Diet Quality and Its Effect on Weight Gain Prevention in Young Adults: A Narrative Review

Possible pitfalls in the prediction of weight gain in middle-aged normal-weight individuals: Results from the NDB-K7Ps-study-2

BACKGROUND

The prevalence of obesity has not decreased worldwide and obesity-related morbidities have been increasing steadily. However, few studies have investigated factors contributing to weight gain in normal-weight individuals. Thus, in this community-based cohort study, we aimed to investigate factors contributing to weight gain in normal-weight participants.

METHODS

Clinical variables and 10 % increase in weight over 10 years (10 %IBW10Y) were retrospectively investigated in apparently healthy 615,077 normal-weight (body mass index (BMI) 21.0-24.9 kg/m2) participants aged 40-64 years who had regularly undergone health checkup. Machine learning and logistic regression analysis (nested case-control study) were used to predict 10 %IBW10Y.

RESULTS

In total, 6.8 % of men and 8.9 % of women had 10 %IBW10Y (P < 0.0001). The prevalence of obesity (BMI ≥25.0 kg/m2) after 10 years and weight gain were higher in participants with 10 %IBW10Y (72.3 %, 8.9 kg) (case-group) versus those without 10 %IBW10Y (11.5 %, -0.4 kg) (control-group), respectively. Machine learning showed positive contributing factors to 10 %IBW10Y were, in descending order, age early 40 s, current smoking, female sex, low serum triglyceride (≤59 mg/dL), and low glycated hemoglobin (HbA1c) (≤4.9 %). Age early 60 s, non-smoking, male sex, high triglyceride (≥200 mg/dL), and HbA1c 6.0 %-6.9 % were negative contributing factors. Logistic regression analysis showed similar results except for high HbA1c (≥7.5 %) as a positive contributing factor.

CONCLUSIONS

In middle-aged individuals with normal weight who undergo regular health check-ups, certain favorable features (female sex, low triglyceride, and low HbA1c), as well as smoking habits that are subject to change in the future, which could lead to weight gain, may be overlooked. 250 ＜250 words.

Dietary and Lifestyle Risk Factors of Obesity Among Young Adults: A Scoping Review of Observational Studies

PURPOSE OF REVIEW

Obesity is a growing public health concern worldwide, especially among young adults. This scoping review aims to identify and summarize the current evidence on dietary and lifestyle risk factors associated with obesity among young adults.

RECENT FINDINGS

A scoping review was performed using the PRISMA-ScR guidelines. A systematic search of five electronic databases published from inception to October 2023 was conducted. A total of 46 observational studies met the inclusion criteria and were included in the review. The findings suggest that high intake of energy-dense foods, unhealthy eating habits, poor sleep quality, and increased screen time were significant risk factors for obesity among young adults. In contrast, the association between obesity and sedentary behavior, low physical activity levels, alcohol consumption, and smoking habits was inconclusive. The reviewed evidence suggests that unhealthy dietary habits and lifestyle behaviors are associated with an increased risk of obesity among young adults. The findings highlight the need for further research on these modifiable risk factors to prevent and manage obesity among young adults.

Machine Learning Approach to Comparing Fatty Acid Profiles of Common Food Products Sold on Romanian Market

Food composition issues represent an increasing concern nowadays, in the context of diverse food commodity varieties. The contents and types of fatty acids are a constant preoccupation among consumers because of their reflections of nutrition and health problems. This study aims to find the best tool for the rapid and reliable identification of similarities and differences among several food items from a fatty acid profile perspective. An acknowledged GC-FID method was considered, while, for a better interpretation of the analytical results, machine learning algorithms were used. It was possible to develop a recognition model able to simultaneously differentiate, with an accuracy of 79.3%, nine product types using the bagged tree ensemble model. The low number of samples or some similarities among the classes could be responsible for the wrong assignments that occurred, especially in the biscuit, wafer and instant soup classes. Better accuracies values of 95, 86.1, and 97.8% were obtained when the products were grouped into three categories: (1) sunflower oil, mayonnaise, margarine, and cream cheese; (2) biscuits, cookies, margarine, and wafers; and (3) sunflower oil, chips, and instant soup.

Deep neural network for prediction of diet quality among doctors and nurses in North China during the COVID-19 pandemic

Objective

The COVID-19 pandemic has placed unprecedented pressure on front-line healthcare workers, leading to poor health status, especially diet quality. This study aimed to develop a diet quality prediction model and determine the predictive effects of personality traits, socioeconomic status, lifestyles, and individual and working conditions on diet quality among doctors and nurses during the COVID-19 pandemic.

Methods

A total of 5,013 doctors and nurses from thirty-nine COVID-19 designated hospitals provided valid responses in north China in 2022. Participants' data related to social-demographic characteristics, lifestyles, sleep quality, personality traits, burnout, work-related conflicts, and diet quality were collected with questionnaires. Deep Neural Network (DNN) was applied to develop a diet quality prediction model among doctors and nurses during the COVID-19 pandemic.

Results

The mean score of diet quality was 46.14 ± 15.08; specifically, the mean scores for variety, adequacy, moderation, and overall balance were 14.33 ± 3.65, 17.99 ± 5.73, 9.41 ± 7.33, and 4.41 ± 2.98, respectively. The current study developed a DNN model with a 21-30-28-1 network framework for diet quality prediction. The DNN model achieved high prediction efficacy, and values of R2, MAE, MSE, and RMSE were 0.928, 0.048, 0.004, and 0.065, respectively. Among doctors and nurses in north China, the top five predictors in the diet quality prediction model were BMI, poor sleep quality, work-family conflict, negative emotional eating, and nutrition knowledge.

Conclusion

During the COVID-19 pandemic, poor diet quality is prevalent among doctors and nurses in north China. Machine learning models can provide an automated identification mechanism for the prediction of diet quality. This study suggests that integrated interventions can be a promising approach to improving diet quality among doctors and nurses, particularly weight management, sleep quality improvement, work-family balance, decreased emotional eating, and increased nutrition knowledge.

Machine Learning Approaches for Predicting Fatty Acid Classes in Popular US Snacks Using NHANES Data

In the US, people frequently snack between meals, consuming calorie-dense foods including baked goods (cakes), sweets, and desserts (ice cream) high in lipids, salt, and sugar. Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) are reasonably healthy; however, excessive consumption of food high in saturated fatty acid (SFA) has been related to an elevated risk of cardiovascular diseases. The National Health and Nutrition Survey (NHANES) uses a 24 h recall to collect information on people's food habits in the US. The complexity of the NHANES data necessitates using machine learning (ML) methods, a branch of data science that uses algorithms to collect large, unstructured, and structured data sets and identify correlations between the data variables. This study focused on determining the ability of ML regression models including artificial neural networks (ANNs), decision trees (DTs), k-nearest neighbors (KNNs), and support vector machines (SVMs) to assess the variability in total fat content concerning the classes (SFA, MUFA, and PUFA) of US-consumed snacks between 2017 and 2018. KNNs and DTs predicted SFA, MUFA, and PUFA with mean squared error (MSE) of 0.707, 0.489, 0.612, and 1.172, 0.846, 0.738, respectively. SVMs failed to predict the fatty acids accurately; however, ANNs performed satisfactorily. Using ensemble methods, DTs (10.635, 5.120, 7.075) showed higher error values for MSE than linear regression (LiR) (9.086, 3.698, 5.820) for SFA, MUFA, and PUFA prediction, respectively. R2 score ranged between -0.541 to 0.983 and 0.390 to 0.751 for models one and two, respectively. Extreme gradient boost (XGR), Light gradient boost (LightGBM), and random forest (RF) performed better than LiR, with RF having the lowest score for MSE in predicting all the fatty acid classes.

Hormonal Gut-Brain Signaling for the Treatment of Obesity

The brain, particularly the hypothalamus and brainstem, monitors and integrates circulating metabolic signals, including gut hormones. Gut-brain communication is also mediated by the vagus nerve, which transmits various gut-derived signals. Recent advances in our understanding of molecular gut-brain communication promote the development of next-generation anti-obesity medications that can safely achieve substantial and lasting weight loss comparable to metabolic surgery. Herein, we comprehensively review the current knowledge about the central regulation of energy homeostasis, gut hormones involved in the regulation of food intake, and clinical data on how these hormones have been applied to the development of anti-obesity drugs. Insight into and understanding of the gut-brain axis may provide new therapeutic perspectives for the treatment of obesity and diabetes.

Dietary diversity and determinants of young adults in central China: A cross-sectional study from 2015 to 2020

Background

Early adulthood is a vulnerable period for improved nutrition at all phases of the life cycle. However, there is limited research on diversity information in young adults from middle-income countries undergoing an apparent nutritional transition. The purpose of this study was to explore dietary diversity and determinants among young adults aged 18–35 years in central China.

Methods

From January 2015 to December 2020, a cross-sectional survey of 49,021 young adults in a health management center of central China was conducted through report and phone-assisted self-report for information. The outcome variable was the Dietary Diversity Score. Independent variables included age, sex, race, material status, education, BMI, taste preference, regular meals, midnight snacks, sugared beverage/coffee consumption, and smoking/drinking status. Multivariate logistic regression was performed.

Results

Of 49,021 young adults, 38,374 (78.3%) reported insufficient dietary diversity, and 422 (0.9%) reported sufficient dietary diversity. Light taste preference [adjusted odds ratio (aOR) = 2.325; 95% CI: 1.779, 3.039] and those who had meals regularly (aOR = 1.241; 95% CI: 1.018, 1.513) and consumed coffee (aOR = 2.765; 95% CI: 2.257, 3.387) were more likely to be associated with sufficient dietary diversity. Midnight snacks (aOR = 0.728; 95% CI: 0.588, 0.901) and sugary beverages (aOR = 0.666; 95% CI: 0.535, 0.829) were less likely to be associated with sufficient dietary diversity. Higher BMI (aOR = 1.092; 95% CI: 1.061, 1.125) was associated with higher odds of sufficient dietary diversity. Additionally, participants who were 18–30 years old, with master or above degree and away from cigarette/alcohol were more likely to report better dietary diversity.

Conclusion

Our results painted a less than ideal nutritional condition affecting young adults. High-fat/sugar/salt dietary practices can lead to low dietary diversity, while high dietary diversity might have adverse BMI outcomes in youth. This study highlighted the importance of increasing the diversity of healthy and selective food items before wide recommendation for dietary diversity.

Added sugars, saturated fat, and sodium intake from snacks among U.S. adolescents by eating location

Snacking away from home is thought to contribute to excess intake of energy, added sugars, saturated fat, and sodium compared to snacking at home. Using data from the National Health and Nutrition Examination Survey 2009-2016, we examined associations between location of snack consumption (at home or away from home) and added sugars, saturated fat, and sodium intake from food and beverage snacks in U.S. adolescents aged 12-19. We also compared top snack contributors to intakes of these nutrients by location of consumption. Nutrient intake (added sugars, saturated fat, and sodium) from food and beverage snacks was estimated by the average intake from two 24-hour dietary recalls, and location of consumption for each snack was reported by participants as at home or away from home. Adjusted mixed effects models were performed to examine associations between nutrient intakes and the location of consumption. Adolescents (n = 3,869) had lower intakes of added sugars (-5.20 g/day), saturated fat (-2.06 g/day) and sodium (-170.15 mg/day) from food snacks consumed away from home compared to at home (p < 0.0001). Similarly, adolescents had lower intake of added sugars (-2.74 g/day), saturated fat (-0.32 g/day) and sodium (-16.04 mg/day) from beverage snacks consumed away from home compared to at home (p < 0.0001). The top contributors to the target nutrients were similar irrespective of location. Taken together, our results demonstrate that adolescents consumed more target nutrients from snacks at home than away from home. Larger snack portion sizes and higher frequency of snacking at home may explain these findings and requires further study.

Obesity Fact Sheet in Korea, 2020: Prevalence of Obesity by Obesity Class from 2009 to 2018

Background

We examined the prevalence of different obesity classes in South Korea based on the 2020 Obesity Fact Sheet.

Methods

Individuals ≥20 years who underwent a health examination provided by the Korean National Health Insurance System between 2009 and 2018 were included and the prevalence of class I, II, and III obesity was calculated for the total sample and age, sex, and region subgroups.

Results

From 2009 to 2018, the prevalence of all obesity classes increased across all sex and age groups and all regions. In the study population as a whole, the prevalence of class I, II, and III obesity was 29.1%, 3.2%, and 0.3% in 2009 and 32.5%, 5.2%, and 0.81% in 2018, respectively. Among young-aged individuals, the prevalence of each obesity class was 23.7%, 3.6%, and 0.44% in 2009 and 28.3%, 6.9%, and 1.61% in 2018, respectively. The prevalence among middle-aged individuals was 31.6%, 3.1%, and 0.24% in 2009 and 33.6%, 4.8%, and 0.59% in 2018; and among elderly individuals was 31.9%, 3.1%, and 0.21% in 2009 and 35.5%, 3.9%, and 0.32% in 2018. The increase in the prevalence of all obesity classes among young adults was dramatic. In particular, the class III obesity prevalence increased up to 3.8- and 3.5-fold between 2009 and 2018 in young men and women.

Conclusion

Based on the 2020 Obesity Fact Sheet, there was a dramatic increase in the prevalence of class II and III obesity from 2009 to 2018 among young adults, as well as the population as a whole. Optimal strategies for the prevention and treatment of obesity are needed considering the recent obesity epidemic in South Korea.

The Relationship of Diet and Physical Activity with Weight Gain and Weight Gain Prevention in Women of Reproductive Age

Reproductive-age women often see increased weight gain, which carries an increased risk of long-term overweight and obesity and adverse maternal and child health outcomes. Supporting women to achieve optimal weight through lifestyle modification (diet and physical activity) is of critical importance to reduce weight gain across key reproductive life-stages (preconception, pregnancy and postpartum). This review comprehensively summarizes the current state of knowledge on the contribution of diet and physical activity to weight gain and weight gain prevention in reproductive-aged women. Suboptimal diets including a higher proportion of discretionary choices or energy intake from fats, added sugars, sweets or processed foods are associated with higher weight gain, whereas increased consumption of core foods including fruits, vegetables and whole grains and engaging in regular physical activity are associated with reduced weight gain in reproductive age women. Diet and physical activity contributing to excessive gestational weight gain are well documented. However, there is limited research assessing diet and physical activity components associated with weight gain during the preconception and postpartum period. This review highlights the need for further research to identify key dietary and physical activity components targeting the critical windows of reproductive life-stages in women to best guide interventions to prevent weight gain.