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Bone metabolism appears to influence insulin secretion and sensitivity, and insulin promotes bone formation in animals, but similar evidence in humans is limited. The objectives of this study are to explore if bone turnover markers were associated with insulin secretion and sensitivity and to determine if bone turnover markers predict changes in insulin secretion and sensitivity. The study population encompassed 576 non-diabetic adult men with normal glucose tolerance (NGT; n = 503) or impaired glucose regulation (IGR; n = 73). Baseline markers of bone resorption (CTX) and formation (P1NP) were determined in the fasting state and after a 2-h hyperinsulinaemic, euglycaemic clamp. An intravenous glucose tolerance test (IVGTT) and a 2-h oral glucose tolerance test (OGTT) were performed at baseline, and the OGTT was repeated after 3 years. There were no differences in bone turnover marker levels between NGT and IGR. CTX and P1NP levels decreased by 8.0% (p < 0.001) and 1.9% (p < 0.01) between baseline and steady-state during the clamp. Fasting plasma glucose was inversely associated with CTX and P1NP both before and after adjustment for recruitment centre, age, BMI, smoking and physical activity. However, baseline bone turnover markers were neither associated with insulin sensitivity (assessed using hyperinsulinaemic euglycaemic clamp and OGTT) nor with insulin secretion capacity (based on IVGTT and OGTT) at baseline or at follow-up. Although inverse associations between fasting glucose and markers of bone turnover were identified, this study cannot support an association between insulin secretion and sensitivity in healthy, non-diabetic men.
Background: We evaluated the sensitivity of the D-statistic, a parsimony-like method widely used to detect gene flow between closely related species. This method has been applied to a variety of taxa with a wide range of divergence times. However, its parameter space and thus its applicability to a wide taxonomic range has not been systematically studied. Divergence time, population size, time of gene flow, distance of outgroup and number of loci were examined in a sensitivity analysis.
Result: The sensitivity study shows that the primary determinant of the D-statistic is the relative population size, i.e. the population size scaled by the number of generations since divergence. This is consistent with the fact that the main confounding factor in gene flow detection is incomplete lineage sorting by diluting the signal. The sensitivity of the D-statistic is also affected by the direction of gene flow, size and number of loci. In addition, we examined the ability of the f-statistics, fˆGf^G and fˆhomf^hom, to estimate the fraction of a genome affected by gene flow; while these statistics are difficult to implement to practical questions in biology due to lack of knowledge of when the gene flow happened, they can be used to compare datasets with identical or similar demographic background.
Conclusions: The D-statistic, as a method to detect gene flow, is robust against a wide range of genetic distances (divergence times) but it is sensitive to population size. The D-statistic should only be applied with critical reservation to taxa where population sizes are large relative to branch lengths in generations.