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Many studies have shown that hyperinsulinemia and/or insulin resistance are related to various metabolic and physiological disorders including hypertension, dyslipidemia, and non-insulin-dependent diabetes mellitus. This syndrome has been termed Syndrome X. An important limitation of previous studies has been that they all have been cross sectional, and thus the presence of insulin resistance could be a consequence of the underlying metabolic disorders rather than its cause. We examined the relationship of fasting insulin concentration (as an indicator of insulin resistance) to the incidence of multiple metabolic abnormalities in the 8-yr follow-up of the cohort enrolled in the San Antonio Heart Study, a population-based study of diabetes and cardiovascular disease in Mexican Americans and non-Hispanic whites. In univariate analyses, fasting insulin was related to the incidence of the following conditions: hypertension, decreased high-density lipoprotein cholesterol concentration, increased triglyceride concentration, and non-insulin-dependent diabetes mellitus. Hyperinsulinemia was not related to increased low-density lipoprotein or total cholesterol concentration. In multivariate analyses, after adjustment for obesity and body fat distribution, fasting insulin continued to be significantly related to the incidence of decreased high-density lipoprotein cholesterol and increased triglyceride concentrations and to the incidence of non-insulin-dependent diabetes mellitus. Baseline insulin concentrations were higher in subjects who subsequently developed multiple metabolic disorders. These results were not attributable to differences in baseline obesity and were similar in Mexican Americans and non-Hispanic whites. These results support the existence of a metabolic syndrome and the relationship of that syndrome to multiple metabolic disorders by showing that elevations of insulin concentration precede the development of numerous metabolic disorders.

BackgroundWe present a potentially useful alternative approach based on support vector machine (SVM) techniques to classify persons with and without common diseases. We illustrate the method to detect persons with diabetes and pre-diabetes in a cross-sectional representative sample of the U.S. population.MethodsWe used data from the 1999-2004 National Health and Nutrition Examination Survey (NHANES) to develop and validate SVM models for two classification schemes: Classification Scheme I (diagnosed or undiagnosed diabetes vs. pre-diabetes or no diabetes) and Classification Scheme II (undiagnosed diabetes or pre-diabetes vs. no diabetes). The SVM models were used to select sets of variables that would yield the best classification of individuals into these diabetes categories.ResultsFor Classification Scheme I, the set of diabetes-related variables with the best classification performance included family history, age, race and ethnicity, weight, height, waist circumference, body mass index (BMI), and hypertension. For Classification Scheme II, two additional variables--sex and physical activity--were included. The discriminative abilities of the SVM models for Classification Schemes I and II, according to the area under the receiver operating characteristic (ROC) curve, were 83.5% and 73.2%, respectively. The web-based tool-Diabetes Classifier was developed to demonstrate a user-friendly application that allows for individual or group assessment with a configurable, user-defined threshold.ConclusionsSupport vector machine modeling is a promising classification approach for detecting persons with common diseases such as diabetes and pre-diabetes in the population. This approach should be further explored in other complex diseases using common variables.

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Recent data indicate that low-birthweight adults are at a higher risk than their high-birthweight peers of developing ischaemic heart disease or a cluster of conditions known as the IRS, which includes dys-lipidaemias, hypertension, unfavourable body fat distribution and NIDDM. Thus far these observations have been limited to Caucasians from the United Kingdom. We extended these observations to a broader segment of the general population by studying the association of birthweight and adult health outcomes in a biethnic population of the United States. We divided a group of 564 young adult Mexican-American and non-Hispanic white men and women participants of the S an Antonio Heart Study into tertiles of birthweight and compared metabolic, anthropometric, haemodynamic, and demographic characteristics across these tertile categories. Additionally, we studied birthweight as a pre-dictor of the clustering of diseases associated with the IRS, defined as any two or more of the following conditions: hypertension, NIDDM or impaired glucose tolerance, dyslipidaemia. Normotensive, non-diabetic individuals whose birthweight was in the lowest tertile had significantly higher levels of fasting serum insulin and a more truncal fat deposition pattern than individuals whose birthweight was in the highest tertile, independently of sex, ethnicity, and current socioeconomic status. Also, the odds of expressing the IRS increased 1.72 times (95% confidence interval: 1.16-2.55) for each tertile decrease in birthweight. These findings were independent of sex, ethnicity, and current levels of socioeconomic status or obesity. In conclusion, low birthweight could be a major independent risk factor for the development of adult chronic conditions commonly associated with insulin resistance in the general population. [Diabetologia (1994) 37: 624-631]

The increasing prevalence of obesity in early life indicates a need for primary prevention. Additional study is needed to determine whether these trends are continuing to accelerate and to examine possible explanations, such as diet and physical activity, for these changes.

Family history is a risk factor for many chronic diseases, including cancer, cardiovascular disease, and diabetes. Professional guidelines usually include family history to assess health risk, initiate interventions, and motivate behavioral changes. The advantages of family history over other genomic tools include a lower cost, greater acceptability, and a reflection of shared genetic and environmental factors. However, the utility of family history in public health has been poorly explored. To establish family history as a public health tool, it needs to be evaluated within the ACCE framework (analytical validity; clinical validity; clinical utility; and ethical, legal, and social issues). Currently, private and public organizations are developing tools to collect standardized family histories of many diseases. Their goal is to create family history tools that have decision support capabilities and are compatible with electronic health records. These advances will help realize the potential of family history as a public health tool.

EWET identifies a syndrome of lipid overaccumulation associated with metabolic risk and accelerated mortality after middle age. Prospective studies should evaluate this simple indicator.