Objective: In contrast to intentionally restricting energy intake, restricting the eating window may be an option for treating obesity. By comparing time-restricted eating (TRE) with an unrestricted (non-TRE) control, it was hypothesized that TRE facilitates weight loss, alters body composition, and improves metabolic measures. Methods: Participants (17 women and 3 men; mean [SD]: 45.5 [12.1] years; BMI 34.1 [7.5] kg/m 2 ) with a prolonged eating window (15.4 [0.9] hours) were randomized to TRE (n = 11: 8-hour window, unrestricted eating within window) versus non-TRE (n = 9: unrestricted eating) for 12 weeks. Weight, body composition (dual x-ray absorptiometry), lipids, blood pressure, 2-hour oral glucose tolerance, 2-week continuous glucose monitoring, and 2-week physical activity (actigraphy assessed) were measured during the pre-and end-intervention periods. Results: The TRE group significantly reduced the eating window (endintervention window: 9.9 [2.0] hours) compared with the non-TRE group (end-intervention window: 15.1 [1.1] hours) (P < 0.01). Compared with non-TRE, TRE decreased the number of eating occasions, weight, lean mass, and visceral fat (all P ≤ 0.05). Compared with preintervention measures, the TRE group reduced the number of eating occasions (−21.9% [30.1%]) and reduced weight (−3.7% [1.8%]), fat mass (−4% [2.9%]), lean mass (−3.0% [2.7%]), and visceral fat (−11.1% [13.4%]) (all P ≤ 0.05). Physical activity and metabolic measures remained unchanged. Conclusions: In the setting of a randomized trial, TRE presents a simplified view of food intake that reduces weight.Obesity (2020) 28, 860-869.

Diet has the potential to be a powerful and cost-effective tool for treatment of type 2 diabetes mellitus (T2D). High-protein diets have shown promise for this purpose. The objective of this systematic review was to evaluate whether high-protein diets improve glycemic outcomes in people with T2D. We conducted a systematic search of literature published prior to 1 February 2018 to find clinical studies of high-protein diet patterns for treatment of T2D in human participants. A high-protein diet was defined as a diet with protein content greater than that of a typical diet in the United States (>16% of total energy as protein). Studies were excluded if weight loss >5% occurred or if no glycemic outcomes were measured. A total of 21 independent articles met our criteria and were included. Most tested diets had a protein content of around 30% of total energy. Many studies supported the use of high-protein diets for patients with T2D, but were limited by small size (n = 8–32) and short duration (1–24 wk). Randomized controlled trials tended to be larger (n = 12–419) and longer (6 wk–2 y), and had mixed results, with many trials showing no difference between a high-protein diet and control. Many randomized controlled trials were limited by low compliance and high dropout rates >15%. There were no consistent beneficial or detrimental effects of high-protein diets on renal or cardiovascular outcomes. Evidence was insufficient to recommend 1 type of protein (plant or animal) over the other. Our review suggests that interventions to improve compliance with diet change over the long term may be equally important as specific macronutrient recommendations for treatment of T2D.

Time-restricted eating (TRE) can facilitate weight loss, yet its effect on eating patterns remains unknown. Twenty adults with BMI ≥ 25 kg/m2 underwent a 12-week randomized trial, examining the effect of an 8-h, time-restricted eating intervention on dietary patterns. Oral intake was documented using a smartphone. Dietary patterns, assessed as frequency of eating occasions (EOs) and types of meals/snacks and beverages, were compared between baseline (T0), early-intervention (T1), and end-intervention (T2). At T1 and T2, both groups had less EOs compared to T0, with greater reduction seen in the TRE group (−28%) than the non-TRE group (−12%) at T2 (p = 0.01 vs. non-TRE). Comparing T1 to T0, the TRE group documented less incomplete meals (−32.5%: p = 0.02), high quality snacks (−23.6%: p = 0.03), and low quality snacks (−36.6%: p = 0.004). Comparing T2 to T0, the TRE group documented less incomplete meals (−33.9%: p = 0.03), high quality snacks (−28.1%: p < 0.001) and low quality snacks (−51.2%: p < 0.001). Caffeinated beverage intake was reduced in the TRE group at T1 (−20.2%) and T2 (−28.8%) vs. T0, but remained unaltered in the non-TRE group. By using a smartphone application to document dietary intake, TRE significantly reduced the number of EOs, snacks, and caffeinated beverages, relative to baseline and relative to the non-TRE.

Background. Coconut oil, a saturated fat comprised mostly of the medium-chain fatty acid, lauric acid, has become increasingly popular over the past few decades due to its touted anti-inflammatory, metabolic, and lipid-lowering properties. There have been many studies with mixed results evaluating the effects of coconut oil consumption on lipid metabolism and cardiometabolic risk. However, the effects on glucose metabolism are less clear. There are few trials on the effects of coconut oil on glucose homeostasis but no case reports prior to the current one. Case. We present a case of a 66-year-old man with a history of type 2 diabetes managed with insulin who developed recurrent hypoglycemia and required reduction in insulin therapy quickly after consuming coconut oil supplementation. Conclusion. This is the first known case report of coconut oil supplementation in a diabetic patient on insulin resulting in hypoglycemia. Review of the literature shows that coconut oil supplementation can have a favorable effect on glycemic control, possibly through phenolic compounds mediating anti-inflammatory effects. This effect is inconsistent throughout the studies reviewed, likely due to variations in types of coconut oil supplementation and scarcity of trials. Further research is required both in animal models and in humans before coconut oil intake is widely advised and popularized. This is especially true in patients with diabetes, who are at increased risk of cardiovascular disease, and in whom reduction in saturated fat intake is advised.

Increased prevalence of obesity may be due to an increase of being sedentary at work. Increasing non-exercise activity thermogenesis (NEAT) using walking workstations may increase total physical activity and promote a leaner physical body composition (or phenotype). The purpose of this study was to test whether walking slowly during work was sufficient to promote a leaner phenotype by increasing physical activity in sedentary desk workers without inducing compensation or a decrease in activity or energy expenditure during the nonworking hours. We conducted a prospective cohort study using a within-subjects crossover design. The design involved two phases each lasting 2 weeks: a treadmill exercise phase in which subjects used a walking workstation for 2.5 hours a day 5 days/week and a control phase in which subjects maintained their normal work activity. Twenty-five sedentary adults working at the Minneapolis VA Health Care System. We measured body weight, body composition, food intake, 24-hour physical activity, and self-reported physical activity with the International Physical Activity Questionnaire (IPAQ). Treadmill exercise caused a leaner phenotype (lean mass gain and fat mass loss) and significantly increased their 24-hour physical activity. Walking workstation use had favorable effects on physical well-being and mental focus and did not adversely affect productivity. Light treadmill exercise during work can increase physical activity and result in a leaner body composition. This is a potentially useful intervention to increase NEAT in the modern sedentary work environment.

The ketogenic diet, which has become an increasingly popular diet, severely restricts carbohydrate intake to shunt metabolism towards fatty acid oxidation and production of ketones as a fuel source. There have been many studies illustrating the positive effects of a ketogenic diet in weight loss and other benefits; however, the long-term effects and potential adverse events of a ketogenic diet have not been well studied or documented in literature. There are a few case reports of ketogenic diet resulting in hypoglycemia. We report a case of hypoglycemia with a blood glucose of 39 mg/dL and ketosis in a 69-year-old woman who strictly followed a ketogenic diet for nearly one year. She presented with malaise, sugar cravings, and mental fogginess, and after intake of alcoholic beverages, was admitted to the hospital with hypoglycemia. She had elevated beta-hydroxybutyrate, and low insulin and C-peptide, all consistent with a starvation ketosis. This case illustrates that adherence to a ketogenic diet for a prolonged period of time, in combination with alcohol intake, can disrupt normal glucose homeostatic mechanisms and result in a significant degree of hypoglycemia. This pattern of hypoglycemia may not present with classic symptoms, most likely partly due to effects of the ketogenic diet on brain function. This case provides insight that supports the need to counsel patients about alcohol intake while on the ketogenic diet. More information is needed on long-term complications of the ketogenic diet on glucose homeostasis in the body as well as in the brain.

Objective Decreased insulin sensitivity and impairment of β‐cell function predate and predict development of type 2 diabetes mellitus. Time‐restricted eating (TRE) might have a benefit for these parameters. The objective of this pilot study was to investigate this possibility. Methods Secondary analysis of a randomized controlled trial comparing 12 weeks of TRE (8‐hour eating window) to unrestricted eating (non‐TRE) was performed. Participants were adults with overweight or obesity and without diabetes. Two‐hour oral glucose tolerance testing was performed at baseline and end‐intervention. Glucose tolerance test‐derived measures of insulin sensitivity, insulin secretion, and β‐cell function were compared between groups. Results Participants (17 women/3 men with mean [SD] age 45.5 [12.1] years and BMI 34.1 [7.5] kg/m2) with a prolonged eating window (15.4 [0.9] hours) were randomized to TRE (n = 11) or non‐TRE (n = 9). The quantitative insulin sensitivity check index (QUICKI), Stumvoll index, Avignon index, insulinogenic index, insulin area under the curve/glucose area under the curve, and oral disposition index did not differ between the TRE and non‐TRE groups at end‐intervention. Conclusions In adults with overweight or obesity and without diabetes, TRE did not significantly alter insulin sensitivity, insulin secretion, or β‐cell function over a 12‐week intervention. Whether TRE is beneficial in adults with prediabetes or type 2 diabetes mellitus warrants further investigation.

Time-restricted eating (TRE) is a form of intermittent fasting that has gained interest in nutrition research and interventions as one of the dietary patterns to promote weight loss and other metabolic benefits. TRE is an eating pattern in which all nutrient intakes occur within a few hours (&lt; 12 hours) every day, with no overt attempt to alter nutrient quality or quantity. The “See Food” study was a randomized, unblinded, controlled, behavioral interventional pilot clinical trial conducted from 2017-2018, that aimed to study the effect of a TRE intervention on metabolic health of overweight and obese participants, using the help of novel digital mobile technology. The objective of the current study is to analyze changes in diet quality among participants of the “See Food” study. Specifically, food intake patterns (frequency and type of meals, snacks and beverages) were compared between subjects in the 8-hour TRE intervention group, versus the unrestricted intake control group. Inclusion criteria included age 18-65, BMI &gt;25 kg/m2, had stable sleep and work schedule and owned a smartphone. Participants with diabetes, cardiovascular disease, uncontrolled pulmonary disease, pregnancy and nursing were excluded. A total of 20 participants with overweight or obesity (9 in control group and 11 in TRE group) were enrolled. Participants were instructed to use the “myCircadianClock” smartphone application to document their time of eating, type of meal and food images at baseline and during the intervention period of 14 weeks. The TRE group was instructed to consume calories within 8 hours each day. We compared the data between 14 days at baseline and 14 days at the end of the intervention. An eating occasion (EO) as defined as an occasion when a food or beverage (other than water) was consumed, and was separate from another EO by at least 15 minutes. Compared to baseline, both the TRE (3.8±0.4 vs 5.3±0.4, p&lt;0.0001) and control group (4.9±0.4 vs 5.6±0.5, p=0.007) had less eating frequency. The TRE group had less eating frequency compared to control group (-1.5±0.2 vs -0.6±0.2, p=0.01) at the end of the study. Meal quality was classified by meal or snack type using a food-based classification system, and included 6 Eos ranging from a complete meal, to a low-quality snack. Beverages were classified separately by type. There were no differences in meal quality between the TRE and control group. The TRE group had less frequency of high-quality snack (-0.5±0.1 vs 0.05±0.1, p=0.008), mixed quality snack (-0.03±0.06 vs -0.3±0.07, p=0.01) and caffeinated beverage (-0.6±0.09 vs -0.008±0.1) compared to the control group. Conclusions: There was a reduction in eating frequency but no change in food quality when following an 8-hour TRE. Estimation of calories intake was limited in this study. A strength of the study is the use of a novel mobile app to track timing of meals. This would be feasible to implement in a real life setting.