Complex patterns of multimorbidity associated with severe COVID-19 and long COVID – Nature.com
							July 11, 2024
							    Here, we systematically investigate the risk conferred by the    presence and potential causal relevance of 1448 diseases for    COVID-19 severity (hospitalisation, severe respiratory failure,    and death) and Long COVID (Fig.1), based on medical    disorder concepts14,16 defined and    collated from >12 million medical records from primary    (general practice), secondary care (hospital admissions), and    disease registry (cancer registry), death certificates, and    patient-reported conditions among 502,460 UKB participants    (Fig.1 and Supplementary    Data1). Incorporating    primary care data more than doubled case numbers for more than    half (n=817; 56.4%) of the diseases we considered    (Supplementary Data1).  
            Scheme of the study design and analysis done,            illustrating our workflow to define disease mechanisms            that may causally contribute to severe COVID-19 or Long            COVID. SNPs Single nucleotide polymorphisms; SPA =            saddle point approximation; MAF = minor allele            frequency; *COVID-19 HGI=COVID-19 Host Genetic            Initiative, but excluding contributions from UK Biobank          
    We identified 1128 significant    (p<1.1105) disease     COVID-19 outcome associations, including almost half    (n=679) of the diseases considered with at least one    of the four COVID-19 outcomes derived (Fig.2    and Supplementary Data2). Pre-existing    diseases were almost exclusively associated with a higher risk    for COVID-19 endpoints (median hazard ratio (HR): 2.39, range:    0.5917.3), only two diseases (benign neoplasm of skin and    varicella infection) were associated with a decreased risk.    Associated diseases spanned almost all chapters of the ICD-10    (17 out of 18) but were consistently enriched in the chapters    respiratory (odds ratio [OR]: 5.96; p-value:    2.7x108), circulatory (OR: 2.95;    p-value: 3.5x107), and    endocrine/metabolic diseases (OR: 2.76; p-value:    9.1104) when associated with severe    COVID-19. In contrast, pre-existing disease-codes classified as    symptoms were more than 13-fold enriched among diseases    associated with an increased risk for Long COVID (OR: 13.2;    p-value: 3.6x108) but also hospitalisation    (OR: 5.53; p-value: 9.9x105) and death    (OR: 3.06; p-value: 7.3x103).  
            Each panel contains association statistics,            p-values (triangles), from Cox-proportional            hazard models (two-sided) testing for an association            between the disease on the x-axis and three different            COVID-19 outcomes, as well as Long COVID. Disease            associations passing the multiple testing correction            (dotted line, p<1.1105) are            depicted by larger triangles of which facing up ones            indicate positive, e.g., increased disease risk,            associations and downward facing vice versa. The            diseases are ordered by ICD-10 chapters (colours) and            the top ten for each endpoint annotated. Underlying            sample numbers and statistics can be found in            Supplementary Data1 and            2.          
    For COVID-19 requiring hospitalisation, we replicated and    refined known associations with serious pre-existing diseases    that have been previously used to identify clinically extremely    vulnerable people. This included respiratory diseases like    pseudomonal pneumonia (HR: 7.53, 95%-CI: 4.7411.97;    p-value<1.2x1017), acute renal    failure (HR: 4.02, 95%-CI: 3.744.32, p-value:    <10300) or type 2 diabetes with renal    complications (HR: 7.44; 95%-CI: 5.679.76; p-value:    1.5x1047), as well as immune deficiencies (e.g.,    deficiency of humoral immunity HR: 6.02; 95%-CI: 4.368.31;    p-value: 1.3x1027) or patients under    immune suppression (e.g., liver transplants HR: 7.25 95%-CI:    4.5111.68, p-value: 3.4x1016). However,    we further observed strong associations with so far less    recognized pre-existing mental health and psychiatric diseases    and conditions with effect sizes comparable to those previously    considered to identify extremely vulnerable people. This    included symptoms of malaise and fatigue (HR: 2.17, 95%-CI:    2.072.27, p-value: 4.4x10222) or suicide    attempts (HR 5.33, 95%-CI: 4.456.39, p-value:    3.6x1073). Most diseases (n=641, 95.5%,    phetero>103) associated with    similar magnitude across all three different definitions of    COVID-19 severity, with different forms of dementias    (phetero<2.1x1024) being among the    few exceptions, associating with hospitalisation (HR: 3.83;    95%-CI: 3.384.34; p-value: 2.3x1097) and    death (HR: 10.82; 95%-CI: 9.1512.80; p-value:    1.4x10170), but not severe respiratory failure    (HR: 1.15; 95%-CI: 0.512.57; p-value: 0.74) due to    COVID-19.  
    In contrast, pre-existing diseases associated with an increased    risk for Long COVID only partially overlapped with those    increasing the risk for severe COVID-19. Most notably, we    replicated associations with anxiety    disorders28 (HR: 2.59;    95%-CI: 2.093.20; p-value:1.8x1018) and    other mental health symptoms, but most prominently with    symptoms of malaise and fatigue (HR: 2.78; 95%-CI: 2.293.37;    p-value:1.5x1025) that are hallmarks of    Long COVID and were also strongly associated with severe    COVID-19.  
    Almost all significant associations (99.8%, n=1126)    were consistent when considering all-cause death as a competing    event (Supplementary Data3), and more than    half (63.6%; n=718) remained statistically significant    (p<4.4x105) when accounting for a    large set of potential confounders in multivariable Cox-models    (Supplementary Data3). This suggests    that potentially unreported associations, such as the increased    risk for severe COVID-19 among patients reporting symptoms of    malaise and fatigue (adjusted HR: 1.66, 95%-CI: 1.58 - 1.74,    p-value=7.3x1092), are not just a    reflection of a general disease burden or other chronic    diseases associated with a greater risk for severe COVID-19.  
    We observed limited evidence for effect modifications by sex    (n=7), non-European ancestry (n=1), or age    (n=8), but not social deprivation, with 16 disease     COVID 19 pairings showing evidence of significant differences    (Supplementary Data4;    p<3.6x106). All included stronger effects in    women compared to men, e.g., gout for hospitalised COVID-19    (women: HR: 2.56, 95%-CI 2.212.96, p-value:    1.3x1036; men: HR: 1.46, 95%-CI: 1.341.58,    p-value: 2.1x1019), among Europeans    reporting vitamin D deficiencies (Europeans: HR: 2.31, 95%-CI:    2.132.51, p-value: 2.1x1087;    non-Europeans: HR: 1.31, 95%-CI: 1.081.60,    p-value=5.5x103), or among younger    participants, e.g., disorders of magnesium metabolism and death    with COVID-19 as a likely result of renal failure (age 65    years: HR: 42.98, 95%-CI: 20.1091.90, p-value:    3.0x1022; age >65 years: HR: 5.35, 95%-CI:    3.518.16, p-value: 5.9x1015).  
    We next derived a disease-disease network18    (Fig.3a) to understand,    whether the large set of diseases associated with an increased    risk for severe COVID-19 act independently or rather reflect an    increased risk among participants suffering from multiple    pre-existing conditions, i.e., multimorbidity. The network    contained a total of 1381 diseases connected through 5212 edges    based on non-random co-occurrence (Supplementary Data    5a, b). Diseases    segregated into 31 communities being more strongly connected    to each other compared to the rest of the network    (Fig.3b, c).  
            a Disease  disease network based on significant            (p<4.8x108) positive partial            correlations (two-sided). Nodes (diseases) are coloured            by ICD-10 chapters and strength of partial correlation            depicted by width of the edges. The underlying data can            be found in Supplementary Data 5ac Same            network, but only highlighting two disease communities            strongly enriched for associations with severe            COVID-19. d Hub score for the 30 diseases with            highest values and associated association statistics,            hazard ratios (rectangle) with 95%-confidence intervals            (lines), from Cox-proportional hazard models            (two-sided). Significant associations are indicated by            filled boxes. Colours according to ICD-10 chapters. All            underlying data can be found in Supplementary            Data2 and            5b.          
    Two disease communities were consistently and strongly enriched    for diseases associated with severe COVID-19. The first (e.g.,    OR: 5.20; p-value=2.2x1010; for severe    respiratory failure) community was strongly enriched for    circulatory (OR: 17.6; p-value: 4.4x1039)    and respiratory (OR: 10.3; p-value:    7.8x1016) diseases, closely resembling the    cardio-respiratory risk profile already described above    (Fig.3b). The second    community consisted of diverse endocrine (OR: 6.19;    p-value: 1.9x1013) and circulatory disease    (OR: 3.75; p-value: 5.4x108), and largely    reflected the renal-diabetic risk profile    (Fig.3c). Accordingly, for    each disease acquired during lifetime within the latter disease    community, participants risk increased by 18% and 20% to be    hospitalised (HR: 1.18; 95%-CI: 1.171.18; p-value:    p<10300) or die with COVID-19 (HR:    1.20; 1.191.20; p-value<10300),    respectively.  
    Diseases increasing the risk for severe COVID-19, but not Long    COVID further significantly correlated with hub status (e.g.,    hospitalisation: r=0.59; p-value:    2.8x10124) in the disease-disease network    (Fig.3d), that is, diseases    that connect a large cluster of diseases to the rest of the    network and might hence be considered as multimorbidity    hotspots. For example, acute renal failure, strongly associated    with severe COVID-19 (Fig.3d), showed strong    partial correlations with 30 other diseases and patients are    hence prone to complex multimorbidity. However, the imperfect    correlation between hub status and disease-association profiles    indicates that certain forms of multimorbidity, such those    related to secondary malignancies of lymph nodes, are possibly    less related to severe COVID-19.  
    We next systematically characterised whether diseases    identified to be associated with COVID-19 severity or Long    COVID shared genetic similarity with host genetic    susceptibility to severe COVID-19 to understand potential    underlying causal mechanisms. We computed genetic correlation    estimates for all 1128 disease  COVID-19 outcome pairs and    observed 75 pairs (6.6%) that showed evidence for significant    (p<4.4x105) and directionally    consistent genetic correlations (Fig.4    and Supplementary Data6), indicating a    putatively causal link of any of 57 unique diseases on severe    COVID-19. We did not observe evidence of convergence for Long    COVID, which might likely be explained by the still low    statistical power for the respective genome-wide association    study13.  
            The first three panels show association statistics,            hazard ratios (rectangle) and 95%-confidence interval            (lines), for 57 diseases with evidence of convergence            with genetic correlation analysis, that are shown in            the last two panels (rectangle  genetic correlation;            lines  95%-confidence intervals). Disease have been            grouped by ICD-10 chapters and coloured accordingly            (see Figs.2 or            3 for legend).            NOS = not elsewhere specified; All underlying data can            be found in Supplementary Data1 (sample            numbers), 2 and 6.          
    The diseases with consistent evidence from survival and genetic    analysis included well-described risk-increasing effects of    pre-existing endocrine (e.g., type 2 diabetes), respiratory    (e.g., respiratory failure), or renal (e.g., chronic kidney    disease) diseases, but also digestive (e.g., gastritis and    duodenitis), or musculoskeletal (e.g., rheumatoid arthritis)    diseases, and further symptoms of malaise and fatigue    (rG=0.26; p-value=4.7106)    and abdominal pain (rG=0.33;    p=2.51011), as well as adverse    reactions to drugs (e.g., poisoning by antibiotics:    rG=0.38; p-value=2.2x106).    Findings that collectively demonstrated the need for a    comprehensive assessment of disease-risk beyond few, selected    common chronic conditions.  
    Among the 41 diseases for which we had sufficient genetic    instruments to perform more stringent Mendelian randomization    (MR) analyses to assess causality, we observed only nominally    significant (p<0.05) evidence for gout and    hospitalisation (OR: 1.03; 95%-CI: 1.011.05, p-value:    0.03), as well as arthropathy not elsewhere specified (OR:    1.28; 95%-CI: 1.061.55; p-value: 0.02) and unspecified    monoarthrtitis (OR: 1.21; 95%-CI: 1.041.41; p-value:    0.02) for severe COVID-19 (Supplementary    Data7). While we might    have been still underpowered for many diseases, this leaves the    possibility that convergence of survival and genetic    correlation analysis might, in part, be explained by shared    risk factors.  
    To finally understand possible molecular mechanisms linking the    diseasome to COVID-19, we systematically profiled disease    associations across 49 independent genomic regions linked to    COVID-19 or Long COVID. We observed strong and robust evidence    of a genetic signal shared between severe COVID-19 and a total    of 33 diseases at nine loci (posterior probability    (PP)>80%) (Fig.5a and Supplementary    Data8). Apart from known    pleiotropic loci, such as ABO and FUT2 coding for    blood group types, this included respiratory risk loci, albeit    with contradicting effect estimates for three loci    (Fig.5b). While COVID-19    risk increasing alleles at LZTFL1 and TRIM4 were    consistently associated with a higher risk for viral pneumonia    and post-inflammatory pulmonary fibrosis, respectively,    risk-increasing alleles at MUC5B, NPNT, and    PSMD3 were inversely associated with post-inflammatory    pulmonary fibrosis and asthma. An observation that extended    even beyond shared loci (Fig.5c) illustrating a    general trend of phenotypic divergence of genetic effects on    diseases that share pathological features with severe COVID-19.  
            a Network representation of significant            (PP>80%) colocalization results. Loci are depicted            as white rectangles and diseases as coloured nodes            according to ICD-10 chapters. Edges represent strong            evidence for colocalization, and solid lines indicate a            risk-increasing effect of the COVID-19 risk increasing            allele, whereas dashed lines indicate protective            effects. Underlying data can be found in Supplementary            Data8. b            Forest plot displaying hazard ratios (rectangle) with            95%-confidence intervals (lines) for each variant and            different COVID-19 and colocalising disease outcomes.            Effect estimates for COVID-19 have been obtained from            the COVID-19 Host Genetic Initiative and effect            estimates for diseases in the present study. All            estimates are derived from logistic regression models.            c Heatmap of effect estimates across 49            independent genetic loci associated with increased risk            for sever COVID-19 and corresponding effects on six            selected traits that showed evidence of colocalization            at least one other locus. Black rectangles indicate            genome-wide significant effects            (p<5108). NOS not elsewhere            specified; All underlying data can be found in            Supplementary Data1 and            8 or is given            in the data availability statement.          
    A notable observation was the TYK2 locus that has    previously been suggested to indicate the efficacy of    successfully repurposed drugs for severe    COVID-1929. Briefly,    TYK2 encodes for tyrosine kinase 2 (TYK2) a protein    partially targeted by Janus kinase (JAK) inhibitors like    baricitinib, that have been approved for rheumatoid arthritis    and successfully repurposed for severe COVID-19, although    predating possible evidence from genetic    studies30,31,32. Accordingly, we    observed that the same genetic variant, rs34536443    (PP=99.8%), associated with the risk for severe COVID-19 was    also associated with, amongst others, the risk of rheumatoid    arthritis, but in opposing effect directions    (Fig.5b). Rs34536443 is a    loss-of-function missense variant (p.Pro1104Ala) for TYK2 and    the functionally impairing minor C allele was associated with a    50% increased risk for severe COVID-19 (odds ratio: 1.50;    95%-CI: 1.40 1.62, p-value= 4.3x1029)    but a 23% reduced risk for rheumatoid arthritis (odds ratio:    0.77; 95%-CI: 0.720.83;    p-value=2.4x1012) as well as other    autoimmune diseases, in particular psoriasis (Supplementary    Data8). While the    discrepancy between the success of the drug and genetic    inference might be explained by the rather weak affinity of    baricitinib for TYK233, patients    undergoing trials with TYK2-inhibitors for    psoriasis34 might be at an    elevated risk for severe COVID-19. This observation seemingly    aligns with studies on Tyk2-/- mouse    models reporting an impaired immune response to viral    infections35.  
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Complex patterns of multimorbidity associated with severe COVID-19 and long COVID - Nature.com