Discrepancy between predicted and measured exercise intensity for eliciting the maximal rate of lipid oxidation.

BACKGROUND AND AIMS: Ectopic lipid storage is implicated in type 2 diabetes pathogenesis; hence, exercise to deplete stores (i.e., at the intensity that allows for maximal rate of lipid oxidation; MLO) might be optimal for restoring metabolic health. This intensity ("Fatmax") is estimated during incremental exercise ("Fatmax test"). However, in "the field" general recommendations exist regarding a range of percentages of maximal heart rate (HR) to elicit MLO. The degree to which this range is aligned with measured Fatmax has not been investigated. We compared measured HR at Fatmax, with maximal HR percentages within the typically recommended range in a sample of 26 individuals (Female: n = 11, European ancestry: n = 17). METHODS AND RESULTS: Subjects completed a modified Fatmax test with a 5-min warmup, followed by incremental stages starting at 15 W with work rate increased by 15 W every 5 min until termination criteria were reached. Pulmonary gas exchange was recorded and average values for V˙ o2 and V˙ co2 for the final minute of each stage were used to estimate substrate-oxidation rates. We modeled lipid-oxidation kinetics using a sinusoidal model and expressed MLO relative to peak V˙ o2 and HR. Bland-Altman analysis demonstrated lack of concordance between HR at Fatmax and at 50%, 70%, and 80% of age-predicted maximum with a mean difference of 23 b·min-1. CONCLUSION: Our results indicate that estimated "fat-burning" heart rate zones are inappropriate for prescribing exercise to elicit MLO and we recommend direct individual exercise lipid oxidation measurements to elicit these values.

Whole body lipid oxidation during exercise is impaired with poor insulin sensitivity but not with obesity per se.

Equivocal findings regarding the influence of overweight/obesity on exercise lipid-oxidizing capacity (EX-LIPOX) might reflect inadequate control of 1) acute energy balance/macronutrient composition of diet; 2) intensity/duration of exercise; and/or 3) insulin sensitivity (IS) of participant. To assess independent/combined influences of IS and overweight/obesity with other factors controlled, we recruited sedentary adults with normal weight (NW; n = 15) or overweight/obesity (O; n = 15) subdivided into metabolically healthy (MH; n = 8) and unhealthy (MU; n = 7) groups (IS; MH > MU). Participants completed a 9-day, weight-stabilizing, controlled-feeding protocol comprising measurements of resting metabolism, body composition, oral glucose tolerance, and maximal exercise capacity. We measured EX-LIPOX during the initial 45 min of "steady state" during constant-work-rate cycling at 70% and 100% of participant gas-exchange threshold (GET). At 70%, average EX-LIPOX in absolute (0.11 ± 0.02 g·min-1) and relative (2.4 ± 0.3 mg·kgFFM-1·min-1) terms was lower for NW-MU than MH regardless of body composition (NW-MH, 0.19 ± 0.02 g·min-1/3.9 ± 0.3 mg·kgFFM-1·min-1; O-MH, 0.19 ± 0.02 g·min-1/3.7 ± 0.3 mg·kgFFM-1·min-1), whereas no difference was present for NW-MU and O-MU (0.15 ± 0.02 g·min-1/2.8 ± 0.3 mg·kgFFM-1·min-1). Multiple regression confirmed that with IS-controlled, overweight/obesity was not associated with decreased EX-LIPOX, whereas decreased EX-LIPOX was associated with decreased IS independent of overweight/obesity. Overweight/obesity also did not influence EX-LIPOX across MH groups or with cohort divided by body-composition classification alone (P > 0.05). Exercise lipid-oxidizing capacity is impaired with poor IS regardless of body composition, but not with overweight/obesity per se.NEW & NOTEWORTHY In this study, we have shown that the capacity to oxidize lipid during exercise is influenced by metabolic health of the exerciser regardless of body composition, but not by body composition per se. This observation refutes the belief that a reduced capacity to oxidize lipid is an obligatory characteristic of the overweight/obese condition while supporting the contention that exercise should be prescribed with specificity based on both absence/presence of overweight/obesity and compromise/lack thereof in metabolic health.

Role of artificial intelligence in critical care nutrition support and research.

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.