), as well as information obtained through a structured interview regarding socioeconomic and cultural profile, lifestyle, and health status, similar to that described by Maia et al. (2013), and application of a Likert scale; (3) Application of the Clinical-Functional Vulnerability Index–20 (IVCF-20), which assesses the risk of vulnerability in older adults (Freitas & Soares 2019); (4) evaluation of hematological, biochemical and oxidative blood markers; (5) These analyses, using the same procedures, were replicated six months after the participant’s inclusion in the study, enabling comparison of potential variations in blood-based functional and oxidative markers, the vulnerability profile assessed by the Clinical-Functional Vulnerability Index–20 (IVCF-20), and COVID-19–related questions structured in the form of a Likert scale.

Figure 1
General experimental design of the study. After the selection of older adults volunteers and the signing of the informed consent form to participate in the research, the following four stages were conducted. The stages included the analysis of sociodemographic aspects and the context of SARS-CoV-2 infection through a structured questionnaire, clinical-functional factor analysis via IVCF-20, blood collection for the evaluation of hematological, biochemical, and oxidative stress parameters, and the development and application of a Likert-type scale for self-perception of sequelae. Hematological, biochemical, and oxidative assessments and the application of the Likert scale were performed at two distinct time points, namely at admission (first assessment) and 6 months later (second assessment).

The study was approved by the Ethics Research Committee of the State University of Amazonas (CEP-UEA, Manaus, Brazil), under the protocol number CAEE: 47914221.1.1001.5016, and was conducted following Resolution No. 340 of the National Commission for Research Ethics (CONEP, Brazil) and the Helsinki Declaration. All older adults participants provided signed informed consent.

Sample Selection Criteria

The study was conducted using a convenience sample of 55 older adults (≥ 60 years) participating in extension activities at the Open University Foundation for the Third Age (FUnATI), located in Manaus, Amazonas, Brazil (an accessible population of approximately 3,000 individuals). As this was a non-probabilistic sampling strategy, the findings should not be generalized to the entire older population served by the institution. The sample size provides greater sensitivity to detect changes of moderate magnitude over time, but may be limited in identifying small effects, particularly in multivariate analyses with multiple predictors.

The inclusion criteria were: (1) older adults with a prior confirmed diagnosis of SARS-CoV-2 infection between 2020 and 2021, based on clinical data obtained from the medical records of the FUnATI health polyclinic; all participants had their infection confirmed by molecular testing using real-time reverse transcription polymerase chain reaction (RT-PCR) performed on nasopharyngeal swab samples; and (2) ability to provide informed consent.

The exclusion criteria were: presence of cognitive impairment preventing adequate understanding or completion of the questionnaire; active acute infection at the time of data collection; and severe decompensated chronic illness that could interfere with participation in the study assessments.

Health Assessment and Sociodemographic Aspects

It is well-known that vulnerable populations were the most affected by COVID-19, and the impact of social inequalities emerges as one of the multiple factors capable of influencing health and well-being (Lewis et al. 2022). Therefore, a structured questionnaire consisting of 30 questions was administered, encompassing general information such as gender and age, as well as socioeconomic and cultural aspects, educational level, and race/ethnicity. Concerning the disease, the questionnaire covered the period of SARS-CoV-2 infection, vaccination status, number of doses received, type of vaccine, the occurrence of hospitalization and length of hospital stay, use and type of ventilatory support, and administration of drugs promoted as “preventive”. Additionally, vital signs of the older adults individuals were measured (blood pressure, temperature, and oxygen saturation), as well as weight, height, and circumference calf and abdominal. Information on previous comorbidities and medications used by the volunteers was collected directly from the participants’ medical records, thereby minimizing the risk of inaccurate reporting.

Application of the Clinical Functional Vulnerability Index (IVCF-20)

The IVCF-20 is a tool with 20 questions distributed across eight categories: age, self-rated health, functional abilities, cognition, emotions, mobility, communication, and additional medical conditions. The maximum possible score is 40 points, and older adults individuals with a score equal to or higher than 7 are classified as pre-frail (Sena et al. 2021).

Blood Collection and Sample Process

Participants were instructed to fast for eight hours, after which 20 mL of blood was collected by venipuncture. Approximately 4 mL of blood was placed in serum gel tubes for biochemical analyses, while the remainder was divided into tubes containing EDTA for hematological and oxidative analyses. Subsequently, the serum gel tubes and EDTA tubes for oxidative analysis were centrifuged at 146 g for 10 min, while the other EDTA tube sample was used for a complete blood count analysis.

Hematological and Biochemical Analytical Standards

Hematological analysis was performed using the automated Counter 29 (Wierner Lab, Rosario, Argentina), which provides the measurement of 25 parameters. The measurement principle involves flow cytometry (FCM) + laser scatter in three angles + chemical staining method for counting and differential analysis in five parts of white blood cells and electrical impedance method for red blood cells and platelets. Biochemical analyses were conducted using the semi-automatic CM-250 (Wiener Lab, Rosario, Argentina). All markers were assessed using commercial kits from the said company, following the manufacturer’s recommendations. For liver function evaluation, the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-Glutamyltransferase (GGT), total bilirubin, direct bilirubin, and indirect bilirubin were measured. In the assessment of kidney function, creatinine and blood urea nitrogen levels were measured. For lipid profile evaluation, levels of total cholesterol, LDL, HDL, triglycerides, and glucose were measured. Protein level analyses were conducted via total proteins and albumin. Ferritin levels were also measured.

Oxidative Markers Quantification

For the assessment of the oxidative profile, the levels of lipid peroxidation were measured using thiobarbituric acid reactive substances (TBARS), protein carbonylation, and ROS production using 2’,7’-Dichlorofluorescein diacetate (DCFH-DA) and uric acid levels. The oxidative analyses were performed with plasma in triplicate. The protocols are described as follows.

(1) The TBARS assay was conducted according to Jentzsch et al. (1996). Lipoperoxidation is a rapid reaction that can be measured by its products, mainly TBARS, such as malondialdehyde (MDA). The analysis of MDA formation, resulting from the breakdown of polyunsaturated fatty acids, is a convenient method for determining the degree of lipid peroxidation. In summary, 25 µL of distilled water was added, followed by 5 µL of 10 mM BHT (Butylated hydroxytoluene), 295 µL of 1% phosphoric acid, 25 µL of 8.1% SDS (sodium dodecyl sulfate) denaturation buffer, 125 µL of 0.6% TBARS and 50 µL of the sample. The samples were incubated for 1h at 95°C and then centrifuged (16 g for 10 min). The reading was performed on a spectrophotometer at 532 nm.

(2) The protein carbonylation test, used to detect and quantify protein oxidation, was performed according to Morabito et al. (2004). Protein carbonylation involves the modification of amino acid side chains, leading to the formation of aldehyde, ketone, amide, carboxyl, and ester groups. The reaction of protein groups with specific carbonylated group reagents provides a method for detecting and quantifying protein oxidation catalyzed by metals and oxidative stress. Classically, 2,4-dinitrophenylhydrazine (DNPH), a reagent that reacts with carbonylated proteins, is used. In summary, samples were prepared by adding 50 µL to 3950 µL of 10 mM Tris/HCL, pH 7.4 (1:80). Then, 500 µL of DNPH was added to 1000 µL of the prepared sample. The tubes were incubated for 1h at room temperature followed by the addition of 500 µL of 3% SDS and 1000 µL of hexane. The tubes were vortexed for 40 seconds and then centrifuged (146 g for 15 min). The supernatant was removed, and the pellet was resuspended in 500 µL of 3% SDS. The samples were incubated at 50°C for 20 min, and the reading was performed using a 96-well plate on a spectrophotometer at 370 nm.

(3) ROS levels were measured using the DCFH-DA assay, as per Barbisan et al. (2014). This assay is commonly used to assess the content of reactive species (more sensitive to hydrogen peroxide), which will be directly proportional to the fluorescence intensity, providing an indirect measure of the total rate of reactive species. In summary, 65 µL of 10 mM Tris/HCl, pH 7.4, were added, followed by 50 µL of the sample and 10 µL of 0.1 mM DCFH-DA. The samples were incubated in the dark at room temperature for 1h, and the reading was performed on a spectrophotometer with emission at 525 nm and excitation at 488 nm.

(4) Analyses of uric acid levels were performed using commercial kits (Wierner Lab, Rosario, Argentina), according to the manufacturer’s recommendations.

Self-Report Instrument for Assessing Functional Sequelae of COVID-19 by

Since, at the time of the study, no self-report instrument had been developed or validated to assess sequelae related to COVID-19 infection, questions were structured in the form of a Likert scale. Likert scales provide a convenient way to measure unobservable constructs, and published tutorials detailing the process of their development have been highly influential (Likert 1932). The Likert-type scale was structured containing 13 questions related to self-perceived functional conditions, autonomy, and health of older adults individuals after COVID-19 aim to assess the occurrence, type, and severity of functional sequelae related to SARS-CoV-2 infection.

The organization of the scale was based on health-related axes and is written in uppercase and within parentheses. This information should not be disclosed to the person being assessed; it serves as a guide only to assist the evaluator. During the assessment, the interviewer should inform the person being assessed that they will present an affirmative statement that should be answered by considering whether they (1) fully agree, (2) neither agree nor disagree, or (3) fully disagree. The scoring for items (1), (2), and (3) will be 2, 1, and 0, respectively. In other words, complete agreement equals two points, while disagreement does not score. At the end of the scale, the maximum score for the person being assessed will be 26 points. A higher score indicates a greater quantity or severity of sequelae. The cutoff point was determined as follows: 0 to 3 points – normal, 4 to 7 – mild, 8 to 13 – moderate, > 13 severe.

Artificial Intelligence Support

Artificial intelligence (ChatGPT, OpenAI) was used to assist in the preparation of tables and in English language revision. All content was critically reviewed and validated by the authors, who take full responsibility for the integrity and accuracy of the manuscript.

Statistical Analysis

The results were organized in an Excel spreadsheet for subsequent statistical analysis using the free R software. Data were initially inspected for completeness and paired structure. All longitudinal analyses were performed using only participants with available measurements at both baseline and the 6-month follow-up for each respective variable. Continuous variables were expressed as mean ± standard deviation (SD) and median values, given the presence of non-normal distributions in several biomarkers. Changes over time were calculated as delta (Δ), defined as the difference between post-infection (6-month follow-up) and baseline values (Δ = post − pre). Normality of the distribution of paired differences (Δ) was assessed using the Shapiro–Wilk test. Variables with p < 0.05 in the Shapiro–Wilk test were considered not normally distributed and were compared using the Wilcoxon signed-rank test. Variables without significant deviation from normality were analyzed using the paired t-test. For each biomarker, 95% confidence intervals (95% CI) for the mean Δ were estimated using non-parametric bootstrap resampling (5,000 iterations), providing robust interval estimation independent of distributional assumptions.

To investigate whether significant longitudinal changes were influenced by potential COVID-19 risk factors, multivariate linear regression models were constructed. For each biomarker that demonstrated a significant change in the univariate paired analysis, the delta (Δ) was used as the dependent variable. Independent variables included male sex, advanced age (>75 years), diabetes, and obesity. Regression coefficients (β), p-values, and adjusted coefficients of determination (adjusted R²) were reported.

All statistical tests were two-tailed, and statistical significance was set at p < 0.05.

RESULTS

The sample consisted predominantly of female older adults, with a mean age of 67.27 ± 6.8 years (range: 60–96 years). The socioeconomic profile of the participants was characterized, and baseline characteristics are presented in Table I. Most volunteers had completed high school or higher education, while smaller proportions reported elementary education or no formal education. The majority self-identified as mixed race, followed by White and Black participants.

Table I
Sociodemographic characteristics of older-adults.

Variable	n	Frequency	Percentual (%)
Education	55	2	Literate (3.6%)
		7	Elementary school (12.7%)
		25	High school (45.5%)
		21	College (38.2%)
Ethnicity	55	12	White (21.8%)
		2	Black (3.6%)
		41	Mixed race (74.5%)
Income	55	9	Did not disclose (16.4%)
		12	Up to 1 minimum wage (21.8%)
		18	2 to 3 minimum wage (32.7%)
		11	4 to 5 minimum wage (20%)
		5	More than 6 minimum wage (9.1%)
Housing	55	50	Own (90.9%)
		5	Rented (9.1%)
Infection period	49	28	In 2020 (57.1%)
		21	In 2021 (42.9%)
Vaccinated	55	55	Yes (100%)
		0	No (0%)
Vaccine doses	55	2	Two (3.6%)
		53	Three or more (96.4%)
Hospitalization	55	40	No (72.7%)
		15	Yes (28.3%)
Duration of hospitalization	55	4	< 7 days (26.7%)
		6	7 a 15 days (40%)
		5	> 15 days (33.3%)
Use of ventilatory support	55	15	(27) Used ventilatory support
		40	(73%) Did not use ventilator support

• The assessment of sociodemographic characteristics was obtained through a questionnaire. The elderly participants were inquired about basic aspects that influence health, well-being, and quality of life (education, income, and housing). Ethnic/racial aspects were also considered. The context involving the disease was evaluated in terms of the period of SARS-CoV-2 infection, hospitalization, and length of hospital stay and use of ventilatory support. The use of unproven-effective medications and vaccination status were also taken into account. The data are presented in a frequency analysis, considering the percentage.

Regarding socioeconomic indicators, most participants reported a monthly income between two and three minimum wages, and the vast majority lived in their own homes. All participants had been vaccinated against COVID-19, with most having received three or more vaccine doses. Infection occurred predominantly during the first two years of the pandemic. A minority required hospitalization, and among those hospitalized, length of stay varied. Approximately one-quarter of the sample required ventilatory support during the acute phase of the disease.

Anthropometric, physiological, and health indicators are presented in Table II. Mean anthropometric values were consistent with overweight and obesity, as reflected by the average BMI. Waist circumference values were elevated in both sexes, suggesting increased cardiometabolic risk, whereas calf circumference measurements were within ranges generally compatible with preserved peripheral muscle mass. Hypertension was the most frequently reported clinical condition, followed by diabetes. Additional comorbidities included dyslipidemia, thyroid disorders, asthma, anxiety, and other less common conditions, while a minority of participants reported no comorbidities.

Table II
General Characteristics of Older Adults Survivors of COVID-19 Pandemic.

Variables		Values
Age (years, mean ± SD)		67.3 ± 6.7
Gender, n (%)	Female	43 (78.2)
	Male	12 (21.8)
Height (cm, mean ± SD)		154 ± 33
Weight (kg, mean ± SD)		72 ± 13
BMI kg/m2 (mean ± SD)		30 ± 11
Waist circunference (cm, mean ± SD )	Female	100 ± 6
	Male	97 ± 3
Calf circunference (cm, mean ± SD)	Female	37.6 ± 2.9
	Male	38.3 ± 9.2
Systolic blood pressure (mm/Hg, mean ± SD)		141 ± 4
Diastolic blood pressure (mm/Hg, mean ± SD)		83 ± 2
Clinical conditions
Hypertension, n (%)		33 (60)
Diabetes, n (%)		12 (21.8)
Obesity, n (%)		calcular
Dyslipidemia, n (%)		04 (7.3)
Thyroid conditions, n (%)		03 (5.4)
Asthma		01 (1.8)
Anxiety		01 (1.8)
Other conditions		02 (3.6)
No comorbidities		07 (12.7)
Vulnerability Risk (IVCF-20)	Lower Risk (0-6 ponts)	32 (57.4)
	Moderate Risk (7-14 ponts)	15 (27.8)
	Higher Risk (> 15 ponts)	08 (14.8)
Medicine Class
Antihypertensive drugs, n (%)*		34 (61.8)
Oral antidiabetic agents, n (%)		12 (21.8)
Insulin therapy, n (%)		01 (1.8)
Statins, n (%)		06 (10.9)
Thyroid hormone replacement , n (%)		03 (5.5)
Antiplatelet agents (AAS) , n (%)		01 (1.8)
Bisphosphonates (bone antiresorptives), n (%)		03 (5.5%)
Vitamin D, n (%)		04 (7.3%)
Calcium, n (%)		2 (3.6%)
Methotrexate, n (%)		01 (1.8)
No medication use, n (%)		07 (12.7%)
Use of so-called “preventive” drugs (e.g., Chloroquine and Ivermectin)		43 (78.2%)

• Variables were analyzed after the volunteers’ research consent forms were signed. The application of the structured questionnaire was used to view the general information and clinical aspects of the selected older adults individuals (age, gender, height, weight, BMI, and abdominal and calf circumferences). Blood pressure was also measured at the time. The obtained data are expressed as mean and standard error. BMI: Body Mass Index; mm/Hg: millimeters of mercury. In gender F is (female) and M is (male). *Antihypertensive drugs include: Losartan, Enalapril, Atenolol, Telmisartan, Valsartan, Amlodipine, Metoprolol (Selozok), Olmesartan, Spironolactone, Hydrochlorothiazide, among others.

Regarding pharmacological treatment, antihypertensive agents were the most commonly used medications, followed by oral antidiabetic drugs and statins. Smaller proportions reported insulin therapy, thyroid hormone replacement, antiplatelet therapy, bisphosphonates, vitamin D and calcium supplementation, and methotrexate use. A subset of participants reported no regular medication use. Notably, a large proportion reported prior use of so-called “preventive” medications, such as chloroquine and ivermectin, during the pandemic period.

As shown in Table II, most participants were classified as having low clinical-functional vulnerability, with the majority not presenting a high risk according to the IVCF-20 classification.

The results of the Likert scale assessing self-perceived COVID-19–related sequelae were then analyzed. At baseline, total scores ranged from 0 to 26 points. The mean score was 5.02 ± 3.28, with a median of 5.0 points. The 95% confidence interval (95% CI) for the mean ranged from 4.18 to 5.86, indicating moderate dispersion around the central tendency.

Comparison between baseline and 6-month follow-up assessments revealed a significant reduction in total Likert score over time (Table III). The mean score decreased from 5.02 ± 3.28 at baseline to 4.11 ± 3.96 at 6 months (p < 0.0001), and the median declined from 5.0 to 4.0 points. The mean change (Δ post–pre) was −0.90 points. As the paired differences did not follow a normal distribution (Shapiro–Wilk p < 0.001), the Wilcoxon signed-rank test was applied, confirming a statistically significant reduction in perceived impact over time (p = 0.004). Overall, these findings indicate a partial subjective recovery in perceived COVID-19 sequelae after six months.

Table III
Self-perception of COVID-19 Sequelae of Older-Adults.

Question	Answer			Assessment
	Agree n (%)	Neither agree nor disagree n (%)	Disagree n (%)
1. Did your overall health get worse than it was after you were infected with SARS-CoV-2?	38 (69.1%)	0 (0%)	17 (30.9%)	Baseline
	25 (51.5%)	7 (14.3%)	17 (34.7%)	6-Month
2. Did your hearing, in general, get worse than it was after you were infected with SARS-CoV-2?	24 (43.6%)	0 (0%)	31 (56.4%)	Baseline
	12 (24.5%)	2 (4.1%)	35 (70%)	6-Month
3. Do you use a greater number of daily medications after being infected with SARS-CoV-2?	17 (30.9)	0 (0%)	38 (69.1%)	Baseline
	16 (32.7%)	2 (4.1%)	31 (63.3%)	6-Month
4. Did the doctor diagnose other diseases that you didn’t have or were unaware of before being infected with SARS-CoV-2?	19 (34.5%)	0 (0%)	36 (65.5%)	Baseline
	16 (32.7%)	2 (4.1%)	31 (66.6%)	6-Month
5. Do you believe that you have become more tired (especially when you wake up in the morning) and have less energy after being infected with SARS-CoV-2?	33 (60%)	2 (3.6%)	20 (36.4%)	Baseline
	22 (44.9%)	1 (2.0%)	26 (53.1%)	6-Month
6. Do you have more difficulty moving around, performing small daily activities, dressing, and bathing without assistance after being infected with SARS-CoV-2?	13 (23.6%)	1 (1.8%)	41 (74.5%)	Baseline
	8 (16.3%)	2 (4.1%)	39 (79.6%)	6-Month
7. Do you feel less hungry or eat much more after being infected with SARS-CoV-2?	31 (56.4%)	3 (5.5%)	21 (38.1%)	Baseline
	26 (53.1%)	6 (12.2%)	17 (34.7%)	6-Month
8. Did you lose a lot of weight when you were infected with SARS-CoV-2 and have not yet regained your previous weight?	17 (30.9%)	3 (5.5%)	35 (63.6%)	Baseline
	14 (28.6%)	1 (2.0%)	34 (69.4%)	6-Month
9. Do you have a reduced sense of taste and smell compared to before the SARS-CoV-2 infection?	21 (38.2%)	0 (0%)	34 (61.8%)	Baseline
	13 (26.5%)	1 (2.0%)	35 (71.4%)	6-Month
10. Do you still experience any of these symptoms that appeared when you were infected with SARS-CoV-2: cough, shortness of breath, gastrointestinal disturbances (diarrhea, constipation, vomiting, nausea), loss or decrease in sense of smell and taste, dizziness, and headaches?	43 (78.2%)	1 (1.8%)	11 (20%)	1st
	26 (53.1%)	2 (4.1%)	21 (42.9%)	2nd
11. Were you hospitalized as a result of SARS-CoV-2 infection and needed ventilatory support or were admitted to the ICU, and do you still have difficulty breathing?	9 (16.4%)	1 (1.8%)	43 (87.8%)	1st
	5 (10.2%)	1 (2.0%)	43 (87.8%)	2nd
12. Did you need to be hospitalized once or more times after recovering from the SARS-CoV-2 infection?	2 (3.6%)	1 (1.8%)	52 (94.5%)	1st
	1 (2.0%)	1 (2.0%)	47 (96%)	2nd
13. As a result of hospitalization related to SARS-CoV-2 infection, did you develop problems with urinary or fecal incontinence, mobility issues, or memory problems?	13 (23.6%)	1 (1.8%)	41 (74.5%)	1st
	2 (4.1%)	1 (2.0%)	46 (93.9%)	2nd

• The Likert scale developed and applied to assess self-perceived sequelae of COVID-19 was structured with 13 questions grouped according to their similarity in the health context. Evaluations were conducted at two different time points (upon admission and 6 months later). Frequency analysis and values were used, considering the percentage. A difference of 10% in the percentage between the first and second assessments was considered statistically significant.

Considering that oxidative markers may be associated with functional alterations reflected by hematological and biochemical parameters, a panel of these variables was initially evaluated in blood samples collected at baseline and after 6 months. Although 55 volunteers were included in the study, analyses were restricted to the 49 (89.1%) older adults with complete laboratory data. For each variable, only participants with paired measurements were considered. Hematological, biochemical, and oxidative markers assessed before and after the 6-month follow-up are presented in Table IV.

Table IV
Baseline and before 6-month follow-up of hematological, biochemical and oxidative markers Biochemical, Hematological Markers of older adults with history of SARS-CoV-2 Infection.

Marker	Baseline	Baseline	6-Month	6-Month	Δ (post-pre)	Δ 95% CI	Normality*	p-value
	Mean ± SD	Median	Mean ± SD	Median	Mean	Bootstrap Mean	Shapiro p
Hemoglobin (g/L)	12.81 ± 1.11	13.00	12.80 ± 1.20	13.00	-0.01	-0.23 to 0.21	0.002	0.9756
Platelet count (μL)	239.96 ± 52.58	226.00	377.53 ± 1006.51	238.00	137.57	-10.57 to 426.43	0.000	0.4296
Leukocytes (mm3)	6082.24 ± 1684.46	5660.00	5939.63 ± 2223.22	5560.00	-142.61	-613.21 to 280.41	0.000	0.7629
Glucose (mg/dL)	119.04 ± 32.27	106.00	134.16 ± 59.55	110.00	15.12	3.35 to 28.47	0.000	0.0792
Triglycerides (mg/dL)	141.55 ± 77.74	125.00	154.24 ± 101.14	121.00	12.69	-4.39 to 30.96	0.099	0.1676
Total Chol (mg/dL)	205.47 ± 43.76	205.00	200.98 ± 40.34	197.00	-4.49	-15.18 to 6.22	0.003	0.4001
Creatinine (mg/dL)	0.95 ± 0.29	1.01	4.21 ± 22.14	1.00	3.25	0.04 to 9.62	0.000	0.0710
Urea (mg/dL)	37.24 ± 14.28	36.00	37.41 ± 10.06	35.00	0.16	-2.43 to 2.92	0.268	0.9050
Uric acid (mg/dL)	5.42 ± 2.01	5.00	4.33 ± 1.29	4.30	-1.09	-1.45 to -0.74	0.940	0.0000
ALT (U/L)	22.84 ± 10.09	22.00	23.39 ± 12.08	20.00	0.55	-1.98 to 3.25	0.000	0.5068
AST (U/L)	23.73 ± 17.00	18.00	26.43 ± 17.83	19.00	2.69	-2.31 to 7.80	0.000	0.0884
Gamma-GT (U/L)	32.80 ± 21.51	25.00	34.22 ± 25.64	27.00	1.43	-3.18 to 6.02	0.000	0.3175
Direct bilirubin (mg/dL)	0.18 ± 0.10	0.20	0.20 ± 0.14	0.20	0.01	-0.02 to 0.04	0.000	0.5963
Indirect bilirubin (mg/dL)	0.31 ± 0.32	0.20	0.24 ± 0.16	0.20	-0.07	-0.14 to -0.03	0.000	0.0034
Total bilirubin (mg/dL)	0.49 ± 0.39	0.40	0.43 ± 0.28	0.30	-0.06	-0.11 to -0.01	0.000	0.0097
Total protein (g/dL)	7.50 ± 0.65	7.00	7.63 ± 0.60	7.70	0.13	-0.11 to 0.36	0.001	0.0818
Albumin (g/dL)	4.36 ± 0.71	4.00	4.18 ± 0.25	4.10	-0.18	-0.42 to 0.01	0.000	0.4634
HDL-Chol (mg/dL)	47.98 ± 9.32	48.00	44.73 ± 14.14	43.00	-3.24	-7.08 to 0.67	0.891	0.1048
LDL-Chol (mg/dL)	129.16 ± 36.15	129.00	124.42 ± 31.75	124.00	-4.75	-15.22 to 5.17	0.031	0.4238
Ferritin (ng/mL)	103.18 ± 53.12	96.00	171.69 ± 106.84	156.00	68.51	47.14 to 91.72	0.039	0.0000
Oxidative markers
TBARS	102.51 ± 51.79	96.10	185.73 ± 38.07	84.09	-16.77	31.11 to -3.31	0.0249	0.0277
PCarb	134.46 ± 87.53	59.60	84.59 ± 63.16	120.40	49.87	-24.83 to 74.31	0.001	0.0000
DCFH-DA	102.35 ± 95.24	77.01	104.71 ± 128.36	38.72	1.24	01.23 to 3.24	0.000	0.079

• *Variables with p < 0.05 in the Shapiro test were considered not normally distributed and were compared using the Wilcoxon signed-rank test. Variables without significant deviation from normality in the Shapiro test were statistically compared using the paired t-test. Blood collection allowed for the assessment of hematological and biochemical parameters. In addition to the red and white blood cell counts, lipid, protein, renal, and hepatic profiles were evaluated. Ferritin levels were also measured. Assessments were conducted at two different time points (admission and 6 months later). The data were analyzed using a paired t-test, and the values were expressed as mean and standard error. HDL: High-Density Lipoprotein; LDL: Low-Density Lipoprotein; AST: Aspartate Aminotransferase; ALT: Alanine Aminotransferase; GGT: Gamma-Glutamyltransferase; PCarb – protein carbonylation; Manufacturer-provided reference ranges (Wiener Lab). Brazilian clinical laboratory standards. Age-adjusted reference ranges when applicable.

Hemoglobin levels did not differ significantly between assessments. Platelet and leukocyte counts also remained stable over time. Fasting glucose showed an upward trend at follow-up; however, the difference did not reach statistical significance. Triglycerides and total cholesterol remained unchanged. Creatinine and urea levels were also stable. In contrast, uric acid levels showed a significant reduction at the second assessment.

Regarding hepatic markers, ALT, AST, and gamma-GT did not change significantly. Direct bilirubin remained stable, whereas indirect and total bilirubin showed significant reductions at follow-up. Total protein and albumin levels did not differ between time points. HDL- and LDL-cholesterol also remained stable. Conversely, ferritin levels increased significantly at the 6-month evaluation.

Among oxidative stress markers, TBARS levels increased significantly over time, whereas protein carbonylation decreased significantly at follow-up. No significant change was observed in DCFH-DA levels.

To determine whether longitudinal changes were influenced by established COVID-19 risk factors, multivariate linear regression models were constructed using the variation (Δ post–pre) of each significant biomarker as the dependent variable and sex (male), age >75 years, diabetes, and obesity as independent predictors.

For uric acid, none of the covariates were significantly associated with the observed reduction (all p > 0.60), and the model demonstrated minimal explanatory capacity (adjusted R² = −0.059), indicating independence from the evaluated risk factors. For indirect bilirubin, male sex was independently associated with its variation (β = −0.186, p = 0.013), whereas diabetes showed a borderline association (β = 0.134, p = 0.068). Age >75 years was not significant (β = −0.075, p = 0.526). The model demonstrated modest explanatory power (adjusted R² = 0.106). For total bilirubin, none of the predictors were statistically significant (all p > 0.17), and explanatory capacity was limited (adjusted R² = 0.017). In the ferritin model, both male sex (β = 58.086, p = 0.0468) and diabetes (β = −63.748, p = 0.0298) were independently associated with longitudinal variation, whereas age >75 years was not significant (β = −5.680, p = 0.9037). The model showed limited but non-negligible explanatory capacity (adjusted R² = 0.086).

For TBARS and protein carbonylation, none of the covariates were significantly associated with changes over time, and both models demonstrated negligible explanatory power, indicating that the observed longitudinal variations were independent of the evaluated risk factors.

Overall, multivariate analysis indicated that most longitudinal biochemical and oxidative changes occurred independently of classical COVID-19 risk factors. The only consistent independent associations were observed for ferritin (male sex and diabetes) and indirect bilirubin (male sex).

To evaluate the association between self-perceived post-COVID sequelae and oxidative and clinical variables, participants were categorized into lower (n = 28) and higher (n = 27) perceived impact groups based on the median baseline Likert score. Associations between demographic, clinical, and IVCF-20 domains and higher perceived impact are presented in Table V.

Table V
Comparison of variables associated with risk of complications, laboratory markers, and vulnerability risk between older adults with higher and lower self-perceived sequelae of COVID-19 infection, as defined by a Likert scale.

Variable	Lower impact (n=27)	Higher impact (n=28)	OR	95% CI	p-value
Male sex	6 (22.2%)	7 (25.0%)	1.21	0.27–5.42	0.802
Age >75 years	3 (11.1%)	6 (21.4%)	0.13	0.01–1.87	0.132
Illiterate	7 (25.9%)	9 (32.1%)	1.35	0.40–4.56	0.648
Income <1 minimum wage	7 (25.9%)	9 (32.1%)	1.28	0.38–4.30	0.732
Diabetes	6 (22.2%)	5 (17.9%)	0.91	0.20–4.11	0.903
Obesity (BMI >30)	8 (29.6%)	10 (35.7%)	1.50	0.44–5.15	0.516
Laboratorial Variables
Direct bilirubin (mg/dL)	0.62 ± 0.24	0.69 ± 0.31	1.78	0.47–6.69	0.401
Total bilirubin (mg/dL)	0.93 ± 0.32	1.02 ± 0.37	1.96	0.58–6.60	0.279
Uric acid (mg/dL)	5.10 ± 1.48	5.36 ± 1.62	1.11	0.76–1.62	0.581
Ferritin (ng/mL)	186.4 ± 142.7	224.8 ± 165.3	1.001	0.998–1.004	0.395
TBARS	3.92 ± 1.74	4.18 ± 1.88	1.09	0.73–1.62	0.668
PCarb	1.12 ± 0.48	1.18 ± 0.53	1.27	0.36–4.42	0.703
IVCF-20 (per point)	4.1 ± 4.0	8.5 ± 5.7	1.23	1.05–1.45	0.012
IVCF-20 Domains
Self-rated poor health	8 (29.6%)	9 (32.1%)	1.15	0.41–3.24	1.000
Instrumental ADL limitation	4 (14.8%)	9 (32.1%)	2.77	0.50–15.42	0.427
Memory complaint (reported)	3 (11.1%)	12 (42.9%)	5.40	1.76–16.53	0.004
Memory worsening	4 (14.8%)	13 (46.4%)	5.00	1.25–20.08	0.033
Functional impairment due to memory	2 (7.4%)	10 (35.7%)	8.62	1.00–74.57	0.054
Depressive symptoms	5 (18.5%)	14 (50.0%)	3.64	1.12–11.85	0.051
Reduced aerobic/muscle capacity	6 (22.2%)	11 (39.3%)	2.72	0.74–9.94	0.215
Mobility limitation (AY)	7 (25.9%)	10 (35.7%)	1.48	0.41–5.37	0.548
Communication limitation (AZ)	2 (7.4%)	4 (14.3%)	1.92	0.33–11.11	0.467
Multiple comorbidities (BA)	6 (22.2%)	8 (28.6%)	1.37	0.39–4.83	0.622
Polypharmacy (BB)	5 (18.5%)	8 (28.6%)	1.74	0.48–6.29	0.396
Recent hospitalization (BC)	4 (14.8%)	7 (25.0%)	1.58	0.42–5.89	0.493

No significant differences were observed between groups regarding sex, age >75 years, literacy status, income below one minimum wage, diabetes, or obesity. Although a greater proportion of older participants was observed in the higher impact group, this difference did not reach statistical significance. No significant associations were observed between oxidative or biochemical markers and higher perceived impact. In contrast, the total IVCF-20 score was significantly associated with higher perceived impact, indicating that greater clinical-functional vulnerability increased the likelihood of belonging to the higher impact group.

Among individual IVCF-20 domains, cognitive-related items demonstrated the strongest associations. Memory complaints reported by relatives or acquaintances and recent worsening of memory were both significantly associated with higher perceived impact. Functional impairment due to memory and depressive symptoms showed borderline associations. Other domains — including instrumental activities of daily living, aerobic or muscular capacity, mobility limitations, communication difficulties, multiple comorbidities, polypharmacy, and recent hospitalization—were not significantly associated.

Overall, although significant longitudinal changes were observed in oxidative stress markers—specifically increased TBARS (lipid peroxidation) and reduced protein carbonylation—these alterations were not associated with self-perceived post-COVID sequelae. Instead, perceived impact was more strongly related to clinical-functional vulnerability, particularly cognitive dimensions.

DISCUSSION

The primary objective of this study was to investigate whether oxidative stress markers were associated with self-perceived post-COVID sequelae in older adults. Our findings do not support a consistent association between these variables. Although significant longitudinal changes were observed in oxidative biomarkers—specifically increased TBARS levels and reduced protein carbonylation—these alterations were not associated with higher self-perceived impact of post-COVID sequelae.

TBARS levels increased over the six-month follow-up, suggesting persistent lipid peroxidation after the acute phase of infection. In contrast, protein carbonylation decreased, indicating possible recovery or compensatory remodeling of protein oxidative damage. Despite these measurable biochemical changes, neither marker demonstrated a significant association with perceived symptom burden. Furthermore, multivariate models confirmed that these oxidative variations occurred independently of classical COVID-19 risk factors and were not influenced by clinical-functional vulnerability status. Importantly, this suggests that systemic oxidative modulation may represent a parallel biological process that does not directly translate into subjective functional impairment. Detectable biochemical redox shifts may occur without necessarily producing clinically perceptible symptoms, particularly when adaptive or compensatory mechanisms are activated.

It is important to contextualize the choice of oxidative stress markers used in this study. We deliberately selected relatively nonspecific and integrative biomarkers—such as TBARS (a proxy for lipid peroxidation), protein carbonylation (a marker of cumulative protein oxidative damage), and DCFH-DA–derived ROS levels—to capture overall systemic redox status rather than pathway-specific oxidative events. These markers provide a composite assessment of redox imbalance across different biomolecular compartments, which is particularly relevant in multifactorial conditions such as post-COVID sequelae. Moreover, these assays are widely employed in epidemiological and clinical research due to their feasibility, relatively low cost, and reproducibility (Grotto et al. 2009, Dalle-Donne et al. 2003, Kalyanaraman et al. 2012). Previous studies, including Mehri et al. (2021), have reported significant associations between oxidative stress markers and the acute phase of COVID-19, supporting the biological plausibility of redox imbalance in SARS-CoV-2 infection. Thus, our methodological approach balanced biological relevance with pragmatic applicability in a real-world aging cohort.

An additional aspect requiring consideration is the apparent dissociation between lipid peroxidation and global ROS levels. While DCFH-DA–derived ROS did not differ significantly between assessments, TBARS levels increased over time. Because TBARS is not highly specific and may react with aldehydes other than malondialdehyde (Ayala et al. 2014), its elevation does not necessarily indicate a generalized systemic rise in reactive oxygen species. Rather, lipid peroxidation products may persist longer than transient ROS fluctuations, reflecting accumulated downstream molecular injury instead of ongoing oxidant bursts. This temporal dissociation may help explain why ROS levels remained stable while TBARS increased. Such patterns have been described in chronic inflammatory and metabolic conditions, where redox imbalance manifests as cumulative biomolecular modification rather than sustained systemic oxidant production. The observed increase in TBARS may therefore reflect residual membrane injury, endothelial dysfunction, mitochondrial alterations, or persistent low-grade inflammation, mechanisms proposed in long COVID pathophysiology (Su et al. 2022).

Regarding biochemical markers that showed significant differences after six months, mean values remained within expected reference ranges. Although statistically significant, these variations did not indicate clinically overt abnormalities. Instead, they may reflect subtle physiological adaptation or post-inflammatory metabolic remodeling following SARS-CoV-2 infection. This distinction between statistical significance and clinical abnormality is particularly relevant in aging research, where modest biochemical shifts may represent dynamic regulatory adjustments rather than disease progression.

A central finding of this study was the significant association between higher clinical-functional vulnerability, as measured by the IVCF-20, and greater self-perceived impact of post-COVID sequelae. Cognitive-related domains—particularly memory complaints reported by relatives and recent memory worsening—showed the strongest associations with higher perceived impact. These findings suggest that cognitive vulnerability plays a central role in shaping the subjective burden of post-COVID symptoms in older adults.

Persistent cognitive impairment has been consistently reported in post-COVID cohorts. Population-based analyses demonstrated measurable deficits in memory, attention, and executive function following SARS-CoV-2 infection (Hampshire et al. 2021). Longitudinal neuroimaging studies identified structural brain changes in regions implicated in memory processing and limbic function, even among non-hospitalized individuals (Douaud et al. 2022). Broader reviews have established cognitive dysfunction as a hallmark of post-acute COVID-19 syndrome (Nalbandian et al. 2021), and recent integrative analyses confirmed persistent alterations in frontotemporal and limbic circuits, along with disrupted functional connectivity and neuroinflammatory signatures (Pettemeridou et al. 2025).

Neuroinflammation, endothelial dysfunction, microvascular injury, and immune-mediated mechanisms have been proposed as central drivers of these cognitive sequelae. Oxidative stress is closely intertwined with these processes at the central nervous system level. However, oxidative processes occurring within neural tissue may not be fully captured by peripheral circulating markers. This compartmentalization hypothesis may explain the dissociation observed in our study: although systemic oxidative modulation was detected, these alterations were not associated with perceived symptom burden. If redox imbalance contributes to cognitive sequelae, it may operate predominantly at the tissue or cellular level—such as through mitochondrial dysfunction, glial activation, or blood–brain barrier disruption—rather than through sustained systemic oxidative activation detectable in plasma.

In older adults, these mechanisms may interact with reduced cognitive reserve and pre-existing frailty. Thus, post-COVID cognitive complaints may reflect both infection-related neurobiological effects and the unmasking or amplification of underlying vulnerability trajectories. The strong association between IVCF-20 cognitive domains and higher perceived impact reinforces the relevance of multidimensional geriatric assessment in post-COVID follow-up.

This study contributes to the literature by simultaneously examining oxidative biomarkers, biochemical parameters, and clinical-functional vulnerability within a longitudinal framework. By integrating biological and functional domains, it provides a more comprehensive perspective on post-COVID manifestations in older adults.

Some limitations should be considered. The sample size was moderate, potentially limiting power to detect smaller associations. The absence of an age-matched non-infected control group restricts causal inference regarding whether oxidative changes were infection-specific or reflect age-related redox dynamics. Oxidative stress was assessed using systemic markers, which may not capture localized central processes. Self-perceived impact was measured using a subjective instrument, which may be influenced by psychosocial factors. Finally, the predominance of community-dwelling individuals with relatively low vulnerability may limit generalizability to more severe populations, although it enhances relevance to real-world aging cohorts.

In conclusion, although systemic oxidative modulation was detectable six months after SARS-CoV-2 infection, these peripheral redox alterations were not associated with self-perceived post-COVID sequelae. Instead, clinical-functional vulnerability, particularly cognitive impairment—emerged as the principal determinant of perceived impact, reinforcing the concept that post-COVID burden in older adults is more strongly driven by neurobiological vulnerability and frailty dynamics than by persistent systemic oxidative activation.

Acknowledgements

The authors would like to thank the funding sources, Conselho Nacional de Desenvolvimento Científico (CNPq) e Fundação de Amparo à Pesquisa do Estado do Amazonas (FAPEAM) and Eduardo Vélez-Martin for statistical support.

Data availability

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

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Correspondence

Correspondence to: Verônica Farina Azzolin [e-mail]

Handling editor

Guilherme Baldo

Publication Dates

• Publication in this collection
  09 Oct 2026
• Date of issue
  2026

History

• Received
  11 June 2025
• Accepted
  01 Apr 2026