Intelligent decision making for energy efficient fog nodes selection and smart switching in the IOT: a machine learning approach.

With the emergence of Internet of Things (IoT) technology, a huge amount of data is generated, which is costly to transfer to the cloud data centers in terms of security, bandwidth, and latency. Fog computing is an efficient paradigm for locally processing and manipulating IoT-generated data. It is difficult to configure the fog nodes to provide all of the services required by the end devices because of the static configuration, poor processing, and storage capacities. To enhance fog nodes' capabilities, it is essential to reconfigure them to accommodate a broader range and variety of hosted services. In this study, we focus on the placement of fog services and their dynamic reconfiguration in response to the end-device requests. Due to its growing successes and popularity in the IoT era, the Decision Tree (DT) machine learning model is implemented to predict the occurrence of requests and events in advance. The DT model enables the fog nodes to predict requests for a specific service in advance and reconfigure the fog node accordingly. The performance of the proposed model is evaluated in terms of high throughput, minimized energy consumption, and dynamic fog node smart switching. The simulation results demonstrate a notable increase in the fog node hit ratios, scaling up to 99% for the majority of services concurrently with a substantial reduction in miss ratios. Furthermore, the energy consumption is greatly reduced by over 50% as compared to a static node.

Statistical analysis of software development models by six-pointed star framework.

Software Development Process Model (SDPM) develops software according to the needs of the client within the defined budget and time. There are many software development models such as waterfall, Iterative, Rapid Application Development (RAD), Spiral, Agile, Z, and AZ model. Each development model follows a series of steps to develop a product. Each model has its strengths and weaknesses. In this study, we have investigated different software development process models using the six-pointed star framework. Six-point star is a framework of project management industry standards maintained by Project Management Body of Knowledge (PMBOK). A survey is designed to evaluate the performance of well-known software process models in the context of factors defined by the six-point star framework. The survey is conducted with experienced users of the software industry. The statistical analysis and comparison of results obtained by the survey are further used to examine the effectiveness of each model for the development of high-quality software concerning lightweight and heavyweight methodologies for small, medium and large scale projects. After exploring the results of all factors of the six-pointed star model, we conclude that lightweight methodology easily handles small-scale projects. The heavyweight methodology is suitable for medium and large-scale projects, whereas the AZ model, which is one of the latest models, works efficiently with both small-scale and large-scale categories of projects.

Pharmacologic Activation of STING in the Bladder Induces Potent Antitumor Immunity in Non-Muscle Invasive Murine Bladder Cancer.

Stimulator of interferon genes (STING) is an innate immune receptor activated by natural or synthetic agonists to elicit antitumoral immune response via type I IFNs and other inflammatory cytokines. Bacillus Calmette-Guerin (BCG) is the standard of care as intravesical therapy for patients with high-risk non-muscle invasive bladder cancer (NMIBC). There are limited options available for patients with NMIBC who developed BCG unresponsiveness. In this study, we characterized in vitro and in vivo antitumor effects of E7766, a macrocyle-bridged STING agonist, via intravesical instillation in two syngeneic orthotopic murine NMIBC tumor models resistant to therapeutic doses of BCG and anti-PD-1 agents. E7766 bound to recombinant STING protein with a Kd value of 40 nmol/L and induced IFNß expression in primary human peripheral blood mononuclear cells harboring any of seven major STING genotypes with EC50 values of 0.15 to 0.79 µmol/L. Intravesical E7766 was efficacious in both NMIBC models with induction of effective immunologic memory in the treated animals. Pharmacologic activation of the STING pathway in the bladder resulted in IFN pathway activation, infiltration of T cells and natural killer (NK) cells, dendritic cell activation, and antigen presentation in bladder epithelium, leading to the antitumor activity and immunity. In addition, measurements of the pharmacodynamic markers, Ifnß1 and CXCL10, in bladder, urine, and plasma, and of STING pathway intactness in cancer cells, supported this mode of action. Taken together, our studies reveal an antitumor immune effect of pharmacologic activation of the STING pathway in bladder epithelium and thus provide a rationale for subsequent clinical studies in patients with NMIBC.

Effect of zinc nanoparticles seed priming and foliar application on the growth and physio-biochemical indices of spinach (Spinacia oleracea L.) under salt stress.

Salt stress is the major risk to the seed germination and plant growth via affecting physiological and biochemical activities in plants. Zinc nanoparticles (ZnNPs) are emerged as a key agent in regulating the tolerance mechanism in plants under environmental stresses. However, the tolerance mechanisms which are regulated by ZnNPs in plants are still not fully understood. Therefore, the observation was planned to explore the role of ZnNPs (applied as priming and foliar) in reducing the harmful influence of sodium chloride (NaCl) stress on the development of spinach (Spinacia oleracea L.) plants. Varying concentrations of ZnNPs (0.1%, 0.2% & 0.3%) were employed to the spinach as seed priming and foliar, under control as well as salt stress environment. The alleviation of stress was observed in ZnNPs-applied spinach plants grown under salt stress, with a reduced rise in the concentration hydrogen peroxide, melondialdehyde and anthocyanin contents. A clear decline in soluble proteins, chlorophyll contents, ascorbic acid, sugars, and total phenolic contents was observed in stressed conditions. Exogenous ZnNPs suppressed the NaCl generated reduction in biochemical traits, and progress of spinach plants. However, ZnNPs spray at 0.3% followed by priming was the most prominent treatment in the accumulation of osmolytes and the production of antioxidant molecules in plants.

Integration of 5G and Block-Chain Technologies in Smart Telemedicine Using IoT.

The Internet of Health Thing (IoHT) has various applications in healthcare. Modern IoHTintegrates health-related things like sensors and remotely observed medical devices for the assessment and managment of a patient's record to provide smarter and efficient health diagnostics to the patient. In this paper, we proposed an IoT with a cloud-based clinical decision support system for prediction and observation of disease with its severity level with the integration of 5G services and block-chain technologies. A block-chain is a system for storing and sharing information that is secure because of its transparency. Block-chain has many applications in healthcare and can improve mobile health applications, monitoring devices, sharing and storing of the electronic media records, clinical trial data, and insurance information storage. The proposed framework will collect the data of patients through medical devices that will be attached to the patient, and these data will be stored in a cloud server with relevant medical records. Deployment of Block-chain and 5G technology allows for sending patient data securely at a fast transmission rate with efficient response time. Furthermore, a Neural Network (NN) classifier is used for the prediction of diseases and their severity level. The proposed model is validated by employing different classifiers. The performance of different classifiers is measured by comparing the values to select the classifier that is the best for the dataset. The NN classifier attains an accuracy of 98.98. Furthermore, the NN is trained for the dataset so that it can predict the result of the dataset class that is not labeled. The trained Neural Network predicts and intelligently shows the results with more accuracy than other classifiers.

E7766, a Macrocycle-Bridged Stimulator of Interferon Genes (STING) Agonist with Potent Pan-Genotypic Activity.

A strategy for creating potent and pan-genotypic stimulator of interferon genes (STING) agonists is described. Locking a bioactive U-shaped conformation of cyclic dinucleotides by introducing a transannular macrocyclic bridge between the nucleic acid bases leads to a topologically novel macrocycle-bridged STING agonist (MBSA). In addition to substantially enhanced potency, the newly designed MBSAs, exemplified by clinical candidate E7766, exhibit broad pan-genotypic activity in all major human STING variants. E7766 is shown to have potent antitumor activity with long lasting immune memory response in a mouse liver metastatic tumor model. Two complementary stereoselective synthetic routes to E7766 are also described.

Comprehensive facts on dynamic antimicrobial properties of polysaccharides and biomolecules-silver nanoparticle conjugate.

Based on progress for the green synthesis of nanoparticle (NPs), the mushrooms have also been utilized extensively for the biogenic synthesis of NPs. In recent years, silver NPs have been fabricated using mushrooms. The antimicrobial drugs are efficient to control the infectious diseases, but due to widespread of drugs, microbes became resistant to drugs, which demands develop of new bioactive agents. The silver NPs have been recognized as efficient broad spectrum antimicrobial agents, which have been fabricated using polysaccharides from mushrooms as reducing and capping agent. This review focused on the comprehensive study that deals silver NPs polysaccharides from Pleurotus mushroom, their synthesis mechanism, action mechanism of silver NPs and their characterization using advanced techniques i.e., ultraviolet-visible (UV-Vis), dynamic light scattering, Fourier transformation infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), transmission electron microscopy (TEM) and XRD. The Pleurotus mushroom showed promising efficiency for the biogenic synthesis of polysaccharides­silver NPS and as-prepared NPs showed excellent antimicrobial activity.

An Intelligent Fire Warning Application Using IoT and an Adaptive Neuro-Fuzzy Inference System.

In the recent past, a few fire warning and alarm systems have been presented based on a combination of a smoke sensor and an alarm device to design a life-safety system. However, such fire alarm systems are sometimes error-prone and can react to non-actual indicators of fire presence classified as false warnings. There is a need for high-quality and intelligent fire alarm systems that use multiple sensor values (such as a signal from a flame detector, humidity, heat, and smoke sensors, etc.) to detect true incidents of fire. An Adaptive neuro-fuzzy Inference System (ANFIS) is used in this paper to calculate the maximum likelihood of the true presence of fire and generate fire alert. The novel idea proposed in this paper is to use ANFIS for the identification of a true fire incident by using change rate of smoke, the change rate of temperature, and humidity in the presence of fire. The model consists of sensors to collect vital data from sensor nodes where Fuzzy logic converts the raw data in a linguistic variable which is trained in ANFIS to get the probability of fire occurrence. The proposed idea also generates alerts with a message sent directly to the user's smartphone. Our system uses small size, cost-effective sensors and ensures that this solution is reproducible. MATLAB-based simulation is used for the experiments and the results show a satisfactory output.

Zinc biofortification of cereals-role of phosphorus and other impediments in alkaline calcareous soils.

Alkaline calcareous soils are deficient in plant nutrients; in particular, phosphorus (P) and zinc (Zn) are least available; their inorganic fertilizers are generally applied to meet the demand of crops. The applied nutrients react with soil constituents as well as with each other, resulting in lower plant uptake. Phosphorus availability is usually deterred due to lime content, while Zn availability is largely linked with alkalinity of the soil. The present manuscript critically discusses the factors associated with physicochemical properties of soil and other interactions in soil-plant system which contribute to the nutrients supply from soil, and affect productivity and quality attributes of cereals. Appropriate measures may possibly lessen the severity of nutritional disorder in cereal and optimize P and Zn concentrations in grain. Foliar Zn spray is found to escape most of the soil reactions; thus, Zn bioavailability is higher either through increase in grain Zn or through decrease in phytate content. The reactivity of nutrients prior to its uptake is deemed as major impediments in Zn biofortification of cereals. The article addresses physiological limitation of plants to accumulate grain Zn and the ways to achieve biofortification in cereals, while molecular mechanism explains how it affects nutritional quality of cereals. Moreover, it highlights the desirable measures for enhancing Zn bioavailability, e.g., manipulation of genetic makeup for efficient nutrient uptake/translocation, and also elucidates agronomic measures that help facilitate Zn supply in soil for plant accumulation.

Phytoremediation strategies for soils contaminated with heavy metals: Modifications and future perspectives.

Presence of heavy metals in agricultural soils is of major environmental concern and a great threat to life on the earth. A number of human health risks are associated with heavy metals regarding their entry into food chain. Various physical, chemical and biological techniques are being used to remove heavy metals and metalloids from soils. Among them, phytoremediation is a good strategy to harvest heavy metals from soils and have been proven as an effective and economical technique. In present review, we discussed various sources and harmful effects of some important heavy metals and metalloids, traditional phytoremediation strategies, mechanisms involved in phytoremediation of these metals, limitations and some recent advances in phytoremediation approaches. Since traditional phytoremediation approach poses some limitations regarding their applications at large scale, so there is a dire need to modify this strategy using modern chemical, biological and genetic engineering tools. In view of above, the present manuscript brings both traditional and advanced phytoremediation techniques together in order to compare, understand and apply these strategies effectively to exclude heavy metals from soil keeping in view the economics and effectiveness of phytoremediation strategies.

Zinc-cadmium interactions: Impact on wheat physiology and mineral acquisition.

Increasing concentration of Cd in soil is of great concern due to risk of its entry into food chain. Zinc (Zn) being antagonist to Cd is an important micronutrient to ameliorate its toxic effects on plants and to limit its entry into food chain. A pot experiment was conducted using Cd contaminated soil (30 mg Cd kg(-1) soil as 3CdSO4 · 8H2O) to investigate the effect of soil and foliar applied Zn on physiological response and Cd concentration in wheat. In soil, Zn was applied at 15 and 30 mg Zn kg(-1) soil as zinc sulfate (ZnSO4 · 7H2O). For foliar applications, 3 and 6 g L(-1) ZnSO4 solution was sprayed on completing eight weeks of growth. Results indicated that Zn application could effectively improve physiological performance and mineral content of wheat grown on Cd contaminated soils. Among different Zn fertilization treatments, foliar application of 3 g L(-1) ZnSO4 solution recorded the maximum soluble proteins and the minimum grain-Cd concentration. Soil application of ZnSO4 or foliar application at 6 g L(-1) did not affect Cd concentration in grains. Zinc application through both the methods significantly increased phosphorus (P), potassium (K) and Zn concentrations in shoots. Concentration of P and K in grains showed positive relationship with that of Zn. In crux, present study suggests that foliar application of Zn at booting stage in a suitable concentration (3 g L(-1) ZnSO4 solution) can effectively ameliorate the adverse effects of Cd and decrease grain-Cd of wheat grown in Cd contaminated soil.

Genetic studies of quantitative MCI and AD phenotypes in ADNI: Progress, opportunities, and plans.

INTRODUCTION: Genetic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) have been crucial in advancing the understanding of Alzheimer's disease (AD) pathophysiology. Here, we provide an update on sample collection, scientific progress and opportunities, conceptual issues, and future plans. METHODS: Lymphoblastoid cell lines and DNA and RNA samples from blood have been collected and banked, and data and biosamples have been widely disseminated. To date, APOE genotyping, genome-wide association study (GWAS), and whole exome and whole genome sequencing data have been obtained and disseminated. RESULTS: ADNI genetic data have been downloaded thousands of times, and >300 publications have resulted, including reports of large-scale GWAS by consortia to which ADNI contributed. Many of the first applications of quantitative endophenotype association studies used ADNI data, including some of the earliest GWAS and pathway-based studies of biospecimen and imaging biomarkers, as well as memory and other clinical/cognitive variables. Other contributions include some of the first whole exome and whole genome sequencing data sets and reports in healthy controls, mild cognitive impairment, and AD. DISCUSSION: Numerous genetic susceptibility and protective markers for AD and disease biomarkers have been identified and replicated using ADNI data and have heavily implicated immune, mitochondrial, cell cycle/fate, and other biological processes. Early sequencing studies suggest that rare and structural variants are likely to account for significant additional phenotypic variation. Longitudinal analyses of transcriptomic, proteomic, metabolomic, and epigenomic changes will also further elucidate dynamic processes underlying preclinical and prodromal stages of disease. Integration of this unique collection of multiomics data within a systems biology framework will help to separate truly informative markers of early disease mechanisms and potential novel therapeutic targets from the vast background of less relevant biological processes. Fortunately, a broad swath of the scientific community has accepted this grand challenge.

Glycated hemoglobin measurement and prediction of cardiovascular disease.

IMPORTANCE: The value of measuring levels of glycated hemoglobin (HbA1c) for the prediction of first cardiovascular events is uncertain. OBJECTIVE: To determine whether adding information on HbA1c values to conventional cardiovascular risk factors is associated with improvement in prediction of cardiovascular disease (CVD) risk. DESIGN, SETTING, AND PARTICIPANTS: Analysis of individual-participant data available from 73 prospective studies involving 294,998 participants without a known history of diabetes mellitus or CVD at the baseline assessment. MAIN OUTCOMES AND MEASURES: Measures of risk discrimination for CVD outcomes (eg, C-index) and reclassification (eg, net reclassification improvement) of participants across predicted 10-year risk categories of low (<5%), intermediate (5% to <7.5%), and high (≥ 7.5%) risk. RESULTS: During a median follow-up of 9.9 (interquartile range, 7.6-13.2) years, 20,840 incident fatal and nonfatal CVD outcomes (13,237 coronary heart disease and 7603 stroke outcomes) were recorded. In analyses adjusted for several conventional cardiovascular risk factors, there was an approximately J-shaped association between HbA1c values and CVD risk. The association between HbA1c values and CVD risk changed only slightly after adjustment for total cholesterol and triglyceride concentrations or estimated glomerular filtration rate, but this association attenuated somewhat after adjustment for concentrations of high-density lipoprotein cholesterol and C-reactive protein. The C-index for a CVD risk prediction model containing conventional cardiovascular risk factors alone was 0.7434 (95% CI, 0.7350 to 0.7517). The addition of information on HbA1c was associated with a C-index change of 0.0018 (0.0003 to 0.0033) and a net reclassification improvement of 0.42 (-0.63 to 1.48) for the categories of predicted 10-year CVD risk. The improvement provided by HbA1c assessment in prediction of CVD risk was equal to or better than estimated improvements for measurement of fasting, random, or postload plasma glucose levels. CONCLUSIONS AND RELEVANCE: In a study of individuals without known CVD or diabetes, additional assessment of HbA1c values in the context of CVD risk assessment provided little incremental benefit for prediction of CVD risk.

Maternal obesity during pregnancy and premature mortality from cardiovascular event in adult offspring: follow-up of 1 323 275 person years.

OBJECTIVES: To determine whether maternal obesity during pregnancy is associated with increased mortality from cardiovascular events in adult offspring. DESIGN: Record linkage cohort analysis. SETTING: Birth records from the Aberdeen Maternity and Neonatal databank linked to the General Register of Deaths, Scotland, and the Scottish Morbidity Record systems. POPULATION: 37 709 people with birth records from 1950 to present day. MAIN OUTCOME MEASURES: Death and hospital admissions for cardiovascular events up to 1 January 2012 in offspring aged 34-61. Maternal body mass index (BMI) was calculated from height and weight measured at the first antenatal visit. The effect of maternal obesity on outcomes in offspring was tested with time to event analysis with Cox proportional hazard regression to compare outcomes in offspring of mothers in underweight, overweight, or obese categories of BMI compared with offspring of women with normal BMI. RESULTS: All cause mortality was increased in offspring of obese mothers (BMI >30) compared with mothers with normal BMI after adjustment for maternal age at delivery, socioeconomic status, sex of offspring, current age, birth weight, gestation at delivery, and gestation at measurement of BMI (hazard ratio 1.35, 95% confidence interval 1.17 to 1.55). In adjusted models, offspring of obese mothers also had an increased risk of hospital admission for a cardiovascular event (1.29, 1.06 to 1.57) compared with offspring of mothers with normal BMI. The offspring of overweight mothers also had a higher risk of adverse outcomes. CONCLUSIONS: Maternal obesity is associated with an increased risk of premature death in adult offspring. As one in five women in the United Kingdom is obese at antenatal booking, strategies to optimise weight before pregnancy are urgently required.

C-reactive protein, fibrinogen, and cardiovascular disease prediction.

BACKGROUND: There is debate about the value of assessing levels of C-reactive protein (CRP) and other biomarkers of inflammation for the prediction of first cardiovascular events. METHODS: We analyzed data from 52 prospective studies that included 246,669 participants without a history of cardiovascular disease to investigate the value of adding CRP or fibrinogen levels to conventional risk factors for the prediction of cardiovascular risk. We calculated measures of discrimination and reclassification during follow-up and modeled the clinical implications of initiation of statin therapy after the assessment of CRP or fibrinogen. RESULTS: The addition of information on high-density lipoprotein cholesterol to a prognostic model for cardiovascular disease that included age, sex, smoking status, blood pressure, history of diabetes, and total cholesterol level increased the C-index, a measure of risk discrimination, by 0.0050. The further addition to this model of information on CRP or fibrinogen increased the C-index by 0.0039 and 0.0027, respectively (P<0.001), and yielded a net reclassification improvement of 1.52% and 0.83%, respectively, for the predicted 10-year risk categories of "low" (<10%), "intermediate" (10% to <20%), and "high" (≥20%) (P<0.02 for both comparisons). We estimated that among 100,000 adults 40 years of age or older, 15,025 persons would initially be classified as being at intermediate risk for a cardiovascular event if conventional risk factors alone were used to calculate risk. Assuming that statin therapy would be initiated in accordance with Adult Treatment Panel III guidelines (i.e., for persons with a predicted risk of ≥20% and for those with certain other risk factors, such as diabetes, irrespective of their 10-year predicted risk), additional targeted assessment of CRP or fibrinogen levels in the 13,199 remaining participants at intermediate risk could help prevent approximately 30 additional cardiovascular events over the course of 10 years. CONCLUSIONS: In a study of people without known cardiovascular disease, we estimated that under current treatment guidelines, assessment of the CRP or fibrinogen level in people at intermediate risk for a cardiovascular event could help prevent one additional event over a period of 10 years for every 400 to 500 people screened. (Funded by the British Heart Foundation and others.).

Lipid-related markers and cardiovascular disease prediction.

CONTEXT: The value of assessing various emerging lipid-related markers for prediction of first cardiovascular events is debated. OBJECTIVE: To determine whether adding information on apolipoprotein B and apolipoprotein A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 to total cholesterol and high-density lipoprotein cholesterol (HDL-C) improves cardiovascular disease (CVD) risk prediction. DESIGN, SETTING, AND PARTICIPANTS: Individual records were available for 165,544 participants without baseline CVD in 37 prospective cohorts (calendar years of recruitment: 1968-2007) with up to 15,126 incident fatal or nonfatal CVD outcomes (10,132 CHD and 4994 stroke outcomes) during a median follow-up of 10.4 years (interquartile range, 7.6-14 years). MAIN OUTCOME MEASURES: Discrimination of CVD outcomes and reclassification of participants across predicted 10-year risk categories of low (<10%), intermediate (10%-<20%), and high (≥20%) risk. RESULTS: The addition of information on various lipid-related markers to total cholesterol, HDL-C, and other conventional risk factors yielded improvement in the model's discrimination: C-index change, 0.0006 (95% CI, 0.0002-0.0009) for the combination of apolipoprotein B and A-I; 0.0016 (95% CI, 0.0009-0.0023) for lipoprotein(a); and 0.0018 (95% CI, 0.0010-0.0026) for lipoprotein-associated phospholipase A2 mass. Net reclassification improvements were less than 1% with the addition of each of these markers to risk scores containing conventional risk factors. We estimated that for 100,000 adults aged 40 years or older, 15,436 would be initially classified at intermediate risk using conventional risk factors alone. Additional testing with a combination of apolipoprotein B and A-I would reclassify 1.1%; lipoprotein(a), 4.1%; and lipoprotein-associated phospholipase A2 mass, 2.7% of people to a 20% or higher predicted CVD risk category and, therefore, in need of statin treatment under Adult Treatment Panel III guidelines. CONCLUSION: In a study of individuals without known CVD, the addition of information on the combination of apolipoprotein B and A-I, lipoprotein(a), or lipoprotein-associated phospholipase A2 mass to risk scores containing total cholesterol and HDL-C led to slight improvement in CVD prediction.

Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies.

BACKGROUND: Persistent inflammation has been proposed to contribute to various stages in the pathogenesis of cardiovascular disease. Interleukin-6 receptor (IL6R) signalling propagates downstream inflammation cascades. To assess whether this pathway is causally relevant to coronary heart disease, we studied a functional genetic variant known to affect IL6R signalling. METHODS: In a collaborative meta-analysis, we studied Asp358Ala (rs2228145) in IL6R in relation to a panel of conventional risk factors and inflammation biomarkers in 125,222 participants. We also compared the frequency of Asp358Ala in 51,441 patients with coronary heart disease and in 136,226 controls. To gain insight into possible mechanisms, we assessed Asp358Ala in relation to localised gene expression and to postlipopolysaccharide stimulation of interleukin 6. FINDINGS: The minor allele frequency of Asp358Ala was 39%. Asp358Ala was not associated with lipid concentrations, blood pressure, adiposity, dysglycaemia, or smoking (p value for association per minor allele ≥0·04 for each). By contrast, for every copy of 358Ala inherited, mean concentration of IL6R increased by 34·3% (95% CI 30·4-38·2) and of interleukin 6 by 14·6% (10·7-18·4), and mean concentration of C-reactive protein was reduced by 7·5% (5·9-9·1) and of fibrinogen by 1·0% (0·7-1·3). For every copy of 358Ala inherited, risk of coronary heart disease was reduced by 3·4% (1·8-5·0). Asp358Ala was not related to IL6R mRNA levels or interleukin-6 production in monocytes. INTERPRETATION: Large-scale human genetic and biomarker data are consistent with a causal association between IL6R-related pathways and coronary heart disease. FUNDING: British Heart Foundation; UK Medical Research Council; UK National Institute of Health Research, Cambridge Biomedical Research Centre; BUPA Foundation.

Diabetes mellitus, fasting glucose, and risk of cause-specific death.

BACKGROUND: The extent to which diabetes mellitus or hyperglycemia is related to risk of death from cancer or other nonvascular conditions is uncertain. METHODS: We calculated hazard ratios for cause-specific death, according to baseline diabetes status or fasting glucose level, from individual-participant data on 123,205 deaths among 820,900 people in 97 prospective studies. RESULTS: After adjustment for age, sex, smoking status, and body-mass index, hazard ratios among persons with diabetes as compared with persons without diabetes were as follows: 1.80 (95% confidence interval [CI], 1.71 to 1.90) for death from any cause, 1.25 (95% CI, 1.19 to 1.31) for death from cancer, 2.32 (95% CI, 2.11 to 2.56) for death from vascular causes, and 1.73 (95% CI, 1.62 to 1.85) for death from other causes. Diabetes (vs. no diabetes) was moderately associated with death from cancers of the liver, pancreas, ovary, colorectum, lung, bladder, and breast. Aside from cancer and vascular disease, diabetes (vs. no diabetes) was also associated with death from renal disease, liver disease, pneumonia and other infectious diseases, mental disorders, nonhepatic digestive diseases, external causes, intentional self-harm, nervous-system disorders, and chronic obstructive pulmonary disease. Hazard ratios were appreciably reduced after further adjustment for glycemia measures, but not after adjustment for systolic blood pressure, lipid levels, inflammation or renal markers. Fasting glucose levels exceeding 100 mg per deciliter (5.6 mmol per liter), but not levels of 70 to 100 mg per deciliter (3.9 to 5.6 mmol per liter), were associated with death. A 50-year-old with diabetes died, on average, 6 years earlier than a counterpart without diabetes, with about 40% of the difference in survival attributable to excess nonvascular deaths. CONCLUSIONS: In addition to vascular disease, diabetes is associated with substantial premature death from several cancers, infectious diseases, external causes, intentional self-harm, and degenerative disorders, independent of several major risk factors. (Funded by the British Heart Foundation and others.).

Separate and combined associations of body-mass index and abdominal adiposity with cardiovascular disease: collaborative analysis of 58 prospective studies.

BACKGROUND: Guidelines differ about the value of assessment of adiposity measures for cardiovascular disease risk prediction when information is available for other risk factors. We studied the separate and combined associations of body-mass index (BMI), waist circumference, and waist-to-hip ratio with risk of first-onset cardiovascular disease. METHODS: We used individual records from 58 cohorts to calculate hazard ratios (HRs) per 1 SD higher baseline values (4.56 kg/m(2) higher BMI, 12.6 cm higher waist circumference, and 0.083 higher waist-to-hip ratio) and measures of risk discrimination and reclassification. Serial adiposity assessments were used to calculate regression dilution ratios. RESULTS: Individual records were available for 221,934 people in 17 countries (14,297 incident cardiovascular disease outcomes; 1.87 million person-years at risk). Serial adiposity assessments were made in up to 63,821 people (mean interval 5.7 years [SD 3.9]). In people with BMI of 20 kg/m(2) or higher, HRs for cardiovascular disease were 1.23 (95% CI 1.17-1.29) with BMI, 1.27 (1.20-1.33) with waist circumference, and 1.25 (1.19-1.31) with waist-to-hip ratio, after adjustment for age, sex, and smoking status. After further adjustment for baseline systolic blood pressure, history of diabetes, and total and HDL cholesterol, corresponding HRs were 1.07 (1.03-1.11) with BMI, 1.10 (1.05-1.14) with waist circumference, and 1.12 (1.08-1.15) with waist-to-hip ratio. Addition of information on BMI, waist circumference, or waist-to-hip ratio to a cardiovascular disease risk prediction model containing conventional risk factors did not importantly improve risk discrimination (C-index changes of -0.0001, -0.0001, and 0.0008, respectively), nor classification of participants to categories of predicted 10-year risk (net reclassification improvement -0.19%, -0.05%, and -0.05%, respectively). Findings were similar when adiposity measures were considered in combination. Reproducibility was greater for BMI (regression dilution ratio 0.95, 95% CI 0.93-0.97) than for waist circumference (0.86, 0.83-0.89) or waist-to-hip ratio (0.63, 0.57-0.70). INTERPRETATION: BMI, waist circumference, and waist-to-hip ratio, whether assessed singly or in combination, do not importantly improve cardiovascular disease risk prediction in people in developed countries when additional information is available for systolic blood pressure, history of diabetes, and lipids. FUNDING: British Heart Foundation and UK Medical Research Council.