CFA findings for youths with either T1 or T2DM were that a single-factor model yielded the worst fit of the CVD risk factor data, compared to four other prespecified models. The best-fitting model incorporated three correlated factors: obesity, lipids, and blood pressure. We suggest that etiologically driven studies of CVD risk factors in youth with diabetes consider obesity, lipids, and blood pressure as separate (but potentially correlated) variables, rather than focus on the metabolic syndrome.
CFA allows for tests of specific hypothesis that a prespecified model (e.g., one latent factor) provides a good fit of the CVD risk factor data, compared to other models.14
Additionally, it has been argued that forcing factors to be uncorrelated, as is done via orthogonal rotation in principal factor analysis, is inconsistent with a priori
knowledge of correlated biologic processes. Here, CFA is useful because the method avoids the need to force independence among factors. Shen and colleagues23
used CFA to test the goodness of fit for a four-factor model. Results confirmed the hypothesis of four factors (insulin resistance, obesity, lipids, and blood pressure), and this was established for men and women across three ethnic groups. In contrast, Pladevall et al.8
used CFA to test the hypothesis that components of the metabolic syndrome were best described by a single common factor versus a four-factor model, and results favored the single common factor.
Pladevall et al.8
criticized prior work due to correlations among variables such as SBP and DBP, triglycerides and HDL, and waist and BMI, suggesting that such correlations would drive results away from finding a single common factor because those highly correlated variables representing essentially the same phenomenon would load together to yield the respective separate phenomenon (e.g., blood pressure, lipids, obesity) rather than loading on a single factor overall. Therefore, unlike previous work, we systematically prespecified five models that allowed not only for the single-factor possibility but also for a priori
knowledge of underlying biology including correlation between measures. Still, consistent with the results of the initial exploratory principal factor analysis, CFA ruled out a single common factor and identified three correlated factors as the best-fitting data structure for both T1DM and T2DM.
Reaven recently suggested that clustering of risk factors would only occur in the presence of insulin resistance.9
Interestingly, in the present data, the three-correlated-factor structure emerged both for youth with T1DM and those with T2DM. It is possible that despite the starkly different prevalence of the risk factors between T1DM and T2DM, the correlation among the three factors in the best-fitting model may be due to unmeasured insulin resistance in both populations. It is of note, however, that the hierarchical model that included one second-order factor (presumably representing insulin resistance given Reaven's argument) also did not fit the data as well as the model of three correlated factors.
Our findings in no way argue against the importance of insulin resistance and traditional components of the metabolic syndrome in the development of cardiovascular disease in either T1DM or T2DM. Among over 200 youths with T1DM, a wide range of insulin resistance as measured by euglycemic clamp has been demonstrated; in this sample, insulin resistance was associated significantly with measures of overall and central adiposity, dyslipidemia, and blood pressure.24
Increased risk for diabetes-related complications and mortality has been associated with metabolic syndrome components and insulin resistance in two large T1DM cohorts.25,26
Interestingly, in the Pittsburgh Epidemiology of Diabetes Complications Study cohort,25
components of three different definitions of metabolic syndrome predicted major diabetes-related complications better than the overall syndrome. In a large group (n
1366) T2DM patients, insulin resistance measured by homeostasis model assessment–insulin resistance (HOMA-IR) was independently associated with lipids, obesity, and hypertension,27
and in the Verona Diabetes Complications Study, the presence of the metabolic syndromewas associated with nearly a five-fold increase in CVD risk.28
We had a limited number of individuals with T2DM, so we were unable to conduct a split sample replication of the CFA among youth with T2DM. This reflects the epidemiology of diabetes in youths in which the major form of diabetes that occurs is T1DM.29
Also, the response rate for youths registered as valid cases in SEARCH to attend the SEARCH in-person visit was not optimal, as detailed in Methods. However, for the issue of nonresponse to impact substantially on the results, one would need to suppose that the multivariate associations within subjects who did not attend the clinic visit differed from the multivariate associations within subjects who did attend. This seems unlikely; we consider the internal validity of these findings to be acceptable.
Under the SEARCH protocol, individuals with clinically diagnosed maturity-onset diabetes of the young (MODY) were not included in the SEARCH in-person visit and therefore were excluded. It is possible that a small number of individuals classified as T2DM under our criteria have MODY, although with the high C-peptide required under our strict definition, this is unlikely. It is also possible that some youths who tested negative for both IA2 and GAD may be positive for an unmeasured diabetes-related autoantibody. Given we also required a very high fasting C-peptide level for those classified as T2DM, we suspect that misclassification due to unmeasured (but positive) DAA would be negligent. Finally, given previously published findings from SEARCH that CVD risk factor prevalence differs according to race/ethnicity even after adjustment for diabetes type,1
it is possible that race/ethnicity played an important role with respect to the emergence of the three-correlated-factor solution identified in the present analyses. We did not have sufficient numbers within specific race/ethnic subgroups according to DM type to evaluate the issue of race/ethnicity rigorously within the analytic methods employed here; however, future work in SEARCH will focus on developing this area as additional subjects are added to the cohort over time.
In summary, for youths with either T1DM or T2DM, the individual components of metabolic syndrome and the metabolic syndrome itself can be used to describe CVD risk status for clinical or public health purposes. However, the present results suggest that for etiologically driven studies of CVD risk profile, metabolic syndrome may not be useful due to heterogeneous phenomena underlying this construct. Studies of the determinants of individually measured CVD risk factors, and of vascular end points, are critically needed to address long-term risk for CVD in youth with diabetes.