Next Article in Journal
Vitamin D—An Effective Antioxidant in an Animal Model of Progressive Multiple Sclerosis
Previous Article in Journal
Nutritional Supplement Use in a UK High-Performance Swimming Club
Previous Article in Special Issue
Dietary Diversity and Inflammatory Diet Associated with All-Cause Mortality and Incidence and Mortality of Type 2 Diabetes: Two Prospective Cohort Studies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 15
Issue 15
10.3390/nu15153307
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Are Energy and Protein Intakes Lower Than Requirements in Older Adults? An Urgent Issue in Hospitals and Nursing Homes

by
Marie Blanquet
1,2,3,*,
Candy Guiguet-Auclair
2,3,
Pauline Berland
2,3,
Guillaume Ducher
4,
Anaïs Sauvage
5,
Sylvain Dadet
6,
Vincent Guiyedi
1,
Nicolas Farigon
7,
Jérôme Bohatier
8,
Laurent Gerbaud
2,3 and
Yves Boirie
7,9
1
Unité de Recherche Clinique, CH Mauriac, 15200 Mauriac, France
2
CNRS, Institut Pascal, Université Clermont Auvergne, CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France
3
Service de Santé Publique, CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France
4
Gériatrie, Clinique La Châtaigneraie, 63110 Beaumont, France
5
Recherche et Développement, Nutriset, 76770 Malaunay, France
6
Médecine, CH Brioude, 43100 Brioude, France
7
Nutrition Clinique, CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France
8
Gériatrie, CH Riom, 63000 Clermont-Ferrand, France
9
Unité de Nutrition Humaine, Université Clermont Auvergne, INRA, UMR 1019, 63000 Clermont-Ferrand, France
*
Author to whom correspondence should be addressed.
Nutrients 2023, 15(15), 3307; https://doi.org/10.3390/nu15153307
Submission received: 30 June 2023 / Revised: 23 July 2023 / Accepted: 24 July 2023 / Published: 26 July 2023
(This article belongs to the Special Issue Sarcopenia, Diabetes and Nutrition: Recent Findings)
Browse Figures
Versions Notes

Abstract

:
Energy and protein intakes lower than requirements are associated with worsening health outcomes. Here we set out to evaluate gaps between energy and protein intakes and requirements in older adults in hospitals and in nursing homes (NH). A cross-sectional study included 360 inpatients and residents aged 75 years and older in two acute care wards; i.e., a multidisciplinary care unit (MCU) and a geriatric care unit (GCU), a geriatric rehabilitation unit (GRU), and two NH. Intakes were measured for three days. Requirements were based on French National Health Authority recommendations. Energy and protein intakes were under the minimum requirement of 30 kcal/kg/day and 1.2 g/kg/day in 89.5% and 100% of MCU patients, respectively, 75.5% and 64.2% of GCU patients, 92.7% and 90.9% of GRU patients, and 83.8% and 83.8 of NH residents. Intake-to-requirement gaps were not significantly associated with malnutrition, except in the GCU group where non-malnourished patients had higher energy gaps than malnourished patients. Intakes fell dramatically short of requirements in older adults in both hospital and NH settings irrespective of malnutrition status. A new paradigm based on a patient-centered approach should be developed to adapt meals served in hospital and in NH.

1. Introduction

Nutritional intake in older adults is a major issue that healthcare professionals should be systematically addressing whatever the patient’s setting, whether at home, in hospital, or in nursing homes (NH) care [1,2,3]. Energy and protein intakes lower than requirements are associated with worsening health outcomes such as frailty, impaired muscle function, healthcare-associated infections, mortality, longer hospital stays, and frequent readmissions [4,5,6,7,8,9,10,11,12].
Studies have measured food intakes in patients hospitalized in acute care wards, but rarely in rehabilitation units or NH and specifically in older adults [11,13,14,15,16,17,18,19,20,21]. Most of these studies estimated food intake based on percentage intake of meal served (0%, 25%, 50%, 75%, or 100%) or on the larger threshold of meal served (<100%, <75% or ≤50%). Three studies calculated energy intake (kcal) and protein intake (g) based on food consumed and compared the results against inpatient requirements [13,14,17]. In these three studies, two were located in the same hospital, using the same structure survey protocol at two different times (1999 and 2008) [13,14]. They aimed to measure the effect of a nutrition permanent improvement process launch in the hospital by measuring 24 hours food intakes and requirements [13, 21]. Measurements were performed by dieticians [14]. Sanson et al. used photography to assess intakes and estimated requirements in older acute-care patients (81.5 ± 11.5 years old) over the first 5 days of a hospital stay [17]. Two further studies estimated intakes based on patient interviews and calculated the proportion of patients that failed to meet their energy and/or protein requirements: Beavan et al. [15] studied patients with a median age of 72 years (range 22–98) in 25 medical and surgical wards, and Vasse et al. [19] measured protein intakes in acute medicine or surgical wards in patients aged at least 65 years old (mean age was 77.4 ± 5.6 in the low-malnutrition-risk group and 78.7 ± 6.4 in the medium/high-risk group).
However, to our knowledge, no study has calculated the gaps between energy/protein intakes and requirements in a geriatric population hospitalized in acute-care and rehabilitation wards and in NH. Previous studies have analyzed factors associated with low intakes, but not the extent of the gaps.
Here, to address this issue, we evaluated energy and protein gaps between intakes and requirements in older adults hospitalized in acute and rehabilitation wards or living in NH with long-term care facilities, in order to compare intake-to-requirement gaps according to malnutrition status, and to identify others factors associated with these gaps.

2. Materials and Methods

2.1. Study Design

A multidepartment cross-sectional study was conducted in the Auvergne region (south-central France) between September 2017 and December 2018 [22]. The units concerned were community hospital units—a multidisciplinary care unit (MCU) and a geriatric rehabilitation unit (GRU)—and a university hospital geriatric care unit (GCU). We also included each NH that had long-term care facilities at these two hospitals. The study was proposed to MCU, GCU, and GRU inpatients during the first 3 days of their hospital stay and to all NH residents. Readmitted patients could not be included a second time around. The Confusion Assessment Method was used to identify confused participants (inpatients or residents), and if any participant showed an acutely confused state or a medical history of dementia, then the study was presented to their primary care person. The primary care person was declared by the patient at the admission in hospital care wards or to the NH. If the patient was unable to declare a primary care person, healthcare professionals checked whether the care person declared at the most recent previous hospitalization remained unchanged. Inpatient data were collected during the first 7 days of the hospital stay, and NH resident data were collected during the 14 days following weight measurement. The study was registered at Clinical Trials.gov under number NCT03196622.

2.2. Participants

Inpatients and NH residents aged 75 years old and above were eligible for inclusion. Exclusion criteria were older adults in end-of-life care (death expected within the next 72 h), inappropriate medical situation (no expected health improvement through monitoring nutritional intakes, as the patients ate for pleasure), inpatient or resident refusal to participate, or refusal of the primary care person to participate if the inpatient or resident was found to show confusion or dementia, vulnerable older adults under guardianship, and older adults fed exclusively via percutaneous endoscopic gastrostomy.

2.3. Nutritional Intakes

Participant nutritional intakes were measured for 3 days as recommended by French guidelines [23]. The care assistant recorded the quantity eaten as 0%, 25%, 50%, 75%, or 100% per dish and per meal for breakfast, lunch, and dinner, plus any between-meal oral nutrition support (ONS) snacks. Three dieticians then calculated energy intakes in kilocalories (kcal) and protein intakes in grams (g) per day for each inpatient and resident that had a complete set of nutritional data. Inpatient energy and protein requirements were calculated based on the French National Health Authority guidelines; i.e., 30–40 kcal/kg/day for energy and 1.2–1.5 g/kg/day for protein [24]. Adjusted weight was calculated for body mass index (BMI) above 30 kg/m2 as adjusted weight = ideal weight (kg) + 0.25 [measured weight (kg) − ideal weight (kg)], where ideal weight (kg) = height (cm) − 100 − ([height (cm) − 150/] n), with n = 2.5 in women and n = 4 in men.

2.4. Malnutrition Diagnosis

The diagnosis of malnutrition was established according to the updated French National Health Authority guidelines that integrate the Global Leadership Initiative on Malnutrition (GLIM) criteria [25,26]. Participants were classified as malnourished if they met at least one phenotypic criterion and at least one etiological criterion. The phenotypic criteria were: unintentional weight loss (>5% in 1 month or ≥10% in 6 months), low BMI (<22 kg/m2), and low muscle mass corresponding to Fat-Free Mass Index (FFMI) < 15 kg/m2 in women and <17 kg/m2 in men. The etiological criteria were reduced food intake or assimilation (≤50% of energy requirements > 1 week, or any food intake reduction for >2 weeks, or any chronic gastrointestinal condition that adversely impacted food assimilation or absorption), or inflammation (related to acute disease/injury or chronic disease).
We considered that all older MCU, GCU, and GRU patients met the etiological criterion for inflammation, as they were all hospitalized for acute disease related or not to a chronic disease. We considered that older adults in NH who had a score of 3 or 4 on one of the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) [27] organ-system categories (excluding the “eyes, ears, nose, throat, and larynx” category) met the etiological criterion for inflammation.
Severity of malnutrition was graded as either stage 1—moderate malnutrition, or stage 2—severe malnutrition, based on the following phenotypic criteria: weight loss (≥10% in 1 month or ≥15% in 6 months), low BMI (<20 kg/m2), albuminemia (≤30 g/L).

2.5. Measurements

Sociodemographic data were collected on age, gender, living situation (at home, in a NH, other), and life situation (alone, with partner, with a family member, other).
Anthropometric measurements for each participant included usual weight (kg), weight at admission (kg), height (m), and BMI (kg/m2). Usual weight was the patient’s regular weight when at home, and weight at admission was the weight measured by healthcare professionals on admission to care. Muscle strength was measured by handgrip strength (HGS) on a Jamar® hydraulic hand dynamometer in sitting position following the Southampton protocol [28]. Muscle mass was estimated by FFMI based on bioelectrical-impedance analysis (BIA) using a Bodystat® 1500 analyzer. Sarcopenia was defined according to the 2019 European Working Group on Sarcopenia in Older People (EWGSOP) revised criteria [29]. Probable sarcopenia was defined as low muscle strength, and sarcopenia was confirmed as low muscle strength combined with low muscle mass. The thresholds for low muscle strength were HGS < 16 kg for women and <27 kg for men.
Oral daily medications were recorded by pharmaceutical molecule.
Comorbidity was measured using the CIRS-G [27], which assesses comorbidity through 14 organ system categories on a severity scale, scored as follows: no problem (0), current mild problem or past significant problem (1), moderate disability or morbidity/requiring ‘first-line’ therapy (2), severe/constant significant disability/uncontrollable chronic problems (3), extremely severe/immediate treatment required/end-organ failure/severe functional impairments (4). The total CIRS-G score is the sum of each of the 14 individual organ system scores, ranging from 0 to 56. Severity index is the mean of the scores of the first 13 categories, excluding psychiatric impairment. Comorbidity index is the number of categories with a score of 2 or higher, including psychiatric impairment.
The Katz Activities of Daily Living (ADL) scale [30] and the Lawton Instrumental Activities of Daily Living (IADL) scale [31] were used to measure inpatient functional ability. The IADL scale was not used in NH residents. The ADL scale explores six dimensions scored as 0 (unable), 0.5 (partially able), or 1 (able); i.e., for bathing, dressing, toileting, transferring, feeding, and continence, giving a total score from 0 to 6. The IADL scale measures 8 dimensions scored as 0 (unable) or 1 (able); i.e., for telephoning, shopping, cooking, housekeeping, laundry, transportation, managing medications, and managing finances, giving a total score from 0 to 8. For the IADL measure, a dimension was scored if the person regularly performed the activity in the past as a common part of their daily activity.
C-reactive protein (CRP) was measured, and CRP > 5 mg/L defined inflammation.

2.6. Statistical Analysis

Qualitative variables were expressed as frequencies and percentages. Continuous variables were expressed as median with interquartile range (IQR).
Analyses were conducted separately in each group of patients; i.e., hospitalized in MCU, GCU, or GRU, or NH residents. We chose to differentiate patients in MCU and GCU, as healthcare providers in GCUs are trained in good clinical practices for geriatric care. Moreover, patients admitted into a GCU have more comorbidities than patients admitted into an MCU.
Descriptive analyses were performed to assess sociodemographic and clinical characteristics. Energy and protein gaps were calculated as requirements minus intakes [25], with positive values indicating that intakes were lower than requirements. Protein intake was expressed in grams and as percentage of daily energy intake, which is the ratio of protein energy to total energy (P/E ratio). Intakes were compared to requirements using the Wilcoxon signed rank test. Intakes-to-requirements were compared according to malnutrition status using the non-parametric Mann–Whitney test. Spearman’s correlation coefficient was used to correlate intake-to-requirement gaps with age, BMI, daily oral medication, CIRS-G scores, ADL scores, and IADL scores. Energy and protein gaps were compared according to gender, life situation, inflammation, and sarcopenia, using the non-parametric Mann–Whitney test.
Two-sided p-values < 0.05 were considered statistically significant. Statistical analyses were performed using SAS statistics software package v 9.4 (SAS Institute Inc., Cary, NC, USA).

3. Results

3.1. Partcipant Characteristics

The study included a total of 360 participants: 104 in the MCU group (women: 56.3%), 119 in the GCU group (women: 76.5%), 83 in the GRU group (women: 69.9%), and 54 in the NH group (women: 74.1%) (Table 1). The median participant age was 86.0 years (IQR 82.0–89.5) in the MCU group, 86.0 years (IQR 83.0–89.0) in the GCU group, 87.0 years (IQR 82.0–89.0) in the GRU group, and 88.0 years (IQR 85.0–93.0) in the NH group. Analysis of nutritional status found that more than 30% of all inpatients and NH residents were malnourished. Prevalence of malnutrition was 35.2% (95% CI: 25.4–45.0) in the MCU, 40.0% (95% CI: 30.8–49.2) in the GCU, 30.6% (95% CI: 19.9–41.2) in the GRU, and 31.3% (95% CI: 18.1–44.4) in the NH. Prevalence of severe malnutrition was 18.7% (95% CI: 10.7–26.7) in the MCU, 23.6% (95% CI: 15.7–31.6) in the GCU, 15.3% (95% CI: 7.0–23.6) in the GRU, and 4.2% (95% CI: 0.0–9.8) in the NH (Table 1).

3.2. Energy and Protein Intakes-to-Requirements

Complete daily energy and protein intake records were available for 36.5% of MCU patients (n = 38), 89.1% of GCU patients (n = 106), 66.3% of GRU patients (n = 55), and 68.5% of NH residents (n = 37). Participants with measured intakes did not have a significantly different malnutrition status to the other participants (p = 0.636 in the MCU group, p = 0.760 in the GCU group, p = 0.718 in the GRU, group, and p = 1.000 in the NH group). In the MCU group, patients with measured intakes needed more help with feeding than patients without measured intakes (36.4% vs. 13.3%, p = 0.010). In the GRU group, patients with measured intakes needed less help with feeding (16.4% vs. 46.4%, p = 0.003) and had more inflammation (78.2% vs. 55.6%, p = 0.034) than patients without measured intakes. GCU inpatients and NH residents with measured intakes were not significantly different to participants without measured intakes.
Daily energy and protein intakes of participants were compared to the minimum energy (Figure 1) and protein (Figure 2) requirements set out by French recommendations.
Energy intakes were lower than the minimum energy requirement of 30 kcal/kg/day in at least 75% of the total participant population; i.e., 89.5% of MCU patients, 75.5% of GCU patients, 92.7% of GRU patients, and 83.8% of NH residents. All participants in all settings had intakes under the maximum energy allowance of 40 kcal/kg/day, except in the GCU group (98.1% of patients). Energy intakes were significantly lower than the minimum requirements of 30 kcal/kg/day in all groups (p < 0.001) (Figure 1). Median gaps between daily energy intakes and requirements (based on a minimum energy requirement of 30 kcal/kg/day) were 680.0 kcal (IQR: 280.0–948.4) in the MCU group, 308.4 kcal (IQR: 6.4–655.0) in the GCU group, 536.5 kcal (IQR: 261.5–765.0) in the GRU group and 385.0 kcal (IQR: 125.7–755.1) in the NH group.
Protein intakes were lower than the minimum protein requirement of 1.2 g/kg/day in a vast majority of participants; i.e., 100% of MCU patients, 64.2% of GCU patients, 90.9% of GRU patients, and 83.8% of NH residents. All participants in all settings had intakes under the maximum protein requirement of 1.5 g/kg/day, except in the GCU group (84.0% of patients). Protein intakes were significantly lower than the minimum requirement of 1.2 g/kg/day in all groups (p < 0.001) (Figure 2). Median gaps between daily protein intakes and requirements (based on a minimum protein requirement of 1.2 g/kg/day) were 32.9 g (IQR: 18.4–42.4) in the MCU group, 6.0 g (IQR: −7.8–22.1) in the GCU group, 20.4 g (IQR: 9.9–31.8) in the GRU group, and 17.2 g (IQR: 5.7–31.4) in the NH group.
Median P/E ratios were 14.2% (IQR: 12.7–15.4) in the MCU group, 17.9% (IQR: 16.2–19.4) in the GCU group, 15.6% (IQR: 14.3–17.5) in the GRU group, and 15.6% (IQR: 13.1–17.0) in the NH group.

3.3. Intake-to-Requirement Gaps and Malnutrition

Energy intakes were significantly lower than the minimum requirement of 30 kcal/kg/day both for non-malnourished patients (p < 0.001 in MCU, GCU and GRU groups, and p = 0.006 in the NH group) and malnourished patients (p < 0.001 in the MCU group, p = 0.015 in the GCU group, p = 0.003 in the GRU group, and p = 0.002 in the NH group) (Figure 1). Energy gaps between intakes and requirements (based on a minimum energy requirement of 30 kcal/kg/day) were not significantly different between malnourished and non-malnourished participants, except in the GCU group (Figure 1 and Table 2). Non-malnourished patients in GCU had higher energy intake-to-requirement gaps than malnourished patients (p = 0.032) (Table 2). NH residents tended to have higher energy intake-to-requirement gaps if they were malnourished (p = 0.091).
Protein intakes were significantly lower than the minimum requirement of 1.2 g/kg/day for non-malnourished patients (p < 0.001 in all groups) (Figure 1). Protein intakes of malnourished patients were significantly lower than requirement, except in the GCU group (p = 0.663) (p < 0.001 in the MCU group, p = 0.007 in the GRU group, and p = 0.004 in the NH group) (Figure 1). Protein intake-to-requirement gaps (based on a minimum protein requirement of 1.2 g/kg/day) and P/E ratios were not significantly different between malnourished and well-nourished participants (Table 2).

3.4. Factors Associated with Energy and Protein Intake-to-Requirement Gaps

In the MCU group, energy intake-to-requirement gaps (based on a minimum energy requirement of 30 kcal/kg/day) were significantly higher in men and were associated with more comorbidities and a lower ADL score (Table 3). MCU patients with probable sarcopenia had significantly higher energy and protein intake-to-requirement gaps. Protein gaps tended to increase with BMI (p = 0.078), more comorbidities (p = 0.060 for CIRS-G total score), and inflammation (p = 0.071).
In the GCU group, energy and protein gaps were higher in men and in patients not living alone, but the difference was only significant (p < 0.05) for energy gaps (Table 4). GCU patients who needed help with feeding had significantly higher energy gaps. Patients with inflammation had significantly higher energy and protein gaps.
In the GRU group, higher energy and protein intake-to-requirement gaps were associated with higher CIRS-G total comorbidity score and severity index, but the difference was only significant (p < 0.05) for protein gap (Table 5). Energy and protein gaps increased with CIRS-G comorbidity index, without statistical significance. GRU patients with inflammation had significantly higher energy and protein intake-to-requirement gaps.
In the NH group, only lower daily functional ability in ADL was associated with higher energy and protein intake-to-requirement gaps, but the difference was only significant (p < 0.05) for energy gap (Table 6).

4. Discussion

This study found that older-adult energy and protein intakes systematically fell short of energy and protein requirements in both hospital and nursing-home settings. Malnutrition status was not associated with the extent of the energy/protein gaps, except in the GRU setting where energy gaps were higher in patients without malnutrition. The factors identified as associated with higher energy gaps were gender, lower daily functional ability, more comorbidities, inflammation, not living alone, needing help with feeding, and probable sarcopenia. The factors identified as associated with higher protein gaps were more comorbidities, inflammation, and probable sarcopenia.
Computed results based on the five studies that had previously estimated dietary provision of nutritional requirements found that 49% to 82.7% of patients had an energy gap and 37% to 74% of patients had a protein gap [13,14,15,17,19]. Our study revealed that intake-to-requirement gaps were higher in the older-adult population. Only one previous study identified factors associated with intake-to-requirement gaps in older adults [17]. It found that age, middle-upper arm circumference, total arm area, albumin, C-reactive protein (CRP), CRP/albumin ratio, and impaired self-feeding at admission were associated with energy intake-to-requirement gaps in acute wards only [17]. It also showed that age, middle-upper arm circumference, total arm area, albumin, C-reactive protein (CRP), lymphocytes, CRP/albumin ratio, and impaired self-feeding at admission were associated with protein intake-to-requirement gaps [17]. Here we identified two comparative factors; i.e., inflammation, and probable sarcopenia, which is close to middle-upper arm circumference as another way to evaluate muscle.
How can we increase intakes in hospitals and in NH? Protein and energy supplementation could improve intakes [32,33]. Several studies have proposed to address malnutrition through various strategies, such as implementing volunteer mealtime assistants [34,35,36,37], improved meal presentation [38,39], room service [40], electronic beside meal ordering [41,42], fortified-food meals [32], or managing the barriers to food intake interruptions at meals [43]. The European Society for Clinical Nutrition and Metabolism (ESPEN) has issued recommendations advocating better integration of aging-related needs in hospitalization, including systematic screening and earlier management of malnutrition, performing training exercises at the beginning of the hospital stay, and better preparing patient discharge to avoid premature readmissions [2].
A new paradigm is needed concerning nutritional care in hospitals and NH. The current nutritional care paradigm is based on one portion of standardized enriched and high-protein meal, irrespective of the patient’s characteristics. First, all older adults should benefit from enriched meals straight from admission to hospital, as they have a high prevalence of malnutrition and their bodies have to contend with acute disease. Second, meal portions should also be adapted. For example, a malnourished older adult will receive greater benefit from a half portion of a high-protein meal than a full portion of an enriched meal, due to their low BMI and low appetite. A patient-centered approach should be developed based on calculating the patient’s specific health status-related requirements (age, malnutrition, and sarcopenia status, obesity, appetite, chronic pathologies, pressure ulcer). Moreover, energy and protein intakes should be monitored precisely and daily, using a computerized device that can alert staff to situations where intakes fall short of requirements.
This study, performed in a multidepartment setting that spanned the continuum of older adult hospitalization, acute care, and rehabilitation wards, as well as a non-hospital living situation, had some limitations. Nutritional intakes were not measured in the whole sample, notably in the MCU. However, participants with measured intakes were not significantly different from other participants in terms of malnutrition status. Second, intakes were measured at the beginning of the hospital stay and during the next 3 days, which corresponded to the worst health state and therefore could have led to overestimate the gaps between intakes and requirements. Intakes should be measured throughout the hospital stay, which could be done using electronic devices.

5. Conclusions

Energy and protein intakes fell dramatically short of energy and protein requirements in older adult hospitalization settings and nursing homes irrespective of malnutrition status. Long-term monitoring of inpatient and resident intakes should be performed to make healthcare professionals more systematically aware of malnutrition issues. Moreover, a new paradigm based on a patient-centered approach should be developed in order to adapt meals served meals to inpatients’ and residents’ physical characteristics and/or food routines so as to prevent or at least not worsen malnutrition.

Author Contributions

Conceptualization, M.B., N.F. and Y.B.; methodology, M.B., L.G. and Y.B.; formal analysis, C.G.-A. and P.B.; investigation, M.B., G.D., S.D., V.G. and J.B.; data curation, C.G.-A. and P.B.; writing—original draft preparation, M.B. and C.G.-A.; writing—review and editing, M.B., C.G.-A., P.B. and Y.B.; visualization, M.B., C.G.-A. and P.B.; supervision, M.B., L.G., and Y.B.; project administration, M.B.; funding acquisition, M.B. and A.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by NUTRISET and NUTRICIA.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Clermont-Ferrand University Hospital (Comité de Protection des Personnes Sud-Est VI, AU 1289, 21 March 2017).

Informed Consent Statement

Written informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The research team thanks the paramedical and medical staff of the two acute care wards, the geriatric rehabilitation unit, and the two nursing homes.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Boirie, Y.; Morio, B.; Caumon, E.; Cano, N.J. Nutrition and protein energy homeostasis in elderly. Mech. Ageing Dev. 2014, 136–137, 76–84. [Google Scholar] [CrossRef]
  2. Volkert, D.; Beck, A.M.; Cederholm, T.; Cruz-Jentoft, A.; Hooper, L.; Kiesswetter, E.; Maggio, M.; Raynaud-Simon, A.; Sieber, C.; Sobotka, L.; et al. ESPEN practical guideline: Clinical nutrition and hydration in geriatrics. Clin. Nutr. 2022, 41, 958–989. [Google Scholar] [CrossRef]
  3. Norman, K.; Hab, U.; Pirlich, M. Malnutrition in older adults—Recent advances and remaining challenges. Nutrients 2021, 13, 2764. [Google Scholar] [CrossRef]
  4. Agarwal, E.; Ferguson, M.; Banks, M.; Batterham, M.; Bauer, J.; Capra, S.; Isenring, E. Malnutrition and poor food intake are associated with prolonged hospital stay, frequent readmissions, and greater in-hospital mortality: Results from the Nutrition Care Day Survey 2010. Clin. Nutr. 2013, 32, 737–745. [Google Scholar] [CrossRef] [Green Version]
  5. Agarwal, E.; Ferguson, M.; Banks, M.; Vivanti, A.; Batterham, M.; Bauer, J.; Capra, S.; Isenring, E. Malnutrition, poor food intake, and adverse healthcare outcomes in non-critically ill obese acute care hospital patients. Clin. Nutr. 2019, 38, 759–766. [Google Scholar] [CrossRef] [Green Version]
  6. Lorenzo-López, L.; Maseda, A.; de Labra, C.; Regueiro-Folgueira, L.; Rodríguez-Villamil, J.L.; Millán-Calenti, J.C. Nutritional determinants of frailty in older adults: A systematic review. BMC Geriatr. 2017, 17, 108. [Google Scholar] [CrossRef] [Green Version]
  7. Coelho-Júnior, H.J.; Rodrigues, B.; Uchida, M.; Marzetti, E. Low protein intake is associated with frailty in older adults: A Systematic Review and Meta-Analysis of Observational Studies. Nutrients 2018, 10, 1334. [Google Scholar] [CrossRef] [Green Version]
  8. Frisoni, G.B.; Franzoni, S.; Rozzini, R.; Ferrucci, L.; Boffelli, S.; Trabucchi, M. Food intake and mortality in the frail elderly. J. Gerontol. A Biol. Sci. Med. Sci. 1995, 50, M203–M210. [Google Scholar] [CrossRef]
  9. Katano, S.; Yano, T.; Kouzu, H.; Ohori, K.; Shimomura, K.; Honma, S.; Nagaoka, R.; Inoue, T.; Takamura, Y.; Ishigo, T.; et al. Energy intake during hospital stay predicts all-cause mortality after discharge independently of nutritional status in elderly heart failure patients. Clin. Res. Cardiol. 2021, 110, 1202–1220. [Google Scholar] [CrossRef]
  10. Doyev, R.; Axelrod, R.; Keinan-Boker, L.; Shimony, T.; Goldsmith, R.; Nitsan, L.; Dichtiar, R.; Sinai, T. Energy intake is highly associated with handgrip strength in community-dwelling elderly adults. J. Nutr. 2021, 151, 1249–1255. [Google Scholar] [CrossRef]
  11. Hiesmayr, M.; Schindler, K.; Pernicka, E.; Schuh, C.; Schoeniger-Hekele, A.; Bauer, P.; Laviano, A.; Lovell, A.D.; Mouhieddine, M.; Schuetz, T.; et al. Decreased food intake is a risk factor for mortality in hospitalised patients: The NutritionDay survey 2006. Clin. Nutr. 2009, 28, 484–491. [Google Scholar] [CrossRef]
  12. Thibault, R.; Makhlouf, A.M.; Kossovsky, M.P.; Iavindrasana, J.; Chikhi, M.; Meyer, R.; Pittet, D.; Zingg, W.; Pichard, C. Healthcare-associated infections are associated with insufficient dietary intake: An observational cross-sectional study. PLoS ONE 2015, 10, e0123695. [Google Scholar] [CrossRef]
  13. Dupertuis, Y.M.; Kossovsky, M.P.; Kyle, U.G.; Raguso, C.A.; Genton, L.; Pichard, C. Food intake in 1707 hospitalised patients: A prospective comprehensive hospital survey. Clin. Nutr. 2003, 22, 115–123. [Google Scholar] [CrossRef]
  14. Thibault, R.; Chikhi, M.; Clerc, A.; Darmon, P.; Chopard, P.; Genton, L.; Kossovsky, M.P.; Pichard, C. Assessment of food intake in hospitalised patients: A 10-year comparative study of a prospective hospital survey. Clin. Nutr. 2011, 30, 289–296. [Google Scholar] [CrossRef]
  15. Beavan, S.; Baker, R.; Sadler, H.; Collinson, A. Improving the nutritional intake of hospital patients: How far have we come? A re-audit. J. Hum. Nutr. Diet. 2019, 32, 372–384. [Google Scholar] [CrossRef]
  16. Curtis, L.J.; Valaitis, R.; Laur, C.; McNicholl, T.; Nasser, R.; Keller, H. Low food intake in hospital: Patient, institutional, and clinical factors. Appl. Physiol. Nutr. Metab. 2018, 43, 1239–1246. [Google Scholar] [CrossRef]
  17. Sanson, G.; Bertocchi, L.; Dal Bo, E.; Di Pasquale, C.L.; Zanetti, M. Identifying reliable predictors of protein-energy malnutrition in hospitalized frail older adults: A prospective longitudinal study. Int. J. Nurs. Stud. 2018, 82, 40–48. [Google Scholar] [CrossRef]
  18. Patel, M.D.; Martin, F.C. Why don’t elderly hospital inpatients eat adequately? J. Nutr. Health Aging 2008, 12, 227–231. [Google Scholar] [CrossRef]
  19. Vasse, E.; Beelen, J.; de Roos, N.M.; Janssen, N.; de Groot, L.C. Protein intake in hospitalized older people with and without increased risk of malnutrition. Eur. J. Clin. Nutr. 2018, 72, 917–919. [Google Scholar] [CrossRef]
  20. Sieske, L.; Janssen, G.; Babel, N.; Westhoff, T.H.; Wirth, R.; Pourhassan, M. Inflammation, appetite and food intake in older hospitalized patients. Nutrients 2019, 11, 1986. [Google Scholar] [CrossRef] [Green Version]
  21. Schindler, K.; Themessl-Huber, M.; Hiesmayr, M.; Kosak, S.; Lainscak, M.; Laviano, A.; Ljungqvist, O.; Mouhieddine, M.; Schneider, S.; de van der Schueren, M.; et al. To eat or not to eat? Indicators for reduced food intake in 91,245 patients hospitalized on nutritionDays 2006–2014 in 56 countries worldwide: A descriptive analysis. Am. J. Clin. Nutr. 2016, 104, 1393–1402. [Google Scholar] [CrossRef] [Green Version]
  22. Blanquet, M.; Ducher, G.; Sauvage, A.; Dadet, S.; Guiyedi, V.; Farigon, N.; Guiguet-Auclair, C.; Berland, P.; Bohatier, J.; Boirie, Y.; et al. Handgrip strength as a valid practical tool to screen early-onset sarcopenia in acute care wards: A first evaluation. Eur. J. Clin. Nutr. 2022, 76, 56–64. [Google Scholar] [CrossRef]
  23. Bouteloup, C.; Thibault, R. Decision tree for nutritional car. Nutr. Clin. Metab. 2014, 28, 52–56. [Google Scholar] [CrossRef]
  24. Haute Autorité de Santé—Stratégie de Prise en Charge en Cas de Dénutrition Protéino-Énergétique Chez la Personne Âgée. Available online: https://www.has-sante.fr/upload/docs/application/pdf/denutrition_personne_agee_2007_-_recommandations.pdf (accessed on 29 June 2023).
  25. Haute Autorité de Santé—Diagnostic de la Dénutrition Chez la Personne de 70 Ans et Plus. Available online: https://www.has-sante.fr/upload/docs/application/pdf/2021-11/reco368_recommandations_denutrition_pa_cd_20211110_v1.pdf (accessed on 29 June 2023).
  26. Cederholm, T.; Jensen, G.L.; Correia, M.I.T.D.; Gonzalez, M.C.; Fukushima, R.; Higashiguchi, T.; Baptista, G.; Barazzoni, R.; Blaauw, R.; Coats, A.J.S.; et al. GLIM criteria for the diagnosis of malnutrition—A consensus report from the global clinical nutrition community. Clin. Nutr. 2019, 38, 1–9. [Google Scholar] [CrossRef] [Green Version]
  27. Salvi, F.; Miller, M.D.; Grilli, A.; Giorgi, R.; Towers, A.L.; Morichi, V.; Spazzafumo, L.; Mancinelli, L.; Espinosa, E.; Rappelli, A.; et al. A manual of guidelines to score the modified cumulative illness rating scale and its validation in acute hospitalized elderly patients. J. Am. Geriatr. Soc. 2008, 56, 1926–1931. [Google Scholar] [CrossRef]
  28. Roberts, H.C.; Denison, H.J.; Martin, H.J.; Patel, H.P.; Syddall, H.; Cooper, C.; Sayer, A.A. A review of the measurement of grip strength in clinical and epidemiological studies: Towards a standardised approach. Age Ageing 2011, 40, 423–429. [Google Scholar] [CrossRef] [Green Version]
  29. Cruz-Jentoft, A.J.; Bahat, G.; Bauer, J.; Boirie, Y.; Bruyère, O.; Cederholm, T.; Cooper, C.; Landi, F.; Rolland, Y.; Sayer, A.A.; et al. Sarcopenia: Revised European consensus on definition and diagnosis. Age Ageing 2019, 48, 16–31. [Google Scholar] [CrossRef] [Green Version]
  30. Katz, S.; Akpom, C.A. A measure of primary sociobiological functions. Int. J. Health Serv. 1976, 6, 493–508. [Google Scholar] [CrossRef]
  31. Lawton, M.P.; Brody, E.M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969, 9, 179–186. [Google Scholar]
  32. Morilla-Herrera, J.C.; Martín-Santos, F.J.; Caro-Bautista, J.; Saucedo-Figueredo, C.; García-Mayor, S.; Morales-Asencio, J.M. Effectiveness of food-based fortification in older people. A systematic review and meta-analysis. J. Nutr. Health Aging 2016, 20, 178–184. [Google Scholar] [CrossRef]
  33. Seguy, D.; Hubert, H.; Robert, J.; Meunier, J.P.; Guérin, O.; Raynaud-Simon, A. Compliance to oral nutritional supplementation decreases the risk of hospitalisation in malnourished older adults without extra health care cost: Prospective observational cohort study. Clin. Nutr. 2020, 39, 1900–1907. [Google Scholar] [CrossRef] [Green Version]
  34. Roberts, H.C.; Pilgrim, A.L.; Jameson, K.A.; Cooper, C.; Sayer, A.A.; Robinson, S. The impact of trained volunteer mealtime assistants on the dietary intake of older female in-patients: The Southampton Mealtime Assistance Study. J. Nutr. Health Aging 2017, 21, 320–328. [Google Scholar] [CrossRef]
  35. Roberts, H.C. Changing the food environment: The effect of trained volunteers on mealtime care for older people in hospital. Proc. Nutr. Soc. 2018, 77, 95–99. [Google Scholar]
  36. Saunders, R.; Seaman, K.; Graham, R.; Christiansen, A. The effect of volunteers’ care and support on the health outcomes of older adults in acute care: A systematic scoping review. J. Clin. Nurs. 2019, 28, 4236–4249. [Google Scholar] [CrossRef] [Green Version]
  37. Howson, F.F.A.; Robinson, S.M.; Lin, S.X.; Orlando, R.; Cooper, C.; Sayer, A.A.P.; Roberts, H.C. Can trained volunteers improve the mealtime care of older hospital patients? An implementation study in one English hospital. BMJ Open 2018, 8, e022285. [Google Scholar] [CrossRef] [Green Version]
  38. Hannan-Jones, M.; Capra, S. Impact of type, size and shape of plates on hospital patients’ perceptions of the quality of meals and satisfaction with foodservices. Appetite 2018, 120, 523–526. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Navarro, D.A.; Boaz, M.; Krause, I.; Elis, A.; Chernov, K.; Giabra, M.; Levy, M.; Giboreau, A.; Kosak, S.; Mouhieddine, M.; et al. Improved meal presentation increases food intake and decreases readmission rate in hospitalized patients. Clin. Nutr. 2016, 35, 1153–1158. [Google Scholar] [CrossRef]
  40. McCray, S.; Maunder, K.; Krikowa, R.; MacKenzie-Shalders, K. Room service improves nutritional intake and increases patient satisfaction while decreasing food waste and cost. J. Acad. Nutr. Diet. 2018, 118, 284–293. [Google Scholar] [CrossRef] [Green Version]
  41. MacKenzie-Shalders, K.; Maunder, K.; So, D.; Norris, R.; McCray, S. Impact of electronic bedside meal ordering systems on dietary intake, patient satisfaction, plate waste and costs: A systematic literature review. Nutr. Diet. 2020, 77, 103–111. [Google Scholar] [CrossRef]
  42. Ottrey, E.; Porter, J. Hospital menu interventions: A systematic review of research. Int. J. Health Care Qual. Assur. 2016, 29, 62–74. [Google Scholar] [CrossRef] [PubMed]
  43. Keller, H.; Allard, J.; Vesnaver, E.; Laporte, M.; Gramlich, L.; Bernier, P.; Davidson, B.; Duerksen, D.; Jeejeebhoy, K.; Payette, H. Barriers to food intake in acute care hospitals: A report of the Canadian Malnutrition Task Force. J. Hum. Nutr. Diet. 2015, 28, 546–557. [Google Scholar] [CrossRef]
Nutrients 15 03307 g001 550
Figure 1. Daily energy intakes versus minimum requirements (30 kcal/kg/day): (a) Multidisciplinary Care Unit (MCU); (b) Geriatric Care Unit (GCU); (c) Geriatric Rehabilitation Unit (GRU); (d) Nursing Homes (NH). Boxplots indicate lower quartile, median, and upper quartile, and whiskers indicate minimum and maximum values. * Intakes significantly lower than requirements.
Figure 1. Daily energy intakes versus minimum requirements (30 kcal/kg/day): (a) Multidisciplinary Care Unit (MCU); (b) Geriatric Care Unit (GCU); (c) Geriatric Rehabilitation Unit (GRU); (d) Nursing Homes (NH). Boxplots indicate lower quartile, median, and upper quartile, and whiskers indicate minimum and maximum values. * Intakes significantly lower than requirements.
Nutrients 15 03307 g001
Nutrients 15 03307 g002 550
Figure 2. Daily protein intakes versus minimum requirements (1.2 g/kg/day): (a) Multidisciplinary Care Unit (MCU); (b) Geriatric Care Unit (GCU); (c) Geriatric Rehabilitation Unit (GRU); (d) Nursing Homes (NH). Boxplots indicate lower quartile, median, and upper quartile, and whiskers indicate minimum and maximum values. * Intakes significantly lower than requirements.
Figure 2. Daily protein intakes versus minimum requirements (1.2 g/kg/day): (a) Multidisciplinary Care Unit (MCU); (b) Geriatric Care Unit (GCU); (c) Geriatric Rehabilitation Unit (GRU); (d) Nursing Homes (NH). Boxplots indicate lower quartile, median, and upper quartile, and whiskers indicate minimum and maximum values. * Intakes significantly lower than requirements.
Nutrients 15 03307 g002
Table
Table 1. Sociodemographic and clinical characteristics of participants.
Table 1. Sociodemographic and clinical characteristics of participants.
CharacteristicsMultidisciplinary Care Unit
(MCU)		Geriatric
Care Unit
(GCU)		Geriatric
Rehabilitation Unit (GRU)		Nursing Homes
(NH)
Participants, n1041198354
Age (years), median (IQR)86.0 (82.0–89.5)86.0 (83.0–89.0)87.0 (82.0–89.0)88.0 (85.0–93.0)
Women, n (%)59 (56.7)91 (76.5)58 (69.9)40 (74.1)
BMI (kg/m2), median (IQR)25.3 (22.2–29.7)26.9 (22.5–31.3)28.4 (24.4–32.2)29.0 (24.5–32.5)
Living situation, n (%) NA
At home93 (89.4)107 (89.9)79 (95.2)
Residential home8 (7.7)8 (6.7)0 (0)-
Other3 (2.9)4 (3.4)4 (4.8)-
Life situation, n (%) NA
Living alone60 (65.2)77 (69.4)56 (67.5)-
With partner18 (19.6)22 (19.8)22 (26.5)-
With a family member14 (15.2)12 (10.8)5 (6.0)-
Daily medications, median (IQR)8.0 (5.0–9.0)7.0 (5.0–9.0)8.0 (5.0–10.0)9.0 (7.0–11.0)
CIRS-G, median (IQR)
Total score 12.0 (9.0–15.0)16.0 (12.0–20.0)17.0 (12.0–22.0)17.0 (14.0–21.0)
Severity index 0.9 (0.7–1.2)1.1 (0.8–1.4)1.2 (0.8–1.5)1.2 (0.8–1.5)
Comorbidity index4.0 (3.0–6.0)5.0 (3.0–6.0)5.0 (3.0–7.0)6.0 (4.0–7.0)
ADL, median (IQR)5.5 (3.5–6.0)5.0 (4.0–6.0)5.5 (4.5–6.0)3.0 (1.5–4.8)
Needing help with feeding *, n (%)20 (21.5)24 (20.2)22 (26.5)20 (38.5)
IADL, median (IQR)4.3 (2.0–7.0)4.0 (3.0–6.0)5.0 (3.0–6.9)NA
Inflammation (CRP > 5), n (%)69 (67.0)83 (69.7)58 (70.7)28 (53.8)
Sarcopenia, n (%)
Probable51 (51.0)74 (66.7)52 (69.3)35 (72.9)
Confirmed18 (20.2)35 (32.1)17 (25.0)13 (28.9)
Malnutrition, n (%)32 (35.2)44 (40.0)22 (30.6)15 (31.3)
Stage 1—Moderate15 (16.5)18 (16.4)11 (15.3)13 (27.1)
Stage 2—Severe17 (18.7)26 (23.6)11 (15.3)2 (4.2)
NA: Not applicable; CIRS-G: Cumulative Illness Rating Scale-Geriatric; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living; IQR: Inter Quartile range. * ADL feeding dimension.
Table
Table 2. Gaps between energy and protein intakes and minimum requirements and P/E ratios stratified by malnutrition status.
Table 2. Gaps between energy and protein intakes and minimum requirements and P/E ratios stratified by malnutrition status.
CharacteristicsMultidisciplinary Care Unit
(MCU)		Geriatric
Care Unit
(GCU)		Geriatric
Rehabilitation Unit (GRU)		Nursing Homes
(NH)
No. 34984833
Energy gap (kcal), median (IQR)
Malnutrition592.5 (220.0–770.0)160.0 (−174.0–543.0)504.3 (122.0–736.0)640.8 (301.0–765.5)
No malnutrition650.0 (280.0–932.0)317.1 (106.4–637.9)598.1 (370.1–769.5)268.6 (−5.6–617.0)
p-value0.8600.0320.2310.091
Protein gap (g), median (IQR)
Malnutrition32.2 (16.4–41.8)1.4 (−15.4–20.4)18.5 (0.7–30.4)24.7 (17.6–36.1)
No malnutrition33.0 (20.9–42.4)7.1 (−2.6–22.1)21.0 (13.9–33.0)12.5 (4.2–26.0)
p-value0.8330.1160.1990.106
P/E ratio (%), median (IQR)
Malnutrition14.2 (13.2–15.0)17.9 (16.2–19.1)16.9 (14.9–17.5)14.5 (12.9–18.2)
No malnutrition14.2 (12.7–15.7)18.1 (16.6–19.5)15.4 (14.3–17.5)15.7 (13.9–16.4)
p-value0.8880.6630.4310.574
Significant p-values are reported in bold text (p < 0.05). Energy gap: requirement of 30 kcal/kg/day—intake; protein gap: requirement of 1.2 g/kg/day—intake; P/E ratio: protein energy/total energy. A positive gap value indicates intake lower than requirements.
Table
Table 3. Factors associated with energy and protein intake-to-requirement gaps in the Multidisciplinary Care Unit group.
Table 3. Factors associated with energy and protein intake-to-requirement gaps in the Multidisciplinary Care Unit group.
Multidisciplinary Care UnitEnergy Gapp-ValueProtein Gapp-Value
Age (years), r0.050.7480.020.908
Gender, median (IQR) 0.034 0.122
Men882.2 (468.3–1162.4) 35.8 (28.1–46.8)
Women584.8 (220.0–766.0) 27.6 (17.6–39.7)
BMI (kg/m2), r0.200.2350.300.078
Life situation, median (IQR) 0.517 0.427
Living alone616.9 (280.0–816.0) 28.4 (18.4–40.0)
With partner or a family member710.0 (190.5–1277.1) 34.0 (15.6–55.0)
Daily medications, r0.140.3980.150.384
CIRS-G, r
Total score 0.340.0470.320.060
Severity index 0.270.1020.270.096
Comorbidity index0.210.2020.290.079
ADL, r−0.350.048−0.290.102
Needing help with feeding *, median (IQR) 0.956 0.566
No650.0 (220.0–1052.0) 33.0 (17.6–46.5)
Yes679.3 (325.3–944.5) 31.1 (23.6–37.3)
IADL, r−0.280.148−0.260.185
Inflammation (CRP > 5), median (IQR) 0.347 0.071
No596.9 (280.0–770.0) 27.3 (16.4–33.0)
Yes738.0 (278.0–1015.6) 37.7 (23.8–45.1)
Probable sarcopenia, median (IQR) 0.015 0.017
No504.3 (220.0–751.0) 26.3 (16.4–35.0)
Yes865.5 (592.5–1052.0) 39.2 (30.0–46.5)
Confirmed sarcopenia, median (IQR) 0.497 0.404
No617.5 (237.0–915.0) 30.0 (17.6–40.0)
Yes670.8 (271.8–1319.8) 37.0 (18.2–55.6)
Data are presented as Spearman’s correlation coefficient (r) or median with interquartile range (IQR). Significant p-values are reported in bold text (p < 0.05). Energy gap: requirement of 30 kcal/kg/day—intake; protein gap: requirement of 1.2 g/kg/day—intake. A positive gap value indicate intake lower than requirements. CIRS-G: Cumulative Illness Rating Scale-Geriatric; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living. * ADL feeding dimension.
Table
Table 4. Factors associated with energy and protein intake-to-requirement gaps in the Geriatric Care Unit group.
Table 4. Factors associated with energy and protein intake-to-requirement gaps in the Geriatric Care Unit group.
Geriatric Care UnitEnergy Gapp-ValueProtein Gapp-Value
Age (years), r0.130.1990.070.456
Gender, median (IQR) 0.031 0.053
Men447.0 (234.0–1011.7) 15.9 (2.1–31.4)
Women233.0 (−21.0–564.0) 3.6 (−9.6–21.0)
BMI (kg/m2), r0.170.0770.120.236
Life situation, median (IQR) 0.031 0.064
Living alone238.5 (−19.0–542.0) 4.5 (−9.6–19.7)
With partner or a family member421.7 (233.0–1011.7) 9.0 (1.2–36.8)
Daily medications, r0.140.1480.080.396
CIRS-G, r
Total score 0.170.0810.110.282
Severity index 0.170.0880.100.291
Comorbidity index0.160.1120.110.268
ADL, r−0.160.113−0.110.247
Needing help with feeding *, median (IQR) 0.033 0.161
No271.0 (−19.0–496.0) 4.9 (−7.8–21.1)
Yes655.0 (65.7–1011.7) 19.7 (−3.0–36.8)
IADL, r−0.100.324−0.110.267
Inflammation (CRP > 5), median (IQR) 0.001 0.034
No20.5 (−162.0–435.0) −2.4 (−12.8–18.2)
Yes408.6 (196.5–732.2) 8.7 (−0.9–25.4)
Probable sarcopenia, median (IQR) 0.683 0.791
No314.5 (5.5–511.1) 7.0 (−7.9–16.7)
Yes265.5 (−12.0–662.0) 4.1 (−8.1–25.4)
Confirmed sarcopenia, median (IQR) 0.359 0.648
No304.0 (49.0–560.6) 5.5 (−5.0–21.0)
Yes234.0 (−165.0–655.0) 3.1 (−12.8–21.4)
Data are presented as Spearman’s correlation coefficient (r) or median with interquartile range (IQR). Significant p-values are reported in bold text (p < 0.05). Energy gap: requirement of 30 kcal/kg/day—intake; protein gap: requirement of 1.2 g/kg/day—intake. A positive gap value indicates intake lower than requirements. CIRS-G: Cumulative Illness Rating Scale-Geriatric; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living. * ADL feeding dimension.
Table
Table 5. Factors associated with energy and protein intake-to-requirement gaps in the Geriatric Rehabilitation Unit group.
Table 5. Factors associated with energy and protein intake-to-requirement gaps in the Geriatric Rehabilitation Unit group.
Geriatric Rehabilitation UnitEnergy Gapp-ValueProtein Gapp-Value
Age (years), r−0.020.9080.080.584
Gender, median (IQR) 0.228 0.382
Men664.0 (487.9–765.0) 21.9 (13.8–38.4)
Women526.9 (213.0–731.0) 20.3 (8.7–30.4)
BMI (kg/m2), r0.170.2210.140.325
Life situation, median (IQR) 0.821 0.898
Living alone575.0 (329.0–769.5) 21.0 (8.7–31.8)
With partner or a family member525.3 (234.0–736.0) 20.4 (13.9–21.6)
Daily medications, r0.120.3920.040.751
CIRS-G, r
Total score 0.260.0580.280.038
Severity index 0.260.0550.280.038
Comorbidity index0.240.0820.250.063
ADL, r0.050.7020.020.876
Needing help with feeding *, median (IQR) 0.549 0.316
No532.5 (329.0–731.0) 20.0 (9.9–31.6)
Yes664.0 (170.0–910.0) 24.2 (17.3–34.3)
IADL, r0.010.942−0.090.519
Inflammation (CRP > 5), median (IQR) 0.027 0.027
No385.2 (58.0–546.1) 11.9 (−1.3–23.1)
Yes602.1 (340.9–798.5) 21.2 (13.9–33.7)
Probable sarcopenia, median (IQR) 0.838 0.599
No466.0 (340.9–694.5) 20.6 (17.3–31.6)
Yes575.0 (189.0–781.8) 19.4 (8.2–33.6)
Confirmed sarcopenia, median (IQR) 0.173 0.123
No598.1 (370.1–769.5) 21.0 (13.9–33.0)
Yes480.0 (122.0–736.0) 18.0 (0.7–30.4)
Data are presented as Spearman’s correlation coefficient (r) or median with interquartile range (IQR). Significant p-values are reported in bold text (p < 0.05). Energy gap: requirement of 30 kcal/kg/day—intake; protein gap: requirement of 1.2 g/kg/day—intake. A positive gap value indicates intake lower than requirements. CIRS-G: Cumulative Illness Rating Scale-Geriatric; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living. * ADL feeding dimension.
Table
Table 6. Factors associated with energy and protein intake-to-requirement gaps in the Nursing Homes group.
Table 6. Factors associated with energy and protein intake-to-requirement gaps in the Nursing Homes group.
Nursing HomesEnergy Gapp-ValueProtein Gapp-Value
Age (years), r0.070.6730.010.966
Gender, median (IQR) 0.095 0.164
Men767.0 (197.2–941.6) 24.3 (11.1–37.9)
Women357.3 (89.0–613.0) 16.0 (4.2–26.0)
BMI (kg/m2), r−0.080.629−0.150.365
Daily medications, r0.000.983−0.030.883
CIRS-G, r
Total score 0.200.2440.160.339
Severity index 0.220.1980.170.324
Comorbidity index0.160.3570.100.540
ADL, r−0.350.040−0.290.088
Needing help with feeding *, median (IQR) 0.186 0.499
No273.5 (−3.9–564.0) 14.6 (1.2–29.1)
Yes563.7 (140.7–765.5) 17.6 (8.2–26.1)
Inflammation (CRP > 5), median (IQR) 0.247 0.489
No246.0 (89.0–474.5) 13.2 (5.7–25.0)
Yes562.9 (140.7–759.0) 16.8 (4.2–35.7)
Probable sarcopenia, median (IQR) 0.411 0.482
No275.1 (−5.6–474.5) 12.1 (5.1–26.0)
Yes330.5 (89.0–758.3) 17.4 (4.2–35.7)
Confirmed sarcopenia, median (IQR) 0.276 0.258
No272.3 (−5.6–617.0) 14.2 (5.1–26.0)
Yes562.0 (246.0–759.0) 26.1 (13.2–36.1)
Data are presented as Spearman’s correlation coefficient (r) or median with interquartile range (IQR). Significant p-values are reported in bold text (p < 0.05). Energy gap: requirement of 30 kcal/kg/day—intake; protein gap: requirement of 1.2 g/kg/day—intake. A positive gap value indicates intake lower than requirements. CIRS-G: Cumulative Illness Rating Scale-Geriatric; ADL: Activities of Daily Living; IADL: Instrumental Activities of Daily Living. * ADL feeding dimension.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Blanquet, M.; Guiguet-Auclair, C.; Berland, P.; Ducher, G.; Sauvage, A.; Dadet, S.; Guiyedi, V.; Farigon, N.; Bohatier, J.; Gerbaud, L.; et al. Are Energy and Protein Intakes Lower Than Requirements in Older Adults? An Urgent Issue in Hospitals and Nursing Homes. Nutrients 2023, 15, 3307. https://doi.org/10.3390/nu15153307

AMA Style

Blanquet M, Guiguet-Auclair C, Berland P, Ducher G, Sauvage A, Dadet S, Guiyedi V, Farigon N, Bohatier J, Gerbaud L, et al. Are Energy and Protein Intakes Lower Than Requirements in Older Adults? An Urgent Issue in Hospitals and Nursing Homes. Nutrients. 2023; 15(15):3307. https://doi.org/10.3390/nu15153307

Chicago/Turabian Style

Blanquet, Marie, Candy Guiguet-Auclair, Pauline Berland, Guillaume Ducher, Anaïs Sauvage, Sylvain Dadet, Vincent Guiyedi, Nicolas Farigon, Jérôme Bohatier, Laurent Gerbaud, and et al. 2023. "Are Energy and Protein Intakes Lower Than Requirements in Older Adults? An Urgent Issue in Hospitals and Nursing Homes" Nutrients 15, no. 15: 3307. https://doi.org/10.3390/nu15153307

APA Style

Blanquet, M., Guiguet-Auclair, C., Berland, P., Ducher, G., Sauvage, A., Dadet, S., Guiyedi, V., Farigon, N., Bohatier, J., Gerbaud, L., & Boirie, Y. (2023). Are Energy and Protein Intakes Lower Than Requirements in Older Adults? An Urgent Issue in Hospitals and Nursing Homes. Nutrients, 15(15), 3307. https://doi.org/10.3390/nu15153307

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop