In this Taiwanese cohort, we found associations of carotid IMT with blood pressure and LDL cholesterol. When their effects were taken into account simultaneously, the positive association between the increase in LDL cholesterol and IMT became attenuated and non-significant. This indicates that change in systolic blood pressure may be a better predictor or a stronger risk factor for IMT.
The effects of blood pressure and lipids on carotid IMT have been reported in previous studies [1
]. In one study based on 2268 adults who undertook health examinations, increased numbers of metabolic risk factors were associated with an increased IMT in different ethnic groups [1
]. Another study based on 1809 young Finnish adults also showed that metabolic components were significantly associated with carotid IMT, and the progress of IMT among these young adults was related to obesity, LDL and insulin resistance [32
]. Another small study in Italy (240 healthy adults) from health checkups also showed an additive synergistic effect of metabolic components for IMT [33
]. Exploratory factor analysis was used to extract three factors; obesity/dyslipidemia, hypertension, and hyperglycemia [33
]. However, the specific effects of the components were unclear in the factor analysis strategy. In addition, these studies were cross-sectional in design, without considering the cumulative effects of both blood pressure and lipids on carotid atherosclerosis. Our findings from the multiple linear regression model showed similar findings to those in the study of Al-Shali and colleagues [6
]. In this study, we used latent growth curve modeling to investigate the associations of both baseline and changes in blood pressure and lipids on carotid IMT. Change in blood pressure seems to have a stronger effect on IMT than change in LDL cholesterol, suggesting the effects of lipid-lowering treatment on carotid IMT regression might be small [34
]. This is consistent with the findings from clinical trials on lipid-lowering therapies in which medications for reducing LDL had inconsistent effects on IMT, as shown by numerous statin studies including METEOR (rosuvastatin) [35
], ACAPS (lovastatin) [36
], KAPS (pravastatin) [37
], PLAC-II (pravastatin) [38
], BCAPS (fluvastatin) [39
], FAST (pravastatin) [40
], and REGRESS (pravastatin) [41
]. The stronger association between changes in blood pressure and IMT may be because the hemodynamic change of blood pressure had a stronger impact on carotid intima media thickness, while injuries caused by LDL changes affected the carotid IMT slowly and did not manifest during the study period. Consequently, the change in IMT caused by carotid blood flow may be an important indicator for endothelial function. Evidence from antihypertensive clinical trials showed that change of BP was related to the severity of carotid IMT regression, and results from our observational study seem to support the same hypothesis that carotid IMT is primarily a mechanism of medial hypertrophy.
Evidence from randomized controlled trial data also showed differential effects of blood pressure and cholesterol lowering on carotid atherosclerosis. Among 508 combined hypertensive and hypercholesterolemic patients under a randomized control trial for 2.6
years, the progression of the carotid IMT were not significant for patients treated with fosinopril (−0.002±0.004
mm) or those with fosinipril and prastatin (−0.002±0.004
]. Adding LDL cholesterol lowering drugs did not seem to affect the change of IMT under the blood pressure lowering effects, which was also consistent with our findings. Antihypertensive drugs were related to the carotid IMT progression or regression, and the mechanism is complex. For example, the net arterial volume expansion developed as a decrease in carotid IMT after antihypertensive medication. Therefore, further investigation on different mechanisms of carotid IMT progress by blood pressure lowering drugs was warranted.
Latent growth curve modeling is a new tool to biomedical and epidemiological research, while it has been widely used in the social sciences. Using software packages for structural equation modeling, latent growth curve modeling provides a flexible statistical framework for the analysis of repeated measures in clinical practice to investigate the baseline and changes of exposure to outcomes [29
]. The main advantage of using latent growth curve modeling instead of traditional approaches is that repeated measurements of multiple variables (e.g. LDL and systolic blood pressure in this study) can be incorporated into one single model, and their association with a distant outcome (e.g. IMT) can then be tested. This method has been successfully applied in psychology and sociology [43
], and could be a very useful tool for longitudinal data analysis in medical research [44
]. In this study, we used latent growth curve modeling to estimate the effects of baseline values and changes from baseline on the carotid IMT. The clinical implication of our findings was two fold: firstly, baseline blood pressure and lipids are risk factors for carotid atherosclerosis severity; and secondly, keeping optimal blood pressure levels may be helpful for reducing carotid IMT.
Several limitations of our study should be mentioned. First, IMT, rather than plaque status, was the outcome for our investigation. Previous studies have shown that the numbers of plaque were related to cardiovascular complications [46
] and events [47
]. Second, we did not include the medications for lipid and blood pressure in our analysis because that information was not available. Third, we did not measure mean carotid IMT in the study participants, because our protocol was designed to measure maximal carotid IMT values [24
]. Finally, carotid IMT magnitude was measured once and we did not estimate the progress of the IMT.