ArticleEndocrine2014
Risk profiles for metabolic syndrome and its transition patterns for the elderly in Beijing, 1992-2009.
Article in Endocrine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Dynamic behavior of metabolic syndrome progression: a comprehensive systematic review on recent discoveries.BMC endocrine disorders · 2021Pooled it
- Predicting the natural history of metabolic syndrome with a Markov-system dynamic model: a novel approach.BMC medical research methodology · 2021Article
- The Impact of BMI Categories on Metabolic Abnormality Development in Chinese Adults Who are Metabolically Healthy: A 7-Year Prospective Study.Diabetes, metabolic syndrome and obesity : targets and therapy · 2020Article
- Dynamic development of metabolic syndrome and its risk prediction in Chinese population: a longitudinal study using Markov model.Diabetology & metabolic syndrome · 2018Article
- Association of high-density lipoprotein with development of metabolic syndrome components: a five-year follow-up in adults.BMC public health · 2015Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
There have been few reports on the development of metabolic disorders, especially when they are considered as a cluster. The purpose of this study was to describe risk profiles for metabolic syndrome (MetS) in elderly dwellers in Beijing, and to find their transition patterns over time. Data were derived from Beijing longitudinal study of aging, a community-based cohort study hosted by Xuanwu hospital. There were 3,257 elderly people aged 55 years or over recruited in 1992. MetS was assessed for the years 1992, 2000, and 2009. Finally, 363 subjects with complete information for components of MetS in the three years were included in the study. The criteria of MetS recommended by the joint interim statement criteria were adopted. Latent transition analysis was used to calculate the transition probabilities between adjacent visits. A risk typology consisting of four time-invariant groups was detected based on the components of MetS for all subjects. Low MetS risk group, BP risk group, BP-HDL risk group, and BP-FPG-TG risk group were found. The probability of staying at the same status was higher at the two intervals across 18 years. Four latent groups were extracted based on three assessments for the components of MetS, together with their transition patterns. Findings suggested various trajectories for MetS components. Different combinations of intervention strategy might be needed for MetS risk groups.
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