1. Epidemiology and Global Health

Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: A 41-year retrospective cohort study of 482,914 patients

  1. Peter Bruun-Rasmussen
  2. Morten Hanefeld Dziegiel
  3. Karina Banasik
  4. Pär Ingemar Johansson
  5. Søren Brunak  Is a corresponding author
  1. Department of Clinical Immunology, Copenhagen University Hospital, Denmark
  2. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark
https://doi.org/10.7554/eLife.83116
Share this article
Cite this article
  1. Peter Bruun-Rasmussen
  2. Morten Hanefeld Dziegiel
  3. Karina Banasik
  4. Pär Ingemar Johansson
  5. Søren Brunak
(2023)
Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: A 41-year retrospective cohort study of 482,914 patients
eLife 12:e83116.
https://doi.org/10.7554/eLife.83116
  2. Download BibTeX
  3. Download .RIS

Abstract

Background:

Whether natural selection may have attributed to the observed blood group frequency differences between populations remains debatable. The ABO system has been associated with several diseases and recently also with susceptibility to COVID-19 infection. Associative studies of the RhD system and diseases are sparser. A large disease-wide risk analysis may further elucidate the relationship between the ABO/RhD blood groups and disease incidence.

Methods:

We performed a systematic log-linear quasi-Poisson regression analysis of the ABO/RhD blood groups across 1,312 phecode diagnoses. Unlike prior studies, we determined the incidence rate ratio for each individual ABO blood group relative to all other ABO blood groups as opposed to using blood group O as the reference. Moreover, we used up to 41 years of nationwide Danish follow-up data, and a disease categorization scheme specifically developed for diagnosis-wide analysis. Further, we determined associations between the ABO/RhD blood groups and the age at the first diagnosis. Estimates were adjusted for multiple testing.

Results:

The retrospective cohort included 482,914 Danish patients (60.4% females). The incidence rate ratios (IRRs) of 101 phecodes were found statistically significant between the ABO blood groups, while the IRRs of 28 phecodes were found statistically significant for the RhD blood group. The associations included cancers and musculoskeletal-, genitourinary-, endocrinal-, infectious-, cardiovascular-, and gastrointestinal diseases.

Conclusions:

We found associations of disease-wide susceptibility differences between the blood groups of the ABO and RhD systems, including cancer of the tongue, monocytic leukemia, cervical cancer, osteoarthrosis, asthma, and HIV- and hepatitis B infection. We found marginal evidence of associations between the blood groups and the age at first diagnosis.

Funding:

Novo Nordisk Foundation and the Innovation Fund Denmark

Editor's evaluation

This important retrospective analysis of nearly 500,000 hospitalized Danish patients sheds light on the possible relationships between blood type and susceptibility to a host of diseases. The Danish National Patient Register is a compelling data source, and the statistical methodology is solid. The findings reported herein provide evidence, supporting information, and potential hypotheses for researchers interested in the causes and etiology of diseases as they relate to blood type.

https://doi.org/10.7554/eLife.83116.sa0

Introduction

Still 100 years after the discovery of the ABO and Rhesus D (RhD) blood group systems, the selective forces that may have attributed to the observed blood group population differences remain elusive (Anstee, 2010). The pathophysiological mechanisms behind the observed relationship between blood groups and diseases are not well understood either. The ABO system has been associated with susceptibility to multiple diseases, including gastrointestinal- and cardiovascular diseases and pancreatic-, gastric-, and ovarian cancers (Vasan et al., 2016; Liumbruno and Franchini, 2014; Wolpin et al., 2010; Groot et al., 2020; Edgren et al., 2010; Dahlén et al., 2021; Li and Schooling, 2020). The ABO system has also been associated with the susceptibility, progression, and severity of COVID-19 (Ellinghaus et al., 2020). In contrast, apart from hemolytic disease of the newborn, reported associations between the RhD blood group and disease development are sparser (Anstee, 2010).

Specifically, higher levels of factor VIII (FVIII) and von Willebrand factor (vWF) observed in individuals with a non-O blood group have been suggested to affect the development of cardiovascular disease (Jenkins and O’Donnell, 2006; Franchini and Lippi, 2015). Additionally, blood group-related antigens have been suggested to be involved in the adhesion of trophoblast, inflammatory cells, and metastatic tumor cells to the endothelial cells of the vasculature (Ravn and Dabelsteen, 2000). The endothelial cells of the vasculature have also been suggested to contribute to the initiation and propagation of severe clinical manifestations of COVID-19 (Teuwen et al., 2020).

Recently, an associative disease-wide risk analysis of the ABO and RhD blood groups was conducted in a large Swedish cohort (Dahlén et al., 2021). The study generated further support for previous findings and suggested new associations. Here, we further uncover the relationship between the ABO and RhD blood groups and disease susceptibility using a Danish cohort of 482,914 patients. In contrast to previous studies, we use up to 41 years of follow-up data, and a disease categorization scheme specifically developed for disease-wide analysis called phecodes (Wu et al., 2019). Further, we determine the uniqueness of each individual ABO blood group as opposed to using blood group O as the reference.

We estimate incidence rate ratios of 1312 phecodes (diagnoses) for the ABO and RhD blood groups. Further, we determine associations between the ABO/RhD blood groups and the age at the first diagnosis to better disclose the temporal life course element of disease development.

Methods

Study design

This retrospective cohort study was based on the integration of the Danish National Patient Registry (DNPR) and data on ABO/RhD blood groups of hospitalized patients. We included Danish patients who had their ABO/RhD blood group determined in the Capital Region or Region Zealand (covering ~45% of the Danish population Nordjylland, 2022), between January 1, 2006, and April 10, 2018. A blood type determination is commonly done for patients who may require a blood transfusion during hospitalization for example, anemic patients and women in labor. In the inclusion period, approximately 90% of the population in the Capital Region and 97% of the Region Zealand population were of European ancestry (Supplementary file 1). The DNPR provided the International Classification of Diseases 8th and 10th revision (ICD-8 and ICD-10) diagnosis codes, dates of diagnosis, date of birth, date of potential emigration, and sex of patients, with records dating back to 1977.

Similar to a case-control study, the patients were included retrospectively. Here, selection into the study was based on an in-hospital ABO/RhD blood group determination. That is, the person-time and the entire disease history back to 1977 of patients hospitalized between 2006 and 2018 with known ABO/RhD blood groups were included retrospectively.

We defined diseased and non-diseased individuals using the phecode mapping from ICD-10 diagnosis codes (Wu et al., 2019). Before categorizing the assigned ICD diagnosis codes into phecodes, the ICD-8 codes were converted to ICD-10 codes (Pedersen et al., 2023). Further, referral diagnoses were excluded. Pregnancy- and perinatal diagnosis (ICD-10 chapters 15–16) assigned before or after age 10 were excluded, or, when possible, rightly assigned to the mother or newborn, respectively. The disease categories of injuries, poisonings, and symptoms were deemed unlikely to be associated with the blood groups and excluded from the analyses (phecode categories: ‘injuries and poisonings’, ‘symptoms’ and phecodes above 999). Only phecodes with at least 100 cases in the study sample were included. The patients were followed from the entry in the DNPR to the date of death, emigration, the first event of the studied phecode, or end study period (April 10, 2018), whichever came first. Thus, follow-up was up to 41 years. The patients were allowed to contribute events and time at risk to multiple phecode analysis.

Diagnosis-wide incidence rate ratios

We used a log-linear quasi-Poisson regression model to estimate incidence rate ratios (IRRs) of each phecode among individuals with blood groups A, B, AB, and O relative to the other blood groups, respectively (e.g. A vs. B, AB, and O) (Dewey et al., 1995; Ver Hoef and Boveng, 2007). Further, we compared individuals with positive RhD type relative to negative RhD type. The analyses of diseases developed by both males and females were adjusted for sex, while analyses of sex-restricted diseases (e.g. cervical cancer) only included a subgroup of individuals of the restricted sex. Sex-restricted diseases were pre-defined by the phecode terminology. Sex was adjusted for as prior studies have found sex differences in the incidence rates of multiple diseases (Westergaard et al., 2019). Further, we adjusted for the year of birth and attained age, both modeled using restricted cubic splines with five knots. Attained age was split into 1 year intervals and treated as a time-dependent covariate, thus allowing individuals to move between categories with time. Herewith, age was used as the underlying time scale. Further, an interaction between attained age and sex was modeled for non-sex-restricted analyses. Patients were excluded from the analysis if they were assigned the phecode under study at the start of the DNPR. For analysis of congenital phecodes (e.g. sickle cell disease), prevalence ratios were estimated instead of IRRs by using the cohort size as the offset (see Supplementary file 2 for a list of the congenital phecodes). The analyses of ABO blood groups were adjusted for RhD type, and the RhD-analyses were adjusted for the ABO blood group. Adjustment for the birth year was done to control for societal changes and was used instead of the calendar period of diagnosis. The robust quasi-Poisson variance formula was used to control for over-dispersion (Ver Hoef and Boveng, 2007). We conducted a supplemental analysis using the same methodology but where blood group O was instead used as the reference to enable direct comparison and meta-analysis with previous studies.

Age of first hospital diagnosis

We estimated differences in age of first phecode of individuals with blood group A, B, AB, and O relative to any other blood group, respectively. Similar analyses were done for RhD-positive individuals relative to RhD negative individuals. We used a linear regression model adjusted for sex and birth year (as a restricted cubic spline with five knots). Analysis of sex-restricted phecodes was not adjusted for sex. Individuals who were assigned the studied phecode at the start date of the DNPR were excluded as the age of diagnosis was uncertain. Further, congenital- and pregnancy-related phecodes were not included.

Statistical analyses were performed in R (version 3.6.2) using the survival and rms package. p-values were two-sided. p-values and confidence intervals were adjusted for multiple testing by the false discovery rate (FDR) approach, accounting for the number of performed tests (5 blood groups times 1312 phecodes; Benjamini and Hochberg, 1995; Altman and Bland, 2011). FDR adjusted p-values <0.05 were deemed statistically significant. The analysis pipeline was made in python (anaconda3/5.3.0) using snakemake for reproducibility (Köster and Rahmann, 2012). The analyses code is available through https://www.github.com/peterbruun/blood_type_study (copy archived at Bruun-Rasmussen, 2023). The manuscript complies with the STROBE reporting guidelines.

Results

In total, 482,914patients (60.4%females) were included and 1312 phecodes (diagnosis codes) were examined (Figure 1, and Table 1). The median follow-up time for all phecode analyses was 17,555,322 person-years (Q1-Q3: 17,324,597–17,615,142). The cohort held a wide age distribution of patients born from 1901 to 2015 (Table 1, and Supplementary file 3). The ABO/RhD blood group distribution of the patients was similar to that of a previously summarized reference population of 2.2 million Danes (Table 1; Barnkob et al., 2020; Banks, 2022).

Figure 1
Download asset Open asset
Selection of patients for the 41-year retrospective cohort study on ABO/RhD blood groups and associations with disease incidence in 482,914 Danish patients.
Table 1
Characteristics of patients in the 41-year retrospective cohort study on ABO/RhD blood groups and associations with disease incidence in 482,914 Danish patients.
N=482,914
ABO, n (%)O197,634 (40.9)
A206,110 (42.7)
AB22,111 (4.6)
B57,059 (11.8)
RhD, n (%)Negative74,150 (15.4)
Positive408,764 (84.6)
Sex, n (%)K291,649 (60.4)
M191,265 (39.6)
Birth year, median [Q1,Q3]1963 [1945,1982]
Age at entry, median [Q1,Q3]13 [0,32]
Follow-up time, median [Q1,Q3]40.8 [33.4,41.3]

Incidence rate ratios

After adjustment for multiple testing, we found the incidence rate ratios (IRRs) of 101 and 28 phecodes (116 unique) to be statistically significant for the ABO and RhD blood groups, respectively. The statistically significant IRRs are given with 95% confidence intervals in Table 2. The estimates of all examined phecodes are given in Supplementary file 4. Further, Manhattan plots of the p-values and disease categories are presented in Figures 2—6 .

Figure 2
Download asset Open asset
Manhattan plot for blood group A with phecodes included by category.

The vertical axis shows the -log10 transformed FDR adjusted p-values on a log10-scale. The horizontal axis shows the phecodes by category. The red line indicates the statistically significant level of <0.05 for FDR adjusted p-values. Associations with p-value >0.8 are not displayed. Coloured and annotated associations were deemed statistically significant. The direction of the triangles indicates positive or inverse associations (upward: IRR >1, downward: IRR <1). The size of the triangles indicates the size of the incidence rate ratio.

Figure 3
Download asset Open asset
Manhattan plot for blood group B with phecodes included by category.

The vertical axis shows the -log10 transformed FDR adjusted p-values on a log10-scale. The horizontal axis shows the phecodes by category. The red line indicates the statistically significant level of <0.05 for FDR adjusted p-values. Associations with p-value >0.8 are not displayed. Coloured and annotated associations were deemed statistically significant. The direction of the triangles indicates positive or inverse associations (upward: IRR >1, downward: IRR <1). The size of the triangles indicates the size of the incidence rate ratio.

Figure 4
Download asset Open asset
Manhattan plot for blood group AB with phecodes included by category.

The vertical axis shows the -log10 transformed FDR adjusted p-values on a log10-scale. The horizontal axis shows the phecodes by category. The red line indicates the statistically significant level of <0.05 for FDR adjusted p-values. Associations with p-value >0.8 are not displayed. Coloured and annotated associations were deemed statistically significant. The direction of the triangles indicates positive or inverse associations (upward: IRR >1, downward: IRR <1). The size of the triangles indicates the size of the incidence rate ratio.

Figure 5
Download asset Open asset
Manhattan plot for blood group O with phecodes included by category.

The vertical axis shows the -log10 transformed FDR adjusted p-values on a log10-scale. The horizontal axis shows the phecodes by category. The red line indicates the statistically significant level of <0.05 for FDR adjusted p-values. Associations with p-value >0.8 are not displayed. Coloured and annotated associations were deemed statistically significant. The direction of the triangles indicates positive or inverse associations (upward: IRR >1, downward: IRR <1). The size of the triangles indicates the size of the incidence rate ratio.

Figure 6
Download asset Open asset
Manhattan plot for the Rhesus D blood group with phecodes included by category.

The vertical axis shows the -log10 transformed FDR adjusted p-values on a log10-scale. The horizontal axis shows the phecodes by category. The red line indicates the statistically significant level of <0.05 for FDR adjusted p-values. Associations with p-value >0.8 are not displayed. Coloured and annotated associations were deemed statistically significant. The direction of the triangles indicates positive or inverse associations (upward: IRR >1, downward: IRR <1). The size of the triangles indicates the size of the incidence rate ratio.

Table 2
Statistically significant incidence rate ratios for each individual blood group A, B, AB, and O relative to any other blood group (e.g. A vs. B, AB, and O combined). Further, also for RhD-positive blood group relative to the RhD negative blood group.
Blood group ABlood group BBlood group ABBlood group 0Blood group RhD
PhecodePhenotypeCasesPerson-yearsIRR (95% CI)p-valueIRR (95% CI)p-valueIRR (95% CI)p-valueIRR (95% CI)p-valueIRR (95% CI)p-value
Infectious Diseases
010Tuberculosis2101176034400.87 (0.74, 1.02)0.0931.4 (1.18, 1.65)<0.0011.07 (0.32, 3.57)0.9150.97 (0.59, 1.57)0.8991.36 (1.12, 1.65)0.002
070Viral hepatitis6596175570780.88 (0.82, 0.93)<0.0011.22 (1.12, 1.34)<0.0011 (0.81, 1.23)0.971.04 (0.9, 1.21)0.5831.12 (0.99, 1.28)0.069
070.2Viral hepatitis B1664176135720.7 (0.62, 0.79)<0.0011.66 (1.46, 1.9)<0.0011.12 (0.38, 3.26)0.8511.06 (0.7, 1.61)0.7941.36 (1.07, 1.71)0.01
071Human immunodeficiency virus [HIV] disease1182176208080.81 (0.63, 1.05)0.1091.18 (0.54, 2.59)0.6881.19 (0.52, 2.73)0.6931.1 (0.67, 1.81)0.7251.49 (1.04, 2.15)0.031
071.1HIV infection, symptomatic1182176208080.81 (0.63, 1.05)0.1091.18 (0.54, 2.59)0.6881.19 (0.52, 2.73)0.6931.1 (0.67, 1.81)0.7251.49 (1.04, 2.15)0.031
110.13Dermatophytosis of the body211176305310.89 (0.35, 2.22)0.8091.64 (1.08, 2.49)0.020.93 (0.03, 30.15)0.970.88 (0.35, 2.23)0.8041.13 (0.2, 6.46)0.897
Neoplasms
145.2Cancer of tongue606176290550.95 (0.51, 1.76)0.8771.13 (0.71, 1.82)0.6161.21 (0.43, 3.38)0.7260.96 (0.46, 2.03)0.930.74 (0.6, 0.92)0.007
157Pancreatic cancer2828176279481.26 (1.16, 1.36)<0.0011.02 (0.34, 3.13)0.971.11 (0.53, 2.33)0.80.76 (0.7, 0.82)<0.0011.01 (0.52, 1.98)0.97
180Cervical cancer and dysplasia12538103088601.05 (0.99, 1.12)0.1180.9 (0.83, 0.97)0.010.96 (0.66, 1.39)0.8391 (0.99, 1.01)0.970.93 (0.86, 1.01)0.083
180.3Cervical intraepithelial neoplasia [CIN] [Cervical dysplasia]10895103271071.06 (0.99, 1.13)0.1150.89 (0.82, 0.97)0.0090.95 (0.67, 1.34)0.7661 (0.84, 1.19)0.970.93 (0.85, 1.01)0.102
195.1Malignant neoplasm, other7383176034240.96 (0.87, 1.05)0.3680.95 (0.77, 1.17)0.6420.96 (0.55, 1.69)0.9061.07 (1, 1.15)0.0380.88 (0.82, 0.94)<0.001
204.3Monocytic leukemia179176313001.04 (0.15, 7.27)0.970.92 (0.06, 13.67)0.9570.25 (0.06, 0.98)0.0471.13 (0.44, 2.91)0.8090.84 (0.37, 1.93)0.698
216Benign neoplasm of skin12993174954311.02 (0.84, 1.23)0.8830.9 (0.82, 0.98)0.0140.99 (0.62, 1.57)0.971.03 (0.93, 1.14)0.5830.95 (0.85, 1.05)0.32
860Bone marrow or stem cell transplant142176313021.01 (0.77, 1.31)0.970.63 (0.15, 2.57)0.5310.15 (0.05, 0.44)<0.0011.37 (1.02, 1.82)0.0334.15 (2.13, 8.09)<0.001
Endocrine/Metabolic
242Thyrotoxicosis with or without goiter9744175274260.93 (0.86, 1.02)0.1270.99 (0.59, 1.65)0.970.9 (0.66, 1.22)0.5051.09 (1.02, 1.18)0.0131.02 (0.7, 1.48)0.927
250Diabetes mellitus36810172950331.01 (0.92, 1.11)0.8491.09 (1.05, 1.13)<0.0011.06 (0.95, 1.18)0.320.95 (0.92, 0.97)<0.0011.07 (1.03, 1.11)<0.001
250.2Type 2 diabetes32505173465331 (0.82, 1.23)0.971.1 (1.06, 1.15)<0.0011.07 (0.96, 1.19)0.2130.94 (0.92, 0.97)<0.0011.07 (1.02, 1.12)0.004
261Vitamin deficiency6674175940820.94 (0.86, 1.03)0.1831.15 (1.05, 1.26)0.0030.93 (0.66, 1.31)0.6831.01 (0.75, 1.36)0.941.06 (0.92, 1.22)0.405
261.4Vitamin D deficiency4105176130130.91 (0.85, 0.97)0.0071.23 (1.14, 1.34)<0.0010.95 (0.56, 1.61)0.861.01 (0.63, 1.63)0.971.14 (1.04, 1.25)0.006
271Disorders of carbohydrate transport and metabolism1306176190380.86 (0.69, 1.07)0.1761.28 (1, 1.64)0.0471.07 (0.23, 4.92)0.9341.01 (0.51, 2)0.971.16 (0.78, 1.72)0.475
271.3Intestinal disaccharidase deficiencies and disaccharide malabsorption1154176213370.85 (0.68, 1.05)0.1351.32 (1.05, 1.67)0.0181.08 (0.24, 4.88)0.9241.01 (0.55, 1.86)0.971.15 (0.73, 1.8)0.562
272Disorders of lipoid metabolism41222173470321.06 (1.03, 1.09)<0.0011.01 (0.72, 1.42)0.971.03 (0.63, 1.69)0.920.93 (0.91, 0.96)<0.0011.02 (0.9, 1.17)0.733
272.11Hypercholesterolemia35565173950121.06 (1.03, 1.09)<0.0011.01 (0.79, 1.29)0.9561.03 (0.88, 1.21)0.7090.93 (0.91, 0.96)<0.0011.03 (0.96, 1.09)0.433
272.13Mixed hyperlipidemia1324176199111.05 (0.66, 1.67)0.8411.04 (0.14, 7.94)0.971.39 (1.01, 1.91)0.040.87 (0.7, 1.08)0.2111.09 (0.58, 2.04)0.809
Hematopoietic
282Hereditary hemolytic anemias947482914*0.76 (0.59, 0.99)**0.0391.36 (0.9, 2.06)**0.1411.42 (0.72, 2.8)**0.3161.03 (0.25, 4.26)**0.971.65 (1.06, 2.56)**0.026
282.8Other hemoglobinopathies557482914*0.65 (0.51, 0.82)**<0.0011.56 (1.16, 2.1)**0.0031.47 (0.77, 2.83)**0.2481.09 (0.58, 2.07)**0.7982.24 (1.52, 3.29)**<0.001
286Coagulation defects4124176067961.14 (1.01, 1.3)0.0350.95 (0.56, 1.6)0.8541.14 (0.67, 1.95)0.6330.87 (0.76, 0.99)0.0290.93 (0.66, 1.33)0.717
286.11Von willebrand’s disease214482914*0.71 (0.35, 1.47)**0.3680.49 (0.17, 1.45)**0.1990.39 (0.02, 8.74)**0.5621.96 (1.32, 2.91)**<0.0010.68 (0.29, 1.6)**0.388
286.3Coagulation defects complicating pregnancy or postpartum2015105024181.15 (1.08, 1.22)<0.0011.02 (0.58, 1.79)0.9461.14 (0.95, 1.38)0.1640.84 (0.79, 0.89)<0.0010.88 (0.8, 0.97)0.013
286.7Other and unspecified coagulation defects1085176214211.23 (1.05, 1.45)0.0130.98 (0.45, 2.16)0.971.52 (1.1, 2.08)0.0110.74 (0.64, 0.85)<0.0011.03 (0.23, 4.72)0.97
Mental Disorders
300.1Anxiety disorder7985176031881.03 (0.93, 1.14)0.5750.97 (0.8, 1.16)0.7350.96 (0.66, 1.41)0.8580.99 (0.76, 1.29)0.9530.92 (0.86, 0.99)0.027
Neurological
333.8Other degenerative diseases of the basal ganglia381176302090.78 (0.57, 1.07)0.1281.05 (0.11, 9.95)0.9670.65 (0.24, 1.78)0.4071.32 (1.02, 1.71)0.0331 (0.95, 1.05)0.97
339Other headache syndromes3466176032691.05 (0.9, 1.22)0.5461.11 (0.95, 1.3)0.2041.03 (0.39, 2.73)0.9570.9 (0.83, 0.99)0.0231.06 (0.82, 1.36)0.688
345Epilepsy, recurrent seizures, convulsions23469172287800.99 (0.77, 1.28)0.970.96 (0.86, 1.08)0.510.97 (0.71, 1.32)0.8531.03 (0.96, 1.1)0.440.94 (0.89, 1)0.039
345.3Convulsions14391173516760.99 (0.76, 1.3)0.970.96 (0.79, 1.18)0.7321 (0.88, 1.13)0.971.02 (0.89, 1.18)0.770.92 (0.85, 0.99)0.036
Sense Organs
361.1Retinal detachment with retinal defect3037175943941.06 (0.87, 1.29)0.5821.07 (0.69, 1.66)0.7871.23 (0.9, 1.69)0.1930.88 (0.79, 0.98)0.0220.97 (0.49, 1.96)0.948
381Otitis media and Eustachian tube disorders22790171445511.07 (1.03, 1.11)<0.0010.94 (0.86, 1.04)0.2261.01 (0.55, 1.87)0.970.95 (0.91, 1)0.0390.96 (0.87, 1.06)0.437
381.1Otitis media12313173640911.09 (1.04, 1.14)<0.0010.95 (0.83, 1.09)0.4490.98 (0.44, 2.22)0.970.94 (0.89, 1)0.0670.97 (0.82, 1.13)0.688
381.2Eustachian tube disorders2358175711181.15 (1.01, 1.31)0.0330.9 (0.62, 1.29)0.5660.87 (0.38, 2.01)0.7650.93 (0.74, 1.17)0.5310.88 (0.69, 1.13)0.32
385.5Tympanosclerosis and middle ear disease related to otitis media530176252151.25 (0.95, 1.64)0.1140.99 (0.55, 1.77)0.971.21 (0.37, 3.96)0.7610.77 (0.6, 0.98)0.0361.08 (0.29, 3.98)0.918
386.9Dizziness and giddiness (Light-headedness and vertigo)1060176240970.84 (0.73, 0.97)0.0161.04 (0.43, 2.55)0.9330.82 (0.42, 1.57)0.5551.2 (1.06, 1.37)0.0051.08 (0.7, 1.68)0.738
389Hearing loss43238171661140.99 (0.88, 1.12)0.8971.01 (0.74, 1.37)0.971.01 (0.78, 1.3)0.971.01 (0.83, 1.21)0.961.06 (1.01, 1.1)0.009
389.3Degenerative and vascular disorders of ear22354174199470.99 (0.81, 1.22)0.941 (0.85, 1.19)0.971.02 (0.53, 1.94)0.9611 (0.83, 1.22)0.971.08 (1.02, 1.15)0.012
Circulatory System
394Rheumatic disease of the heart valves8422175776581.08 (1.01, 1.16)0.0290.98 (0.61, 1.57)0.9240.93 (0.63, 1.38)0.7440.94 (0.86, 1.03)0.20.96 (0.78, 1.19)0.729
411.2Myocardial infarction25905174111931.03 (0.96, 1.11)0.4341.06 (0.97, 1.16)0.1881.03 (0.58, 1.8)0.9320.94 (0.9, 0.98)0.0081.02 (0.83, 1.26)0.867
415Pulmonary heart disease10870175653691.2 (1.15, 1.26)<0.0011.07 (0.9, 1.27)0.4751.14 (0.96, 1.36)0.1250.78 (0.75, 0.82)<0.0010.95 (0.82, 1.09)0.482
415.11Pulmonary embolism and infarction, acute1533176124651.39 (1.21, 1.59)<0.0011.11 (0.56, 2.18)0.7811.14 (0.44, 2.98)0.7980.65 (0.57, 0.75)<0.0010.88 (0.59, 1.32)0.562
425.12Other hypertrophic cardiomyopathy466176288200.81 (0.61, 1.07)0.1380.83 (0.35, 1.98)0.6831.11 (0.12, 10.23)0.931.3 (1.03, 1.64)0.0280.91 (0.32, 2.6)0.877
428.1Congestive heart failure (CHF) NOS8357175953281.03 (0.94, 1.13)0.51.02 (0.76, 1.37)0.9061.12 (0.96, 1.3)0.140.94 (0.89, 0.99)0.0281.01 (0.7, 1.46)0.961
440Atherosclerosis10901175547041.05 (0.95, 1.16)0.3451.1 (0.96, 1.25)0.1691.12 (0.91, 1.37)0.30.9 (0.85, 0.95)<0.0011.04 (0.88, 1.23)0.675
440.2Atherosclerosis of the extremities8348175703361.06 (0.96, 1.18)0.2581.09 (0.9, 1.32)0.4061.08 (0.75, 1.55)0.6860.9 (0.84, 0.96)0.0021.03 (0.8, 1.33)0.823
442.2Aneurysm of iliac artery326176306600.75 (0.64, 0.89)<0.0011.13 (0.63, 2.04)0.691.2 (0.61, 2.38)0.6071.21 (1, 1.47)0.0450.72 (0.6, 0.87)<0.001
443Peripheral vascular disease13791175467961.05 (0.98, 1.12)0.1531.01 (0.59, 1.73)0.971.03 (0.71, 1.5)0.8720.94 (0.89, 1)0.0351.03 (0.91, 1.18)0.63
443.7Peripheral angiopathy in diseases classified elsewhere3677176114141.02 (0.68, 1.51)0.941.1 (0.9, 1.34)0.371.1 (0.78, 1.55)0.6030.93 (0.83, 1.04)0.2141.18 (1.05, 1.31)0.004
444Arterial embolism and thrombosis2390176146191.14 (0.98, 1.34)0.0931.11 (0.77, 1.61)0.5831.28 (0.93, 1.76)0.1340.79 (0.7, 0.89)<0.0011 (0.92, 1.08)0.97
444.1Arterial embolism and thrombosis of lower extremity artery1286176238721.13 (0.78, 1.63)0.5331.13 (0.61, 2.13)0.7061.38 (0.86, 2.23)0.1850.78 (0.63, 0.97)0.0220.98 (0.35, 2.76)0.97
451Phlebitis and thrombophlebitis16748174797091.22 (1.18, 1.25)<0.0011.15 (1.09, 1.21)<0.0011.29 (1.18, 1.4)<0.0010.73 (0.7, 0.75)<0.0010.97 (0.84, 1.13)0.717
451.2Phlebitis and thrombophlebitis of lower extremities15650174895281.23 (1.19, 1.27)<0.0011.14 (1.08, 1.2)<0.0011.31 (1.2, 1.42)<0.0010.72 (0.69, 0.74)<0.0010.97 (0.84, 1.12)0.678
452Other venous embolism and thrombosis4275176071941.24 (1.16, 1.32)<0.0011.21 (1.07, 1.36)0.0021.31 (1.1, 1.56)0.0030.69 (0.64, 0.74)<0.0011 (0.89, 1.11)0.97
452.8Postphlebitic syndrome341176294251.35 (1.08, 1.68)0.0091.26 (0.76, 2.08)0.3781.82 (1.13, 2.94)0.0140.55 (0.45, 0.67)<0.0011.18 (0.54, 2.56)0.688
454Varicose veins16500173819711.1 (1.01, 1.2)0.0320.98 (0.57, 1.68)0.9461.04 (0.51, 2.1)0.930.91 (0.83, 1)0.050.98 (0.67, 1.42)0.906
455Hemorrhoids9001175239620.95 (0.84, 1.08)0.4810.91 (0.78, 1.08)0.2790.91 (0.67, 1.24)0.5621.1 (1.03, 1.18)0.0050.99 (0.67, 1.46)0.97
456Chronic venous insufficiency [CVI]925176267091.24 (1.07, 1.44)0.0041.04 (0.23, 4.7)0.9671.37 (0.91, 2.05)0.1310.73 (0.64, 0.83)<0.0010.94 (0.49, 1.79)0.858
459Other disorders of circulatory system2555176167131.12 (0.99, 1.27)0.0761.12 (0.85, 1.48)0.4331.15 (0.75, 1.75)0.5280.82 (0.75, 0.9)<0.0011.08 (0.8, 1.46)0.636
459.9Circulatory disease NEC2174176189301.16 (1.02, 1.31)0.0241.13 (0.86, 1.5)0.3811.22 (0.85, 1.74)0.2790.78 (0.71, 0.86)<0.0011.02 (0.38, 2.74)0.966
Respiratory
474Acute and chronic tonsillitis41427168176021.1 (1.08, 1.13)<0.0010.93 (0.88, 0.97)0.0021.01 (0.63, 1.63)0.970.94 (0.91, 0.96)<0.0010.97 (0.9, 1.05)0.466
474.2Chronic tonsillitis and adenoiditis27077170304801.14 (1.11, 1.17)<0.0010.89 (0.84, 0.93)<0.0011.02 (0.75, 1.4)0.8970.92 (0.89, 0.94)<0.0010.96 (0.89, 1.03)0.259
477Epistaxis or throat hemorrhage12337175067940.93 (0.89, 0.98)0.0060.92 (0.83, 1.02)0.1180.94 (0.71, 1.23)0.6481.12 (1.08, 1.16)<0.0011.01 (0.73, 1.41)0.948
495Asthma31106172387381.04 (1, 1.08)0.0280.94 (0.88, 0.99)0.0231 (1, 1)0.970.99 (0.9, 1.08)0.7770.99 (0.78, 1.27)0.97
Digestive
530.11GERD7461175821580.99 (0.69, 1.43)0.9661.12 (1.03, 1.22)0.0071.04 (0.62, 1.72)0.8970.95 (0.88, 1.03)0.2231.12 (1.03, 1.21)0.007
530.7Gastroesophageal laceration-hemorrhage syndrome597176239001.16 (0.8, 1.66)0.4391.04 (0.15, 7.23)0.970.43 (0.22, 0.83)0.0120.94 (0.44, 2.02)0.8831.09 (0.39, 2.99)0.883
531Peptic ulcer (excl. esophageal)16678174437040.9 (0.87, 0.94)<0.0011 (0.94, 1.07)0.970.92 (0.75, 1.14)0.4711.12 (1.08, 1.16)<0.0011.02 (0.83, 1.25)0.86
531.1Hemorrhage from gastrointestinal ulcer6277175837510.91 (0.84, 0.98)0.0090.89 (0.75, 1.04)0.1490.91 (0.6, 1.4)0.6881.17 (1.11, 1.25)<0.0011.07 (0.91, 1.27)0.421
531.2Gastric ulcer6745175553120.91 (0.85, 0.98)0.0081.01 (0.63, 1.63)0.970.92 (0.62, 1.36)0.6811.11 (1.04, 1.18)0.0020.97 (0.73, 1.28)0.838
531.3Duodenal ulcer4534175556840.88 (0.8, 0.96)0.0071.06 (0.71, 1.57)0.8030.97 (0.21, 4.56)0.971.12 (1.01, 1.24)0.0281.05 (0.77, 1.43)0.789
531.5Gastrojejunal ulcer586176285390.83 (0.61, 1.11)0.2131.41 (1.08, 1.84)0.0121.12 (0.23, 5.41)0.8971.01 (0.64, 1.6)0.970.85 (0.56, 1.28)0.439
550Abdominal hernia47761169760731.01 (0.94, 1.09)0.7570.94 (0.89, 0.99)0.0210.97 (0.81, 1.17)0.7651.02 (0.96, 1.08)0.5751.01 (0.77, 1.32)0.97
562Diverticulosis and diverticulitis16569175155680.94 (0.87, 1.03)0.190.92 (0.82, 1.03)0.1380.91 (0.7, 1.18)0.4811.11 (1.06, 1.17)<0.0010.98 (0.75, 1.28)0.884
562.1Diverticulosis16569175155680.94 (0.87, 1.03)0.190.92 (0.82, 1.03)0.1380.91 (0.7, 1.18)0.4811.11 (1.06, 1.17)<0.0010.98 (0.75, 1.28)0.884
571.81Portal hypertension1101176276080.94 (0.66, 1.35)0.7661.17 (0.83, 1.63)0.3760.62 (0.43, 0.9)0.0111.06 (0.76, 1.47)0.7460.99 (0.52, 1.88)0.97
574Cholelithiasis and cholecystitis31530172992061.05 (1.02, 1.09)0.0040.98 (0.85, 1.13)0.7660.99 (0.64, 1.53)0.970.96 (0.92, 1)0.0621.01 (0.73, 1.39)0.957
574.1Cholelithiasis24028173649171.05 (1.01, 1.1)0.0290.98 (0.81, 1.18)0.8090.99 (0.49, 1.96)0.970.96 (0.91, 1.02)0.1831.01 (0.75, 1.35)0.97
574.12Cholelithiasis with other cholecystitis2650176056421.12 (1, 1.25)0.0440.88 (0.69, 1.12)0.320.91 (0.48, 1.7)0.770.95 (0.76, 1.2)0.6880.96 (0.61, 1.52)0.878
574.2Calculus of bile duct8401175609371.07 (1, 1.15)0.0420.95 (0.78, 1.17)0.6510.98 (0.46, 2.06)0.9530.95 (0.87, 1.05)0.331.07 (0.94, 1.21)0.32
575.2Obstruction of bile duct1593176266841.24 (1.1, 1.41)<0.0010.97 (0.28, 3.43)0.971.04 (0.18, 5.91)0.970.8 (0.7, 0.91)0.0011.1 (0.71, 1.69)0.688
578Gastrointestinal hemorrhage20111175022000.96 (0.9, 1.03)0.2960.94 (0.83, 1.08)0.3930.91 (0.78, 1.07)0.2491.08 (1.03, 1.12)<0.0011.01 (0.77, 1.33)0.93
578.8Hemorrhage of rectum and anus11095175553310.96 (0.87, 1.06)0.4130.93 (0.79, 1.1)0.3950.92 (0.71, 1.18)0.5051.09 (1.03, 1.15)0.0041 (0.8, 1.26)0.97
Genitourinary
614.51Cervicitis and endocervicitis660104887910.97 (0.52, 1.8)0.9181.1 (0.7, 1.73)0.6961.4 (1.03, 1.91)0.0330.93 (0.64, 1.34)0.7061.16 (0.86, 1.57)0.326
622.2Mucous polyp of cervix1401104988021.17 (1.03, 1.34)0.020.99 (0.64, 1.53)0.971.12 (0.56, 2.21)0.7660.83 (0.73, 0.95)0.0070.97 (0.43, 2.18)0.948
626.12Excessive or frequent menstruation10504103758230.92 (0.86, 0.99)0.0260.96 (0.74, 1.24)0.7571.02 (0.48, 2.14)0.971.1 (1.03, 1.17)0.0040.97 (0.76, 1.23)0.811
Pregnancy Complications
634.3Ectopic pregnancy5034104269670.97 (0.87, 1.08)0.5921.11 (1.01, 1.21)0.0221.11 (0.93, 1.34)0.250.96 (0.86, 1.08)0.5240.99 (0.65, 1.5)0.957
643Excessive vomiting in pregnancy4314104703230.91 (0.85, 0.97)0.0051.17 (1.08, 1.27)<0.0011.12 (0.83, 1.51)0.481 (0.87, 1.14)0.971 (0.89, 1.11)0.97
643.1Hyperemesis gravidarum3558104884460.9 (0.84, 0.96)0.0021.15 (0.97, 1.37)0.1051.11 (0.91, 1.37)0.3131.01 (0.76, 1.36)0.9271.05 (0.78, 1.4)0.777
647.3Major puerperal infection274105046891.04 (0.34, 3.13)0.9521.37 (0.98, 1.92)0.0680.78 (0.25, 2.45)0.6880.86 (0.59, 1.25)0.4390.72 (0.56, 0.93)0.01
649.1Diabetes or abnormal glucose tolerance complicating pregnancy5053104806820.92 (0.64, 1.33)0.6791.22 (0.95, 1.56)0.1181.17 (0.75, 1.82)0.5040.95 (0.55, 1.64)0.8661.26 (1.03, 1.55)0.026
654.1Abnormality of organs and soft tissues of pelvis complicating pregnancy, childbirth, or the puerperium5842104795330.94 (0.89, 0.99)0.0161.09 (1.01, 1.16)0.0190.93 (0.76, 1.13)0.4671.04 (0.96, 1.12)0.3781.08 (1.01, 1.16)0.03
654.2Rhesus isoimmunization in pregnancy808105031901.12 (0.91, 1.38)0.2940.8 (0.61, 1.06)0.1180.82 (0.4, 1.71)0.6121.01 (0.65, 1.57)0.970.26 (0.24, 0.29)<0.001
656.1Isoimmunization of fetus or newborn508482914*2.6 (1.57, 4.33)**<0.0011.57 (0.53, 4.61)**0.420.4 (0.01, 11.01)**0.5990.24 (0.11, 0.51)**<0.0011.03 (0.21, 5.13)**0.97
Dermatologic
704Diseases of hair and hair follicles4102175783530.99 (0.62, 1.59)0.971.18 (1.04, 1.34)0.011.03 (0.25, 4.3)0.970.93 (0.82, 1.06)0.2731.04 (0.68, 1.59)0.876
704.2Hirsutism1778176088880.94 (0.7, 1.26)0.6881.32 (1.15, 1.5)<0.0011.26 (0.96, 1.65)0.0930.88 (0.75, 1.04)0.131.13 (0.87, 1.45)0.369
707Chronic ulcer of skin2121176175330.94 (0.74, 1.19)0.6171.23 (1.04, 1.47)0.0170.94 (0.29, 3.07)0.9270.98 (0.51, 1.88)0.9591.01 (0.59, 1.74)0.97
Musculoskeletal
722.7Intervertebral disc disorder with myelopathy747176254610.95 (0.61, 1.47)0.821.26 (1.01, 1.59)0.0420.65 (0.31, 1.35)0.2481.01 (0.58, 1.76)0.970.97 (0.28, 3.31)0.961
727Other disorders of synovium, tendon, and bursa30487172727531.02 (0.95, 1.09)0.6270.94 (0.89, 0.99)0.0160.99 (0.57, 1.7)0.971.01 (0.92, 1.11)0.8090.97 (0.9, 1.04)0.352
727.7Contracture of tendon (sheath)391176280060.91 (0.45, 1.84)0.81.14 (0.38, 3.43)0.8261.11 (0.05, 24.01)0.9511.02 (0.46, 2.25)0.970.65 (0.47, 0.9)0.009
728Disorders of muscle, ligament, and fascia11338175107631.08 (1.02, 1.13)0.0080.93 (0.83, 1.04)0.1990.88 (0.74, 1.05)0.1640.98 (0.85, 1.12)0.7660.93 (0.85, 1.01)0.085
740Osteoarthrosis53711171877481.01 (0.94, 1.09)0.8150.95 (0.9, 0.99)0.0211.02 (0.79, 1.31)0.9111.01 (0.93, 1.1)0.8380.97 (0.92, 1.01)0.132
740.11Osteoarthrosis, localized, primary40386173330611.01 (0.94, 1.09)0.7790.94 (0.9, 0.99)0.0120.99 (0.72, 1.35)0.941.01 (0.95, 1.08)0.6630.97 (0.92, 1.02)0.188
741Symptoms and disorders of the joints5085175784500.99 (0.66, 1.48)0.970.85 (0.73, 0.99)0.0320.93 (0.52, 1.69)0.831.09 (0.99, 1.2)0.0721 (0.82, 1.24)0.97
742Derangement of joint, non-traumatic16652174381761.03 (0.96, 1.11)0.3570.9 (0.83, 0.97)0.0040.98 (0.61, 1.56)0.9241.02 (0.88, 1.17)0.8380.95 (0.87, 1.04)0.291
742.9Other derangement of joint13669174901821.02 (0.9, 1.16)0.7570.91 (0.83, 0.99)0.0230.98 (0.47, 2.06)0.971.02 (0.91, 1.15)0.7140.95 (0.86, 1.04)0.26
743Osteoporosis, osteopenia and pathological fracture15875175364241.05 (1, 1.11)0.0490.94 (0.85, 1.05)0.260.92 (0.76, 1.12)0.4050.99 (0.85, 1.14)0.8581 (0.98, 1.02)0.97
743.11Osteoporosis NOS13633175539761.06 (1.02, 1.1)0.0030.93 (0.87, 1)0.0390.94 (0.78, 1.13)0.5140.98 (0.9, 1.07)0.681 (0.82, 1.21)0.97
Congenital Anomalies
747Cardiac and circulatory congenital anomalies15297482914*1.07 (1.01, 1.14)**0.0290.94 (0.8, 1.11)**0.4751.02 (0.47, 2.23)**0.9610.95 (0.88, 1.03)**0.2230.98 (0.77, 1.25)**0.884
747.1Cardiac congenital anomalies1621482914*1.19 (1.02, 1.39)**0.0230.77 (0.6, 0.98)**0.0361.03 (0.27, 3.86)**0.970.93 (0.69, 1.25)**0.6351.02 (0.48, 2.16)**0.97
755Congenital anomalies of limbs6557482914*0.99 (0.65, 1.5)**0.9570.96 (0.71, 1.29)**0.7780.96 (0.46, 1.97)**0.911.04 (0.89, 1.23)**0.6270.88 (0.8, 0.97)**0.013
755.61Congenital hip dysplasia and deformity2823482914*1.01 (0.57, 1.78)**0.970.87 (0.66, 1.16)**0.3550.89 (0.42, 1.88)**0.7661.07 (0.85, 1.35)**0.570.81 (0.71, 0.94)**0.004

  1. a. Statistically significant IRRs are marked with bold (FDR adjusted P-value <0.05).

  2. b. The IRRs are adjusted for age, sex, interaction between age and sex, and birth year.

  3. c. All other ABO blood groups and the RhD negative blood group was used as a reference, respectively.

  4. d. FDR-adjusted p-values and 95% confidence intervals are presented.

  5. e. FDR adjusted p-values above 0.97 were set to 0.97 to avoid exploding adjusted confidence intervals.

  6. e. Phecodes are divided by PheWAS disease categories.

  7. f. The number of events and the follow-up time in person-years for each phecode is presented.

  8. g. For study results of congenital phecodes estimates marked with ** are prevalence ratios instead of IRRs and the corresponding person-year marked with * are the size of the cohort.

Table 2—source data 1

Associations between the ABO/RhD blood groups and phecode incidence rate ratios.

https://cdn.elifesciences.org/articles/83116/elife-83116-table2-data1-v2.zip

The number of statistically significant IRRs for A, B, AB, O, and RhD were 50, 38, 11, 53, and 28, respectively. However, a between blood group comparison on the number of statistically significant IRRs is problematic because the analyses of blood group A and O had the highest power given that these blood groups were most frequent in the study sample (Table 1). For 13 phecodes, an association was found for both the ABO blood group and the RhD blood group. The ABO blood groups were found positively associated with 75 phecodes and inversely associated with 67 phecodes. The RhD-positive blood group was found to have 16 positive- and 12 inverse associations. Blood groups A and O were associated with diseases of the circulatory and digestive system. Blood group B was associated with several infectious, metabolic, and musculoskeletal diseases. The associations of the RhD blood group included cancers, infectious diseases, and pregnancy complications. The results of the supplementary analyses where blood group O was used as the reference is shown in Supplementary files 6 and 7.

Age at first diagnosis

We found the B blood group to be associated with a later diagnosis of viral infection. Further, blood group O was associated with a later diagnosis of phlebitis and thrombophlebitis (Table 3 and Supplementary file 5). The RhD-positive group was associated with a later diagnosis of acute and chronic tonsilitis diagnosis.

Table 3
Statistically significant associations between the ABO/RhD blood groups and the age of the first diagnosis.
Blood group ABlood group BBlood group ABBlood group 0Blood group RhD
PhecodePhenotypeNEstimate (95% CI)p-valueEstimate (95% CI)p-valueEstimate (95% CI)p-valueEstimate (95% CI)p-valueEstimate (95% CI)p-value
079Viral infection25075–0.26 (–3.06, 2.54)0.8640.92 (0.13, 1.71)0.0220.53 (–6.18, 7.23)0.887–0.23 (–3.22, 2.75)0.8870.27 (–5.22, 5.75)0.93
451Phlebitis and thrombophlebitis16748–0.58 (–1.02,–0.13)0.011–0.24 (–5.71, 5.22)0.936–0.6 (–5.87, 4.66)0.8330.91 (0.57, 1.25)<0.0010.01 (–0.33, 0.34)0.97
451.2Phlebitis and thrombophlebitis of lower extremities15650–0.53 (–1.08, 0.01)0.055–0.27 (–5.85, 5.31)0.93–0.7 (–6.35, 4.95)0.820.9 (0.55, 1.25)<0.0010.08 (–3.89, 4.06)0.97
474Acute and chronic tonsillitis41428–0.29 (–1.45, 0.87)0.6340.42 (–1.97, 2.8)0.7440.38 (–5.71, 6.48)0.9090.05 (–2.15, 2.24)0.970.67 (0.15, 1.19)0.011
474.1Acute tonsillitis181620.1 (–4.42, 4.61)0.970.41 (–7.17, 7.98)0.9230.75 (–7.23, 8.74)0.864–0.42 (–3.76, 2.93)0.821.34 (0.64, 2.04)<0.001

  1. a. Statistically significant effect estimates are marked with bold (FDR adjusted P-value <0.05).

  2. b. FDR adjusted p-values and 95% confidence intervals are presented.

  3. c. FDR adjusted p-values above 0.97 were set to 0.97 to enable estimation of adjusted confidence intervals.

  4. d. Estimates represent increases or decreases in years of age of first diagnosis.

Table 3—source data 1

Associations between the ABO/RhD blood groups and the age of the first diagnosis.

https://cdn.elifesciences.org/articles/83116/elife-83116-table3-data1-v2.zip

Discussion

We found the ABO/RhD blood groups to be associated with a wide spectrum of diseases including cancers and musculoskeletal-, genitourinary-, endocrinal-, infectious-, cardiovascular-, and gastrointestinal diseases. Associations of the ABO blood groups included monocytic leukemia, tonsilitis, renal dialysis, diseases of the female reproductive system, and osteoarthrosis. Associations of the RhD blood group included cancer of the tongue, malignant neoplasm (other), tuberculosis-, HIV-, hepatitis B infection, type 2 diabetes, hereditary hemolytic anemias, major puerperal infection, anxiety disorders, and contracture of tendon.

The blood groups may reflect their corresponding genetic markers; thus, our findings may indicate an association between disease and the ABO locus on chromosome 9 and the RH locus on chromosome 1, respectively. Alternatively, the associations may indicate that the blood groups are involved in disease mechanisms at the molecular level mediated either through the blood group antigens or by the blood group reactive antibodies. However, our findings have a compromised causal interpretation given the retrospective inclusion of individuals (and person-time) after an in-hospital blood group test.

Our results support several previously observed associations including positive associations between the non-O blood groups and prothrombotic diseases of the circulatory system (phecodes: 411.1–459.9), associations with gastroduodenal ulcers, associations of blood group O and lower risk of type 2 diabetes, and positive association between blood group B and tuberculosis (Vasan et al., 2016; Edgren et al., 2010; Dahlén et al., 2021; Fagherazzi et al., 2015; Rao, 2012). Further, our results support findings associating non-O blood groups with increased risk of pancreatic cancer (Liumbruno and Franchini, 2014). The role of the ABO blood group in HIV susceptibility remains controversial; we only observed a positive association for the RhD-positive blood group (Davison et al., 2020).

We found blood group B to be positively associated with ‘ectopic pregnancy’, ‘excessive vomiting in pregnancy, and ‘abnormality of organs and soft tissues of pelvis complicating pregnancy’ indicating that blood group B mothers may be more likely to experience pregnancy complications. Further, we found positive associations of blood group A with both ‘mucous polyp of cervix’, and blood group AB with ‘cervicitis and endocervicitis’. Taken together these findings may indicate that the ABO blood groups are associated with diseases of the female reproductive system. However, the study design does not allow for any causal interpretation.

Only a few statistically significant associations were found for the analyses of the age of the first diagnosis; thus, indicating that the blood group’s involvement in disease onset may be marginal. However, we assumed a linear relationship with age because assessing potential non-linear relationships for each disease would be unfeasible given the large number of tests performed. The linearity assumption may not hold for all analyses which limits the interpretation of the estimates.

A strength of our approach is that we utilized the phecode disease classification scheme that is specifically developed for disease-wide risk analyses (Wu et al., 2019) The phecode mapping scheme combines ICD-10 codes that clinical domain experts have deemed to cover the same disease. For example, respiratory tuberculosis (A16), tuberculosis of nervous system (A17), and miliary tuberculosis (A19), are combined into the phecode tuberculosis (phecode 10). Phecodes may therefore provide increased power and precision compared with using ICD-10 categories (Denny et al., 2010). Further, contrary to previous studies, we compared each blood group to all other blood groups, instead of determining effect estimates relative to blood group O. Thus, here we better capture the uniqueness of each individual ABO blood group.

Limitations

Our study has some important limitations, firstly, the retrospective inclusion of patients and person-time may have introduced an immortal time bias from deaths before enrollment (in-hospital ABO/RhD blood group test) (Yadav and Lewis, 2021). The findings are therefore conditioned on patients surviving until the enrollment period. This implies, for example, that if a specific blood group causes a higher incidence of a deadly disease, then patients with such blood group are more likely to have died before enrollment, and therefore fewer individuals having both that blood group and the disease will be present in our cohort. If so, the direction of the estimates for deadly diseases strongly related to any blood group will have been lowered or even flipped, relative to any causal relationship. The study design, however, enabled 41 year of follow-up and was deemed reasonable because the blood groups have not been associated with mortality differences. Moreover, the blood group distribution in our cohort was found to be almost identical to a reference population of 2.2 million Danish blood donors. Further, we replicated several findings of associations between the blood groups and severe diseases, including pancreatic cancer (Vasan et al., 2016; Liumbruno and Franchini, 2014). This may indicate that the potential bias was less prevalent. Further, by controlling for year of birth, the potential effects of immortal time bias were likely reduced, however, this could not be tested. Immortal time biases are potentially applicable in many biobanks studies, e.g. when using the UK Biobank for retrospective studies (Yadav and Lewis, 2021).

The generalizability of our findings is limited further because our cohort solely included hospitalized patients with known ABO and RhD blood groups. These are patients whom the treating doctor has deemed likely to potentially require a blood transfusion during hospitalization. The patients under study might therefore suffer from other diseases than patients without a determined blood group, and than never hospitalized individuals. Further, diseases that do not require hospitalization could not be examined. If the effect sizes are modified by factors which are more common in our cohort than in the general population then the estimates may not be generalizable. However, it is unclear if such effect modifier exists. Lastly, it was not possible to adjust for possible confounding from the geographical distribution or ethnicity of the patients (Anstee, 2010). This may have biased some estimates because the distribution of blood groups varies between ethnicities while ethnicity is also associated with differences in disease susceptibility. Particularly, ethnicity has been associated with differences in prevalence of infectious-, cardiovascular-, sickle cell disease, and thasalamia (Kurian and Cardarelli, 2007; McQuillan et al., 2004). Thus, the estimate of these disease groups should be interpreted with caution. The Danish population is however quite homogenous and approximately 94% of Danes have European ancestry (Supplementary file 1). Therefore, a potential bias from ethnicity may be less prevalent in our cohort as compared with studies in populations of more admixed origin.

In conclusion, we found the ABO/RhD blood groups to be associated with a wide spectrum of diseases, including cardiovascular-, infectious-, gastrointestinal- and musculoskeletal diseases. This may indicate that some of the potential selective pressure on the blood groups can be attributed to disease susceptibility differences. We found few associations between the blood groups and age of first diagnosis.

Data availability

Anonymized patient data was used in this study. Due to national and EU regulations, the data cannot be shared with the wider research community. However, data can be accessed upon relevant application to the Danish authorities. The Danish Patient Safety Authority and the Danish Health Data Authority have permitted the use of the data in this study; whilst currently, the appropriate authority for journal data use in research is the regional committee ("Regionsråd"). The statistical summary data used to create the tables and graphs are available as Table 2—source data 1 and Table 3—source data 1. The analysis code is publicly available through https://www.github.com/peterbruun/blood_type_study (copy archived at Bruun-Rasmussen, 2023).

References

  3. Website
    1. Banks TDB
    (2022) Blodtypes
    Accessed August 31, 2022.
    https://bloddonor.dk/fakta-om-blod/blodtyper/
    1. Dewey M
    2. Clayton D
    3. Hills M
    (1995) Statistical models in epidemiology
    Journal of the Royal Statistical Society. Series A Statistics in Society 158:343.
    https://doi.org/10.2307/2983301
    1. Ellinghaus D
    2. Degenhardt F
    3. Bujanda L
    4. Buti M
    5. Albillos A
    6. Invernizzi P
    7. Fernández J
    8. Prati D
    9. Baselli G
    10. Asselta R
    11. Grimsrud MM
    12. Milani C
    13. Aziz F
    14. Kässens J
    15. May S
    16. Wendorff M
    17. Wienbrandt L
    18. Uellendahl-Werth F
    19. Zheng T
    20. Yi X
    21. de Pablo R
    22. Chercoles AG
    23. Palom A
    24. Garcia-Fernandez A-E
    25. Rodriguez-Frias F
    26. Zanella A
    27. Bandera A
    28. Protti A
    29. Aghemo A
    30. Lleo A
    31. Biondi A
    32. Caballero-Garralda A
    33. Gori A
    34. Tanck A
    35. Carreras Nolla A
    36. Latiano A
    37. Fracanzani AL
    38. Peschuck A
    39. Julià A
    40. Pesenti A
    41. Voza A
    42. Jiménez D
    43. Mateos B
    44. Nafria Jimenez B
    45. Quereda C
    46. Paccapelo C
    47. Gassner C
    48. Angelini C
    49. Cea C
    50. Solier A
    51. Pestaña D
    52. Muñiz-Diaz E
    53. Sandoval E
    54. Paraboschi EM
    55. Navas E
    56. García Sánchez F
    57. Ceriotti F
    58. Martinelli-Boneschi F
    59. Peyvandi F
    60. Blasi F
    61. Téllez L
    62. Blanco-Grau A
    63. Hemmrich-Stanisak G
    64. Grasselli G
    65. Costantino G
    66. Cardamone G
    67. Foti G
    68. Aneli S
    69. Kurihara H
    70. ElAbd H
    71. My I
    72. Galván-Femenia I
    73. Martín J
    74. Erdmann J
    75. Ferrusquía-Acosta J
    76. Garcia-Etxebarria K
    77. Izquierdo-Sanchez L
    78. Bettini LR
    79. Sumoy L
    80. Terranova L
    81. Moreira L
    82. Santoro L
    83. Scudeller L
    84. Mesonero F
    85. Roade L
    86. Rühlemann MC
    87. Schaefer M
    88. Carrabba M
    89. Riveiro-Barciela M
    90. Figuera Basso ME
    91. Valsecchi MG
    92. Hernandez-Tejero M
    93. Acosta-Herrera M
    94. D’Angiò M
    95. Baldini M
    96. Cazzaniga M
    97. Schulzky M
    98. Cecconi M
    99. Wittig M
    100. Ciccarelli M
    101. Rodríguez-Gandía M
    102. Bocciolone M
    103. Miozzo M
    104. Montano N
    105. Braun N
    106. Sacchi N
    107. Martínez N
    108. Özer O
    109. Palmieri O
    110. Faverio P
    111. Preatoni P
    112. Bonfanti P
    113. Omodei P
    114. Tentorio P
    115. Castro P
    116. Rodrigues PM
    117. Blandino Ortiz A
    118. de Cid R
    119. Ferrer R
    120. Gualtierotti R
    121. Nieto R
    122. Goerg S
    123. Badalamenti S
    124. Marsal S
    125. Matullo G
    126. Pelusi S
    127. Juzenas S
    128. Aliberti S
    129. Monzani V
    130. Moreno V
    131. Wesse T
    132. Lenz TL
    133. Pumarola T
    134. Rimoldi V
    135. Bosari S
    136. Albrecht W
    137. Peter W
    138. Romero-Gómez M
    139. D’Amato M
    140. Duga S
    141. Banales JM
    142. Hov JR
    143. Folseraas T
    144. Valenti L
    145. Franke A
    146. Karlsen TH
    147. Severe Covid-19 GWAS Group
    (2020) Genomewide association study of severe covid-19 with respiratory failure
    The New England Journal of Medicine 383:1522–1534.
    https://doi.org/10.1056/NEJMoa2020283
    1. Kurian AK
    2. Cardarelli KM
    (2007)
    Racial and ethnic differences in cardiovascular disease risk factors: A systematic review
    Ethnicity & Disease 17:143–152.
  23. Software
    1. Pedersen MK
    2. Eriksson R
    3. Pedersen HK
    4. Collin C
    5. Reguant R
    6. Simon C
    7. Sørup FKH
    8. Damgaard KA
    9. Birch AM
    10. Larsen M
    11. Nielsen AP
    12. Kirstine Belling SB
    (2023)
    A unidirectional mapping of ICD-8 to ICD-10 codes, for harmonized longitudinal analysis of diseases
    Under Rev.

Article and author information

Author details

  1. Peter Bruun-Rasmussen

    1. Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
    2. Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
    Contribution
    Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3595-1311

  2. Morten Hanefeld Dziegiel

    Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
    Contribution
    Conceptualization, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8034-1523

  3. Karina Banasik

    Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    No competing interests declared

  4. Pär Ingemar Johansson

    Department of Clinical Immunology, Copenhagen University Hospital, Copenhagen, Denmark
    Contribution
    Conceptualization, Supervision, Writing – review and editing
    Competing interests
    has received grants from the AP Møller Foundation, Innovation Fund Denmark and Novo Nordisk Foundation. The author has been issued the following patents: Publication no: 20110201553, 20110268732, 20130040898, 20130261177, 20150057325, 20160113891, 9381166, 9381243, 20160250164, 9433589, 20160303040 and US20090053193A1. PI Johansson reports ownership of stocks in Trial-Lab AB, Endothel Pharma ApS, TissueLink ApS, and MoxieLab ApS. PI Johansson declares that the financial interests listed have no impact on the submitted work. The author has no other competing interests to declare. The author declares that the financial interests listed have no impact on the submitted work

  5. Søren Brunak

    Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
    Contribution
    Conceptualization, Resources, Supervision, Writing – review and editing
    For correspondence
    soren.brunak@cpr.ku.dk
    Competing interests
    participates on the Danish National Genome Center advisory board and is the Chairman for the data infrastructure board. The author has stock in Intomics A/S, Hoba Therapeutics Aps, Novo Nordisk A/S, Lundbeck A/S and ALK Abello. The author participates on the board of directors for both Proscion A/S and Intomics A/S. The author has no other competing interests to declare. SB declares that the financial interests listed have no impact on the submitted work
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0316-5866

Funding

Novo Nordisk Fonden (NNF14CC0001)

  • Søren Brunak

Novo Nordisk Fonden (NNF17OC0027594)

  • Søren Brunak

Innovation Fund Denmark (5153-00002B)

  • Søren Brunak

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was performed as a part of the CAG (Clinical Academic Group) Center for Endotheliomics under the Greater Copenhagen Health Science Partners (GCHSP). Sources of Funding The study was supported by the Novo Nordisk Foundation (grants NNF14CC0001 and NNF17OC0027594) and the Innovation Fund Denmark (grant 5153-00002B). The funders played no role in the conduct of the study. Funding Novo Nordisk Foundation and the Innovation Fund Denmark

Ethics

Human subjects: This is a register-based study and informed consent for such studies is waived by the Danish Data Protection Agency. Data access was approved by the Danish Patient Safety Authority (3-3013-1731), the Danish Data Protection Agency (DT SUND 2016-50 and 2017-57) and the Danish Health Data Authority (FSEID 00003092 and FSEID 00003724).

Copyright

© 2023, Bruun-Rasmussen et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,227
    views
  • 204
    downloads
  • 7
    citations

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

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Peter Bruun-Rasmussen
  2. Morten Hanefeld Dziegiel
  3. Karina Banasik
  4. Pär Ingemar Johansson
  5. Søren Brunak
(2023)
Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: A 41-year retrospective cohort study of 482,914 patients
eLife 12:e83116.
https://doi.org/10.7554/eLife.83116

Share this article

https://doi.org/10.7554/eLife.83116

Categories and tags

Research organism

Further reading

    1. Epidemiology and Global Health

    Lifestyles and their relative contribution to biological aging across multiple-organ systems: Change analysis from the China Multi-Ethnic Cohort study

    Yuan Zhang, Dan Tang ... Xing Zhao
    Research Article

    Background:

    Biological aging exhibits heterogeneity across multi-organ systems. However, it remains unclear how is lifestyle associated with overall and organ-specific aging and which factors contribute most in Southwest China.

    Methods:

    This study involved 8396 participants who completed two surveys from the China Multi-Ethnic Cohort (CMEC) study. The healthy lifestyle index (HLI) was developed using five lifestyle factors: smoking, alcohol, diet, exercise, and sleep. The comprehensive and organ-specific biological ages (BAs) were calculated using the Klemera–Doubal method based on longitudinal clinical laboratory measurements, and validation were conducted to select BA reflecting related diseases. Fixed effects model was used to examine the associations between HLI or its components and the acceleration of validated BAs. We further evaluated the relative contribution of lifestyle components to comprehension and organ systems BAs using quantile G-computation.

    Results:

    About two-thirds of participants changed HLI scores between surveys. After validation, three organ-specific BAs (the cardiopulmonary, metabolic, and liver BAs) were identified as reflective of specific diseases and included in further analyses with the comprehensive BA. The health alterations in HLI showed a protective association with the acceleration of all BAs, with a mean shift of –0.19 (95% CI −0.34, –0.03) in the comprehensive BA acceleration. Diet and smoking were the major contributors to overall negative associations of five lifestyle factors, with the comprehensive BA and metabolic BA accounting for 24% and 55% respectively.

    Conclusions:

    Healthy lifestyle changes were inversely related to comprehensive and organ-specific biological aging in Southwest China, with diet and smoking contributing most to comprehensive and metabolic BA separately. Our findings highlight the potential of lifestyle interventions to decelerate aging and identify intervention targets to limit organ-specific aging in less-developed regions.

    Funding:

    This work was primarily supported by the National Natural Science Foundation of China (Grant No. 82273740) and Sichuan Science and Technology Program (Natural Science Foundation of Sichuan Province, Grant No. 2024NSFSC0552). The CMEC study was funded by the National Key Research and Development Program of China (Grant No. 2017YFC0907305, 2017YFC0907300). The sponsors had no role in the design, analysis, interpretation, or writing of this article.

    1. Epidemiology and Global Health
    2. Microbiology and Infectious Disease

    Protection afforded by post-infection SARS-CoV-2 vaccine doses: A cohort study in Shanghai

    Bo Zheng, Bronner P Gonçalves ... Caoyi Xue
    Research Article

    Background:

    In many settings, a large fraction of the population has both been vaccinated against and infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, quantifying the protection provided by post-infection vaccination has become critical for policy. We aimed to estimate the protective effect against SARS-CoV-2 reinfection of an additional vaccine dose after an initial Omicron variant infection.

    Methods:

    We report a retrospective, population-based cohort study performed in Shanghai, China, using electronic databases with information on SARS-CoV-2 infections and vaccination history. We compared reinfection incidence by post-infection vaccination status in individuals initially infected during the April–May 2022 Omicron variant surge in Shanghai and who had been vaccinated before that period. Cox models were fit to estimate adjusted hazard ratios (aHRs).

    Results:

    275,896 individuals were diagnosed with real-time polymerase chain reaction-confirmed SARS-CoV-2 infection in April–May 2022; 199,312/275,896 were included in analyses on the effect of a post-infection vaccine dose. Post-infection vaccination provided protection against reinfection (aHR 0.82; 95% confidence interval 0.79–0.85). For patients who had received one, two, or three vaccine doses before their first infection, hazard ratios for the post-infection vaccination effect were 0.84 (0.76–0.93), 0.87 (0.83–0.90), and 0.96 (0.74–1.23), respectively. Post-infection vaccination within 30 and 90 days before the second Omicron wave provided different degrees of protection (in aHR): 0.51 (0.44–0.58) and 0.67 (0.61–0.74), respectively. Moreover, for all vaccine types, but to different extents, a post-infection dose given to individuals who were fully vaccinated before first infection was protective.

    Conclusions:

    In previously vaccinated and infected individuals, an additional vaccine dose provided protection against Omicron variant reinfection. These observations will inform future policy decisions on COVID-19 vaccination in China and other countries.

    Funding:

    This study was funded the Key Discipline Program of Pudong New Area Health System (PWZxk2022-25), the Development and Application of Intelligent Epidemic Surveillance and AI Analysis System (21002411400), the Shanghai Public Health System Construction (GWVI-11.2-XD08), the Shanghai Health Commission Key Disciplines (GWVI-11.1-02), the Shanghai Health Commission Clinical Research Program (20214Y0020), the Shanghai Natural Science Foundation (22ZR1414600), and the Shanghai Young Health Talents Program (2022YQ076).

    1. Epidemiology and Global Health

    Serum metabolome indicators of early childhood development in the Brazilian National Survey on Child Nutrition (ENANI-2019)

    Marina Padilha, Victor Nahuel Keller ... Gilberto Kac
    Research Article Updated

    Background:

    The role of circulating metabolites on child development is understudied. We investigated associations between children’s serum metabolome and early childhood development (ECD).

    Methods:

    Untargeted metabolomics was performed on serum samples of 5004 children aged 6–59 months, a subset of participants from the Brazilian National Survey on Child Nutrition (ENANI-2019). ECD was assessed using the Survey of Well-being of Young Children’s milestones questionnaire. The graded response model was used to estimate developmental age. Developmental quotient (DQ) was calculated as the developmental age divided by chronological age. Partial least square regression selected metabolites with a variable importance projection ≥1. The interaction between significant metabolites and the child’s age was tested.

    Results:

    Twenty-eight top-ranked metabolites were included in linear regression models adjusted for the child’s nutritional status, diet quality, and infant age. Cresol sulfate (β=–0.07; adjusted-p <0.001), hippuric acid (β=–0.06; adjusted-p <0.001), phenylacetylglutamine (β=–0.06; adjusted-p <0.001), and trimethylamine-N-oxide (β=–0.05; adjusted-p=0.002) showed inverse associations with DQ. We observed opposite directions in the association of DQ for creatinine (for children aged –1 SD: β=–0.05; pP=0.01;+1 SD: β=0.05; p=0.02) and methylhistidine (–1 SD: β = - 0.04; p=0.04;+1 SD: β=0.04; p=0.03).

    Conclusions:

    Serum biomarkers, including dietary and microbial-derived metabolites involved in the gut-brain axis, may potentially be used to track children at risk for developmental delays.

    Funding:

    Supported by the Brazilian Ministry of Health and the Brazilian National Research Council.