A question of food intake: The impact of living arrangement and meal factors on total daily caloric intake

There has been dramatic weight gain among college students during their collegiate years. A food diary can give much insight of a college student's life. The purpose of this study is to analyze the food intake of college-aged students taking in factors such as the size of meal, the foods being eaten, the location of the meal, and if the meal was eaten with others. The results of this study suggest that being male, eating breakfast, and eating more snacks relative to the number meals increases daily caloric intake. On the other hand, being female and eating more meals at home will result in a lower daily caloric intake.

Predictive equations versus measured energy expenditure by indirect calorimetry: A retrospective validation

BACKGROUND & AIMS

Measuring resting energy expenditure (REE) via indirect calorimetry (IC) in intensive care unit (ICU) patient is the gold standard recommended by guidelines. However technical difficulties hinder its use and predictive equations are largely used instead. We sought to validate commonly used equations using a large cohort of patients.

METHODS

Patients hospitalized from 2003 to 2015 in a 16-bed ICU at a university-affiliated, tertiary care hospital who had IC measurement to assess caloric targets were included. Data was drawn from a computerized system and included REE and other variables required by equations. Measurements were restricted to 5 REE per patient to avoid bias. Equation performance was assessed by comparing means, standard deviations, correlation, concordance and agreement, which was defined as a measurement within 85-115% of measured REE. A total of 8 equations were examined.

RESULTS

A total of 3573 REE measurements in 1440 patients were included. Mean patient age was 58 years and 65% were male. A total of 562 (39%) patients had >2 REE measurements. Standard deviation of REE ranged from 430 to 570 kcal. The Faisy equation had the least mean difference (90 Kcal); Harris-Benedict had the highest correlation (52%) and agreement (50%) and Jolliet the highest concordance (62%). Agreement within 10% of caloric needs was met only in a third of patients.

CONCLUSIONS

Predictive equations have low performance when compared to REE in ICU patients. We therefore suggest that predictive equations cannot wholly replace indirect calorimetry for the accurate estimation of REE in this population.

The Association Between Artificial Sweeteners and Obesity

PURPOSE OF REVIEW

The purpose of this paper is to review the epidemiology of obesity and the evolution of artificial sweeteners; to examine the latest research on the effects of artificial sweeteners on the host microbiome, the gut-brain axis, glucose homeostasis, and energy consumption; and to discuss how all of these changes ultimately contribute to obesity.

RECENT FINDINGS

Although artificial sweeteners were developed as a sugar substitute to help reduce insulin resistance and obesity, data in both animal models and humans suggest that the effects of artificial sweeteners may contribute to metabolic syndrome and the obesity epidemic. Artificial sweeteners appear to change the host microbiome, lead to decreased satiety, and alter glucose homeostasis, and are associated with increased caloric consumption and weight gain. Artificial sweeteners are marketed as a healthy alternative to sugar and as a tool for weight loss. Data however suggests that the intended effects do not correlate with what is seen in clinical practice. Future research should focus on the newer plant-based sweeteners, incorporate extended study durations to determine the long-term effects of artificial sweetener consumption, and focus on changes in the microbiome, as that seems to be one of the main driving forces behind nutrient absorption and glucose metabolism.

Resting energy expenditure, calorie and protein consumption in critically ill patients: a retrospective cohort study

Background

Intense debate exists regarding the optimal energy and protein intake for intensive care unit (ICU) patients. However, most studies use predictive equations, demonstrated to be inaccurate to target energy intake. We sought to examine the outcome of a large cohort of ICU patients in relation to the percent of administered calories divided by resting energy expenditure (% AdCal/REE) obtained by indirect calorimetry (IC) and to protein intake.

Methods

Included patients were hospitalized from 2003 to 2015 at a 16-bed ICU at a university affiliated, tertiary care hospital, and had IC measurement to assess caloric targets. Data were drawn from a computerized system and included the % AdCal/REE and protein intake and other variables. A Cox proportional hazards model for 60-day mortality was used, with the % AdCal/REE modeled to accommodate non-linearity. Length of stay (LOS) and length of ventilation (LOV) were also assessed.

Results

A total of 1171 patients were included. The % AdCal/REE had a significant non-linear (p < 0.01) association with mortality after adjusting for other variables (p < 0.01). Increasing the percentage from zero to 70 % resulted in a hazard ratio (HR) of 0.98 (CI 0.97–0.99) pointing to reduced mortality, while increases above 70 % suggested an increase in mortality with a HR of 1.01 (CI 1.01–1.02). Increasing protein intake was also associated with decreased mortality (HR 0.99, CI 0.98–0.99, p = 0.02). An AdCal/REE >70 % was associated with an increased LOS and LOV.

Conclusions

The findings of this study suggest that both underfeeding and overfeeding appear to be harmful to critically ill patients, such that achieving an Adcal/REE of 70 % had a survival advantage. A higher caloric intake may also be associated with harm in the form of increased LOS and LOV. The optimal way to define caloric goals therefore requires an exact estimate, which is ideally performed using indirect calorimetry. These findings may provide a basis for future randomized controlled trials comparing specific nutritional regimens based on indirect calorimetry measurements.