Abstract
Aims: We attempted to clarify whether the multiple criteria for metabolic syndrome (MetS) can sufficiently predict cardiovascular disease, whether waist circumference (WC) should be required, and whether sex-specific thresholds for each component are necessary. Only a few large-scale studies among East Asians have addressed the ability of MetS to predict cardiovascular disease.
Methods: We analyzed the data of 330,051 men and 235,028 women aged 18–74 years with no history of coronary artery disease (CAD) or cerebrovascular disease (CVD) from a nationwide Japanese claims database accumulated during 2008–2016. The association of each MetS component with CAD or CVD (CAD/CVD), MetS associated with CAD/CVD according to various criteria, and utility of modified criteria with more specific optimal values for each component were examined using multivariate Cox regression and receiver operating characteristic (ROC) analysis.
Results: During the study, 3,934 men (1.19%) and 893 women (0.38%) developed CAD/CVD. For each current MetS criteria, there was a 1.3- to 2.9-fold increased risk of CAD/CVD. Optimal thresholds for predicting CAD/CVD were WCs of 83 and 77 cm, triglycerides levels of 130 and 90 mg/dl, high-density lipoprotein cholesterol levels of 50 and 65 mg/dl, blood pressures of 130/80 and 120/80 mmHg, and fasting plasma glucose levels of 100 and 90 mg/dl for men and women, respectively. The existing MetS criteria and modified criteria were not significantly different in predicting CAD/CVD, but using the modified criteria markedly increased the prevalence of MetS and percentage of people with MetS developing CAD/CVD.
Conclusions: Although various criteria for MetS similarly predicted CAD/CVD, the new criteria greatly reduced the number of high-risk individuals, especially women, overlooked by the current criteria.
See editorial vol. 34: 349-350
Introduction
Cases of cardiovascular disease, mainly ischemic heart disease and stroke, has been increasing worldwide for the past 30 years1), making it essential to appropriately screen high-risk groups for prevention. Metabolic syndrome (MetS) is proposed as a high-risk condition for cardiovascular disease other than smoking and elevated low-density lipoprotein cholesterol (LDL-C), and includes hyperglycemia, dyslipidemia, and hypertension. The diagnostic criteria for MetS were published by the World Health Organization (WHO) in 1999 2), followed by the National Cholesterol Education Program–Adult Treatment Panel III (NCEP-ATP III) in 2001 3), and the International Diabetes Federation (IDF) in 2005 4); the latter two are currently in use. In Japan, as with the IDF, diagnostic criteria requiring waist circumference (WC) were established in 2005 5). The main difference between these criteria is whether the inclusion of WC is mandatory and whether there are differences in criteria involving values for risk factors between men and women.
The MetS criteria are still being debated, with issues including 1) whether it is possible to adequately assess the risk of cardiovascular disease; 2) whether WC should be a required criterion; and 3) whether cutoff values based on sex and ethnicity are also needed for all items6-12). These issues remain unresolved. In a meta-analysis including different diagnostic criteria, the increased risk of developing coronary artery disease (CAD) in those with MetS was reported to have an odds ratio of 4.03 13) and that for developing stroke had a relative risk (RR) of 1.70 14). However, there is no comparison of the risk of developing CAD and cerebrovascular disease (CVD) according to the presence of MetS in the same population.
The prevalence of MetS in the Asia–Pacific region ranges from 11.9% to 37.1%, which is comparable to other regions in the world and has increased over time15). The predictive capacity of MetS for cardiovascular disease varies widely, depending on an individual’s background, such as race and comorbidities16, 17). In a Japanese cohort study using the Japanese diagnostic criteria, the risk of developing cardiovascular disease, both CAD and CVD combined, was only about 1.3 times higher in men and 2 times higher in women with MetS than in those without MetS18-20). However, a large-scale study of this issue has not been conducted. The hazard of cardiovascular death was reported to be 1.39 times higher in those with MetS than in those without it21). Although the development of both CAD and CVD significantly reduces the quality of life, to our knowledge, no large-scale studies have examined the extent to which the current MetS criteria predict cardiovascular disease, which includes both CAD and CVD, in East Asians.
WC is not a mandatory item in the NCEP-ATP III criteria based on the concept of cardiometabolic risk in Europe and in the United States, but it is a mandatory item in the IDF and Japanese criteria, emphasizing insulin resistance caused by visceral fat accumulation and central obesity22, 4). In particular, compared with Europeans, Asians have more visceral adipose tissue relative to WC23, 24). The prevalence of nonobese individuals with multiple risk factors is higher among Japanese, suggesting that excluding nonobese individuals from the diagnosis of MetS may result in overlooking their cardiovascular disease risk25, 26). Furthermore, the use of ethnicity-specific cutoff values for WC has been recommended, with the NCEP-ATP III and IDF criteria using ≥ 90 cm and ≥ 80 cm for Asian men and women, respectively4, 27). In Japan, a WC of ≥ 85 cm for men and ≥ 90 cm for women, corresponding to ≥ 100 cm2 of visceral fat stores, which also corresponds to ≥ 100 cm2 of visceral adipose tissue, have been adopted. These differences indicate the need to reconsider optimal cutoff values for WC in predicting the development of cardiovascular disease. We previously showed that a modification of a simple definition can increase the predictive power of cardiovascular disease in a limited population of Japanese patients with type 2 diabetes28). However, optimal universal definitions remain unknown for the entire Japanese population. Furthermore, it is not known to what extent optimized cutoff values will improve determinations of the prevalence of MetS and the sensitivity and specificity of predicting cardiovascular disease on the basis of the existing criteria.
We comprehensively examined the extent to which the established criteria for MetS differ in their ability to predict cardiovascular disease and to what extent optimizing the criteria improves the usefulness of the MetS criteria, including its predictive ability, sensitivity, and specificity, in the Japanese population. These findings are important to revisit the significance of MetS in the current context and to apply the findings to strategies to prevent cardiovascular diseases.
Methods
Study Participants
We retrospectively analyzed data from a nationwide claims-based database containing information on 805,992 Japanese company employees and their dependents who are covered by health insurance. Details of the claims data and classifications were described elsewhere29-31). Individuals aged 18–74 years who were monitored for at least 3 years from April 1, 2008, to July 31, 2016, were included in this analysis and continued to be monitored until August 31, 2019. We excluded individuals with CAD or CVD at baseline, with type 1 diabetes, and without health screening data, including blood tests. Finally, 565,079 individuals (330,051 men and 235,028 women) with no history of either or both CAD and CVD (CAD/CVD) were included in this study.
Definitions
Data on age, sex, body mass index (BMI), blood pressure (BP), laboratory values, and information from questionnaires were acquired on the earliest annual check-up day. The use of medications for diabetes, hypertension, dyslipidemia, and antiplatelet agents was defined according to the usage status during the baseline period based on the claims data. Current smoking information was obtained from the questionnaire provided at health checkups and was designated as either “yes” or “no.” All facilities measured BP in accordance with the guidelines of the Japanese Society of Hypertension. For medical checkups, these guidelines recommended measuring BP twice using the oscillometric method and averaging the results. Trained examiners measured WC at the level of the umbilicus in the late exhalation phase, with the participant in standing position.
The presence of CAD was determined according to claims using the ICD-10 codes for cardiac events but excluding heart failure and procedure codes for medical interventions, such as percutaneous coronary intervention and coronary artery bypass grafting. The presence of CVD was determined according to claims using the ICD-10 codes for cerebrovascular events and procedure codes for medical interventions, such as thrombolytic therapy and endovascular recanalization.
In the present study, we tested the following five sets of diagnostic criteria for MetS: the Japanese Committee for the Diagnostic Criteria of MetS (Japanese criteria), modified NCEP-ATP III criteria, IDF criteria for Asians, threshold optimization criteria with a WC requirement (Optimized criteria-1), and threshold optimization criteria with no WC requirement (Optimized criteria-2). The threshold optimization criteria were created by determining each optimal cutoff value using the method described below.
Statistical Analysis
Categorical variables were expressed as numerals and percentages and were compared with χ2 tests. Continuous variables were expressed as mean±SD or median and interquartile range. Continuous variables were compared using the unpaired Student’s t-test. Receiver operating characteristic (ROC) curve analyses were performed to calculate optimal WC, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP), and fasting plasma glucose (FPG) cutoffs for predicting CAD/CVD in men and women, respectively. The ROC analysis treated the onset of CAD/CVD as binary cases. The optimal cutoff value was determined using the Youden index (sensitivity＋specificity –1), defined as the maximum vertical distance of the ROC curve from a point (x, y) on the diagonal line (chance line)32). A Cox proportional hazards regression model identified the association between the incidence of CAD or CVD or CAD/CVD by risk factor items within the diagnostic criteria for MetS. Similarly, Cox multivariate regression models were used to examine the risk of the occurrence of an event in the presence of the three different MetS criteria (Japanese, IDF, and modified NCEP-ATP III criteria) and both threshold optimization criteria (Optimized criteria-1 and -2). Covariates included age. We determined the sensitivity and specificity of event onset for each diagnostic criterion.
Analyses were performed using SPSS 27 software (IBM, Armonk, NY). Statistical significance was set at p＜0.05. The Ethics Committee of Niigata University approved this study.
Results
The baseline characteristics of the participants in this study are summarized in Supplemental Table 1. Among 330,051 men and 235,028 women, 3,934 (1.19%) men and 893 (0.38%) women developed CAD/CVD. The median follow-up period was 5.2 years. Compared with the CAD/CVD− group, the CAD/CVD+ group had a significantly higher age, BMI, WC, SBP, DBP, TG, LDL-C, FPG, and HbA1c and significantly lower HDL-C. The cutoff values for WC, TG, HDL-C, SBP, DBP, and FPG for predicting CAD/CVD in men and women were calculated using the ROC curve analysis and are shown in Supplemental Table 2. The cutoff values for all, except HDL-C, were similar to or lower than those of the current criteria. Two Optimized criteria based on these cutoff values are shown in Table 1, along with the three current criteria.
Supplemental Table 1.Baseline characteristics of study participants with and without incidence of CAD/CVD in men and women
|
Men |
Women |
|
All
n = 330051
|
CAD/CVD(-)
n = 326117
|
CAD/CVD(+)
n = 3934
|
p value
|
All
n = 235028
|
CAD/CVD(-)
n = 234135
|
CAD/CVD(+)
n = 893
|
p value
|
| Age (years) |
46±9 |
46±9 |
52±8 |
＜0.001 |
45±9 |
45±9 |
51±9 |
＜0.001 |
| BMI (kg/m^2) |
23.6±3.4 |
23.6±3.4 |
24.7±3.5 |
＜0.001 |
21.5±3.5 |
21.5±3.5 |
23.0±4.2 |
＜0.001 |
| WC (cm) |
84±9 |
84±9 |
87±9 |
＜0.001 |
77±9 |
77±9 |
81±11 |
＜0.001 |
| SBP (mmHg) |
122±15 |
122±15 |
131±17 |
＜0.001 |
114±16 |
114±16 |
126±20 |
＜0.001 |
| DBP (mmHg) |
77±11 |
77±11 |
83±12 |
＜0.001 |
69±11 |
69±11 |
77±14 |
＜0.001 |
| TG (mg/dL) |
101 (70-148) |
100 (70-148) |
123 (84-179) |
＜0.001 |
65 (49-91) |
65 (49-91) |
82 (59-123) |
＜0.001 |
| HDL-C (mg/dL) |
58±15 |
58±15 |
54±14 |
＜0.001 |
71±16 |
71±16 |
68±17 |
＜0.001 |
| LDL-C (mg/dL) |
124±31 |
124±31 |
134±34 |
＜0.001 |
117±31 |
117±31 |
129±34 |
＜0.001 |
| FPG (mg/dL) |
97±19 |
97±19 |
108±34 |
＜0.001 |
90±13 |
90±13 |
97±27 |
＜0.001 |
| HbA1c (%) |
5.5±0.6 |
5.5±0.6 |
6.0±1.2 |
＜0.001 |
5.4±0.5 |
5.4±0.5 |
5.7±0.9 |
＜0.001 |
| Current smoking (%) |
124393 (38) |
122477 (38) |
1916 (49) |
＜0.001 |
20195 (8.6) |
20064 (8.6) |
131 (14.7) |
＜0.001 |
|
Medication for
diabetes (n (%))
|
10512 (3.2) |
10050 (3.1) |
462 (11.7) |
＜0.001 |
2169 (0.9) |
2132 (0.9) |
37 (4.1) |
＜0.001 |
|
Medication for
hypertension (n (%))
|
30099 (9.1) |
29171 (8.9) |
928 (23.6) |
＜0.001 |
9805 (4.2) |
9647 (4.1) |
158 (17.7) |
＜0.001 |
|
Medication for
dyslipidemia (n (%))
|
20460 (6.2) |
19870 (6.1) |
590 (15.0) |
＜0.001 |
8716 (3.7) |
8619 (3.7) |
97 (10.9) |
＜0.001 |
CAD/CVD, coronary artery disease (CAD) or cerebrovascular disease (CVD) Data are presented as mean±SD or median (interquartile range), n (%).
BMI, body mass index; WC, waist circumference; BP, blood pressure; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; LDL-C, low- density lipoprotein cholesterol; FPG, fasting plasma glucose
Supplemental Table 2.Calculated optimal cutoff values of MetS components for incident CAD/ CVD in men and women
|
|
Men |
Women |
| WC |
ROC-AUC |
0.600 |
0.624 |
| (cm) |
Set reference value |
83 |
77 |
| TG |
ROC-AUC |
0.598 |
0.628 |
| (mg/dL) |
Set reference value |
130 |
90 |
| HDL-C |
1－ROC-AUC |
0.594 |
0.568 |
| (mg/dL) |
Set reference value |
50 |
65 |
| SBP |
ROC-AUC |
0.665 |
0.694 |
| (mmHg) |
Set reference value |
130 |
120 |
| DBP |
ROC-AUC |
0.658 |
0.670 |
| (mmHg) |
Set reference value |
80 |
80 |
| FPG |
ROC-AUC |
0.618 |
0.607 |
| (mg/dL) |
Set reference value |
100 |
90 |
CAD/CVD, coronary artery disease (CAD) or cerebrovascular disease (CVD)
WC, waist circumference; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose
ROC, receiver operating characteristic; AUC, area under curve
Table 1.Current and optimized criteria for MetS
| Criteria |
Japanese |
IDF |
Modified NCEP ATP III |
Optimized-1 |
Optimized-2 |
| Definition of MetS |
high WC plus ≥ 2 other components |
high WC plus ≥ 2 other components |
≥ 3 of the following components |
high WC plus ≥ 2 other components |
≥ 3 of the following components |
| WC (cm) |
≥ 85 in men |
≥ 90 in men |
≥ 90 in men |
≥ 83 in men |
| ≥ 90 in women |
≥ 80 in women |
≥ 80 in women |
≥ 77 in women |
| TG (mg/dL) |
TG ≥ 150 and/or HDL-C ＜40
or on drug treatment
|
≥ 150
or on drug treatment
|
≥ 150
or on drug treatment
|
TG ≥ 130 and/or HDL-C ＜50 in men TG ≥ 90 and/or HDL-C ＜65 in women or on drug treatment |
| HDL-C (mg/dL) |
＜40 in men
＜50 in women
or on drug treatment
|
＜40 in men
＜50 in women
or on drug treatment
|
| BP (mmHg) |
≥ 130/85
or on drug treatment
|
≥ 130/85
or on drug treatment
|
≥ 130/85
or on drug treatment
|
≥ 130/80 in men
≥ 120/80 in women or on drug treatment
|
| FPG (mg/dL) |
≥ 110 or on drug treatment |
≥ 100 or previously diagnosed
type 2 diabetes
|
≥ 100 |
≥ 100 in men
≥ 90 in women
or previously diagnosed type 2 diabetes
|
IDF, International Diabetes Federation criteria for Asians; modified NCEP ATP III, modified National Cholesterol Education Program–Adult Treatment Panel III criteria for Asians; Optimized- 1, threshold optimization criteria with a WC requirement; Optimized-2, threshold optimization criteria with no WC requirement
WC, waist circumference; TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; FPG, fasting plasma glucose
Table 2 shows hazard ratios (HRs, 95% confidence intervals (CIs)) by Cox regression analysis for each risk factor within the MetS diagnostic criteria and for CAD, CVD, and CAD/CVD. In men, the presence of each risk factor increased the risk of CAD/CVD by 1.6–2.4-fold, with variations dependent on cutoff values. In women, the degree of increased risk according to each risk factor was 1.3–2.9 times, with a particularly large increase in CAD risk. FPG had a greater effect on the risk of CAD in women than in men. The HRs for cardiovascular disease for the presence of MetS by each criterion are shown in Table 3. There were no large differences in HRs between each diagnostic criterion for both men and women, with a 2.0–2.7-fold increased risk for CAD/CVD. The HRs for CAD/CVD in the presence of MetS and for elevated LDL-C and smoking were similar.
Table 2.Hazard ratios (95% confidence intervals) for incidence of CAD/CVD in participants with and without each component of MetS
|
CAD |
CVD |
CAD/CVD |
| Unadjusted |
Age-adjusted |
Unadjusted |
Age-adjusted |
Unadjusted |
Age-adjusted |
| Men
|
|
|
|
|
|
|
| Waist circumference |
|
|
|
|
|
|
| ≥ 83 cm |
2.22 (2.02-2.44) |
1.93 (1.76-2.12) |
1.62 (1.49-1.76) |
1.41 (1.30-1.54) |
1.84 (1.72-1.96) |
1.61 (1.50-1.72) |
| ≥ 85 cm |
2.05 (1.88-2.24) |
1.83 (1.68-2.00) |
1.58 (1.46-1.71) |
1.41 (1.31-1.53) |
1.77 (1.66-1.88) |
1.59 (1.49-1.69) |
| ≥ 90 cm |
1.90 (1.73-2.08) |
1.79 (1.63-1.96) |
1.61 (1.48-1.76) |
1.52 (1.39-1.65) |
1.75 (1.64-1.87) |
1.66 (1.55-1.77) |
| Dyslipidemia |
|
|
|
|
|
|
| TG ≥ 150 mg/dL or on drug treatment |
2.47 (2.27-2.70) |
2.13 (1.95-2.32) |
1.64 (1.51-1.78) |
1.41 (1.30-1.53) |
2.01 (1.89-2.14) |
1.74 (1.64-1.86) |
| HDL ＜40 mg/dL or on drug treatment |
3.32 (3.03-3.64) |
2.61 (2.38-2.87) |
1.96 (1.78-2.16) |
1.53 (1.38-1.69) |
2.55 (2.37-2.74) |
2.03 (1.89-2.18) |
| TG ≥ 130 and/or HDL ＜50 mg/dL or on drug treatment |
2.84 (2.57-3.12) |
2.61 (2.37-2.88) |
1.58 (1.45-1.71) |
1.45 (1.34-1.58) |
2.07 (1.93-2.21) |
1.91 (1.79-2.04) |
| TG ≥ 150 and/or HDL ＜40 mg/dL or on drug treatment |
2.70 (2.48-2.95) |
2.36 (2.16-2.57) |
1.65 (1.52-1.79) |
1.44 (1.33-1.56) |
2.11 (1.98-2.25) |
1.86 (1.75-1.98) |
| Blood pressure |
|
|
|
|
|
|
| BP ≥ 130/80 mmHg or on drug treatment |
3.24 (2.93-3.57) |
2.36 (2.13-2.61) |
3.31 (3.02-3.63) |
2.47 (2.25-2.72) |
3.24 (3.02-3.48) |
2.41 (2.24-2.59) |
| BP ≥ 130/85 mmHg or on drug treatment |
3.27 (2.98-3.58) |
2.33 (2.12-2.56) |
3.45 (3.16-3.75) |
2.53 (2.32-2.76) |
3.29 (3.08-3.51) |
2.39 (2.24-2.56) |
| Dysglycemia |
|
|
|
|
|
|
| FPG ≥ 100 mg/dL |
2.61 (2.40-2.85) |
1.82 (1.66-1.99) |
1.98 (1.83-2.15) |
1.39 (1.28-1.51) |
2.22 (2.09-2.37) |
1.56 (1.46-1.66) |
| FPG ≥ 100 mg/dL or previously diagnosed type 2 diabetes |
2.66 (2.44-2.90) |
1.85 (1.69-2.02) |
2.03 (1.88-2.21) |
1.42 (1.31-1.55) |
2.26 (2.13-2.41) |
1.58 (1.48-1.69) |
| FPG ≥ 110 mg/dL or on drug treatment |
3.79 (3.46-4.15) |
2.53 (2.31-2.78) |
2.34 (2.14-2.57) |
1.56 (1.42-1.72) |
2.94 (2.74-3.15) |
1.97 (1.83-2.11) |
| Risk factors not included in MetS definitions |
|
|
|
|
|
|
| Current smoking |
1.70 (1.56-1.85) |
1.93 (1.77-2.11) |
1.34 (1.24-1.45) |
1.51 (1.39-1.64) |
1.53 (1.44-1.63) |
1.72 (1.61-1.83) |
| LDL-C ≥ 120 mg/dL or on drug treatment |
3.25 (2.91-3.64) |
2.78 (2.49-3.11) |
1.24 (1.14-1.35) |
1.06 (0.97-1.15) |
1.90 (1.77-2.04) |
1.63 (1.52-1.75) |
| LDL-C ≥ 140 mg/dL or on drug treatment |
3.11 (2.85-3.40) |
2.66 (2.43-2.91) |
1.34 (1.23-1.45) |
1.14 (1.05-1.23) |
2.03 (1.91-2.16) |
1.74 (1.64-1.86) |
| BMI |
|
|
|
|
|
|
| ≥ 22.0 kg/m^2 |
2.31 (2.06-2.59) |
2.17 (1.93-2.43) |
1.41 (1.29-1.55) |
1.33 (1.21-1.45) |
1.73 (1.60-1.87) |
1.63 (1.51-1.75) |
| ≥ 23.6 kg/m^2 |
2.10 (1.92-2.30) |
2.04 (1.87-2.24) |
1.48 (1.36-1.60) |
1.43 (1.32-1.55) |
1.74 (1.63-1.85) |
1.69 (1.59-1.80) |
| ≥ 25.0 kg/m^2 |
1.97 (1.80-2.14) |
1.99 (1.82-2.17) |
1.46 (1.34-1.58) |
1.46 (1.35-1.59) |
1.68 (1.58-1.79) |
1.70 (1.59-1.81) |
| Women
|
|
|
|
|
|
|
| Waist circumference |
|
|
|
|
|
|
| ≥ 77 cm |
4.49 (2.98-6.76) |
2.90 (1.91-4.38) |
1.92 (1.67-2.20) |
1.46 (1.27-1.68) |
2.19 (1.91-2.52) |
1.65 (1.44-1.90) |
| ≥ 80 cm |
3.78 (2.66-5.36) |
2.43 (1.70-3.47) |
1.92 (1.68-2.19) |
1.46 (1.27-1.67) |
2.14 (1.88-2.44) |
1.61 (1.41-1.84) |
| ≥ 90 cm |
3.29 (2.23-4.84) |
2.22 (1.50-3.28) |
2.33 (1.96-2.76) |
1.83 (1.54-2.17) |
2.49 (2.11-2.93) |
1.93 (1.63-2.28) |
| Dyslipidemia |
|
|
|
|
|
|
| TG ≥ 150 mg/dL or on drug treatment |
5.66 (3.99-8.02) |
2.41 (1.66-3.50) |
2.93 (2.49-3.44) |
1.69 (1.42-2.00) |
3.14 (2.68-3.67) |
1.77 (1.50-2.09) |
| HDL ＜50 mg/dL or on drug treatment |
5.54 (3.93-7.80) |
2.98 (2.08-4.27) |
2.33 (1.98-2.75) |
1.58 (1.33-1.87) |
2.58 (2.2-3.02) |
1.73 (1.47-2.04) |
| TG ≥ 90 and/or HDL ＜65 mg/dL or on drug treatment |
4.94 (3.23-7.57) |
3.54 (2.30-5.44) |
1.75 (1.53-2.01) |
1.45 (1.26-1.66) |
1.88 (1.64-2.15) |
1.54 (1.35-1.77) |
| TG ≥ 150 and/or HDL ＜40 mg/dL or on drug treatment |
5.38 (3.80-7.62) |
2.35 (1.62-3.41) |
2.91 (2.48-3.42) |
1.72 (1.45-2.03) |
3.08 (2.63-3.59) |
1.78 (1.51-2.09) |
| Blood pressure |
|
|
|
|
|
|
| BP ≥ 120/80 mmHg or on drug treatment |
7.63 (5.02-11.62) |
4.07 (2.64-6.30) |
3.53 (3.07-4.06) |
2.44 (2.11-2.83) |
3.81 (3.31-4.38) |
2.59 (2.24-3.00) |
| BP ≥ 130/85 mmHg or on drug treatment |
8.68 (6.10-12.33) |
4.37 (3.01-6.34) |
4.03 (3.52-4.60) |
2.64 (2.29-3.05) |
4.45 (3.91-5.08) |
2.88 (2.50-3.32) |
| Dysglycemia |
|
|
|
|
|
|
| FPG ≥ 90 mg/dL or previously diagnosed type 2 diabetes |
3.08 (2.13-4.44) |
1.72 (1.18-2.52) |
1.91 (1.67-2.19) |
1.34 (1.17-1.54) |
1.94 (1.7-2.22) |
1.34 (1.16-1.54) |
| FPG ≥ 100 mg/dL |
5.45 (3.89-7.65) |
2.63 (1.84-3.75) |
2.19 (1.86-2.57) |
1.33 (1.13-1.58) |
2.47 (2.12-2.89) |
1.49 (1.26-1.75) |
| FPG ≥ 100 mg/dL or previously diagnosed type 2 diabetes |
5.26 (3.75-7.39) |
2.52 (1.77-3.60) |
2.20 (1.88-2.58) |
1.34 (1.13-1.58) |
2.45 (2.1-2.86) |
1.47 (1.25-1.72) |
| FPG ≥ 110 mg/dL or on drug treatment |
11.66 (8.13-16.72) |
4.99 (3.40-7.31) |
3.09 (2.49-3.84) |
1.69 (1.35-2.11) |
3.77 (3.09-4.6) |
2.03 (1.65-2.49) |
| Risk factors not included in MetS definitions |
|
|
|
|
|
|
| Current smoking |
2.23 (1.43-3.46) |
2.48 (1.60-3.86) |
1.70 (1.40-2.06) |
1.80 (1.48-2.18) |
1.84 (1.53-2.21) |
1.95 (1.62-2.35) |
| LDL-C ≥ 120 mg/dL or on drug treatment |
4.33 (2.92-6.43) |
1.96 (1.30-2.98) |
2.03 (1.77-2.33) |
1.23 (1.06-1.43) |
2.20 (1.92-2.52) |
1.31 (1.13-1.51) |
| LDL-C ≥ 140 mg/dL or on drug treatment |
5.10 (3.63-7.18) |
2.29 (1.59-3.30) |
2.21 (1.93-2.53) |
1.29 (1.12-1.49) |
2.49 (2.18-2.84) |
1.43 (1.24-1.65) |
| BMI |
|
|
|
|
|
|
| ≥ 22.0 kg/m^2 |
2.74 (1.95-3.85) |
2.00 (1.42-2.81) |
1.90 (1.67-2.17) |
1.56 (1.36-1.78) |
2.02 (1.77-2.30) |
1.64 (1.43-1.87) |
| ≥ 25.0 kg/m^2 |
2.76 (1.92-3.95) |
2.17 (1.51-3.11) |
1.77 (1.51-2.08) |
1.51 (1.29-1.78) |
1.96 (1.68-2.29) |
1.67 (1.43-1.94) |
CAD, coronary artery disease; CVD, cerebrovascular disease; CAD/CVD, CAD and/or CVD
TG, triglycerides; HDL-C, high-density lipoprotein cholesterol; BP, blood pressure; FPG, fasting plasma glucose; LDL-C, low-density lipoprotein cholesterol; BMI, body mass index.
Table 3.Hazard ratios (95% confidence intervals) for incidence of CAD/CVD according to presence or absence of each set of criteria for MetS
| Criteria |
Japanese |
IDF |
Modified NCEP ATP III |
Optimized-1 |
Optimized-2 |
| Men |
|
|
|
|
|
| CAD |
Unadjusted |
3.63 (3.32-3.97) |
2.74 (2.49-3.02) |
3.76 (3.45-4.10) |
3.33 (3.05-3.63) |
3.83 (3.50-4.18) |
|
Age-adjusted |
2.76 (2.52-3.02) |
2.28 (2.07-2.52) |
2.81 (2.57-3.07) |
2.58 (2.36-2.82) |
2.89 (2.64-3.16) |
| CVD |
Unadjusted |
2.49 (2.28-2.72) |
2.05 (1.86-2.26) |
2.40 (2.21-2.61) |
2.31 (2.13-2.50) |
2.48 (2.29-2.69) |
|
Age-adjusted |
1.90 (1.73-2.08) |
1.72 (1.56-1.89) |
1.79 (1.64-1.95) |
1.80 (1.66-1.95) |
1.88 (1.73-2.04) |
| CAD/CVD |
Unadjusted |
2.94 (2.75-3.15) |
2.37 (2.20-2.55) |
2.99 (2.80-3.19) |
2.70 (2.54-2.88) |
2.99 (2.81-3.19) |
|
Age-adjusted |
2.25 (2.10-2.41) |
1.99 (1.85-2.14) |
2.25 (2.10-2.40) |
2.11 (1.98-2.25) |
2.27 (2.13-2.42) |
| Women |
|
|
|
|
|
| CAD |
Unadjusted |
9.29 (5.98-14.43) |
8.38 (5.98-11.73) |
9.00 (6.45-12.57) |
7.17 (4.96-10.35) |
8.86 (5.94-13.2) |
|
Age-adjusted |
4.21 (2.68-6.63) |
3.84 (2.68-5.51) |
3.98 (2.77-5.72) |
3.96 (2.70-5.80) |
4.76 (3.14-7.23) |
| CVD |
Unadjusted |
3.73 (2.90-4.80) |
3.34 (2.85-3.92) |
3.52 (3.02-4.10) |
2.65 (2.32-3.03) |
2.82 (2.46-3.22) |
|
Age-adjusted |
2.22 (1.72-2.87) |
1.98 (1.67-2.35) |
2.02 (1.71-2.38) |
1.81 (1.57-2.08) |
1.88 (1.63-2.16) |
| CAD/CVD |
Unadjusted |
4.55 (3.61-5.72) |
3.95 (3.39-4.59) |
4.05 (3.50-4.69) |
3.05 (2.67-3.47) |
3.19 (2.80-3.64) |
|
Age-adjusted |
2.66 (2.11-3.37) |
2.32 (1.97-2.72) |
2.30 (1.97-2.70) |
2.05 (1.79-2.36) |
2.11 (1.83-2.42) |
CAD, coronary artery disease; CVD, cerebrovascular disease; CAD/CVD, CAD and/or CVD
IDF, International Diabetes Federation criteria for Asians; Modified NCEP ATP III, Modified National Cholesterol Education Program–Adult Treatment Panel III criteria for Asians; Optimized-1, threshold optimization criteria with a WC requirement; Optimized-2, threshold optimization criteria with no WC requirement
The prevalence, sensitivity, and specificity of each MetS criterion are shown in Table 4. The sensitivity of the Japanese criteria was very low (31.2% in men and 9.0% in women) especially in women, while specificity was high (86.6% in men and 97.8% in women). In contrast, the sensitivity of both Optimized criteria increased to 50%–55% in both men and women while specificity decreased to 70%–74%.
Table 4.Prevalence, sensitivity, specificity of each MetS criterion according to the incidence of CAD, CVD or CAD/CVD
| Criteria |
Japanese |
IDF |
Modified NCEP ATP III |
Optimized-1 |
Optimized-2 |
| CAD |
CVD |
CAD/CVD |
CAD |
CVD |
CAD/CVD |
CAD |
CVD |
CAD/CVD |
CAD |
CVD |
CAD/CVD |
CAD |
CVD |
CAD/CVD |
| Men |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| prevalence (%) |
13.6 |
11.9 |
17.9 |
27.6 |
29.8 |
| sensitivity (%) |
36.3 |
28.4 |
31.2 |
26.8 |
21.7 |
23.7 |
44.5 |
34.3 |
38.5 |
55.8 |
46.9 |
50.3 |
61.8 |
51.4 |
55.5 |
| specificity (%) |
86.4 |
86.1 |
86.6 |
88.1 |
87.9 |
88.2 |
82.1 |
81.8 |
82.4 |
72.4 |
72.2 |
72.7 |
70.2 |
69.9 |
70.5 |
| Women |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| prevalence (%) |
2.3 |
8.4 |
9.2 |
26.1 |
28.7 |
| sensitivity (%) |
17.3 |
7.6 |
9.0 |
42.4 |
22.4 |
25.2 |
46.8 |
25.2 |
27.7 |
71.2 |
47.5 |
50.8 |
77.7 |
52.2 |
55.2 |
| specificity (%) |
97.7 |
97.7 |
97.8 |
91.6 |
91.6 |
91.7 |
90.7 |
90.7 |
90.8 |
73.8 |
73.9 |
74.0 |
71.2 |
71.3 |
71.4 |
CAD, coronary artery disease; CVD, cerebrovascular disease; CAD/CVD, CAD and/or CVD
IDF, International Diabetes Federation criteria for Asians; Modified NCEP ATP III, Modified National Cholesterol Education Program–Adult Treatment Panel III criteria for Asians; Optimized-1, threshold optimization criteria with a WC requirement; Optimized-2, threshold optimization criteria with no WC requirement
The absolute risk of developing CAD/CVD was higher in men than in women: 1.19% in men and 0.38% in women overall, and 2.38%–2.73% in men and 1.14%–1.50% in women using the current MetS criteria. Also shown was that the risk of CAD/CVD was higher with the presence of MetS regardless of differences in criteria. For both optimized criteria, the rates were 1.54%–1.66% in men and 0.73%–0.74% in women.
BMI was also evaluated by replacing WC with BMI. The cutoff for BMI based on the ROC analysis was 23.6 kg/m2 for men and 22.0 kg/m2 for women. The HRs for CAD, CVD, and CAD/CVD in MetS using BMI-required (BMI-Optimized-1) and non-required (BMI-Optimized-2) criteria, in which WC was replaced by BMI, did not differ significantly from those for other existing criteria or the Optimized criteria (Supplemental Table 3). Prevalence and sensitivity were lower for the BMI-Optimized criteria than for the Optimized criteria but were higher than for the Japanese criteria (Supplemental Table 4). The HRs for CAD/CVD for BMI alone were similar to those for WC and other MetS components regardless of cutoff values.
Supplemental Table 3.Hazard ratios (95% confidence intervals) for incidence of CAD/CVD for BMI-Optimized-MetS
| Criteria |
BMI-Optimized-1 |
BMI-Optimized-2 |
| Men
|
|
|
| CAD |
Unadjusted |
3.20 (2.94-3.49) |
3.78 (3.46-4.13) |
|
Age-adjusted |
2.63 (2.41-2.87) |
2.94 (2.69-3.22) |
| CVD |
Unadjusted |
2.12 (1.95-2.30) |
2.43 (2.24-2.64) |
|
Age-adjusted |
1.75 (1.61-1.90) |
1.90 (1.75-2.06) |
| CAD/CVD |
Unadjusted |
2.59 (2.43-2.75) |
2.97 (2.79-3.16) |
|
Age-adjusted |
2.14 (2.01-2.28) |
2.33 (2.19-2.49) |
| Women
|
|
|
| CAD |
Unadjusted |
6.63 (4.74-9.26) |
7.74 (5.51-10.89) |
|
Age-adjusted |
3.73 (2.64-5.28) |
4.18 (2.92-5.98) |
| CVD |
Unadjusted |
2.76 (2.40-3.18) |
2.89 (2.52-3.32) |
|
Age-adjusted |
1.90 (1.64-2.19) |
1.92 (1.66-2.22) |
| CAD/CVD |
Unadjusted |
3.14 (2.74-3.60) |
3.25 (2.84-3.71) |
|
Age-adjusted |
2.13 (1.85-2.46) |
2.14 (1.85-2.46) |
CAD, coronary artery disease; CVD, cerebrovascular disease; CAD/CVD, CAD and/or CVD
BMI-Optimized-1, threshold optimization criteria with a BMI requirement; BMI-Optimized-2, threshold optimization criteria with no BMI requirement
Supplemental Table 4.Prevalence, sensitivity, specificity of BMI-Optimized-MetS criteria according to the incidence of CAD, CVD or CAD/CVD
| Criteria |
BMI-Optimized-1 |
BMI-Optimized-2 |
| CAD |
CVD |
CAD/CVD |
CAD |
CVD |
CAD/CVD |
| Men
|
|
|
|
|
|
|
| prevalence (%) |
|
25.3 |
|
|
28.2 |
|
| sensitivity (%) |
52.0 |
42.0 |
46.4 |
59.6 |
49.0 |
53.4 |
| specificity (%) |
74.7 |
74.5 |
74.9 |
71.8 |
71.5 |
72.1 |
| Women
|
|
|
|
|
|
|
| prevalence (%) |
|
16.1 |
|
|
17.2 |
|
| sensitivity (%) |
55.4 |
33.8 |
36.6 |
61.2 |
36.7 |
39.3 |
| specificity (%) |
83.9 |
83.9 |
84.0 |
82.7 |
82.8 |
82.9 |
CAD, coronary artery disease; CVD, cerebrovascular disease; CAD/CVD, CAD and/or CVD
BMI-Optimized-1, threshold optimization criteria with a BMI requirement; BMI-Optimized-2, threshold optimization criteria with no BMI requirement
The Net Reclassification Improvement was used to evaluate improvements in risk prediction when the Japanese criteria were changed to the Optimized criteria. Risk predictions by switching to Optimized criteria-1 and -2 were significantly improved in men at 5.2% (95% CI: 3.8–6.6) (p＜0.01) and 8.2% (95% CI: 6.6–9.7) (p＜0.01)), respectively, and in women at 18.1% (95% CI: 13.9–22.4) (p＜0.01) and 19.9% (95% CI: 15.4–24.3) (p＜0.01)), respectively (Supplemental Table 5).
Supplemental Table 5.Reclassification from Japanese criteria to Optimized criteria for MetS
| Men
|
Optimized- 1 |
|
Optimized- 2 |
| MetS (－) |
MetS (＋) |
MetS (－) |
MetS (＋) |
|
CADCVD (－) |
|
|
|
CADCVD (－) |
|
|
| Japanese |
MetS (－) |
237007 |
45377 |
Japanese |
MetS (－) |
229873 |
52511 |
|
MetS (＋) |
0 |
43733 |
|
MetS (＋) |
0 |
43733 |
|
CADCVD (＋) |
|
|
|
CADCVD (＋) |
|
|
| Japanese |
MetS (－) |
1955 |
752 |
Japanese |
MetS (－) |
1752 |
955 |
|
MetS (＋) |
0 |
1227 |
|
MetS (＋) |
0 |
1227 |
|
|
NRI (%)(95%CI) 5.2% (3.8-6.6) (p＜0.01)
|
|
|
NRI (%)(95%CI) 8.2% (6.6-9.7) (p＜0.01)
|
| Women
|
Optimized- 1 |
|
Optimized- 2 |
| MetS (－) |
MetS (＋) |
MetS (－) |
MetS (＋) |
|
CADCVD (－) |
|
|
|
CADCVD (－) |
|
|
| Japanese |
MetS (－) |
173303 |
55571 |
Japanese |
MetS (－) |
167146 |
61728 |
|
MetS (＋) |
0 |
5261 |
|
MetS (＋) |
0 |
5261 |
|
CADCVD (＋) |
|
|
|
CADCVD (＋) |
|
|
| Japanese |
MetS (－) |
439 |
374 |
Japanese |
MetS (－) |
400 |
413 |
|
MetS (＋) |
0 |
80 |
|
MetS (＋) |
0 |
80 |
|
|
NRI (%)(95%CI) 18.1% (13.9-22.4) (p＜0.01)
|
|
|
NRI (%)(95%CI) 19.9% (15.4-24.3) (p＜0.01)
|
CAD/CVD, coronary artery disease (CAD) or cerebrovascular disease (CVD)
Optimized-1, threshold optimization criteria with a WC requirement; Optimized-2, threshold optimization criteria with no WC requirement; NRI, net reclassification improvement.
Discussion and Conclusion
This is the first study to simultaneously compare the risk of CAD and CVD according to the MetS definitions using both conventional and threshold-optimized diagnostic criteria. Surprisingly, the predictive ability of the MetS criteria for CAD, CVD, and CAD/CVD did not differ significantly according to the diagnostic criteria. Nevertheless, the criteria with optimized cutoff values for CAD, CVD, and CAD/CVD risk significantly increased the prevalence and sensitivity without a significant decrease in specificity compared with the current criteria. This improvement is in line with the original and important purpose of the recognition of MetS, which is to screen for high-risk individuals.
Can the MetS Criteria Evaluate Cardiovascular Risk Sufficiently?
MetS is a condition based on the pathophysiological association between insulin resistance and atherosclerosis. Its clinical role is to screen individuals at high risk for cardiovascular disease or diabetes, although in practice, the association between MetS and cardiovascular disease has been reported to be modest in Western populations7, 8). Conversely, elevated LDL-C and smoking, which are not included in the MetS definitions, have been major classic risk factors for atherosclerosis since before the development of the concept of MetS. The significance of the treatment of the components of MetS has already been established. In this study, the HRs of MetS, LDL-C, and smoking for CAD/CVD were nearly the same. Therefore, at least in the Japanese population, MetS is important for the assessment of cardiovascular risk, suggesting the existence of ethnic differences in its clinical significance. In addition, MetS was more strongly associated with CAD/CVD than any single risk factor other than elevated BP, although there were differences in the cutoff and diagnostic criteria in this study. However, this result differed from that of a previous Western study10, 33), which found that the presence of MetS did not present a greater risk than each individual MetS component. Although elevated BP is an important risk factor for CAD/CVD, MetS may have a significance beyond the risk associated with each component in a predictive capacity for cardiovascular disease events in the Japanese population. Further studies from other East Asian countries are expected.
Should WC be a Mandatory Element in the Diagnosis of MetS?
One of the most significant differences between the various MetS diagnostic criteria is whether or not WC is a mandatory item. WC is not a mandatory item in the NCEP-ATP III criteria, but it is in the IDF and Japanese criteria4, 5, 27). There has been much debate on the important and fundamental issue of whether to make WC a requirement in all MetS criteria. There is a fundamental difference in whether abdominal obesity is considered essential to the MetS disease concept, which could be related to prevalence. Contrary to expectations, however, the risk of CAD/CVD was approximately 2-fold higher for MetS across all criteria, whether WC was mandatory or not, and was similar regardless of diagnostic criteria. The Japanese MetS criteria are based on the idea that the accumulation of visceral and ectopic fat is a major factor in cardiovascular disease and metabolic risk, and that medical care by reducing visceral fat for groups for which this trait is a key player is needed to reduce the risk22). However, even with the same WC, Asians have a greater amount of visceral adipose tissue compared with Westerners23, 24), and a pooled analysis of 10 prospective cohort studies in Japan reported high risks of CVD in the presence of multiple risk factors even without visceral fat accumulation26). Because of the high prevalence of nonobese individuals with multiple risk factors for MetS in the Japanese population25), making WC a required item may overlook the population at high risk for CVD among nonobese individuals.
Are Sex- and Ethnicity-Specific Cutoff Values Essential for each Component of MetS?
Since the risk of cardiovascular disease due to abdominal obesity varies by race, cutoff values for obesity control have been determined for each ethnic group4). Compared with the NCEP-ATP III and IDF criteria for Asians, the cutoff for WC is higher for women than for men under the Japanese criteria. However, the results of this study based on actual cardiovascular disease risk showed that the optimized cutoff value for WC predictive of CAD/CVD was 83 and 77 cm for men and women, respectively, which differed significantly from the current Japanese criteria. A Korean study of WC predicting cardiovascular disease in 20 million people showed similar results to the present study; that is, 84 and 78 cm for men and women, respectively, for myocardial infarction and 85 and 78 cm for men and women, respectively, for ischemic stroke34). On the basis of actual risk, there may be a need to reconsider cutoff values that would identify high-risk individuals. In addition, it was reported that the risk of myocardial infarction increased with increasing WC cutoff values in women compared with men35). Also, it was shown that women have a higher area under the curve than men and need to pay attention to the risk of CVD according to an increase in their WC36). This suggests that the current criteria, especially in women, may be overlooking those at higher risk.
It is also necessary to consider whether to differentiate between men and women for items other than WC. Elevated BP and FPG presented an increased risk for CAD rather than CVD for women rather than men. In a meta-analysis, it was reported that impaired glucose tolerance increased the risks of CAD and CVD37), but the risk was greater for CAD than for CVD38). The tendency for diabetes to increase the risk of CAD is also stronger in women. The RR for fatal CAD associated with diabetes is 50% higher in women than in men39). Therefore, a lower glycemic cutoff value for women than for men could be considered.
The absolute risk of developing CAD/CVD was lower in women than in men, especially for CAD in women. The incidence of cardiovascular disease in women is known to be 25% lower than in men (4.1 vs. 6.4 (/1,000 person-years)40). Additionally, to differences in the background environment and the influence of female hormones, it has been suggested that primary prevention and healthy lifestyle behaviors are more prevalent in women than in men, which may contribute to the lower risk of heart disease in women41). The absolute risk of developing CAD/CVD increased approximately 2-fold for men and 4-fold for women with MetS under the current criteria for MetS. As to sensitivity, while the difference between men and women was smaller, the sensitivity of the risk of developing CAD/CVD was lower, resulting in approximately 90% of women being missed. In the past, it was pointed out that with the current Japanese criteria for MetS, many women are missed41). Our results suggest that many high-risk groups that were previously not included since they were diagnosed as having a normal risk would no longer be missed by changing the cutoff values. This would allow for greater risk interventions, potentially leading to event prevention that is currently the case. Therefore, the threshold optimization criteria may be consistent with the original goal of preventing events by increasing opportunities to receive health guidance by widening the identification and criteria of those at risk. In addition, although BMI is widely used and is useful as a marker of obesity, basing obesity on the BMI may indicate the presence of abdominal obesity even in the absence of overall obesity. There is increasing evidence in favor of visceral adiposity as a marker of cardiovascular risk42). In this study, optimized WC was more useful as a risk marker than optimized BMI for cardiovascular disease risk assessment.
Strengths and Limitations
The strengths of this study are its large sample size and its ability to accurately capture the diagnosis of CAD and CVD based on ICD-10 codes and procedures, combined with information from physical examinations and databases of claims. It is also important to note that the participants in this study are of the generation most affected by economic activity. However, this study has several limitations. The first is the possible introduction of selection bias. Because of the nature of employer-sponsored health care plans, patients older than age 75 were not included, and only life-insured individuals who underwent a medical examination were included. Therefore, our findings should be interpreted cautiously. Second, the health examinations were based on the declaration of whether the test data were obtained on an empty stomach, and there may be variations in the time since the previous meal. Third, because each risk factor was measured at a single point in time, it was not possible to identify participants whose situations improved or worsened during the follow-up period and to examine associated changes in risk. Fourth, the usefulness of other indices of central obesity, such as the body-shape-index, waist-to-hip ratio, and waist-to-height ratio, has also been reported43). However, data to calculate these factors were not available in the database used. Fifth, although we optimized only the items used in the existing MetS diagnostic criteria, major CVD risk factors such as nonalcoholic fatty liver disease and chronic kidney disease have been newly identified, and their addition may further improve the predictive ability44).
In conclusion
Our results show that MetS is more highly associated with CAD/CVD risk by any of the diagnostic criteria than elevated LDL-C or smoking. Also, HRs were not significantly affected by the different diagnostic criteria, including optimized ones. However, a new criteria would greatly improve sensitivity without the extreme sacrifice of specificity and would significantly reduce the number of high-risk individuals, especially women, who are missed by the current criteria.
Acknowledgements
Y.Y. and K.F. developed the study design, researched the data, contributed to discussions, wrote the manuscript, and reviewed and edited the manuscript. T.S., M.H.Y., Y.Y., Y.M., T.Y., S.K., K.K., and H.S. researched the data, contributed to discussions, wrote the manuscript and reviewed and edited the manuscript. H.S. planned and supervised this research, researched the data, contributed to discussions, wrote the manuscript, and reviewed and edited the manuscript. H.S. developed the study design, contributed to discussions, and reviewed and edited the manuscript. Y.Y. and K.F. are the guarantor of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
The authors thank Mami Haga and Yoko Chino, Niigata University Faculty of Medicine, for excellent secretarial assistance.
Disclosure Summary
The authors declare that there is no duality of interest associated with the manuscript. The authors have nothing to disclose.
Funding
This work is supported by JMDC Inc. and the Japan Society for Promotion of Science (JSPS). The sponsor had no role in the design and conduct of the study.
Footnotes to Text
BMI Body mass index
BP Blood pressure
CAD Coronary artery disease
CVD Cerebrovascular disease
CAD/CVD Coronary artery disease and/or Cerebrovascular disease
DBP Diastolic blood pressure
FPG Fasting plasma glucose
HDL-C High-density lipoprotein cholesterol
HRs Hazard ratios
ICD-10 International Statistical Classification of Diseases and Related Health Problems, 10th revision
IDF International Diabetes Federation
LDL-C Low-density lipoprotein cholesterol
MetS Metabolic syndrome
NCEP ATP III National Cholesterol Education Program–Adult Treatment Panel III
NGSP National Glycohemoglobin Standardization Program
PCI Percutaneous coronary intervention
ROC Receiver operating characteristics
SBP Systolic blood pressure
TG Triglycerides
WC Waist circumference
WHO World Health Organization
References
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