National policies for mitigating the COVID-19 pandemic include stricter measures for people considered to be at high risk of severe and potentially fatal cases of the disease. Although older age and pre-existing health conditions are strong risk factors, it is poorly understood why susceptibility varies so widely in the population.

People with cardiometabolic diseases, such as diabetes and liver diseases, or chronic inflammation are at higher risk of severe COVID-19 and other infections including pneumonia. These conditions alter the molecules circulating in the blood, providing potential ‘biomarkers’ to determine whether a person is more likely to develop a fatal infection. Uncovering these blood biomarkers could help to identify people who are prone to life-threatening infections despite not having ever been diagnosed with a cardiometabolic disease.

To find these biomarkers, Julkunen et al. studied blood samples that had been collected from 105,000 healthy individuals in the United Kingdom over ten years ago. The data showed that individuals with biomarkers linked to low-grade inflammation and cardiometabolic disease were more likely to have died or been hospitalised with pneumonia.

A score based on 25 of these biomarkers provided the best predictor of severe pneumonia. This biomarker score performed up to four times better within the first few years after blood sampling compared to predicting cases of pneumonia a decade later. The same blood biomarker changes were also linked with developing severe COVID-19 over ten years after the blood samples had been collected. The predictive value of the biomarker score was similar for both severe COVID-19 and the long-term risk of severe pneumonia.

Julkunen et al. propose that the metabolic biomarkers reflect inhibited immunity that impairs response to infections. The results from over 100,000 individuals suggest that these blood biomarkers may help to identify people at high risk of severe COVID-19 or other infectious diseases.

The coronavirus disease 2019 (COVID-19) pandemic affects societies and healthcare systems worldwide. Protection of those individuals who are most susceptible to a severe and potentially fatal COVID-19 disease course is a prime component of national policies, with stricter social distancing and other preventative means recommended mainly for elderly people and individuals with pre-existing disease conditions. The prominent susceptibility to severe COVID-19 for people at high age has been linked with impaired immune response due to chronic inflammation caused by ageing processes (Akbar and Gilroy, 2020). However, large numbers of seemingly healthy middle-aged individuals also suffer from severe COVID-19 (Zhou et al., 2020; Atkins et al., 2020; Williamson et al., 2020); this could partly be due to similar molecular processes related to impaired immunity. A better understanding of the molecular factors predisposing to severe COVID-19 outcomes may help to explain the risk elevation ascribed to pre-existing disease conditions. From a translational point of view, this might also complement the identification of highly susceptible individuals in general population settings beyond current risk factor assessment.

Pneumonia is a life-threatening complication of COVID-19 and the most common diagnosis in severe COVID-19 patients. As for COVID-19, the main factors that increase the susceptibility for severe community-acquired pneumonia are high age and pre-existing respiratory and cardiometabolic diseases, which can weaken the lungs and the immune system (Almirall et al., 2017). Based on analyses of large blood sample collections of healthy individuals, biomarkers associated with the risk for severe COVID-19 are largely shared with the biomarkers associated with the risk for severe pneumonia, including elevated markers of impaired kidney function and inflammation and lower HDL cholesterol (Ho et al., 2020). This may indicate that these molecular markers may reflect an overall susceptibility to severe complications after contracting an infectious disease.

Comprehensive profiling of metabolic biomarkers, also known as metabolomics, in prospective population studies have suggested a range of blood biomarkers for cardiovascular disease and diabetes to also be reflective of the susceptibility for severe infectious diseases (Ritchie et al., 2015; Deelen et al., 2019). Metabolic profiling could therefore potentially identify biomarkers that reflect the susceptibility to severe COVID-19 among initially healthy individuals. However, such studies require measurement of vast numbers of blood samples collected prior to the COVID-19 pre-pandemic. Conveniently, a broad panel of metabolic biomarkers have recently been measured using nuclear magnetic resonance (NMR) spectroscopy in over 100,000 plasma samples from the UK Biobank.

Here, we examined if NMR-based metabolic biomarkers from blood samples collected a decade before the COVID-19 pandemic associate with the risk of severe infectious disease in UK general population settings. Exploiting the shared risk factor relation between susceptibility to severe COVID-19 and pneumonia (Ho et al., 2020), we used well-powered statistical analyses of biomarkers with severe pneumonia events to develop a multi-biomarker score that condenses the information from the metabolic measures into a single multi-biomarker score. Taking advantage of the time-resolved information on the occurrence of severe pneumonia events in the UK Biobank, we mimicked the influence of the decade lag from blood sampling to the COVID-19 pandemic on the biomarker associations, and used analyses with short-term follow-up to interpolate to a scenario of identifying individuals susceptible to severe COVID-19 in a preventative screening setting. Our primary aim was to improve the molecular understanding on how metabolic risk markers may contribute to increased predisposition to severe COVID-19 and other infections.

A flow diagram of eligible study participants and case numbers is shown in Figure 1. Clinical characteristics of the study population are listed in Table 1. Among the 105,146 UK Biobank study participants with complete data on metabolic biomarkers and severe pneumonia outcomes, and no prior history of diagnosed pneumonia, there were 2507 severe pneumonia events recorded in hospital or death registries after the baseline blood sampling (median follow-up time 8.1 years).

Figure 1 with 1 supplement see all

Download asset Open asset

For the severe COVID-19 analyses, there were 652 PCR-confirmed positive cases diagnosed in hospital (inferred as severe cases in this study) among the 92,725 individuals with COVID-19 data linkage available per 3rd of February 2021. The number of severe COVID-19 cases in the UK Biobank closely followed the trends in hospitalised individuals for COVID-19 in England (Figure 1—figure supplement 1). In February 2021, the age range of study participants was 49–84 years. The median duration from blood sampling to the COVID-19 pandemic was 11.2 years (interquartile range 10.0–12.6). The prevalence of chronic respiratory and cardiometabolic diseases was similar for study participants who developed severe pneumonia and those who contracted COVID-19 and required hospitalisation, with the exception of COPD. There were 33 overlapping cases between severe pneumonia and COVID-19.

Metabolic biomarkers and severe pneumonia risk

Figure 2A shows the associations of 37 biomarkers with severe pneumonia events occurring during the follow-up in the entire study population (n = 105 146). The biomarkers highlighted here are those with a regulatory approval for diagnostics use in the Nightingale Health NMR platform. These biomarkers span most of the different metabolic pathways captured with the NMR platform; results for all 249 metabolic measures quantified are shown in Figure 2—figure supplements 1–3. Strong associations were observed across several metabolic pathways: increased plasma concentrations of cholesterol measures, omega-3 and omega-6 fatty acid levels, histidine, branched-chain amino acids and albumin were associated with lower susceptibility to contracting severe pneumonia. Increased concentrations of monounsaturated and saturated fatty acids, as well glycoprotein acetyls (GlycA, a marker of low-grade inflammation) were associated with elevated susceptibility to contracting severe pneumonia.

Figure 2 with 3 supplements see all

Download asset Open asset

Figure 2—source data 1

Numerical tabulation of odds ratios, betas, standard errors, and p-values for results shown in Figure 2.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig2-data1-v2.csv

Download elife-63033-fig2-data1-v2.csv

Since all the biomarkers are quantified in the same single measurement, we examined if even stronger associations with severe pneumonia could be obtained using a combination of multiple biomarkers. We derived this multi-biomarker combination, denoted ‘infectious disease score’, using logistic regression with LASSO for variable selection, considering the 37 clinically validated biomarkers in a half of the study population as the training set. This resulted in an infectious disease score comprised of the weighted sum of 25 biomarkers, with the weights selected by the machine learning algorithm (Supplementary file 1). Broadly similar results were obtained using all 249 metabolic measures quantified in the Nightingale Health NMR platform to derive the multi-biomarker score.

The multi-biomarker infectious disease score was then tested for association with severe pneumonia in the other half of the study population. The magnitude of association for the infectious disease score was approximately twice as strong with severe pneumonia compared to any of the individual biomarkers (Figure 2B). The odds for contracting severe pneumonia was increased 67% per 1-SD increment in the infectious disease score. This corresponds to close to fourfold higher risk for contracting severe pneumonia among people in the highest quintile of the infectious disease score, compared to those with a score in the lowest quintile.

To assess the robustness of the multi-biomarker score association with severe pneumonia, we adjusted the analyses for prevalent diseases and performed analyses stratified by age and sex (Figure 3). The association was attenuated by ~10% in magnitude when adjusting for, or omitting, individuals with a diagnosis of prevalent diseases at time of blood sampling (cardiovascular diseases, diabetes, lung cancer, COPD, liver diseases, renal failure, and dementia; panels 3A and 3B). The association was similar across age groups, and also for men and women analysed separately (panels 3C and 3D).

Figure 3

Download asset Open asset

Figure 3—source data 1

Numerical tabulation of odds ratios, betas, standard errors, and p-values for results shown in Figure 3.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig3-data1-v2.csv

Download elife-63033-fig3-data1-v2.csv

To mimic the influence of the decade-long lag from blood sample collection to the COVID-19 pandemic, we tested the association of the multi-biomarker infectious disease score with severe pneumonia events occurring during 7–11 years after the blood sampling (Figure 4A). Since there were only few severe pneumonia events recorded with more than 9 years of follow-up, we could not fully mimic the decade long time lag to the COVID-19 pandemic. The risk elevation observed in this time-lag accounting scenario was only approximately half of that observed for severe pneumonia events occurring within the first 7 years (odds ratio 1.43 vs 1.75 per 1-SD, respectively; and 2.59 vs 4.27 for individuals in the highest vs lowest quintile of the infectious disease score).

Figure 4 with 1 supplement see all

Download asset Open asset

Figure 4—source data 1

Numerical tabulation of odds ratios, betas, standard errors, and p-values for results shown in Figure 4.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig4-data1-v2.csv

Download elife-63033-fig4-data1-v2.csv

To interpolate to a screening scenario conducted today, we also tested the association with short-term risk of severe pneumonia by analysing events occurring within the first 2 years after the blood sampling (Figure 4B). The association magnitude in this analysis of short-term risk scenario was approximately twice as strong as for severe pneumonia events occurring more than 2 years after blood sampling (odds ratio 2.21 vs 1.59 per 1-SD; 7.95 vs 3.35 for individuals in the highest vs lowest quintile of the infectious disease score). The elevated susceptibility to severe pneumonia associated with the multi-biomarker score was therefore three to four times stronger when examining short-term risk as compared to risk of severe pneumonia events occurring almost a decade after the blood sampling.

The elevation in the short-term risk for severe pneumonia for high levels of the infectious disease multi-biomarker score remained strong when adjusting for BMI, smoking and prevalent diseases (odds ratio 6.10 for individuals in the highest vs lowest quintile; Figure 4—figure supplement 1).

We further explored the risk gradient for a future onset of severe pneumonia along increasing levels of the infectious disease score, since non-linear effects could potentially facilitate the identification of thresholds for individuals at high susceptibility. Figure 5A shows the increase in the proportion of individuals who contracted severe pneumonia according to percentiles of the score. The risk increased prominently in the highest quintile, and particularly for the highest few percentiles. The time-resolved plot of the cumulative probability of severe pneumonia during follow-up is shown in Figure 5B. The susceptibility to severe pneumonia was particularly elevated among individuals with the very highest levels of the multi-biomarker infectious disease score. This was observed already during the first few years of follow-up, corroborating the results for long-term and short-term risk shown in Figure 3. The prominent and immediate elevation in susceptibility to severe pneumonia was also observed when limiting analyses to individuals without chronic respiratory and cardiometabolic diseases at the time of blood sampling (Figure 5—figure supplement 1).

Figure 5 with 1 supplement see all

Download asset Open asset

Figure 5—source data 1

Numerical tabulation of event rates for each percentile in Figure 5A.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig5-data1-v2.csv

Download elife-63033-fig5-data1-v2.csv

Metabolic biomarkers and severe COVID-19

Figure 6 shows the associations of the 37 clinically validated biomarkers and the infectious disease score with the future onset of severe COVID-19 (defined as PCR-confirmed positive inpatient diagnosis). Many of the individual biomarkers had significant associations (p-value<0.001) with increased risk for severe COVID-19. These biomarkers for susceptibility to severe COVID-19 include lower levels of omega-3 omega-6 fatty acids as well as albumin, and higher levels of GlycA. We observed a high concordance in the overall pattern of COVID-19 biomarker associations with severe pneumonia (Figure 2A), with a Spearman correlation of 0.89 between the overall biomarker association signatures for severe pneumonia and severe COVID-19 (Figure 7).

Figure 6

Download asset Open asset

Figure 6—source data 1

Numerical tabulation of odds ratios, betas, standard errors, and p-values for results shown in Figure 6.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig6-data1-v2.csv

Download elife-63033-fig6-data1-v2.csv

Figure 7

Download asset Open asset

Figure 7—source data 1

Numerical tabulation of odds ratios, and 95% confidence intervals for results shown in Figure 7.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig7-data1-v2.csv

Download elife-63033-fig7-data1-v2.csv

The multi-biomarker infectious disease score derived for the future onset of severe pneumonia was also robustly associated with the future onset of severe COVID-19. The odds ratio was 1.40 per 1-SD increment and 2.90 for comparing individuals in the highest quintile of the multi-biomarker infectious disease score to those in the lowest quintile. This magnitude of association with susceptibility to severe COVID-19 was similar to that observed with severe pneumonia events occurring during the interval of 7–11 years after the blood sampling.

We further examined the association of the multi-biomarker infectious disease score with severe COVID-19 after adjustment or exclusion for prevalent diseases, and conducted stratified analyses for age and sex (Figure 8). The association with severe COVID-19 was attenuated, but remained significant when adjusted for BMI, smoking and prevalent diseases (panel 7A). The association magnitudes were approximately 20% weaker when limiting the COVID-19 analyses to individuals without prevalent diseases at time of blood sampling (panel 7B). There was no robust evidence of differences in association magnitude according to age (panel 7C) and odd ratios were broadly similar for men and women (panel 7D).

Figure 8

Download asset Open asset

Figure 8—source data 1

Numerical tabulation of odds ratios, betas, standard errors, and p-values for results shown in Figure 8.

https://cdn.elifesciences.org/articles/63033/elife-63033-fig8-data1-v2.csv

Download elife-63033-fig8-data1-v2.csv

Finally, we examined the technical repeatability and biological stability of measuring the multi-biomarker infectious disease score. The measurement repeatability was high (Pearson correlation 0.94 in blind duplicate samples; Figure 9A). Even though the blood samples were primarily non-fasting, the levels of the infectious disease score remained broadly stable during 4 years based on blood samples from repeat visits (Pearson correlation 0.61 between baseline and repeat visit measurements; Figure 9B).

Figure 9

Download asset Open asset

Most biomarker studies on COVID–19 have focused on characterising already infected patients and their disease prognosis (Kermali et al., 2020; Shen et al., 2020; Messner et al., 2020; Dierckx et al., 2020). In contrast, in the largest blood metabolic profiling study to date, we explored biomarker associations for susceptibility to severe pneumonia and COVID-19 in general population settings. We developed a multi-biomarker score for increased susceptibility to a severe infectious disease course, and demonstrated that this biomarker score captures an increased risk for COVID-19 hospitalisation a decade after the blood sampling.

The overall signature of biomarker associations was similar for the susceptibility to severe COVID-19 and to severe pneumonia (Figure 7). The proportions of individuals with existing cardiometabolic diseases were also consistent for both of these infectious diseases (Table 1). We used these observations of a shared risk factor basis to draw an analogy between susceptibility to severe pneumonia and severe COVID-19, and hereby infer potential implications for preventative screening. We therefore exploited the strong statistical power and time-resolved information on severe pneumonia events for more detailed analyses than was feasible with COVID-19. This led to three important observations. First, the infectious disease multi-biomarker score was largely independent of prevalent chronic respiratory and cardiometabolic diseases (Figure 3). Second, the susceptibility to severe pneumonia was drastically elevated in the extreme tail of the multi-biomarker infectious disease score, with 5–10 times higher risk compared to individuals with normal levels of the multi-biomarker score (Figure 5). Such features might aid in establishing thresholds for identifying individuals most susceptible to a severe disease course. Third, the odds ratio of the multi-biomarker score for severe pneumonia events occurring after 7–11 years closely matched that of severe COVID-19, for which all events occurred over decade after blood sampling (Figure 4A). Yet, screening for the susceptibility to severe COVID-19 would require a strong association with the short-term risk. When confining the analyses of severe pneumonia to events occurring within the first 2 years after blood sampling, the short-term risk elevation was over four times stronger than that observed for long-term risk — individuals with high levels of the multi-biomarker score were almost 7-times more susceptible than people with low levels (Figure 4B). If similar enhancement in short-term risk extend to COVID-19, our results could potentially indicate applications for identification of individuals at high susceptibility to a severe COVID-19 disease course. However, the unavailability of metabolic biomarker data from blood samples drawn shortly prior to the pandemic prevents us from examining biomarker associations with short-term COVID-19 susceptibility, and our results should therefore be considered of hypothesis generating nature.

We observed multiple blood biomarkers commonly linked with the risk for cardiovascular disease and diabetes (Soininen et al., 2015; Würtz et al., 2017; Holmes et al., 2018; Ahola-Olli et al., 2019) to also be associated with increased susceptibility to both severe pneumonia and severe COVID-19. The biomarkers span multiple metabolic pathways, including low concentrations of lipoprotein lipids, impaired fatty acid balance, decreased amino acid levels and high chronic inflammation. This is the first study to show that many of these blood biomarkers associate with susceptibility to severe infections, potentially indicating that fatty acids and amino acids should not be considered only as biomarkers for cardiometabolic risk. The associations of omega-3 and other fatty acids with the risk for severe COVID-19 may be particularly important, as these measures are more directly modifiable by lifestyle means than common markers of inflammation. The overall pattern of biomarker associations followed a characteristic metabolic signature reflective of an increased susceptibility to a severe infectious disease. This pattern of biomarker associations is broadly similar to what has previously been reported with the risk for all-cause mortality in smaller prospective cohort studies (Deelen et al., 2019). It is therefore unlikely that the identified biomarker signature is specific to the risk for severe pneumonia and COVID-19, or even specific to infectious diseases in general. We propose that the overall metabolic biomarker perturbations observed here reflect molecular signals of low-grade inflammation that exacerbate disease severity, in case of both infectious and chronic diseases (Akbar and Gilroy, 2020; Bonafè et al., 2020). In line with this, prior studies have demonstrated that elevated levels of GlycA, the biomarker with the strongest weight in the infectious disease score, is associated with increased neutrophil activity and the long-term risk for fatal infections (Ritchie et al., 2015). Such over-activity of immune response from pneumonia or COVID-19 infection is known to cause tissue damage and organ dysfunction through cytokine storm, a common complication of severe COVID-19 (Mangalmurti and Hunter, 2020). While the specific biological mechanisms underpinning the blood metabolic biomarker associations with chronic and infectious diseases remain poorly understood, we emphasize that the observational character of our study does not allow us to conclude whether the biomarkers are contributing causally to increase the risk or are merely indirect risk markers.

Replication of novel biomarker associations is a key aspect in observational studies. We are not aware of other prospective studies with sufficient COVID-19 hospitalisation events and NMR-based metabolic biomarker data to address this. However, a preprint of the present study featured analysis of 195 severe COVID-19 cases, based on data available in UK Biobank back in June 2020 (Julkunen et al., 2020). In the present updated analyses, with over three times the number of cases, all biomarker associations with susceptibility to severe COVID-19 were similar or stronger, and hereby provide a within-cohort replication of our initial findings. In addition, a recent study used the same metabolic biomarker panel in three cohorts of hospitalised patients and observed similar overall biomarker perturbations to be predictive of COVID-19 severity (Dierckx et al., 2020). The study also reported the multi-biomarker infectious disease score to be among the strongest biomarkers for discriminating COVID-19 severity among already hospitalised patients.

Our study has both strengths and limitations. Strengths include the large sample size, which enabled the analysis of biomarkers for susceptibility to severe COVID-19 based on pre-pandemic blood samples from general population settings. We used a validated metabolic profiling platform that enables simultaneous quantification of numerous metabolic biomarkers in a scalable low-cost setup. Although the number of hospitalised COVID-19 cases was in line with the prevalence in England, we acknowledge that the statistical power was limited for prediction analyses even with close to 100,000 samples linked with COVID-19 outcome data. Furthermore, the UK Biobank study participants are not fully representative of the UK population by demographic characteristics; the individuals were enrolled on a volunteer basis and are therefore more representative of healthier individuals than average (Sudlow et al., 2015; Fry et al., 2017). Even though this is generally not a concern for investigating risk associations (Keyes and Westreich, 2019), it does limit the statistical power to explore effects of ethnicity and old age. Other limitations include the decade long duration from blood sampling to the COVID-19 pandemic. While this limits inference on how well the biomarkers predict short-term risk for severe COVID-19, our analogy with long-term risk for severe pneumonia indicates that the time lag likely attenuates the biomarker association magnitudes substantially. Conversely, the remarkably strong associations for short-term risk of severe pneumonia led us to speculate that similar enhancements in association magnitudes could also hold for severe COVID-19. However, this inference should be further tested, in particular in the light of the bacterial origin of many severe pneumonia cases and the viral origin of COVID-19. Weaker biomarker associations for severe COVID-19 compared to severe pneumonia may also arise from the UK Biobank COVID-19 data being influenced by ascertainment bias in terms of differential healthcare seeking and differential testing (Griffith et al., 2020), whereas pneumonia is anticipated to have nearly complete case ascertainment (Ho et al., 2020).

In conclusion, a metabolic signature of perturbed blood biomarkers is associated with an increased susceptibility to both severe pneumonia and COVID-19 in blood samples collected a decade before the pandemic. The multi-biomarker score captures an elevated susceptibility to severe pneumonia within few years after blood sampling that is several times stronger than the risk elevation associated with many pre-existing health conditions, such as obesity and diabetes (Ho et al., 2020). If the three- to fourfold elevation in short-term risk compared to long-term risk of severe pneumonia also applies to severe COVID-19, then the metabolic biomarker profiling could potentially complement existing tools for identifying individuals most susceptible to a severe COVID-19 disease course. Regardless of the translational prospects, these results provide novel understanding on how metabolic biomarkers may reflect the susceptibility of severe COVID-19 and other infections.

The data are available for approved researchers from UK Biobank. The metabolic biomarker data has been released to the UK Biobank resource in March 2021.

1. 1. Cichońska A
   2. Julkunen H
   3. Würtz P

   (2021) Biobank

   ID 220. UK Biobank Nightingale biomarker data.

   https://biobank.ndph.ox.ac.uk/showcase/label.cgi?id=220

1. 1. Ahola-Olli AV
   2. Mustelin L
   3. Kalimeri M
   4. Kettunen J
   5. Jokelainen J
   6. Auvinen J
   7. Puukka K
   8. Havulinna AS
   9. Lehtimäki T
   10. Kähönen M
   11. Juonala M
   12. Keinänen-Kiukaanniemi S
   13. Salomaa V
   14. Perola M
   15. Järvelin MR
   16. Ala-Korpela M
   17. Raitakari O
   18. Würtz P

   (2019) Circulating metabolites and the risk of type 2 diabetes: a prospective study of 11,896 young adults from four finnish cohorts

   Diabetologia 62:2298–2309.

   https://doi.org/10.1007/s00125-019-05001-w

   • PubMed
   • Google Scholar
2. 1. Akbar AN
   2. Gilroy DW

   (2020) Aging immunity may exacerbate COVID-19

   Science 369:256–257.

   https://doi.org/10.1126/science.abb0762

   • PubMed
   • Google Scholar
3. 1. Almirall J
   2. Serra-Prat M
   3. Bolíbar I
   4. Balasso V

   (2017) Risk factors for Community-Acquired pneumonia in adults: a systematic review of observational studies

   Respiration 94:299–311.

   https://doi.org/10.1159/000479089

   • PubMed
   • Google Scholar
4. 1. Atkins JL
   2. Masoli JAH
   3. Delgado J
   4. Pilling LC
   5. Kuo C-L
   6. Kuchel GA
   7. Melzer D

   (2020) Preexisting comorbidities predicting COVID-19 and mortality in the UK biobank community cohort

   The Journals of Gerontology: Series A 75:2224–2230.

   https://doi.org/10.1093/gerona/glaa183

   • Google Scholar
5. 1. Bonafè M
   2. Prattichizzo F
   3. Giuliani A
   4. Storci G
   5. Sabbatinelli J
   6. Olivieri F

   (2020) Inflamm-aging: why older men are the most susceptible to SARS-CoV-2 complicated outcomes

   Cytokine & Growth Factor Reviews 53:33–37.

   https://doi.org/10.1016/j.cytogfr.2020.04.005

   • PubMed
   • Google Scholar
6. 1. Deelen J
   2. Kettunen J
   3. Fischer K
   4. van der Spek A
   5. Trompet S
   6. Kastenmüller G
   7. Boyd A
   8. Zierer J
   9. van den Akker EB
   10. Ala-Korpela M
   11. Amin N
   12. Demirkan A
   13. Ghanbari M
   14. van Heemst D
   15. Ikram MA
   16. van Klinken JB
   17. Mooijaart SP
   18. Peters A
   19. Salomaa V
   20. Sattar N
   21. Spector TD
   22. Tiemeier H
   23. Verhoeven A
   24. Waldenberger M
   25. Würtz P
   26. Davey Smith G
   27. Metspalu A
   28. Perola M
   29. Menni C
   30. Geleijnse JM
   31. Drenos F
   32. Beekman M
   33. Jukema JW
   34. van Duijn CM
   35. Slagboom PE

   (2019) A metabolic profile of all-cause mortality risk identified in an observational study of 44,168 individuals

   Nature Communications 10:3346.

   https://doi.org/10.1038/s41467-019-11311-9

   • PubMed
   • Google Scholar
7. Preprint

   1. Dierckx T
   2. van Elslande J
   3. Salmela H

   (2020) The metabolic fingerprint of COVID-19 severity

   medRxiv.

   https://doi.org/10.1101/2020.11.09.20228221

   • Google Scholar
8. 1. Fry A
   2. Littlejohns TJ
   3. Sudlow C
   4. Doherty N
   5. Adamska L
   6. Sprosen T
   7. Collins R
   8. Allen NE

   (2017) Comparison of Sociodemographic and Health-Related characteristics of UK biobank participants with those of the general population

   American Journal of Epidemiology 186:1026–1034.

   https://doi.org/10.1093/aje/kwx246

   • PubMed
   • Google Scholar
9. 1. Griffith GJ
   2. Morris TT
   3. Tudball MJ
   4. Herbert A
   5. Mancano G
   6. Pike L
   7. Sharp GC
   8. Sterne J
   9. Palmer TM
   10. Davey Smith G
   11. Tilling K
   12. Zuccolo L
   13. Davies NM
   14. Hemani G

   (2020) Collider Bias undermines our understanding of COVID-19 disease risk and severity

   Nature Communications 11:5749.

   https://doi.org/10.1038/s41467-020-19478-2

   • PubMed
   • Google Scholar
10. 1. Ho FK
    2. Celis-Morales CA
    3. Gray SR
    4. Katikireddi SV
    5. Niedzwiedz CL
    6. Hastie C
    7. Ferguson LD
    8. Berry C
    9. Mackay DF
    10. Gill JM
    11. Pell JP
    12. Sattar N
    13. Welsh P

    (2020) Modifiable and non-modifiable risk factors for COVID-19, and comparison to risk factors for influenza and pneumonia: results from a UK biobank prospective cohort study

    BMJ Open 10:e040402.

    https://doi.org/10.1136/bmjopen-2020-040402

    • PubMed
    • Google Scholar
11. 1. Holmes MV
    2. Millwood IY
    3. Kartsonaki C
    4. Hill MR
    5. Bennett DA
    6. Boxall R
    7. Guo Y
    8. Xu X
    9. Bian Z
    10. Hu R
    11. Walters RG
    12. Chen J
    13. Ala-Korpela M
    14. Parish S
    15. Clarke RJ
    16. Peto R
    17. Collins R
    18. Li L
    19. Chen Z
    20. China Kadoorie Biobank Collaborative Group

    (2018) Lipids, lipoproteins, and metabolites and Risk of Myocardial Infarction and Stroke

    Journal of the American College of Cardiology 71:620–632.

    https://doi.org/10.1016/j.jacc.2017.12.006

    • PubMed
    • Google Scholar
12. Preprint

    1. Julkunen H
    2. Cichońska A
    3. Nightingale Health UK Biobank Initiative

    (2020) Blood biomarker score identifies individuals at high risk for severe COVID-19 a decade prior to diagnosis: metabolic profiling of 105,000 adults in the UK biobank

    medRxiv.

    https://doi.org/10.1101/2020.07.02.20143685

    • Google Scholar
13. 1. Kermali M
    2. Khalsa RK
    3. Pillai K
    4. Ismail Z
    5. Harky A

    (2020) The role of biomarkers in diagnosis of COVID-19 - A systematic review

    Life Sciences 254:117788.

    https://doi.org/10.1016/j.lfs.2020.117788

    • PubMed
    • Google Scholar
14. 1. Keyes KM
    2. Westreich D

    (2019) UK Biobank, big data, and the consequences of non-representativeness

    The Lancet 393:1297.

    https://doi.org/10.1016/S0140-6736(18)33067-8

    • Google Scholar
15. 1. Khera AV
    2. Chaffin M
    3. Aragam KG
    4. Haas ME
    5. Roselli C
    6. Choi SH
    7. Natarajan P
    8. Lander ES
    9. Lubitz SA
    10. Ellinor PT
    11. Kathiresan S

    (2018) Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations

    Nature Genetics 50:1219–1224.

    https://doi.org/10.1038/s41588-018-0183-z

    • PubMed
    • Google Scholar
16. 1. Mangalmurti N
    2. Hunter CA

    (2020) Cytokine storms: understanding COVID-19

    Immunity 53:19–25.

    https://doi.org/10.1016/j.immuni.2020.06.017

    • PubMed
    • Google Scholar
17. 1. Messner CB
    2. Demichev V
    3. Wendisch D
    4. Michalick L
    5. White M
    6. Freiwald A
    7. Textoris-Taube K
    8. Vernardis SI
    9. Egger AS
    10. Kreidl M
    11. Ludwig D
    12. Kilian C
    13. Agostini F
    14. Zelezniak A
    15. Thibeault C
    16. Pfeiffer M
    17. Hippenstiel S
    18. Hocke A
    19. von Kalle C
    20. Campbell A
    21. Hayward C
    22. Porteous DJ
    23. Marioni RE
    24. Langenberg C
    25. Lilley KS
    26. Kuebler WM
    27. Mülleder M
    28. Drosten C
    29. Suttorp N
    30. Witzenrath M
    31. Kurth F
    32. Sander LE
    33. Ralser M

    (2020) Ultra-High-Throughput clinical proteomics reveals classifiers of COVID-19 infection

    Cell Systems 11:11–24.

    https://doi.org/10.1016/j.cels.2020.05.012

    • PubMed
    • Google Scholar
18. Website

    1. Resource UKBiobankD

    (2020) COVID-19 test results data

    Accessed February 3, 2021.

    http://biobank.ctsu.ox.ac.uk/crystal/exinfo.cgi?src=COVID19_tests;
19. 1. Ritchie SC
    2. Würtz P
    3. Nath AP
    4. Abraham G
    5. Havulinna AS
    6. Fearnley LG
    7. Sarin AP
    8. Kangas AJ
    9. Soininen P
    10. Aalto K
    11. Seppälä I
    12. Raitoharju E
    13. Salmi M
    14. Maksimow M
    15. Männistö S
    16. Kähönen M
    17. Juonala M
    18. Ripatti S
    19. Lehtimäki T
    20. Jalkanen S
    21. Perola M
    22. Raitakari O
    23. Salomaa V
    24. Ala-Korpela M
    25. Kettunen J
    26. Inouye M

    (2015) The biomarker GlycA is associated with chronic inflammation and predicts Long-Term risk of severe infection

    Cell Systems 1:293–301.

    https://doi.org/10.1016/j.cels.2015.09.007

    • PubMed
    • Google Scholar
20. 1. Shen B
    2. Yi X
    3. Sun Y
    4. Bi X
    5. Du J
    6. Zhang C
    7. Quan S
    8. Zhang F
    9. Sun R
    10. Qian L
    11. Ge W
    12. Liu W
    13. Liang S
    14. Chen H
    15. Zhang Y
    16. Li J
    17. Xu J
    18. He Z
    19. Chen B
    20. Wang J
    21. Yan H
    22. Zheng Y
    23. Wang D
    24. Zhu J
    25. Kong Z
    26. Kang Z
    27. Liang X
    28. Ding X
    29. Ruan G
    30. Xiang N
    31. Cai X
    32. Gao H
    33. Li L
    34. Li S
    35. Xiao Q
    36. Lu T
    37. Zhu Y
    38. Liu H
    39. Chen H
    40. Guo T

    (2020) Proteomic and metabolomic characterization of COVID-19 patient sera

    Cell 182:59–72.

    https://doi.org/10.1016/j.cell.2020.05.032

    • Google Scholar
21. 1. Soininen P
    2. Kangas AJ
    3. Würtz P
    4. Suna T
    5. Ala-Korpela M

    (2015) Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

    Circulation: Cardiovascular Genetics 8:192–206.

    https://doi.org/10.1161/CIRCGENETICS.114.000216

    • PubMed
    • Google Scholar
22. 1. Sudlow C
    2. Gallacher J
    3. Allen N
    4. Beral V
    5. Burton P
    6. Danesh J
    7. Downey P
    8. Elliott P
    9. Green J
    10. Landray M
    11. Liu B
    12. Matthews P
    13. Ong G
    14. Pell J
    15. Silman A
    16. Young A
    17. Sprosen T
    18. Peakman T
    19. Collins R

    (2015) UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age

    PLOS Medicine 12:e1001779.

    https://doi.org/10.1371/journal.pmed.1001779

    • PubMed
    • Google Scholar
23. 1. Williamson EJ
    2. Walker AJ
    3. Bhaskaran K

    (2020) OpenSAFELY: factors associated with COVID-19 death in 17 million patients

    Nature 584:430–436.

    https://doi.org/10.1038/s41586-020-2521-4

    • Google Scholar
24. 1. Würtz P
    2. Kangas AJ
    3. Soininen P
    4. Lawlor DA
    5. Davey Smith G
    6. Ala-Korpela M

    (2017) Quantitative Serum Nuclear Magnetic Resonance Metabolomics in Large-Scale Epidemiology: A Primer on -Omic Technologies

    American Journal of Epidemiology 186:1084–1096.

    https://doi.org/10.1093/aje/kwx016

    • PubMed
    • Google Scholar
25. 1. Zhou F
    2. Yu T
    3. Du R
    4. Fan G
    5. Liu Y
    6. Liu Z
    7. Xiang J
    8. Wang Y
    9. Song B
    10. Gu X
    11. Guan L
    12. Wei Y
    13. Li H
    14. Wu X
    15. Xu J
    16. Tu S
    17. Zhang Y
    18. Chen H
    19. Cao B

    (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

    The Lancet 395:1054–1062.

    https://doi.org/10.1016/S0140-6736(20)30566-3

    • PubMed
    • Google Scholar

Author details

1. Heli Julkunen

   Nightingale Health Plc, Helsinki, Finland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   HJ is employee and holds stock options with Nightingale Health Plc.

   "This ORCID iD identifies the author of this article:" 0000-0002-4282-0248

2. Anna Cichońska

   Nightingale Health Plc, Helsinki, Finland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Investigation, Visualization, Methodology, Writing - original draft, Project administration

   Competing interests

   AC is employee and holds stock options with Nightingale Health Plc.

3. P Eline Slagboom

   1. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands
   2. Max Planck Institute for Biology of Ageing, Cologne, Germany

   Contribution

   Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

4. Peter Würtz

   Nightingale Health Plc, Helsinki, Finland

   Contribution

   Conceptualization, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   For correspondence

   peter.wurtz@nightingalehealth.com

   Competing interests

   PW is employee and shareholder of Nightingale Health Plc.

   "This ORCID iD identifies the author of this article:" 0000-0002-5832-0221

5. Nightingale Health UK Biobank Initiative

   Nightingale Health Plc, Helsinki, Finland

• 5,199

  views

• 489

  downloads

• 132

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.