Biomedical subjects

Robert Yang

Publications and source records attributed to Robert Yang.

4 recordsLinked to original sources

PubMed2026

Genetically Proxied Leukocyte Telomere Length and Epigenetic Age Acceleration in Relation to Healthspan: A Mendelian Randomization Study.

BACKGROUND: Leukocyte telomere length (LTL) and epigenetic age acceleration (EAA) are widely studied biomarkers of biological aging, but their potential roles in healthspan remain unclear. We evaluated whether genetically proxied LTL and EAA show evidence of potential effects on healthspan. METHODS: We conducted a two-sample Mendelian randomization study. Genetic instruments for LTL and four EAA biomarkers were obtained from published genome-wide association studies, including up to 472,174 individuals for LTL and approximately 35,000 individuals for each EAA biomarker. Summary statistics for healthspan, defined as age at first diagnosis of any of eight major chronic conditions or death, were derived from 300,447 unrelated European-ancestry participants in the UK Biobank. We used inverse-variance-weighted (IVW) models for the main analysis, with complementary MR estimators and sensitivity analyses to evaluate consistency, pleiotropy, instrument heterogeneity, and robustness. RESULTS: Genetically proxied longer LTL was associated with extended healthspan (IVW β = 0.106; 95% CI: 0.054-0.158; p = 6.9 × 10-5). The association was robust across multiple sensitivity analyses. In contrast, the four genetically proxied EAA biomarkers did not show consistent MR evidence of an association with healthspan. CONCLUSIONS: These findings provide genetic evidence consistent with a potential role of LTL in healthspan, while providing little support for comparable associations involving the genetically proxied components of the evaluated EAA biomarkers. The findings do not exclude potential associations with environmentally or physiologically acquired EAA.

Mendelian randomization↗

PubMed2006

The impact of accepting living kidney donors with mild hypertension or proteinuria on transplantation rates.

BACKGROUND: As waiting times for kidney transplantation increase, individuals with hypertension or proteinuria may be considered as eligible living donors. We set out to determine how frequently donors are excluded because of hypertension or proteinuria and to what extent accepting such donors would increase transplantation rates. METHODS: Wait lists from 4 Canadian transplantation centers were examined for causes of living kidney donor exclusion. Donors with hypertension (clinic blood pressure >140/90 mm Hg or requiring antihypertensive medication) or proteinuria historically have been excluded at these centers. We define potentially acceptable hypertension as a clinic blood pressure less than 150/100 mm Hg or less than 140/90 mm Hg if administered a single antihypertensive medication and define acceptable proteinuria as protein of 0.15 to 0.3 g/d. RESULTS: Only 35% (124 of 352 patients) of wait-listed patients had a living donor evaluated (n = 180 potential donors). Primary reasons for donor exclusion were immunologic: a positive cross-match (32%; n = 59) or blood group type incompatibility (22%; n = 40). Hypertension or proteinuria were less common (17%; n = 31). Of 31 donors excluded for hypertension or proteinuria, only 13 had results in the acceptable range. Acceptance of these donors would have resulted in transplantation of 3% (12 of 352 patients) of the wait-list population. CONCLUSION: Accepting living donors with mild hypertension and proteinuria will lead to a slight increase in transplantation rates. Efforts to improve living donor awareness and overcome immunologic barriers to transplantation may have a greater impact.

Cross-Sectional Studies↗

PubMed2005

Dynamic cellular automata: an alternative approach to cellular simulation.

A wide variety of approaches, ranging from Petri nets to systems of partial differential equations, have been used to model very specific aspects of cellular or biochemical functions. Here we describe how an agent-based or dynamic cellular automata (DCA) approach can be used as a very simple, yet very general method to model many different kinds of cellular or biochemical processes. Specifically, using simple pairwise interaction rules coupled with random object moves to simulate Brownian motion, we show how the DCA approach can be used to easily and accurately model diffusion, viscous drag, enzyme rate processes, metabolism (the Kreb's cycle), and complex genetic circuits (the repressilator). We also demonstrate how DCA approaches are able to accurately capture the stochasticity of many biological processes. The success and simplicity of this technique suggests that many other physical properties and significantly more complicated aspects of cellular behavior could be modeled using DCA methods. An easy-to-use, graphically-based computer program, called SimCell, was developed to perform the DCA simulations described here. It is available at http://wishart.biology.ualberta.ca/SimCell/.

Algorithms↗

PubMed2004

Decoding randomly ordered DNA arrays.

We have developed a simple and efficient algorithm to identify each member of a large collection of DNA-linked objects through the use of hybridization, and have applied it to the manufacture of randomly assembled arrays of beads in wells. Once the algorithm has been used to determine the identity of each bead, the microarray can be used in a wide variety of applications, including single nucleotide polymorphism genotyping and gene expression profiling. The algorithm requires only a few labels and several sequential hybridizations to identify thousands of different DNA sequences with great accuracy. We have decoded tens of thousands of arrays, each with 1520 sequences represented at approximately 30-fold redundancy by up to approximately 50,000 beads, with a median error rate of <1 x 10(-4) per bead. The approach makes use of error checking codes and provides, for the first time, a direct functional quality control of every element of each array that is manufactured. The algorithm can be applied to any spatially fixed collection of objects or molecules that are associated with specific DNA sequences.

Algorithms↗