Statistical analysis of the associations of hereditary factors with the risk of Type 1 diabetes and Diabetic Nephropathy based on the familial data collected through ascertaiment from population-based registers

In genetic epidemiology, population-based disease registries are commonly used to collect genotype or other risk factor information concerning affected subjects and their relatives. This work presents two new approaches for the statistical inference of ascertained data: a conditional and full likelihood approaches for the disease with variable age at onset phenotype using familial data obtained from population-based registry of incident cases. The aim is to obtain statistically reliable estimates of the general population parameters. The statistical analysis of familial data with variable age at onset becomes more complicated when some of the study subjects are non-susceptible, that is to say these subjects never get the disease. A statistical model for a variable age at onset with long-term survivors is proposed for studies of familial aggregation, using latent variable approach, as well as for prospective studies of genetic association studies with candidate genes. In addition, we explore the possibility of a genetic explanation of the observed increase in the incidence of Type 1 diabetes (T1D) in Finland in recent decades and the hypothesis of non-Mendelian transmission of T1D associated genes. Both classical and Bayesian statistical inference were used in the modelling and estimation. Despite the fact that this work contains five studies with different statistical models, they all concern data obtained from nationwide registries of T1D and genetics of T1D. In the analyses of T1D data, non-Mendelian transmission of T1D susceptibility alleles was not observed. In addition, non-Mendelian transmission of T1D susceptibility genes did not make a plausible explanation for the increase in T1D incidence in Finland. Instead, the Human Leucocyte Antigen associations with T1D were confirmed in the population-based analysis, which combines T1D registry information, reference sample of healthy subjects and birth cohort information of the Finnish population. Finally, a substantial familial variation in the susceptibility of T1D nephropathy was observed. The presented studies show the benefits of sophisticated statistical modelling to explore risk factors for complex diseases.

The reproducibility of acquiring three dimensional gait and plantar pressure data using established protocols in participants with and without type 2 diabetes and foot ulcers

Background Several prospective studies have suggested that gait and plantar pressure abnormalities secondary to diabetic peripheral neuropathy contributes to foot ulceration. There are many different methods by which gait and plantar pressures are assessed and currently there is no agreed standardised approach. This study aimed to describe the methods and reproducibility of three-dimensional gait and plantar pressure assessments in a small subset of participants using pre-existing protocols. Methods Fourteen participants were conveniently sampled prior to a planned longitudinal study; four patients with diabetes and plantar foot ulcers, five patients with diabetes but no foot ulcers and five healthy controls. The repeatability of measuring key biomechanical data was assessed including the identification of 16 key anatomical landmarks, the measurement of seven leg dimensions, the processing of 22 three-dimensional gait parameters and the analysis of four different plantar pressures measures at 20 foot regions. Results The mean inter-observer differences were within the pre-defined acceptable level (<7 mm) for 100 % (16 of 16) of key anatomical landmarks measured for gait analysis. The intra-observer assessment concordance correlation coefficients were > 0.9 for 100 % (7 of 7) of leg dimensions. The coefficients of variations (CVs) were within the pre-defined acceptable level (<10 %) for 100 % (22 of 22) of gait parameters. The CVs were within the pre-defined acceptable level (<30 %) for 95 % (19 of 20) of the contact area measures, 85 % (17 of 20) of mean plantar pressures, 70 % (14 of 20) of pressure time integrals and 55 % (11 of 20) of maximum sensor plantar pressure measures. Conclusion Overall, the findings of this study suggest that important gait and plantar pressure measurements can be reliably acquired. Nearly all measures contributing to three-dimensional gait parameter assessments were within predefined acceptable limits. Most plantar pressure measurements were also within predefined acceptable limits; however, reproducibility was not as good for assessment of the maximum sensor pressure. To our knowledge, this is the first study to investigate the reproducibility of several biomechanical methods in a heterogeneous cohort.

Born-ratio type data normalization improves quantitation in photoacoustic tomography

In this report, we present a Born-ratio type of data normalization for reconstruction of initial acoustic pressure distribution in photoacoustic tomography (PAT). The normalized Born-ratio type of data is obtained as a ratio of photoacoustic pressure obtained with tissue sample in a coupling medium to the one obtained using purely coupling medium. It is shown that this type of data normalization improves the quantitation (intrinsic contrast) of the reconstructed images in comparison to the traditional techniques (unnormalized) that are currently available in PAT. Studies are carried out using various tissue samples. The robustness of the proposed method is studied at various noise levels added to the collected data. The improvement in quantitation can enable accurate estimation of pathophysiological parameter (optical absorption coefficient, a) of tissue sample under investigation leading to better sensitivity in PAT.

Using electronic health record data for substance use Screening, Brief Intervention, and Referral to Treatment among adults with type 2 diabetes: Design of a National Drug Abuse Treatment Clinical Trials Network study.

BACKGROUND: The Affordable Care Act encourages healthcare systems to integrate behavioral and medical healthcare, as well as to employ electronic health records (EHRs) for health information exchange and quality improvement. Pragmatic research paradigms that employ EHRs in research are needed to produce clinical evidence in real-world medical settings for informing learning healthcare systems. Adults with comorbid diabetes and substance use disorders (SUDs) tend to use costly inpatient treatments; however, there is a lack of empirical data on implementing behavioral healthcare to reduce health risk in adults with high-risk diabetes. Given the complexity of high-risk patients' medical problems and the cost of conducting randomized trials, a feasibility project is warranted to guide practical study designs. METHODS: We describe the study design, which explores the feasibility of implementing substance use Screening, Brief Intervention, and Referral to Treatment (SBIRT) among adults with high-risk type 2 diabetes mellitus (T2DM) within a home-based primary care setting. Our study includes the development of an integrated EHR datamart to identify eligible patients and collect diabetes healthcare data, and the use of a geographic health information system to understand the social context in patients' communities. Analysis will examine recruitment, proportion of patients receiving brief intervention and/or referrals, substance use, SUD treatment use, diabetes outcomes, and retention. DISCUSSION: By capitalizing on an existing T2DM project that uses home-based primary care, our study results will provide timely clinical information to inform the designs and implementation of future SBIRT studies among adults with multiple medical conditions.

Birthweight and the risk of childhood-onset type 1 diabetes: a meta-analysis of observational studies using individual patient data

Aims/hypothesis: We investigated whether children who are heavier at birth have an increased risk of type 1 diabetes. Methods: Relevant studies published before February 2009 were identified from literature searches using MEDLINE, Web of Science and EMBASE. Authors of all studies containing relevant data were contacted and asked to provide individual patient data or conduct pre-specified analyses. Risk estimates of type 1 diabetes by category of birthweight were calculated for each study, before and after adjustment for potential confounders. Meta-analysis techniques were then used to derive combined ORs and investigate heterogeneity between studies. Results: Data were available for 29 predominantly European studies (five cohort, 24 case-control studies), including 12,807 cases of type 1 diabetes. Overall, studies consistently demonstrated that children with birthweight from 3.5 to 4 kg had an increased risk of diabetes of 6% (OR 1.06 [95% CI 1.01-1.11]; p=0.02) and children with birthweight over 4 kg had an increased risk of 10% (OR 1.10 [95% CI 1.04-1.19]; p=0.003), compared with children weighing 3.0 to 3.5 kg at birth. This corresponded to a linear increase in diabetes risk of 3% per 500 g increase in birthweight (OR 1.03 [95% CI 1.00-1.06]; p=0.03). Adjustments for potential confounders such as gestational age, maternal age, birth order, Caesarean section, breastfeeding and maternal diabetes had little effect on these findings. Conclusions/interpretation: Children who are heavier at birth have a significant and consistent, but relatively small increase in risk of type 1 diabetes. © 2010 Springer-Verlag.


--------------------------------------------------------------------------------

Reaxys Database Information|

--------------------------------------------------------------------------------

Type II-P supernovae as standardized candles: improvements using near-infrared data

We present the first near-infrared Hubble diagram for Type II-P supernovae (SNe), to further explore their value as distance indicators. We use a modified version of the standardized candle method, which relies on the tight correlation between the absolute magnitudes of Type II-P SNe and their expansion velocities during the plateau phase. Although our sample contains only 12 II-P SNe and they are necessarily local (z

Breast-feeding and childhood-onset type 1 diabetes:A pooled analysis of individual participant data from 43 observational studies

OBJECTIVE: To investigate if there is a reduced risk of type 1 diabetes in children breastfed or exclusively breastfed by performing a pooled analysis with adjustment for recognized confounders.
RESEARCH DESIGN AND METHODS: Relevant studies were identified from literature searches using MEDLINE, Web of Science, and EMBASE. Authors of relevant studies were asked to provide individual participant data or conduct prespecified analyses. Meta-analysis techniques were used to combine odds ratios (ORs) and investigate heterogeneity between studies.
RESULTS: Data were available from 43 studies including 9,874 patients with type 1 diabetes. Overall, there was a reduction in the risk of diabetes after exclusive breast-feeding for >2 weeks (20 studies; OR = 0.75, 95% CI 0.64-0.88), the association after exclusive breast-feeding for >3 months was weaker (30 studies; OR = 0.87, 95% CI 0.75-1.00), and no association was observed after (nonexclusive) breast-feeding for >2 weeks (28 studies; OR = 0.93, 95% CI 0.81-1.07) or >3 months (29 studies; OR = 0.88, 95% CI 0.78-1.00). These associations were all subject to marked heterogeneity (I(2) = 58, 76, 54, and 68%, respectively). In studies with lower risk of bias, the reduced risk after exclusive breast-feeding for >2 weeks remained (12 studies; OR = 0.86, 95% CI 0.75-0.99), and heterogeneity was reduced (I(2) = 0%). Adjustments for potential confounders altered these estimates very little.
CONCLUSIONS: The pooled analysis suggests weak protective associations between exclusive breast-feeding and type 1 diabetes risk. However, these findings are difficult to interpret because of the marked variation in effect and possible biases (particularly recall bias) inherent in the included studies.

Investigating the efficiency of fitting Coxian phase-type distributions to health care data

Coxian phase-type distributions are becoming a popular means of representing survival times within a health care environment. They are favoured as they show a distribution as a system of phases and can allow for an easy visual representation of the rate of flow of patients through a system. Difficulties arise, however, in determining the parameter estimates of the Coxian phase-type distribution. This paper examines ways of making the fitting of the Coxian phase-type distribution less cumbersome by outlining different software packages and algorithms available to perform the fit and assessing their capabilities through a number of performance measures. The performance measures rate each of the methods and help in identifying the more efficient. Conclusions drawn from these performance measures suggest SAS to be the most robust package. It has a high rate of convergence in each of the four example model fits considered, short computational times, detailed output, convergence criteria options, along with a succinct ability to switch between different algorithms.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.