The aging process is often accompanied by high risk of malnutrition and elevated levels of growth differentiation factor 15 (GDF15). GDF15 is an increasingly recognized biomarker for regulation of metabolism, but few studies have investigated the connection between GDF15 and malnutrition in older age and how it relates to other features of aging such as decreased appetite and physical function. Therefore, we investigated the associations between GDF15 levels and nutritional status, appetite, and physical function in acutely admitted older adults. Plasma GDF15 levels were measured using immunoassays in 302 older adults (≥65 years) admitted to the emergency department (ED). Nutritional status was evaluated with the Mini Nutritional Assessment Short-Form (MNA®-SF), appetite was evaluated with the Simplified Nutritional Appetite Questionnaire (SNAQ), and physical function was evaluated with handgrip strength (HGS), 30-s chair stand test (30s-RSS), and gait speed (GS). Associations between GDF15 and each outcome was determined by logistic regression adjusted for age, sex, and C-reactive protein (CRP). Each doubling in plasma GDF15 level was associated with an adjusted odds ratio (OR) (95% confidence interval) of 1.59 (1.10-2.29, P = 0.01) for risk of malnutrition compared to normal nutrition and 1.19 (0.85-1.69, P = 0.3)) for malnutrition compared to risk of malnutrition. Each doubling in GDF15 was associated with an adjusted OR of 1.63 (1.21-2.23)) for having poor appetite, 1.46 (1.07-1.99) for having low HGS, 1.74 (1.23-2.51) for having low 30s-RSS, and 1.99 (1.39-2.94) for having low GS. Among older adults admitted to the ED, higher GDF15 levels were significantly associated with malnutrition, poor appetite, and low physical function independent of age, sex, and CRP.

The accuracy of multi-frequency (MF) bioelectrical impedance analysis (BIA) to estimate low muscle mass in older hospitalized patients remains unclear. This study aimed to describe the ability of MF-BIA to identify low muscle mass as proposed by The Global Leadership Initiative on Malnutrition (GLIM) and The European Working Group on Sarcopenia in Older People (EWGSOP-2) and examine the association between muscle mass, dehydration, malnutrition, and poor appetite in older hospitalized patients. In this prospective exploratory cohort study, low muscle mass was estimated with MF-BIA against dual-energy X-ray absorptiometry (DXA) in 42 older hospitalized adults (≥65 years). The primary variable for muscle mass was appendicular skeletal muscle mass (ASM), and secondary variables were appendicular skeletal muscle mass index (ASMI) and fat-free mass index (FFMI). Cut-off values for low muscle mass were based on recommendations by GLIM and EWGSOP-2. MF-BIA was evaluated against DXA on the ability to estimate absolute values of muscle mass by mean bias, limits of agreement (LOA), and accuracy (5% and 10% levels). Agreement between MF-BIA and DXA to identify low muscle mass was evaluated with sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). The association between muscle mass, dehydration, malnutrition, and poor appetite was visually examined with boxplots. MF-BIA overestimated absolute values of ASM with a mean bias of 0.63 kg (CI: −0.20:1.46, LOA: −4.61:5.87). Agreement between MF-BIA and DXA measures of ASM showed a sensitivity of 86%, specificity of 94%, PPV of 75% and NPV of 97%. Boxplots indicate that ASM is lower in patients with malnutrition. This was not observed in patients with poor appetite. We observed a tendency toward higher ASM in patients with dehydration. Estimation of absolute ASM values with MF-BIA should be interpreted with caution, but MF-BIA might identify low muscle mass in older hospitalized patients.

Aim: Cannabis‐based medicines are gaining interest and being explored for new therapeutic indications, many of which commonly affect older medical patients. As most previous studies of the population pharmacokinetics of cannabinoids have been performed in healthy adults, this study aimed to investigate the population pharmacokinetics of Δ ⁹ ‐tetrahydrocannabinol (THC) and its active metabolite 11‐hydroxy‐Δ ⁹ ‐tetrahydrocannabinol (THC‐OH) in older medical patients with poor appetite. Methods: We administered two fixed doses of Sativex® oromucosal spray to 20 patients, each dose consisting of two or three sprays (2.7 mg THC and 2.5 mg cannabidiol per spray), with a dosing interval of 4 h. Blood samples were collected for up to 8 h to obtain plasma concentration‐time data for non‐linear mixed‐effects modelling. Population pharmacokinetic models were developed for THC and THC‐OH sequentially, resulting in a combined parent‐metabolite model. Results: We found a one‐compartment model and a two‐compartment model to be the best fits for THC and THC‐OH, respectively, with apparent clearance of THC through conversion to THC‐OH (765 L h ⁻¹ ) and other pathways (162 L h ⁻¹ ). Absorption of THC was modelled with a delay through three transit compartments. The inter‐ and intra‐individual variability on pharmacokinetic parameters was generally large (CV% = 40.2‐152%). Using the parent‐metabolite model, it was investigated whether physiological characteristics such as kidney function and body composition influence THC pharmacokinetics. Conclusion: The parent‐metabolite model describes and quantifies the pharmacokinetics of oromucosally administered THC in older medical patients with poor appetite. The covariate analysis did not show any clinically significant effect on pharmacokinetic parameters of THC or THC‐OH.

Medication review in the hospital is an effective tool for identifying inappropriate prescribing, but not every patient can receive one due to resource constraints. Thus, it is important to identify patients who stand to benefit most from medication review. Patients are typically identified for medication review based on number of medications and age, but this may not be the most efficient strategy for identifying patients at high risk of future adverse clinical outcomes. In this exploratory study of 27 873 acutely admitted medical patients (≥18 years) at Copenhagen University Hospital Hvidovre, Denmark, we investigated whether other clinical factors could improve the prediction of adverse clinical outcomes and, therefore, be incorporated into patient stratification for medication review. The outcomes of interest were acute readmission or mortality within 90 days post‐discharge. Medication risk score (MERIS), renal function and anticholinergic cognitive burden did not improve prediction of adverse clinical outcomes compared to number of medications and age. However, addition of a frailty index based on routine biomarkers (FI‐OutRef) or plasma soluble urokinase plasminogen activator receptor (suPAR) did improve prediction of outcomes. Use of these practical proxies of disease burden may improve the identification of acutely hospitalized patients who would benefit most from a medication review.