Data from the ASU Arizona Insulin Registry (AIR) registry and the USC Study of Latino Adolescents at Risk for diabetes project were used to test the cross-sectional and prospective utility of novel biomarkers to identify youth at risk for type 2 diabetes. Pediatric and adult data from the ASU AIR registry were assessed to examine the association of single nucleotide polymorphisms (SNPs) with type 2 diabetes risk. Three KCNQ1 SNPs (rs151290; rs2237892; rs2237895) were examined as novel genetic variants for type 2 diabetes in Latinos.
Latino youth with a biphasic response in the AIR registry exhibited significantly better β-cell function (P < 0.05) compared to youth with a monophasic response. Additionally, Latino youth with a 1-hr glucose ≥155 mg/dL exhibited a significantly greater decline in β-cell function over 8 years compared with the <155 mg/dL group (β=-327.8±126.2, P = 0.01). Moreover, a 1-hr glucose ≥155 mg/dL was associated with a 2.5 times greater risk for developing prediabetes over time (P = 0.0001). 1-hr glucose was the most powerful predictor of prediabetes (area under the receiver operating characteristic curve=0.73) when compared to the traditional biomarkers including HbA1c (0.58), fasting (0.67), and 2-hr glucose (0.64). Two KCNQ1 SNPs (rs151290 and rs2237892) exhibited significant associations with type 2 diabetes risk factors. For the novel glycemic markers, 15 SNPs were associated with the glucose response curve, while 18 SNPs were associated with 1-hr glucose.
These data suggest that glucose response curve and 1-hr glucose during an OGTT independently differentiate type 2 diabetes risk among Latino youth. Furthermore, it was successful to replicate the association of type 2 diabetes risk with 2 KCNQ1 SNPs in a Latino population. Data suggest that novel glycemic biomarkers are influenced by genetic background in this high-risk population.
What's a profession without a code of ethics? Being a legitimate profession almost requires drafting a code and, at least nominally, making members follow it. Codes of ethics (henceforth “codes”) exist for a number of reasons, many of which can vary widely from profession to profession - but above all they are a form of codified self-regulation. While codes can be beneficial, it argues that when we scratch below the surface, there are many problems at their root. In terms of efficacy, codes can serve as a form of ethical window dressing, rather than effective rules for behavior. But even more that, codes can degrade the meaning behind being a good person who acts ethically for the right reasons.
Online communities are becoming increasingly important as platforms for large-scale human cooperation. These communities allow users seeking and sharing professional skills to solve problems collaboratively. To investigate how users cooperate to complete a large number of knowledge-producing tasks, we analyze Stack Exchange, one of the largest question and answer systems in the world. We construct attention networks to model the growth of 110 communities in the Stack Exchange system and quantify individual answering strategies using the linking dynamics on attention networks. We identify two answering strategies. Strategy A aims at performing maintenance by doing simple tasks, whereas strategy B aims at investing time in doing challenging tasks. Both strategies are important: empirical evidence shows that strategy A decreases the median waiting time for answers and strategy B increases the acceptance rate of answers. In investigating the strategic persistence of users, we find that users tends to stick on the same strategy over time in a community, but switch from one strategy to the other across communities. This finding reveals the different sets of knowledge and skills between users. A balance between the population of users taking A and B strategies that approximates 2:1, is found to be optimal to the sustainable growth of communities.
Background: Healthy individuals on the lower end of the insulin sensitivity spectrum also have a reduced gene expression response to exercise for specific genes. The goal of this study was to determine the relationship between insulin sensitivity and exercise-induced gene expression in an unbiased, global manner.
Methods and Findings: Euglycemic clamps were used to measure insulin sensitivity and muscle biopsies were done at rest and 30 minutes after a single acute exercise bout in 14 healthy participants. Changes in mRNA expression were assessed using microarrays, and miRNA analysis was performed in a subset of 6 of the participants using sequencing techniques. Following exercise, 215 mRNAs were changed at the probe level (Bonferroni-corrected P<0.00000115). Pathway and Gene Ontology analysis showed enrichment in MAP kinase signaling, transcriptional regulation and DNA binding. Changes in several transcription factor mRNAs were correlated with insulin sensitivity, including MYC, r=0.71; SNF1LK, r=0.69; and ATF3, r= 0.61 (5 corrected for false discovery rate). Enrichment in the 5’-UTRs of exercise-responsive genes suggested regulation by common transcription factors, especially EGR1. miRNA species of interest that changed after exercise included miR-378, which is located in an intron of the PPARGC1B gene.
Conclusions: These results indicate that transcription factor gene expression responses to exercise depend highly on insulin sensitivity in healthy people. The overall pattern suggests a coordinated cycle by which exercise and insulin sensitivity regulate gene expression in muscle.