Metabolomics and Lipidomics Signatures of Insulin Resistance and Abdominal Fat Depots in People Living with Obesity

The liver, skeletal muscle, and adipose tissue are major insulin target tissues and key players in glucose homeostasis. We and others have described diverse insulin resistance (IR) phenotypes in people at risk of developing type 2 diabetes. It is postulated that identifying the IR phenotype in a patient may guide the treatment or the prevention strategy for better health outcomes in populations at risk. Here, we performed plasma metabolomics and lipidomics in a cohort of men and women living with obesity not complicated by diabetes (mean [SD] BMI 36.0 [4.5] kg/m2, n = 62) to identify plasma signatures of metabolites and lipids that align with phenotypes of IR (muscle, liver, or adipose tissue) and abdominal fat depots. We used 2-step hyperinsulinemic-euglycemic clamp with deuterated glucose, oral glucose tolerance test, dual-energy X-ray absorptiometry and abdominal magnetic resonance imaging to assess muscle-, liver- and adipose tissue- IR, beta cell function, body composition, abdominal fat distribution and liver fat, respectively. Spearman’s rank correlation analyses that passed the Benjamini–Hochberg statistical correction revealed that cytidine, gamma-aminobutyric acid, anandamide, and citrate corresponded uniquely with muscle IR, tryptophan, cAMP and phosphocholine corresponded uniquely with liver IR and phenylpyruvate and hydroxy-isocaproic acid corresponded uniquely with adipose tissue IR (p < 7.2 × 10−4). Plasma cholesteryl sulfate (p = 0.00029) and guanidinoacetic acid (p = 0.0001) differentiated between visceral and subcutaneous adiposity, while homogentisate correlated uniquely with liver fat (p = 0.00035). Our findings may help identify diverse insulin resistance and adiposity phenotypes and enable targeted treatments in people living with obesity

Validation of Case-Finding Algorithms Derived from Administrative Data for Identifying Adults Living with Human Immunodeficiency Virus Infection

OBJECTIVE: We sought to validate a case-finding algorithm for human immunodeficiency virus (HIV) infection using administrative health databases in Ontario, Canada. METHODS: We constructed 48 case-finding algorithms using combinations of physician billing claims, hospital and emergency room separations and prescription drug claims. We determined the test characteristics of each algorithm over various time frames for identifying HIV infection, using data abstracted from the charts of 2,040 randomly selected patients receiving care at two medical practices in Toronto, Ontario as the reference standard. RESULTS: With the exception of algorithms using only a single physician claim, the specificity of all algorithms exceeded 99%. An algorithm consisting of three physician claims over a three year period had a sensitivity and specificity of 96.2% (95% CI 95.2%-97.9%) and 99.6% (95% CI 99.1%-99.8%), respectively. Application of the algorithm to the province of Ontario identified 12,179 HIV-infected patients in care for the period spanning April 1, 2007 to March 31, 2009. CONCLUSIONS: Case-finding algorithms generated from administrative data can accurately identify adults living with HIV. A relatively simple "3 claims in 3 years" definition can be used for assembling a population-based cohort and facilitating future research examining trends in health service use and outcomes among HIV-infected adults in Ontario

Management of high-risk corneal grafts

MACMILLAN BUILDING,4 CRINAN ST, LONDON, ENGLAND, N1 9X

Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium

BACKGROUND: Health administrative data are frequently used for health services and population health research. Comparative research using these data has been facilitated by the use of a standard system for coding diagnoses, the International Classification of Diseases (ICD). Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes. This paper presents a list of high-priority methodological areas for researchers using health administrative data. METHODS: A group of researchers and users of health administrative data from Canada, the United States, Switzerland, Australia, China and the United Kingdom came together in June 2005 in Banff, Canada to discuss and identify high-priority methodological research areas. The generation of ideas for research focussed not only on matters relating to the use of administrative data in health services and population health research, but also on the challenges created in transitioning from ICD-9 to ICD-10. After the brain-storming session, voting took place to rank-order the suggested projects. Participants were asked to rate the importance of each project from 1 (low priority) to 10 (high priority). Average ranks were computed to prioritise the projects. RESULTS: Thirteen potential areas of research were identified, some of which represented preparatory work rather than research per se. The three most highly ranked priorities were the documentation of data fields in each country's hospital administrative data (average score 8.4), the translation of patient safety indicators from ICD-9 to ICD-10 (average score 8.0), and the development and validation of algorithms to verify the logic and internal consistency of coding in hospital abstract data (average score 7.0). CONCLUSION: The group discussions resulted in a list of expert views on critical international priorities for future methodological research relating to health administrative data. The consortium's members welcome contacts from investigators involved in research using health administrative data, especially in cross-jurisdictional collaborative studies or in studies that illustrate the application of ICD-10

A classification of diabetic foot infections using ICD-9-CM codes: application to a large computerized medical database

<p>Abstract</p> <p>Background</p> <p>Diabetic foot infections are common, serious, and varied. Diagnostic and treatment strategies are correspondingly diverse. It is unclear how patients are managed in actual practice and how outcomes might be improved. Clarification will require study of large numbers of patients, such as are available in medical databases. We have developed and evaluated a system for identifying and classifying diabetic foot infections that can be used for this purpose.</p> <p>Methods</p> <p>We used the (VA) Diabetes Epidemiology Cohorts (DEpiC) database to conduct a retrospective observational study of patients with diabetic foot infections. DEpiC contains computerized VA and Medicare patient-level data for patients with diabetes since 1998. We determined which ICD-9-CM codes served to identify patients with different types of diabetic foot infections and ranked them in declining order of severity: Gangrene, Osteomyelitis, Ulcer, Foot cellulitis/abscess, Toe cellulitis/abscess, Paronychia. We evaluated our classification by examining its relationship to patient characteristics, diagnostic procedures, treatments given, and medical outcomes.</p> <p>Results</p> <p>There were 61,007 patients with foot infections, of which 42,063 were classifiable into one of our predefined groups. The different types of infection were related to expected patient characteristics, diagnostic procedures, treatments, and outcomes. Our severity ranking showed a monotonic relationship to hospital length of stay, amputation rate, transition to long-term care, and mortality.</p> <p>Conclusions</p> <p>We have developed a classification system for patients with diabetic foot infections that is expressly designed for use with large, computerized, ICD-9-CM coded administrative medical databases. It provides a framework that can be used to conduct observational studies of large numbers of patients in order to examine treatment variation and patient outcomes, including the effect of new management strategies, implementation of practice guidelines, and quality improvement initiatives.</p

Establishment of Functioning Human Corneal Endothelial Cell Line with High Growth Potential

Hexagonal-shaped human corneal endothelial cells (HCEC) form a monolayer by adhering tightly through their intercellular adhesion molecules. Located at the posterior corneal surface, they maintain corneal translucency by dehydrating the corneal stroma, mainly through the Na+- and K+-dependent ATPase (Na+/K+-ATPase). Because HCEC proliferative activity is low in vivo, once HCEC are damaged and their numbers decrease, the cornea begins to show opacity due to overhydration, resulting in loss of vision. HCEC cell cycle arrest occurs at the G1 phase and is partly regulated by cyclin-dependent kinase inhibitors (CKIs) in the Rb pathway (p16-CDK4/CyclinD1-pRb). In this study, we tried to activate proliferation of HCEC by inhibiting CKIs. Retroviral transduction was used to generate two new HCEC lines: transduced human corneal endothelial cell by human papillomavirus type E6/E7 (THCEC (E6/E7)) and transduced human corneal endothelial cell by Cdk4R24C/CyclinD1 (THCEH (Cyclin)). Reverse transcriptase polymerase chain reaction analysis of gene expression revealed little difference between THCEC (E6/E7), THCEH (Cyclin) and non-transduced HCEC, but cell cycle-related genes were up-regulated in THCEC (E6/E7) and THCEH (Cyclin). THCEH (Cyclin) expressed intercellular molecules including ZO-1 and N-cadherin and showed similar Na+/K+-ATPase pump function to HCEC, which was not demonstrated in THCEC (E6/E7). This study shows that HCEC cell cycle activation can be achieved by inhibiting CKIs even while maintaining critical pump function and morphology

Health status and quality of life among older adults in rural Tanzania

BACKGROUND\ud \ud Increasingly, human populations throughout the world are living longer and this trend is developing in sub-Saharan Africa. In developing African countries such as Tanzania, this demographic phenomenon is taking place against a background of poverty and poor health conditions. There has been limited research on how this process of ageing impacts upon the health of older people within such low-income settings.\ud \ud OBJECTIVE\ud \ud The objective of this study is to describe the impacts of ageing on the health status, quality of life and well-being of older people in a rural population of Tanzania.\ud \ud DESIGN\ud \ud A short version of the WHO Survey on Adult Health and Global Ageing questionnaire was used to collect information on the health status, quality of life and well-being of older adults living in Ifakara Health and Demographic Surveillance System, Tanzania, during early 2007. Questionnaires were administered through this framework to 8,206 people aged 50 and over.\ud \ud RESULTS\ud \ud Among people aged 50 and over, having good quality of life and health status was significantly associated with being male, married and not being among the oldest old. Functional ability assessment was associated with age, with people reporting more difficulty in performing routine activities as age increased, particularly among women. Reports of good quality of life and well-being decreased with increasing age. Women were significantly more likely to report poor quality of life (odds ratio 1.31; p<0.001, 95% CI 1.15-1.50).\ud \ud CONCLUSIONS\ud \ud Older people within this rural Tanzanian setting reported that the ageing process had significant impacts on their health status, quality of life and physical ability. Poor quality of life and well-being, and poor health status in older people were significantly associated with marital status, sex, age and level of education. The process of ageing in this setting is challenging and raises public health concerns

Gene expression microarray analysis of early oxygen-induced retinopathy in the rat

Different inbred strains of rat differ in their susceptibility to oxygen-induced retinopathy (OIR), an animal model of human retinopathy of prematurity. We examined gene expression in Sprague–Dawley (susceptible) and Fischer 344 (resistant) neonatal rats after 3 days exposure to cyclic hyperoxia or room air, using Affymetrix rat Genearrays. False discovery rate analysis was used to identify differentially regulated genes. Such genes were then ranked by fold change and submitted to the online database, DAVID. The Sprague–Dawley list returned the term “response to hypoxia,” absent from the Fischer 344 output. Manual analysis indicated that many genes known to be upregulated by hypoxia-inducible factor-1α were downregulated by cyclic hyperoxia. Quantitative real-time RT-PCR analysis of Egln3, Bnip3, Slc16a3, and Hk2 confirmed the microarray results. We conclude that combined methodologies are required for adequate dissection of the pathophysiology of strain susceptibility to OIR in the rat