How to analyze many contingency tables simultaneously in genetic association studies

We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chi-squared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are show

Lower Fasting Muscle Mitochondrial Activity Relates to Hepatic Steatosis in Humans

OBJECTIVE Muscle insulin resistance has been implicated in the development of steatosis and dyslipidemia by changing the partitioning of postprandial substrate fluxes. Also, insulin resistance may be due to reduced mitochondrial function. We examined the association between mitochondrial activity, insulin sensitivity, and steatosis in a larger human population. RESEARCH DESIGN AND METHODS We analyzed muscle mitochondrial activity from ATP synthase flux (fATP) and ectopic lipids by multinuclei magnetic resonance spectroscopy from 113 volunteers with and without diabetes. Insulin sensitivity was assessed from M values using euglycemic-hyperinsulinemic clamps and/or from oral glucose insulin sensitivity (OGIS) using oral glucose tolerance tests. RESULTS Muscle fATP correlated negatively with hepatic lipid content and HbA1c. After model adjustment for study effects and other confounders, fATP showed a strong negative correlation with hepatic lipid content and a positive correlation with insulin sensitivity and fasting C-peptide. The negative correlation of muscle fATP with age, HbA1c, and plasma free fatty acids was weakened after adjustment. Body mass, muscle lipid contents, plasma lipoproteins, and triglycerides did not associate with fATP. CONCLUSIONS The association of impaired muscle mitochondrial activity with hepatic steatosis supports the concept of a close link between altered muscle and liver energy metabolism as early abnormalities promoting insulin resistance

Association of cardiac autonomic dysfunction with higher levels of plasma lipid metabolites in recent-onset type 2 diabetes

Aims/hypothesis!#!Emerging evidence suggests that in addition to hyperglycaemia, dyslipidaemia could represent a contributing pathogenetic factor to diabetic neuropathy, while obesity and insulin resistance play a role in the development of diabetic cardiac autonomic neuropathy (CAN) characterised by reduced heart rate variability (HRV), particularly in type 2 diabetes. We hypothesised that distinct lipid metabolites are associated with diminished HRV in recent-onset type 2 diabetes rather than type 1 diabetes.!##!Methods!#!We analysed 127 plasma lipid metabolites (11 acylcarnitines, 39 NEFA, 12 sphingomyelins (SMs), 56 phosphatidylcholines and nine lysophosphatidylcholines) using MS in participants from the German Diabetes Study baseline cohort recently diagnosed with type 1 (n = 100) and type 2 diabetes (n = 206). Four time-domain HRV indices (number of normal-to-normal (NN) intervals >50 ms divided by the number of all NN intervals [pNN50]; root mean square of successive differences [RMSSD]; SD of NN intervals [SDNN]; and SD of differences between adjacent NN intervals) and three frequency-domain HRV indices (very-low-frequency [VLF], low-frequency [LF] and high-frequency [HF] power spectrum) were computed from NN intervals recorded during a 3 h hyperinsulinaemic-euglycaemic clamp at baseline and in subsets of participants with type 1 (n = 60) and type 2 diabetes (n = 95) after 5 years.!##!Results!#!In participants with type 2 diabetes, after Bonferroni correction and rigorous adjustment, SDNN was inversely associated with higher levels of diacyl-phosphatidylcholine (PCaa) C32:0, PCaa C34:1, acyl-alkyl-phosphatidylcholine (PCae) C36:0, SM C16:0 and SM C16:1. SD of differences between NN intervals was inversely associated with PCaa C32:0, PCaa C34:1, PCaa C34:2, PCae C36:0 and SM C16:1, and RMSSD with PCae C36:0. For VLF power, inverse associations were found with PCaa C30:0, PCaa C32:0, PCaa C32:1, PCaa C34:2 and SM C16:1, and for LF power inverse associations were found with PCaa C32:0 and SM C16:1 (r = -0.242 to r = -0.349; p ≤ 0.0005 for all correlations). In contrast, no associations of lipid metabolites with measures of cardiac autonomic function were noted in participants recently diagnosed with type 1 diabetes. After 5 years, HRV declined due to ageing rather than diabetes, whereby prediction analyses for lipid metabolites were hampered.!##!Conclusions/interpretation!#!Higher plasma levels of specific lipid metabolites are closely linked to cardiac autonomic dysfunction in recent-onset type 2 diabetes but not type 1 diabetes, suggesting a role for perturbed lipid metabolism in the early development of CAN in type 2 diabetes. Graphical abstract

How to analyze many contingency tables simultaneously in genetic association studies

We study exact tests for (2 x 2) and (2 x 3) contingency tables, in particular exact chi-squared tests and exact tests of Fisher type. In practice, these tests are typically carried out without randomization, leading to reproducible results but not exhausting the significance level. We discuss that this can lead to methodological and practical issues in a multiple testing framework when many tables are simultaneously under consideration as in genetic association studies.Realized randomized p-values are proposed as a solution which is especially useful for data-adaptive (plug-in) procedures. These p-values allow to estimate the proportion of true null hypotheses much more accurately than their non-randomized counterparts. Moreover, we address the problem of positively correlated p-values for association by considering techniques to reduce multiplicity by estimating the "effective number of tests" from the correlation structure.An algorithm is provided that bundles all these aspects, efficient computer implementations are made available, a small-scale simulation study is presented and two real data examples are shown

Health behaviour in children and adolescents with type 1 diabetes compared to a representative reference population.

We provide a population-based overview of health behaviours of children and adolescents with type 1 diabetes in comparison to the general population, and analyse their relevance for glycaemic control and self-rated health status.Data from questionnaires of 11- to 17-year-old children and adolescents with diabetes (n = 629) were compared to a representative sample (n = 6,813).Children and adolescents with type 1 diabetes had a significantly increased odds of infrequent physical activity (adjusted OR 1.56), short overall duration of physical activity per week (OR 1.55, difference -1.3 hours/week), and high daily computer use (OR 2.51). They had a lower odds of active and passive smoking (OR 0.31 and OR 0.29), and high daily television time (OR 0.68). The odds of an at least good and excellent self-rated health status was increased with intense physical activity, and decreased with active smoking and prolonged daily use of computer and television. Active smoking and prolonged daily use of computer were associated with higher HbA1c.Children and adolescents with type 1 diabetes showed a different profile of health behaviour. Their overall health may improve if their education stresses specifically frequent physical activity with longer overall duration and less frequent television or computer use

Direct diabetes-related costs in young patients with early-onset, long-lasting type 1 diabetes.

OBJECTIVE: To estimate diabetes-related direct health care costs in pediatric patients with early-onset type 1 diabetes of long duration in Germany. RESEARCH DESIGN AND METHODS: Data of a population-based cohort of 1,473 subjects with type 1 diabetes onset at 0-4 years of age within the years 1993-1999 were included (mean age 13.9 (SD 2.2) years, mean diabetes duration 10.9 (SD 1.9) years, as of 31.12.2007). Diabetes-related health care services utilized in 2007 were derived from a nationwide prospective documentation system (DPV). Health care utilization was valued in monetary terms based on inpatient and outpatient medical fees and retail prices (perspective of statutory health insurance). Multiple regression models were applied to assess associations between direct diabetes-related health care costs per patient-year and demographic and clinical predictors. RESULTS: Mean direct diabetes-related health care costs per patient-year were €3,745 (inter-quartile range: 1,943-4,881). Costs for glucose self-monitoring were the main cost category (28.5%), followed by costs for continuous subcutaneous insulin infusion (25.0%), diabetes-related hospitalizations (22.1%) and insulin (18.4%). Female gender, pubertal age and poor glycemic control were associated with higher and migration background with lower total costs. CONCLUSIONS: Main cost categories in patients with on average 11 years of diabetes duration were costs for glucose self-monitoring, insulin pump therapy, hospitalization and insulin. Optimization of glycemic control in particular in pubertal age through intensified care with improved diabetes education and tailored insulin regimen, can contribute to the reduction of direct diabetes-related costs in this patient group

Comparison of Liver Fat Indices for the Diagnosis of Hepatic Steatosis and Insulin Resistance

<div><p>Context</p><p>Hepatic steatosis, defined as increased hepatocellular lipid content (HCL), associates with visceral obesity and glucose intolerance. As exact HCL quantification by ¹H-magnetic resonance spectroscopy (¹H-MRS) is not generally available, various clinical indices are increasingly used to predict steatosis.</p><p>Objective</p><p>The purpose of this study was to test the accuracy of NAFLD liver fat score (NAFLD-LFS), hepatic steatosis index (HSI) and fatty liver index (FLI) against ¹H-MRS and their relationships with insulin sensitivity and secretion.</p><p>Design, Setting and Participants</p><p>Ninety-two non-diabetic, predominantly non-obese humans underwent clinical examination, ¹H-MRS and an oral glucose tolerance test (OGTT) to calculate insulin sensitivity and β-cell function. Accuracy of indices was assessed from the area under the receiver operating characteristic curve (AROC).</p><p>Results</p><p>Median HCL was 2.49% (0.62;4.23) and correlated with parameters of glycemia across all subjects. NAFLD-LFS, FLI and HSI yielded AROCs of 0.70, 0.72, and 0.79, respectively, and related positively to HCL, insulin resistance, fasting and post-load β-cell function normalized for insulin resistance. Upon adjustment for age, sex and HCL, regression analysis revealed that NAFLD-LFS, FLI and HSI still independently associated with both insulin sensitivity and β-cell function.</p><p>Conclusion</p><p>The tested indices offer modest efficacy to detect steatosis and cannot substitute for fat quantification by ¹H-MRS. However, all indices might serve as surrogate parameters for liver fat content and also as rough clinical estimates of abnormal insulin sensitivity and secretion. Further validation in larger collectives such as epidemiological studies is needed.</p></div

Association of indices with insulin sensitivity and β-cell function after adjustment for age, sex, HCL and LRA.

<p>*, p<0.05;</p><p>**, p<0.01;</p><p>***, p<0.001;</p><p>B-cell func, B-cell function.</p

Description of the two study populations.

a<p>Percentages and means (SD).</p>b<p>Weighted percentages and weighted means (SD).</p>c<p>χ²-Test, t-Test.</p><p>Description of the two study populations.</p