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GUIDELINES/CLINICAL TRIALS/META-ANALYSIS (WJ KOSTIS, SECTION EDITOR)
Efficacy of Probiotics in Patients of Cardiovascular Disease
Risk: a Systematic Review and Meta-analysis
Asher Dixon
1,2,3
&Kai Robertson
4
&Amanda Yung
2
&Michael Que
2
&Hayden Randall
2,3
&Don Wellalagodage
2,3
&
Tynan Cox
1
&Dylan Robertson
1
&Cheng Chi
1
&Jing Sun
1
#Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
This meta-analysis examined the effect of probiotics on outcomes associated with cardiovascular disease risk factors (high blood
pressure, overweight BMI, high cholesterol and triglycerides, elevated HbA1c and serum glucose). All randomised controlled
trials publish on PubMed, Scopus, Embase, Grey Literature and the Cochrane Central Register of Controlled Trials (CENTRAL)
from 1990 to 2020 were systematically searched. The PEDro scale was used to assess the quality of studies. A total of 34 studies
with 2177 adults were selected for inclusion in the analysis. The mean difference and effect size with a 95% confidence interval
(CI) were analysed for the pooled results. Statistically significant pooled effects of probiotics were found in the reduction of
systolic and diastolic blood pressure, total cholesterol, LDL-C, serum glucose, HbA1C and BMI; and elevation of HDL-C. No
significant changes were observed in the outcome of triglycerides. Subgroup analysis revealed statistically significant effects of
probiotics on the treatment of risk factors, with results favouring longer duration of treatment (> 1.5 months), use of alternate
formulations (kefir and powder), higher dosage of probiotics (> 1.0 × 10
9
CFU), lower rate of study attrition (< 15%), double
blinding of the study, diabetic patients and female populations. In summary, our meta-analysis showed a highly significant
reduction in SBP, DBP associated with type 2 diabetes and in patients with diabetes mellitus, milk intake and more than
1.5 months duration intake. The effect on the reduction of total cholesterol LDL-C was associated with diabetes, hypertension,
hypercholesterolemia, yoghurt intake and less than 1.5 months probiotic intake. The effect on the reduction of glucose and
HbA1c was associated with diabetes, small dosage of probiotics, milk type and less than 1.5 months duration intake.
Additionally, probiotic supplement had a beneficial effect in reducing BMI associated with obesity, higher dosage intake of
probiotics and more than 1.5 months duration of intake.
Keywords Probiotics .Cardiovascular Disease .Hypertension .Diabetes .Dyslipidaemia .Nutrition
Introduction
Cardiovascular disease (CVD) is the leading cause of death
worldwide. In 2016, CVDs were responsible for
approximately 17.9 million deaths, or 31% of all deaths glob-
ally [1]. By 2025, over 5 million men and 2.8 million women
are projected to die prematurely from CVD [2]. The most
prominent physiological risk factors for CVDs are a cluster
of medical conditions known as metabolic syndrome. This
“deadly quartet”of metabolic abnormalities includes obesity,
glucose intolerance/insulin resistance, dyslipidaemia and hy-
pertension [3].
The presence of cardiovascular risk factors, or any one of
its components, confers a greatly increased risk of developing
CVD, including ischaemic heart disease and stroke [4]. While
there are variable definitions of cardiovascular disease risk
such as metabolic syndrome, it is estimated to be prevalent
in approximately one-quarter of the world population, putting
a significant proportion of humanity at risk of developing
CVD [5]. Thus, CVDs and its associated comorbidities repre-
sent a significant burden to world health.
This article is part of the Topical Collection on Guidelines/Clinical Trials/
Meta-Analysis
*Jing Sun
j.sun@griffith.edu.au
1
School of Medicine, Griffith University, Gold Coast Campus, Gold
Coast, Queensland Q4222, Australia
2
Sydney Medical School, University of Sydney, Camperdown, NSW,
Australia
3
St Paul’s College Graduate House, Camperdown, Australia
4
School of Medical Science, Griffith University, Gold
Coast, Australia
Current Hypertension Reports (2020) 22:74
https://doi.org/10.1007/s11906-020-01080-y
Currently, CVD risk reduction is commonly managed by
medications such as statins and antihypertensives [6]. Control
of risk factors for CVD can also be achieved through lifestyle
modification, especially through diet and exercise. For in-
stance, lowering salt intake has been shown to reduce blood
pressure substantially [7]. However, due to the immense and
ever-growing burden of CVDs, alternative approaches are re-
quired in the coming years to reduce morbidity and mortality
associated with CVDs.
The past decade has seen an upsurge of interest in
the contribution of the human microbiome to health and
disease. The human gastrointestinal microbiota is incred-
ibly diverse [8] and plays key roles in maintaining the
gastrointestinal epithelium, modulation of the immune
system, digestion and nutrient metabolism [9]
Alteration of the microbiota in a pathological fashion,
known as dysbiosis, may be involved in the pathogene-
sis of many diseases, including inflammatory bowel dis-
ease [10], cancer [11], obesity [12] and diabetes
mellitus [13]. Dysbiosis itself can be caused by medical
and lifestyle factors such as infection, host genetics,
antibiotic use and a high-fat, low-fibre diet [14].
Crucially, there have also been studies linking dysbiosis
to hypertension, insulin resistance, dyslipidaemia and
increased adiposity [15,16], thus raising the possibility
of managing CVD risk factors by treating dysbiosis.
One method of treating dysbiosis is by probiotics.
Probiotics, formally defined as “live microorganisms that,
when administered in adequate amounts, confer a health ben-
efit on the host”[17], have shown efficacy in helping prevent
or alleviate gut-related disorders such as irritable bowel syn-
drome [18], diarrhoea [19,20], colorectal cancer [21] and
lactose intolerance [22]. The most common strains used in
probiotics are lactic acid bacteria belonging to genera
Lactobacillus,Enterococcus and Bifidobacterium [23].
Possible mechanisms by which probiotics exert their effect
include restoring microbiota diversity, decreasing cholesterol
absorption, reducing the production of pro-inflammatory mol-
ecules and the synthesis of various metabolites [24,25].
Probiotics are most frequently consumed in the form of food
or drink, such as yoghurts, or as dietary supplement pills [26].
Probiotic studies have alsoshown promising results in treating
hypertension, dyslipidaemia, insulin resistance and obesity,
with potential to have protective and therapeutic effects
against heart failure and myocardial infarction [27,28•,29].
Patients with CVD risk factors, such as metabolic syn-
drome, face a greatly increased risk of cardiovascular events
and may therefore have the greatest potential for benefit from
probiotic therapy. Thus, our systematic review and meta-
analysis aims to elucidate whether probiotic therapy is effec-
tive in lowering the physiological and biomedical risk factors
of CVD (namely obesity, blood pressure, blood glucose and
lipids) in patients who are afflicted by cardiovascular
comorbidities such as hypertension, type II diabetes, obesity
and dyslipidaemia.
Methods
Search Methods
The review protocol was registered with the Prospero
International Prospective Register of Systematic Reviews
(http://www.crd.york.ac.uk/prospero/display_record.php?
ID=CRD42014010591); registration number
CRD2014010591 [29]. The Population, Intervention,
Control, Outcome (PICO) principle was used to search the
literature [30]. The population was hypertensive adults >
18 years of age with cardiovascular disease risk, metabolic
syndrome, type II diabetes mellitus, hypertension, obesity or
hypercholesterolaemia. The intervention was probiotic sup-
plementation treatment within the intervention group; the con-
trol included patients receiving no treatment, placebo or alter-
nate cardiovascular medications; the primary outcomes used
were blood pressure, cholesterol, triglycerides, fasting blood
glucose, H1bA1C, LDL-C and HDL-C and the secondary
outcome was BMI. The keywords used to identify relevant
studies through advanced searches were as follows:
Hypertension AND (Cardiovascular Disease OR Metabolic
Syndrome OR Diabetes) AND Probiotic Agent AND
Randomised Controlled Trial. For cases where data was not
adequately reported, authors were contacted in order to re-
trieve the necessary information.
Inclusion Criteria
Inclusion of studies within the meta-analysis followed strict
inclusion criteria as follows: (1) Published in a peer-reviewed
journal in the past 30 years or grey literature (February 1990
until February 2020); (2) study design was an RCT; (3) par-
ticipants were adult patients with hypertension, obesity, car-
diovascular disease, metabolic syndrome, type II diabetes or
hypercholesterolaemia; (4) probiotics were used as an inter-
vention approach; (5) primary outcome variables included
blood pressure, total cholesterol, HbA1C, serum glucose,
LDL-C, HDL-C with BMI as an additional outcome; (6) if
multiple studies were published on the same population, only
the most recent study is to be included.
Data Collection
Studies that reported insufficient or incomplete data had their
corresponding authors contacted, seeking acquisition of nec-
essary additional data. No authors provided information on
outcomes for studies and their values were excluded in the
analysis.
74 Page 2 of 27 Curr Hypertens Rep (2020) 22:74
Data Extraction
Two authors completed the initial search with review of
each search strategy, and three authors resolved dis-
agreements. AD completed the first search with AY
completing a paralleled search. DR participated in dis-
cussion when disagreements were raised. All citations
and abstracts were downloaded to EndNote X9 for re-
view. Duplicates were deleted and the titles and ab-
stracts of the remaining articles were screened for inclu-
sion and checked by a second examiner (Fig. 1outlines
this process). Several databases were used to identify
articles for inclusion such as PubMed, Scopus,
Embase, Grey Literature and the Cochrane Central
Register of Controlled Trials (CENTRAL). The follow-
ing data were extracted from each of the included arti-
cles: study setting, design location, study blinding, par-
ticipant demographics, attrition rate, comorbidities, par-
ticipant age and gender, probiotic treatment duration and
route, strain and form of probiotic used for intervention,
dosage of intervention treatment, control used, measured
outcomes, information for assessment of the risk of bias
and reported major findings.
Quality Assessment
The Physiotherapy Evidence Database tool (PEDro scale) was
used to rapidly assess the internal validity (criteria 2–9), ex-
ternal validity (criteria 1) and sufficiency of statistical infor-
mation (criteria 10–11) of randomised clinical trials. The
Pedro tool categorises the quality of studies into three levels:
high quality (≥8points),moderatequality(4–7 points) and
low-quality (≤3 points). Criteria 1–11 are as follows: eligibil-
ity criteria specified, subject randomisation, allocation con-
cealment, the similarity of baseline prognosis between groups,
blinding of subjects, therapists and assessors, a primary out-
come measurement on ≥85% of initial subjects, use of
intention-to-treat analysis, use of variability measures and
use of between-group comparison methods. Each paper was
assessed independently by two reviewers and any discrepan-
cies were resolved via discussion (Table 1).
Statistical Analysis
The effect of probiotics on CV health outcome variables was
described with the use of a pooled effect size method. Random
effect meta-analyses were performed to describe the overall
Fig. 1 PRISMA flow chart
Curr Hypertens Rep (2020) 22:74 Page 3 of 27 74
Table 1 PEDro analysis of remaining studies
Trial Specified
eligibility
criteria
Random
allocation
Allocation
concealment
Blinding of
subjects
Blinding of
assessors
Blinding of
researchers
Similar at
baseline
Key outcome
measurement 85%
Intention
to treat
Between group
comparison
Measure of
variability
Total
score
Abbasi et al. (2018)
[31]
X X X X X X X X - X X 10/11
Agerholm-Larsen et al.
(2000) [32]
XX-XX--X -XX7/11
Asemi et al. (2013)
[33]
XX-XX-XX -X X8/11
Cavallini et al. (2016)
[34]
X X X X X X X X - X X 10/11
Ebrahimi et al. (2017)
[35]
X X X X X X X X - X X 10/11
Ejtahed et al. (2012)
[36]
X X X X X X X X - X X 10/11
Feizollahzadeh et al.
(2017) [37•]
XX-XX-X- -X X7/11
Firouzi et al. (2017)
[38••]
X X X X X X X - X X X 10/11
Gomes et al. (2017)
[39]
X X X X X X X - X X X 10/11
Hata et al. (1996) [40] X X - X - - X - - X X 6/11
Higashikawa et al.
(2016) [41]
X X X X X X X X X X X 11/11
Inoue et al. (2003) [42] X X - X - - X X - X X 7/11
Ivey et al. (2015) [43] X X - - - - X X - X X 6/11
Jones et al. (2012) [44] X X - X X - X X X X X 9/11
Jung et al. (2013) [45] X X X X X - X - X X X 9/11
Kadooka et al. (2010)
[46]
XX-XX-XX -X X8/11
Kassaian et al. (2018)
[47•]
XXXXXXX- -X X9/11
Kim et al. (2017) [48••] X X X X X - X X - X X 9/11
Madjd et al. (2016)
[49]
XXXX--XX XXX9/11
Mafi et al. (2018) [50•]X X X X X - X X X X X 10/11
Mizushima et al.
(2004) [51]
XX-XX-XX -XX8/11
Mobini et al. (2017)
[52•]
XX-XXXXX -XX9/11
Mohamadshahi et al.
(2018) [53]
XX-XX-XX -XX8/11
Mohammadi-Sartang
et al. (2018) [54]
XX-XX-XX -XX8/11
Naito et al. (2018) [55]X X X X X X X X - X X 10/11
Ostadrahimi et al.
(2015) [56]
XXXXX-XX -XX9/11
X X X X X X X X X X X 11/11
74 Page 4 of 27 Curr Hypertens Rep (2020) 22:74
effect size. Mean difference was used to present the effect size
for blood pressure, total cholesterol, LDL-C, HDL-C, triglyc-
erides, serum glucose, HbA1c and BMI. CIs and the weighted
mean difference were calculated for each outcome variable.
Analysis of each study to assess for inconsistency included
visual assessment of CI for overlap and I
2
statistics of hetero-
geneity. Subgroup analyses were performed including gender
ratio (female > male, male > female), dosage of probiotics (<
1.0 × 10
9
CFU compared with > 1.0 × 10
9
CFU), attrition (>
15% compared with < 15%), form of probiotic (capsule, milk,
yoghurt, other), study blinding (SB, DB and NB) and duration
of study (< 1.5 months compared with > 1.5 months). It can be
noted, study numbers within the subgroup Tables 3,4,5,6
and 7represent the total number of studies included within
each paper. As such individual papers may have included
more than one study. Publication bias was assessed using
Egger’stest(Table8).
Results
The initial database search yielded 901 potential articles and
15 additional articles were found through the reference lists of
published articles. Duplicate articles (28) were removed. A
total of 888 studies remained for screening through abstract
and title for relevant articles to the study. Application of the
Physiotherapy Evidence Database process confirmed the re-
maining selection of 34 studies to be included in the final
Meta-Analysis (Table 1).
Participants
An outline of included studies is provided in Table 2.
Outcomes were reported on 2177 adults receiving probiotic
intervention (1176) or control (1001) with removal of dupli-
cate controls. Probiotics treatment was used to assess different
cardiovascular outcomes, with ten studies measuring blood
pressure (38, 43, 45, 51, 52, 55, 57, 59, 60, 64); twenty-four
studies measuring total cholesterol (31, 33, 34, 35, 38, 39, 41,
42, 43, 44, 45, 49, 50, 51, 52, 55, 56, 57, 59, 60, 61, 62, 63);
twenty-one studies measuring LDL-C (31, 32, 33, 34 35, 39,
41,43, 44, 45, 49, 50, 52, 55, 56, 57, 58, 59, 60, 61, 63);
twenty-three studies measuring LDL-C (31, 32, 33, 34 35,
39, 40, 41,43, 44, 45, 49, 50, 51, 52, 55, 56, 57, 58, 59, 60,
61, 63); twenty-five studies measuring triglycerides (31, 33,
34, 35, 37, 38,39, 41, 42, 43, 44, 45, 49, 50, 51, 52, 55, 56, 57,
59, 60, 61, 62, 63); twenty studies measuring serum glucose
(33, 35, 36, 37, 38, 41, 42, 45, 47, 49, 50, 52, 53, 55, 56, 57,
58, 5, 61, 62, 63); sixteen studies measuring HbA1C (33, 35,
36, 38, 39, 41, 45, 47, 49, 50, 52, 53, 54, 55, 56, 59); and
nineteen studies measuring BMI (33, 35, 38, 39, 41, 45, 46,
48, 49, 50, 52, 53, 54, 57, 58, 60, 61, 63, 64). These studies
were conducted in 11 different locations, including Australia
Table 1 (continued)
Trial Specified
eligibility
criteria
Random
allocation
Allocation
concealment
Blinding of
subjects
Blinding of
assessors
Blinding of
researchers
Similar at
baseline
Key outcome
measurement 85%
Intention
to treat
Between group
comparison
Measure of
variability
Total
score
Raygan et al. (2018)
[57]
Razmpoosh et al.
(2019) [58]
XXXXXX-X -X X9/11
Rezaei et al. (2017)
[59••]
XXXXXX-X -X X9/11
Sharafedtinov et al.
(2013) [60]
XXXXX--- -X X7/11
Shakeri et al. (2014)
[61]
XX-XX-XX XX X9/11
Szulinska et al. (2018)
[62•]
XXXXXX-X -X X9/11
Tajabadi-Ebrahimi
et al. (2017) [63]
X X X X X X X X X X X 11/11
Zarrati et al. (2014)
[64]
XXXXX-XX -XX9/11
X = characteristic within study, - = characteristic absent within study
Curr Hypertens Rep (2020) 22:74 Page 5 of 27 74
Table 2 Study characteristics
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
Abbasi et al. (2018)
[31]
DB; Iran 44 4 (9.0) OW 56.9/53.6 Not
mentioned
Soymilk Lactobacillus
plantarum A7
2×10
7
Chol, TAG Consumption of
probiotic soymilk
improved LDL and
serum TAG
significantly more
than conventional
soymilk (P=0.01
and P=0.007,
respectively).
Agerholm-Larsen
et al. (2000) [32]
DB; Denmark 36 0 (.0) OW, OB 38.6/38.3 4/12, 3/7 Calcium lactate
placebo
Streptococcus
thermophilus,
Lactobacillus
acidophilus
4.5 × 10
11
SBP, DBP, Chol,
TG
Cholesterol, SBP and
DBP pressure were
significantly
decreased (P< 0.05)
by probiotic yoghurt
products
Asemi et al. (2013)
[33]
DB; Iran 54 0 (0) T2D 50.5/52.6 9/18, 9/18 Placebo capsule L. acidophilus,
L. casei,
L. rhamnosu-
s,
L. bulgaricus,
B. breve,
B. longum,
Streptococcus
thermophilus
9.52 × 10
10
HbA1c, TC,
TAG, BMI
Consumption of
probiotic
supplements
prevented a rise in
plasma glucose
(P= 0.01).
Cavallini et al.
(2016) [34]
DB; Brazil 49 0 (0) HCL (1) 48.1/45.4, (2)
45.1/45.4
(1) 17/0, (2)
17/0, 15/0
Placebo
unfermented
soy product
(1) E. faecium,
L. helveticus,
without
Isoflavin,(2)
E. faecium,
L. helveticus,
with Isoflavin
(genistin,
genistein,
daidzin,
daidzein)
(1) 2 × 10
10
(2) 2 × 10
10
TC, TAG The probiotic with
Isoflavin group
experienced
significantly reduced
total cholesterol and
LDL (P<0.05and
P<0.05,
respectively), but not
for the group without
Isoflavin.
Ebrahimi et al.
(2017) [35]
DB; Iran 70 0 (0) T2D 58.7/58.6 23/12, 19/26 Placebo capsule Lactobacillus
family,
Bifidobacteri-
um family,
Streptococcus
thermophilus
10
9
FBG, Hb1c,
BMI, TC,
TAG
Probiotic
supplementation
significantly
decreased plasma
glucose and HbA1c
levels (P=0.05and
P<0.01,
respectively).
74 Page 6 of 27 Curr Hypertens Rep (2020) 22:74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
Ejtahed et al. (2012)
[36]
DB; Iran 64 4 (6.25) T2D 50.9/51.0 11/19, 12/18 Conventional
yoghurt
B. lactis,
L. acidophil-
us
2.03 × 10
10
FBG, HbA1c Consumption of
probiotic yoghurt
significantly
decreased plasma
glucose and HbA1c
levels (P<0.01and
P<0.05,
respectively).
Feizollahzadeh et al.
(2017) [37•]
DB; Iran 48 8 (16.7) T2D 56.9/53.6 9/11, 10/10 Soymilk L. plantarum A7 2×10
7
FBG, TG In the probiotic group,
there was a
significant decrease
in LDL (P= 0.023)
but no significant
changes in plasma
glucose.
Firouzi et al. (2017)
[38••]
DB; Malaysia 136 35 (25.74) T2DM 52.9/54.2 31/17, 34/19 Placebo sachet
with water
L. acidophilus,
L. casei,
L. lactis,
B. bifidum,
B. longum,
B. infantis
3×10
10
All Outcomes No difference between
probiotics and
control group in all
variables
Gomes et al. (2017)
[39]
DB; Brazil 60 17 (28.3) OW/OB 20–59/20–59 0/21. 0/22 Placebo sachet L. acidophilus
LA-14,
L. casei
LC-11,
L. lactis
LL-23,
B. bifidum
BB-06,
B. lactis BL-4
1×10
9
BMI, HBA1C,
TC, TAG
The probiotic group
showed a greater
decrease in BMI
(P<0.001).No
significant
differences were
observed in LDL or
triglyceride levels.
Hata et al. (1996)
[40]
SB; Japan 35 6 (16.0) OW, OB 76.5/73 4/13, 4/9 Placebo L. helveticus.
S. cerevisiae
7.025 × 10
10
SBP, DBP, Chol,
TG
Daily consumption of
the commercial
probiotic drink
“Calpis”
significantly reduced
SBP by 14 mmHg
and DBP by
7 mmHg from
baseline, whereas
placebo had no
significant effect.
However, there was
no difference
Curr Hypertens Rep (2020) 22:74 Page 7 of 27 74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
between probiotic
and placebo.
Higashikawa et al.
(2016) [41]
DB; Japan 62 4 (6.45) OW (1) 52.5/52.8, (2)
55.5/52.8
(1) 8/13, 7/13
(2) 8/13,
7/13
Placebo powder (1) Pediococcus
pentosaceus
LP28,(2)
heat-killed
Pediococcus
pentosaceus
LP28
(1) 1 × 10
11
(2)
1×10
11
BMI, FPG,
HBA1c, TC,
TG
For the heat-killed
probiotic group only,
there was a
significant decrease
in BMI compared
with placebo
(P= 0.035). There
were no changes in
plasma glucose,
HbA1c or serum
lipids.
Inoue et al. (2003)
[42]
SB; Japan 39 4 (10.26) HTN 56.2/53.9 10/8, 10/7 Placebo milk L. casei strain
Shirota,
L. lactis YIT
2027
<10×10
10
TC, TAG, FBG,
SBP, DBP
The probiotic group
experienced a
significant decrease
in SBP (P< 0.05).
Ivey et al. (2015)
[43]
DB; Australia 116 0 (.0) OW, OB (1) 68.0/68.0, (2)
65.0/65.0
(1) 25/15,
23/17 (2)
23/13,
23/17
Control milk with
placebo
capsule
(1)
(L. acidophil-
us La5 and B.
animalis
subsp. lactis
Bb12 dose
Yoghurt)+
(L. acidophil-
us La5 and B.
animalis
subsp. lactis
Bb12 dose
capsule), (2)
L. acidophil-
us La5 and B.
animalis
subsp. lactis
Bb12 dose +
control milk
(1)1.2 × 10
10
(2)
6×10
9
SBP, DBP, Chol,
BMI
No difference between
probiotics and
control group in all
variables
Jones et al. (2012)
[44]
DB; Czech
Republic
120 11 (9.2) HCL 51.9/48.7 21/34, 20/38 Placebo yoghurt L. reuteri 5×10
9
Chol, TAG The probiotic group
experienced
significant decreases
in LDL and total
cholesterol
(P= 0.016 and
P=0.031,
respectively).
74 Page 8 of 27 Curr Hypertens Rep (2020) 22:74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
Jung et al. (2013)
[45]
DB; Korea 62 8 (12.9) OW/OB 19–60/19–60 14/14, 9/20 Placebo capsule L. gasseri
BNR17
6×10
10
All Outcomes No difference between
probiotics and
control group in all
variables
Kadooka et al.
(2010) [46]
DB; Japan 87 0 (0) OB 48.3/49.2 19/14, 30/14 Placebo
fermented milk
L. gasseri
SBT2055
(LG2055)
5×10
10
BMI Consumption of
probiotics
significantly
decreased BMI
(P<0.001).
Kassaian et al.
(2018) [47•]
DB; Iran 91 35 (29.2) Pre-T2DM (1) 53.0/53.0, (2)
53.0/53.0
(1) 19/14,
12/16 (2)
13/17,
12/16
Placebo
maltodextrin
powder
(1) Probiotic
(L. acidophil-
us,B.lactis,
B. bifidum,
B. longum)
(2) Synbiotic
(same
bacteria)
(1) 1 × 10
9
(2)
1×10
9
HBA1c, FPG Probiotic
supplementation
resulted in a
significant decrease
in HbA1c
(P=0.005).
Synbiotic
supplementation
resulted in a
significantly greater
decrease in plasma
glucose and HbA1c
(P=0.01and
P=0.008
respectively).
Kim et al. (2017)
[48••]
DB; Korea 120 54 (45) OW Not mentioned Not
mentioned
Placebo powder L. curvatus
HY7601,
L. plantarum
KY1032
2.5 × 10
9
BMI Probiotics resulted in a
significant reduction
in BMI (P= 0.080).
Madjd et al. (2016)
[49]
SB; Iran 89 0 (0) OB 32.2/31.8 0/44, 0/45 Low-fat yoghurt,
S. thermophil-
es and
L. bulgaricus
(starter
cultures),
hypo-energetic
diet
S. thermophiles,
L. bulgaricus,
L. acidophil-
us LA5,
B. lactis
BB12
1×10
7
TC, HBA1C,
FPG, TAG,
BMI
The probiotic group had
significantly greater
decrease in total
cholesterol and LDL
(P= 0.024 and
P=0.018,
respectively). There
was no significant
difference in plasma
glucose.
Mafi et al. (2018)
[50•]
DB; Iran 60 6 (10) T2D 58.9/60.9 Not
mentioned
Placebo capsule L. acidophilus
strain ZT-L1,
B. bifidum
strain ZT-B1,
L. reuteri
strain ZT-Lre,
8×10
9
BMI, FPG,
HBA1C,
TAG, TC
Probiotic
supplementation
resulted in a
significant decrease
in plasma glucose
and total
Curr Hypertens Rep (2020) 22:74 Page 9 of 27 74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
L. fermentum
strain ZT-L3
triglycerides
(P=0.01and
P=0.001,
respectively).
Mizushima et al.
(2004) [51]
DB; Japan 46 4 (8.7) HTN 44.3/10.3 23/0, 23/0 Placebo milk
(acidified)
L. helveticus. S.
cerevisiae
7×10
11
,
2.5 × 10
9
SBP, DBP, TC,
TAG
The probiotic group had
a significant change
in DBP (P= 0.039).
Mobini et al. (2017)
[52•]
DB; Sweden 44 0 (0) T2D, OB (1) 66.0/65.0, (2)
64.0/65.0
(1) 12/3, 11/4
(2) 11/3,
11/4
Placebo powder (1) Lactobacillus
reuteri DSM
17938,(2)
Lactobacillus
reuteri DSM
17938
(1) 1 × 10
8
(2)
1×10
10
All Outcomes No difference between
probiotics and
control group in all
variables
Mohamadshahi
et al. (2018) [53]
DB; Iran 44 2 (4.55) T2M, OW or OB 53.0/49.0 Not
mentioned
Placebo
conventional
yoghurt
(Lactobacillus
delbrueckii-
subsp.
bulgaricus and
Streptococcus
thermophilus)
Bifidobacterium
animalis
subsp. lactis
Bb12 (DSM
10140)and
Lactobacillus
acidophilus
strain La5
3.7 × 10
6
FBG, BMI,
HBA1C
Consumption of
probiotic yoghurt
significantly
decreased HbA1c
levels (P= 0.032).
Mohammadi-Sarta-
ng et al. (2018)
[54]
DB; Iran 44 2 (4.7) T2D, OW, OB 53.0/49.0 5/17, 5/17 Placebo yoghurt B. animal lactis
Bb12 + L.
acidophilus
La4
7.4 × 10
6
HBA1C, BMI Consumption of
probiotic yoghurt
resulted in a
significant decrease
in triglyceride levels
(P=0.003).
Naito et al. (2018)
[55]
DB; Japan 100 2 (2.0) OB, pre-T2D 48.6/47.4 48/0. 50.0 Milk placebo Milk fermented
with LcS YIT
9029
1×10
11
All Outcomes Daily administration of
a fermented milk
product significantly
reduced plasma
glucose,
glycoalbumin and
HbA1c at 8 weeks
compared with
baseline (P< 0.05).
However, this
improvement was
only significantly
better than placebo
for glycoalbumin.
Subgroup analysis
revealed improved
74 Page 10 of 27 Curr Hypertens Rep (2020) 22:74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
benefits for patients
with severe glucose
intolerance. LDL
was significantly
loweredinthe
probiotic group
compared with
control.
Ostadrahimi et al.
(2015) [56]
DB; Iran 60 0 (0) T2D 35–65/5–65 Not
mentioned
Placebo
conventional
fermented milk
L. acidophilus,
L. casei,
B. lactis
25 × 10
6
,
15 × 10
6
,
8×10
6
TC, TAG, FPG,
HBA1C
The probiotic group had
a significant decrease
in blood glucose and
HbA1c (P=0.01and
P=0.02,
respectively).
Raygan et al. (2018)
[57]
DB: Iran 60 0 (0) T2D, CHD 60.7/61.8 Not
mentioned
Placebo capsules B. bifidum, L.
casei,
L. acidophil-
us
2×10
9
SBP, DBP, BMI,
FPG, TAG,
TC
Probiotic
supplementation
significantly
decreased plasma
glucose (P=0.005).
Razmpoosh et al.
(2019) [58]
DB; Iran 68 8 (11.8) T2D 58.6/61.3 17/13, 16/14 Placebo capsule L. acidophilus,
L. casei,
L. rhamnosu-
s,
L. bulgaricus,
B. breve,
B. longum,
S. thermophi-
lus
1.9 × 10
11
FPG,BMI,TC,
TAG
There was a significant
decrease in plasma
glucose for the
probiotic group
(P=0.001).
Rezaei et al. (2017)
[59••]
DB; Iran 90 0 (0) T2D 50.5/50.1 23/22, 21/24 Placebo yoghurt,
starter cultures
S. thermophil-
us, L.
bulgaricus
L. acidophilus
La5,B. lactis
Bb12
2.22 × 10
9
SBP, DBP, FBG,
TC, TAG,
HBA1C
The probiotic group
experienced
significant decreases
in plasma glucose,
LDL, triglycerides,
DBP and HbA1c
levels (P<0.05for
all).
Shakeri et al. (2014)
[61]
DB; Iran 98 6 (8.33) T2D (1) 52.3/53.1, (2)
52.3/53.1
Not
mentioned
Control bread (1)
L. sporogene-
s,inulin
(HPX), (2)
L. sporogenes
(1) 1.2 × 10
10
(2)
1.2 × 10
10
BMI, FPG,
TAG, TC,
Consumption of
probiotic group with
insulin resulted in a
significant decrease
in triglycerides
(P=0.005).
Sharafedtinov et al.
(2013) [60]
DB; Estonia 40 4 (10) MetS 10.9/12.1 9/16, 4/11 Conventional
cheese
L. plantarum
TENSIA
2.55 × 10
10
SBP, DBP, BMI,
Chol, TG
Probiotics significantly
lowered BMI
Curr Hypertens Rep (2020) 22:74 Page 11 of 27 74
Table 2 (continued)
Study (year) (ref.) Design;
location
Participants
at beginning
n(P/C)
Attrition
rate n
(%)
Comorbidities Age, P/C,
multiple P
groups labelled
as (1) and (2)
Sex, n(P,
C), (M/F)
Control Probiotic
strain(s)
Dose, CFU/g Measured
outcomes
Key results
(P= 0.03), as well as
DBP (P=0.02);SBP
was lowered, though
not significant
Szulinska et al.
(2018) [62•]
SB; Poland 71 5 (12.3) OB,
(postmeno-
pausal)
(1) 55.2/58.7, (2)
56.4/58.7
(1) 0/23, 0/24
(2) 0/24,
0/24
Placebo powder (1) B. bifidum,
B. lactis W51,
B. lactis W52,
L. acidophil-
us,L. brevis,
L. casei,
L. salivarius,
L. lactis W19,
L. lactis W58,
(2)Same as
previous
(1) 1 × 10
10
(2)
2.5 × 10
9
TC, BMI, TAG,
FPG
Both high and low
doses of probiotics
significantly
decreased SBP
(P=0.0498 and
P= 0.0359,
respectively).
Tajabadi-Ebrahimi
et al. (2017) [63]
DB; Iran 60 5 (8.3) OW, T2D, CHD 64.2/64.0 Not
mentioned
Placebo capsule L. acidophilus,
L. casei,
B. bifidum
6×10
9
BMI, FPG, TC,
TAG
Consumption of
probiotics
significantly
decreased plasma
glucose (P=0.03).
Zarrati et al. (2014)
[64]
DB; Iran 75 0 (0) OW/OB 36.0/36.0 Not
mentioned
Placebo yoghurt
(starter cultures
of
S. thermophil-
us and
L. bulgaricus.)
S. thermophilus.
L. bulgaricus,
L. acidophilus
LA5,L. casei
DN001,
B. lactis
BB12
1×10
7
BMI, SBP, DBP Probiotic yoghurt
resulted in a
significant reduction
in BMI (P< 0.01).
74 Page 12 of 27 Curr Hypertens Rep (2020) 22:74
(43), Brazil (34, 39), Czech Republic (44), Denmark (32),
Estonia (60), Iran (31,33 35, 36, 37, 47, 49, 50, 53, 54, 56,
57, 58, 59, 61, 63, 64), Japan (40, 41, 42, 46, 51, 55), Korea
(45, 58), Malaysia (38), Poland (62) and Sweden (52).
Effect on Blood Pressure
Ten studies with a total sample of 755 adults measured the
mean difference of blood pressure between the intervention
and control groups. The meta-analysis showed there was a
statistically significant difference in the mean systolic and
diastolic blood pressure, with an overall difference of −
1.31 mmHg (95% CI: −2.23, −0.39) in SBP (P= 0.005)
and −1.87 mmHg (95% CI: −2.41, −1.33) in DBP
(P< 0.001) in favour of probiotic intervention. The heteroge-
neity was not statistically significant (I
2
=0%, P=0.48). The
forest plot of the effect is presented in Fig. 2A and B.A
subgroup analysis (Table 3) found the effect of probiotics on
SBP and DBP was more statistically significant in patients
with diabetes mellitus demonstrating a 5.82 mmHg reduction
in SBP (P= 0.02) and a 3.81 mmHg reduction in DBP
(P< 0.001). Probiotics were more effective when in yoghurt
form, reducing SBP and DBP by 3.56 mmHg (P= 0.07) and
3.54 mmHg (P= 0.001), respectively. SBP reductions of
2.38 mmHg (P= 0.04) were observed in patients treated for
> 1.5 months. Probiotics appeared more effective on SBP and
DBP in male populations demonstrating reductions of
2.36 mmHg (P=0.05) and 0.81 mmHg (P= 0.007), respec-
tively. A reduction in DBP was observed in both long- and
short-duration patient groups, with DBP reductions of
1.98 mmHg (P=0.01) and 2.14 mmHg (P= 0.001), respec-
tively. Subgroup comparisons between dosage, blinding, at-
trition and hypertensive or hypercholesterolaemic patients
could not be calculated due to lack of data.
Effect on Total Cholesterol
Twenty-four studies with a total sample of 1727 adults mea-
sured the mean difference in cholesterol between the interven-
tion and control. The meta-analysis showed there was a sta-
tistically significant difference in the mean cholesterol, with
an overall difference of −6.05 mg/dL (95% CI: −8.49, −
3.61) in total cholesterol (P< 0.001) in favour of probiotic
intervention. The heterogeneity was not statistically signifi-
cant (I
2
=0%, P= 0.80). The forest plot of the effect is pre-
sented in Fig. 2C. A subgroup analysis (Table 4) detected a
cholesterol reduction of 16.54 mg/dL (P< 0.001) in hypercho-
lesterolaemic patients. A significant reduction in serum cho-
lesterol was recorded in patients treated for < 1.5 months, −
11.61 mg/dL (P< 0.001), and > 1.5 months, −4.84 mg/dL
(P< 0.001). Patients that received probiotics in yoghurt, milk
and other forms (e.g. kefir and powder) had significant reduc-
tions in serum cholesterol: −9.48 mg/dL (P= 0.006), −
8.74 mg/dL (P=0.004)and−5.22 mg/dL (P= 0.003), respec-
tively. Probiotics were more effective in reducing cholesterol
in predominantly male patient groups than predominantly fe-
male patient groups with reductions of 6.44 mg/dL (P=0.01)
and 5.59 mg/dL (P= 0.002), respectively. Double blinded
studies reported a statistically significant reduction in choles-
terol of 6.43 mg/dL (P< 0.001). A reduction in cholesterol
was apparent in patients receiving higher dosages (> 1.0 ×
10
9
CFU) of probiotics, −6.38 mg/dL (P< 0.001). Studies
with lower attrition rates (< 15%) reported a more significant
reduction in serum cholesterol, −6.01 mg/dL (P<0.001).
Subgroup comparisons in non-blinded studies could not be
calculated due to a lack of data.
Effect on LDL-C
Twenty-one studies with a total sample of 1526 adults mea-
sured the mean difference in LDL between the intervention
and control. The meta-analysis showed there was a statistical-
ly significant difference in LDL, with an overall difference of
−8.77 mg/dL (95% CI: −11.86, −5.69) (P<0.001)infavour
of probiotic treatment. There is evidence of moderate hetero-
geneity (I
2
=78%, P< 0.001). The forest plot of the effect is
presented in Fig. 2H. A subgroup analysis (Table 5) found the
effect of probiotics on LDL-C was more statistically signifi-
cant in patients with hypercholesterolaemia demonstrating a
−20.67 mg/dL reduction (P= 0.001). Patients with diabetes
and obesity were close to follow with reductions in LDL-C of
−8.09 mg/dL (P= 0.03) and −7.27 mg/dL (P= 0.001), re-
spectively. When administered as yoghurt, probiotics demon-
strated a significant decline in LDL-C of −18.06 mg/dL (P=
0.001). Milk and capsule form similarly showed significant
reductions in LDL-C of −9.82 mg/dL (P= 0.007) and −
4.35 mg/dL (P= 0.001). Shorter durations of treatment (<
1.5 months) proved to have significant reductions in LDL-C
−14.01 mg/dL (P= 0.001). Studies with treatment duration
>1.5 months similarly proved to reduce LDL-C (−7.08 mg/
dL, P= 0.001). A greater reduction in LDL-C was apparent in
patients receiving higher dosages (> 1.0 × 10
9
CFU) of
probiotics, −8.38 mg/dL (P= 0.001). Probiotics were more
effective in lowering LDL-C in patient groups that were pre-
dominantly female, demonstrating a −9.87 mg/dL (P=0.001)
Subgroup comparisons on the blinding of studies and attrition
could not be calculated due to a lack of data.
Effect on HDL-C
Twenty-three studies with a total sample of 1602 adults mea-
sured the mean difference in HDL-C between the intervention
and control. The meta-analysis showed there was a statistical-
ly significant difference in HDL-C, with an overall difference
of 1.05 mg/dL (95% CI: −0.33, 2.43) (P< 0.001) in favour of
probiotic treatment. There is evidence of sever heterogeneity
Curr Hypertens Rep (2020) 22:74 Page 13 of 27 74
a
b
Effect of Probiotics on SBP
Effect of Probiotics DBP
cEffect of Probiotics on Cholesterol
Fig. 2 Forest plots of the effects of probiotics on systolic blood pressure (A), diastolic blood pressure (B), cholesterol (C), triglycerides (D), serum
glucose (E), HbA1C (F), BMI (G)
74 Page 14 of 27 Curr Hypertens Rep (2020) 22:74
d
e
Effect of Probiotics on Triglycerides
Effect of Probiotics on Glucose
fEffect of Probiotics on HbA1C
Fig. 2 (continued)
Curr Hypertens Rep (2020) 22:74 Page 15 of 27 74
gEffect of Probiotics on BMI
h
i
Effect of Probiotics on LDL
Effect of Probiotics on HDL
Fig. 2 (continued)
74 Page 16 of 27 Curr Hypertens Rep (2020) 22:74
Table 3 Subgroup analysis of SBP and DBP
Subgroups SBP DBP
Studies, nParticipants, nI
2
Q-test Mean difference 95% CI PStudies, nParticipants, nI
2
Q-test Mean Difference 95% CI P
Health
DM 2 191 28 0.24 −5.82 −10.52, −1.12 0.02 2 191 28 0.45 −3.81 −5.68, −1.93 0.0001
Obesity 9 408 0 0.75 −2.38 −0.59, −0.18 0.03 9 408 0 0.38 −2.07 −3.75, −0.38 0.01
HTN
Hypercholesterolemia
Other 4 144 0 0.92 −0.78 −1.85, 0.29 0.15 4 144 0 0.75 −1.64 −2.24, −1.03 0.0001
Sex
F > M 7 273 68 0.04 −2.64 −7.09, 2.81 0.25 7 273 68 0.06 −2.29 −4.30, −0.27 0.03
F < M 7 406 0 0.82 −2.36 −4.74, 0.03 0.05 7 406 0 0.81 −0.91 −3.30, −0.53 0.007
Dosage
<1.0×10
9
CFU
>1.0×10
9
CFU
Attrition
> 15%
<15%
Form
Capsule 5 173 0 0.92 −0.31 −4.99, 0.36 0.90 5 173 0 0.80 −0.25 −3.37, 2.88 0.88
Milk 4 214 0 0.42 −2.68 −5.81, 0.44 0.09 4 214 0 0.34 −1.97 −4.52, 0.58 0.13
Yoghurt 3 217 48 0.15 −3.56 −7.43, 0.31 0.07 3 217 48 0.60 −3.54 −5.13, −1.96 0.001
Other 4 185 0 0.71 −0.88 −0.94, 0.17 0.10 4 185 0 0.77 −1.68 −2.28, −1.08 0.001
Blinding
SB
DB
NB
Duration
< 1.5 months 5 293 41 0.15 −2.00 −4.37, 0.37 0.10 5 293 41 0.21 −2.14 −3.39, −0.89 0.001
> 1.5 months 11 496 0 0.84 −2.38 −4.60, −0.16 0.04 11 496 0 0.64 −1.98 −3.57, −0.38 0.01
Curr Hypertens Rep (2020) 22:74 Page 17 of 27 74
Table 4 Subgroup analysis of TC and TAG
Subgroups Total cholesterol Triglycerides
Studies, nParticipants, nI
2
Q-test Mean difference 95% CI PStudies, nParticipants, nI
2
Q-test Mean difference 95% CI P
Health
DM 8 507 0 0.46 −7.53 −13.59, −1.47 0.01 9 547 22 0.26 −14.17 −26.26, −2.08 0.02
Obesity 15 683 0 0.88 −4.19 −7.28, −1.11 0.008 15 663 74 0.01 7.51 −2.36, 17.38 0.14
HTN 2 81 10 0.29 −6.14 −20.05, 7.77 0.39 2 81 0 0.90 16.10 −41.50, 73.71 0.58
Hypercholesterolemia 3 163 0 0.48 −16.54 −25.46, −7.62 0.0003 3 163 0 0.61 −33.69 −91.36, 23.98 0.25
Other 6 264 0 0.96 −7.31 −14.94, 0.32 0.06 6 264 37 0.16 −7.19 −27.64, 13.27 0.49
Sex
F > M 15 726 9 0.36 −5.59 −9.08, −2.10 0.002 16 746 74 0.001 7.26 −5.65, 20.18 0.27
F < M 12 620 0 0.61 −6.44 −11.32, −1.57 0.01 12 620 2 0.42 2.32 −3.06, 7.70 0.40
Dosage
<1.0×10
9
CFU 4 194 0 0.83 −3.67 −10.73, 3.39 0.31 5 234 0 0.97 1.04 −9.12, 11.19 0.84
>1.0×10
9
CFU 30 1504 0 0.68 −6.38 −8.98, −3.78 0.0001 30 1484 68 0.001 −2.70 −11.49, 6.09 0.55
Attrition
> 15% 3 174 0 0.35 −6.90 −18.43, 4.62 0.24 4 214 0 0.85 −9.95 −23.51, 3.61 0.15
< 15% 31 1524 0 0.75 −6.01 −8.51, −3.52 0.0001 31 1504 63 0.001 −0.86 −9.01, 7.30 0.84
Form
Capsule 10 477 0 0.72 −3.00 −9.08, 3.07 0.33 10 477 23 0.26 −7.18 −22.15, 7.79 0.35
Milk 9 398 0 0.58 −8.74 −14.62, −2.86 0.004 10 438 0 00.88 −6.99 −26.34, 12.36 0.48
Yoghurt 5 393 0 0.31 −9.48 −16.17, −2.48 0.006 5 393 40 0.15 1.61 −6.50, 9.71 0.70
Other 10 430 0 0.85 −5.22 −8.63, −1.82 0.003 10 410 84 0.001 −1.36 −20.86, 18.14 0.89
Blinding
SB 3 154 0 0.63 −1.42 −10.29, 7.46 0.75 3 154 0 0.62 0.38 −10.27, 11.03 0.94
DB 31 1544 0 0.78 −6.43 −8.97, −3.89 0.0001 32 1564 63 0.001 −2.11 −10.49, 6.27 0.62
NB
Duration
< 1.5 months 9 516 0 0.44 −11.61 −17.37, −5.85 0.0001 9 516 12 0.34 2.84 −3.78, 9.45 0.40
> 1.5 months 25 1182 0 0.96 −4.84 −7.53, −2.14 0.0004 26 1202 65 0.001 −2.65 −12.42, 7.12 0.60
74 Page 18 of 27 Curr Hypertens Rep (2020) 22:74
Table 5 Subgroup analysis of LDL-C and HDL-C
Subgroups LDL HDL
Studies, nParticipants, nI
2
Q-test Mean difference 95% CI PStudies, nParticipants, nI
2
Q-test Mean difference 95% CI P
Health
DM 7 406 90 0.0001 −8.09 −15.52, −0.65 0.03 7 406 85 0.001 2.03 0.39, 3.66 0.01
Obesity 12 562 60 0.01 −7.27 −10.55, −3.99 0.001 13 592 21 0.26 −1.21 −1.93, −0.50 0.001
HTN
Hypercholesterolemia 3 163 49 0.14 −20.67 −31.00, −10.34 0.001 3 163 41 0.18 6.36 0.15, 12.57 0.04
Other 6 264 0 0.58 −5.61 −11.37, 0.15 0.06 6 264 76 0.001 1.26 −2.23, 4.76 0.48
Sex
F > M 12 605 79 0.001 −9.87 −13.66, −6.08 0.001 13 635 41 0.09 −1.02 −1.88, −0.17 0.02
F < M 9 438 53 0.03 −6.35 −13.47, 0.77 0.08 10 484 30 0.17 1.22 −0.99, 3.43 0.28
Dosage
<1.0×10
9
CFU 3 159 86 0.001 −10.73 −25.58, 4.13 0.16 2 159 0 0.99 0.25 −1.20, 1.70 0.74
>1.0×10
9
CFU 25 1236 78 0.001 −8.38 −11.62, −5.13 0.001 28 1312 91 0.001 1.16 −0.36, 2.68 0.13
Attrition
> 15%
< 15%
Form
Capsule 10 477 0 0.83 −4.35 −6.87, −1.84 0.007 10 477 85 0.001 1.95 −0.73, 4.62 0.15
Milk 6 287 64 0.02 −9.82 −18.28, −1.36 0.02 8 363 52 0.05 0.86 −2.07, 3.80 0.56
Yoghurt 4 370 52 0.10 −18.06 −24.88, −11.24 0.001 4 370 0 0.99 0.25 −1.82, 2.32 0.81
Other 8 261 88 0.001 −7.67 −13.16, −2.19 0.006 8 261 97 0.001 0.32 −2.41, 3.06 0.82
Blinding
SB
DB
NB
Duration
< 1.5 months 8 470 53 0.04 −14.01 −20.67, −7.35 0.001 9 516 49 0.05 0.61 −2.21, 3.44 0.67
> 1.5 months 20 925 81 0.001 −7.08 −10.50, −3.66 0.001 21 955 94 0.001 1.08 −0.56, 2.72 0.20
Curr Hypertens Rep (2020) 22:74 Page 19 of 27 74
(I
2
=90%,P< 0.001). The forest plot of the effect is presented
in Fig. 2I. A subgroup analysis (Table 5) found that probiotics
were most effective in hypercholesterolaemic and diabetic pa-
tients with an increase in HDL-C of 6.36 mg/dL (P=0.04)
and 2.03 mg/dL (P= 0.01), respectively. It can be noted that
obese patients sustained a reduction in HDL-C of −1.21 mg/
dL (P= 0.001). Similarly, patient groups that were predomi-
nately female indicated significant reductions of HDL-C −
1.02 mg/dL (P= 0.02). The remaining data revealed that pro-
biotic treatment did not elicit a significant difference in HDL-
C between subgroups: Dosage, formulation, study duration.
Subgroup comparisons on the blinding of studies and attrition
could not be calculated due to a lack of data.
Effect on Triglycerides
Twenty-five studies with a total sample of 1590 adults mea-
sured the mean difference in cholesterol between the interven-
tion and control. The meta-analysis showed there was no sta-
tistically significant difference in the mean serum triglycerides
in patients taking probiotics with an overall difference of
3.24 mg/dL (95% CI: −0.07, 6.54) (P= 0.06). There is evi-
dence of moderate heterogeneity (I
2
=61%,P<0.001)within
the study sample. The forest plot of the effect is presented in
Fig. 2D. A subgroup analysis (Table 4) detected a triglyceride
reduction of 14.17 mg/dL (P= 0.02) in diabetes mellitus pa-
tients treated with probiotics. The remaining data revealed that
probiotic treatment did not elicit a significant difference in
serum triglycerides between subgroups: sex, dosage, attrition,
formulation, blinding or study duration. Subgroup compari-
sons in non-blinded studies could not be calculated due to a
lack of data.
Effect on Fasting Glucoses
Twenty studies with a total sample of 1481 adults measured
the mean difference in serum fasting glucose between the
intervention and control. The meta-analysis showed there
was a statistically significant difference in serum glucose, with
an overall difference of −4.92 mg/dL (95% CI: −6.15, −
3.69) (P< 0.001) in favour of probiotic treatment. There is
evidence of substantial heterogeneity (I
2
= 83%, P< 0.001)
within the study sample. The forest plot of the effect is pre-
sented in Fig. 2E. A subgroup analysis (Table 6) detected a
serum fasting glucose reduction of 16.15 mg/dL (P=0.001)in
short-term treatment models where patients were treated for <
1.5 months with probiotics. Patients that received probiotics in
capsule, yoghurt and other forms (e.g. kefir and powder) had
significant reductions in serum fasting glucose: −13.47 mg/
dL (P=0.03),−12.88 mg/dL (P=0.03) and 4.41 mg/dL (P=
0.001), respectively. It was also observed that probiotics re-
duced serum fasting glucose by 12.94 mg/dL (P<0.001) in
diabetes mellitus patients treated with probiotics. Subgroup
analysis revealed a greater reduction of serum fasting glucose
in predominantly male patient groups, −5.69 mg/dL (P=
0.001). A greater reduction in serum glucose was apparent
in patients receiving higher dosages (> 1.0 × 10
9
CFU) of
probiotics, −5.29 mg/dL (P= 0.001). Double blinded studies
reported a serum fasting glucose reduction of 5.27 mg/dL
(P= 0.001). No significant changes in fasting glucose could
be identified between groups of attrition, with a −5.63 mg/dL
(P= 0.001) reduction in > 15% attrition and a −5.29 mg/dL
(P= 0.001) reduction in < 15% attrition. Subgroup compari-
sons in non-blinded studies and patients with hypertension or
hypercholesterolaemia could not be calculated due to a lack of
data.
Effect on HbA1C
Sixteen studies with a total sample of 1122 adults mea-
sured the mean difference in HbA1C between the inter-
vention and control. The meta-analysis showed there was
a statistically significant difference in HbA1C, with an
overall difference of −0.18% (95% CI: −0.31, −0.06)
(P= 0.005) in favour of probiotic treatment. There is ev-
idence of substantial heterogeneity (I
2
=92%, P<0.001)
within the study sample. The forest plot of the effect is
presented in Fig. 2F. A subgroup analysis (Table 6)dem-
onstrated that yoghurt probiotic formulations reduced
HbA1C by 55% (P= 0.03), and statistically significant
reductions were not seen in other formulations. It was
also found probiotics were more effective in reducing
HbA1C in patients treated for < 1.5 months, −50%
(P= 0.001). Furthermore, serum HbA1C was reduced
by 32% (P= 0.04) in patients within the “other”health
group (e.g. multiple comorbidities, such as metabolic
syndrome). Studies with higher attrition rates (> 15%)
demonstrated a slightly larger reduction in HbA1C, −
20% (P=0.01),whencomparedwithlowerattritionrate
studies (< 15%), −19% (P= 0.001). Probiotics were
more effective at reducing HbA1C in patient groups that
were predominantly female, with a reduction of 18%
(P< 0.001). Subgroup analysis revealed there was a
more promising trend in the reduction of HbA1C in
higher doses (> 1 × 10
9
CFU) of probiotics, −12%
(P= 0.06), than lower doses. Subgroup comparisons of
study blinding and patients with hypertension or hyper-
cholesterolaemia could not be calculated due to a lack
of data.
Effect on BMI
Nineteen studies with a total sample of 1205 adults measured
the mean difference in BMI between the intervention and
control. The meta-analysis showed there was a statistically
significant difference in BMI, with an overall difference of
74 Page 20 of 27 Curr Hypertens Rep (2020) 22:74
Table 6 Subgroup analysis of fasting glucose and HbA1C
Subgroups Fasting glucose HbA1C
Studies, nParticipants, nI
2
Q-test Mean difference 95% CI PStudies, nParticipants, nI
2
Q-test Mean difference 95% CI P
Health
DM 10 607 48 0.04 −12.94 −20.75, −5.13 0.001 6 435 94 0.001 −0.35 −1.09, 0.39 0.36
Obesity 7 377 63 0.01 −1.60 −3.61, 0.41 0.12 6 349 0 0.64 −0.02 −0.05, 0.02 0.31
HTN
Hypercholesterolemia
Other 8 351 55 0.03 −5.69 −6.30, −5.09 0.001 7 275 91 0.001 −0.32 −0.62, −0.01 0.04
Sex
F > M 12 680 81 0.001 −4.17 −5.12, −3.23 0.001 11 584 92 0.001 −0.18 −0.32, −0.05 0.007
F < M 7 408 55 003 −5.69 −6.30, −5.09 0.001 5 313 76 0.002 0.13 −0.17, 0.43 0.40
Dosage
<1.0×10
9
CFU 5 236 0 0.88 −1.16 −4.24, 1.92 0.46 4 205 93 0.001 −0.43 −1.01, 0.15 0.15
>1.0×10
9
CFU 21 1134 85 0.001 −5.29 −6.57, −4.01 0.001 15 854 90 0.001 −0.12 −0.24, 0.01 0.06
Attrition
> 15% 4 226 0 0.97 −5.63 −5.82, −5.43 0.001 4 229 0 0.51 −0.19 −0.25, −0.14 0.001
< 15% 22 1144 85 0.001 −5.29 −6.57, −4.01 0.001 15 830 92 0.001 −0.20 −0.36, −0.04 0.01
Form
Capsule 7 421 67 0.006 −13.47 −25.70, −1.24 0.03 4 241 83 0.001 0.14 −0.15, 0.44 0.34
Milk 4 233 95 0.001 −7.54 −23.84, 8.76 0.36 2 158 98 0.001 −0.61 −0.81, 0.58 0.31
Yoghurt 5 304 62 0.01 −12.88 −24.54, −1.22 0.03 5 325 90 0.001 −0.55 −1.03, −0.06 0.03
Other 10 412 80 0.001 −4.41 −5.32, −3.51 0.001 8 335 77 0.001 −0.08 −0.17, 0.02 0.12
Blinding
SB 2 124 32 0.23 −3.15 −11.48, 5.17 0.46
DB 24 1246 83 0.001 −5.27 −6.54, −4.00 0.001
NB
Duration
< 1.5 months 3 210 0 0.84 −16.15 −26.88, −5.42 0.003 2 150 0 0.64 −0.50 −0.87, −0.13 0.009
> 1.5 months 23 1160 85 0.001 −4.77 −6.01, −3.54 0.001 17 909 92 0.001 −0.16 −0.29, −0.03 0.02
Curr Hypertens Rep (2020) 22:74 Page 21 of 27 74
−0.31 kg/m
2
(95% CI: −0.41, −0.21) (P<0.001)infavourof
probiotic treatment. The heterogeneity was not statistically
significant (I
2
=0%, P= 0.51). The forest plot of the effect is
presented in Fig. 2G. A subgroup analysis (Table 7) found that
probiotics were able to reduce BMI by 0.50 kg/m
2
(P=0.001)
when administered in other forms (e.g. kefir and powder).
Probiotics were also able to reduce BMI by 0.48 kg/m
2
(P=
0.001) in patients with obesity. A greater reduction in BMI
was apparent in patients receiving higher dosages (> 1.0 ×
10
9
CFU) of probiotics, −0.41 kg/m
2
(P= 0.001). Probiotics
were more effective in lowering BMI in patient groups that
were predominantly female, demonstrating a 0.35 kg/m
2
(P=
0.001) reduction in BMI in these groups. It was also found
probiotics were more effective in lowering BMI in patients
treated for > 1.5 months, −0.31 kg/m
2
(P= 0.001). Studies
with lower attrition rates (< 15%) reported a significant reduc-
tion in BMI, −0.29 kg/m
2
(P= 0.001). Subgroup comparisons
on the blinding of studies could not be calculated due to a lack
of data.
Publication Bias
Findings from Egger’stest(Table8)(SBP:P=0.205;DBP:
P= 0.611; total cholesterol: P= 0.637; triglycerides: P=.533;
Table 7 Subgroup analysis of BMI
Subgroups BMI
Studies, nParticipants, nI
2
Q-test Mean difference 95% CI P
Health
DM 6 357 0 0.99 −0.33 −1.28, 0.62 0.49
Obesity 11 623 0 0.84 −0.48 −0.62, −0.35 0.001
HTN
Hypercholesterolemia
Other 8 350 0 0.99 −0.09 −0.22, 0.05 0.21
Sex
F > M 11 526 47 0.04 −0.35 −0.55, −0.15 0.001
F < M 7 460 0 0.98 −0.45 −1.03, 0.14 0.14
Dosage
<1.0×10
9
CFU 4 205 0 0.99 −0.08 −0.23, 0.07 0.31
>1.0×10
9
CFU 21 1125 0 0.91 −0.41 −0.53, −0.29 0.001
Attrition
> 15% 3 210 0 0.72 −0.40 −1.01, 0.21 0.20
< 15% 22 1120 7 0.37 −0.29 −0.41, −0.18 0.001
Form
Capsule 7 421 0 0.99 −0.11 −0.37, 0.15 0.42
Milk
Yoghurt 5 248 0 0.96 −0.09 −0.24, 0.06 0.24
Other 11 476 0 0.95 −0.50 −0.64, −0.36 0.001
Blinding
SB
DB
NB
Duration
< 1.5 months 2 100 0 0.91 −0.28 −1.92, 1.36 0.74
> 1.5 months 23 1230 6 0.38 −0.30 −0.41, −0.18 0.001
Table 8 Egger’stest
Variable Studies, nt 95% CI P
SBP 13 1.36 −1.03, 4.22 0.205
DBP 13 −0.53 −6.21, 3.84 0.611
Total cholesterol 28 −0.48 −2.15, 1.34 0.637
Triglycerides 29 0.63 −2.06, 3.89 0.533
Glucose 22 −5.96 −12.54, −6.09 0.001
HbA1C 16 −0.86 −8.61, 3.67 0.403
BMI 21 −1.63 −5.45, 0.65 0.117
LDL 21 −1.93 −8.53, 0.28 0.066
HDL 23 1.27 −1.80, 7.61 0.215
74 Page 22 of 27 Curr Hypertens Rep (2020) 22:74
glucose: P=0.001; HbA1C: P= 0.403; and BMI: 0.117;
LDL-C: P= 0.066; HDL-C: P= 0.215) supported that there
was no publication bias, except within glucose. The sensitivity
analysis for glucose was conducted removing any study did
not change the overall results.
Discussion
The pooled effects based on the meta-analysis found that
probiotics improved the health outcomes in patients at risk
of cardiovascular disease. This included an overall statistically
significant reduction in systolic and diastolic blood pressure,
serum cholesterol, serum triglycerides, serum glucose,
HbA1C, LDL-C and BMI. It can also be noted there was an
overall increase in HDL-C although it is not statistically sig-
nificant. All outcomes demonstrated a statistically significant
reduction in cardiovascular risk factors except HDL-C. These
findings are highly relevant in patients at risk of cardiovascu-
lar disease, with polypharmacy becoming an increasing issue
in global health [65]. The capacity to reduce cardiovascular
risk factors in conjunction of prescription of pharmaceuticals
indicates a significant advance in the field of the human
microbiome. Our results are consistent with previously pub-
lished reviews regarding the effects of probiotics in reducing
blood pressure [66], cholesterol [67], serum triglycerides [68,
69], serum glucose [70], HbA1C [71]andBMI[72]. Despite
correlations, these previous studies focused on mono-morbid
patient groups rather than a whole society at risk of cardiovas-
cular disease with comorbidities or measured only one out-
come variable. None of these studies provided subgroup anal-
ysis on our key variables.Our studies included RCTs focusing
on adults at risk of cardiovascular disease with comorbidities
(diabetes, dyslipidaemia, metabolic syndrome, hypercholes-
terolaemia, hypertension).
Effects of Probiotics on BP, TC, LDL-C, HDL-C TAG,
Glucose, HbA1C and BMI
Subgroup analyses revealed the beneficial effects of probiotics
may be related to patient morbidity. Diabetic patients
displayed the greatest reduction in blood pressure, fasting glu-
cose and triglycerides out of all of the health groups, with a
5.82 mmHg reduction in SBP (P= 0.02) and a 3.81 mmHg
reductioninDBP(P< 0.001). Similarly, fasting glucose
displayed the most significant reduction in diabetics with
12.94 mg/dL (P< 0.001). Triglycerides and HDL-C also dem-
onstrated the most beneficial effects in diabetic patients with a
14.17 mg/dL (P= 0.02) reduction and 2.03 mg/dL (P=0.01)
mean difference between the intervention and control group.
Outcome variables further indicated analyses may be relat-
ed to morbidity with hypercholesterolaemic patients
displaying the greatest reduction in total cholesterol
(16.54 mg/dL, P<0.001) and LDL-C (−20.67 mg/dL, P=
0.001), as did obese patients in BMI (0.48 kg/m
2
,P=0.001).
These results may be explained by the inherent association
between these morbidities and the outcome variable, given
each patient subgroup showed the most significant reduction
in the variable that was most related to their condition. This is
a potential form of selection bias [73], whereby it can be
expected that each of these subgroups would have a correla-
tion with a given outcome measure.
Probiotics in yoghurt form demonstrated the most signifi-
cant reductions in the cardiovascular risk factors of total cho-
lesterol (−9.48 mg/dL), fasting glucose (−12.88 mg/dL),
HbA1C (−0.55%) and LDL-C (−18.06 mg/dL); reductions
in blood pressure were also observed. Probiotics within the
“other forms”category (Kefir and powder) demonstrated a
significant reduction in BMI (−0.5 kg/m
2
). One mechanism
for explaining this characteristic is the greater diversity of
probiotic species found in kefir and their capacity to stimulate
gastric emptying [74]. As such studies have shown a correla-
tion between probiotic strain diversity and gastric motility
[75], hence reducing the time for absorption and digestion.
Studies with greater proportions of female patients indicat-
ed more significant reductions in outcome measures, specifi-
cally total cholesterol (−5.59 mg/dL, P= 0.002), fasting glu-
cose (−5.63 mg/dL, P=0.001), HbA1C (−0.18%,P=
0.007), LDL-C (−9.87 mg/dL, P=0.001)andBMI(−
0.35 kg/m
2
,P= 0.001). One explanation for this is the rela-
tionship between the oestrogen-gut microbiome axis, whereby
women are at greater risk of microbiota decline [76], thus
providing a physiological template for microbiota correction
and reduction in CVD risk factors.
Longer treatment duration (> 1.5 months) was favoured
holistically within the outcome of blood pressure, with reduc-
tions of −2.38 mmHg in SBP (P= 0.04) and −1.98 mmHg in
DBP (P= 0.01). Reduction over extended period of time
could be expected, as resting blood pressure has been shown
to require long-term lifestyle intervention [77].
Shorter treatment duration (< 1.5 months) was statistically
favoured for reduction in total cholesterol (−11.61 mg/dL,
P<0.001), LDL-C (−14.01 mg/dL, P= 0.001), fasting glu-
cose (−16.15 mg/dL, P= 0.003) and HbA1C (−0.5%,
P= 0.009) One mechanism for this can be explained
by initial increase in gastric motility providing the ma-
jority of the effect in total cholesterol reduction [78]by
a reduced absorption time, this may have residual ef-
fects with metabolism.
Subgroup analyses revealed higher treatment doses (> 1 ×
10
9
CFU) were more effective in reducing BMI (0.41 kg/m
2
,
P= 0.001), total cholesterol (6.38 mg/dL, P< 0.001), HbA1C
(12%, P= 0.06) and fasting glucose (16.15 mg/dL, P=
0.001), respectively. It can also be said, results were trending
towards higher treatment doses in triglycerides outcome
groups for greater effect.
Curr Hypertens Rep (2020) 22:74 Page 23 of 27 74
Strengths, Limitations and Implications
Our review used a thorough and comprehensive search
strategy in identifying and assessing each article’s quality
for inclusion within the meta-analysis. Reviewer bias was
minimised through individual and independent screening
of potential articles before collaboration for final inclusion.
One strength of this meta-analysis was the use of broad
search terms to allow for manual inclusion of all relevant
articles, maximising the power of the study. All articles
included were RCTs and from the last 30 years, providing
a relevant and up-to date review. Any missing data from
studies prompted contacting of authors, and thus, all pos-
sible data was included in this meta-analysis at the time of
publication. All studies included adults at risk of cardio-
vascular disease and therefore, the effect of probiotics
could be assessed. Subgroup analysis provided statistical
insight into formulations, dosages and patient groups most
susceptible to treatment. This meta-analysis strengthens
prior published findings. For example, a meta-analysis
[27•], which included 15 studies (788 adults with CVD
risk), reported statistically significant reduction in choles-
terol and BMI, whereas our study had 34 RCTs (n=2177
adults with CVD risk) which found probiotics significantly
reduce the risk of cardiovascular disease in high-risk pa-
tient groups. A limitation of this study is the inconsistent
use of strain types among studies, disallowing for accurate
assessment of individual strain efficacy. However, this
allowed for cross analysis of the relationship between
strain number and therapeutic efficacy. Another limitation
of this study is the small population samples within each
study; these were primarily mitigated through inclusion of
all available studies; however, a larger sample size could
improve the statistical power of the study. These limita-
tions are primarily due to a lack of literature available.
Further RCTs observing the effects of single and multiple
strain use on patients with cardiovascular risk factors are
required to further clarify findings.
Authors Contribution The authors’responsibilities were as follows: AD:
conceptualised and designed the study, developed search strategy, col-
lected and extracted the data, interpreted the data, all table formulation,
referencing, PEDro table development, article drafting, edited and sub-
mitted manuscript, critically revised the article –KR: data extraction –
AY: data collection, data extraction, formulated Table 2–MQ: data
extraction, referencing, table formulation, edited manuscript –HR: data
extraction, formulated Table 2–DW: PEDro table development –TC:
data extraction, referencing, subgroup table formatting, edited –DR: data
extraction –CC: statistical analysis and subgroup calculations –JS: de-
signed the study, statistical analysis and subgroup calculations.
Compliance with Ethical Standards
Conflict of Interest The authors declare no conflicts of interest relevant
to this manuscript.
Human and Animal Rights and Informed Consent This article does not
contain any studies with human or animal subjects performed by any of
the authors.
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