6 individuals for a mean period of 4.8 years and found that the presence of both CKD and MetS conferred a higher risk for CVD events (HR, 3.58; 95% CI, 2.14–5.95) compared to the no CKD/no MetS group. Our ﬁndings are a validation and extension of these ﬁndings in a multiethnic cohort free of cardiovascular disease at baseline. In contrast to the study by Martins, we found no signiﬁcant association in the CKD only group but found signiﬁcant association in the MetS only group which is perhaps due to small numbers of CKD only individuals. Second, our study included a cohort with both with and without hypertension. Our study has several limitations. First, MESA did not directly measure glomerular ﬁltration rate (GFR); there- fore, we cannot be certain that the association between elevated cystatin C level and CVD events are solely caused by its approximation of impaired GFR. This approach can lead to misclassiﬁcation of individuals due to biased estimates. Second, although eﬀorts were made to adjust for known confounders, there remains a possibility of failure to adjust for unknown confounders or inadequate adjustment of established risk factors (severity and duration of hypertension, diabetes) resulting in spurious results due to residual confounding. Third, some studies suggest that corticosteroids [32] and thyroid function [33] are associated with cystatin C, and since adjustment with these measures was not performed, results should be interpreted with caution in this subset of individuals. The distribution of metabolic syndrome components may vary from population to population, which may have an impact on the external validity of ﬁndings if the joint association/interaction is mostly due to one of the components. The results of the study are limited to individuals without cardiovascular disease and may not be generalizable to a population with known coronary artery disease due to selection bias. Also, due to the cross-sectional nature of the risk factors, the association between CKD, MetS, and CVD events could potentially be due to post assessment residual confounding. 5. Conclusion This study shows that the co-occurrence of CKD and MetS results in an increased hazard for cardiovascular events in a multiethnic population. Although, no multiplicative interaction was demonstrated, there is signiﬁcant presence of additive interaction. Both CKD and MetS are independent predictors of CVD but their combination furthers the risk independent of conventional risk factors. From the clinical viewpoint, physicians should become more cognizant that concomitant CKD and MetS lead to increased risk for CVD events. Additionally, assessment of renal function in individ- uals with MetS and vice versa may lead to improved risk stratiﬁcation for cardiovascular disease in clinical practice. More studies are needed in the future to explore the temporal relationship between CKD, MetS and cardiovascular disease. Additionally, studies are needed to explore whether novel and aggressive pharmacological and behavioral modiﬁcations in individuals with both CKD and MetS, will lead to reduction in CVD risk. Cardiology Research and Practice Disclosures Appendix The authors had full access to the data and take responsibility for the integrity of the data. All authors have read and agree to the study as written. Using the output from Tables 4 and 5, the calculations of RERI and the test for the additive interaction are illustrated below. (1) Relative Excess Risk due to Interaction (RERI) = HR11 − HR10 − HR01 + 1, where HR11is hazard due to both CKD and MetS, HR10 is hazard due to CKD alone, and HR01is hazard due to MetS alone. RERI = e β1+ β2+ β3 − eβ1 − eβ2 + 1 = 5.56 − 1.43 − 2.40 + 1 = 2.73, VAR(RERI) = a2 1 × varβ1 + a22 × varβ2 + a23 × varβ3 + 2 a1 a2 × Cov β1 β2 + a1a3 ×Cov β1β3 + a2a3 × Cov β2β3 , eβ2−1 a1 = eβ1+β2+β3 , e β1−1 a2 = eβ1+β2+β3 , e β1+β2−1 a3 = eβ1+β2+β3 . a1 = a3 − a2 = a3 − a3 = e β1 eβ1+β2 − 2 , eβ2 eβ1+β2 − 2 , eβ1+β2+ β3 eβ1+β2+β3 − 1 . a1 = eβ1+β2+β3 − e β1 = 5.56 − 1.43 = 4.13, a2 = eβ1+β2+β3 − eβ2 = 5.56 − 2.40 = 3.16, a3 = eβ1+β2+β3 = 5.56. (A.1) (A.2) (A.3) VAR (RERI) = 1.19, SE (RERI) = 1.09. Hence, t = RERI/VAR (RERI) = 2.73/1.19 = 2.28, P = 0.02, and a 95% conﬁdence interval estimate for RERI, RERI ± 1.96 × SE (RERI) = (0.57 − 4.85): (2) Attributable portion (AP) = (eβ1+β2+β3 − e β1 − eβ2 + 1)/(eβ1+β2+β3) = 2.73/5.56 = 0.49: VAR(AP) = 0.016, SE(AP) = 0.126. Hence, 95% conﬁdence interval estimate for AP, AP ± 1.96 × SE(AP) = (0.24 − 0.74). (3) Synergy index (SI) = eβ1+β2+β3 − 1/eβ1+β2 − 2 = 4.56/1.83 = 2.49.