Study on Dynamic Progression and Risk Assessment of Metabolic Syndrome Based on Multi-State Markov Model

Abstract

Aim Metabolic syndrome (MetS) coexists with the occurrence and even death of cardiovascular disease and diabetes mellitus. It is essential to study the factors in the dynamic progression of MetS in the interest of prevention and control. Purpose The aim of this study was to analyze the dynamic progression of Mets and explore the potential factors influencing the progression or reversal of MetS. Patients and Methods This study involved 5581 individuals from two waves of the China Health and Retirement Longitudinal Study: 2011 and 2015. A multistate Markov model containing 4 states (free of metabolic disorder (FMD), mild metabolic disorder (MMD), severe metabolic disorder (SMD) and MetS) was adopted to study the dynamic progression of MetS and its influencing factors. Results After follow-up, a total of 2862 cases (50.28% of the total number) had disease state transition. The intensity of transition from MetS to SMD is the same as that from SMD to MMD, and is greater than that from MMD to Mets (0.06 vs 0.05). For the MetS state, a mean of 1/0.08=12.5 years was spent in the MetS state before recovery. The exercise, smoke, drink, BMI level, hyperuricemia had statistically significant effects on progression of MetS status ( P <0.05). The obesity or overweight, little exercise, smoke, drink and hyperuricemia increased the risk of forward progression of MetS disease status. There were significant nonmodifiable (age, gender) and modifiable factors (exercise, drink, BMI level, or high HbA1c) associated with reversion of MetS state. Conclusion The likelihood of progression from MMD to MetS is less likely than that of reversion from MetS to SMD and SMD to MMD. Old females were more resistant to recover from worse states than males. Prevention and intervention measures should be adopted early when MMD or SMD onset occurs.

Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data

“…Based on the multi-state Markov models of dynamic progression of MetS in previous studies 10 , we found that obesity or overweight, physical inactivity, smoking, alcohol consumption and hyperuricaemia increase the risk of forward progression of MetS, females, obesity/overweight, high HbA1c, history of alcohol consumption, and low physical activity are all risk factors that impede recovery from MetS. On this basis, this study will further confirm the causal relationship between these factors and disease progression and recovery using Bayesian network modelling.…”

“…Studies have shown that individuals with adverse behavioral patterns, such as sedentary 28 , high-fat diet 29 , smoking and alcohol consumption 10 , have a high prevalence of MetS, and that most of these adverse lifestyle conditions occur simultaneously 30 . The Bayesian network model found that exercise status indirectly influences the progression and recovery of MetS by directly affecting BMI levels.…”

“…The ID merge resulted in 5581 valid cases through the strict exclusion process. The detailed inclusion and exclusion of study subjects have been reported in our previous studies 10 . The previous study was conducted to analyse the influencing factors on the dynamic development of MetS in 5581 study subjects, but the influencing factors were not necessarily causally related.…”

“…Based on the definition of five metabolic component abnormalities the Joint Interim Statement 38 :①central obesity: waist circumference ≥85cm for men and ≥80cm for women ② hypertension: SBP/DBP ≥130/85 mmHg or those who have been diagnosed with and treated for hypertension; ③high fasting glucose: FBG ≥5.6 mmol/L or those who have been diagnosed with and treated for high glucose; ④ hypertriglyceride: TG ≥1.7 mmol/L; ⑤ low high density lipoprotein cholesterol: <1.03 mmol/L in men and <1.29 mmol/L in women, or those who have received treatment for this condition. Disease states were classified into four disease states: free of metabolic disorder(FMD, no abnormal component), mild metabolic disorder(MMD, one abnormal component), severe metabolic disorder(SMD, two abnormal components) and MetS (Three or more abnormal components) 10 . The dynamic transition is shown in Figure 7 .…”

“…Our Subject Group used longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS), previous study that analyzed the influencing factors associated with the positive progression and reversal of MetS, and concluded that the gender, age, exercise, smoke, drink, hyperuricemia, high HbA1c, functional loss, and BMI level acted as the key factors influencing their history of disease progression 10 . Therefore, on this basis, the above influencing factors associated with positive MetS progression and reversal were used as candidate variables in the causal inference model.…”