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Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease logoLink to Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
. 2024 Mar 27;13(8):e031616. doi: 10.1161/JAHA.123.031616

Frailty and Metabolic Vulnerability in Heart Failure: A Community Cohort Study

Sant Kumar 1, Katherine M Conners 2, Joseph J Shearer 2, Jungnam Joo 3, Sarah Turecamo 2, Maureen Sampson 4, Anna Wolska 4, Alan T Remaley 4, Margery A Connelly 5, James D Otvos 5, Nicholas B Larson 6, Suzette J Bielinski 7, Véronique L Roger 2,[Link],✉
PMCID: PMC11262513  PMID: 38533960

Abstract

Background

Frailty is common in heart failure (HF) and is associated with death but not routinely captured clinically. Frailty is linked with inflammation and malnutrition, which can be assessed by a novel plasma multimarker score: the metabolic vulnerability index (MVX). We sought to evaluate the associations between frailty and MVX and their prognostic impact.

Methods and Results

In an HF community cohort (2003–2012), we measured frailty as a proportion of deficits present out of 32 physical limitations and comorbidities, MVX by nuclear magnetic resonance spectroscopy, and collected extensive longitudinal clinical data. Patients were categorized by frailty score (≤0.14, >0.14 and ≤0.27, >0.27) and MVX score (≤50, >50 and ≤60, >60 and ≤70, >70). Cox models estimated associations of frailty and MVX with death, adjusted for Meta‐Analysis Global Group in Chronic Heart Failure (MAGGIC) score and NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide). Uno's C‐statistic measured the incremental value of MVX beyond frailty and clinical factors. Weibull's accelerated failure time regression assessed whether MVX mediated the association between frailty and death. We studied 985 patients (median age, 77; 48% women). Frailty and MVX were weakly correlated (Spearman's ρ=0.21). The highest frailty group experienced an increased rate of death, independent of MVX, MAGGIC score, and NT‐proBNP (hazard ratio, 3.3 [95% CI, 2.5–4.2]). Frailty improved Uno's c‐statistic beyond MAGGIC score and NT‐proBNP (0.69–0.73). MVX only mediated 3.3% and 4.5% of the association between high and medium frailty groups and death, respectively.

Conclusions

In this HF cohort, frailty and MVX are weakly correlated. Both independently contribute to stratifying the risk of death, suggesting that they capture distinct domains of vulnerability in HF.

Keywords: frailty, heart failure, inflammation, malnutrition, metabolomics, death

Subject Categories: Heart Failure, Epidemiology, Aging

Nonstandard Abbreviations and Acronyms

MAGGIC

Meta‐Analysis Global Group in Chronic Heart Failure

MVX

metabolic vulnerability index

Clinical Perspective.

What Is New?

  • The metabolic vulnerability index and frailty capture unique domains of vulnerability and independently improve mortality risk stratification in patients with heart failure.

What Are the Clinical Implications?

  • A more comprehensive assessment of vulnerability in elderly patients with heart failure is needed.

Heart failure (HF) disproportionately affects adults aged ≥65 years, 1 an age range when frailty, defined by a state of vulnerability to poor resolution of homeostasis after a stressor event, becomes increasingly prevalent. 2 Frailty is associated with an increased risk of death in HF. 3 , 4 The 2022 American Heart Association/American College of Cardiology/Heart Failure Society of America guidelines recommend measuring frailty to assist in risk stratification of patients with HF. 5 Frailty can be measured in several ways, 6 including the biological phenotype, 7 the Groningen Frailty Indicator, 8 and the Rockwood Index, which measures frailty by assessing the accumulation of health deficits defined as symptoms, signs, disabilities, and diseases. 9 Unlike other frailty indices, the feasibility and the reproducibility of the Rockwood Index are attractive 10 ; however, it does not lend itself to easy integration in clinical workflows. In this context, the availability of a blood biomarker that could serve as a surrogate to measure frailty at the point of care would be particularly appealing.

The biological underpinnings of frailty are often linked to inflammation and malnutrition, both associated with adverse outcomes in HF. 11 , 12 , 13 The metabolic vulnerability index (MVX), which can be measured in plasma by nuclear magnetic resonance, 14 is a composite biomarker score consisting of 2 inflammatory biomarkers (GlycA and small high‐density lipoprotein particles) 15 , 16 , 17 and 4 metabolic malnutrition biomarkers (valine, leucine, isoleucine, and citrate). 14 , 16 MVX has been associated with an increased risk of death in patients at high risk for cardiovascular disease 14 and among patients enrolled in an HF community cohort. 18 Given the association between inflammation, malnutrition, and frailty, we hypothesized that MVX could serve as a biomarker of frailty and tested this hypothesis in a population‐based cohort of patients with HF. To do so, we measured frailty using the Rockwood Index, examined its relationship with MVX, and compared the prognostic value of frailty and of MVX, as well as its incremental contribution to risk stratification beyond established prognostic factors in HF.

Methods

The data that support the findings of this study are available from the corresponding author upon reasonable request. We prospectively enrolled 1389 community residents with HF from September 2, 2003, through June 16, 2012. Patients with HF were identified via the Rochester Epidemiology Project, a medical record linkage system that captures nearly all clinical diagnoses, procedures, results, and outcomes in its catchment area. 19 , 20 Information on case ascertainment, cohort assembly, and data collection has been published. 21 , 22 Briefly, natural language processing was used to search for patients aged ≥20 years residing in Olmsted, Dodge, and Fillmore Counties in Minnesota. Trained research nurses reviewed and confirmed HF cases using Framingham criteria. 23 Patients were then approached and consented to participate in the study, including a blood draw. The Mayo Clinic and Olmsted Medical Center Institutional Review Boards approved of this study.

Nurse abstractors collected clinical information from inpatient and outpatient records. Clinical information included cardiovascular risk factors such as hypertension, diabetes, and hyperlipidemia, as well as comorbid conditions included in the Charlson comorbidity index. 24 Body mass index (BMI) was calculated using weight (kilograms) from the last outpatient visit before enrollment divided by their earliest recorded adult height (meters) squared. Left ventricular ejection fraction was obtained from the last echocardiogram performed either within 6 months before or 2 months following the date of enrollment. We calculated the Meta‐Analysis Global Group on Chronic Heart Failure (MAGGIC) score, a clinically validated mortality risk score, using age, sex, ejection fraction, systolic blood pressure, BMI, creatinine, New York Heart Association class, smoking status, diabetes, chronic obstructive pulmonary disease, and HF diagnosis >18 months ago, and use of a β blocker, angiotensin‐converting enzyme inhibitor, or angiotensin receptor blocker. 25 Plasma samples collected at enrollment and stored at −80°C were used to measure NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) concentrations using a Mesoscale multiplex assay (https://www.mesoscale.com/en).

We calculated the Rockwood Index as a proportion of deficits present out of 32 deficits captured (Table S1). Patients were excluded if they did not have information on at least 30 deficits. 9 , 26 The majority of patients were excluded due to missing information on their activities of daily living, as these were not routinely assessed until after 2005. If an individual had information on only 31 or 30 deficits available, we calculated their frailty score as a proportion of deficits present out of the 31 or 30 considered. Fourteen deficits were self‐reported activities of daily living collected from a patient‐provided questionnaire completed up to 60 days after the enrollment date. The remaining 18 deficits were conditions from the Charlson comorbidity index, 24 as well as hypertension, hyperlipidemia, depression, and BMI. To calculate the Rockwood Index, patients were given 1 point if a deficit was present and 0 points if not present. For the activity of daily living “climbing 2 flights of stairs without rest,” 0 points were given for the response “yes, with no difficulty”; 0.5 points were given for the response “yes, with difficulty”; and 1 point was given for a response of “no.” 9 For BMI, patients were given 0 points for a BMI of 18.5 to <25 kg/m2, 0.5 points for a BMI of 25 to <30 kg/m2, and 1 point for a BMI <18.5 or ≥30 kg/m2. Patients were divided into low, medium, and high frailty groups on the basis of frailty scores and cut points were determined by performing the recursive partitioning for survival trees method 27 (R package rpart) using 10 000 bootstrap resampling, which yielded cut points consistent with previous studies on frailty as captured by the Rockwood Index. 28 , 29 , 30 Low frailty scores were ≤0.14, medium frailty scores ranged from >0.14 to ≤0.27, and high frailty scores were >0.27.

The calculation of MVX scores has been described previously. 18 In brief, NMR LipoProfile analyses of frozen EDTA plasma collected from study participants at enrollment were performed on the high‐throughput 400 MHz Vantera Clinical Analyzer platform (Labcorp, Morrisville, NC) at the National Heart, Lung, and Blood Institute Lipoprotein Metabolism Laboratory (Bethesda, MD), and sex‐specific MVX scores were calculated using the MVX software algorithm. 14 , 31 MVX scores range from 1 to 100, with a higher score indicating greater metabolic vulnerability. Patients were divided into 4 groups based on their MVX scores as previously published in this cohort (group 1: ≤50; group 2: >50 and ≤60; group 3: >60 and ≤70; group 4: >70). 18

Baseline characteristics were reported as frequencies (percent) for categorical variables and medians (interquartile range) for continuous variables and compared across frailty groups using χ2 and Kruskal–Wallis tests, respectively. Missing clinical information was minimal (BMI, 2.5%; ejection fraction, 2.0%; NYHA class, 0.5%; and HF duration, 0.1%) and addressed using 10 multiple imputations by chain equations to calculate MAGGIC scores. 32 Spearman's correlation coefficient (ρ) was used to assess the correlation between frailty and MVX scores.

Patients were followed through March 31, 2021, via the Rochester Epidemiology Project, which obtains death information from participating health care providers, from State of Minnesota death certificates, and from linkage of patient records to the National Death Index. Cause of death information is available from Minnesota death certificates and from the National Death Index. Patients alive at the end of follow‐up were censored on March 31, 2021, or date of last known health care contact, whichever was earlier.

Median follow‐up time was calculated using the reverse Kaplan–Meier method. 33 Survival was estimated using the Kaplan–Meier method and compared across frailty groups using a log‐rank test. Cox proportional hazard regression was used to examine the association between frailty groups and all‐cause death adjusted for (1) MAGGIC score+NT‐proBNP and (2) MAGGIC score+NT‐proBNP + MVX group. NT‐proBNP values were log2‐transformed for survival analyses. Linear trend tests were performed by assigning increasing numeric scores to the 3 frailty groups (1–3) and comparing hazard ratios across groups. Uno's C‐statistics and 95% CIs were calculated to estimate the incremental prognostic value of frailty groups and MVX group beyond the MAGGIC score and NT‐proBNP at 3 years as the MAGGIC score was originally validated to estimate mortality risk 1 to 3 years after HF diagnosis. 25 To examine whether the effects of frailty on death operated through MVX, we conducted a mediation analysis based on Weibull accelerated failure time regression. 34 , 35

Hospitalizations were ascertained through the linked Rochester Epidemiology Project medical records, and the risk of hospitalization was assessed using a Fine–Gray subdistribution hazard model, with all‐cause death as the competing risk. Time to first hospitalization that occurred >7 days after enrollment were considered.

Analyses were performed using R version 4.10 (R Foundation for Statistical Computing, Vienna, Austria), with a 2‐sided P value <0.05 considered statistically significant.

Results

Our study included 985 (71%) of the 1382 patients enrolled in the population‐based cohort with nuclear magnetic resonance measurements. Patients were excluded if they did not have data on at least 30 Rockwood Index deficits (n=397). The median age of the cohort was 77 (interquartile range [IQR], 67–84) years, 48% were women, and there was a near‐even split between patients presenting with ejection fraction >50% and ≤50% (Table 1). Demographic and HF characteristics were similar between the cohort with nuclear magnetic resonance measurements and our subset (Table S2).

Table 1.

Baseline Characteristics of Patients With Heart Failure by Rockwood Group

Overall cohort Low‐frailty group ≤0.14 Medium‐frailty group >0.14 and ≤0.27 High‐frailty group >0.27 P value
n=985 n=134 n=447 n=404
Demographics
Age, y 77 (67–84) 68 (57–81) 77 (67–83) 80 (72–86) <0.001
Women 471 (48) 51 (38) 210 (47) 210 (52) 0.018
Cardiovascular risk factors
Hypertension 906 (92) 107 (80) 407 (91) 392 (97) <0.001
Diabetes 352 (36) 13 (10) 141 (32) 198 (49) <0.001
Hyperlipidemia 813 (83) 76 (57) 380 (85) 357 (88) <0.001
Body mass index, kg/m2 28 (25–34) 28 (24–32) 28 (25–33) 29 (25–35) 0.058
HF characteristics
NYHA class III and IV 668 (68) 82 (62) 297 (67) 289 (72) 0.072
MAGGIC score 24 (19–28) 19 (15–25) 23 (19–27) 26 (22–30) <0.001
Ejection fraction >50% 505 (52) 44 (33) 237 (54) 224 (57) <0.001
NT‐proBNP, pg/mL 8913 (4188–16 213) 8182 (2734–13 053) 7978 (3715–14 802) 10 703 (5110–18 949) <0.001
Risk indices
Metabolic vulnerability index 65 (56–72) 60 (53–69) 63 (55–71) 67 (59–74) <0.001
Charlson comorbidity index 7 (5–9) 3 (2–5) 6 (5–8) 8 (7–10) <0.001
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Values are N (%) or median (IQR).

HF indicates heart failure; IQR interquartile range; MAGGIC, Meta‐Analysis Global Group in Chronic Heart Failure; NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide; and NYHA, New York Heart Association.

The cohort's median frailty score was 0.24 (IQR, 0.17–0.33) (Figure 1). There were 134 patients with low frailty score, 447 with medium frailty, and 404 with high frailty. The median MVX score in our cohort was 65 (IQR, 56–72). Frailty and MVX scores were only weakly correlated (Spearman's ρ=0.21; P<0.001) (Figure 2). The overlap between frailty and MVX groups is further summarized in Figure S1. Patients with high frailty (versus low and medium frailty) were older and were more likely to be women, have diabetes, hypertension, hyperlipidemia, an ejection fraction >50% and have higher NYHA class, MAGGIC score, NT‐proBNP, and MVX score (Table 1).

Figure 1. Distribution of frailty scores among 985 community‐dwelling people with heart failure.

Figure 1

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Low‐frailty group (blue): ≤0.14, N=134; medium‐frailty group (green): >0.14 and ≤0.27, N=447; high‐frailty group (orange): >0.27, N=404. IQR indicates interquartile range.

Figure 2. Spearman's correlation plot between frailty and MVX scores.

Figure 2

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MVX indicates metabolic vulnerability index.

Patients were followed for a median of 13.1 (IQR, 10.9–14.5) years, with an overall 5‐year mortality rate of 50.2% (95% CI, 46.9–53.2). There was a graded positive association between frailty and death (Figure 3). The 5‐year mortality rate was lowest among patients with low frailty (21.7% [95% CI, 14.4–28.4]), and highest among those with high frailty (70.5% [95% CI, 65.7–74.7]).

Figure 3. Survival by frailty group.

Figure 3

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Low‐frailty group (blue): ≤0.14, N=134; medium‐frailty group (green): >0.14 and ≤0.27, N=447; high‐frailty group (orange): >0.27, N=404.

Patients with high frailty had a 4‐fold increased mortality rate compared with those with low frailty (hazard ratio [HR], 4.2 [95% CI, 3.3–5.4]) in an unadjusted model. The increased mortality rate remained after adjustment for MAGGIC score and NT‐proBNP (HR, 3.3 [95% CI, 2.6–4.3]) and further adjustment for the MVX group (HR, 3.3 [95% CI, 2.5–4.2]) (Table 2). The addition of MVX to models with MAGGIC score alone performed similar across frailty groups. Discrimination analysis results showed the addition of frailty to a reference model with MAGGIC score and NT‐proBNP provided a significant increase in the Uno c‐statistic (0.69–0.73; P<0.001) (Table 3). Further addition of MVX group to the model provided an additional modest but statistically significant improvement in the Uno's c‐statistic (0.73–0.74; P<0.001). MVX only mediated 4.5% of the association between medium frailty and death and 3.3% of the association between high frailty and death.

Table 2.

Frailty Group and Mortality

Model Low‐frailty group ≤0.14 Medium‐frailty group >0.14 and ≤0.27 High‐frailty group >0.27 P‐trend
n=134 n=447 n=404
Unadjusted Reference group 1.9 (1.5–2.5) 4.2 (3.3–5.4) <0.001
Adjusted for MAGGIC score and NT‐proBNP Reference group 1.7 (1.3–2.2) 3.3 (2.6–4.3) <0.001
Adjusted for MAGGIC score, NT‐proBNP and MVX group Reference group 1.7 (1.3–2.2) 3.3 (2.5–4.2) <0.001
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Values are presented as hazard ratios and 95% CIs. MAGGIC indicates Meta‐Analysis Global Group in Chronic Heart Failure; MVX, metabolic vulnerability index; and NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.

Table 3.

Model Performance at 3 Years

Model Uno's c‐statistic (95% CI) P value
MAGGIC score+NT‐proBNP 0.69 (0.67, 0.72) N/A
MAGGIC score+NT‐proBNP + Rockwood group 0.73 (0.70, 0.75) <0.001
MAGGIC score+NT‐proBNP + Rockwood group + MVX group 0.74 (0.71, 0.76) 0.0148
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P value indicates significant improvement in c‐statistic from previous model.

MAGGIC indicates Meta‐Analysis Global Group in Chronic Heart Failure; MVX, metabolic vulnerability index; and NT‐proBNP, N‐terminal pro‐brain natriuretic peptide.

In our cohort, 895 (91%) patients had at least 1 hospitalization >7 days after study enrollment. Only 16 (1.6%) patients did not have a hospitalization or death during the study period. We observed a graded positive association between frailty and rate of hospitalization in a model adjusted for MVX, MAGGIC score, and NT‐proBNP (Table 4).

Table 4.

Frailty and MVX Group Associations With Hospitalization

Hazard ratio (95% CI) P value
Low‐frailty group Frailty group reference N/A
Medium‐frailty group 1.2 (1.0–1.4) 0.1
High‐frailty group 1.3 (1.1–1.6) 0.0023
MVX group 1 MVX group reference N/A
MVX group 2 1.0 (0.8–1.3) 0.76
MVX group 3 1.1 (0.9–1.4) 0.46
MVX group 4 1.0 (0.8–1.3) 0.87
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Model was adjusted for NT‐proBNP and MAGGIC score. MAGGIC indicates Meta‐Analysis Global Group in Chronic Heart Failure; MVX, metabolic vulnerability index; N/A, not applicable; and NT‐proBNP, N‐terminal pro‐B‐type natriuretic peptide.

Patients in the high‐frailty group had an increased rate of hospitalization compared with patients in the low‐frailty group (HR, 1.3 [95% CI, 1.1–1.6]). We did not detect any association between MVX and rate of hospitalizations.

Discussion

In this HF community cohort, we measured frailty by the Rockwood Index and categorized patients using cut points consistent with previous reports. 28 , 29 , 30 We identified that a high level of frailty was highly prevalent and that, contrary to the hypothesis that we set out to test, frailty was only weakly correlated with MVX. Frailty was associated with a large increased rate of death, and, importantly, this association was independent of MVX. Both frailty and MVX provided incremental value in risk stratification beyond established clinical risk factors and MVX mediated only a small proportion of the association between frailty and death.

Previous work from our group has shown a strong, graded positive association between the MVX group and death, independent of the MAGGIC score and NT‐proBNP. 18 The weak correlation between MVX and frailty observed herein suggests that they capture different domains of vulnerability, both of high relevance to risk stratification in HF. MVX provides, with 1 blood test, measures of systemic inflammation and metabolic dysfunction, both physiological underpinnings of frailty. 3 MVX can thus inform on metabolic frailty, thereby complementing measures of physical frailty by the Rockwood Index. Combining both domains conceptually provides a more comprehensive assessment of frailty.

HF can affect people of all ages but primarily impacts older individuals. 36 The 2022 American Heart Association/American College of Cardiology/Heart Failure Society of America guidelines recommend the assessment of frailty and other domains of vulnerability for the purpose of risk stratification of patients with HF. 5 Specifically for the evaluation of frailty, several methods exist. Prior work from our team indicated that the biological frailty phenotype and the Rockwood Index both have prognostic value in a community HF cohort. 26 The Rockwood Index is appealing, as it lends itself to secondary data collection from medical records as opposed to the biological frailty phenotype, which must be prospectively evaluated clinically. 26 Other measures of frailty include the Groningen Frailty Index 8 ; the Tilburg Frailty Indicator 37 ; the Brief Risk Identification of Geriatric Health Tool 38 ; the strength, assistance in walking, rising from a chair, climbing stairs, and falls screening tool 39 ; and the Strawberg Questionnaire. 40 Of these assessments, the Groningen Frailty Index 41 ; the Tilburg Frailty Indicator 42 ; and the strength, assistance in walking, rising from a chair, climbing stairs, and falls screening tool 43 , 44 have been assessed in patients with HF. The strength, assistance in walking, rising from a chair, climbing stairs, and falls screening tool has been shown to have low to moderate sensitivity for detecting frailty compared with other measures of sarcopenia like the Mini Sarcopenia Risk Assessment Questionaire. 45 , 46 The Groningen Frailty Index and Tilburg Frailty Indicator rely on self‐report, raising concerns with accurate recall of their physical and cognitive impairments. 41 We used the Rockwood Index as our measure of frailty due to its multimodal frailty assessment and the fact that it is not fully reliant on patient self‐reported data.

Frailty encompasses several biological domains, including metabolic dysregulation associated with malnutrition. 3 “Metabolic malnutrition” is characterized by weight and muscle mass loss, resulting in increased susceptibility to disability. 3 Studies have reported increased frailty among people with inadequate nutrition. 47 , 48 , 49 , 50 The Metabolic Malnutrition Index, 14 which measures metabolites related to sarcopenia and cachexia and is part of the MVX score, has been shown previously to be associated with an increased mortality rate among patients with HF. 18

Frailty is also associated with inflammation 51 , 52 as measured by CRP (C‐reactive protein), interleukin‐6, and tumor necrosis factor. 1 , 2 , 3 However, inflammation encompasses multiple complex mechanisms and pathways. The inflammatory markers in MVX include GlycA and small high‐density lipoprotein particles. Reports of associations between CRP and GlycA in obesity and insulin‐resistant states demonstrated 2 key points: the mediocre correlation between CRP and GlycA and the fact that both CRP and GlycA retained independent prognostic value in multivariable models. 53 , 54 Our findings resonate with these reports and underscore that GlycA and small high‐density lipoprotein particles provide prognostic information independent of frailty, likely reflecting inflammatory pathways different than those captured by CRP.

Our data underscore that MVX contributes to risk stratification over and above known clinical risk factors and frailty as measured by the Rockwood Index. These findings in turn suggest that MVX captures a domain of vulnerability distinct from frailty. Indeed, MVX could be conceptualized as reflecting metabolic and physiological health, while frailty as measured by the Rockwood Index is more likely indicative of physical capabilities. This observation is consistent with the concept that multiple health indicators are involved in the aging process. 55 Our results therefore offer a novel avenue toward a more comprehensive assessment of domains of vulnerability in the elderly patient with HF.

Limitations and Strengths

Some limitations must be acknowledged to aid in the interpretation of our results. First, some patients with HF were missing Rockwood Index values. However, our study population had similar demographic characteristics, comorbidities, and distribution of MVX scores when compared with the original population‐based cohort. Second, the population studied was primarily of European ancestry, such that the present findings will need replication in different populations. To our knowledge, the present study is the first population‐based study that has examined both MVX and frailty, highlighting the need for future validation studies. Third, additional biomarker studies are needed to examine how MVX compares with other markers of metabolic frailty to further define the mechanistic boundaries of distinct frailty domains.

Our study has several strengths. We report on a community‐based population that represents “real life” practice across the HF syndrome. We analyzed a rich clinical data set with long‐term follow‐up that includes nearly all inpatient and outpatient encounters as part of the Rochester Epidemiology Project. This allowed us to control key potential confounders including clinical characteristics not readily available in most cohorts. Finally, our findings of a novel metabolic marker providing incremental information above that obtained by evaluating frailty is of important clinical relevance to risk stratification among patients with HF.

Conclusions

In this community cohort of patients with HF, frailty and MVX constitute distinct and useful risk stratification indicators in HF, suggesting that they both capture different domains of vulnerability, relevant to the outcomes of HF.

Sources of Funding

The investigators were supported by the Intramural Research program of the National Heart, Lung, and Blood Institute of the National Institutes of Health (ZIAHL006278). This study also used in part the resources from the Rochester Epidemiology Project medical records linkage system, which is supported by the National Institute on Aging (AG 058738), by the Mayo Clinic Research Committee, and by fees paid annually by Rochester Epidemiology Project users. The funding institution did not play a role in the design, conduct, analysis, or reporting nor in the decision to submit this manuscript for publication.

Disclosures

M.A. Connelly is an employee of and holds stock in LabCorp. J.D. Otvos is a consultant, stockholder, and former employee of LabCorp. The remaining authors have no disclosures to report.

Supporting information

Data S1

JAH3-13-e031616-s001.pdf (111.7KB, pdf)

Acknowledgments

The authors thank Mary Walter, Yuhai Dai, the National Institute of Diabetes and Digestive and Kidney Diseases Clinical Laboratory Core, and Rebecca Oyetoro of the National Heart, Lung, and Blood Institute for their role in measuring key clinical laboratory variables.

*

S. Kumar and K. M. Conners contributed equally to this article.

For Sources of Funding and Disclosures, see page 8.

REFERENCES

  • 1. Tsao CW, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker‐Smith CM, Beaton AZ, Boehme AK, Buxton AE, et al. Heart disease and stroke statistics‐2023 update: a report from the American Heart Association. Circulation. 2023;147:e93–e621. doi: 10.1161/CIR.0000000000001123 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet. 2013;381:752–762. doi: 10.1016/S0140-6736(12)62167-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Ijaz N, Buta B, Xue QL, Mohess DT, Bushan A, Tran H, Batchelor W, deFilippi CR, Walston JD, Bandeen‐Roche K, et al. Interventions for frailty among older adults with cardiovascular disease: JACC state‐of‐the‐art review. J Am Coll Cardiol. 2022;79:482–503. doi: 10.1016/j.jacc.2021.11.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Uchmanowicz I, Lee CS, Vitale C, Manulik S, Denfeld QE, Uchmanowicz B, Rosińczuk J, Drozd M, Jaroch J, Jankowska EA. Frailty and the risk of all‐cause mortality and hospitalization in chronic heart failure: a meta‐analysis. ESC Heart Failure. 2020;7:3427–3437. doi: 10.1002/ehf2.12827 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5. Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, Deswal A, Drazner MH, Dunlay SM, Evers LR, et al. 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association joint committee on clinical practice guidelines. Circulation. 2022;145:e895–e1032. doi: 10.1161/CIR.0000000000001063 [DOI] [PubMed] [Google Scholar]
  • 6. Bouillon K, Kivimaki M, Hamer M, Sabia S, Fransson EI, Singh‐Manoux A, Gale CR, Batty GD. Measures of frailty in population‐based studies: an overview. BMC Geriatr. 2013;13:64. doi: 10.1186/1471-2318-13-64 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–M156. doi: 10.1093/gerona/56.3.M146 [DOI] [PubMed] [Google Scholar]
  • 8. Steverink N. Measuring frailty: developing and testing the GFI (Groningen Frailty Indicator). The Gerontologist. 2001;41:236. [Google Scholar]
  • 9. Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;2008(8):1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007;62:738–743. doi: 10.1093/gerona/62.7.738 [DOI] [PubMed] [Google Scholar]
  • 11. Gargiulo P, Marsico F, Renga F, Dell'Aversana S, Esposito I, Marciano C, Dellegrottaglie S, Perrone‐Filardi P, Paolillo S. The metabolic syndrome in heart failure: insights to specific mechanisms. Heart Fail Rev. 2020;25:1–7. doi: 10.1007/s10741-019-09838-6 [DOI] [PubMed] [Google Scholar]
  • 12. Schumacher SM, Naga Prasad SV. Tumor necrosis factor‐α in heart failure: an updated review. Curr Cardiol Rep. 2018;20:117. doi: 10.1007/s11886-018-1067-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Shah RV, Chen‐Tournoux AA, Picard MH, van Kimmenade RRJ, Januzzi JL. Galectin‐3, cardiac structure and function, and long‐term mortality in patients with acutely decompensated heart failure. Eur J Heart Fail. 2010;12:826–832. doi: 10.1093/eurjhf/hfq091 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Otvos JD, Shalaurova I, May HT, Muhlestein JB, Wilkins JT, McGarrah RW, Kraus WE. Multimarkers of metabolic malnutrition and inflammation and their association with mortality risk in cardiac catheterisation patients: a prospective, longitudinal, observational, cohort study. Lancet Healthy Longev. 2023;4:e72–e82. doi: 10.1016/S2666-7568(23)00001-6 [DOI] [PubMed] [Google Scholar]
  • 15. Otvos JD, Shalaurova I, Wolak‐Dinsmore J, Connelly MA, Mackey RH, Stein JH, Tracy RP. GlycA: a composite nuclear magnetic resonance biomarker of systemic inflammation. Clin Chem. 2015;61:714–723. doi: 10.1373/clinchem.2014.232918 [DOI] [PubMed] [Google Scholar]
  • 16. Otvos JD, Guyton JR, Connelly MA, Akapame S, Bittner V, Kopecky SL, Lacy M, Marcovina SM, Muhlestein JB, Boden WE. Relations of GlycA and lipoprotein particle subspecies with cardiovascular events and mortality: a post hoc analysis of the AIM‐HIGH trial. J Clin Lipidol. 2018;12:348–355. doi: 10.1016/j.jacl.2018.01.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Rohatgi A, Westerterp M, von Eckardstein A, Remaley A, Rye KA. HDL in the 21st century: a multifunctional roadmap for future HDL research. Circulation. 2021;143:2293–2309. doi: 10.1161/CIRCULATIONAHA.120.044221 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Conners KM, Shearer JJ, Joo J, Park H, Manemann SM, Remaley AT, Otvos JD, Connelly MA, Sampson M, Bielinski SJ, et al. The metabolic vulnerability index: a novel marker for mortality prediction in heart failure. JACC Heart Fail. 2024;12:290–300. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Rocca WA, Yawn BP, St Sauver JL, Grossardt BR, Melton LJ. History of the Rochester epidemiology project: half a century of medical records linkage in a US population. Mayo Clin Proc. 2012;87:1202–1213. doi: 10.1016/j.mayocp.2012.08.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20. Rocca WA, Grossardt BR, Brue SM, Bock‐Goodner CM, Chamberlain AM, Wilson PM, Finney Rutten LJ, St Sauver JL. Data resource profile: expansion of the Rochester epidemiology project medical records‐linkage system (E‐REP). Int J Epidemiol. 2018;47:368–368j. doi: 10.1093/ije/dyx268 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Dunlay SM, Manemann SM, Chamberlain AM, Cheville AL, Jiang R, Weston SA, Roger VL. Activities of daily living and outcomes in heart failure. Circ Heart Fail. 2015;8:261–267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Dunlay SM, Gerber Y, Weston SA, Killian JM, Redfield MM, Roger VL. Prognostic value of biomarkers in heart failure. Circulation. Heart Failure. 2009;2:393–400. doi: 10.1161/CIRCHEARTFAILURE.109.849299 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of congestive heart failure: the Framingham study. N Engl J Med. 1971;285:441–1446. doi: 10.1056/NEJM197112232852601 [DOI] [PubMed] [Google Scholar]
  • 24. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373–383. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
  • 25. Pocock SJ, Ariti CA, McMurray JJV, Maggioni A, Køber L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013;34:1404–1413. doi: 10.1093/eurheartj/ehs337 [DOI] [PubMed] [Google Scholar]
  • 26. McNallan SM, Chamberlain AM, Gerber Y, Singh M, Kane RL, Weston SA, Dunlay SM, Jiang R, Roger VL. Measuring frailty in heart failure: a community perspective. Am Heart J. 2013;166:768–774. doi: 10.1016/j.ahj.2013.07.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Onodera W, Hara N, Aoki S, Asahi T, Sawamura N. Phylogenetic tree reconstruction via graph cut presented using a quantum‐inspired computer. Mol Phylogenet Evol. 2023;178:107636. doi: 10.1016/j.ympev.2022.107636 [DOI] [PubMed] [Google Scholar]
  • 28. Song X, Mitnitski A, Rockwood K. Prevalence and 10‐year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc. 2010;58:681–687. doi: 10.1111/j.1532-5415.2010.02764.x [DOI] [PubMed] [Google Scholar]
  • 29. Rockwood K, Song X, Mitnitski A. Changes in relative fitness and frailty across the adult lifespan: evidence from the Canadian National Population Health Survey. CMAJ. 2011;183:E487–E494. doi: 10.1503/cmaj.101271 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Romero‐Ortuno R. An alternative method for Frailty Index cut‐off points to define frailty categories. Eur Geriatr Med. 2013;4:299–303. doi: 10.1016/j.eurger.2013.06.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Garcia E, Bennett DW, Connelly MA, Jeyarajah EJ, Warf FC, Shalaurova I, Matyus SP, Wolak‐Dinsmore J, Oskardmay DN, Young RM, et al. The extended lipid panel assay: a clinically‐deployed high‐throughput nuclear magnetic resonance method for the simultaneous measurement of lipids and apolipoprotein B. Lipids Health Dis. 2020;19:247. doi: 10.1186/s12944-020-01424-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Azur MJ, Stuart EA, Frangakis C, Leaf PJ. Multiple imputation by chained equations: what is it and how does it work? Int J Methods Psychiatr Res. 2011;20:40–49. doi: 10.1002/mpr.329 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Altman DG, De Stavola BL, Love SB, Stepniewska KA. Review of survival analyses published in cancer journals. Br J Cancer. 1995;72(2):511–518. doi: 10.1038/bjc.1995.364 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34. VanderWeele TJ. Causal mediation analysis with survival data. Epidemiology. 2011;22:582–585. doi: 10.1097/EDE.0b013e31821db37e [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Gelfand LA, MacKinnon DP, DeRubeis RJ, Baraldi AN. Mediation analysis with survival outcomes: accelerated failure time vs. proportional hazards models. Front Psychol. 2016;7:423. doi: 10.3389/fpsyg.2016.00423 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Gorodeski EZ, Goyal P, Hummel SL, Krishnaswami A, Goodlin SJ, Hart LL, Forman DE, Wenger NK, Kirkpatrick JN, Alexander KP. Domain management approach to heart failure in the geriatric patient. J Am Coll Cardiol. 2018;71:1921–1936. doi: 10.1016/j.jacc.2018.02.059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Gobbens RJ, van Assen MA, Luijkx KG, Wijnen‐Sponselee MT, Schols JM. The Tilburg frailty indicator: psychometric properties. J Am Med Dir Assoc. 2010;11:344–355. doi: 10.1016/j.jamda.2009.11.003 [DOI] [PubMed] [Google Scholar]
  • 38. Kerse N, Boyd M, Mclean C, Koziol‐Mclain J, Robb G. The BRIGHT tool. Age Ageing. 2008;37:553–588. doi: 10.1093/ageing/afn145 [DOI] [PubMed] [Google Scholar]
  • 39. Morley JE, Rolland Y, Tolson D, Vellas B. Increasing awareness of the factors producing falls: the mini falls assessment. J Am Med Dir Assoc. 2012;13:87–90. doi: 10.1016/j.jamda.2011.11.002 [DOI] [PubMed] [Google Scholar]
  • 40. Matthews M, Lucas A, Boland R, Hirth V, Odenheimer G, Wieland D, Williams H, Eleazer GP. Use of a questionnaire to screen for frailty in the elderly: an exploratory study. Aging Clin Exp Res. 2004;16:34–40. doi: 10.1007/BF03324529 [DOI] [PubMed] [Google Scholar]
  • 41. Drubbel I, Bleijenberg N, Kranenburg G, Eijkemans RJC, Schuurmans MJ, de Wit NJ, Numans ME. Identifying frailty: do the frailty index and groningen frailty indicator cover different clinical perspectives? A cross‐sectional study. BMC Fam Pract. 2013;14:64. doi: 10.1186/1471-2296-14-64 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Uchmanowicz I, Wleklik M, Gobbens RJ. Frailty syndrome and self‐care ability in elderly patients with heart failure. Clin Interv Aging. 2015;10:871–877. doi: 10.2147/CIA.S83414 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Zhao W, Lu M, Wang X, Guo Y. The role of sarcopenia questionnaires in hospitalized patients with chronic heart failure. Aging Clin Exp Res. 2021;33:339–344. doi: 10.1007/s40520-020-01561-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Tanaka S, Kamiya K, Hamazaki N, Matsuzawa R, Nozaki K, Maekawa E, Noda C, Yamaoka‐Tojo M, Matsunaga A, Masuda T, et al. Utility of SARC‐F for assessing physical function in elderly patients with cardiovascular disease. J Am Med Dir Assoc. 2017;18:176–181. doi: 10.1016/j.jamda.2016.10.019 [DOI] [PubMed] [Google Scholar]
  • 45. Cesari M, Marzetti E, Calvani R. Sarcopenia and SARC‐F: “Perfect is the Enemy of Good”. J Am Med Dir Assoc. 2021;22:1862–1863. doi: 10.1016/j.jamda.2021.07.030 [DOI] [PubMed] [Google Scholar]
  • 46. Bahat G, Erdoğan T, İlhan B. SARC‐F and other screening tests for sarcopenia. Curr Opin Clin Nutr Metab Care. 2022;25:37–42. doi: 10.1097/MCO.0000000000000801 [DOI] [PubMed] [Google Scholar]
  • 47. Collins J, Longhurst G, Roschel H, Gualano B. Resistance training and co‐supplementation with creatine and protein in older subjects with frailty. J Frailty Aging. 2016;5:126–134. doi: 10.14283/jfa.2016.85 [DOI] [PubMed] [Google Scholar]
  • 48. Niccoli S, Kolobov A, Bon T, Rafilovich S, Munro H, Tanner K, Pearson T, Lees SJ. Whey protein supplementation improves rehabilitation outcomes in hospitalized geriatric patients: a double blinded, randomized controlled trial. J Nutr Gerontol Geriatr. 2017;36:149–165. doi: 10.1080/21551197.2017.1391732 [DOI] [PubMed] [Google Scholar]
  • 49. Bauer JM, Verlaan S, Bautmans I, Brandt K, Donini LM, Maggio M, McMurdo MET, Mets T, Seal C, Wijers SL, et al. Effects of a vitamin D and leucine‐enriched whey protein nutritional supplement on measures of sarcopenia in older adults, the PROVIDE study: a randomized, double‐blind, placebo‐controlled trial. J Am Med Dir Assoc. 2015;16:740–747. doi: 10.1016/j.jamda.2015.05.021 [DOI] [PubMed] [Google Scholar]
  • 50. Talegawkar SA, Bandinelli S, Bandeen‐Roche K, Chen P, Milaneschi Y, Tanaka T, Semba RD, Guralnik JM, Ferrucci L. A higher adherence to a Mediterranean‐style diet is inversely associated with the development of frailty in community‐dwelling elderly men and women. J Nutr. 2012;142:2161–2166. doi: 10.3945/jn.112.165498 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Soysal P, Stubbs B, Lucato P, Luchini C, Solmi M, Peluso R, Sergi G, Isik AT, Manzato E, Maggi S, et al. Inflammation and frailty in the elderly: a systematic review and meta‐analysis. Ageing Res Rev. 2016;31:1–8. doi: 10.1016/j.arr.2016.08.006 [DOI] [PubMed] [Google Scholar]
  • 52. Ferrucci L, Fabbri E. Inflammageing: chronic inflammation in ageing, cardiovascular disease, and frailty. Nat Rev Cardiol. 2018;15(9):505–522. doi: 10.1038/s41569-018-0064-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Lorenzo C, Festa A, Hanley AJ, Rewers MJ, Escalante A, Haffner SM. Novel protein glycan–derived markers of systemic inflammation and C‐reactive protein in relation to glycemia, insulin resistance, and insulin secretion. Diabetes Care. 2017;40:375–382. doi: 10.2337/dc16-1569 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54. Levine JA, Han JM, Wolska A, Wilson SR, Patel TP, Remaley AT, Periwal V, Yanovski JA, Demidowich AP. Associations of GlycA and high‐sensitivity C‐reactive protein with measures of lipolysis in adults with obesity. J Clin Lipidol. 2020;14:667–674. doi: 10.1016/j.jacl.2020.07.012 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55. Lu W, Pikhart H, Sacker A. Domains and measurements of healthy aging in epidemiological studies: a review. The Gerontologist. 2019;59:e294–e310. doi: 10.1093/geront/gny029 [DOI] [PMC free article] [PubMed] [Google Scholar]

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