Type 2 diabetes (T2D) and increased liver fat content (LFC) alter lipoprotein profile and composition and impair liver substrate uptake. Exercise training mitigates T2D and reduces LFC, but the benefits of different training intensities in terms of lipoprotein classes and liver substrate uptake are unclear. The aim of this study was to evaluate the effects of moderate-intensity continuous training (MICT) or sprint interval training (SIT) on LFC, liver substrate uptake, and lipoprotein profile in subjects with normoglycemia or prediabetes/T2D. We randomized 54 subjects (normoglycemic group, n = 28; group with prediabetes/T2D, n = 26; age = 40–55 yr) to perform either MICT or SIT for 2 wk and measured LFC with magnetic resonance spectroscopy, lipoprotein composition with NMR, and liver glucose uptake (GU) and fatty acid uptake (FAU) using PET. At baseline, the group with prediabetes/T2D had higher LFC, impaired lipoprotein profile, and lower whole body insulin sensitivity and aerobic capacity compared with the normoglycemic group. Both training modes improved aerobic capacity (P < 0.001) and lipoprotein profile (reduced LDL and increased large HDL subclasses; all P < 0.05) with no training regimen (SIT vs. MICT) or group effect (normoglycemia vs. prediabetes/T2D). LFC tended to be reduced in the group with prediabetes/T2D compared with the normoglycemic group posttraining (P = 0.051). When subjects were divided according to LFC (high LFC, >5.6%; low LFC, <5.6%), training reduced LFC in subjects with high LFC (P = 0.009), and only MICT increased insulin-stimulated liver GU (P = 0.03). Short-term SIT and MICT are effective in reducing LFC in subjects with fatty liver and in improving lipoprotein profile regardless of baseline glucose tolerance. Short-term MICT is more efficient in improving liver insulin sensitivity compared with SIT. NEW & NOTEWORTHY In the short term, both sprint interval training and moderate-intensity continuous training (MICT) reduce liver fat content and improve lipoprotein profile; however, MICT seems to be preferable in improving liver insulin sensitivity.

Despite the recent studies on structural and functional adaptations of the right ventricle (RV) to exercise training, adaptations of its metabolism remain unknown. We investigated the effects of short-term, high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on RV glucose and fat metabolism. Twenty-eight untrained, healthy 40–55 yr-old-men were randomized into HIIT ( n = 14) and MICT ( n = 14) groups. Subjects performed six supervised cycle ergometer training sessions within 2 wk (HIIT session: 4–6 × 30 s all-out cycling/4-min recovery; MICT session: 40–60 min at 60% peak O₂ uptake). Primary outcomes were insulin-stimulated RV glucose uptake (RVGU) and fasted state RV free fatty acid uptake (RVFFAU) measured by positron emission tomography. Secondary outcomes were changes in RV structure and function, determined by cardiac magnetic resonance. RVGU decreased after training (−22% HIIT, −12% MICT, P = 0.002 for training effect), but RVFFAU was not affected by the training ( P = 0.74). RV end-diastolic and end-systolic volumes, respectively, increased +5 and +7% for HIIT and +4 and +8% for MICT ( P = 0.002 and 0.005 for training effects, respectively), but ejection fraction mildly decreased (−2% HIIT, −4% MICT, P = 0.034 for training effect). RV mass and stroke volume remained unaltered. None of the observed changes differed between the training groups ( P > 0.12 for group × training interaction). Only 2 wk of physical training in previously sedentary subjects induce changes in RV glucose metabolism, volumes, and ejection fraction, which precede exercise-induced hypertrophy of RV. Listen to this article’s corresponding podcast at http://ajpheart.podbean.com/e/right-ventricular-metabolic-adaptations-to-exercise-training/ .

Type 2 diabetes mellitus (T2DM) is associated with reduced myocardial glucose uptake (GU) and increased free fatty acid uptake (FFAU). Sprint interval training (SIT) improves physical exercise capacity and metabolic biomarkers, but effects of SIT on cardiac function and energy substrate metabolism in diabetic subjects are unknown. We tested the hypothesis that SIT is more effective than moderate-intensity continuous training (MICT) on adaptations in left and right ventricle (LV and RV) glucose and fatty acid metabolism in diabetic subjects. Twenty-six untrained men and women with T2DM or prediabetes were randomized into two-week-long SIT (n = 13) and MICT (n = 13) interventions. Insulin-stimulated myocardial GU and fasted state FFAU were measured by positron emission tomography and changes in LV and RV structure and function by cardiac magnetic resonance. In contrast to our hypothesis, SIT significantly decreased GU compared to MICT in LV. FFAU of both ventricles remained unchanged by training. RV end-diastolic volume (EDV) and RV mass increased only after MICT, whereas LV EDV, LV mass, and RV and LV end-systolic volumes increased similarly after both training modes. As SIT decreases myocardial insulin-stimulated GU compared to MICT which may already be reduced in T2DM, SIT may be metabolically less beneficial than MICT for a diabetic heart.

Chemical-genomic and genetic interaction profiling approaches are widely used to study mechanisms of drug action and resistance. However, there exist a number of scoring algorithms customized to different experimental assays, the relative performance of which remains poorly understood, especially with respect to different types of chemogenetic assays. Using yeast Saccharomyces cerevisiae as a test bed, we carried out a systematic evaluation among the main drug target analysis approaches in terms of predicting global drug–target interaction networks. We found drastic differences in their performance across different chemical-genomic assay types, such as those based on heterozygous and homozygous diploid or haploid deletion mutant libraries. Moreover, a relatively small overlap in the predicted targets was observed between those approaches that use either chemical-genomic screening alone or combined with genetic interaction profiling. A rank-based integration of the complementary scoring approaches led to improved overall performance, demonstrating that genetic interaction profiling provides added information on drug target prediction. Optimal performance was achieved when focusing specifically on the negative tail of the genetic interactions, suggesting that combining synthetic lethal interactions with chemical–genetic interactions provides highest information on drug–target interactions. A network view of rapamycin-interacting genes, pathways and complexes was used as an example to demonstrate the benefits of such integrated and optimized analysis of chemogenetic assays in yeast.