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Nutritional status is associated with physical function and disability in older adults with chronic heart failure
European Journal of Internal Medicine xxx (xxxx) xxx–xxx
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Nutritional status is associated with physical function and disability in older adults with chronic heart failure ⁎
Diana Lellia, , Stefano Toloneb, Giovanni Pulignanob, Maria Denitza Tintib, Donatella Del Sindacoc, Giulia Dipasquale Mazzillib, Raffaele Antonelli Incalzia, Claudio Pedonea a
Area di Geriatria, Campus Bio-Medico University, via Alvaro del Portillo 200, 00128 Rome, Italy Unità di Cardiologia 1, A.O. S. Camillo-Forlanini, Circonvallazione Gianicolense 87, 00152 Rome, Italy c Unità di Cardiologia, Ospedale Nuovo Regina Margherita, Via Emilio Morosini 30, 00153 Rome, Italy b
A R T I C LE I N FO
A B S T R A C T
Keywords: Aged Congestive heart failure Disability Activities of daily living Walking speed Nutritional assessment
Background: The association between nutritional status (NS) and physical performance and disability in older adults with chronic heart failure (CHF) is not well established. We aimed at evaluating whether NS, estimated using the Mini Nutritional Assessment (MNA), is associated with gait speed (GS) and disability (ADL/IADL impairment) in this population and to assess whether energy intake (EI) and appendicular skeletal muscle mass index (ASMMI) influence this relationship. Methods: In this cross-sectional study we enrolled 88 older adults admitted to a cardiology outpatient clinic for CHF. MNA was analyzed both as continuous and categorical variable (risk of malnutrition [RM]/well-nourished [WN]). The association between NS and GS and disability was assessed using linear and logistic regression models, respectively, crude, adjusted firstly for age, sex, ejection fraction, and mood status, and then for EI and ASMMI. Results: Mean age was 77.8 years, 73% were men. MNA score was positively associated with GS: β adjusted = 0.022, P = 0.035; the coefficient was unaffected by adjustment for EI and ASMMI (β = 0.022, P = 0.052). Compared to WN, RM participants had a lower gait speed (0.82 vs 0.99 m/s, P = 0.006); the difference was attenuated after adjustment for potential confounders (β − = 0.138, P = 0.055). MNA score was inversely associated with ADL impairment (Adjusted OR: 0.80, 95%CI 0.64–0.98), but not with IADL impairment (Adjusted OR: 0.94, 95%CI 0.78–1.13). Conclusion: Reduced MNA score is associated with poorer physical function and ADL impairment in older adults affected by CHF, independently of EI and ASMMI. Routinely evaluation of NS should be performed in this population.
1. Introduction Heart failure (HF) has a high prevalence worldwide, that increases with age [1], and a natural history characterized, beside hospitalizations [3] and death, also by a progressive functional decline [2] and disability [3,4], that severely influence quality of life [5] and clinical outcomes [6,7]. Malnutrition is a common feature of this disease, especially in advanced stages: its prevalence ranges from 16% to 62.4% in stable HF [8] and increases up to 80% in advanced HF [9]. This large range of prevalence estimates may be related to different nutritional screening tools used and to the different mean age of the study participants: older adults affected by HF are at higher risk of malnutrition compared to younger people because to the HF-related mechanisms,
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such as low nutritional intake due to intestinal edema and anorexia [10], liver disfunction [11], and cytokine-induced hyper-catabolism [12], add up age-related factors, such as comorbidities, polytherapy, and social factors. Regardless of HF, in older adults malnutrition is associated with sarcopenia [13], poor physical performance [14,15] and disability [16], and even the risk of malnutrition is associated with poorer physical function [15], although this association is not as strong as the one with overt malnutrition. The association between nutritional status and physical performance or disability in patients affected by HF may be more complex compared to people without this disease, because HF symptoms and natural history may be the principal determinants of these outcomes [17], and nutritional status may have only a minor role in this population.
Corresponding author. E-mail address: [email protected] (D. Lelli).
https://doi.org/10.1016/j.ejim.2019.12.007 Received 26 October 2019; Received in revised form 4 December 2019; Accepted 15 December 2019 0953-6205/ © 2019 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Diana Lelli, et al., European Journal of Internal Medicine, https://doi.org/10.1016/j.ejim.2019.12.007
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mass index, fat mass index, ASMMI) was evaluated using bio-impedance analysis (BIA 101 New Edition, Akern), early in the morning, after at least 8 hours of fasting. Physical function was evaluated measuring the gait speed (4 meters at usual pace). It is a quick, reliable measure of functional capacity with well-documented predictive value for major health-related outcomes in older adults [33–35]. Finally, blood examination including creatinine, electrolytes, complete blood count, and brain natriuretic peptide (BNP) was performed within 2 weeks of clinical evaluation.
The role of nutritional status in influencing physical function and disability in patients affected by HF has been poorly investigated: only few data are available on the association with physical function [18,19], and only one study, with a relatively small sample size, was focused on older adults, documenting an association between malnutrition and a poorer physical function in patients admitted to rehabilitation for HF [20]. To the best of our knowledge, no studies focused on older adults affected by stable chronic HF or on the relationship between nutritional status and disability are available. Furthermore, to the best of our knowledge, no study investigated the role of caloric intake and reduced appendicular skeletal muscle mass (ASMMI), two of the most important factors implicated in the association between malnutrition and physical function [21–23], in influencing this relationship. The objective of this study was to analyse the association between nutritional status and physical function and disability in older adults affected by chronic HF and to analyse the role of caloric intake and ASMMI in influencing these relationships.
2.3. Statistical analysis The characteristics of the study sample were reported using descriptive statistics (mean and standard deviation or proportions, as appropriate), according to MNA classes (Malnourished: <17 points; At risk of malnutrition: 17–23.5 points; Well nourished: 24–30 points). Only one patient was categorized as malnourished and was thus excluded from the analysis. The MNA was analysed both as continuous and categorical variable (at risk of malnutrition/well-nourished, using well-nourished as the reference group). Visual inspection of the MNA distribution revealed a departure from normality. Thus, the association between nutritional status and physical function (gait speed) was evaluated using the Spearman's rank correlation test and regression models. The goodness of fit of a linear and non-linear (squared) regression model was evaluated analysing the adjusted R-squared and the residuals plots. The linear model provided a satisfactory fit and was used for the analysis. The evaluation of diagnostic plots did not reveal violation of the assumptions; in particular, we found no trend of residuals over fitted values, homoskedasticity, and normal distribution of the residuals. The model was then adjusted for potential confounders, selected on the basis of clinical significance, prior knowledge, and results of the univariate analysis (age, sex, ejection fraction, GDS). At a second step the model was adjusted for ASMMI and daily caloric intake indexed for ideal body weight (calculated using the Lorenz formula [36]). The association between nutritional status and disability was evaluated using the chi-squared test and logistic regression models, both crude and adjusted for potential confounders. All the analyses were performed using R version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria).
2. Material and methods 2.1. Study design and setting In this cross-sectional study, we enrolled patients aged 65 years or older affected by HF in NYHA class II-IV, attending an outpatient HF clinic at S. Camillo-Forlanini Hospital in Rome between January 2016 and September 2018. We excluded patients with acute cardiovascular events (history of myocardial infarction or acute HF in the previous month), severe dementia, visual or hearing impairment or severe functional limitation (not able to walk 4 meters). We also excluded participants affected by chronic kidney failure requiring hemo-dialysis, active cancer, not compensated thyroid disease, terminal diseases (life expectancy < 1 year) or malabsorption diseases. The study was performed according to the World Medical Association Declaration of Helsinki and was approved by the Ethic Committee of the S. CamilloForlanini Hospital (1578/CE Lazio 1). 2.2. Measurements All participants performed a cardiological visit, during which cardiological history, comorbidities, and pharmacological therapy were collected. An echocardiogram was performed by a trained cardiologist with a Philips IE 33; left ventricular ejection fraction (4 chambers Simpson method) and filling pressures (E/E’ ratio), tricuspid annular plane systolic excursion (TAPSE), chambers dimensions, evidences of valvular defects and inferior cava vein collapsibility were systematically evaluated. Participants underwent also a geriatric multidimensional evaluation, including assessment of cognitive function (Montreal Cognitive Assessment [24]), mood (Geriatric Depression Scale [25]), quality of life (EuroQOL [26]), and disability (Basic Activities of Daily Living [ADL] [27] and Instrumental Activities of Daily Living [IADL] [28]). Disability was defined as a reduction of at least one point in ADL or in IADL score and was analyzed separately for basic and instrumental activities of daily living [29]. Nutritional status was evaluated using the Mini Nutritional Assessment (MNA), a multidimensional evaluation tool approved by the American Society for Parenteral and Enteral Nutrition and validated in older adults [30]. The dietary intake was evaluated using the EPIC questionnaire [31], that investigates intake frequency over the previous year of 236 specific foods, along with the average size of the servings, selected from a range as shown in photographs. The information derived from the questionnaire was automatically converted into data on energy, micro- and macronutrient intake by a specifically designed software. The EPIC nutritional assessment has been successfully validated in an older adult population [32]. The body composition (free fat
3. Results 3.1. General characteristics of the population The mean age of the 88 study participants at baseline was 77.8 years (SD 7.1); 73% were male. The mean ejection fraction was 40.6% (SD 10.3); 72.7% of the participants was in NYHA class II and 27.3% in NYHA class III; no participants were classified in NYHA class IV. Fiftynine participants were classified according to MNA as WN and 29 as RM. There were no differences in age and comorbidities between the groups, except for anaemia, that was more prevalent in the RM group (31% vs 10%, P = 0.032), and COPD, that was more prevalent in the WN group (31% vs. 7%, P = 0.027). RM participants were less frequently male (52% vs 83%, P = 0.004). There were no differences between groups in BNP serum concentration (WN: 276.8 pg/ml, SD 360.3; RM: 274.3 pg/ml, SD 239; P = 0.973), nor in pharmacological therapy. Daily energy and protein intake standardized for the ideal body weight did not differed between the groups. The RM group had a lower BMI (24.3 kg/m2, SD 2.9 vs 26.2 kg/m2, SD 3.6, P = 0.010), fat free mass index (18.4 kg/m2, SD 1.9 vs 20.5 kg/m2, SD 2.4; P < 0.001) and appendicular skeletal muscle mass index (6.7 kg/m2, SD 0.8 vs 8.1 kg/m2, SD 2.9, P = 0.002), while there was no difference in fat mass index. RM participants were more frequently in NYHA class III (41.4% vs 20.3%, P = 0.067). There were no differences between 2
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Table 1 General characteristic of the population according to nutritional status.
Age (years) Male sex (%) Hypertension (%) Type II diabetes (%) Anemia (%) Atrial fibrillation (%) IHD (%) Dyslipidemia (%) Stroke (%) COPD (%) ACE-inhibitors or ARBs (%) β-blockers Antiplatelet agents Pump proton inhibitors Nitrates NAO (%) Warfarin Creatinine (mg/dl) eGFR (CKD-EPI, mL/min/1.73 mq) BNP (pg/ml) Total cholesterol (mmol/l) Kcal/ideal weight (kcal/kg) Proteins/ideal weight (g/kg) Fat mass index (kg/m2) Fat free mass index (kg/m2) Appendicular skeletal muscle mass index (kg/m2) BMI (kg/m2) NYHA class II (%) NYHA class III (%) Ejection fraction Medium E/E’ ratio TAPSE (mm) sPAP (mmHg) MOCA GDS EuroQOL Gait speed (m/s) Reduced ADL (%) Reduced IADL (%)
Well nourishedN: 59
At risk of malnutritionN: 29
AllN: 88
P-value
77.5 (6.9) 83 59 25 10 39 58 45 2 31 76 88 54 70 15 15 44 1.3 (0.4) 55.7 (17.9) 276.8 (360.3) 3.95 (1.04) 29.2 (9.9) 1.1 (0.3) 5.7 (2.5) 20.5 (2.4) 8.1 (2.9) 26.2 (3.6) 79.7 20.3 41.1 (10) 13.7 (7.7) 18.7 (3.5) 38 (9.8) 20.7 (4.6) 3.6 (3.2) 64.5 (17) 0.99 (0.26) 16.1 39.3
78.5 (7.7) 52 69 24 31 38 59 35 3 7 69 97 52 66 10 28 21 1.2 (0.6) 59.3 (23.7) 274.3 (239) 4.3 (1.06) 27.5 (6.1) 1.2 (0.3) 6.1 (2.6) 18.4 (1.9) 6.7 (0.8) 24.3 (2.9) 58.6 41.4 39.5 (10.9) 16.9 (9.8) 20 (4.7) 39.5 (11.9) 19 (5.6) 5.8 (3.9) 63.8 (19.1) 0.82 (0.25) 42.9 57.1
77.8 (7.1) 73 62 25 17 39 58 41 2 23 74 91 53 68 14 19 36 1.3 (0.5) 56.9 (19.9) 276 (325.2) 4.07 (1.06) 28.6 (8.8) 1.1 (0.3) 5.8 (2.5) 19.9 (2.5) 7.6 (2.5) 25.6 (3.5) 72.7 27.3 40.6 (10.3) 14.7 (8.4) 19.1 (3.9) 38.5 (10.5) 20.2 (5) 4.3 (3.6) 64.2 (17.6) 0.93 (0.27) 25 45.2
0.553 0.004 0.520 1 0.032 1 1 0.556 1 0.027 0.635 0.370 1 0.894 0.764 0.276 0.056 0.452 0.483 0.973 0.174 0.314 0.451 0.459 <0.001 0.002 0.010 0.067 0.500 0.192 0.206 0.588 0.221 0.015 0.868 0.006 0.016 0.188
Abbreviations: IHD: ischemic heart disease; COPD: chronic obstructive pulmonary disease; eGFR: estimated glomerular filtration rate; ARBs: angiotensin II receptor blockers; BNP: brain natriuretic peptide; BMI: body mass index; NYHA: New York Health Association; TAPSE: tricuspid annular plane systolic excursion; sPAP: systolic pulmonary artery pressure; MOCA: Montreal Cognitive Assessment; GDS: Geriatric Depression Scale; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living.
compared to the WN group (42.9% vs 16.1%, P = 0.016) (Table 1, Fig. 3); this result was confirmed by a logistic regression model adjusted for potential confounders (OR 3.36, 95% CI 1.07–10.97). Similar results, albeit with wider confidence intervals, were found in models adjusted for ASMMI and caloric intake (OR 3.33, 95% CI 0.92–12.54) (Table 3). Analyzing the MNA as a continuous variable, each 1-point increase in the MNA score was associated with a 25% reduction of the odds for ADL impairment (95% CI 0.62–0.88). These results were confirmed after adjustment for potential confounders (OR 0.79, 95% CI 0.64–0.95), and the results did not change including in the model also ASMMI and caloric intake (OR 0.80, 95% CI 0.64–0.98) (Table 3). There were no differences between groups in proportion of patients with IADL impairment (Table 1, Fig. 3). These results were confirmed also after adjustment for potential confounders and analysing the MNA as a continuous variable (Table 3).
groups in mean ejection fraction (RM: 39.5%, SD 10.9; WM: 41.1, SD 10; P = 0.500), nor in E/E’ ratio (RM: 16.9, SD 9.8; WN: 13.7, SD 7.7; P = 0.192) (Table 1). RM group had a higher GDS score (5.8, SD 3.9 vs 3.6, SD 3.2, P = 0.015), while there were no differences between groups in quality of life and cognitive function (Table 1). 3.2. Association between nutritional status and gait speed Respect to WN group, RM participants had a lower gait speed (0.82 vs 0.99 m/s, p = 0.006) (Table 1, Fig. 1). This finding was confirmed after adjustment for potential confounders, either excluding (adjusted β −0.122, P = 0.059), or including ASMMI and caloric intake (adjusted β −0.138, P = 0.055) (Table 2). Analysing the MNA as a continuous variable, it was positively associated with the gait speed (Rho: 0.272, P = 0.012) (Fig. 2). This association was confirmed after adjustment for potential confounders in a linear regression model (β 0.022, P = 0.035). Adjusting the model also for ASMMI and caloric intake, the regression coefficient did not change (β 0.022, P = 0.052) (Table 2).
4. Discussion Our sample of older adults affected by chronic HF was globally wellnourished according to both BMI and MNA. Our main finding is that, even in the absence of evident nutritional problems, people at risk of malnutrition had a poorer physical function and a higher risk of impairment in basic activities of daily living, independently of muscular
3.3. Association between nutritional status and disability The prevalence of ADL impairment was higher in the RM group 3
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Fig. 1. Nutritional status and physical performance – Patients at risk of malnutrition had a worse physical performance with respect to well-nourished. Table 2 Linear regression models of the association between nutritional status and gait speed. MNA categorical (Reference: well-nourished) β Crude (Pvalue) At risk of malnutrition Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight
−0.167 (0.005)) – – – – – –
MNA continuous (1-point increase) β Crude (Pvalue) MNA Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight
0.027 (0.007) – – – – – –
β Adjusted 1 (Pvalue)
β Adjusted 2 (Pvalue)
−0.122 (0.059)
−0.138 (0.055)
−0.005 (0.183) 0.108 (0.143) −0.001 (0.712) −0.006 (0.478) – –
−0.006 (0.194) 0.179 (0.065) 0.0001 (0.962) −0.003 (0.746) −0.023 (0.151) 0.002 (0.600)
β Adjusted 1 (Pvalue)
β Adjusted 2 (Pvalue)
0.022 (0.035) −0.005 (0.201) 0.102 (0.171) −0.001 (0.642) −0.006 (0.537) – –
0.022 (0.052) −0.005 (0.219) 0.114 (0.181) −0.001 (0.828) −0.004 (0.669) −0.012 (0.294) 0.0004 (0.902)
Abbreviations: GDS: geriatric depression scale; ASMMI: appendicular skeletal muscle mass index.
Fig. 2. Association between Mini Nutritional Assessment score and physical performance – Mini Nutritional Assessment score was positively associated with gait speed.
mass and caloric intake. The association between poor nutritional status and reduced physical function in people with HF has been previously reported, but only in patients hospitalized for rehabilitation [19,20] or for a primary diagnosis of HF with preserved ejection fraction [18]: in a sample of 145 patients with age ≥65 years old admitted for cardiologic rehabilitation, Katano et al. found that malnutrition, diagnosed using the MNA short form (MNA-SF), was an independent predictor of functional dependence at discharge, evaluated using the Barthel index [20]. In a similar setting, in a sample of 105 patients with a mean age of about 73 years, Matsuo et al. documented that MNA-SF was linearly associated with the Barthel index [19]. These results were confirmed also in patients
affected by HF with preserved ejection: moderate or severe nutritional risk, evaluated using the Geriatric Nutrition Risk Index (GNRI), was associated with a lower Barthel index at discharge (results not adjusted for potential confounders) [18]. However, only the study by Katano et al was focused on older adults, and the association between nutritional status and physical function in older adults affected by chronic HF has not been previously investigated. Our results are in line with the above-mentioned studies and extend their results: we confirmed the previously described association 4
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between MNA score and physical function in older adults affected by HF, and we found that in this population not only malnutrition (as previously reported) but also the risk of malnutrition is associated with a poorer physical function respect to well-nutrition. Furthermore, for the first time in this population, we documented that nutritional status is associated with ADL impairment. The lack of association with IADL impairment found in our study could be explained by the high baseline risk of IADL impairment in this population; thus, the additional effect of nutritional status on this outcome could not be caught in these patients. Finally, our study analysed the role of energy intake and appendicular skeletal muscle mass index in influencing the association between nutritional status and physical function. Interestingly, differently from what would have been expected, nor energy intake, nor appendicular skeletal muscle mass significantly influenced this association. The explanation of these results could lie in the fact that MNA is a multidimensional tool, that take into account not only the dietary intake and the BMI, but also other factors, such as polypharmacy, mood, and cognitive impairment [30]. Compared to other nutritional tools, this approach could be more effective in older adults, that are characterized by comorbidities, polypharmacy and higher risk of disability, and in whom the association between nutritional status and physical function may not be exclusively related to a reduced energy intake or reduced muscle mass, but also to multiple other factors, such as social factors, polypharmacy, recent acute events, and depression, that together contribute to the outcome. Our results must be interpreted taking into account some limitations. Being a cross-sectional study, reverse causation (i.e., functional status influences nutritional status, and not the other way around) cannot be ruled out. In fact, nutritional status is one of the multiple elements that influence the onset and the course of a functional disability; on the other side, disability itself may contribute to malnutrition onset and worsening. However, the bulk of the available evidence documents that nutritional status influences both physical function and disability [37]. Furthermore, the sample size is relatively small, and the study involved only one centre, thus potentially impacting on the robustness of the study, although the sample and study design are similar to that of most of the studies on this topic. Third, malnourished patients were not represented in our population and thus were not studied. However, considering that risk of malnutrition is considered an intermediate condition between well-nutrition and malnutrition, we expect that these patients would have had a stronger association with our outcomes of interest. Fourth, physical function was evaluated exclusively with gait speed; nevertheless, it has the advance of being quick and predictive of health-related outcomes in older adults [33,34]. Finally, the information on orthopaedic disorders, that could potentially influence the gait speed, was not available; however, no patients needed assistance in performing the gait speed test.
Fig. 3. Nutritional status and disability – Patients at risk of malnutrition had a higher prevalence of ADL impairment while the prevalence of IADL impairment did not change between the groups. Table 3 Regression models of the association between nutritional status (MNA) and disability. ADL impairment MNA categorical (Reference: well-nourished) OR Crude(95% CI) MNA
3.80 (1.42–10.66) – – – – – –
Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight MNA continuous (1-point increase) OR Crude (95% CI) MNA 0.75 (0.62-0.88) Age – Sex – Ejection fraction – GDS – ASMMI – – Caloric intake/ideal body weight IADL impairment MNA categorical (Reference: well-nourished) OR Crude (95% CI) At risk of malnutrition 1.67 (0.69–4.09) Age – Sex – Ejection fraction – GDS – ASMMI – Caloric intake/ideal – body weight MNA continuous (1-point increase) OR Crude (95% CI) MNA 0.92 (0.79-1.06) Age – Sex – Ejection fraction – GDS – ASMMI – Caloric intake/ideal – body weight
OR Adjusted 1(95%CI)
OR Adjusted 2(95% CI)
3.36 (1.07–10.97) 1.04 (0.97–1.12) 0.66 (0.19–2.31) 1.02 (0.97–1.07) 1.15 (0.99–1.34) – –
3.33 (0.92–12.54) 1.04 (0.95–1.15) 0.66 (0.12–4.98) 1.02 (0.95–1.09) 1.09 (0.91–1.33) 0.77 (0.32–1.07) 0.91 (0.81–1)
OR Adjusted 1 (95%CI) 0.79 (0.64–0.95) 1.05 (0.96–1.14) 0.33 (0.08–1.30) 0.99 (0.93–1.05) 1.03 (0.86–1.22) – –
OR Adjusted 2 (95% CI) 0.80 (0.64–0.98) 1.05 (0.96–1.17) 0.45 (0.08–3.47) 1.01 (0.94–1.08) 1.05 (0.86–1.28) 0.79 (0.33–1.06) 0.92 (0.83–1.01)
OR Adjusted 1 (95%CI) 1.45 (0.48–4.35)
OR Adjusted 2 (95% CI) 1.80 (0.55–5.89)
1.05 0.59 1.02 1.13 – –
1.04 0.42 1.01 1.06 1.09 0.97
(0.98–1.12) (0.17–2.02) (0.97–1.07) (0.97–1.31)
OR Adjusted 1 (95%CI) 0.97 (0.81–1.16) 1.04 (0.97–1.12) 0.66 (0.19–2.31) 1.02 (0.97–1.07) 1.15 (0.99–1.34) – –
(0.97–1.12) (0.09–1.65) (0.96–1.07) (0.91–1.24) (0.87–1.84) (0.91–1.03)
5. Conclusions Our results suggest that an early assessment of nutritional status using the MNA should be performed in older adults affected by CHF to identify patients at higher risk of poorer physical function and disability. The importance of our results lies in the fact that we showed that a poorer nutritional status is associated with poor physical function and disability, which are in turn associated with negative outcomes such as hospitalizations [6] and death [7] and have a significant impact on quality of life of these patients [5]. Longitudinal studies are needed to confirm these results and intervention trials should investigate whether multidimensional approach for the treatment of malnutrition and risk of malnutrition including nutritional supplementation can improve physical function and reduce the risk of disability in this population.
OR Adjusted 2 (95% CI) 0.94 (0.78–1.13) 1.04 (0.97–1.12) 0.46 (0.10–1.82) 1.01 (0.96–1.07) 1.08 (0.92–1.27) 1.08 (0.87–1.80) 0.97 (0.91–1.03)
Abbreviations: GDS: geriatric depression scale; ASMMI: appendicular skeletal muscle mass index.
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Funding [15]
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
[16]
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
[17] [18]
Acknowledgments We would like to acknowledge dr. Ilaria Aquilea and dr. Wanda Rizza for their support in the enrollment of the patients.
[19]
[20]
Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejim.2019.12.007.
[21]
[22]
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European Journal of Internal Medicine journal homepage: www.elsevier.com/locate/ejim
Nutritional status is associated with physical function and disability in older adults with chronic heart failure ⁎
Diana Lellia, , Stefano Toloneb, Giovanni Pulignanob, Maria Denitza Tintib, Donatella Del Sindacoc, Giulia Dipasquale Mazzillib, Raffaele Antonelli Incalzia, Claudio Pedonea a
Area di Geriatria, Campus Bio-Medico University, via Alvaro del Portillo 200, 00128 Rome, Italy Unità di Cardiologia 1, A.O. S. Camillo-Forlanini, Circonvallazione Gianicolense 87, 00152 Rome, Italy c Unità di Cardiologia, Ospedale Nuovo Regina Margherita, Via Emilio Morosini 30, 00153 Rome, Italy b
A R T I C LE I N FO
A B S T R A C T
Keywords: Aged Congestive heart failure Disability Activities of daily living Walking speed Nutritional assessment
Background: The association between nutritional status (NS) and physical performance and disability in older adults with chronic heart failure (CHF) is not well established. We aimed at evaluating whether NS, estimated using the Mini Nutritional Assessment (MNA), is associated with gait speed (GS) and disability (ADL/IADL impairment) in this population and to assess whether energy intake (EI) and appendicular skeletal muscle mass index (ASMMI) influence this relationship. Methods: In this cross-sectional study we enrolled 88 older adults admitted to a cardiology outpatient clinic for CHF. MNA was analyzed both as continuous and categorical variable (risk of malnutrition [RM]/well-nourished [WN]). The association between NS and GS and disability was assessed using linear and logistic regression models, respectively, crude, adjusted firstly for age, sex, ejection fraction, and mood status, and then for EI and ASMMI. Results: Mean age was 77.8 years, 73% were men. MNA score was positively associated with GS: β adjusted = 0.022, P = 0.035; the coefficient was unaffected by adjustment for EI and ASMMI (β = 0.022, P = 0.052). Compared to WN, RM participants had a lower gait speed (0.82 vs 0.99 m/s, P = 0.006); the difference was attenuated after adjustment for potential confounders (β − = 0.138, P = 0.055). MNA score was inversely associated with ADL impairment (Adjusted OR: 0.80, 95%CI 0.64–0.98), but not with IADL impairment (Adjusted OR: 0.94, 95%CI 0.78–1.13). Conclusion: Reduced MNA score is associated with poorer physical function and ADL impairment in older adults affected by CHF, independently of EI and ASMMI. Routinely evaluation of NS should be performed in this population.
1. Introduction Heart failure (HF) has a high prevalence worldwide, that increases with age [1], and a natural history characterized, beside hospitalizations [3] and death, also by a progressive functional decline [2] and disability [3,4], that severely influence quality of life [5] and clinical outcomes [6,7]. Malnutrition is a common feature of this disease, especially in advanced stages: its prevalence ranges from 16% to 62.4% in stable HF [8] and increases up to 80% in advanced HF [9]. This large range of prevalence estimates may be related to different nutritional screening tools used and to the different mean age of the study participants: older adults affected by HF are at higher risk of malnutrition compared to younger people because to the HF-related mechanisms,
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such as low nutritional intake due to intestinal edema and anorexia [10], liver disfunction [11], and cytokine-induced hyper-catabolism [12], add up age-related factors, such as comorbidities, polytherapy, and social factors. Regardless of HF, in older adults malnutrition is associated with sarcopenia [13], poor physical performance [14,15] and disability [16], and even the risk of malnutrition is associated with poorer physical function [15], although this association is not as strong as the one with overt malnutrition. The association between nutritional status and physical performance or disability in patients affected by HF may be more complex compared to people without this disease, because HF symptoms and natural history may be the principal determinants of these outcomes [17], and nutritional status may have only a minor role in this population.
Corresponding author. E-mail address: [email protected] (D. Lelli).
https://doi.org/10.1016/j.ejim.2019.12.007 Received 26 October 2019; Received in revised form 4 December 2019; Accepted 15 December 2019 0953-6205/ © 2019 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.
Please cite this article as: Diana Lelli, et al., European Journal of Internal Medicine, https://doi.org/10.1016/j.ejim.2019.12.007
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mass index, fat mass index, ASMMI) was evaluated using bio-impedance analysis (BIA 101 New Edition, Akern), early in the morning, after at least 8 hours of fasting. Physical function was evaluated measuring the gait speed (4 meters at usual pace). It is a quick, reliable measure of functional capacity with well-documented predictive value for major health-related outcomes in older adults [33–35]. Finally, blood examination including creatinine, electrolytes, complete blood count, and brain natriuretic peptide (BNP) was performed within 2 weeks of clinical evaluation.
The role of nutritional status in influencing physical function and disability in patients affected by HF has been poorly investigated: only few data are available on the association with physical function [18,19], and only one study, with a relatively small sample size, was focused on older adults, documenting an association between malnutrition and a poorer physical function in patients admitted to rehabilitation for HF [20]. To the best of our knowledge, no studies focused on older adults affected by stable chronic HF or on the relationship between nutritional status and disability are available. Furthermore, to the best of our knowledge, no study investigated the role of caloric intake and reduced appendicular skeletal muscle mass (ASMMI), two of the most important factors implicated in the association between malnutrition and physical function [21–23], in influencing this relationship. The objective of this study was to analyse the association between nutritional status and physical function and disability in older adults affected by chronic HF and to analyse the role of caloric intake and ASMMI in influencing these relationships.
2.3. Statistical analysis The characteristics of the study sample were reported using descriptive statistics (mean and standard deviation or proportions, as appropriate), according to MNA classes (Malnourished: <17 points; At risk of malnutrition: 17–23.5 points; Well nourished: 24–30 points). Only one patient was categorized as malnourished and was thus excluded from the analysis. The MNA was analysed both as continuous and categorical variable (at risk of malnutrition/well-nourished, using well-nourished as the reference group). Visual inspection of the MNA distribution revealed a departure from normality. Thus, the association between nutritional status and physical function (gait speed) was evaluated using the Spearman's rank correlation test and regression models. The goodness of fit of a linear and non-linear (squared) regression model was evaluated analysing the adjusted R-squared and the residuals plots. The linear model provided a satisfactory fit and was used for the analysis. The evaluation of diagnostic plots did not reveal violation of the assumptions; in particular, we found no trend of residuals over fitted values, homoskedasticity, and normal distribution of the residuals. The model was then adjusted for potential confounders, selected on the basis of clinical significance, prior knowledge, and results of the univariate analysis (age, sex, ejection fraction, GDS). At a second step the model was adjusted for ASMMI and daily caloric intake indexed for ideal body weight (calculated using the Lorenz formula [36]). The association between nutritional status and disability was evaluated using the chi-squared test and logistic regression models, both crude and adjusted for potential confounders. All the analyses were performed using R version 3.5.0 (R Foundation for Statistical Computing, Vienna, Austria).
2. Material and methods 2.1. Study design and setting In this cross-sectional study, we enrolled patients aged 65 years or older affected by HF in NYHA class II-IV, attending an outpatient HF clinic at S. Camillo-Forlanini Hospital in Rome between January 2016 and September 2018. We excluded patients with acute cardiovascular events (history of myocardial infarction or acute HF in the previous month), severe dementia, visual or hearing impairment or severe functional limitation (not able to walk 4 meters). We also excluded participants affected by chronic kidney failure requiring hemo-dialysis, active cancer, not compensated thyroid disease, terminal diseases (life expectancy < 1 year) or malabsorption diseases. The study was performed according to the World Medical Association Declaration of Helsinki and was approved by the Ethic Committee of the S. CamilloForlanini Hospital (1578/CE Lazio 1). 2.2. Measurements All participants performed a cardiological visit, during which cardiological history, comorbidities, and pharmacological therapy were collected. An echocardiogram was performed by a trained cardiologist with a Philips IE 33; left ventricular ejection fraction (4 chambers Simpson method) and filling pressures (E/E’ ratio), tricuspid annular plane systolic excursion (TAPSE), chambers dimensions, evidences of valvular defects and inferior cava vein collapsibility were systematically evaluated. Participants underwent also a geriatric multidimensional evaluation, including assessment of cognitive function (Montreal Cognitive Assessment [24]), mood (Geriatric Depression Scale [25]), quality of life (EuroQOL [26]), and disability (Basic Activities of Daily Living [ADL] [27] and Instrumental Activities of Daily Living [IADL] [28]). Disability was defined as a reduction of at least one point in ADL or in IADL score and was analyzed separately for basic and instrumental activities of daily living [29]. Nutritional status was evaluated using the Mini Nutritional Assessment (MNA), a multidimensional evaluation tool approved by the American Society for Parenteral and Enteral Nutrition and validated in older adults [30]. The dietary intake was evaluated using the EPIC questionnaire [31], that investigates intake frequency over the previous year of 236 specific foods, along with the average size of the servings, selected from a range as shown in photographs. The information derived from the questionnaire was automatically converted into data on energy, micro- and macronutrient intake by a specifically designed software. The EPIC nutritional assessment has been successfully validated in an older adult population [32]. The body composition (free fat
3. Results 3.1. General characteristics of the population The mean age of the 88 study participants at baseline was 77.8 years (SD 7.1); 73% were male. The mean ejection fraction was 40.6% (SD 10.3); 72.7% of the participants was in NYHA class II and 27.3% in NYHA class III; no participants were classified in NYHA class IV. Fiftynine participants were classified according to MNA as WN and 29 as RM. There were no differences in age and comorbidities between the groups, except for anaemia, that was more prevalent in the RM group (31% vs 10%, P = 0.032), and COPD, that was more prevalent in the WN group (31% vs. 7%, P = 0.027). RM participants were less frequently male (52% vs 83%, P = 0.004). There were no differences between groups in BNP serum concentration (WN: 276.8 pg/ml, SD 360.3; RM: 274.3 pg/ml, SD 239; P = 0.973), nor in pharmacological therapy. Daily energy and protein intake standardized for the ideal body weight did not differed between the groups. The RM group had a lower BMI (24.3 kg/m2, SD 2.9 vs 26.2 kg/m2, SD 3.6, P = 0.010), fat free mass index (18.4 kg/m2, SD 1.9 vs 20.5 kg/m2, SD 2.4; P < 0.001) and appendicular skeletal muscle mass index (6.7 kg/m2, SD 0.8 vs 8.1 kg/m2, SD 2.9, P = 0.002), while there was no difference in fat mass index. RM participants were more frequently in NYHA class III (41.4% vs 20.3%, P = 0.067). There were no differences between 2
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Table 1 General characteristic of the population according to nutritional status.
Age (years) Male sex (%) Hypertension (%) Type II diabetes (%) Anemia (%) Atrial fibrillation (%) IHD (%) Dyslipidemia (%) Stroke (%) COPD (%) ACE-inhibitors or ARBs (%) β-blockers Antiplatelet agents Pump proton inhibitors Nitrates NAO (%) Warfarin Creatinine (mg/dl) eGFR (CKD-EPI, mL/min/1.73 mq) BNP (pg/ml) Total cholesterol (mmol/l) Kcal/ideal weight (kcal/kg) Proteins/ideal weight (g/kg) Fat mass index (kg/m2) Fat free mass index (kg/m2) Appendicular skeletal muscle mass index (kg/m2) BMI (kg/m2) NYHA class II (%) NYHA class III (%) Ejection fraction Medium E/E’ ratio TAPSE (mm) sPAP (mmHg) MOCA GDS EuroQOL Gait speed (m/s) Reduced ADL (%) Reduced IADL (%)
Well nourishedN: 59
At risk of malnutritionN: 29
AllN: 88
P-value
77.5 (6.9) 83 59 25 10 39 58 45 2 31 76 88 54 70 15 15 44 1.3 (0.4) 55.7 (17.9) 276.8 (360.3) 3.95 (1.04) 29.2 (9.9) 1.1 (0.3) 5.7 (2.5) 20.5 (2.4) 8.1 (2.9) 26.2 (3.6) 79.7 20.3 41.1 (10) 13.7 (7.7) 18.7 (3.5) 38 (9.8) 20.7 (4.6) 3.6 (3.2) 64.5 (17) 0.99 (0.26) 16.1 39.3
78.5 (7.7) 52 69 24 31 38 59 35 3 7 69 97 52 66 10 28 21 1.2 (0.6) 59.3 (23.7) 274.3 (239) 4.3 (1.06) 27.5 (6.1) 1.2 (0.3) 6.1 (2.6) 18.4 (1.9) 6.7 (0.8) 24.3 (2.9) 58.6 41.4 39.5 (10.9) 16.9 (9.8) 20 (4.7) 39.5 (11.9) 19 (5.6) 5.8 (3.9) 63.8 (19.1) 0.82 (0.25) 42.9 57.1
77.8 (7.1) 73 62 25 17 39 58 41 2 23 74 91 53 68 14 19 36 1.3 (0.5) 56.9 (19.9) 276 (325.2) 4.07 (1.06) 28.6 (8.8) 1.1 (0.3) 5.8 (2.5) 19.9 (2.5) 7.6 (2.5) 25.6 (3.5) 72.7 27.3 40.6 (10.3) 14.7 (8.4) 19.1 (3.9) 38.5 (10.5) 20.2 (5) 4.3 (3.6) 64.2 (17.6) 0.93 (0.27) 25 45.2
0.553 0.004 0.520 1 0.032 1 1 0.556 1 0.027 0.635 0.370 1 0.894 0.764 0.276 0.056 0.452 0.483 0.973 0.174 0.314 0.451 0.459 <0.001 0.002 0.010 0.067 0.500 0.192 0.206 0.588 0.221 0.015 0.868 0.006 0.016 0.188
Abbreviations: IHD: ischemic heart disease; COPD: chronic obstructive pulmonary disease; eGFR: estimated glomerular filtration rate; ARBs: angiotensin II receptor blockers; BNP: brain natriuretic peptide; BMI: body mass index; NYHA: New York Health Association; TAPSE: tricuspid annular plane systolic excursion; sPAP: systolic pulmonary artery pressure; MOCA: Montreal Cognitive Assessment; GDS: Geriatric Depression Scale; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living.
compared to the WN group (42.9% vs 16.1%, P = 0.016) (Table 1, Fig. 3); this result was confirmed by a logistic regression model adjusted for potential confounders (OR 3.36, 95% CI 1.07–10.97). Similar results, albeit with wider confidence intervals, were found in models adjusted for ASMMI and caloric intake (OR 3.33, 95% CI 0.92–12.54) (Table 3). Analyzing the MNA as a continuous variable, each 1-point increase in the MNA score was associated with a 25% reduction of the odds for ADL impairment (95% CI 0.62–0.88). These results were confirmed after adjustment for potential confounders (OR 0.79, 95% CI 0.64–0.95), and the results did not change including in the model also ASMMI and caloric intake (OR 0.80, 95% CI 0.64–0.98) (Table 3). There were no differences between groups in proportion of patients with IADL impairment (Table 1, Fig. 3). These results were confirmed also after adjustment for potential confounders and analysing the MNA as a continuous variable (Table 3).
groups in mean ejection fraction (RM: 39.5%, SD 10.9; WM: 41.1, SD 10; P = 0.500), nor in E/E’ ratio (RM: 16.9, SD 9.8; WN: 13.7, SD 7.7; P = 0.192) (Table 1). RM group had a higher GDS score (5.8, SD 3.9 vs 3.6, SD 3.2, P = 0.015), while there were no differences between groups in quality of life and cognitive function (Table 1). 3.2. Association between nutritional status and gait speed Respect to WN group, RM participants had a lower gait speed (0.82 vs 0.99 m/s, p = 0.006) (Table 1, Fig. 1). This finding was confirmed after adjustment for potential confounders, either excluding (adjusted β −0.122, P = 0.059), or including ASMMI and caloric intake (adjusted β −0.138, P = 0.055) (Table 2). Analysing the MNA as a continuous variable, it was positively associated with the gait speed (Rho: 0.272, P = 0.012) (Fig. 2). This association was confirmed after adjustment for potential confounders in a linear regression model (β 0.022, P = 0.035). Adjusting the model also for ASMMI and caloric intake, the regression coefficient did not change (β 0.022, P = 0.052) (Table 2).
4. Discussion Our sample of older adults affected by chronic HF was globally wellnourished according to both BMI and MNA. Our main finding is that, even in the absence of evident nutritional problems, people at risk of malnutrition had a poorer physical function and a higher risk of impairment in basic activities of daily living, independently of muscular
3.3. Association between nutritional status and disability The prevalence of ADL impairment was higher in the RM group 3
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Fig. 1. Nutritional status and physical performance – Patients at risk of malnutrition had a worse physical performance with respect to well-nourished. Table 2 Linear regression models of the association between nutritional status and gait speed. MNA categorical (Reference: well-nourished) β Crude (Pvalue) At risk of malnutrition Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight
−0.167 (0.005)) – – – – – –
MNA continuous (1-point increase) β Crude (Pvalue) MNA Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight
0.027 (0.007) – – – – – –
β Adjusted 1 (Pvalue)
β Adjusted 2 (Pvalue)
−0.122 (0.059)
−0.138 (0.055)
−0.005 (0.183) 0.108 (0.143) −0.001 (0.712) −0.006 (0.478) – –
−0.006 (0.194) 0.179 (0.065) 0.0001 (0.962) −0.003 (0.746) −0.023 (0.151) 0.002 (0.600)
β Adjusted 1 (Pvalue)
β Adjusted 2 (Pvalue)
0.022 (0.035) −0.005 (0.201) 0.102 (0.171) −0.001 (0.642) −0.006 (0.537) – –
0.022 (0.052) −0.005 (0.219) 0.114 (0.181) −0.001 (0.828) −0.004 (0.669) −0.012 (0.294) 0.0004 (0.902)
Abbreviations: GDS: geriatric depression scale; ASMMI: appendicular skeletal muscle mass index.
Fig. 2. Association between Mini Nutritional Assessment score and physical performance – Mini Nutritional Assessment score was positively associated with gait speed.
mass and caloric intake. The association between poor nutritional status and reduced physical function in people with HF has been previously reported, but only in patients hospitalized for rehabilitation [19,20] or for a primary diagnosis of HF with preserved ejection fraction [18]: in a sample of 145 patients with age ≥65 years old admitted for cardiologic rehabilitation, Katano et al. found that malnutrition, diagnosed using the MNA short form (MNA-SF), was an independent predictor of functional dependence at discharge, evaluated using the Barthel index [20]. In a similar setting, in a sample of 105 patients with a mean age of about 73 years, Matsuo et al. documented that MNA-SF was linearly associated with the Barthel index [19]. These results were confirmed also in patients
affected by HF with preserved ejection: moderate or severe nutritional risk, evaluated using the Geriatric Nutrition Risk Index (GNRI), was associated with a lower Barthel index at discharge (results not adjusted for potential confounders) [18]. However, only the study by Katano et al was focused on older adults, and the association between nutritional status and physical function in older adults affected by chronic HF has not been previously investigated. Our results are in line with the above-mentioned studies and extend their results: we confirmed the previously described association 4
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between MNA score and physical function in older adults affected by HF, and we found that in this population not only malnutrition (as previously reported) but also the risk of malnutrition is associated with a poorer physical function respect to well-nutrition. Furthermore, for the first time in this population, we documented that nutritional status is associated with ADL impairment. The lack of association with IADL impairment found in our study could be explained by the high baseline risk of IADL impairment in this population; thus, the additional effect of nutritional status on this outcome could not be caught in these patients. Finally, our study analysed the role of energy intake and appendicular skeletal muscle mass index in influencing the association between nutritional status and physical function. Interestingly, differently from what would have been expected, nor energy intake, nor appendicular skeletal muscle mass significantly influenced this association. The explanation of these results could lie in the fact that MNA is a multidimensional tool, that take into account not only the dietary intake and the BMI, but also other factors, such as polypharmacy, mood, and cognitive impairment [30]. Compared to other nutritional tools, this approach could be more effective in older adults, that are characterized by comorbidities, polypharmacy and higher risk of disability, and in whom the association between nutritional status and physical function may not be exclusively related to a reduced energy intake or reduced muscle mass, but also to multiple other factors, such as social factors, polypharmacy, recent acute events, and depression, that together contribute to the outcome. Our results must be interpreted taking into account some limitations. Being a cross-sectional study, reverse causation (i.e., functional status influences nutritional status, and not the other way around) cannot be ruled out. In fact, nutritional status is one of the multiple elements that influence the onset and the course of a functional disability; on the other side, disability itself may contribute to malnutrition onset and worsening. However, the bulk of the available evidence documents that nutritional status influences both physical function and disability [37]. Furthermore, the sample size is relatively small, and the study involved only one centre, thus potentially impacting on the robustness of the study, although the sample and study design are similar to that of most of the studies on this topic. Third, malnourished patients were not represented in our population and thus were not studied. However, considering that risk of malnutrition is considered an intermediate condition between well-nutrition and malnutrition, we expect that these patients would have had a stronger association with our outcomes of interest. Fourth, physical function was evaluated exclusively with gait speed; nevertheless, it has the advance of being quick and predictive of health-related outcomes in older adults [33,34]. Finally, the information on orthopaedic disorders, that could potentially influence the gait speed, was not available; however, no patients needed assistance in performing the gait speed test.
Fig. 3. Nutritional status and disability – Patients at risk of malnutrition had a higher prevalence of ADL impairment while the prevalence of IADL impairment did not change between the groups. Table 3 Regression models of the association between nutritional status (MNA) and disability. ADL impairment MNA categorical (Reference: well-nourished) OR Crude(95% CI) MNA
3.80 (1.42–10.66) – – – – – –
Age Sex Ejection fraction GDS ASMMI Caloric intake/ideal body weight MNA continuous (1-point increase) OR Crude (95% CI) MNA 0.75 (0.62-0.88) Age – Sex – Ejection fraction – GDS – ASMMI – – Caloric intake/ideal body weight IADL impairment MNA categorical (Reference: well-nourished) OR Crude (95% CI) At risk of malnutrition 1.67 (0.69–4.09) Age – Sex – Ejection fraction – GDS – ASMMI – Caloric intake/ideal – body weight MNA continuous (1-point increase) OR Crude (95% CI) MNA 0.92 (0.79-1.06) Age – Sex – Ejection fraction – GDS – ASMMI – Caloric intake/ideal – body weight
OR Adjusted 1(95%CI)
OR Adjusted 2(95% CI)
3.36 (1.07–10.97) 1.04 (0.97–1.12) 0.66 (0.19–2.31) 1.02 (0.97–1.07) 1.15 (0.99–1.34) – –
3.33 (0.92–12.54) 1.04 (0.95–1.15) 0.66 (0.12–4.98) 1.02 (0.95–1.09) 1.09 (0.91–1.33) 0.77 (0.32–1.07) 0.91 (0.81–1)
OR Adjusted 1 (95%CI) 0.79 (0.64–0.95) 1.05 (0.96–1.14) 0.33 (0.08–1.30) 0.99 (0.93–1.05) 1.03 (0.86–1.22) – –
OR Adjusted 2 (95% CI) 0.80 (0.64–0.98) 1.05 (0.96–1.17) 0.45 (0.08–3.47) 1.01 (0.94–1.08) 1.05 (0.86–1.28) 0.79 (0.33–1.06) 0.92 (0.83–1.01)
OR Adjusted 1 (95%CI) 1.45 (0.48–4.35)
OR Adjusted 2 (95% CI) 1.80 (0.55–5.89)
1.05 0.59 1.02 1.13 – –
1.04 0.42 1.01 1.06 1.09 0.97
(0.98–1.12) (0.17–2.02) (0.97–1.07) (0.97–1.31)
OR Adjusted 1 (95%CI) 0.97 (0.81–1.16) 1.04 (0.97–1.12) 0.66 (0.19–2.31) 1.02 (0.97–1.07) 1.15 (0.99–1.34) – –
(0.97–1.12) (0.09–1.65) (0.96–1.07) (0.91–1.24) (0.87–1.84) (0.91–1.03)
5. Conclusions Our results suggest that an early assessment of nutritional status using the MNA should be performed in older adults affected by CHF to identify patients at higher risk of poorer physical function and disability. The importance of our results lies in the fact that we showed that a poorer nutritional status is associated with poor physical function and disability, which are in turn associated with negative outcomes such as hospitalizations [6] and death [7] and have a significant impact on quality of life of these patients [5]. Longitudinal studies are needed to confirm these results and intervention trials should investigate whether multidimensional approach for the treatment of malnutrition and risk of malnutrition including nutritional supplementation can improve physical function and reduce the risk of disability in this population.
OR Adjusted 2 (95% CI) 0.94 (0.78–1.13) 1.04 (0.97–1.12) 0.46 (0.10–1.82) 1.01 (0.96–1.07) 1.08 (0.92–1.27) 1.08 (0.87–1.80) 0.97 (0.91–1.03)
Abbreviations: GDS: geriatric depression scale; ASMMI: appendicular skeletal muscle mass index.
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Funding [15]
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
[16]
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Acknowledgments We would like to acknowledge dr. Ilaria Aquilea and dr. Wanda Rizza for their support in the enrollment of the patients.
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Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejim.2019.12.007.
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