OBJECTIVES:Nonalcoholic fatty liver disease is the most common chronic liver disease. Fatty pancreas has also been described but is diffi cult to assess. It is now possible to measure pancreatic and liver fat accurately with magnetic resonance imaging (MRI). We aimed to defi ne the normal range of pancreatic fat and identify factors associated with fatty pancreas. In addition, the effect of fatty liver and fatty pancreas on insulin resistance (IR) and pancreatic  -cell function was studied. METHODS:Fat-water MRI and proton-magnetic resonance spectroscopy were performed on 685 healthy volunteers from the general population to measure pancreatic and liver fat, respectively. On the basis of fasting plasma glucose and insulin levels, the IR and  -cell function were assessed using the homeostasis model assessment (HOMA). RESULTS:Among subjects without signifi cant alcohol consumption or any component of metabolic syndrome, 90 % had pancreatic fat between 1.8 and 10.4 % . Using the upper limit of normal of 10.4 % , 110 (16.1 % ; 95 % confi dence interval 13.3 -18.8 % ) subjects had fatty pancreas. On multivariable analysis, high serum ferritin, central obesity, and hypertriglyceridemia were independent factors associated with fatty pancreas. Subjects with both fatty pancreas and fatty liver had higher HOMA-IR than did those with either condition alone. Fatty pancreas was not associated with HOMA-β after adjusting for liver fat and body mass index.CONCLUSIONS: In all, 16.1 % of this community cohort of adult Hong Kong Chinese volunteers had a fatty pancreas by our defi nition. Central obesity, hypertriglyceridemia, and hyperferritinemia are associated with fatty pancreas. Individuals with fatty pancreas have increased IR.
OBJECTIVE -We sought to develop stroke risk equations for Chinese patients with type 2 diabetes in Hong Kong. RESEARCH DESIGN AND METHODS -A total of 7,209Hong Kong Chinese type 2 diabetic patients without a history of stroke at baseline were analyzed. The data were randomly and evenly divided into the training subsample and the test subsample. In the training subsample, stepwise Cox models were used to develop the risk equation. Validation of the U.K. Prospective Diabetes Study (UKPDS) stroke risk engine and the current stroke equation was performed in the test dataset. The life-table method was used to check calibration, and the area under the receiver operating characteristic curve (aROC) was used to check discrimination.RESULTS -A total of 372 patients developed incident stroke during a median of 5.37 years (interquartile range 2.88 -7.78) of follow-up. Age, A1C, spot urine albumin-to-creatinine ratio (ACR), and history of coronary heart disease (CHD) were independent predictors. The performance of the UKPDS stroke engine was suboptimal in our cohort. The newly developed risk equation defined by these four predictors had adequate performance in the test subsample. The predicted stroke-free probability by the current equation was within the 95% CI of the observed probability. The aROC was 0.77 for predicting stroke within 5 years. The risk score was computed as follows: 0.0634 ϫ age (years) ϩ 0.0897 ϫ A1C ϩ 0.5314 ϫ log 10 (ACR) (mg/mmol) ϩ 0.5636 ϫ history of CHD (1 if yes). The 5-year stroke probability can be calculated by: 1 Ϫ 0.9707 EXP (Risk Score Ϫ 4.5674) .CONCLUSIONS -Although the risk equation performed reasonably well in Chinese type 2 diabetic patients, external validation is required in other populations. Diabetes Care 30:65-70, 2007S troke is among the most common causes of death worldwide (1). Chinese individuals have a higher incidence of stroke and related mortality than Caucasians, as shown in the World Health Organization MONICA project (2). Diabetic patients have a two-to fivefold increased risk of stroke, in part due to interactions between multiple risk factors (3). The Framingham Study (4) and U.K. Prospective Diabetes Study (UKPDS) (5) have developed risk equations based on data collected from the Caucasian community and diabetic patients. Although a stroke risk equation has been developed in a small cohort of Chinese men recruited from a workforce (6), there is currently no risk equation applicable to Chinese individuals with diabetes, despite this number being projected to 42.3 million by 2030 (7). In this study, we validate and develop stroke risk equations to predict first stroke in Chinese type 2 diabetic patients based on data from the Hong Kong Diabetes Registry. RESEARCH DESIGN AND METHODS-Since 1995, all newly referred diabetic patients to the Prince of Wales Hospital in Hong Kong underwent comprehensive assessments of complications and risk factors based on the European DiabCare protocol (7a). Patients with hospital admissions within 6 -8 weeks before assessment accounted for...
OBJECTIVE -The purpose of this study was to compare the predictive value for coronary heart disease (CHD) of the International Diabetes Federation (IDF) definition (with Asian criteria for central obesity) of the metabolic syndrome with existing criteria of the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) in Chinese subjects with type 2 diabetes.RESEARCH DESIGN AND METHODS -Subjects with type 2 diabetes and without macrovascular diseases or end-stage renal disease were categorized by the criteria of the IDF and the NCEP ATP III. CHD was defined as myocardial infarction, ischemic heart disease, coronary revascularization, heart failure, and death related to CHD. CONCLUSIONS -With established type 2 diabetes, the IDF definition of the metabolic syndrome failed to identify a subgroup of patients who had the highest risk for CHD. Practitioners must recognize the appropriate setting for its application. RESULTS Diabetes Care 30:1206 -1211, 2007C oexistence of glucose intolerance, central obesity, insulin resistance, hypertension, dyslipidemia, proinflammatory state, gout, and albuminuria is associated with premature atherosclerosis and coronary heart disease (CHD) (1-3) as well as type 2 diabetes (4,5). The constellation of these conditions is known as the metabolic syndrome. Various criteria have been proposed by the World Health Organization (WHO) (6), the National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) (7), and the European Group for the Study of Insulin Resistance (8) to define the clustering of such risk factors in individuals. There are essential components that are common to all definitions, such as glucose intolerance, obesity, hypertension, and dyslipidemia, although the exact criteria differ among definitions. For subjects without diabetes, the need for assessment of insulin resistance by either an oral glucose tolerance test or the hyperinsulinemic-euglycemic clamp implies that the WHO definition is more appropriate for clinical research purposes. In contrast, the NCEP ATP III definition is better suited for clinical practice because it only requires measurement of fasting blood glucose (9). Furthermore, given the difference in adiposity among different populations, the cutoff points for obesity in the WHO and NCEP ATP III definitions have been questioned (10,11). To provide a more clinician-friendly definition for the metabolic syndrome than the original P.C.Y.T. had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.Abbreviations: ACR, albumin-to-creatinine ratio; ATP III, Adult Treatment Panel III; CHD, coronary heart disease; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; IDF, International Diabetes Federation; NCEP, National Cholesterol Education Program; WHO, World Health Organization.A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.
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