Association of dietary patterns with obesity and metabolically healthy obesity phenotype in Chinese population: a cross-sectional analysis of China Multi-Ethnic Cohort Study

Association of dietary behavior patterns of middle-aged and older adults with their obesity metabolic phenotype: a cross-sectional study

BACKGROUND

Middle-aged and elderly individuals are the most susceptible groups for metabolic diseases, with their dietary behaviors being significant influencing factors. Exploring the association between overall dietary behaviors and obesity metabolic phenotypes is crucial for early prevention and control of chronic diseases, precision treatment and personalized interventions.

METHODS

We conducted a cross-sectional study of 15,160 middle-aged and older adults between June 2019 and August 2021 to collect information on their body mass index (BMI), biochemical indices and disease history. The population was classified into four categories by the criteria of obesity metabolic phenotypes: metabolically healthy non-obesity (MHNO), metabolically unhealthy non-obesity (MUNO), metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUO). Scores were calculated based on compliance with healthy eating behavior patterns (appropriate or light dietary taste, moderately soft and hard food, slightly hot food temperature, medium or slow eating speed, daily intake of dietary supplements and eating with others), and the population was categorized into subgroups 0-2 (did not meet and met only 1 or 2), 3-4 (met 3 or 4), 5-6 (met 5 or 6). The relationship between dietary behavior patterns and different obesity metabolic phenotypes in middle-aged and elderly people were analyzed by multi-categorical logistic regression model.

RESULTS

Compared with the 5-6 subgroup, the dietary behavior patterns of 0-2 and 3-4 scores were risk factors for MUNO, MHO and MUO (P < 0.05), and the lower the scores of the dietary behavior patterns, the higher the multiplicity of the occurrence of MUNO, MHO and MUO, especially for females and adults between 45-60 years old. Appropriate or light dietary taste, moderately soft and hard food, and slightly hot food temperature were protective factors for MUNO and MUO (P < 0.05); medium or slow eating speed and daily intake of dietary supplements were protective factors for MUNO, MHO and MUO (P < 0.05).

CONCLUSION

Dietary behavior patterns in middle-aged and older adults are associated with different obesity metabolic phenotypes, and healthy dietary behaviors may be beneficial for the prevention and control of MUNO, MHO and MUO.

Adherence to Mediterranean-Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay Diet in Relation to Serum Brain-Derived Neurotrophic Factor Concentrations and Metabolic Health Status in Adults

Background

There is a lack of data regarding the Mediterranean-Dietary Approaches to Stop Hypertension Intervention for Neurodegenerative Delay (MIND) diet and metabolic health.

Objectives

This study assessed the relation between MIND diet and metabolic health status relative to serum brain-derived neurotrophic factor (BDNF) concentrations.

Methods

This was a cross-sectional study of 527 adults (286 males and 241 females) recruited from 20 schools in 6 different educational districts of Isfahan, Iran. Dietary intakes of participants were collected by a validated 168-item food frequency questionnaire, and MIND diet score was estimated. Anthropometric indices, blood pressure, biochemical parameters, and BDNF concentrations were assessed for all participants. The metabolically unhealthy (MU) phenotype was determined based on blood pressure, glycemic and lipid profiles, chronic inflammation, and insulin resistance.

Results

The frequency of MU phenotype among obese/overweight and normal-weight individuals was 79.5 % and 20.5 %, respectively. After adjustment for confounders, individuals in the top tertile of the MIND diet scores had 58 % lower odds of having the MU phenotype than individuals in the bottom tertile (odds ratios [ORs]: 0.42; 95 % confidence interval [CI]: 0.20, 0.90). In the fully adjusted model, females and normal-weight individuals had 81 % (OR: 0.19; 95 % CI: 0.04, 0.83) and 89 % (OR: 0.11; 95 % CI: 0.02, 0.69) lower chance of developing the MU phenotype, respectively. In addition, significant inverse associations between adherence to the MIND diet and high-blood pressure and hypertriglyceridemia were found. No significant association was found between adherence to MIND diet and odds of low BDNF concentrations.

Conclusions

Adherence to MIND diet is inversely associated with odds of MU phenotype, especially among women and normal-weight individuals. BDNF concentration is not associated with MIND diet and MU status.

Ethnic differences in eating patterns and their associations with obesity among adults in West China

Despite observed ethnic differences in eating patterns and obesity, evidence in China is limited. This study examined ethnic differences in eating patterns and their associations with weight outcomes among multi-ethnic adults in West China. A cross-sectional survey collected self-reported data on demographics, eating behaviours, weight and height in 2021. Principal component analysis and multivariate regression were conducted to identify eating patterns and examine their associations with weight outcomes. In total, 4407 subjects aged ≥ 18 years were recruited across seven provinces in West China. Four eating patterns were identified: 'meat-lover' - characterised by frequent consumption of meat and dairy products, 'indulgent' - by frequent intakes of added salt, sugar, alcohol and pickled food, 'diversified-eating' - by frequently consuming food with diversified cooking methods and eating out and 'nutri-health-concerned' - by good food hygiene behaviours and reading food labels. Ethnic differences in eating patterns were observed. Compared with Han, Hui were less likely to exhibit meat-lover or diversified-eating patterns; Tibetans were less likely to have meat-lover or nutri-health-concerned patterns; Mongolians were more likely to have indulgent pattern. BMI was positively associated with meat-lover pattern in both genders (exp(β): 1·029; 95 % CI: 1·001, 1·058 for men; 1·018; 1·000, 1·036 for women) and negatively associated with nutri-health-concerned pattern in women (0·983; 0·966, 1·000). Mongolians were two times more likely to be overweight/obese than Han (OR: 3·126; 1·688, 5·790). Considerable ethnic differences existed in eating patterns in West China. Mongolians were more likely to be overweight/obese, which was associated with their indulgent eating patterns. Ethnic-specific healthy eating intervention programs are needed.

Risk factor analysis and risk prediction study of obesity in steelworkers: model development based on an occupational health examination cohort dataset

BACKGROUND

Obesity is increasingly recognized as a grave public health concern globally. It is associated with prevalent diseases including coronary heart disease, fatty liver, type 2 diabetes, and dyslipidemia. Prior research has identified demographic, socioeconomic, lifestyle, and genetic factors as contributors to obesity. Nevertheless, the influence of occupational risk factors on obesity among workers remains under-explored. Investigating risk factors specific to steelworkers is crucial for early detection, prediction, and effective intervention, thereby safeguarding their health.

METHODS

This research utilized a cohort study examining health impacts on workers in an iron and steel company in Hebei Province, China. The study involved 5469 participants. By univariate analysis, multifactor analysis, and review of relevant literature, predictor variables were found. Three predictive models-XG Boost, Support Vector Machine (SVM), and Random Forest (RF)-were employed.

RESULTS

Univariate analysis and cox proportional hazard regression modeling identified age, gender, smoking and drinking habits, dietary score, physical activity, shift work, exposure to high temperatures, occupational stress, and carbon monoxide exposure as key factors in the development of obesity in steelworkers. Test results indicated accuracies of 0.819, 0.868, and 0.872 for XG Boost, SVM, and RF respectively. Precision rates were 0.571, 0.696, and 0.765, while recall rates were 0.333, 0.592, and 0.481. The models achieved AUCs of 0.849, 0.908, and 0.912, with Brier scores of 0.128, 0.105, and 0.104, log losses of 0.409, 0.349, and 0.345, and calibration-in-the-large of 0.058, 0.054, and 0.051, respectively. Among these, the Random Forest model demonstrated superior performance.

CONCLUSIONS

The research indicates that obesity in steelworkers results from a combination of occupational and lifestyle factors. Of the models tested, the Random Forest model exhibited superior predictive ability, highlighting its significant practical application.

Is metabolic-healthy obesity associated with risk of dementia? An age-stratified analysis of the Whitehall II cohort study

BACKGROUND

Metabolically healthy obesity is hypothesized to be a benign condition but whether this is the case for dementia remains debated. We examined the role of age at assessment of metabolic-obesity phenotypes in associations with incident dementia.

METHODS

Obesity (body mass index ≥ 30 kg/m2) and poor metabolic health (≥ 2 of elevated serum triglycerides, low HDL-C, elevated blood pressure, and elevated serum fasting glucose) were used to define four metabolic-obesity phenotypes (metabolically healthy (MHNO) and unhealthy non-obesity (MUNO), metabolically healthy (MHO) and unhealthy obesity (MUO)) at < 60, 60 to < 70, and ≥ 70 years using 6 waves of data from the Whitehall II study and their associations with incident dementia was examined using Cox regression.

RESULTS

Analyses with exposures measured < 60, 60 to < 70, and ≥ 70 years involved 410 (5.8%), 379 (5.6%), and 262 (7.4%) incident dementia cases over a median follow-up of 20.8, 10.3, and 4.2 years respectively. In analyses of individual components, obesity before 60 years (HR 1.41, 95% CI: [1.08, 1.85]) but not at older ages was associated with dementia; unhealthy metabolic status when present < 60 years (HR 1.33, 95% CI: [1.08, 1.62]) and 60 to < 70 years (HR 1.32, 95% CI: [1.07, 1.62]) was associated with dementia. Compared to the metabolically healthy non-obesity group, the risk of dementia was higher in those with metabolically healthy obesity before 60 years (1.69; 95% CI: [1.16, 2.45]); this was not the case when metabolic-obesity phenotype was present at 60 to < 70 years or ≥ 70 years. Analyses at older ages were on smaller numbers due to death and drop-out but inverse probability weighting to account for missing data yielded similar results.

CONCLUSIONS

Individuals with metabolically healthy obesity before age 60 had a higher risk of incident dementia over a 27-year follow-up; the excess risk dissipates when metabolic health and obesity are measured after 70 years.

Inflammatory markers may mediate the relationship between processed meat consumption and metabolic unhealthy obesity in women: a cross sectional study

Metabolically healthy obesity (MHO) and metabolically unhealthy obesity (MUHO) are known to be affected by diet and inflammatory factors (such as TGF-β1, IL-β1, MCP1). We sought to survey that consume of processed meat could effect on MHO and MUHO phenotypes, mediated through inflammatory markers, in overweight and obese Iranian women. The current cross-sectional study was done on 224 women 18-48 years, with a body mass index (BMI) ≥ 25 kg/m². A 147- item food frequency questionnaire (FFQ) was used to evaluate dietary intake. In all participants, anthropometric indices and biochemical factors, as well as metabolic health phenotypes based on Karelis score, were evaluated. According to results, 22.6% of participants had MHO and 75.7% had MUHO phenotypes. There was an association between higher adherence to processed meats and increasing odds of MUHO phenotype in Iranian women (OR:2.54; 95% CI 0.009, 7.51; P = 0.05). Moreover, we found that the relation can be affected by agents such as TGF-β1, IL-β1, and MCP1; however, more research is needed to confirm these results and finding.

Metabolically Healthy Obesity: Are Interventions Useful?

PURPOSE OF REVIEW

This review aims to detail the current global research state of metabolically healthy obesogenesis with regard to metabolic factors, disease prevalence, comparisons to unhealthy obesity, and targeted interventions to reverse or delay progression from metabolically healthy to unhealthy obesity.

RECENT FINDINGS

As a long-term condition with increased risk of cardiovascular, metabolic, and all-cause mortality risks, obesity threatens public health on a national level. The recent discovery of metabolically healthy obesity (MHO), a transitional condition during which obese persons carry comparatively lower health risks, has added to confusion about the true effect of visceral fat and subsequent long-term health risks. In this context, the evaluation of fat loss interventions, such as bariatric surgery, lifestyle changes (diet/exercise), and hormonal therapies require re-evaluation in light of evidence that progression to high-risk stages of obesity relies on metabolic status and that strategies to protect the metabolism may be useful in the prevention of metabolically unhealthy obesity. Typical calorie-based exercise and diet interventions have failed to reduce the prevalence of unhealthy obesity. Holistic lifestyle, psychological, hormonal, and pharmacological interventions for MHO, on the other hand, may at least prevent progression to metabolically unhealthy obesity.

Association of major dietary patterns and different obesity phenotypes in Southwest China: the China Multi-Ethnic Cohort (CMEC) Study

PURPOSE

Dietary behavior is an important part of lifestyle interventions for obesity and its cardiovascular comorbidities. However, little is known about associations between dietary patterns and obesity phenotypes in Southwest China, a region with unique dietary patterns and significant heterogeneity in obesity.

METHODS

Data from the baseline survey of the China Multi-Ethnic Cohort in Southwest China were analyzed (n = 64,448). Dietary intakes during the past year were measured with the semi-quantitative Food Frequency Questionnaire (s-FFQ). Principal component factor analysis (PCFA) was used to identify dietary patterns. Multinomial logistic regressions were used to examine the associations between dietary patterns and obesity phenotypes and stratified analyses were performed to assess whether the associations differed across demographic variables.

RESULTS

Three dietary patterns were identified and then named according to their apparent regional gathering characteristics: the Sichuan Basin dietary pattern (characterized by high intakes of various foods), the Yunnan-Guizhou Plateau dietary pattern (characterized by agricultural lifestyles), and the Qinghai-Tibet Plateau dietary pattern (characterized by animal husbandry lifestyles), respectively. Higher adherence to the Sichuan Basin dietary pattern was positively associated with metabolically healthy overweight/obesity (MHO, OR 1.13, 95% CI 1.05-1.21) but negatively associated with metabolically unhealthy normal weight (MUNW, OR 0.78, 95% CI 0.65-0.95). Higher adherence to the other two dietary patterns was positively associated with MHO and metabolically unhealthy overweight/obesity (MUO). Besides, differences in socioeconomic status also affected the relationship between dietary patterns and obesity phenotypes.

CONCLUSIONS

Adherence to the more diverse Sichuan basin dietary pattern performed a mixed picture, while the other two may increase the risk of obesity phenotypes, which indicates nutritional interventions are urgently needed.