Pulse-sampling fluorescence lifetime imaging: evaluation of photon economy.

This Letter presents an experimental study comparing the photon rate and photon economy of pulse sampling fluorescence lifetime imaging (PS-FLIm) with the conventional time-correlated single photon counting (TCSPC) technique. We found that PS-FLIm has a significantly higher photon detection rate (200 MHz) compared with TCSPC (2-8 MHz) but lower photon economy (4-5 versus 1-1.3). The main factor contributing to the lower photon economy in PS-FLIm is laser pulse variability. These results demonstrate that PS-FLIm offers 25× faster imaging speed than TCSPC while maintaining room light rejection in clinical settings. This makes PS-FLIm a robust technique for clinical applications.

Clinical Practice Experiences Using a Professional Diabetes Management Ecosystem During COVID.

BACKGROUND: Challenges of patient care in diabetes were exacerbated by COVID, undermining the ability of patients to engage in-person with health care professionals (HCPs). To combat this, there has been accelerated adoption of telemedicine to support patient and provider connectivity. METHODS: We collated survey information regarding telemedicine from 21 European clinical institutions. Health care professionals joined virtual meetings focusing on the OneTouch Reveal (OTR) ecosystem and its utility for conducting telemedicine. Selected HCPs provided clinical case studies to explain how the OTR ecosystem supported patient care. RESULTS: Remote consultations increased by nearly 50% in 21 European clinics during the pandemic (Belgium [24%], Iberia [65%], Germany [34%], Italy [54%]). In all, 52% of people with diabetes using OTR app to connect remotely with HCPs had type 1 diabetes and 48% had type 2 diabetes. Remote connection methods included telephone (60%), email (19%), video chat (10%), text only (3%), or a mix of these methods (8%). Health care professionals usually reviewed patient data during consultations (45%) rather than before consultations (25%). Fifty-five percent of HCPs indicated digital ecosystems like OTR ecosystem would become their standard of care for diabetes management. In-depth conversations with HCPs provided a deeper understanding of how a digital ecosystem integrated into clinical practice and population management. In addition, five patient case studies using OTR ecosystem were provided by a selection of our HCPs. CONCLUSION: Diabetes management solutions, such as OTR ecosystem, supported telemedicine during the pandemic and will continue to play a valuable role in patient care beyond the pandemic.

Assessment of Singularities in the EEG During A-Phases of Sleep Based on Wavelet Decomposition.

Electroencephalography (EEG) signals convey information related to different processes that take place in the brain. From the EEG fluctuations during sleep, it is possible to establish the sleep stages and identify short events, commonly related to a specific physiological process or pathology. Some of these short events (called A-phases) present an organization and build up the concept of the Cyclic Alternating Pattern (CAP) phenomenon. In general, the A-phases abruptly modify the EEG fluctuations, and a singular behavior could occur. With the aim to quantify the abrupt changes during A-phases, in this work the wavelet analysis is considered to compute Hölder exponents, which measure the singularity strength. We considered time windows of 2s outside and 5s inside A-phases onset (or offset). A total number of 5121 A-phases from 9 healthy participants and 10 patients with periodic leg movements were analyzed. Within an A-phase the Hölder numerical value tends to be 0.6, which implies a less abrupt singularity. Whereas outside of A-phases, it is observed that the Hölder value is approximately equal to 0.3, which implies stronger singularities, i.e., a more evident discontinuity in the signal behavior. In addition, it seems that the number of singularities increases inside of A-phases. The numerical results suggest that the EEG naturally conveys singularities modified by the A-phase occurrence, and this information could help to conceptualize the CAP phenomenon from a new perspective based on the sharpness of the EEG instead of the oscillatory way.

Raman Spectroscopy Study of Skin Biopsies from Patients with Parkinson's Disease: Trends in Alpha-Synuclein Aggregation from the Amide I Region.

Parkinson's disease (PD) is one of the most common neurological pathologies with a high prevalence worldwide. PD is characterized by Lewy bodies, whose major component is the aggregates of α-synuclein (αSyn) protein. Interestingly, recent works have demonstrated that skin biopsy studies are a promising diagnostic tool for evaluating α-synucleinopathies. In this sense, this work focuses on the detection of αSyn in skin biopsies employing Raman spectroscopy, using three different approaches: (i) the in vitro Raman spectrum of α-synuclein, (ii) the ex vivo Raman spectra of human skin biopsies from healthy and Parkinson's disease patients, and (iii) theoretical calculations of the Raman spectra obtained from different model αSyn fragments using density functional theory (DFT). Significant differences in the intensity and location of Raman active frequencies in the amide I region were found when comparing healthy and PD subjects related to α-synuclein conformational changes and variations in their aggregation behavior. In samples from healthy patients, we identified well-known Raman peaks at 1655, 1664, and 1680 cm-1 associated with the normal state of the protein. In PD subjects, shifted Raman bands and intensity variations were found at 1650, 1670, and 1687 cm-1 associated with aggregated forms of the protein. DFT calculations reveal that the shape of the amide I Raman peak in model αSyn fragments strongly depends on the degree of aggregation. Sizable frequency shifts and intensity variations are found within the highly relevant 1600-1700 cm-1 domain, revealing the sensitivity of the amide I Raman band to the changes in the local atomic environment. Interestingly, we obtain that the presence of surrounding waters also affects the structure of the amide I band, leading to the appearance of new peaks on the low-frequency side and a notable broadening of the Raman spectra. These results strongly suggest that, through Raman spectroscopy, it is possible to infer the presence of aggregated forms of αSyn in skin biopsies, a result that could have important implications for understanding α-synuclein related diseases.

Cutaneous Manifestations in COVID-19: Report on 31 Cases from Five Countries.

The increasingly frequent cutaneous manifestations of coronavirus disease (COVID-19) remain to pose a problem to clinicians. Herein, we aimed to describe the clinical and pathological findings of skin lesions in patients with COVID-19. The case series, which was based on the International Dermatological Registry circulated to dermatologists worldwide, was conducted across organizations and societies belonging to five different countries. We documented 31 patients with dermatologic manifestations associated with COVID-19, including maculopapular rashes (16.10%), urticarial lesions (26.80%), pseudochilblains (22.60%), petechiae/purpura (6.50%), distal ischaemia and necrosis (6.50%), livedo racemosa (12.90%), and others (9.70%). Twenty-six cases (83.90%) were qRT-PCR-confirmed COVID-19 cases, two (6.50%) were serologically confirmed, while two others (9.7%) were suspected cases owing to previous contact with COVID-19-positive patients. Therefore, our findings indicate that a febrile rash or even a rash in an afebrile state in the early stages of the disease may be the only clinical manifestation of COVID-19. In the future, we recommend close monitoring of all patients with skin lesions not attributable to other causal factors; in the diagnostic perspective, clinicians should aim to confirm if the skin lesions are associated with COVID-19.

Probabilistic Multiple Sclerosis Lesion Detection using Superpixels and Markov Random Fields / Detección Probabilística de Lesiones de Esclerosis Múltiple usando Superpixeles y Campos Aleatorios de Markov

Abstract Multiple Sclerosis (MS) is the most common neurodegenerative disease among young adults. Diagnosis and monitoring of MS is performed with T2-weighted or T2 FLAIR magnetic resonance imaging, where MS lesions appear as hyperintense spots in the white matter. In recent years, multiple algorithms have been proposed to detect these lesions with varying success rates, which greatly depend on the amount of a priori information required by each algorithm, such as the use of an atlas or the involvement of an expert to guide the segmentation process. In this work, a fully automatic method that does not rely on a priori anatomical information is proposed and evaluated. The proposed algorithm is based on an over-segmentation in superpixels and their classification by means of Gauss-Markov Measure Fields (GMMF). The main advantage of the over-segmentation is that it preserves the borders between tissues, while the GMMF classifier is robust to noise and computationally efficient. The proposed segmentation is then applied in two stages: first to segment the brain region and then to detect hyperintense spots within the brain. The proposed method is evaluated with synthetic images from BrainWeb, as well as real images from MS patients. The proposed method produces competitive results with respect to other algorithms in the state of the art, without requiring user assistance nor anatomical prior information.

Resumen La Esclerosis Múltiple (MS) es una de las enfermedades neurodegenerativas más comunes en adultos jóvenes. El diagnóstico y su monitoreo se realiza generalmente mediante imágenes de resonancia magnética T2 o T2 FLAIR, donde se observan regiones hiperintensas relacionadas a lesiones cerebrales causadas por la MS. En años recientes, múltiples algoritmos han sido propuestos para detectar estas lesiones con diferentes tasas de éxito las cuales dependen en gran medida de la cantidad de información a priori que requiere cada algoritmo, como el uso de un atlas o el involucramiento de un experto que guíe el proceso de segmentación. En este trabajo, se propone un método automático independiente de información anatómica. El algoritmo propuesto está basado en una sobresegmentación en superpixeles y su clasificación mediante un proceso de Campos Aleatorios de Markov de Medidas Gaussianas (GMMF). La principal ventaja de la sobresegmentación es que preserva bordes entre tejidos, además que tiene un costo reducido en tiempo de ejecución, mientras que el clasificador GMMF es robusto a ruido y computacionalmente eficiente. La segmentación propuesta es aplicada en dos etapas: primero para segmentar el cerebro y después para detectar las lesiones en él. El método propuesto es evaluado usando imágenes sintéticas de BrainWeb, así como también imágenes reales de pacientes con MS. Con respecto a los resultados, el método propuesto muestra un desempeño competitivo respecto a otros métodos en el estado del arte, tomando en cuenta que éste no requiere de asistencia o información a priori.

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These events can be classified in three groups (A1, A2, and A3) according to their spectral contents, and are thought to play a role in the transitions between the different sleep stages. A-phase detection and classification is usually performed manually by a trained expert, but it is a tedious and time-consuming task. In the past two decades, various researchers have designed algorithms to automatically detect and classify the A-phases with varying degrees of success, but the problem remains open. In this paper, a different approach is proposed: instead of attempting to design a general classifier for all subjects, we propose to train ad-hoc classifiers for each subject using as little data as possible, in order to drastically reduce the amount of time required from the expert. The proposed classifiers are based on deep convolutional neural networks using the log-spectrogram of the EEG signal as input data. Results are encouraging, achieving average accuracies of 80.31% when discriminating between A-phases and non A-phases, and 71.87% when classifying among A-phase sub-types, with only 25% of the total A-phases used for training. When additional expert-validated data is considered, the sub-type classification accuracy increases to 78.92%. These results show that a semi-automatic annotation system with assistance from an expert could provide a better alternative to fully automatic classifiers. Graphical abstract A/N Deep Learning Classifier.

Improved Vancouver Raman Algorithm Based on Empirical Mode Decomposition for Denoising Biological Samples.

A novel method based on the Vancouver Raman algorithm (VRA) and empirical mode decomposition (EMD) for denoising Raman spectra of biological samples is presented. The VRA is one of the most used methods for denoising Raman spectroscopy and is composed of two main steps: signal filtering and polynomial fitting. However, the signal filtering step consists in a simple mean filter that could eliminate spectrum peaks with small intensities or merge relatively close spectrum peaks into one single peak. Thus, the result is often sensitive to the order of the mean filter, so the user must choose it carefully to obtain the expected result; this introduces subjectivity in the process. To overcome these disadvantages, we propose a new algorithm, namely the modified-VRA (mVRA) with the following improvements: (1) to replace the mean filter step by EMD as an adaptive parameter-free signal processing method; and (2) to automate the selection of polynomial degree. The denoising capabilities of VRA, EMD, and mVRA were compared in Raman spectra of artificial data based on Teflon material, synthetic material obtained from vitamin E and paracetamol, and biological material of human nails and mouse brain. The correlation coefficient (ρ) was used to compare the performance of the methods. For the artificial Raman spectra, the denoised signal obtained by mVRA (ρ>0.91) outperforms VRA (ρ>0.86) for moderate to high noise levels whereas mVRA outperformed EMD (ρ>0.90) for high noise levels. On the other hand, when it comes to modeling the underlying fluorescence signal of the samples (i.e., the baseline trend), the proposed method mVRA showed consistent results (ρ>0.94). For Raman spectra of synthetic material, good performance of the three methods (ρ=0.99 for VRA, ρ=0.93 for EMD, and ρ=0.99 for mVRA) was obtained. Finally, in the biological material, mVRA and VRA showed similar results (ρ=0.96 for VRA, ρ=0.85 for EMD, and ρ=0.91 for mVRA); however, mVRA retains valuable information corresponding to relevant Raman peaks with small amplitude. Thus, the application of EMD as a filter in the VRA method provides a good alternative for denoising biological Raman spectra, since the information of the Raman peaks is conserved and parameter tuning is not required. Simultaneously, EMD allows the baseline correction to be automated.

A new Probabilistic Active Contour region-based method for multiclass medical image segmentation.

In medical imaging, the availability of robust and accurate automatic segmentation methods is very important for a user-independent and time-saving delineation of regions of interest. In this work, we present a new variational formulation for multiclass image segmentation based on active contours and probability density functions demonstrating that the method is fast, accurate, and effective for MRI brain image segmentation. We define an energy function assuming that the regions to segment are independent. The first term of this function measures how much the pixels belong to each class and forces the regions to be disjoint. In order for this term to be outlier-resistant, probability density functions were used allowing to define the structures to be segmented. The second one is the classical regularization term which constrains the border length of each region removing inhomogeneities and noise. Experiments with synthetic and real images showed that this approach is robust to noise and presents an accuracy comparable to other classical segmentation approaches (in average DICE coefficient over 90% and ASD below one pixel), with further advantages related to segmentation speed. Graphical Abstract.

Presence of human papillomavirus DNA in breast cancer: a Spanish case-control study.

BACKGROUND: Breast cancer is one of the most important neoplasia among women. It was recently suggested that biological agents could be the etiological cause, particularly Human Papilloma Virus (HPV). The aim of this study was to explore the presence of HPV DNA in a case-control study. METHODS: We performed our study including 251 cases (breast cancer) and 186 controls (benign breast tumors), using three different molecular techniques with PCR (GP5/GP6, CLART® and DIRECT FLOW CHIP®). RESULTS: HPV DNA was evidenced in 51.8% of the cases and in 26.3% of the controls (p < 0.001). HPV-16 was the most prevalent serotype. The odds ratio (OR) of HPV within a multivariate model, taking into account age and breastfeeding, was 4.034. CONCLUSIONS: Our study, with methodological rigour and a sample size not previously found in the literature, demonstrate a significant presence of HPV DNA in breast cancer samples. A possible causal relationship, or mediation or not as a cofactor, remains to be established by future studies.

Population-Based National Prevalence of Thyroid Dysfunction in Spain and Associated Factors: Di@bet.es Study.

BACKGROUND: The aim of this study was to investigate the national prevalence of thyroid dysfunction in Spain and its association with various clinical, environmental, and demographic variables. METHODS: The study included 4554 subjects (42.4% men) with a mean age of 50 years (range 18-93 years), who were participants in a national, cross-sectional, population-based survey conducted in 2009-2010. Data gathered included clinical and demographic characteristics, physical examination, and blood sampling. Thyrotropin, free thyroxine, free triiodothyronine, and thyroid peroxidase antibody (TPOAb) concentrations were analyzed by electrochemiluminescence. Urinary iodine (UI) levels were measured in an isolated urine sample. RESULTS: The prevalence of treated hypothyroidism, untreated subclinical hypothyroidism, and untreated clinical hypothyroidism was 4.2% [confidence interval (CI) 3.6-4.9%], 4.6% [CI 4.0-5.2%], and 0.3% [CI 0.1-0.5%], respectively. The prevalence of total hypothyroidism (including all fractions) was 9.1% [CI 8.2-10.0%]. The prevalence of total hyperthyroidism was 0.8% [CI 0.6-1.1]. A total of 7.5% [CI 6.7-8.3%] of the population tested positive for TPOAbs (≥50 IU/mL). In multivariate logistic regression models, TPOAbs were strongly associated with both hypothyroidism (p < 0.001) and hyperthyroidism (p = 0.005), whereas high UI levels (>200 µg/g creatinine) were associated with hypothyroidism (p < 0.001). The positive association between UI and hypothyroidism remained for both treated (p < 0.001) and untreated (p < 0.05) hypothyroidism, whereas it was especially significant for non-autoimmune (TPOAbs negative) forms (p < 0.001). At UI levels ≥200 µg/g, there was a positive correlation between UI and thyrotropin levels (ß = 0.152, p < 0.001) and a negative correlation between UI and free triiodothyronine levels (ß = -0.134, p = 0.001). CONCLUSION: According to the data, a large proportion (10%) of the Spanish population has some evidence of thyroid dysfunction. High TPOAb concentrations were associated with both hypo- and hyperthyroidism, whereas high UI concentrations were associated with hypothyroidism.

Low Physical Activity and Its Association with Diabetes and Other Cardiovascular Risk Factors: A Nationwide, Population-Based Study.

Low physical activity (PA), or sedentary lifestyle, is associated with the development of several chronic diseases. We aimed to investigate current prevalence of sedentariness and its association with diabetes and other cardiovascular risk factors. PA was evaluated in a population-based, cross-sectional, randomly sampled study conducted in 2009-2010 in Spain. International Physical Activity Questionnaire (SF-IPAQ) was used to assess PA. 4991 individuals (median age 50 years, 57% women) were studied. Prevalence of sedentariness was 32.3% for men and 39% for women (p < 0.0001). Sex differences were particularly notable (age*sex interaction, p = 0.0024) at early and older ages. Sedentary individuals had higher BMI (28 vs. 27 kg/m2) and obesity prevalence (37 vs. 26%). Low PA was present in 44, 43, and 38% of individuals with known diabetes (KDM), prediabetes/unknown-diabetes (PREDM/UKDM), and normal glucose regulation (p = 0.0014), respectively. No difference between KDM and PREDM/UKDM (p = 0.72) was found. Variables independently associated (p < 0.05) with sedentariness were age, sex, BMI, central obesity, Mediterranean diet adherence, smoking habit, HDL-cholesterol, triglycerides and dyslipidemia. Low PA is on the rise in Spain, especially among women. Sedentariness is associated with several cardiovascular risk factors and may be responsible for the increasing prevalence of obesity and diabetes in this country.

Prevalence, Diagnosis, Treatment, and Control of Hypertension in Spain. Results of the Di@bet.es Study.

INTRODUCTION AND OBJECTIVES: Di@bet.es is a national study designed to estimate the prevalence of diabetes mellitus and other cardiovascular risk factors in the Spanish adult population. The prevalence of hypertension and the degree to which it is recognized, treated, and controlled are described. METHODS: The study included a sample of the Spanish population with 5048 adults aged ≥ 18 years. Patients were questioned and examined, with 3 blood pressure readings while seated and at rest to calculate the mean of the 3 readings. Hypertension was defined as systolic blood pressure ≥ 140 mmHg and/or diastolic blood pressure ≥ 90 mmHg and/or prescription for antihypertensive drug therapy. RESULTS: Hypertension was found in 42.6% of the Spanish adult population aged ≥ 18 years and was more common among men (49.9%) than women (37.1%). The prevalence was higher among prediabetics (67.9%) and diabetics (79.4%). Undiagnosed hypertension was identified in 37.4% of patients and was more common in men (43.3%) than in women (31.5%). Among patients with known hypertension, 88.3% were receiving drug therapy. Well-controlled blood pressure was found in only 30% and was more common among women (24.9%) than men (16%). CONCLUSIONS: The prevalence of hypertension in Spain is high, and a considerable percentage of hypertensive patients have still not been diagnosed. Hypertension is associated with diabetes and prediabetes, and although drug therapy is increasingly common, the degree of control has not improved and remains low. Population campaigns should be developed and promoted for hypertension prevention, detection, and treatment.

Analysis of A-phase transitions during the cyclic alternating pattern under normal sleep.

An analysis of the EEG signal during the B-phase and A-phases transitions of the cyclic alternating pattern (CAP) during sleep is presented. CAP is a sleep phenomenon composed by consecutive sequences of A-phases (each A-phase could belong to a possible group A1, A2 or A3) observed during the non-REM sleep. Each A-phase is separated by a B-phase which has the basal frequency of the EEG during a specific sleep stage. The patterns formed by these sequences reflect the sleep instability and consequently help to understand the sleep process. Ten recordings from healthy good sleepers were included in this study. The current study investigates complexity, statistical and frequency signal properties of electroencephalography (EEG) recordings at the transitions: B-phase--A-phase. In addition, classification between the onset-offset of the A-phases and B-phase was carried out with a kNN classifier. The results showed that EEG signal presents significant differences (p < 0.05) between A-phases and B-phase for the standard deviation, energy, sample entropy, Tsallis entropy and frequency band indices. The A-phase onset showed values of energy three times higher than B-phase at all the sleep stages. The statistical analysis of variance shows that more than 80% of the A-phase onset and offset is significantly different from the B-phase. The classification performance between onset or offset of A-phases and background showed classification values over 80% for specificity and accuracy and 70% for sensitivity. Only during the A3-phase, the classification was lower. The results suggest that neural assembles that generate the basal EEG oscillations during sleep present an over-imposed coordination for a few seconds due to the A-phases. The main characteristics for automatic separation between the onset-offset A-phase and the B-phase are the energy at the different frequency bands.

Fluorescence background removal method for biological Raman spectroscopy based on empirical mode decomposition.

Raman spectroscopy of biological tissue presents fluorescence background, an undesirable effect that generates false Raman intensities. This paper proposes the application of the Empirical Mode Decomposition (EMD) method to baseline correction. EMD is a suitable approach since it is an adaptive signal processing method for nonlinear and non-stationary signal analysis that does not require parameters selection such as polynomial methods. EMD performance was assessed through synthetic Raman spectra with different signal to noise ratio (SNR). The correlation coefficient between synthetic Raman spectra and the recovered one after EMD denoising was higher than 0.92. Additionally, twenty Raman spectra from skin were used to evaluate EMD performance and the results were compared with Vancouver Raman algorithm (VRA). The comparison resulted in a mean square error (MSE) of 0.001554. High correlation coefficient using synthetic spectra and low MSE in the comparison between EMD and VRA suggest that EMD could be an effective method to remove fluorescence background in biological Raman spectra.

Non-linear analysis of EEG and HRV signals during sleep.

The sleep phenomenon is a complex process that involves fluctuations of autonomic functions such as the blood pressure, temperature and brain function. These fluctuations change their properties through the different sleep stages with specific relations among the different systems. In order to understand the relation between the cardiovascular and central nervous system at the different sleep stages, we applied different non-linear methods to the energy of electroencephalographic signal (EEG) and the heart rate fluctuations. The EEG was divided in the Delta, Theta, Alpha and Beta frequency bands and the mean energy of these bands was computed at each heart rate interval. Thus, the non-linear relation was evaluated between the energy of the EEG bands and the heart rate fluctuations using Cross-Correlation, Cross-Sample Entropy and Recurrence Quantification Analysis in segments of 5 minutes grouped by sleep stage. The results showed that a relation exists between the changes of the energy in the Delta band and the Heart rate fluctuations.

Heart rate variability in cyclic alternating pattern during sleep in healthy and Nocturnal Front Lobe Epilepsy patients.

The aim of this paper is to assess heart rate variability (HRV) during the cyclic alternating pattern, which is a sleep phenomenon, composed by cortical events that interrupt the basal oscillation of the NREM sleep stage. These cortical events are called A-phases and classified into three subtypes: A1, A2, A3. In addition, a comparison between healthy and Nocturnal Front Lobe Epilepsy (NFLE) patients was carried out. HRV was assessed by means of a time-varying autoregressive (TVAR) model with an adaptive filtering prediction scheme and by the time-varying square root of the mean of the sum of the squares of differences (RMSSD) of the RR intervals. For each A-phase, two signal segments were evaluated, the first one before the onset of the A-phase and the second one after the onset of the A-phase. An increase in the sympathetic outflow was suggested by higher values of low-frequency (LF) power in the three A-phases after the onset of A-phases, where A3-phases showed the largest changes. Increases in LF power were related with increases in heart rate in the same signal segments. These differences were found in both healthy and NFLE patients. LF/HF ratio and normalized LF power in A1-phases resulted significantly higher in NFLE patients in comparison with healthy subjects, which could suggest that there is a shift in the sympatho-vagal balance towards a more sympathetically mediated control of heart rate involving A1-phases in NFLE patients.

Ambient temperature and prevalence of obesity in the Spanish population: The Di@bet.es study.

OBJECTIVE: The aim of this study was to examine possible associations between ambient temperature and obesity in the Spanish population using an ecological focus. METHODS: The Di@bet.es study is a national, cross-sectional, population-based survey of cardiometabolic risk factors and their association with lifestyle. SAMPLE: 5,061 subjects in 100 clusters. VARIABLES: Clinical, demographic and lifestyle survey, physical examination, and blood sampling. The mean annual temperature (°C) for each study site was collected from the Spanish National Meteorology Agency (1971-2000). RESULTS: The prevalence rates of obesity in the different geographical areas divided according to mean annual temperature quartiles were 26.9% in quartile 1 (10.4-14.5°C), 30.5% in quartile 2 (14.5-15.5°C), 32% in quartile 3 (15.5-17.8°C), and 33.6% in quartile 4 (17.8-21.3°C) (P = 0.003). Logistic regression analyses including multiple socio-demographic (age, gender, educational level, marital status) and lifestyle (physical activity, Mediterranean diet score, smoking) variables showed that, as compared with quartile 1, the odd ratios for obesity were 1.20 (1.01-1.42), 1.35 (1.12-1.61), and 1.38 (1.14-1.67) in quartiles 2, 3, and 4, respectively (P = 0.001 for difference, P < 0.001 for trend). CONCLUSIONS: Our study reports an association between ambient temperature and obesity in the Spanish population controlled for known confounders.

Variable patterns of obesity and cardiometabolic phenotypes and their association with lifestyle factors in the Di@bet.es study.

BACKGROUND AND AIM: Prevalence rates of "metabolically healthy obese" (MHO) subjects vary depending on the criteria used. This study examined the prevalence and characteristics of MHO subjects and metabolically abnormal normal-weight subjects and compared the findings with the NHANES 1999-2004 study. The aims of the present study were, first, to determine the prevalence rates of MHO and MNHNO subjects using the same criteria as those of the National Health and Nutrition Examination Survey (NHANES) (1999-2004) study, and second to compare the prevalence and correlates of obese subjects who are resistant to the development of adiposity-associated cardiometabolic abnormalities (CA) and normal-weight individuals who display cardiometabolic risk factor clustering between the Spanish and the US populations. METHODS AND RESULTS: Di@bet.es study is a national, cross-sectional population-based survey of 5728 adults conducted in 2009-2010. Clinical, metabolic, sociodemographic, and anthropometric data and information about lifestyle habits, such as physical activity, smoking habit, alcohol intake and food consumption, were collected. Subjects were classified according to their body mass index (BMI) (normal-weight, <25 kg/m(2); overweight, 25-29.9 kg/m(2); and obese, >30 kg/m(2)). CA included elevated blood pressure; elevated levels of triglycerides, fasting glucose, and high-sensitivity C-reactive protein (hs-CRP); and elevated homeostasis model assessment of insulin resistance (HOMA-IR) value and low high-density lipoprotein cholesterol (HDL-c) level. Two phenotypes were defined: metabolically healthy phenotype (0-1 CA) and metabolically abnormal phenotype (≥2 CA). The prevalence of metabolically abnormal normal-weight phenotype was slightly lower in the Spanish population (6.5% vs. 8.1%). The prevalence of metabolically healthy overweight and MHO subjects was 20.9% and 7.0%, respectively, while in NHANES study it was 17.9% and 9.7%, respectively. Cigarette smoking was associated with CA in each phenotype, while moderate physical activity and moderate alcohol intake were associated with being metabolically healthy. Olive oil intake was negatively associated with the prevalence of CA. CONCLUSIONS: Smoking, physical activity level, and alcohol intake contribute to the explanation of the prevalence of CA in the Spanish population, as in the US population. However in Spain, olive oil intake contributes significantly to the explanation of the variance in the prevalence of CA.

Prevalence of plasma lipid abnormalities and its association with glucose metabolism in Spain: the di@bet.es study.

INTRODUCTION: Dyslipidemia is a significant contributor to the elevated CVD risk observed in type 2 diabetes mellitus. We assessed the prevalence of dyslipidemia and its association with glucose metabolism status in a representative sample of the adult population in Spain and the percentage of subjects at guideline-recommended LDL-C goals. MATERIAL AND METHODS: The di@bet.es study is a national, cross-sectional population-based survey of 5728 adults. RESULTS: A total of 4776 subjects were studied. Dyslipidemia was diagnosed in 56.8% of subjects; only 13.2% of subjects were treated with lipid lowering drugs. Lipid abnormalities were found in 56.8% of Spanish adults: 23.3% with high LDL-C, 21.5% high TG, 35.8% high non-HDL-C, and 17.2% low HDL-C. Most normal subjects showed an LDL-C ≤ 3.36 mmol/l. Pre-diabetics presented similar proportion when considering a goal of 3.36 mmol/l, but only 35% of them reached an LDL-C goal ≤ 2.6 mmol/l. Finally, 45.3% of diabetics had an LDL-C ≤ 2.6 mmol/l, and only 11.3% achieved an LDL-C ≤ 1.8 mmol/l. CONCLUSIONS: Our study demonstrates a high prevalence of dyslipidemia in the adult Spanish population, and a low use of lipid-lowering drugs. Moreover, the number of subjects achieving their corresponding LDL-C goal is small, particularly in subjects at high cardiovascular risk, such as diabetics.