Journal of clinical lipidology · 2025
Prevalence, patterns, and geographical distribution of cardiometabolic multimorbidity and its association with unhealthy behaviors among Chinese adults: Evidence from the China National Nutrition Health Survey (2015).
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Moeng Eva, Nget Musa, Bestman Prince L, Luo Miyang, Kolleh Edwina M, Ding Mengyu, Yu Yiyang, Luo Jiayou
Аннотация
BACKGROUND: Cardiometabolic multimorbidity (CMM) is an emerging public health challenge in China, yet no nationwide study has examined its patterns and associations with combined unhealthy behaviors using nationally representative data.
OBJECTIVE: (1) to assess the prevalence, patterns, and geographical distribution of CMM among Chinese adults, (2) to investigate the association between individual and combined unhealthy behaviors and the risk of CMM, and (3) to examine the clustering role of individual cardiometabolic diseases, particularly dyslipidemia, within multimorbidity patterns.
METHODS: This study used secondary data from the 2015 China National Nutrition Health Survey involving 118,489 adults aged ≥18 years. We determined the prevalence and geographical distribution of CMM, identified patterns using association rule mining (ARM), and analyzed the relationships between unhealthy behaviors and CMM using multivariable logistic regression.
RESULTS: The prevalence of CMM was 16.2% among Chinese adults (n = 19,840). ARM revealed dyslipidemia as a key clustering component, appearing in 56.8% of multimorbid cases despite representing only 16.1% individual prevalence. The most common dyad was dyslipidemia-hypertension (9.3% of the total population), followed by hypertension-diabetes (7.3%). Dyslipidemia showed strong bidirectional associations with diabetes (lift = 2.10), while hypertension was central to 80% of multimorbidity clusters. Geographic analysis showed that CMM prevalence peaked in northeastern provinces and was lowest in southern and western regions. Unhealthy behaviors showed dose-dependent associations with CMM risk: daily alcohol use (adjusted odds ratios [aOR] = 1.44), smoking (aOR = 2.58), and poor diet (aOR = 1.29). Combined unhealthy behaviors demonstrated progressive associations with multimorbidity development.
CONCLUSION: This nationally representative study revealed dyslipidemia as a central clustering component in CMM, suggesting targeted lipid management could help prevent multimorbidity progression.
OBJECTIVE: (1) to assess the prevalence, patterns, and geographical distribution of CMM among Chinese adults, (2) to investigate the association between individual and combined unhealthy behaviors and the risk of CMM, and (3) to examine the clustering role of individual cardiometabolic diseases, particularly dyslipidemia, within multimorbidity patterns.
METHODS: This study used secondary data from the 2015 China National Nutrition Health Survey involving 118,489 adults aged ≥18 years. We determined the prevalence and geographical distribution of CMM, identified patterns using association rule mining (ARM), and analyzed the relationships between unhealthy behaviors and CMM using multivariable logistic regression.
RESULTS: The prevalence of CMM was 16.2% among Chinese adults (n = 19,840). ARM revealed dyslipidemia as a key clustering component, appearing in 56.8% of multimorbid cases despite representing only 16.1% individual prevalence. The most common dyad was dyslipidemia-hypertension (9.3% of the total population), followed by hypertension-diabetes (7.3%). Dyslipidemia showed strong bidirectional associations with diabetes (lift = 2.10), while hypertension was central to 80% of multimorbidity clusters. Geographic analysis showed that CMM prevalence peaked in northeastern provinces and was lowest in southern and western regions. Unhealthy behaviors showed dose-dependent associations with CMM risk: daily alcohol use (adjusted odds ratios [aOR] = 1.44), smoking (aOR = 2.58), and poor diet (aOR = 1.29). Combined unhealthy behaviors demonstrated progressive associations with multimorbidity development.
CONCLUSION: This nationally representative study revealed dyslipidemia as a central clustering component in CMM, suggesting targeted lipid management could help prevent multimorbidity progression.