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.)

Adult height has directionally opposing relationships with risk of death from several different major causes of chronic diseases.

Next-generation sequencing technologies are making it possible to study the role of rare variants in human disease. Many studies balance statistical power with cost-effectiveness by (a) sampling from phenotypic extremes and (b) utilizing a two-stage design. Two-stage designs include a broad-based discovery phase and selection of a subset of potential causal genes/variants to be further examined in independent samples. We evaluate three parameters: first, the gain in statistical power due to extreme sampling to discover causal variants; second, the informativeness of initial (Phase I) association statistics to select genes/variants for follow-up; third, the impact of extreme and random sampling in (Phase 2) replication. We present a quantitative method to select individuals from the phenotypic extremes of a binary trait, and simulate disease association studies under a variety of sample sizes and sampling schemes. First, we find that while studies sampling from extremes have excellent power to discover rare variants, they have limited power to associate them to phenotype—suggesting high false-negative rates for upcoming studies. Second, consistent with previous studies, we find that the effect sizes estimated in these studies are expected to be systematically larger compared with the overall population effect size; in a well-cited lipids study, we estimate the reported effect to be twofold larger. Third, replication studies require large samples from the general population to have sufficient power; extreme sampling could reduce the required sample size as much as fourfold. Our observations offer practical guidance for the design and interpretation of studies that utilize extreme sampling.

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This paper presents the design of an embedded automated digital video surveillance system with real-time performance. Hardware accelerators for video segmentation, morphological operations, labeling and feature extraction are required to achieve the real-time performance while tracking will be handled in software in an embedded processor. By implementing a complete embedded system, bottlenecks in computational complexity and memory requirements can be identified and addressed. Accordingly, a memory reduction scheme for the video segmentation unit, reducing bandwidth with more than 70%, and a low complexity morphology architecture that only requires memory proportional to the input image width, have been developed. On a system level, it is shown that a labeling unit based on a contour tracing technique does not require unique labels, resulting in more than 50% memory reduction. The hardware accelerators provide the tracking software with image objects properties, i.e. features, thereby decoupling the tracking algorithm from the image stream. A prototype of the embedded system is running in real-time, 25 fps, on a field programmable gate array development board. Furthermore, the system scalability for higher image resolution is evaluated.