Chronologic distribution of stroke after minimally invasive versus conventional coronary artery bypass

AbstractObjectivesWe sought to investigate whether the chronologic distribution of the onset of stroke occurring after coronary artery bypass graft surgery (CABG) without cardiopulmonary bypass (off-pump CABG) is different from the conventional on-pump approach (CABG with cardiopulmonary bypass).BackgroundOff-pump CABG has been associated with a lower stroke rate, compared with conventional on-pump CABG. However, it is unknown whether the chronologic distribution of the onset of stroke is different between the two approaches.MethodsWe evaluated the chronologic distribution of postoperative stroke in patients undergoing CABG from June 1996 to August 2001 (n = 10,573). Preoperative risk factors for stroke were identified using the Northern New England preoperative estimate of stroke risk. Multivariate logistic regression analysis was used to determine the independent predictors of early stroke and to delineate the association between the surgical approach and the chronologic distribution of the onset of stroke.ResultsStroke occurred in 217 patients (2%, n = 10,573). A total of 44 (20%) and 173 (80%) of these patients had stroke after off-pump CABG and on-pump CABG, respectively. The median time for the onset of stroke was two days (range 0 to 11 days) after on-pump CABG versus four days (range 0 to 14 days) after off-pump CABG (p < 0.01). On-pump CABG was associated with a higher risk of early stroke (odds ratio 5.3, 95% confidence interval 2.6 to 10.9; p < 0.01) compared with off-pump CABG.ConclusionsCompared with off-pump CABG, on-pump CABG is associated with an earlier onset of postoperative stroke during the recovery phase, suggesting different mechanisms in the pathogenesis of stroke between the two surgical approaches

The impact of salsalate treatment on serum levels of advanced glycation end products in type 2 diabetes.

OBJECTIVE Salsalate is a nonacetylated salicylate that lowers glucose levels in people with type 2 diabetes (T2D). Here we examined whether salsalate also lowered serum-protein-bound levels of early and advanced glycation end products (AGEs) that have been implicated in diabetic vascular complications. RESEARCH DESIGN AND METHODS Participants were from the Targeting Inflammation Using Salsalate for Type 2 Diabetes (TINSAL-T2D) study, which examined the impact of salsalate treatment on hemoglobin A1c (HbA1c) and a wide variety of other parameters. One hundred eighteen participants received salsalate, 3.5 g/day for 48 weeks, and 109 received placebo. Early glycation product levels (HbA1c and fructoselysine [measured as furosine]) and AGE levels (glyoxal and methylglyoxal hydroimidazolones [G-(1)H, MG-(1)H], carboxymethyllysine [CML], carboxyethyllysine [CEL], pentosidine) were measured in patient serum samples. RESULTS Forty-eight weeks of salsalate treatment lowered levels of HbA1c and serum furosine (P \u3c 0.001) and CML compared with placebo. The AGEs CEL and G-(1)H and MG-(1)H levels were unchanged, whereas pentosidine levels increased more than twofold (P \u3c 0.001). Among salsalate users, increases in adiponectin levels were associated with lower HbA1c levels during follow-up (P \u3c 0.001). Changes in renal and inflammation factor levels were not associated with changes in levels of early or late glycation factors. Pentosidine level changes were unrelated to changes in levels of renal function, inflammation, or cytokines. CONCLUSIONS Salsalate therapy was associated with a reduction in early but not late glycation end products. There was a paradoxical increase in serum pentosidine levels suggestive of an increase in oxidative stress or decreased clearance of pentosidine precursor

Targeting inflammation using salsalate in patients with type 2 diabetes: effects on flow-mediated dilation (TINSAL-FMD).

OBJECTIVE: To test whether inhibiting inflammation with salsalate improves endothelial function in patients with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted an ancillary study to the National Institutes of Health-sponsored, multicenter, randomized, double-masked, placebo-controlled trial evaluating the safety and efficacy of salsalate in targeting inflammation to improve glycemia in patients with T2D. Flow-mediated, endothelium-dependent dilation (FMD) and endothelium-independent, nitroglycerin-mediated dilation (NMD) of the brachial artery were assessed at baseline and 3 and 6 months following randomization to either salsalate 3.5 g/day or placebo. The primary end point was change in FMD at 6 months. RESULTS: A total of 88 participants were enrolled in the study, and data after randomization were available for 75. Patients in the treatment and control groups had similar ages (56 years), BMI (33 kg/m(2)), sex (64% male), ethnicity, current treatment, and baseline HbA1c (7.7% [61 mmol/mol]). In patients treated with salsalate versus placebo, HbA1c was reduced by 0.46% (5.0 mmol/mol; P \u3c 0.001), fasting glucose by 16.1 mg/dL (P \u3c 0.001), and white blood cell count by 430 cells/µL (P \u3c 0.02). There was no difference in the mean change in either FMD (0.70% [95% CI -0.86 to 2.25%]; P = 0.38) or NMD (-0.59% [95% CI -2.70 to 1.51%]; P = 0.57) between the groups treated with salsalate and placebo at 6 months. Total and LDL cholesterol were 11 and 16 mg/dL higher, respectively, and urinary albumin was 2.0 µg/mg creatinine higher in the patients treated with salsalate compared with those treated with placebo (all P \u3c 0.009). CONCLUSIONS: Salsalate does not change FMD in peripheral conduit arteries in patients with T2D despite lowering HbA1c. This finding suggests that salsalate does not have an effect on vascular inflammation, inflammation does not cause endothelial dysfunction in T2D, or confounding effects of salsalate mitigate favorable effects on endothelial function

Genetic Predictors of Weight Loss and Weight Regain After Intensive Lifestyle Modification, Metformin Treatment, or Standard Care in the Diabetes Prevention Program

OBJECTIVE: We tested genetic associations with weight loss and weight regain in the Diabetes Prevention Program, a randomized controlled trial of weight loss–inducing interventions (lifestyle and metformin) versus placebo. RESEARCH DESIGN AND METHODS: Sixteen obesity-predisposing single nucleotide polymorphisms (SNPs) were tested for association with short-term (baseline to 6 months) and long-term (baseline to 2 years) weight loss and weight regain (6 months to study end). RESULTS: Irrespective of treatment, the Ala12 allele at PPARG associated with short- and long-term weight loss (−0.63 and −0.93 kg/allele, P ≤ 0.005, respectively). Gene–treatment interactions were observed for short-term (LYPLAL1 rs2605100, P$_{lifestyle*SNP}$ = 0.032; GNPDA2 rs10938397, P$_{lifestyle*SNP}$ = 0.016; MTCH2 rs10838738, P$_{lifestyle*SNP}$ = 0.022) and long-term (NEGR1 rs2815752, P$_{metformin*SNP}$ = 0.028; FTO rs9939609, P$_{lifestyle*SNP}$ = 0.044) weight loss. Three of 16 SNPs were associated with weight regain (NEGR1 rs2815752, BDNF rs6265, PPARG rs1801282), irrespective of treatment. TMEM18 rs6548238 and KTCD15 rs29941 showed treatment-specific effects (P$_{lifestyle*SNP}$ < 0.05). CONCLUSIONS: Genetic information may help identify people who require additional support to maintain reduced weight after clinical intervention

Genetic risk of progression to type 2 diabetes and response to intensive lifestyle or metformin in prediabetic women with and without a history of gestational diabetes mellitus.

OBJECTIVE The Diabetes Prevention Program (DPP) trial investigated rates of progression to diabetes among adults with prediabetes randomized to treatment with placebo, metformin, or intensive lifestyle intervention. Among women in the DPP, diabetes risk reduction with metformin was greater in women with prior gestational diabetes mellitus (GDM) compared with women without GDM but with one or more previous live births. RESEARCH DESIGN AND METHODS We asked if genetic variability could account for these differences by comparing β-cell function and genetic risk scores (GRS), calculated from 34 diabetes-associated loci, between women with and without histories of GDM. RESULTS β-Cell function was reduced in women with GDM. The GRS was positively associated with a history of GDM; however, the GRS did not predict progression to diabetes or modulate response to intervention. CONCLUSIONS These data suggest that a diabetes-associated GRS is associated with development of GDM and may characterize women at risk for development of diabetes due to β-cell dysfunction

Genetic modulation of lipid profiles following lifestyle modification or metformin treatment: The Diabetes Prevention Program

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene $SLC47A1$ with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene $STK11$, the AMPK subunit genes $PRKAA1$ and $PRKAA2$, and a missense SNP in $SLC22A1$, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene $PRKAG2$ (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10$^{−4}$). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest $P$ values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples

Effects of Genetic Variants Previously Associated with Fasting Glucose and Insulin in the Diabetes Prevention Program

Common genetic variants have been recently associated with fasting glucose and insulin levels in white populations. Whether these associations replicate in pre-diabetes is not known. We extended these findings to the Diabetes Prevention Program, a clinical trial in which participants at high risk for diabetes were randomized to placebo, lifestyle modification or metformin for diabetes prevention. We genotyped previously reported polymorphisms (or their proxies) in/near G6PC2, MTNR1B, GCK, DGKB, GCKR, ADCY5, MADD, CRY2, ADRA2A, FADS1, PROX1, SLC2A2, GLIS3, C2CD4B, IGF1, and IRS1 in 3,548 Diabetes Prevention Program participants. We analyzed variants for association with baseline glycemic traits, incident diabetes and their interaction with response to metformin or lifestyle intervention. We replicated associations with fasting glucose at MTNR1B (P<0.001), G6PC2 (P = 0.002) and GCKR (P = 0.001). We noted impaired β-cell function in carriers of glucose-raising alleles at MTNR1B (P<0.001), and an increase in the insulinogenic index for the glucose-raising allele at G6PC2 (P<0.001). The association of MTNR1B with fasting glucose and impaired β-cell function persisted at 1 year despite adjustment for the baseline trait, indicating a sustained deleterious effect at this locus. We also replicated the association of MADD with fasting proinsulin levels (P<0.001). We detected no significant impact of these variants on diabetes incidence or interaction with preventive interventions. The association of several polymorphisms with quantitative glycemic traits is replicated in a cohort of high-risk persons. These variants do not have a detectable impact on diabetes incidence or response to metformin or lifestyle modification in the Diabetes Prevention Program

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVE: Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS: We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS: We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS: We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples