Analysis of persistent COVID-19 subtypes and their impact on quality of life

Analysis of persistent COVID-19 subtypes and their impact on quality of life | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Analysis of persistent COVID-19 subtypes and their impact on quality of life Andrea Domínguez-Martín, Nuria Suárez-Moreno, Alicia Navarro-Cáceres, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455995/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Persistent COVID-19 (PC) affects individuals who have survived the acute phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present prolonged and fluctuating symptoms. These symptoms are multisystemic and significantly impair quality of life. The high degree of clinical variability hinders classification and management in clinical practice. This study aimed to classify patients according to their predominant symptoms and explore their impact on their quality of life, taking into account sociodemographic variables, personal history, and lifestyle. Methods: This was a cross-sectional observational study. The subjects came from the dedicated persistent COVID-19 clinic of internal medicine at Salamanca Hospital and from primary care clinics in Salamanca. Clinical, sociodemographic, and symptom data were collected via standardised questionnaires. Quality of life was assessed via the SF-36 questionnaire. Symptom grouping was performed via nonparametric statistical techniques. Results : The study included 305 individuals (68.2% women) with a mean age of 52.7 ± 11.91 years. Eighty-two percent were infected before completing primary vaccination. Fatigue was the most common symptom (71.4%), along with other symptoms, such as a lack of energy, memory loss, dyspnea, and sleep disturbance. Women presented more symptoms than men did. Five clusters were identified: the largest, Cluster 1 (51.8%), with respiratory/cardiovascular, systemic, and musculoskeletal symptoms. With respect to quality of life, Cluster 5 presented the highest scores, and Cluster 1 presented the lowest scores, especially for the physical components. Significant differences were observed between clusters on the SF-36 questionnaire scales and domains, highlighting a poorer quality of life in the most symptomatic clusters. Conclusions : This study identified five subgroups of patients with PC, and those with more symptoms presented poorer quality of life. Dyspnea and fatigue are indicators of this deterioration, with women being more affected. Cluster 1 reported the worst quality of life, whereas Cluster 5 had the best quality of life, highlighting the need for individualised therapeutic approaches. Trial registration: Registered on Clinicaltrials.gov with identifier NCT05819840. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Signs and symptoms long COVID-19 quality of life symptom clusters fatigue dyspnea Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Both acute SARS-CoV-2 infection and its long-term effects have been the subject of extensive research. A significant proportion of patients who have recovered from the infection have been observed to experience debilitating symptoms for weeks, months, and even years after the acute infection has resolved. Numerous terms have been used to refer to this new phenomenon (1), the most common being persistent COVID (PC) and long COVID-19 (2–4). Currently, the most accepted definition of PC is that provided by the WHO (5). This condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection and typically continues three months after the onset of COVID-19, with symptoms lasting at least two months and remaining unexplained by an alternative diagnosis. Symptoms may appear at any time after recovery or may persist from the onset of the disease. Fluctuating symptoms and periods of relapse are common. PC presents as a multiorgan disorder with a wide variety of manifestations. Among the more than 200 symptoms identified are fatigue, dyspnea, cognitive impairment, sleep disorders, anxiety, depression, myalgia, difficulty concentrating, anosmia/dysgeusia, or headache with limited functional capacity. All of these symptoms lead to a deterioration in the quality of life of these patients (2,6–8). Their quality of life is also influenced by related mental health issues, such as posttraumatic stress syndrome, anxiety, depression and insomnia (9–11). A further condition influencing the limitation of quality of life and functionality is the predominance of neurocognitive impairment, which manifests specifically through attention and memory problems (78.2% and 72.6% of cases in Spain, respectively) (12). The duration and severity of symptoms vary widely from patient to patient, resulting in different impacts on quality of life for each individual. Given the wide variability in symptom clusters, there is no universal classification of PC subtypes. In some studies, they are divided into groups on the basis of the affected system: neurological, cardiopulmonary, gastrointestinal, upper respiratory, and other aging-related comorbidities (13,14). Other approaches use a classification by individual symptoms or heterogeneous groups, considering factors such as the coexistence of symptoms, arthralgia or neurological disorders; some even highlight fatigue as a main symptom associated with autonomic dysfunction and psychiatric symptoms (15,16). In summary, there is no clear identification of PC symptoms or a classification that groups patients according to these symptoms, which hinders the approach to treating this disease. There are studies that agree on classifying some subtypes on the basis of the presence of the most common symptoms (13,14,17) but with disparate criteria. Identifying subgroups of clinical manifestations can help direct resources more efficiently and in a personalised manner, as well as help develop more effective prevention and treatment strategies, thereby enhancing the quality of care and, consequently, the quality of life of these patients. The main aim of the present study was therefore to develop a clinical classification of predominant symptoms to explore differences in quality of life overall and by sex and how these differences are related to lifestyle. The graphical abstract (Fig.1) provides a general illustration of the approach and the main findings of this study. 2 Methods 2.1 Design This is a cross-sectional, descriptive, observational study conducted between April 2022 and August 2023 at the Primary Care Research Unit of Salamanca (APISAL). The results of this study are part of the BioICOPER study project on the relationships between endothelial structure, function, and damage and between vascular aging and the biopsychological situation in adults diagnosed with PC (Identifier ClinicalTrials.org: NCT05819840. Registered in April 2023). 2.2 Participants Participants were selected from patients who had a PC diagnosis recorded in their medical records, both in the internal medicine clinic specialising in persistent COVID-19 and in primary care clinics. Subjects were included by consecutive sampling if they met the following set of inclusion criteria: presented with PC as per the WHO definition; had a history of probable or confirmed SARS-CoV-2 infection with symptoms lasting at least two months and unexplained by an alternative diagnosis; and provided signed informed consent. Figure 2 The exclusion criteria were people with terminal illnesses, those unable to visit the health center for a visit, those with a history of cardiovascular disease (ischemic heart disease or cerebrovascular disease), and those with a glomerular filtration rate less than 30%. The sample size was calculated via free GRANMO software (https://www.datarus.eu/ca/aplications/granmo/). Accepting an alpha risk of 0.05 and a beta risk of less than 0.2 in a two-sided contrast and assuming a standard deviation of 20, a total of 258 individuals were required to detect a seven-point difference in the SF36 score between the subgroup with the most PC symptoms and the subgroup with the least. Therefore, the recruitment of 305 individuals was considered sufficient. 2.3 Variables and measuring instruments All measurements were collected in face‒to-face interviews via various self- and peer-reported questionnaires following a standardised protocol, with quality control by an independent researcher(18). The analysis variables were as follows: Sociodemographic variables and personal and family history, such as age, sex, marital status, educational level, and employment status, were recorded. Cardiovascular risk factors: Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded via three measurements taken with an OMRON M10-IT sphygmomanometer (Omron Healthcare, Kyoto, Japan), and the average of the last two measurements was recorded. This was done in line with the recommendations of the European Society of Hypertension (ESH) (19). The participant was seated, and after resting for at least five minutes, three measurements were taken on the dominant and nondominant arms. The mean arterial pressure (MAP) was calculated via the formula MAP = (2DBP + SBP)/3. The heart rate (HR) value was also taken from the OMRON BP monitor. Participants were considered hypertensive if they were taking antihypertensive drugs or had BP values ≥ 140/90 mmHg. Dyslipidemia: Participants were considered to have this condition if they were taking lipid-lowering drugs or had fasting total cholesterol ≥ 200 mg/dL, high-density lipoprotein cholesterol (HDL-C) < 40 mg/dL in men and < 50 mg/dL in women, or triglycerides ≥ 150 mg/dL. Diabetes: Participants who received oral antidiabetic agents or insulin therapy or who had fasting plasma glucose levels ≥ 126 mg/dL or HbA1c ≥ 6.5% were considered to have diabetes mellitus. Smokers/nonsmokers: Participants were recorded as nonsmokers if they had never smoked or had not smoked in the past year. Obesity was diagnosed if the BMI was ≥ 30 kg/m2 (20). o Alcohol: Those who consumed any alcoholic beverages during the follow-up week recorded in the self-administered questionnaires were considered alcohol drinkers. The vaccination status, number and date of COVID-19 infection, clinical symptoms and laboratory tests (PCR or Ag) formed the basis for the diagnosis of the disease, and the variant that caused the acute infection was associated with the onset of PC. The variable was obtained on the basis of the date of infection and the predominant variant at that time in the province of Salamanca. These data were collected from the participants’ medical history and from face‒to-face interviews. Complete primary vaccination was recorded for patients who had received two doses of the same or a different vaccine and a single dose of the Janssen vaccine (21). A physical examination was performed, and the following variables were recorded: Height was measured in cm using a wall-mounted stadiometer during inspiration, with the participant barefoot with heels against the wall. The waist circumference was recorded with a flexible tape measure parallel to the floor, above the iliac crest, at the end of exhalation, with the participant standing and wearing underwear. Hip circumference was measured at the point at which the maximum circumference passed through the greater trochanter of both femurs. Weight was determined via an InBody 230 monitor (InBody Co., Ltd., Seoul, South Korea), with the participant fasting for at least 2 hours, barefoot, wearing light clothing, and having an empty bladder. Body mass index was calculated by dividing body weight (kg) by height squared (m2). PC symptoms were reported by the participants during their anamnesis at the time of the study, including during the acute phase, months after the acute phase, and at the time of the interview. The following symptoms were recorded: Systemic manifestations: fatigue, lack of energy, fever Neurocognitive manifestations include memory loss, difficulty concentrating, brain fog or confusion. Respiratory/cardiovascular manifestations: dyspnea, chest tightness, cough, and sore throat. Musculoskeletal manifestations: Myalgia, arthralgia, and mobility limitations. Neurological/neuromuscular manifestations: headache, altered taste or smell, loss of reflexes or paresthesia. Psychosocial or psychiatric manifestations: depression, anxiety, and sleep disturbance. Quality of life was assessed via the SF-36 health questionnaire. This questionnaire is divided into 9 subscales: Physical functioning: assesses the degree to which health limits daily physical activities such as self-care, walking, climbing stairs, bending, lifting or carrying weights, and moderate and intense exertion. Role-Physical: measures how physical health interferes with work and other daily activities, negatively influences performance, limits the types of activity, or makes them difficult to perform. Bodily pain: estimates the intensity of pain and its effect on routine work, both in the workplace and at home. General health: personal assessment of health, including current health, future health prospects, and resistance to illness. Vitality: assesses feelings of energy and vitality versus feelings of tiredness and exhaustion. Social Functioning: This measures the degree to which emotional problems interfere with work or other activities of daily living, including reduced time spent on these activities, lower-than-desired performance, and decreased quality of work. Mental health assesses mental health, including depression, anxiety, behavioural control, emotional control, and overall positive affect. Reported Health Trend: This refers to the quantification of current health compared with that one year prior (22). From these subscales, two more scores can be obtained, which group the subscales that measure the physical component (PCS) and the subscales that evaluate the mental component (MCS). 2.4 Statistical analysis Data were collected via the REDCap (Research Electronic Data Capture) platform with a data collection questionnaire designed for the project (18). The variables are presented as the means and standard deviations for quantitative variables and as numbers and percentages for categorical variables. The Kolmogorov‒Smirnov test was used to verify the normal distribution of the variables. Odds ratios were calculated to assess the probability of presenting symptoms between sexes. Subgroups of the symptoms present were identified via cluster analysis via hierarchical clustering with Jaccard's distance and Ward's methods. The Kruskal‒Wallis test was used for comparisons across clusters, and the Mann‒Whitney U test was used for pairwise comparisons of clusters. For multiple comparisons, p values adjusted by Bonferroni correction were used. The data were analysed via SPSS for Windows, version 26.0 (IBM, Armonk, New York: IBM Corp.) and R (R Foundation for Statistical Computing, Vienna, Austria). 3 Results Among the 305 participants, one was considered missing because they did not complete the symptom and quality of life questionnaires. The mean age was 52.71 ± 11.91 years, and 68.2% of the participants (n = 208) were women. The general characteristics of the study population and their sex distributions are shown in Table 1. The majority of participants were employed (n = 186, 61.2%). Among these participants, 231 (75%) were infected before completing primary vaccination. The primary variant responsible for infection in these patients was EU1 (63.8%), with similar rates in men and women. During the acute phase, 29.7% of the total patients were hospitalised, with men being the predominant group requiring hospitalisation. The mean BMI was 27.99 ± 5.55, and 32.6% (n = 99) were obese. With respect to personal history, 35.9% were hypertensive, and 201 patients (66.1%) had dyslipidemia. Regarding alcohol use, 46.4% of the patients drank some alcohol. All variables related to cardiovascular risk were present at higher rates in men than in women. Table 1 Sample description General Men (N=97) Women (N=207) P value Sex (n, %) 97 (31.8%) 207 (68.1%) <0.001 Age (years, mean, SD) 52.71 ± 11.91 55.70 ± 12.28 51.41 ± 11.51 0.003 Employment 0.795 In work 186 (61.2%) 57 (58.8%) 129 (62.3%) Out of work 79 (26.0%) 26 (26.8%) 53 (25.6%) Sick leave/Permanent disability 39 (12.9%) 14 (14.4%) 25 (12.1%) COVID-19 Variant responsible for infection 0.397 EU1 194 (63.8%) 58 (59.8%) 136 (65.7%) Alpha 51 (16.8%) 20 (20.6%) 31 (15.0%) Delta 16 (5.3%) 7 (7.2%) 9 (4.3%) Omicron 43 (14.1%) 12 (12.5%) 31 (15.0%) Complete primary vaccination 278 (91.4%) 89 (91.8%) 189 (91.3%) 0.896 Infection before completing primary vaccination 228 (82.0%) 74 (83.1%) 154 (81.5%) 0.736 Hospitalised during the acute phase 83 (29.7%) 41 (44.8%) 42 (22.6%) <0.001 Cardiovascular risk factors BMI (kg/cm 2 , mean, SD) 27.99 ± 5.55 29.60 ± 4.64 27.23 ± 5.78 <0.001 Obesity, n (%) 99 (32.6) 44 (44,4) 55 (26.6%) 0.001 HBP (n, %) 109 (35.9%) 52 (53.6%) 57 (27.5%) <0.001 Dyslipidemia (n, %) 201 (66.1%) 71 (73.2%) 130 (62.8%) 0.074 DM (n, %) 37 (12.1%) 22 (22.7%) 15 (7.2%) <0.001 Smoker (n, %) 16 (5.3%) 8 (8.2%) 8 (3.8%) 0.111 Alcohol user (n, %) 141 (46.4%) 61 (62.9%) 80 (38.6%) <0.001 BMI: Body Mass Index; HBP: High Blood Pressure; DM: Diabetes Mellitus 3.1 Persistent COVID-19 symptoms Table 2 shows the symptoms overall and by sex. The predominant symptom was fatigue, reported by 71.4% of patients (62.9% of men and 75.4% of women). A lack of energy (67.4%), memory loss (59.9%), dyspnea (58.2%), and sleep disturbances (60.5%) were also common. Table 2: Prevalence of the symptoms studied in the entire population and by sex. . General Men (N=97) Women (N=207) Odds ratio (IC95%) (M/H) Systemic manifestations Fatigue, n (%) 217 (71.4) 61 (62.9%) 156 (75.4%) 1.805* Lack energy 205 (67.4%) 55 (56.7%) 150 (72.5%) 2.010* General malaise 127 (41.8%) 29 (29.9%) 98 (47.3%) 2.108* Fever 16 (5.3%) 4 (4.1%) 12 (5.8%) 1.431 Neurocognitive manifestations Memory loss 182 (59.9%) 46 (47.4%) 136 (65.7%) 2.124* Difficulty concentrating 171 (56.3%) 39 (40.2%) 132 (63.8%) 2.617** Brain fog or confusion 133 (43.8%) 30 (30.9%) 103 (49.8%) 2.212* Respiratory/cardiovascular manifestations Dyspnea 177 (58.2%) 49 (50.5%) 128 (61.8%) 1.587 Chest tightness 95 (31.3%) 22 (22.7%) 73 (35.3%) 1.857* Cough 79 (26.0%) 31 (32.0%) 48 (23.2%) 0.643 Sore throat 63 (20.7%) 17 (17.5%) 46 (22.2%) 1.345 Musculoskeletal manifestations Myalgia 169 (55.6%) 36 (37.1%) 133 (64.3%) 3.045** Arthralgia 165 (54.3%) 38 (39.2%) 127 (61.4%) 2.465** Mobility limitations 73 (24.0%) 16 (16.5%) 57 (27.5%) 1.924* Neurological/neuromuscular manifestations Headache 121 (39.8%) 29 (29.9%) 92 (44.4%) 1.876* Altered taste or smell 80 (26.3%) 23 (23.7%) 57 (27.5%) 1.223 Loss of reflexes or parestesia 122 (40.1%) 41 (42.3%) 81 (39.1%) 0.878 Psychosocial or psychiatric manifestations Depression 131 (43.1%) 31 (32.0%) 100 (48.3%) 1.990* Anxiety 135 (44.4%) 32 (33.0%) 103 (49.8%) 2.012* Sleep disturbance 184 (60.5%) 44 (45.4%) 140 (67.6%) 2.517** *p valor <0.05 **p valor <0.001 Overall, women had a greater frequency of symptoms than men did. In particular, fatigue (75.4% vs. 62.9%, p = 0.006) and lack of energy (72.5% vs. 56.7%, p = 0.006) were significantly more common in women. The same was true for other symptoms, such as malaise, memory and concentration problems, brain fog, chest tightness, muscle and joint pain, difficulty moving, headache, depression, anxiety, and sleep disturbances. The odds ratio analysis revealed that women were more likely to present with most of the symptoms than men were, except for cough (OR = 0.643) and loss of reflexes (OR = 0.878). 3.2 Clusters by symptom groups Since symptoms occurred simultaneously, a cluster analysis was performed to identify possible patterns. Thus, the 18 symptoms were previously organised into six groups according to their origin or clinical relationship: systemic, neurocognitive, respiratory/cardiovascular, musculoskeletal, neurological/neuromuscular, and psychological-psychiatric (see Table 2). A patient was considered to belong to a group if they reported at least one of its symptoms. The results of the analysis identified five main clusters, as shown in Figure 3. The clusters resulting from the combination of different symptoms were as follows: Cluster 1 - Respiratory/cardiovascular with muscular and systemic involvement: predominance of respiratory/cardiovascular symptoms with systemic and musculoskeletal symptoms. Cluster 2 - Predominantly respiratory/cardiovascular: showing respiratory/cardiovascular symptoms without neurological/neuromuscular or psychological-psychiatric involvement. In these patients, the predominant symptoms are respiratory/cardiovascular. Cluster 3 - Without respiratory or cardiovascular involvement: showing any symptoms without respiratory/cardiovascular symptoms. Cluster 4 - Respiratory/cardiovascular with psychological-psychiatric involvement: generally showing respiratory/cardiovascular and psychological-psychiatric symptoms but without musculoskeletal involvement. Cluster 5 - Asymptomatic: no symptoms at the current time. Cluster 1 was the largest. The study included 158 patients (51.8%) with respiratory symptoms with muscular and systemic involvement. This was followed by Cluster 3, with 75 participants (24.6%). Cluster 5 comprised 10 patients who were asymptomatic at the time of assessment but met the PC criteria. This group of patients presented symptoms such as weakness and fatigue (90%), headache (80%), fever, malaise, muscle pain, cough and taste disturbance (70%) during the acute phase, while symptoms decreased as the disease progressed. At the time of the study visit, the predominant symptoms were fatigue and dyspnea, which were among the three most frequent symptoms in Clusters 1, 2, and 4, with percentages exceeding 50%. In Cluster 3, the most frequent symptoms were memory loss, sleep disturbance, and decreased concentration, with percentages of 58.67%, 56%, and 53.33%, respectively. The distributions of men and women in the defined clusters were similar in all of them except for Cluster 5, with 8 male participants (80%). With respect to employment status, Cluster 1 had the highest percentage of sick leave or disability, with 29 (18.2%) participants. Cluster 3 had the highest proportion of individuals out of work, with 50 (33.3%). The clusters with the highest proportion of active participants were Clusters 2 and 5, with 16 (72.7%) and 8 (80%) patients, respectively. Regarding the impact of COVID-19 during the acute phase, more than 60% of patients in all subgroups were infected before completing primary vaccination, with the highest percentages in Clusters 1, 3, and 4, with infection rates of 82.4%, 84.1%, and 89.2%, respectively. No group had high hospitalisation rates, and the groups were evenly distributed. In all the groups, the number of fully vaccinated participants at the time of the interview was high, exceeding 90% in most cases. The variant predominantly responsible for infection in all clusters was EU1. With respect to cardiovascular risk factors, all the subgroups had a mean BMI above the normal weight range, yet no group presented high levels of obesity. Dyslipidemia was the most common factor, with percentages over 50% in all subgroups. Smoking was not particularly prevalent in any cluster, with low or no smoking rates. With respect to alcohol, Cluster 5 had the highest rate of alcohol use (80%), with Cluster 1 drinking the least (37.3%). The characteristics of each cluster are shown in Table 3. Table 3. Cluster characteristics Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Size 158 (51.8%) 22 (7.2%) 75 (24.6%) 39 (12.8%) 10 (3.3%) Sex (n, %) Men 40 (25.3%) 10 (45.5%) 26 (34.7%) 13 (33.3%) 8 (80.0%) Women 118 (74.7%) 12 (54.5%) 49 (65.3%) 26 (66.7%) 2 (20.0%) Age (years, mean, SD) 52.59 ± 10.83 52.05 ± 14.09 54.41 ± 11.95 52.03 ± 13.95 47.90 ± 14.73 Employment In work 94 (59.5%) 16 (72.7%) 44 (58.7%) 25 (64.1%) 7 (70.0%) Out of work 35 (22.2%) 4 (18.2%) 50 (33.3%) 12(30.1%) 3 (30%) Sick leave/Permanent disability 29 (18.2%) 2 (9.1%) 6 (8%) 2 (5.1%) 0 (0.0%) Most frequent symptoms in order of frequency 1. Fatigue (93.0%) 1. Dyspnea (90.5%) 1.Memory impairment (59.5%) 1. Dyspnea (76.3%) 2. Weakness (87.3%) 2. Fatigue (52.4%) 2. Sleep disturbance (56.8%) 2 Sleep disturbance (76.3%) 3. Dyspnea (82.2%) 3. Cough (42.9%) 3. Concentration difficulties (54.1%) 3. Fatigue (68.4%) Variant responsible for COVID-19 infection EU1 105 (66.5%) 11 (50.0%) 47 (62.7%) 27 (69.2%) 4 (40.0%) Alpha 24 (15.2%) 3 (13.6%) 15 (20.0%) 7 (17.9%) 2 (20.0%) Delta 7 (4.4%) 2 (9.1%) 2 (2.7%) 3 (7.7%) 2 (20.0%) Omicron 22 (13.9%) 6 (27.5%) 11 (14.7%) 2 (5.2%) 2 (20.0%) Complete primary vaccination 142 (89.9%) 21 (95.5%) 69 (92.0%) 37 (94.9%) 9 (90.0%) Infected before primary vaccination 117 (82.4%) 13 (61.9%) 58 (84.1%) 33 (89.2%) 7 (77.8%) Hospitalised during acute phase 40 (28.8%) 7 (33.3%) 20 (29.0%) 13 (36.1%) 1 (12.5%) Cardiovascular risk factors Body Mass Index (kg/cm2, mean, SD) 28.41 ± 6.12 28.88 ± 5.09 26.67 ± 4.31 28.10 ± 5.47 28.88 ± 4.79 HBP (n, %) 62 (39.2%) 6 (27.3%) 21 (28.0%) 15 (38.5%) 5 (50.0%) Dyslipidemia (n, %) 104 (65.8%) 14 (63.6%) 48 (64.0%) 30 (76.9%) 5 (50.0%) DM (n, %) 26 (16.5%) 3 (13.6%) 4 (5.3%) 3 (7.7%) 1 (10.0%) Smoker (n, %) 8 (5.1%) 0 (0.0%) 7 (9.3%) 0 (0.0%) 1 (10.0%) Alcohol user (n, %) 59 (37.3%) 11 (50.0%) 39 (52.0%) 24 (61.5%) 8 (80.0%) Obesity (n, %) 55 (34.8%) 7 (31.8%) 18 (24.0%) 15 (38.5%) 4 (40.0%) 3.3 Impact on quality of life In the analysis comparing the five identified clusters, differences were apparent in most of the physical and mental health components assessed with the SF36 (p < 0.001), indicating the existence of distinct clinical profiles. Cluster 1 had the lowest scores in all dimensions, indicating greater physical and mental impairment (physical functioning: 40.7 ± 10.3; mental health: 42.3 ± 9.9). This group also had the lowest levels of vitality, role-physical, and social functioning. Cluster 2 had the best overall scores on nearly all scales (physical functioning: 50.5 ± 6.4; mental health: 53.2 ± 6.1), suggesting better overall health and less symptomatic impact. Clusters 3 and 4 had intermediate scores. While Cluster 3 showed better results in general health and physical functioning, Cluster 4 was characterised by greater impairment in vitality and role-emotional. Cluster 5 had high scores for both physical and mental components (physical functioning: 53.5 ± 3.4; role-emotional: 51.6 ± 4.7), as well as the best subjective state-of-health assessment (67.5 ± 31.3). Together, these findings confirm that the groups derived from the cluster analysis reflect different degrees of functional and emotional impact, with Cluster 1 being the most affected and Cluster 5 having the highest scores in the physical domain and the second highest in the mental domain. All scores on the SF-36 scale are collated in Table 4 and displayed in Figures 4 and 5. Table 4: Cluster scores on the scales and domains of the SF-36 quality-of-life questionnaire Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 p Scales Physical functioning 40.71 ± 10.25 50.52 ± 6.41 47.15 ± 9.58 46.39 ± 8.34 53.46 ± 3.43 <0.001 Role-physical 36.29 ± 11.66 49.70 ± 9.57 42.94 ± 12.38 43.66 ± 12.39 50.51 ± 9.59 <0.001 Bodily pain 35.25 ± 8.18 44.62 ± 8.14 40.66 ± 8.63 42.60 ± 8.41 48.28 ± 9.49 <0.001 General health 36.89 ± 8.22 47.90 ± 6.69 42.07 ± 9.00 39.46 ± 9.09 48.25 ± 10.69 <0.001 Vitality 34.79 ± 8.49 47.31 ± 8.90 41.42 ± 10.05 38.23 ± 10.16 46.20 ± 7.16 <0.001 Social functioning 33.53 ± 12.88 47.81 ± 9.12 39.12 ± 14.14 39.57 ± 13.29 49.33 ± 8.56 <0.001 Role-emotional 40.29 ± 14.91 51.15 ± 5.97 40.50 ± 14.90 42.15 ± 13.90 51.57 ± 4.67 0.006 Mental health 42.34 ± 9.90 53.24 ± 6.08 44.18 ± 9.54 44.71 ± 8.95 50.75 ± 7.21 <0.001 Subjective health assessment 46.85 ± 26.03 48.81 ± 21.62 55.33 ± 25.43 50.64 ± 21.06 67.50 ± 31.29 0.109 Domains Physical components 35.58 ± 10.05 46.47 ± 7.59 43.91 ± 10.08 43.10 ± 9.63 49.78 ± 9.92 <0.001 Mental components 40.49 ± 12.86 51.97 ± 7.50 40.97 ± 13.81 41.43 ± 12.60 49.67 ± 7.73 <0.001 A comparison of the SF-36 questionnaire subscales across clusters revealed significant differences (p < 0.05), primarily in the physical domain but also in some mental domains. With respect to physical functioning, significant differences were found between Cluster 1 and all the other clusters (Clusters 2, 3, 4, and 5; p < 0.001 in most cases), indicating that this cluster presents poorer quality of life in this component of the scale. In contrast, the differences between the other clusters were not significant. For role-physical and bodily pain, a similar trend emerged: Cluster 1 was the only cluster presenting differences in comparison with the other clusters (p < 0.01). The general health dimension significantly differed between Cluster 1 and Clusters 2, 3, and 5 (p < 0.01) and between Cluster 2 and Cluster 4 (p = 0.008), whereas the remaining comparisons were not significant. The pattern observed in the physical dimensions was repeated in mental health, with differences between Cluster 1 and Cluster 2 (p < 0.001), as well as between Cluster 2 and Cluster 3 (p = 0.001) and between Cluster 4 (p = 0.008), indicating notable differences in psychological aspects. In contrast, there were no significant differences in self-reported health assessments across the clusters (p > 0.05 in all cases), which could indicate that all patient groups had similar self-assessments of their health, regardless of the symptoms they presented. The physical and mental components follow the pattern observed in the categories analysed previously. There were differences between Cluster 1 and the other categories (p < 0.01) in the physical components, whereas the mental components only differed between Cluster 1 and Cluster 2 (p = 0.001) and between Clusters 2 and 3 and 4 (p = 0.005 and 0.017, respectively). This suggests that the quality of life of Cluster 1 is influenced mainly by physical characteristics, whereas Clusters 2, 3, 4 and 5 share greater similarity with each other, with some specific exceptions in dimensions generally related to mental components. The summary figure (Fig 6) illustrates the main results and findings of this study. 4 Discussion The present study subdivided a cross-sectional sample of 304 participants by symptoms of persistent COVID-19. Cluster analysis yielded five clusters with distinct symptom groupings, with differences observed in the subcategories of quality of life and physical and mental components. The analysis by sex revealed no differences between the variants, vaccination status, or infection status prior to primary vaccination. However, women were at greater risk of experiencing more PC symptoms. This finding is in line with those of previous studies, which reported that women have a greater number of symptoms, regardless of severity, in the acute phase (23–26). This greater susceptibility has been attributed to immunological differences between sexes, which are linked to an increase in proinflammatory cytokines and greater immunological dysfunction in females (27,28). Moreover, men follow similar patterns to those of the general population during the acute phase, presenting more hospitalisations than women do (29,30). Furthermore, men had a higher rate of unemployment, sick leave, or disability, despite having fewer symptoms. This finding is relevant since lower rates of employment have been linked to a greater presence of symptoms (26). One possible explanation could be related to sociocultural factors, as well as the type or severity of symptoms, which could have differential effects on men’s and women’s functional capacity and ability to cope at work. These differences underscore the importance of considering sex and sociocultural factors when studying and managing PC. These factors can play important roles in the clinical and functional expression of the disease, highlighting the importance of multidisciplinary and personalised approaches to optimise PC patient care. Understanding the PC symptom profile and the differences between sexes is vital in assessing its impact on quality of life. Fatigue and lack of energy were the symptoms most frequently reported by patients, affecting more than half of the patients, with differences observed between sexes. Other common symptoms included memory loss, difficulty concentrating, dyspnea, myalgia, arthralgia, and sleep disturbance; only dyspnea did not vary by sex. These findings are consistent with previous meta-analyses where fatigue, dyspnoea, memory loss, and difficulty concentrating were identified as the main symptoms of PC (31–33). Fatigue and dyspnoea are associated with greater negative impacts on work capacity and worse quality of life. Other studies have confirmed that these symptoms are associated with poorer quality of life and reduced work activity (26,34,35). Fatigue also shares clinical and pathophysiological characteristics with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), a condition closely related to persistent COVID-19, with groups of similar symptoms and more than half of the cases meeting the diagnostic criteria for CFS/ME (36,37). In addition, neuromuscular alterations have been observed to occur with arthralgia and myalgia, which suggests that they have similar pathophysiologies (38). Therefore, the high prevalence of symptoms shared with CFS/ME suggests that the clinical course of these patients may lead to a significant proportion of patients with a CFS/ME-like condition. Neurocognitive symptoms and sleep disturbances predominate in patients with severe acute illness, whereas fatigue and dyspnea are more common in patients with mild illness. Finally, hospitalisation during the acute phase is associated with poorer quality of life in patients with persistent symptoms, especially fatigue and dyspnoea. Taken together, these findings reinforce the importance of looking beyond the initial severity of the illness to focus on symptomatic assessment in patients with persistent COVID-19. Identifying symptom profiles can facilitate the design of tailored clinical and psychosocial strategies aimed at improving patients' quality of life and functional reintegration. To identify these profiles, a cluster classification was performed with symptom groupings on the basis of potential etiopathogenic pathways. This allowed the identification of five clinical subgroups and quality of life assessment via the SF-36 questionnaire. Cluster 1 was characterised by the largest sample size and the highest proportion of patients on sick leave or with disability. This group also had the lowest quality of life scores, with significant differences compared with the other clusters. Cluster 2 included patients with a greater proportion of individuals at work and high quality of life scores. The absence of neuromuscular or cognitive limitations could suggest a less severe condition. Neurocognitive symptoms predominated in Cluster 3, which appeared to negatively impact vitality levels and the physical and mental components of quality of life. Cluster 4 included patients who presented with greater severity during the acute phase and a high rate of hospitalisation. Despite having relatively better scores than those of other clusters did, they showed notable declines in general health and vitality. Finally, Cluster 5, considered the asymptomatic group, comprised young patients, with a high proportion in work and low hospitalisation rates. Although these patients cannot be considered fully recovered because of the fluctuating nature of long COVID-19, they presented the best quality of life, likely related to the absence of symptoms at the time of assessment. Several studies have analysed the distribution of PCs, with mixed results. In some studies, clusters were based on the severity of the acute phase (17), the distribution of symptoms (16,39), the systems affected (17,40) or the severity of PC (41). In general, these classifications have been correlated with sociodemographic variables and preexisting conditions (17,39,40), with hospitalisation or severity during the acute phase (17,40) and with the impact on functional status, occupational status, or quality of life. Gentilotti et al. (42) identified four distinct clinical phenotypes related to the course and treatment of the acute phase. While direct comparison of these studies is difficult, given differences in the populations analysed, the methodologies employed are similar and tend to identify comparable subgroups. Except for the asymptomatic subgroup, the phenotypes described in the ORCHESTRA cohort (42) are similar in our population with regard to the most predominant symptoms. In terms of quality of life, as assessed in both studies with the SF-36 questionnaire, our sample presented lower scores in some subgroups, indicating poorer quality of life. In both our study and that of Gentilotti et al. (42), asymptomatic patients consistently reported a better quality of life, with an increase in symptoms being associated with worsening quality of life. Taken together, these findings reinforce the usefulness of cluster analysis as a tool for identifying distinct clinical profiles within PCs. This allows for a better understanding of the heterogeneity of the disease and its functional impact, which could contribute to the development of more effective diagnostic and therapeutic strategies. One of the main limitations of this study is that it focused exclusively on symptomatic individuals who consulted for these symptoms. This makes it impossible to estimate the true prevalence of the condition in the general population. It is likely that milder cases have gone undetected, as these individuals do not usually seek medical attention for their symptoms. This remains a matter of controversy, since the lack of specific diagnostic methods or objective markers of the disease contributes to significant underdiagnosis (43). Furthermore, social determinants of health, such as economic or psychosocial factors, which are recognised as possible cofactors in the risk and evolution of PC, were not included (44). Therefore, some occupational variables, characteristics of the acute phase, and the vaccination status of the participants were considered. In the case of Spain, free and universal access to healthcare limits the impact of access to care. As in other studies with large samples, some statistically significant differences may not have a relevant clinical impact, so clinical interpretation of these data is necessary. While the sample was neither randomised nor gender balanced, it reflects the known distribution of the disease, with a greater impact on women (27,28). Finally, the cross-sectional, nonrandomised design makes it difficult to establish causality or ensure population representativeness, although the results obtained allow for the identification of clinical patterns and the generation of hypotheses for future longitudinal research. The strengths of this study are the recruitment strategies from primary care and hospital clinics, providing a broader perspective of the disease; the relatively high number of symptoms recorded, providing a comprehensive view of patients' health status; and a thorough assessment of demographic and clinical characteristics, which allowed for the adjustment of some analyses for comorbidities or characteristics of the acute infection responsible for the PC. The SF-36, a questionnaire validated in the Spanish population, was also used to assess quality of life. The grouping of symptoms into clusters permits the identification of patients who may benefit most from future health interventions and treatment approaches. This study also helps focus future research in this regard, with the goal of improving these patients’ quality of life. The first analysis identified different symptom clusters possibly associated with distinct pathogenetic pathways, and the second analysis indicated two possible distinct subpopulations characterised not only by different symptom profiles but also by different demographics, baseline characteristics, and timings and severities of acute infection. The results of the cluster analysis are potentially relevant for future clinical research targeting treatment on the basis of the cluster. 5 Conclusion The present study identified five clinical subgroups within the persistent COVID-19 spectrum, demonstrating that the presence of more symptoms is associated with worse quality of life. Dyspnea and fatigue are the main markers of this deterioration, especially in women, who are more affected. Cluster 1 presented the lowest levels of quality of life, whereas Cluster 5 presented the highest, underscoring the importance of developing personalised therapeutic strategies. These findings contribute to current knowledge about persistent COVID-19, supporting its clinically heterogeneous nature, greater susceptibility in women, and the usefulness of symptomatic classification into subgroups, allowing a differentiated assessment of the impact on quality of life. Abbreviations PC: Persistent COVID-19 SARS-CoV-2: syndrome coronavirus 2 APISAL: Primary Care Research Unit of Salamanca SBP: Systolic blood pressure DBP: Diastolic blood pressure ESH: European Society of Hypertension MAP: Mean arterial pressure HR: Heart rate HDL-C: high-density lipoprotein cholesterol PCS: Physical component MCS: Mental component BMI : Body Mass Index HBP : High Blood Pressure DM: Diabetes Mellitus OR: Odds Ratio CFS/ME: Chronic fatigue syndrome/myalgic encephalomyelitis Declarations 6.1 Ethics and informed consent The project was approved by the Drug Research Ethics Committee of Salamanca (CEIm), 27/06/2022. Before the start of the study, all the participants signed informed consent forms. Throughout the study, the Declaration of Helsinki and the WHO guidelines for observational studies were followed. The subjects were informed of the project aims and the risks and benefits of the examinations performed. The confidentiality of the subjects included was guaranteed at all times in accordance with Organic Law 3/2018, of December 5, on the Protection of Personal Data and the General Data Protection Regulation (GDPR) (EU) 2016/679 of the European Parliament and European Council, April 27, 2016. Consent for publication Not applicable 6.2 Availability of material data The data supporting the findings of this study are available at ZENODO under the DOI. https://doi.org/10.5281/zenodo.14282872 . It may be necessary to consult the Ethics Committee for permission to share. 6.3 Conflicts of interest Not applicable 6.4 Funding This study was funded by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ISCIII). RD24/0005/0018: Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) is funded by the European Union-Next Generation EU, Recovery and Resilience Facility (RRF), through the PI21/00454 project funded by the Instituto de Salud Carlos III (ISCIII), cofunded by the European Union and the CIBER CB22/06/00035 from the area of respiratory diseases. The government of Castilla y León also collaborated with the funding of this study through research projects (GRS 2501/B/22). 6.5 Author contributions Writing: review and editing, A. D.-M., A.S.-M., C.D.-M, C.L.-S, and P.G.-M.; Supervision C.L.-S.; E.N.-M., M.A.G.-M., E.R.-S. and L.G.-O.; Writing the original draft, A.D.-M. and C.L.-S.; Research N.S-M., A.N.-C., S.A.-R., A.D.-M., O.T.-M., S.G.-S. and A.B.C.-R. All the authors have read and agreed with the published version of the manuscript. 6.6 Acknowledgements We would like to thank all the members of the BIOICOPER research team and all the members of the team on the Primary Care Research Unit of Salamanca (APISAL) for their collaboration on the project. We would also like to thank all those who participated in this study. Their willingness, time, and commitment made data collection and the development of the BIOICOPER project possible. BIO-ICOPER investigators group: Carmen Patino Alonso, Alicia Hortega Andrés, Jesús F. Bermejo Martín, David González Calle, Teresa Muñoz Ciudad, David Cembrero Fuciños, Ángel García García, Manuel Á. Gómez Marcos, Raquel Jiménez Gómez, José A. 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Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort. eClinicalMedicine. 2023 Jul 21;62:102107. doi: 10.1016/j.eclinm.2023.102107 Global Burden of Disease Long COVID Collaborators, Wulf Hanson S, Abbafati C, Aerts JG, Al-Aly Z, Ashbaugh C, et al. Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA. 2022 Oct 25;328(16):1604–15. doi: 10.1001/jama.2022.18931 van den Houdt SCM, Slurink IAL, Mertens G. Long COVID is not a uniform syndrome: evidence from person-level symptom clusters using latent class analysis. J Infect Public Health. 2024 Feb;17(2):321–8. doi: 10.1016/j.jiph.2023.12.010 Additional Declarations No competing interests reported. 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Gómez-Marcos","email":"","orcid":"","institution":"Salamanca Primary Care Management (SACyL)","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"A.","lastName":"Gómez-Marcos","suffix":""}],"badges":[],"createdAt":"2025-08-25 17:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7455995/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7455995/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102941790,"identity":"02cc714a-226b-43db-862a-14f2f4c59983","added_by":"auto","created_at":"2026-02-18 17:21:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":326755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy summary\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/524e2ad238d75fa92b508760.png"},{"id":102941789,"identity":"955036af-02e7-45d7-8722-74b2258f1974","added_by":"auto","created_at":"2026-02-18 17:21:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60062,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the participant selection process\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/42488bffa9522d1a48eecd76.png"},{"id":102964631,"identity":"6fbe4973-6c88-4e8b-bc07-5c47b0f00b9b","added_by":"auto","created_at":"2026-02-19 04:23:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":229645,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadial dendrogram showing symptomatic classification of patients by cluster\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/20c0e41935b4fa6a0e3ca092.png"},{"id":102941794,"identity":"d2e32ced-b42a-47be-8519-ddc87c3734b4","added_by":"auto","created_at":"2026-02-18 17:21:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":155356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpider plot of mean SF-36 questionnaire subscale scores by cluster. \u003c/strong\u003eEach radial axis represents a subscale of the SF-36 questionnaire, with higher scores for outer values ​​and lower scores for inner values. Each polygon represents the mean score for each cluster.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/2a2295f50d932626664f18cf.png"},{"id":102941793,"identity":"3f991ec1-83e9-4966-9cdf-595dc0bb5d4d","added_by":"auto","created_at":"2026-02-18 17:21:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":169498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViolin plot comparing the SF-36 questionnaire domains by cluster\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/40949fcbbcd06784378c0c01.png"},{"id":102964111,"identity":"b4fffee7-39d6-4878-9833-ecf7d1b11558","added_by":"auto","created_at":"2026-02-19 04:21:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":325967,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSummary figure\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/cfa39434262bd4e1316a2453.png"},{"id":108635899,"identity":"62522313-40e8-4ee7-9c13-74ee02300a6e","added_by":"auto","created_at":"2026-05-06 17:55:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1822281,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7455995/v1/c90da13f-bd73-40a1-9242-2789eb731b38.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of persistent COVID-19 subtypes and their impact on quality of life","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBoth acute SARS-CoV-2 infection and its long-term effects have been the subject of extensive research. A significant proportion of patients who have recovered from the infection have been observed to experience debilitating symptoms for weeks, months, and even years after the acute infection has resolved. Numerous terms have been used to refer to this new phenomenon (1), the most common being persistent COVID (PC) and long COVID-19 (2–4). Currently, the most accepted definition of PC is that provided by the WHO (5). This condition occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection and typically continues three months after the onset of COVID-19, with symptoms lasting at least two months and remaining unexplained by an alternative diagnosis. Symptoms may appear at any time after recovery or may persist from the onset of the disease. Fluctuating symptoms and periods of relapse are common.\u003c/p\u003e\n\u003cp\u003ePC presents as a multiorgan disorder with a wide variety of manifestations. Among the more than 200 symptoms identified are fatigue, dyspnea, cognitive impairment, sleep disorders, anxiety, depression, myalgia, difficulty concentrating, anosmia/dysgeusia, or headache with limited functional capacity. All of these symptoms lead to a deterioration in the quality of life of these patients (2,6–8). Their quality of life is also influenced by related mental health issues, such as posttraumatic stress syndrome, anxiety, depression and insomnia (9–11). A further condition influencing the limitation of quality of life and functionality is the predominance of neurocognitive impairment, which manifests specifically through attention and memory problems (78.2% and 72.6% of cases in Spain, respectively) (12). The duration and severity of symptoms vary widely from patient to patient, resulting in different impacts on quality of life for each individual.\u003c/p\u003e\n\u003cp\u003eGiven the wide variability in symptom clusters, there is no universal classification of PC subtypes. In some studies, they are divided into groups on the basis of the affected system: neurological, cardiopulmonary, gastrointestinal, upper respiratory, and other aging-related comorbidities (13,14). Other approaches use a classification by individual symptoms or heterogeneous groups, considering factors such as the coexistence of symptoms, arthralgia or neurological disorders; some even highlight fatigue as a main symptom associated with autonomic dysfunction and psychiatric symptoms (15,16).\u003c/p\u003e\n\u003cp\u003eIn summary, there is no clear identification of PC symptoms or a classification that groups patients according to these symptoms, which hinders the approach to treating this disease. There are studies that agree on classifying some subtypes on the basis of the presence of the most common symptoms (13,14,17) but with disparate criteria. Identifying subgroups of clinical manifestations can help direct resources more efficiently and in a personalised manner, as well as help develop more effective prevention and treatment strategies, thereby enhancing the quality of care and, consequently, the quality of life of these patients.\u003c/p\u003e\n\u003cp\u003eThe main aim of the present study was therefore to develop a clinical classification of predominant symptoms to explore differences in quality of life overall and by sex and how these differences are related to lifestyle.\u003c/p\u003e\n\u003cp\u003eThe graphical abstract (Fig.1) \u0026nbsp;provides a general illustration of the approach and the main findings of this study.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a cross-sectional, descriptive, observational study conducted between April 2022 and August 2023 at the Primary Care Research Unit of Salamanca (APISAL). The results of this study are part of the BioICOPER study project on the relationships between endothelial structure, function, and damage and between vascular aging and the biopsychological situation in adults diagnosed with PC (Identifier ClinicalTrials.org: NCT05819840. Registered in April 2023).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were selected from patients who had a PC diagnosis recorded in their medical records, both in the internal medicine clinic specialising in persistent COVID-19 and in primary care clinics. Subjects were included by consecutive sampling if they met the following set of inclusion criteria: presented with PC as per the WHO definition; had a history of probable or confirmed SARS-CoV-2 infection with symptoms lasting at least two months and unexplained by an alternative diagnosis; and provided signed informed consent. Figure 2\u003c/p\u003e\n\u003cp\u003eThe exclusion criteria were people with terminal illnesses, those unable to visit the health center for a visit, those with a history of cardiovascular disease (ischemic heart disease or cerebrovascular disease), and those with a glomerular filtration rate less than 30%.\u003c/p\u003e\n\u003cp\u003eThe sample\u0026nbsp;size was calculated via free GRANMO software (https://www.datarus.eu/ca/aplications/granmo/). Accepting an alpha risk of 0.05 and a beta risk of less than 0.2 in a two-sided contrast and assuming a standard deviation of 20, a total of 258 individuals were required to detect a seven-point difference in the SF36 score between the subgroup with the most PC symptoms and the subgroup with the least. Therefore, the recruitment of 305 individuals was considered sufficient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Variables and measuring instruments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll measurements were collected in face‒to-face interviews via various self- and peer-reported questionnaires following a standardised protocol, with quality control by an independent researcher(18). The analysis variables were as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eSociodemographic variables and personal and family history, such as age, sex, marital status, educational level, and employment status, were recorded.\u003c/li\u003e\n \u003cli\u003eCardiovascular risk factors:\u003cul class=\"decimal_type\"\u003e\n \u003cli\u003eSystolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded via three measurements taken with an OMRON M10-IT sphygmomanometer (Omron Healthcare, Kyoto, Japan), and the average of the last two measurements was recorded. This was done in line with the recommendations of the European Society of Hypertension (ESH) (19). The participant was seated, and after resting for at least five minutes, three measurements were taken on the dominant and nondominant arms. The mean arterial pressure (MAP) was calculated via the formula MAP = (2DBP + SBP)/3. The heart rate (HR) value was also taken from the OMRON BP monitor. Participants were considered hypertensive if they were taking antihypertensive drugs or had BP values \u0026ge; 140/90 mmHg.\u003c/li\u003e\n \u003cli\u003eDyslipidemia: Participants were considered to have this condition if they were taking lipid-lowering drugs or had fasting total cholesterol \u0026ge; 200 mg/dL, high-density lipoprotein cholesterol (HDL-C) \u0026lt; 40 mg/dL in men and \u0026lt; 50 mg/dL in women, or triglycerides \u0026ge; 150 mg/dL.\u003c/li\u003e\n \u003cli\u003eDiabetes: Participants who received oral antidiabetic agents or insulin therapy or who had fasting plasma glucose levels \u0026ge; 126 mg/dL or HbA1c \u0026ge; 6.5% were considered to have diabetes mellitus.\u003c/li\u003e\n \u003cli\u003eSmokers/nonsmokers: Participants were recorded as nonsmokers if they had never smoked or had not smoked in the past year.\u003c/li\u003e\n \u003cli\u003eObesity was diagnosed if the BMI was \u0026ge; 30 kg/m2 (20).\u003c/li\u003e\n \u003cli\u003eo Alcohol: Those who consumed any alcoholic beverages during the follow-up week recorded in the self-administered questionnaires were considered alcohol drinkers.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eThe vaccination\u0026nbsp;status, number and date of COVID-19 infection, clinical symptoms and laboratory tests (PCR or Ag) formed the basis for the diagnosis of the disease, and the variant that caused the acute infection was associated with the onset of PC. The variable was obtained on the basis of the date of infection and the predominant variant at that time in the province of Salamanca. These data were collected from the participants\u0026rsquo; medical history and from face‒to-face interviews. Complete primary vaccination was recorded for patients who had received two doses of the same or a different vaccine and a single dose of the Janssen vaccine\u0026nbsp;(21).\u003c/li\u003e\n \u003cli\u003eA physical examination was performed, and the following variables were recorded:\u003cul\u003e\n \u003cli\u003eHeight was measured in cm using a wall-mounted stadiometer during inspiration, with the participant barefoot with heels against the wall.\u003c/li\u003e\n \u003cli\u003eThe waist\u0026nbsp;circumference was recorded with a flexible tape measure parallel to the floor, above the iliac crest, at the end of exhalation, with the participant standing and wearing underwear. Hip circumference was measured at the point at which the maximum circumference passed through the greater trochanter of both femurs.\u003c/li\u003e\n \u003cli\u003eWeight was determined via an InBody 230 monitor (InBody Co., Ltd., Seoul, South Korea), with the participant fasting for at least 2 hours, barefoot, wearing light clothing, and having an empty bladder. Body mass index was calculated by dividing body weight (kg) by height squared (m2).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003ePC symptoms were reported by the participants during their anamnesis at the time of the study, including during the acute phase, months after the acute phase, and at the time of the interview. The following symptoms were recorded:\u003cul\u003e\n \u003cli\u003eSystemic manifestations: fatigue, lack of energy, fever\u003c/li\u003e\n \u003cli\u003eNeurocognitive manifestations include memory loss, difficulty concentrating, brain fog or confusion.\u003c/li\u003e\n \u003cli\u003eRespiratory/cardiovascular manifestations: dyspnea, chest tightness, cough, and sore throat.\u003c/li\u003e\n \u003cli\u003eMusculoskeletal manifestations: Myalgia, arthralgia, and mobility limitations.\u003c/li\u003e\n \u003cli\u003eNeurological/neuromuscular manifestations: headache, altered taste or smell, loss of reflexes or paresthesia.\u003c/li\u003e\n \u003cli\u003ePsychosocial or psychiatric manifestations: depression, anxiety, and sleep disturbance.\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n \u003cli\u003eQuality of life was assessed via the SF-36 health questionnaire. This questionnaire is divided into 9 subscales:\u003cul\u003e\n \u003cli\u003ePhysical functioning: assesses the degree to which health limits daily physical activities such as self-care, walking, climbing stairs, bending, lifting or carrying weights, and moderate and intense exertion.\u003c/li\u003e\n \u003cli\u003eRole-Physical: measures how physical health interferes with work and other daily activities, negatively influences performance, limits the types of activity, or makes them difficult to perform.\u003c/li\u003e\n \u003cli\u003eBodily pain: estimates the intensity of pain and its effect on routine work, both in the workplace and at home.\u003c/li\u003e\n \u003cli\u003eGeneral health: personal assessment of health, including current health, future health prospects, and resistance to illness.\u003c/li\u003e\n \u003cli\u003eVitality: assesses feelings of energy and vitality versus feelings of tiredness and exhaustion.\u003c/li\u003e\n \u003cli\u003eSocial Functioning: This measures the degree to which emotional problems interfere with work or other activities of daily living, including reduced time spent on these activities, lower-than-desired performance, and decreased quality of work.\u003c/li\u003e\n \u003cli\u003eMental health assesses mental health, including depression, anxiety, behavioural control, emotional control, and overall positive affect.\u003c/li\u003e\n \u003cli\u003eReported Health Trend: This refers to the quantification of current health compared with that one year prior (22).\u003c/li\u003e\n \u003cli\u003eFrom these subscales, two more scores can be obtained, which group the subscales that measure the physical component (PCS) and the subscales that evaluate the mental component (MCS).\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;2.4\u0026nbsp;\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected via the REDCap (Research Electronic Data Capture) platform with a data collection questionnaire designed for the project (18). The variables are presented as the means and standard deviations for quantitative variables and as numbers and percentages for categorical variables. The Kolmogorov‒Smirnov test was used to verify the normal distribution of the variables. Odds ratios were calculated to assess the probability of presenting symptoms between sexes. Subgroups of the symptoms present were identified via cluster analysis via hierarchical clustering with Jaccard\u0026apos;s distance and Ward\u0026apos;s methods. The Kruskal‒Wallis test was used for comparisons across clusters, and the Mann‒Whitney U test was used for pairwise comparisons of clusters. For multiple comparisons, p values adjusted by Bonferroni correction were used. The data were analysed via SPSS for Windows, version 26.0 (IBM, Armonk, New York: IBM Corp.) and R (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e"},{"header":" 3\tResults","content":"\u003cp\u003eAmong\u0026nbsp;the 305 participants, one was considered missing because they did not complete the symptom and quality of life questionnaires. The mean age was 52.71 \u0026plusmn; 11.91 years, and 68.2% of the participants (n = 208) were women. The general characteristics of the study population and their sex distributions are shown in Table 1. The majority of participants were employed (n = 186, 61.2%). Among these participants, 231 (75%) were infected before completing primary vaccination. The primary variant responsible for infection in these patients was EU1 (63.8%), with similar rates in men and women. During the acute phase, 29.7% of the total patients were hospitalised, with men being the predominant group requiring hospitalisation.\u003c/p\u003e\n\u003cp\u003eThe mean BMI was 27.99 \u0026plusmn; 5.55, and 32.6% (n = 99) were obese. With respect to personal history, 35.9% were hypertensive, and 201 patients (66.1%) had dyslipidemia. Regarding alcohol use, 46.4% of the patients drank some alcohol. All variables related to cardiovascular risk were present at higher rates in men than in women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Sample description\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 30px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMen \u0026nbsp; \u0026nbsp;(N=97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eWomen (N=207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eSex (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e97 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e207 (68.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eAge (years, mean, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e52.71 \u0026plusmn; 11.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e55.70 \u0026plusmn; 12.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e51.41 \u0026plusmn; 11.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.795\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eIn work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e186 (61.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e57 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e129 (62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eOut of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e79 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e26 (26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e53 (25.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003eSick leave/Permanent disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e39 (12.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e14 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e25 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cu\u003eCOVID-19\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003eVariant responsible for infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 30px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;EU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e194 (63.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e58 (59.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e136 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Alpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e51 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e20 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Delta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e16 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e7 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e9 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Omicron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e43 (14.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e12 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eComplete primary vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e278 (91.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e89 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e189 (91.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eInfection before completing primary vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e228 (82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e74 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e154 (81.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHospitalised during the acute phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e83 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e41 (44.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42 (22.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cu\u003eCardiovascular risk factors\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eBMI (kg/cm\u003csup\u003e2\u003c/sup\u003e, mean, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e27.99 \u0026plusmn; 5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e29.60 \u0026plusmn; 4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e27.23 \u0026plusmn; 5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eObesity, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e99 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e44 (44,4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eHBP (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e109 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e52 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eDyslipidemia\u0026nbsp;(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e201 (66.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e71 (73.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e130 (62.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eDM (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e37 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e22 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e15 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eSmoker (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e16 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e8 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eAlcohol user (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e141 (46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e61 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e80 (38.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI: Body Mass Index; HBP: High Blood Pressure; DM: Diabetes Mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Persistent COVID-19 symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows the symptoms overall and by sex. The predominant symptom was fatigue, reported by 71.4% of patients (62.9% of men and 75.4% of women). A lack of energy (67.4%), memory loss (59.9%), dyspnea (58.2%), and sleep disturbances (60.5%) were also common.\u003c/p\u003e\u003ch4\u003e\u003cstrong\u003eTable 2:\u003c/strong\u003e \u003cstrong\u003ePrevalence of the symptoms studied in the entire population and by sex.\u003c/strong\u003e.\u003c/h4\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"71%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003eGeneral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eMen\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(N=97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eWomen (N=207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eOdds ratio\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(IC95%) (M/H)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eSystemic manifestations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eFatigue, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e217 (71.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e61 (62.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e156 (75.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.805*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eLack energy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e205 (67.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e55 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e150 (72.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.010*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eGeneral malaise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e127 (41.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e98 (47.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.108*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e16 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e12 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNeurocognitive manifestations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMemory loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e182 (59.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e136 (65.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.124*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eDifficulty concentrating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e171 (56.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39 (40.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e132 (63.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.617**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eBrain fog or confusion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e133 (43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e30 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e103 (49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.212*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eRespiratory/cardiovascular manifestations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e177 (58.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e49 (50.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e128 (61.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eChest tightness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e95 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e22 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e73 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.857*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eCough\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e79 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e48 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eSore throat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e63 (20.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e17 (17.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e46 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMusculoskeletal manifestations\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMyalgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e169 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e36 (37.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e133 (64.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3.045**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eArthralgia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e165 (54.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e38 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e127 (61.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.465**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eMobility limitations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e73 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e16 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.924*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eNeurological/neuromuscular manifestations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eHeadache\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e121 (39.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29 (29.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e92 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.876*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAltered taste or smell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e80 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e23 (23.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e57 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eLoss of reflexes or parestesia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e122 (40.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e41 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e81 (39.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e0.878\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003ePsychosocial or psychiatric manifestations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eDepression\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e131 (43.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e31 (32.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e100 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.990*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAnxiety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e135 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32 (33.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e103 (49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.012*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 3px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eSleep disturbance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e184 (60.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e44 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e140 (67.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2.517**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e*p valor \u0026lt;0.05 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;**p valor \u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, women had a greater frequency of symptoms than men did. In particular, fatigue (75.4% vs. 62.9%, p = 0.006) and lack of energy (72.5% vs. 56.7%, p = 0.006) were significantly more common in women. The same was true for other symptoms, such as malaise, memory and concentration problems, brain fog, chest tightness, muscle and joint pain, difficulty moving, headache, depression, anxiety, and sleep disturbances.\u003c/p\u003e\n\u003cp\u003eThe odds ratio analysis revealed that women were more likely to present with most of the symptoms than men were, except for cough (OR = 0.643) and loss of reflexes (OR = 0.878).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Clusters by symptom groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSince symptoms occurred simultaneously, a cluster analysis was performed to identify possible patterns. Thus, the 18 symptoms were previously organised into six groups according to their origin or clinical relationship: systemic, neurocognitive, respiratory/cardiovascular, musculoskeletal, neurological/neuromuscular, and psychological-psychiatric (see Table 2). A patient was considered to belong to a group if they reported at least one of its symptoms. The results of the analysis identified five main clusters, as shown in Figure 3.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe clusters resulting from the combination of different symptoms were as follows:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eCluster 1 - Respiratory/cardiovascular with muscular and systemic involvement: predominance of respiratory/cardiovascular symptoms with systemic and musculoskeletal symptoms.\u003c/li\u003e\n \u003cli\u003eCluster 2 - Predominantly respiratory/cardiovascular: showing respiratory/cardiovascular symptoms without neurological/neuromuscular or psychological-psychiatric involvement. In these patients, the predominant symptoms are respiratory/cardiovascular.\u003c/li\u003e\n \u003cli\u003eCluster 3 - Without respiratory or cardiovascular involvement: showing any symptoms without respiratory/cardiovascular symptoms.\u003c/li\u003e\n \u003cli\u003eCluster 4 - Respiratory/cardiovascular with psychological-psychiatric involvement: generally showing respiratory/cardiovascular and psychological-psychiatric symptoms but without musculoskeletal involvement.\u003c/li\u003e\n \u003cli\u003eCluster 5 - Asymptomatic: no symptoms at the current time.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCluster 1 was the largest. The study included 158 patients (51.8%) with respiratory symptoms with muscular and systemic involvement. This was followed by Cluster 3, with 75 participants (24.6%). Cluster 5 comprised 10 patients who were asymptomatic at the time of assessment but met the PC criteria. This group of patients presented symptoms such as weakness and fatigue (90%), headache (80%), fever, malaise, muscle pain, cough and taste disturbance (70%) during the acute phase, while symptoms decreased as the disease progressed. At the time of the study visit, the predominant symptoms were fatigue and dyspnea, which were among the three most frequent symptoms in Clusters 1, 2, and 4, with percentages exceeding 50%. In Cluster 3, the most frequent symptoms were memory loss, sleep disturbance, and decreased concentration, with percentages of 58.67%, 56%, and 53.33%, respectively.\u003c/p\u003e\n\u003cp\u003eThe distributions of men and women in the defined clusters were similar in all of them except for Cluster 5, with 8 male participants (80%). With respect to employment status, Cluster 1 had the highest percentage of sick leave or disability, with 29 (18.2%) participants. Cluster 3 had the highest proportion of individuals out of work, with 50 (33.3%). The clusters with the highest proportion of active participants were Clusters 2 and 5, with 16 (72.7%) and 8 (80%) patients, respectively.\u003c/p\u003e\n\u003cp\u003eRegarding the impact of COVID-19 during the acute phase, more than 60% of patients in all subgroups were infected before completing primary vaccination, with the highest percentages in Clusters 1, 3, and 4, with infection rates of 82.4%, 84.1%, and 89.2%, respectively. No group had high hospitalisation rates, and the groups were evenly distributed. In all the groups, the number of fully vaccinated participants at the time of the interview was high, exceeding 90% in most cases. The variant predominantly responsible for infection in all clusters was EU1.\u003c/p\u003e\n\u003cp\u003eWith respect to\u0026nbsp;cardiovascular risk factors, all the subgroups had a mean BMI above the normal weight range, yet no group presented high levels of obesity. Dyslipidemia was the most common factor, with percentages over 50% in all subgroups. Smoking was not particularly prevalent in any cluster, with low or no smoking rates. With respect to alcohol, Cluster 5 had the highest rate of alcohol use (80%), with Cluster 1 drinking the least (37.3%). The characteristics of each cluster are shown in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Cluster characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e158 (51.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e22 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e75 (24.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e39 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e10 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSex (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e40 (25.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e10 (45.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e26 (34.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e13 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e118 (74.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e12 (54.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e49 (65.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e26 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAge (years, mean, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e52.59 \u0026plusmn; 10.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e52.05 \u0026plusmn; 14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e54.41 \u0026plusmn; 11.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e52.03 \u0026plusmn; 13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e47.90 \u0026plusmn; 14.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eEmployment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eIn work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e94 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e16 (72.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e44 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e25 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e7 (70.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eOut of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e35 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e50 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e12(30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e3 (30%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 4px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eSick leave/Permanent disability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e29 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e6 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eMost frequent symptoms in order of frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e1. Fatigue (93.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1. Dyspnea (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e1.Memory impairment (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1. Dyspnea\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e2. Weakness (87.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2. Fatigue (52.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2. Sleep disturbance\u003c/p\u003e\n \u003cp\u003e(56.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 Sleep disturbance (76.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e3. Dyspnea (82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3. Cough\u003c/p\u003e\n \u003cp\u003e(42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e3. Concentration difficulties (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3. Fatigue\u003c/p\u003e\n \u003cp\u003e(68.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eVariant responsible for COVID-19 infection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eEU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e105 (66.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e47 (62.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e27 (69.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAlpha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e24 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e15 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e7 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eDelta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e7 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2 (9.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e2 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eOmicron\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e22 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6 (27.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e11 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2 (5.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e2 (20.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eComplete primary vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e142 (89.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e21 (95.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e69 (92.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e37 (94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e9 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eInfected before primary vaccination\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e117 (82.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e13 (61.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e58 (84.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e33 (89.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e7 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eHospitalised during acute phase\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e40 (28.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e7 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e20 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e13 (36.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cu\u003eCardiovascular risk factors\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eBody Mass Index (kg/cm2, mean, SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e28.41 \u0026plusmn; 6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e28.88 \u0026plusmn; 5.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e26.67 \u0026plusmn; 4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e28.10 \u0026plusmn; 5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e28.88 \u0026plusmn; 4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eHBP (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e62 (39.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e21 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e15 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eDyslipidemia\u0026nbsp;(n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e104 (65.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e14 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e48 (64.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e30 (76.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e5 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eDM (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e26 (16.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e4 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e3 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eSmoker (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e8 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e7 (9.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e0 (0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e1 (10.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eAlcohol user (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e59 (37.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e39 (52.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e24 (61.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e8 (80.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eObesity (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e55 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e7 (31.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e18 (24.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e15 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9px;\"\u003e\n \u003cp\u003e4 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Impact on quality of life\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the analysis comparing the five identified clusters, differences were apparent in most of the physical and mental health components assessed with the SF36 (p \u0026lt; 0.001), indicating the existence of distinct clinical profiles.\u003c/p\u003e\n\u003cp\u003eCluster 1 had the lowest scores in all dimensions, indicating greater physical and mental impairment (physical functioning: 40.7 \u0026plusmn; 10.3; mental health: 42.3 \u0026plusmn; 9.9). This group also had the lowest levels of vitality, role-physical, and social functioning.\u003c/p\u003e\n\u003cp\u003eCluster 2 had the best overall scores on nearly all scales (physical functioning: 50.5 \u0026plusmn; 6.4; mental health: 53.2 \u0026plusmn; 6.1), suggesting better overall health and less symptomatic impact.\u003c/p\u003e\n\u003cp\u003eClusters 3 and 4 had intermediate scores. While Cluster 3 showed better results in general health and physical functioning, Cluster 4 was characterised by greater impairment in vitality and role-emotional.\u003c/p\u003e\n\u003cp\u003eCluster 5 had high scores for both physical and mental components (physical functioning: 53.5 \u0026plusmn; 3.4; role-emotional: 51.6 \u0026plusmn; 4.7), as well as the best subjective state-of-health assessment (67.5 \u0026plusmn; 31.3).\u003c/p\u003e\n\u003cp\u003eTogether, these findings confirm that the groups derived from the cluster analysis reflect different degrees of functional and emotional impact, with Cluster 1 being the most affected and Cluster 5 having the highest scores in the physical domain and the second highest in the mental domain. All scores on the SF-36 scale are collated in Table 4 and displayed in Figures 4 and 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Cluster scores on the scales and domains of the SF-36 quality-of-life questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScales\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical functioning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.71 \u0026plusmn; 10.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e50.52 \u0026plusmn; 6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e47.15 \u0026plusmn; 9.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e46.39 \u0026plusmn; 8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e53.46 \u0026plusmn; 3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRole-physical\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e36.29 \u0026plusmn; 11.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e49.70 \u0026plusmn; 9.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.94 \u0026plusmn; 12.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e43.66 \u0026plusmn; 12.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.51 \u0026plusmn; 9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBodily pain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e35.25 \u0026plusmn; 8.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e44.62 \u0026plusmn; 8.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.66 \u0026plusmn; 8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.60 \u0026plusmn; 8.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e48.28 \u0026plusmn; 9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeneral health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e36.89 \u0026plusmn; 8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e47.90 \u0026plusmn; 6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.07 \u0026plusmn; 9.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e39.46 \u0026plusmn; 9.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e48.25 \u0026plusmn; 10.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVitality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e34.79 \u0026plusmn; 8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e47.31 \u0026plusmn; 8.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e41.42 \u0026plusmn; 10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e38.23 \u0026plusmn; 10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e46.20 \u0026plusmn; 7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial functioning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e33.53 \u0026plusmn; 12.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e47.81 \u0026plusmn; 9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e39.12 \u0026plusmn; 14.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e39.57 \u0026plusmn; 13.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e49.33 \u0026plusmn; 8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRole-emotional\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.29 \u0026plusmn; 14.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e51.15 \u0026plusmn; 5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.50 \u0026plusmn; 14.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.15 \u0026plusmn; 13.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e51.57 \u0026plusmn; 4.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental health\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e42.34 \u0026plusmn; 9.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e53.24 \u0026plusmn; 6.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e44.18 \u0026plusmn; 9.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e44.71 \u0026plusmn; 8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.75 \u0026plusmn; 7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubjective health assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e46.85 \u0026plusmn; 26.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e48.81 \u0026plusmn; 21.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e55.33 \u0026plusmn; 25.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e50.64 \u0026plusmn; 21.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e67.50 \u0026plusmn; 31.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysical components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e35.58 \u0026plusmn; 10.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e46.47 \u0026plusmn; 7.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e43.91 \u0026plusmn; 10.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e43.10 \u0026plusmn; 9.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e49.78 \u0026plusmn; 9.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental components\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.49 \u0026plusmn; 12.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e51.97 \u0026plusmn; 7.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e40.97 \u0026plusmn; 13.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e41.43 \u0026plusmn; 12.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e49.67 \u0026plusmn; 7.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eA comparison\u0026nbsp;of the SF-36 questionnaire subscales across clusters revealed significant differences (p \u0026lt; 0.05), primarily in the physical domain but also in some mental domains.\u003c/p\u003e\n\u003cp\u003eWith respect to\u0026nbsp;physical functioning, significant differences were found between Cluster 1 and all the other clusters (Clusters 2, 3, 4, and 5; p \u0026lt; 0.001 in most cases), indicating that this cluster presents poorer quality of life in this component of the scale. In contrast, the differences between the other clusters were not significant.\u003c/p\u003e\n\u003cp\u003eFor role-physical and bodily pain, a similar trend emerged: Cluster 1 was the only cluster presenting differences in comparison with the other clusters (p \u0026lt; 0.01). The general health dimension significantly differed between Cluster 1 and Clusters 2, 3, and 5 (p \u0026lt; 0.01) and between Cluster 2 and Cluster 4 (p = 0.008), whereas the remaining comparisons were not significant.\u003c/p\u003e\n\u003cp\u003eThe pattern observed in the physical dimensions was repeated in mental health, with differences between Cluster 1 and Cluster 2 (p \u0026lt; 0.001), as well as between Cluster 2 and Cluster 3 (p = 0.001) and between Cluster 4 (p = 0.008), indicating notable differences in psychological aspects.\u003c/p\u003e\n\u003cp\u003eIn contrast, there were no significant differences in self-reported health assessments across the clusters (p \u0026gt; 0.05 in all cases), which could indicate that all patient groups had similar self-assessments of their health, regardless of the symptoms they presented. The physical and mental components follow the pattern observed in the categories analysed previously. There were differences between Cluster 1 and the other categories (p \u0026lt; 0.01) in the physical components, whereas the mental components only differed between Cluster 1 and Cluster 2 (p = 0.001) and between Clusters 2 and 3 and 4 (p = 0.005 and 0.017, respectively). This suggests that the quality of life of Cluster 1 is influenced mainly by physical characteristics, whereas Clusters 2, 3, 4 and 5 share greater similarity with each other, with some specific exceptions in dimensions generally related to mental components.\u003c/p\u003e\n\u003cp\u003eThe summary figure (Fig 6) illustrates the main results and findings of this study.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe present study subdivided a cross-sectional sample of 304 participants by symptoms of persistent COVID-19. Cluster analysis yielded five clusters with distinct symptom groupings, with differences observed in the subcategories of quality of life and physical and mental components.\u003c/p\u003e\n\u003cp\u003eThe analysis by sex revealed no differences between the variants, vaccination status, or infection status prior to primary vaccination. However, women were at greater risk of experiencing more PC symptoms. This finding is in line with those of previous studies, which reported that women have a greater number of symptoms, regardless of severity, in the acute phase (23–26). This greater susceptibility has been attributed to immunological differences between sexes, which are linked to an increase in proinflammatory cytokines and greater immunological dysfunction in females (27,28). Moreover, men follow similar patterns to those of the general population during the acute phase, presenting more hospitalisations than women do (29,30). Furthermore, men had a higher rate of unemployment, sick leave, or disability, despite having fewer symptoms. This finding is relevant since lower rates of employment have been linked to a greater presence of symptoms (26). One possible explanation could be related to sociocultural factors, as well as the type or severity of symptoms, which could have differential effects on men’s and women’s functional capacity and ability to cope at work. These differences underscore the importance of considering sex and sociocultural factors when studying and managing PC. These factors can play important roles in the clinical and functional expression of the disease, highlighting the importance of multidisciplinary and personalised approaches to optimise PC patient care.\u003c/p\u003e\n\u003cp\u003eUnderstanding the PC symptom profile and the differences between sexes is vital in assessing its impact on quality of life. Fatigue and lack of energy were the symptoms most frequently reported by patients, affecting more than half of the patients, with differences observed between sexes. Other common symptoms included memory loss, difficulty concentrating, dyspnea, myalgia, arthralgia, and sleep disturbance; only dyspnea did not vary by sex. These findings are consistent with previous meta-analyses where fatigue, dyspnoea, memory loss, and difficulty concentrating were identified as the main symptoms of PC (31–33). Fatigue and dyspnoea are associated with greater negative impacts on work capacity and worse quality of life. Other studies have confirmed that these symptoms are associated with poorer quality of life and reduced work activity (26,34,35). Fatigue also shares clinical and pathophysiological characteristics with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), a condition closely related to persistent COVID-19, with groups of similar symptoms and more than half of the cases meeting the diagnostic criteria for CFS/ME (36,37). In addition, neuromuscular alterations have been observed to occur with arthralgia and myalgia, which suggests that they have similar pathophysiologies (38). Therefore, the high prevalence of symptoms shared with CFS/ME suggests that the clinical course of these patients may lead to a significant proportion of patients with a CFS/ME-like condition. Neurocognitive symptoms and sleep disturbances predominate in patients with severe acute illness, whereas fatigue and dyspnea are more common in patients with mild illness. Finally, hospitalisation during the acute phase is associated with poorer quality of life in patients with persistent symptoms, especially fatigue and dyspnoea. Taken together, these findings reinforce the importance of looking beyond the initial severity of the illness to focus on symptomatic assessment in patients with persistent COVID-19. Identifying symptom profiles can facilitate the design of tailored clinical and psychosocial strategies aimed at improving patients' quality of life and functional reintegration.\u003c/p\u003e\n\u003cp\u003eTo identify these profiles, a cluster classification was performed with symptom groupings on the basis of potential etiopathogenic pathways. This allowed the identification of five clinical subgroups and quality of life assessment via the SF-36 questionnaire. Cluster 1 was characterised by the largest sample size and the highest proportion of patients on sick leave or with disability. This group also had the lowest quality of life scores, with significant differences compared with the other clusters. Cluster 2 included patients with a greater proportion of individuals at work and high quality of life scores. The absence of neuromuscular or cognitive limitations could suggest a less severe condition. Neurocognitive symptoms predominated in Cluster 3, which appeared to negatively impact vitality levels and the physical and mental components of quality of life. Cluster 4 included patients who presented with greater severity during the acute phase and a high rate of hospitalisation. Despite having relatively better scores than those of other clusters did, they showed notable declines in general health and vitality. Finally, Cluster 5, considered the asymptomatic group, comprised young patients, with a high proportion in work and low hospitalisation rates. Although these patients cannot be considered fully recovered because of the fluctuating nature of long COVID-19, they presented the best quality of life, likely related to the absence of symptoms at the time of assessment. Several studies have analysed the distribution of PCs, with mixed results. In some studies, clusters were based on the severity of the acute phase (17), the distribution of symptoms (16,39), the systems affected\u0026nbsp;(17,40) or the severity of PC (41). In general, these classifications have been correlated with sociodemographic variables and preexisting conditions\u0026nbsp;(17,39,40), with hospitalisation or severity during the acute phase\u0026nbsp;(17,40)\u0026nbsp;and with the impact on functional status, occupational status, or quality of life. Gentilotti et al.\u0026nbsp;(42)\u0026nbsp;identified four distinct clinical phenotypes related to the course and treatment of the acute phase. While direct comparison of these studies is difficult, given differences in the populations analysed, the methodologies employed are similar and tend to identify comparable subgroups. Except for the asymptomatic subgroup, the phenotypes described in the ORCHESTRA cohort\u0026nbsp;(42)\u0026nbsp;are similar in our population with regard to the most predominant symptoms. In terms of quality of life, as assessed in both studies with the SF-36 questionnaire, our sample presented lower scores in some subgroups, indicating poorer quality of life. In both our study and that of Gentilotti et al.\u0026nbsp;(42), asymptomatic patients consistently reported a better quality of life, with an increase in symptoms being associated with worsening quality of life. Taken together, these findings reinforce the usefulness of cluster analysis as a tool for identifying distinct clinical profiles within PCs. This allows for a better understanding of the heterogeneity of the disease and its functional impact, which could contribute to the development of more effective diagnostic and therapeutic strategies.\u003c/p\u003e\n\u003cp\u003eOne of the main limitations of this study is that it focused exclusively on symptomatic individuals who consulted for these symptoms. This makes it impossible to estimate the true prevalence of the condition in the general population. It is likely that milder cases have gone undetected, as these individuals do not usually seek medical attention for their symptoms. This remains a matter of controversy, since the lack of specific diagnostic methods or objective markers of the disease contributes to significant underdiagnosis (43). Furthermore, social determinants of health, such as economic or psychosocial factors, which are recognised as possible cofactors in the risk and evolution of PC, were not included (44). Therefore, some occupational variables, characteristics of the acute phase, and the vaccination status of the participants were considered. In the case of Spain, free and universal access to healthcare limits the impact of access to care. As in other studies with large samples, some statistically significant differences may not have a relevant clinical impact, so clinical interpretation of these data is necessary. While the sample was neither randomised nor gender balanced, it reflects the known distribution of the disease, with a greater impact on women (27,28). Finally, the cross-sectional, nonrandomised design makes it difficult to establish causality or ensure population representativeness, although the results obtained allow for the identification of clinical patterns and the generation of hypotheses for future longitudinal research.\u003c/p\u003e\n\u003cp\u003eThe strengths of this study are the recruitment strategies from primary care and hospital clinics, providing a broader perspective of the disease; the relatively high number of symptoms recorded, providing a comprehensive view of patients' health status; and a thorough assessment of demographic and clinical characteristics, which allowed for the adjustment of some analyses for comorbidities or characteristics of the acute infection responsible for the PC. The SF-36, a questionnaire validated in the Spanish population, was also used to assess quality of life. The grouping of symptoms into clusters permits the identification of patients who may benefit most from future health interventions and treatment approaches. This study also helps focus future research in this regard, with the goal of improving these patients’ quality of life. The first analysis identified different symptom clusters possibly associated with distinct pathogenetic pathways, and the second analysis indicated two possible distinct subpopulations characterised not only by different symptom profiles but also by different demographics, baseline characteristics, and timings and severities of acute infection. The results of the cluster analysis are potentially relevant for future clinical research targeting treatment on the basis of the cluster.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThe present study identified five clinical subgroups within the persistent COVID-19 spectrum, demonstrating that the presence of more symptoms is associated with worse quality of life. Dyspnea and fatigue are the main markers of this deterioration, especially in women, who are more affected. Cluster 1 presented the lowest levels of quality of life, whereas Cluster 5 presented the highest, underscoring the importance of developing personalised therapeutic strategies. These findings contribute to current knowledge about persistent COVID-19, supporting its clinically heterogeneous nature, greater susceptibility in women, and the usefulness of symptomatic classification into subgroups, allowing a differentiated assessment of the impact on quality of life.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePC: Persistent COVID-19\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSARS-CoV-2: syndrome coronavirus 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAPISAL: Primary Care Research Unit of Salamanca\u003c/p\u003e\n\u003cp\u003eSBP: Systolic blood pressure\u003c/p\u003e\n\u003cp\u003eDBP: Diastolic blood pressure\u003c/p\u003e\n\u003cp\u003eESH: European Society of Hypertension\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMAP: Mean arterial pressure\u003c/p\u003e\n\u003cp\u003eHR: Heart rate\u003c/p\u003e\n\u003cp\u003eHDL-C: high-density lipoprotein cholesterol\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCS: Physical component\u003c/p\u003e\n\u003cp\u003eMCS: Mental component\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp;: Body Mass Index\u003c/p\u003e\n\u003cp\u003eHBP\u0026nbsp;: High Blood Pressure\u003c/p\u003e\n\u003cp\u003eDM: Diabetes Mellitus\u003c/p\u003e\n\u003cp\u003eOR: Odds Ratio\u003c/p\u003e\n\u003cp\u003eCFS/ME: Chronic fatigue syndrome/myalgic encephalomyelitis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6.1\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eEthics and informed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was approved by the Drug Research Ethics Committee of Salamanca (CEIm), 27/06/2022. Before the start of the study, all the participants signed informed consent forms. Throughout the study, the Declaration of Helsinki and the WHO guidelines for observational studies were followed. The subjects were informed of the project aims and the risks and benefits of the examinations performed. The confidentiality of the subjects included was guaranteed at all times in accordance with Organic Law 3/2018, of December 5, on the Protection of Personal Data and the General Data Protection Regulation (GDPR) (EU) 2016/679 of the European Parliament and European Council, April 27, 2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.2\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of material data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available at ZENODO under the DOI.\u0026nbsp;https://doi.org/10.5281/zenodo.14282872\u003c/a\u003e. It may be necessary to consult the Ethics Committee for permission to share.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.4\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (ISCIII). RD24/0005/0018: Network for Research on Chronicity, Primary Care, and Health Promotion (RICAPPS) is funded by the European Union-Next Generation EU, Recovery and Resilience Facility (RRF), through the PI21/00454 project funded by the Instituto de Salud Carlos III (ISCIII), cofunded by the European Union and the CIBER CB22/06/00035 from the area of respiratory diseases. The government of Castilla y León also collaborated with the funding of this study through research projects (GRS 2501/B/22).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.5\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting: review and editing, A. D.-M., A.S.-M., C.D.-M, C.L.-S, and P.G.-M.; Supervision C.L.-S.; E.N.-M., M.A.G.-M., E.R.-S. and L.G.-O.; Writing the original draft, A.D.-M. and C.L.-S.; Research N.S-M., A.N.-C., S.A.-R., A.D.-M., O.T.-M., S.G.-S. and A.B.C.-R. All the authors have read and agreed with the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.6\u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the members of the BIOICOPER research team and all the members of the team on the Primary Care Research Unit of Salamanca (APISAL) for their collaboration on the project. We would also like to thank all those who participated in this study. Their willingness, time, and commitment made data collection and the development of the BIOICOPER project possible.\u003c/p\u003e\n\u003cp\u003eBIO-ICOPER investigators group: Carmen Patino Alonso, Alicia Hortega Andrés, Jesús F. Bermejo Martín, David González Calle, Teresa Muñoz Ciudad, David Cembrero Fuciños, Ángel García García, Manuel Á. Gómez Marcos, Raquel Jiménez Gómez, José A. Maderuelo Fernández, Andrea Domínguez Martín, Miguel Marcos Martín, José I. Martín González, José A. Martín Oterino, Nadia García Mateo, Elena Navarro Matías, Olaya Tamayo Morales, Nuria Suárez Moreno, Alicia Navarro Cáceres, Silvia Arroyo Romero, Luis García Ortiz, Ana Belén Castro Rivero, Pedro L. Sánchez Fernández, Cristina Lugones Sánchez, Emiliano Rodríguez Sánchez, Marta Gómez Sánchez; Leticia Gómez Sánchez and Susana González Sánchez.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eChaichana U, Man KKC, Chen A, Wong ICK, George J, Wilson P, et al. Definition of post-COVID-19 condition among published research studies. JAMA Netw Open. 2023 Apr 3;6(4). doi: 10.1001/jamanetworkopen.2023.5856Anex\u003c/li\u003e\n\u003cli\u003eAbrignani MG, Maloberti A, Temporelli PL, Binaghi G, Cesaro A, Ciccirillo F, et al. [Long COVID: nosographic aspects and clinical epidemiology]. G Ital Cardiol (Rome). 2022 Sep;23(9):651\u0026ndash;62. doi: 10.1714/3793.37795.\u003c/li\u003e\n\u003cli\u003eLopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo PA, Cuapio A, et al. More than 50 long-term effects of COVID-19: a systematic review and meta-analysis. Sci Rep. 2021 Aug 9;11(1):16144. doi: 10.1038/s41598-021-95565-8.\u003c/li\u003e\n\u003cli\u003eSugiyama A, Miwata K, Kitahara Y, Okimoto M, Abe K, E B, et al. Long COVID occurrence in COVID-19 survivors. Sci Rep. 2022 Apr 11;12(1):6039. doi: 10.1038/s41598-022-09929-5.\u003c/li\u003e\n\u003cli\u003eSoriano JB, Murthy S, Marshall JC, Relan P, Diaz JV; WHO Clinical Case Definition Working Group on Post-COVID-19 Condition. A clinical case definition of post-COVID-19 condition by a Delphi consensus. Lancet Infect Dis. 2022 Apr;22(4):e102\u0026ndash;7. doi: 10.1016/S1473-3099(21)00703-9.\u003c/li\u003e\n\u003cli\u003eStein SR, Ramelli SC, Grazioli A, Chung JY, Singh M, Yinda CK, et al. 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QJM. 2021 Apr 27;114(2):95\u0026ndash;8. doi: 10.1093/qjmed/hcaa292.\u003c/li\u003e\n\u003cli\u003eMandal S, Barnett J, Brill SE, Brown JS, Denneny EK, Hare SS, et al. \u0026lsquo;Long-COVID\u0026rsquo;: a cross-sectional study of persisting symptoms, biomarker and imaging abnormalities following hospitalisation for COVID-19. Thorax. 2021 Apr;76(4):396\u0026ndash;8. doi: 10.1136/thoraxjnl-2020-215818.\u003c/li\u003e\n\u003cli\u003eMazza MG, De Lorenzo R, Conte C, Poletti S, Vai B, Bollettini I, et al. Anxiety and depression in COVID-19 survivors: role of inflammatory and clinical predictors. Brain Behav Immun. 2020 Oct;89:594\u0026ndash;600. doi: 10.1016/j.bbi.2020.07.037.\u003c/li\u003e\n\u003cli\u003eRodriguez Ledo P. Gu\u0026iacute;a cl\u0026iacute;nica para la atenci\u0026oacute;n al paciente long COVID/COVID persistente [Internet]. 2021 May 5 [cited 2025 Feb 25]. 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Rev Med Virol. 2022;32(4):e2315. doi: 10.1002/rmv.2315.\u003c/li\u003e\n\u003cli\u003eMoniz M, Ruivinho C, Goes AR, Soares P, Leite A. Long COVID is not the same for everyone: a hierarchical cluster analysis of Long COVID symptoms 9 and 12 months after SARS-CoV-2 test. BMC Infect Dis. 2024 Sep 19;24(1):1001. doi: 10.1186/s12879-024-09896-8\u003c/li\u003e\n\u003cli\u003eReese JT, Blau H, Casiraghi E, Bergquist T, Loomba JJ, Callahan TJ, et al. 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Un protocolo de investigaci\u0026oacute;n de un estudio transversal. Frente Physiol. 2023 Sep 12;14:1236430. doi: 10.3389/fphys.2023.1236430. PMID: 37772064; PMCID: PMC10523018.\u003c/li\u003e\n\u003cli\u003eWilliams B, Mancia G, Spiering W, Agabiti Rosei E, Azizi M, Burnier M, et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Cardiology and the European Society of Hypertension. J Hypertens. 2018 Oct;36(10):1953\u0026ndash;2041. doi: 10.1097/HJH.0000000000001904\u003c/li\u003e\n\u003cli\u003eMancia G, Fagard R, Narkiewicz K, Redon J, Zanchetti A, B\u0026ouml;hm M, et al. 2013 Practice guidelines for the management of arterial hypertension of the European Society of Hypertension (ESH) and the European Society of Cardiology (ESC). J Hypertens. 2013 Oct;31(10):1925\u0026ndash;38. doi: 10.1097/HJH.0b013e328364ca4c\u003c/li\u003e\n\u003cli\u003eMinisterio de Sanidad. Estrategia de vacunaci\u0026oacute;n frente a COVID-19 en Espa\u0026ntilde;a. Actualizaci\u0026oacute;n 11 [Internet]. Madrid: Ministerio de Sanidad; 2021 [cited 2025 Mar 24]. Available from: https://www.sanidad.gob.es/areas/promocionPrevencion/vacunaciones/covid19/Actualizaciones_Estrategia_Vacunacion/docs/COVID-19_Actualizacion11_EstrategiaVacunacion.pdf\u003c/li\u003e\n\u003cli\u003eAlonso J, Prieto L, Ant\u0026oacute; JM. [The Spanish version of the SF-36 Health Survey (the SF-36 health questionnaire): an instrument for measuring clinical results]. Med Clin (Barc). 1995 May 27;104(20):771\u0026ndash;6. doi: 10.1016/S0025-7753(95)71168-6\u003c/li\u003e\n\u003cli\u003eFern\u0026aacute;ndez-de-Las-Pe\u0026ntilde;as C, Mart\u0026iacute;n-Guerrero JD, Pellicer-Valero \u0026Oacute;J, Navarro-Pardo E, G\u0026oacute;mez-Mayordomo V, Cuadrado ML, et al. Female sex is a risk factor associated with long-term post-COVID related-symptoms but not with COVID-19 symptoms: the LONG-COVID-EXP-CM multicenter study. J Clin Med. 2022 Jan 14;11(2):413. doi: 10.3390/jcm11020413\u003c/li\u003e\n\u003cli\u003eTsampasian V, Elghazaly H, Chattopadhyay R, Debski M, Naing TKP, Garg P, et al. Risk factors associated with post-COVID-19 condition: a systematic review and meta-analysis. JAMA Intern Med. 2023 Jun 1;183(6):566\u0026ndash;80. doi: 10.1001/jamainternmed.2023.0517\u003c/li\u003e\n\u003cli\u003ePillay J, Rahman S, Guitard S, Wingert A, Hartling L. Risk factors and preventive interventions for post-COVID-19 condition: systematic review. Emerg Microbes Infect. 2022 Dec;11(1):2762\u0026ndash;80. doi: 10.1080/22221751.2022.2115850\u003c/li\u003e\n\u003cli\u003eFloridia M, Giuliano M, Weimer LE, Ciardi MR, Agostoni P, Palange P, et al. Symptom profile, case and symptom clustering, clinical and demographic characteristics of a multicentre cohort of 1297 patients evaluated for long COVID. BMC Med. 2024 Nov 14;22(1):532. doi: 10.1186/s12916-024-02633-8\u003c/li\u003e\n\u003cli\u003eJansen EB, Ostadgavahi AT, Hewins B, Buchanan R, Thivierge BM, Sganzerla Martinez G, et al. PASC (Post Acute Sequelae of COVID-19) is associated with decreased neutralising antibody titres in both biological sexes and increased ANG-2 and GM-CSF in females. Sci Rep. 2024 Apr 29;14(1):9854. doi: 10.1038/s41598-024-66241-z\u003c/li\u003e\n\u003cli\u003eHamlin RE, Pienkos SM, Chan L, Stabile MA, Pinedo K, Rao M, et al. Sex differences and immune correlates of Long COVID development, symptom persistence, and resolution. Sci Transl Med. 2024 Nov 13;16(773). doi: 10.1126/scitranslmed.adr1032\u003c/li\u003e\n\u003cli\u003ePijls BG, Jolani S, Atherley A, Dijkstra JIR, Franssen GHL, Hendriks S, et al. Temporal trends of sex differences for COVID-19 infection, hospitalisation, severe disease, intensive care unit (ICU) admission and death: a meta-analysis of 229 studies covering over 10 M patients. F1000Res. 2022;11:5. doi: 10.12688/f1000research.76468.2\u003c/li\u003e\n\u003cli\u003ePeckham H, de Gruijter NM, Raine C, Radziszewska A, Ciurtin C, Wedderburn LR, et al. Male sex identified by global COVID-19 meta-analysis as a risk factor for death and ITU admission. Nat Commun. 2020 Dec 9;11(1):6317. doi: 10.1038/s41467-020-19741-6\u003c/li\u003e\n\u003cli\u003eMarjenberg Z, Leng S, Tascini C, Garg M, Misso K, El Guerche Seblain C, et al. Risk of long COVID main symptoms after SARS-CoV-2 infection: a systematic review and meta-analysis. Sci Rep. 2023 Sep 15;13(1):15332. doi: 10.1038/s41598-023-42220-2\u003c/li\u003e\n\u003cli\u003eAlkodaymi MS, Omrani OA, Fawzy NA, Shaar BA, Almamlouk R, Riaz M, et al. Prevalence of postacute COVID-19 syndrome symptoms at different follow-up periods: a systematic review and meta-analysis. Clin Microbiol Infect. 2022 May;28(5):657\u0026ndash;66. doi: 10.1016/j.cmi.2022.02.022\u003c/li\u003e\n\u003cli\u003eCeban F, Ling S, Lui LMW, Lee Y, Gill H, Teopiz KM, et al. Fatigue and cognitive impairment in post-COVID-19 syndrome: a systematic review and meta-analysis. Brain Behav Immun. 2022 Mar;101:93\u0026ndash;135. doi: 10.1016/j.bbi.2022.01.021\u003c/li\u003e\n\u003cli\u003eMalesevic S, Sievi NA, Baumgartner P, Roser K, Sommer G, Schmidt D, et al. Impaired health-related quality of life in long-COVID syndrome after mild to moderate COVID-19. Sci Rep. 2023 May 12;13(1):7717. doi: 10.1038/s41598-023-34310-5\u003c/li\u003e\n\u003cli\u003eMalik P, Patel K, Pinto C, Jaiswal R, Tirupathi R, Pillai S, et al. Postacute COVID-19 syndrome (PCS) and health-related quality of life (HRQoL): a systematic review and meta-analysis. J Med Virol. 2022 Jan;94(1):253\u0026ndash;62. doi: 10.1002/jmv.27309\u003c/li\u003e\n\u003cli\u003eDehlia A, Guthridge MA. The persistence of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) after SARS-CoV-2 infection: a systematic review and meta-analysis. J Infect [Internet]. 2024 Dec 1 [cited 2025 Apr 8];89(6). Available from: https://www.journalofinfection.com/article/S0163-4453(24)00231-7/fulltext\u003c/li\u003e\n\u003cli\u003eRetornaz F, Rebaudet S, Stavris C, Jammes Y. Long-term neuromuscular consequences of SARS-CoV-2 and their similarities with myalgic encephalomyelitis/chronic fatigue syndrome: results of the retrospective CoLGEM study. J Transl Med. 2022 Sep 24;20(1):429. doi: 10.1186/s12967-022-03639-9\u003c/li\u003e\n\u003cli\u003eCornelissen MEB, Bloemsma LD, Vaes AW, Baalbaki N, Deng Q, Beijers RJHCG, et al. Fatigue and symptom-based clusters in post-COVID-19 patients: a multicentre, prospective, observational cohort study. J Transl Med. 2024 Feb 21;22(1):191. doi: 10.1186/s12967-024-03333-y\u003c/li\u003e\n\u003cli\u003eKenny G, McCann K, O\u0026rsquo;Brien C, Savinelli S, Tinago W, Yousif O, et al. Identification of distinct long COVID clinical phenotypes through cluster analysis of self-reported symptoms. Open Forum Infect Dis. 2022 Apr;9(4). doi: 10.1093/ofid/ofac060\u003c/li\u003e\n\u003cli\u003eZhang H, Zang C, Xu Z, Zhang Y, Xu J, Bian J, et al. Data-driven identification of postacute SARS-CoV-2 infection subphenotypes. Nat Med. 2023;29(1):226\u0026ndash;35. doi: 10.1038/s41591-022-02144-1\u003c/li\u003e\n\u003cli\u003eFischer A, Badier N, Zhang L, Elb\u0026eacute;ji A, Wilmes P, Oustric P, et al. Long COVID classification: findings from a clustering analysis in the Predi-COVID cohort study. Int J Environ Res Public Health. 2022 Nov 30;19(23):16018. doi: 10.3390/ijerph192316018\u003c/li\u003e\n\u003cli\u003eGentilotti E, G\u0026oacute;rska A, Tami A, Gusinow R, Mirandola M, Rodr\u0026iacute;guez-Ba\u0026ntilde;o J, et al. Clinical phenotypes and quality of life to define post-COVID-19 syndrome: a cluster analysis of the multinational, prospective ORCHESTRA cohort. eClinicalMedicine. 2023 Jul 21;62:102107. doi: 10.1016/j.eclinm.2023.102107\u003c/li\u003e\n\u003cli\u003eGlobal Burden of Disease Long COVID Collaborators, Wulf Hanson S, Abbafati C, Aerts JG, Al-Aly Z, Ashbaugh C, et al. Estimated global proportions of individuals with persistent fatigue, cognitive, and respiratory symptom clusters following symptomatic COVID-19 in 2020 and 2021. JAMA. 2022 Oct 25;328(16):1604\u0026ndash;15. doi: 10.1001/jama.2022.18931\u003c/li\u003e\n\u003cli\u003evan den Houdt SCM, Slurink IAL, Mertens G. Long COVID is not a uniform syndrome: evidence from person-level symptom clusters using latent class analysis. J Infect Public Health. 2024 Feb;17(2):321\u0026ndash;8. doi: 10.1016/j.jiph.2023.12.010\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"long COVID-19, quality of life, symptom clusters, fatigue, dyspnea","lastPublishedDoi":"10.21203/rs.3.rs-7455995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7455995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePersistent COVID-19 (PC) affects individuals who have survived the acute phase of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present prolonged and fluctuating symptoms. These symptoms are multisystemic and significantly impair quality of life. The high degree of clinical variability hinders classification and management in clinical practice. This study aimed to classify patients according to their predominant symptoms and explore their impact on their quality of life, taking into account sociodemographic variables, personal history, and lifestyle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This was a cross-sectional observational study. The subjects came from the dedicated persistent COVID-19 clinic of internal medicine at Salamanca Hospital and from primary care clinics in Salamanca. Clinical, sociodemographic, and symptom data were collected via standardised questionnaires. Quality of life was assessed via the SF-36 questionnaire. Symptom grouping was performed via nonparametric statistical techniques.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: The study included 305 individuals (68.2% women) with a mean age of 52.7 ± 11.91 years. Eighty-two percent were infected before completing primary vaccination. Fatigue was the most common symptom (71.4%), along with other symptoms, such as a lack of energy, memory loss, dyspnea, and sleep disturbance. Women presented more symptoms than men did. Five clusters were identified: the largest, Cluster 1 (51.8%), with respiratory/cardiovascular, systemic, and musculoskeletal symptoms. With respect to quality of life, Cluster 5 presented the highest scores, and Cluster 1 presented the lowest scores, especially for the physical components. Significant differences were observed between clusters on the SF-36 questionnaire scales and domains, highlighting a poorer quality of life in the most symptomatic clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: This study identified five subgroups of patients with PC, and those with more symptoms presented poorer quality of life. Dyspnea and fatigue are indicators of this deterioration, with women being more affected. Cluster 1 reported the worst quality of life, whereas Cluster 5 had the best quality of life, highlighting the need for individualised therapeutic approaches.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration: \u003c/strong\u003eRegistered on Clinicaltrials.gov with identifier NCT05819840.\u003c/p\u003e","manuscriptTitle":"Analysis of persistent COVID-19 subtypes and their impact on quality of life","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 17:20:58","doi":"10.21203/rs.3.rs-7455995/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"eb65c034-84d1-42cb-b2bb-b683e79ee687","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62962331,"name":"Health sciences/Diseases"},{"id":62962332,"name":"Health sciences/Health care"},{"id":62962333,"name":"Health sciences/Medical research"},{"id":62962334,"name":"Health sciences/Signs and symptoms"}],"tags":[],"updatedAt":"2026-05-06T17:54:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 17:20:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7455995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7455995","identity":"rs-7455995","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}