Abstract
Blood is critical for health, supporting key functions like immunity and oxygen transport. While studies have found links between common blood clinical indicators and COVID-19, they cannot provide causal inference due to residual confounding and reverse causality. To identify indicators affecting COVID-19, we analyzed clinical data (n = 2,293, aged 18–65 years) from Guangzhou Medical University’s first affiliated hospital (2022-present), identifying 34 significant indicators differentiating COVID-19 patients from healthy controls. Utilizing bidirectional Mendelian randomization analyses, integrating data from over 2.46 million participants from various large-scale studies, we established causal links for six blood indicators with COVID-19 risk, five of which is consistent with our observational findings. Specifically, elevated Troponin I and Platelet Distribution Width levels are linked with increased COVID-19 susceptibility, whereas higher Hematocrit, Hemoglobin, and Neutrophil counts confer a protective effect. Reverse MR analysis confirmed four blood biomarkers influenced by COVID-19, aligning with our observational data for three of them. Notably, COVID-19 exhibited a positive causal relationship with Troponin I (Tnl) and Serum Amyloid Protein A, while a negative association was observed with Plateletcrit. These findings may help identify high-risk individuals and provide further direction on the management of COVID‐19.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12920-024-01844-4.
Keywords: Body fluids, COVID-19, Clinical indicators, Mendelian randomization
Introduction
COVID-19, a complicated multi-system syndrome caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first emerged in December 2019 and was declared a global pandemic by the World Health Organization (WHO) in March 2020 [1, 2]. Despite isolation measures, the COVID-19 pandemic has spread globally with over 772 million confirmed cumulative cases, and more than 6.9 million cumulative deaths, as reported by the WHO on December 17, 2023. The constant emergence of SARS-CoV-2 variants and the ongoing outbreak of COVID-19 have already consumed a large number of medical resources, leading to a considerable global healthcare crisis [2, 3]. Thus, identifying the causal factors of COVID-19 is crucial for reducing its disease burden. In particular, a deeper awareness of the causality and magnitude of the effects of different clinical risk factors may help identify high-risk individuals and provide further direction on the mechanisms of COVID-19.
Blood is critical for human health, underpinning various physiological processes such as immune response, oxygen transport, and maintenance of homeostasis, which when impaired cause a considerable health burden [4, 5]. Recently, several observational studies have increasingly revealed the correlation between COVID-19 and the commonly used clinical indicators of blood testing [6–8]. Nevertheless, no causal inference can be made from these results due to unmeasured confounding and reverse causality in observational studies [9, 10]. Mendelian randomization (MR) can help fill this gap as alleles are randomly assigned and allelic randomization antedates the onset of disease [10]. As a useful alternative to randomized control trials, MR is widely used to infer the causal nature between the exposure and the outcome in recent years [9–11]. Importantly, bidirectional MR allows for the evaluation of reverse causality, determining whether changes in blood markers are a consequence of COVID-19, thereby providing a comprehensive causal framework.
In the current study, we first collected clinical data on 58 common blood indicators from both healthy individuals and COVID-19 patients, covering a range of indicators impacting human health. We then sought to identify these indicators affecting COVID-19. Subsequently, we obtained the summary-level statistical data for these candidate blood clinical factors from large-scale GWAS and sought to infer potential causal associations of these candidate indicators on COVID‐19 using large‐sample statistical data sets of three COVID‐19 subtypes (COVID-19 infection, hospitalized COVID‐19, and severe COVID‐19). Complementing this, bidirectional MR analyses were conducted to discern not only the effect of blood indicators on COVID-19 risk but also the potential influence of COVID-19 on the levels of these blood indicators, thus providing a thorough exploration of their interplay.
Methods
Study design
We retrospectively collected clinical data from both healthy individuals and COVID-19 patients at the First Affiliated Hospital of Guangzhou Medical University to identify blood clinical indicators affecting COVID-19. Summary-level data for 58 blood indicators identified by the observational study were obtained from the large-scale Genome-wide association studies. Two-sample Mendelian randomization (MR) analyses were applied to infer the causal nature of host genetic factors on COVID‐19 risk by exploiting single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) of exposure [10, 12]. All MR analyses should meet the following three assumptions. First, the selected IVs were robustly associated with the exposure. Second, the used IVs should not be related to potential confounders. Third, the IVs only affect the outcome only through the exposure. MR analysis was excluded if pleiotropy was detected. Furthermore, bidirectional MR was incorporated to assess the potential reciprocal causal relationships between COVID-19 and the blood indicators, providing a comprehensive analysis of directionality in these associations. The workflow of the study design is presented in Fig. 1.
Fig. 1.
The workflow of designed analysis. MR, Mendelian randomization; COVID-19, coronavirus disease 2019; SNP, single nucleotide polymorphism
Human cohort information
To identify the candidate risk factors of COVID-19, we retrospectively collected clinical data on 58 common blood indicators by reviewing the patients' electronic health records from the first affiliated hospital of Guangzhou medical university from January 2022 to the present. These indicators were divided into five major groups: hematological traits, hepatic and renal function markers, myocardial injury markers, metabolic indexes, and inflammatory parameters. Patients aged 18–65 years with a diagnosis of COVID-19 by reverse transcription-polymerase chain reaction (PCR) were defined as the COVID-19 group. Healthy adults (aged 18–65 years) who tested negative for COVID-19 and attended a medical checkup were defined as the healthy control group. Participants who had missing information were eliminated from the study and the final analytic sample consisted of 1,325 healthy individuals and 901 COVID-19 patients who had provided informed consent to participate in this study. The summary of the demographic information of participants is listed in Table S1. Given the retrospective nature of our research, the need for ethical approval and the informed consent statement was waived by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (China).
Genome‐wide association study (GWAS) summary datasets preparation
All GWAS summary statistics were obtained from IEU Open GWAS database. The exposures were obtained from publicly available GWAS summary results (https://gwas.mrcieu.ac.uk/datasets/), including those on (COVID-19 infection, 38,984 cases, and 1,644,784 controls), hospitalized COVID‐19 (9,986 cases and 1,887,658 controls), and severe COVID‐19 (5,101 cases and 1,383,342 controls) [13]. Where feasible, independent samples were utilized to mitigate the risk of sample overlap between the COVID-19 dataset and the UK Biobank GWAS dataset. Despite these precautions, we acknowledge that complete exclusion of sample overlap cannot be guaranteed due to the shared nature of some data sources. The summary-level statistical data for white blood cell count (WBC), basophil cell count (BASO), lymphocyte cell count (LYM), eosinophil cell count (EOS), plateletcrit and hematocrit were downloaded from the Blood Cell Consortium [4, 14]. The summary-level statistical data for neutrophil count (NEU), red blood cell count (RBC), hemoglobin concentration (HGB), mean platelet volume (MPV), platelet distribution width (PDW), alkaline phosphatase (ALP), albumin, total bilirubin (Tbil), HDL cholesterol (HDLC), total cholesterol (TC), apolipoprotein A-I (ApoA-I), c-reactive protein (CRP), and hemoglobin A1c (HbA1c) were obtained from UK Biobank GWAS [15, 16]. The summary-level statistical mixed-population data for glomerular filtration rate (GFR) was obtained from a large GWAS [17]. The summary-level statistical mixed-population data for troponin I (Tnl), D-dimer (DD), and Serum amyloid A-1 protein (SAA) were obtained from the INTERVAL study [18]. The summary-level statistical data for the levels of myoglobin [19] were obtained from large GWASs.
Genetic instrument selection
The candidate IVs were selected by a series of quality control steps. Instrumental SNPs meeting quality control criteria were identified using R software. Initially, SNPs were selected as IVs if they met the genome-wide significance threshold (P < 5 × 10^-8). However, for exposures where fewer than three SNPs met this threshold, we extended the criteria to include SNPs with a P-value < 5 × 10^-6 to ensure a sufficient number of instruments for robust Mendelian randomization analysis. Considering that SNPs which directly affect the outcome variable could violate the assumptions of the instrumental variable, any IVs not included in the outcome GWAS and those significantly associated with the outcome (P > 5 × 10–5) were removed [20]. Furthermore, the number of above-selected IVs selected from the outcome is not less than three. Subsequently, phenoscanner2 was used to evaluate whether any exposure-related IVs were associated with confounders of COVID-19. Palindromic and incompatible IVs were then removed by harmonization to ensure that the effect of these IVs on exposure corresponded to the same allele as the effect on the outcome. F statistic was applied to test whether there was a weak instrumental variable bias. The F-statistics were calculated by the formula of F = R2/ (1 − R2) *(n − k − 1)/ k (R2 = 2*MAF*(1-MAF) *Beta2; n, sample size; k, number of instrumental variables; and MAF, minor allele frequency). Moreover, the statistical power of MR analysis to detect causal association was calculated by mRnd [21].
Statistical analyses
The Chi-square test was applied to detect differences in categorical variables, which were reported by percentage (%). Continuous variables were compared using t-tests or non-parametric Wilcoxon rank sum tests after testing the normality of the distribution using the Shapiro–Wilk test. Mean ± standard deviation (SD) was applied to describe the normally distributed continuous variables, while the median and interquartile range (IQR) was expressed for these variables that did not meet the normality assumptions. To maintain the integrity of our statistical analysis and avoid the bias associated with listwise deletion, we implemented a mean imputation strategy for missing values. This method was selected based on its appropriateness for our data structure and the minimal impact it has on the distribution of observed data. The “TwoSampleMR” package based on R (Version 4.0.2) was used to conduct MR analysis. The conventional inverse-variance weighted (IVW) was deemed the most reliable model because it provides the most persuasive estimates when there is no evidence of directional pleiotropy [22–24]. Moreover, MR-Egger and weighted-median methods were implemented as sensitivity analysis approaches to ensure the robustness of the results [23, 24]. The MR-Egger test for directional pleiotropy and Cochran’s Q statistics were applied to identify whether significant heterogeneity or directional pleiotropy was present.
Results
Distinct blood indicators profiles in COVID patients compared to healthy controls
In our retrospectively analysis, we evaluated 1,325 healthy individuals and 852 COVID-19 patients from the first affiliated hospital of Guangzhou medical university (Table S1). The COVID-19 cohort was significantly older than the controls, with a balanced gender distribution (Table 1). We assessed 58 blood biomarkers, commonly used in clinical settings, for potential associations with COVID-19 (Fig. 2A, Table S1). Following FDR correction, we found significant disparities between the groups across a range of biomarkers, including counts of various blood cells (white, basophil, lymphocyte, eosinophil, neutrophil), red blood cell parameters (count, hemoglobin concentration, hematocrit), platelet metrics (mean volume, plateletcrit, distribution width), coagulation profiles (prothrombin time, activated partial thromboplastin time, thrombin time, fibrinogen, D-dimer), metabolic and organ function indicators (uric acid, urea nitrogen, alkaline phosphatase, albumin, bilirubins, bile acids, myocardial enzymes), lipid profiles (HDL cholesterol, total cholesterol, apolipoprotein A), glycemic control marker (hemoglobin A1c), and inflammation markers (C-reactive protein, serum amyloid protein A) (Table 2, Fig. 2B, C). These data revealed a broad spectrum of altered blood biomarkers in COVID-19 patients compared to healthy controls.
Table 1.
Baseline characteristics of participants
| Healthy individuals | COVID-19 patients | P | |
|---|---|---|---|
| N = 1390 | N = 852 | ||
| Age[mean(SE)] | 37.65 ± 12.12 | 43.30 ± 13.94 | < 0.001* |
| Gender(%) | 0.22 | ||
| Male | 54.32% | 51.60% | |
| Female | 45.68% | 48.40% |
Normally distributed continuous variables are presented as means ± SDs
Categorical variables are expressed as numbers (percentages)
*Significantly different between healthy individuals and COVID-19 patients (p < 0.05)
Fig. 2.
Difference of the clinical blood indicators between COVID-19 patients and healthy individuals. A Summary of the commonly used clinical indicators of blood testing. B The geographical locations of the first affiliated hospital of Guangzhou medical university. C Log 2 the fold changes of clinical indicators between COVID-19 patients and healthy individuals. Bars in red represent up-regulated traits in COVID-19 patients compared to healthy individuals while bars in blue indicate down-regulated traits in COVID-19 patients compared to healthy individuals. The depth of color refers to the Log 10 expression of the p-value
Table 2.
Risk factors for COVID-19 patients from the first affiliated hospital of Guangzhou medical university
| Risk factors | Healthy individuals | COVID-19 patients | P | FDR |
|---|---|---|---|---|
| White blood cell count | 6.63 (5.51,8.04) | 5.95 (4.15,7.69) | 0.002* | 0.004142857* |
| Basophil count | 0.03 (0.00,0.05) | 0.02 (0.01,0.03) | < 0.001* | 0.002230769* |
| Monocyte cell count | 0.50 (0.40, 0.60) | 0.46 (0.34, 0.61) | 0.595 | 0.651132075 |
| Lymphocyte cell count | 2.10 (1.74,2.50) | 1.65 (1.10,2.10) | < 0.001* | 0.002230769* |
| Eosinophil cell count | 0.12 (0.10,0.22) | 0.08 (0.02,0.15) | < 0.001* | 0.002230769* |
| Neutrophil cell count | 3.70 (2.90,4.60) | 3.28 (2.30, 4.30) | < 0.001* | 0.002230769* |
| Red blood cell count | 4.81 (4.38,5.21) | 4.16 (3.59, 4.57) | < 0.001* | 0.002230769* |
| Hemoglobin concentration | 143.00 (130.00,153.00) | 133.00 (121.00,146.75) | < 0.001* | 0.002230769* |
| Hematocrit | 0.42 (0.39,0.45) | 0.39 (0.35,0.43) | < 0.001* | 0.002230769* |
| Mean corpuscular hemoglobin concentration | 341.00 (332.00,349.00) | 341.00 (336.00,346.00) | 0.911 | 0.926982456 |
| Mean corpuscular hemoglobin | 30.10 (28.90,31.20) | 29.80 (28.73,30.90) | 0.095 | 0.134390244 |
| Mean corpuscular volume | 88.00 (84.40,90.85) | 87.20 (84.40, 90.10) | 0.104 | 0.14027907 |
| Red cell distribution width | 13.00 (12.60,13.60) | 12.90 (12.50,13.50) | 0.04* | 0.064444444 |
| Mean platelet volume | 8.90 (8.10,9.73) | 8.40 (7.80,9.20) | < 0.001* | 0.002230769* |
| Plateletcrit | 0.22 (0.19,0.25) | 0.20 (0.16,0.24) | < 0.001* | 0.002230769* |
| Platelet count | 246.00 (209.00,287.50) | 236.00 (183.25,296.25) | 0.079 | 0.117487179 |
| Platelet distribution width | 16.40 (15.80,16.80) | 16.60 (16.20,17.10) | < 0.001* | 0.002230769* |
| Prothrombin time | 12.95 (12.50,13.60) | 13.35 (13.00,13.90) | 0.001* | 0.002230769* |
| Activated partial thromboplastin time | 35.50 (33.90,38.55) | 37.45 (35.15,40.88) | 0.004* | 0.007483871* |
| Thrombin time | 16.70 (16.35,17.45) | 18.10 (17.10,19.50) | < 0.001* | 0.002230769* |
| Fibrinogen | 3.05 (2.66,3.40) | 4.64 (3.48, 5.81) | < 0.001* | 0.002230769* |
| D-dimer | 236(172,804) | 594.00(366.00,1309.00) | < 0.001* | 0.002230769* |
| Serum cystatin C | 0.82 ± 0.14 | 1.33 ± 0.69 | 0.111 | 0.146318182 |
| Serum creatinine | 82.00 (70.18,92.43) | 79.70 (67.70, 94.10) | 0.467 | 0.531098039 |
| Uric acid | 381.40 (313.40,476.40) | 319.10 (264.03,390.45) | < 0.001* | 0.002230769* |
| Urea nitrogen | 5.00 (4.10,5.70) | 4.50 (3.70,5.90) | 0.008* | 0.0145* |
| β2-microglobulin | 1.69 (1.66,2.11) | 2.25 (1.86,3.21) | 0.114 | 0.146933333 |
| Alkaline phosphatase | 65.40 (53.40,79.10) | 58.70 (49.15,70.70) | 0.014* | 0.023882353* |
| Alanine aminotransferase | 20.80 (13.20,32.80) | 19.10 (12.40,29.15) | 0.077 | 0.117487179 |
| Aspartate aminotransferase | 22.50 (18.60,27.20) | 22.30 (19.10,29.70) | 0.299 | 0.361291667 |
| Gamma glutamyltransferase | 23.30 (14.90,39.83) | 23.00 (15.75,43.30) | 0.212 | 0.261617021 |
| Albumin | 44.10 (42.60,45.90) | 43.40 (37.10,46.65) | < 0.001* | 0.002230769* |
| Total bilirubin | 10.70 (8.80,13.70) | 13.10 (10.40,17.10) | < 0.001* | 0.002230769* |
| Direct bilirubin | 2.30 (1.70,3.10) | 2.00 (1.70,2.70) | 0.002* | 0.004142857* |
| Total bile acid | 4.30 (3.20,6.13) | 3.15 (1.87,4.93) | < 0.001* | 0.002230769* |
| Total protein | 75.60 (73.00,78.30) | 76.00 (68,45,80.25) | 0.573 | 0.639115385 |
| Myoglobin levels | 14.70 (11.50,21.00) | 18.80 (14.30,28.40) | 0.001* | 0.002230769* |
| Troponin I | 2.40 (1.60,3.60) | 3.50 (2.50,6.15) | < 0.001* | 0.002230769* |
| Creatine Kinase | 111.70 (79.60,153.70) | 78.85 (53.43,115.23) | < 0.001* | 0.002230769* |
| creatine kinase isoenzymes | 9.00 (7.00,12.00) | 9.00 (7.00,11.00) | 0.767 | 0.823814815 |
| Lactate dehydrogenase | 177.50 (152.80,199.55) | 205.00 (185.20,248.48) | < 0.001* | 0.002230769* |
| Hydroxybutyrate Dehydrogenase | 112.00 (96.65,128.90) | 135.00 (121.00,152.00) | < 0.001* | 0.002230769* |
| Glucose | 5.25 (4.93,5.68) | 5.31 (4.89,6.21) | 0.064 | 0.100324324 |
| HDL cholesterol | 1.24 (1.08,1.45) | 1.13 (0.96,1.33) | < 0.001* | 0.002230769* |
| LDL cholesterol | 3.41 (2.80,3.90) | 3.37 (2.89,3.85) | 0.986 | 0.986 |
| Total cholesterol | 5.33 (4.57,6.12) | 5.04 (4.44,5.67) | 0.014* | 0.023882353* |
| Triglyceride | 1.35 (0.90,2.14) | 1.36 (0.98,2.05) | 0.884 | 0.915571429 |
| Apolipoprotein A | 1.30 (1.16,1.50) | 0.74 (0.58,1.06) | < 0.001* | 0.002230769* |
| Apolipoprotein B | 0.90 ± 0.28 | 0.75 ± 0.35 | 0.45 | 0.522 |
| Hemoglobin A1a | 0.99 (0.89,1.10) | 1.09 (0.90,1.33) | 0.101 | 0.13947619 |
| Hemoglobin A1b | 0.94 (0.85,1.10) | 1.10 (0.86,1.20) | 0.156 | 0.196695652 |
| Hemoglobin A1c | 5.45 (5.14,5.80) | 5.96 (5.43,7.55) | 0.004* | 0.007483871* |
| C-Reactive protein | 0.32 (0.09,1.31) | 0.68 (0.10,6.38) | < 0.001* | 0.002230769* |
| Serum amyloid protein | 5.00 (5.00,6.21) | 93.83 (26.62,127.22) | 0.004* | 0.007483871* |
| Complement C3 | 0.88 ± 0.18 | 0.99 ± 0.20 | 0.34 | 0.40244898 |
| Complement C4 | 0.21 (0.15,0.27) | 0.29 (0.23,0.34) | 0.034* | 0.056342857 |
| Transferrin | 2.29 ± 0.33 | 1.71 ± 0.28 | 0.815 | 0.859454545 |
| Ceruloplasmin | 0.31 ± 0.03 | 0.34 ± 0.08 | 0.085 | 0.12325 |
Continuous variables with nonnormal data distribution are presented as medians (interquartile range)
Normally distributed continuous variables are presented as means ± SDs
*Significantly different between healthy individuals and COVID-19 patients (p < 0.05)
Genetic instrumentation and GWAS data for COVID-19 risk factors
To assess the causal nature of potential risk factors on COVID‐19, we sourced the summary-level statistics for 25 candidate markers from extensive GWASs. Post LD pruning, we identified 3 to 510 genetic instruments for blood indicators, all demonstrating strong genetic instruments (F-statistics > 10, Table 3). We targeted three COVID-19 subtypes—infection, hospitalization, and severity—as outcomes, reflecting the spectrum of SARS-CoV-2 infection impacts (Table 4). Detailed information on COVID-19 independent SNPs (after the clumping process) for candidate markers were listed in Tables S2- S6.
Table 3.
Characters of 28 candidate risk factors in European ancestry
| Category | Phenotype_name | Abbreviation | GWAS id | Sample Size | N_SNPs | F |
|---|---|---|---|---|---|---|
| Hematological traits | White blood cell (leukocyte) count | WBC | ieu-b-30 | 563,946 | 507 | 107.69 |
| Basophil cell count | BASO | ieu-b-29 | 563,946 | 201 | 109.61 | |
| Lymphocyte cell count | LYM | ieu-b-32 | 563,946 | 510 | 113.99 | |
| Eosinophil cell count | EOS | ieu-b-33 | 563,947 | 448 | 140.17 | |
| Neutrophil count | NEU | ukb-d-30140_irnt | 349,856 | 296 | 87.90 | |
| Red blood cell count | RBC | ukb-d-30010_irnt | 350,475 | 358 | 79.17 | |
| Hemoglobin concentration | HGB | ukb-d-30020_irnt | 350,474 | 301 | 56.93 | |
| Hematocrit | HCT | ebi-a-GCST90002304 | 562,259 | 463 | 101.22 | |
| Mean platelet (thrombocyte) volume | MPV | ukb-d-30100_irnt | 350,470 | 440 | 251.48 | |
| Plateletcrit | PCT | ebi-a-GCST004607 | 164,339 | 227 | 98.83 | |
| Platelet distribution width | PDW | ukb-d-30110_irnt | 350,470 | 350 | 177.40 | |
| D-dimer | DD | prot-a-1086 | 3,301 | 12 | 24.66 | |
| Hepatic and renal function markers | Alkaline phosphatase | ALP | ukb-d-30610_raw | 344292 | 252 | 48848.18 |
| Albumin | ALB | ukb-d-30600_raw | 315,268 | 201 | 525.67 | |
| Total bilirubin | Tbil | ukb-d-30840_raw | 342829 | 89 | 3594.81 | |
| Myocardial injury markers | Myoglobin levels | MB | ebi-a-GCST90012047 | 21,758 | 25 | 28.51 |
| Troponin I, cardiac muscle | Tnl | prot-a-3066 | 3,301 | 9 | 24.18 | |
| Creatine kinase B-type | CKB | prot-a-565 | 3,301 | 17 | 24.81 | |
| Creatine kinase M-type | CKM | prot-a-566 | 3,301 | 3 | 46.67 | |
| Metabolic indexes | HDL cholesterol | HDLC | ieu-b-109 | 403,943 | 362 | 112.33 |
| Total cholesterol | TC | met-d-Total_C | 115,078 | 64 | 130.66 | |
| apolipoprotein A-I | ApoA | ieu-b-107 | 393,193 | 299 | 112.57 | |
| Hemoglobin A1c | HbA1c | ukb-d-30750_raw | 344,182 | 190 | 4703.55 | |
| Inflammatory parameters | C-Reactive protein | CRP | ukb-d-30710_raw | 343,524 | 53 | 2842.86 |
| Serum amyloid A-1 protein | SAA | prot-a-2623 | 3301 | 15 | 139.63 |
Table 4.
Description of COVID-19 subtypes in European ancestry
| Trait_name | N_SNPs | N_case | N_control | Sample_size |
|---|---|---|---|---|
| COVID-19 infection | 7 | 38984 | 1644784 | 1683768 |
| hospitalized COVID‐19 | 5 | 9986 | 1887658 | 1897644 |
| Severe COVID‐19 | 8 | 5101 | 1383342 | 1388443 |
The MR analyses revealed the causal roles of blood indicators on COVID-19
Our MR analyses explored the causal imapct of 25 candidate factors on COVID‐19 outcomes (Tables S7- 9). Notably, genetic liability to higher basophil cell counts (BASO) was linked to a reduced risk of hospitalization due to COVID-19, with an odds ratio (OR) of 0.85 for each standard deviation (SD) increase in BASO (95% CI: 0.73–0.99, Figs. 3, 4, Table S8). Elevated Troponin I (Tnl) levels were associated with an increased risk of severe COVID-19, while higher BASO, hemoglobin concentration (HGB), and hematocrit (HCT) levels were associated with reduced risk (Figs. 3, 4). Specifically, ORs for severe COVID-19 were 1.21 for Tnl, 0.74 for BASO, 0.76 for HGB, and 0.83 for HCT per SD increase (95% CIs: 1.02–1.43, 0.60–0.93, 0.61–0.94, and 0.70–0.97, respectively; Table S9). Our MR analysis confirmed four blood biomarkers affecting COVID-19, aligning with our observational data for all of them (Fig. 5).
Fig. 3.
Overview of the associations of 28 candidate markers with three subtypes of COVID-19. IWV indicates an Inverse variance‐weighted method
Fig. 4.
The causal association of candidate blood indicators on COVID-19. Forest plot for causal effects of blood indicators on three subtypes of COVID-19 (COVID-19 infection, hospitalization, and severity).The red diamonds indicated higher odds of COVID-19 (P < 0.05), the blue diamonds indicated lower odds of COVID-19 (P < 0.05), and the grey diamonds indicated the odds of COVID-19 (P > 0.05)
Fig. 5.
Summary of the causal association between COVID-19 and blood indicators
The MR revealed the causal roles of COVID-19 on blood indicators
To obtain a deeper understanding of the potential causal mechanisms between COVID-19 and blood factors, we employed bidirectional MR analyses to test if there is a causal effect of disease on these biomarkers (Tables S10- 12). Genetic liability to higher creatine kinase B-type (CKB) and total bilirubin (Tbil) were linked to a incresed risk of COVID-19 infection, while higher platelet distribution width (PDW) and albumin were associated with reduced risk. Specifically, ORs for severe COVID-19 infection were 1.30 for CKB, 1.12 for Tbil, 0.93 for PDW, and 0.80 for albumin per SD increase (95% CIs: 1.01–1.67, 1.01–1.23, 0.87–0.99, and 0.66–0.97, respectively; Table S10). Notably, genetic liability to higher albumin was linked to a reduced risk of severe COVID-19, with an odds ratio (OR) of 0.96 for each SD increase in albumin (95% CI: 0.93–1.00, Table S12). Our reverse MR analysis confirmed four blood biomarkers influenced by COVID-19, aligning with our observational data for two of them (Fig. 5).
Notably, COVID-19 exhibited a positive causal relationship with Troponin I (Tnl) and Serum Amyloid Protein A, while a negative association was observed with Plateletcrit.
Discussion
According to our knowledge, our research is the first comprehensive inference of the causal nature between the common clinical blood indicators and COVID-19. We found significant variations in 36 blood indicators when comparing COVID-19 patients to healthy controls. Subsequent analysis of 25 candidate indicators from GWAS data, using MR-analytic methods, established causal links for 4 blood markers with COVID-19. Notably, all of these markers (Troponin I, hematocrit, hemoglobin concentration, and basophil cell count) corroborate our observational findings. Additionally, reverse MR analysis affirmed the influence of COVID-19 on four blood biomarkers, with two (albumin and total bilirubin) reflecting our observational data trends. These findings might provide novel insight into the pathophysiology of COVID-19 and may aid in the development of new diagnostic and therapeutic strategies for COVID-19.
Our study has confirmed Troponin I (TnI) as heritable causal risk factors for COVID-19. The linkage of myocardial injury markers like TnI with higher mortality rates in COVID-19 patients is well-documented, with myocardial troponin recognized as a critical prognostic tool for assessing COVID-19 severity and mortality, supported by prior research and clinical bulletins from leading cardiology associations [6, 25–28]. Moreover, COVID-19 appears to elevate levels of total bilirubin (Tbil), indicating potential pathways for disease impact beyond respiratory symptoms, including liver involvement. Studies have shown liver abnormalities in COVID-19 patients, with elevated Tbil being a significant indicator [29, 30]. It is noteworthy that elevated levels of Tbil may predict adverse outcomes in COVID-19 patients [31, 32], as it reflects the severity of liver damage, which is associated with an increased mortality rate among these patients [33]. Beyond its correlation with the progression and prognosis of COVID-19, TBIL has also been observed to relate to the incidence of ARDS and acute myocardial injury in affected patients [34], with higher TBIL levels corresponding to increased hs-cTnI levels, suggesting that elevated TBIL may be indicative of cardiac injury [35].
In our investigation, we identified basophil cell count, hematocrit, and hemoglobin concentration (HGB) as heritable causal protective factors of COVID-19. Additionally, our findings indicate that COVID-19 may lead to a decrease in albumin levels. Leukocytes are crucial for immune balance and defense against SARS-CoV-2, as evidenced by lower counts in COVID-19 patients compared to controls, which is consistent with multiple reports highlighting their prognostic value [36–40]. Our MR analysis specifically underscores the protective role of basophil cell, suggesting their monitoring could be pivotal in managing COVID-19. Moreover, we observed that anemia, marked by reduced hematocrit and HGB levels, may contribute to the susceptibility to COVID-19—a finding echoed by recent studies indicating anemia as a prevalent condition in COVID-19 patients [39, 41, 42]. Anemia can be a manifestation of malnutrition, which, in turn, may lead to decreased levels of albumin [43, 44]. HGB has been regarded as a more sensitive indicator of anemia than hematocrit. Nevertheless, recent studies show conflicting results on the association between HGB and COVID-19 [39]. Some papers observed similar HGB in deceased patients and those who survived COVID-19 [45], or in ICU patients and those who had been not admitted to ICU [46], whereas others reported lower HGB levels in COVID-19 patients [39, 47]. These findings suggest that our study results may inform the development of targeted therapeutic interventions that target these blood indicators to reduce the risk of COVID-19.
Our study's strength lies in integrating observational data with Mendelian randomization (MR) analyses, which mutually reinforce the robustness of our findings. The observational study provided a broad scope, while MR analyses reduced confounding and reverse causation, although the possibility of false negatives remains. The utilization of electronic health records facilitated the retrospective collection of extensive data, enhancing our analysis with a substantial sample size. Additionally, employing summary-level GWAS data strengthened the causality inference through genetic instruments. Nevertheless, there are also several limitations to consider when interpreting our results. The study's reliance on European and Chinese data may not be representative of other ethnicities, potentially limiting the broader applicability of our findings. Furthermore, We also did not apply a range of MR methods, such as MR-PRESSO, which may have left certain pleiotropic effects unaddressed, thus impacting our conclusions. Furthermore, the potential for sample overlap in the large-scale genomic data used, including between COVID-19 datasets and the UK Biobank, is acknowledged as a challenge that could introduce analytical bias. The issue of pleiotropy, with single genetic variants influencing multiple traits, also remains a concern for the validity of our results.
While our findings shed light on the potential links between blood indicators and COVID-19, we recognize the limitation of not conducting a multivariable Mendelian randomization analysis to assess each indicator's independent effect. Acknowledging this, we suggest that future studies incorporate multivariable approaches to more definitively determine the relationships observed. Moreover, in cases where genetic instruments could not be identified or were not sufficiently reliable, we chose not to proceed with MR analysis to avoid potential biases and misinterpretations. Additionally, examination of the biological pathways linking blood indicators to COVID-19 risk would provide a deeper understanding of the mechanisms underlying these associations. This could involve conducting functional genomics studies to identify the specific genes and molecular pathways that are affected by these blood indicators, as well as conducting animal and cell-based studies to further explore the causal relationships identified in this study.Comprehensive approaches, including the consideration of sample overlap, will be crucial to further validate our findings and enhance the reliability of potential interventions derived from these insights.
Supplementary Information
Acknowledgements
Not applicable.
Authors’ contributions
Zhenglin Chang: Conceptualization, Investigation, Visualization, Writing—Original Draft. Suilin Wang: Formal analysis, Writing—Review & Editing. Kemin Liu: Investigation, Formal analysis. Runpei Lin: Visualization, Formal analysis. Changlian Liu: Writing—Original Draft, Formal analysis. Jiale Zhang: Formal analysis. Daqiang Wei: Visualization. Yuxi Nie: Visualization. Yuerong Chen: Writing—Review & Editing, Jiawei He: Writing—Review & Editing. Haiyang Li: supervision. Zhangkai J. Cheng: Writing—Review & Editing. Baoqing Sun: Project administration, Funding acquisition.
Funding
This work was supported by the National Natural Science Foundation of China (No. 82302607), Guangdong Basic and Applied Basic Research Foundation (2022A1515110555, 2023A1515010932), State Key Laboratory of Respiratory Disease (SKLRD-Z-202410), the Emergency Key Project of Guangzhou Laboratory (No. EKPG21-30–2), Guangzhou Laboratory Emergency Research Project (NO: EKPG21-302), Zhong Nanshan Medical Foundation of Guangdong Province (ZNSXS-20220015) and GuangDong Basic and Applied Basic Research Foundation (2021B1515230008).
Availability of data and materials
All data in this study are publicly available summary statistics. All accession numbers listed in Table 3 can be directly linked to their corresponding indicators by searching on the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk).
Declarations
Ethics approval and consent to participate
Given the retrospective nature of our research, the need for ethical approval and the informed consent statement was waived by the Ethics Committee of the First Affiliated Hospital of Guangzhou Medical University (China).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zhenglin Chang, Suilin Wang, Kemin Liu, Runpei Lin and Changlian Liu contributed equally to this work and share the first authorship.
Contributor Information
Haiyang Li, Email: haiyang.li@mrc-bsu.cam.ac.uk.
Zhangkai J. Cheng, Email: jasontable@gmail.com
Baoqing Sun, Email: sunbaoqing@vip.163.com.
References
- 1.Cucinotta D, Vanelli M. WHO Declares COVID-19 a Pandemic. Acta Biomed. 2022;91(1):157–160. doi: 10.23750/abm.v91i1.9397. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Hu B, Guo H, Zhou P, Shi ZL. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. 2021;19(3):141–154. doi: 10.1038/s41579-020-00459-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Lee VH, Adham M, Ben Kridis W, et al. International recommendations for plasma Epstein-Barr virus DNA measurement in nasopharyngeal carcinoma in resource-constrained settings: lessons from the COVID-19 pandemic. Lancet Oncol. 2022;23(12):e544–e551. doi: 10.1016/s1470-2045(22)00505-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Vuckovic D, Bao EL, Akbari P, et al. The Polygenic and Monogenic basis of blood traits and diseases. Cell. 2020;182(5):1214–1231 e11. doi: 10.1016/j.cell.2020.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Dumas SJ, Meta E, Borri M, et al. Phenotypic diversity and metabolic specialization of renal endothelial cells. Nat Rev Nephrol. 2021;17(7):441–464. doi: 10.1038/s41581-021-00411-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Metkus TS, Sokoll LJ, Barth AS, et al. Myocardial Injury in Severe COVID-19 Compared With Non-COVID-19 Acute Respiratory Distress Syndrome. Circulation. 2021;143(6):553–565. doi: 10.1161/circulationaha.120.050543. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Yun H, Sun Z, Wu J, Tang A, Hu M, Xiang Z. Laboratory data analysis of novel coronavirus (COVID-19) screening in 2510 patients. Clin Chim Acta. 2020;507:94–97. doi: 10.1016/j.cca.2020.04.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Kamel AM, Sobhy M, Magdy N, Sabry N, Farid S. Anticoagulation outcomes in hospitalized Covid-19 patients: A systematic review and meta-analysis of case-control and cohort studies. Rev Med Virol. 2021;31(3):e2180. doi: 10.1002/rmv.2180. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sakaue S, Kanai M, Karjalainen J, et al. Trans-biobank analysis with 676,000 individuals elucidates the association of polygenic risk scores of complex traits with human lifespan. Nat Med. 2020;26(4):542–548. doi: 10.1038/s41591-020-0785-8. [DOI] [PubMed] [Google Scholar]
- 10.Smith GD, Ebrahim S. 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol. 2003;32(1):1–22. doi: 10.1093/ije/dyg070. [DOI] [PubMed] [Google Scholar]
- 11.Soremekun O, Soremekun C, Machipisa T, et al. Genome-Wide Association and Mendelian Randomization Analysis Reveal the Causal Relationship Between White Blood Cell Subtypes and Asthma in Africans. Front Genet. 2021;12:749415. doi: 10.3389/fgene.2021.749415. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Emdin CA, Khera AV, Kathiresan S. Mendelian Randomization. JAMA. 2017;318(19):1925–1926. doi: 10.1001/jama.2017.17219. [DOI] [PubMed] [Google Scholar]
- 13.Mapping the human genetic architecture of COVID-19. Nature. 2021;600(7889):472–477. 10.1038/s41586-021-03767-x [DOI] [PMC free article] [PubMed]
- 14.Chen MH, Raffield LM, Mousas A, et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell. 2020;182(5):1198–1213 e14. doi: 10.1016/j.cell.2020.06.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med. 2020;17(3):e1003062. doi: 10.1371/journal.pmed.1003062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Backman JD, Li AH, Marcketta A, et al. Exome sequencing and analysis of 454,787 UK Biobank participants. Nature. 2021;599(7886):628–634. doi: 10.1038/s41586-021-04103-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Pattaro C, Teumer A, Gorski M, et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat Commun. 2016;7:10023. doi: 10.1038/ncomms10023. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Sun BB, Maranville JC, Peters JE, et al. Genomic atlas of the human plasma proteome. Nature. 2018;558(7708):73–79. doi: 10.1038/s41586-018-0175-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Folkersen L, Gustafsson S, Wang Q, et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab. 2020;2(10):1135–1148. doi: 10.1038/s42255-020-00287-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Chang Z, An L, Lei M, et al. The genetic associations of COVID-19 on genitourinary symptoms. Front Immunol. 2023;14:1216211. doi: 10.3389/fimmu.2023.1216211. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497–1501. doi: 10.1093/ije/dyt179. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Holmes MV, Ala-Korpela M, Smith GD. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol. 2017;14(10):577–590. doi: 10.1038/nrcardio.2017.78. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Park S, Lee S, Kim Y, et al. Atrial fibrillation and kidney function: a bidirectional Mendelian randomization study. Eur Heart J. 2021;42(29):2816–2823. doi: 10.1093/eurheartj/ehab291. [DOI] [PubMed] [Google Scholar]
- 24.Baranova A, Cao H, Zhang F. Causal effect of COVID-19 on Alzheimer's disease: A Mendelian randomization study. J Med Virol. 2022; 10.1002/jmv.28107. [DOI] [PMC free article] [PubMed]
- 25.Shi S, Qin M, Shen B, et al. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan. China. JAMA Cardiol. 2020;5(7):802–810. doi: 10.1001/jamacardio.2020.0950. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Du RH, Liang LR, Yang CQ, et al. Predictors of mortality for patients with COVID-19 pneumonia caused by SARS-CoV-2 a prospective cohort study. Eur Respir J. 2020;55(5):2000524. doi: 10.1183/13993003.00524-2020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Hu L, Chen S, Fu Y, et al. Risk Factors Associated With Clinical Outcomes in 323 Coronavirus Disease 2019 (COVID-19) Hospitalized Patients in Wuhan. China. Clin Infect Dis. 2020;71(16):2089–2098. doi: 10.1093/cid/ciaa539. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Inciardi RM, Adamo M, Lupi L, et al. Characteristics and outcomes of patients hospitalized for COVID-19 and cardiac disease in Northern Italy. Eur Heart J. 2020;41(19):1821–1829. doi: 10.1093/eurheartj/ehaa388. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sultan S, Altayar O, Siddique SM, et al. AGA institute rapid review of the gastrointestinal and liver manifestations of COVID-19, meta-analysis of international data, and recommendations for the consultative management of patients with COVID-19. Gastroenterology. 2020;159(1):320–334.e27. doi: 10.1053/j.gastro.2020.05.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Cai Q, Huang D, Yu H, et al. COVID-19: Abnormal liver function tests. J Hepatol. 2020;73(3):566–574. doi: 10.1016/j.jhep.2020.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Andrade JA, Muzykovsky K, Truong J. Risk factors for mortality in COVID-19 patients in a community teaching hospital. J Med Virol. 2021;93(5):3184–3193. doi: 10.1002/jmv.26885. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Lei F, Liu YM, Zhou F, et al. Longitudinal Association Between Markers of Liver Injury and Mortality in COVID-19 in China. Hepatology. 2020;72(2):389–398. doi: 10.1002/hep.31301. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Yip TC, Wong VW, Lui GC, et al. Current and Past Infections of HBV Do Not Increase Mortality in Patients With COVID-19. Hepatology. 2021;74(4):1750–1765. doi: 10.1002/hep.31890. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Fan Z, Chen L, Li J, et al. Clinical features of COVID-19-related liver functional abnormality. Clin Gastroenterol Hepatol. 2020;18(7):1561–1566. doi: 10.1016/j.cgh.2020.04.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zhou F, Yu T, Du R, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet. 2020;395(10229):1054–1062. doi: 10.1016/s0140-6736(20)30566-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Seyit M, Avci E, Nar R, et al. Neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio and platelet to lymphocyte ratio to predict the severity of COVID-19. Am J Emerg Med. 2021;40:110–114. doi: 10.1016/j.ajem.2020.11.058. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Tong X, Cheng A, Yuan X, et al. Characteristics of peripheral white blood cells in COVID-19 patients revealed by a retrospective cohort study. BMC Infect Dis. 2021;21(1):1236. doi: 10.1186/s12879-021-06899-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Parsons IT, Parsons AT, Balme E, et al. The use of routine blood tests to assist the diagnosis of COVID-19 in symptomatic hospitalized patients. Ann Clin Biochem. 2021;58(4):318–326. doi: 10.1177/0004563221999076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Bergamaschi G, de Borrelli Andreis F, Aronico N, et al. Anemia in patients with Covid-19 pathogenesis and clinical significance. Clin Exp Med. 2021;21(2):239–246. doi: 10.1007/s10238-020-00679-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Qin C, Zhou L, Hu Z, et al. Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan. China. Clin Infect Dis. 2020;71(15):762–768. doi: 10.1093/cid/ciaa248. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Tao Z, Xu J, Chen W, et al. Anemia is associated with severe illness in COVID-19: A retrospective cohort study. J Med Virol. 2021;93(3):1478–1488. doi: 10.1002/jmv.26444. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Oh SM, Skendelas JP, Macdonald E, et al. On-admission anemia predicts mortality in COVID-19 patients: A single center, retrospective cohort study. Am J Emerg Med. 2021;48:140–147. doi: 10.1016/j.ajem.2021.03.083. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Eckart A, Struja T, Kutz A, et al. Relationship of nutritional status, inflammation, and Serum Albumin levels during acute illness: a prospective study. Am J Med. 2020;133(6):713–722 e7. doi: 10.1016/j.amjmed.2019.10.031. [DOI] [PubMed] [Google Scholar]
- 44.Zhang Z, Pereira SL, Luo M, Matheson EM. Evaluation of blood biomarkers associated with risk of malnutrition in older adults: a systematic review and meta-Analysis. Nutrients. 2017;9(8):829. doi: 10.3390/nu9080829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Zhou M, Qi J, Li X, et al. The proportion of patients with thrombocytopenia in three human-susceptible coronavirus infections: a systematic review and meta-analysis. Br J Haematol. 2020;189(3):438–441. doi: 10.1111/bjh.16655. [DOI] [PubMed] [Google Scholar]
- 46.Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China. Lancet. 2020;395(10223):497–506. doi: 10.1016/s0140-6736(20)30183-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Wang C, Deng R, Gou L, et al. Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters. Ann Transl Med. 2020;8(9):593. doi: 10.21037/atm-20-3391. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data in this study are publicly available summary statistics. All accession numbers listed in Table 3 can be directly linked to their corresponding indicators by searching on the IEU OpenGWAS project (https://gwas.mrcieu.ac.uk).