Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance. OBJECTIVE: To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study. DESIGN: Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured. RESULTS: Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%. CONCLUSIONS: A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss

Adipose tissue transcriptome reflects variations between subjects with continued weight loss and subjects regaining weight 6 mo after caloric restriction independent of energy intake

BACKGROUND: The mechanisms underlying body weight evolution after diet-induced weight loss are poorly understood. OBJECTIVE: We aimed to identify and characterize differences in the subcutaneous adipose tissue (SAT) transcriptome of subjects with different weight changes after energy restriction-induced weight loss during 6 mo on 4 different diets. DESIGN: After an 8-wk low-calorie diet (800 kcal/d), we randomly assigned weight-reduced obese subjects from 8 European countries to receive 4 diets that differed in protein and glycemic index content. In addition to anthropometric and plasma markers, SAT biopsies were taken at the beginning [clinical investigation day (CID) 2] and end (CID3) of the weight follow-up period. Microarray analysis was used to define SAT gene expression profiles at CID2 and CID3 in 22 women with continued weight loss (successful group) and in 22 women with weight regain (unsuccessful group) across the 4 dietary arms. RESULTS: Differences in SAT gene expression patterns between successful and unsuccessful groups were mainly due to weight variations rather than to differences in dietary macronutrient content. An analysis of covariance with total energy intake as a covariate identified 1338 differentially expressed genes. Cellular growth and proliferation, cell death, cellular function, and maintenance were the main biological processes represented in SAT from subjects who regained weight. Mitochondrial oxidative phosphorylation was the major pattern associated with continued weight loss. CONCLUSIONS: The ability to control body weight loss independent of energy intake or diet composition is reflected in the SAT transcriptome. Although cell proliferation may be detrimental, a greater mitochondrial energy gene expression is suggested as being beneficial for weight control

Origins of the Ambient Solar Wind: Implications for Space Weather

The Sun's outer atmosphere is heated to temperatures of millions of degrees, and solar plasma flows out into interplanetary space at supersonic speeds. This paper reviews our current understanding of these interrelated problems: coronal heating and the acceleration of the ambient solar wind. We also discuss where the community stands in its ability to forecast how variations in the solar wind (i.e., fast and slow wind streams) impact the Earth. Although the last few decades have seen significant progress in observations and modeling, we still do not have a complete understanding of the relevant physical processes, nor do we have a quantitatively precise census of which coronal structures contribute to specific types of solar wind. Fast streams are known to be connected to the central regions of large coronal holes. Slow streams, however, appear to come from a wide range of sources, including streamers, pseudostreamers, coronal loops, active regions, and coronal hole boundaries. Complicating our understanding even more is the fact that processes such as turbulence, stream-stream interactions, and Coulomb collisions can make it difficult to unambiguously map a parcel measured at 1 AU back down to its coronal source. We also review recent progress -- in theoretical modeling, observational data analysis, and forecasting techniques that sit at the interface between data and theory -- that gives us hope that the above problems are indeed solvable.Comment: Accepted for publication in Space Science Reviews. Special issue connected with a 2016 ISSI workshop on "The Scientific Foundations of Space Weather." 44 pages, 9 figure

Reproducibility in the absence of selective reporting : An illustration from large-scale brain asymmetry research

Altres ajuts: Max Planck Society (Germany).The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p-hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left-right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta-analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an "ideal publishing environment," that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically-used sample sizes

Het Verzekerdenpanel: basisrapport met informatie over het panel 2018 - update.

Wat is het Verzekerdenpanel? Het Verzekerdenpanel is opgericht in 2006 en is een samenwerking tussen VGZ (hieronder vallen de labels: Univé, VGZ, ZEKUR, Bewuzt, IZZ, IZA GezondSamen, UMC, Zorgzaam, SZVK en UnitedConsumers) en het Nivel (Nederlands instituut voor onderzoek van de gezondheidszorg). Sinds 2010 is de samenwerking tussen het Nivel en VGZ uitgebreid met de Open Universiteit (OU) en is de Academische Onderzoekswerkplaats Zorgverzekeraars (AOZ) opgericht. Alle leden van het Verzekerdenpanel zijn verzekerd bij één van de labels van VGZ en hebben zich bereid verklaard regelmatig vragen te beantwoorden die betrekking hebben op de gezondheidszorg en/of zorgverzekeringen. Doel Het doel van het Verzekerdenpanel is om inzicht te krijgen in de wensen van verzekerden met betrekking tot de zorg en de diensten en producten van hun zorgverzekeraar en in het keuzegedrag van verzekerden op de zorgverzekeringsmarkt. Samenstelling van het panel en werkwijze Op het moment dat dit rapport geschreven wordt, bestaat het panel uit 15.139 personen van 18 jaar en ouder die verzekerd zijn bij VGZ. Het panel wordt regelmatig aangevuld en ververst. Leden werden t/m half 2016 geworven door middel van een oproep in het VGZ verzekerdenblad en door verzekerden van Univé, IZA, Trias en ZEKUR telefonisch te benaderen. Vanaf medio 2016 ontvangen klanten van VGZ die met een VGZ label bellen, mailen of schrijven en die interesse hebben in het panel een uitnodiging per mail. De panelleden worden maximaal drie tot vier keer per jaar benaderd om vragen te beantwoorden over actuele en belangrijke thema’s binnen de gezondheidszorg en de zorgverzekeringsmarkt. In totaal worden er binnen het Verzekerdenpanel zeven tot tien onderzoeken per jaar gedaan. De vragenlijsten kunnen zowel schriftelijk als online via e‐mail worden afgenomen. Ook kunnen er telefonische interviews of focusgroepgesprekken worden gehouden. Wat wordt er met het onderzoek gedaan? De onderzoeken zijn bedoeld voor het beantwoorden van algemene beleidsmatige en wetenschappelijke vragen over de zorg en het zorgstelsel. De hoofdthema’s van de peilingen zijn: keuzegedrag van verzekerden, loyaliteit aan de zorgverzekeraar, exit en voice, collectieve zorgverzekeringen, zorginkoop, kwaliteit van dienstverlening en wisselen van zorgverzekeraar. Over de peilingen wordt openbaar gepubliceerd. Er wordt beoogd wetenschappelijke kennis en praktijkkennis te combineren, wat leidt tot goede en implementeerbare resultaten. (aut. ref.

Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index

Weight regain after weight loss is common. In the Diogenes dietary intervention study, high protein and low glycemic index (GI) diet improved weight maintenance. OBJECTIVE: To identify blood predictors for weight change after weight loss following the dietary intervention within the Diogenes study. DESIGN: Blood samples were collected at baseline and after 8-week low caloric diet-induced weight loss from 48 women who continued to lose weight and 48 women who regained weight during subsequent 6-month dietary intervention period with 4 diets varying in protein and GI levels. Thirty-one proteins and 3 steroid hormones were measured. RESULTS: Angiotensin I converting enzyme (ACE) was the most important predictor. Its greater reduction during the 8-week weight loss was related to continued weight loss during the subsequent 6 months, identified by both Logistic Regression and Random Forests analyses. The prediction power of ACE was influenced by immunoproteins, particularly fibrinogen. Leptin, luteinizing hormone and some immunoproteins showed interactions with dietary protein level, while interleukin 8 showed interaction with GI level on the prediction of weight maintenance. A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%. CONCLUSIONS: A selected panel of blood proteins/steroids can predict the weight change after weight loss. ACE may play an important role in weight maintenance. The interactions of blood factors with dietary components are important for personalized dietary advice after weight loss