Maria Vasiloglou

Dr. Maria Vasiloglou

Nestle Institute of Health Sciences, 1000 Lausanne, Switzerland
orcid

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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EREmilie Reber
Emilie Reber
FGFilomena Gomes
Filomena Gomes
MVMaria Vasiloglou
Maria Vasiloglou
PSPhilipp Schuetz
Philipp Schuetz
ZSZeno Stanga
Zeno Stanga
Malnutrition is an independent risk factor that negatively influences patients’ clinical outcomes, quality of life, body function, and autonomy. Early identification of patients at risk of malnutrition or who are malnourished is crucial in order to start a timely and adequate nutritional support. Nutritional risk screening, a simple and rapid first-line tool to detect patients at risk of malnutrition, should be performed systematically in patients at hospital admission. Patients with nutritional risk should subsequently undergo a more detailed nutritional assessment to identify and quantify specific nutritional problems. Such an assessment includes subjective and objective parameters such as medical history, current and past dietary intake (including energy and protein balance), physical examination and anthropometric measurements, functional and mental assessment, quality of life, medications, and laboratory values. Nutritional care plans should be developed in a multidisciplinary approach, and implemented to maintain and improve patients’ nutritional condition. Standardized nutritional management including systematic risk screening and assessment may also contribute to reduced healthcare costs. Adequate and timely implementation of nutritional support has been linked with favorable outcomes such as a decrease in length of hospital stay, reduced mortality, and reductions in the rate of severe complications, as well as improvements in quality of life and functional status. The aim of this review article is to provide a comprehensive overview of nutritional screening and assessment methods that can contribute to an effective and well-structured nutritional management (process cascade) of hospitalized patients.

26 September 2015 in Diabetologia
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SPStergios A Polyzos
Stergios A Polyzos
Konstantinos N AronisKonstantinos N Aronis
Konstantinos N Aronis
JKJannis Kountouras
Jannis Kountouras
DRDimitrios D. Raptis
Dimitrios D. Raptis
MVMaria Vasiloglou
Maria Vasiloglou
CMChristos S. Mantzoros
Christos S. Mantzoros

1 September 2014 in The Journal of Clinical Endocrinology & Metabolism
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AAAthanasios D. Anastasilakis
Athanasios D. Anastasilakis
SPStergios A Polyzos
Stergios A Polyzos
ZSZacharias G. Saridakis
Zacharias G. Saridakis
GKGeorgios Kynigopoulos
Georgios Kynigopoulos
ESElpida C. Skouvaklidou
Elpida C. Skouvaklidou
CMChristos S. Mantzoros
Christos S. Mantzoros
The myokine irisin may increase energy expenditure and affect metabolism. The objective of the study was to elucidate predictors of irisin and study whether circulating irisin may have day-night rhythm in humans. This was an observational, cross-sectional study with an additional 24-hour prospective observational arm (day-night rhythm substudy) and two prospective interventional arms (mixed meal substudy and exercise substudy). The study was conducted at the Hellenic Military School of Medicine (Thessaloniki, Greece). One hundred twenty-two healthy, young individuals were subjected to anthropometric and body composition measurements, and their eating and exercise behavior profiles were assessed with validated questionnaires. Subgroups were subjected to day-night rhythm, standardized meal ingestion, and 30-minute aerobic exercise studies. Circulating irisin levels were measured. Ιrisin levels were lower in males than females (P = .02) after adjustment for lean body mass, which was its major determinant. Irisin levels followed a day-night rhythm (P < .001) with peak at 9:00 pm. Irisin levels were increased at the end of exercise (84.1 ± 10.0 vs 105.8 ± 14.3 ng/mL; P < .001). Irisin levels were not affected by intake of a standardized meal and were not associated with caloric intake or diet quality. In healthy, young individuals, circulating irisin displays a day-night rhythm, is correlated with lean body mass, and increases acutely after exercise.

19 March 2024 in Nature Metabolism
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MMMathieu Membrez
Mathieu Membrez
EMEugenia Migliavacca
Eugenia Migliavacca
SCStefan Christen
Stefan Christen
KYKeisuke Yaku
Keisuke Yaku
JTJennifer Trieu
Jennifer Trieu
JFJerome Feige
Jerome Feige
Mitochondrial dysfunction and low nicotinamide adenine dinucleotide (NAD+) levels are hallmarks of skeletal muscle ageing and sarcopenia1–3, but it is unclear whether these defects result from local changes or can be mediated by systemic or dietary cues. Here we report a functional link between circulating levels of the natural alkaloid trigonelline, which is structurally related to nicotinic acid4, NAD+ levels and muscle health in multiple species. In humans, serum trigonelline levels are reduced with sarcopenia and correlate positively with muscle strength and mitochondrial oxidative phosphorylation in skeletal muscle. Using naturally occurring and isotopically labelled trigonelline, we demonstrate that trigonelline incorporates into the NAD+ pool and increases NAD+ levels in Caenorhabditis elegans, mice and primary myotubes from healthy individuals and individuals with sarcopenia. Mechanistically, trigonelline does not activate GPR109A but is metabolized via the nicotinate phosphoribosyltransferase/Preiss–Handler pathway5,6 across models. In C. elegans, trigonelline improves mitochondrial respiration and biogenesis, reduces age-related muscle wasting and increases lifespan and mobility through an NAD+-dependent mechanism requiring sirtuin. Dietary trigonelline supplementation in male mice enhances muscle strength and prevents fatigue during ageing. Collectively, we identify nutritional supplementation of trigonelline as an NAD+-boosting strategy with therapeutic potential for age-associated muscle decline.

5 July 2024 in npj Digital Medicine
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BSBen Singh
Ben Singh
MAMavra Ahmed
Mavra Ahmed
Amanda StaianoAmanda Staiano
Amanda Staiano
CGClaire Gough
Claire Gough
JPJasmine Petersen
Jasmine Petersen
CMCarol A. Maher
Carol A. Maher
The aim of this meta-meta-analysis was to systematically review randomised controlled trial (RCT) evidence examining the effectiveness of e- and m-Health interventions designed to improve physical activity, sedentary behaviour, healthy eating and sleep. Nine electronic databases were searched for eligible studies published from inception to 1 June 2023. Systematic reviews with meta-analyses of RCTs that evaluate e- and m-Health interventions designed to improve physical activity, sedentary behaviour, sleep and healthy eating in any adult population were included. Forty-seven meta-analyses were included, comprising of 507 RCTs and 206,873 participants. Interventions involved mobile apps, web-based and SMS interventions, with 14 focused on physical activity, 3 for diet, 4 for sleep and 26 evaluating multiple behaviours. Meta-meta-analyses showed that e- and m-Health interventions resulted in improvements in steps/day (mean difference, MD = 1329 [95% CI = 593.9, 2065.7] steps/day), moderate-to-vigorous physical activity (MD = 55.1 [95% CI = 13.8, 96.4] min/week), total physical activity (MD = 44.8 [95% CI = 21.6, 67.9] min/week), sedentary behaviour (MD = −426.3 [95% CI = −850.2, −2.3] min/week), fruit and vegetable consumption (MD = 0.57 [95% CI = 0.11, 1.02] servings/day), energy intake (MD = −102.9 kcals/day), saturated fat consumption (MD = −5.5 grams/day), and bodyweight (MD = −1.89 [95% CI = −2.42, −1.36] kg). Analyses based on standardised mean differences (SMD) showed improvements in sleep quality (SMD = 0.56, 95% CI = 0.40, 0.72) and insomnia severity (SMD = −0.90, 95% CI = −1.14, −0.65). Most subgroup analyses were not significant, suggesting that a variety of e- and m-Health interventions are effective across diverse age and health populations. These interventions offer scalable and accessible approaches to help individuals adopt and sustain healthier behaviours, with implications for broader public health and healthcare challenges.

31 July 2020 in Sensors
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YLYa Lu
Ya Lu
TSThomai Stathopoulou
Thomai Stathopoulou
MVMaria Vasiloglou
Maria Vasiloglou
LPLillian F. Pinault
Lillian F. Pinault
CKColleen Kiley
Colleen Kiley
Stavroula MougiakakouStavroula Mougiakakou
Stavroula Mougiakakou
Accurate estimation of nutritional information may lead to healthier diets and better clinical outcomes. We propose a dietary assessment system based on artificial intelligence (AI), named goFOODTM. The system can estimate the calorie and macronutrient content of a meal, on the sole basis of food images captured by a smartphone. goFOODTM requires an input of two meal images or a short video. For conventional single-camera smartphones, the images must be captured from two different viewing angles; smartphones equipped with two rear cameras require only a single press of the shutter button. The deep neural networks are used to process the two images and implements food detection, segmentation and recognition, while a 3D reconstruction algorithm estimates the food’s volume. Each meal’s calorie and macronutrient content is calculated from the food category, volume and the nutrient database. goFOODTM supports 319 fine-grained food categories, and has been validated on two multimedia databases that contain non-standardized and fast food meals. The experimental results demonstrate that goFOODTM performed better than experienced dietitians on the non-standardized meal database, and was comparable to them on the fast food database. goFOODTM provides a simple and efficient solution to the end-user for dietary assessment.