Edograms: recording the microstructure of meal intake in humans-a window on appetite mechanisms

Evaluation of Dietary and Alcohol Drinking Patterns in Patients with Excess Body Weight in a Spanish Cohort: Impact on Cardiometabolic Risk Factors

Unhealthy dietary habits and sedentarism coexist with a rising incidence of excess weight and associated comorbidities. We aimed to analyze the dietary and drinking patterns of patients with excess weight, their main characteristics, plausible gender differences and impact on cardiometabolic risk factors, with a particular focus on the potential contribution of beer consumption. Data from 200 consecutive volunteers (38 ± 12 years; 72% females) living with overweight or class I obesity attending the obesity unit to lose weight were studied. Food frequency questionnaires and 24 h recalls were used. Reduced-rank regression (RRR) analysis was applied to identify dietary patterns (DPs). Anthropometry, total and visceral fat, indirect calorimetry, physical activity level, comorbidities and circulating cardiometabolic risk factors were assessed. Study participants showed high waist circumference, adiposity, insulin resistance, dyslipidemia, pro-inflammatory adipokines and low anti-inflammatory factors like adiponectin and interleukin-4. A low-fiber, high-fat, energy-dense DP was observed. BMI showed a statistically significant (p < 0.05) correlation with energy density (r = 0.80) as well as percentage of energy derived from fat (r = 0.61). Excess weight was associated with a DP low in vegetables, legumes and whole grains at the same time as being high in sweets, sugar-sweetened beverages, fat spreads, and processed meats. RRR analysis identified a DP characterized by high energy density and saturated fat exhibiting negative loadings (>-0.30) for green leafy vegetables, legumes, and fruits at the same time as showing positive factor loadings (>0.30) for processed foods, fat spreads, sugar-sweetened beverages, and sweets. Interestingly, for both women and men, wine represented globally the main source of total alcohol intake (p < 0.05) as compared to beer and distillates. Beer consumption cannot be blamed as the main culprit of excess weight. Capturing the DP provides more clinically relevant and useful information. The focus on consumption of single nutrients does not resemble real-world intake behaviors.

Integrating Effects of Human Physiology, Psychology, and Individual Variations on Satiety–An Exploratory Study

Satiety can influence food intake, and as a consequence has the potential to affect weight and obesity. Human factors such as physiology and psychology are likely to be important in determining satiety. However, it is not well-understood how these factors (individual variations) alone or combined contribute to satiety feelings. In addition, there have been limited or no attempts to use a holistic approach to evaluate satiety. In this study, three plant-based foods were used as mid-morning snack for 52 participants to evaluate satiety response (during three consecutive days, one-day-one-food type). The foods were served ad libitum until participants felt comfortably full prior to satiety monitoring. The study explored diverse human factors (n = 30) that might contribute to satiety including those related to oral physiology, metabolic factors, body composition and psychology. It identified important variables for satiety as well as the interactions among them and the influences of age, gender, and low satiety phenotype (consistently lower reported fullness scores) on satiety. Overall, combinations of factors rather than individual ones contributed to self-reported satiety. Food factors (e.g., type, composition) had limited effects, but there were only three types used in the study. The combination of metabolic factors [respiratory quotient, age, and body energy usage type (e.g., carbohydrate or fat)], oral sensitivity & processing, personality traits (agreeableness, conscientiousness, and neuroticism), and eating behavior (e.g., emotional and external eating) were the most important for explaining individual satiety responses. Older participants had significantly higher reported satiety than younger participants, associated with significant differences in oral physiology, increased body fat, and mature psychological characters. Moreover, different satiety phenotypes had significant differences in relationships with body fat, oral physiology, personalities, food neophobia, and eating behaviors. The results of this study indicate that much greater insights into the factors determining satiety responses can be obtained by combining multiple food and human physiological and psychological characteristics. This study used more diverse measures of individual variation than previous studies of satiety and points the way toward a more holistic approach to understanding the (control of) perceptions of fullness at both individual and group levels.

The Universal Eating Monitor (UEM): objective assessment of food intake behavior in the laboratory setting

The Universal Eating Monitor was a term used to describe a device used in a laboratory setting that enabled investigators to measure, with the same instrument, the rate of eating either solids or liquids, hence the term “universal”. It consisted of an electronic balance placed in a false panel under a table cloth on which could be placed a food reservoir that contained either solid or liquefied food. The device was created in order to determine whether rates of eating differed in pattern between solid and liquid foods. An acceptable mixture of foods of identical composition that could be served as either solid or blended as a liquid was used to test the hypothesis that eating rate and intake were affected by physical composition. A best-fitting mathematical function (intake was quadratic function of time, with coefficients varying among foods used and experimental conditions), quantified intake rates. The device was used to test a variety of mechanisms underlying food intake control. Eating rates were linear when solid foods were used, but negatively accelerated with liquids. Overall, intake did not differ between solid and liquefied food of identical composition. Satiation on a calorie for calorie basis was different among foods, but physical composition interacted with energy density. Hormones and gastric distension were strong influences on food intake and rate of eating. Individuals with bulimia nervosa and binge eating disorder ate more than individuals without these disturbances. Intake in social and individual contexts was identical, but the rate of eating was slower when two individuals dined together. The eating monitor has been a useful instrument for elucidating controls of food intake and describing eating pathology.

Assessment of real life eating difficulties in Parkinson's disease patients by measuring plate to mouth movement elongation with inertial sensors

Parkinson’s disease (PD) is a neurodegenerative disorder with both motor and non-motor symptoms. Despite the progressive nature of PD, early diagnosis, tracking the disease’s natural history and measuring the drug response are factors that play a major role in determining the quality of life of the affected individual. Apart from the common motor symptoms, i.e., tremor at rest, rigidity and bradykinesia, studies suggest that PD is associated with disturbances in eating behavior and energy intake. Specifically, PD is associated with drug-induced impulsive eating disorders such as binge eating, appetite-related non-motor issues such as weight loss and/or gain as well as dysphagia—factors that correlate with difficulties in completing day-to-day eating-related tasks. In this work we introduce Plate-to-Mouth (PtM), an indicator that relates with the time spent for the hand operating the utensil to transfer a quantity of food from the plate into the mouth during the course of a meal. We propose a two-step approach towards the objective calculation of PtM. Initially, we use the 3D acceleration and orientation velocity signals from an off-the-shelf smartwatch to detect the bite moments and upwards wrist micromovements that occur during a meal session. Afterwards, we process the upwards hand micromovements that appear prior to every detected bite during the meal in order to estimate the bite’s PtM duration. Finally, we use a density-based scheme to estimate the PtM durations distribution and form the in-meal eating behavior profile of the subject. In the results section, we provide validation for every step of the process independently, as well as showcase our findings using a total of three datasets, one collected in a controlled clinical setting using standardized meals (with a total of 28 meal sessions from 7 Healthy Controls (HC) and 21 PD patients) and two collected in-the-wild under free living conditions (37 meals from 4 HC/10 PD patients and 629 meals from 3 HC/3 PD patients, respectively). Experimental results reveal an Area Under the Curve (AUC) of 0.748 for the clinical dataset and 0.775/1.000 for the in-the-wild datasets towards the classification of in-meal eating behavior profiles to the PD or HC group. This is the first work that attempts to use wearable Inertial Measurement Unit (IMU) sensor data, collected both in clinical and in-the-wild settings, towards the extraction of an objective eating behavior indicator for PD.