Estimating causality between smoking and abdominal obesity by Mendelian randomization.

BACKGROUND AND AIMS: Smokers tend to have a lower body weight than non-smokers, but also more abdominal fat. It remains unclear whether or not the relationship between smoking and abdominal obesity is causal. Previous Mendelian randomization (MR) studies have investigated this relationship by relying upon a single genetic variant for smoking heaviness. This approach is sensitive to pleiotropic effects and may produce imprecise causal estimates. We aimed to estimate causality between smoking and abdominal obesity using multiple genetic instruments. DESIGN: MR study using causal analysis using summary effect estimates (CAUSE) and latent heritable confounder MR (LHC-MR) methods that instrument smoking using genome-wide data, and also two-sample MR (2SMR) methods. SETTING: Genome-wide association studies (GWAS) summary statistics from participants of European ancestry, obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), Genetic Investigation of Anthropometric Traits (GIANT) Consortium and the UK Biobank. PARTICIPANTS: We used GWAS results for smoking initiation (n = 1 232 091), life-time smoking (n = 462 690) and smoking heaviness (n = 337 334) as exposure traits, and waist-hip ratio (WHR) and waist and hip circumferences (WC and HC) (n up to 697 734), with and without adjustment for body mass index (adjBMI), as outcome traits. MEASUREMENTS: Smoking initiation, life-time smoking, smoking heaviness, WHR, WC, HC, WHRadjBMI, WCadjBMI and HCadjBMI. FINDINGS: Both CAUSE and LHC-MR indicated a positive causal effect of smoking initiation on WHR (0.13 [95% confidence interval (CI) = 0.10, 0.16 and 0.49 (0.41, 0.57), respectively] and WHRadjBMI (0.07 (0.03, 0.10) and 0.31 (0.26, 0.37). Similarly, they indicated a positive causal effect of life-time smoking on WHR [0.35 (0.29, 0.41) and 0.44 (0.38, 0.51)] and WHRadjBMI [0.18 (0.13, 0.24) and 0.26 (0.20, 0.31)]. In follow-up analyses, smoking particularly increased visceral fat. There was no evidence of a mediating role by cortisol or sex hormones. CONCLUSIONS: Smoking initiation and higher life-time smoking may lead to increased abdominal fat distribution. The increase in abdominal fat due to smoking is characterized by an increase in visceral fat. Thus, efforts to prevent and cease smoking can have the added benefit of reducing abdominal fat.

Emotion recognition through Spanish prosody in people with schizophrenia.

People with schizophrenia have deficits in the ability to identify emotions. An area of important dysfunction is the understanding of affective prosody, which can limit communication and social functionality. The objective of this study is to compare emotional recognition through prosody between a group of people with schizophrenia versus a control group without pathology, through the Reading the Mind in the Voice - Spanish Version scale (RMV-SV).

Reconocimiento de las emociones a través de la prosodia en español en personas con esquizofrenia / Emotion recognition through Spanish prosody in people with schizophrenia

Introducción: Las personas con esquizofrenia presentan déficits en lahabilidad para identificar emociones. Un área de importante disfunción es la comprensión de la prosodia afectiva, que puede limitar la comunicación y la funcionalidad social. Elobjetivo de este estudio es comparar el reconocimiento emocional a través de la prosodia entre un grupo de personas con esquizofrenia frente a un grupo control sin patología, através de la escala Reading the Mind in the Voice – SpanishVersion (RMV-SV).Metodología:Se reclutó un grupo de personas con esquizofrenia otrastorno esquizo-afectivo, clínicamente estables (n = 62).Se compararon con un grupo control (n = 63) en las características sociodemográficas, clínicas, el coeficiente intelectualy el rendimiento en la escala RMV-SV.Resultados:El grupo de casos presentó puntuaciones más bajas enla RMV-SV, con diferencias estadísticamente significativas (p< ,001) frente a los controles. En 17/22 ítems de respuestaserróneas, los enunciados contenían emociones negativas. Lacorrelación fue positiva entre RMV-SV y CI. Se correlacionó de forma inversa el RMV-SV y PANSS, principalmente lasubescala negativa, y la edad.Conclusiones:La escala RMV-SV, validada en autismo, permite detectarlas alteraciones del reconocimiento prosódico en español enla esquizofrenia, postulándose como una herramienta evaluadora de este dominio de la cognición social. (AU)

Introduction: People with schizophrenia have deficits in the ability toidentify emotions. An area of important dysfunction is theunderstanding of affective prosody, which can limit communication and social functionality. The objective of this studyis to compare emotional recognition through prosody between a group of people with schizophrenia versus a controlgroup without pathology, through the Reading the Mind inthe Voice - Spanish Version scale (RMV-SV).MethodA group of people with clinically stable schizophrenia orschizoaffective disorder was recruited (n = 62). They werecompared with a control group (n = 63) in sociodemographic, clinical characteristics, intelligence quotient, and performance on RMV-SV scale. ResultsThe case group presented lower scores on the RMV-SV,with statistically significant differences (p < .001) comparedto controls. In 17/22 items of wrong answers, the statementscontained negative emotions. The correlation was positivebetween RMV-SV and IQ. RMV-SV and PANSS, mainly thenegative subscale, and age were inversely correlated.ConclusionsThe RMV-SV scale, validated in autism, allows detectingthe alterations of prosodic recognition in Spanish in schizophrenia, postulating itself as an evaluating tool of this domain of social cognition. (AU)

VARSE: Android app for real-time acquisition and analysis of heart rate signals.

BACKGROUND AND OBJECTIVE: nowadays, numerous mobile applications capable of measuring the Heart Rate (HR) are continuously being launched. However, these tools do not allow to record and label the acquired HR signals while users are doing activities such as practising sports or viewing images. They do not allow to perform simultaneous HR acquisition and real-time HRV analysis, either. VARSE is an app for Android mobile devices capable of acquiring and labelling HR signals and of performing real-time HRV analysis. METHODS: VARSE was developed for Android devices. It includes functionalities to acquire HR signals from any Bluetooth device that implements the Heart Rate Profile specification (such as a chest strap), while labelling segments of the HR data in different situations. Not only can these signals be stored, but also time and frequency HRV analyses can be carried out over them. The application is distributed under the MIT license [1], and it is also available to be installed via Google Play [2]. Functionality, ease-of-use and friendliness of VARSE were evaluated employing an User Experience Questionnaire (UEQ). Its reliability was proven by comparative studies against other existing acquisition (gVARVI) or HRV analysis (RHRV) software. RESULTS: high values were obtained for all the dimensions evaluated in the UEQ, proving the quality of the application, as well as its ease-of-use and efficiency. Both HR signal acquisition and HRV analysis yielded results similar to the ones obtained using other applications. CONCLUSIONS: VARSE is a tool with complete functionality, that can be easily downloaded or installed on Android mobile devices. It can be used by anyone who wishes to record HR signals while performing different activities, and also by the medical scientific community to perform real-time HRV analyses easily. Future versions will improve its capabilities and allow its integration with other open source applications.

Evidence Based Recommendations for Designing Heart Rate Variability Studies.

Heart rate variability (HRV) analysis is a powerful instrument that provides information about the heart conditions. However, there exist some limitations in the use of HRV in the clinical practice. Examples are the lack of reference values for healthy populations, different HR (Heart Rate) acquisition systems, and varying software packages. Other factors that affect HRV values are the influence of lifestyle, drugs and alcohol consumption, and pollution. In this work, recommendations to perform HRV-based experiments were established. These suggestions refer to best moment of the day to record data, the optimal body position, and the quality and duration of the recorded signals. In this way, HR data from 6 healthy subjects (2 women, 4 men), with median age of 50 years old, were recorded during 15 days, 3 times a day. Recordings were performed in the following situations: both supine and sitting body positions, in the morning, in the afternoon and at night. Data were processed and HRV analysis was performed. Distorting factors affecting HRV have been determined. The most stable HRV indexes (less variation over the days) have also been established. For this task, a variation coefficient was calculated for each parameter, as the ratio between the standard deviation and the mean value. Results indicated that HR data should be recorded in the morning, the sitting position. Related to signals duration, when comparing HR signals, they should be of equal length (same recording time). In addition, HRVi (HRV triangular index) and MADRR (median of the absolute differences between adjacent RR intervals) resulted in the most robust indexes in both low and high frequency domains. For global indexes, the ApEn (approximate entropy) measure emerged as the most stable one. As a conclusion, researchers must be extremely cautious in studies involving HRV analysis; the moment of the day to record data, the body position, or the quality of recorded data will produce different HR signals, and thus, the values of the HRV parameters will be different in each case. This may clearly bias the conclusions of the study.

A Background Modeling and Foreground Detection Algorithm Using Scaling Coefficients Defined With a Color Model Called Lightness-Red-Green-Blue.

This paper presents an algorithm for background modeling and foreground detection that uses scaling coefficients, which are defined with a new color model called lightness-red-green-blue (LRGB). They are employed to compare two images by finding pixels with scaled lightness. Three backgrounds are used: 1) verified background with pixels that are considered as background; 2) testing background with pixels that are tested several times to check if they belong to the background; and 3) final background that is a combination of the testing and verified background (the testing background is used in places, where the verified background is not defined). If a testing background pixel matches pixels from previous frames (the match is tested using scaling coefficients), it is copied to the verified background, otherwise the pixel is set as the weighted average of the corresponding pixels of the last input images. After the background is computed, foreground objects are detected by using the scaling coefficients and additional criteria. The algorithm was evaluated using the SABS data set, Wallflower data set and a subset of the CDnet 2014 data set. The average F measure and sensitivity with the SABS Data set were 0.7109 and 0.8725, respectively. In the Wallflower data set, the total number of errors was 5280 and the total F-measure was 0.9089. In the CDnet 2014 data set, the F-measure for the baseline test case was 0.8887 and for the shadow test case was 0.8300.

Diagnosing Psychopathy through Emotional Regulation Tasks: Heart Rate Variability versus Implicit Association Test.

BACKGROUND: Nowadays, the assessment of psychopathy relies on semistructured interviews plus file reviews. In order to improve the predictive validity of psychopathy at the individual level, tools that are not based on the rating of signs and symptoms are in great need. SAMPLING AND METHODS: The present study was conducted in a representative sample of 204 Spanish sentenced inmates. These inmates have served at least 6 months of their sentence at the Pereiro de Aguiar (Ourense, Spain) penitentiary. Psychopathy signs and symptoms were scored through interview and file review. The Implicit Association Test (IAT) and heart rate variability (HRV) experiments were also conducted. The Iowa Gambling Task (IGT) was performed as a control measure. RESULTS: Spectral HRV indices were able to detect psychopathic inmates at a significant level, while IAT experiments and the IGT could not discriminate them. HRV indices showed a more significant difference when assessing the affective-interpersonal dimensions of psychopathy. CONCLUSIONS: An HRV experiment is better than IAT in order to detect psychopathy in a representative sample of Spanish inmates.

Influence of visual elicitation over emotion regulation: An investigation employing the heart rate variability.

Several works studied the elicitation of emotions through the exposure of individuals to relevant stimuli, using spectral analysis of Heart Rate Variability (HRV) when people are subject to emotional elicitation. If correlation exists between HRV and emotional responses, spectral analysis can be used to study emotion regulation under external stimuli. In this work, we studied the relationship between visual elicitation and emotion regulation, employing HRV. Images (with pleasant, unpleasant and neutral emotional content) were selected from the IAPS (International Affective Picture System) dataset. Ninety-eight participants were enrolled, and subject to view all images, displayed in random order for each participant. Heart rate was recorded during the experiment, and HRV analysis was performed. Spectral values were studied for the different images. The presentation order of images was relevant, mainly when unpleasant images were viewed in first place; this significantly affects HRV values. Spectral values were higher for men, being this difference stronger when pleasant pictures were displayed. Age and gender dependences of spectral indexes were found. The influence of visual elicitation, with different emotional contents, over HRV, was assessed. Results indicate that HRV parameters are affected when individuals are subject to external, emotional-based stimuli.

Detection of premature ventricular contractions using the RR-interval signal: a simple algorithm for mobile devices.

BACKGROUND: Premature ventricular contractions (PVCs) are cardiac abnormalities that may occur in subjects with/without cardiovascular disorder. Detection is usually performed from electrocardiograms (ECGs); heart activity for a long period of time must be recorded at hospital or with ambulatory electrocardiography. An alternative with a common mobile device would be very interesting, because a simple heart rate sensor should be sufficient. OBJECTIVE: To develop an algorithm to detect PVCs using the RR-interval (distance between consecutive beats) extracted from ECGs or from the heart rate signal captured by mobile devices. METHODS: Feature extraction and classification techniques were included: 1) two timing interval features (prematurity and compensatory pause) were extracted. 2) A linear classifier was applied. To validate the method, the MIT-BIH Arrhythmia Database was used. Considering the existence of unbalanced classes (normal beats and PVCs) at different decision costs, validation was performed with receiver operating characteristic (ROC) analysis. RESULTS: A sensitivity of 90.13% and a specificity percentage of 82.52% were achieved. The area under the ROC curve (AUC) was 0.928. CONCLUSIONS: The method is advantageous since it only uses the RR-interval signal for PVC detection, and results compare well with more complex methods that use ECG recording.

Heart rate variability in patients with severe chronic obstructive pulmonary disease in a home care program.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) patients present functional and structural changes of the respiratory system that have a profound influence on cardiac autonomic dysfunction. OBJETIVE: To analyse heart rate variability in COPD patients under stable condition and during acute exacerbation episodes (AECOPD). METHODS: Twenty three severe COPD male patients, 69.6 ± 7.3 years, in stable condition were followed up for two years. Home visits were carried out by a nurse every month, and home or hospital visits were arranged on demand. Every three months an ECG, oxygen saturation and spirometric recording was obtained for each patient. If the patient presented AECOPD compatible clinical data the same measurements were performed before any change of treatment. Spectral parameters of heart rate variability in time and frequency domains were obtained from ECG. The time evolution of power in low frequency (LF) and high frequency (HF) bands were obtained from the spectrogram. In addition, we calculated the LF/HF ratio and total heart rate variability power (POW). RESULTS: We analysed 154 patient-visit records during the follow up, pertaining to 23 patients and 8 controls; 19 of the patients had experienced at least one AECOPD. Stable COPD patients had higher HF values than control subjects. No significant differences were found in LF, LF/HF ratio or POW variables. AECOPD patients had higher LF, HF and POW than the stable COPD and control groups. CONCLUSION: AECOPD patients exhibited signs of increased autonomic activity compared with stable COPD.

Nocturnal evolution of heart rate variability indices in sleep apnea.

Heart rate variability (HRV) is a valuable clinical tool in diagnosing multiple diseases. This paper presents the results of a spectral HRV analysis conducted with 46 patients. HRV indices for the whole night show differences among patients with severe and mild apnea, and healthy subjects. These differences also appear when performing the analysis over 5-min intervals, regarding apneas being present or not in the intervals. Differences were also observed when analyzing the HRV nocturnal evolution. Results are consistent with the hypothesis that cardiovascular risk remains constant for OSA patients while it increases towards the end of the night for healthy subjects.

Detecting sleep apnea by heart rate variability analysis: assessing the validity of databases and algorithms.

Obstructive sleep apnea (OSA) is a serious disorder caused by intermittent airway obstruction which may have dangerous impact on daily living activities. Heart rate variability (HRV) analysis could be used for diagnosing OSA, since this disease affects HRV during sleep. In order to validate different algorithms developed for detecting OSA employing HRV analysis, several public or proprietary data collections have been employed for different research groups. However, for validation purposes, it is obvious and evident the lack of a common standard database, worldwide recognized and accepted by the scientific community. In this paper, different algorithms employing HRV analysis were applied over diverse public and proprietary databases for detecting OSA, and the outcomes were validated in terms of a statistical analysis. Results indicate that the use of a specific database may strongly affect the performance of the algorithms, due to differences in methodologies of processing. Our results suggest that researchers must strongly take into consideration the database used when quoting their results, since selected cases are highly database dependent and would bias conclusions.

Application of the iris filter for automatic detection of pulmonary nodules on computed tomography images.

We have developed a computer-aided diagnosis (CAD) system to detect pulmonary nodules on thin-slice helical computed tomography (CT) images. We have also investigated the capability of an iris filter to discriminate between nodules and false-positive findings. Suspicious regions were characterized with features based on the iris filter output, gray level and morphological features, extracted from the CT images. Functions calculated by linear discriminant analysis (LDA) were used to reduce the number of false-positives. The system was evaluated on CT scans containing 77 pulmonary nodules. The system was trained and evaluated using two completely independent data sets. Results for a test set, evaluated with free-response receiver operating characteristic (FROC) analysis, yielded a sensitivity of 80% at 7.7 false-positives per scan.

Selecting variables in non-parametric regression models for binary response. An application to the computerized detection of breast cancer.

In many biomedical applications, interest lies in being able to distinguish between two possible states of a given response variable, depending on the values of certain continuous predictors. If the number of predictors, p, is high, or if there is redundancy among them, it then becomes important to decide on the selection of the best subset of predictors that will be able to obtain the models with greatest discrimination capacity. With this aim in mind, logistic generalized additive models were considered and receiver operating characteristic (ROC) curves were applied in order to determine and compare the discriminatory capacity of such models. This study sought to develop bootstrap-based tests that allow for the following to be ascertained: (a) the optimal number q < or = p of predictors; and (b) the model or models including q predictors, which display the largest AUC (area under the ROC curve). A simulation study was conducted to verify the behaviour of these tests. Finally, the proposed method was applied to a computer-aided diagnostic system dedicated to early detection of breast cancer.

Categorical variables, interactions and generalized additive models. Applications in computer-aided diagnosis systems.

Recently, the generalized additive models (GAMs) have been presented as a novel statistical approach to distinguish lesion/non-lesion in computer-aided diagnosis (CAD) systems. In this paper, we present an extension of the GAM that allows for the introduction of factors and their interactions with continuous variables, for reducing false positives in a CAD system for detecting clustered microcalcifications in digital mammograms. The results obtained have shown an increase in the sensitivity from 83.12% to 85.71%, while the false positive rate was drastically reduced from 1.46 to 0.74 false detections per image.

Using generalized additive models for construction of nonlinear classifiers in computer-aided diagnosis systems.

Several investigators have pointed out the possibility of using computer-aided diagnosis (CAD) schemes, as second readers, to help radiologists in the interpretation of images. One of the most important aspects to be considered when the diagnostic imaging systems are analyzed is the evaluation of their diagnostic performance. To perform this task, receiver operating characteristic curves are the method of choice. An important step in nearly all CAD systems is the reduction of false positives, as well as the classification of lesions, using different algorithms, such as neural networks or feature analysis, and several statistical methods. A statistical model more often employed is linear discriminant analysis (LDA). However, LDA implies several limitations in the type of variables that it can analyze. In this work, we have developed a novel approach, based on generalized additive models (GAMs), as an alternative to LDA, which can deal with a broad variety of variables, improving the results produced by using the LDA model. As an application, we have used GAM techniques for reducing the number of false detections in a computerized method to detect clustered microcalcifications, and we have compared this with the results obtained when LDA was applied. Employing LDA, the system achieved a sensitivity of 80.52% at a false-positive rate of 1.90 false detections per image. With the GAM, the sensitivity increased to 83.12% and 1.46 false positives per image.

Effects of JPEG2000 data compression on an automated system for detecting clustered microcalcifications in digital mammograms.

The functionalities of the JPEG2000 standard have led to its incorporation into digital imaging and communications in medicine (DICOM), which makes this compression method available for medical systems. In this study, we evaluated the compression of mammographic images with JPEG2000 (16 : 1, 20 : 1, 40 : 1, 60.4 : 1, 80: 1, and 106 : 1) for applications with a computer-aided detection (CAD) system for clusters of microcalcifications. Jackknife free-response receiver operating characteristic (JAFROC) analysis indicated that differences in the detection of clusters of microcalcifications were not statistically significant for uncompressed versus 16: 1 (T = -0.7780; p = 0.4370), 20 : 1 (T = 1.0361; p = 0.3007), and 40 : 1 (T = 1.6966; p = 0.0904); and statistically significant for uncompressed versus 60.4 : 1 (T = 5.8883; p < 0.008), 80 : 1 (T = 7.8414; p < 0.008), and 106 : 1 (T = 17.5034; p = < 0.008). Although there is a small difference in peak signal-to-noise ratio (PSNR) between compression ratios, the true-positive (TP) and false-positive (FP) rates, and the free-response receiver operating characteristic (FROC), figure of merit values considerably decreased from a 60 : 1 compression ratio. The performance of the CAD system is significantly reduced when using images compressed at ratios greater than 40 : 1 with JPEG2000 compared to uncompressed images. Mammographic images compressed up to 20 : 1 provide a percentage of correct detections by our CAD system similar to uncompressed images, regardless of the characteristics of the cluster. Further investigation is required to determine how JPEG2000 affects the detectability of clusters of microcalcifications as a function of their characteristics.