PCA (Fig. 3A) was computed from C/P ratio, BMI, triglycerides, HDL cholesterol, fasting glucose, systolic BP, diastolic BP, smoking status, alcohol consumption, physical activity, low-density lipoprotein (LDL) cholesterol, total cholesterol, white blood cell count (WBC), alanine-aminotransferase (ALAT), and aspartate-aminotransferase (ASAT). All variables were assessed for normality, and C/P ratio, BMI, and triglyceride levels were log-transformed to ensure normality in the data distribution. Subjects with a WBC serum levels >109 cells/L, or ALAT or ASAT levels >50 mmol/L, were excluded from the PCA (n = 161) to secure a representative presentation of the metabolic risk factor distribution in the cohort, so that subjects with extreme WBC, ALAT, and ASAT values would not distort the analysis. After centering and scaling the data, we obtained the principal components (PCs) describing the systematic variation in data across the 15 variables, hence revealing the metabolic profiles in the dataset. The differences between the PC1 components of the four groups were compared using one-way analysis of variance with 95% confidence limits. Tukey's test was applied as post hoc analysis to determine pairwise differences between groups (Fig. 3B). The relationship between subjects defined with MetS compared to the 3 non-MetS subgroups was also analyzed using a Kruskal–Wallis test (Supplemental Digital Content 1, http://links.lww.com/MD/B253). For P values less than 0.05, a post hoc test for pairwise comparison of subgroups, according to Conover, was performed.
3.1 Metabolic syndrome in elderly women
Among the elderly women in the PERF cohort, we found that 20.9% were defined having MetS (n = 818) (Table 1). The demographic characteristics, education level, and lifestyle did not vary among subjects with MetS and controls except for physical activity level, which was greater in the control group (P < 0.001).
Serum LDL and total cholesterol, which are lipid parameters not used in the MetS definition, varied significantly between the two groups (P < 0.001 and P = 0.007, respectively). This was also the case for WBC and the liver function markers ALAT and ASAT (P < 0.001 for all three variables).
We found a 3.6-fold increased risk of developing T2DM (hazard ratio (HR) = 3.63, 95% confidence interval: [2.93–4.48]) and a 1.3-fold increased risk of a CVD event (HR = 1.29 [1.16–1.43]) after 12.7 ± 3.0 years of follow-up for subjects with MetS compared to controls without defined MetS. Given the strong effects of MetS on disease risk, we further investigated the relationship between the individual MetS risk factors and subsequent T2DM or CVD events (Fig. 2).
Central obesity was the only MetS risk factor contributing to increased risk of both outcomes with a 2-fold increased risk of T2DM (HR = 1.98 [1.57–2.48]) and a 1.5-fold increased risk of a CVD event (HR = 1.48 [1.30–1.68]) (Fig. 2). Elevated fasting glucose was only related to the development of T2DM (HR = 3.38 [2.71–4.22]) and did not contribute to an increased risk of CVD. Conversely, high blood pressure was a contributor to the development of CVD events (HR = 1.19 [1.09–1.30]) but did not contribute to an increased risk of T2DM. Neither HDL cholesterol nor triglyceride levels contributed to an increased risk of T2DM and CVD in this cohort of elderly Caucasian women.
3.2 Subgrouping the control group consisting of subjects with heterogeneous MetS risk factor profiles
Since central obesity alone contributed to increased risk of both T2DM and CVD, we speculated if the subjects with central obesity in the control group would take part in reducing the prediction of future disease prevalence within defined MetS subjects. To examine this question, we divided the heterogeneous control group into 3 subgroups: subjects with central obesity, and up to 1 additional MetS risk factor, but not defined with MetS; subjects without central obesity, but with other risk factors for the MetS; and subjects with no MetS risk factors.
To capture the multivariate features of the dataset, we used PCA to visualize the differences between the MetS group and the three control subgroups (Fig. 3A). We observed a distinct separation between subjects with defined MetS (orange) and the control group comprising subjects with no risk factors for MetS (green), while the non-MetS subjects with central obesity (purple) and subjects with other MetS risk factors (gray) cut in between the non-MetS risk factor controls and MetS subjects in the PCA score plot. Based on the group distributions, the multivariate analysis indicated that subjects with central obesity and up to 1 MetS risk factor are metabolically more similar to MetS subjects, while subjects with other MetS risk factors than central obesity are more similar to the reference group with no MetS risk factors. The 4 subgroups were found to statistically separate in PC1 (Fig. 3B), meaning that all subgroups differed in the parameters pulling in the PC1 direction within the loading plot. The parameters driving this separation are mainly MetS classification parameters such as C/P ratio, BMI, fasting glucose, HDL cholesterol, triglycerides, and blood pressure. Smoking, LDL cholesterol, and ASAT had no influence on the separation of the subjects in PC1.
Since the PCA indicated that the three subgroups from the former control group showed differentiated metabolic profiles, we used Cox regression analysis to investigate whether these subjects also showed different risk profiles for T2DM and CVD. We found that controls with central obesity without MetS had a 2.2-fold increased risk of T2DM (HR = 2.21 [1.25–3.93]) and a 1.5-fold increased risk of CVD (HR = 1.51 [1.25–1.83]) compared to the reference group with no risk factors for MetS (Fig. 4A). Likewise, controls with other MetS risk factors than central obesity had a 1.8-fold increased risk of T2DM (HR = 1.75 [1.04–2.96]) and a 1.4-fold increased risk of CVD (HR = 1.36 [1.15–1.60]). Moreover, the stratification of the former control group also affected the disease risk in MetS subjects, as subjects with defined MetS showed a 6.3-fold increased risk of developing T2DM (HR = 6.29 [3.74–10.50]) and a 1.7-fold increased risk of a CVD event (HR = 1.72 [1.44–2.05]), when specifically compared to the reference group without MetS risk factors.
Further, we explored the effect of the risk factor distribution further by analyzing the relationship between the cumulated sum of risk factors and subsequent disease events. The average number of MetS risk factors for all subjects in the analytical sample was 1.8 ± 1.2. T2DM risk was increased for subjects with ≥2 MetS risk factors compared to subjects with no risk factors; 1 risk factor: HR = 1.20 (0.69–2.09), 2 risk factors: HR = 2.44 (1.43–4.17), 3 risk factors: HR = 4.70 (2.77–7.98), 4 risk factors: HR = 7.27 (4.19–12.61), and 5 risk factors: HR = 11.57 (6.12–21.88), respectively (Fig. 4B). An increased risk of a CVD event was found with ≥1 risk factor for MetS: HR = 1.33 (1.12–1.58), HR = 1.47 (1.24–1.75), HR = 1.55 (1.29–1.86), HR = 1.75 (1.41–2.18), and HR = 2.52 (1.83–3.46), respectively, as illustrated in Fig. 4B. The incidence rates shown in Table 2 further manifested the differentiated risk within the metabolic subgroups when stratified either based on metabolic definitions or based on number of risk factors. The lowest incidence was found in the control group holding no risk factors for MetS, with an incidence of 2.8 (1.7–4.6) per 1000 person-years for T2DM and an incidence of 36.3 (31.2–42.2) per 1000 person-years for CVD. The highest incidence was found in the group holding 5 risk factors for MetS, with an incidence of 36.2 (24.3–54.1) per 1000 person-years for T2DM and an incidence of 96.1 (72.8–126.8) per 1000 person-years for CVD.
Elderly women with MetS proved to have an increased risk of developing T2DM and CVD when compared to women not defined with the syndrome. The increased risk of 3.6-fold for T2DM and 1.3-fold for CVD found in this study correlated well with findings reported in previous studies using a heterogeneous control group, although these results mostly originate from cohorts of middle-aged men and women.[10,17–19] We further refined these results by highlighting how a control group with heterogeneous MetS risk profiles in women without defined MetS can lead to a distortion of the hazard estimations associated with the MetS. We showed how specifically comparing subjects with defined MetS to subjects with no risk factors for MetS increased the risk estimate of future T2DM from 3.6 to 6.3-fold and the risk of a future CVD event from 1.3 to 1.7-fold. This clearly suggests that the risk of developing T2DM and CVD in women with defined MetS is much greater than previously proposed and further, that the risk of T2DM and CVD also was greater in women not defined with the syndrome but still holding some risk factors for MetS. To our knowledge, this type of risk assessment of the MetS has not previously been reported. In addition, the analysis of cumulating MetS risk factors showed increasing risk of later disease with increasing number of risk factors; with 5 MetS risk factors resulting in 11.6-fold increased the risk of T2DM development and 2.5-fold risk of CVD. This underlines the value of identifying subjects with MetS risk factors in the elderly population as well.
Central obesity was the only MetS risk factor that independently contributed to the risk of both future T2DM and CVD (2- and 1.5-fold, respectively). As central obesity is consistently highlighted as a key contributor to risk in any definition of the MetS, our finding is congruent with this prominent role of central obesity in the MetS definition. By partitioning the control group of non-MetS subjects into 3 subgroups, we repeated our finding of a 2-fold increased risk of T2DM and 1.5-fold for CVD outcomes in subjects with central obesity without MetS. Furthermore, the PCA revealed that subjects with central obesity displayed a higher degree of similarity to MetS subjects than the 2 other subgroups without this risk factor, emphasizing the role of central obesity as a key driver of both T2DM and CVD. While we clearly demonstrated the predictive value of the MetS in relation to later risk of T2DM and CVD in elderly Caucasian women, we also showed that women not fulfilling the full MetS criteria likewise have a higher risk of developing T2DM and CVD later in life, if they have one or more of the MetS risk factors at baseline. This was further illustrated in the differentiated incidence rates found within the subdivided reference group. Further, the calculated incidence rates also underlined how the incidence of both T2DM and CVD increased with increasing numbers of risk factors.
The prognostic importance of the MetS compared to the prognostic capability of the sum of the individual MetS risk factors has previously been challenged by others.[21–23] With the PCA and risk estimates presented in this study, we add to this debate by assessing the risk of the individual components, highlighting the heterogeneity in the metabolic profiles of subjects not defined with MetS, and determining the predictive ability of the cumulating sum of risk factors constituting the MetS. Other studies have compared the predictive ability for CVD using both the MetS definition and the Framingham Risk Score[24–26] finding similar results for the two scoring systems, and further found the Diabetes Prediction Model to be superior to the MetS definition in predicting the risk of diabetes development. Similarly, the findings in our study indicated that defining the MetS does not supersede the risk estimated when summing the risk of the individual risk factors. Consequently, our findings add to the questioning of applying a MetS definition to commonly cooccurring risk factors will provide auxiliary value in the general practice. Thus, it might be more practical to focus on developing a classification scheme that reflects both the degree and sum of risk factor abnormalities instead of using the current MetS definition. This suggestion is founded on the assumption that cooccurring factors indeed enhance the risk of adverse outcomes, as was also the result of our current cumulating risk factor analysis.
Regardless of focusing on MetS as a joined definition or on the sum of risk factors, it is known that the prevalence of the risk factors for MetS increases with age, reaching a prevalence of 40% in people aged >60 years. The initial indicator of a high-risk metabolic profile is central obesity, and our present study coherently points to the high priority of this risk factor in the elderly segment of the population, when focusing on preventing T2DM and CVD and in advancing efforts to regulate the obesity epidemic.
The strengths of this study include its longitudinal design, detailed assessment of metabolic risk factors, and exclusion of subjects with T2DM and CVD at baseline. The study's follow-up information was derived from Danish registry data, which is of high quality based on the use of a unique personal identifier and nationwide electronic patient records, and thus results in limited loss of data from baseline to follow-up. The cohort consists of a large group of women in Denmark, where the homogenous population with equal access to primary care (tax-paid, not individually paid) may limit extrapolations to other populations. However, the hazard ratios found in this study are comparable to associations found in similar cohorts, though with different age distributions, which indicates that such generalizations are indeed plausible. By applying PCA as a multivariate tool to assess risk profiles, we introduce a possible confounder, as we subdivide the study population before PCA based on central obesity. With this common denominator being present in both the MetS group and the non-MetS group with central obesity, we potentially skew these 2 subgroups toward each other compared to the non-MetS group holding other risk factors for MetS, as this subgroup may be regarded as being more heterogeneous (by not having obesity as a common denominator). However, based on the MetS definition, it is not possible to circumvent this type of limitation. In this study, central obesity was determined by DEXA scan rather than waist circumference originally proposed by IDF. However, IDF does highlight that DEXA scan can be used as an additional factor in research of the MetS, which can allow further modification of the definition if necessary.
Elderly Caucasian women fulfilling the MetS criteria set by the IDF showed increased risk of future T2DM or CVD diagnosis; however, subjects who did not fulfill the criteria for MetS but presented one or more of the components of MetS were also at increased risk. A further subdivision of the reference group proved to increase the risk of T2DM to 6.3-fold (from 3.6-fold) and 1.7-fold for CVD (from 1.3-fold) for MetS subjects when compared to a reference group only including subjects with no MetS risk factors. In clinical practice, employment of the MetS in elderly women should be focused as a tool for identifying subjects with metabolic high-risk profiles. However, the sum of risk factors are proposed to be equally considered, as subjects not fitting the MetS-criterion, but still holding one or more risk factors for MetS, were here identified also to be at increased risk of T2DM and CVD.
We acknowledge the Danish Research Foundation (Den Danske Forskningsfond) for funding the PERF study. The foundation had no role in study design, data interpretation, or submission of this manuscript.
CC serves as a board member and stock owner in Nordic Bioscience. MAK and KH hold stocks in Nordic Bioscience.
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