Association between the consumption of ultra-processed foods and the incidence of peptic ulcer disease in the SUN project: a Spanish prospective cohort study.

PURPOSE: Consumption of ultra-processed foods (UPF) has increased despite potential adverse health effects. Recent studies showed an association between UPF consumption and some gastrointestinal disorders. We evaluated the association between UPF consumption and peptic ulcer disease (PUD) in a large Spanish cohort. METHODS: We conducted a prospective analysis of 18,066 participants in the SUN cohort, followed every two years. UPF was assessed at baseline and 10 years after. Cases of PUD were identified among participants reporting a physician-made diagnosis of PUD during follow-ups. Cases were only partially validated against medical records. Cox regression was used to assess the association between baseline UPF consumption and PUD risk. Based on previous findings and biological plausibility, socio-demographic and lifestyle variables, BMI, energy intake, Helicobacter pylori infection, gastrointestinal disorders, aspirin and analgesic use, and alcohol and coffee consumption were included as confounders.We fitted GEE with repeated dietary measurements at baseline and after 10 years of follow-up. Vanderweele's proposed E value was calculated to assess the sensitivity of observed associations to uncontrolled confounding. RESULTS: During a median follow-up of 12.2 years, we recorded 322 new PUD cases (1.56 cases/1000 person-years). Participants in the highest baseline tertile of UPF consumption had an increased PUD risk compared to participants in the lowest tertile (HR = 1.52, 95% CI: 1.15, 2.00, Ptrend=0.002). The E-values for the point estimate supported the observed association. The OR using repeated measurements of UPF intake was 1.39 (95% CI: 1.03, 1.87) when comparing extreme tertiles. CONCLUSION: The consumption of UPF is associated with an increased PUD risk.

Long-term health outcome and quality of life in children with multisystem inflammatory syndrome: findings from multidisciplinary follow-up at an Italian tertiary-care paediatric hospital.

Multisystem inflammatory syndrome is a severe complication of SARS-CoV-2 infection in children (MIS-C). To date, data on long-term sequelae mainly concern cardiac outcomes. All ≤ 18 year olds consecutively admitted to the Buzzi Children's Hospital with a diagnosis of MIS-C between October 1, 2020, and May 31, 2022, were followed up for up to 12 months by a dedicated multidisciplinary team. They underwent laboratory tests, multi-organ clinical and instrumental assessments, and psychosocial evaluation. 56/62 patients, 40 M, mean age 8.7 years (95% CI 7.7, 9.7), completed the follow-up. Cardiological, gastroenterological, pneumological, and neurological evaluations, including IQ and EEG, were normal. Alterations of HOMA-IR index and/or TyG index, observed in almost all patients during hospitalisation, persisted in about a third of the population at 12 months. At 6 and 12 months respectively, impairment of adaptive functions was observed in 38/56 patients (67.9%) and 25/56 (44.6%), emotional and behavioural problems in 10/56 (17.9%) and 9/56 (16.1%), and decline in QoL in 14/56 (25.0%) and 9/56 (16.1%). Psychosocial well-being impairment was significantly more frequent in the subgroup with persistent glycometabolic dysfunction at 12 months (75% vs. 40.9% p < 0.001). CONLUSION: The mechanisms that might explain the long-term persistence of both metabolic alterations and neuro-behavioural outcomes and their possible relationship are far from being clarified. Our study points out to the potential long-term effects of pandemics and to the importance of a multidisciplinary follow-up to detect potential negative sequelae in different areas of health, both physical and psychosocial. WHAT IS KNOWN: • Multisystem inflammatory syndrome in children (MIS-C) is a severe complication of SARS-CoV-2 infection. • Few data exist on the medium- and long-term outcomes of MIS-C, mostly focused on cardiac involvement. Emerging evidence shows neurological and psychological sequelae at mid- and long-term follow-up. WHAT IS NEW: • This study reveals that MIS-C may lead to long-term glycometabolic dysfunctions joined to impairment in the realm of general well-being and decline in quality of life, in a subgroup of children. • This study highlights the importance of a long-term multidisciplinary follow-up of children hospitalised with MIS-C, in order to detect the potential long-term sequelae in different areas of health, both physical and psychosocial well-being.

Aluminum Nitride Thin Film Piezoelectric Pressure Sensor for Respiratory Rate Detection.

In this study, we propose a low-cost piezoelectric flexible pressure sensor fabricated on Kapton® (Kapton™ Dupont) substrate by using aluminum nitride (AlN) thin film, designed for the monitoring of the respiration rate for a fast detection of respiratory anomalies. The device was characterized in the range of 15-30 breaths per minute (bpm), to simulate moderate difficult breathing, borderline normal breathing, and normal spontaneous breathing. These three breathing typologies were artificially reproduced by setting the expiratory to inspiratory ratios (E:I) at 1:1, 2:1, 3:1. The prototype was able to accurately recognize the breath states with a low response time (~35 ms), excellent linearity (R2 = 0.997) and low hysteresis. The piezoelectric device was also characterized by placing it in an activated carbon filter mask to evaluate the pressure generated by exhaled air through breathing acts. The results indicate suitability also for the monitoring of very weak breath, exhibiting good linearity, accuracy, and reproducibility, in very low breath pressures, ranging from 0.09 to 0.16 kPa. These preliminary results are very promising for the future development of smart wearable devices able to monitor different patients breathing patterns, also related to breathing diseases, providing a suitable real-time diagnosis in a non-invasive and fast way.

A Deep Learning-Based Platform for Workers' Stress Detection Using Minimally Intrusive Multisensory Devices.

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals' overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.

Effect of acute meal and long-term intake of a Mediterranean Diet providing different amounts of carbohydrates on physical performance and biomarkers in non-professional strength athletes.

The study aims to evaluate the effect of an acute meal and long-term intake of Mediterranean Diet (MD) on different parameters such as strength, physical performance, body composition and blood markers in a group of non-professional athletes who practice a strength activity. Thirteen volunteers completed two 8-week dietary interventions in a randomised, cross-over design. Also an acute study was performed. Subjects received a MD High in carbohydrates, characterised by at least five portions of pasta/week and an average 55-60% of daily energy derived from carbohydrates, versus an MD reduced in carbohydrates, with less than two portions of pasta/week and an average of 40-45% of daily energy provided by carbohydrates. Mainly, data did not show significant differences for the parameters analysed, except for Elbow Flexor maximum voluntary contraction (p = .039). Results enlighten that increasing total carbohydrates intake, as typically in the MD, does not negatively affect physical performance, body composition and strength.

Longitudinal Anthropometry and Body Composition in Children With SARS-CoV-2-Associated Multisystem Inflammatory Syndrome.

OBJECTIVES: Acute coronavirus disease 2019 infection has been shown to negatively affect body composition among adult and malnourished or obesity children. Our aim is to longitudinally evaluate body composition in children affected by the Multisystem Inflammatory Syndrome (MIS-C). METHODS: In this cohort study, we recruited 40 patients affected by MIS-C, aged 2-18 years old, who were admitted in our clinic between December 2020 and February 2021. Physical examination for each participant included weight, height, body mass index (BMI) z score, circumferences, and skinfolds assessment. The same measurements were repeated during outpatient follow-up at 10 (T2), 30 (T3), 90 (T4), and 180 (T5) days after hospital discharge. Fat mass and fat free mass were calculated according to skinfolds predictive equations for children and adolescents. A control group was randomly selected among patients attending a pediatric nutritional outpatient clinic. RESULTS: BMI z score significantly decrease between preadmission and hospital discharge. Similarly, arm circumference z score, arm muscular area z score, and arm fat area z score significantly decreased, during hospital stay. Fat mass index (FMI) significantly increased over time, peaking at T3. Fat free mass index decreased during hospitalization. CONCLUSIONS: To the best of our knowledge, this is the first study to assess body composition in a numerically large pediatric MIS-C population from acute infection to 6 months after triggering event. FMI and anthropometric parameters linked to fat deposits were significantly higher 6 months after acute event. Thus, limiting physical activity and having sedentary lifestyle may lead to an accumulation of adipose tissue even in healthy children who experienced MIS-C and long hospitalization.

Integrating Abnormal Gait Detection with Activities of Daily Living Monitoring in Ambient Assisted Living: A 3D Vision Approach.

Gait analysis plays a crucial role in detecting and monitoring various neurological and musculoskeletal disorders early. This paper presents a comprehensive study of the automatic detection of abnormal gait using 3D vision, with a focus on non-invasive and practical data acquisition methods suitable for everyday environments. We explore various configurations, including multi-camera setups placed at different distances and angles, as well as performing daily activities in different directions. An integral component of our study involves combining gait analysis with the monitoring of activities of daily living (ADLs), given the paramount relevance of this integration in the context of Ambient Assisted Living. To achieve this, we investigate cutting-edge Deep Neural Network approaches, such as the Temporal Convolutional Network, Gated Recurrent Unit, and Long Short-Term Memory Autoencoder. Additionally, we scrutinize different data representation formats, including Euclidean-based representations, angular adjacency matrices, and rotation matrices. Our system's performance evaluation leverages both publicly available datasets and data we collected ourselves while accounting for individual variations and environmental factors. The results underscore the effectiveness of our proposed configurations in accurately classifying abnormal gait, thus shedding light on the optimal setup for non-invasive and efficient data collection.

Review on Human Action Recognition in Smart Living: Sensing Technology, Multimodality, Real-Time Processing, Interoperability, and Resource-Constrained Processing.

Smart living, a concept that has gained increasing attention in recent years, revolves around integrating advanced technologies in homes and cities to enhance the quality of life for citizens. Sensing and human action recognition are crucial aspects of this concept. Smart living applications span various domains, such as energy consumption, healthcare, transportation, and education, which greatly benefit from effective human action recognition. This field, originating from computer vision, seeks to recognize human actions and activities using not only visual data but also many other sensor modalities. This paper comprehensively reviews the literature on human action recognition in smart living environments, synthesizing the main contributions, challenges, and future research directions. This review selects five key domains, i.e., Sensing Technology, Multimodality, Real-time Processing, Interoperability, and Resource-Constrained Processing, as they encompass the critical aspects required for successfully deploying human action recognition in smart living. These domains highlight the essential role that sensing and human action recognition play in successfully developing and implementing smart living solutions. This paper serves as a valuable resource for researchers and practitioners seeking to further explore and advance the field of human action recognition in smart living.

Human Action Recognition in Smart Living Services and Applications: Context Awareness, Data Availability, Personalization, and Privacy.

Smart living, an increasingly prominent concept, entails incorporating sophisticated technologies in homes and urban environments to elevate the quality of life for citizens. A critical success factor for smart living services and applications, from energy management to healthcare and transportation, is the efficacy of human action recognition (HAR). HAR, rooted in computer vision, seeks to identify human actions and activities using visual data and various sensor modalities. This paper extensively reviews the literature on HAR in smart living services and applications, amalgamating key contributions and challenges while providing insights into future research directions. The review delves into the essential aspects of smart living, the state of the art in HAR, and the potential societal implications of this technology. Moreover, the paper meticulously examines the primary application sectors in smart living that stand to gain from HAR, such as smart homes, smart healthcare, and smart cities. By underscoring the significance of the four dimensions of context awareness, data availability, personalization, and privacy in HAR, this paper offers a comprehensive resource for researchers and practitioners striving to advance smart living services and applications. The methodology for this literature review involved conducting targeted Scopus queries to ensure a comprehensive coverage of relevant publications in the field. Efforts have been made to thoroughly evaluate the existing literature, identify research gaps, and propose future research directions. The comparative advantages of this review lie in its comprehensive coverage of the dimensions essential for smart living services and applications, addressing the limitations of previous reviews and offering valuable insights for researchers and practitioners in the field.

Human Postures Recognition by Accelerometer Sensor and ML Architecture Integrated in Embedded Platforms: Benchmarking and Performance Evaluation.

Embedded hardware systems, such as wearable devices, are widely used for health status monitoring of ageing people to improve their well-being. In this context, it becomes increasingly important to develop portable, easy-to-use, compact, and energy-efficient hardware-software platforms, to enhance the level of usability and promote their deployment. With this purpose an automatic tri-axial accelerometer-based system for postural recognition has been developed, useful in detecting potential inappropriate behavioral habits for the elderly. Systems in the literature and on the market for this type of analysis mostly use personal computers with high computing resources, which are not easily portable and have high power consumption. To overcome these limitations, a real-time posture recognition Machine Learning algorithm was developed and optimized that could perform highly on platforms with low computational capacity and power consumption. The software was integrated and tested on two low-cost embedded platform (Raspberry Pi 4 and Odroid N2+). The experimentation stage was performed on various Machine Learning pre-trained classifiers using data of seven elderly users. The preliminary results showed an activity classification accuracy of about 98% for the four analyzed postures (Standing, Sitting, Bending, and Lying down), with similar accuracy and a computational load as the state-of-the-art classifiers running on personal computers.

Benchmarking of Contactless Heart Rate Measurement Systems in ARM-Based Embedded Platforms.

Heart rate monitoring is especially important for aging individuals because it is associated with longevity and cardiovascular risk. Typically, this vital parameter can be measured using wearable sensors, which are widely available commercially. However, wearable sensors have some disadvantages in terms of acceptability, especially when used by elderly people. Thus, contactless solutions have increasingly attracted the scientific community in recent years. Camera-based photoplethysmography (also known as remote photoplethysmography) is an emerging method of contactless heart rate monitoring that uses a camera and a processing unit on the hardware side, and appropriate image processing methodologies on the software side. This paper describes the design and implementation of a novel pipeline for heart rate estimation using a commercial and low-cost camera as the input device. The pipeline's performance was tested and compared on a desktop PC, a laptop, and three different ARM-based embedded platforms (Raspberry Pi 4, Odroid N2+, and Jetson Nano). The results showed that the designed and implemented pipeline achieved an average accuracy of about 96.7% for heart rate estimation, with very low variance (between 1.5% and 2.5%) across processing platforms, user distances from the camera, and frame resolutions. Furthermore, benchmark analysis showed that the Odroid N2+ platform was the most convenient in terms of CPU load, RAM usage, and average execution time of the algorithmic pipeline.

Multi-Sensor Platform for Predictive Air Quality Monitoring.

Air quality monitoring is a very important aspect of providing safe indoor conditions, and carbon dioxide (CO2) is one of the pollutants that most affects people's health. An automatic system able to accurately forecast CO2 concentration can prevent a sudden rise in CO2 levels through appropriate control of heating, ventilation and air-conditioning (HVAC) systems, avoiding energy waste and ensuring people's comfort. There are several works in the literature dedicated to air quality assessment and control of HVAC systems; the performance maximisation of such systems is typically achieved using a significant amount of data collected over a long period of time (even months) to train the algorithm. This can be costly and may not respond to a real scenario where the habits of the house occupants or the environment conditions may change over time. To address this problem, an adaptive hardware-software platform was developed, following the IoT paradigm, with a high level of accuracy in forecasting CO2 trends by analysing only a limited window of recent data. The system was tested considering a real case study in a residential room used for smart working and physical exercise; the parameters analysed were the occupants' physical activity, temperature, humidity and CO2 in the room. Three deep-learning algorithms were evaluated, and the best result was obtained with the Long Short-Term Memory network, which features a Root Mean Square Error of about 10 ppm with a training period of 10 days.

Ultra-processed foods and obesity and adiposity parameters among children and adolescents: a systematic review.

PURPOSE: According to the NOVA classification, ultra-processed foods are products made through physical, biological and chemical processes and typically with multiple ingredients and additives, in which whole foods are mostly or entirely absent. From a nutritional point of view, they are typically energy-dense foods high in fat, sugar, and salt and low in fiber. The association between the consumption of ultra-processed food and obesity and adiposity measurements has been established in adults. However, the situation remains unclear in children and adolescents. METHODS: We carried out a systematic review, in which we summarize observational studies investigating the association between the consumption of ultra-processed food, as defined by NOVA classification, and obesity and adiposity parameters among children and adolescents. A literature search was performed using PUBMED and Web of Science databases for relevant articles published prior to May 2021. RESULTS: Ten studies, five longitudinal and five cross-sectional, mainly conducted in Brazil, were included in this review. Four longitudinal studies in children with a follow-up longer than 4 years found a positive association between the consumption of ultra-processed food and obesity and adiposity parameters, whereas cross-sectional studies failed to find an association. CONCLUSION: These data suggest that a consistent intake of ultra-processed foods over time is needed to impact nutritional status and body composition of children and adolescents. Further well-designed prospective studies worldwide are needed to confirm these findings considering country-related differences in dietary habits and food production technologies.

How to mimic a type A intramural hematoma: A case of IgG4-related aortitis associated with cerebral hemorrhage.

IgG4-related aortitis is an inflammatory condition of the aorta, characterized by aortic wall thickening and periaortic soft-tissue involvement. Therefore, this condition can mimic an aortic intramural hematoma (IMH), due to similar radiological findings. We hereby report the case of an IgG4-related aortitis misdiagnosed as an IMH, associated with cerebral hemorrhage, possibly due to cerebral vascular system involvement.

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features.

Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient's behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one's behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

Ambient and Wearable Sensor Technologies for Energy Expenditure Quantification of Ageing Adults.

COVID-19 has affected daily life in unprecedented ways, with dramatic changes in mental health, sleep time and level of physical activity. These changes have been especially relevant in the elderly population, with important health-related consequences. In this work, two different sensor technologies were used to quantify the energy expenditure of ageing adults. To this end, a technological platform based on Raspberry Pi 4, as an elaboration unit, was designed and implemented. It integrates an ambient sensor node, a wearable sensor node and a coordinator node that uses the information provided by the two sensor technologies in a combined manner. Ambient and wearable sensors are used for the real-time recognition of four human postures (standing, sitting, bending and lying down), walking activity and for energy expenditure quantification. An important first aim of this work was to realize a platform with a high level of user acceptability. In fact, through the use of two unobtrusive sensors and a low-cost processing unit, the solution is easily accessible and usable in the domestic environment; moreover, it is versatile since it can be used by end-users who accept being monitored by a specific sensor. Another added value of the platform is the ability to abstract from sensing technologies, as the use of human posture and walking activity for energy expenditure quantification enables the integration of a wide set of devices, provided that they can reproduce the same set of features. The obtained results showed the ability of the proposed platform to automatically quantify energy expenditure, both with each sensing technology and with the combined version. Specifically, for posture and walking activity classification, an average accuracy of 93.8% and 93.3% was obtained, respectively, with the wearable and ambient sensor, whereas an improvement of approximately 4% was reached using data fusion. Consequently, the estimated energy expenditure quantification always had a relative error of less than 3.2% for each end-user involved in the experimentation stage, classifying the high level information (postures and walking activities) with the combined version of the platform, justifying the proposed overall architecture from a hardware and software point of view.

Comparative Analysis of Supervised Classifiers for the Evaluation of Sarcopenia Using a sEMG-Based Platform.

Sarcopenia is a geriatric condition characterized by a loss of strength and muscle mass, with a high impact on health status, functional independence and quality of life in older adults. [d=TT, ]To reduce the effects of the disease, just the diagnostic is not enough, it is necessary more than recognition.To reduce the effects of the disease, it is important to recognize the level and progression of sarcopenia early. Surface electromyography is becoming increasingly relevant for the prevention and diagnosis of sarcopenia, also due to a wide diffusion of smart and minimally invasive wearable devices suitable for electromyographic monitoring. The purpose of this work is manifold. The first aim is the design and implementation of a hardware/software platform. It is based on the elaboration of surface electromyographic signals extracted from the Gastrocnemius Lateralis and Tibialis Anterior muscles, useful to analyze the strength of the muscles with the purpose of distinguishing three different "confidence" levels of sarcopenia. The second aim is to compare the efficiency of state of the art supervised classifiers in the evaluation of sarcopenia. The experimentation stage was performed on an "augmented" dataset starting from data acquired from 32 patients. The latter were distributed in an unbalanced manner on 3 "confidence" levels of sarcopenia. The obtained results in terms of classification accuracy demonstrated the ability of the proposed platform to distinguish different sarcopenia "confidence" levels, with highest accuracy value given by Support Vector Machine classifier, outperforming the other classifiers by an average of 7.7%.

Nutrient intake in school-aged children with food allergies: a case-control study.

Most studies assessed nutrient intake of young children with food allergy (FA) compared to healthy children. We aimed to compare macro- and micronutrient intake of school-aged children with FA to non-allergic children. This case-control study included 93 Italian children (52 with FA and 41 controls, median age 7.5 and 8.3 years, respectively). Macro- and micronutrient intake was assessed by a three-day food dietary record. Anthropometric measurements were also collected. The median height z-score was significantly lower in the FA group, despite a similar daily energy and protein intake. Calcium, iron and vitamin D intake was suboptimal in both groups, while protein intake was higher than recommended in both groups. Unexpectedly, children with FA consume more protein than controls, while having lower micronutrient intake, especially calcium. Our data suggest the importance of nutritional counseling for children with FA to ensure a balanced nutrient intake while on elimination diet.

Isocaloric Ketogenic Diet in Adults with High-Grade Gliomas: A Prospective Metabolic Study.

The use of the ketogenic diet (KD) as an adjuvant therapy in high-grade gliomas (HGG) is supported by preclinical studies, but clinical data on its effects on metabolism are currently lacking. In this study, we describe the effects of a KD on glucose profile, ketonemia, energy metabolism, and nutritional status, in adults affected by HGG. This was a single-arm prospective study. An isocaloric 3:1 KD was administered for 1 mo. Glucose profile was assessed by using fasting glycemia, insulin, and glycated hemoglobin. To evaluate ketonemia changes, a hand-held ketone meter was used from home. Energy metabolism was assessed by indirect calorimetry. Nutritional status was evaluated through changes in body composition and in lipid and hepatic profile. No changes in fasting glycemia were observed; however, insulinemia dropped to half of baseline levels. The KD shifted the metabolism, rising ketonemia and decreasing glucose oxidation rate to a quarter of the initial values. Moreover, the KD was generally safe. One-month intervention with the KD was able to act upon key metabolic substrates potentially involved in HGG metabolism. The lack of a significant reduction in fasting glycemia should be investigated in future studies.

Predictive Fat Mass Equations for Children With Inflammatory Bowel Disease.

OBJECTIVE: Evaluate accuracy of skinfold thicknesses and body mass index (BMI) for the prediction of fat mass percentage (FM%) in paediatric inflammatory bowel disease (IBD) and to develop population-specific formulae based on anthropometry for estimation of FM%. METHODS: IBD children (n = 30) and healthy controls (HCs, n = 144) underwent anthropometric evaluation and dual-energy X-ray absorptiometry (DEXA) scan, as the clinical reference for measurement of body composition. Body FM% estimated with skinfolds thickness was compared with FM% measured with DEXA. By means of 4 prediction models, population specific formulae for estimation of FM% were developed. RESULTS: No significant difference in terms of FM% measured by DEXA was found between IBD population and HCs (FM% 29.6% vs 32.2%, P = 0.108). Triceps skinfold thickness (TSF, Model 2) was better than BMI (Model 1) at predicting FM% (82% vs 68% of variance). The sum of 2 skinfolds (biceps + triceps; SF2, Model 3) showed an improvement in the prediction of FM% as compared with TSF, Model 2 (86% vs 82% of variance). The sum of 4 skinfolds (biceps + triceps + suprailiac + subscapular; Model 4) showed further improvement in the prediction of FM% as compared with SF2 (88% vs 86% of variance). CONCLUSIONS: The sum of 4 skinfolds is the most accurate in predicting FM% in paediatric IBD. The sum of 2 skinfolds is less accurate but more feasible and less prone to error. The newly developed population-specific formulae could be a valid tool for estimation of body composition in IBD population and an alternative to DEXA measurement.