Exercise Intervention on Insomnia in Patients with a Cancer: A Systematic Review of the Literature.

Cancer is associated with increased muscle weakness, reduced physical functioning, increased fatigue, but also sleep disturbances, including insomnia, that affect quality of life (QoL). Physical activity demonstrated benefits on functional capacity, resilience and cancer-related fatigue, but there is a paucity of available data regarding its effects on insomnia in patients with cancer. This systematic review aims to examine the efficacy of exercise levels with insomnia in cancer patients. A systematic search was performed for articles published in PubMed and Cochrane Library databases from December 2013 to February 2023. Included studies explored insomnia during or after cancer treatment, with various exercise interventions. The search identified nine studies included in this review. Due to substantial heterogeneity in the interventions across studies, meta-analysis was not performed. Three studies reported positive results for insomnia reduction by self-reported outcomes under a supervised aerobic exercise program alone or combined with strength training. The present systematic review establishes the role of exercise interventions for reducing cancer-related insomnia. Further studies are indeed warranted to improve the level of evidence for exercise interventions for implementation in the care of cancer-related insomnia.

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.

Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds. While these methods have produced promising results in some cases, they do not shed light on how representations of compounds are transformed in hidden layers, which constitute how neural networks learn. We present a novel technique to interpret neural networks which identifies chemical substructures in training data found to be responsible for the activation of hidden neurons. For individual test compounds, the importance of hidden neurons is determined, and the associated substructures are leveraged to explain the model prediction. Using structural alerts for mutagenicity from the Derek Nexus expert system as ground truth, we demonstrate the validity of the approach and show that model explanations are competitive with and complementary to explanations obtained from an established feature attribution method.

Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.

De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.

A randomized trial to evaluate the effects of a supervised exercise program on insomnia in patients with non-metastatic breast cancer undergoing chemotherapy: design of the FATSOMCAN study.

BACKGROUND: Up to 70% of breast cancer patients report symptoms of insomnia during and after treatment. Despite the ubiquity of insomnia symptoms, they are under-screened, under-diagnosed and poorly managed in breast cancer patients. Sleep medications treat symptoms but are ineffective to cure insomnia. Other approaches such as cognitive behavioral therapy for insomnia, relaxation through yoga and mindfulness are often not available for patients and are complex to implement. An aerobic exercise program could be a promising treatment and a feasible option for insomnia management in breast cancer patients, but few studies have investigated the effects of such a program on insomnia. METHODS: This multicenter, randomized clinical trial evaluate the effectiveness of a moderate to high intensity physical activity program (45 min, 3 times per week), lasting 12 weeks, in minimizing insomnia, sleep disturbances, anxiety/depression, fatigue, and pain, and in enhancing cardiorespiratory fitness. Patients with breast cancer be recruited from six hospitals in France and randomly allocated to either the "training" or the "control" group. Baseline assessments include questionnaires [Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index questionnaire (PSQI), Hospital Anxiety Depression Scale (HADS), Epworth Sleepiness Scale (ESS)], home polysomnography (PSG), and 7-day actigraphy coupled with completion of a sleep diary. Assessments are repeated at the end of training program and at six-month follow-up. DISCUSSION: This clinical trial will provide additional evidence regarding the effectiveness of physical exercise in minimizing insomnia during and after chemotherapy. If shown to be effective, exercise intervention programs will be welcome addition to the standard program of care offered to patients with breast cancer receiving chemotherapy. TRIAL REGISTRATION: National Clinical Trials Number (NCT04867096).

Gradual Advance of Sleep-Wake Schedules Before an Eastward Flight and Phase Adjustment After Flight in Elite Cross-Country Mountain Bikers: Effects on Sleep and Performance.

ABSTRACT: Garbellotto, L, Petit, E, Brunet, E, Guirronnet, S, Clolus, Y, Gillet, V, Bourdin, H, and Mougin, F. Gradual advance of sleep-wake schedules before an eastward flight and phase adjustment after flight in elite cross-country mountain bikers: effects on sleep and performance. J Strength Cond Res 37(4): 872-880, 2023-Strategies, for alleviating jet lag, specifically targeted to competitive athletes have never been studied, in ecological conditions. This study aimed to assess the effects of a phase advance before a 7-hour eastward flight followed by a strategy of resynchronization at destination on sleep and physical performance in professional mountain bikers. Six athletes participated in this study divided into 4 periods: (i) baseline (usual sleep-wake rhythm); (ii) phase advance (advance sleep-wake schedules of 3 hours for 6 days); (iii) travel (flight: Paris-Tokyo); and (iv) phase adjustment (resynchronization of sleep-wake schedules). Melatonin pills and light therapy were administrated during the phase advance and phase adjustment. Sleep was recorded by polysomnography and actigraphy, core body temperature (CBT) rhythm was assessed by ingestible capsules, and physical performances were tested by the Wingate and 5-minute maximal exercise tests. Results showed that bedtime was advanced by 2.9 hours at the end of the phase advance ( p ≤ 0.01) with a batyphase of CBT advanced by 2.5 hours ( p = 0.07). Bedtime was similar at destination compared with baseline. Total sleep time and sleep composition were unchanged at the end of the phase advance or at destination, compared with baseline. Physical performances were maintained after phase advance and at destination. The phase advance enabled to preshift part of the time zones without disturbing sleep and physical performances and contributed to preserving them once at destination. A phase advance before eastward travel represents an effective strategy to counter harmful effects of jet lag.

Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction.

Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are able to use experimental activity values for auxiliary assays when predicting the activity of a test compound on a specific assay. In this study, we tested three different multi-task imputation techniques on three classification-based toxicity datasets: two of small scale (12 assays each) and one large scale with 417 assays. Moreover, we analyzed in detail the improvements shown by the imputation models. We found that test compounds that were dissimilar to training compounds, as well as test compounds with a large number of experimental values for other assays, showed the largest improvements. We also investigated the impact of sparsity on the improvements seen as well as the relatedness of the assays being considered. Our results show that even a small amount of additional information can provide imputation methods with a strong boost in predictive performance over traditional single task and multi-task predictive models.

RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design.

Reaction-based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction-based de novo design tool, which is based on a pseudo-retrosynthetic fragmentation of a reference ligand and an inside-out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool.

Complete sleep evaluation of top professional cross-country mountain bikers' athletes.

BACKGROUND: Despite growing interest in athletes' sleep, few studies have focused on professional athletes, especially in individual sports. Moreover, limited investigations included female athletes. This study aimed to evaluate sleep chronotype, as well as objective and subjective sleep characteristics in male and female professional cross-country mountain bikers. METHODS: Thirteen athletes (7 males and 6 females) of the French national team took part in this study. The Chronotype was assessed by the Horne and Östberg Morningness-Eveningness Questionnaire and sleep by actigraphy for one month, by ambulatory polysomnography (PSG) for one night and by the Pittsburgh Sleep Quality Index. RESULTS: Most athletes (77%) are classified as moderately morning type and a minority of athletes (23%) are intermediate type. Athletes sleep on average 8 hours per night and during the night recorded by PSG, N3 and REM sleep stages represented 21.2±3.4% and 20.9±3.1% of the total sleep time, respectively. These good sleep parameters were confirmed by subjective data with 77% good sleepers. Except the poorer subjective sleep quality in female athletes (5.7±1.6) compared to male athletes (2.6±1.7, P<0.05), no significant sex difference was found for all characteristics evaluated. CONCLUSIONS: The professional status of these athletes and the organization of mountain bike calendar may explain their good sleep characteristics.

Amyloid binding and beyond: a new approach for Alzheimer's disease drug discovery targeting Aßo-PrPC binding and downstream pathways.

Amyloid ß oligomers (Aßo) are the main toxic species in Alzheimer's disease, which have been targeted for single drug treatment with very little success. In this work we report a new approach for identifying functional Aßo binding compounds. A tailored library of 971 fluorine containing compounds was selected by a computational method, developed to generate molecular diversity. These compounds were screened for Aßo binding by a combined 19F and STD NMR technique. Six hits were evaluated in three parallel biochemical and functional assays. Two compounds disrupted Aßo binding to its receptor PrPC in HEK293 cells. They reduced the pFyn levels triggered by Aßo treatment in neuroprogenitor cells derived from human induced pluripotent stem cells (hiPSC). Inhibitory effects on pTau production in cortical neurons derived from hiPSC were also observed. These drug-like compounds connect three of the pillars in Alzheimer's disease pathology, i.e. prion, Aß and Tau, affecting three different pathways through specific binding to Aßo and are, indeed, promising candidates for further development.

A multidisciplinary weight loss intervention in obese adolescents with and without sleep-disordered breathing improves cardiometabolic health, whether SDB was normalized or not.

OBJECTIVES: Pediatric obesity and sleep-disordered breathing (SDB) are strongly associated, and both promote metabolic impairments. However, the effects of a lifestyle intervention on the overall metabolic syndrome (MetS) are unknown. The objectives were i) to evaluate the effects of a lifestyle intervention on cardiometabolic risk (CMR), assessed with a dichotomous (MetS) and a continuous (MetScoreFM) instrument, in obese adolescents with and without SDB and ii) to compare the post-intervention cardiometabolic responses between adolescents with persistent (apnea-hypopnea index; AHI≥2) or normalized-SDB (AHI<2). METHODS: Seventy-six adolescents with obesity recruited from two specialized institutions underwent a 9-12month diet and exercise intervention. Sleep and SDB (AHI≥2) were studied by polysomnography. Anthropometric parameters, fat mass (FM), glucose, insulin, lipid and leptin profiles, blood pressure (BP), MetScoreFM and MetS were assessed pre- and post-intervention. We performed comparisons between Non-SDB and SDB groups and between Normalized-SDB and Persistent-SDB subgroups. RESULTS: Fifty participants completed the study. Pre-intervention, twenty youth had SDB (40%) with higher insulin concentrations and systolic BP than Non-SDB participants (p < 0.01), for a similar degree of obesity. Post-intervention, MetScoreFM (p < 0.001) and MetS prevalence (p < 0.05) were decreased in both groups. Eleven participants (55%) normalized SDB along with a decrease in insulin concentrations and BP (p < 0.05). Triglycerides, total cholesterol and LDL-cholesterol concentrations (p < 0.01) improved equally in the Normalized and Persistent-SDB subgroups. CONCLUSION: SDB was associated with lower insulin sensitivity and higher BP but did not affect the lipid profile. A diet and exercise lifestyle intervention is effective in decreasing the CMR whether or not SDB was normalized in obese adolescents.

Enhancing reaction-based de novo design using a multi-label reaction class recommender.

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.

Sleep-disordered breathing in adolescents with obesity: When does it start to affect cardiometabolic health?

BACKGROUND AND AIMS: Pediatric obesity and sleep-disordered breathing (SDB) are associated with cardiometabolic risk (CMR), but the degree of severity at which SDB affects cardiometabolic health is unknown. We assessed the relationship between the CMR and the apnea-hypopnea index (AHI), to identify a threshold of AHI from which an increase in the CMR is observed, in adolescents with obesity. We also compared the clinical, cardiometabolic and sleep characteristics between adolescents presenting a high (CMR+) and low CMR (CMR-), according to the threshold of AHI. METHODS AND RESULTS: 114 adolescents with obesity were recruited from three institutions specialized in obesity management. Sleep and SDB as assessed by polysomnography, anthropometric parameters, fat mass (FM), glucose and lipid profiles, and blood pressure (BP) were measured at admission. Continuous (MetScoreFM) and dichotomous (metabolic syndrome, MetS) CMR were determined. Associations between MetScoreFM and AHI adjusted for BMI, sex and age were assessed by multivariable analyses. Data of 82 adolescents were analyzed. Multivariable analyses enabled us to identify a threshold of AHI = 2 above which we observed a strong and significant association between CMR and AHI (Cohen's d effect-size = 0.57 [0.11; 1.02] p = 0.02). Adolescents with CMR+ exhibited higher MetScoreFM (p < 0.05), insulin resistance (p < 0.05), systolic BP (p < 0.001), sleep fragmentation (p < 0.01) and intermittent hypoxia than CMR- group (p < 0.0001). MetS was found in 90.9% of adolescents with CMR+, versus 69.4% in the CMR- group (p < 0.05). CONCLUSIONS: The identification of a threshold of AHI ≥ 2 corresponding to the cardiometabolic alterations highlights the need for the early management of SDB and obesity in adolescents, to prevent cardiometabolic diseases. CLINICAL TRIALS: NCT03466359, NCT02588469 and NCT01358773.

Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature.

Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for "dynamic" reaction fingerprinting to maximize the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organizes labels into four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction data sets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationships between reaction classes.

Identification of compounds that rescue otic and myelination defects in the zebrafish adgrg6 (gpr126) mutant.

Adgrg6 (Gpr126) is an adhesion class G protein-coupled receptor with a conserved role in myelination of the peripheral nervous system. In the zebrafish, mutation of adgrg6 also results in defects in the inner ear: otic tissue fails to down-regulate versican gene expression and morphogenesis is disrupted. We have designed a whole-animal screen that tests for rescue of both up- and down-regulated gene expression in mutant embryos, together with analysis of weak and strong alleles. From a screen of 3120 structurally diverse compounds, we have identified 68 that reduce versican b expression in the adgrg6 mutant ear, 41 of which also restore myelin basic protein gene expression in Schwann cells of mutant embryos. Nineteen compounds unable to rescue a strong adgrg6 allele provide candidates for molecules that may interact directly with the Adgrg6 receptor. Our pipeline provides a powerful approach for identifying compounds that modulate GPCR activity, with potential impact for future drug design.

Beneficial effects of a lifestyle intervention program on C-reactive protein: impact of cardiorespiratory fitness in obese adolescents with sleep disturbances.

The objectives of this study were to assess the relationship between inflammation and obstructive sleep apnea (OSA) and determine whether the lifestyle program's effects on inflammatory markers are associated with changes in anthropometric parameters, cardiorespiratory fitness, sleep duration, and OSA severity in severely obese adolescents. Participants were aged 14.6 (SD 1.2) yr, with a body mass index (BMI) of 40.2 (SD 6.5) kg/m2. Sleep, anthropometric parameters, glucose metabolism, inflammatory profile, and cardiorespiratory fitness [V̇o2peak relative to body weight (V̇o2peakBW) and fat-free mass (V̇o2peakFFM)] were assessed at admission and at the end of a 9-mo lifestyle intervention program (LIP). Associations between C-reactive protein (CRP) concentrations and BMI, sex, oxygen desaturation index (ODI), sleep fragmentation, total sleep time (TST), and V̇o2peak were assessed via ANCOVA. Twenty-three subjects completed the study. OSA subjects ( n = 13) exhibited higher CRP concentrations and a trend for higher BMI than non-OSA subjects ( P = 0.09) at admission. After intervention, OSA was normalized in six subjects, and CRP significantly decreased in the OSA group and in the whole population. In both groups, leptin levels significantly decreased, whereas adiponectin concentrations increased. At admission, BMI adjusted for sex, arousal index, ODI, TST, and V̇o2peakBW was associated with CRP levels (adjusted r2 = 0.32, P < 0.05). The decrease in CRP concentrations postintervention was associated with enhanced V̇o2peakFFM adjusted for sex, weight loss, and changed sleep parameters (adjusted r2 = 0.75, P < 0.05). Despite higher amounts of CRP in OSA subjects, obesity severity outweighs the proinflammatory effects of OSA, short sleep duration, and low cardiorespiratory fitness. However, enhanced cardiorespiratory fitness is associated with the decrease of inflammation after controlling for the same parameters.

Alignment-Free Molecular Shape Comparison Using Spectral Geometry: The Framework.

A framework is presented for the calculation of novel alignment-free descriptors of molecular shape. The methods are based on the technique of spectral geometry which has been developed in the field of computer vision where it has shown impressive performance for the comparison of deformable objects such as people and animals. Spectral geometry techniques encode shape by capturing the curvature of the surface of an object into a compact, information-rich representation that is alignment-free while also being invariant to isometric deformations, that is, changes that do not distort distances over the surface. Here, we adapt the technique to the new domain of molecular shape representation. We describe a series of parametrization steps aimed at optimizing the method for this new domain. Our focus here is on demonstrating that the basic approach is able to capture a molecular shape into a compact and information-rich descriptor. We demonstrate improved performance in virtual screening over a more established alignment-free method and impressive performance compared to a more accurate, but much more computationally demanding, alignment-based approach.

Obstructive Sleep Apnea and Sleep Architecture in Adolescents With Severe Obesity: Effects of a 9-Month Lifestyle Modification Program Based on Regular Exercise and a Balanced Diet.

STUDY OBJECTIVES: Physical exercise and lifestyle modification are recognized as adjunct therapy for obstructive sleep apnea (OSA) in overweight adults. The objectives of this study were to investigate the effects of long-term physical exercise combined with a balanced diet on sleep architecture, sleep duration, and OSA in adolescents with severe obesity. METHODS: This interventional study was conducted in a nursing institution. Participants were aged 14.6 ± 1.2 years with obesity (body mass index (BMI) = 40.2 ± 6.5 kg/m2). At admission and at 9 months, participants underwent ambulatory polysomnography and incremental maximal exercise testing to determine cardiorespiratory fitness. RESULTS: Twenty-four subjects completed the study. Analyses were performed on the whole population and on a subgroup of subjects with OSA (OSA-subgroup). OSA, defined as obstructive apnea-hypopnea index (OAHI) ≥ 2 events/h, was diagnosed in 58.3% of the population. OAHI was only associated with fat mass in males (r = .75, P < .05). At 9 months postintervention, weight loss (-11.1 kg, P < .0001) and improved cardiorespiratory fitness (VO2peak: +4.9 mL/min/kg, P < .001) were found in the whole population. Sleep duration was increased (+34 minutes, P < .05) and sleep architecture was changed with an increase of rapid eye movement sleep (+2.5%, P < .05) and a decrease of stage N3 sleep (-3.1%, P < .001). Similar results were found in the OSA subgroup. However, OAHI remained unchanged (P = .18). CONCLUSIONS: A combination of supervised aerobic exercise and a balanced diet led to weight loss, improved aerobic capacity, and modified sleep architecture without changes in OSA. COMMENTARY: A commentary on this article appears in this issue on page 907. CLINICAL TRIAL REGISTRATION: Registry: ClinicalTrials.gov, Title: Exercise and Venous Compression on Upper Airway Resistance in Obese Teenagers With OSA (OBESOMAC), URL: https://clinicaltrials.gov/ct2/show/NCT02588469, Identifier: NCT02588469.

Effect of missing data on multitask prediction methods.

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.e., not all compound-target combinations have experimental values. There has been little research on the effect of missing data on the performance of multitask methods. We have used two complete data sets to simulate sparseness by removing data from the training set. Different models to remove the data were compared. These sparse sets were used to train two different multitask methods, deep neural networks and Macau, which is a Bayesian probabilistic matrix factorization technique. Results from both methods were remarkably similar and showed that the performance decrease because of missing data is at first small before accelerating after large amounts of data are removed. This work provides a first approximation to assess how much data is required to produce good performance in multitask prediction exercises.

Bioisosteric Replacements Extracted from High-Quality Structures in the Protein Databank.

Bioisosterism is an important concept in the lead optimisation phase of drug discovery where the aim is to make modifications to parts of a molecule in order to improve some properties while maintaining others. We present an analysis of bioisosteric fragments extracted from the ligands in an established data set consisting of 121 protein targets. A pairwise analysis is carried out of all ligands for a given target. The ligands are fragmented using the BRICS fragmentation scheme and a pair of fragments is deemed to be bioisosteric if they occupy a similar volume of the protein binding site. We consider two levels of generality, one which does not consider the number of attachment points in the fragments and a more restricted case in which both fragments are required to have the same number of attachments. We investigate the extent to which the bioisosteric pairs that are found are common across different target.

Cardiometabolic risk is associated with the severity of sleep-disordered breathing in children with obesity.

BACKGROUND: The alarming progression of pediatric obesity is associated with the development of sleep-disordered breathing (SDB), and both exhibit similar adverse cardiometabolic health outcomes. Physical activity level (PAL) may counteract sleep and metabolic disturbances. The present study investigates i) the association between the metabolic syndrome in childhood obesity and SDB, ii) the impact of SDB severity on cardiometabolic risk scores and PAL in children with obesity. METHODS: Maturation status (Tanner stages), anthropometric (height, weight, body mass index, waist circumference, body adiposity index) and cardiometabolic characteristics (systolic and diastolic blood pressure, lipid and glycemic profiles) were assessed in 83 obese children (mean±SD, age: 10.7±2.7years). PAL and SDB were investigated with a step test and interviews, and an overnight sleep monitor, respectively. The presence or absence of metabolic syndrome (MS) was established and continuous cardiometabolic risk scores were calculated (MetScoreBMI and MetScoreWC). RESULTS: Obese children with (61.4%) and without (38.6%) MS present similar SDB. SDB severity is associated with increased insulin concentrations, MetScoreBMI and MetScoreWC (p<0.05) in obese children. There is no association between SDB and PAL. CONCLUSIONS: In a context where no consensus exists for SDB diagnosis in children, our results suggest the influence of SDB severity on cardiometabolic risk factors. Further studies are needed to explore the association between PAL and both metabolic and sleep alterations in obese children.