Challenges in identifying malnutrition in obesity; An overview of the state of the art and directions for future research.

(Protein-energy) malnutrition in individuals living with obesity presents complex diagnostic challenges due to the distinctive physiological characteristics of obesity. This narrative review critically examines the identification of malnutrition within the population with obesity, distinguishing malnutrition in obesity from related conditions such as sarcopenic obesity. While noting some shared features, the review highlights key differences between these conditions. The review also highlights the limitations of current malnutrition screening tools, which are not designed for individuals living with obesity. These tools primarily rely on anthropometric measurements, neglecting (among others) nutrient intake assessment, which hinders accurate malnutrition detection. Additionally, this review discusses limitations in existing diagnostic criteria, including the Global Leadership Initiative on Malnutrition (GLIM) criteria, when applied to individuals living with obesity. Challenges include the identification of appropriate cut-off values for phenotypic criteria (unintentional weight loss, low body mass index and muscle mass) and aetiological criteria such as reduced food intake and inflammation for the population with obesity. Overall, this review emphasises the need for modified screening tools and diagnostic criteria to recognise and assess malnutrition in obesity, leading to improved clinical outcomes and overall wellbeing.

Diseases, Health-Related Problems, and the Incidence of Malnutrition in Long-Term Care Facilities.

Certain diseases and malnutrition are known to co-occur in residents of long-term care facilities (LTCF). We assessed which diseases and health-related problems are associated with malnutrition at admission or with incident malnutrition during stays and how different definitions of malnutrition affect these associations. Data of Dutch LTCF residents were obtained from the InterRAI-LTCF instrument (2005-2020). We analyzed the association of diseases (diabetes, cancer, pressure ulcers, neurological, musculoskeletal, psychiatric, cardiac, infectious and pulmonary diseases) and health-related problems (aspiration, fever, peripheral edema, aphasia, pain, supervised/assisted eating, balance, psychiatric, GI tract, sleep, dental and locomotion problems) with malnutrition (recent weight loss (WL), low age-specific BMI (BMI), and ESPEN 2015 definition (ESPEN)) at admission (n = 3713), as well as with incident malnutrition during stay (n = 3836, median follow-up ~1 year). Malnutrition prevalence at admission ranged from 8.8% (WL) to 27.4% (BMI); incident malnutrition during stay ranged from 8.9% (ESPEN) to 13.8% (WL). At admission, most diseases (except cardiometabolic diseases) and health-related problems were associated with higher prevalence of malnutrition based on either criterion, but strongest with WL. This was also seen in the prospective analysis, but relationships were less strong compared to the cross-sectional analysis. A considerable number of diseases and health-related problems are associated with an increased prevalence of malnutrition at admission and incident malnutrition during stays in LTCFs. At admission, low BMI is a good indicator of malnutrition; during stays, we advise use of WL.

GLIM in nursing homes; practical implications.

BACKGROUND: Only very few papers have described malnutrition prevalence rates according to the Global Leadership Initiative on Malnutrition (GLIM) criteria in nursing homes, likely due to practical reasons such as missing data on body composition, dietary intake, or acute disease/inflammation. METHODS: Data was collected in 5 different nursing homes. Food intake measurements took place over 3 days of observations, and intakes below 90% of energy or protein requirements were regarded as insufficient. The GLIM diagnosis was based on body weight loss and/or low BMI in combination with insufficient food intake. Additionally, we also studied the sensitivity of GLIM with the question from the Mini Nutritional Assessment Short Form (MNA-SF) on insufficient food intake (GLIMMNA) versus GLIM with measured food intake. RESULTS: Out of 176 participants, 21.0% were categorized as malnourished according to GLIM. Observations revealed an insufficient food intake in 81.3% (N = 143) of residents; only 39% of those (N = 56) scored positive on the MNA-SF question regarding low food intake. GLIMMNA diagnosed 17.0% of residents as malnourished. Sensitivity of GLIMMNA for GLIM was 62.2%, and specificity 95.0% (kappa = 0.61). CONCLUSION: Twenty-one percent of nursing home residents were diagnosed malnourished based on a limited set of GLIM criteria. The MNA question on insufficient food intake missed ∼60% of residents with a truly low food intake. Herewith, malnutrition prevalence rates with GLIMMNA decreased to 17%. We advise measuring food intake for studies, and to be aware of too low prevalence rates of GLIM when an estimate of reduced food intake is applied.

Protein Intake among Community-Dwelling Older Adults: The Influence of (Pre-) Motivational Determinants.

An adequate protein intake is important for healthy ageing, yet nearly 50% of Dutch community-dwelling older adults do not meet protein recommendations. This study explores protein intake in relation to eight behavioral determinants (I-Change model) among Dutch community-dwelling older adults. Data were collected through an online questionnaire from October 2019-October 2020. Protein intake was assessed by the Protein Screener 55+, indicating a high/low chance of a low protein intake (<1.0 g/kg body weight/day). The behavioral determinants of cognizance, knowledge, risk perception, perceived cues, attitude, social support, self-efficacy and intention were assessed by evaluating statements on a 7-point Likert scale. A total of 824 Dutch community-dwelling older adults were included, recruited via online newsletters, newspapers and by personal approach. Poisson regression was performed to calculate quartile-based prevalence ratios (PRs). Almost 40% of 824 respondents had a high chance of a low protein intake. Univariate analyses indicated that lower scores for all different behavioral determinants were associated with a higher chance of a low protein intake. Independent associations were observed for knowledge (Q4 OR = 0.71) and social support (Q4 OR = 0.71). Results of this study can be used in future interventions aiming to increase protein intake in which focus should lie on increasing knowledge and social support.

What do screening tools measure? Lessons learned from SCREEN II and SNAQ65.

BACKGROUND: Over the last decade, different screening tools for malnutrition have been developed. Within these tools, a distinction can be made between tools that assess nutritional risk and tools that assess protein energy malnutrition. Insights in differences in characteristics of participants at risk and in differences in prevalence rates will aid in deciding which tool(s) to use in daily practice. METHODS: Dutch community-dwelling older adults (n = 200, 78.2 ± 6.9 years), not known to have specific nutrition problems, were recruited to participate in this cross-sectional study. SNAQ65+ (low risk vs moderate/high risk) was used to assess risk of protein energy malnutrition and SCREEN II was used to assess nutrition risk (score <54 out of 64). Chi-square tests were used to test associations between demographic, health, physical and social factors and outcome of SNAQ65+ and SCREEN II. RESULTS: Of all participants 69.0% were at nutrition risk (SCREEN II), while 13.5% were at risk of protein energy malnutrition (SNAQ65+). Agreement between the two tools was poor (kappa < 0.20). Gender, BMI, living status, income, activity level and protein/energy intake were associated with SCREEN II; age, BMI, comorbidities, medication use, help at home, activity level and low basic mobility were associated with SNAQ65+. CONCLUSION: SCREEN II and SNAQ65+ measure different concepts of malnutrition and therefore identify different persons at risk. SCREEN II is more inclusive and comprises both undernutrition and overnutrition as well as different determinants that can impact on food intake, while SNAQ65+ is solely focused on protein-energy malnutrition.

Cross-Country Differences and Similarities in Undernutrition Prevalence and Risk as Measured by SCREEN II in Community-Dwelling Older Adults.

Undernutrition is highly prevalent among community-dwelling older adults. Early identification of nutrition risk is important to prevent or treat undernutrition. This study describes the prevalence rates of nutrition risk in community-dwelling older adults (aged ≥ 65) using the same validated tool across different countries and aims to identify differences in nutritional risk factors. Cross-sectional data was obtained from three datasets including participants from the Netherlands (NL), Canada (CA) and New Zealand (NZ). Seniors in the Community Risk Evaluation for Eating and Nutrition II (SCREEN II) was used to assess nutritional risk factors and prevalence of risk. Differences between countries were tested with logistic and linear regression. Sensitivity analyses were conducted to test the influence of sampling strategy. A total of 13,340 participants were included, and 66.3% were found to be at high nutrition risk. After stratifying the data for method of data sampling, prevalence rates showed some differences across countries (NL: 61.5%, NZ: 68.2%, CA: 70.1%). Risk factor items that contributed to nutrition risk also differed among countries: NZ and CA participants scored higher for weight change, skipping meals, problems with meal preparation, use of meal replacements, problems with biting and chewing, low fluid intake and problems with doing groceries, as compared to participants in NL. Low intake of fruits and vegetables and meat were more prevalent in NL. In conclusion: nutrition risk is a worldwide, highly prevalent problem among community-dwelling older adults, but risk factors contributing to nutrition risk differ by country.

The ConsuMEER study: a randomised trial towards the effectiveness of protein-rich ready-made meals and protein-rich dairy products in increasing protein intake of community-dwelling older adults after switching from self-prepared meals towards ready-made meals.

The risk of undernutrition in older community-dwelling adults increases when they are no longer able to shop or cook themselves. Home-delivered products could then possibly prevent them from becoming undernourished. This single-blind randomised trial tested the effectiveness of home-delivered protein-rich ready-made meals and dairy products in reaching the recommended intake of 1·2 g protein/kg body weight (BW) per d and ≥25 g of protein per meal. Community-dwelling older adults (n 98; mean age 80·4 (sd 6·8) years) switched from self-prepared to home-delivered hot meals and dairy products for 28 d. The intervention group received ready-made meals and dairy products high in protein; the control group received products lower in protein. Dietary intake was measured at baseline, after 2 weeks (T1), and after 4 weeks (T2). Multilevel analyses (providing one combined outcome for T1 and T2) and logistic regressions were performed. Average baseline protein intake was 1·09 (se 0·05) g protein/kg BW per d in the intervention group and 0·99 (se 0·05) g protein/kg BW per d in the control group. During the trial, protein intake of the intervention group was 1·12 (se 0·05) g protein/kg BW per d compared with 0·87 (se 0·03) g protein/kg BW per d in the control group (between-group differences P < 0·05). More participants of the intervention group reached the threshold of ≥25 g protein at dinner compared with the control group (intervention T1: 84·8 %, T2: 88·4 % v. control T1: 42·9 %, T2: 40·5 %; P < 0·05), but not at breakfast and lunch. Our findings suggest that switching from self-prepared meals to ready-made meals carries the risk of a decreasing protein intake, unless extra attention is given to protein-rich choices.

Prevalence of protein-energy malnutrition risk in European older adults in community, residential and hospital settings, according to 22 malnutrition screening tools validated for use in adults ≥65 years: A systematic review and meta-analysis.

This systematic review and meta-analysis assesses the prevalence of protein-energy malnutrition risk across different health-care settings in European older adults, using 22 malnutrition screening tools recently validated for use in older adults. Systematic searches were performed in six electronic databases (2006 through 2017). Included were studies which reported malnutrition risk in adults aged ≥65y in Europe. Frequency of high and moderate malnutrition risk for each malnutrition screening tool was collated. Meta-analyses of malnutrition risk using a random-effects model were performed where data from at least 10 study samples were available. Of 21,465 studies, 196 studies were available for data extraction, representing 223 study samples from 24 European countries and 583,972 older adults. Pooled prevalence rates of high malnutrition risk across all countries and malnutrition screening tools were 28.0% (n = 127 study samples), 17.5% (n = 30), and 8.5% (n = 32), for the hospital, residential care and community settings respectively. Using meta-regression, prevalence rates were higher in adults aged >80y (p < 0.0001), in women (p = 0.03) and in patients with one or multiple comorbidities (p < 0.0001). Prevalence rates differed by country, from 15.2% in Spain to 37.7% in Switzerland, and by screening tool, from 14.9% using MNA-SF to 40.6% using NRS-2002. In conclusion, the prevalence of high malnutrition risk in European older adults varies widely between countries and across health-care settings. Malnutrition risk is associated with older age, gender and presence of disease. As prevalence rates differ depending on the screening tool used, the use of one preferred malnutrition screening tool per setting is strongly recommended.

Prevalence and Determinants of Undernutrition in A Sample of Dutch Community-Dwelling Older Adults: Results from Two Online Screening Tools.

To stimulate undernutrition screening among Dutch community-dwelling adults, a website was developed with general information on healthy eating for healthy aging and self-tests. Based on cross-sectional data obtained from the self-tests, we studied nutritional risk factors (early determinants) as well as risk of undernutrition (late symptoms). SCREEN II (n = 2470) was used to asses nutritional risk factors. This tool consists of 16 items regarding nutritional intake, perception of body weight, appetite, oral health and meal preparation. An adjusted SNAQ65+ (n = 687) was used to assess risk of undernutrition. This four-item tool contains questions on weight loss, appetite, walking stairs and body mass index. Differences between age-groups (65-74, 75-84, ≥85) were tested by logistic regression. Overall prevalence of nutritional risk factors was 84.1%, and increased risk of undernutrition was 56.8%. Participants aged ≥85 scored worst on almost all items of the SCREEN II and the SNAQ65+. In conclusion: A large proportion of older adults reported early determinants for increased nutrition risk, while a smaller, yet remarkable proportion scored positive on undernutrition risk. Internet screening may be a useful, contemporary, and easy, accessible way to reach older adults who are at nutritional risk and may thus contribute to early identification and prevention of undernutrition.