Publications

Genetic susceptibility to cardiovascular disease in type 2 diabetes is influenced by abnormal metabolic status in the affected diabetic patients; and the implicated genes may differ from those in the non-diabetic populations. We conducted 3-stage genome-wide association analysis to detect the genetic factors for coronary heart disease (CHD) in existing type 2 diabetes. We analyzed 2.4 million SNPs in 1,249 diabetic women from the Nurses’ Health Study (273 CHD cases and 976 controls) and 984 diabetic men from the Health Professionals’ Follow-up Study (319 CHD cases and 665 controls), all of European ancestry. The first-stage replication was performed in Joslin Heart Study (420 CHD cases and 431 controls); and the second-stage replication was performed in Gargano Heart Study (314 CHD cases and 384 controls), and Catanzaro study (114 CHD cases and 215 controls). All the replication samples were of European ancestry and also affected by type 2 diabetes. We observed associations of genome-wide significance (the best p=2.04 * 10 -8 ) between CHD and SNPs on chromosome 1, between genes ZNF648 ( zinc finger protein 648; 1q25 .3) and GLUL (glutamate-ammonia ligase; 1q31). eQTL analysis indicates suggestive association between the associated SNPs and a potential CHD gene PPAP2B on chromosome 1 with marginal significance (p=1 * 10 -5 ). The identified SNP showed a significant association with the plasma levels of vascular cell adhesion molecule 1 ( VCAM1 ) in patients with type 2 diabetes but not in non-diabetic healthy controls. Our findings indicate that specific genetic predisposition may determine cardiovascular risk in patients with type 2 diabetes. The findings may inform the development of novel therapies for reduction of cardiovascular complications in diabetes.

The design architecture of a multidisciplinary problem-solving environment (MPSE) for supporting an efficient prediction capability for the response of continuous interacting systems under multiphysics conditions is presented. The system will be referred to as a Data Driven Environment for Multiphysics Applications (DDEMA) and will be primarily differentiated from previous MPSE efforts on its usage of data for improved confidence of simulation prediction. Its architecture takes into consideration information technologies, coupled multiphysics sciences, and data-driven practices to achieve steering of adaptive modeling and simulation of the underlying systemic behavior. Special emphasis is given on middleware implementation issues based on actor-agent abstractions. A description of the design objectives and architectural variations of DDEMA are also given in the context of two multidisciplinary applications related to material/structure design of supersonic platforms and fire/material/environment interaction monitoring, assessment and management. Validation of the architecture will also be attempted in terms of the same two applications.

There is an error in Table 2.The square brackets that indicate the effect allele are shown in error.Please see the correct Table 2 here.S2 Table is a duplicate of S6 Table.Please view the correct S2 Table below.

Background: Recent evidence suggests that having normal weight at time of diagnosis of type 2 diabetes (T2D) is associated with an increased risk of premature death, compared to being overweight or obese; these studies were limited in their sample size and ability to adequately address residual confounding by smoking and reverse causation. Objective: To prospectively evaluate the association between body mass index (BMI) at T2D diagnosis and mortality in two large cohorts. Methods: Women and men with incident T2D from the Nurses’ Health Study (1978-2010; N=8,984) and Health Professionals Follow-up Study (1988-2010; N=2,443) were included if they were free of major chronic disease (cardiovascular disease [CVD], cancer) at T2D diagnosis. Participants’ self-reported body weight preceding diagnosis (mean 11 months) and height was used to calculate BMI (kg/m2). Cox proportional hazards models estimated the relative risk (HR) and 95% confidence interval (CI) for mortality across BMI categories. Multivariable models adjusted for age, smoking, baseline comorbidities (high blood pressure, cholesterol), and several other lifestyle factors. Fixed-effects meta-analyses were used to combine individual cohort estimates. Results: In all, 3,119 total deaths were observed over a follow-up of 36 years in women (18.7 deaths/1,000 person-years) and 26 years in men (25.2 deaths/1,000 person-years). A J-shaped association was observed across BMI categories (18.5-22.4, 22.5-24.9, 25.0-27.4, 27.5-29.9, 30.0-34.9, >35.0) and all-cause mortality (HR [CI] by category: 1.26[1.03, 1.55], 1[reference], 1.11[0.97, 1.28], 1.08[0.94, 1.25], 1.19[1.04, 1.37], 1.33[1.15, 1.55]). After stratifying by smoking status, a direct linear association was present among never smokers and a J-shaped relationship persisted among ever smokers. Excluding deaths in the first 4 years of follow-up and adjusting for BMI change prior to diagnosis further accentuated the linear relationship between BMI and all-cause mortality among never smokers (0.90[0.57, 1.43], 1.00 [reference], 1.19[0.91, 1.57], 1.20[0.91, 1.58], 1.27[0.97, 1.65], 1.50[1.14, 1.99]; p-trend<0.001). The association across BMI categories and CVD mortality was also linear among never smokers, and flat among ever smokers. In addition, among ever smokers, cancer mortality was highest among those with BMI 18.5-22.5. No clear trend was observed between BMI and mortality due to other causes. Excluding insulin users did not appreciably modify the associations. Conclusions: We found no evidence to support lower mortality rates among diabetics who were overweight or obese at diagnosis, compared to their normal-weight counterparts. In contrast, after accounting for confounding by smoking, we observed direct linear relationships between BMI and both all-cause and CVD mortality in our cohorts. Reducing other biases strengthened these relationships.

Low HDL-C is a major risk factor for CVD. To elucidate potential novel pathways behind low HDL-C, we have employed 3 different omics: genomics, transcriptomics, and lipidomics. We performed GWAS on 450 Finns with low or high HDL-C (90th percentiles). Out of 54 individuals, we obtained subcutaneous (SC) fat biopsies for transcriptome analysis, and isolated plasma HDL particles for lipidomics analysis with MS technology. We first conducted gene network analysis for loci associated with low HDL-C observing that SNPs within 4 inflammatory pathways (e.g. antigen-presentation) were enriched among the low HDL-C associated genes (p=10^-5). Also, these inflammatory pathways were over-expressed in SC fat of low-HDL-subjects (p=10^-10). We then calculated genetic risk scores based on low HDL-C associating SNPs from these pathways, observing that high risk score resulted in both decreased HDL-C-levels and increased expression of these inflammatory pathways. Moreover, individual genes of these pathways (e.g. HLA-DRB1 [p=10^-7, TAP2 p=10^-6) exhibited cis-eQTLs, their expression inversely correlating with HDL-C. Consistent with this, the inflammatory nature of the HDL particle of low HDL-C subjects was further highlighted by the elevation of proinflammatory ceramides and reduction of anti-oxidative plasmalogens in the lipidomic analysis, which was also genetic risk score dependent. In a replication analysis of 5 Finnish population cohorts (n=11,211) the low HDL-C associated SNPs were not associated with HDL-C as quantitative trait, but only after using 75, 90, and 95 percentiles as low/high HDL-C criteria. Interestingly, the effect sizes of individual SNPs became stronger when more extreme phenotype criteria for low HDL-C were implemented (e.g. HLA-DRB1; OR for low HDL-C 75 % = 1.11, OR for 90 % = 1.38, OR for 95 % = 1.77). Our findings imply that genetic variation influences inflammatory processes associated with low HDL-cholesterol on both expression and metabolite levels, suggesting more inflammatory and less vasoprotective role for HDL particles in low-HDL-subjects. Using the extreme phenotype approach, we also demonstrate the presence of ‘low HDL genes’ in addition to the population GWAS identified ‘HDL genes.’

OBJECTIVE We evaluated the associations of both maternal and paternal smoking during pregnancy with the risk of type 2 diabetes in daughters and explored whether any association was explained by weight at birth or BMI throughout life. RESEARCH DESIGN AND METHODS We used data from 34,453 participants of the Nurses’ Health Study II. We used Cox proportional hazards models to examine the associations of maternal and paternal smoking during pregnancy with incidence of type 2 diabetes in daughters between 1989 and 2009. RESULTS Maternal smoking during the first trimester only was associated with the risk of type 2 diabetes in the offspring, independent of confounders, birth weight, and later-life BMI (fully adjusted hazard ratio 1.34 [95% CI 1.01, 1.76]). In the age-adjusted models, both continued maternal smoking during pregnancy and paternal smoking tended to be associated with an increased risk of type 2 diabetes in daughters. Perinatal and adult life variables did not explain these associations, but additional adjustment for current BMI fully attenuated the effect estimates. CONCLUSIONS The associations of maternal and paternal smoking during pregnancy with the risk of type 2 diabetes in daughters were largely explained by BMI throughout the life course. Further studies are needed to explore the role of first-trimester-only maternal smoking on insulin resistance in the offspring. Also, similar effect estimates for maternal and paternal smoking suggest that the associations reflect shared family-based or lifestyle-related factors.

The nature of the processes at the origin of life that selected specific classes of molecules for broad incorporation into cells is controversial. Among those classes selected were polyisoprenoids and their derivatives. This paper tests the hypothesis that polyisoprenoids were early contributors to membranes in part because they (or their derivatives) could facilitate charge transport by quantum tunneling. It measures charge transport across self-assembled monolayers (SAMs) of carboxyl-terminated monoterpenoids (O2 C(C9 HX)) and alkanoates (O2 C(C7 HX)) with different degrees of unsaturation, supported on silver (AgTS ) bottom electrodes, with Ga2 O3 /EGaIn top electrodes. Measurements of current density of SAMs of linear length-matched hydrocarbons-both saturated and unsaturated-show that completely unsaturated molecules transport charge faster than those that are completely saturated by approximately a factor of ten. This increase in relative rates of charge transport correlates with the number of carbon-carbon double bonds, but not with the extent of conjugation. These results suggest that polyisoprenoids-even fully unsaturated-are not sufficiently good tunneling conductors for their conductivity to have favored them as building blocks in the prebiotic world.

No abstract is provided for this article.