• 沒有找到結果。

The effectiveness of IDF and ATP-III in identifying metabolic syndrome and the usefulness of these tools for nealth-promotion in older Taiwanese

N/A
N/A
Protected

Academic year: 2021

Share "The effectiveness of IDF and ATP-III in identifying metabolic syndrome and the usefulness of these tools for nealth-promotion in older Taiwanese"

Copied!
1
0
0

加載中.... (立即查看全文)

全文

(1)

The effectiveness of IDF and ATP-III in identifying metabolic syndrome and the usefulness of these tools for health-promotion in older Taiwanese

M.M. CHEN1, and A.C. TSAI1,2,3*

1Department of Healthcare Administration, Asia University

500 Liufeng Road, Wufeng, Taichung 41354, Taiwan

2Department of Health Services Management, China Medical University,

91 Hsueh-Shih Road, Taichung 40402, Taiwan

3Associate Professor emeritus, School of Public Health, University of Michigan, Ann Arbor,

MI 48109, USA

*Corresponding author: Dr. Alan C. Tsai, Professor

Department of Healthcare Administration, Asia University, 500 Liufeng Rd., Wufeng, Taichung 41354, Taiwan. Phone: (886-4) 2332 3456, ext.1943

Fax: (886-4) 2332 1206

(2)

Abstract

Objectives: The aim of this study was to compare the effectiveness of IDF (International

Diabetes Federation) and ATP-III (National Cholesterol Education Program-Adult Treatment Panel III) for predicting metabolic syndrome, and to evaluate the usefulness of these

definitions for health promotion.

Design: A cross-sectional study Setting: A national random sample

Participants: A population representative sample of 1021 54-91 year-old Taiwanese. Measurements: Subjects were measured for anthropometric and biochemical indicators and

rated for the presence of metabolic syndrome using the two definitions. We evaluated the effectiveness of the two definitions in predicting MetS among those who had specific metabolic disorders. Results were analyzed with Student t-test and McNemar’s test.

Results: Among the 918 subjects who had one or more MetS-item disorders, ATP-III rated

greater proportions of subjects as having MetS than IDF, but both definitions predicted less than 50% (37.7% and 45.4%, respectively) as having MetS.

Conclusion: Compared to IDF, ATP-III rated a greater proportion of subjects as having

MetS, but both definitions missed more than 50% of subjects who had metabolic disorder(s). Since those who are missed have as much need for lifestyle intervention, the definitions appear not appropriate for health promotion.

(3)

Introduction

The term "Metabolic syndrome" was originally introduced to focus attention on the pathophysiological interrelationship among a number of metabolic disorders including hypertension, hyperglycemia and dyslipidemia in insulin-resistance, not for clinical diagnosis or health promotion (1). However, with the increasing prevalence of the metabolic disorders in many populations, the term has become more widely used even in health promotion. Several definitions of MetS have been proposed. The updated-National Cholesterol

Education Program-Adult Treatment Panel III (ATP-III) (2) and the International Diabetes Federation (IDF) (3) are two most widely used definitions. Lately, the revised definitions have become more concordant. Both definitions include the same component items. However, some differences remain.

Like many other countries, Taiwan has high prevalence rates of metabolic disorders (4). Recently, the government has used the concept of MetS attempting to improve citizens' awareness of the health risk of metabolic disorders. Although MetS is useful for integrating the etiological interrelationship of the obesity-related metabolic disorders, its usefulness in clinical practice or health promotion is debatable. Since all MetS definitions require the presence of a minimum of 3 metabolic disorders and the IDF further requires abdominal obesity as an essential condition, individuals who have 1 or 2 disorders (or, up to 4 disorders, if not centrally obese, for IDF) would not be classified as having MetS. One may wonder whether those who have metabolic disorder(s) but not meeting the criteria of MetS should receive less attention or treatment. Hence, the present study was undertaken to compare the ability of IDF and ATP-III for predicting metabolic syndrome, and to evaluate the

effectiveness of these definitions for health promotion in older adults.

(4)

Source of data

The study analyzed data of the Social Environment and Biomarker Aging Study (SEBAS), a subsample of the Survey of Health and Living Status of the Elderly in Taiwan (SHLSET). SHLSET is a nationally representative cohort study initiated in 1989 with a stratified, multi-stage probability sample of 4049 Taiwanese aged ≥60 years (5). In 1996, a second sampling of 2462 50-67 year-old persons was drawn with the same procedure to include near-old population. Subjects in the original or combined cohort were interviewed every 3 or 4 years. In 2000, a subsample of1023 respondents was randomly selected for SEBAS which included a face-to-face interview and a comprehensive physical examination (including measurements of biochemical indicators) (6).

SEBAS used a structured-questionnaire to elicit personal, socio-demographic, lifestyle and health-related data. Each subject received a comprehensive physical examination, and provided an over-night fasting blood specimen and a 24-hour urinary sample for

measurement of biochemical indicators in a government-approved clinical laboratory. SEBAS was conducted according to the guidelines laid down in the Declaration of Helsinki and government-appointed representatives approved the study protocol. All participants gave written consents. The detail of SEBAS has also been described elsewhere (7).

Screening for metabolic syndrome

The present study rated the presence of MetS with IDF and ATP-III definitions. Both definitions were based on the same five indicators including (a) waist circumference ≥90 cm for men and ≥80 cm for women; (b) serum triglyceride (TG) ≥150 mg/dL or on medication; (c) high-density lipoprotein cholesterol (HDL-C) <40 mg/dL for men and <50mg/dL for women or on medication; (d) blood pressure (BP) ≥85/≥130 mmHg or on medication; and (e) fasting plasma glucose (FPG) ≥100 mg/dL or on medication. ATP-III classifies a person having any 3 of the 5 metabolic disorders as having MetS, whereas the IDF requires a person

(5)

to have abdominal obesity (excessive WC) plus any 2 of the 4 remaining disorders to be classified as having MetS.

Other metabolic disorders

In addition to the five MetS component items, the study also analyzed the ability of the definitions to identify those who had non-MetS metabolic disorders including over-weight/ obesity (BMI ≥24 kg/m2), elevated glycated hemoglobin (HbA1c ≥5.7%),

hypercholesterolemia (>200 mg/dL) and low glomerular filtration rate (GFR <60

ml/min/1.73m2). GFR was calculated according to the Simplified Modification of Diet in

Renal Disease (MDRD) equation (8) with a cutoff <60 ml/min/1.73m2.

Data analyses

All statistical analyses were weighting-adjusted according to the study design and using SPSS 15.0 (SPSS, Inc., Chicago, IL, USA). Descriptive data were analyzed with simple statistics. Within-definition differences were determined with Student's t-test for independent samples while between-definition differences were analyzed with McNemar’s nonparametric test. Cohen’s kappa statistics were used to evaluate the agreement between results rated with the two definitions. Statistical significance for all analyses was set at α=0.05.

Results

Table 1 shows the characteristics of subjects. The sample included more men (57.7%) than women, which reflected the composition of the specific age group in Taiwan. Subjects averaged 68±8.5 years old (54-91 years old). Most subjects (73.7%) received ≤6 years of formal education; 22.3% were current smokers; 23.3% drank alcohol ≥1 time/week; and 53.6% exercised ≥3 times/week. Two subjects were excluded due to incomplete data.

Table 2 shows the proportions of subjects identified as having MetS or specific

(6)

346 (33.9%) and ATP-III rated 417 (40.8%) as having MetS (Cohen’s kappa=0.852). Among subjects who had excessive WC, low HDL-C, hypertension, hyperglycemia or

hypertriglyceridemia, IDF identified 68.0, 59.3, 40.9, 56.9 and 68.9%; whereas ATP-III identified 68.0, 75.8, 49.5, 71.4 and 85.7%, respectively, as having MetS. The differences in diastolic blood pressure, fasting blood glucose, serum total triglyceride, HDL-C, total Cholesterol and GFR between those who were and those who were not identified as having MetS by the IDF were not statistically significant. Likewise, the differences in blood pressure, serum total triglyceride, HDL-C (in women), total cholesterol and GFR between those who were and those who were not identified by ATP-III were not significant. Compared to IDF, ATP-III rated significantly greater proportions of subjects as having abnormal DBP, SBP, BMI, FPG, HbA1c, and total C, HDL-C (in men) or GFR according to McNemar’s test (all p<0.05). Among 918 subjects who had any MetS-item disorders, IDF identified 346 (37.7%) and ATP-III identified 417 (45.4%) subjects as having MetS.

Discussion

Predicting metabolic syndrome by IDF and ATP-III

Results show that compared to IDF, ATP-III is more effective in predicting MetS among subjects who have metabolic disorders. ATP-III rates greater proportions of subjects who have specific metabolic disorders (except WC where the two definitions were the same) as having MetS than IDF. These results are in line with the findings of some earlier studies. In Eastern Asian populations, the ATP-III is more effective in predicting MetS than IDF (9-11). However, in Western populations it is the opposite (12, 13). Most MetS definitions adopt different criteria for different world populations. Thus, the ability of a MetS definition in predicting MetS is dependent on the population and the version of the definition.

(7)

where the only difference is whether WC is an essential component, the ATP-III (which does not require excessive WC as an essential component) always out-performs IDF. In the present study, both definitions miss a sizeable portion of those who have metabolic disorders (30.1-66.3% for IDF and 14.3-56.0% for ATP-III). Studies have shown that individuals who have single component disorders may not have less risk in cardiovascular mortality than those having MetS (14, 15). Thomas et al. (16)also observed that hypertension but not MetS was a strong predictor for all-cause mortality in people older than 65 years of age. Wang et al. (14) observed that the single MetS component items predicted CVD mortality with equal or higher hazardous ratios than various MetS definitions in 65-74-year old non-diabetic Finns. Guembe et al. (15) found that the construct of the MetS might not be better than its single components in addressing cardiovascular risk. Further, several metabolic disorders including excessive BMI (17), hypercholesterolemia (18, 19), elevated HbA1c (20), and lower GFR (8) which are known risk factors for coronary heart disease (21), stroke and chronic renal disease, are not the component items of MetS.

Appropriateness of MetS definitions for health promotion

Thus, it is clear that using MetS definition as a criterion for health promotion will miss a great proportion of people who have metabolic disorders. Among 969 subjects who had one or more metabolic disorders, IDF identified 346 (35.7%) while ATP-III identified 417 (43%) as having MetS. Obviously, individuals with 1 or 2 disorders (or up to 4 disorders without central obesity when rated with IDF) will not be classified as having MetS. MetS definitions also do not differentiate severity of the disorders. This is evidenced by the fact that many mean values of the metabolic indicators are not significantly different between those who are and those who are not identified as having MetS. Those who are missed are not necessarily at less risk of developing the related chronic diseases or premature death (14, 16). Moreover, those who were not predicted may get a false sense of normalcy and delay treatment.

(8)

Additionally the concept of MetS is not easy for clinicians or public health workers to convey, nor is it easy for patients to comprehend (22, 23).

Strengths and limitations

A major strength of this study is that the study sample is a population-representative sample, thus, the results is applicable to the Taiwanese or those Chinese populations who share the common characteristics as the Taiwanese. A major limitation is that data are from a cross-sectional study that limits our ability to evaluate the longitudinal association of MetS with the related diseases such as DM or CVD, or premature death.

Conclusion

ATP-III identified a greater proportion of older Taiwanese who had metabolic disorders as having MetS than the IDF. However, both definitions missed more than 50% of those who had one or more metabolic disorders. Since those who are missed have as much need for lifestyle intervention to reduce the risk of developing the associated non-communicable diseases, MetS definitions appear not appropriate for health promotion.

Financial discloser: The author(s) received no financial support for the research and/or

authorship of this article.

Conflict of interest: None of the authors has any personal or financial conflicts of interest.

Author contributions: MMC performed statistical analysis and drafted the manuscript; ACT

conceived the idea and reviewed the manuscript.

Acknowledgements

(9)
(10)

References

1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes. 1988;37(12):1595-607.

2. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart

Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation. 2005;112(17):2735-52.

3. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med.

2006;23(5):469-80.

4. Hwang LC, Bai CH, Chen CJ. Prevalence of obesity and metabolic syndrome in Taiwan. J Formos Med Assoc. 2006;105(8):626-35.

5. Hermalin AL, Liang J, Chang MC. 1989 survey of the health and living status of the elderly in Taiwan: questionnaire and survey design. Comparative study of the elderly in Asia research report 89-1. Ann Arbor: University of Michigan.; 1989.

6. Seeman T, Glei D, Goldman N, Weinstein M, Singer B, Lin YH. Social relationships and allostatic load in Taiwanese elderly and near elderly. Soc Sci Med. 2004;59(11):2245-57. 7. Goldman N, Lin IF, Weinstein M, Lin YH. Evaluating the quality of self-reports of

hypertension and diabetes. J Clin Epidemiol. 2003;56(2):148-54.

8. Cheng TY, Wen SF, Astor BC, Tao XG, Samet JM, Wen CP. Mortality risks for all causes and cardiovascular diseases and reduced GFR in a middle-aged working population in Taiwan. Am J Kidney Dis. 2008;52(6):1051-60.

9. Lee J, Ma S, Heng D, Tan CE, Chew SK, Hughes K, et al. Should central obesity be an optional or essential component of the metabolic syndrome? Ischemic heart disease risk in the Singapore Cardiovascular Cohort Study. Diabetes Care. 2007;30(2):343-7.

10. Moy FM, Bulgiba A. The modified NCEP ATP III criteria maybe better than the IDF criteria in diagnosing Metabolic Syndrome among Malays in Kuala Lumpur. BMC Public Health. 2010;10:678.

11. Tong PC, Kong AP, So WY, Yang X, Ho CS, Ma RC, et al. The usefulness of the International Diabetes Federation and the National Cholesterol Education Program's Adult Treatment Panel III definitions of the metabolic syndrome in predicting coronary heart disease in subjects with type 2 diabetes. Diabetes Care. 2007;30(5):1206-11. 12. Moebus S, Hanisch JU, Aidelsburger P, Bramlage P, Wasem J, Jockel KH. Impact of 4

(11)

different definitions used for the assessment of the prevalence of the Metabolic Syndrome in primary healthcare: The German Metabolic and Cardiovascular Risk Project (GEMCAS). Cardiovasc Diabetol. 2007;6:22.

13. Santos AC, Barros H. Impact of metabolic syndrome definitions on prevalence estimates: a study in a Portuguese community. Diab Vasc Dis Res. 2007;4(4):320-7.

14. Wang J, Ruotsalainen S, Moilanen L, Lepisto P, Laakso M, Kuusisto J. The metabolic syndrome predicts cardiovascular mortality: a 13-year follow-up study in elderly non-diabetic Finns. Eur Heart J. 2007;28(7):857-64.

15. Guembe MJ, Toledo E, Barba J, Martinez-Vila E, Gonzalez-Diego P, Irimia P, et al. Association between metabolic syndrome or its components and asymptomatic cardiovascular disease in the RIVANA-study. Atherosclerosis. 2010;211(2):612-7. 16. Thomas F, Pannier B, Benetos A, Vischer UM. The impact of the metabolic syndrome -

but not of hypertension - on all-cause mortality disappears in the elderly. J Hypertens. 2011;29(4):663-8.

17. He Y, Jiang B, Wang J, Feng K, Chang Q, Zhu S, et al. BMI versus the metabolic syndrome in relation to cardiovascular risk in elderly Chinese individuals. Diabetes Care. 2007;30(8):2128-34.

18. Hata J, Doi Y, Ninomiya T, Fukuhara M, Ikeda F, Mukai N, et al. Combined effects of smoking and hypercholesterolemia on the risk of stroke and coronary heart disease in Japanese: the Hisayama study. Cerebrovasc Dis. 2011;31(5):477-84.

19. Arboix A, Garcia-Eroles L, Oliveres M, Targa C, Balcells M, Massons J. Pretreatment with statins improves early outcome in patients with first-ever ischaemic stroke: a pleiotropic effect of statins or a beneficial effect of hypercholesterolemia? BMC Neurol. 2010;10:47.

20. Succurro E, Marini MA, Arturi F, Grembiale A, Fiorentino TV, Andreozzi F, et al. Usefulness of Hemoglobin A1c as a Criterion to Define the Metabolic Syndrome in a Cohort of Italian Nondiabetic White Subjects. Am J Cardiol. 2011(170):1650-5.

21. Ravikiran M, Bhansali A, Ravikumar P, Bhansali S, Dutta P, Thakur JS, et al. Prevalence and risk factors of metabolic syndrome among Asian Indians: a community survey. Diabetes Res Clin Pract. 2010;89(2):181-8.

22. Simmons RK, Alberti KG, Gale EA, Colagiuri S, Tuomilehto J, Qiao Q, et al. The metabolic syndrome: useful concept or clinical tool? Report of a WHO Expert Consultation. Diabetologia. 2010;53(4):600-5.

(12)
(13)

Table 1. Characteristics of subjects (N = 1021) ____________________________________________ Item n (%) Age (y)a 54-64 382 (37.4) 65-74 371 (36.3) ≥ 75 268 (26.2)

Formal education (y)

0-6 752 (73.7) 7-9 102 (10.0) 10-12 95 (9.3) > 12 72 (7.1) Current smoker 227 (22.3) Alcohol drinking Do not drink 783 (76.7) Drink occasionally 183 (17.9)

Drink nearly daily 52 (5.1)

Betel-nut chewing 45 (4.4)

Exercise (times/wk.)

0 377 (36.9)

1-2 96 (9.4)

≥ 3 547 (53.6) Two subjects were excluded due to incomplete data.

(14)

Table 2. The proportion of subjects identified as having metabolic syndrome or specific disorders by IDF or ATP-III, respectively (N = 1021).

_________________________________________________________________________________________________________________________________________ According to IDF According to ATP- III

Item N (%) Identified Not identified Identified Not identified

n (%)a Mean ± SD n (%)a Mean ± SD n (%)a Mean ± SD n (%)a Mean ± SD p b Identified as having MetSc

All 1021 (100) 346 (33.9) 675 (66.1) 417 (40.8) 604 (59.2)

Men 589 (57.7) 151 (25.6) 438 (74.4) 197 (33.4) 392 (66.6)

Women 432 (42.3) 195 (45.1) 237 (54.9) 220 (50.9) 212 (49.1)

Identified as having MetS disorders Waist circumference Men (≥90 cm) 230 (22.5) 151 (65.7) 96.7 ± 6.0 79 (34.3) 94.2 ± 3.8***d 151 (65.7) 96.7 ± 6.0 79 (34.3) 94.2 ± 3.8***d ns Women (≥80 cm) 279 (27.3) 195 (69.9) 89.7 ± 7.4 84 (30.1) 86.6 ± 6.2*** 195 (69.9) 89.7 ± 7.4 84 (30.1) 86.6 ± 6.2*** ns Low HDL-cholesterol Men <40 (mg/dL) 189 (18.5) 93 (49.2) 33.1 ± 4.5 96 (50.8) 34.2 ± 4.2 135 (71.4) 33.2 ± 4.6 54 (28.6) 34.7 ± 3.5* * Woman <50 (mg/dL) 211 (20.7) 144 (68.2) 40.1 ± 5.9 67 (31.8) 40.8 ± 6.6 168 (79.6) 40.0 ± 6.0 43 (20.4) 41.5 ± 6.4 ns Hypertension DBP ≥85 (mmHg)e 527 (51.6) 230 (66.5) 88.8 ± 10.6 297 (56.4) 89.4 ± 9.8 270 (51.2) 88.6 ± 10.5 257 (48.8) 89.7 ± 9.8 * SBP ≥130 (mmHg)e 701 (68.7) 292 (41.7) 150.4 ± 18.2 409 (58.3) 146.6 ± 16.0** 352 (50.3) 149.5 ± 17.8 349 (49.8) 147.0 ± 16.1 * ≥85/130 (mmHg)e 733 (71.8) 300 (40.9) 433 (59.1) 363 (49.5) 370 (50.5) FPG ≥100 (mg/dL)e 413 (40.5) 235 (56.9) 135.6 ± 52.2 178 (43.1) 126.7 ± 43.0 295 (71.4) 135.5 ± 51.3 118 (28.6) 122.7 ± 39.7** * TG ≥150 (mg/dL)e 244 (23.9) 168 (68.9) 236.7 ± 139.7 76 (31.1) 215.4 ± 93.0 209 (85.7) 234.3 ± 133.1 35 (14.3) 205.1 ± 80.2 ns With any MetS disorder 918 (89.9) 346 (37.7) 572 (62.3) 417 (45.4) 501 (54.6)

With any non-MetS disorders

(15)

Total C >200 (mg/dL) 475 (46.5) 189 (40.6) 235.2 ± 31.7 286 (59.4) 233.1 ± 25.6 219 (47.1) 235.1 ± 31.2 256 (52.9) 232.9 ± 25.4 * GFRf <60 (ml/min/1.73m2) 83 (8.13) 28 (33.7) 48.3 ± 11.8 55 (66.3) 49.1 ± 11.1 36 (43.4) 47.9 ± 12.1 47 (56.6) 49.5 ± 10.7 * With any of above 4 items 790 (77.4) 334 (42.3) 456 (57.7) 392 (49.6) 398 (50.4)

With any of the 9 disorders 969 (75.3) 346 (35.7) 623 (64.3) 417 (43.0) 552 (57.0)

MetS: metabolic syndrome, WC: waist circumference, DBP: diastolic blood pressure, SBP: systolic blood pressure, FPG: fasting plasma glucose, TG: triglyceride, HbA1c: glycosylated hemoglobin, C: cholesterol, GFR: glomerular filtration rate.

a Number of subjects identified (or missed) and as percent of subjects having the specific disorder.

b Distributions significantly different between that predicted by IDF and ATP-III on the basis of McNemar’s Test c The agreement between the two definitions is Cohen's kappa 0.852 (p<0.001).

d Mean values significantly different from those who were “identified” on the basis of Student t-test for independent samples. e Or on medication

f Calculated according to MDRD-S (Simplified Modification of Diet in Renal Disease) = 186*Scr - 1.154*Age - 0.203* 0.742; (if female)*1.227 *p <0.05, **p <0.01, ***p <0.001

數據

Table 1. Characteristics of subjects (N = 1021)   ____________________________________________ Item                                                        n (%)              Age (y) a 54-64 382 (37.4) 65-74 371 (36.3) ≥ 75  268 (26.2)
Table 2. The proportion of subjects identified as having metabolic syndrome or specific disorders by IDF or ATP-III, respectively (N = 1021)

參考文獻

相關文件

You are given the wavelength and total energy of a light pulse and asked to find the number of photons it

Reading Task 6: Genre Structure and Language Features. • Now let’s look at how language features (e.g. sentence patterns) are connected to the structure

 Promote project learning, mathematical modeling, and problem-based learning to strengthen the ability to integrate and apply knowledge and skills, and make. calculated

Robinson Crusoe is an Englishman from the 1) t_______ of York in the seventeenth century, the youngest son of a merchant of German origin. This trip is financially successful,

fostering independent application of reading strategies Strategy 7: Provide opportunities for students to track, reflect on, and share their learning progress (destination). •

Strategy 3: Offer descriptive feedback during the learning process (enabling strategy). Where the

Now, nearly all of the current flows through wire S since it has a much lower resistance than the light bulb. The light bulb does not glow because the current flowing through it

Wang, Solving pseudomonotone variational inequalities and pseudocon- vex optimization problems using the projection neural network, IEEE Transactions on Neural Networks 17