Literature DB >> 20680014

Asian Americans have greater prevalence of metabolic syndrome despite lower body mass index.

L P Palaniappan1, E C Wong, J J Shin, S P Fortmann, D S Lauderdale.   

Abstract

OBJECTIVE: To examine the relationship between body mass index (BMI) and metabolic syndrome for Asian Americans and non-Hispanic Whites (NHWs), given that existing evidence shows racial/ethnic heterogeneity exists in how BMI predicts metabolic syndrome. RESEARCH DESIGN AND METHODS: Electronic health records of 43,507 primary care patients aged 35 years and older with self-identified race/ethnicity of interest (Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese or NHW) were analyzed in a mixed-payer, outpatient-focused health-care organization in the San Francisco Bay Area.
RESULTS: Metabolic syndrome prevalence is significantly higher in Asians compared with NHWs for every BMI category. For women at the mean age of 55 and BMI of 25  kg  m(-2), the predicted prevalence of metabolic syndrome is 12% for NHW women compared with 30% for Asians; similarly for men, the predicted prevalence of metabolic syndrome is 22% for NHWs compared with 43% of Asians. Compared with NHW women and men with a BMI of 25  kg  m(-2), comparable prevalence of metabolic syndrome was observed at BMI of 19.6  kg  m(-2) for Asian women and 19.9  kg  m(-2) for Asian men. A similar pattern was observed in disaggregated Asian subgroups.
CONCLUSIONS: In spite of the lower BMI values and lower prevalence of overweight/obesity than NHWs, Asian Americans have higher rates of metabolic syndrome over the range of BMI. Our results indicate that BMI ranges for defining overweight/obesity in Asian populations should be lower than for NHWs.

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Mesh:

Year:  2010        PMID: 20680014      PMCID: PMC2989340          DOI: 10.1038/ijo.2010.152

Source DB:  PubMed          Journal:  Int J Obes (Lond)        ISSN: 0307-0565            Impact factor:   5.095


Introduction

Metabolic syndrome is a constellation of multiple metabolic risk factors that greatly increases risk for cardiovascular mortality1 and Type 2 diabetes.2 Metabolic syndrome is currently defined by the National Cholesterol Education Program Expert Panel in Adult Treatment Panel III (NCEP ATP III) as the presence of three or more of the following factors: elevated fasting glucose (≥100 mg/dL), elevated blood pressure (≥130/85 mmHg), reduced high-density lipoprotein (HDL) (men <40 mg/dL, women <50 mg/dL), and elevated triglycerides (≥150 mg/dL), and an elevated waist circumference (≥40 inches for men, ≥35 inches for women).3 Prospective population studies of people with metabolic syndrome have shown a two-fold increase in relative risk for cardiovascular disease (CVD) events4 and a five-fold increase for developing diabetes compared to people without the syndrome.2 Therefore, the components of metabolic syndrome are important treatment targets for reducing CVD and diabetes. The NCEP ATP III identifies overweight, obesity and physical inactivity, along with genetic factors that enhance insulin resistance, as the root causes of the metabolic syndrome.3 The San Antonio Heart Study and the Insulin Resistance Atherosclerosis Study, both population-based longitudinal studies, showed both body mass index (BMI) and waist circumference to be highly predictive of the onset of metabolic syndrome.5,6 Subjects with a baseline BMI >30 kg/m2 were between 3 and 8 times more likely to develop metabolic syndrome than those with a BMI <25 kg/m2.5 Thus, targeting obesity and obesity-related factors seems to be the most potent strategy to prevent or manage metabolic syndrome. Currently, the Centers for Disease Control and Prevention (CDC) defines overweight as having a BMI ≥25 kg/m2 and obese as having a BMI ≥30 kg/m2. These BMI ranges serve to “assess someone’s likelihood of developing overweight- or obesity-related diseases.”7 However, there is evidence that BMI predicts metabolic syndrome differently across racial/ethnic groups. Racial/ethnic differences in the relationship between BMI and metabolic syndrome have been documented among non-Hispanic Whites (NHWs), African Americans, and Hispanics. Non-obese African American women have higher levels of fasting glucose at the same BMI levels as non-obese NHW women.8,9 Overweight Hispanic women have higher levels of fasting glucose when compared to overweight NHW and African American women.9 Hispanic adults are also reported to have greater fasting insulin levels and resistance than NHWs after adjusting for age and BMI.10 The relationship between BMI and metabolic syndrome has not been explored in Asian Americans. Asians in general have been documented to have higher risk of cardiovascular disease and Type 2 diabetes at lower BMI than European populations. International studies conducted among different Asian subgroups in China, Korea, Philippines, Singapore, and Taiwan have shown this increased risk.11–20 Based on this evidence, a World Health Organization (WHO) expert consultation recommended lower BMI ranges for overweight and obese in Asian populations.21 Specifically, they recommended 23–27.4 kg/m2 for overweight, and 27.5 kg/m2 and higher for obese. However, Asians living in their countries of origin may have very different nutritional and physical activity profiles than Asians who have immigrated to and are living in the US. Due to these important environmental differences, it is unclear whether these international guidelines for Asians are appropriate for Asian Americans. We sought to examine the relationship between BMI and metabolic syndrome for Asian Americans and NHWs in a large, ethnically diverse healthcare organization to evaluate whether there is evidence that ethnic-specific BMI ranges for overweight and obesity are more appropriate for Asian Americans than the CDC BMI ranges.

Patients and Methods

Setting

The Palo Alto Medical Foundation (PAMF) is a mixed-payer, outpatient-focused healthcare organization in the San Francisco bay area of northern California. PAMF has 30 clinics and provides care for approximately 600 000 active patients.

Inclusion/Exclusion

A cross-sectional sample of patient electronic health records from January 1, 2006 – December 31, 2008 was studied. A record was eligible for analysis if the patient: 1) had been seen at least once in a primary care department during the cross-sectional period; 2) was aged 35 years and older as of January 1, 2006; and 3) had a self-identified race/ethnicity22 of: Asian Indian, Chinese, Filipino, Japanese, Korean, Vietnamese or NHWs. Additionally, patients with a distinctly high number of visits (>29 visits in 3 years, 1% of the population), and measurements taken during pregnancy (0.2%) were excluded. No patients were contacted for the study, which received approval from the Palo Alto Medical Foundation Institutional Review Board on August 14, 2008.

Clinical Definitions

The definition of metabolic syndrome was based on the NCEP ATP III guidelines.3 Waist circumference was not consistently available in the electronic health records, so BMI was used to define overweight (BMI: 25.0–29.9 kg/m2) or obese (BMI ≥30.0 kg/m2), as demonstrated in previous national studies.23–25 To avoid circularity when describing the association between BMI and metabolic syndrome, we excluded the BMI criterion from the five criteria and instead defined metabolic syndrome as any two of the remaining four criteria. The electronic health record data elements for the definition are shown in Table 1. Metabolic syndrome was additionally defined as having an ICD-9 code 277.7 (physician-diagnosed metabolic syndrome, which was present in only 2% of total participants with metabolic syndrome). While our redefinition of metabolic syndrome reduces the comparability of our prevalence rates to other studies, our focus is on the internal comparison between Asian Americans and NHWs, for whom it is similarly defined.
Table 1

Definition of Metabolic Syndrome

Metabolic Syndrome ComponentDefinition
Diabetes or Impaired fasting glucose (IFG)ICD-9 250.X, 250.X0, 250.X2, 790.21;-OR- ICD-9 277.7
Fasting glucose ≥100 mg/dL;
Random glucose ≥200 mg/dL; OR
Use of hypoglycemic medications
High blood pressureICD-9 401.0, 401.1, 401.9, 796.2;
At least one blood pressure ≥130/85 mmHg; OR
Use of blood pressure medications
Low high-density lipoprotein cholesterol (HDL)At least one HDL <50 mg/dL (women) or <40 mg/dL (men)
HypertriglyceridemiaAt least one triglyceride ≥150 mg/dL
Obesity*Overweight (BMI: 25.0–29.9 kg/m2) or obese (BMI ≥30 kg/m2)

Obesity is excluded from metabolic syndrome criteria when examining association between BMI and metabolic syndrome

Statistical Analysis

Univariate

Patient demographics and characteristics were described using means and proportions. Means were compared using ANOVA and Tukey comparisons. Proportions of individual metabolic syndrome components were calculated with binomial confidence intervals.

Multivariate

First, we sought to compare the age-adjusted prevalence rates of metabolic syndrome across different Asian subgroups and BMI categories compared to NHWs. Multiple anthropometric measures and laboratory results were available for each patient. To take advantage of all available measures, we needed to account for the correlation of repeated measures from the same patient. To address this, we used generalized estimating equations (GEE) with a logit link function and unstructured working correlation structure. Prevalence rates were calculated using the predicted probability from the GEE model and termed predicted prevalence rates. Models included age, age2, and racial/ethnic group. Terms including age were centered to the mean, and the age2 covariate was included to allow for non-linear contributions of age. We reported the age-adjusted prevalence rate for four separate BMI categories: normal (BMI: 18.5–24.9 kg/m2), overweight (BMI: 25.0–29.9 kg/m2), class I obesity (BMI: 30.0–34.9 kg/m2) and class II obesity (BMI: 35.0–39.9 kg/m2) (Figure 1).
Figure 1

Age-Adjusted Prevalence of Metabolic Syndrome for Separate BMI Categories by Racial/Ethnic Subgroup

Normal (BMI: 18.5–24.9 kg/m2), Overweight (BMI: 25.0–29.9 kg/m2), Class I Obesity (BMI: 30.0–34.9 kg/m2), Class II Obesity (BMI: 35.0–39.9 kg/m2). The 95% confidence intervals are included.

Next, we investigated the relationship of metabolic syndrome with BMI, by racial/ethnic group. Models included age, age2, BMI, racial/ethnic group, and BMI*racial/ethnic group. The interaction terms between BMI and each racial/ethnic group are included in the models so that we can examine whether the effect of BMI on the outcomes differs by race/ethnicity. For example, the additional effect if any of being Asian with high BMI values beyond being Asian or having high BMI values separately. The predicted prevalence was estimated at fixed values of age and plotted against BMI. Statistical significance was determined using the standardized difference in predicted prevalences between racial/ethnic groups on the logit scale. To investigate the relationship of metabolic syndrome and BMI at different ages, results were presented at the representative ages of 35 years, the mean age, and 65 years. We repeated this investigation on metabolic syndrome components. Prevalence rates are presented with 95% confidence intervals or bands. Since 320 models were examined during this study, a Bonferroni type correction was applied and statistical significance was determined at P<0.00016. All statistical analyses were performed using SAS 9.2 (Cary, NC).

Results

Electronic health record data from 43 507 unique patients were included in the analyses (Table 2). The number of unique patients in each racial/ethnic group ranged from 413 in the Korean group to 32 406 in the NHW group. The proportion of women ranged from 46% in Asian Indians to 68% in Japanese. Asian patients as a group were younger than NHWs and had lower BMIs than NHWs. The exception was Filipinos who had BMIs similar to NHWs. The proportion overweight (BMI: 25–30 kg/m2) among Asians was lower than for NHWs, with the exceptions of Asian Indians and Filipinos. The proportion obese (BMI ≥30 kg/m2) was lower for all Asian groups than for NHWs. Type 2 diabetes or impaired fasting glucose (IFG), low HDL, and hypertriglyceridemia were all more common among Asians than NHWs.
Table 2

Patient Characteristics

CharacteristicsNHWAsian (all)Asian IndianChineseFilipinoJapaneseKoreanVietnamese
Sample size32,40611,1012,6795,3101,379837413483
Women, (%)58%58%46% +*60%65% +68% +*68% +64%
Age, mean (SD), y57 (13.4)50 (12.0) +46 (10.1) +*51 (12.3) +*50 (11.0) +56 (14.1) *47 (11.2) +48 (10.5) +
BMI, mean (SD), kg/m226.5 (4.6)24.6 (3.8) +25.8 (3.7) +*23.8 (3.3) +*26.5 (4.1) *24.4 (4.1) +24.0 (3.7) +23.5 (3.2) +*
Prop. of Overweight (%)49%43% +53% *36% +*54% *42%37% +37% +
Prop. of Obese (%)26%12% +17% +*7% +*24% *14% +8% +6% +*
BP-Sys, mean (SD), mmHg124 (14)119 (14) +118 (13) +118 (14) +*124 (13) *123 (14) *117 (14) +116 (13) +
BP-Dia, mean (SD), mmHg74 (8)73 (9) +74 (8) +72 (9) +*76 (8) +*74 (9)73 (9)73 (8) +
Prop. of High BP (%)69%54% +52% +50% +*74% *65% *53% +46% +
Glucose (fasting), mean (SD), mg/dL97 (17)97 (16)97 (18)96 (14)102 (22) +*97 (12)98 (15)94 (13)
Glucose (random), mean (SD), mg/dL99 (21)101 (23) +101 (25) +99 (18)107 (31) +*101 (20)99 (18)97 (18)
Prop. of Diabetes or IFG (%)19%24% +26% +21%35% +*25%20%20%
Total Cholesterol, mean (SD), mg/dL193 (35)187 (34) +183 (32) +*186 (32) +193 (37) *196 (35) *188 (32)194 (34)
HDL, mean (SD), mg/dL58 (16)53 (14) +47 (12) +*55 (14) +*54 (14) +61 (16) +*57 (15)54 (13)
LDL, mean (SD), mg/dL114 (30)110 (29) +110 (28) +108 (29) +112 (32)111 (30)109 (27)115 (29)
Prop. of low HDL (%)25%35% +51% +29% +*37% +21% *28%36% +
Triglycerides, mean (SD), mg/dL110 (66)124 (76) +133 (77) +*117 (72) +*133 (79) +126 (85) +117 (80)127 (74) +
Prop. of Hypertriglyceridemia (%)25%34% +39% +30% +41% +36% +30%34%

Statistically Significant with NHW at p<0.00016

Statistically Significant with Asian (all) at p<0.00016

Age-adjusted metabolic syndrome prevalence rates are presented for four BMI categories (Figure 1). Metabolic syndrome is higher in Asians overall than NHWs within every BMI category. The differences are particularly noteworthy in the overweight category where the rates are 25% for NHWs and 48% for Asians (P<0.00016). Among the Asian subgroups, Asian Indians and Filipinos have uniformly higher prevalence of metabolic syndrome for every BMI category. Figure 2 shows predicted prevalence of metabolic syndrome in NHWs and all Asians over the continuum of BMI. Results were presented for the representative ages of 35 years, the mean age of 55, and 65 years. Compared to NHWs, the predicted prevalence of metabolic syndrome was higher for all Asians at every BMI level over a range of ages. These differences were more pronounced at BMI values 25–30 kg/m2 but were still significantly different over BMI values from 18–35 kg/m2.
Figure 2

Predicted Prevalence of Metabolic Syndrome by BMI

Asians are compared to NHWs (women, men) for ages 35, 55, and 65 years. The 95% confidence bands are included.

For a fixed BMI value, the predicted prevalence of metabolic syndrome can be compared. At the mean ages, a BMI of 25 kg/m2 in women corresponded to a predicted prevalence of metabolic syndrome of 12% for NHWs, compared to 30% for Asians. For men with a BMI of 25 kg/m2, the probability of metabolic syndrome is 22% for NHWs compared to 43% for Asians. Additionally, for a fixed BMI value in NHWs, the corresponding predicted prevalence of metabolic syndrome has a different BMI value in Asians. Compared to NHWs with a BMI of 25 kg/m2, comparable prevalence was seen at a BMI of 19.6 kg/m2 for Asian women and 19.9 kg/m2 for Asian men. At higher levels of obesity, the racial/ethnic differences are just as evident. Compared to NHWs with a BMI of 30 kg/m2, Asian women with a BMI of 23.9 kg/m2 and Asian men with a BMI of 24.3 kg/m2 had comparable prevalences. Similar patterns were observed across all Asian subgroups (not shown). Figure 3 shows the predicted prevalence of individual metabolic syndrome components for NHWs and all Asians, over the continuum of BMI and at the mean age. For diabetes or IFG, Asians had uniformly higher predicted prevalence than NHWs across all BMI values in women and men. For high blood pressure, Asians had higher predicted prevalence than NHWs for BMI values greater than 25 kg/m2 in women and men. For both low HDL and hypertriglyceridemia, Asian women had uniformly higher predicted prevalence for BMI less than 36 kg/m2 and Asian men had higher predicted prevalence than NHWs for BMIs less than 31 kg/m2.
Figure 3

Predicted Prevalence of Metabolic Syndrome Components by BMI

Diabetes or IFG, high blood pressure, low high density lipoprotein, and hypertriglyceridemia are compared in Asians and NHWs. The 95% confidence bands are included.

Discussion

Obesity, as measured by BMI, is a well-known risk factor for metabolic syndrome. There are many studies that question the validity of BMI as a marker of obesity in Asian populations, given data suggesting higher adiposity at similar levels of BMI.17,26,27 While more precise measures of adiposity such as waist circumference, CT scans, and DEXA scans may be preferable to BMI to define obesity, BMI is without question the most cost-effective approach. Waist circumference commonly uses four body sites for measurement, and no consensus exists on the optimum site for measurement, especially since it may differ by sex.28–30 Another study in which both waist circumference and BMI are available for all participants shows that BMI and waist circumference are highly correlated in specific racial/ethnic populations (NHWs, African Americans, and Hispanics), although Asians or Asian subgroups were not examined.31 CT scans and DEXA scans have been used in research settings, but are too expensive to be routinely employed in clinical or public health settings. BMI has proved to be a reliable measure of adiposity, or total body fat, irrespective of height.26,32–34 Moreover, the National Institutes of Health35 and the World Health Organization36 have proposed using BMI as a method for defining overweight and obesity. Given the expense, difficulty, and debatable additional utility of more specialized measures of obesity, it may be preferable to customize BMI ranges to better define overweight/obesity for specific subpopulations than propose more accurate measurement approaches. Racial/ethnic differences in the relationship between BMI and metabolic syndrome risk have previously been documented among African Americans and Hispanics in the US.8,9,37–39 Our study is the first to examine the relationship between BMI and metabolic syndrome in Asian Americans, and in Asian subgroups in the US. Given the heterogeneity of the Asian American population, future studies of Asians in the US should strive to disaggregate these diverse populations. International studies conducted among different Asian national populations in China, Korea, Philippines, Singapore, and Taiwan have shown increased risk of Type 2 diabetes and cardiovascular disease at lower BMI than European populations.11–20 Other international societies, such as the International Diabetes Federation,40 suggest population-specific cut points for obesity, recognizing increased metabolic risk for some populations (Japanese and South Asians) despite similar levels of obesity. Our findings in Asian populations in the US corroborate this increased disease risk for metabolic syndrome at lower BMI. While there is limited sample size to make robust comparisons among the Asian American subgroups, our data strongly suggest heterogeneity among Asian American subgroups. For example, Japanese patients in our population have higher HDL than the other Asian subgroups. Confirming the contrasts among Asian populations that we observe in other study groups would advance our understanding of this apparent heterogeneity. A strength of our study is that the subgroups examined have similar healthcare access, socioeconomic status, and geographic location. The distribution of Asian subgroups in the PAMF population is approximately equal to the proportions observed in the US.41 Previous work in the PAMF population has shown sufficient validity of the electronic health records42 to provide accurate disease estimates. Limitations of this study include it being conducted a single geographic area with somewhat limited sample size in the smaller Asian subgroups (i.e. Korean and Vietnamese populations). The PAMF population is insured (30% PPO, 60% HMO, 10% Medicare), and thus under-represents the medically underserved. However, these limitations that affect the generalizability of the sample also minimize unmeasured confounding and therefore improve the internal validity of our comparisons; any racial/ethnic differences observed in the PAMF clinical population will likely only be magnified in the general population. We found that Asian Americans generally have lower mean BMI values and lower prevalence of overweight and obesity than NHWs when using traditional BMI ranges in our population of insured northern Californians. However, despite lower BMI, Asian Americans have higher rates of metabolic syndrome for each BMI category (normal, overweight, class I obesity, class II obesity). Over the BMI continuum, Asians are more likely to manifest metabolic syndrome than NHWs. This is true for most of the components of the metabolic syndrome as well. These findings support the WHO recommendation for lower BMI ranges for defining overweight and obesity in Asian populations, including Asian Americans, than for NHWs. Using lower BMI ranges for Asian Americans would allow for earlier and more appropriate screening for the components of metabolic syndrome and may improve preventive efforts. Asian Americans have higher prevalence of metabolic syndrome than NHWs at each level of BMI. Accurate and early diagnosis of overweight and obesity in Asian Americans is critical to stem the rising tide of metabolic syndrome and Type 2 diabetes in this rapidly growing population within the US. Our work adds to the growing body of evidence that population-specific BMI ranges are necessary for accurate, timely diagnosis of overweight and obesity to prevent metabolic diseases.
  39 in total

1.  Cardiovascular risk factors in confirmed prediabetic individuals. Does the clock for coronary heart disease start ticking before the onset of clinical diabetes?

Authors:  S M Haffner; M P Stern; H P Hazuda; B D Mitchell; J K Patterson
Journal:  JAMA       Date:  1990-06-06       Impact factor: 56.272

2.  Identifying patients for weight-loss treatment: an empirical evaluation of the NHLBI obesity education initiative expert panel treatment recommendations.

Authors:  M Kiernan; M A Winkleby
Journal:  Arch Intern Med       Date:  2000-07-24

3.  Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese.

Authors:  G T Ko; J C Chan; C S Cockram; J Woo
Journal:  Int J Obes Relat Metab Disord       Date:  1999-11

4.  Cardiovascular morbidity and mortality associated with the metabolic syndrome.

Authors:  B Isomaa; P Almgren; T Tuomi; B Forsén; K Lahti; M Nissén; M R Taskinen; L Groop
Journal:  Diabetes Care       Date:  2001-04       Impact factor: 19.112

5.  Body mass index as a measure of adiposity among children and adolescents: a validation study.

Authors:  A Pietrobelli; M S Faith; D B Allison; D Gallagher; G Chiumello; S B Heymsfield
Journal:  J Pediatr       Date:  1998-02       Impact factor: 4.406

6.  Interpretation of low body mass indices: Australian aborigines.

Authors:  N G Norgan
Journal:  Am J Phys Anthropol       Date:  1994-06       Impact factor: 2.868

7.  Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study.

Authors:  David E Laaksonen; Hanna-Maaria Lakka; Leo K Niskanen; George A Kaplan; Jukka T Salonen; Timo A Lakka
Journal:  Am J Epidemiol       Date:  2002-12-01       Impact factor: 4.897

8.  How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups?

Authors:  D Gallagher; M Visser; D Sepúlveda; R N Pierson; T Harris; S B Heymsfield
Journal:  Am J Epidemiol       Date:  1996-02-01       Impact factor: 4.897

9.  Collecting patient race/ethnicity and primary language data in ambulatory care settings: a case study in methodology.

Authors:  Latha P Palaniappan; Eric C Wong; Jessica J Shin; Maria R Moreno; Regina Otero-Sabogal
Journal:  Health Serv Res       Date:  2009-06-22       Impact factor: 3.402

10.  What is the best way to measure waist circumference?

Authors:  Mary C J Rudolf; Jenny Walker; Tim J Cole
Journal:  Int J Pediatr Obes       Date:  2007
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  87 in total

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Authors:  Latha P Palaniappan; Maria Rosario G Araneta; Themistocles L Assimes; Elizabeth L Barrett-Connor; Mercedes R Carnethon; Michael H Criqui; Gordon L Fung; K M Venkat Narayan; Hamang Patel; Ruth E Taylor-Piliae; Peter W F Wilson; Nathan D Wong
Journal:  Circulation       Date:  2010-08-23       Impact factor: 29.690

2.  Physical Activity Among Asian American Adults in Houston, Texas: Data from the Health of Houston Survey 2010.

Authors:  Dennis Kao; Amy Carvalho Gulati; Rebecca E Lee
Journal:  J Immigr Minor Health       Date:  2016-12

Review 3.  Cardiovascular Health of Filipinos in the United States: A Review of the Literature.

Authors:  Carol Jean Abesamis; Sharon Fruh; Heather Hall; Trey Lemley; Kimberly R Zlomke
Journal:  J Transcult Nurs       Date:  2015-08-04       Impact factor: 1.959

4.  Ethnic disparities in the prevalence of the metabolic syndrome in American adults: data from the Examination of National Health and Nutrition Examination Survey 1999-2010.

Authors:  Lilly Ramphal; Jun Zhang; Sumhiro Suzuki
Journal:  Proc (Bayl Univ Med Cent)       Date:  2014-04

5.  Ethnic disparities in the association of body mass index with the risk of hypertension and diabetes.

Authors:  Robert J Wong; Christina Chou; Sidhartha R Sinha; Ahmad Kamal; Aijaz Ahmed
Journal:  J Community Health       Date:  2014-06

6.  A mini-network balance model for evaluating the progression of cardiovascular complications in Goto-Kakizaki rats.

Authors:  Hao Jiang; Yu-Hao Wang; Chun-Xiang Wei; Xue Zhang; Hao-Chen Liu; Xiao-Quan Liu
Journal:  Acta Pharmacol Sin       Date:  2017-01-02       Impact factor: 6.150

7.  Health fair report of Asian Americans in Michigan.

Authors:  Janilla Lee; Judy Chen; Lili Deng; Prasanna Parasurama
Journal:  J Immigr Minor Health       Date:  2015-02

Review 8.  Cardiorenal metabolic syndrome and cardiometabolic risks in minority populations.

Authors:  Keith C Ferdinand; Fatima Rodriguez; Samar A Nasser; A Enrique Caballero; Gary A Puckrein; Farhad Zangeneh; Michael Mansour; JoAnne Micale Foody; Priscilla E Pemu; Elizabeth O Ofili
Journal:  Cardiorenal Med       Date:  2013-12-31       Impact factor: 2.041

9.  Relationship of ethnicity and body mass index with the development of hypertension and hyperlipidemia.

Authors:  James Davis; Deborah Juarez; Krista Hodges
Journal:  Ethn Dis       Date:  2013       Impact factor: 1.847

10.  Ambient Volatile Organic Compounds and Racial/Ethnic Disparities in Gestational Diabetes Mellitus: Are Asian/Pacific Islander Women at Greater Risk?

Authors:  Andrew D Williams; Katherine L Grantz; Cuilin Zhang; Carrie Nobles; Seth Sherman; Pauline Mendola
Journal:  Am J Epidemiol       Date:  2019-02-01       Impact factor: 4.897

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