Muscle-aging drives sarcopenia and is a major public health issue. Mice are frequently used as a model for human muscle-aging, however, research investigating their translational value is limited. In addition, mechanisms underlying muscle-aging may have sex-specific features in humans, but it is not yet assessed whether these are recapitulated in mice. Here, we studied the effects of aging on a functional, histological and transcriptional level at multiple timepoints in male and female mice (4, 17, 21 and 25 months), with particular emphasis on sex-differences. The effects of natural aging on the transcriptome of quadriceps muscle were compared to humans on pathway level. Significant loss of muscle mass occurred late, at 25 months, in both male (-17%, quadriceps) and female mice (-10%, quadriceps) compared to young control mice. Concomitantly, we found in female, but not male mice, a slower movement speed in the aged groups compared to the young mice (P < 0.001). Consistently, weighted gene co-expression network analysis revealed a stronger association between the aging-related reduction of movement and aging-related changes in muscle transcriptome of female compared to male mice (P < 0.001). In male, but not female mice, major distinctive aging-related changes occurred in the last age group (25 months), which highlights the necessity for careful selection of age using mice as a muscle-aging model. Furthermore, contrasting to humans, more aging-related changes were found in the muscle transcriptome of male mice compared to female mice (4090 vs. 2285 differentially expressed genes at 25 months, respectively). Subsequently, male mice recapitulated more muscle-aging related pathways characteristic for both male and female humans. In conclusion, our data show that sex has a critical effect on the mouse muscle-aging trajectory, although these do not necessarily reflect sex differences observed in the human muscle-aging trajectory.

The prevalence of sarcopenia is increasing while it is often challenging, expensive and time-consuming to test the effectiveness of interventions against sarcopenia. Translational mouse models that adequately mimic underlying physiological pathways could accelerate research but are scarce. Here, we investigated the translational value of three potential mouse models for sarcopenia, namely partial immobilized (to mimic sedentary lifestyle), caloric restricted (CR; to mimic malnutrition) and a combination (immobilized & CR) model. C57BL/6J mice were calorically restricted (-40%) and/or one hindleg was immobilized for two weeks to induce loss of muscle mass and function. Muscle parameters were compared to those of young control (4 months) and old reference mice (21 months). Transcriptome analysis of quadriceps muscle was performed to identify underlying pathways and were compared with those being expressed in aged human vastus lateralis muscle-biopsies using a meta-analysis of five different human studies. Caloric restriction induced overall loss of lean body mass (-15%, p<0.001), whereas immobilization decreased muscle strength (-28%, p<0.001) and muscle mass of hindleg muscles specifically (on average -25%, p<0.001). The proportion of slow myofibers increased with aging in mice (+5%, p<0.05), and this was not recapitulated by the CR and/or immobilization models. The diameter of fast myofibers decreased with aging (-7%, p<0.05), and this was mimicked by all models. Transcriptome analysis revealed that the combination of CR and immobilization recapitulated more pathways characteristic for human muscle-aging (73%) than naturally aged (21 months old) mice (45%). In conclusion, the combination model exhibits loss of both muscle mass (due to CR) and function (due to immobilization) and has a remarkable similarity with pathways underlying human sarcopenia. These findings underline that external factors such as sedentary behavior and malnutrition are key elements of a translational mouse model and favor the combination model as a rapid model for testing the treatments against sarcopenia.

Lifestyle interventions remain the treatment of choice for patients with obesity and metabolic complications, yet are difficult to maintain and often lead to cycles of weight loss and regain (weight cycling). Literature on weight cycling remains controversial and we therefore investigated the association between weight cycling and metabolic complications using preexistent obese mice. Ldlr−/−.Leiden mice received a high‐fat diet (HFD) for 20 weeks to induce obesity. Subsequently, weight‐cycled mice were switched between the healthy chow diet and HFD for four 2‐week periods and compared to mice that received HFD for the total study period. Repeated weight cycling tended to decrease body weight and significantly reduced fat mass, whereas adipose tissue inflammation was similar relative to HFD controls. Weight cycling did not significantly affect blood glucose or plasma insulin levels yet significantly reduced plasma free fatty acid and alanine transaminase/aspartate transaminase levels. Hepatic macrovesicular steatosis was similar and microvesicular steatosis tended to be increased upon weight cycling. Weight cycling resulted in a robust decrease in hepatic inflammation compared to HFD controls while hepatic fibrosis and atherosclerosis development were not affected. These results argue against the postulate that repeated weight cycling leads to unfavorable metabolic effects, when compared to a continuous unhealthy lifestyle, and in fact revealed beneficial effects on hepatic inflammation, an important hallmark of non‐alcoholic steatohepatitis.

Blood-based biomarkers allow monitoring of an individual’s health status and provide insights into metabolic and inflammatory processes in conditions like obesity, cardiovascular, and liver diseases. However, selecting suitable biomarkers and optimizing analytical assays presents challenges, is time-consuming and laborious. Moreover, knowledge of potential sex differences remains incomplete as research is often carried out in men. This study aims at enabling researchers to make informed choices on the type of biomarkers, analytical assays, and dilutions being used. More specifically, we analyzed plasma concentrations of >90 biomarkers using commonly available ELISA or electrochemiluminescence-based multiplex methods, comparing normal weight (BMI < 25; n = 40) with obese (BMI > 30; n = 40) adult blood donors of comparable age. To help choose optimal biomarker sets, we grouped frequently employed biomarkers into biological categories (e.g., adipokines, acute-phase proteins, complement factors, cytokines, myokines, iron metabolism, vascular inflammation), first comparing normal-weight with obese persons, and thereafter exploratively comparing women and men within each BMI group. Many biomarkers linked to chronic inflammation and dysmetabolism were elevated in persons with obesity, including several adipokines, interleukins, chemokines, acute-phase proteins, complement factors, and oxidized LDL. Further exploration suggests sex disparities in biomarker levels within both normal-weight and obese groups. This comprehensive dataset of biomarkers across diverse biological domains constitutes a reference resource that may provide valuable guidance for researchers in selecting appropriate biomarkers and analytical assays for own studies. Moreover, the dataset highlights the importance of taking possible sex differences into account.

Background: A digital-first strategy is increasingly implemented to reduce participant burden, accelerate recruitment, collect real-world data, and increase the diversity of the study population. However, fully remote studies lack face-to-face interaction, which may affect motivation, particularly in the delivery of personalized nutritional advice. Additionally, self-reported data may vary in terms of standardization and completeness. Objective: The study’s primary objective is to evaluate the feasibility of conducting a fully remote, fully digital randomized controlled nutritional intervention, including participant experience and the capability to perform do-it-yourself anthropometric measurements at home. Secondary objectives are to determine whether self-collected data could detect changes in body weight and other anthropometric outcomes, and to compare the effectiveness of generic versus personalized nutrition advice, with and without personalized food boxes. Methods: We conducted a fully online, 3-arm randomized controlled trial including adults with overweight or obesity who were motivated to lose weight. Participants were assigned to a control group that received generic advice (n=43), a personalized intervention group that received personalized advice only (n=40), or a personalized intervention plus group that received personalized advice plus personalized food boxes (n=39). The 6-week intervention was delivered entirely digitally, and all anthropometric measurements, questionnaires, and dietary data were self-collected at home. Feasibility was assessed using adherence metrics, completion of self-measurements, and a user-experience questionnaire. Secondary analyses evaluated weight loss, changes in anthropometry, and exploratory associations, including sex differences. Results: Feasibility was high—102 out of 122 (83.6%) participants found the self-measured anthropometric assessments easy to perform, and 112 (91.8%) participants reported that completing questionnaires from home was easy. For secondary outcomes, participants receiving personalized, but not generic, nutritional advice significantly lost body weight (–1.0 kg; P=.002). Participants receiving personalized food boxes in addition to personalized nutritional advice lost significantly more body weight than the other 2 groups (–2.5 kg; P=.001) and also showed a decrease in hip circumference (–2.9 cm; P=.01). Personalized advice was not easier or more enjoyable to implement than generic nutritional advice, whereas the addition of personalized food boxes improved the ease of implementing personalized nutritional advice (P<.001). All participants, irrespective of the intervention arm, reduced intake of unhealthy food groups, including ready-made meals (113.6 g vs 78.5 g, –30.9%); sauces and gravy (18.8 g vs 10.0 g, –46.8%); sweet snacks (84.8 g vs 64.1 g, –24.4%); savory snacks (50.5 g vs 40.0 g, –20.1%); bread, pasta, rice, and wraps (nutritional quality score of 1.9 vs 1.7, –10.5%); and vegetables (129.0 g vs 118.7 g, –8.0%); and replaced coffee with tea. Conclusions: This study demonstrates that fully remote, participant-led nutritional intervention studies are feasible, with participants able to independently perform anthropometric measurements and self-report data of sufficient quality to detect meaningful effects. Personalized nutritional advice resulted in greater weight loss than generic advice, and the addition of personalized food boxes further enhanced the beneficial anthropometric effects of the intervention. We conclude that such nutritional intervention studies can be conducted fully online, resulting in measurable anthropometric effects after 6 weeks. Trial Registration: ClinicalTrials.gov NCT06547983; https://clinicaltrials.gov/ct2/show/NCT06547983

: Frailty is characterized by loss of physical function and is preferably diagnosed at an early stage (e.g., during pre-frailty). Unfortunately, sensitive tools that can aid early detection are lacking. Blood-based biomarkers, reflecting pathophysiological adaptations before physical symptoms become apparent, could be such tools. We identified candidate biomarkers using a mechanism-based computational approach which integrates a priori defined database-derived clinical biomarkers and skeletal muscle transcriptome data. Identified candidate biomarkers were used as input for a sex-specific correlation analysis, using individual gene expression data from female (n = 24) and male (n = 28) older adults (all 75 + years, ranging from fit to pre-frail) and three frailty-related physical parameters. Male and female groups were matched based on age, BMI, and Fried frailty index. The best correlating candidate biomarkers were evaluated, and selected biomarkers were measured in serum. In females, myostatin and galectin-1 and, in males, cathepsin B and thrombospondin-4 serum levels were significantly different between the physically weakest and fittest participants (all p < 0.05). Logistic regression confirmed the added value of these biomarkers in conjunction with age and BMI to predict whether the subjects belonged to the weaker or fittest group (AUC = 0.80 in females and AUC = 0.83 in males). In conclusion, both in silico and in vivo analyses revealed the sex-specificity of candidate biomarkers, and we identified a selection of potential biomarkers which could be used in a biomarker panel for early detection of frailty. Further investigation is needed to confirm these leads for early detection of frailty.