Time-restricted feeding (TRF) is a form of intermittent fasting that involves having a longer daily fasting period. Preliminary studies report that TRF improves cardiometabolic health in rodents and humans. Here, we performed the first study to determine how TRF affects gene expression, circulating hormones, and diurnal patterns in cardiometabolic risk factors in humans. Eleven overweight adults participated in a 4-day randomized crossover study where they ate between 8 am and 2 pm (early TRF (eTRF)) and between 8 am and 8 pm (control schedule). Participants underwent continuous glucose monitoring, and blood was drawn to assess cardiometabolic risk factors, hormones, and gene expression in whole blood cells. Relative to the control schedule, eTRF decreased mean 24-hour glucose levels by 4 ± 1 mg/dl (p = 0.0003) and glycemic excursions by 12 ± 3 mg/dl (p = 0.001). In the morning before breakfast, eTRF increased ketones, cholesterol, and the expression of the stress response and aging gene SIRT1 and the autophagy gene LC3A (all p < 0.04), while in the evening, it tended to increase brain-derived neurotropic factor (BNDF; p = 0.10) and also increased the expression of MTOR (p = 0.007), a major nutrient-sensing protein that regulates cell growth. eTRF also altered the diurnal patterns in cortisol and the expression of several circadian clock genes (p < 0.05). eTRF improves 24-hour glucose levels, alters lipid metabolism and circadian clock gene expression, and may also increase autophagy and have anti-aging effects in humans.

A lifetime of change: Measurements of total and basal energy in a large cohort of subjects at ages spanning from before birth to old age document distinct changes that occur during a human lifetime. Pontzer et al . report that energy expenditure (adjusted for weight) in neonates was like that of adults but increased substantially in the first year of life (see the Perspective by Rhoads and Anderson). It then gradually declined until young individuals reached adult characteristics, which were maintained from age 20 to 60 years. Older individuals showed reduced energy expenditure. Tissue metabolism thus appears not to be constant but rather to undergo transitions at critical junctures. —LBR

Metabolic flexibility is the capacity for the organism to adapt fuel oxidation to fuel availability. The inability to modify fuel oxidation in response to changes in nutrient availability has been implicated in the accumulation of intramyocellular lipid and insulin resistance. The metabolic flexibility assessed by the ability to switch from fat to carbohydrate oxidation is usually impaired during a hyperinsulinemic clamp in insulin-resistant subjects; however, this “metabolic inflexibility” is mostly the consequence of impaired cellular glucose uptake. Indeed, after controlling for insulin-stimulated glucose disposal rate (amount of glucose available for oxidation), metabolic flexibility is not altered in obesity regardless of the presence of type 2 diabetes. To understand how intramyocellular lipids accumulate and cause insulin resistance, the assessment of metabolic flexibility to high-fat diets is more relevant than metabolic flexibility during a hyperinsulinemic clamp. An impaired capacity to upregulate muscle lipid oxidation in the face of high lipid supply may lead to increased muscle fat accumulation and insulin resistance. Surprisingly, very few studies have investigated the response to high-fat diets. In this review, we discuss the role of glucose disposal rate, adipose tissue lipid storage, and mitochondrial function on metabolic flexibility. Additionally, we emphasize the bias of using the change in respiratory quotient to calculate metabolic flexibility and propose novel approaches to assess metabolic flexibility. On the basis of current evidence, one cannot conclude that impaired metabolic flexibility is responsible for the accumulation of intramyocellular lipid and insulin resistance. We propose to study metabolic flexibility in response to high-fat diets in individuals having contrasting degree of insulin sensitivity and/or mitochondrial characteristics.

OBJECTIVE This study was designed to determine a cutoff point for identifying insulin resistance from hyperinsulinemic-euglycemic clamp studies performed at 120 mU/m² ⋅ min in a white population and to generate equations from routinely measured clinic and blood variables for predicting clamp-derived glucose disposal rate (GDR), i.e., insulin sensitivity. RESEARCH DESIGN AND METHODS We assembled data from hyperinsulinemic-euglycemic clamps (120 mU/m² ⋅ min insulin dose) performed at the Pennington Biomedical Research Center between 2001 and 2011. Subjects were divided into subjects with diabetes (n = 51) and subjects without diabetes (n = 116) by self-report and/or fasting glucose ≥126 mg/dL. RESULTS We found that 75% of individuals with a GDR 5.9 or 2.8< HOMA-IR <5.9 with HDL <51 mg/dL. CONCLUSIONS We developed a cutoff for defining insulin resistance from hyperinsulinemic-euglycemic clamps. Moreover, we now provide classification trees for predicting insulin resistance from routinely measured clinical and biochemical markers. These findings extend the clamp from a research tool to providing a clinically meaningful message for participants in research studies, potentially providing greater opportunity for earlier recognition of insulin resistance.

A recent Perspective article described the “carbohydrate-insulin model (CIM)” of obesity, asserting that it “better reflects knowledge on the biology of weight control” as compared with what was described as the “dominant energy balance model (EBM),” which fails to consider “biological mechanisms that promote weight gain.” Unfortunately, the Perspective conflated and confused the principle of energy balance, a law of physics that is agnostic as to obesity mechanisms, with the EBM as a theoretical model of obesity that is firmly based on biology. In doing so, the authors presented a false choice between the CIM and a caricature of the EBM that does not reflect modern obesity science. Here, we present a more accurate description of the EBM where the brain is the primary organ responsible for body weight regulation operating mainly below our conscious awareness via complex endocrine, metabolic, and nervous system signals to control food intake in response to the body's dynamic energy needs as well as environmental influences. We also describe the recent history of the CIM and show how the latest “most comprehensive formulation” abandons a formerly central feature that required fat accumulation in adipose tissue to be the primary driver of positive energy balance. As such, the new CIM can be considered a special case of the more comprehensive EBM but with a narrower focus on diets high in glycemic load as the primary factor responsible for common obesity. We review data from a wide variety of studies that address the validity of each model and demonstrate that the EBM is a more robust theory of obesity than the CIM.

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood^1–3. Here, the Molecular Transducers of Physical Activity Consortium⁴ profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository (https://motrpac-data.org/).