It has been reported that a distinct ‘old person smell’ can develop with advancing age, however, this odour has not yet been sufficiently described in previous research. Sensory evaluation by a ...trained panel might be useful to describe alterations with age in body odour (BO). To evaluate the alterations and achieve first insights into the ‘old person smell’, this pilot study determined the odour profiles of BO samples from both a younger and an older age group with a trained panel. In addition, we aimed to assess whether the panellists can recognize the age group based on the smell of the BO samples. Eight younger (20–28 years) and eight older (80–83 years) participants sampled their BO by wearing a cotton T‐shirt for one night. The samples were sensorially evaluated by a trained panel, including ratings of total intensity and pleasantness. Additionally, an age labelling task was performed as a forced‐choice decision. Results revealed that the odour profiles of the BO samples were very similar for both age groups. Nevertheless, trained panellists were able to predict the age group with significantly higher accuracy (p = .042) than expected by chance (61% mean accuracy over all panellists). Furthermore, a linear support vector machine (SVM) classifier achieved an average accuracy of 69%. This finding indicates that the age of a person affects the BO, though it is not reflected in significantly distinct odour profiles.
Trained panellists established odour profiles for axillary body odour samples donated by younger and older persons and were able to predict the age group with significantly higher accuracy than expected by chance. By training a linear SVM classifier based on the trained panellists' ratings, higher accuracy was achieved. This finding indicates that hints towards the age of the body odour donor were present in the body odour samples.
Protein intake in older people Kiesswetter, Eva; Sieber, Cornel C.; Volkert, Dorothee
Zeitschrift für Gerontologie und Geriatrie,
07/2020, Volume:
53, Issue:
4
Journal Article
Peer reviewed
The protein intake of older people has gained increasing scientific interest as a potential factor to delay the age-associated decline in muscle mass and consequently to counteract the development of ...sarcopenia. The skeletal muscle of older people seems less responsive to the anabolic stimulus of protein intake. Therefore, higher protein needs are discussed to overcome this anabolic resistance and to maintain muscle mass as far as possible. Besides the total amount of protein consumed, the distribution, quality and timing in relation to physical exercise are considered relevant; however, deriving clear recommendations for clinical practice is still difficult as positive results of protein intake on muscle metabolism found in experimental trials cannot simply be transferred to everyday conditions and randomized controlled trials often failed to show improvements in muscular outcomes related to protein supplementation. The effectiveness of protein supplementation may depend on functional resources of the older persons and the habitual protein intake. There is still a need for studies with well-defined protocols and populations to further elucidate the role of protein in the prevention and treatment of sarcopenia.
Frailty is common in nursing home (NH) residents, but its prevalence in German institutions is unknown. Valid and easy-to-use screening tools are needed to identify frail residents. We used the ...FRAIL-NH scale and the Clinical Frailty Scale (CFS) to (1) obtain the prevalence of frailty, (2) investigate the agreement between both instruments, and (3) evaluate their predictive validity for adverse health events in German NH residents.
Prospective cohort study.
German NH residents (n = 246, age 84 ± 8 years, 67% female).
Frailty status was categorized according to FRAIL-NH (nonfrail, frail, most frail) and CFS (not frail, mild to moderately frail, severely frail). Agreement between instruments was examined by Spearman correlation, an area under the receiver operating characteristic curve (AUC) with 95% CI, and sensitivity and specificity using the "most frail" category of FRAIL-NH as reference standard. Adverse health events (death, hospital admissions, falls) were recorded for 12 months, and multivariate cox and logistic regression models calculated.
According to FRAIL-NH, 71.1% were most frail, 26.4% frail, and 2.5% nonfrail. According to CFS, 66.3% were severely frail, 26.8% mild to moderately frail, and 6.9% not frail. Both scales correlated significantly (r = 0.78; R
= 60%). The AUC was 0.92 (95% CI 0.88-0.96). Using a CFS cutoff of 7 points, sensitivity was 0.90 and specificity 0.92. The frailest groups according to both instruments had an increased risk of death FRAIL-NH hazard ratio (HR) 2.19, 95% CI 1.21-3.99; CFS HR 2.56, 95% CI 1.43-4.58 and hospital admission FRAIL-NH odds ratio (OR) 1.95, 95% CI 1.06-3.58; CFS OR 1.79, 95% CI 1.01-3.20 compared to less frail residents. The FRAIL-NH predicted recurrent faller status (OR 2.57, 95% CI 1.23-5.39).
Frailty is highly prevalent in German NH residents. Both instruments show good agreement despite different approaches and are able to predict adverse health outcomes. Based on our findings and because of its simple administration, CFS may be an alternative to FRAIL-NH for assessing frailty in NHs.
The Protein Screener 55 + (Pro55 + ) is a brief food questionnaire to screen older community-dwelling adults for low protein intake. The result is the predicted probability of protein intake <1.0 ...g/kg adjusted body weight (aBW)/d ranging from 0-1. For purposes of cross-cultural validation, we translated the Pro55+ into German and tested its discriminative accuracy in detecting low protein intake of older community-dwelling people in Germany.
After translation and pilot-testing, the Pro55+ and the reference standard (3-day dietary record) were completed by 144 participants (81.6 ± 3.9 years, 61.8% female). Discriminative properties were tested by receiver operating characteristic curves and by calculating sensitivity and specificity for different cut-offs of predicted probability (>0.3/>0.5/>0.7) using <1.0 or <0.8 g/kg aBW/d to define low protein intake.
Protein intake was <1.0 g/kg aBW/d in 39.6% of the sample and <0.8 g/kg aBW/d in 17.4%. Area under the curve was 62.0% (95%CI 52.6-71.5) and 68.8% (58.1-79.4), respectively. Specificity was 82-90% using probability cut-offs of 0.5 and 0.7 for both protein thresholds. Sensitivity was poor for protein threshold of 1.0 g/kg aBW/d regardless of the used probability cut-offs. For protein threshold of <0.8 g/kg aBW/d, sensitivity was 88.0% (71.8-96.9) using a probability cut-off of 0.09.
The overall discriminative accuracy of the German Pro55+ to identify older community-dwelling people with low protein intake was poor. However, applying different probability cut-offs allows increasing specificity and sensitivity for 0.8 g/kg aBW/d to levels justifying the use for certain purposes e.g. excluding individuals with adequate protein intake. Further validation is needed.
Effective policies to address poor food choices and dietary patterns need to consider the complex set of motives affecting eating behavior. This study examined how different eating motives are ...associated with anthropometry, body composition, and dietary intake. Our analysis is based on a cross-sectional sample with 429 healthy adults in three different age groups collected in Germany from 2016 to 2018. Dietary intake, Body Mass Index (BMI), waist circumference (WC), and fat-free mass (FFM) were measured by standardized methods. Eating motives were measured using The Eating Motivation Scale (TEMS). Regressing dietary intakes and anthropometric indicators on TEMS motives, we identify the main sources of variation in diet and nutritional status separately for men and women. Results indicated the Health motive to be positively associated with FFM (B±SE=1.72±0.44) and negatively with WC (B±SE=−3.23±0.81) for men. For women, the Need & Hunger motive was positively associated with FFM (B±SE=1.63±0.44) and negatively with WC (B±SE=−2.46±0.81). While Liking and Habits were the most frequently stated eating motives, we did not find them to be significantly related to the nutritional status. Other motives were associated with dietary intake but not anthropometry or body composition. The Price motive was positively and the Convenience motive was negatively associated with energy (B±SE=63.77±19.98;B±SE=−46.96±17.12) and carbohydrate intake (B±SE=7.15±2.65;B±SE=−5.98±2.27) for men. The results highlight the need for more differentiated analyses of eating motives, beyond comparing the relative importance of motives based on mean values, towards the association of motives with dietary intake and nutritional status.
Nursing home (NH) residents with (risk of) malnutrition are at particular risk of low protein intake (PI). The aim of the present analysis was (1) to characterize usual PI (total amount/day (d) and ...meal, sources/d and meal) of NH residents with (risk of) malnutrition and (2) to evaluate the effects of an individualized nutritional intervention on usual PI. Forty residents (75% female, 85 ± 8 years) with (risk of) malnutrition and inadequate dietary intake received 6 weeks of usual care followed by 6 weeks of intervention. During the intervention phase, an additional 29 ± 11 g/d from a protein-energy drink and/or 2 protein creams were offered to compensate for individual energy and/or protein deficiencies. PI was assessed with two 3-day-weighing records in each phase and assigned to 4 meals and 12 sources. During the usual care phase, mean PI was 41 ± 10 g/d. Lunch and dinner contributed 31 ± 11% and 32 ± 9% to daily intake, respectively. Dairy products (median 9 (interquartile range 6–14) g/d), starchy foods (7 (5–10) g/d) and meat/meat products (6 (3–9) g/d) were the main protein sources in usual PI. During the intervention phase, an additional 18 ± 10 g/d were consumed. Daily PI from usual sources did not differ between usual care and intervention phase (41 ± 10 g/d vs. 42 ± 11 g/d, p = 0.434). In conclusion, daily and per meal PI were very low in NH residents with (risk of) malnutrition, highlighting the importance of adequate intervention strategies. An individualized intervention successfully increased PI without affecting protein intake from usual sources.
The health effects of dairy products are still a matter of scientific debate owing to inconsistent findings across trials. Therefore, this systematic review and network meta-analysis (NMA) aimed to ...compare the effects of different dairy products on markers of cardiometabolic health. A systematic search was conducted in 3 electronic databases MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL), and Web of Science; search date: 23 September 2022. This study included randomized controlled trials (RCTs) with a ≥12-wk intervention comparing any 2 of the eligible interventions e.g., high dairy (≥3 servings/d or equal amount in grams per day), full-fat dairy, low-fat dairy, naturally fermented milk products, and low dairy/control (0–2 servings/d or usual diet). A pairwise meta-analysis and NMA using random-effects model was performed in the frequentist framework for 10 outcomes body weight, BMI, fat mass, waist circumference, low-density lipoprotein cholesterol, high-density lipoprotein (HDL) cholesterol, triglycerides, fasting glucose, glycated hemoglobin, and systolic blood pressure. Continuous outcome data were pooled using mean differences (MDs) and dairy interventions ranked using the surface under the cumulative ranking curve. Nineteen RCTs with 1427 participants were included. High-dairy intake (irrespective of fat content) showed no detrimental effects on anthropometric outcomes, blood lipids, and blood pressure. Both low-fat and full-fat dairy improved systolic blood pressure (MD: −5.22 to −7.60 mm Hg; low certainty) but, concomitantly, may impair glycemic control (fasting glucose—MD: 0.31–0.43 mmol/L; glycated hemoglobin—MD: 0.37%–0.47%). Full-fat dairy may increase HDL cholesterol compared with a control diet (MD: 0.26 mmol/L; 95% CI: 0.03, 0.49 mmol/L). Yogurt improved waist circumference (MD: −3.47 cm; 95% CI: −6.92, −0.02 cm; low certainty), triglycerides (MD: −0.38 mmol/L; 95% CI: −0.73, −0.03 mmol/L; low certainty), and HDL cholesterol (MD: 0.19 mmol/L; 95% CI: 0.00, 0.38 mmol/L) compared with milk. In conclusion, our findings indicate that there is little robust evidence that a higher dairy intake has detrimental effects on markers of cardiometabolic health.
This review was registered at PROSPERO as CRD42022303198.
Summary
Obesity and sarcopenic obesity (SO) are characterized by excess body fat with or without low muscle mass affecting bio‐psycho‐social health, functioning, and subsequently quality of life in ...older adults. We mapped outcomes addressed in randomized controlled trials (RCTs) on lifestyle interventions in community‐dwelling older people with (sarcopenic) obesity. Systematic searches in Medline, Embase, Cochrane Central, CINAHL, PsycInfo, Web of Science were conducted. Two reviewers independently performed screening and extracted data on outcomes, outcome domains, assessment methods, units, and measurement time. A bubble chart and heat maps were generated to visually display results. Fifty‐four RCTs (7 in SO) reporting 464 outcomes in the outcome domains: physical function (n = 42), body composition/anthropometry (n = 120), biomarkers (n = 190), physiological (n = 30), psychological (n = 47), quality of life (n = 14), pain (n = 4), sleep (n = 2), medications (n = 3), and risk of adverse health events (n = 5) were included. Heterogeneity in terms of outcome definition, assessment methods, measurement units, and measurement times was found. Psychological and quality of life domains were investigated in a minority of studies. There is almost no information beyond 52 weeks. This evidence map is the first step of a harmonization process to improve comparability of RCTs in older people with (sarcopenic) obesity and facilitate the derivation of evidence‐based clinical decisions.
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