Prognostic significance of the Complex "Visceral Adiposity Index" vs. simple anthropometric measures: Tehran lipid and glucose study

There is no consensus on the definition of obesity or on specific aspects of obesity that contribute to the risk of CVD [1]. The precise measurement of the total amount of body fat and its regional distribution is possible by using computed tomography (CT), dual-energy X-ray absorption [2]. Magnetic resonance imaging (MRI), like CT, can separate visceral fat from subcutaneous fat and since there is no radiation involved, it can perform a total body scan for maximal accuracy and fat distribution. However, these methods are primarily used at the research level. Besides, they are time-consuming, costly, and not routinely available. Accordingly there is a need for simple techniques that can discriminate regional fat. Amato et al. have recently individuated a novel sex-specific index based on waist circumference, body mass index (BMI), triglycerides (TGs), high-density lipoprotein cholesterol (HDL-C), and indirectly expressing visceral fat [3] and termed it the visceral adiposity index (VAI). VAI had significant correlation with visceral adiposity and its increase was strongly associated with cardiometabolic risk. However, the prospective relation between VAI and CVD is less clear [3]. Clinical importance of visceral adiposity lies in its association with health risks like CVD. Therefore, from clinical point-of-view, indices developed to measure visceral adiposity should be examined with respect to their ability to predict risks known to be associated with it [4, 5].

Using data from a large community-based study, we examined if VAI would improve CVD prediction currently made by multivariate algorithms and if VAI could add to the predictive ability of the simple anthropometric measures of adiposity i.e. BMI, waist-to-height ratio (WHtR) or waist-to-hip ratio (WHpR).

For a median follow up of 9 years, 6,407 (3,629 women) adult participants of the TLGS contributed to a total of 54,950 person-years follow up. We documented 534 CVD events with the annual incidence rate of CVD events being 9.7 (95% 8.9-10.6) per 1000 person: women 7.3 (6.4-8.3) and men 13.0 (11.7-14.6).

Participants' characteristics are shown according to baseline VAI quintiles in Tables 1 and 2. In general, CVD risk factors levels at baseline increased in stepwise fashion across VAI quintiles; except for smoking. The annual incidence rate of CVD events showed an increasing trend across quintiles of VAI among women (P < 0.001). Age-adjusted survival functions for quintiles of VAI have been compared in Figures 1 and 2. Age-adjusted CVD-free survival probability decreased significantly across VAI quintiles among both men and women (P values < 0.001).

Risk of future CVD increased with increasing levels of VAI among both men and women. Among women, but not men, the increased risk resisted adjustments for the CVD risk factors (Table 3). As shown in Table 3, HRs for CVD of WHpR and WHtR were consistently higher than those of VAI among both men and women. These superiorities, however, did not reach statistical significance.

We observed that among men, 0.7% (95% CIs 0.09-1.9%) of variations in CVD-free survival time was explained VAI. The corresponding figures were 4.5% (95% CIs 2.4-7.2%) for WHtR, 6.1 (95% CIs 3.6-9.0%) for WHpR, and 0.8% (95% CIs 0.1-2.2%) for BMI. Among women, 5.0% (95% CIs 2.7-7.8%) of variations in CVD-free survival time was explained VAI. The corresponding figures were 7.3% (95% CIs 4.5-10.7%) for WHtR, 10.0 (95% CIs 6.6-13.9%) for WHpR, and 1.4% (95% CIs 0.4-3.0%) for BMI.

Harrell's C (95% CIs) and Nam-D'Agostino χ² (P for lack of fit) for the CVD risk based on the Framingham general CVD algorithm were 0.777 (0.754-0.802) and 15.8 (0.070) among men; the corresponding figures were 0.778 (0.704-0.802) and 13.6 (0.135) for VAI, 0.712 (0.700-0.729) and 12.2 (0.200) for BMI, 0.778 (0.704-0.802) and 17.0 (< 0.0.048) for WHtR, and 0.778 (0.704-0.802) and 14.6 (0.103) for WHpR. The Harrell's C (95% CIs) and NamD'Agostino χ² (P for lack of fit) for the CVD risk based on the Framingham general CVD algorithm were 0.841 (0.820-0.862) and 13.9 (0.126) among women. The corresponding figures were 0.841 (0.820-0.862) and 11.1 (0.266) for VAI, 0.835 (0.814-0.858) and 35.9 (< 0.001) for BMI, 0.838 (0.816-0.859) and 42.0 (< 0.0001) for WHtR, and 0.839 (0.8170.860) and 23.2 (0.005) for WHpR.

As shown in Table 4, VAI failed to add to the predictive ability of the Framingham general CVD algorithm. Moreover, among women, when VAI was directly compared to the WHtR, WHpR, and BMI, NRI and IDI statistics were negative, indicating that WHpR, WHtR, and BMI predicted CVD better than did VAI. The only exception insinuated to the findings was that, among men, VAI outperformed BMI as denoted by all indices of added predictive abilities.

Multivariate restricted cubic splines regression analysis demonstrated that VAI-CVD dose- response relations had no threshold and yielded straight lines when risk of disease was plotted on a logarithmic scale (Figure 3). The incident CVD risk corresponding to VAI = 2.3 was null; above this value VAI conferred hazard for incident CVD in a linear fashion. VAI values below 2.3 appeared to provide some protection against CVD.

HRs for incident CVD of lifestyle modification intervention measures was 0.89 (95% CIs 0.70-1.12, P value = 0.324) among men and 1.11 (95% 0.85-1.45, P value = 0.435) among women. Intervention measures did not contribute to the risk of CVD; neither did the intervention measures modify the effects of VAI on the risk of incident CVD (P for interaction: men 0.304 and women 0.711).

VIFs were all < 10 and therefore collinearity did not appear to be a problem.

In this prospective cohort of men and women, we found associations between higher VAI and risk of incident CVD as compared to simple, commonly available anthropometric measures. This community-based study demonstrated that a 1-SD increase in VAI carries 18-27% increase in age-adjusted risk of future CVD. After adjustment for traditional CVD risk factors however, it was only among women that VAI retained its predictability for incident CVD events. VAI failed to add to the predictive ability of the Framingham's "general CVD algorithm," neither it did so to those of BMI, WHpR, or WHtR. The interesting finding of our study was that using VAI instead of simple anthropometric measures of adiposity may lead to loss of considerable information needed for predicting incident CVD. WHtR and WHpR explained greater part of variations in the CVD-free survival time than VAI did.

Establishing the shape of associations between risk factors and CVD is important to gauge the potential for prevention [22, 23]. We observed that dose-response association between VAI and CVD risk had no threshold. The incident CVD risk corresponding to the VAI = 2.3 was null. However, the slope of the dose-response association was steeper for VAI values smaller than 2.3 than those greater than 2.3. VAI values below 2.3 appeared to provide some protection against CVD. Above this value VAI conferred hazard for incident CVD in a linear fashion. As such, irrespective of the level of the VAI, a given increase in VAI levels above 2.3 would be accompanied by the same proportional increase in risk of incident CVD regardless of the initial risk. Meanwhile, a given decrease in VAI levels below 2.3 would be accompanied by the same proportional reduction in risk of incident CVD regardless of the initial risk. Our findings support previous reports showing the best cut-off point for VAI to be around 2.2 [24].

Adjusting for potential intermediates in the association between adiposity and CVD generally attenuates relative risks for the various indices [25, 26]. We observed that after adjustment for CVD risk factors, men's VAI levels were no longer associated with risk of incident CVD. Moreover, VAI did not improve the predictive ability of the Framingham general CVD risk prediction rule; WHtR and in particular WHpR were both superior to VAI in predicting risk of incident CVD, particularly among women. We also estimated the improvements across different categories of risk of the Framingham risk algorithm and observed negligible improvements. Only one percent of participants with moderate CVD risk were correctly reclassified (data are available upon the request). It has been shown that measures of adiposity are correlated with cardiovascular risk although no single adiposity measure can be identified as the best predictor [27]. In line with our findings, however, WHtR and WHpR have been shown to be superior to BMI [28‐31]. Across quintiles of VAI, contribution of WHtR and WHpR to risk of incident CVD remained essentially the same (data available upon the request from authors); indicating that VAI did not modify effects of these risk factors on the risk of CVD. The finding that VAI was not better than WHpR or WHtR, at least in part, could possibly be explained by high correlation between WHpR and WHtR and components of VAI i.e. BMI, waist circumference, HDL-C, and TGs [32]. Anthropometric measures were previously demonstrated to keep pace with a combination of TGs and waist circumference, "lipid accumulation product," in predicting incident CVD [5]. Technological developments including computer tomography scans and MRI, made it possible to precisely measure specific adipose tissue depots such as visceral adipose tissue mass [33]. We are aware of one prospective study in which visceral adiposity mass has directly been measured and compared to its anthropometric indices with respect to the CVD prediction [34]. Interestingly, clinical measurements of abdominal obesity were reported to be better predictors of CVD progression than tomography assessment in women [34]. These findings underscore the fact that there is so much room for investigations on adiposity measures that could improve CVD prediction above and beyond what have already been achieved by simple anthropometric measures.

The hypothesis that visceral adiposity would explain more variance in CVD risk factors than general adiposity was not supported in a relatively large sample of black and white adolescents [35].

"Is the visceral adiposity worth the trouble or expenses involved in accurate measurements for prediction of future CVD?" While current evidences point to the importance of using clinical measurements of abdominal obesity to identify individuals at increased risk for atherosclerosis [34], evidences are not firm enough to recommend precise measurement of the visceral adiposity mass for predicting incident CVD [27]. Moreover, Moebus et al. have demonstrated that the importance of different combinations of metabolic syndrome changes with age and between sexes putting emphasis on a tailored approach towards very young or very old subjects [36]. Sex- and age-specific VAI, thus, may confer stronger predictive capacity.

Strengths of the present study lie in its prospective nature, the use of a large population-based-cohort of both sexes, accurate and valid data on risk factors at baseline, continuous surveillance of mortality and CVD events based on standard criteria.

Some limitations to our study merit mentioning. First, in this study, no data was available about TGs lowering drugs. Second, the population studied was of Persian ancestry, our results, thus, cannot be readily extrapolated to other populations. The debate over how best to define visceral adiposity is complicated by observations suggesting that surrogates of adiposity may each perform better in predicting CVD risk in specific populations, depending on sex, age, and ethnicity [1].

We demonstrated that the VAI was independently associated with an increased risk of incident CVD among women and that the magnitude of this risk due to VAI was not significantly higher than those due to BMI, WHpR, or WHtR. However, among men, after controlling common CVD risk factors, we observed that VAI was not associated with any significant increased risk of incident CVD. The increased risk observed among women, however, resisted all adjustment. Using VAI instead of simple anthropometric measures of adiposity may lead to loss of considerable information needed for predicting incident CVD.