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.

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

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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

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Table 2

				Blood group A		Blood group B		Blood group AB		Blood group 0		Blood group RhD
Phecode	Phenotype	Cases	Person-years	IRR (95% CI)	p-value	IRR (95% CI)	p-value	IRR (95% CI)	p-value	IRR (95% CI)	p-value	IRR (95% CI)	p-value
Infectious Diseases
010	Tuberculosis	2101	17603440	0.87 (0.74, 1.02)	0.093	1.4 (1.18, 1.65)	<0.001	1.07 (0.32, 3.57)	0.915	0.97 (0.59, 1.57)	0.899	1.36 (1.12, 1.65)	0.002
070	Viral hepatitis	6596	17557078	0.88 (0.82, 0.93)	<0.001	1.22 (1.12, 1.34)	<0.001	1 (0.81, 1.23)	0.97	1.04 (0.9, 1.21)	0.583	1.12 (0.99, 1.28)	0.069
070.2	Viral hepatitis B	1664	17613572	0.7 (0.62, 0.79)	<0.001	1.66 (1.46, 1.9)	<0.001	1.12 (0.38, 3.26)	0.851	1.06 (0.7, 1.61)	0.794	1.36 (1.07, 1.71)	0.01
071	Human immunodeficiency virus [HIV] disease	1182	17620808	0.81 (0.63, 1.05)	0.109	1.18 (0.54, 2.59)	0.688	1.19 (0.52, 2.73)	0.693	1.1 (0.67, 1.81)	0.725	1.49 (1.04, 2.15)	0.031
071.1	HIV infection, symptomatic	1182	17620808	0.81 (0.63, 1.05)	0.109	1.18 (0.54, 2.59)	0.688	1.19 (0.52, 2.73)	0.693	1.1 (0.67, 1.81)	0.725	1.49 (1.04, 2.15)	0.031
110.13	Dermatophytosis of the body	211	17630531	0.89 (0.35, 2.22)	0.809	1.64 (1.08, 2.49)	0.02	0.93 (0.03, 30.15)	0.97	0.88 (0.35, 2.23)	0.804	1.13 (0.2, 6.46)	0.897
Neoplasms
145.2	Cancer of tongue	606	17629055	0.95 (0.51, 1.76)	0.877	1.13 (0.71, 1.82)	0.616	1.21 (0.43, 3.38)	0.726	0.96 (0.46, 2.03)	0.93	0.74 (0.6, 0.92)	0.007
157	Pancreatic cancer	2828	17627948	1.26 (1.16, 1.36)	<0.001	1.02 (0.34, 3.13)	0.97	1.11 (0.53, 2.33)	0.8	0.76 (0.7, 0.82)	<0.001	1.01 (0.52, 1.98)	0.97
180	Cervical cancer and dysplasia	12538	10308860	1.05 (0.99, 1.12)	0.118	0.9 (0.83, 0.97)	0.01	0.96 (0.66, 1.39)	0.839	1 (0.99, 1.01)	0.97	0.93 (0.86, 1.01)	0.083
180.3	Cervical intraepithelial neoplasia [CIN] [Cervical dysplasia]	10895	10327107	1.06 (0.99, 1.13)	0.115	0.89 (0.82, 0.97)	0.009	0.95 (0.67, 1.34)	0.766	1 (0.84, 1.19)	0.97	0.93 (0.85, 1.01)	0.102
195.1	Malignant neoplasm, other	7383	17603424	0.96 (0.87, 1.05)	0.368	0.95 (0.77, 1.17)	0.642	0.96 (0.55, 1.69)	0.906	1.07 (1, 1.15)	0.038	0.88 (0.82, 0.94)	<0.001
204.3	Monocytic leukemia	179	17631300	1.04 (0.15, 7.27)	0.97	0.92 (0.06, 13.67)	0.957	0.25 (0.06, 0.98)	0.047	1.13 (0.44, 2.91)	0.809	0.84 (0.37, 1.93)	0.698
216	Benign neoplasm of skin	12993	17495431	1.02 (0.84, 1.23)	0.883	0.9 (0.82, 0.98)	0.014	0.99 (0.62, 1.57)	0.97	1.03 (0.93, 1.14)	0.583	0.95 (0.85, 1.05)	0.32
860	Bone marrow or stem cell transplant	142	17631302	1.01 (0.77, 1.31)	0.97	0.63 (0.15, 2.57)	0.531	0.15 (0.05, 0.44)	<0.001	1.37 (1.02, 1.82)	0.033	4.15 (2.13, 8.09)	<0.001
Endocrine/Metabolic
242	Thyrotoxicosis with or without goiter	9744	17527426	0.93 (0.86, 1.02)	0.127	0.99 (0.59, 1.65)	0.97	0.9 (0.66, 1.22)	0.505	1.09 (1.02, 1.18)	0.013	1.02 (0.7, 1.48)	0.927
250	Diabetes mellitus	36810	17295033	1.01 (0.92, 1.11)	0.849	1.09 (1.05, 1.13)	<0.001	1.06 (0.95, 1.18)	0.32	0.95 (0.92, 0.97)	<0.001	1.07 (1.03, 1.11)	<0.001
250.2	Type 2 diabetes	32505	17346533	1 (0.82, 1.23)	0.97	1.1 (1.06, 1.15)	<0.001	1.07 (0.96, 1.19)	0.213	0.94 (0.92, 0.97)	<0.001	1.07 (1.02, 1.12)	0.004
261	Vitamin deficiency	6674	17594082	0.94 (0.86, 1.03)	0.183	1.15 (1.05, 1.26)	0.003	0.93 (0.66, 1.31)	0.683	1.01 (0.75, 1.36)	0.94	1.06 (0.92, 1.22)	0.405
261.4	Vitamin D deficiency	4105	17613013	0.91 (0.85, 0.97)	0.007	1.23 (1.14, 1.34)	<0.001	0.95 (0.56, 1.61)	0.86	1.01 (0.63, 1.63)	0.97	1.14 (1.04, 1.25)	0.006
271	Disorders of carbohydrate transport and metabolism	1306	17619038	0.86 (0.69, 1.07)	0.176	1.28 (1, 1.64)	0.047	1.07 (0.23, 4.92)	0.934	1.01 (0.51, 2)	0.97	1.16 (0.78, 1.72)	0.475
271.3	Intestinal disaccharidase deficiencies and disaccharide malabsorption	1154	17621337	0.85 (0.68, 1.05)	0.135	1.32 (1.05, 1.67)	0.018	1.08 (0.24, 4.88)	0.924	1.01 (0.55, 1.86)	0.97	1.15 (0.73, 1.8)	0.562
272	Disorders of lipoid metabolism	41222	17347032	1.06 (1.03, 1.09)	<0.001	1.01 (0.72, 1.42)	0.97	1.03 (0.63, 1.69)	0.92	0.93 (0.91, 0.96)	<0.001	1.02 (0.9, 1.17)	0.733
272.11	Hypercholesterolemia	35565	17395012	1.06 (1.03, 1.09)	<0.001	1.01 (0.79, 1.29)	0.956	1.03 (0.88, 1.21)	0.709	0.93 (0.91, 0.96)	<0.001	1.03 (0.96, 1.09)	0.433
272.13	Mixed hyperlipidemia	1324	17619911	1.05 (0.66, 1.67)	0.841	1.04 (0.14, 7.94)	0.97	1.39 (1.01, 1.91)	0.04	0.87 (0.7, 1.08)	0.211	1.09 (0.58, 2.04)	0.809
Hematopoietic
282	Hereditary hemolytic anemias	947	482914*	0.76 (0.59, 0.99)**	0.039	1.36 (0.9, 2.06)**	0.141	1.42 (0.72, 2.8)**	0.316	1.03 (0.25, 4.26)**	0.97	1.65 (1.06, 2.56)**	0.026
282.8	Other hemoglobinopathies	557	482914*	0.65 (0.51, 0.82)**	<0.001	1.56 (1.16, 2.1)**	0.003	1.47 (0.77, 2.83)**	0.248	1.09 (0.58, 2.07)**	0.798	2.24 (1.52, 3.29)**	<0.001
286	Coagulation defects	4124	17606796	1.14 (1.01, 1.3)	0.035	0.95 (0.56, 1.6)	0.854	1.14 (0.67, 1.95)	0.633	0.87 (0.76, 0.99)	0.029	0.93 (0.66, 1.33)	0.717
286.11	Von willebrand’s disease	214	482914*	0.71 (0.35, 1.47)**	0.368	0.49 (0.17, 1.45)**	0.199	0.39 (0.02, 8.74)**	0.562	1.96 (1.32, 2.91)**	<0.001	0.68 (0.29, 1.6)**	0.388
286.3	Coagulation defects complicating pregnancy or postpartum	2015	10502418	1.15 (1.08, 1.22)	<0.001	1.02 (0.58, 1.79)	0.946	1.14 (0.95, 1.38)	0.164	0.84 (0.79, 0.89)	<0.001	0.88 (0.8, 0.97)	0.013
286.7	Other and unspecified coagulation defects	1085	17621421	1.23 (1.05, 1.45)	0.013	0.98 (0.45, 2.16)	0.97	1.52 (1.1, 2.08)	0.011	0.74 (0.64, 0.85)	<0.001	1.03 (0.23, 4.72)	0.97
Mental Disorders
300.1	Anxiety disorder	7985	17603188	1.03 (0.93, 1.14)	0.575	0.97 (0.8, 1.16)	0.735	0.96 (0.66, 1.41)	0.858	0.99 (0.76, 1.29)	0.953	0.92 (0.86, 0.99)	0.027
Neurological
333.8	Other degenerative diseases of the basal ganglia	381	17630209	0.78 (0.57, 1.07)	0.128	1.05 (0.11, 9.95)	0.967	0.65 (0.24, 1.78)	0.407	1.32 (1.02, 1.71)	0.033	1 (0.95, 1.05)	0.97
339	Other headache syndromes	3466	17603269	1.05 (0.9, 1.22)	0.546	1.11 (0.95, 1.3)	0.204	1.03 (0.39, 2.73)	0.957	0.9 (0.83, 0.99)	0.023	1.06 (0.82, 1.36)	0.688
345	Epilepsy, recurrent seizures, convulsions	23469	17228780	0.99 (0.77, 1.28)	0.97	0.96 (0.86, 1.08)	0.51	0.97 (0.71, 1.32)	0.853	1.03 (0.96, 1.1)	0.44	0.94 (0.89, 1)	0.039
345.3	Convulsions	14391	17351676	0.99 (0.76, 1.3)	0.97	0.96 (0.79, 1.18)	0.732	1 (0.88, 1.13)	0.97	1.02 (0.89, 1.18)	0.77	0.92 (0.85, 0.99)	0.036
Sense Organs
361.1	Retinal detachment with retinal defect	3037	17594394	1.06 (0.87, 1.29)	0.582	1.07 (0.69, 1.66)	0.787	1.23 (0.9, 1.69)	0.193	0.88 (0.79, 0.98)	0.022	0.97 (0.49, 1.96)	0.948
381	Otitis media and Eustachian tube disorders	22790	17144551	1.07 (1.03, 1.11)	<0.001	0.94 (0.86, 1.04)	0.226	1.01 (0.55, 1.87)	0.97	0.95 (0.91, 1)	0.039	0.96 (0.87, 1.06)	0.437
381.1	Otitis media	12313	17364091	1.09 (1.04, 1.14)	<0.001	0.95 (0.83, 1.09)	0.449	0.98 (0.44, 2.22)	0.97	0.94 (0.89, 1)	0.067	0.97 (0.82, 1.13)	0.688
381.2	Eustachian tube disorders	2358	17571118	1.15 (1.01, 1.31)	0.033	0.9 (0.62, 1.29)	0.566	0.87 (0.38, 2.01)	0.765	0.93 (0.74, 1.17)	0.531	0.88 (0.69, 1.13)	0.32
385.5	Tympanosclerosis and middle ear disease related to otitis media	530	17625215	1.25 (0.95, 1.64)	0.114	0.99 (0.55, 1.77)	0.97	1.21 (0.37, 3.96)	0.761	0.77 (0.6, 0.98)	0.036	1.08 (0.29, 3.98)	0.918
386.9	Dizziness and giddiness (Light-headedness and vertigo)	1060	17624097	0.84 (0.73, 0.97)	0.016	1.04 (0.43, 2.55)	0.933	0.82 (0.42, 1.57)	0.555	1.2 (1.06, 1.37)	0.005	1.08 (0.7, 1.68)	0.738
389	Hearing loss	43238	17166114	0.99 (0.88, 1.12)	0.897	1.01 (0.74, 1.37)	0.97	1.01 (0.78, 1.3)	0.97	1.01 (0.83, 1.21)	0.96	1.06 (1.01, 1.1)	0.009
389.3	Degenerative and vascular disorders of ear	22354	17419947	0.99 (0.81, 1.22)	0.94	1 (0.85, 1.19)	0.97	1.02 (0.53, 1.94)	0.961	1 (0.83, 1.22)	0.97	1.08 (1.02, 1.15)	0.012
Circulatory System
394	Rheumatic disease of the heart valves	8422	17577658	1.08 (1.01, 1.16)	0.029	0.98 (0.61, 1.57)	0.924	0.93 (0.63, 1.38)	0.744	0.94 (0.86, 1.03)	0.2	0.96 (0.78, 1.19)	0.729
411.2	Myocardial infarction	25905	17411193	1.03 (0.96, 1.11)	0.434	1.06 (0.97, 1.16)	0.188	1.03 (0.58, 1.8)	0.932	0.94 (0.9, 0.98)	0.008	1.02 (0.83, 1.26)	0.867
415	Pulmonary heart disease	10870	17565369	1.2 (1.15, 1.26)	<0.001	1.07 (0.9, 1.27)	0.475	1.14 (0.96, 1.36)	0.125	0.78 (0.75, 0.82)	<0.001	0.95 (0.82, 1.09)	0.482
415.11	Pulmonary embolism and infarction, acute	1533	17612465	1.39 (1.21, 1.59)	<0.001	1.11 (0.56, 2.18)	0.781	1.14 (0.44, 2.98)	0.798	0.65 (0.57, 0.75)	<0.001	0.88 (0.59, 1.32)	0.562
425.12	Other hypertrophic cardiomyopathy	466	17628820	0.81 (0.61, 1.07)	0.138	0.83 (0.35, 1.98)	0.683	1.11 (0.12, 10.23)	0.93	1.3 (1.03, 1.64)	0.028	0.91 (0.32, 2.6)	0.877
428.1	Congestive heart failure (CHF) NOS	8357	17595328	1.03 (0.94, 1.13)	0.5	1.02 (0.76, 1.37)	0.906	1.12 (0.96, 1.3)	0.14	0.94 (0.89, 0.99)	0.028	1.01 (0.7, 1.46)	0.961
440	Atherosclerosis	10901	17554704	1.05 (0.95, 1.16)	0.345	1.1 (0.96, 1.25)	0.169	1.12 (0.91, 1.37)	0.3	0.9 (0.85, 0.95)	<0.001	1.04 (0.88, 1.23)	0.675
440.2	Atherosclerosis of the extremities	8348	17570336	1.06 (0.96, 1.18)	0.258	1.09 (0.9, 1.32)	0.406	1.08 (0.75, 1.55)	0.686	0.9 (0.84, 0.96)	0.002	1.03 (0.8, 1.33)	0.823
442.2	Aneurysm of iliac artery	326	17630660	0.75 (0.64, 0.89)	<0.001	1.13 (0.63, 2.04)	0.69	1.2 (0.61, 2.38)	0.607	1.21 (1, 1.47)	0.045	0.72 (0.6, 0.87)	<0.001
443	Peripheral vascular disease	13791	17546796	1.05 (0.98, 1.12)	0.153	1.01 (0.59, 1.73)	0.97	1.03 (0.71, 1.5)	0.872	0.94 (0.89, 1)	0.035	1.03 (0.91, 1.18)	0.63
443.7	Peripheral angiopathy in diseases classified elsewhere	3677	17611414	1.02 (0.68, 1.51)	0.94	1.1 (0.9, 1.34)	0.37	1.1 (0.78, 1.55)	0.603	0.93 (0.83, 1.04)	0.214	1.18 (1.05, 1.31)	0.004
444	Arterial embolism and thrombosis	2390	17614619	1.14 (0.98, 1.34)	0.093	1.11 (0.77, 1.61)	0.583	1.28 (0.93, 1.76)	0.134	0.79 (0.7, 0.89)	<0.001	1 (0.92, 1.08)	0.97
444.1	Arterial embolism and thrombosis of lower extremity artery	1286	17623872	1.13 (0.78, 1.63)	0.533	1.13 (0.61, 2.13)	0.706	1.38 (0.86, 2.23)	0.185	0.78 (0.63, 0.97)	0.022	0.98 (0.35, 2.76)	0.97
451	Phlebitis and thrombophlebitis	16748	17479709	1.22 (1.18, 1.25)	<0.001	1.15 (1.09, 1.21)	<0.001	1.29 (1.18, 1.4)	<0.001	0.73 (0.7, 0.75)	<0.001	0.97 (0.84, 1.13)	0.717
451.2	Phlebitis and thrombophlebitis of lower extremities	15650	17489528	1.23 (1.19, 1.27)	<0.001	1.14 (1.08, 1.2)	<0.001	1.31 (1.2, 1.42)	<0.001	0.72 (0.69, 0.74)	<0.001	0.97 (0.84, 1.12)	0.678
452	Other venous embolism and thrombosis	4275	17607194	1.24 (1.16, 1.32)	<0.001	1.21 (1.07, 1.36)	0.002	1.31 (1.1, 1.56)	0.003	0.69 (0.64, 0.74)	<0.001	1 (0.89, 1.11)	0.97
452.8	Postphlebitic syndrome	341	17629425	1.35 (1.08, 1.68)	0.009	1.26 (0.76, 2.08)	0.378	1.82 (1.13, 2.94)	0.014	0.55 (0.45, 0.67)	<0.001	1.18 (0.54, 2.56)	0.688
454	Varicose veins	16500	17381971	1.1 (1.01, 1.2)	0.032	0.98 (0.57, 1.68)	0.946	1.04 (0.51, 2.1)	0.93	0.91 (0.83, 1)	0.05	0.98 (0.67, 1.42)	0.906
455	Hemorrhoids	9001	17523962	0.95 (0.84, 1.08)	0.481	0.91 (0.78, 1.08)	0.279	0.91 (0.67, 1.24)	0.562	1.1 (1.03, 1.18)	0.005	0.99 (0.67, 1.46)	0.97
456	Chronic venous insufficiency [CVI]	925	17626709	1.24 (1.07, 1.44)	0.004	1.04 (0.23, 4.7)	0.967	1.37 (0.91, 2.05)	0.131	0.73 (0.64, 0.83)	<0.001	0.94 (0.49, 1.79)	0.858
459	Other disorders of circulatory system	2555	17616713	1.12 (0.99, 1.27)	0.076	1.12 (0.85, 1.48)	0.433	1.15 (0.75, 1.75)	0.528	0.82 (0.75, 0.9)	<0.001	1.08 (0.8, 1.46)	0.636
459.9	Circulatory disease NEC	2174	17618930	1.16 (1.02, 1.31)	0.024	1.13 (0.86, 1.5)	0.381	1.22 (0.85, 1.74)	0.279	0.78 (0.71, 0.86)	<0.001	1.02 (0.38, 2.74)	0.966
Respiratory
474	Acute and chronic tonsillitis	41427	16817602	1.1 (1.08, 1.13)	<0.001	0.93 (0.88, 0.97)	0.002	1.01 (0.63, 1.63)	0.97	0.94 (0.91, 0.96)	<0.001	0.97 (0.9, 1.05)	0.466
474.2	Chronic tonsillitis and adenoiditis	27077	17030480	1.14 (1.11, 1.17)	<0.001	0.89 (0.84, 0.93)	<0.001	1.02 (0.75, 1.4)	0.897	0.92 (0.89, 0.94)	<0.001	0.96 (0.89, 1.03)	0.259
477	Epistaxis or throat hemorrhage	12337	17506794	0.93 (0.89, 0.98)	0.006	0.92 (0.83, 1.02)	0.118	0.94 (0.71, 1.23)	0.648	1.12 (1.08, 1.16)	<0.001	1.01 (0.73, 1.41)	0.948
495	Asthma	31106	17238738	1.04 (1, 1.08)	0.028	0.94 (0.88, 0.99)	0.023	1 (1, 1)	0.97	0.99 (0.9, 1.08)	0.777	0.99 (0.78, 1.27)	0.97
Digestive
530.11	GERD	7461	17582158	0.99 (0.69, 1.43)	0.966	1.12 (1.03, 1.22)	0.007	1.04 (0.62, 1.72)	0.897	0.95 (0.88, 1.03)	0.223	1.12 (1.03, 1.21)	0.007
530.7	Gastroesophageal laceration-hemorrhage syndrome	597	17623900	1.16 (0.8, 1.66)	0.439	1.04 (0.15, 7.23)	0.97	0.43 (0.22, 0.83)	0.012	0.94 (0.44, 2.02)	0.883	1.09 (0.39, 2.99)	0.883
531	Peptic ulcer (excl. esophageal)	16678	17443704	0.9 (0.87, 0.94)	<0.001	1 (0.94, 1.07)	0.97	0.92 (0.75, 1.14)	0.471	1.12 (1.08, 1.16)	<0.001	1.02 (0.83, 1.25)	0.86
531.1	Hemorrhage from gastrointestinal ulcer	6277	17583751	0.91 (0.84, 0.98)	0.009	0.89 (0.75, 1.04)	0.149	0.91 (0.6, 1.4)	0.688	1.17 (1.11, 1.25)	<0.001	1.07 (0.91, 1.27)	0.421
531.2	Gastric ulcer	6745	17555312	0.91 (0.85, 0.98)	0.008	1.01 (0.63, 1.63)	0.97	0.92 (0.62, 1.36)	0.681	1.11 (1.04, 1.18)	0.002	0.97 (0.73, 1.28)	0.838
531.3	Duodenal ulcer	4534	17555684	0.88 (0.8, 0.96)	0.007	1.06 (0.71, 1.57)	0.803	0.97 (0.21, 4.56)	0.97	1.12 (1.01, 1.24)	0.028	1.05 (0.77, 1.43)	0.789
531.5	Gastrojejunal ulcer	586	17628539	0.83 (0.61, 1.11)	0.213	1.41 (1.08, 1.84)	0.012	1.12 (0.23, 5.41)	0.897	1.01 (0.64, 1.6)	0.97	0.85 (0.56, 1.28)	0.439
550	Abdominal hernia	47761	16976073	1.01 (0.94, 1.09)	0.757	0.94 (0.89, 0.99)	0.021	0.97 (0.81, 1.17)	0.765	1.02 (0.96, 1.08)	0.575	1.01 (0.77, 1.32)	0.97
562	Diverticulosis and diverticulitis	16569	17515568	0.94 (0.87, 1.03)	0.19	0.92 (0.82, 1.03)	0.138	0.91 (0.7, 1.18)	0.481	1.11 (1.06, 1.17)	<0.001	0.98 (0.75, 1.28)	0.884
562.1	Diverticulosis	16569	17515568	0.94 (0.87, 1.03)	0.19	0.92 (0.82, 1.03)	0.138	0.91 (0.7, 1.18)	0.481	1.11 (1.06, 1.17)	<0.001	0.98 (0.75, 1.28)	0.884
571.81	Portal hypertension	1101	17627608	0.94 (0.66, 1.35)	0.766	1.17 (0.83, 1.63)	0.376	0.62 (0.43, 0.9)	0.011	1.06 (0.76, 1.47)	0.746	0.99 (0.52, 1.88)	0.97
574	Cholelithiasis and cholecystitis	31530	17299206	1.05 (1.02, 1.09)	0.004	0.98 (0.85, 1.13)	0.766	0.99 (0.64, 1.53)	0.97	0.96 (0.92, 1)	0.062	1.01 (0.73, 1.39)	0.957
574.1	Cholelithiasis	24028	17364917	1.05 (1.01, 1.1)	0.029	0.98 (0.81, 1.18)	0.809	0.99 (0.49, 1.96)	0.97	0.96 (0.91, 1.02)	0.183	1.01 (0.75, 1.35)	0.97
574.12	Cholelithiasis with other cholecystitis	2650	17605642	1.12 (1, 1.25)	0.044	0.88 (0.69, 1.12)	0.32	0.91 (0.48, 1.7)	0.77	0.95 (0.76, 1.2)	0.688	0.96 (0.61, 1.52)	0.878
574.2	Calculus of bile duct	8401	17560937	1.07 (1, 1.15)	0.042	0.95 (0.78, 1.17)	0.651	0.98 (0.46, 2.06)	0.953	0.95 (0.87, 1.05)	0.33	1.07 (0.94, 1.21)	0.32
575.2	Obstruction of bile duct	1593	17626684	1.24 (1.1, 1.41)	<0.001	0.97 (0.28, 3.43)	0.97	1.04 (0.18, 5.91)	0.97	0.8 (0.7, 0.91)	0.001	1.1 (0.71, 1.69)	0.688
578	Gastrointestinal hemorrhage	20111	17502200	0.96 (0.9, 1.03)	0.296	0.94 (0.83, 1.08)	0.393	0.91 (0.78, 1.07)	0.249	1.08 (1.03, 1.12)	<0.001	1.01 (0.77, 1.33)	0.93
578.8	Hemorrhage of rectum and anus	11095	17555331	0.96 (0.87, 1.06)	0.413	0.93 (0.79, 1.1)	0.395	0.92 (0.71, 1.18)	0.505	1.09 (1.03, 1.15)	0.004	1 (0.8, 1.26)	0.97
Genitourinary
614.51	Cervicitis and endocervicitis	660	10488791	0.97 (0.52, 1.8)	0.918	1.1 (0.7, 1.73)	0.696	1.4 (1.03, 1.91)	0.033	0.93 (0.64, 1.34)	0.706	1.16 (0.86, 1.57)	0.326
622.2	Mucous polyp of cervix	1401	10498802	1.17 (1.03, 1.34)	0.02	0.99 (0.64, 1.53)	0.97	1.12 (0.56, 2.21)	0.766	0.83 (0.73, 0.95)	0.007	0.97 (0.43, 2.18)	0.948
626.12	Excessive or frequent menstruation	10504	10375823	0.92 (0.86, 0.99)	0.026	0.96 (0.74, 1.24)	0.757	1.02 (0.48, 2.14)	0.97	1.1 (1.03, 1.17)	0.004	0.97 (0.76, 1.23)	0.811
Pregnancy Complications
634.3	Ectopic pregnancy	5034	10426967	0.97 (0.87, 1.08)	0.592	1.11 (1.01, 1.21)	0.022	1.11 (0.93, 1.34)	0.25	0.96 (0.86, 1.08)	0.524	0.99 (0.65, 1.5)	0.957
643	Excessive vomiting in pregnancy	4314	10470323	0.91 (0.85, 0.97)	0.005	1.17 (1.08, 1.27)	<0.001	1.12 (0.83, 1.51)	0.48	1 (0.87, 1.14)	0.97	1 (0.89, 1.11)	0.97
643.1	Hyperemesis gravidarum	3558	10488446	0.9 (0.84, 0.96)	0.002	1.15 (0.97, 1.37)	0.105	1.11 (0.91, 1.37)	0.313	1.01 (0.76, 1.36)	0.927	1.05 (0.78, 1.4)	0.777
647.3	Major puerperal infection	274	10504689	1.04 (0.34, 3.13)	0.952	1.37 (0.98, 1.92)	0.068	0.78 (0.25, 2.45)	0.688	0.86 (0.59, 1.25)	0.439	0.72 (0.56, 0.93)	0.01
649.1	Diabetes or abnormal glucose tolerance complicating pregnancy	5053	10480682	0.92 (0.64, 1.33)	0.679	1.22 (0.95, 1.56)	0.118	1.17 (0.75, 1.82)	0.504	0.95 (0.55, 1.64)	0.866	1.26 (1.03, 1.55)	0.026
654.1	Abnormality of organs and soft tissues of pelvis complicating pregnancy, childbirth, or the puerperium	5842	10479533	0.94 (0.89, 0.99)	0.016	1.09 (1.01, 1.16)	0.019	0.93 (0.76, 1.13)	0.467	1.04 (0.96, 1.12)	0.378	1.08 (1.01, 1.16)	0.03
654.2	Rhesus isoimmunization in pregnancy	808	10503190	1.12 (0.91, 1.38)	0.294	0.8 (0.61, 1.06)	0.118	0.82 (0.4, 1.71)	0.612	1.01 (0.65, 1.57)	0.97	0.26 (0.24, 0.29)	<0.001
656.1	Isoimmunization of fetus or newborn	508	482914*	2.6 (1.57, 4.33)**	<0.001	1.57 (0.53, 4.61)**	0.42	0.4 (0.01, 11.01)**	0.599	0.24 (0.11, 0.51)**	<0.001	1.03 (0.21, 5.13)**	0.97
Dermatologic
704	Diseases of hair and hair follicles	4102	17578353	0.99 (0.62, 1.59)	0.97	1.18 (1.04, 1.34)	0.01	1.03 (0.25, 4.3)	0.97	0.93 (0.82, 1.06)	0.273	1.04 (0.68, 1.59)	0.876
704.2	Hirsutism	1778	17608888	0.94 (0.7, 1.26)	0.688	1.32 (1.15, 1.5)	<0.001	1.26 (0.96, 1.65)	0.093	0.88 (0.75, 1.04)	0.13	1.13 (0.87, 1.45)	0.369
707	Chronic ulcer of skin	2121	17617533	0.94 (0.74, 1.19)	0.617	1.23 (1.04, 1.47)	0.017	0.94 (0.29, 3.07)	0.927	0.98 (0.51, 1.88)	0.959	1.01 (0.59, 1.74)	0.97
Musculoskeletal
722.7	Intervertebral disc disorder with myelopathy	747	17625461	0.95 (0.61, 1.47)	0.82	1.26 (1.01, 1.59)	0.042	0.65 (0.31, 1.35)	0.248	1.01 (0.58, 1.76)	0.97	0.97 (0.28, 3.31)	0.961
727	Other disorders of synovium, tendon, and bursa	30487	17272753	1.02 (0.95, 1.09)	0.627	0.94 (0.89, 0.99)	0.016	0.99 (0.57, 1.7)	0.97	1.01 (0.92, 1.11)	0.809	0.97 (0.9, 1.04)	0.352
727.7	Contracture of tendon (sheath)	391	17628006	0.91 (0.45, 1.84)	0.8	1.14 (0.38, 3.43)	0.826	1.11 (0.05, 24.01)	0.951	1.02 (0.46, 2.25)	0.97	0.65 (0.47, 0.9)	0.009
728	Disorders of muscle, ligament, and fascia	11338	17510763	1.08 (1.02, 1.13)	0.008	0.93 (0.83, 1.04)	0.199	0.88 (0.74, 1.05)	0.164	0.98 (0.85, 1.12)	0.766	0.93 (0.85, 1.01)	0.085
740	Osteoarthrosis	53711	17187748	1.01 (0.94, 1.09)	0.815	0.95 (0.9, 0.99)	0.021	1.02 (0.79, 1.31)	0.911	1.01 (0.93, 1.1)	0.838	0.97 (0.92, 1.01)	0.132
740.11	Osteoarthrosis, localized, primary	40386	17333061	1.01 (0.94, 1.09)	0.779	0.94 (0.9, 0.99)	0.012	0.99 (0.72, 1.35)	0.94	1.01 (0.95, 1.08)	0.663	0.97 (0.92, 1.02)	0.188
741	Symptoms and disorders of the joints	5085	17578450	0.99 (0.66, 1.48)	0.97	0.85 (0.73, 0.99)	0.032	0.93 (0.52, 1.69)	0.83	1.09 (0.99, 1.2)	0.072	1 (0.82, 1.24)	0.97
742	Derangement of joint, non-traumatic	16652	17438176	1.03 (0.96, 1.11)	0.357	0.9 (0.83, 0.97)	0.004	0.98 (0.61, 1.56)	0.924	1.02 (0.88, 1.17)	0.838	0.95 (0.87, 1.04)	0.291
742.9	Other derangement of joint	13669	17490182	1.02 (0.9, 1.16)	0.757	0.91 (0.83, 0.99)	0.023	0.98 (0.47, 2.06)	0.97	1.02 (0.91, 1.15)	0.714	0.95 (0.86, 1.04)	0.26
743	Osteoporosis, osteopenia and pathological fracture	15875	17536424	1.05 (1, 1.11)	0.049	0.94 (0.85, 1.05)	0.26	0.92 (0.76, 1.12)	0.405	0.99 (0.85, 1.14)	0.858	1 (0.98, 1.02)	0.97
743.11	Osteoporosis NOS	13633	17553976	1.06 (1.02, 1.1)	0.003	0.93 (0.87, 1)	0.039	0.94 (0.78, 1.13)	0.514	0.98 (0.9, 1.07)	0.68	1 (0.82, 1.21)	0.97
Congenital Anomalies
747	Cardiac and circulatory congenital anomalies	15297	482914*	1.07 (1.01, 1.14)**	0.029	0.94 (0.8, 1.11)**	0.475	1.02 (0.47, 2.23)**	0.961	0.95 (0.88, 1.03)**	0.223	0.98 (0.77, 1.25)**	0.884
747.1	Cardiac congenital anomalies	1621	482914*	1.19 (1.02, 1.39)**	0.023	0.77 (0.6, 0.98)**	0.036	1.03 (0.27, 3.86)**	0.97	0.93 (0.69, 1.25)**	0.635	1.02 (0.48, 2.16)**	0.97
755	Congenital anomalies of limbs	6557	482914*	0.99 (0.65, 1.5)**	0.957	0.96 (0.71, 1.29)**	0.778	0.96 (0.46, 1.97)**	0.91	1.04 (0.89, 1.23)**	0.627	0.88 (0.8, 0.97)**	0.013
755.61	Congenital hip dysplasia and deformity	2823	482914*	1.01 (0.57, 1.78)**	0.97	0.87 (0.66, 1.16)**	0.355	0.89 (0.42, 1.88)**	0.766	1.07 (0.85, 1.35)**	0.57	0.81 (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

Download 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.

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.

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).

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Decision letter after peer review:

Thank you for submitting your article "Associations of ABO and Rhesus D Blood Groups with Phenome-Wide Disease Incidence: A 41-year Retrospective Cohort Study of 482,914 Patients" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including Philip Boonstra as Reviewing Editor and Reviewer #3, and the evaluation has been overseen by a Senior Editor.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1) Reviewers 1 and 3 both point out an issue regarding the A and O subgroups dominating the analyses due to their sample size. This seems to be a very important caveat to include in the comparison of the number of statistically significant findings per blood group.

2) Reviewer 1 comments on the lack of adjustment for patient ethnicity as a confounder (or a surrogate for other confounders). Please engage with this comment, which may involve explaining why this is unlikely, or which may involve actually trying to incorporate patient ethnicity into your models.

3) All of the reviewers raise many other good points in their Comments to the Authors, which I encourage you to read and engage with, potentially adjusting your analyses if you believe appropriate.

Reviewer #1 (Recommendations for the authors):

This study aims to address the important question of how different blood types are related to disease risk and age at diagnosis.

However, a major concern is that as per the comments in the public review, the lack of adjustment for confounding due to ethnicity represents a highly substantial limitation of this work. While it is briefly mentioned in the manuscript, this is a very major limitation that leads to very limited interpretability of the results. Incorporating ethnicity as a covariate into the analyses would be crucial.

Reviewer #2 (Recommendations for the authors):

Abstract/Intro

– "we determined the uniqueness" is a bit vague, could you more explicitly say you perform tests with A, AB, B, and O blood groups each as reference group as opposed to only O as the reference group?

– "diagnosis-wide" or "disease-wide" was used but perhaps more accurate to say "phenome wide" as in the title? Both disease-wide and diagnosis-wide are also used in the introduction before phecodes are introduced, and consistency might be better here.

– Age of disease onset specifically, not just disease onset, and perhaps age at first diagnosis is the most accurate (as is used in the introduction).

Methodological considerations

– ICD9 wasn't used? Most of the phecode mapping was done in ICD9/ICD10. Am I understanding ref 16 is in preparation? It would be important to describe how this is done and how it may bias your phecodes, particularly if ref 16 is not pre-printed yet. A good sanity check is how the prevalence/incidence of a handful of traits with this mapping compares to any other population-wide prevalence/incidence measures.

– It would be great to see a sensitivity analysis using either a mixed model to adjust for cryptic relatedness, close family structure, and population structure (presuming like other countries, the DNPR has many relatives). This may not statistically work with the quasi-Poisson model, so perhaps just restricting it to <3 degree individuals and comparing findings (of course this will decrease power, but nice to see if some of the main findings remain).

– I see it in the limitations, but please comment earlier in the manuscript on who gets a blood group determination in the hospital (e.g. people who made need a transplant sooner). Is it possible to characterize the disease prevalence in the subsection of the DNPR with a blood type and without so you can identify any major disease group biases?

– Was a power calculation used to determine the need for 100 cases?

– How is emigration recorded? National registers?

– The interquartile range would be a better descriptor of follow-up time rather than just the maximum (41 years) in the methods and in the limitations section.

– Why use a log-linear quasi-Poisson regression to estimate incidence rate ratios as opposed to logistic regression and odds ratios? It could be a valuable addition to the paper to provide odds ratios as well.

– It's good to adjust for ABO and RhD when testing RhD and ABO respectively, but is it possible to use interaction terms and consider these as well?

– It's great to use birth year to adjust for any "cohort effects" in society over time. Is attained age the age at end of the study period? Wouldn't birth year and attained age be too highly correlated to use both in the model? What is the rationale for using cubic splines for these variables rather than the numerical variables themselves? I see from the code that 20-year increments were used for the knots, any rationale for this methodology would be helpful.

– By excluding patients assigned a phecode at the start of the DNPR, would these be people who had the diagnosis previously and were recorded upon the inauguration of the register? Is there any kind of washout period you are using to define "start" of the DNPR?

– What is the ancestral breakdown of the cohort? Mostly European ancestries? While our current labels for genetic ancestry are quite rough, I think this is an important piece of information given the different distributions of blood groups across global populations.

– Excellent availability of code and summary data for tables/graphs.

Results

– The audience may be less interested in the number of significant phecodes and more in patterns. It could be good to comment on the shared phecodes between the 50, 38, 11, 53, and 28 found. What large-scale disease groupings (phewas disease categories) do these tend to fall in? Does one blood group have far more cardiovascular phecodes than another? Are any phecodes significant for more than 3 blood groups? Etc.

– Personally, I prefer p-values in scientific notation rather than <0.001 but I understand Table 2 is a lot of data to present.

– The figures could benefit from larger labels for readability.

– For the Manhattan plots, it would be good to specify -log10 FDR transformed adjusted p-values on the y-axis in addition to the figure legend.

– Were any blood groups associated with an earlier onset of outcomes?

Discussion

– What was used to identify "novel associations"? A systematic literature review? Comparison to Dahlén? I would refrain from using novel unless you define specifically how it was determined to be novel.

– A systematic comparison with what seems to be the closest study, Dahlén et al., would be beneficial as a type of replication.

– I would refrain from using the term linkage in the discussion as that may lead the reader to think of chromosomal linkage, but I think the authors mean a causal association.

– I don't think the findings support the discussion point on the selective pressure.

Reviewer #3 (Recommendations for the authors):

1. Could the authors justify why choosing to fit separate models comparing one blood type against all others, e.g. A vs. all others then AB vs. all others, is the more sensible choice than fitting one model that jointly tests for A vs. AB vs. B vs. O? I understand that there are various interpretative and statistical challenges to both, but fitting separate models is not internally consistent. The 'A vs. all others' model implicitly assumes that there is no difference in incidence in the AB, B, and O groups, but then the next model ('AB vs. all others') makes a different assumption, namely that there is no difference in incidence in the A, B, and O groups.

2. A natural limitation to this analysis is that there are more statistically significant findings in the O and A blood groups because they are the more prevalent groups, and statistical significance is driven by sample size. In this sense, it would be interesting if there were a way to account for the differences in sample size between the blood groups. Is it possible to investigate whether any of the groups have disproportionately more statistically significant findings after accounting for sample size?

3. Page 6, line 124: I think the use of the word 'confounder' here is not quite right in the technical sense, as I do not read this sentence to be claiming that sex is influencing blood type.

4. Regarding the legend for Figure 2:

a. It should have triangles rather than circles. Assuming this plot was made in ggplot2, this can be done using the override.aes argument in the guides function.

b. it would be helpful to show more than 3 values on the legend.

c. It would be helpful to use the same scale across the subfigures. What I mean is, in the bloodgroup AB figure, there is no discernible difference in size between a 1.1 and a 4.0 rate ratio.

d. I realize this is very pedantic but I believe the legend is technically not showing rate ratios but rather max(rate_ratio, 1/rate_ratio).

5. Do the authors have any intuition why Figure 1 is bimodal? My interpretation of this figure is that, among those who were hospitalized in Denmark between 2006 and 2018, the plurality was born either in the immediate post-WW2 era (makes sense to me) or the 80s (doesn't make as much sense to me).

6. Page 7, line 152: reference 20 is not related to FDR. Can the authors provide a reference for their specific approach to controlling FDR?

Essential revisions:

1) Reviewers 1 and 3 both point out an issue regarding the A and O subgroups dominating the analyses due to their sample size. This seems to be a very important caveat to include in the comparison of the number of statistically significant findings per blood group.

This has now been mentioned in the lines where the number of statistically significant findings per blood groups are mentioned (lines 191-194). Further, a supplemental analysis has been included where blood group O is used as the reference (Supplementary files 6–7.

2) Reviewer 1 comments on the lack of adjustment for patient ethnicity as a confounder (or a surrogate for other confounders). Please engage with this comment, which may involve explaining why this is unlikely, or which may involve actually trying to incorporate patient ethnicity into your models.

We unfortunately do not have access to information on ethnicity. We have further elaborated on this limitation in the manuscript and pointed out that the Danish population is quite homogenous why this is less of a concern in our study as compared with studies of populations with more admixed origin (lines 341-349). For a more detailed discussion on this please see our response to the reviewers’ comments.

3) All of the reviewers raise many other good points in their Comments to the Authors, which I encourage you to read and engage with, potentially adjusting your analyses if you believe appropriate.

We have engaged with all the comments and have changed the manuscript and figures accordingly.

Reviewer #1 (Recommendations for the authors):

This study aims to address the important question of how different blood types are related to disease risk and age at diagnosis.

However, a major concern is that as per the comments in the public review, the lack of adjustment for confounding due to ethnicity represents a highly substantial limitation of this work. While it is briefly mentioned in the manuscript, this is a very major limitation that leads to very limited interpretability of the results. Incorporating ethnicity as a covariate into the analyses would be crucial.

We agreed that ethnicity may introduce bias in the analysis of some diseases. We have now further elaborated on this limitation in lines 341-349. Unfortunately, we do not have information on ethnicity, and therefore an analysis adjusting for ethnicity is not possible with the available data. While we agree that ethnicity is a concern, it should be noted that ethnicity will only introduce confounding for the analysis of diagnosis which are more common for certain ethnicities and where at the same time the distribution of the ABO/RhD blood groups are different between ethnicities. This may e.g. be the case for infectious diseases, cardiovascular diseases and bleeding disorders as has now been mentioned in the limitations. Further, the Danish population is very homogeneous (approximately 94% have European origin). Therefore, a potential bias from not being able to adjust for ethnicity will be less distinct in our study. In Supplement Figure 1, we have now provided a graph showing the ancestry of the complete Danish population, the Capital Region of Denmark, and Region Zealand from 2008-2016 (Supplemenary file 1). From the graph it can be seen that approximately 90% of the population in the Capital Region and 97% of the Region Zealand population were of European origin. This information has now been added in the method section lines 99-102.

Reviewer #2 (Recommendations for the authors):

Abstract/Intro

– "we determined the uniqueness" is a bit vague, could you more explicitly say you perform tests with A, AB, B, and O blood groups each as reference group as opposed to only O as the reference group?

The formulation has now been changed to “we determined the incidence rate ratios for each individual ABO blood group relative to all other ABO blood groups…” (lines 37-38).

– "diagnosis-wide" or "disease-wide" was used but perhaps more accurate to say "phenome wide" as in the title? Both disease-wide and diagnosis-wide are also used in the introduction before phecodes are introduced, and consistency might be better here.

We have chosen not to use the term “phenome wide” in the manuscript text because “phenome wide” without the mentioning of disease is to be understood as a PheWAS analysis where genetic information of a specific SNP is used. The title is combining “phenome-wide” with “disease incidence” to underline that it is not a classic PheWAS study. As suggested by the reviewer we have now consistently used “disease-wide” in the introduction.

– Age of disease onset specifically, not just disease onset, and perhaps age at first diagnosis is the most accurate (as is used in the introduction).

We agree that age at first diagnosis is the most accurate formulation. This has now been changed throughout the manuscript.

Methodological considerations

– ICD9 wasn't used? Most of the phecode mapping was done in ICD9/ICD10. Am I understanding ref 16 is in preparation? It would be important to describe how this is done and how it may bias your phecodes, particularly if ref 16 is not pre-printed yet. A good sanity check is how the prevalence/incidence of a handful of traits with this mapping compares to any other population-wide prevalence/incidence measures.

ICD9 codes was never used in Denmark. Ref 16 is currently under review. I have received a copy of the manuscript by the authors which I have now uploaded as a “related manuscript file” for the reviewers to see. The authors do unfortunately not wish to make it available as preprint.

– It would be great to see a sensitivity analysis using either a mixed model to adjust for cryptic relatedness, close family structure, and population structure (presuming like other countries, the DNPR has many relatives). This may not statistically work with the quasi-Poisson model, so perhaps just restricting it to <3 degree individuals and comparing findings (of course this will decrease power, but nice to see if some of the main findings remain).

This is indeed an interesting suggestion. Unfortunately, we do not have access to information on population or family structure. However, we do not believe that family structure would impose a significant amount of bias given the large sample size. As mentioned in the limitation’s ethnicity may however impose a bias which have now been further elaborated in the limitations section.

– I see it in the limitations, but please comment earlier in the manuscript on who gets a blood group determination in the hospital (e.g. people who made need a transplant sooner). Is it possible to characterize the disease prevalence in the subsection of the DNPR with a blood type and without so you can identify any major disease group biases?

We have now commented earlier on in the manuscript on the kinds of patients which were included in the study (lines 99-100). Further, we have in the limitation elaborated on what this means for generalizability (lines 336-339). With the available data is it unfortunately not possible to identify any major disease group differences between the hospitalized patients with a blood group determination and those without.

– Was a power calculation used to determine the need for 100 cases?

A power calculation was not used. Similarly to the study by Dahlén et al. the number of cut-off cases was arbitrarily picked. We have now changed the formulation in line 111-112 so that is cannot be understood as if a power calculation had been done for each of the 5x1300 analysis.

– How is emigration recorded? National registers?

Yes. This information has now been added to the manuscript (line 104).

– The interquartile range would be a better descriptor of follow-up time rather than just the maximum (41 years) in the methods and in the limitations section.

In the methods and limitation we referred to the length of the study period which is the maximum length of follow-up. The median and interquartile range of follow-up time in the cohort is given in Table 1.

– Why use a log-linear quasi-Poisson regression to estimate incidence rate ratios as opposed to logistic regression and odds ratios? It could be a valuable addition to the paper to provide odds ratios as well.

A logistic regression would not take time-to-event into account. Thus, incorporating person-time using a log-linear Poisson regression enriches the analysis as compared with a logistic regression.

– It's good to adjust for ABO and RhD when testing RhD and ABO respectively, but is it possible to use interaction terms and consider these as well?

Similar to the study by Dahlén et al. we chose not to consider interactions between the ABO and RhD blood groups as there is little evidence that such interaction exists.

– It's great to use birth year to adjust for any "cohort effects" in society over time. Is attained age the age at end of the study period? Wouldn't birth year and attained age be too highly correlated to use both in the model? What is the rationale for using cubic splines for these variables rather than the numerical variables themselves? I see from the code that 20-year increments were used for the knots, any rationale for this methodology would be helpful.

Attained age is not the age at the end of the study period. If so attained age and birth year would indeed be highly correlated. Attained age is the underlying person-time. In a classic logistic regression time is not incorporated. However, we are doing a time-to-event study using a log-linear Possion regression incorporating person-time. Attained age is thus the age of each individual at each time period in the study. Thus, we are comparing individuals of the same age over time. As explained in the methods section, attained age is divide into 1-year time intervals and used as the underlying time.

We did use the numerical variables in the models however instead of assuming linear relationships we used cubic splines to allow flexible modeling which better adjusts for potential non-linear relationships. For a more elaborated discussion on the benefits modelling using restricted cubic splines the reviewer is referred to: Gauthier, J., Q. V. Wu, and T. A. Gooley. "Cubic splines to model relationships between continuous variables and outcomes: a guide for clinicians." Bone marrow transplantation 55.4 (2020): 675-680.

– By excluding patients assigned a phecode at the start of the DNPR, would these be people who had the diagnosis previously and were recorded upon the inauguration of the register? Is there any kind of washout period you are using to define "start" of the DNPR?

This is likely to be people who had the diagnosis previously which was then registered at time the registry was created. Few is also likely to be individuals who got the diagnosis at the very start of the registry. Because this is unknown, we chose to exclude these patients from the analysis as this information was regarded too noisy. There is no wash-out period. The “start” of the DNPR is defined as the official start of the registry. Few patient were excluded from this procedure and thus the effect on the analysis is very limited.

– What is the ancestral breakdown of the cohort? Mostly European ancestries? While our current labels for genetic ancestry are quite rough, I think this is an important piece of information given the different distributions of blood groups across global populations.

The Danish population is very homogeneous and mostly of European ancestry. We have now added a table in Supplementary file 1 showing the ancestral distribution in Denmark in the inclusion period. We do unfortunately not have any information available on ancestry or ethnicity of the study sample. This fact has now been further elaborated in the limitations section lines 341-348.

– Excellent availability of code and summary data for tables/graphs.

Thanks. We have tried to make the analysis code easy accessible to allow for replication in other cohorts.

Results

– The audience may be less interested in the number of significant phecodes and more in patterns. It could be good to comment on the shared phecodes between the 50, 38, 11, 53, and 28 found. What large-scale disease groupings (phewas disease categories) do these tend to fall in? Does one blood group have far more cardiovascular phecodes than another? Are any phecodes significant for more than 3 blood groups? Etc.

We have now commented on the phewas disease groupings of the blood groups (lines 198-201).

– Personally, I prefer p-values in scientific notation rather than <0.001 but I understand Table 2 is a lot of data to present.

This was also something we considered. However, we decided that we would rather direct the reader’s attention to the confidence intervals than the p-value. Therefore, we used the <0.001 notation.

– The figures could benefit from larger labels for readability.

Larger labels have now been added to the figures.

– For the Manhattan plots, it would be good to specify -log10 FDR transformed adjusted p-values on the y-axis in addition to the figure legend.

This has now been specified on the y-axis.

– Were any blood groups associated with an earlier onset of outcomes?

In table 3 we show the findings from the analysis on the ABO and RhD blood groups association with age at the first diagnosis.

Discussion

– What was used to identify "novel associations"? A systematic literature review? Comparison to Dahlén? I would refrain from using novel unless you define specifically how it was determined to be novel.

We did a literature search ourselves but agree that without a reference to a published systematic literature review the term novel should not be used. We have now removed the term “novel” lines 266 and 268.

– A systematic comparison with what seems to be the closest study, Dahlén et al., would be beneficial as a type of replication.

We agree with the reviewer that such comparison would be beneficial. However, a systematic comparison would be very comprehensive, and we believe it would be more appropriate as a manuscript on its own. In the discussion we have instead chosen to discuss a few selected findings some of which have also been found in the study by Dahlén et al. as indicated by a reference to the study by Dahlén et al. For example, we highlight associations of pancreatic cancer, gastroduodenal ulcers, type 2 diabetes, and cardiovascular diseases which was also observed by Dahlén et al. Lastly, it should be noted that the two studies cannot be compared directly as Dahlén et al. used blood groups O as the reference and we used all other blood groups as the reference.

– I would refrain from using the term linkage in the discussion as that may lead the reader to think of chromosomal linkage, but I think the authors mean a causal association.

The term linkage has now been changed to relationship (line 273).

– I don't think the findings support the discussion point on the selective pressure.

We agree, this point has now been removed from the discussion and conclusion.

Reviewer #3 (Recommendations for the authors):

1. Could the authors justify why choosing to fit separate models comparing one blood type against all others, e.g. A vs. all others then AB vs. all others, is the more sensible choice than fitting one model that jointly tests for A vs. AB vs. B vs. O? I understand that there are various interpretative and statistical challenges to both, but fitting separate models is not internally consistent. The 'A vs. all others' model implicitly assumes that there is no difference in incidence in the AB, B, and O groups, but then the next model ('AB vs. all others') makes a different assumption, namely that there is no difference in incidence in the A, B, and O groups.

The aim of the study was to determine how each individual ABO blood group is distinct from all other ABO blood groups in terms of disease susceptibility. This question can only be answered by fitting separate models defining the patients as either having the blood group under study or not. An alternative, which would answer a different research question, would be to do a pairwise comparison between each ABO blood group would require 8x1,312 analyses (see response 1 to reviewer #1s comment). We do not believe that one model which jointly tests for all pair-wise comparisons can be fitted, the model would need to define a reference which in previous studies have been blood group O. The research question which has been asked in previous studies is thus how does blood group A, B and AB differ from blood group O in terms of disease susceptibility, respectively. We believe it to be more informative to ask how each blood group differs from all the other blood groups. However, we agree with the reviewer that this has its limitations because of the difference in the frequency of the individual ABO blood groups. We have now added a Supplementary Analysis where blood group O has been used as the reference (lines 145-147). The results of the supplemental analysis are presented in Supplementary files 6–7.

2. A natural limitation to this analysis is that there are more statistically significant findings in the O and A blood groups because they are the more prevalent groups, and statistical significance is driven by sample size. In this sense, it would be interesting if there were a way to account for the differences in sample size between the blood groups. Is it possible to investigate whether any of the groups have disproportionately more statistically significant findings after accounting for sample size?

We agree that this is a natural limitation to any study concerning blood groups and we have now commented on this in line 192-195. The only way to be able to better find associations for the less prevalent blood groups would be to include more patients, potentially by conducting a similar study combining cohorts from several countries to increase the power. We do not know of a way to determine if any of the blood groups have disproportionately more statistically significant findings while accounting for sample size.

3. Page 6, line 124: I think the use of the word 'confounder' here is not quite right in the technical sense, as I do not read this sentence to be claiming that sex is influencing blood type.

We agree with the reviewer that this was an incorrect formulation. We have now changed the statement.

4. Regarding the legend for Figure 2:

a. It should have triangles rather than circles. Assuming this plot was made in ggplot2, this can be done using the override.aes argument in the guides function.

We thank the reviewer for this tip. The circles have now been replaced by triangles.

b. it would be helpful to show more than 3 values on the legend.

We have now added two more legends so that five values are shown. However, for the comparison of the IRRs the readers should instead use table 2.

c. It would be helpful to use the same scale across the subfigures. What I mean is, in the bloodgroup AB figure, there is no discernible difference in size between a 1.1 and a 4.0 rate ratio.

We have now applied the same legends in all plots.

d. I realize this is very pedantic but I believe the legend is technically not showing rate ratios but rather max(rate_ratio, 1/rate_ratio).

This is correct. It is showing the rate ratio if rate ratio >= 1 and 1/rate ratio if the rate ratio <1. This makes it possible to compare the sizes of the rate ratios for both positive and negative associations. The direction of the triangles shows if it is a positive or inverse association as mentioned in the figure text.

5. Do the authors have any intuition why Figure 1 is bimodal? My interpretation of this figure is that, among those who were hospitalized in Denmark between 2006 and 2018, the plurality was born either in the immediate post-WW2 era (makes sense to me) or the 80s (doesn't make as much sense to me).

We agree with the reviewers first point. The reason that the distribution is bimodal is likely because the older population will be hospitalized in the inclusion period corresponding to the first peak. The second peak is likely caused by pregnant women who would be hospitalized when given birth and who commonly have their blood type determined in the hospital.

6. Page 7, line 152: reference 20 is not related to FDR. Can the authors provide a reference for their specific approach to controlling FDR?

Thanks for spotting this error. We have now also added a reference to the specific approach used to control for FDR.

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

   "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

   "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

   "This ORCID iD identifies the author of this article:" 0000-0003-0316-5866

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