136 research outputs found

    Risk of Cardiovascular Events and Death—Does Insurance Matter?

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    BACKGROUND: Many Americans lack health insurance. Despite good evidence that lack of insurance compromises access to care, few prospective studies examine its relationship to health outcomes. OBJECTIVE: To determine the relationship between insurance and cardiovascular outcomes and the relationship between insurance and selected process measures. DESIGN AND PARTICIPANTS: We used data from 15,792 participants in the Atherosclerosis Risk in Communities Study, a prospective cohort study. Participants were enrolled in 1987–1989 and returned for follow-up visits every 3 years, for a total of 4 visits. MAIN OUTCOME MEASURES: We estimated the hazard of myocardial infarction, stroke, and death associated with insurance status using Cox proportional hazard modeling. We used generalized estimating equations to examine the association between insurance status and risk of (1) reporting no routine physical examinations, (2) being unaware of a personal cardiovascular risk condition, and (3) inadequate control of cardiovascular risk conditions. RESULTS: Persons without insurance had higher rates of stroke (adjusted hazard ratio, 95% CI 1.22–2.22) and death (adjusted hazard ratio 1.26, 95% CI 1.03–1.53), but not myocardial infarction, than those who were insured. The uninsured were less likely to report routine physical examinations (adjusted risk ratio 1.13, 95% CI 1.08–1.18); more likely to be unaware of hypertension (adjusted risk ratio 1.12, 95% CI 1.00–1.25) and hyperlipidemia (adjusted risk ratio 1.11, 95% CI 1.03–1.19); and more likely to have poor blood pressure control (adjusted risk ratio 1.23, 95% CI 1.08–1.39). CONCLUSIONS: Lack of health insurance is associated with increased rates of stroke and death and with less awareness and control of cardiovascular risk conditions. Health insurance may improve cardiovascular risk factor awareness, control and outcomes

    New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

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    Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

    A Federated Database for Obesity Research:An IMI-SOPHIA Study

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    Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

    A Federated Database for Obesity Research:An IMI-SOPHIA Study

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    Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders

    Matrix Metalloproteinase-10 Is Required for Lung Cancer Stem Cell Maintenance, Tumor Initiation and Metastatic Potential

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    Matrix metalloproteinases (Mmps) stimulate tumor invasion and metastasis by degrading the extracellular matrix. Here we reveal an unexpected role for Mmp10 (stromelysin 2) in the maintenance and tumorigenicity of mouse lung cancer stem-like cells (CSC). Mmp10 is highly expressed in oncosphere cultures enriched in CSCs and RNAi-mediated knockdown of Mmp10 leads to a loss of stem cell marker gene expression and inhibition of oncosphere growth, clonal expansion, and transformed growth in vitro. Interestingly, clonal expansion of Mmp10 deficient oncospheres can be restored by addition of exogenous Mmp10 protein to the culture medium, demonstrating a direct role for Mmp10 in the proliferation of these cells. Oncospheres exhibit enhanced tumor-initiating and metastatic activity when injected orthotopically into syngeneic mice, whereas Mmp10-deficient cultures show a severe defect in tumor initiation. Conversely, oncospheres implanted into syngeneic non-transgenic or Mmp10−/− mice show no significant difference in tumor initiation, growth or metastasis, demonstrating the importance of Mmp10 produced by cancer cells rather than the tumor microenvironment in lung tumor initiation and maintenance. Analysis of gene expression data from human cancers reveals a strong positive correlation between tumor Mmp10 expression and metastatic behavior in many human tumor types. Thus, Mmp10 is required for maintenance of a highly tumorigenic, cancer-initiating, metastatic stem-like cell population in lung cancer. Our data demonstrate for the first time that Mmp10 is a critical lung cancer stem cell gene and novel therapeutic target for lung cancer stem cells

    A Federated Database for Obesity Research:An IMI-SOPHIA Study

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    Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.</p

    The influence of body weight on the pulmonary oxygen uptake kinetics in pre-pubertal children during moderate- and heavy intensity treadmill exercise

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    To assess the influence of obesity on the oxygen uptake (V˙O2) kinetics of pre-pubertal children during moderate- and heavy intensity treadmill exercise. We hypothesised that obese (OB) children would demonstrate significantly slower V˙O2 kinetics than their normal weight (NW) counterparts during moderate- and heavy intensity exercise. 18 OB (9.8 ± 0.5 years; 24.1 ± 2.0 kg m2) and 19 NW (9.7 ± 0.5 years; 17.6 ± 1.0 kg m2) children completed a graded-exercise test to volitional exhaustion and two submaximal constant work rate treadmill tests at moderate (90 % gas exchange threshold) and heavy (∆40 %) exercise intensities. Bodyweight significantly influenced the V˙O2 kinetics during both moderate- and heavy exercise intensities (P < 0.05). During moderate intensity exercise, the phase II τ (OB: 30 ± 13 cf. NW: 22 ± 7 s), mean response time (MRT; OB: 35 ± 16 cf. NW: 25 ± 10 s), phase II gain (OB: 156 ± 21 cf. NW: 111 ± 18 mLO2 kg−1 km−1) and oxygen deficit (OB: 0.36 ± 0.11 cf. NW: 0.20 ± 0.06 L) were significantly higher in the OB children (all P < 0.05). During heavy intensity exercise, the τ (OB: 33 ± 9 cf. NW: 27 ± 6 s; P < 0.05) and phase II gain (OB: 212 ± 61 cf. NW: 163 ± 23 mLO2 kg−1 km−1; P < 0.05) were similarly higher in the OB children. A slow component was observed in all participants during heavy intensity exercise, but was not influenced by weight status. In conclusion, this study demonstrates that weight status significantly influences the dynamic V˙O2 response at the onset of treadmill exercise in children and highlights that the deleterious effects of being obese are already manifest pre-puberty

    No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels.

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    Gene-lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13-0.31], P = 1.63 × 10(-6)). All SNPs were associated with 2-h glucose (β = 0.06-0.12 mmol/allele, P ≤ 1.53 × 10(-7)), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene-lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions
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