Comparison of coronary heart disease risk assessments among individuals with metabolic syndrome using three diagnostic definitions: a cross-sectional study from China

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

U-shaped association of serum uric acid with cardiovascular disease risk scores and the modifying role of sex among Chinese adults

BACKGROUND AND AIMS

Serum uric acid (SUA) is involved in the development of cardiovascular disease (CVD). However, information on the dose-response relationship between SUA and CVD is limited in the Chinese population. This study aimed to investigate the potential nonlinear dose-response association of SUA with CVD risk in a Chinese population and to explore the effect of sex on these associations.

METHODS AND RESULTS

Cross-sectional data, from 6252 Chinese adults aged 30-74 years who participated in the China Health and Nutrition Survey 2009, were stratified by SUA deciles. The 10-year risk of CVD was determined using the Framingham risk score. A restricted cubic spline (RCS) was incorporated into the logistic models to assess the nonlinear relationship between SUA and CVD. Among the participants, 65%, 20%, and 15% had low, moderate, and high 10-year CVD risks, respectively. Compared with the reference SUA strata of 225 to <249 μmol/L, CVD risk was significantly increased at SUA ≥294 μmol/L, with adjusted ORs ranging from 2.39 (1.33-4.33) to 4.25 (2.37-7.65). An increasingly higher nonsignificant CVD risk was found at SUA <225 μmol/L and showed a nonlinear U-shaped association. In the fitted RCS model, an approximate U-shaped association between SUA and CVD risk scores was found in women, but this significant nonlinear relationship was not found in men.

CONCLUSION

This study showed that both lower and higher SUA levels were associated with a higher 10-year CVD risk among Chinese adults, forming a U-shaped relationship, and this pattern was particularly pronounced for women.

A Bayesian Network Analysis of the Probabilistic Relationships Between Various Obesity Phenotypes and Cardiovascular Disease Risk in Chinese Adults: Chinese Population-Based Observational Study

BACKGROUND

Cardiovascular disease (CVD) risk among individuals with different BMI levels might depend on their metabolic health. The extent to which metabolic health status and BMI affect CVD risk, either directly or through a mediator, in the Chinese population remains unclear.

OBJECTIVE

In this study, the Bayesian network (BN) perspective is adopted to characterize the multivariable probabilistic connections between CVD risk and metabolic health and obesity status and identify potential factors that influence these relationships among Chinese adults.

METHODS

The study population comprised 6276 Chinese adults aged 30 to 74 years who participated in the China Health and Nutrition Survey 2009. BMI was used to categorize participants as normal weight, overweight, or obese, and metabolic health was defined by the Adult Treatment Panel-3 criteria. Participants were categorized into 6 phenotypes according to their metabolic health and BMI categorization. The 10-year risk of CVD was determined using the Framingham Risk Score. BN modeling was used to identify the network structure of the variables and compute the conditional probability of CVD risk for the different metabolic obesity phenotypes with the given structure.

RESULTS

Of 6276 participants, 64.67% (n=4059), 20.37% (n=1279), and 14.95% (n=938) had a low, moderate, and high 10-year CVD risk. An averaged BN with a stable network structure was constructed by learning 300 bootstrapped networks from the data. Using BN reasoning, the conditional probability of high CVD risk increased as age progressed. The conditional probability of high CVD risk was 0.43% (95% CI 0.2%-0.87%) for the 30 to 40 years age group, 2.25% (95% CI 1.75%-2.88%) for the 40 to 50 years age group, 16.13% (95% CI 14.86%-17.5%) for the 50 to 60 years age group, and 52.02% (95% CI 47.62%-56.38%) for those aged ≥70 years. When metabolic health and BMI categories were instantiated to their different statuses, the conditional probability of high CVD risk increased from 7.01% (95% CI 6.27%-7.83%) for participants who were metabolically healthy normal weight to 10.47% (95% CI 7.63%-14.18%) for their metabolically healthy obese (MHO) counterparts and up to 21.74% and 34.48% among participants who were metabolically unhealthy normal weight and metabolically unhealthy obese (MUO), respectively. Sex was a significant modifier of the conditional probability distribution of metabolic obesity phenotypes and high CVD risk, with a conditional probability of high CVD risk of only 2.02% and 22.7% among MHO and MUO women, respectively, compared with 21.92% and 48.21% for their male MHO and MUO counterparts, respectively.

CONCLUSIONS

BN modeling was applied to investigate the relationship between CVD risk and metabolic health and obesity phenotypes in Chinese adults. The results suggest that both metabolic health and obesity status are important for CVD prevention; closer attention should be paid to BMI and metabolic status changes over time.

Potential of four definitions of metabolic syndrome to discriminate individuals with different 10-year cardiovascular disease risk scores: a cross-sectional analysis of an Iranian cohort

OBJECTIVE

We aimed to reveal the potential of four different metabolic syndrome (Mets) definitions to differentiate subjects according to 10-year risk of cardiovascular disease.

DESIGN

A cross-sectional analysis of a prospective cohort.

SETTING

This study used baseline data from the Shiraz Heart Study, a prospective cohort study in Shiraz, Iran. Participants were screened against Mets definitions including modified WHO, National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III), American Heart Association (AHA) and International Diabetes Federation (IDF). Also, Framingham risk score (FRS) and atherosclerotic cardiovascular disease (ASCVD) risk score were determined for each participant.

PARTICIPANTS

A total number of 7225 participants of both genders entered the study. They were selected through defined family physician centres in different geographical areas. Urban residents with no migration plan were included. Those who were far from study centres or with disabilities that made them incapable to cooperate were excluded.

RESULTS

Participants were 47.68% (N=3445) male with the mean age of 52.13±8.00 years. The number of subjects with Mets identified by WHO was the lowest (N=1676), while the percentage of subjects with high risk score was the highest, 17.1% (N=282) in FRS and 9.8% (N=162) in ASCVD risk score. There were statistically significant differences in the mean risk scores between participants with and without Mets according to AHA, WHO and NCEP ATP III definitions (p<0.001). In IDF definition, the risk scores of subjects with Mets were not statistically different compared with peers without Mets, neither based on FRS (p=0.247) nor ASCVD risk score (p=0.193).

CONCLUSIONS

IDF was not the appropriate definition for discrimination of subjects with Mets and/or those at high risk of future cardiovascular events. AHA, WHO and NCEP ATP III definitions were effective to discriminate subjects with Mets from peers without Mets.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.