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Arq Bras Cardiol. 2023; 120(5):e20220523
Original Article
Prognostic Significance of Nutrition-Associated Markers in Heart
Failure with Preserved Ejection Fraction: A Systematic Review and
Meta-Analysis
Ying Meng,1 Zhengyi Zhang,1 Tong Zhao,2 Dekui Zhang3
Departamento de Medicina Geral, Segundo Hospital da Universidade de Lanzhou,1 Lanzhou, Gansu – China
Departamento de Ortopedia, Primer Hospital da Universidade de Lanzhou,2 Lanzhou, Gansu – China
Departamento de Gastroenterologia, Segundo Hospital da Universidade de Lanzhou,3 Lanzhou, Gansu – China
Mailing Address: Dekui Zhang •
Department of Gastroenterology, Lanzhou University Second Hospital,
Lanzhou University, Gansu 730030 – China
Email: zhangdk8616@126.com
Manuscript received July 26, 2022, revised manuscript December 23, 2022,
accepted February 15, 2023
DOI: https://doi.org/10.36660/abc.20220523
Abstract
Background: The prognostic significance of nutrition indicators in patients with heart failure with preserved ejection
fraction (HFpEF) is unclear.
Objectives: This systematic review and meta-analysis aimed to assess the prognostic value of serum albumin (SA), the
geriatric nutritional risk index (GNRI), and the prognostic nutritional index (PNI) in patients with HFpEF.
Methods: Databases of PubMed, Embase, The Cochrane Library, and Web of Science were systematically searched for
all studies published up to January 2022. The prognostic significance of SA, GNRI, and PNI for HFpEF was explored.
Pooled hazard ratio (HR) and 95% confidence interval (CI) were estimated using the STATA 15.0 software. The Quality
of Prognosis Studies tool was used to assess the quality of studies.
Results: Nine studies met the inclusion criteria, and 5603 adults with HFpEF were included in the meta-analysis. The
analyses showed that a decreased SA or GNRI was significantly related to high all-cause mortality (HR: 1.98; 95% CI:
1.282–3.057; p = 0.002; and HR: 1.812;95% CI: 1.064–3.086; p = 0.029, respectively). Furthermore, a lower SA indicates
a bad composite outcome of all-cause mortality and HF rehospitalization (HR: 1.768; 95% CI: 1.483–2.108; p = 0.000),
and a lower GNRI was significantly associated with high cardiovascular mortality (HR: 1.922; 95% CI: 1.504–2.457;
p = 0.000). However, a lower PNI did not correlate with all-cause mortality (HR: 1.176; 95% CI: 0.858–1.612, p=0.314).
Conclusions: Our meta-analysis indicates that SA and GNRI may be useful indicators to predict the prognosis of patients
with HFpEF.
Keywords: Heart Failure; Prognosis; Malnutrition; Strpke Volume; Systematic Reviews; Epidemiology; Mortality.
malnutrition,3-5 and nutritional problems related to a
worse HF. Malnutrition leads to systemic inflammation via
activated cytokines that can stimulate the nervous system.6-8
All these are greatly associated with the progression of HF.
A variety of indicators can be used to assess nutritional risk.
Serum albumin (SA) is a common indicator of nutritional
assessment but is susceptible to variations in systemic
diseases. Geriatric nutritional risk index (GNRI) is used to
assess the nutritional status based on the weight, height,
and level of SA,9 and prognostic nutritional index (PNI) is
used to assess the nutritional status based on SA level and
the lymphocyte count.10 These multidimensional indices
are considered more accurate and comprehensive. Several
studies have shown the predictive value of these indicators
for various clinical outcomes.11-15 Research on HF has
shown that these indices can also predict outcomes in
patients with heart failure with reduced ejection fraction
(HFrEF).16-19 However, the prognostic significance of
nutritional indicators in patients with HFpEF has not been
determined, and studies investigating the clinical value of
SA in predicting the outcome of HFpEF have conflicting
results.20,21 There are no systematic reviews showing the
relationship between nutritive indexes and the prognosis of
Introduction
Heart failure with preserved ejection fraction (HFpEF)
has become an increasingly common form of heart
failure (HF). Epidemiological studies have shown that the
proportion of HFpEF in the HF population has increased
from 41% in 1985-1994 to 56.17% in 2005-2014.1
Meanwhile, observational studies suggest that HFpEF is
associated with high morbidity and rate of hospitalization.2
This condition has become a severe public health burden,
but unfortunately, no effective therapeutic strategies exist.
Patients with HFpEF are usually elderly with many
complications, including hypertension, diabetes,
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Arq Bras Cardiol. 2023; 120(5):e20220523
Original Article
Meng et al.
Influence of Nutrition Markers in HFpEF
HFpEF. Therefore, our systematic review and meta-analysis
were designed to evaluate the prognostic value of SA, GNRI,
and PNI in patients with HFpEF.
Methods
This review followed the Preferred Reporting Items
for Systematic Reviews and Meta-Analyses guidelines.22
It was registered in the International Prospective
Register of Systematic Reviews under the registration ID:
CRD42021238546.
Study search
PubMed, Embase, The Cochrane Library, and Web of
Science databases were systematically searched for all studies
about the prognostic significance of nutritional parameters
among patients with HFpEF published till January 2022. The
following search terms were used (“albumin” OR “ALB” OR
“hypoalbuminemia” OR “geriatric nutritional risk index” OR
“GNRI” OR “prognostic nutritional index” OR “PNI”) AND
(“heart failure with preserved ejection fraction” OR “HFpEF”
OR “diastolic heart failure” OR “heart failure with normal
ejection fraction”). We additionally screened the reference
lists of selected studies and related systematic reviews to
identify relevant studies.
Selection criteria
Two authors (MY and ZT) independently performed
the study selection process, and any disagreement was
discussed. The inclusion criteria were as follows: 1. Adult
patients (>18 years old) with HFpEF (the left ventricular
EF [LVEF] of HFpEF subjects included in this study was
≥ 40%); and 2. Studies with prognostic information on one
of the nutritional assessment indicators (SA, GNRI, or PNI).
The exclusion criteria were: 1. Patients with severe heart
valve disease; 2. Patients with congenital heart diseases;
3. Patients with acute myocardial infarction; 4. Patients
with cor pulmonale; 5. Pregnant women; 6. Incomplete
data even after contacting the authors; and 7. Case reports
and conference abstracts.
Data extraction and quality assessment
Two authors (MY and ZT) independently extracted
the following data from the included studies: the year of
publication, first author, sample size, study design, follow-up
duration, mean/median age of the study population, mean
ejection fraction, nutritional indicators, endpoint data, hazard
ratio (HR), and corresponding 95% confidence intervals (CIs).
The Quality of Prognosis Studies Tool was used to assess
the risk of bias,15 using 6 parameters (study participation,
Central Illustration: Prognostic Signicance of Nutrition-Associated Markers in Heart Failure with
Preserved Ejection Fraction: A Systematic Review and Meta-Analysis ABC Cardiol
Journal of Brazilian Society of Cardiology
Assessing the prognostic value of SA, GNRI, PNI
in patients with HFpEF
Database searching and screening
Analysis results
Studies included is qualitative synthesis
(meta-analysis) (n=9)
SA
Lower SA Signicantly related
with high all-cause mortality,
and indicates a bad composite
outcome of all-cause mortality
and HF rehospitalization
GNRI
Lower GNRI signicantly
related with high all-
cause mortality and high
cardiovascular mortality
PNI
Lower PNI showed no
correlation with all-cause
mortality
Arq Bras Cardiol. 2023; 120(5):e20220523
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Influence of Nutrition Markers in HFpEF
study attrition, prognostic factor measurement, outcome
measurement, study confounding, and statistical analysis and
reporting), and the studies were rated as high, moderate, or
low risk of bias.
Statistical analyses
We performed the statistical analysis using STATA version
15.0 (Stata Corporation, College Station, TX, USA). HR and
95% CI were considered concerning the effect size of each
study. When the HR was unavailable, we reconstructed the HR
estimate and its variance from the Kaplan-Meier survival curves
by Engauge Digitizer. Statistical heterogeneity was evaluated
using the chi-squared Q test and I2 statistic,23 where I2 > 50%
and p<0.05 indicated heterogeneity between studies. A
fixed-effects model was applied if there was no significant
heterogeneity; otherwise, a random-effects model was used.
Egger’s test evaluated publication bias. P values of less than
0.05 were considered statistically significant.
Results
Study search and characteristics
Figure 1 provides a detailed search selection of studies
for this meta-analysis. We identified 1536 publications
through an online database search; 661 were excluded due
to duplication. After screening the titles and abstracts, we
excluded 848 records. The full text of the remaining 27 studies
was reviewed and evaluated in detail. Finally, we included
9 articles in this meta-analysis.20,24-31
The characteristics of the studies included are listed in
Table 1. Of the nine studies, five were prospective, and four
were retrospective. All studies were published between 2012
and 2020, six were conducted in Asia, and three were in
North America. The analysis included 5603 adults who were
followed up for one year to 5.8 years on average. The subjects’
average ages ranged from 32 to 98 years. These studies used
various LVEF cut-offs in the HFpEF population ranging from
40% to 50%. Two studies used a threshold of 40%, one used
45%, and six used 50%. Three nutritive indexes were used
in these selected studies; five studies measured the SA, four
studies measured the GNRI, and two studies measured the
PNI to assess malnutrition.
Meta-analysis result
SA
Three studies analyzed all-cause mortality with SA. After
combining HR, lower SA predicted higher all-cause mortality
in the random effects model (HR = 1.98; 95% CI = 1.282–
3.057, p = 0.002; I2 = 83.6%; Figure 2A), and the Egger’s test
(p = 0.584) did not identify publication bias. Three studies
analyzed the composite endpoint of all-cause mortality
and HF rehospitalization with SA, a fixed effects model
(HR = 1.768; 95% CI = 1.483–2.108, p = 0.000; I2 = 22.3%;
Fig 2B) was statistically significant, and Egger’s test (p = 0.661)
showed no publication bias.
GNRI
Four studies analyzed all-cause mortality with GNRI. After
combining HR, the lower GNRI and the worse all-cause
mortality were predicted. Since a significant heterogeneity was
observed between individual studies (I2 = 90.4%, p < 0.01), a
random effects model was used to obtain the pooled estimate
effect. The meta-analysis revealed a significantly increased all-
cause mortality (HR: 1.812; 95% CI: 1.064–3.086, p = 0.029;
Figure 2C) for HFpEF patients with lower GNRI. However,
there may be publication bias, as supported by Egger’s test
(p = 0.014). This was tested further by Trim and Fill analysis,
and the result of pooled HR did not change. The bias did not
affect the evaluation result.
Cardiovascular mortality was analyzed in three studies with
GNRI. Comprehensive data showed that lower GNRIs were
related to higher cardiovascular mortality, and the fixed effects
model (HR = 1.922; 95% CI = 1.504–2.457, p = 0.000;
I2 = 0.00%; Figure 2D) was statistically significant, and Egger’s
test (p = 0.41) showed there was no publication bias.
PNI
PNI was estimated using a random model in two studies,
and the pooled HR revealed no statistical difference in all-
cause mortality between the patients with a high and low
level of PNI (HR: 1.176; 95% CI: 0.858–1.612, p = 0.314,
I2 =80.6%; Figure 2E), and Egger’s test (p < 0.05) showed
certain publication bias, as seen in some studies.
Study quality
The quality of these studies was assessed according to
the Quality of Prognosis Studies Tool; seven studies ranked
moderate quality, and two studies were ranked high quality.
Figure 1 – Flow chart of literature selection.
Records identied through database searching (n=1536)
Included Eligibility Screening Identication
Records after duplicates removed (n=875)
Records excluded by titles and
abstracts (n=848)
Full-text articles assessed for eligibilty (n=27)
Studies included in systematic review (n=9)
Studies included in meta-analysis (n=9)
Full-text articles excluded for the
reasons below:
Without extractable data (n=9)
Other language than English (n=1)
Wrong subjct (n=2)
Non-conforming endpoints (n=5)
Repeated study (n=1)
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Original Article
Meng et al.
Influence of Nutrition Markers in HFpEF
Table 1 – Characteristics of studies included in the meta-analysis
Liu Vasiliki Toshiyuki Stuart Yoshiharu Isao Masatoshi Shih-Chieh Yu-Lun
Year 2012 2018 2018 2020 2013 2019 2019 2019 2017
Country China USA Japan USA Japan Japan USA China China
Study design Prospective Retrospective Prospective Prospective Retrospective Prospective Retrospective Retrospective Prospective
Inclusion period
June
2006-December
2009
January 2012-April
2012
November
2012-March
2015
-January 2004-April
2011
June
2012-March
2015
August
2006-January 2012
March
2021-December
2014
October
2003-December
2012
Follow up 12m 2y 731d 57.6m 2.1y 503.5d 2.9y 1255d 31.5m
Number 576 445 535 118 152 110 1677 1120 870
Women, % 64% 57.80% 50% 92% 46.10% 46.40% 50.80% 60.60% -
Mean age 77±10 73(63,83) 80(73-84) 65.42 ±9.49 77±11 78.5±7.2 72.12±2.49 77.2 -
NYHA class II-III, % 83% 24% 74% - 83.40% 90.90% -86.20% -
LEVF ≥50% >40% ≥50% >50% ≥40% ≥50% ≥45% ≥50% ≥50%
Nutritional index SA SA SA SA GNRI GNRI GNRI SA, GNRI, PNI PNI
Cut off 34g/L 34g/L -35g/L 92 92 - SA:35g/L;
GNRI:92; PNI:38 39.3
Outcome, HR(95% CI) ACM,3.18
(2.27–4.45)
ACM,1.67 (1.28–2.18)
CEP,1.69(1.13-2.53)
CEP,2.27(1.59-
3.23)
CEP,1.61
(1.29-2.06)
ACM,2.667(1.527-
4.651); CM,
2.469(1.248-4.902)
ACM,3.202
(1.295–
7.918)
ACM:1.79
(1.33–2.42);
CM:2.06(1.40–3.03)
A C M :
SA:1.49
(1.18–2.22),
GNRI:1.02
(1.01–1.03),
PNI:1.03 (1.01–
1.05);
C M:
GNRI:1.69
(1.19–2.44)
ACM,1.43(1.08-
1.90)
Adjustment variables
Age, male, CR
levels, SBP, history
of CVD, history of
DM, BUN levels,
HB levels, use of
ACEIs/ARBs
Age and sex - - - Age and sex
Nyha functional
class, hypertension,
DM, HF
hospitalization,
MI, stroke, AF, any
cancer, use of ACEIs/
ARBs, beta-blockers,
HB levels, serum
sodium levels,
bilirubin levels, EGFR
Age, sex, HMI,
systolic blood
pressure, heart
rate, prior HF,
hypertension,
cardiovascular
disease,
diabetes, and
atrial fibrillation
-
ACM: all-cause mortality; CM: cardiovascular mortality; CEP: composite endpoint (all-cause mortality and HF rehospitalization); y: year; m: month; d: day.
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Influence of Nutrition Markers in HFpEF
Four studies did not record or control confounding factors related
to the evaluation results, and three did not provide information
on losses to follow-up. Details are provided in Table 2.
Discussion
Malnutrition may result in energy deficiency, immunologic
hypofunction, and tissue and organ damage.32 Compared with
well-nourished patients, malnourished patients have longer
hospital stays, higher readmission rates, and mortality.33 The
imbalances of anabolism and catabolism in the development
of HF can also lead to malnutrition. Research suggests that
50% of patients with chronic HF developed some degree of
malnutrition.25 The imbalance between nutrient supply and
energy needs results in impaired cellular energy metabolism
and impacts the whole body’s metabolic systems. Significant
body energy consumption can cause cardiac cachexia; it
has been reported that 15% of patients with HF manifested
cachexia.25 At the same time, cardiac cachexia is considered
a risk factor for mortality in patients with HF.34
As the significance of malnutrition in patients with
HFpEF has not yet been fully assessed, we evaluated the
Figure 2 – A) Forrest plot of hazard ratio (HR) for the association between serum albumin (SA) and all-cause mortality; B) Forrest plot of the hazard ratio for
the association between SA and the composite endpoint of all-cause mortality and HF rehospitalization; C) Forrest plot of the hazard ratio for the association
between geriatric nutritional risk index (GNRI) and all-cause mortality; D) Forrest plot of the hazard ratio for the association between GNRI and cardiovascular
mortality; E) Forrest plot of the hazard ratio for the association between prognostic nutritional index (PNI) and all-cause mortality. Heterogeneity among studies
was determined using I2 statistics at a significance level of p < 0.05. CI: confidence interval; HR: hazard ratio.
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Influence of Nutrition Markers in HFpEF
role of different nutritional indicators (SA, GNRI, and PNI)
in predicting the disease prognosis in patients with HFpEF.
We found that lower SA and GNRI scores were significantly
associated with higher all-cause mortality, and lower SA is also
associated with increased composite outcomes of all-cause
mortality and HF hospitalization rates. However, there was no
correlation between lower PNI and all-cause mortality. The
findings suggest that SA and GNRI may be helpful indicators
for prognosis assessment in a patient with HFpEF.
SA is a simple and objective indicator of nutritional
evaluation, and it can better reflect muscle mass and protein
storage.35,36 It is also considered an inflammatory marker.37
SA plays an important role in many physiological processes,
including maintaining a stable colloid osmotic pressure
and microvascular integrity, delivering substance in the
body as a carrier protein, and scavenging free radicals and
anticoagulant activities.38 According to a survey by Liu et al.,20
SA deficiency was observed in 30% of patients with chronic
H F. 20 Hypoproteinemia can promote the development of HF
by causing pulmonary and myocardial edema, fluid retention,
diuretic resistance, oxidative stress, and inflammation.39 A
multicenter study including adults without HF has shown
the important role of SA in the development of HF, in which
baseline hypoalbuminemia is associated with an increased
risk of developing HF during the 10-year follow-up period.40
However, studies have yielded conflicting results on the ability
of SA to predict the prognosis of patients with HFpEF. Liu et
al.20 suggest that hypoalbuminemia was significantly related
to the increased risk of death for patients with HFpEF.20
However, Shanmugam et al. show that hypoalbuminemia had
no obvious relationship to 1-year mortality in patients with
HFpEF.21 Our meta-analysis reveals that hypoalbuminemia
was significantly associated with a high all-cause mortality
rate and HF hospitalizations in patients with HFpEF, which
support that SA is a strong predictor of adverse outcome in
patients with HFpEF.
GNRI was proposed by Bouillanne et al.,41 and its basic
parameters are SA and body mass index (BMI). It was initially
used to assess nutritional risk in the elderly. However, it
was also found to predict clinical outcomes under different
pathological conditions.42,43 Seoudy et al.44 suggest that
compared to healthy individuals, the level of cardiovascular
biomarkers increased markedly, and the prevalence of chronic
HF was higher in patients with low GNRI.44 In addition,
research showed that GNRI was associated with volume
overload,45 higher cardiovascular death, and higher rates of
rehospitalization46,47 in patients with HF. Our meta-analysis also
indicates that low GNRI correlates with a high cardiovascular
mortality rate in patients with HFpEF. Some researchers believe
GNRI represents the patients’ frail state caused by various
stressors under multiple systems disorders.48,49 Studies have
shown that HFpEF patients have a higher mortality rate when
they have low BMI and poor protein reserve,35,50 and this poor
nutritional status may represent the progression of HFpEF.
PNI is a synthetically nutritional evaluation index
representing protein synthesis and the body’s immune
function.51 Nutritional state may affect the metabolism
and function of immune cells, and malnutrition can lead
to immunosuppression and affects prognosis in patients.52
PNI was originally used to assess the perioperative risk of
gastrointestinal surgery patients.53 However, recent research
shows that PNI is an effective prognostic marker in patients
with various malignant tumors,54 acute HF,25 and pulmonary
embolism.55 In our analysis, only two studies could be used for
the combined analysis of the impact of low PNI on all-cause
mortality of HFpEF, and the results failed to show a correlation
between PNI and HFpEF. This lack of correlation may be due
to clinical heterogeneity, as the cut-off points for PNI are
not uniform. However, due to the small number of included
studies and the unavailability of further subgroup analysis,
high-quality studies are needed to evaluate the predictive
value of PNI on the prognosis of HFpEF.
As HFpEF is a disease with high heterogeneity and
complicated pathological processes caused by multiple
comorbidities that can affect the development of HFpEF,
a single nutritional index may not accurately predict the
outcome in all patients. Comprehensive assessment of various
nutri tion indicators can provide complete prognostic information,
Table 2 – The Quality of Prognosis Studies Tool for assessing the quality of selected studies
Study, year Study
participation Study attrition
Prognostic
factor
measurement
Outcome
measurement
Study
confounding
Statistical
analysis and
reporting
Total
MingLiu, 2012 M LM M HM M
Yoshiharu, 2013 M LM M LM M
Yu-Lu, 2017 M M M M LM M
Vasiliki, 2018 M LMMMMM
Toshiyuki, 2018 M M M M LM M
Isao, 2019 M M HHHHH
Shih Chieh, 2019 M M HHHHH
Masatoshi, 2019 M M M M M M M
Stuart, 2020 M M H H L M M
L: low quality; M: middle quality; H: high quality.
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Arq Bras Cardiol. 2023; 120(5):e20220523
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Meng et al.
Influence of Nutrition Markers in HFpEF
and it would increase the ability to predict and risk stratification
of HFpEF. At the same time, such risk identification may lead to
improved clinical decision-making to delay disease progression,
and formulating nutritional intervention plans may also help
improve the clinical outcome of such patients. It has been shown
that nutritional supplements are good for patients with chronic
H F, 56,57 but further clinical studies are needed to verify whether it
is directly related to the prognosis of patients with HFpEF.
Limitations
There are some limitations in our study. There are relatively
few related studies; therefore, we could not include as many
assessable studies as possible. In our meta-analysis, we defined
HFpEF as an LVEF≥40%, which can cause a difference to some
extent in the results. In some studies, the HR and 95% CIs were
estimated by Kaplan–Meier survival curves, which may lead to
potential error. Moreover, studies have a certain heterogeneity,
which may be associated with the inconsistency of cut-off value
and adjusted confounding factors when calculating HR in the
included studies. In addition, as systemic diseases can affect
nutritional status, this will also increase the heterogeneity of the
study. Because of the limited number and quality of the studies,
further studies are needed to evaluate the role of nutritional
indicators in predicting the prognosis of HFpEF.
Conclusion
As summarized in the central illustration, this meta-analysis
provides evidence of the correlation between the nutritional
indices, SA and GNRI, and the prognosis of HFpEF patients,
showing that HFpEF patients with low SA have a higher risk
of all-cause death and a higher risk of composite endpoint
events of all-cause death and rehospitalization, and HFpEF
patients with low GNRI have a higher risk of all-cause death
and cardiovascular death. These results indicate the predictive
value of SA and GNRI in the prognosis of HFpEF patients,
and they may be useful reference indicators for the prognosis
evaluation of HFpEF.
Author Contributions
Conception and design of the research, Acquisition of data,
Obtaining financing and Writing of the manuscript:
Meng Y;
Analysis and interpretation of the data:
Zhang Z, Zhao T; Statistical
analysis:
Zhao T; Critical revision of the manuscript for important
intellectual content:
Zhang Z, Zhang D.
Potential conflict of interest
No potential conflict of interest relevant to this article was
reported.
Sources of funding
This study was partially funded by Cuiying Scientific and
Technological Innovation Program of Lanzhou University Second
Hospital (No.2020QN-11) and the Natural Science Foundation
of Gansu (21JR7RA397).
Study association
This study is not associated with any thesis or dissertation work.
Ethics approval and consent to participate
This article does not contain any studies with human
participants or animals performed by any of the authors.
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