BACKGROUND: Cardiovascular risk factors cluster in the metabolic syndrome (MS), but it is not known whether the risk associated with the syndrome is higher than the sum of its parts. In this study, we explored the relationship between clustering cardiometabolic risk factors and carotid intima-media thickness (c-IMT). METHODS AND RESULTS: Cardiovascular parameters and c-IMT were determined in 240 middle-aged healthy participants, divided into groups according to their number of MS components. Higher number of MS components were associated with higher mean c-IMT. Analysis of synergy revealed that c-IMT increase at component clustering fitted an additive model. Redefinition of cutpoints for MS traits, optimized to detect high c-IMT, did not improve the interaction between components. When metabolic factors were rendered independent, a synergistic interaction between factors in increasing the likelihood of having a high c-IMT was detected. Synergic as well was the interaction between metabolic factors with other risk factors that are not consequence of insulin resistance, such as low-density lipoprotein-cholesterol level and smoking habit. CONCLUSION: A stepwise approach reveals that the lack of synergy in the interactions between MS components is attributable to their mutual interdependence, possibly owing to the common pathophysiological background. Indeed, if MS is a unique clinical entity, it should be no more than the sum of its parts.
BACKGROUND: Cardiovascular risk factors cluster in the metabolic syndrome (MS), but it is not known whether the risk associated with the syndrome is higher than the sum of its parts. In this study, we explored the relationship between clustering cardiometabolic risk factors and carotid intima-media thickness (c-IMT). METHODS AND RESULTS: Cardiovascular parameters and c-IMT were determined in 240 middle-aged healthy participants, divided into groups according to their number of MS components. Higher number of MS components were associated with higher mean c-IMT. Analysis of synergy revealed that c-IMT increase at component clustering fitted an additive model. Redefinition of cutpoints for MS traits, optimized to detect high c-IMT, did not improve the interaction between components. When metabolic factors were rendered independent, a synergistic interaction between factors in increasing the likelihood of having a high c-IMT was detected. Synergic as well was the interaction between metabolic factors with other risk factors that are not consequence of insulin resistance, such as low-density lipoprotein-cholesterol level and smoking habit. CONCLUSION: A stepwise approach reveals that the lack of synergy in the interactions between MS components is attributable to their mutual interdependence, possibly owing to the common pathophysiological background. Indeed, if MS is a unique clinical entity, it should be no more than the sum of its parts.