Ken Williams

National Cholesterol Education Program Versus World Health Organization Metabolic Syndrome in Relation to All-Cause and Cardiovascular Mortality in the San Antonio Heart Study

Background—To assess the utility of clinical definitions of the metabolic syndrome (MetS) to identify individuals with increased cardiovascular risk, we examined the relation between the MetS, using both the National Cholesterol Education Program (NCEP) and the World Health Organization definitions, and all-cause and cardiovascular mortality in San Antonio Heart Study participants enrolled between 1984 and 1988. Methods and Results—Among 2815 participants, 25 to 64 years of age at enrollment, 509 met both criteria, 197 met NCEP criteria only, and 199 met WHO criteria only. Over an average of 12.7 years, 229 deaths occurred (117 from cardiovascular disease). Moreover, in the primary prevention population of 2372 participants (ie, those without diabetes or cardiovascular disease at baseline), 132 deaths occurred (50 from cardiovascular disease). In the primary prevention population, the only significant association adjusted for age, gender, and ethnic group was between NCEP-MetS and cardiovascular mortality (hazard ratio [HR], 2.01; 95% CI, 1.13–3.57). In the general population, all-cause mortality HRs were 1.47 (95% CI, 1.13–1.92) for NCEP-MetS and 1.27 (95% CI, 0.97–1.66) for WHO-MetS. Furthermore, for cardiovascular mortality, there was evidence that gender modified the predictive ability of the MetS. For women and men, respectively, HRs for NCEP-MetS were 4.65 (95% CI, 2.35–9.21) and 1.82 (95% CI, 1.14–2.91), whereas HRs for WHO-MetS were 2.83 (95% CI, 1.55–5.17) and 1.15 (95% CI, 0.72–1.86). Conclusions—In summary, although both definitions were predictive in the general population, the simpler NCEP definition tended to be more predictive in lower-risk subjects.

Pioglitazone for Diabetes Prevention in Impaired Glucose Tolerance

BACKGROUND Impaired glucose tolerance is associated with increased rates of cardiovascular disease and conversion to type 2 diabetes mellitus. Interventions that may prevent or delay such occurrences are of great clinical importance. METHODS We conducted a randomized, double-blind, placebo-controlled study to examine whether pioglitazone can reduce the risk of type 2 diabetes mellitus in adults with impaired glucose tolerance. A total of 602 patients were randomly assigned to receive pioglitazone or placebo. The median follow-up period was 2.4 years. Fasting glucose was measured quarterly, and oral glucose tolerance tests were performed annually. Conversion to diabetes was confirmed on the basis of the results of repeat testing. RESULTS Annual incidence rates for type 2 diabetes mellitus were 2.1% in the pioglitazone group and 7.6% in the placebo group, and the hazard ratio for conversion to diabetes in the pioglitazone group was 0.28 (95% confidence interval, 0.16 to 0.49; P<0.001). Conversion to normal glucose tolerance occurred in 48% of the patients in the pioglitazone group and 28% of those in the placebo group (P<0.001). Treatment with pioglitazone as compared with placebo was associated with significantly reduced levels of fasting glucose (a decrease of 11.7 mg per deciliter vs. 8.1 mg per deciliter [0.7 mmol per liter vs. 0.5 mmol per liter], P<0.001), 2-hour glucose (a decrease of 30.5 mg per deciliter vs. 15.6 mg per deciliter [1.6 mmol per liter vs. 0.9 mmol per liter], P<0.001), and HbA(1c) (a decrease of 0.04 percentage points vs. an increase of 0.20 percentage points, P<0.001). Pioglitazone therapy was also associated with a decrease in diastolic blood pressure (by 2.0 mm Hg vs. 0.0 mm Hg, P=0.03), a reduced rate of carotid intima-media thickening (31.5%, P=0.047), and a greater increase in the level of high-density lipoprotein cholesterol (by 7.35 mg per deciliter vs. 4.5 mg per deciliter [0.4 mmol per liter vs. 0.3 mmol per liter], P=0.008). Weight gain was greater with pioglitazone than with placebo (3.9 kg vs. 0.77 kg, P<0.001), and edema was more frequent (12.9% vs. 6.4%, P=0.007). CONCLUSIONS As compared with placebo, pioglitazone reduced the risk of conversion of impaired glucose tolerance to type 2 diabetes mellitus by 72% but was associated with significant weight gain and edema. (Funded by Takeda Pharmaceuticals and others; ClinicalTrials.gov number, NCT00220961.).

Identification of Persons at High Risk for Type 2 Diabetes Mellitus: Do We Need the Oral Glucose Tolerance Test?

Context Lifestyle and pharmaceutical interventions can prevent overt diabetes in people with impaired glucose tolerance. Oral glucose tolerance testing is the reference standard for identifying impaired glucose tolerance, but it is inconvenient and relatively expensive. Contribution The authors developed multivariable models that use readily available clinical variables to predict the development of diabetes. The models were more accurate than oral glucose tolerance testing alone. Adding results of oral glucose tolerance testing did not substantially improve the models predictions. Cautions More than half the study sample was Mexican American. Validation in other populations and translation for bedside calculation is needed before clinicians can use the model. The Editors In 1979, the National Diabetes Data Group defined an entity called impaired glucose tolerance, which reflects a degree of glucose tolerance that, although abnormal, is considered insufficient to merit a diagnosis of diabetes mellitus (1). This entity, which was later endorsed by the World Health Organization (WHO) (2, 3) and the American Diabetes Association (4), requires a 2-hour oral glucose tolerance test for its detection. It is important to emphasize that impaired glucose tolerance is by itself entirely asymptomatic and unassociated with any functional disability. Indeed, insulin secretion is typically greater in response to a mixed meal than in response to a pure glucose load (5); as a result, most persons with impaired glucose tolerance are rarely, if ever, hyperglycemic in their daily lives (5, 6), except when they undergo diagnostic glucose tolerance tests. Thus, the importance of impaired glucose tolerance resides exclusively in its ability to identify persons at increased risk for future disease. The standard method for identifying persons at high risk for developing diabetes mellitus has been to identify persons with impaired glucose tolerance without regard to other diabetes risk factors. For example, nearly all of the clinical trials on prevention of type 2 diabetes have used impaired glucose tolerance as the principal enrollment criterion. Three of these trialsthe Finnish Diabetes Prevention Study (7), the Diabetes Prevention Program (8), and the Study To Prevent Non-Insulin-Dependent Diabetes Mellitus (STOP-NIDDM) (9)have recently reported positive results. Therefore, a need has arisen to identify persons at high risk for diabetes so that physicians can offer them preventive interventions. Because the 2-hour oral glucose tolerance test, which is necessary for the diagnosis of impaired glucose tolerance, is time consuming, costly, and inconvenient, it becomes relevant to ask whether other, more efficient means exist for identifying persons at high risk for diabetes. A popular method of assessing the predictive discrimination of a test is to use a receiver-operating characteristic (ROC) curve (10) that plots the sensitivity of the test against the corresponding false-positive rate. In the present context, sensitivity refers to the percentage of persons whose initial values were above a given cut point among all persons who later developed diabetes; false-positive rate refers to the percentage of persons whose initial values were above the cut point among persons who nevertheless remained free of diabetes. The area under the ROC curve is a measure of how well a continuous variable can predict the outcome of interest: If the sensitivity increases steeply as the threshold for diagnosis is relaxed, with only a relatively slow accumulation of false-positive results, the area under the ROC curve will be large; conversely, if the sensitivity increases slowly as the threshold for diagnosis is relaxed, with a rapid accumulation of false-positive results, the area under the ROC curve will be correspondingly smaller. The differences in the areas under two curves may be tested to see whether the apparent superiority of one continuous variable over another is statistically significant. We have used this approach to compare the 2-hour glucose value after an oral glucose load to various multivariable models for predicting future diabetes. Methods Participants Our analyses are based on data gathered in the San Antonio Heart Study. The methods of this study have been described elsewhere (11-13). Briefly, households were randomly sampled from three types of neighborhoods: low, middle, and high income. Residents of these households were eligible if they were 25 to 64 years of age and not pregnant. Because the number of nonMexican-American persons residing in the low-income areas was negligible, only Mexican Americans were recruited from these neighborhoods. Stratified random sampling was used to recruit an approximately equal number of Mexican Americans and non-Hispanic whites from the middle-income and high-income neighborhoods. The baseline data were collected in two phases, from 1979 to 1982 and from 1984 to 1988. A total of 5158 participants were enrolled in these two phases, representing a response rate of 65.3% of all eligible participants from the selected households. A follow-up examination was performed 7 to 8 years after the baseline examination on 3682 persons, representing 73.7% of the 4998 surviving study participants. The Institutional Review Board of the University of Texas Health Science Center at San Antonio approved the protocol. All participants gave informed consent. Measurements Fasting plasma glucose levels were measured for all participants; they then drank a standardized 75-g glucose load (Koladex or Orangedex, Custom Laboratories, Baltimore, Maryland). The plasma glucose level was measured again 2 hours later. Although various protocols have been used for oral glucose tolerance testing, the glucose level obtained 2 hours after the administration of the oral glucose load is the only postload value that has been used as the basis for diagnostic categories (1-4); therefore, this was the only postload value that we considered. In line with common usage, we refer to this value as the 2-hour glucose value. Diabetes was diagnosed according to WHO criteria (fasting glucose level 7.0 mmol/L [ 126 mg/dL] or 2-hour glucose level 11.1 mmol/L [ 200 mg/dL]) (3). Persons who reported a history of diabetes diagnosed by a physician and who reported current use of insulin or an oral antidiabetic agent were considered to have diabetes regardless of their plasma glucose levels. Participants were asked to bring to the examination center a list of all prescription medications that they were receiving or the containers in which the medications had been dispensed. Participants who did not adhere to this request were subsequently contacted by telephone to verify their medications. Participants were classified as having diabetes if they met at least one of the above three criteria (fasting glucose value, 2-hour glucose value, or antidiabetic medications), even if all three variables were not recorded. Participants were classified as nondiabetic if all three variables were recorded and none met the criterion for diabetes. Persons with diabetes who were not taking insulin were considered to have type 2 diabetes. Participants with diabetes who used insulin were considered to have type 2 diabetes if they were at least 30 years of age at diabetes onset and if their body mass index (BMI) (weight in kg divided by height in meters squared) was greater than 27.0 kg/m2. Nondiabetic persons were classified as having impaired glucose tolerance if their 2-hour plasma glucose level was 7.8 mmol/L or higher ( 140 mg/dL) but less than 11.1 mmol/L (<200 mg/dL) (1-4). Height; weight; blood pressure; plasma glucose level; serum total, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) cholesterol levels; and serum triglyceride level were measured by using previously reported methods (11, 12). Statistical Analysis Baseline characteristics of the study sample according to sex and ethnicity were adjusted for age by analysis of covariance using SAS software (14). We developed a multiple logistic regression model with incident diabetes as the dependent variable and a panel of baseline characteristics that are ordinarily available in a routine clinical setting as independent variables. We refer to this modelwhich includes (with or without selected interactions, as explained in the Results section) age; sex; ethnicity; fasting and 2-hour glucose levels; systolic and diastolic blood pressures; total, LDL, and HDL cholesterol levels; triglyceride level; body mass index; and parental or sibling history of diabetesas the full model. In addition to testing the statistical significance of each interaction term, we also used likelihood ratio tests to globally compare models with and without interactions. We assessed the importance of the 2-hour glucose value for predicting diabetes by comparing the full model with a nested model that excluded the 2-hour glucose value. We also examined a simplified model based on widely recognized diabetes risk factors, which we call the clinical model. We believe that clinicians would more readily accept this simplified model because it entails fewer variables. The variables used in the clinical model were age, sex, ethnicity, fasting glucose level, systolic blood pressure, HDL cholesterol level, body mass index, and parental or sibling history of diabetes. This model was also examined with and without selected interactions and with and without 2-hour glucose value. We assessed the goodness of fit of all models by using the HosmerLemeshow test (14). We compared the predictive discrimination of the multivariable models to the predictive discrimination of the 2-hour glucose measurement by using ROC curves. The cutoff point defining impaired glucose tolerance represents only one of many possible cutoff points along the 2-hour glucose curve. The ROC curves were calculated for the multivariable models and for 2-hour glucose concentrati

Fueling the Obesity Epidemic? Artificially Sweetened Beverage Use and Long-term Weight Gain

We have examined the relationship between artificially sweetened beverage (ASB) consumption and long‐term weight gain in the San Antonio Heart Study. From 1979 to 1988, height, weight, and ASB consumption were measured among 5,158 adult residents of San Antonio, Texas. Seven to eight years later, 3,682 participants (74% of survivors) were re‐examined. Outcome measures were incidence of overweight/obesity (OW/OBinc) and obesity (OBinc) (BMI ≥ 25 and ≥ 30 kg/m2, respectively), and BMI change by follow‐up (ΔBMI, kg/m2). A significant positive dose‐response relationship emerged between baseline ASB consumption and all outcome measures, adjusted for baseline BMI and demographic/behavioral characteristics. Consuming >21 ASBs/week (vs. none) was associated with almost‐doubled risk of OW/OB (odds ratio (OR) = 1.93, P = 0.007) among 1,250 baseline normal‐weight (NW) individuals, and doubled risk of obesity (OR = 2.03, P = 0.0005) among 2,571 individuals with baseline BMIs <30 kg/m2. Compared with nonusers (+1.01 kg/m2), ΔBMIs were significantly higher for ASB quartiles 2–4: +1.46 (P = 0.003), +1.50 (P = 0.002), and +1.78 kg/m2 (P < 0.0001), respectively. Overall, adjusted ΔBMIs were 47% greater among artificial sweetner (AS) users than nonusers (+1.48 kg/m2 vs. +1.01 kg/m2, respectively, P < 0.0001). In separate analyses—stratified by gender; ethnicity; baseline weight category, dieting, or diabetes status; or exercise‐change category—ΔBMIs were consistently greater among AS users. These differences, though not significant among exercise increasers, or those with baseline diabetes or BMI >30 kg/m2 (P = 0.069), were significant in all 13 remaining strata. These findings raise the question whether AS use might be fueling—rather than fighting—our escalating obesity epidemic.

Relations of Change in Plasma Levels of LDL-C, Non-HDL-C and apoB With Risk Reduction From Statin Therapy: A Meta-Analysis of Randomized Trials

Background Identifying the best markers to judge the adequacy of lipid‐lowering treatment is increasingly important for coronary heart disease (CHD) prevention given that several novel, potent lipid‐lowering therapies are in development. Reductions in LDL‐C, non‐HDL‐C, or apoB can all be used but which most closely relates to benefit, as defined by the reduction in events on statin treatment, is not established. Methods and Results We performed a random‐effects frequentist and Bayesian meta‐analysis of 7 placebo‐controlled statin trials in which LDL‐C, non‐HDL‐C, and apoB values were available at baseline and at 1‐year follow‐up. Summary level data for change in LDL‐C, non‐HDL‐C, and apoB were related to the relative risk reduction from statin therapy in each trial. In frequentist meta‐analyses, the mean CHD risk reduction (95% CI) per standard deviation decrease in each marker across these 7 trials were 20.1% (15.6%, 24.3%) for LDL‐C; 20.0% (15.2%, 24.7%) for non‐HDL‐C; and 24.4% (19.2%, 29.2%) for apoB. Compared within each trial, risk reduction per change in apoB averaged 21.6% (12.0%, 31.2%) greater than changes in LDL‐C (P<0.001) and 24.3% (22.4%, 26.2%) greater than changes in non‐HDL‐C (P<0.001). Similarly, in Bayesian meta‐analyses using various prior distributions, Bayes factors (BFs) favored reduction in apoB as more closely related to risk reduction from statins compared with LDL‐C or non‐HDL‐C (BFs ranging from 484 to 2380). Conclusions Using both a frequentist and Bayesian approach, relative risk reduction across 7 major placebo‐controlled statin trials was more closely related to reductions in apoB than to reductions in either non‐HDL‐C or LDL‐C.

Linkage of high-density lipoprotein-cholesterol concentrations to a locus on chromosome 9p in Mexican Americans.

High-density lipoproteins (HDLs) are anti-atherogenic lipoproteins that have a major role in transporting cholesterol from peripheral tissues to the liver, where it is removed. Epidemiologic studies have shown that low levels of high-density lipoprotein–cholesterol (HDL-C) are associated with an increased incidence of coronary heart disease and an increased mortality rate, indicating a protective role of high concentrations of HDL-C against atherogenesis and the development of coronary heart disease. HDL-C level is influenced by several genetic and nongenetic factors. Nongenetic factors include smoking, which has been shown to decrease the HDL-C level. Exercise and alcohol have been shown to increase HDL-C levels. Decreased HDL-C is often associated with other coronary heart disease risk factors such as obesity, hyperinsulinemia and insulin resistance, hypertriglyceridemia and hypertension. Although several genes have been identified for rare forms of dyslipidemia, the genes accounting for major variation in HDL-C levels have yet to be identified. Using a multipoint variance components linkage approach, we found strong evidence of linkage (lod score=3.4; P=0.00004) of a quantitative trait locus (QTL) for HDL-C level to a genetic location between markers D9S925 and D9S741 on chromosome 9p in Mexican Americans. A replication study in an independent set of Mexican American families confirmed the existence of a QTL on chromosome 9p.

Concordance between the CC chemokine receptor 5 genetic determinants that alter risks of transmission and disease progression in children exposed perinatally to human immunodeficiency virus

If CC chemokine receptor 5 (CCR5)-dependent mechanisms at the time of initial virus exposure are important determinants of virus entry and disease outcome, then the polymorphisms in CCR5 that influence risk of transmission and disease progression should be similar; this hypothesis was tested in a cohort of 649 Argentinean children exposed perinatally to human immunodeficiency virus type 1 (HIV-1). Two lines of evidence support this hypothesis. First, CCR5 haplotype pairs associated with enhanced risk of transmission were the chief predictors of a faster disease course. Second, some of the haplotype pairs associated with altered rates of transmission and disease progression in children were similar to those that we previously found influenced outcome in European American adults. This concordance suggests that CCR5 haplotypes may serve as genetic rheostats that influence events occurring shortly after initial virus exposure, dictating not only virus entry but, by extension, also the extent of early viral replication.

Genetic pattern of prostate cancer progression

Genetic alterations in primary prostate cancer (CaP) have been extensively studied, yet little is known about the genetic mechanisms underlying progression of primary CaP to metastatic prostate cancer. As a result, it is not possible to distinguish clinically indolent localized disease from potentially life‐threatening tumors with high metastatic potential. To address this question, we collected tissue from 34 autopsy‐derived metastases, samples rarely analyzed in previous studies. These were compared to a separate set of 17 prostatectomy specimens containing 22 foci of CaP associated with 49 examples of high‐grade prostatic intraepithelial neoplasia (PIN), a histological precursor of CaP. We compared the loss of heterozygosity (LOH) profiles of high‐grade PIN, primary CaP and metastases by analyzing 33 microsatellite markers previously found to have high frequencies of LOH in primary CaP. These markers were on chromosomes 5q, 6q, 7q, 8p, 9p, 10q, 11p, 13q, 16q, 17, 18q and 21q. In addition, markers on chromosomes 4p, 11q, 14q and 20q with no reported LOH in primary CaP were analyzed to determine the frequency of background LOH. In PIN lesions, the rate of LOH was significant only at D5S806 (20%) and D16S422 (29%). In addition, different PIN lesions within the same prostate gland were genetically diverse, indicating divergent evolution of synchronous neoplastic precursor lesions. LOH frequency was progressively higher in primary CaP and metastatic lesions. In primary CaP, significant losses occurred at the 8p, 10q, 11p, 16q, 17p, 18q and 21q loci (range 17–43%). Distinct patterns of LOH frequencies were observed in primary CaP compared with metastases. Although some loci (D16S422, D17S960, D21S156) showed similar frequencies of LOH in primary CaP and metastatic CaP, most other loci showed up to 7‐fold metastasis‐related increases. The metastatic samples revealed previously unrecognized prostate cancer LOH at D5S806, D6S262, D9S157, D13S133 and D13S227. These significant stage‐specific differences in LOH frequency specify genetic loci that may play key roles in CaP progression and could represent clinically useful biomarkers for CaP aggressiveness. Int. J. Cancer 81:219–224, 1999.

Elevated Carotid Artery Intima-Media Thickness Levels in Individuals Who Subsequently Develop Type 2 Diabetes

Objective—We examined whether B-mode ultrasound–detected carotid artery intima-media thickness (IMT) was elevated before the onset of clinical diabetes. Methods and Results—The study population for these analyses included 1127 nondiabetic participants, 66 prediabetic participants, and 303 diabetic participants with a mean age of 49.8 years who participated in the Mexico City Diabetes Study, a prospective cohort study. Common carotid artery (CCA) and internal carotid artery (ICA) IMTs were measured bilaterally by B-mode ultrasound. Age- and sex-adjusted mean ICA and CCA IMTs were both significantly higher among prediabetic individuals {0.81 mm [95% confidence interval (CI), 0.75–0.88] and 0.72 mm [95% CI, 0.69–0.75], respectively} than in individuals who remained free of diabetes [0.71 mm (95% CI, 0.69–0.72) and 0.69 mm (95% CI, 0.68–0.69), respectively]. However, after adjustment for established cardiovascular risk factors, ICA IMT, but not CCA IMT, remained significantly higher among prediabetic individuals [0.81 mm (95% CI, 0.75–0.88) and 0.71 mm (95% CI, 0.68–0.74)] than in individuals who remained free of diabetes [0.71 mm (95% CI, 0.69–0.72) and 0.69 mm (95% CI, 0.68–0.70)]. Conclusions—The present study provides direct evidence at the vascular level that atherosclerosis levels are elevated before the clinical onset of diabetes.

Which Obesity Index Best Explains Prevalence Differences in Type 2 Diabetes Mellitus

Objective: Obesity drives the diabetes epidemic. However, it is not known which obesity index best explains variations in type 2 diabetes mellitus prevalence across populations.