Rodolfo Valdez

Prospective Analysis of The Insulin-Resistance Syndrome (Syndrome X)

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.

Birthweight and adult health outcomes in a biethnic population in the USA

SummaryRecent 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 dyslipidaemias, 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 San 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 predictor 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.

Decreased Testosterone and Dehydroepiandrosterone Sulfate Concentrations Are Associated With Increased Insulin and Glucose Concentrations in Nondiabetic Men

Although many studies indicate that increased androgenicity is associated with insulin resistance and hyperinsulinemia in both premenopausal and postmenopausal women, relatively few data are available on this relationship in men. We examined the association of sex hormone-binding globulin (SHBG), total and free testosterone, dehydroepiandrosterone sulfate (DHEA-SO4), and estradiol to glucose and insulin concentrations before and during an oral glucose tolerance test in 178 men from the San Antonio Heart Study, a population-based study of diabetes and cardiovascular disease. Total and free testosterone and DHEA-SO4 were significantly inversely associated with insulin concentrations. Free testosterone and DHEA-SO4 were also significantly inversely correlated with glucose concentrations. SHBG was weakly positively associated with glucose concentrations. Estradiol was not related to glucose or insulin concentrations. After adjustment for age, obesity, and body fat distribution, insulin concentrations remained significantly inversely correlated with free testosterone (r = -.23), total testosterone (r = -.21), and DHEA-SO4 (r = -.21; all P < .01). In conclusion, we observed that increased testosterone and DHEA-SO4 are associated with lower insulin concentrations in men. This is in striking contrast to women, where increased androgenicity is associated with insulin resistance and hyperinsulinemia.

Family History in Public Health Practice: A Genomic Tool for Disease Prevention and Health Promotion*

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.

Family History and Prevalence of Diabetes in the U.S. Population

OBJECTIVE—We sought to test the association between stratified levels of familial risk of diabetes and the prevalence of the disease in the U.S. population. RESEARCH DESIGN AND METHODS—This study includes 16,388 adults interviewed for the National Health and Nutrition Examination Survey between 1999 and 2004. Fasting glucose was available for a subsample of 6,004 participants. Familial risk of diabetes was classified as average, moderate, or high. The prevalence and the odds of having diabetes were estimated for each risk class after accounting for other risk factors. RESULTS—Overall, 69.8% of the U.S. adults were in the average, 22.7% in the moderate, and 7.5% in the high familial risk for diabetes. The crude prevalence of diabetes for each risk class was 5.9, 14.8, and 30%, respectively. The graded association between familial risk and prevalence of diabetes remained even after accounting for sex, race/ethnicity, age, BMI, hypertension, income, and education. Versus people in the average risk class, independently of other risk factors considered, the odds of having diabetes for people in the moderate and high familial risk categories were, respectively, 2.3 and 5.5 times higher. CONCLUSIONS—In the U.S. population, family history of diabetes has a significant, independent, and graded association with the prevalence of diabetes. This association not only highlights the importance of shared genes and environment in diabetes but also opens the possibility of formally adding family history to public health strategies aimed at detecting and preventing the disease.

Family history of type 2 diabetes: A population-based screening tool for prevention?

Purpose: To evaluate the use of self-reported family medical history as a potential screening tool to identify people at-risk for diabetes.Methods: The HealthStyles 2004 mail survey comprises 4345 US adults who completed a questionnaire to ascertain personal and family history of diabetes, perceived risk of diabetes, and practice of risk-reducing behaviors. Using number and type of affected relatives, respondents were ranked into three familial risk levels. Adjusted odds ratios (AORs) were obtained to evaluate associations between familial risk and prevalent diabetes, perceived risk of disease, and risk-reducing behaviors. Validity of family history as a screening tool was examined by calculating sensitivity, specificity, and positive and negative predictive values.Results: Compared to those of average risk, people with moderate and high familial risk of diabetes were more likely to report a diagnosis of diabetes (AOR: 3.6, 95% CI: 2.8, 4.7; OR: 7.6, 95% CI: 5.9, 9.8, respectively), a higher perceived risk of diabetes (AOR: 4.6, 95% CI: 3.7, 5.7; OR: 8.5, 95% CI: 6.6, 17.7, respectively), and making lifestyle changes to prevent diabetes (AOR: 2.2, 95% CI: 1.8, 2.7; OR: 4.5, 95% CI: 3.6, 5.6, respectively). A positive familial risk of diabetes identified 73% of all respondents with diabetes and correctly predicted prevalent diabetes in 21.5% of respondents.Conclusion: Family history of diabetes is not only a risk factor for the disease but is also positively associated with risk awareness and risk-reducing behaviors. It may provide a useful screening tool for detection and prevention of diabetes.

Application of support vector machine modeling for prediction of common diseases: the case of diabetes and pre-diabetes

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.

Relationship of Proinsulin and Insulin to Cardiovascular Risk Factors in Nondiabetic Subjects

Recent data suggest that proinsulin is strongly associated with cardiovascular risk factors in diabetic subjects. However, this relationship has not been examined in nondiabetic subjects. Therefore, we examined the relation of proinsulin to lipids, obesity (body mass index), and waist-to-hip ratio in 260 nondiabetic individuals from the San Antonio Heart Study, a population-based study of diabetes and cardiovascular disease. Proinsulin was measured by radioimmunoassay, and insulin was measured by a Linco radioimmunoassay that does not cross-react with proinsulin. Fasting insulin was significantly associated with body mass index (0.42), waist-to-hip ratio (r = 0.30), triglyceride (r = 0.29), high-density lipoprotein cholesterol (r = −0.20), and systolic blood pressure (r = 0.16) but not significantly related to diastolic blood pressure (r = 0.11). Fasting proinsulin was significantly associated with body mass index (r = 0.19), waist-to-hip ratio (r = 0.25), triglyceride (r = 0.41), systolic blood pressure (r = 0.19), and diastolic blood pressure (r = 0.15). Proinsulin was more strongly related to increased triglyceride than insulin despite its weaker relationship to obesity. In multivariate analyses, proinsulin continued to be significantly related to triglyceride concentrations (explaining 23.1% of the variance) and to systolic blood pressure (explaining 4.0% of the variance), even after adjusting for insulin. These observations suggest that proinsulin should be measured in addition to insulin in epidemiological studies. Proinsulin may be a marker for metabolic decompensation in prediabetic subjects.

Incidence of NIDDM and impaired glucose tolerance in hypertensive subjects: The San Antonio heart study

Hypertension often occurs in association with NIDDM and IGT. We examined the association of hypertension at baseline to the 8-yr incidence of NIDDM and IGT in 1471 subjects who participated in the San Antonio Heart Study. Subjects who were hypertensive at baseline had a higher incidence of NIDDM (8.9 vs. 4.9%, P = 0.041) and IGT (25.2 vs. 10.0%, P < 0.001) than subjects who were normotensive at baseline. After adjusting for age, sex, ethnicity, obesity, body fat distribution, fasting glucose, and insulin, this excess was eliminated for NIDDM, but not for IGT. Specifically, the adjusted OR for NIDDM in hypertensive versus normotensive patients was 0.73 (95% Cl 0.34–1.58), whereas the adjusted OR for IGT was 1.87 (95% Cl 1.08–3.22). The excess risk of NIDDM in hypertensive patients can be explained by their greater age, obesity, more unfavorable body fat distribution, and hyperinsulinemia, whereas the excess risk of IGT is independent of these factors.

Endogenous sex hormones: Impact on lipids, lipoproteins, and insulin

Estrogen use has been reported to decrease triglyceride and low-density lipoprotein cholesterol (LDL-C) and increase high-density lipoprotein cholesterol (HDL-C). Estrogen use increases the secretion of large, very low-density lipoprotein cholesterol (VLDL-C) and also stimulates the uptake of VLDL-C by the liver and increases the catabolism of LDL-C in the liver. Sex hormones may affect several enzymes involved in the metabolism of HDL-C and triglyceride and may also affect lipolysis. In both pre- and postmenopausal women, several studies have shown that increased glucose and insulin concentrations are associated with increased free testosterone and decreased sex hormone binding globulin. The temporal direction of this relationship in premenopausal women is not clear, however. In contrast to women, increased androgen concentrations in men do not seem to be associated with increased cardiovascular risk factors, although testosterone concentrations are associated with increased HDL-C and decreased insulin concentrations. Dehydroepiandrosterone (DHEA) and dehydroepiandrosterone sulfate (DHEAS) appear to be associated with improved cardiovascular risk factors in men, but this connection in women is less clear.