Skip to main content
PMC home page
JACC: Advances logoLink to JACC: Advances
. 2023 Sep 28;2(8):100635. doi: 10.1016/j.jacadv.2023.100635

Cardiovascular Outcomes in Acute Coronary Syndrome and Malnutrition

A Meta-Analysis of Nutritional Assessment Tools

Angeline RX Lai a,∗, Manish Warrier a,∗, Ethel ZX Ng a, Chaoxing Lin a, Yip Han Chin a,∗, Gwyneth Kong a, Vickram V Anand b, Ethan CZ Lee b, Haoxing Lai a, Hung Wei Ng a, Rachel SJ Goh a, Bryan Chong a, Mark D Muthiah a,c,d, Chin Meng Khoo a,e, Jiong-Wei Wang a,f,g, Gary Tse h,i, Poay Huan Loh a,j, Anurag Mehta k, Adrian Brown l,m,n, Georgios K Dimitriadis o,p, Mark Y Chan a,j, Nicholas WS Chew j,∗∗
PMCID: PMC11198676  PMID: 38938362

Abstract

Background

There is emerging evidence that malnutrition is associated with poor prognosis among patients with acute coronary syndrome (ACS).

Objectives

This study seeks to elucidate the prognostic impact of malnutrition in patients with ACS and provide a quantitative review of most commonly used nutritional assessment tools.

Methods

Medline and Embase were searched for studies reporting outcomes in patients with malnutrition and ACS. Nutritional screening tools of interest included the Prognostic Nutrition Index, Geriatric Nutritional Risk Index, and Controlling Nutritional Status. A comparative meta-analysis was used to estimate the risk of all-cause mortality and cardiovascular events based on the presence of malnutrition and stratified according to ACS type, ACS intervention, ethnicity, and income.

Results

Thirty studies comprising 37,303 patients with ACS were included, of whom 33.5% had malnutrition. In the population with malnutrition, the pooled mortality rate was 20.59% (95% CI: 14.95%-27.67%). Malnutrition was significantly associated with all-cause mortality risk after adjusting for confounders including age and left ventricular ejection fraction (adjusted HR: 2.66, 95% CI: 1.78-3.96, P = 0.004). There was excess mortality in the group with malnutrition regardless of ACS type (P = 0.132), ethnicity (P = 0.245), and income status (P = 0.058). Subgroup analysis demonstrated no statistically significant difference in mortality risk between individuals with and without malnutrition (P = 0.499) when using Controlling Nutritional Status (OR: 7.80, 95% CI: 2.17-28.07, P = 0.011), Geriatric Nutritional Risk Index (OR: 4.30, 95% CI: 2.78-6.66, P < 0.001), and Prognostic Nutrition Index (OR: 4.67, 95% CI: 2.38-9.17, P = 0.023).

Conclusions

Malnutrition was significantly associated with all-cause mortality risk following ACS, regardless of ACS type, ethnicity, and income status, underscoring the importance of screening and interventional strategies for patients with malnutrition.

Key words: acute coronary syndrome, coronary artery disease, malnutrition, nutritional assessment tools, prognosis, systematic review and meta-analysis

Central Illustration

graphic file with name ga1.jpg

Open in a new tab

Mortality following acute coronary syndrome (ACS) remains high, and life-threatening coronary artery disease is often predisposed by cardiovascular disease (CVD) risk factors such as type 2 diabetes mellitus, hypertension, and dyslipidemia.1 Emerging evidence has shown a possible J-curve association between nutrition and CVD, and the identification of this modifiable risk factor may potentially allow for better risk stratification of patients with ACS.1,2 Various nutritional assessment tools used to categorize malnutrition, such as the Prognostic Nutrition Index (PNI), Geriatric Nutritional Risk Index (GNRI), and Controlling Nutritional Status (CONUT), have been developed and demonstrated to possess prognostic utility in the prognostication of ACS.3, 4, 5 These past studies have postulated that hypoalbuminemia is correlated with a proinflammatory environment, although the exact cause-and-effect mechanism between them is unclear.3,4 Moreover, the immunocompromised state indicated by lymphocytopenia in these patients may further contribute to the poor prognosis.3,5

With 10% or more of patients with ACS suffering from malnutrition,6 early identification and management of malnutrition have the potential to improve outcomes for this vulnerable group of patients. However, while malnutrition is a risk factor that can be readily intervened,7 it is often overlooked in clinical practice.8 This is in part due to the lack of a standardized definition or method to assess nutritional status and the numerous different types of tools available.9,10 There is limited evidence in the current literature suggesting which tool is superior prognostically, a relevant factor that may help clinicians in the selection of tool to use.

While studies have examined malnutrition in CVD, few have focused specifically on patients with ACS.11, 12, 13, 14 Furthermore, current meta-analyses addressing the prognostic impact of malnutrition on patients with ACS often examine individual nutritional indices but do not compare different widely used nutritional assessment tools.8,9 Hence, this paper seeks to elucidate the prognostic impact of malnutrition in patients with ACS by examining the outcomes of all-cause mortality, major adverse cardiovascular outcomes, and other cardiovascular outcomes, as well as medication usage across the total follow-up duration reported in the studies. Moreover, we aim to comprehensively consolidate and compare the utility of the most commonly used nutritional assessment tools.

Methods

Search strategy

This study was conducted with reference to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses15 and was registered with PROSPERO (CRD42022334482). Ethical approval was not required because this meta-analysis utilized data from published studies. A search was conducted on Medline and Embase to identify literature published from inception to May 18, 2022, relating to the outcomes of patients with ACS and malnutrition compared to patients without malnutrition. Keywords included in the search strategy were “malnutrition”, “nutritional assessment”, and “acute coronary syndrome” (Supplemental Appendix). References were imported into Endnote (Version 20, Clarivate) for removal of duplicates.

Study selection and data extraction

Title and abstract sieve, and full text review were conducted independently by 2 authors (A.L.R.X. and E.N.Z.X.). Disputes were resolved by a senior author (N.C.W.S.). The inclusion criteria were: 1) cohort studies examining the outcomes of adult (aged 18 and above) participants; 2) populations with ACS; and 3) utilized a nutritional index to classify patients as with or without malnutrition. A description of the various nutritional indices can be found in Supplemental Table 1 inclusive of PNI,16 GNRI,17 CONUT,18 Nutrition Risk Screening-2002,19 Malnutrition Screening Tool,20 Mini Nutritional Assessment,21 Mini-Nutritional Assessment-Short Form,22 and Triglyceride, Total Cholesterol and Body Weight Index.23 ACS was defined in accordance with the clinical practice guidelines, inclusive of ST-segment elevation myocardial infarction (STEMI), and non-ST-segment elevation ACS (NSTE-ACS).24 Meta-analyses, systematic reviews, case reports, position papers, commentaries, conference abstracts, and randomized controlled trials were excluded. Studies were also excluded if they involved special patient groups such as cancer or pediatric patients. For studies that reported on the same patient pool in overlapping time periods, we included the most comprehensive article.

Data was extracted independently by 3 authors (A.L.R.X., M.W., and E.N.Z.X.). The extracted information included study characteristics and patient demographics. In addition, we extracted survival and cardiovascular outcomes of interest, namely all-cause mortality, in-hospital mortality, cardiac mortality, major adverse cardiovascular events (MACE), cerebrovascular accidents, and coronary revascularization. In most articles, MACE was defined as the composite of all-cause mortality, myocardial infarction, revascularizations, and ischemic stroke, while coronary revascularization included percutaneous coronary intervention and coronary artery bypass graft (CABG). Transformation of values from median and interquartile range to mean and standard deviation were carried out when they were not stated.25 The primary outcome was all-cause mortality. Secondary outcomes included in-hospital mortality, cardiac mortality, MACE, cerebrovascular accidents, and coronary revascularization. Unless otherwise stated, the follow-up time period used for analysis was the entire follow-up time period reported in the various studies.

Statistical analysis and quality assessment

Statistical analysis was conducted using RStudio (Version 4.1.0). For the analysis of variables reported as proportions, a single-arm meta-analysis of proportions was conducted using a generalized linear mixed model with Clopper-Pearson intervals in order to stabilize the variance using the metaprop function.26, 27, 28 To estimate the effects of malnutrition on patient outcomes, the OR was determined using a generalized linear model using the metabin function.29,30 Next, the inverse variance method was used to conduct a meta-analysis of means for the analysis of data reported as continuous variables using the metamean function. Cochrane Review Manager (Version 5.4.1) was used to pool HRs using the inverse variance method.31 For this analysis, we pooled the unadjusted HR and adjusted HR of all-cause mortality separately. Where studies reported more than one set of adjusted HR, the results of the most comprehensive analysis that included the most covariates were extracted. The random effects model was utilized since the pooled studies differed in trial characteristics such as inclusion and exclusion criteria and study duration.32 Hartung-Knapp adjustments were used to adjust for confidence intervals.33 Results of the statistical analysis were considered significant if they had a value of P ≤ 0.05. Small, moderate, and large amounts of heterogeneity were defined with I2 cutoffs of 25%, 50%, and 75%, respectively.34 Publication bias was assessed via a funnel plot.

Subgroup analysis was conducted to analyze the difference in outcomes by nutritional assessment tools (CONUT, GNRI, PNI, others), mean age (<65 years, ≥65 years), ethnicity (predominantly Asian, predominantly White), duration of follow-up (<2 years, ≥2 years), type of ACS (ACS, STEMI, NSTE-ACS), and income group (high, upper middle). Subgroup analysis was done with the byvar argument in Rstudio, and P values from the test of subgroup difference were reported. In the respective subgroups, the results of the statistical analysis comparing patients with malnutrition to patients without malnutrition were also reported in terms of OR and its corresponding P value. We followed the World Bank classification of countries for income group, and Asian ethnicity was defined as individuals from East Asia, Central Asia, South Asia, and Middle East heritage.35 In addition, we also conducted a separate analysis involving studies that followed the more widely adopted guidelines for malnutrition for the primary outcome of all-cause mortality. The cutoffs used were CONUT ≥2, GNRI <98, and NRS-2002 ≥3 (Supplemental Table 1).

Risk of bias assessment

Risk of bias assessment was carried out using the Newcastle-Ottawa Scale by 3 independent authors (A.L.R.X., M.W., and E.N.Z.X.). In the event of disagreements, a fourth independent author (Y.H.C.) was recruited to resolve disputes. The 3 domains of bias assessed are: selection of study groups; comparability of these groups; and ascertainment of exposure or outcome of interest.36 The maximum possible score is 9.

Results

Summary of included articles

The search yielded 3,786 results, of which 776 duplicates were removed. A total of 2,776 studies were excluded after title and abstract sieve, with 234 studies selected for full text review. A final total of 30 studies were included in this meta-analysis (Figure 1). Of these 30 studies, 20 were from Asia, 9 were from Europe, and 1 was from South America. A total of 37,303 individuals were included, of whom 12,494 (33.5%) had malnutrition and 24,621 did not. The mean ages of the patients with and without malnutrition were 72 years and 65 years, respectively, and they were predominantly males (67.1% and 74.5%, respectively). The mean body mass index of the patients with and without malnutrition was 24.4 kg/m2 and 26.7 kg/m2, respectively, and the proportion of smokers was 36.1% and 43.8%, respectively. A summary of the included articles can be found in Supplemental Table 2.

Figure 1.

Figure 1

Open in a new tab

PRISMA Flow Diagram

A total of 3,786 articles were identified from Medline and Embase, of which 30 articles were included. PRISMA = Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Baseline characteristics

The comparison of baseline characteristics can be found in Supplemental Table 3. The prevalence of dyslipidemia was significantly higher in the group without malnutrition (39.6%, 95% CI: 25.6%-55.6%) compared to the group with malnutrition (33.8%, 95% CI: 19.6%-51.3%, P = 0.017). The prevalence of hypertension and type 2 diabetes mellitus was similar between the 2 groups. When comparing laboratory markers of nutrition, the group with malnutrition had significantly lower serum albumin levels and lymphocyte counts. They also had significantly lower levels of low-density lipoprotein, total cholesterol, and triglyceride.

Primary outcomes

There were 25 studies comprising 34,593 patients reporting data on all-cause mortality with a mean follow-up duration of 33 months (range: 9-43 months) (Table 1). The temporal trend of all-cause mortality is presented in Supplemental Table 4 and Figure 2. In the group with malnutrition, the pooled mortality rate was 20.59% (95% CI: 14.95%-27.67%), increasing from 3.22% (95% CI: 1.33%-7.59%) at 30 days, 12.48% (95% CI: 7.37%-20.38%) at 1 year, 16.49% (95% CI: 6.65%-35.36%) at 2 years, to 37.92% (95% CI: 25.06%-52.74%) at 5 years. In contrast, the pooled mortality rate was lower in the group without malnutrition (5.41%, 95% CI: 3.44%-8.39%), with smaller increases in mortality rates over time: 1.02% (95% CI: 0.47%-2.24%) at 30 days, 2.44% (95% CI: 1.25%-4.70%) at 1 year, 2.32% (95% CI: 0.79%-6.58%) at 2 years, and 12.38% (95% CI: 8.40%-17.87%) at 5 years. Patients with malnutrition following ACS had a higher risk of all-cause mortality compared to their counterparts (unadjusted HR: 4.36, 95% CI: 3.06-6.22, P < 0.001). Malnutrition was significantly associated with all-cause mortality risk even after adjusting for possible confounders such as age and left ventricular ejection fraction (adjusted HR: 2.66, 95% CI: 1.78-3.96, P = 0.004) (Supplemental Table 5). The model covariates used by the individual studies for analysis are reported in Supplemental Table 2. Funnel plot analysis showed no publication bias (Supplemental Figure 1).

Table 1.

Prognostic Outcomes Following Acute Coronary Syndrome Across the Entire Follow-Up Duration, Pooled and With Stratification by Subgroups

Studies With Malnutrition
 Without Malnutrition
 Comparison of Patients With Malnutrition Against Without Malnutrition
Proportion (95% CI) I2 P Value Proportion (95% CI) I2 P Value OR (95% CI) I2 P Value
All-cause mortality
 Overall 25 20.59% (14.95%-27.67%) 96.0% 5.41% (3.44%-8.39%) 97.0% 4.51 (3.33-6.12) 87.0% <0.001
 Nutritional assessment tool 0.717 0.181 0.499
 CONUT 5 15.14% (5.43%-35.66%) 96.4% 2.18% (0.52%-8.61%) 94.5% 7.80 (2.17-28.07) 87.3% 0.011
 GNRI 10 21.33% (10.47%-38.61%) 96.6% 5.90% (2.34%-8.61%) 95.8% 4.30 (2.78-6.66) 82.2% <0.001
 PNI 5 24.38% (11.72%-43.90%) 97.0% 6.75% (3.11%-14.02%) 97.9% 4.67 (2.38-9.17) 86.1% 0.023
 Others 5 21.66% (13.65%-32.58%) 89.2% 7.79% (2.97%-18.94%) 98.4% 3.29 (1.31-8.31) 91.6% 0.003
 Mean age, y 0.166 0.002 0.052
 <65 7 16.38% (7.51%-32.11%) 95.8% 2.89% (1.41%-5.86%) 91.0% 6.93 (3.39-14.15) 83.8% <0.001
 ≥65 17 25.28% (19.39%-32.11%) 94.8% 8.37% (5.58%-12.37%) 96.4% 3.67 (2.69-5.01) 83.5% <0.001
 Type of ACS 0.293 0.099 0.132
 ACS 15 22.82% (16.63%-30.46%) 96.0% 7.47% (4.42%-12.36%) 97.4% 3.61 (2.58-5.05) 85.7% <0.001
 STEMI 6 23.51% (8.86%-49.30%) 95.7% 4.34% (1.58%-11.33%) 93.2% 7.21 (3.05-17.04) 84.8% 0.002
 NSTE-ACS 4 10.70% (2.00%-41.24%) 96.0% 2.12% (0.32%-12.93%) 82.9% 4.99 (2.38-10.46) 29.6% 0.006
 Duration of follow-up 0.986 0.742 0.560
 2 y or less 12 20.40% (16.19%-25.37%) 85.5% 4.96% (3.04%-7.97%) 97.4% 4.93 (2.91-8.37) 87.8% <0.001
 More than 2 y 13 20.50% (10.88%-35.28%) 97.4% 5.73% (2.47%-12.72%) 96.5% 4.16 (2.88-8.37) 86.9% <0.001
 Ethnicity 0.036 0.008 0.245
 Predominantly Asiana 19 18.19% (12.04%-26.55%) 96.3% 3.23% (2.46%-7.16%) 93.1% 5.03 (3.54-7.16) 80.5% <0.001
 Predominantly White 6 29.51% (20.31%-40.75%) 96.2% 11.10% (5.63%-20.72%) 97.0% 3.37 (1.68-6.74) 92.6% 0.006
 Prospective/retrospective 0.158 0.286 0.636
 Prospective 8 15.89% (10.22%-23.86%) 90.5% 4.11% (2.19%-7.60%) 87.9% 4.10 (2.92-5.75) 45.4% <0.001
 Retrospective 17 23.11% (15.03%-33.82%) 96.0% 6.20% (3.40%-11.02%) 97.1% 4.62 (2.99-7.13) 90.7% <0.001
 Income group 0.317 0.043 0.058
 High 12 23.79% (17.33%-31.74%) 95.7% 8.21% (4.61%-14.21%) 97.0% 3.50 (2.25-5.43) 87.6% <0.001
 Upper middle 13 17.81% (9.80%-30.18%) 96.3% 3.71% (1.94%-7.00%) 92.3% 5.84 (3.93-8.67) 78.1% <0.001
Other outcomes
 Cardiac mortality 7 11.37% (4.35%-26.59%) 95.9% 3.47% (1.79%-6.64%) 87.0% 3.74 (1.77-7.88) 78.6% 0.005
 In-hospital mortality 8 16.40% (10.85%-24.02%) 95.8% 3.24% (2.09%-4.99%) 84.1% 5.83 (3.29-10.33) 89.6% <0.001
 MACE 11 27.48% (19.01%-37.96%) 97.8% 11.89% (7.86%-17.60%) 98.6% 2.74 (2.14-3.51) 86.6% <0.001
 Cerebrovascular accident 5 3.43% (1.15%-9.72%) 81.9% 1.47% (0.64%-3.31%) 70.5% 3.42 (1.98-5.93) 0.0% 0.034
 Coronary revascularization 8 16.35% (3.86%-48.74%) 98.8% 18.04% (3.87%-54.60%) 99.4% 0.98 (0.61-1.57) 86.1% 0.933
Medications
 Aspirin 9 92.36% (73.69%-98.12%) 97.7% 93.78% (75.52%-98.66%) 99.2% 0.92 (0.70-1.21) 33.6% 0.510
 Beta-blockers 12 70.96% (50.26%-85.52%) 97.3% 74.77% (55.47%-87.58%) 98.8% 0.78 (0.67-0.90) 45.4% 0.004
 ARB 12 68.68% (49.99%-82.79%) 98.4% 69.79% (50.22%-84.10%) 98.8% 0.99 (0.83-1.17) 69.2% 0.853
 Statins 13 89.38% (71.69%-96.55%) 97.2% 94.00% (74.28%-98.84%) 99.3% 0.71 (0.41-1.23) 91.5% 0.200
Open in a new tab

Bold values indicate P value of <0.05 and it is taken as statistical significance.

ACS = acute coronary syndrome; ARB = angiotensin receptor blocker; CABG = coronary artery bypass graft; CONUT = controlling nutritional status; GNRI = Geriatric Nutritional Risk Index; MACE = major adverse cardiovascular outcomes; NSTE-ACS = non-ST-segment elevation acute coronary syndrome; NSTEMI = non-ST-segment elevation myocardial infarction; PNI = Prognostic Nutrition Index; STEMI = ST-segment elevation myocardial infarction.

a

Predominantly Asian included all Asian ethnicities.

Figure 2.

Figure 2

Open in a new tab

Bar Plot of All-Cause Mortality, Stratified by Follow-Up Duration

(A) Proportion, (B) odds ratio. Regardless of the follow-up duration (mean: 33 months, range: 9-43 months), there was increased odds of all-cause mortality in patients with malnutrition.

Baseline demographics

There was no difference in all-cause mortality (P = 0.245) between Asian (OR: 4.87, 95% CI: 3.44-6.91, P < 0.001) and White (OR: 3.37, 95% CI: 1.68-6.74, P = 0.006) ethnicities. Similarly, the upper-middle (5.84, 95% CI: 3.93-8.67, P < 0.001) and high (3.50, 95% CI: 2.25-5.43) income groups had similar mortality risks (P = 0.058). In the subgroup analysis based on age cutoff of 65 years old, patients with malnutrition who were older did not have higher mortality risk (OR: 6.93, 95% CI: 3.39-14.15, P < 0.001) compared to those who were younger (OR: 3.67, 95% CI: 2.69-5.01, P < 0.001). However, the group with malnutrition had higher mortality rates in both the STEMI (OR: 7.21, 95% CI: 3.05-17.04, P = 0.002) and NSTE-ACS subgroups (OR: 4.99, 95% CI: 2.38-10.46, P = 0.006).

Type of nutritional score

Subgroup analysis based on nutritional assessment tools demonstrated no difference in their prognosticating ability of all-cause mortality between patients with and without malnutrition (P = 0.499) when using CONUT (OR: 7.80, 95% CI: 2.17-28.07, P = 0.011), GNRI (OR: 4.30, 95% CI: 2.78-6.66, P < 0.001), and PNI (OR: 4.67, 95% CI: 2.38-9.17, P = 0.023). When comparing across studies that adhered to the more widely used nutritional tool cutoffs (as defined in Supplemental Table 1), higher odds of mortality (P = 0.018) were noted for CONUT (OR: 3.78, 95% CI: 2.97-4.82, P < 0.001), while statistical significance was not achieved using GNRI (OR: 6.90, 95% CI: 0.44-107.95, P = 0.071) (Supplemental Table 6). The forest plots of all-cause mortality and subgroup analysis can be found in Figure 3.

Figure 3.

Figure 3

Open in a new tab

Summary Plot of All-Cause Mortality, With Stratification by Selected Subgroups

All-cause mortality was higher in patients with malnutrition (mean follow-up duration: 33 months, range: 9-43 months), with no significant difference when comparing the various subgroups.

Secondary outcomes

The summary of secondary outcomes is presented in Table 1 and Figure 4. Compared to the group without malnutrition, the group with malnutrition had a higher risk of MACE following ACS (OR: 2.74, 95% CI: 2.14-3.51, P < 0.001, mean follow-up duration: 39 months, range: 20-43 months), in-hospital mortality (OR: 5.83, 95% CI: 3.29-10.33, P < 0.001, mean follow-up duration: 20 months, range: 12-33 months), cardiac mortality (OR: 3.74, 95% CI: 1.77-7.88, P = 0.005, mean follow-up duration: 24 months, range: 11-32 months), and cerebrovascular accidents (OR: 3.42, 95% CI: 1.98-5.93, P = 0.003, mean follow-up duration: 31 months, range: 20-39 months). There was no difference in coronary revascularization rates between the 2 groups (mean follow-up duration: 23 months, range: 9-39 months).

Figure 4.

Figure 4

Open in a new tab

Summary Plot of Outcomes and Medications

Patients with malnutrition had higher rates of cardiovascular outcomes and a lower rate of beta-blocker usage when comparing against the entire follow-up duration.

Guideline-directed medical therapy

In terms of guideline-directed medical therapy following ACS, the group with malnutrition had a lower use of beta-blockers (OR: 0.78, 95% CI: 0.67-0.90, P = 0.004) compared to the group without malnutrition, but there were no differences in the use of aspirin, angiotensin receptor blockers, and statins between the 2 groups (Table 1).

Discussion

In this meta-analysis, we showed that malnutrition was significantly associated with all-cause mortality risk in patients with ACS, even after adjusting for important confounders.7,37 Two in 5 patients do not survive beyond 5 years after the index ACS event, emphasizing the importance of addressing poor nutritional status in the post-ACS care bundle. Moreover, with the various nutritional tools available, it is important to harmonize the diagnosis of malnutrition in patients with CVD. Importantly, this study highlighted similar prognostic capabilities across the various tools, which may suggest the consideration of other factors, such as the ease and availability of the prognostic biomarkers, when deciding the preferred tools to be adopted for use in clinical practice (Central Illustration).

Central Illustration.

Central Illustration

Open in a new tab

Our Study Included 37,303 Patients, of Which 33.5% Had Malnutrition

The adjusted hazard ratio for the primary outcome of all-cause mortality was 2.66 when comparing against the entire follow-up duration, and there was no significant difference in the prognostic capability of nutritional screening tools. ∗∗Adjusted for age, sex, type 2 diabetes mellitus, hypertension and baseline cardiovascular diseases.

Compared to the group without malnutrition, the patients with malnutrition had significantly lower cholesterol levels, body mass index, and smoking prevalence,38 but they had excess mortality post-ACS throughout the various time periods, regardless of ACS type, ethnicity, and income status. Moreover, individuals with malnutrition and ACS had an increased risk of other complications including stroke and MACE. Given the anti-inflammatory and antioxidative properties of serum albumin,39 studies have postulated that the lowered levels of albumin in patients with malnutrition9,40 may exacerbate the inflammatory process,41 resulting in higher atherosclerotic burden42 and predisposing to various comorbidities.43, 44, 45 In line with the recommendations of present clinical practice guidelines, nutrition is an important modifiable risk factor not to be neglected in clinical settings, though scientific evidence in this patient population is lacking,46 and this calls for greater awareness among clinicians in the evaluation of nutritional status in all patients with ACS.47 Indeed, there are emerging interests in the applicability of nutritional assessment tools used in patients with CVD48 beyond its traditional utility in the geriatric or cancer cohorts.49,50 The importance of malnutrition screening in patients with ACS in the early identification and nutritional management of these individuals should not be understated and aggressively screened for. Exercise training has been highlighted as a crucial interventional strategy in patients with malnutrition, which has a net positive effect on muscle function and renutrition.51 However, there are barriers to the implementation of dietary and exercise interventions in various patient groups.52 Hence, the multidisciplinary approach involving dietitians and other allied healthcare workers remains paramount in achieving optimal nutritional status in the ACS care bundle.53 Beyond the need for global efforts for the implementation of malnutrition-based patient stratification, it is pertinent to investigate the most appropriate and efficacious nutritional intervention to improve patient outcomes.

Malnutrition and obesity are not mutually exclusive and may coexist.54 Complicating the matter is the obesity paradox, in which the presence of obesity has been suggested to confer a favorable prognosis in patients with CVDs.55 A greater physiological reserve has been proposed as a plausible reason,55 but this may not exist in patients with malnutrition. Future research should ascertain the impact of the double burden of malnutrition and obesity in patients with CVDs. Next, our study notes the fewer prescriptions of beta-blockers observed in the group with malnutrition, which may have been eschewed due to pre-existing hypotension, chronic obstructive pulmonary disease, or frailty in patients with malnutrition.56 Aggressive implementation of guideline-directed medical and nutritional therapy in patients needs to be the focus in order to improve their chances of morbidity-free survival.

Despite the various nutritional assessment tools available, with the most used scores being CONUT, GNRI, and PNI, there is currently no guidance on the suitability of these scores in the context of ACS. Preceding any consensus on the determination of the ideal nutritional assessment tool in the cohort with ACS, the comparison of the commonly used nutritional tools in terms of their prognostic utility in the prognosis of patients with ACS will be the next important step. With the present study’s large sample size, we were not able to detect any significant differences across the various scores used when examining the odds of all-cause mortality in the groups with malnutrition compared to those without malnutrition. This may be due to overlaps in the methods of calculation among the various tools, where most studies have included measures relating to albumin and body weight as markers of malnutrition. Clinicians need to be cognizant of the practicality of administering these tools in the acute setting, with the advantage of CONUT, GNRI, PNI, and Triglyceride, Total Cholesterol and Body Weight Index using readily available laboratory tests, while others such as Mini Nutritional Assessment and Malnutrition Universal Screening Tool57 require more holistic evaluation with the use of comprehensive questionnaires. With the practical challenges surrounding the qualitative assessment of nutritional status, especially in the setting of ACS, there also remains a lack of research data using these approaches, and hence our study was unable to include these tools in the analysis. Moreover, only 8 out of the 25 included studies analyzed adhered to the widely used malnutrition cutoffs, while the others had their own definitions. Though the possibility of reporting bias from the adjustment of thresholds cannot be completely ruled out,58 the heterogeneity in the cutoffs used may be attempts in contextualizing the measures to the specific population. Similar to how the definition of overweight was adjusted for Asian populations,59 adjustment of cutoff values for baseline demographics may be necessary in the context of malnutrition. This calls for the concerted effort in harmonizing the definition of malnutrition in CVD and establishing the appropriate nutritional assessment tools and their respective tool-specific cutoffs.

Strengths and limitations

This study provides a comprehensive consolidation of the prognostic capability of commonly used nutritional assessment tools in populations with ACS and malnutrition, providing important comparisons by the type of tool as well as survival trends across various subgroups based on epidemiological or clinical factors. However, the study has its limitations. Firstly, there was a paucity of data on baseline demographics such as B-type natriuretic peptide and the NYHA functional class, limiting our ability to depict the health status of patients. Similarly, we were unable to conduct participant-level subgroup analyses based on important variables such as sex. Nonetheless, the significantly higher adjusted hazard ratio of all-cause mortality highlights that malnutrition is correlated with poorer prognosis in patients with ACS. Next, nutritional status may change over time. However, examining temporal trends in the degree of malnutrition was not possible in this study, given the paucity of granular data such as nutritional supplementation. In addition, due to the lack of data, the paper was unable to evaluate nutritional assessment tools such as the Malnutrition Universal Screening Tool, thus limiting the applicability of the results to clinical settings that adopt these tools. Nevertheless, the paper was able to capture most of the commonly used tools such as GNRI, CONUT, and PNI. However, the inclusion of various tools in our analysis increases heterogeneity. Nonetheless, most of the tools seek to evaluate similar parameters in patients, and subgroup analysis based on the type of nutritional assessment tools showed similar prognostic efficacy. Lastly, this paper was unable to establish the potential correlation between sarcopenia, specific fat depots, and malnutrition due to the lack of data reported in the included articles.

Conclusions

Malnutrition was significantly associated with all-cause mortality risk following ACS, with a 2.6-fold higher risk of mortality compared to the patients without malnutrition. Excess mortality was observed in patients with malnutrition regardless of ACS type, ethnicity, and income status, thus underscoring the importance of malnutrition screening for early identification. Although nutritional screening tools such as GNRI, CONUT, and PNI are readily available with similar prognostic capabilities, clinical interpretation still needs to be tailored in the context of the tool used. Concerted efforts in the harmonization of the screening tools and definition of malnutrition in patients with CVD will be the next important step.

PERSPECTIVES.

COMPETENCY IN PATIENT CARE: Malnutrition was significantly associated with all-cause mortality risk following ACS, and commonly used nutritional screening tools share similar prognostic capabilities. Aggressive implementation of guideline-directed nutritional therapy is needed.

TRANSLATIONAL OUTLOOK: Concerted effort in harmonizing the definition of malnutrition in ACS is the next important step.

Funding support and author disclosures

This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-profit sectors. Dr Brown has received honoraria from Novo Nordisk, Office of Health Improvement and Disparity, Johnson and Johnson, and Obesity UK outside the submitted work and is on the Medical Advisory Board; and is shareholder of Reset Health Clinics Ltd. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Footnotes

The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.

Appendix

For the Search strategy for Medline as well as supplemental tables and figures, please see the online version of this paper.

Contributor Information

Yip Han Chin, Email: c.yiphan@u.nus.edu.

Nicholas W.S. Chew, Email: nicholas_ws_chew@nuhs.edu.sg.

Supplementary data

Supplementary data
mmc1.docx (144.7KB, docx)

References

  • 1.Balakumaran V., Namrata H., Anirudhya N.R. Analysis of complications of acute coronary syndrome and their outcomes in India. Int J Clin Cardiol. 2020;7(4):1–8. [Google Scholar]
  • 2.Powell-Wiley T.M., Poirier P., Burke L.E., et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984–e1010. doi: 10.1161/CIR.0000000000000973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Chen Q.J., Qu H.J., Li D.Z., et al. Prognostic nutritional index predicts clinical outcome in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention. Sci Rep. 2017;7(1):3285. doi: 10.1038/s41598-017-03364-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Raposeiras Roubin S., Abu Assi E., Cespon Fernandez M., et al. Prevalence and prognostic significance of malnutrition in patients with acute coronary syndrome. J Am Coll Cardiol. 2020;76(7):828–840. doi: 10.1016/j.jacc.2020.06.058. [DOI] [PubMed] [Google Scholar]
  • 5.Nakamura T., Haraguchi Y., Matsumoto M., Ishida T., Momomura S.I. Prognostic impact of malnutrition in elderly patients with acute myocardial infarction. Heart Vessels. 2022;37(3):385–391. doi: 10.1007/s00380-021-01922-y. [DOI] [PubMed] [Google Scholar]
  • 6.Lizancos Castro A., Parada Barcia J.A., Abu-Assi E., et al. Prevalence and prognostic significance of malnutrition among patients with acute coronary syndrome. Eur Heart J. 2020;41(Supplement_2) [Google Scholar]
  • 7.Freeman A.M., Morris P.B., Barnard N., et al. Trending cardiovascular nutrition controversies. J Am Coll Cardiol. 2017;69(9):1172–1187. doi: 10.1016/j.jacc.2016.10.086. [DOI] [PubMed] [Google Scholar]
  • 8.Tonet E., Campo G., Maietti E., et al. Nutritional status and all-cause mortality in older adults with acute coronary syndrome. Clin Nutr. 2020;39(5):1572–1579. doi: 10.1016/j.clnu.2019.06.025. [DOI] [PubMed] [Google Scholar]
  • 9.Arero G., Arero A.G., Mohammed S.H., Vasheghani-Farahani A. Prognostic potential of the controlling nutritional status (CONUT) score in predicting all-cause mortality and major adverse cardiovascular events in patients with coronary artery disease: a meta-analysis. Front Nutr. 2022;9 doi: 10.3389/fnut.2022.850641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Fan Y., He L., Zhou Y., Man C. Predictive value of geriatric nutritional risk index in patients with coronary artery disease: a meta-analysis. Front Nutr. 2021;8 doi: 10.3389/fnut.2021.736884. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sze S., Pellicori P., Kazmi S., et al. Prevalence and prognostic significance of malnutrition using 3 scoring Systems among outpatients with heart failure: a comparison with body mass index. J Am Coll Cardiol HF. 2018;6(6):476–486. doi: 10.1016/j.jchf.2018.02.018. [DOI] [PubMed] [Google Scholar]
  • 12.Goldfarb M., Lauck S., Webb J.G., et al. Malnutrition and mortality in frail and non-frail older adults undergoing aortic valve replacement. Circulation. 2018;138(20):2202–2211. doi: 10.1161/CIRCULATIONAHA.118.033887. [DOI] [PubMed] [Google Scholar]
  • 13.Raposeiras-Roubin S., Abu-Assi E., Paz R.C., et al. Impact of malnutrition in the embolic-haemorrhagic trade-off of elderly patients with atrial fibrillation. Europace. 2020;22(6):878–887. doi: 10.1093/europace/euaa017. [DOI] [PubMed] [Google Scholar]
  • 14.Lv S., Ru S. The prevalence of malnutrition and its effects on the all-cause mortality among patients with heart failure: a systematic review and meta-analysis. PLoS One. 2021;16(10) doi: 10.1371/journal.pone.0259300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Page M.J., McKenzie J.E., Bossuyt P.M., et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. doi: 10.1136/bmj.n71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Buzby G.P., Mullen J.L., Matthews D.C., Hobbs C.L., Rosato E.F. Prognostic nutritional index in gastrointestinal surgery. Am J Surg. 1980;139(1):160–167. doi: 10.1016/0002-9610(80)90246-9. [DOI] [PubMed] [Google Scholar]
  • 17.Bouillanne O., Morineau G., Dupont C., et al. Geriatric nutritional risk index: a new index for evaluating at-risk elderly medical patients. Am J Clin Nutr. 2005;82(4):777–783. doi: 10.1093/ajcn/82.4.777. [DOI] [PubMed] [Google Scholar]
  • 18.Ignacio de Ulíbarri J., González-Madroño A., de Villar N.G., et al. CONUT: a tool for controlling nutritional status. First validation in a hospital population. Nutr Hosp. 2005;20(1):38–45. [PubMed] [Google Scholar]
  • 19.Kondrup J., Rasmussen H.H., Hamberg O.L.E., Stanga Z. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003;22(3):321–336. doi: 10.1016/s0261-5614(02)00214-5. [DOI] [PubMed] [Google Scholar]
  • 20.Ferguson M., Capra S., Bauer J., Banks M. Development of a valid and reliable malnutrition screening tool for adult acute hospital patients. Nutrtition. 1999;15(6):458–464. doi: 10.1016/s0899-9007(99)00084-2. [DOI] [PubMed] [Google Scholar]
  • 21.Vellas B., Guigoz Y., Garry P.J., et al. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrtition. 1999;15(2):116–122. doi: 10.1016/s0899-9007(98)00171-3. [DOI] [PubMed] [Google Scholar]
  • 22.Kaiser M.J., Bauer J.M., Ramsch C., et al. Validation of the mini nutritional assessment short-form (MNA®-SF): a practical tool for identification of nutritional status. J Nutr Health Aging. 2009;13(9):782. doi: 10.1007/s12603-009-0214-7. [DOI] [PubMed] [Google Scholar]
  • 23.Doi S., Iwata H., Wada H., et al. A novel and simply calculated nutritional index serves as a useful prognostic indicator in patients with coronary artery disease. Int J Cardiol. 2018;262:92–98. doi: 10.1016/j.ijcard.2018.02.039. [DOI] [PubMed] [Google Scholar]
  • 24.Collet J.-P., Thiele H., Barbato E., et al. 2020 ESC guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: the Task Force for the Management of Acute Coronary Syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC) Eur Heart J. 2021;42(14):1289–1367. doi: 10.1093/eurheartj/ehaa575. [DOI] [PubMed] [Google Scholar]
  • 25.Wan X., Wang W., Liu J., Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14(1):135. doi: 10.1186/1471-2288-14-135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Clopper C.J., Pearson E.S. The Use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413. [Google Scholar]
  • 27.Schwarzer G., Chemaitelly H., Abu-Raddad L.J., Rucker G. Seriously misleading results using inverse of Freeman-Tukey double arcsine transformation in meta-analysis of single proportions. Res Synth Methods. 2019;10(3):476–483. doi: 10.1002/jrsm.1348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Balduzzi S., Rucker G., Schwarzer G. How to perform a meta-analysis with R: a practical tutorial. Evid Based Ment Health. 2019;22(4):153–160. doi: 10.1136/ebmental-2019-300117. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Low C.J., Leow A.S., Syn N.L., et al. Outcomes of left ventricular thrombosis in post-acute myocardial infarction patients stratified by antithrombotic strategies: a meta-analysis with meta-regression. Int J Cardiol. 2021;329:36–45. doi: 10.1016/j.ijcard.2020.12.087. [DOI] [PubMed] [Google Scholar]
  • 30.Lange S., Probst C., Rehm J., Popova S. National, regional, and global prevalence of smoking during pregnancy in the general population: a systematic review and meta-analysis. Lancet Glob Health. 2018;6(7):e769–e776. doi: 10.1016/S2214-109X(18)30223-7. [DOI] [PubMed] [Google Scholar]
  • 31.Parmar M.K.B., Torri V., Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med. 1998;17(24):2815–2834. doi: 10.1002/(sici)1097-0258(19981230)17:24<2815::aid-sim110>3.0.co;2-8. [DOI] [PubMed] [Google Scholar]
  • 32.Deeks J.J., Higgins J.P.T., Altman D.G. In: Cochrane Handbook for systematic reviews of interventions version 6.3 (updated February 2022) Higgins J.P.T., Thomas J., Chandler J., et al., editors. Cochrane; 2022. Chapter 10: analysing data and undertaking meta-analyses. [Google Scholar]
  • 33.Harrer M., Cuijpers P., Furukawa T.A., Ebert D.D. 1st ed. Chapman & Hall/CRC Press; 2021. Doing Meta-Analysis With R: A Hands-on Guide. [Google Scholar]
  • 34.Higgins J.P., Thompson S.G., Deeks J.J., Altman D.G. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.The World Bank The World by income and region. https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html
  • 36.Wells G.A., Wells G., Shea B., et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of Nonrandomised studies in meta-analyses. https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
  • 37.Yoo S.H., Kook H.Y., Hong Y.J., Kim J.H., Ahn Y., Jeong M.H. Influence of undernutrition at admission on clinical outcomes in patients with acute myocardial infarction. J Cardiol. 2017;69(3):555–560. doi: 10.1016/j.jjcc.2016.05.009. [DOI] [PubMed] [Google Scholar]
  • 38.Balagopal P., de Ferranti S.D., Cook S., et al. Nontraditional risk factors and biomarkers for cardiovascular disease: Mechanistic, research, and clinical considerations for Youth. Circulation. 2011;123(23):2749–2769. doi: 10.1161/CIR.0b013e31821c7c64. [DOI] [PubMed] [Google Scholar]
  • 39.Arques S. Human serum albumin in cardiovascular diseases. Eur J Intern Med. 2018;52:8–12. doi: 10.1016/j.ejim.2018.04.014. [DOI] [PubMed] [Google Scholar]
  • 40.Keller U. Nutritional laboratory markers in malnutrition. J Clin Med. 2019;8(6):775. doi: 10.3390/jcm8060775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Kamaruzzaman S.B. The magnitude of malnutrition among Hospitalized elderly patients in University Malaya Medical Centre. J Environ Health. 2010;1(2):64–72. [Google Scholar]
  • 42.Kang S.H., Song H.N., Moon J.Y., et al. Prevalence and prognostic significance of malnutrition in patients with acute coronary syndrome treated with percutaneous coronary intervention. Medicine (Baltimore) 2022;101(34) doi: 10.1097/MD.0000000000030100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Das U.N. Albumin infusion for the critically ill—is it beneficial and, if so, why and how? Crit Care. 2015;19:156. doi: 10.1186/s13054-015-0862-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Caraceni P., Domenicali M., Tovoli A., et al. Clinical indications for the albumin use: still a controversial issue. Eur J Intern Med. 2013;24(8):721–728. doi: 10.1016/j.ejim.2013.05.015. [DOI] [PubMed] [Google Scholar]
  • 45.Folsom A.R., Lutsey P.L., Heckbert S.R., Cushman M. Serum albumin and risk of venous thromboembolism. Thromb Haemost. 2010;104(1):100–104. doi: 10.1160/TH09-12-0856. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Piepoli M.F., Hoes A.W., Agewall S., et al. 2016 European Guidelines on Cardiovascular Disease Prevention in Clinical Practice: the Sixth Joint Task Force of the European Society of Cardiology and other Societies on Cardiovascular Disease Prevention in Clinical Practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European Association for Cardiovascular Prevention & Rehabilitation (EACPR) Eur Heart J. 2016;37(29):2315–2381. doi: 10.1093/eurheartj/ehw106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Tobert C.M., Mott S.L., Nepple K.G. Malnutrition diagnosis during adult Inpatient Hospitalizations: analysis of a Multi-Institutional Collaborative Database of Academic medical Centers. J Acad Nutr Diet. 2018;118(1):125–131. doi: 10.1016/j.jand.2016.12.019. [DOI] [PubMed] [Google Scholar]
  • 48.Kalyoncuoglu M., Katkat F., Biter H.I., Cakal S., Tosu A.R., Can M.M. Predicting one-year deaths and major adverse vascular events with the controlling nutritional status score in elderly patients with non-ST-Elevated myocardial infarction undergoing percutaneous coronary intervention. J Clin Med. 2021;10(11):2247. doi: 10.3390/jcm10112247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Caccialanza R., Pedrazzoli P., Cereda E., et al. Nutritional Support in cancer patients: a position paper from the Italian Society of medical Oncology (AIOM) and the Italian Society of Artificial Nutrition and Metabolism (SINPE) J Cancer. 2016;7(2):131–135. doi: 10.7150/jca.13818. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Ahmed T., Haboubi N. Assessment and management of nutrition in older people and its importance to health. Clin Interv Aging. 2010;5:207–216. doi: 10.2147/cia.s9664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Hébuterne X., Bermon S., Schneider S.M. Ageing and muscle: the effects of malnutrition, re-nutrition, and physical exercise. Curr Opin Clin Nutr Metab Care. 2001;4(4):295–300. doi: 10.1097/00075197-200107000-00009. [DOI] [PubMed] [Google Scholar]
  • 52.Folta S.C., Paul L., Nelson M.E., et al. Changes in diet and physical activity resulting from the Strong Hearts, Healthy Communities randomized cardiovascular disease risk reduction multilevel intervention trial. Int J Behav Nutr Phys Act. 2019;16(1):91. doi: 10.1186/s12966-019-0852-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Ji T., Zhang L., Han R., et al. Management of malnutrition based on multidisciplinary team decision-making in Chinese older adults (3M study): a prospective, multicenter, randomized, controlled study protocol. Front Nutr. 2022;9 doi: 10.3389/fnut.2022.851590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Kobylińska M., Antosik K., Decyk A., Kurowska K. Malnutrition in obesity: is it possible? Obes Facts. 2022;15(1):19–25. doi: 10.1159/000519503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Giri Ravindran S., Saha D., Iqbal I., et al. The obesity paradox in chronic heart disease and chronic obstructive pulmonary disease. Cureus. 2022;14(6) doi: 10.7759/cureus.25674. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Kraut R., Lundby C., Babenko O., Kamal A., Sadowski C.A. Antihypertensive medication in frail older adults: a narrative review through a deprescribing lens. Am Heart J Cardiol Res Pract. 2022;17 doi: 10.1016/j.ahjo.2022.100166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Stratton R.J., Hackston A., Longmore D., et al. Malnutrition in hospital outpatients and inpatients: prevalence, concurrent validity and ease of use of the ‘malnutrition universal screening tool’ (‘MUST’) for adults. Br J Nutr. 2004;92(5):799–808. doi: 10.1079/bjn20041258. [DOI] [PubMed] [Google Scholar]
  • 58.Rifai N., Altman D.G., Bossuyt P.M. Reporting bias in diagnostic and prognostic studies: time for action. Clin Chem. 2008;54(7):1101–1103. doi: 10.1373/clinchem.2008.108993. [DOI] [PubMed] [Google Scholar]
  • 59.World Health Organization Regional Office for the Western Pacific. The Asia-Pacific perspective : redefining obesity and its treatment. Sydney: Health Communications Australia. Accessed September 11, 2022. https://apps.who.int/iris/handle/10665/206936

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary data
mmc1.docx (144.7KB, docx)

Articles from JACC: Advances are provided here courtesy of Elsevier

RESOURCES