Mild-to-Moderate Kidney Dysfunction and Cardiovascular Disease: Observational and Mendelian Randomization Analyses

BACKGROUND: End-stage renal disease is associated with a high risk of cardiovascular events. It is unknown, however, whether mild-to-moderate kidney dysfunction is causally related to coronary heart disease (CHD) and stroke. METHODS: Observational analyses were conducted using individual-level data from 4 population data sources (Emerging Risk Factors Collaboration, EPIC-CVD [European Prospective Investigation into Cancer and Nutrition-Cardiovascular Disease Study], Million Veteran Program, and UK Biobank), comprising 648 135 participants with no history of cardiovascular disease or diabetes at baseline, yielding 42 858 and 15 693 incident CHD and stroke events, respectively, during 6.8 million personyears of follow-up. Using a genetic risk score of 218 variants for estimated glomerular filtration rate (eGFR), we conducted Mendelian randomization analyses involving 413 718 participants (25917 CHD and 8622 strokes) in EPIC-CVD, Million Veteran Program, and UK Biobank. RESULTS: There were U-shaped observational associations of creatinine-based eGFR with CHD and stroke, with higher risk in participants with eG FR values 105 mL.min(-1).1.73 m(-2), compared with those with eG FR between 60 and 105 mL.min(-1).1.73 m(-2). Mendelian randomization analyses for CHD showed an association among participants with eGFR 105 mL.min(-1).1.73 m(-2). Results were not materially different after adjustment for factors associated with the eGFR genetic risk score, such as lipoprotein(a), triglycerides, hemoglobin Alc, and blood pressure. Mendelian randomization results for stroke were nonsignificant but broadly similar to those for CHD. CONCLUSIONS: In people without manifest cardiovascular disease or diabetes, mild-to-moderate kidney dysfunction is causally related to risk of CHD, highlighting the potential value of preventive approaches that preserve and modulate kidney function

World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions

BACKGROUND: To help adapt cardiovascular disease risk prediction approaches to low-income and middle-income countries, WHO has convened an effort to develop, evaluate, and illustrate revised risk models. Here, we report the derivation, validation, and illustration of the revised WHO cardiovascular disease risk prediction charts that have been adapted to the circumstances of 21 global regions. METHODS: In this model revision initiative, we derived 10-year risk prediction models for fatal and non-fatal cardiovascular disease (ie, myocardial infarction and stroke) using individual participant data from the Emerging Risk Factors Collaboration. Models included information on age, smoking status, systolic blood pressure, history of diabetes, and total cholesterol. For derivation, we included participants aged 40-80 years without a known baseline history of cardiovascular disease, who were followed up until the first myocardial infarction, fatal coronary heart disease, or stroke event. We recalibrated models using age-specific and sex-specific incidences and risk factor values available from 21 global regions. For external validation, we analysed individual participant data from studies distinct from those used in model derivation. We illustrated models by analysing data on a further 123 743 individuals from surveys in 79 countries collected with the WHO STEPwise Approach to Surveillance. FINDINGS: Our risk model derivation involved 376 177 individuals from 85 cohorts, and 19 333 incident cardiovascular events recorded during 10 years of follow-up. The derived risk prediction models discriminated well in external validation cohorts (19 cohorts, 1 096 061 individuals, 25 950 cardiovascular disease events), with Harrell's C indices ranging from 0·685 (95% CI 0·629-0·741) to 0·833 (0·783-0·882). For a given risk factor profile, we found substantial variation across global regions in the estimated 10-year predicted risk. For example, estimated cardiovascular disease risk for a 60-year-old male smoker without diabetes and with systolic blood pressure of 140 mm Hg and total cholesterol of 5 mmol/L ranged from 11% in Andean Latin America to 30% in central Asia. When applied to data from 79 countries (mostly low-income and middle-income countries), the proportion of individuals aged 40-64 years estimated to be at greater than 20% risk ranged from less than 1% in Uganda to more than 16% in Egypt. INTERPRETATION: We have derived, calibrated, and validated new WHO risk prediction models to estimate cardiovascular disease risk in 21 Global Burden of Disease regions. The widespread use of these models could enhance the accuracy, practicability, and sustainability of efforts to reduce the burden of cardiovascular disease worldwide. FUNDING: World Health Organization, British Heart Foundation (BHF), BHF Cambridge Centre for Research Excellence, UK Medical Research Council, and National Institute for Health Research

Cross-Modal Perception in the Framework of Non-Riemannian Sensory Space

Though human sensations, such as the senses of hearing, sight, etc., are independent each other, the interference between two of them is sometimes observed, and is called cross-modal perception[1]. Hitherto we studied unimodal perception of visual sensation[2] and auditory sensation[3] respectively by differential geometry[4]. We interpreted the parallel alley and the distance alley as two geodesics under different conditions in a visual space, and depicted the trace of continuous vowel speech as the geodesics through phonemes on a vowel plane. In this work, cross-modal perception is similarly treated from the standpoint of non-Riemannian geometry, where each axis of a cross-modal sensory space represents unimodal sensation. The geometry allows us to treat asymmetric metric tensor and hence a non-Euclidean concept of anholonomic objects, representing unidirectional property of cross-modal perception. The McGurk effect in audiovisual perception[5] and ‘rubber hand’ illusion in visual tactile perception[6] can afford experimental evidence of torsion tensor. The origin of ‘bouncing balls’ illusion[7] is discussed from the standpoint of an audiovisual cross-modal sensory space in a qualitative manner

Sequences in mRNA Precursors by a Position-Tree Method

Several methods to predict 5 ’ splice sites of mRNA precursors (pre-mRNAs) have been proposed [1, 2]. In our previous paper, we proposed a subclass method, and could predict correctly 5 ’ splice sites from unknown sequences with a discrimination rate of over ninety percent [3]. However, there were some sequences that were not correct 5 ’ splice site sequences but were similar to 5 ’ splice site sequences

Multidimensional Curve Classification Using Passing-Through Regions

A new method is proposed for classifying sets of a variable number of points and curves in a multi-dimensional space as time series. Almost all classifiers proposed so far assume that there is a constant number of features and they cannot treat a variable number of features. To cope with this difficulty, we examine a fixed number of questions like "how many points are in a certain range of a certain dimension," and we convert the corresponding answers into a binary vector with a fixed length. These converted binary vectors are used as the basis for our classification. With respect to curve classification, many conventional methods are based on a frequency analysis such as Fourier analysis, a predictive analysis such as autoregression, or a hidden Markov model. However, their resulting classification rules are difficult to interpret. In addition, they also rely on the global shape of curves and cannot treat cases in which only one part of a curve is important for classification. We prop..

MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification

. A new method is proposed for selection of the optimal number of components of a mixture model for pattern classification. We approximate a class-conditional density by a mixture of Gaussian components. We estimate the parameters of the mixture components by the EM (Expectation Maximization) algorithm and select the optimal number of components on the basis of the MDL (Minimum Description Length) principle. We evaluate the goodness of an estimated model in a tradeoff between the number of the misclassified training samples and the complexity of the model. 1 Introduction In pattern recognition, we often apply clustering techniques over the training samples to understand the spatial structure of the samples and to reduce the computational costs of design of classifiers. In approximating a class-conditional density by a mixture of Gaussian components, we encounter the situation of finding initial components for an iterative procedure called EM algorithm [1]. In this situation, two probl..