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Joanna M.M. Howson Jason D. Cooper Deborah J. Smyth Neil M. Walker Helen Stevens Jin-Xiong She George S. Eisenbarth Marian Rewers John A. Todd Beena Akolkar Patrick Concannon Henry A. Erlich Cécile Julier Grant Morahan J?rn Nerup Concepcion Nierras Flemming Pociot Stephen S. Rich and the Type Diabetes Genetics Consortium 《Diabetes》2012,61(11):3012-3017
The common genetic loci that independently influence the risk of type 1 diabetes have largely been determined. Their interactions with age-at-diagnosis of type 1 diabetes, sex, or the major susceptibility locus, HLA class II, remain mostly unexplored. A large collection of more than 14,866 type 1 diabetes samples (6,750 British diabetic individuals and 8,116 affected family samples of European descent) were genotyped at 38 confirmed type 1 diabetes-associated non-HLA regions and used to test for interaction of association with age-at-diagnosis, sex, and HLA class II genotypes using regression models. The alleles that confer susceptibility to type 1 diabetes at interleukin-2 (IL-2), IL2/4q27 (rs2069763) and renalase, FAD-dependent amine oxidase (RNLS)/10q23.31 (rs10509540), were associated with a lower age-at-diagnosis (P = 4.6 × 10−6 and 2.5 × 10−5, respectively). For both loci, individuals carrying the susceptible homozygous genotype were, on average, 7.2 months younger at diagnosis than those carrying the protective homozygous genotypes. In addition to protein tyrosine phosphatase nonreceptor type 22 (PTPN22), evidence of statistical interaction between HLA class II genotypes and rs3087243 at cytotoxic T-lymphocyte antigen 4 (CTLA4)/2q33.2 was obtained (P = 7.90 × 10−5). No evidence of differential risk by sex was obtained at any loci (P ≥ 0.01). Statistical interaction effects can be detected in type 1 diabetes although they provide a relatively small contribution to our understanding of the familial clustering of the disease.Knowledge of the genetic architecture of type 1 diabetes has increased recently owing to large-scale genome-wide association (GWA) studies (1–3). Estimates of the contributions of the HLA region and numerous non-HLA loci across the genome now account for a sizeable proportion of familial clustering of the disorder (4–6). However, there remains substantial familial clustering that is not explained by the known loci (likely to be in excess of 40%) (4–6). Interactions between risk loci beyond that of a multiplicative model on the odds ratio (OR) scale (or additive on the log odds scale (7)) could account for some of the “missing heritability.” In addition, the existence of differential effects according to age-at-diagnosis and sex remains relatively unexplored.The HLA region on chromosome 6p21 is the major source of familial clustering in type 1 diabetes (4). HLA-DRB1 and HLA-DQB1 are associated with ORs in excess of 10 for susceptible genotypes (or less than 0.1 for protective genotypes) (8). The risk genotype HLA-DRB1*03/HLA-DRB1*04-HLA-DQB1*0302 (referred to as DR3/DR4-DQ302) with greatest effect has been shown to have the highest frequency in the individuals with youngest onset (9). An age-at-diagnosis interaction has also been reported for HLA-DRB1*04 (10) and the HLA class I alleles HLA-A*24 and HLA-B*39 (11,12).In contrast, reports of age-at-diagnosis interaction effects at non-HLA loci are contradictory, with positive reports largely confined to studies involving small sample sets (3,13–15). Similarly, reports of gene–gene interaction of type 1 diabetes–associated regions are also mainly conflicting (16–19), we presume due to inadequate sample sizes, with most positive reports likely to be false because the false-discovery rate would be high in these underpowered studies. The only convincing gene–gene interaction reported, is between a major non-HLA locus, protein tyrosine phosphatase nonreceptor type 22 (PTPN22) and DR3/DR4-DQ302 genotypes (20–23).The incidence of childhood type 1 diabetes is similar in males and females, unlike other autoimmune diseases such as Graves disease, celiac disease, or multiple sclerosis. Despite similar frequencies of childhood type 1 diabetes by sex, there have been reports of genetic risk factors differing between males and females (22,24).Given that most studies of gene–gene interaction, age-at-diagnosis effects, and sex effects on type 1 diabetes risk have not been addressed in sufficiently well-powered studies, the Type 1 Diabetes Genetics Consortium (T1DGC) has collected more than 16,000 type 1 diabetes–affected samples and tested them for interaction effects with sex and age-at-diagnosis at 38 non-HLA type 1 diabetes–associated regions (Supplementary Table 2). Gene–gene interaction was also tested between HLA class II and the 38 non-HLA loci. With this very large sample set, the study had at least 80% power to detect effects as small as an interaction OR = 1.12 for sex and 1.19 for interactions with age-at-diagnosis or HLA. These calculations assume a multiplicative (log additive) effects model, an OR = 1.15 for association with type 1 diabetes for the test locus and a minor allele frequency of 0.2 and α = 0.0004. In contrast, with 5,000 samples, which is twice as large as any other study testing for interaction effects in type 1 diabetes published to date, the study would only be powered at 80% to detect interaction effects larger than an OR = 1.3 with sex (with the same assumptions as above). For age-at-diagnosis interaction, an OR ≥ 1.37 could be detected; for HLA interaction, an OR ≥ 1.38 could be detected (Supplementary Figs. 1–6). 相似文献
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Thomas H. Meek Mauricio D. Dorfman Miles E. Matsen Jonathan D. Fischer Alexis Cubelo Monica R. Kumar Gerald J. Taborsky Jr. Gregory J. Morton 《Diabetes》2015,64(7):2376-2387
Several lines of evidence implicate excess glucagon secretion in the elevated rates of hepatic glucose production (HGP), hyperglycemia, and ketosis characteristic of uncontrolled insulin-deficient diabetes (uDM), but whether hyperglucagonemia is required for hyperglycemia in this setting is unknown. To address this question, adult male Wistar rats received either streptozotocin (STZ) to induce uDM (STZ-DM) or vehicle and remained nondiabetic. Four days later, animals received daily subcutaneous injections of either the synthetic GLP-1 receptor agonist liraglutide in a dose-escalating regimen to reverse hyperglucagonemia or its vehicle for 10 days. As expected, plasma glucagon levels were elevated in STZ-DM rats, and although liraglutide treatment lowered glucagon levels to those of nondiabetic controls, it failed to attenuate diabetic hyperglycemia, elevated rates of glucose appearance (Ra), or increased hepatic gluconeogenic gene expression. In contrast, it markedly reduced levels of both plasma ketone bodies and hepatic expression of the rate-limiting enzyme involved in ketone body production. To independently confirm this finding, in a separate study, treatment of STZ-DM rats with a glucagon-neutralizing antibody was sufficient to potently lower plasma ketone bodies but failed to normalize elevated levels of either blood glucose or Ra. These data suggest that in rats with uDM, hyperglucagonemia is required for ketosis but not for increased HGP or hyperglycemia. 相似文献
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Jaime P. Almandoz Ekta Singh Lisa A. Howell Karen Grothe Danielle T. Vlazny Almira Smailovic Brian A. Irving Robert H. Nelson John M. Miles 《Diabetes》2013,62(6):1897-1903
Spillover of lipoprotein lipase-generated fatty acids from chylomicrons into the plasma free fatty acid (FFA) pool is an important source of FFA and reflects inefficiency in dietary fat storage. We measured spillover in 13 people with type 2 diabetes using infusions of a [3H]triolein-labeled lipid emulsion and [U-13C]oleate during continuous feeding, before and after weight loss. Body fat was measured with dual energy X-ray absorptiometry and computed tomography. Participants lost ∼14% of body weight. There was an ∼38% decrease in meal-suppressed FFA concentration (P < 0.0001) and an ∼23% decrease in oleate flux (P = 0.007). Fractional spillover did not change (P = NS). At baseline, there was a strong negative correlation between spillover and leg fat (r = −0.79, P = 0.001) and a positive correlation with the trunk-to-leg fat ratio (R = 0.56, P = 0.047). These correlations disappeared after weight loss. Baseline leg fat (R = −0.61, P = 0.027) but not trunk fat (R = −0.27, P = 0.38) negatively predicted decreases in spillover with weight loss. These results indicate that spillover, a measure of inefficiency in dietary fat storage, is inversely associated with lower body fat in type 2 diabetes.Free fatty acids (FFAs) mediate insulin resistance (1,2), drive VLDL triglyceride synthesis in the liver (3), and play an important role in the pathogenesis of hypertension (4,5) and diabetes (6). Spillover of lipoprotein lipase (LPL)-generated fatty acids from chylomicrons into the plasma FFA pool is an important source of FFA (7–10) and reflects inefficiency in dietary fat storage. Previous work has shown that the amount of fat taken up in leg fat per gram of tissue increases as a function of leg fat mass, whereas it actually decreases as a function of visceral fat mass and does not change in upper body subcutaneous fat (11). However, it is not clear whether these findings reflect changes in rates of LPL-mediated meal fat hydrolysis, changes in fractional spillover, or both. We therefore undertook a study in people with type 2 diabetes to determine the effects of weight loss on spillover and to investigate potential associations between spillover and body fat depots. 相似文献
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2 α (PGF) and cisapride were investigated during the early postoperative period in 26 patients who underwent abdominal surgery.
Records of intestinal motility were made using an infusion catheter. PGF, 0.4 μg/kg per minute, given intravenously over 60
min, and cisapride, 5 mg, given intraintestinally, were administered to 13 patients each, first immediately after the operation,
and then after the migrating motor complexes (MMCs) had reappeared following a period of intestinal quiescence. The MMCs were
reestablished within the first postoperative day. Both PGF and cisapride stimulated irregular, high-amplitude contractions;
however, the MMCs reappeared following these induced contractions only if the drugs were administered just after the postoperative
MMCs became evident. These prokinetic drugs did not affect gastrointestinal hormone concentrations, but induced contractile
activity even in the early postoperative period. Although the findings of this study demonstrate that these drugs may be useful
as prokinetic agents to promote recovery from postoperative ileus just after the reappearance of MMCs in the early postoperative
period, their precise mode of action has not been established.
(Received for publication on May 7, 1997; accepted on Nov. 6, 1997) 相似文献
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Thomas E. Delea Oleg Sofrygin James L. Palmer Helen Lau Veronica C. Munk Jennifer Sung Alan Charney Hans-Henrik Parving Sean D. Sullivan 《Journal of the American Society of Nephrology : JASN》2009,20(10):2205-2213
The Aliskiren in the Evaluation of Proteinuria in Diabetes (AVOID) trial demonstrated that adding aliskiren, an oral direct renin inhibitor, at a dosage of 300 mg/d to the highest approved dosage of losartan and optimal antihypertensive therapy reduces albuminuria over 6 mo among patients with type 2 diabetes, hypertension, and albuminuria. The cost-effectiveness of this therapy, however, is unknown. Here, we used a Markov model to project progression to ESRD, life years, quality-adjusted life years, and lifetime costs for aliskiren plus losartan versus losartan. We used data from the AVOID study and the Irbesartan in Diabetic Nephropathy Trial (IDNT) to estimate probabilities of progression of renal disease. We estimated probabilities of mortality for ESRD and other comorbidities using data from the US Renal Data System, US Vital Statistics, and published studies. We based pharmacy costs on wholesale acquisition costs and based costs of ESRD and transplantation on data from the US Renal Data System. We found that adding aliskiren to losartan increased time free of ESRD, life expectancy, and quality-adjusted life expectancy by 0.1772, 0.1021, and 0.0967 yr, respectively. Total expected lifetime health care costs increased by $2952, reflecting the higher pharmacy costs of aliskiren and losartan ($7769), which were partially offset by savings in costs of ESRD ($4860). We estimated the cost-effectiveness of aliskiren to be $30,500 per quality-adjusted life year gained. In conclusion, adding aliskiren to losartan and optimal therapy in patients with type 2 diabetes, hypertension, and albuminuria may be cost-effective from a US health care system perspective.Recent clinical trials have shown that reductions in albuminuria with agents that inhibit the renin-angiotensin-aldosterone system (RAAS; e.g., angiotensin-converting enzyme inhibitors or angiotensin receptor blockers), as defined by a urinary albumin-creatinine ratio (UACR) or urinary albumin excretion rate (UAER), can delay progression of renal disease in patients with hypertension and type 2 diabetes.1–5 Albuminuria is associated with fatal and nonfatal cardiovascular events and progression toward ESRD,1,2,6 and reductions in albuminuria have been widely used as surrogate markers of renoprotection.7,8The Aliskiren in the Evaluation of Proteinuria in Diabetes (AVOID) trial was a multicenter, randomized, double-blind study to assess the efficacy and safety of adding aliskiren, an oral direct renin inhibitor, at a dosage of 300 mg/d to the highest approved dosage of losartan (100 mg/d) and optimal antihypertensive therapy in patients with type 2 diabetes, hypertension, and albuminuria.9 At the end of 6 mo of follow-up, aliskiren 300 mg/d significantly reduced mean UACR by 20% (P = 0.0009) and overnight UAER by 18% versus placebo (P = 0.009). Although the AVOID trial demonstrated that adjunctive treatment with aliskiren 300 mg/d reduces albuminuria during 6 mo in these patients, it did not examine the potential long-term clinical and economic consequences of such treatment.The objective of this study was to evaluate, from the US health care system perspective, the potential cost-effectiveness of lifetime treatment with aliskiren 300 mg/d plus losartan 100 mg/d and optimal antihypertensive therapy (aliskiren plus losartan) versus lifetime treatment with losartan 100 mg/d and optimal antihypertensive therapy alone (losartan only) in patients with type 2 diabetes, hypertension, and albuminuria. We evaluated cost-effectiveness using a Markov model (Figure 1).10 The initial distribution of the population across disease states and probabilities of progression from microalbuminuria (MA) and early overt nephropathy (EON) to advanced overt nephropathy (AON) were from the AVOID trial (11Open in a separate windowFigure 1.Markov state transition model. Ovals represent health states. Patients are assumed to transition among states every 6 mo on the basis of transition probabilities in Health State Cycles/Ages Aliskiren + Losartan Losartan Only Source or Reference Beginning of Cycle End of Cycle MA EON First 6-mo cycle 0.2716 0.3291 AVOID Subsequent cycles 0.2774 0.3510 AVOID AON First 6-mo cycle 0.0108 0.0266 AVOID Subsequent cycles 0.0000 0.0000 AVOID Death US age-/gender-specific rates × 2.0 16,30 EON MA First 6-mo cycle 0.2503 0.1446 AVOID Subsequent cycles 0.0000 0.0000 Assumption AON First 6-mo cycle 0.0794 0.0987 AVOID Subsequent cycles 0.0804 0.0995 AVOID Death US age-/gender-specific rates × 4.4 16,30 AON MA First 6-mo cycle 0.0972 0.0624 AVOID Subsequent cycles 0.0000 0.0000 Assumption EON First 6-mo cycle 0.3547 0.3754 AVOID Subsequent cycles 0.0000 0.0000 Assumption DSC Year 1a 0.0351 0.0351 16 Year 2a 0.0230 0.0230 16 Year 3a 0.0214 0.0214 16 Year 4+a 0.0159 0.0159 16 ESRD-dialysis Year 1a 0.0157 0.0157 16 Year 2a 0.0104 0.0104 16 Year 3a 0.0125 0.0125 16 Year 4+a 0.0129 0.0129 16 Death US age-/gender-specific rates × 4.4 16,30 DSC ESRD-dialysis 0.3200 0.3200 16 Death US age-/gender-specific rates × 4.4 16,30 ESRD-dialysis ESRD-transplant 0.0253 0.0253 16 Death Aged 50 to 59 yr 0.0857 0.0857 29 Aged 60 to 64 yr 0.1039 0.1039 29 Aged 65 to 69 yr 0.1206 0.1206 29 Aged 70 to 79 yr 0.1564 0.1564 29 Aged ≥80 yr 0.2122 0.2122 29 ESRD-transplant ESRD-dialysis 0.0609 0.0609 16 Death Aged 50 to 59 yr 0.0270 0.0270 29 Aged 60 to 64 yr 0.0376 0.0376 29 Aged 65 to 69 yr 0.0499 0.0499 29 Aged 70 to 79 yr 0.0615 0.0615 29 Aged ≥80 yr 0.1081 0.1081 29