(2024) 22:206
Liu et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03423-x

BMC Medicine

RESEARCH ARTICLE

Open Access

ABO and Rhesus blood groups and multiple
health outcomes: an umbrella review
of systematic reviews with meta‑analysesof observational studies
Fang‑Hua Liu1,2†, Jia‑Kai Guo1,3†, Wei‑Yi Xing1,2†, Xue‑Li Bai1,4†, Yu‑Jiao Chang1,2†, Zhao Lu1,5, Miao Yang1,6,
Ying Yang1,7, Wen‑Jing Li1,8, Xian‑Xian Jia1,6, Tao Zhang1,5, Jing Yang1,9, Jun‑Tong Chen10, Song Gao4, Lang Wu11,
De‑Yu Zhang4*, Chuan Liu4*, Ting‑Ting Gong4* and Qi‑Jun Wu1,2,4,12*   

Abstract
Background Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh)blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associa‑
tions is still lacking.
Methods We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases
from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of obser‑
vational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For eachassociation, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval,
heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grad‑ing scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according
to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evalu‑
ated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews(AMSTAR). AMSTAR contains 11 items, which were scored as high (8–11), moderate (4–7), and low (0–3) quality. We
have gotten the registration for protocol on the PROSPERO database (CRD42023409547).
Results The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations.
We re-calculated each association and found only one convincing evidence (Class I) for an association between bloodgroup B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28†

Fang-Hua Liu, Jia-Kai Guo, Wei-Yi Xing, Xue-Li Bai, and Yu-Jiao Chang
contributed equally to this work.
*Correspondence:
De‑Yu Zhang
zhangdy@sj-hospital.org
Chuan Liu
fkzlMDT@163.com
Ting‑Ting Gong
gongtt@sj-hospital.org
Qi‑Jun Wu
wuqj@sj-hospital.org
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecom‑mons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Liu et al. BMC Medicine(2024) 22:206

Page 2 of 13(95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity (I2 = 13%) and 95%
prediction intervals excluding the null value, and without hints of small-study effects (P for Egger’s test > 0.10,
but the largest study effect was not more conservative than the summary effect size) or excess of significance(P < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodologicalquality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate,and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE.
Conclusions Our findings suggest a potential relationship between ABO and Rh blood groups and adverse healthoutcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.
Keywords ABO blood group, Meta-analysis, Observational study, Rhesus blood group, Umbrella review

Background
Blood groups can be categorized based on different systems, such as the ABO blood group system, the Rhesus(Rh) blood group system, and the MN blood group system [1]. ABO blood group system is the most frequently
applied [2]. Each of the two alleles possesses antigen A,
B, or neither. These alleles come together to be a combination, determining an individual’s blood type phenotype, thus perform as the type of O, A, B, or AB. The
Rh blood group system is more polymorphic than others among human blood groups, which is composed ofnumerous antigens and next to ABO. The ABO and Rh
blood group system are extensively utilized in clinicalpractice, affecting host susceptibility [3, 4].
The previous study suggested that blood groups are
involved in disease mechanisms at the molecular level
mediated either through the blood group antigens orby the blood group reactive antibodies [5]. In addition, J. Höglund et al. found 39 plasma proteins wereassociated with variation at the ABO locus. For example, proteins with functions related to tumorigenesis(CA9, Gal-9, and KLK6) and pro-inflammatory or antiinflammatory functions (IFN-gamma-R1, IL-18BP, andMARCO) [6]. Generally, the overexpression of these
proteins leads to an abnormal cell proliferation or cell
growth. Thus, blood group may influence disease development through protein expression levels.
Numerous systematic reviews with meta-analyses
have been published, which explored correlations
between ABO and Rh blood groups with various healthoutcomes [7–9]. However, to date, the association
between these blood groups and human health outcomes remains controversial [10–12]. Most of them
have primarily concentrated on one single disease endpoint, lacking a comprehensive evaluation of the aforementioned relationships. In addition, the strength andreliability of the evidence remains unclear. To overcome the inherent limitations of systematic reviews
with meta-analyses and provide a comprehensive
overview of the claimed associations of ABO and Rh

blood groups with health outcomes, in the form of anumbrella review (UR), is necessary.
UR synthesizes evidence from various systematic
reviews with meta-analyses on a subject, appraising thecertainty, precision, and potential bias of the correlations,thus facilitating evidence grading based on well-defined
criteria [13]. We set out to conduct an UR to comprehensively evaluate systematic reviews with meta-analyses ofobservational studies, which examined associations of
ABO and Rh blood groups with a range of health outcomes. This endeavor was aimed at presenting an overview of the breadth and validity for aforementioned
associations. We thus hoped to provide both clinicians
and policy makers with robust data to identify high-risk
groups and inform clinical practice and guidelines.

Methods
Protocol registration

We have gotten the registration for the protocol of thisUR with the International Prospective Register of Systematic Reviews (PROSPERO; registration number
CRD42023409547). The study followed the Preferred
Reporting Items for Systematic Reviews and Meta-analyses reporting guideline [14] (Additional file 1: Table S1)and the Meta-analysis of Observational Studies in Epidemiology reporting guideline [15] (Additional file 1:
Table S2).
Search strategy

We systematically searched PubMed, Web of Science,
Embase, Scopus, Cochrane Library, and several regional
databases (Latin American and Caribbean Health Sciences Literature, Western Pacific Region Index Medicus,
Index Medicus for South-East Asia Region, Index Medicus for the Eastern Mediterranean Region, and African
Index Medicus) on the date from inception until Feb 16,
2024, to identify systematic reviews with meta-analysesof observational studies evaluating associations between
ABO as well as Rh blood groups and diverse health outcomes. We used the keywords (“ABO” OR “blood group”
OR “blood type” OR “Rh”) AND (“meta-analysis” ORLiu et al. BMC Medicine(2024) 22:206“systematic review” OR “systematic overview”) (Additional file 1: Table S3) to search. Besides, the literaturesearch was reviewed by hand-checking the reference listsof all systematic reviews with meta-analyses.
Eligibility criteria

Articles were selected based on the following PECOS(Population, Exposure, Comparison, Outcome, Study
design) strategy:(1) Population: population with ABO or Rh bloodgroups;(2) Exposure: ABO (blood types A, B, O, and AB) andRh (Rh positive [Rh +] and Rh negative [Rh −])blood groups (any method used to assess blood
type, including genetic tests and forward/reverse
agglutination tests, was accepted);(3) Comparison: different blood groups;(4) Outcome: any health outcome (e.g., cancer, coronavirus disease 2019 [COVID-19], coronary arterydisease, etc.). Ascertained health outcomes using
self-report, observed (e.g., clinical diagnoses) or
objective [e.g., biomarkers, certified mortality] criteria); and(5) Study design: systematic reviews with meta-analysesof observational studies (cohort, case–control, or
cross-sectional studies).
The exclusion criteria were established as follows: (1)systematic reviews without quantitative analysis, (2) systematic reviews with meta-analyses without study-level
data (e.g., effect sizes, 95% confidence intervals [CIs],
the number of cases, and participants/control), (3) studies on genetic polymorphisms, animal studies, laboratory
studies, conference abstracts and randomized controlled
trials, or (4) systematic reviews with meta-analyses conducted in languages other than English.
Given the requirement for a minimum of three original studies to calculate 95% prediction intervals (PIs), we
incorporated meta-analyses comprising at least 3 original
studies [16]. Associations were considered to overlap if
they assessed the same research topic and were examinedin more than one systematic review with meta-analysis[17]. The inclusion of primary studies once or more may
be led by incorporating results of reviews with overlapping associations, and biased findings and estimates
could be caused by incorporating results as well [18,19]. Therefore, the systematic review with meta-analysis
which contained the largest number of primary studies
was picked up if two or more systematic reviews withmeta-analyses overlapped, while the one with the largest
sample size of participants if more than one systematic

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review with meta-analysis kept the same numbered primary studies.
To ascertain the eligible articles, four experienced
investigators (Y-JC, J-KG, J-TC, and YY) matched inpairs and screened titles, abstracts, and full texts independently. We also checked the references of relevant
studies to confirm any other eligible articles by hand. If
there were any discrepancies, they would be made out by
a third reviewer (Q-JW).
Data extraction

Ten trained investigators (Y-JC, J-KG, YY, X-XJ, W-JL,
T-Z, YY, MY, ZL, and X-LB) were paired to extract data
independently, discrepancies were settled by a third
reviewer (Q-JW) when it was needed. From every metaanalysis we identified, it was abstracted of the contentson the name of the first author, journal, publication year,
exposures of interest, outcomes of interest, comparison,meta-analysis metrics (RR [risk ratio], OR [odds ratio],or HR [hazard ratio]), and the number of studies considered. From the individual studies included in every metaanalysis, it was extracted of the name of the first author,
publication year, epidemiological study design, numberof cases and controls in the observational case–control
studies or total population in the observational cohort
studies, maximally adjusted risk estimates, and 95% CIs.
Data analysis

Estimation of summary effect—We utilized a randomeffects model for each meta-analysis to do a calculation
for the summary effect size and corresponding 95% CI[20].
Estimation of prediction interval—We got the 95% prediction intervals (PIs) for the summary random effectsizes, because it can explain heterogeneity between varied studies and the uncertainty for the effect, with an
expectation in another study concerning on the same
relationship [21].
Assessment of heterogeneity—We evaluated heterogeneity with the I2 metric. And I2 value exceeding 50% isjudged large heterogeneity, and 75% is judged very large
heterogeneity similarly [22]. We also produced τ2 statistic
to assess the heterogeneity.
Assessment of small study effects—Through Egger’s
regression asymmetry test [23], we evaluated small-study
effects (i.e., whether larger studies are more likely to giveindirectly smaller estimates of effect size when compared with smaller ones) [24]. Reasons for distinctions
between small and large studies such as publication andother reporting biases, genuine heterogeneity, chance,or other conditions are revealed through small study
effects [24]. They were considered to exist when the largest study effect was more conservative than the summary

Liu et al. BMC Medicine(2024) 22:206

effect size in the meta-analysis and it was found that
P value < 0.10 in the regression asymmetry test.
Evaluation of excess significance—We assessed excess
significance bias by analyzing whether the number ofobserved studies (O) with nominally statistically significant results (“positive” studies, P < 0.05) was larger thanthe expected number of studies (E) with statistically significant results using the chi-square test [25]. The effectsize of the largest study (that is, the smallest standard
error) in a meta-analysis assessed the strength of thestudy which needed to use a noncentral t distribution[26, 27]. The excess significance test was judged positive
when it comes to both O > E and P < 0.10 [22].
Strength of evidence—According to the establishedcriteria applied in previously published URs [13, 28–30]
and based on our calculation, significant associations(P < 0.05) between ABO and Rh blood groups and healthoutcomes were divided into 4 levels of evidence strength(convincing [Class I], highly suggestive [Class II], suggestive [Class III], or weak [Class IV] evidence) to draw
conclusions. This criterion was evaluated based on statistical significance, number of cases, heterogeneity, largest
study, 95% PI, small-study effect, and excess significancebias. P value ≥ 0.05 demonstrated a statistically non-significant association (Additional file 1: Table S4).
Certainty of the evidence—The credibility of the evidence was qualitatively assessed by two reviewers (W-YX
and X-LB) using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations)
method. As recommended by GRADE, the level of evidence was graded the high, moderate, low, and very low
determined by risk of bias, inconsistency, indirectness,
imprecision, and publication bias.
Sensitivity analyses—To verify the robustness of ourfindings, we conducted sensitivity analyses to assess theconcordance of the summary associations, which were
initially graded as convincing (Class I) or highly suggestive (Class II) evidence. The sensitivity analyses wererealized by excluding small-sample studies (< 25th percentile) from meta-analyses with evidence of small-study
effects and primary studies with low-quality evidence(Newcastle–Ottawa Scale < 6 [31], Agency for Healthcare
Research and Quality < 8 [32], or effective public healthpractice project guideline rating moderate and low rather
than strong quality [33]. Further sensitivity analysis was
performed with the meta-analyses due to overlap in themain analysis. All statistical analyses were conducted in
STATA version 17 and RStudio version 3.6.2.
Assessment of the methodological qualityof meta‑analyses

We used A Measurement Tool to Assess Systematic
Reviews (AMSTAR) to evaluate the quality of systematic

Page 4 of 13

reviews and meta-analyses, which was considered asa valid and dependable measurement tool [34]. This
instrument contains a total of 11 items. A “yes” scores
one point, and the other answers score 0 points. The
AMSTAR was graded as low (0–3 points), moderate (4–7
points), or high quality (8–11 points) [34]. Ten trained
investigators (Y-CS, Z-PN, W-YX, YY, W-JL, ZL, JY,
X-LB, MY, and J-NS) matched in pairs, and AMSTAR
was used independently to assess the eligible systematic
reviews with meta-analyses on methodological quality.
Disagreements were made the final decision by the third
author (Q-JW).

Results
Literature identification and selection

We retrieved 6474 records from PubMed, Web of Science, Embase, Scopus, Cochrane Library, and several
regional databases. According to the criterion, 159 fulltext articles were retrieved and checked for inclusion
after duplicate removal, title, and abstract screening.
There were no additional eligible articles found by handchecking the reference lists of all systematic reviews.
Overall, 51 systematic reviews with meta-analyses corresponded to 270 unique associations were included [7,9, 11, 12, 31–33, 35–78] (Fig. 1). The two pairs of four
investigators showed high consistency in terms of study
selection, with kappa values of 0.893 and 0.926, respectively. The excluded articles and the reasons behind theirremoval are provided in Additional file 1: Table S5. For
meta-analyses excluded due to a lack of data relating toquantitative synthesis, we further summarized their findings in Additional file 1: Table S6.
Characteristics of included meta‑analyses

The 51 systematic reviews with meta-analyses corresponded to 270 unique associations: 105 on cancer outcomes (39%), 91 on infectious outcomes (34%), 25 on
cardiovascular outcomes (9%), 22 on oral-related outcomes (8%), 12 on pregnancy-related outcomes (4%),
5 on metabolic disease (2%), and 10 on other outcomes(4%) (Fig. 2). The systematic reviews with meta-analyses included in this UR were published from 2007 until
2023. The number of studies per association ranged from
3 to 49. One hundred and ninety-five meta-analyses
included ≥ 1000 cases (Additional file 1: Table S7).
Summary findings

Among 270 associations included in our UR. Eightynine associations (33%) presented a nominally statistically significant effect (P < 0.05). Of these, 41 (46%) werein conformity with the principle of statistical significance at P < ­10 −3, and 24 (27%) reached P < ­10 −6. Whencalculating the effect size of the largest data study of theLiu et al. BMC Medicine(2024) 22:206

Fig. 1 PRISMA flow chart. Flow chart of included and excluded systematic reviews and meta-analyses

Fig. 2 Map of 270 blood group related outcomes: percentage of outcomes per outcome category for all studies

Page 5 of 13

Liu et al. BMC Medicine(2024) 22:206

associations, 61 (69%) of the 89 associations showed statistical significance. After estimating the 95% PI, 66 (74%)
contained null values. Twenty-three (26%) and 19 (21%)
associations had significant (50% < I2 ≤ 75%) and considerable (I2 > 75%) heterogeneity estimates, respectively.
Twenty-one (24%) of the 89 associations presented evidence for small-study effects, and 29 (33%) associations
presented evidence for excess significance bias.
Cancer outcomesWe summarized 105 associations between blood groupand cancer outcomes. The magnitude of the observed
summary random effects estimates ranged from 0.65 to
1.54 (Additional file 2: Fig. S1). Thirty-one meta-analyses(30%) presented a nominally statistically significant effect(P < 0.05). Of these, 14 associations were graded as suggestive or above evidence (Fig. 3, Additional file 1: Tables
S7–8).

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Esophageal cancer

We found blood group B was associated with a higher
risk of esophageal cancer (OR = 1.20; 95% CI: 1.10, 1.31),
compared with blood group non-B. And the association
was graded as suggestive evidence.
Gastric cancer

Blood group A was associated with a higher risk of gastric cancer, both compared with blood group non-A(OR = 1.11; 95% CI: 1.07, 1.15) and blood group O (OR =1.19; 95% CI: 1.14, 1.24). However, blood group O wasassociated with a lower risk of gastric cancer (OR = 0.91;
95% CI: 0.89, 0.94), compared with blood group non-O.
These above associations were graded as highly suggestive evidence.
Pancreatic cancer

Compared with blood group O, blood group A was associated with higher risk of pancreatic cancer (OR = 1.33;

Fig. 3 Forest plot showing studies investigating the association between blood group and health outcomes. CI, confidence interval; CagA,
cytotoxin-associated gene A

Liu et al. BMC Medicine(2024) 22:206

95% CI: 1.27, 1.40), cytotoxin-associated gene A (CagA)
endemic pancreatic cancer (OR = 1.46; 95% CI: 1.24,1.50) and CagA-nonendemic pancreatic cancer (OR =1.43; 95% CI: 1.24, 1.64); blood group B was associatedwith higher risk of pancreatic cancer (OR = 1.20; 95%
CI: 1.10, 1.31) and CagA-nonendemic pancreatic cancer (OR = 1.42; 95% CI: 1.19, 1.69); blood group AB was
associated with higher risk of CagA-nonendemic pancreatic cancer (OR = 1.54; 95% CI: 1.26, 1.88); Blood group
non-O was also associated with higher risk of pancreaticcancer (OR = 1.31; 95% CI: 1.22, 1.42). Compared withblood group non-O, blood group O was associated with ahigher risk of pancreatic cancer (OR = 1.32; 95% CI: 1.22,
1.42), CagA endemic pancreatic cancer (OR = 1.20; 95%
CI: 1.11, 1.30), and CagA-nonendemic pancreatic cancer(OR = 1.42; 95% CI: 1.28, 1.59). According to the UR criteria, the associations between blood group A and pancreatic cancer, CagA endemic, and CagA-nonendemic
pancreatic cancer risk, blood group O and pancreaticcancer and CagA-nonendemic pancreatic cancer, bloodgroup non-O and pancreatic cancer were graded ashighly suggestive evidence. The remaining associations
were graded as suggestive evidence.
Infectious disease outcomes

Ninety-one associations between blood group and infectious disease outcomes were investigated. The magnitude of the observed summary random effects estimates
ranged from 0.50 to 47.85 (Additional file 2: Fig. S2).
Overall, 27 (32%) of 85 associations reached a statisticallysignificant value at P < 0.05. Ten associations were supported by suggestive or above evidence (Fig. 3, Additional
file 1: Tables S7–8).
Coronavirus disease 2019 (COVID‑19)

We found blood group A (OR = 1.25; 95% CI: 1.14, 1.37)
and blood group B (OR = 1.15; 95% CI: 1.07, 1.22) wereassociated with an increased risk of COVID-19 infection,compared with blood group O. But blood group O wasassociated with a decreased risk of COVID-19 infection(OR = 0.88; 95% CI: 0.82, 0.94), compared with bloodgroup O. The association between blood group O and
COVID-19 infection was supported by highly suggestive
evidence. The other two associations were supported bysuggestive evidence.

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and RR = 47.85; 95% CI: 44.01, 52.03; blood group O
versus blood group non-O). And these four associations
were supported by highly suggestive evidence.
P. falciparum

Blood group A (OR = 1.68; 95% CI:1.32, 2.14), bloodgroup B (OR = 1.97; 95% CI:1.49, 2.59), and blood group
non-O (OR = 1.86; 95% CI:1.49, 2.33) were associatedwith an increased risk of P. falciparum infection, compared with blood group O. All of these associations were
supported by suggestive evidence.
Cardiovascular and cerebrovascular outcomes

Twenty-five associations between blood group and cardiovascular and cerebrovascular outcomes were summarized. The magnitude of the observed summary
random effects estimates ranged from 0.58 to 2.55 (Additional file 2: Fig. S3). Of which, 21 (84%) associations
gave a show on statistically significant effect nominally(P < 0.05), and 7 associations reached suggestive or above
evidence (Fig. 3, Additional file 1: Tables S7–8).
Myocardial infarction (MI)Blood group A (OR = 1.29; 95% CI: 1.16, 1.45) andblood group non-O (OR = 1.25; 95% CI:1.14, 1.37) had
an increased risk of MI compared with blood group O.
Another association showed blood group O had anincreased risk of MI (OR = 1.28; 95% CI:1.17, 1.40) compared with blood group non-O. All three associations
reached suggestive evidence.
Peripheral vascular disease (PVD)

Compared with blood group O, blood group A (OR =1.44; 95% CI: 1.19, 1.74) and blood group non-O (OR =1.45; 95% CI:1.35, 1.56) had an increased risk of PVD.
The associations between blood group A and bloodgroup non-O and PVD risk were reached suggestive andhighly suggestive evidence, respectively.
Venous thromboembolism (VTE)

Blood group A (OR = 1.63; 95% CI: 1.40, 1.89) andblood group non-O (OR = 2.10; 95% CI:1.83, 2.40) had
an increased risk of VTE compared with blood group
O. And the two associations reached highly suggestive
evidence.

Human immunodeficiency virus (HIV)

Four blood groups of ABO blood group system wereassociated with an increased risk of HIV infection (RR =24.25; 95% CI: 21.60, 27.23; blood group A versus bloodgroup non-A, RR = 21.29; 95% CI: 18.62, 24.36; bloodgroup B versus blood group non-B, RR = 5.44; 95% CI:
4.10, 7.22; blood group AB versus blood group non-AB,

Oral‑related outcome

Twenty-two associations between blood group and oralrelated outcomes were summarized. The magnitude of
the observed summary random effects estimates ranged
from 0.70 to 1.36 (Additional file 2: Fig. S4). Only one
association gave a show on statistically significant effectLiu et al. BMC Medicine(2024) 22:206

nominally (P < 0.05). No association reached suggestiveor above evidence (Additional file 1: Tables S7–8).
Pregnancy‑related outcomesWe summarized twelve associations between bloodgroup and pregnancy-related outcomes. The summary
random effects estimate magnitude ranged from 0.90 to
1.49 (Additional file 2: Fig. S5). Only two associations
were statistically significant at P < 0.05. And no association reached suggestive or above evidence (Additional
file 1: Tables S7–8).
Metabolic disease outcomesWe summarized 5 associations between blood groupand metabolic disease outcomes. The magnitude of theobserved summary random effects estimates ranged
from 0.91 to 1.28 (Additional file 2: Fig. S6). Only 2 (40%)
of 5 associations were nominally statistically significant
at a P < 0.05 level. One association was supported by suggestive or above evidence (Fig. 3, Additional file 1: Tables
S7–8).
Type 2 diabetes mellitus incidence (T2DM)

Blood group B, compared with blood group non-B, had agreater risk of T2DM (OR = 1.28; 95% CI: 1.17, 1.40), andthe association was supported by convincing evidence.
Other outcomes

Ten associations between blood group and other outcomes (such as bleeding complication, decreased ovarian
reserve) were summarized. The summary random effects
estimate magnitude ranged from 0.84 to 1.33 (Additional
file 2: Fig. S7). Only one association was statistically significant at P < 0.05. And which was supported by highlysuggestive evidence (Fig. 3, Additional file 1: Tables
S7–8).

Page 8 of 13

as comparison with blood group O, both blood group A(OR = 1.63; 95% CI: 1.40, 1.89) and non-O blood group(OR = 2.10; 95% CI: 1.83, 2.40) increased the risk of VTE
incidence. In addition, we found 14 associations wererated as suggestive evidence. Fifty-six associations wererated as weak evidence and the remaining 181 associations were not significant (Additional file 1: Tables S7–8).
Methodological quality of the meta‑analyses

With the measurement tool AMSTAR, 18 (35%) articles
were categorized as high quality. Of the 51 articles, 32(63%) articles and only 1 (2%) article were categorized asmoderate and low quality, respectively (Additional file 1:
Table S9).
Certainty of the evidence

Based on the GRADE approach, no health outcomesreached high credibility criteria. Nine of 89 health outcomes met the moderate certainty criteria. Thirty-three
and 47 of 89 health outcomes met the low and very
low certainty criteria, respectively (Additional file 1:
Table S10).
Sensitivity analyses

Findings from sensitivity analyses are reported in Additional file 1: Tables S11–13. Removal of small-sized studies from the meta-analyses with evidence of small-study
effects, these evidence ratings were not modified. When
excluding low-quality studies, the associations between
blood group A and gastric cancer, pancreatic cancer,
and VTE and blood group O and pancreatic cancer
retained their highly suggestive evidence ratings. When
we focused on the associations excluded due to overlap,
twelve associations were downgraded because of random
P value.

Discussion

Bleeding complication

Main findings

Blood group O was associated with a higher risk of bleeding complication (OR = 1.33; 95% CI: 1.25, 1.42), compared with blood group non-O, which was supported byhighly suggestive evidence.
In summary, we found an association between bloodgroup B and an increased risk of T2DM incidence (OR =1.28; 95% CI: 1.17, 1.40) was rated as convincing evidence
when it was taken as comparison for blood group nonB, by owing over 1000 cases, random P value < ­10–6, notlarge heterogeneity (I2 < 50%), 95% PI excluding the nullvalue, no hints for small-study effects and excess significance bias (Fig. 3). Eighteen associations were rated ashighly suggestive evidence, they reached a statisticallysignificant value at P < ­10 −6, had more 1000 cases, and
the P value of the largest study was less than 0.05, such

This is the first UR to provide a comprehensive overview
of the observational data assessing associations between
the ABO and Rh blood groups and multiple health outcomes. And we found 89 statistically significant associations. Convincing (Class I) evidence was only presented
for the association between blood group B and T2DM
risk. Highly suggestive (Class II) evidence was presented
for 18 associations, such as HIV and VTE.
Comparison with previous studies

The positive association between blood group B andthe risk of T2DM detected in this UR was supported
by a prospective cohort study. This study included
82,104 women and followed for 18 years in France,
throughout which 3553 women had a validated

Liu et al. BMC Medicine(2024) 22:206

diagnosis of T2DM. After adjustment for potential confounders, blood group B increased the riskof T2DM compared with blood group O (HR = 1.21;
95% CI: 1.07, 1.36) [79]. A comparative cross-sectional
study, including 326 participants (163 T2DM patientsand 163 age and sex-matched healthy individuals),confirmed the harmful association of blood group
B with T2DM risks (OR = 1.96; 95% CI: 1.05, 3.65),
compared with the non-B blood group [80]. A metaanalysis revealed blood group B was significantly associated with an increased risk of T2DM (RR = 1.05; 95%
CI: 0.93, 1.18), compared with the non-B blood group[81]. Nevertheless, caution is warranted in interpretingthe observed association between blood group B and
T2DM risk. Despite our result being consistent withfindings from a prospective cohort study conducted
in France, it is important to note that they exclusively
included women. Subgroup analysis stratified by gender is needed in the future. In addition, the above
studies have different control groups, sample sizes,
and study designs. Further well-designed, large-scale
prospective studies are needed to clarify the association between blood group B and T2DM.
The association between blood groups and HIV
infection wase debated. In our UR, we found all ABO
blood group was associated with an increased riskof HIV infection, and all of them were supported byhighly suggestive evidence. A cross-sectional study
conducted in Nairobi, Kenya among 280 female sex
workers showed blood group A (OR = 1.56; 95% CI:
1.06, 2.28) was associated with HIV infection, compared with blood group O. However, blood group B(OR = 1.63; 95% CI: 0.94, 2.80) and blood group AB(OR = 1.50; 95% CI: 0.57, 3.93) were not associatedwith HIV infection [82]. A previous cross-sectional
study conducted in South Africa investigated the associations between ABO blood groups and HIV infection among blood donors. The results suggested that
the ABO blood group was not related to HIV infection. However, the point estimate for OR assesses
blood group AB and HIV infection is 1.03 [83]. Crosssectional studies cannot be used to infer causality andpotential biases should be considered in the observational studies. Further well-designed longitudinalstudies and controlling for different sources of bias are
warranted to assess causality.
The harmful association between blood group A andnon-O blood group and VTE incidence observed in our
UR was supported by previous studies. For example, aprevious study that included 7830 patients found bloodgroup A was associated with VTE incidence (OR =2.16; 95% CI: 1.10, 4.24) [84] in comparison with theblood group.

Page 9 of 13

Biological plausibility

Multifactorial mechanisms might explain the increased
risk of T2DM associated with blood groups. The previous
study showed ABO blood group is in association withthe level of plasma soluble intercellular adhesion molecule-1 and tumor necrosis factor receptor-2 [85]. And
the above markers are identified to contribute a higher
risk of T2DM. Moreover, a study suggested that the ABO
blood group, being a gene-determined host factor, modulated the composition of the intestinal microbiota [86],which played an important role in influencing metabolism including glucose metabolism, energy balance, andlow-grade inflammation [87].
For potential mechanisms between blood group andHIV infection, some studies indicated that expressionof glycosyltransferase could be induced due to HIV andfurther synthetization of antigens of blood type on lymphocyte surfaces [88, 89]. Therefore, apart from releasing
new virion particles from lymphocytes, HIV could also
integrate antigens of the blood group into its envelope
surface [89]. The presence of these antigens sensitizes thevirus against neutralizing antibodies and complements
specific blood groups, potentially influencing the virus’stransmission between individuals and different bloodgroups [88].
It has not been thoroughly clear of the exact mechanism revealing the ABO blood group and VTE. The
most likely hypothesis is that ABO plays a role in dominating the glycosylation degree of von Willebrand factor via modifying GT expressions [90]. von Willebrandfactor multimeric composition is regulated in plasma by
ADAMTS13. Proteolysis is enhanced by von Willebrandfactor deglycosylation by ADAMTS13 [91]. In addition,individuals with blood group A1 and blood group B areat the level of 20% higher circulating Willebrand factor
on average, which factor VIII levels than for O or A2 [92,93], high plasma levels of Willebrand factor, and factor
VIII having association with increased VTE risk [94–97].
Strengths and limitations

To our knowledge, this is the first UR that systematically and comprehensively appraises the hierarchy ofevidence relating blood groups to various health outcomes. Beyond summarizing the findings for a seriesof health end-point, we further an inquiry into bias andheterogeneity in the observational blood group literature.
Compared with an individual systematic review or metaanalysis. This UR helped to summarize the complicatedand vast amounts of research by comparing and contrasting the results of individual reviews, which provided anefficient overview of the findings for a particular problem[98]. Moreover, we adhered to a systematic methodology
involving a search strategy in electronic databases andLiu et al. BMC Medicine(2024) 22:206

study selection and extraction conducted by two separate
researchers. We also used standard approaches to evaluate the methodological quality and epidemiological evidence strength of the included studies.
UR provides top-tier evidence and important insights,
but several limitations should be considered. First, somesystematic reviews and meta-analyses did not acquire thelevel of evidence because they did not provide the number of cases. Second, we used I2 (an estimate of the proportion of variance reflecting true differences in effectsize) and τ2 (an estimate of true variation in the summary
estimate) to evaluate statistical heterogeneity. According
to UR criteria, I2 < 50% was applied as one of the criteria
for convincing evidence in our UR, assigning the best
evidence grade to robust associations. Several systematic
reviews with meta-analyses examined the clinical andmethodological heterogeneity by performing subgroup
analyses stratified by these characteristics. Of note, we
also extracted this information and analyzed it in thepresent UR. For example, within the subgroup comprising pancreatic cancer patients classified as either CagAnonendemic or CagA endemic and COVID-19 patientswith hospitalization, we found the results from subgroup
analyses were consistent with the main findings. Futurestudies should better explore clinical and methodological heterogeneity to verify the association between bloodgroups and various health outcomes. Third, for the same
health outcome (e.g., COVID-19 infection), the comparison group is different (e.g., A vs B, A vs AB, A vs O, andA vs non-A). Therefore, the findings between the bloodgroup and health outcome in our study should be interpreted with caution. Fourth, we identified studies frompublished systematic reviews with meta-analyses, which
may have omitted some individual studies for not in thesystematic reviews with meta-analyses above. However,the systematic reviews with meta-analyses included in
the current study were those of included the largest number of primary studies, which was unlikely to affect our
results. Fifth, the reliability of the UR relies directly on
the incorporated systematic reviews with meta-analyses.
However, some included systematic reviews with metaanalyses existed risk of bias, which might decrease therobustness of statistical analyses. The study did not adjust
for confounding factors that could have mediated associations between blood group and outcomes, because
adjustment for potential confounders was unavailable inpublished systematic reviews with meta-analyses. Sixth,
as this UR only included observational data, limitations
common to this approach may influence the results ofthis review, such as information bias and residual confounding. There was a limited number of systematic
reviews with meta-analyses that exclusively included
prospective study designs, where information bias wasPage 10 of 13

reduced. However, case–control and cross-sectional
study designs were more common than prospective study
designs and were associated with a higher potential for
information bias and reverse causation.

Conclusions
This comprehensive UR will help investigators to judge
the relative priority of health outcomes related to the
ABO blood group and RH blood group for future
research and clinical management of the disease. In summary, compared with the non-B blood group, we foundthe association between blood group B and increased
risk of T2DM incidence (OR = 1.28; 95% CI: 1.17, 1.40)
was supported by convincing evidence. We also found 18
associations, such as blood group A and the risk of VTE
incidence (OR = 1.63; 95% CI: 1.40, 1.89) and non-O
blood group and the risk of VTE incidence (OR = 2.10;
95% CI: 1.83, 2.40), were supported by highly suggestive
evidence. To enhance the quality of evidence regardingthese associations and be able to give strong recommendations, future studies should consider several aspects.
For example, set the same control group to increase thecomparability of results, use standard definition of exposure or outcome to reduce clinical heterogeneity, andmatch the characteristics between cases and controls to
reduce the impact of potential confounding. In addition,
future studies understanding mechanisms between bloodgroups and various health outcomes are needed.
Abbreviations
AMSTAR​	A Measurement Tool to Assess Systematic Reviews
CIs	Confidence intervalsHIV	Human immunodeficiency virus infectionHR	Hazard ratioOR	Odds ratioPIs	Prediction intervals
PVD	Peripheral vascular diseaseRh	Rhesus
RR	Risk ratio
T2DM	Type 2 diabetes mellitus
UR	Umbrella review
VTE	Venous thromboembolism

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03423-x.
Additional file 1: Tables S1-13. Table S1-PRISMA checklist of items
to include when reporting a systematic review or meta-analysis;Table S2-MOOSE checklist for meta-analyses of observational studies;
Table S3-Search strategy; Table S4-Criteria for categorizing the credibilityof evidence in the umbrella review; Table S5-The list of the excluded
records during the process of full-text review; Table S6-The summary
results of meta-analyses excluded due to lack of data for quantitativesynthesis; Table S7-Description of 270 associations investigating the asso‑
ciations between ABO and Rhesus blood groups and multiple health out‑
comes; Table S8-Strength assessment of evidence from 270 associations
examining associations between ABO and Rhesus blood groups and mul‑
tiple health outcomes; Table S9-Methodological quality assessment of the
included articles with AMSTAR; Table S10-The results of GRADE assessment

Liu et al. BMC Medicine(2024) 22:206of the evidence certainty on the associations between ABO and Rhesus
blood groups and multiple health outcomes; Table S11-Sensitivity analysis
results of omission of small-sized studies (< 25th percentile) from thosemeta-analyses with evidence of small-study effects; Table S12-Sensitivity
analysis results of omission of primary studies with low-quality evidence;
Table S13-Sensitivity analysis results of excluded meta-analyses due tooverlap.
Additional file 2: Fig. S1-7. Fig. S1-Summary effects sizes with inverse of
the variance of association between blood group and cancer outcomes;
Fig. S2-Summary effects sizes with inverse of the variance of association
between blood group and infectious disease outcomes; Fig. S3-Summary
effects sizes with inverse of the variance of association between bloodgroup and cardiovascular and cerebrovascular outcomes; Fig. S4-Sum‑
mary effects sizes with inverse of the variance of association between
blood group and oral related outcomes; Fig. S5-Summary effects sizes
with inverse of the variance of association between blood group andpregnancy related outcomes; Fig. S6-Summary effects sizes with inverse of
the variance of association between blood group and metabolic disease
outcomes; Fig. S7-Summary effects sizes with inverse of the variance of
association between blood group and other outcomes.
Acknowledgements
Not applicable.
Authors’ contributions
F-HL, J-KG, T-TG, and Q-JW contributed to the study design. J-KG, ZL, MY, X-LB,
YY, W-JL, X-XJ, and TZ collection of data. F-HL and W-YX analysis of data. F-HL,
J-KG, Y-JC, YJ, J-TC, SG, LW, D-YZ, CL, T-TG, and Q-JW wrote the first draft of themanuscript and edited the manuscript. All authors read and approved thefinal manuscript. F-HL, J-KG, W-YX, X-LB, and Y-JC contributed equally to thiswork.
Funding
This work was supported by the National Key Research and Development Pro‑
gram of China (No. 2022YFC2704205 to Wu QJ), the Natural Science Founda‑tion of China (No. 82073647 and No. 82373674 to Wu QJ and No.82103914 toGong TT), Outstanding Scientific Fund of Shengjing Hospital (Wu QJ), and 345
Talent Project of Shengjing Hospital of China Medical University (Gong TT).
Availability of data and materials
Not applicable.

Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Clinical Epidemiology, Shengjing Hospital of China Medical
University, Shenyang, China. 2 Clinical Research Center, Shengjing Hospitalof China Medical University, Shenyang, China. 3 Hospital Management Office,Shengjing Hospital of China Medical University, Shenyang, China. 4 Depart‑
ment of Obstetrics and Gynecology, The Fourth Affiliated Hospital of China
Medical University, Shenyang, China. 5 Department of Radiology, Shengjing
Hospital of China Medical University, Shenyang, China. 6 Department of Pedi‑
atrics, Shengjing Hospital of China Medical University, Shenyang, China.
7
Department of Hematology, Shengjing Hospital of China Medical University,
Shenyang, China. 8 Department of Otolaryngology Head and Neck Surgery,
Shengjing Hospital of China Medical University, Shenyang, China. 9 Depart‑
ment of Endocrinology, Shengjing Hospital of China Medical University,
Shenyang, China. 10 School of Medicine, Zhejiang University, Hangzhou, China.
11
Cancer Epidemiology Division, Population Sciences in the Pacific Program,

Page 11 of 13

University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu,
HI, USA. 12 NHC Key Laboratory of Advanced Reproductive Medicine and Fertil‑ity (China Medical University), National Health Commission, Shenyang, China.
Received: 29 November 2023 Accepted: 9 May 2024

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