Dietary Incorporation of Lentils to Improve Metabolic Health

Overview

Approximately 40% of Americans are pre-diabetic or diabetic, mostly in the form of type 2 diabetes (T2D), which is heavily influenced by diet. Three interrelated factors driving the progression of T2D are large glycemic and lipidemic responses after a meal, consumption of excess calories, and increased fat within the abdominal compartment, referred to as visceral adipose tissue (VAT). Available research suggests that these problems may be attenuated with pulse consumption both at the time of consumption and at the next meal, in what is referred to as the second meal effect. Associations between pulse consumption and metabolic health have been measured in observational studies; unfortunately, randomized clinical trials data to establish cause and effect in humans are typically short in duration (≤ 4 weeks), limited to a single dose of pulse consumption (none exclusively for lentils), and not designed to strategically exploit the well-established second meal effect. We expect the impact of lentil intake will be greatest if consumed at the midday meal to offset the magnitude of the response to the large caloric intake typical in the evening. Our overarching hypothesis is that midday lentil consumption in individuals at greater risk for metabolically driven diseases will improve metabolic health. The purpose of this proposal is to determine whether eight weeks of 0, 300, or 600 grams per week of lentils by individuals with elevated VAT will improve insulin sensitivity, hepatic insulin resistance, lipid profiles (total cholesterol, triglycerides, LDL and HDL lipoproteins), inflammation, appetite and satiety, body mass, body composition, and volume of VAT.

Investigators utilized a parallel intervention of two separate dietary lentil doses versus control (no lentils) treatments for eight weeks in adults with elevated waist circumference. Enrolled participants were randomized to LOW (300 g lentils/week), HIGH (600 g lentils/week), or CONTROL (0 g lentils/week) treatment groups using block randomization. Outcome variables were assessed before and after 8 weeks of the dietary intervention. Procedures: Anthropometrics. Measurements were collected from participants before and after the intervention using the validated segmental multifrequency bioelectrical impedance analysis (SECA mBCA515, Hamburg, Germany) (cite). Fat mass (%) and estimated visceral adipose (L) were used for analysis. Glycemic Challenge. An oral glucose tolerance test (OGTT) was administered as a meal which contained white bread containing 75 g or available carbohydrate. Water was provided with the meal. Caffeinated black tea was provided for participants who identified as habitual morning caffeine consumers. Participants were instructed to avoid alcohol consumption and strenuous physical activity in the 24 hours before their visit and to complete an overnight fast (10 -12 hours) before blood collection. Fasting blood samples were collected by venipuncture by a trained phlebotomist before ingestion of the glycemic challenge meal. A fingerstick was used to obtain blood for glucose and insulin prior to ingestion of bread and every 30 minutes in the 2-hour postprandial period. Dietary Intervention: Experimental diets were provided to participants in the form of five pre-made midday meals containing 0, 60, or 120 g per meal matched across treatment groups for total energy and protein. Midday meals were targeted in this study to exploit the second meal effect of lower caloric intake at the next meal, the evening meal. Participants were instructed to consume all of the meal provided to them at the midday meal and then proactively reduce portion sizes and not eat beyond fullness at the evening meal. Participants were instructed to eat the meals Monday through Friday, and to eat any missed meals on Saturday or Sunday. Compliance was checked with random emails once per week and verbal reporting when picking up weekly meals. Assessment of Satisfaction with Meal Provided: An email or text message questionnaire was sent to each participant for them to complete in real time at 4:00 pm one evening per week. Day of the week was varied so that each of the five weekdays was sampled at least once. Participants were asked to rate the following question "How much do you like your meal provided today?" on a scale from 1 (dislike extremely) to 9 (like extremely). Assessment of Satiety: An email or text message questionnaire was sent to each participant for them to complete in real time at 4:00 pm one evening per week. Day of the week was varied so that each of the five weekdays was sampled at least once. To assess the elements of satiety, participants rated each of the following questions on a scale of 0 to 10: How hungry are you? (0 = not at all, 10 = extremely); How full are you? (0 = not at all, 10 = extremely); How satisfied do you feel? (0 = not at all, 10 = extremely); How strong is your desire to eat? (0 = very weak, 10 = very strong); How much do you think you could (or would want to) eat right now? (0 = nothing, 10 = very large amount) Assessment of Gastrointestinal Comfort: An email or text message questionnaire was sent to each participant for them to complete in real time at 8:00 pm one evening per week. Day of the week was varied so that each of the five weekdays was sampled at least once. To assess gastrointestinal comfort, participants rated the following symptoms as none, mild, moderate, or severe: flatulence, bloating, cramping, abdominal discomfort. Blood analysis: Whole blood in serum separating tubes was allowed to clot for 15 minutes before centrifugation at 1200 RPM for 15 minutes with resulting serum aliquoted and stored at -80ºC until analysis.Determination of blood markers. Blood markers of metabolic syndrome as defined by the World Health Organization were determined from whole blood run on Picollo Xpress Chemistry Analyzer lipid panels (Abaxis, Union City, USA). Glycated hemoglobin (HbA1c) was determined using the DCA Vantage Analyzer (Siemens Medical Solutions Diagnostics, Cergy-Pontoise, France) performed according to manufacturer instructions. Insulin concentrations from fasting and postprandial samples were determined through a high-sensitivity insulin ELISA kit according to manufacturer instructions (ALPCO, Salem, NH, United States). Fasting insulin and glucose concentrations were used to calculate homeostasis model assessment of insulin resistance (HOMA-IR). The Matsuda Index was calculated from fasting and postprandial glucose and insulin concentrations. The postprandial glucose and insulin concentrations in response to the OGTT were input into the incremental area under the curved (AUC) method.