This single-blind, crossover study aimed to measure and evaluate the short-term metabolic responses to continuous and intermittent hypoxic patterns in individuals with obesity. Indirect calorimetry was used to quantify changes in resting metabolic rate (RMR), carbohydrate (CHO, %CHO), and fat oxidation (FAT, %FAT) in nine individuals with obesity pre and post: ) breathing normoxic air [normoxic sham control (NS-control)], ) breathing continuous hypoxia (CH), or ) breathing intermittent hypoxia (IH). A mean peripheral oxygen saturation ([Formula: see text]) of 80-85% was achieved over a total of 45 min of hypoxia. Throughout each intervention, pulmonary gas exchanges, oxygen consumption (V̇o) carbon dioxide production (V̇co), and deoxyhemoglobin concentration (Δ[HHb]) in the vastus lateralis were measured. Both RMR and CHO measured pre- and postinterventions were unchanged following each treatment: NS-control, CH, or IH (all > 0.05). Conversely, a significant increase in FAT was evident between pre- and post-IH (+44%, = 0.048). Although the mean Δ[HHb] values significantly increased during both IH and CH ( < 0.05), the greatest zenith of Δ[HHb] was achieved in IH compared with CH ( = 0.002). Furthermore, there was a positive correlation between Δ[HHb] and the shift in FAT measured pre- and postintervention. It is suggested that during IH, the increased bouts of muscle hypoxia, revealed by elevated Δ[HHb], coupled with cyclic periods of excess posthypoxia oxygen consumption (EPHOC, inherent to the intermittent pattern) played a significant role in driving the increase in FAT post-IH.
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http://dx.doi.org/10.1152/ajpregu.00153.2023 | DOI Listing |
Lancet Reg Health West Pac
January 2025
Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
Background: Existing studies have not provided robust evidence about the CVD risk of non-smoking patients with restrictive spirometric pattern (RSP) or airflow obstruction (AFO), and how the risk is modified by body shape. We aimed to bridge the gap.
Methods: We used never-smokers' data from the China Kadoorie Biobank (CKB) and performed Cox models by sex (278,953 females and 50,845 males).
J Diabetes Metab Disord
June 2025
Department of Community Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, P. O. Box: 1416643931, Tehran, Iran.
Objectives: An efficient approach to monitor the risks associated with chronic diseases is to use a dietary diversity score (DDS). To our knowledge, there has been no study conducted on the correlation between DDS and cardiovascular risk factors in individuals with diabetes. Hence, the objective of this study is to ascertain the correlation between these traits.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
Department of Cardiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Background: Adrenal Vein Sampling (AVS) is the gold standard for categorizing primary aldosteronism (PA). However, catheterization of the right adrenal vein (RAV) can be technically challenging. This study aimed to investigate the validity of the right renal vertebral contour as fluoroscopic landmarks to help RAV orifice localization during AVS.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
January 2025
Department of Endocrinology, Genetics and Metabolism, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China.
Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) has become one of the most prevalent chronic liver diseases worldwide. The serum uric acid-to-high-density lipoprotein cholesterol ratio (UHR) has been recognized as a novel marker for metabolic diseases, including MASLD. However, all previous studies were performed in adults.
View Article and Find Full Text PDFFront Public Health
January 2025
Karolinska Institutet, Department of Medicine Solna, Division of Clinical Epidemiology, Stockholm, Sweden.
Background: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate success, possibly from relying on knowledge extracted using limited types of statistical data analysis methods.
Purpose: To explore if multimodal machine learning can enhance identifying predictive features from obesogenic environments and investigating complex disease or social patterns, using the Mexican National Health and Nutrition Survey.
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