Lipid measures for prediction of incident cardiovascular disease in diabetic and non-diabetic adults: results of the 8.6 years follow-up of a population based cohort study

<p>Abstract</p> <p>Background</p> <p>Diabetes is a strong risk factor for cardiovascular disease (CVD).The relative role of various lipid measures in determining CVD risk in diabetic patients is still a subject of debate. We aimed to compare performance of different lipid measures as predictors of CVD using discrimination and fitting characteristics in individuals with and without diabetes mellitus from a Middle East Caucasian population.</p> <p>Methods</p> <p>The study population consisted of 1021 diabetic (men = 413, women = 608) and 5310 non-diabetic (men = 2317, women = 2993) subjects, aged ≥ 30 years, free of CVD at baseline. The adjusted hazard ratios (HRs) for CVD were calculated for a 1 standard deviation (SD) change in total cholesterol (TC), log-transformed triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), non-HDL-C, TC/HDL-C and log-transformed TG/HDL-C using Cox proportional regression analysis. Incident CVD was ascertained over a median of 8.6 years of follow-up.</p> <p>Results</p> <p>A total of 189 (men = 91, women = 98) and 263(men = 169, women = 94) CVD events occurred, in diabetic and non-diabetic population, respectively. The risk factor adjusted HRs to predict CVD, except for HDL-C, TG and TG/HDL-C, were significant for all lipid measures in diabetic males and were 1.39, 1.45, 1.36 and 1.16 for TC, LDL-C, non- HDL-C and TC/HDL-C respectively. In diabetic women, using multivariate analysis, only TC/HDL-C had significant risk [adjusted HR1.31(1.10-1.57)].Among non-diabetic men, all lipid measures, except for TG, were independent predictors for CVD however; a 1 SD increase in HDL-C significantly decreased the risk of CVD [adjusted HR 0.83(0.70-0.97)].In non-diabetic women, TC, LDL-C, non-HDL-C and TG were independent predictors.</p> <p>There was no difference in the discriminatory power of different lipid measures to predict incident CVD in the risk factor adjusted models, in either sex of diabetic and non-diabetic population.</p> <p>Conclusion</p> <p>Our data according to important test performance characteristics provided evidence based support for WHO recommendation that along with other CVD risk factors serum TC vs. LDL-C, non-HDL-C and TC/HDL-C is a reasonable lipid measure to predict incident CVD among diabetic men. Importantly, HDL-C did not have a protective effect for incident CVD among diabetic population; given that the HDL-C had a protective effect only among non- diabetic men.</p

"Predictability of body mass index for diabetes: Affected by the presence of metabolic syndrome?"

<p>Abstract</p> <p>Background</p> <p>Metabolic syndrome (MetS) and body mass index (BMI, kg.m^-2) are established independent risk factors in the development of diabetes; we prospectively examined their relative contributions and joint relationship with incident diabetes in a Middle Eastern cohort.</p> <p>Method</p> <p>participants of the ongoing Tehran lipid and glucose study are followed on a triennial basis. Among non-diabetic participants aged≥ 20 years at baseline (8,121) those with at least one follow-up examination (5,250) were included for the current study. Multivariate logistic regression models were used to estimate sex-specific adjusted odd ratios (ORs) and 95% confidence intervals (CIs) of baseline BMI-MetS categories (normal weight without MetS as reference group) for incident diabetes among 2186 men and 3064 women, aged ≥ 20 years, free of diabetes at baseline.</p> <p>Result</p> <p>During follow up (median 6.5 years); there were 369 incident diabetes (147 in men). In women without MetS, the multivariate adjusted ORs (95% CIs) for overweight (BMI 25-30 kg/m2) and obese (BMI≥30) participants were 2.3 (1.2-4.3) and 2.2 (1.0-4.7), respectively. The corresponding ORs for men without MetS were 1.6 (0.9-2.9) and 3.6 (1.5-8.4) respectively. As compared to the normal-weight/without MetS, normal-weight women and men with MetS, had a multivariate-adjusted ORs for incident diabetes of 8.8 (3.7-21.2) and 3.1 (1.3-7.0), respectively. The corresponding ORs for overweight and obese women with MetS reached to 7.7 (4.0-14.9) and 12.6 (6.9-23.2) and for men reached to 3.4(2.0-5.8) and 5.7(3.9-9.9), respectively.</p> <p>Conclusion</p> <p>This study highlights the importance of screening for MetS in normal weight individuals. Obesity increases diabetes risk in the absence of MetS, underscores the need for more stringent criteria to define healthy metabolic state among obese individuals. Weight reduction measures, thus, should be encouraged in conjunction with achieving metabolic targets not addressed by current definition of MetS, both in every day encounter and public health setting.</p

The influence of a covariate on optimal designs in longitudinal studies with discrete-time survival endpoints

Longitudinal intervention studies on event occurrence can measure the timing of an event at discrete points in time. To design studies of this kind as inexpensively and efficiently as possible, researchers need to decide on the number of subjects and the number of measurements for each subject. Different combinations of these design factors may produce the same level of power, but each combination can have different costs. When applying a cost function, the optimal design gives the optimal number of subjects and measurements, thus maximizing the power for a given budget and achieving sufficient power at minimal costs. Only very limited research has been conducted on the effect of a predictive covariate on optimal designs for a treatment effect estimator. Here, we go one step further than previous studies on optimal designs and demonstrate the extent to which a binary covariate influences the optimal design. An examination of various covariate effects and prevalences shows how substantially the covariate affects the optimal design and this effect is partly associated with the cost ratio between sampling subjects and measurements, and the survival pattern. So since the optimal design is sensitive to misspecification of these factors, we advise researchers to carefully specify the covariate effect and prevalence

Optimal designs in longitudinal trials with varying treatment effects and discrete-time survival endpoints

It is plausible to assume that the treatment effect in a longitudinal study will vary over time. It can become either stronger or weaker as time goes on. Here, we extend previous work on optimal designs for discrete-time survival analysis to trials with the treatment effect varying over time. In discrete-time survival analysis, subjects are measured in discrete time intervals, while they may experience the event at any point in time. We focus on studies where the width of time intervals is fixed beforehand, meaning that subjects are measured more often when the study duration increases. The optimal design is defined as the optimal combination of the number of subjects, the number of measurements for each subject, and the optimal proportion of subjects assigned to the experimental condition. We study optimal designs for different optimality criteria and linear cost functions. We illustrate the methodology of finding optimal designs using a clinical trial that studies the effect of an outpatient mental health program on reducing substance abuse among patients with severe mental illness. We observe that optimal designs depend to some extent on the rate at which group differences vary across time intervals and the direction of these changes over time. We conclude that an optimal design based on the assumption of a constant treatment effect is not likely to be efficient if the treatment effect varies across time

The influence of a covariate on optimal designs in longitudinal studies with discrete-time survival endpoints

Longitudinal intervention studies on event occurrence can measure the timing of an event at discrete points in time. To design studies of this kind as inexpensively and efficiently as possible, researchers need to decide on the number of subjects and the number of measurements for each subject. Different combinations of these design factors may produce the same level of power, but each combination can have different costs. When applying a cost function, the optimal design gives the optimal number of subjects and measurements, thus maximizing the power for a given budget and achieving sufficient power at minimal costs. Only very limited research has been conducted on the effect of a predictive covariate on optimal designs for a treatment effect estimator. Here, we go one step further than previous studies on optimal designs and demonstrate the extent to which a binary covariate influences the optimal design. An examination of various covariate effects and prevalences shows how substantially the covariate affects the optimal design and this effect is partly associated with the cost ratio between sampling subjects and measurements, and the survival pattern. So since the optimal design is sensitive to misspecification of these factors, we advise researchers to carefully specify the covariate effect and prevalence

The Design of Cluster Randomized Trials With Random Cross-Classifications

Data from cluster randomized trials do not always have a pure hierarchical structure. For instance, students are nested within schools that may be crossed by neighborhoods, and soldiers are nested within army units that may be crossed by mental health–care professionals. It is important that the random cross-classification is taken into account while planning a cluster randomized trial. This article presents sample size equations, such that a desired power level is achieved for the test on treatment effect. Furthermore, it also presents optimal sample sizes given a budgetary constraint, with a special focus on conditional optimal designs where one of the sample sizes is fixed beforehand. The optimal design methodology is illustrated using a postdeployment training to reduce ill-health in armed forces personnel

Optimal number of accrual groups and accrual group sizes in longitudinal trials with discrete-time survival endpoints

In longitudinal trials, the number of accrual groups and their sizes should carefully be chosen to ensure a desired power to detect a specified treatment effect. Methods are proposed to obtain a cost-effective combination of the number and size of accrual groups that provides high efficiency at minimal cost. We focus on trials where an event occurs at any point in time, but it is recorded on a discrete scale. The Weibull survival function is considered for modeling the underlying time to event. By using a cost function, it is shown that the ratio of the cost of recruiting and treating subjects to the cost of measuring them and also the survival pattern highly influence the optimal combination of the number and size of accrual groups. A maximin approach is further presented to obtain robust designs with respect to poor specification of these modeling parameters. We also show the application of the proposed optimal design methodology using real examples

Continuous Adequate Iodine Supplementation in Fars Province: The 2007 Goiter and Urinary Iodine Excretion Survey in Schoolchildren

Background: The iodine deficiency elimination program thatbegan two decades ago resulted in Iran becoming an iodinedeficiency disorders free country in the Middle East region.The present study was performed to evaluate the adequacy ofiodine supplementation after 17 years of universal salt iodizationin Fars province.Methods: In a cross-sectional study, 1200 schoolchildren (480girls and 720 boys) aged 8 to10 years, were randomly selectedfrom Fars province and evaluated in 2007. Goiter prevalence,urinary iodine excretion, and iodine content of household saltswere measured and the data were compared with those obtainedin 1996 and 2001.Results: Total prevalence of goiter was 1.3% (CI: 0.53-2.47)and no grade 2 goiter was found. One-tenth of the childrenenrolled for goiter assessment, were randomly selected forurinary iodine measurement. The median urinary iodine inthese 120 schoolchildren was 159.4 μg/L (85.6-252.3), with14.8% having urinary iodine excretion less than 50 μg/L. 98%of households were using purified iodized salt. 70% of householdshad appropriate salt storage and none of the householdsalts contained less than 15 μg iodide.Conclusion: Goiter prevalence has significantly decreased inthe Fars province, 17 years after universal salt iodization. Themedian urinary iodine of schoolchildren was adequate as thatreported in 1996 and 2001, indicating a well established sustainableiodine deficiency elimination program in the province