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High intensity interval training (HIIT) involves repeated bouts of relatively hard work, commonly at intensities eliciting ≥ 80% of maximal heart rate (HR max ), interspersed with recovery periods. Anchoring intensity to HR max can elicit a wide range of acute metabolic responses to exercise. Expressing intensity relative to metabolic thresholds such as critical power (CP) may reduce this variability. We therefore examined whether anchoring HIIT to CP reduced variability in change in [blood lactate] (ΔBLa − ) compared to HR max -based approach. Nineteen adults aged 23 ± 4 years completed two 4 × 4-min HIIT trials in a randomized, crossover manner at intensities equal to CP + 10% of work prime (CP HIIT ) or ≥ 80% HR max (HR HIIT ). Variability in [ΔBLa − ] from rest to exercise was not different between CP HIIT and HR HIIT (1.37 (0.42–1.62) vs. 1.32 (0.77–1.97) mM; p  = 0.75). Workload was higher in CP HIIT vs. HR HIIT (192 ± 39 W vs. 180 ± 43 W; p  = 0.001), as was exercise oxygen consumption, ventilation, respiratory exchange ratio, heart rate, and rating of perceived exertion (all p  < 0.05). A CP-based HIIT protocol did not reduce variability of change in [ΔBLa − ] compared to a traditional approach anchored to %HR max . However, anchoring HIIT intensity to CP resulted in participants achieving higher workloads, eliciting higher cardiorespiratory and perceived stress which could translate to divergent training-induced responses.

Background Metabolic abnormalities such as dyslipidemia, glucose and high blood pressure are common in diabetic patients. Visit-to-visit variabilities in these measures have been reported as potential residual cardiovascular risk factors. However, the relationship between these variabilities and their effects on cardiovascular prognosis have not been studied. Methods A total of 22,310 diabetic patients with ≥ 3 measurements of systolic blood pressure (SBP), blood glucose, total cholesterol (TC), and triglyceride (TG) levels during a minimum of three years at three tertiary general hospitals were selected. They were divided into high/low variability groups for each variable based on the coefficient of variation (CV) values. The primary outcome was the incidence of major adverse cardiovascular events (MACE), a composite of cardiovascular death, myocardial infarction, and stroke. Results All high CV groups had a higher incidence of MACE than those with low CV (6.0% vs. 2.5% for SBP-CV groups, 5.5% vs. 3.0% for TC-CV groups, 4.7% vs. 3.8% for TG-CV groups, 5.8% vs. 2.7% for glucose-CV groups). In multivariable Cox regression analysis,, high SBP-CV (HR 1.79 [95% CI 1.54–2.07], p < 0.01), high TC-CV (HR 1.54 [95% CI 1.34–1.77], p < 0.01), high TG-CV (HR 1.15 [95% CI 1.01–1.31], p = 0.040) and high glucose-CV (HR 1.61 [95% CI 1.40–1.86], p < 0.01) were independent predictors of MACE. Conclusion Variability of SBP, TC, TG and glucose are important residual risk factors for cardiovascular events in diabetic patients.

Fish mucus has important biological and ecological roles such as defense against fish pathogens and chemical mediation among several species. A non-targeted liquid chromatography–mass spectrometry metabolomic approach was developed to study gill mucus of eight butterflyfish species in Moorea (French Polynesia), and the influence of several fish traits (geographic site and reef habitat, species taxonomy, phylogeny, diet and parasitism levels) on the metabolic variability was investigated. A biphasic extraction yielding two fractions (polar and apolar) was used. Fish diet (obligate corallivorous, facultative corallivorous or omnivorous) arose as the main driver of the metabolic differences in the gill mucus in both fractions, accounting for 23% of the observed metabolic variability in the apolar fraction and 13% in the polar fraction. A partial least squares discriminant analysis allowed us to identify the metabolites (variable important in projection, VIP) driving the differences between fish with different diets (obligate corallivores, facultative corallivores and omnivorous). Using accurate mass data and fragmentation data, we identified some of these VIP as glycerophosphocholines, ceramides and fatty acids. Level of monogenean gill parasites was the second most important factor shaping the gill mucus metabolome, and it explained 10% of the metabolic variability in the polar fraction and 5% in the apolar fraction. A multiple regression tree revealed that the metabolic variability due to parasitism in the polar fraction was mainly due to differences between non-parasitized and parasitized fish. Phylogeny and butterflyfish species were factors contributing significantly to the metabolic variability of the apolar fraction (10 and 3%, respectively) but had a less pronounced effect in the polar fraction. Finally, geographic site and reef habitat of butterflyfish species did not influence the gill mucus metabolome of butterflyfishes.

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

In the past, determination of absolute values of cerebral metabolic rate of glucose (CMRGlc) in clinical routine was rarely carried out due to the invasive nature of arterial sampling. With the advent of combined PET/MR imaging technology, CMRGlc values can be obtained non-invasively, thereby providing the opportunity to take advantage of fully quantitative data in clinical routine. However, CMRGlc values display high physiological variability, presumably due to fluctuations in the intrinsic activity of the brain at rest. To reduce CMRGlc variability associated with these fluctuations, the objective of this study was to determine whether functional connectivity measures derived from resting-state fMRI (rs-fMRI) could be used to correct for these fluctuations in intrinsic brain activity. We studied 10 healthy volunteers who underwent a test-retest dynamic [18F]FDG-PET study using a fully integrated PET/MR system (Siemens Biograph mMR). To validate the non-invasive derivation of an image-derived input function based on combined analysis of PET and MR data, arterial blood samples were obtained. Using the arterial input function (AIF), parametric images representing CMRGlc were determined using the Patlak graphical approach. Both directed functional connectivity (dFC) and undirected functional connectivity (uFC) were determined between nodes in six major networks (Default mode network, Salience, L/R Executive, Attention, and Sensory-motor network) using either a bivariate-correlation (R coefficient) or a Multi-Variate AutoRegressive (MVAR) model. In addition, the performance of a regional connectivity measure, the fractional amplitude of low frequency fluctuations (fALFF), was also investigated. The average intrasubject variability for CMRGlc values between test and retest was determined as (14 ±8%) with an average inter-subject variability of 25% at test and 15% at retest. The average CMRGlc value (umol/100 g/min) across all networks was 39 ±10 at test and increased slightly to 43 ±6 at retest. The R, MVAR and fALFF coefficients showed relatively large test-retest variability in comparison to the inter-subjects variability, resulting in poor reliability (intraclass correlation in the range of 0.11-0.65). More importantly, no significant relationship was found between the R coefficients (for uFC), MVAR coefficients (for dFC) or fALFF and corresponding CMRGlc values for any of the six major networks. Measurement of functional connectivity within established brain networks did not provide a means to decrease the inter- or intrasubject variability of CMRGlc values. As such, our results indicate that connectivity measured derived from rs-fMRI acquired contemporaneously with PET imaging are not suited for correction of CMRGlc variability associated with intrinsic fluctuations of resting-state brain activity. Thus, given the observed substantial inter- and intrasubject variability of CMRGlc values, the relevance of absolute quantification for clinical routine is presently uncertain.

Patterns in functional diversity of organisms at large spatial scales can provide insight into possible responses to future climate change, but it remains a challenge to link large-scale patterns at the population or species level to their underlying physiological mechanisms at the individual level. The climate variability hypothesis predicts that temperate ectotherms will be less vulnerable to climate warming compared with tropical ectotherms, due to their superior acclimatization capacity. However, metabolic acclimatization occurs over multiple levels, from the enzyme and cellular level, through organ systems, to whole-organism metabolic rate (from this point forwards biological hierarchy). Previous studies have focused on one or a few levels of the biological hierarchy, leaving us without a general understanding of how metabolic acclimatization might differ between tropical and temperate species. Here, we investigated thermal acclimation of three species of Takydromus lizards distributed along a broad latitudinal gradient in China, by studying metabolic modifications at the level of the whole organism, organ, mitochondria, metabolome, and proteome. As predicted by the climate variability hypothesis, the two temperate species T. septentrionalis and T. wolteri had an enhanced acclimation response at the whole organism level compared with the tropical species T. sexlineatus, as measured by respiratory gas exchange rates. However, the mechanisms by which whole organism performance was modified was strikingly different in the two temperate species: widespread T. septentrionalis modified organ sizes, whereas the narrowly distributed T. wolteri relied on mitochondrial, proteomic and metabolomic regulation. We suggest that these two mechanisms of thermal acclimatization may represent general strategies used by ectotherms, with distinct ecological costs and benefits. Lacking either of these mechanisms of thermal acclimatization capacity, the tropical species is likely to have increased vulnerability to climate change.

Physiological plasticity is crucial for survival in fluctuating environments. The climate variability hypothesis (CVH) proposes that physiological plasticity scales with climatic variation across geographical gradients, yet its generality remains debated. Furthermore, the mediating role of prior thermal history is largely unexplored. This study examines how altitude and thermal variation shape phenotypic plasticity in resting metabolic rate (RMR) and maximum metabolic rate (MMR) of Asiatic toads (Bufo gargarizans). We found that RMR plasticity, but not MMR plasticity, varied altitudinally and was influenced by thermal conditions. Compared to low‐altitude toads, high‐altitude individuals exhibited reduced RMR plasticity, contradicting the CVH. This difference was amplified under higher thermal variability. In contrast, MMR plasticity showed no altitudinal variation or response to thermal variability. However, warm acclimation significantly increased MMR thermal sensitivity. Metabolic substrate choice depended on pre‐acclimation thermal experience. Our results indicate that RMR plasticity, rather than MMR plasticity, primarily underpins altitudinal adaptation, and increased thermal fluctuation may disrupt this adaptive pattern. This research provides novel insights into macrophysiological responses to global warming. This study shows that the plasticity of the metabolic floor, but not the metabolic ceiling, varies along altitudinal gradients and is influenced by the thermal context prior to thermal acclimatization in Bufo gargarizans. The data suggest that maintenance of metabolic plasticity is likely to be the primary strategy for adaptation to altitudinal gradients and that increased thermal variability due to climate warming may disrupt this adaptive pattern.

Genome-scale metabolic modeling has become widespread for analyzing microbial metabolism. Extending this established paradigm to more complex microbial communities is emerging as a promising way to unravel the interactions and biochemical repertoire of these omnipresent systems. While several modeling techniques have been developed for microbial communities, little emphasis has been placed on the need to impose a time-averaged constant growth rate across all members for a community to ensure co-existence and stability. In the absence of this constraint, the faster growing organism will ultimately displace all other microbes in the community. This is particularly important for predicting steady-state microbiota composition as it imposes significant restrictions on the allowable community membership, composition and phenotypes. In this study, we introduce the SteadyCom optimization framework for predicting metabolic flux distributions consistent with the steady-state requirement. SteadyCom can be rapidly converged by iteratively solving linear programming (LP) problem and the number of iterations is independent of the number of organisms. A significant advantage of SteadyCom is compatibility with flux variability analysis. SteadyCom is first demonstrated for a community of four E. coli double auxotrophic mutants and is then applied to a gut microbiota model consisting of nine species, with representatives from the phyla Bacteroidetes, Firmicutes, Actinobacteria and Proteobacteria. In contrast to the direct use of FBA, SteadyCom is able to predict the change in species abundance in response to changes in diets with minimal additional imposed constraints on the model. By randomizing the uptake rates of microbes, an abundance profile with a good agreement to experimental gut microbiota is inferred. SteadyCom provides an important step towards the cross-cutting task of predicting the composition of a microbial community in a given environment.

BackgroundWhether short sleep duration or high sleep variability may predict less weight loss and reduction in measures of adiposity in response to lifestyle interventions is unknown. The aim of this study was to compare the 12-month changes in weight and adiposity measures between those participants with short or adequate sleep duration and those with low or high sleep variability (intra-subject standard deviation of the sleep duration) in PREvención con DIeta MEDiterránea (PREDIMED)-Plus, a primary prevention trial based on lifestyle intervention programs.MethodsProspective analysis of 1986 community-dwelling subjects (mean age 65 years, 47% females) with overweight/obesity and metabolic syndrome from the PREDIMED-Plus trial was conducted. Accelerometry-derived sleep duration and sleep variability and changes in average weight, body mass index (BMI), and waist circumference (WC) attained after 12-month interventions were analyzed.ResultsThe adjusted difference in 12-month changes in weight and BMI in participants in the third tertile of sleep variability was 0.5 kg (95% CI 0.1 to 0.9; p = 0.021) and 0.2 kg/m2 (0.04 to 0.4; p = 0.015), respectively, as compared with participants in the first tertile. The adjusted difference in 12-month changes from baseline in WC was −0.8 cm (−1.5 to −0.01; p = 0.048) in participants sleeping <6 h, compared with those sleeping between 7 and 9 h.ConclusionsOur findings suggest that the less variability in sleep duration or an adequate sleep duration the greater the success of the lifestyle interventions in adiposity.

The RISKMet project aims to: (1) identify risk factors for metabolic syndrome (MetS) by comparing patients with and without MetS; (2) characterise patients treated with second-generation antipsychotics (SGAs) about MetS diagnosis; (3) study behavioural patterns, including physical activity (PA) and dietary habits, in patients and healthy individuals using a prospective cohort design. The RISKMet project investigates MetS in individuals treated with SGAs, focusing on both adult and paediatric populations. The study utilizes a case-control design to examine potential risk factors for MetS, categorizing participants as MetS+ considered as \"Cases\" and MetS- considered as \"Controls\" matched by sex and age. The evaluation of factors such as MetS, lifestyle habits, and environmental influences is conducted at two time points, T0 and T3, after 3 months. Subsequently, the project aims to assess body parameters, including physical examinations, and blood, and stool sample collection, to evaluate metabolic markers and the impact of SGAs. The analysis includes pharmacological treatment data and genetic variability. Behavioural markers related to lifestyle, eating behaviour, PA, and mood are assessed at both T0 and T3 using interviews, accelerometers, and a mobile app. The study aims to improve mental and physical well-being in SGA-treated individuals, establish a biobank for MetS research, build an evidence base for physical health programs, and develop preventive strategies for SGA-related comorbidities. This project innovates MetS monitoring in psychiatry by using intensive digital phenotyping, identifying biochemical markers, assessing familial risks, and including genetically similar healthy controls. ISRCTN18419418 at www.isrctn.com.