10-years disease trajectories and genetic insights into long COVID susceptibility

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While individual comorbidities have been studied, the role of coexisting chronic conditions remains underexplored. This study investigates whether pre-pandemic disease trajectories—sequential patterns of chronic conditions—affect long COVID risk and symptom profiles, and explores shared genetic susceptibility. Methods We analysed 8,322 participants (58.6% women) from the COVICAT, followed between 2021 and 2023. Disease trajectories were reconstructed from electronic health records (2010–2019), focusing on sequences of two chronic conditions found in ≥ 1% of the cohort. We evaluated shared genetic architecture and polygenic risk scores (PRS) for predictive capacity. Results Thirty-eight disease trajectories were associated with increased long COVID risk. These trajectories primarily involved mental and neurological disorders (e.g., depression, anxiety, migraine), respiratory diseases (e.g., asthma, allergic rhinitis), and cardiometabolic or digestive conditions (e.g., hypertension, lipidaemia, obesity, gastroesophageal reflux). No significant genetic correlations with long COVID were detected, but polygenic risk scores for two nervous system and musculoskeletal conditions showed modest associations with increased risk. Conclusions Disease trajectories were significantly associated with long COVID, highlighting the importance of multimorbidity and the temporal sequence of conditions. While no strong overall genetic correlations were found, modest polygenic associations suggest a role for shared susceptibility in nervous system and musculoskeletal disorders. From a public health perspective, identifying high-risk multimorbid individuals may inform targeted prevention and care strategies. Long COVID multimorbidity disease trajectories polygenic risk scores genetic correlation Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Long COVID, also known as post COVID-19 condition or post-acute sequela of COVID-19, is the persistence of symptoms or sequela after the recovery of SARS-CoV-2 infection. Its prevalence is estimated to be between 10–30% in mild COVID-19 cases, 50–70% in severe cases and 10–12% in vaccinated individuals [ 1 ]. Long COVID has been associated with female sex, middle age, and lower educational attainment [ 2 ]. Pre-existing conditions such as hypertension, obesity, diabetes, chronic kidney disease, cerebrovascular and cardiovascular diseases, chronic obstructive pulmonary disease, as well as depression and anxiety, have also been identified as risk factors [ 3 , 4 ]. While multiple studies have confirmed prior conditions as risk factors for long COVID, the impact of sequential combinations of chronic diseases remains unexplored. It is well established that chronic diseases impact differently when occurring in isolation versus in combination with other conditions, a phenomenon known as multimorbidity. Co-occurrence leads to an increased disease severity, higher healthcare utilization, reduced quality of life, and elevated mortality [ 5 ]. Likewise, this sequence co-occurrence is not random, as the presence of a particular disease in an individual modify the risk of suffering a second condition [ 6 ]. This is particularly relevant in older age, where multimorbidity is shaped by specific conditions, named root diseases, and shared genetic factors that may act as a catalyst for the development of multiple coexisting disorders [ 7 ]. Diseases occurring in a specific order, referred to as disease trajectories [ 8 ], could help understand uncover disease mechanisms and improve patient prediction and classification. Long COVID is a heterogeneous condition comprising distinct subtypes with varying symptom profiles and differing impacts on quality of life [ 9 ]. Identifying these differential mechanisms may be crucial for preventing worse outcomes and improving prognosis. While chronic disease burden has been described as a risk factor, we hypothesize that specific temporal sequences of disease may uncover shared biological pathways and common genetic susceptibilities, contributing to a more precise characterization of long COVID risk trajectories. In this study, we examine the impact of disease trajectories—defined as a sequence of two chronic diseases preceding the COVID-19 pandemic—on the development of long COVID and its associated symptoms. Additionally, we investigate shared genetic risk factors underlying multimorbidity and genetic susceptibility to long COVID Methods Study Population This study utilizes data from COVICAT, an adult COVID-19 population-based cohort in Catalonia [ 2 , 10 , 11 ], established in 2020 to assess the health impact of the COVID-19 pandemic on the population of Catalonia. COVICAT is a nested study survey from the GCAT cohort [ 12 ]. The GCAT is a population-based cohort of nearly 20,000 participants from Catalonia, who voluntarily enrolled the study (2014–2018) with the unique restriction of being between 40 and 65 years old and be living in Catalonia. This cohort is linked to Electronic Health Records (EHR) from the public healthcare system providing more than 15 years of retrospective follow-up. Participants from the GCAT cohort were invited to complete COVID-19-related surveys during the following periods: June–November 2020 (baseline), June–August 2021 (first follow-up), and February–May 2023 (second follow-up), with a participation rate of 74% [ 2 ]. Ethical approval was obtained by the ethics committees at the Hospital Universitari Germans Trias i Pujol (CEI no. PI-13-020, PI-20–182). All participants provided informed consent, agreed to potential re-contact and can opt-out or withdraw their consent for specific areas of research. Inclusion and exclusion criteria From COVICAT participants with data from the 2021 and 2023 surveys (n = 8,918), we excluded those without linked electronic health record (EHR) data (n = 399), individuals who had moved outside Catalonia (n = 42), and those with no diagnosis information in their EHR (n = 99). Additionally, 56 participants were excluded due to technical reasons, including revoked consent and technical issues (n = 2), as well as birthdate discrepancies (n = 54). The final analytic sample comprised 8,322 individuals, of whom 4,874 (58.6%) were female. Long COVID definition We first defined long COVID following based on WHO guidelines and the 2024 NASEM definition as: (i) SARS-CoV-2 infection (as described below), and (ii) self-report of at least one symptom or sequel lasting at least three months, unexplained by other causes. These symptoms or sequelae included fever or low-grade fever; abdominal pain; headache; tiredness or unusual fatigue; chills, vertigo, or dizziness; tingling sensation; loss of taste or smell; cardiovascular, respiratory, renal, dermatological, cognitive/neurological, psychological/psychiatric, muscular, haematological, endocrine, digestive problems; dry eyes or mouth; and menstrual disturbances. Consistent with our previous study [ 2 ], long COVID cases were identified by combining symptom reports from both the first (2021) or second (2023) follow-ups. To maximize statistical power in this case-control study, we used population controls, including individuals with and without a confirmed SARS-CoV-2 infection who were not classified as cases. SARS-CoV-2 infection defined based on self-reported COVID-19 diagnosis. COVID severity was classified according to WHO guidelines as asymptomatic, mild/moderate, or severe/critical (the latter defined by self-reported hospitalization or intensive care unit admission). For analysis, severity was dichotomized into a binary variable: severe/critical versus asymptomatic and mild/moderate cases. Clinical diagnoses To analyse multimorbidity, we used prevalent chronic conditions diagnosed prior to the pandemic. Disease diagnoses were obtained from primary care, hospital and emergencies EHR. Disease diagnoses are encoded with the International Classification of Diseases (ICD) version 10 and 9, and converted into ICD-10 using a conversion table [ 13 ]. To avoid the influence of COVID-19, we excluded all diagnoses recorded after 2019, which resulted in 6,161 distinct ICD-10 codes. Acute diagnoses were filtered out using the Chronic Conditions Indicator (CCI 2023), leaving 1,776 chronic ICD-10 diagnoses. For recurrent diagnosis, only the first diagnosis date was retained. Diagnoses were then grouped at the three-digit ICD-10 level into 536 categories, characterized by diagnostic homogeneity, grouping conditions with similar clinical, anatomical, or etiological features. Sex-specific diagnoses (N40–N98, O00–O99, E28–E29, and C50–C63) were excluded from the overall analysis. Finally, a frequency filter retaining diagnoses present in at least 1% of participants yielded 106 diseases (79 in both sexes, 92 in women, and 79 in men). These diagnoses were used to model disease trajectories and to investigate their association with long COVID. Disease trajectories Disease trajectories were assessed by analysing the temporal sequence and co-occurrence of diseases. To obtain them we followed the guidelines from previous studies [ 8 ], by computing a disease history vector per individual, which is a vector of selected diseases in order of appearance. Then, common disease trajectories among all individuals were computed by pairwise comparison between all individuals’ disease history vectors, keeping those diagnoses that appeared in both individuals in the same order (equal or ascendant dates, as more than one diagnosis can be presented in the same date). For disease trajectories filtering, we removed those trajectories with less than 1% prevalence as well as disease trajectories of more than two diseases, for simplicity. We then applied Fisher’s exact test and corrected for multiple testing using Benjamini-Hochberg False Discovery Rate (FDR) [ 14 ] to obtain enriched pairs of diagnosis. These trajectories were then analysed for their association with long COVID incidence. Association analysis Statistical association between long COVID as the outcome and (i) selected diseases or (ii) selected disease trajectories as predictors was performed through logistic regression using age and sex (in the overall analysis) as covariates, and reporting Odds Ratio (OR), standard error (SE), 95% confidence interval (CI) and p-value. Multiple test correction was performed using FDR. For each associated disease and disease trajectory derived from the first analysis, we then examined its association with long COVID symptoms to gain a more detailed understanding of the relationship, including the types and severity of long COVID. In addition, we performed sex-stratified analysis to assess sex-specific associations. To assess whether the observed associations between disease trajectories and long COVID could have occurred by chance we performed 10,000 random simulations of long COVID case-control status, maintaining the same sex distribution (769 female and 293 male cases), generating a null distribution of significant associations expected randomly, setting the 99th percentile of the simulations as the significance threshold. Estimates were derived and used to assess sex-based differences and determine statistical significance. We also assessed the overrepresentation of ICD-10 chapters, as a proxy of broadly coherent clinical groupings, among associated diseases compared to the initially selected ones. These comparisons were performed using Fisher’s exact test and corrected by FDR. Finally, given that COVID-19 severity is a well-established predictor of long COVID and may mediate some of the observed associations, we conducted an additional analysis adjusting for disease severity. Specifically, hospitalization due to COVID-19 was included as a covariate to identify associations independent of acute severity. All analyses were conducted using R [ 15 ]. Genetic Correlation and PRS analysis Genotypic data were available for 2,108 of the included individuals (25.3%). These data were generated beforehand by our group, as described by Galvan-Femenía et al. [ 16 ], with detailed information provided in the supplementary data. Using this sample, we assessed the genetic contribution to long COVID by analysing all associated diseases, whether identified through trajectories or as individual conditions. First, we performed genetic correlations to identify shared genetic determinants with long COVID, using publicly available GWAS summary statistics; (1) for each associated disease from FinnGen [ 17 ] ( Sup Table S1 ) and (2) for long COVID, from the Host Genetics Initiative [ 18 ]. Pairwise genetic correlations between all diseases, including long COVID, were estimated using LD Score Regression (LDSC) [ 19 ], and p-values were corrected for multiple testing using FDR. Secondly, using publicly available GWAS summary statistics, we computed polygenic risk scores (PRS) for our participants with available genetic data. Posterior SNP effect sizes for those summary statistic data were computed using PRScs [ 20 ] and individual scores were calculated using PLINK1.9 [ 21 ] as the cumulative sum of SNP dosages weighted by their posterior effect sizes. The resulting raw PRS were standardized into z-scores to enable comparison of odds ratios across analyses. These scores were used to test its association with long COVID through generalised linear models using age and sex as covariates and associations were corrected using FDR. Results A total of 8,322 individuals from the COVICAT cohort were analysed, of whom 1,029 (12.4%) were classified as long COVID cases. The prevalence was higher in women (15.1%) and in those with severe COVID-19 (66.7%) (Table 1 ). Table 1 Summary of the individuals included in the analysis according to their Long COVID status. Characteristic Overall N = 8,322 No LC N = 7,293 1 LC N = 1,029 1 Sex Female 4,874 4,138 (84.9%) 736 (15.1%) Male 3,448 3,155 (91.5%) 293 (8.5%) Age (mean (SD)) 57.2 (7.0) 57.4 (7.0) 56.4 (6.4) COVID-19 severity 129 43 (33.3%) 86 (66.7%) Female 48 11 (22.9%) 37 (77.1%) Male 81 32 (39.5%) 49 (60.5%) 1 n (%) Prior chronic conditions and long COVID incidence Among 106 prior chronic conditions examined, 23 showed significant associations with long COVID incidence. In the overall analysis, 22 conditions were significant (28%), with 13 significant in females (14%) and 3 in males (4%) (Fig. 1 , Sup Table S2 ). Women exhibited a higher comorbidity burden, averaging 4.6 diagnoses (SD = 3.71) per person, compared to 3.6 (SD = 3.13) in men. Analysis by ICD-10 disease domains revealed no overrepresentation of any specific category ( Sup Table S3 ). However, the most prevalent conditions were mental, behavioural, and neurodevelopmental disorders (23.7%), followed by diseases of the nervous system (13.2%), digestive system (13.2%), musculoskeletal system and connective tissue (13.2%), endocrine and metabolic diseases (10.5%), and respiratory diseases (10.5%). Symptom analysis showed distinct patterns linked to prior conditions. Mental health conditions such as depression was inversely associated with loss of taste and smell. Anxiety and severe stress correlated with psychological symptoms and fatigue; notably, severe stress was also associated with respiratory problems. Neurological conditions (e.g., migraine, headache syndromes) were linked to digestive symptoms, while mononeuropathies of the upper limb corresponded with tingling sensations. Finally, internal derangement of the knee correlated with cardiovascular and respiratory symptoms in long COVID. Although fewer associations reached statistical significance in women, the overall patterns remained consistent with those observed in the full cohort ( Sup Table S4 ). Disease trajectories and long COVID incidence A total of 162 distinct disease trajectories were identified: 68 overall, 127 among women, and 54 among men. Most trajectories involved mental health (33.3%), metabolic diseases (27.7%), and nervous system conditions (8.2%). Thirty-eight trajectories (23%) were significantly associated with increased long COVID risk: 23 overall (34%), 34 among women (27%), and none among men (Fig. 2 , Sup Table S5 ). Respiratory disease trajectories were overrepresented among risk patterns. In women, digestive system diseases were also overrepresented, while endocrine and metabolic disorders were underrepresented ( Sup Table S6 ). No significant associations emerged between trajectories and specific long COVID symptoms ( Sup Table S7 ). These results surpassed the simulation-based significance thresholds, confirming their robustness ( Sup Figure S4 ). The mean number of disease trajectories associated with long COVID in the simulations was 0.06 (SD = 0.29) overall, 0.06 (SD = 0.30) for females, and 0.08 (SD = 0.36) for males. Most long COVID–associated trajectories involved transitions between different ICD chapters, while 29.8% involved pairs within the same chapter (Fig. 3 ). To help improve the interpretation of multimorbid mechanisms, three clusters were defined based on the most frequent chapter transitions among disease trajectories associated with long COVID. Three main clusters emerged: Mental Health and Neuromuscular (MHNM, 45.6%), Cardiometabolic and Digestive (CMD, 19.3%), and Respiratory (RESP, 7%). Disease trajectories with both conditions from the same cluster will be referred to as cis trajectories. Among the trans trajectories (transitioning from one cluster to another one), the most common transitions were CMD → MHNM (19.3%), MHNM → CMD (5.3%), MHNM → RESP (1.8%), and RESP → MHNM (1.8%). Independent Effect of COVID-19 Severity Adjusting for hospitalization (Table 2 ) reduced the significance of several diseases and trajectories, suggesting their link to long COVID is partly driven by acute infection severity. After adjustment, 14 diseases remained significantly associated with long COVID in the overall cohort ( Sup Figure S1 , Sup Table S8 ), including 11 in women and 3 in men. Conversely, associations with obesity, lipidaemia, mononeuropathies of the upper limb, otitis media, hearing loss, gastritis, other liver diseases, and knee derangement lost significance in the overall analysis. In the sex-stratified analysis, specified mood affective disorders and other liver diseases also lost significance in women, while no changes were observed in men. Regarding disease trajectories, 11 previously significant trajectories lost significance after hospitalization adjustment ( Sup Figure S2 , Sup Table S9 ). Notably, a new trajectory—obesity followed by knee derangement—emerged specifically in women. In the disease flow, we observe a reduction in Endocrine → Mental health and Mental health → Mental health associated trajectories ( Sup Figure S3 ). Table 2 . Summary of the number of selected and long COVID (LC) associated diseases and disease trajectories, with (adjusted) and without (unadjusted) correcting by COVID-19 severity in the overall and sex-specific analysis. Clusters of disease trajectories are specified. Overall (N=8,322) Women (N=4,874) Men (N=3,448) Selected diseases 79 92 79 LC unadjusted 22 13 3 LC adjusted 14 11 3 Selected disease trajectories 68 127 54 LC unadjusted 23 34 0 CMD 6 5 0 CMD – MHNM 5 6 0 MHNM – CMD 1 2 0 MHNM 9 17 0 MHNM – RESP 0 1 0 RESP – MHNM 0 1 0 RESP 2 2 0 LC adjusted 15 27 0 CMD 5 5 0 CMD – MHNM 2 4 0 MHNM – CMD 1 2 0 MHNM 5 12 0 MHNM – RESP 0 1 0 RESP – MHNM 0 1 0 RESP 2 2 0 Genetic correlation Genetic correlation analysis did not find significant correlations between long COVID and associated diseases, though some (e.g., hearing loss, asthma) showed moderate correlations. In contrast, strong positive genetic correlations were observed among many associated diseases, especially within mental health, musculoskeletal, and digestive clusters. Notably, cardiovascular and metabolic diseases formed a smaller cluster, with high correlation between clusters (Fig. 5 ). PRS of diseases as a long COVID predictor Polygenic risk score (PRS) analysis revealed few associations with long COVID diagnosis. The strongest was the PRS for long COVID itself, indicating a genetic predisposition. Among disease-specific PRSs, those for intervertebral disc disorders and headache syndromes showed significant associations in women and the overall cohort, but not in men ( Sup Table S11 ). Discussion We analysed a 15-year prospective cohort to identify disease trajectories preceding the pandemic around 10-years before, and examine their impact on long COVID risk and symptomatology. We identified 38 out of 162 disease trajectories (23%) associated with long COVID, representing a shift in understanding long COVID risk by revealing that not only the presence but the interaction and order of diseases critically modulate susceptibility and severity. We also explored shared genetic risk factors underlying multimorbidity and genetic predisposition to long COVID, observing low genetic correlation with long COVID. Additionally, our genetic analysis revealed that two disease-specific polygenic risk scores (PRS)—for diseases of the nervous system and musculoskeletal and connective tissue disorders—were significantly associated with long COVID susceptibility. Among the identified disease trajectories, the Mental Health and Neuromuscular (MHNM) cluster was the most prevalent, representing nearly half of all risk trajectories. While the overall ICD-10 category for mental health was not broadly enriched, specific mental health diagnoses exhibited the strongest associations with long COVID, indicating a fundamental neurocognitive vulnerability [ 22 , 23 ]. This MHNM cluster encompasses a diverse range of conditions—including mental health disorders, neurological issues, ear diseases, musculoskeletal problems, and female genitourinary conditions—that are united by their impact on brain, nerve, and cognitive functions. Its prominence in our results supports earlier findings and shows that mental health should be a key part of long COVID assessment and care. Importantly, MHNM trajectories often overlapped with CMD conditions (in 24.6% of associated trajectories). This group includes circulatory diseases (e.g., hypertension), endocrine and metabolic conditions (e.g., obesity, lipidaemia), and digestive disorders (e.g., gastroesophageal reflux), with a 19.3% of associated cis-trajectories. This pattern suggests a gut-brain axis dysregulation [ 24 , 25 ], linking nervous and gastrointestinal systems via microbiome, immune, and neural pathways, potentially explaining frequent transitions between cardiometabolic/digestive and mental health/neuromuscular conditions in long COVID. In contrast to the larger CMD and MHNM clusters, respiratory disease cis-trajectories formed a smaller but distinct cluster RESP (7%) with minimal overlap, highlighting significant biological and clinical diversity in long COVID. The only notable overlap was between vasomotor and allergic rhinitis bidirectionally co-occurring with migraine in women, conditions that share common underlying mechanisms [ 26 ]. These cluster patterns highlight that the sequence and interaction of diseases, more than isolated conditions, drive long COVID risk. Seven diseases (32%) were associated with long COVID only in the presence of other conditions, underscoring the role of multimorbidity in susceptibility; conversely, eight diseases (35%) were independently linked and did not cluster with others. These findings suggest that certain disease interactions—potentially involving mechanisms such as the gut-brain axis observed in CMD-MHNM trans-trajectories—underlie distinct long COVID risk patterns. Our results highlight the importance of disease temporality in long COVID prognosis. While some diagnosis pairs showed similar associations regardless of order (26.3%; e.g., anxiety disorders and migraine, anxiety disorders and intervertebral disc disorders), remarkably, 73.7% of the trajectories demonstrated that only a specific order of disease onset increases long COVID risk, emphasizing that temporal progression—not just co-occurrence—of conditions determines vulnerability. The broader range of second diagnoses in significant trajectories indicates that after an initial condition, patients often develop diverse follow-up diseases. This highlights how disease progression pathways, rather than isolated conditions, shape the complexity and outcomes of long COVID. The symptoms analysis reveals complex interactions between neurological and mental health comorbidities that influence long COVID severity. For example, individuals with a previous diagnosis of depression were less likely to report loss of taste and smell. This may be because such sensory changes are already common in depression due to neurotransmitter imbalances, functional and structural brain changes and impaired neurogenesis [ 27 , 28 ], rendering new symptoms less noticeable. Additionally, anxiety and severe stress were linked to a greater number and severity of long COVID symptoms, consistent with prior findings associating mental health disorders with worse COVID outcomes [ 29 , 30 ]. Beyond mental health, physical comorbidities also contribute to long COVID heterogeneity. The association between migraine and digestive symptoms suggests dysregulation of the gut–brain axis as a contributor to symptom persistence, as previously discussed. Clinical studies further indicate disrupted respiratory and gastrointestinal microbiota homeostasis in hospitalized COVID-19 patients [ 31 ], supporting a potential causal role of the gut-brain axis in long COVID. Altered hypothalamic–pituitary–adrenal axis function, which is closely regulated by the gut microbiota, has been reported in other suspected post-viral syndromes [ 32 ], though not typically prior to infection. Mononeuropathies such as carpal tunnel syndrome may exacerbate neurological symptoms through pre-existing nerve sensitization worsened by viral-induced neuroinflammation. Finally, chronic pain and reduced physical activity resulting from musculoskeletal conditions like knee internal derangement may contribute to more severe cardiovascular and respiratory long COVID symptoms. Collectively, these associations illustrate how interconnected disease patterns underlie the diverse manifestations of long COVID. While some comorbidities appear to increase long COVID risk by exacerbating acute COVID-19 severity, others influence risk independently or through combined disease pathways. Adjusting for COVID-19 severity had a stronger impact in the overall cohort reducing the number of diseases associated with long COVID, while the impact in women was lower due to the lower rates of severe COVID-19, and in men as they had less long COVID associated diseases and disease trajectories. Among established severity risk factors for COVID and long COVID [ 2 ], obesity lost significance after adjustment, supporting a role as a mediator. Some other trajectories lost their association with long COVID after adjustment, especially mental health-related trajectories, indicating that their effects were largely mediated by acute COVID-19 phase. An exception was the disease trajectory from obesity to knee derangement in women, which gained significance despite individual diseases losing theirs—implying a combined or sequential effect influencing long COVID risk uniquely in this subgroup. Since knee derangement often leads to reduced physical activity—a known risk factor for long COVID—this progression may contribute to more severe long COVID symptoms. To further elucidate potential underlying mechanisms, we examined the genetic correlations between long COVID and the comorbid diseases identified in our study. We found no significant genetic correlations between long COVID and associated diseases, consistent with GWAS studies showing low heritability for long COVID, likely reflecting its heterogeneity [ 18 ]. The lack of significant genetic correlations between long COVID and associated diseases is consistent with recent research showing that, despite limited genetic overlap, shared biological pathways may underlie these conditions [ 33 ]. This highlights the importance of exploring non-genetic factors and gene-environment interactions to better understand long COVID susceptibility. However, many of the comorbid diseases showed strong genetic correlations among themselves, consistent with prior studies and suggesting shared genetic architecture within disease clusters, such as cardiovascular and metabolic disorders. In addition, polygenic risk scores (PRS) for certain conditions—including intervertebral disc disorders and headache syndromes—were significantly associated with long COVID incidence. These findings suggest that these PRS might serve as genetic biomarkers or reflect gene-environment interactions influencing susceptibility. This indicates that, although long COVID itself has limited direct genetic overlap with comorbid diseases, genetic factors related to these associated conditions could indirectly affect long COVID risk through complex pathways or interactions. In addition to genetic factors, sex-related biological differences emerged as a significant aspect influencing disease patterns and long COVID risk. Although the low number of male long COVID cases may bias results, we observed clear sex-related biological differences in disease patterns and risk. Women in the cohort had, on average, one more comorbidity than men, consistent with literature showing women are diagnosed with more chronic conditions over their lifetime. This may reflect women's more frequent healthcare use, resulting in earlier and more diagnoses, without increased mortality [ 34 ]. Biological and hormonal factors also likely increase the incidence of some conditions, including long COVID [ 35 ]. Despite these insights, several limitations must be considered to contextualize our findings and guide future research. Although we used a large cohort with detailed EHR data, limited long COVID cases—particularly among men and genotyped individuals—reduce power to detect sex-specific and genetic effects confidently. EHR data have limitations: diagnoses before 2010 are incomplete, and private healthcare records are missing, possibly causing under-ascertainment. Additionally, the use of three-digit ICD-10 codes to classify diseases can group heterogeneous conditions, limiting diagnostic specificity and potentially masking important subtypes. Changing clinical definitions and diagnostic criteria of long COVID during the study period complicate interpretation and may cause misclassification bias. As an observational study, unmeasured confounders may influence findings, limiting the ability to draw causal conclusions. These limitations highlight the need for further research in larger, more diverse cohorts with detailed phenotyping and longer-term follow-up to validate and extend these findings. Conclusions In summary, long COVID risk is shaped by complex multimorbid disease trajectories where the sequence and interaction of mental health, cardiometabolic, and respiratory conditions are decisive. Some comorbidities increase risk by worsening acute COVID-19, while others act independently or synergistically, with clear sex-specific patterns. Although direct genetic overlap is limited, polygenic risk scores for neurological and musculoskeletal disorders suggest indirect genetic influences. Our findings highlight the necessity of incorporating disease temporality and multimorbidity into personalized risk assessment and management of long COVID. Abbreviations SARS-CoV-2: severe acute respiratory syndrome coronavirus 2 COVID-19: Coronavirus disease 2019 EHR: Electronic health records ICD: International Classification of Diseases FDR: False discovery rate OR: Odds ratio SE: Standard error CI: Confidence interval PRS: polygenic risk score EGA: European Genome-phenome Archive MHNM: Mental health and neuromuscular CMD: Cardiometabolic and digestive RESP: Respiratory Declarations Ethics approval and consent to participate All participants contacted had consented in the past to be re-contacted and had provided informed consent. Ethical approval was obtained from the Hospital Universitari Germans Trias I Pujol Ehtincs Committee (CEI no. PI-20-182). Consent for publication Not applicable Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. Competing interests The authors declare that they have no competing interests. Funding La Caixa Foundation (SR20-01024), La Marató TV3 (167/C/2021), the Spanish Ministry of Health (PI18/01512) Horizon Europe END-VOC (GA:101046314). Authors’ contributions NB and RdC contributed to the study conceptualization and design. RdC and MK participated in the funding acquisition. JGA, GCV, MK and RdC participated in data acquisition. NB performed data collection and curation. NB and XF performed formal analysis, interpretation and visualization. NB and RdC drafted the manuscript, and ALL authors participated in the review and editing of the final manuscript. Acknowledgements We express our sincere gratitude to the GCAT cohort study volunteers and the Blood and Tissue Bank team. We also wish to extend our special appreciation to the GCAT project investigators, particularly Anna Carreras, Beatriz Cortés. Anonymized data were provided by the Catalan Agency for Quality and Health Assessment (PADRIS Program). 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FinnGen provides genetic insights from a well-phenotyped isolated population. Nature. 2023;613(7944):508–18. Lammi V, Nakanishi T, Jones SE, Andrews SJ, Karjalainen J, Cortés B, O’Brien HE, Fulton-Howard BE, Haapaniemi HH, Schmidt A et al. Genome-wide Association Study of Long COVID. medRxiv 2023:2023.2006.2029.23292056.. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Psychiatric Genomics C, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control, Duncan C. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41. Ge T, Chen CY, Ni Y, Feng YA, Smoller JW. Polygenic prediction via Bayesian regression and continuous shrinkage priors. Nat Commun. 2019;10(1):1776. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75. Reiss AB, Greene C, Dayaramani C, Rauchman SH, Stecker MM, De Leon J, Pinkhasov A. Long COVID, the Brain, Nerves, and Cognitive Function. Neurol Int. 2023;15(3):821–41. Wang HI, Doran T, Crooks MG, Khunti K, Heightman M, Gonzalez-Izquierdo A, Qummer Ul Arfeen M, Loveless A, Banerjee A, Van Der Feltz-Cornelis C. Prevalence, risk factors and characterisation of individuals with long COVID using Electronic Health Records in over 1.5 million COVID cases in England. J Infect. 2024;89(4):106235. Gareau MG, Barrett KE. Role of the microbiota-gut-brain axis in postacute COVID syndrome. Am J Physiol Gastrointest Liver Physiol. 2023;324(4):G322–8. Li Z, Xia Q, Feng J, Chen X, Wang Y, Ren X, Wu S, Yang R, Li J, Liu Y, et al. The causal role of gut microbiota in susceptibility of Long COVID: a Mendelian randomization study. Front Microbiol. 2024;15:1404673. Davis JV, Ramanathan P, Prabu T. Prevalence of Migraine Headache among Patients with Allergic Rhinitis in a Tertiary Care Hospital, Puducherry. Indian J Otolaryngol Head Neck Surg. 2024;76(5):4216–21. Hur K, Choi JS, Zheng M, Shen J, Wrobel B. Association of alterations in smell and taste with depression in older adults. Laryngoscope Investig Otolaryngol. 2018;3(2):94–9. Taalman H, Wallace C, Milev R. Olfactory Functioning and Depression: A Systematic Review. Front Psychiatry. 2017;8:190. Kananian S, Nemani A, Stangier U. Risk and protective factors for the severity of long COVID - A network analytic perspective. J Psychiatr Res. 2024;178:291–7. Sorberg Wallin A, Ohlis A, Dalman C, Ahlen J. Risk of severe COVID-19 infection in individuals with severe mental disorders, substance use disorders, and common mental disorders. Gen Hosp Psychiatry. 2022;75:75–82. Wang B, Zhang L, Wang Y, Dai T, Qin Z, Zhou F, Zhang L. Alterations in microbiota of patients with COVID-19: potential mechanisms and therapeutic interventions. Signal Transduct Target Ther. 2022;7(1):143. Tanriverdi F, Karaca Z, Unluhizarci K, Kelestimur F. The hypothalamo-pituitary-adrenal axis in chronic fatigue syndrome and fibromyalgia syndrome. Stress. 2007;10(1):13–25. Smeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. Nat Rev Neurol. 2020;16(7):366–79. Oksuzyan A, Juel K, Vaupel JW, Christensen K. Men: good health and high mortality. Sex differences in health and aging. Aging Clin Exp Res. 2008;20(2):91–102. Peluso MJ, Deeks SG. Mechanisms of long COVID and the path toward therapeutics. Cell. 2024;187(20):5500–29. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6936765","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":481112137,"identity":"9ffacbf1-2098-4d24-83d2-bdbe2389fb58","order_by":0,"name":"Natalia Blay","email":"","orcid":"","institution":"Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol","correspondingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Blay","suffix":""},{"id":481112138,"identity":"e5940e0e-fa67-46ad-b30c-f04fdc56331d","order_by":1,"name":"Xavier Farré","email":"","orcid":"","institution":"Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol","correspondingAuthor":false,"prefix":"","firstName":"Xavier","middleName":"","lastName":"Farré","suffix":""},{"id":481112139,"identity":"833c5679-a191-4ae9-add4-245de845cf99","order_by":2,"name":"Judith Garcia-Aymerich","email":"","orcid":"","institution":"Barcelona Institute for Global Health","correspondingAuthor":false,"prefix":"","firstName":"Judith","middleName":"","lastName":"Garcia-Aymerich","suffix":""},{"id":481112140,"identity":"c61b2713-c99d-4e6a-a72e-05d16f938130","order_by":3,"name":"Gemma Castaño-Vinyals","email":"","orcid":"","institution":"Barcelona Institute for Global Health","correspondingAuthor":false,"prefix":"","firstName":"Gemma","middleName":"","lastName":"Castaño-Vinyals","suffix":""},{"id":481112141,"identity":"87385bf8-b40f-4dd8-8772-cd0e94acfa8b","order_by":4,"name":"Manolis Kogevinas","email":"","orcid":"","institution":"Barcelona Institute for Global Health","correspondingAuthor":false,"prefix":"","firstName":"Manolis","middleName":"","lastName":"Kogevinas","suffix":""},{"id":481112142,"identity":"b6f585f2-16c5-4df4-a1e7-1339efe3a298","order_by":5,"name":"Rafael Cid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYFCCBCBis+EhWUsaDwMbmGdApBYGtsMMxGsxb08++OFB2XkZ+fjeAww/av4Q1iJz5lmyRMK52zyGx/gSGHuOEWGLhESOGUNiG1BLG48BMwMbUVryvwG1nINq+UecLWxALQd45NmAWhjbiNHC88wY6JdkHgO2vISDvX3GRGhhT3748UeZnb1889mDD358kyOsBQ4MDvAwHCBBPRDIN5CUZkbBKBgFo2AkAQDaLjLtGD4KjgAAAABJRU5ErkJggg==","orcid":"","institution":"Barcelona Institute for Global Health","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Cid","suffix":""}],"badges":[],"createdAt":"2025-06-20 08:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6936765/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6936765/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12916-025-04427-x","type":"published","date":"2025-10-27T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86141464,"identity":"d560b562-5ae0-4dfa-bbf2-0250147970df","added_by":"auto","created_at":"2025-07-07 08:26:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71890,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between diagnoses and long COVID. In the y-axis, the ICD-10 code and description of the diagnoses, and in the x-axis the odds ratio. Colours represent ICD-10 chapters.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/25594f77d7a8d7e786221c15.png"},{"id":86141467,"identity":"a3da5c94-e60d-401c-834c-76c2d6271f74","added_by":"auto","created_at":"2025-07-07 08:26:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":121541,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between disease trajectories and long COVID. In the y-axis, the ICD-10 code and description of the trajectories, and in the x-axis the odds ratio. Colours represent trajectories clusters.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/4a14f146f87b19f95745c10b.png"},{"id":86142147,"identity":"e04a2b48-0f2c-475d-86b4-5225a86a7126","added_by":"auto","created_at":"2025-07-07 08:34:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":147733,"visible":true,"origin":"","legend":"\u003cp\u003eDisease trajectories associated with long COVID depicting the chapter of the first and second diagnosis for the aggregated analysis (left) and for women-only (right). The width of the lines represents the number of individuals with the trajectory and long COVID, and the colour the cluster of the trajectories.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/95155eb1a098d306043709cf.png"},{"id":86141485,"identity":"3d8d2a3c-b0e3-457a-be27-5fb940f8546a","added_by":"auto","created_at":"2025-07-07 08:26:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":224838,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 5\u003c/strong\u003e. Heatmap of the genetic correlation among the selected diseases and long COVID. Colour gradient indicates correlation, and asterisk indicates a p-value \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/e69c939c5540e984340bd680.png"},{"id":95039961,"identity":"0419a8af-1ef0-4b96-a6ca-409b4162b76f","added_by":"auto","created_at":"2025-11-03 16:06:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265446,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/12385b0b-4bfe-4a09-a6d6-fd8e79b3ca72.pdf"},{"id":86141469,"identity":"f5f53934-c02a-4f2c-838b-f5cc9f3cd2e4","added_by":"auto","created_at":"2025-07-07 08:26:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":544836,"visible":true,"origin":"","legend":"","description":"","filename":"DTLCSupFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/80dbfc5cc7b634494b3651c1.docx"},{"id":86141474,"identity":"78f04f33-bf97-45f0-9702-5497e98ae45e","added_by":"auto","created_at":"2025-07-07 08:26:16","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":320352,"visible":true,"origin":"","legend":"","description":"","filename":"DTLCsuptablesv2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6936765/v1/154732809279a3cf2f645058.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"10-years disease trajectories and genetic insights into long COVID susceptibility","fulltext":[{"header":"Background","content":"\u003cp\u003eLong COVID, also known as post COVID-19 condition or post-acute sequela of COVID-19, is the persistence of symptoms or sequela after the recovery of SARS-CoV-2 infection. Its prevalence is estimated to be between 10\u0026ndash;30% in mild COVID-19 cases, 50\u0026ndash;70% in severe cases and 10\u0026ndash;12% in vaccinated individuals [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Long COVID has been associated with female sex, middle age, and lower educational attainment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Pre-existing conditions such as hypertension, obesity, diabetes, chronic kidney disease, cerebrovascular and cardiovascular diseases, chronic obstructive pulmonary disease, as well as depression and anxiety, have also been identified as risk factors [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile multiple studies have confirmed prior conditions as risk factors for long COVID, the impact of sequential combinations of chronic diseases remains unexplored. It is well established that chronic diseases impact differently when occurring in isolation versus in combination with other conditions, a phenomenon known as multimorbidity. Co-occurrence leads to an increased disease severity, higher healthcare utilization, reduced quality of life, and elevated mortality [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Likewise, this sequence co-occurrence is not random, as the presence of a particular disease in an individual modify the risk of suffering a second condition [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This is particularly relevant in older age, where multimorbidity is shaped by specific conditions, named root diseases, and shared genetic factors that may act as a catalyst for the development of multiple coexisting disorders [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Diseases occurring in a specific order, referred to as disease trajectories [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], could help understand uncover disease mechanisms and improve patient prediction and classification.\u003c/p\u003e\u003cp\u003eLong COVID is a heterogeneous condition comprising distinct subtypes with varying symptom profiles and differing impacts on quality of life [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Identifying these differential mechanisms may be crucial for preventing worse outcomes and improving prognosis. While chronic disease burden has been described as a risk factor, we hypothesize that specific temporal sequences of disease may uncover shared biological pathways and common genetic susceptibilities, contributing to a more precise characterization of long COVID risk trajectories.\u003c/p\u003e\u003cp\u003eIn this study, we examine the impact of disease trajectories\u0026mdash;defined as a sequence of two chronic diseases preceding the COVID-19 pandemic\u0026mdash;on the development of long COVID and its associated symptoms. Additionally, we investigate shared genetic risk factors underlying multimorbidity and genetic susceptibility to long COVID\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Population\u003c/h2\u003e\u003cp\u003eThis study utilizes data from COVICAT, an adult COVID-19 population-based cohort in Catalonia [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], established in 2020 to assess the health impact of the COVID-19 pandemic on the population of Catalonia. COVICAT is a nested study survey from the GCAT cohort [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The GCAT is a population-based cohort of nearly 20,000 participants from Catalonia, who voluntarily enrolled the study (2014\u0026ndash;2018) with the unique restriction of being between 40 and 65 years old and be living in Catalonia. This cohort is linked to Electronic Health Records (EHR) from the public healthcare system providing more than 15 years of retrospective follow-up. Participants from the GCAT cohort were invited to complete COVID-19-related surveys during the following periods: June\u0026ndash;November 2020 (baseline), June\u0026ndash;August 2021 (first follow-up), and February\u0026ndash;May 2023 (second follow-up), with a participation rate of 74% [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEthical approval was obtained by the ethics committees at the Hospital Universitari Germans Trias i Pujol (CEI no. PI-13-020, PI-20\u0026ndash;182). All participants provided informed consent, agreed to potential re-contact and can opt-out or withdraw their consent for specific areas of research.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInclusion and exclusion criteria\u003c/h3\u003e\n\u003cp\u003eFrom COVICAT participants with data from the 2021 and 2023 surveys (n\u0026thinsp;=\u0026thinsp;8,918), we excluded those without linked electronic health record (EHR) data (n\u0026thinsp;=\u0026thinsp;399), individuals who had moved outside Catalonia (n\u0026thinsp;=\u0026thinsp;42), and those with no diagnosis information in their EHR (n\u0026thinsp;=\u0026thinsp;99). Additionally, 56 participants were excluded due to technical reasons, including revoked consent and technical issues (n\u0026thinsp;=\u0026thinsp;2), as well as birthdate discrepancies (n\u0026thinsp;=\u0026thinsp;54). The final analytic sample comprised 8,322 individuals, of whom 4,874 (58.6%) were female.\u003c/p\u003e\n\u003ch3\u003eLong COVID definition\u003c/h3\u003e\n\u003cp\u003e We first defined long COVID following based on WHO guidelines and the 2024 NASEM definition as: (i) SARS-CoV-2 infection (as described below), and (ii) self-report of at least one symptom or sequel lasting at least three months, unexplained by other causes. These symptoms or sequelae included fever or low-grade fever; abdominal pain; headache; tiredness or unusual fatigue; chills, vertigo, or dizziness; tingling sensation; loss of taste or smell; cardiovascular, respiratory, renal, dermatological, cognitive/neurological, psychological/psychiatric, muscular, haematological, endocrine, digestive problems; dry eyes or mouth; and menstrual disturbances. Consistent with our previous study [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], long COVID cases were identified by combining symptom reports from both the first (2021) or second (2023) follow-ups. To maximize statistical power in this case-control study, we used population controls, including individuals with and without a confirmed SARS-CoV-2 infection who were not classified as cases.\u003c/p\u003e\u003cp\u003eSARS-CoV-2 infection defined based on self-reported COVID-19 diagnosis. COVID severity was classified according to WHO guidelines as asymptomatic, mild/moderate, or severe/critical (the latter defined by self-reported hospitalization or intensive care unit admission). For analysis, severity was dichotomized into a binary variable: severe/critical versus asymptomatic and mild/moderate cases.\u003c/p\u003e\n\u003ch3\u003eClinical diagnoses\u003c/h3\u003e\n\u003cp\u003eTo analyse multimorbidity, we used prevalent chronic conditions diagnosed prior to the pandemic. Disease diagnoses were obtained from primary care, hospital and emergencies EHR. Disease diagnoses are encoded with the International Classification of Diseases (ICD) version 10 and 9, and converted into ICD-10 using a conversion table [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To avoid the influence of COVID-19, we excluded all diagnoses recorded after 2019, which resulted in 6,161 distinct ICD-10 codes. Acute diagnoses were filtered out using the Chronic Conditions Indicator (CCI 2023), leaving 1,776 chronic ICD-10 diagnoses. For recurrent diagnosis, only the first diagnosis date was retained. Diagnoses were then grouped at the three-digit ICD-10 level into 536 categories, characterized by diagnostic homogeneity, grouping conditions with similar clinical, anatomical, or etiological features. Sex-specific diagnoses (N40\u0026ndash;N98, O00\u0026ndash;O99, E28\u0026ndash;E29, and C50\u0026ndash;C63) were excluded from the overall analysis. Finally, a frequency filter retaining diagnoses present in at least 1% of participants yielded 106 diseases (79 in both sexes, 92 in women, and 79 in men). These diagnoses were used to model disease trajectories and to investigate their association with long COVID.\u003c/p\u003e\n\u003ch3\u003eDisease trajectories\u003c/h3\u003e\n\u003cp\u003eDisease trajectories were assessed by analysing the temporal sequence and co-occurrence of diseases. To obtain them we followed the guidelines from previous studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], by computing a disease history vector per individual, which is a vector of selected diseases in order of appearance. Then, common disease trajectories among all individuals were computed by pairwise comparison between all individuals\u0026rsquo; disease history vectors, keeping those diagnoses that appeared in both individuals in the same order (equal or ascendant dates, as more than one diagnosis can be presented in the same date). For disease trajectories filtering, we removed those trajectories with less than 1% prevalence as well as disease trajectories of more than two diseases, for simplicity. We then applied Fisher\u0026rsquo;s exact test and corrected for multiple testing using Benjamini-Hochberg False Discovery Rate (FDR) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] to obtain enriched pairs of diagnosis. These trajectories were then analysed for their association with long COVID incidence.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAssociation analysis\u003c/h2\u003e\u003cp\u003eStatistical association between long COVID as the outcome and (i) selected diseases or (ii) selected disease trajectories as predictors was performed through logistic regression using age and sex (in the overall analysis) as covariates, and reporting Odds Ratio (OR), standard error (SE), 95% confidence interval (CI) and p-value. Multiple test correction was performed using FDR. For each associated disease and disease trajectory derived from the first analysis, we then examined its association with long COVID symptoms to gain a more detailed understanding of the relationship, including the types and severity of long COVID. In addition, we performed sex-stratified analysis to assess sex-specific associations.\u003c/p\u003e\u003cp\u003eTo assess whether the observed associations between disease trajectories and long COVID could have occurred by chance we performed 10,000 random simulations of long COVID case-control status, maintaining the same sex distribution (769 female and 293 male cases), generating a null distribution of significant associations expected randomly, setting the 99th percentile of the simulations as the significance threshold. Estimates were derived and used to assess sex-based differences and determine statistical significance.\u003c/p\u003e\u003cp\u003eWe also assessed the overrepresentation of ICD-10 chapters, as a proxy of broadly coherent clinical groupings, among associated diseases compared to the initially selected ones. These comparisons were performed using Fisher\u0026rsquo;s exact test and corrected by FDR.\u003c/p\u003e\u003cp\u003eFinally, given that COVID-19 severity is a well-established predictor of long COVID and may mediate some of the observed associations, we conducted an additional analysis adjusting for disease severity. Specifically, hospitalization due to COVID-19 was included as a covariate to identify associations independent of acute severity. All analyses were conducted using R [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGenetic Correlation and PRS analysis\u003c/h3\u003e\n\u003cp\u003eGenotypic data were available for 2,108 of the included individuals (25.3%). These data were generated beforehand by our group, as described by Galvan-Femen\u0026iacute;a et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], with detailed information provided in the supplementary data. Using this sample, we assessed the genetic contribution to long COVID by analysing all associated diseases, whether identified through trajectories or as individual conditions.\u003c/p\u003e\u003cp\u003eFirst, we performed genetic correlations to identify shared genetic determinants with long COVID, using publicly available GWAS summary statistics; (1) for each associated disease from FinnGen [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] (\u003cb\u003eSup Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e) and (2) for long COVID, from the Host Genetics Initiative [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Pairwise genetic correlations between all diseases, including long COVID, were estimated using LD Score Regression (LDSC) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], and p-values were corrected for multiple testing using FDR.\u003c/p\u003e\u003cp\u003eSecondly, using publicly available GWAS summary statistics, we computed polygenic risk scores (PRS) for our participants with available genetic data. Posterior SNP effect sizes for those summary statistic data were computed using PRScs [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] and individual scores were calculated using PLINK1.9 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] as the cumulative sum of SNP dosages weighted by their posterior effect sizes. The resulting raw PRS were standardized into z-scores to enable comparison of odds ratios across analyses. These scores were used to test its association with long COVID through generalised linear models using age and sex as covariates and associations were corrected using FDR.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 8,322 individuals from the COVICAT cohort were analysed, of whom 1,029 (12.4%) were classified as long COVID cases. The prevalence was higher in women (15.1%) and in those with severe COVID-19 (66.7%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of the individuals included in the analysis according to their Long COVID status.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;8,322\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo LC\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;7,293\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLC\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;1,029\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,138 (84.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e736 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3,155 (91.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e293 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (mean (SD))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e57.2 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.4 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56.4 (6.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOVID-19 severity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e43 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e86 (66.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37 (77.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32 (39.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49 (60.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sup\u003e n (%)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003ePrior chronic conditions and long COVID incidence\u003c/h2\u003e\u003cp\u003eAmong 106 prior chronic conditions examined, 23 showed significant associations with long COVID incidence. In the overall analysis, 22 conditions were significant (28%), with 13 significant in females (14%) and 3 in males (4%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eSup Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Women exhibited a higher comorbidity burden, averaging 4.6 diagnoses (SD\u0026thinsp;=\u0026thinsp;3.71) per person, compared to 3.6 (SD\u0026thinsp;=\u0026thinsp;3.13) in men. Analysis by ICD-10 disease domains revealed no overrepresentation of any specific category (\u003cb\u003eSup Table S3\u003c/b\u003e). However, the most prevalent conditions were mental, behavioural, and neurodevelopmental disorders (23.7%), followed by diseases of the nervous system (13.2%), digestive system (13.2%), musculoskeletal system and connective tissue (13.2%), endocrine and metabolic diseases (10.5%), and respiratory diseases (10.5%).\u003c/p\u003e\u003cp\u003eSymptom analysis showed distinct patterns linked to prior conditions. Mental health conditions such as depression was inversely associated with loss of taste and smell. Anxiety and severe stress correlated with psychological symptoms and fatigue; notably, severe stress was also associated with respiratory problems. Neurological conditions (e.g., migraine, headache syndromes) were linked to digestive symptoms, while mononeuropathies of the upper limb corresponded with tingling sensations. Finally, internal derangement of the knee correlated with cardiovascular and respiratory symptoms in long COVID. Although fewer associations reached statistical significance in women, the overall patterns remained consistent with those observed in the full cohort (\u003cb\u003eSup Table S4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDisease trajectories and long COVID incidence\u003c/h2\u003e\u003cp\u003eA total of 162 distinct disease trajectories were identified: 68 overall, 127 among women, and 54 among men. Most trajectories involved mental health (33.3%), metabolic diseases (27.7%), and nervous system conditions (8.2%). Thirty-eight trajectories (23%) were significantly associated with increased long COVID risk: 23 overall (34%), 34 among women (27%), and none among men (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eSup Table S5\u003c/b\u003e). Respiratory disease trajectories were overrepresented among risk patterns. In women, digestive system diseases were also overrepresented, while endocrine and metabolic disorders were underrepresented (\u003cb\u003eSup Table S6\u003c/b\u003e). No significant associations emerged between trajectories and specific long COVID symptoms (\u003cb\u003eSup Table S7\u003c/b\u003e). These results surpassed the simulation-based significance thresholds, confirming their robustness (\u003cb\u003eSup Figure S4\u003c/b\u003e). The mean number of disease trajectories associated with long COVID in the simulations was 0.06 (SD\u0026thinsp;=\u0026thinsp;0.29) overall, 0.06 (SD\u0026thinsp;=\u0026thinsp;0.30) for females, and 0.08 (SD\u0026thinsp;=\u0026thinsp;0.36) for males.\u003c/p\u003e\u003cp\u003eMost long COVID\u0026ndash;associated trajectories involved transitions between different ICD chapters, while 29.8% involved pairs within the same chapter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To help improve the interpretation of multimorbid mechanisms, three clusters were defined based on the most frequent chapter transitions among disease trajectories associated with long COVID. Three main clusters emerged: Mental Health and Neuromuscular (MHNM, 45.6%), Cardiometabolic and Digestive (CMD, 19.3%), and Respiratory (RESP, 7%). Disease trajectories with both conditions from the same cluster will be referred to as cis trajectories. Among the trans trajectories (transitioning from one cluster to another one), the most common transitions were CMD \u0026rarr; MHNM (19.3%), MHNM \u0026rarr; CMD (5.3%), MHNM \u0026rarr; RESP (1.8%), and RESP \u0026rarr; MHNM (1.8%).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIndependent Effect of COVID-19 Severity\u003c/h2\u003e\u003cp\u003eAdjusting for hospitalization (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) reduced the significance of several diseases and trajectories, suggesting their link to long COVID is partly driven by acute infection severity. After adjustment, 14 diseases remained significantly associated with long COVID in the overall cohort (\u003cb\u003eSup Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Sup Table S8\u003c/b\u003e), including 11 in women and 3 in men. Conversely, associations with obesity, lipidaemia, mononeuropathies of the upper limb, otitis media, hearing loss, gastritis, other liver diseases, and knee derangement lost significance in the overall analysis. In the sex-stratified analysis, specified mood affective disorders and other liver diseases also lost significance in women, while no changes were observed in men. Regarding disease trajectories, 11 previously significant trajectories lost significance after hospitalization adjustment (\u003cb\u003eSup Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Sup Table S9\u003c/b\u003e). Notably, a new trajectory\u0026mdash;obesity followed by knee derangement\u0026mdash;emerged specifically in women. In the disease flow, we observe a reduction in Endocrine → Mental health and Mental health → Mental health associated trajectories (\u003cb\u003eSup Figure S3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Summary of the number of selected and long COVID (LC) associated diseases and disease trajectories, with (adjusted) and without (unadjusted) correcting by COVID-19 severity in the overall and sex-specific analysis. Clusters of disease trajectories are specified.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"546\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u0026nbsp;\u003c/strong\u003e(N=8,322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWomen\u0026nbsp;\u003c/strong\u003e(N=4,874)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMen\u0026nbsp;\u003c/strong\u003e(N=3,448)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelected diseases\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; LC unadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; LC adjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSelected disease trajectories\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4202%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u003cstrong\u003eLC unadjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eCMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eCMD \u0026ndash; MHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM \u0026ndash; CMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM \u0026ndash; RESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eRESP \u0026ndash; MHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eRESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;LC adjusted\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eCMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eCMD \u0026ndash; MHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM \u0026ndash; CMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eMHNM \u0026ndash; RESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eRESP \u0026ndash; MHNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 35.5246%;\"\u003e\n \u003cp\u003eRESP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.8241%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 23.0082%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.9587%;\"\u003e\n \u003cp\u003e0\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\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eGenetic correlation\u003c/h2\u003e\u003cp\u003eGenetic correlation analysis did not find significant correlations between long COVID and associated diseases, though some (e.g., hearing loss, asthma) showed moderate correlations. In contrast, strong positive genetic correlations were observed among many associated diseases, especially within mental health, musculoskeletal, and digestive clusters. Notably, cardiovascular and metabolic diseases formed a smaller cluster, with high correlation between clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003ePRS of diseases as a long COVID predictor\u003c/h2\u003e\u003cp\u003ePolygenic risk score (PRS) analysis revealed few associations with long COVID diagnosis. The strongest was the PRS for long COVID itself, indicating a genetic predisposition. Among disease-specific PRSs, those for intervertebral disc disorders and headache syndromes showed significant associations in women and the overall cohort, but not in men (\u003cb\u003eSup Table S11\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe analysed a 15-year prospective cohort to identify disease trajectories preceding the pandemic around 10-years before, and examine their impact on long COVID risk and symptomatology. We identified 38 out of 162 disease trajectories (23%) associated with long COVID, representing a shift in understanding long COVID risk by revealing that not only the presence but the interaction and order of diseases critically modulate susceptibility and severity. We also explored shared genetic risk factors underlying multimorbidity and genetic predisposition to long COVID, observing low genetic correlation with long COVID. Additionally, our genetic analysis revealed that two disease-specific polygenic risk scores (PRS)\u0026mdash;for diseases of the nervous system and musculoskeletal and connective tissue disorders\u0026mdash;were significantly associated with long COVID susceptibility.\u003c/p\u003e\u003cp\u003eAmong the identified disease trajectories, the Mental Health and Neuromuscular (MHNM) cluster was the most prevalent, representing nearly half of all risk trajectories. While the overall ICD-10 category for mental health was not broadly enriched, specific mental health diagnoses exhibited the strongest associations with long COVID, indicating a fundamental neurocognitive vulnerability [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This MHNM cluster encompasses a diverse range of conditions\u0026mdash;including mental health disorders, neurological issues, ear diseases, musculoskeletal problems, and female genitourinary conditions\u0026mdash;that are united by their impact on brain, nerve, and cognitive functions. Its prominence in our results supports earlier findings and shows that mental health should be a key part of long COVID assessment and care.\u003c/p\u003e\u003cp\u003eImportantly, MHNM trajectories often overlapped with CMD conditions (in 24.6% of associated trajectories). This group includes circulatory diseases (e.g., hypertension), endocrine and metabolic conditions (e.g., obesity, lipidaemia), and digestive disorders (e.g., gastroesophageal reflux), with a 19.3% of associated cis-trajectories. This pattern suggests a gut-brain axis dysregulation [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], linking nervous and gastrointestinal systems via microbiome, immune, and neural pathways, potentially explaining frequent transitions between cardiometabolic/digestive and mental health/neuromuscular conditions in long COVID.\u003c/p\u003e\u003cp\u003eIn contrast to the larger CMD and MHNM clusters, respiratory disease cis-trajectories formed a smaller but distinct cluster RESP (7%) with minimal overlap, highlighting significant biological and clinical diversity in long COVID. The only notable overlap was between vasomotor and allergic rhinitis bidirectionally co-occurring with migraine in women, conditions that share common underlying mechanisms [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThese cluster patterns highlight that the sequence and interaction of diseases, more than isolated conditions, drive long COVID risk. Seven diseases (32%) were associated with long COVID only in the presence of other conditions, underscoring the role of multimorbidity in susceptibility; conversely, eight diseases (35%) were independently linked and did not cluster with others. These findings suggest that certain disease interactions\u0026mdash;potentially involving mechanisms such as the gut-brain axis observed in CMD-MHNM trans-trajectories\u0026mdash;underlie distinct long COVID risk patterns. Our results highlight the importance of disease temporality in long COVID prognosis. While some diagnosis pairs showed similar associations regardless of order (26.3%; e.g., anxiety disorders and migraine, anxiety disorders and intervertebral disc disorders), remarkably, 73.7% of the trajectories demonstrated that only a specific order of disease onset increases long COVID risk, emphasizing that temporal progression\u0026mdash;not just co-occurrence\u0026mdash;of conditions determines vulnerability. The broader range of second diagnoses in significant trajectories indicates that after an initial condition, patients often develop diverse follow-up diseases. This highlights how disease progression pathways, rather than isolated conditions, shape the complexity and outcomes of long COVID.\u003c/p\u003e\u003cp\u003eThe symptoms analysis reveals complex interactions between neurological and mental health comorbidities that influence long COVID severity. For example, individuals with a previous diagnosis of depression were less likely to report loss of taste and smell. This may be because such sensory changes are already common in depression due to neurotransmitter imbalances, functional and structural brain changes and impaired neurogenesis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], rendering new symptoms less noticeable. Additionally, anxiety and severe stress were linked to a greater number and severity of long COVID symptoms, consistent with prior findings associating mental health disorders with worse COVID outcomes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond mental health, physical comorbidities also contribute to long COVID heterogeneity. The association between migraine and digestive symptoms suggests dysregulation of the gut\u0026ndash;brain axis as a contributor to symptom persistence, as previously discussed. Clinical studies further indicate disrupted respiratory and gastrointestinal microbiota homeostasis in hospitalized COVID-19 patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], supporting a potential causal role of the gut-brain axis in long COVID. Altered hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal axis function, which is closely regulated by the gut microbiota, has been reported in other suspected post-viral syndromes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], though not typically prior to infection. Mononeuropathies such as carpal tunnel syndrome may exacerbate neurological symptoms through pre-existing nerve sensitization worsened by viral-induced neuroinflammation. Finally, chronic pain and reduced physical activity resulting from musculoskeletal conditions like knee internal derangement may contribute to more severe cardiovascular and respiratory long COVID symptoms. Collectively, these associations illustrate how interconnected disease patterns underlie the diverse manifestations of long COVID.\u003c/p\u003e\u003cp\u003eWhile some comorbidities appear to increase long COVID risk by exacerbating acute COVID-19 severity, others influence risk independently or through combined disease pathways. Adjusting for COVID-19 severity had a stronger impact in the overall cohort reducing the number of diseases associated with long COVID, while the impact in women was lower due to the lower rates of severe COVID-19, and in men as they had less long COVID associated diseases and disease trajectories.\u003c/p\u003e\u003cp\u003eAmong established severity risk factors for COVID and long COVID [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], obesity lost significance after adjustment, supporting a role as a mediator. Some other trajectories lost their association with long COVID after adjustment, especially mental health-related trajectories, indicating that their effects were largely mediated by acute COVID-19 phase. An exception was the disease trajectory from obesity to knee derangement in women, which gained significance despite individual diseases losing theirs\u0026mdash;implying a combined or sequential effect influencing long COVID risk uniquely in this subgroup. Since knee derangement often leads to reduced physical activity\u0026mdash;a known risk factor for long COVID\u0026mdash;this progression may contribute to more severe long COVID symptoms.\u003c/p\u003e\u003cp\u003eTo further elucidate potential underlying mechanisms, we examined the genetic correlations between long COVID and the comorbid diseases identified in our study. We found no significant genetic correlations between long COVID and associated diseases, consistent with GWAS studies showing low heritability for long COVID, likely reflecting its heterogeneity [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The lack of significant genetic correlations between long COVID and associated diseases is consistent with recent research showing that, despite limited genetic overlap, shared biological pathways may underlie these conditions [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. This highlights the importance of exploring non-genetic factors and gene-environment interactions to better understand long COVID susceptibility. However, many of the comorbid diseases showed strong genetic correlations among themselves, consistent with prior studies and suggesting shared genetic architecture within disease clusters, such as cardiovascular and metabolic disorders.\u003c/p\u003e\u003cp\u003eIn addition, polygenic risk scores (PRS) for certain conditions\u0026mdash;including intervertebral disc disorders and headache syndromes\u0026mdash;were significantly associated with long COVID incidence. These findings suggest that these PRS might serve as genetic biomarkers or reflect gene-environment interactions influencing susceptibility. This indicates that, although long COVID itself has limited direct genetic overlap with comorbid diseases, genetic factors related to these associated conditions could indirectly affect long COVID risk through complex pathways or interactions.\u003c/p\u003e\u003cp\u003eIn addition to genetic factors, sex-related biological differences emerged as a significant aspect influencing disease patterns and long COVID risk. Although the low number of male long COVID cases may bias results, we observed clear sex-related biological differences in disease patterns and risk. Women in the cohort had, on average, one more comorbidity than men, consistent with literature showing women are diagnosed with more chronic conditions over their lifetime. This may reflect women's more frequent healthcare use, resulting in earlier and more diagnoses, without increased mortality [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Biological and hormonal factors also likely increase the incidence of some conditions, including long COVID [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite these insights, several limitations must be considered to contextualize our findings and guide future research. Although we used a large cohort with detailed EHR data, limited long COVID cases\u0026mdash;particularly among men and genotyped individuals\u0026mdash;reduce power to detect sex-specific and genetic effects confidently. EHR data have limitations: diagnoses before 2010 are incomplete, and private healthcare records are missing, possibly causing under-ascertainment. Additionally, the use of three-digit ICD-10 codes to classify diseases can group heterogeneous conditions, limiting diagnostic specificity and potentially masking important subtypes. Changing clinical definitions and diagnostic criteria of long COVID during the study period complicate interpretation and may cause misclassification bias. As an observational study, unmeasured confounders may influence findings, limiting the ability to draw causal conclusions. These limitations highlight the need for further research in larger, more diverse cohorts with detailed phenotyping and longer-term follow-up to validate and extend these findings.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, long COVID risk is shaped by complex multimorbid disease trajectories where the sequence and interaction of mental health, cardiometabolic, and respiratory conditions are decisive. Some comorbidities increase risk by worsening acute COVID-19, while others act independently or synergistically, with clear sex-specific patterns. Although direct genetic overlap is limited, polygenic risk scores for neurological and musculoskeletal disorders suggest indirect genetic influences. Our findings highlight the necessity of incorporating disease temporality and multimorbidity into personalized risk assessment and management of long COVID.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSARS-CoV-2: severe acute respiratory syndrome coronavirus 2\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eEHR: Electronic health records\u003c/p\u003e\n\u003cp\u003eICD: International Classification of Diseases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR: False discovery rate\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u003c/p\u003e\n\u003cp\u003eSE: Standard error\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u003c/p\u003e\n\u003cp\u003ePRS: polygenic risk score\u003c/p\u003e\n\u003cp\u003eEGA: European Genome-phenome Archive\u003c/p\u003e\n\u003cp\u003eMHNM: Mental health and neuromuscular\u003c/p\u003e\n\u003cp\u003eCMD: Cardiometabolic and digestive\u003c/p\u003e\n\u003cp\u003eRESP: Respiratory\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants contacted had consented in the past to be re-contacted and had provided informed consent. Ethical approval was obtained from the Hospital Universitari Germans Trias I Pujol Ehtincs Committee (CEI no. PI-20-182).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLa Caixa Foundation (SR20-01024), La Marat\u0026oacute; TV3 (167/C/2021), \u0026nbsp;the Spanish Ministry of Health (PI18/01512) Horizon Europe END-VOC (GA:101046314).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNB and RdC contributed to the study conceptualization and design. RdC and MK participated in the funding acquisition. JGA, GCV, MK and RdC participated in data acquisition. NB performed data collection and curation. NB and XF performed formal analysis, interpretation and visualization. NB and RdC drafted the manuscript, and ALL authors participated in the review and editing of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe express our sincere gratitude to the GCAT cohort study volunteers and the Blood and Tissue Bank team. We also wish to extend our special appreciation to the GCAT project investigators, particularly Anna Carreras, Beatriz Cort\u0026eacute;s. Anonymized data were provided by the Catalan Agency for Quality and Health Assessment (PADRIS Program). For a complete list of investigators, please visit www.genomesforlife.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDavis HE, McCorkell L, Vogel JM, Topol EJ. 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The hypothalamo-pituitary-adrenal axis in chronic fatigue syndrome and fibromyalgia syndrome. Stress. 2007;10(1):13\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmeland OB, Frei O, Dale AM, Andreassen OA. The polygenic architecture of schizophrenia - rethinking pathogenesis and nosology. Nat Rev Neurol. 2020;16(7):366\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOksuzyan A, Juel K, Vaupel JW, Christensen K. Men: good health and high mortality. Sex differences in health and aging. Aging Clin Exp Res. 2008;20(2):91\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeluso MJ, Deeks SG. Mechanisms of long COVID and the path toward therapeutics. Cell. 2024;187(20):5500\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Long COVID, multimorbidity, disease trajectories, polygenic risk scores, genetic correlation","lastPublishedDoi":"10.21203/rs.3.rs-6936765/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6936765/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLong COVID refers to the persistence of symptoms after SARS-CoV-2 infection. While individual comorbidities have been studied, the role of coexisting chronic conditions remains underexplored. This study investigates whether pre-pandemic disease trajectories\u0026mdash;sequential patterns of chronic conditions\u0026mdash;affect long COVID risk and symptom profiles, and explores shared genetic susceptibility.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe analysed 8,322 participants (58.6% women) from the COVICAT, followed between 2021 and 2023. Disease trajectories were reconstructed from electronic health records (2010\u0026ndash;2019), focusing on sequences of two chronic conditions found in \u0026ge;\u0026thinsp;1% of the cohort. We evaluated shared genetic architecture and polygenic risk scores (PRS) for predictive capacity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThirty-eight disease trajectories were associated with increased long COVID risk. These trajectories primarily involved mental and neurological disorders (e.g., depression, anxiety, migraine), respiratory diseases (e.g., asthma, allergic rhinitis), and cardiometabolic or digestive conditions (e.g., hypertension, lipidaemia, obesity, gastroesophageal reflux). No significant genetic correlations with long COVID were detected, but polygenic risk scores for two nervous system and musculoskeletal conditions showed modest associations with increased risk.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDisease trajectories were significantly associated with long COVID, highlighting the importance of multimorbidity and the temporal sequence of conditions. While no strong overall genetic correlations were found, modest polygenic associations suggest a role for shared susceptibility in nervous system and musculoskeletal disorders. From a public health perspective, identifying high-risk multimorbid individuals may inform targeted prevention and care strategies.\u003c/p\u003e","manuscriptTitle":"10-years disease trajectories and genetic insights into long COVID susceptibility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-07 08:26:11","doi":"10.21203/rs.3.rs-6936765/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-07T13:31:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T11:18:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-05T12:40:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280861331326518466209606690728963019720","date":"2025-07-21T07:50:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126267826212665642921665975439880819712","date":"2025-07-19T00:27:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-12T14:52:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196706519376293489935777187794355079262","date":"2025-07-05T16:24:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-03T11:14:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-20T09:26:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-20T08:23:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2025-06-20T08:06:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e3e71235-8dd7-49ac-ad81-d609989d121d","owner":[],"postedDate":"July 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-03T16:01:03+00:00","versionOfRecord":{"articleIdentity":"rs-6936765","link":"https://doi.org/10.1186/s12916-025-04427-x","journal":{"identity":"bmc-medicine","isVorOnly":false,"title":"BMC Medicine"},"publishedOn":"2025-10-27 15:57:07","publishedOnDateReadable":"October 27th, 2025"},"versionCreatedAt":"2025-07-07 08:26:11","video":"","vorDoi":"10.1186/s12916-025-04427-x","vorDoiUrl":"https://doi.org/10.1186/s12916-025-04427-x","workflowStages":[]},"version":"v1","identity":"rs-6936765","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6936765","identity":"rs-6936765","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}