Metabolic flexibility to lipid during exercise is not associated with metabolic health outcomes in individuals without obesity

Participants

We included individuals that met the following eligibility criteria: [a] man or woman; [b] 40 to 64 years old; [c] normal weight or moderately overweight as indicated by a body mass index between 18.5 and 27.0 kg/m²; [d] self-reported stability of body weight (< 3 kg of change in the last three months); [e] free of diagnosed chronic diseases (metabolic risk factors such as impaired glycemia were allowed); [f] low-risk alcohol consumption as assessed by the Alcohol Use Disorders Identification Test²¹; [g] level of moderate-vigorous physical activity ≤1,200 MET×min/week as assessed by the Global Physical Activity Questionnaire²²; [h] not taking medications; and [i] not pregnant (in women). All participants signed a written informed consent form before participating. The Scientific Ethics Committee at Universidad Finis Terrae approved the study (#76-18-2021). All procedures were performed in accordance with the Declaration of Helsinki.

Design

This was an observational, cross-sectional, analytical study conducted between 2019 and 2022. Supplementary Table 1 shows the STROBE checklist for observational studies. Potential participants were recruited via flyers, social media, and word of mouth. Interested individuals completed an online survey to pre-assess the eligibility criteria. Pre-selected individuals were invited to a screening visit. Individuals arrived after 10–14 h of fasting, and the necessary questionnaires along with standard clinical measurements were conducted (anthropometry, arterial blood pressure, electrocardiogram, temperature, and others). A venous blood sample was obtained to measure biochemical and lipid profiles, hemogram, circulating concentrations of thyroid stimulating hormone and free thyroxine, prothrombin time, and plasma electrolytes. Selected participants were invited to participate in four additional visits to: [a] determine the VO₂max and daily time spent on physical behaviors; [b] assess MetF-lip; [c] determine body composition; and [d] obtain a skeletal muscle biopsy. Body weight was measured in each visit to ensure weight stability throughout the study (±2 kg from the screening).

VO₂max and daily time spent on physical behaviors

VO₂max was determined with an exercise test of incremental intensity in a cyclo-ergometer. The test began at 0 W for 2 min, followed by 50 W for 2 min, and then increments of 25 W every 2 min until exhaustion. Participants had to maintain a cadence of 55–65 rpm. Breath-by-breath VO₂ and VCO₂ (Ergocard, Medisoft), and heart rate (H10, Polar) were measured throughout the test. Participants were considered to reach exhaustion if were unable to continue pedaling even after verbal encouragement, or if were unable to maintain the minimum cadence. The last 30 s of gas exchange at each workload, along with the heart rate at the end of each workload, were used to determine whether the participant achieved the VO₂max. Following the criteria described by Howley et al.²³, participants were considered to achieve their VO₂max if attaining at least one of these: [a] a plateau in VO₂ as indicated by an increase < 150 mL/min in successive, incremental workloads; [b] RER > 1.10; or [c] heart rate within 10 bpm of the maximal heart rate predicted as 202 − 0.72 × age in years²⁴. After confirming that the VO₂max was reached, we computed a linear regression between VO₂ and workload to calculate the workload (in W) that required 50% VO₂max. Such a workload was then used in the exercise challenge to measure MetF-lip.

After the test, participants received an accelerometer to be worn on the hip for at least one week (90 Hz sampling frequency; wGT3X-BT, Actigraph). They were instructed to wear it while awake, except while showering or swimming. ActiLife version 6.13.4 (Actigraph) was used to process the raw data in 60-s epochs using the normal filter. Wear time was estimated according to Choi et al.²⁵. A day was considered valid if had ≥10 h of recording. Thus, our participants wore the accelerometer a median [25th percentile, 75th percentile] of 13.8 [13.0, 14.6] hours/day for a minimum of 6 days. Time spent on physical behaviors was estimated as previously described²⁶. In brief, sedentary behavior according to Kozey-Keadle et al.²⁷, moderate-vigorous physical activity according to Sasaki et al.²⁸, and light-intensity physical activity as the remaining wear time. Time spent on each physical behavior was weighted by weekdays and weekend days as described before²⁹.

MetF-lip

MetF-lip was measured during an exercise challenge in cyclo-ergometer adapted from our previous protocol¹⁷. Participants were requested to avoid medication, supplements, alcohol, cigarettes, energy drinks, coffee, tea, and vigorous physical activity the day before this visit. They had to be free of injuries or other conditions that could impair exercise performance. Participants received a meal to be eaten the evening before the visit. The meal contained 30% of daily energy requirements estimated using sex-specific equations that consider age, weight, height, waist circumference, and resting metabolic rate³⁰ (resting metabolic rate estimated from age and height³¹). 60% of the energy was provided as carbohydrates in an attempt to match glycogen stores in skeletal muscle among participants. Then, participants had to sleep at least seven hours and arrive at the laboratory after 10–14 h of overnight fasting. A venous blood sample was drawn while resting before exercise. Participants then warmed up at 0 W for 2 min in the cycle-ergometer. The exercise challenge began at the power (W) calculated to require 50% VO₂max. After 8 min of exercise, power was adjusted to approach the expected VO₂. Then, participants maintained the power until completing 120 min of exercise. The cadence was maintained at 55–65 rpm throughout the exercise. VO₂ and VCO₂ (Ergocard, Medisoft), along with heart rate (H10, Polar) were measured at 8, 15, 30, 60, 90, and 120 min of exercise (average of 3 min in each time point). RER was calculated from VO₂ and VCO₂ measurements. Venous blood samples were obtained at 15, 30, 60, 90, and 120 min of exercise.

MetF is the capacity to adapt fuel oxidation to fuel availability during metabolic challenges¹. The adaptation in fuel oxidation was measured as the change in RER during exercise. This was computed as the difference between the RER at the final of the exercise and the maximum RER measured (ΔRER = RERfinal – RERmax). Negative ΔRER values indicate a drop in RER, which represents an increase in lipid oxidation relative to carbohydrate oxidation³². The adaptation in fuel availability was measured as the change in the circulating concentrations of NEFA. This was computed as the difference between the NEFA at the final of the exercise and the minimum NEFA measured (ΔNEFA = NEFAfinal – NEFAmin). Positive ΔNEFA values indicate an increase in the circulating availability of lipids. Thus, MetF was calculated as the ΔRER during exercise, adjusted for ΔNEFA if required (see Results). As the drop in RER during exercise is mostly driven by muscle activity, our method is deemed to measure MetF-lip in skeletal muscle.

Response of energy metabolism and circulating markers to the exercise challenge

Energy expenditure (in kcal/min) and the contribution of lipids and carbohydrates at each time point were calculated according to Elia and Livesey³². Null contribution of proteins was assumed. Total energy expenditure during exercise was computed as the AUC of energy expenditure assuming a constant expenditure during the first 8 min. Lipid and carbohydrate oxidation (in g/min) was calculated by assuming 9.41 kcal/g of lipid and 4.18 kcal/g of carbohydrates³². Circulating markers included plasma glucose (by dry chemistry), plasma lactate (by enzymatic/colorimetric analysis), serum insulin (by chemiluminescence), serum NEFA (NEFA-HR[2], Wako Diagnostics, Richmond, VA, USA), serum glycerol (MAK117, Sigma- Aldrich, St. Louis, MO, USA), serum triglycerides (by dry chemistry), and serum β-hydroxybutyrate (βOHB; #700190, Cayman Chemical, Ann Arbor, MI, USA).

Metabolic health outcomes

HOMA-IR was calculated from the blood sample withdrawn before beginning the exercise challenge as: plasma glucose [mM] × serum insulin [mIU/L] / 22.5. The components of the metabolic syndrome were obtained during the screening visit. These components included: waist circumference (midpoint between lower rib and iliac crest), serum HDL-C, serum triglycerides, systolic and diastolic blood pressure (by mercury sphygmomanometer), and plasma glucose concentration. Mean arterial pressure was computed as: diastolic [mmHg] + (systolic [mmHg] – diastolic [mmHg]) / 3. A metabolic syndrome z-score specific for Chilean adults was computed by integrating all metabolic syndrome components, as described before⁸. Fat mass, fat-free mass, fat percentage, trunk fat, arms fat, and gluteofemoral fat were determined by dual-energy X-ray absorptiometry (Lunar iDXA, GE Healthcare). Trunk-to-appendicular fat was computed as: trunk fat [kg] / (arms fat [kg] + gluteofemoral fat [kg]). VO₂max was also considered a metabolic health outcome.

Skeletal muscle biopsy and inflammatory signaling pathway

Participants arrived at the laboratory after 10–14 h of overnight fasting. A muscle biopsy was taken at rest from the Vastus lateralis muscle using the Bergstrom technique with suction under local anesthesia (2% lidocaine). The sample was immediately frozen in liquid nitrogen and then stored at -80 °C. For the protein extraction, muscle biopsies were homogenized in a lysis buffer containing 20 mM Tris-HCl (pH 7.5), 1% Triton X-100, 2 mM EDTA, 20 mM NaF, 1 mM Na₂PO₇, 10% glycerol, 150 mM NaCl, 10 mM Na₃VO₄, 1 mM PMSF, and a protease inhibitor cocktail (Complete TM, Roche Applied Science). Protein concentration was measured using the Micro BCA™ Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA).

For western blotting, 40–50 mg of proteins were subjected to SDS-PAGE and transferred onto polyvinylidene difluoride membranes (Thermo Fisher Scientific, Waltham, MA, USA). Two gels were processed in parallel to accommodate all the samples each time. The immunoblotting was developed with these primary antibodies from Santa Cruz (Dallas, TX, USA): mouse anti-TLR4 (1:1000, SC-293072), mouse anti-GAPDH (1:2000, SC-47724), and mouse anti-phospho-NFkB^Ser536 (1:1000, SC-136548); or with these primary antibodies from Cell Signaling (Danvers, MA, USA): rabbit anti-Myd88 (1:1000, 4283 S), rabbit anti-phospho-TAK1^Ser412 (1:1000, 9339 S), rabbit anti-TAK1 (1:500, 4505 S), rabbit anti-phospho-p38^{Thr180/Tyr182} (1:1000, 9215 S), rabbit anti-p38 (1:1000, 9212 S), rabbit anti-phospho-ERK1/2^{Thr202/Tyr204} (1:1000, 9101 S), rabbit anti-ERK1/2 (1:1000, 4377 S), rabbit anti-phospho-JNK^{Thr183/Tyr185} (1:1000, 9251 S), rabbit anti-JNK (1:1000, 9252 S), and rabbit anti-NFkB (1:1000, 8242 S). The membranes were then incubated with an appropriate secondary antibody (Santa Cruz, Dallas, TX, USA): goat anti-mouse IgGHRP (1:3000) or mouse anti-rabbit IgG-HRP (1:3000). The immunoreaction was visualized by enhanced chemiluminescence (Thermo Scientific, Waltham, MA, USA).

Images were acquired using the Fotodyne FOTO/Analyst Luminary Workstation Systems (Fisher Scientific, St. Waltham, MA, USA). Bands density was obtained with the ImageJ software (National Institutes of Health, Bethesda, MD, USA). The same two control samples were charged in the extremes of each gel, and the ratio of their density was used to normalize the other samples within the gel: sample density / (control 1 density / control 2 density). Protein abundance was then expressed relative to a control protein and expressed in arbitrary units.

Statistics

Analyses were conducted in R version 4.3.3. A P-value lower than 0.05 was considered statistically significant. As normality tests have low power with sample sizes as the one in our current study³³, we used non-parametric tests because they do not have the assumption of normally distributed data. Data are presented as median with interquartile ranges (25th percentile, 75th percentile). Friedman test for repeated measurements was used to determine the changes in metabolic variables during exercise; in the case of statistical significance, the Sign post-hoc test (two-sided) was used to compare the first time point versus the other time points. The Bonferroni correction was applied to adjust for multiple comparisons (adjusted P-value = 0.05 / 5 comparisons = 0.01). Spearman rho was used to test the correlations between variables.