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      Deriving Ethnic-Specific BMI Cutoff Points for Assessing Diabetes Risk

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      , MSC 1 , 2 , , PHD 1 , 3 , , MD, MSC 1 , 3 , 4 , 5 , 6 , , MD, PHD 1 , 7 , , MD, PHD 1 , 2 , 3 , 8
      Diabetes Care
      American Diabetes Association

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          Abstract

          OBJECTIVE

          The definition of obesity (BMI ≥30 kg/m 2), a key risk factor of diabetes, is widely used in white populations; however, its appropriateness in nonwhite populations has been questioned. We compared the incidence rates of diabetes across white, South Asian, Chinese, and black populations and identified equivalent ethnic-specific BMI cutoff values for assessing diabetes risk.

          RESEARCH DESIGN AND METHODS

          We conducted a multiethnic cohort study of 59,824 nondiabetic adults aged ≥30 years living in Ontario, Canada. Subjects were identified from Statistics Canada’s population health surveys and followed for up to 12.8 years for diabetes incidence using record linkages to multiple health administrative databases.

          RESULTS

          The median duration of follow-up was 6 years. After adjusting for age, sex, sociodemographic characteristics, and BMI, the risk of diabetes was significantly higher among South Asian (hazard ratio 3.40, P < 0.001), black (1.99, P < 0.001), and Chinese (1.87, P = 0.002) subjects than among white subjects. The median age at diagnosis was lowest among South Asian (aged 49 years) subjects, followed by Chinese (aged 55 years), black (aged 57 years), and white (aged 58 years) subjects. For the equivalent incidence rate of diabetes at a BMI of 30 kg/m 2 in white subjects, the BMI cutoff value was 24 kg/m 2 in South Asian, 25 kg/m 2 in Chinese, and 26 kg/m 2 in black subjects.

          CONCLUSIONS

          South Asian, Chinese, and black subjects developed diabetes at a higher rate, at an earlier age, and at lower ranges of BMI than their white counterparts. Our findings highlight the need for designing ethnically tailored prevention strategies and for lowering current targets for ideal body weight for nonwhite populations.

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          Most cited references18

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          Asians are different from Caucasians and from each other in their body mass index/body fat per cent relationship.

          The objective was to study the relationship between body mass index (BMI) and body fat per cent (BF%) in different population groups of Asians. The study design was a literature overview with special attention to recent Asian data. Specific information is provided on Indonesians (Malays and Chinese ancestry), Singaporean Chinese, Malays and Indians, and Hong Kong Chinese. The BMI was calculated from weight and height and the BF% was determined by deuterium oxide dilution, a chemical-for-compartment model, or dual-energy X-ray absorptiometry. All Asian populations studied had a higher BF% at a lower BMI compared to Caucasians. Generally, for the same BMI their BF% was 3-5% points higher compared to Caucasians. For the same BF% their BMI was 3-4 units lower compared to Caucasians. The high BF% at low BMI can be partly explained by differences in body build, i.e. differences in trunk-to-leg-length ratio and differences in slenderness. Differences in muscularity may also contribute to the different BF%/BMI relationship. Hence, the relationship between BF% and BMI is ethnic-specific. For comparisons of obesity prevalence between ethnic groups, universal BMI cut-off points are not appropriate.
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            Are Asians at greater mortality risks for being overweight than Caucasians? Redefining obesity for Asians.

            To assess whether overweight Asians, assessed on the basis of WHO criteria, are at greater mortality risk than overweight Caucasians, and to determine whether alternative cut-off points (BMI = 23.0-24.9 kg/m2 for overweight and BMI >or= 25.0 kg/m2 for obesity) suggested by the WHO Western Pacific Regional Office are appropriate. The cohort was followed prospectively until the end of 2001. All-cause and CVD mortality risks of the overweight and obese group, relative to the reference group (BMI = 18.5-24.9 or 18.5-22.9 kg/m2), were assessed using Cox regression analysis, adjusting for age, smoking and gender. Excess deaths were estimated with a method proposed by the US Centers for Disease Control and Prevention. National Health Interview Survey (NHIS 2001) and a middle-aged perspective cohort in Taiwan. Subjects comprised 36 386 civil servants and school teachers, aged 40 years and older, who underwent a medical examination during 1989-1992. In the WHO-defined overweight group, Asians showed a significant increase in all-cause mortality risk compared with Caucasians. Asians showed risks equivalent to Caucasians' at lower BMI (around 5 units). Every unit of BMI increase, at 25.0 kg/m2 or above, was associated with a 9 % increase in relative mortality risk from all causes. Applying a cut-off point of 25.0 kg/m2 for obesity would result a prevalence of 27.1 %, while the traditional WHO cut-off point of 30.0 kg/m2 yielded obesity prevalence of 4.1 %. Excess deaths due to obesity accounted for 8.6 % of all deaths and 21.1 % of CVD deaths, based on the alternative cut-offs. In this Asian population, significant mortality risks started at BMI >or= 25.0 kg/m2, rather than at BMI >or= 30.0 kg/m2. The study supports the use of BMI >or= 25.0 kg/m2 as a new cut-off point for obesity and BMI = 23.0-24.9 kg/m2 for overweight. The magnitude of obesity-attributable deaths has been hitherto under-appreciated among Asians.
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              Accuracy of administrative databases in identifying patients with hypertension

              Background Traditionally, the determination of the occurrence of hypertension in patients has relied on costly and time-consuming survey methods that do not allow patients to be followed over time. Objectives To determine the accuracy of using administrative claims data to identify rates of hypertension in a large population living in a single-payer health care system. Methods Various definitions for hypertension using administrative claims databases were compared with 2 other reference standards: (1) data obtained from a random sample of primary care physician offices throughout the province, and (2) self-reported survey data from a national census. Results A case-definition algorithm employing 2 outpatient physician billing claims for hypertension over a 3-year period had a sensitivity of 73% (95% confidence interval [CI] 69%–77%), a specificity of 95% (CI 93%–96%), a positive predictive value of 87% (CI 84%–90%), and a negative predictive value of 88% (CI 86%–90%) for detecting hypertensive adults compared with physician-assigned diagnoses. Compared with self-reported survey data, the algorithm had a sensitivity of 64% (CI 63%–66%), a specificity of 94%(CI 93%–94%), a positive predictive value of 77% (76%–78%), and negative predictive value of 89% (CI 88%–89%). When this algorithm was applied to the entire province of Ontario, the age- and sex-standardized prevalence of hypertension in adults older than 35 years increased from 20% in 1994 to 29% in 2002. Conclusions It is possible to use administrative data to accurately identify from a population sample those patients who have been diagnosed with hypertension. Given that administrative data are already routinely collected, their use is likely to be substantially less expensive compared with serial cross-sectional or cohort studies for surveillance of hypertension occurrence and outcomes over time in a large population.
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                Author and article information

                Journal
                Diabetes Care
                diacare
                dcare
                Diabetes Care
                Diabetes Care
                American Diabetes Association
                0149-5992
                1935-5548
                August 2011
                16 July 2011
                : 34
                : 8
                : 1741-1748
                Affiliations
                [1] 1Institute for Clinical Evaluative Sciences, Ontario, Canada
                [2] 2Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
                [3] 3Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
                [4] 4Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
                [5] 5Statistics Canada, Ottawa, Ontario, Canada
                [6] 6Department of Family Medicine, University of Ottawa, Ottawa, Ontario, Canada
                [7] 7Department of Medicine, University of Toronto, Toronto, Ontario, Canada
                [8] 8Schulich Heart Centre, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
                Author notes
                Corresponding author: Jack V. Tu, tu@ 123456ices.on.ca
                Article
                2300
                10.2337/dc10-2300
                3142051
                21680722
                62fb126b-a786-4750-b474-05a9880dd6cf
                © 2011 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

                History
                : 7 December 2010
                : 28 April 2011
                Categories
                Original Research
                Epidemiology/Health Services Research

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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