Patient-reported outcomes following islet cell or pancreas transplantation (alone or after kidney) in type 1 diabetes: a systematic review

Aims For selected individuals with complex Type 1 diabetes, pancreatic islet transplantation (IT) offers the potential of excellent glycaemic controlwithout significant hypoglycaemia, balanced by the need for ongoing systemic immunosuppression. Increasingly, patient-reported outcomes (PROs) are considered alongside biomedical outcomes as a measure of transplant success. PROs in IT have not previously been compared directlywith the closest alternate treatment option, pancreas transplant alone (PTA) or pancreas after kidney (PAK).

Methods We used a Population, Intervention, Comparisons, Outcomes (PICO) strategy to search Scopus and screened 314 references for inclusion.

Results Twelve studies [including PRO assessment of PAK, PTA, islet-after kidney (IAK) and islet transplant alone (ITA); n = 7–205] used a total of nine specified and two unspecified PRO measures. Results were mixed but identified some benefits which remained apparent up to 36 months post-transplant, including improvements in fear of hypoglycaemia, as well as some aspects of diabetes-specific quality of life (QoL) and general health status. Negative outcomes included short-term pain associated with the procedure, immunosuppressant side effects and depressed mood associated with loss of graft function.

Conclusions The mixed resultsmay be attributable to limited sample sizes. Also, some PROmeasures may lack sensitivity to detect actual changes, as they exclude issues and domains of life likely to be important forQoL post-transplantation and when patients may no longer perceive themselves to have diabetes. Thus, the full impact of islet ⁄ pancreas transplantation (alone or after kidney) on QoL is unknown. Furthermore, no studies have assessed patient satisfaction, which may highlight further advantages and disadvantages of transplantation.

Analysis of protein sequence and interaction data for candidate disease gene prediction

Linkage analysis is a successful procedure to associate diseases with specific genomic regions. These regions are often large, containing hundreds of genes, which make experimental methods employed to identify the disease gene arduous and expensive. We present two methods to prioritize candidates for further experimental study: Common Pathway Scanning (CPS) and Common Module Profiling (CMP). CPS is based on the assumption that common phenotypes are associated with dysfunction in proteins that participate in the same complex or pathway. CPS applies network data derived from protein–protein interaction (PPI) and pathway databases to identify relationships between genes. CMP identifies likely candidates using a domain-dependent sequence similarity approach, based on the hypothesis that disruption of genes of similar function will lead to the same phenotype. Both algorithms use two forms of input data: known disease genes or multiple disease loci. When using known disease genes as input, our combined methods have a sensitivity of 0.52 and a specificity of 0.97 and reduce the candidate list by 13-fold. Using multiple loci, our methods successfully identify disease genes for all benchmark diseases with a sensitivity of 0.84 and a specificity of 0.63. Our combined approach prioritizes good candidates and will accelerate the disease gene discovery process.

Disease gene prediction database

This database includes gene predictions for disease phenotypes based on published Genome-Wide Association Data. May be used to choose primers for phenotype-specific resquencing of patient DNA.
For each prediction for following data is listed: phenotype, predicted gene, significant SNP, datasource, datasource reference.

Home hemodialysis : a successful option for obese and bariatric people with end-stage kidney disease

The increasing prevalence of obesity in developed countries is reflected in the chronic kidney disease, dialysis, and transplant populations. The added risk factor of obesity increases the risk of vascular events, inflammation, insulin resistance, blood pressure, dyslipidemia, and mortality risk. Nephrology center policies may exclude obese people from transplantation programs resulting in many years of dialysis. The case of a 215-kg Australian male who has successfully dialyzed at home for more than 8 years will be used to illustrate the important considerations and clinical support that these people require for successful home dialysis treatment. The aim of this paper is to report on a program that has successfully trained 23 obese (body mass index >30) people who commenced on home hemodialysis between 2001 and 2009. Body weight ranged between 94.0 and 215 kg (mean 126, SD 26.19) and body mass index ranged between 34.9 and 71 (mean 43.38, SD 9.99) at the start of home training. During the 8.5 years of follow-up, average time on home dialysis was 43.7 months. Home hemodialysis is a feasible treatment for obese people to facilitate longer and more frequent dialysis, resulting in improved hemodynamic stability and improved quality of life. For obese people with end-stage kidney disease, home hemodialysis has shown to be cost-effective and can result in greater treatment efficacy than in-center hospital dialysis.

A nurse managed kidney disease program in regional and remote Australia

Health services that aim to prevent and manage chronic kidney disease (CKD) in rural and remote Aboriginal communities in Australia, including the Goldfields region of Western Australia (WA), require innovative approaches. Nursing roles can significantly improve access to renal services in rural and remote areas as they are able to address a range of renal health promotion and prevention activities, and provide renal clinical education and support to Aboriginal people. The Goldfields Kidney Disease Nursing Management Program (GKDNMP), funded through the Council of Australian Governments (COAG) National Partnership Agreements, was developed to provide a comprehensive approach to primary health care that incorporates a range of health promotion and disease management activities. In the first year, the program increased home dialysis rates and decreased patient travel due to expanded access to renal care within the region. Context-specific health programs generated in response to local needs can be successful in addressing specific health care challenges in rural and remote health.

Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease

Current single-locus-based analyses and candidate disease gene prediction methodologies used in genome-wide association studies (GWAS) do not capitalize on the wealth of the underlying genetic data, nor functional data available from molecular biology. Here, we analyzed GWAS data from the Wellcome Trust Case Control Consortium (WTCCC) on coronary artery disease (CAD). Gentrepid uses a multiple-locus-based approach, drawing on protein pathway- or domain-based data to make predictions. Known disease genes may be used as additional information (seeded method) or predictions can be based entirely on GWAS single nucleotide polymorphisms (SNPs) (ab initio method). We looked in detail at specific predictions made by Gentrepid for CAD and compared these with known genetic data and the scientific literature. Gentrepid was able to extract known disease genes from the candidate search space and predict plausible novel disease genes from both known and novel WTCCC-implicated loci. The disease gene candidates are consistent with known biological information. The results demonstrate that this computational approach is feasible and a valuable discovery tool for geneticists.