Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning algorithms: a population-based prospective cohort study over 40 years in China.

Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models.

Predictive modeling the probability of suffering from metabolic syndrome using machine learning: A population-based study.

Background: There is an increasing trend of Metabolic syndrome (MetS) prevalence, which has been considered as an important contributor for cardiovascular disease (CVD), cancers and diabetes. However, there is often a long asymptomatic phase of MetS, resulting in not diagnosed and intervened so timely as needed. It would be very helpful to explore tools to predict the probability of suffering from MetS in daily life or routinely clinical practice.

Development and validation of a machine learning-augmented algorithm for diabetes screening in community and primary care settings: A population-based study.

Background: Opportunely screening for diabetes is crucial to reduce its related morbidity, mortality, and socioeconomic burden. Machine learning (ML) has excellent capability to maximize predictive accuracy. We aim to develop ML-augmented models for diabetes screening in community and primary care settings.

Catch-up fat in male adults induces low testosterone and consequently promotes metabolic abnormalities and cognitive impairment.

Background: Catch-up fat in adults (CUFA) caused by rapid nutrition promotion after undernutrition plays an important role in the epidemic of insulin resistance (IR)-related diseases in developing societies. Insulin resistance is considered to be closely associated with reduced testosterone levels and cognitive function. However, the effects of CUFA on testosterone levels and cognitive function are unclear in males.

Not performing an OGTT results in underdiagnosis, inadequate risk assessment and probable cost increases of (pre)diabetes in Han Chinese over 40 years: a population-based prospective cohort study.

Objective: To explore the influence by not performing an oral glucose tolerance test (OGTT) in Han Chinese over 40 years.

Design: Overall, 6682 participants were included in the prospective cohort study and were followed up for 3 years.

Methods: Fasting plasma glucose (FPG), 2-h post-load plasma glucose (2h-PG), FPG and 2h-PG (OGTT), and HbA1c testing using World Health Organization (WHO) or American Diabetes Association (ADA) criteria were employed for strategy analysis.

Non-high-density lipoprotein cholesterol: High-density lipoprotein cholesterol ratio is an independent risk factor for diabetes mellitus: Results from a population-based cohort study.

Background: Dyslipidemia predicts the development and progression of diabetes. A higher non-high-density lipoprotein cholesterol (HDL-C):  HDL-C ratio is reportedly associated with metabolic syndrome and insulin resistance, but its relationship with glycemic levels and diabetes remains unclear.

Methods: In all, 4882 subjects aged ≥40 years without diabetes and not using lipid-lowering drugs were enrolled in the study.