Examination of the relationship of diet quality with cardiometabolic risk factors in apparently healthy college students

Prevalence and determinants of overweight or obesity among medical students over a 2-year observation

Introduction

University students are a special population group characterized by changes in BMI values over the subsequent years of education, with an upward tendency to BMI. The presented study aims to evaluate the prevalence of overweight and obesity and their determinants in medical students during the 2-year follow-up observation.

Materials and methods

We analyzed data collected from the first follow-up of the cohort study named "POLLEK" conducted among medical students at the Medical University of Silesia in Katowice. Students were followed at two points of time: in their inaugural year of studies (the academic year 2021/2022, T1, N = 427), and subsequently in their second year (the academic year 2022/2023, T2, N = 335).

Results

In the initial year of evaluation, 371 individuals (86.9%) exhibited normal body weight, 47 (11.0%) were overweight, and 9 (2.1%) were classified as obese. Subsequent assessments during the second year revealed the following distribution: 277 students (84.2%) with normal body weight, 40 (12.2%) classified as overweight, and 12 (3.6%) identified as obese. In summary, regardless of the academic year, an increased risk of being overweight or obese was significantly associated with dissatisfaction with personal health, financial strain, and a diet abundant in animal products.

Conclusion

The results of our study confirmed an increase in the prevalence of overweight or obesity among medical students during the 2-year follow-up observation. Significant determinants of overweight or obesity among medical students were: dissatisfaction with individual health status, male sex, financial deficiencies, and a diet abundant in meat consumption.

Diet Quality among Students Attending an Australian University is Compromised by Food Insecurity and Less Frequent Intake of Home Cooked Meals. A Cross-Sectional Survey Using the Validated Healthy Eating Index for Australian Adults (HEIFA-2013)

Poor diet quality is commonly reported in young adults. This study aimed to measure the diet quality of students attending a large Australian university (including domestic and international students), and to examine the effect of food security status and other key factors likely to impact their diet quality. Using the Automated Self-Administered 24-h recall Australian version, a cross-sectional survey collected dietary recalls from domestic and international students in one university in Sydney. Diet quality was assessed using the validated Healthy Eating Index for Australian Adults (HEIFA-2013) which gives a score out of 100. Food security status was measured by the 18-item Household Food Security Survey Module. Differences in the mean HEIFA-2013 scores by student characteristics were determined by analysis of covariance. A total of 141 students completed one dietary recall. The mean HEIFA-2013 score for students was low (mean 52.4, 95% CI 50.0–54.8). Food-insecure students had a poorer diet quality (mean 43.7, 95% CI 35.7–51.8) than their food-secure peers (mean 53.2, 95% CI 50.8–55.7, p = 0.027). The mean HEIFA-2013 score was similar in domestic (mean 52.5, 95% CI 49.9–55.2) and international students (mean 51.9, 95% CI 46.3–57.5, p = 0.845). Those reporting self-perceived excellent cooking skills and higher cooking frequency had better diet quality. Interventions to improve food and nutrition knowledge and skills and address food insecurity may help tertiary education students cook more frequently and achieve better diet quality.

The Association between Diet and Sleep Quality among Spanish University Students

While it has long been recognized that diet is a leading behavioral risk factor for human health, recent scientific findings have also suggested that diet and sleep quality may be connected. The purpose of the present study is to evaluate the association between diet and sleep quality among a group of Spanish university students. To do so, a cross-sectional study of 868 students was carried out. Sleep quality was assessed using the Spanish version of the Pittsburgh Sleep Quality Index (PSQI), while diet was assessed using the Spanish Healthy Eating Index (SHEI). The study revealed a noteworthy rate of bad sleepers (51.6%) and students whose diet needed modifications (82.2%). Unhealthy eaters were more likely to have poor sleep quality (aOR = 4.20; CI 95%: 2.07-8.52). The unbalanced intake of vegetables (aOR = 1.63; CI 95%: 1.14-2.34), fruits (aOR = 4.08; CI 95%: 2.90-5.74), dairy products (aOR = 1.96; CI 95%: 1.41-2.72), lean meats (aOR = 1.82; CI 95%: 1.19-2.78), legumes (aOR = 1.43; CI 95%: 1.00-2.02), sweets (aOR = 1.60; CI 95%: 1.13-2.25) and sugary soft drinks (aOR = 1.46; CI 95%: 1.07-1.99) was associated with lower sleep quality.

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults

The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically from existing datasets, or a combination of the two. We used these methods to derive diet quality indexes (DQI), generated from a novel dietary assessment, and to evaluate relationships with cardiometabolic risk factors in young adults with (n = 106) or without (n = 106) diagnosed depression (62% female, mean age = 21). Participants completed a liking survey (proxy for usual dietary consumption). Principle component analysis of plasma (insulin, glucose, lipids) and adiposity (BMI, Waist-to-Hip ratio) measures formed a continuous cardiometabolic risk factor score (CRFS). DQIs were created: theoretically (food/beverages grouped, weighted conceptually), empirically (grouping by factor analysis, weights empirically-derived by ridge regression analysis of CRFS), and hybrid (food/beverages conceptually-grouped, weights empirically-derived). The out-of-sample CRFS predictability for the DQI was assessed by two-fold and five-fold cross validations. While moderate consistencies between theoretically- and empirically-generated weights existed, the hybrid outperformed theoretical and empirical DQIs in cross validations (five-fold showed DQI explained 2.6% theoretical, 2.7% empirical, and 6.5% hybrid of CRFS variance). These pilot data support a liking survey that can generate reliable/valid DQIs that are significantly associated with cardiometabolic risk factors, especially theoretically- plus empirically-derived DQI.