Cardiometabolic health impacts of time-restricted eating : Implications for type 2 diabetes, cancer and cardiovascular diseases

Purpose of review Time-restricted eating (TRE) entails consuming energy intake within a 4- to 10-h window, with the remaining time spent fasting. Although studies have reported health benefits from TRE, little is known about the impact of TRE on common chronic diseases such as type 2 diabetes, cancer and cardiovascular disease. This review summarizes and critically evaluates the most recent TRE research findings relevant to managing and treating these chronic diseases. Recent findings Most recent TRE studies have been in populations with overweight/obesity or metabolic syndrome; two have been in populations with diabetes, three in cancer survivors and none in populations with cardiovascular disease. Collectively, these studies showed that participants could adhere to TRE and TRE is well tolerated. These studies also showed preliminary efficacy for improved glucose regulation and insulin sensitivity, a reduction in body fat and blood pressure, reduced cardiovascular risk scores and increased quality of life. More research is required to define the most effective TRE protocol (i.e. length and timing of eating window, intervention duration). Summary TRE has demonstrated benefits on cardiovascular, metabolic and clinical outcomes relevant to the underlying pathophysiology, but there are limited data on TRE implemented specifically within populations with diabetes, cancer or cardiovascular disease

The Intra- or Extracellular Redox State Was Not Affected by a High vs. Low Glycemic Response Diet in Mice

<div><p>A low glycemic response (LGR) vs. high glycemic response (HGR) diet helps curtail the development of obesity and diabetes, though the mechanisms are unknown. We hypothesized that consumption of a HGR vs. a LGR diet would lead to a more oxidized circulating redox state and predicted that a HGR diet would increase fat accumulation, reduce insulin sensitivity, and impair metabolic acclimation to a high fat diet in a mouse model. Hence, male C57BL/6 mice consumed a HGR or LGR diet for 16 weeks and a subset of the mice subsequently consumed a high fat diet for 4 weeks. We found that body mass increased at a faster rate for those consuming the HGR diet. Percent body fat was greater and percent lean mass was lesser in the HGR group starting at 12 weeks. However, the groups did not differ in terms of glucose tolerance at week 14 and metabolic parameters (respiratory exchange ratio, heat production, activity) at weeks 4 or 15. Moreover, mice on either diet did not show differences in metabolic acclimation to the high fat leg of the study. At the termination of the study, the groups did not differ in terms of redox pairs (lactate/pyruvate and β-hydroxybutyrate/acetoacetate) or thioredoxin reductase activity in blood. Also, total and oxidized glutathione levels and lipid peroxidation were similar in blood and liver. Correlations between baseline measures, longitudinal parameters, environmental conditions, and terminal metrics revealed that individual mice have innate propensities to metabolic regulation that may be difficult to perturb with diet alone; for example, starting mass correlated negatively with energy expenditure 4 weeks into the study and total hepatic glutathione at the end of the study. In conclusion, these data suggest that the mechanism by which HGR carbohydrates contributes to obesity is not via prolonged oxidation of the circulating redox state.</p></div

Body composition, as measured by MRI, vs. time.

<p>Percent fat mass (A), lean mass (B), and total body water (C) over the first 15 weeks of the experiment. <i>n</i> = 17 in the low glycemic response diet group, white, and <i>n</i> = 15 in the high glycemic response diet group, gray. There are effects of Group, Time, and Group × Time (<i>p</i> < 0.009) for all three measures (ANOVA). Data represent avg ± SE.</p

Metabolic parameters during the light and dark cycles at weeks 4 and 15.

<p>A) The respiratory exchange ratio (RER), B) Heat production (or total energy expenditure) normalized to body mass, C) Heat production normalized to fat free mass (FFM), and D) The total activity counts for day and night periods for mice on the low (white) and high (gray) glycemic response diets at night (dark cycle) and during the day (light cycle) for weeks 4 and 15 of the diet. <i>n</i> = 16 on the high glycemic index diet and <i>n</i> = 16 on the low glycemic index diet. Data represent avg ± SE.</p

Experimental design.

<p>^<i>a</i>Magnetic resonance imaging</p><p>^<i>b</i>Respiratory exchange ratio</p><p>Experimental design.</p

<i>Ex vivo</i> redox measures.

<p>At the termination of the study, blood and liver were harvested for redox analysis. Mice that were on the low glycemic response diet are in white and those on the high glycemic response diet are in gray. Mice that were terminated during the 16^th week are labeled “Low Fat” while those subjected to the high fat diet for 4 weeks succeeding the low fat leg are labeled “High Fat”. Data represent avg ± SE. A) Lactate/Pyruvate ratio; B) βOHB/Acoc ratio; C) TBARS- blood; D) TBARS- liver; E) Total glutathione- blood; F) Oxidized glutathione- blood; G) Total glutathione- liver; H) Oxidized glutathione- liver; I) Thioredoxin reductase activity in liver.</p

Correlations between baseline, longitudinal, and end point measures.

<p>A) Correlation matrix chromatically depicting correlations between all parameters. Blue indicates a positive correlation (<i>r</i> = 1) and red indicates a negative correlation (<i>r</i> = -1). B) Correlation between heat generation (energy expenditure) during the dark cycle at week 4 and the mouse starting mass, C) Correlation between total glutathione in the liver at the termination of the study and the starting mouse, D) Correlation between the average temperature in the housing facility and food intake at week 15, E) Correlation between TBARS (lipid peroxidation) in the liver and the β-hydroxybutyrate:acetoacetate (B/A) ratio in the serum, both at the end of the study. Starting mass = Mass at the start of the study; RateGain = the rate at which weight was gained during the study; RER4 = average resting energy expenditure during the metabolic cage experiment during 24 hrs during week 4 (kcal/kg/min); Food4 = Food consumed during the fourth week of the experiment (g/g body weight/day); Water4 = Water consumed during the fourth week of the experiment (mL/g body weight/day); Heat4 = average heat generated during the metabolic cage experiment during the dark cycle during week 4 (kcal/kg/min); Activity4 = Total x, y, and z beam breaks during 24 hrs in the metabolic cages; Fat4 = percent fat, as measured by MRI, at week 4; Lean4 = percent fat, as measured by MRI, at week 4; TotWater4 = percent total water, as measured by MRI, at week 4; Glc6 = random blood glucose measurement at week 6; Fat12, Lean12, and TotWater12 = body composition as measured by MRI at week 12; FastGlc14 = fasting blood glucose concentration at week 14; AUC14 = area under the curve of a blood glucose vs. time plot during a glucose tolerance test at week 14; Food15, Water15, RER15, Heat15, Activity15 = metabolic measures during the metabolic cage experiment during week 15 as in week 4; Fat15, Lean15, TotWater15 = body composition as measured by MRI at week 15; the following measures are post-mortem: Liver = liver mass at the end of the study; SubQ = mass of subcutaneous fat mass at the end of the study; Epi = mass of epididymal fat mass at the end of the study; TrxR = thioredoxin reductase activity in the liver; Glycogen = glycogen content of the liver; Lactate = serum lactate concentration; Pyruvate = serum pyruvate concentration; L:P = serum lactate:pyruvate ratio; bOHB = serum β-hydroxybutyrate ratio; Acoc = serum acetoacetate ratio; B:A = serum β-hydroxybutyrate:acetoacetate ratio; TBARSblood = TBARS (lipid peroxidation) in the blood; TBARSliver = TBARS (lipid peroxidation) in the liver; TotGSHB = total glutathione concentration in the blood; GSSGB = oxidized glutathione concentration in the blood; TotGSHL = total glutathione concentration in the liver; GSSGL = total oxidized glutathione concentration in the liver; AvgTemp = average temperature in the facility during the entire experiment; AvgHum = average humidity in the facility.</p

Daily food and water intake at weeks 4 and 15.

<p>A) Mice on the low glycemic response diet (white) tended to eat more than those on the high glycemic response diet (gray; <i>p</i> = 0.054, repeated measures ANOVA). B) Mice drank less water at week 15 compared to week 4 (<i>p</i> = 0.007). <i>n</i> = 16 for those on the low glycemic response diet and <i>n</i> = 15 for those on the high glycemic response diet; repeated measures ANOVA. Data represent avg ± SE.</p

Macronutrient composition of the experimental diets.

<p>Macronutrient composition of the experimental diets.</p

Mechanisms, Mediators, and Moderators of the Effects of Exercise on Chemotherapy-Induced Peripheral Neuropathy

Chemotherapy-induced peripheral neuropathy (CIPN) is an adverse effect of neurotoxic antineoplastic agents commonly used to treat cancer. Patients with CIPN experience debilitating signs and symptoms, such as combinations of tingling, numbness, pain, and cramping in the hands and feet that inhibit their daily function. Among the limited prevention and treatment options for CIPN, exercise has emerged as a promising new intervention that has been investigated in approximately two dozen clinical trials to date. As additional studies test and suggest the efficacy of exercise in treating CIPN, it is becoming more critical to develop mechanistic understanding of the effects of exercise in order to tailor it to best treat CIPN symptoms and identify who will benefit most. To address the current lack of clarity around the effect of exercise on CIPN, we reviewed the key potential mechanisms (e.g., neurophysiological and psychosocial factors), mediators (e.g., anti-inflammatory cytokines, self-efficacy, and social support), and moderators (e.g., age, sex, body mass index, physical fitness, exercise dose, exercise adherence, and timing of exercise) that may illuminate the relationship between exercise and CIPN improvement. Our review is based on the studies that tested the use of exercise for patients with CIPN, patients with other types of neuropathies, and healthy adults. The discussion presented herein may be used to (1) guide oncologists in predicting which symptoms are best targeted by specific exercise programs, (2) enable clinicians to tailor exercise prescriptions to patients based on specific characteristics, and (3) inform future research and biomarkers on the relationship between exercise and CIPN