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Articles by L. E. Wagenknecht
Total Records ( 2 ) for L. E. Wagenknecht
  B. I. Freedman , D. W. Bowden , S. S. Rich , J. Xu , L. E. Wagenknecht , J. Ziegler , P. J. Hicks and C. D. Langefeld
  Aims/hypothesis  Glomerular filtration rate (GFR), end-stage renal disease and albuminuria are highly heritable. We performed a genome-wide linkage scan in 416 Diabetes Heart Study (DHS) families to detect loci that contributed to renal function and albuminuria.

Materials and methods  A total of 1067 individuals (900 with Type 2 diabetes mellitus) from 348 European American and 68 African American DHS families had measures of urine albumin : creatinine ratio (ACR), serum creatinine concentration and Modification of Diet in Renal Disease estimated GFR (eGFR). Variance components quantitative trait linkage analysis (using SOLAR) was computed.

Results  Participants had mean ± sd age 61.4 ± 9.4 years; diabetes duration 10.5 ± 7.4 years; eGFR 1.15 ± 0.32 ml/sec; and urine ACR 15.8 ± 67.2 mmol/l (median 1.4). In all families, significant evidence for linkage of GFR was observed on chromosome 2p16 (log of the odds; LOD = 4.31 at 72.0 cM, ATA47C04P/D2S1352) and 1p36 (LOD = 3.81 at 45.0 cM, D1S3669/D1S3720), with suggestive evidence on 7q21 (LOD = 2.42 at 99.0 cM, D7S820/D7S821) and 13q13 (LOD = 2.28 at 28.0 cM, D13S1493/D13S894). The evidence for linkage to ACR was far weaker, on 13q21-q22 (LOD = 1.84 at 50 cM, D13S1807/D13S800), 3p24-p23 (LOD = 1.81 at 58 cM, D3S3038/D3S2432) and 10p11 (LOD = 1.78 at 71.0 cM, D10S1208/D10S1221).

Conclusions/interpretations  The eGFR linkage peaks on 2p16, 7q21 and 13q13 closely overlap with nephropathy peaks identified in family studies enriched for severe kidney disease. These diabetes-enriched families provide an opportunity to map genes regulating renal function, potentially leading to the identification of genes producing nephropathy susceptibility in subjects with Type 2 diabetes.

  S. M. Watkins , M. W. Rowe , J. A. Kolberg , L. E. Wagenknecht and R. N. Bergman
  Aims  Insulin sensitivity and acute insulin response measure key components of Type 2 diabetes aetiology that contribute independently to risk in the Insulin Resistance Atherosclerosis Study. As insulin sensitivity and acute insulin response are not routinely measured in a clinical setting, we evaluated three fasting biomarker models, homeostasis model assessment of insulin sensitivity (HOMA-%S), β-cell function (HOMA-%B) and a Diabetes Risk Score, as potential surrogates for risk associated with insulin sensitivity, acute insulin response and the interaction of these two measures, the disposition index.

Methods  Models were calculated from baseline plasma biomarker concentrations for 664 participants who underwent a frequently sampled intravenous glucose tolerance test. To assess relationships among biomarker models and test measures, we calculated improvement in risk estimation gained by combining each fasting measure with each frequently sampled intravenous glucose tolerance test measure using logistic regression.

Results  The strongest correlates of acute insulin response, insulin sensitivity and disposition index were HOMA-%B (rs2 = 0.23), HOMA-%S (rs2 = 0.48) and Diabetes Risk Score (rs2 = 0.34), respectively. Individual areas under the curves for prediction of diabetes were 0.549 (HOMA-%B), 0.694 (HOMA-%S), 0.700 (insulin sensitivity), 0.714 (acute insulin response), 0.756 (Diabetes Risk Score) and 0.817 (disposition index). Models combining acute insulin response with Diabetes Risk Score (area under the curve 0.798) or HOMA-%S (area under the curve 0.805) nearly equalled disposition index, outperforming other individual measures (P < 0.05). Insulin sensitivity plus Diabetes Risk Score (area under the curve 0.760) was better than insulin sensitivity (P = 0.03), but not better than Diabetes Risk Score alone. HOMA-%S plus insulin sensitivity (area under the curve 0.704) was not significantly better than either alone.

Conclusions  The Diabetes Risk Score and HOMA-%S were excellent surrogates for insulin sensitivity, capturing the predictive power of insulin sensitivity. Diabetes Risk Score captured some of the additional predictive power of acute insulin response, but the HOMA models did not. No fasting model was as predictive as disposition index, but the Diabetes Risk Score was the best surrogate.

 
 
 
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