Metabolically healthy obesity, transition to unhealthy phenotypes, and type 2 diabetes in 0.5 million Chinese adults: the China Kadoorie Biobank

Associations between life's essential 8 and metabolic health among us adults: insights of NHANES from 2005 to 2018

BACKGROUND

Metabolic unhealth (MUH) is closely associated with cardiovascular disease (CVD). Life's Essential 8 (LE8), a recently updated cardiovascular health (CVH) assessment, has some overlapping indicators with MUH but is more comprehensive and complicated than MUH. Given the close relationship between them, it is important to compare these two measurements.

METHODS

This population-based cross-sectional survey included 20- to 80-year-old individuals from 7 National Health and Nutrition Examination Survey (NHANES) cycles between 2005 and 2018. Based on the parameters provided by the American Heart Association, the LE8 score (which ranges from 0 to 100) was used to classify CVH into three categories: low (0-49), moderate (50-79), and high (80-100). The MUH status was evaluated by blood glucose, blood pressure, and blood lipids. The associations were assessed by multivariable regression analysis, subgroup analysis, restricted cubic spline models, and sensitivity analysis.

RESULTS

A total of 22,582 participants were enrolled (median of age was 45 years old), among them, 11,127 were female (weighted percentage, 49%) and 16,595 were classified as MUH (weighted percentage, 73.5%). The weighted median LE8 scores of metabolic health (MH) and MUH individuals are 73.75 and 59.38, respectively. Higher LE8 scores were linked to lower risks of MUH (odds ratio [OR] for every 10 scores increase, 0.53; 95% CI 0.51-0.55), and a nonlinear dose-response relationship was seen after the adjustment of potential confounders. This negative correlation between LE8 scores, and MUH was strengthened among elderly population.

CONCLUSIONS

Higher LE8 and its subscales scores were inversely and nonlinearly linked with the lower presence of MUH. MUH is consistent with LE8 scores, which can be considered as an alternative indicator when it is difficult to collect the information of health behaviors.

Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study

CONTEXT

Evidence regarding the association between metabolically healthy overweight or obesity (MHOO) and diabetes is controversial, and mostly ignores the dynamic change of metabolic health status and obesity.

OBJECTIVE

To explore the association between transitions of metabolic health status and obesity over 5 years and diabetes incidence.

METHODS

We examined 17 309 participants derived from the Dongfeng-Tongji cohort and followed from 2008 to 2018 (median follow-up 9.9 years). All participants were categorized into 4 phenotypes based on body mass index (BMI) and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOO, and metabolically unhealthy overweight or obesity (MUOO). The associations of changes in BMI-metabolic health status (2008-2013) with diabetes incidence (2018) were performed among 12 206 individuals with 2 follow-up examinations.

RESULTS

Compared with stable MHNW, stable MHOO (hazard ratio [HR] 1.76; 95% CI 1.26, 2.45) and transition from MHOO to metabolically unhealthy phenotypes were associated with higher risk for diabetes (HR 2.97; 95% CI 1.79, 4.93 in MHOO to MUNW group and HR 3.38; 95% CI 2.54, 4.49 in MHOO to MUOO group). Instead, improvements to metabolic healthy phenotypes or weight loss occurring in MUOO reduced the risk of diabetes compared with stable MUOO, changing from MUOO to MHNW, MUNW, and MHOO resulted in HRs of 0.57 (95% CI 0.37, 0.87), 0.68 (95% CI 0.50, 0.93), and 0.45 (95% CI 0.34, 0.60), respectively.

CONCLUSION

People with MHOO, even stable MHOO, or its transition to metabolically unhealthy phenotypes were at increased risk of diabetes. Metabolic improvements and weight control may reduce the risk of diabetes.

Metabolic health and adiposity transitions and risks of type 2 diabetes and cardiovascular diseases: a systematic review and meta-analysis

BACKGROUND

Metabolic health status and levels of adiposity are prone to change over time. Mixed results have been reported regarding the extent by which changes in metabolic health and weight affect cardiometabolic risks. This systematic review and meta-analysis aims to examine the association between transitions in metabolic health and adiposity status on risk of incident type 2 diabetes (T2DM) and cardiovascular disease (CVD) events.

METHODS

A systematic literature search was conducted on MEDLINE and EMBASE through August 2022 for prospective cohort studies examining transitions in metabolic health and adiposity status and risk of incident T2DM and CVDs without restrictions on language or publication status. Meta-analysis was performed to summarize hazard ratios for T2DM and composite CVD events separately using random-effects model.

RESULTS

A total of 17 studies were included. Compared to stable metabolically healthy status, transition to metabolically unhealthy status significantly increased the risk of incident T2DM and composite CVD events among individuals with normal weight and individuals with overweight/obesity. Compared to stable metabolically unhealthy status, transition to metabolically healthy status significantly lowered the risk among individuals with normal weight and individuals with overweight/obesity. When metabolic health status remained unchanged, progression from normal weight to overweight/obesity significantly increased risk of CVDs but not risk of T2DM.

CONCLUSION

The impact of change in metabolic health on the risks of T2DM and CVD is more prominent than that of change to body mass index category. Obesity treatment should consider prioritizing improvement in metabolic health parameters over focusing on the extent of weight loss only.

The relationship between islet β-cell function and metabolomics in overweight patients with Type 2 diabetes

A cross-sectional study was performed using metabolomics in overweight patients with Type 2 diabetes (T2D) at different stages of the disease. We aimed to identify potential metabolites for assessing islet β-cell function in order to investigate the correlation between islet β-cell dysfunction and metabolite changes in overweight patients with T2D. We selected 60 overweight adults (24 ≤ body mass index [BMI] < 28 kg/m2) with T2D who had been admitted to our hospital. The participants were equally divided into three groups according to disease duration: H1 (duration ≤ 5 years), H2 (5 years < duration ≤ 10 years), and H3 (duration > 10 years). Questionnaires, physical examinations, laboratory tests, and imaging studies were administered to all participants. The modified homeostasis model of assessment (HOMA) index was calculated using fasting C-peptide levels, and metabolite assays were performed using mass spectrometry. The results showed that HOMA-β and visceral fat area (VFA) were negatively correlated with diabetes duration. The VFA was positively correlated with arginine, cysteine, methionine, proline, and succinyl/methylmalonylcarnitine levels. The HOMA-β was negatively correlated with the serine and tetradecanoyldiacylcarnitine levels, and positively correlated with the aspartic acid, cysteine, homocysteine, piperamide, proline, and valine levels. The HOMA-IR was negatively correlated with hydroxypalmitoylcarnitine levels and positively correlated with the myristoylcarnitine levels. Thus, at different stages of T2D progression in overweight patients, serine, aspartic acid, cysteine, homocysteine, piperamide, proline, valine, and tetradecanoyldiacylcarnitine may be associated with HOMA-β and represent potential novel biomarkers for evaluating islet β-cell function.

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.

Transition patterns of metabolism-weight phenotypes over time: A longitudinal study using the multistate Markov model in China

Background

A change in weight or metabolic status is a dynamic process, yet most studies have focused on metabolically healthy obesity (MHO) and the transition between MHO and metabolically unhealthy obesity (MUO); therefore, they have not fully revealed the nature of all possible transitions among metabolism-weight phenotypes over the years.

Methods

This was a longitudinal study based on a retrospective health check-up cohort. A total of 9,742 apparently healthy individuals aged 20-60 years at study entry were included and underwent at least two health check-ups. Six metabolism-weight phenotypes were cross-defined by body mass index (BMI) categories and metabolic status as follows: metabolically healthy normal weight (MHNW), metabolically healthy overweight (MHOW), MHO, metabolically unhealthy normal weight (MUNW), metabolically unhealthy overweight (MUOW), and MUO. A multistate Markov model was used to analyse all possible transitions among these phenotypes and assess the effects of demographic and blood indicators on the transitions.

Results

The transition intensity from MUNW to MHNW was the highest (0.64), followed by the transition from MHO to MUO (0.56). The greatest sojourn time appeared in the MHNW state (3.84 years), followed by the MUO state (2.34 years), and the shortest sojourn time appeared in the MHO state (1.16 years). Transition intensities for metabolic improvement gradually decreased with BMI level as follows: 0.64 for MUNW to MHNW, 0.44 for MUOW to MHNW, and 0.27 for MUO to MHO; however, transition intensities for metabolic deterioration, including MHNW to MUNW, MHOW to MUOW, and MHO to MUO, were 0.15, 0.38, and 0.56, respectively. In the middle-aged male group, elevated alanine aminotransferase (ALT), aspartate aminotransferase (AST), and uric acid (UA) increased the risk of deterioration in weight and metabolic status and decreased the possibility of improvement.

Conclusion

Maintaining a normal and stable BMI is important for metabolic health. More attention should be given to males and elderly people to prevent their progression to an unhealthy metabolic and/or weight status. MHO is the most unstable phenotype and is prone to convert to the MUO state, and individuals with abnormal ALT, AST and UA are at an increased risk of transitioning to an unhealthy weight and/or metabolic status; therefore, we should be alert to abnormal indicators and MHO. Intervention measures should be taken early to maintain healthy weight and metabolic status.

Body fat and muscle were associated with metabolically unhealthy phenotypes in normal weight and overweight/obesity in Yi people: A cross-sectional study in Southwest China

This study aimed to determine the association between the absolute mass, distribution, and relative ratio of body fat and muscle with the metabolically unhealthy (MU) phenotypes in normal weight and overweight/obesity in Yi people in China. The cross-sectional data from the Yi Migrants Study was used, which included 3,053 Yi people aged 20-80 years from the rural and urban sets. Participants were classified according to body mass index and metabolic status. Body composition including body fat percentage (BFP), fat mass index (FMI), visceral fat grade (VFG), muscle mass index (MMI), and muscle/fat ratio (M/F) were measured by bioelectrical impedance analysis. Restricted cubic spline and logistics regression models were used to test the associations between body composition parameters with MU phenotypes. Receiver-operating characteristic curves (ROC) were used to analyze the predictive value of MU phenotypes. Among the normal weight and overweight/obesity, 26.31% (497/1,889) and 52.15% (607/1,164) were metabolically unhealthy. Stratified by BMI, covariance analysis showed higher body fat (BFP, FMI, and VFG) and MMI in MU participants than in healthy participants. BFP, FMI, VFG, and MMI were positively associated with MU phenotypes both in normal weight and overweight/obesity after adjustment. M/F was significantly lower than MU participants and was negatively associated with MU phenotypes. BFP, FMI, VFG, and M/F could better predict MU phenotypes than BMI. We concluded that BFP, FMI, and VFG were positively associated with MU phenotypes, while M/F was negatively associated with MU phenotypes across the BMI categories in Yi people. Body fat and muscle measurement could be a valuable approach for obesity management.

Type 2 Diabetes Mellitus Facilitates Shift of Adipose-Derived Stem Cells Ex Vivo Differentiation toward Osteogenesis among Patients with Obesity

Objective: Sedentary behavior with overnutrition provokes the development of obesity, insulin resistance, and type 2 diabetes mellitus (T2DM). The main progenitor cells of adipose tissue are adipose-derived stem cells (ADSCs) which can change differentiation, metabolic, and secretory phenotypes under obesity conditions. The purpose of this study was to evaluate ADSC osteogenesis activity among patients with obesity in normal glucose tolerance (NGT) and T2DM conditions. Methods: In the study, ADSCs from donors with obesity were used. After clinical characterization, all patients underwent bariatric surgery and ADSCs were isolated from subcutaneous fat biopsies. ADSCs were subjected to osteogenic differentiation, stained with Alizarin Red S, and harvested for real-time PCR and Western blotting. Cell senescence was evaluated with a β-galactosidase-activity-based assay. Results: Our results demonstrated the significantly increased calcification of ADSC on day 28 of osteogenesis in the T2DM group. These data were confirmed by the statistically significant enhancement of RUNX2 gene expression, which is a master regulator of osteogenesis. Protein expression analysis showed the increased expression of syndecan 1 and collagen I before and during osteogenesis, respectively. Moreover, T2DM ADSCs demonstrated an increased level of cellular senescence. Conclusion: We suggest that T2DM-associated cellular senescence can cause ADSC differentiation to shift toward osteogenesis, the impaired formation of new fat depots in adipose tissue, and the development of insulin resistance. The balance between ADSC adipo- and osteogenesis commitment is crucial for the determination of the metabolic fate of patients and their adipose tissue.