Metabolomics and Human Health: Progress, Insights, Challenges and the Concept of a Healthy Metabolome

Multiomic profiling can reveal population heterogeneity for both health and disease states. Obesity drives a myriad of metabolic perturbations and is a risk factor for multiple chronic diseases. Here we report an atlas of cross-sectional and longitudinal changes in 1,111 blood analytes associated with variation in body mass index (BMI), as well as multiomic associations with host polygenic risk scores and gut microbiome composition, from a cohort of 1,277 individuals enrolled in a wellness program (Arivale). Machine learning model predictions of BMI from blood multiomics captured heterogeneous phenotypic states of host metabolism and gut microbiome composition better than BMI, which was also validated in an external cohort (TwinsUK). Moreover, longitudinal analyses identified variable BMI trajectories for different omics measures in response to a healthy lifestyle intervention; metabolomics-inferred BMI decreased to a greater extent than actual BMI, whereas proteomics-inferred BMI exhibited greater resistance to change. Our analyses further identified blood analyte–analyte associations that were modified by metabolomics-inferred BMI and partially reversed in individuals with metabolic obesity during the intervention. Taken together, our findings provide a blood atlas of the molecular perturbations associated with changes in obesity status, serving as a resource to quantify metabolic health for predictive and preventive medicine.

Objective: This study aims to examine the influence of financial literacy, lifestyle, and income on the financial behavior of accounting students at Wijaya Kusuma University Surabaya. It seeks to determine the extent to which these factors contribute to shaping students' financial decision-making and management practices, highlighting the role of financial literacy in fostering responsible financial behavior. Methods: The research adopts a quantitative approach, utilizing primary data collected through questionnaires distributed via Google Forms. A sample of 44 accounting students was selected using the judgment sampling method. Data collection employed a Likert scale to measure variables, and the analysis was conducted using SmartPLS 4.0 software to test the relationships between financial literacy, lifestyle, income, and financial behavior. Results: The analysis reveals that financial literacy (X1) positively influences financial behavior (Y), indicating that students with higher financial literacy demonstrate better financial decision-making. Conversely, lifestyle (X2) and income (X3) do not significantly affect financial behavior. These findings suggest that financial behavior is more strongly linked to knowledge and understanding of financial principles rather than lifestyle choices or income levels. Novelty: This research contributes to the understanding of financial behavior among university students by emphasizing the critical role of financial literacy. Unlike many studies that focus on broader populations, this study specifically examines accounting students, who are presumed to have some foundational knowledge of finance. The findings highlight gaps in the influence of lifestyle and income, providing a basis for further exploration of external factors and educational interventions.

Obese persons (those with a body mass index [BMI] ≥30 kg/m2) tend to underestimate their weight, leading to an underestimation of their true (measured) BMI and obesity prevalence.1,2 In contrast, underweight people (BMI <18.5 kg/m2) tend to report themselves heavier, resulting in a higher BMI compared with measured BMI and an underestimation of underweight prevalence.

Effects of 12-week treatment with dapagliflozin and gliclazide on faecal microbiome: Results of a double-blind randomized trial in patients with type 2 diabetes

A 3-year Longitudinal Study on Interaction Effects Between Self-perceived Weight Status and Lifestyle Behaviors on Obesity: The Children Obesity Study in China Mega-cities (P21-044-19)

Machine learning analysis of metabolomic biomarkers for diagnosis of heart failure

Consumption behavior in the current digitalization era is high because shopping platforms make it easier for everyone to access online shopping through several online shopping platforms. For students, uncontrolled consumptive behavior will burden their finances and cause financial problems to arise in the future. The purpose of this study was to determine (1) the effect of financial literacy on student consumptive behavior on online shopping platforms, (2) the effect of pocket money on student consumptive behavior on online shopping platforms, (3) the effect of lifestyle on student consumptive behavior on online shopping platforms (4 ) the effect of financial literacy, pocket money and lifestyle on student consumptive behavior on online shopping platforms. This study uses descriptive quantitative research with correlational methods using the help of the IBM SPSS Statistics 25 for Windows application program. The population in the study was 136, and in determining the sample using a purposive sampling technique, were 101 respondents in the sample. Data collection for financial literacy used tests for pocket money variables, lifestyle and consumptive behavior using questionnaires. Based on the results of the analysis, it can be concluded that (1) there is an effect of financial literacy on consumptive behavior, (2) there is an effect of pocket money on consumptive behavior, (3) there is an influence of lifestyle on consumptive behavior(4) there is a simultaneous effect of financial literacy, pocket money and lifestyle towards consumptive behavior.

This study aimed to examine the association between lifestyle behaviors and body mass index (BMI) along with their potential interactions with the severity of blood pressure (BP) classifications among older adults with hypertension. Among 17,441 participants, lifestyle behaviors were assessed, including smoking, drinking, dietary patterns, physical activity, and sleeping. Multinomial logistic regression was used to examine the association between lifestyle behaviors and BMI with BP classifications, and multiplicative interactions were included to estimate potential interactions. To explore variations, analyses were also stratified by BMI. High-risk dietary pattern and obesity were negatively associated with lower BP classifications, with odds ratios (ORs) and 95% confidence intervals (CIs) for normal BP, high-normal BP, and grade 1 hypertension in high-risk dietary pattern were 0.74 (0.57-0.95), 0.69 (0.54-0.90), 0.75 (0.59-0.95), and the ORs (95% CIs) in obesity were 0.61 (0.53-0.69), 0.77 (0.67-0.89), 0.82 (0.73-0.93). Compared with never drinking, former drinkers had higher odds of having normal BP (OR: 1.45, 95% CI: 1.15-1.82), high-normal BP (OR: 1.31, 95% CI: 1.02-1.67), and grade 1 hypertension (OR: 1.26, 95% CI: 1.01-1.58). The multiplicative interaction between drinking status and BMI was found on BP classifications (P forinteraction <0.05), and the effects of former drinking and low PA level on BP control were significant in overweight and underweight groups, respectively (P < 0.05). Poor diet and obesity are associate with severe BP, particularly among non-drinking older adults, suggesting targeted interventions in rural primary care.

Background:The risk of cardiovascular diseases (CVDs) increases with age. The objective was to determine whether lifestyle and dietary behaviors and anthropometric measures, which are affected by these behaviors, contribute to the increase of CVD risk factors across age categories of 20–50-year-old.Methods:In a cross-sectional design, 437 adults aged 20–50-year-old were selected from households living in Shiraz. Risk factors of CVD, including body mass index (BMI), waist-to-height ratio (WHtR), blood pressure, fasting blood glucose (FBG), serum triglycerides, total cholesterol, and low- and high-density lipoprotein cholesterol (LDL-C and HDL-C, respectively) as well as lifestyle behaviors (physical activity and smoking), dietary habits, and food intakes were assessed across the age categories of 20–29, 30–39, and 40–50 years. Linear regression was used to examine the contribution of different variables to the age-related increase of CVD risk factors.Results:All CVD risk factors, except for HDL-C, significantly increased across age categories. Older subjects had healthier dietary habits and food intakes, but they possessed nonsignificantly lower physical activity and higher smoking rate compared to younger adults. Adjusting for physical activity, smoking, and BMI did not change the significant positive association between age and CVD risk factors but adjusting for WHtR disappeared associations for blood pressure, triglycerides, and metabolic syndrome although significant associations remained for FBG and total and LDL-C.Conclusions:Age-related increase of CVD risk factors occurred independent of lifestyle habits. WHtR, but not BMI, may partially contribute to the age-related increase in CVD risk factors.

Young adulthood is a crucial period marked by significant changes in dietary habits and lifestyle patterns.The present study evaluates the dietary habits, lifestyle behaviours and health status of young adults (18-25 years) and analyses their association with Body Mass Index (BMI).A sample of 60 young adults was selected using simple random sampling.Data were collected through a structured questionnaire covering socio-demographic details, dietary practices, lifestyle indicators and health issues.Anthropometric measurements were obtained using standard procedures.The findings revealed that age was significantly associated with BMI, whereas dietary habits, physical activity, sleep duration, fruit and vegetable intake, junk food consumption and health symptoms showed no significant association.The study suggests that BMI in young adults may be more influenced by age-related metabolic factors rather than isolated lifestyle or dietary behaviours.

The 2010 Centers for Disease Control and Prevention (CDC) report indicates that 63.4% of Delaware's adult population is overweight and 28% is obese. Here, the authors reveal analyses acquired from detailed investigations about the importance of gender, and other lifestyle factors and behaviors on the Body Mass Index (BMI) trends amongst an indiscriminate sample of the Wesley College (Wesley) undergraduate population. A 25-question paper-format survey was distributed to 307 randomly chosen Wesley undergraduates. The accrued qualitative (or categorical) data were transferred to an Excel spreadsheet to construct and observe frequency distributions. A Chi-square test of independence (χ2) was performed between BMI status (normal, overweight, obese) and the following factors: gender, diet plan, adherence to the United States Department of Agriculture (USDA) MyPlate nutrition guide, use of the seasonal flu shot, weekly workout schedule, supplement usage, participation on athletic teams, questioning of label nutritional facts, and the use of added salt in food. A 2-sample proportion test was performed between students who were overweight or obese for the same factors. Also performed were t-tests for mean BMI for those who followed USDA MyPlate guidelines and for those who did not. An analysis of 278 completed surveys show that 29.5% of the Wesley respondents are overweight and 19.8% are obese. The mean BMI for males was statistically higher than the mean BMI for females. The mean BMI for students living on-campus was statistically higher than the mean BMI for students living off-campus. The results also demonstrate that adhering to the USDA dietary recommendations for fruit and dairy can be important factors in reducing the risk of obesity.

The present study aimed to examine the relationship between screen time (ST), sleep time (SLT), physical fitness (PF), Mediterranean diet (MD) adherence, eating behaviours, and body mass index (BMI) in a sample of pre-schoolers from Granada (Spain). In order to address this aim, an explanatory model was developed to examine existing relationships between ST, SLT, PF, MD, pro-intake (PRO-I) and anti-intake (ANT-I) behaviours, and BMI. Further, the proposed structural model was examined via multi-group analysis as a function of sex and BMI. A cross-sectional study was conducted with 653 three- to six-year-old pre-schoolers attending 18 different schools invited to take part in the present study. Structural equation modelling (SEM) was employed to analyse relationships between study variables as a function of sex and BMI. SEM analysis revealed negative associations between ST and PF (p < 0.005), ST and MD adherence (p < 0.005), ST and SLT (p < 0.005), MD adherence and ANT-I behaviours (p < 0.005), and MD adherence and BMI (p = 0.033). In contrast, positive associations emerged between SLT and MD adherence (p < 0.005), and PRO-I behaviours and BMI (p < 0.005). SEM revealed differences according to sex and BMI. The study highlights significant relationships between lifestyle behaviours and physical and dietary outcomes in pre-schoolers from southern Spain, with variations based on sex and BMI. These findings suggest the need for interventions aimed at reducing ST and promoting better sleep, PF, and dietary habits in order to limit weight-related and general health risks in pre-schoolers from southern Spain.

Few studies have attempted to disentangle the complex relationship between socioeconomic status (SES) and ethnicity and its influence on body mass index (BMI) and on change in BMI over a decade. The present study highlights the influence of ethnicity (country of birth) and SES on BMI, adjusting for certain lifestyle factors. The longitudinal self-reported data, based on a simple random sample, were obtained from 1972 women and 1871 men, aged 25-74, interviewed in 1980/81 and re-interviewed in 1988/89 using the same questionnaire. The influence of the independent variables on BMI was analysed by the Generalized Estimation Equation (GEE) method. The change in BMI for different states of the independent variables was analysed in a linear model with the difference in BMI between 1988/89 and 1980/81 as dependent variable, by sex. There was a graded relationship between male SES, defined as educational status, and BMI when adjusted for background variables, while for females, only a low educational level was associated with a higher BMI. Southern European men and Finnish women showed a higher increase in BMI than the reference group (Swedes and West Europeans) when adjusted for age, smoking, exercise, education, marital status, and health status. Not taking exercise was related to a higher BMI compared with those who were taking exercise. The BMI levels and smoking habits were little changed between 1980/81 and 1988/89, while exercise habits showed a more variable pattern. When the focus was on change in BMI, former smokers had a larger increase in BMI than never smokers. Further, men who ceased taking exercise had a larger increase in BMI than those who were taking regular exercise on both occasions. When adjusted for smoking and the other background variables, SES and ethnicity are two separate independent factors influencing BMI in men and women. When the focus was on change in BMI, former smokers had a larger increase in BMI than never smokers. Further, men who ceased taking exercise had a larger increase in BMI than those who took regular exercise on both occasions.

Background: Unhealthy lifestyle behaviors, including poor diet, physical inactivity, and excessive alcohol consumption, are well-established risk factors for hypertension. Aims: This study aimed to assess the association of perceived stress, lifestyle and dietary behaviors with hypertension among university personnel. Methods: A cross-sectional survey was conducted on 250 randomly selected university workers was conducted. Data on perceived stress, eating behavior, dietary patterns, smoking habits, alcohol consumption, and physical activity levels were collected employing validated instruments. Blood pressure and nutritional anthropometric measurements were performed using standard procedures. The data were analyzed using descriptive statistics. A Pearson’s Chi-square, correlation and logistic regression analyses were conducted. A p-value of less than 0.05 was considered statistically significant. Results: The median age of the respondents was 40 years, with median values for waist circumference, height, and weight of 90.0 cm, 1.69 m, and 69.0 kg, respectively. The prevalence of hypertension was 49.6%, with overweight (26.8%), abdominal obesity (32.8%), alcohol consumption (26.0%), smoking (15.2%), and low physical activity levels (64.4%) also being common. The majority of respondents (92.8 %) reported moderate stress levels, and only one-fifth exhibited adequate dietary habits. Cognitive restraint and emotional eating were prevalent, with high consumption of fried and sugary snacks (70.0%) and carbonated drinks (60.4 %), along with frequent meal skipping. In the logistic regression model, age, body mass index, quantity of alcohol consumed, and cognitive restraint eating behavior were significantly (p &lt; 0.05) associated with hypertension. Conclusions: The prevalence of unhealthy lifestyle behaviors and hypertension is high among university personnel. The findings highlight the critical need for interventions that target stress reduction, dietary modifications, and physical activity promotion are crucial to mitigate the burden of hypertension in this population. Keywords: Hypertension Risk; Lifestyle Behavior; Eating Behavior; Perceived Stress.

The Health Risks of Obesity Have Been Exaggerated.