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Funding | The trAPP-study is funded by a grant from
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Ethical approval | Erasmus | The local medical ethics committee of the Erasmus MC University Medical Center Rotterdam approved the study (MEC 2014-250). The trial was registered at the Dutch Trial Registration (NTR 4765). | PMC10755994 |
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Data | The data that support the findings of this study are available from the corresponding author on reasonable request. | PMC10755994 |
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Provenance | Freely submitted; externally peer reviewed. | PMC10755994 |
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Competing interests | The authors have declared no competing interests. | PMC10755994 |
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Discuss this article: | PMC10755994 |
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References | PMC10755994 |
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Background | heart failure | HEART FAILURE | In a large randomized controlled trial (PARADIGM-HF), ARNI has been shown to significantly reduce cardiovascular mortality and hospitalization for patients with reduced ejection fraction in heart failure. This study analyzed the efficacy and safety of ARNI on the basis of various types of heart failure patients in southwestern Sichuan Province. | PMC10334639 |
Methods | heart failure | HEART FAILURE | This study included patients with heart failure who were treated at the Affiliated Hospital of North Sichuan Medical College from July 2017 to June 2021. This study analyzed the efficacy and safety of ARNI in the treatment of heart failure, and analyzed the risk factors for readmission after ARNI treatment. | PMC10334639 |
Results | readmission, heart failure, HF | ADVERSE EVENTS, HEART FAILURE | After propensity score matching, a total of 778 patients were included in the study. The readmission rate for heart failure in patients treated with ARNI (8.7%) was significantly lower than that in the standard treatment group (14.5%) (P = 0.023). Both the proportion of patients with increased LVEF and with decreased LVEF were higher in the ARNI treatment group than in the conventional therapy group. Compared with receiving standard medical treatment, combined ARNI treatment resulted in a greater reduction in SBP (-10.00, 95%CI: -24.00-1.50 vs. -7.00, 95%CI: -20.00-4.14; P = 0.016) in HF patients. Combination ARNI therapy did not increase the risk of adverse events. The study found that age (> 65 vs. ≤65 years) (OR = 4.038, 95%CI: 1.360-13.641, P = 0.013) and HFrEF (OR = 3.162, 95%CI: 1.028–9.724, P = 0.045) were independent predictors of readmission in HF patients treated with ARNI. | PMC10334639 |
Conclusion | readmission, heart failure, HF | HEART FAILURE | Patients with heart failure treated with ARNI can improve clinical symptoms and reduce the risk of readmitted hospital admission. Age > ~ 65 years and HFrEF were independent predictors of readmission in HF patients treated in ARNI group. | PMC10334639 |
Keywords | PMC10334639 |
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Introduction | Heart failure, heart failure, HF | HEART FAILURE, HEART FAILURE | Heart failure (HF) is a major public health problem that imposes an enormous social and economic burden on the world, and about 64.3 million people in the world suffer from HF [In China, there is currently a lack of real-world studies analyzing drug therapy in patients with heart failure. To explore the efficacy and safety of ARNI in heart failure patients in Southwest Sichuan Province, we conducted a real-world study based on medical data from the Affiliated Hospital of North Sichuan Medical College in Sichuan Province, China. | PMC10334639 |
Methods | PMC10334639 |
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Patients | Heart Failure, heart failure | HEART FAILURE, HEART FAILURE | This study is a real-world study involving patients with heart failure who were treated at the Affiliated Hospital of North Sichuan Medical College from July 2017 to June 2021. Inclusion criteria include meeting the diagnosis and treatment standards for HF in the “Chinese Heart Failure Diagnosis and Treatment Guidelines 2018”; receiving heart failure drug treatment, including S-V (ARNI), ARB, ACEI, etc.; follow-up for at least 6 months; patients ≥ 18 years old. The study excluded patients with contraindications to the use of HF medications, patients with follow-up less than half a year and patients with missing follow-up data. All patients were divided into standard treatment group and ARNI group according to whether they received combined ARNI treatment or not (Fig.
Flow chart of the sduty | PMC10334639 |
Variables extraction | HEART | The data used in this study were extracted from a database constructed by combining information from multiple data sources, including the Hospital Information System, Laboratory Information Management System, Picture archiving and communication systems, and Electronic Medical Record of the Affiliated Hospital of North Sichuan Medical College. Variables included in the study included patients’ sociodemographic information, drinking history, smoking history, previous medical history, comorbidities, HF-related characteristics, New York Heart Association (NYHA) functional class, laboratory parameters, medication, and other treatments. | PMC10334639 |
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Outcomes and definition | heart failure, hypotension | HEART FAILURE, RENAL IMPAIRMENT | The outcomes of interest in this study were readmission rate, hypotension, and renal impairment. The definition of heart failure refers to the description of heart failure in “Chinese guidelines for the diagnosis and treatment of heart failure 2018“ [ | PMC10334639 |
Statistical analysis | REGRESSION | Propensity score matching (PSM) was used to balance measurable confounders between standard treatment group and ARNI group. The propensity score was calculated by logistic regression model with the following covariates age, gender, height, weight, smoking, drinking, medical history, type of HF and NYHA classification. The matching was performed using a 1:2 nearest-neighbor matching protocol with caliper width 0.1. After PSM, statistical analyses were performed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). Figures were plotted using GraphPad Prism 8.02 (GraphPad Software Inc., San Diego, CA, USA). Continuous variables were described as median and interquartile. Categorical variables were described as number and percentage. Comparisons between standard treatment group and ARNI group were conducted by Wilcoxon rank sum test or Chi-square test. Factors association with readmission rate in ARNI group were screened using univariate logistic regression model, and variables with P value < 0.1 in univariate logistic regression model were further analyzed using multivariate logistic regression model. All tests were two-tail, P value < 0.05 were considered statistically significant. | PMC10334639 |
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Results | PMC10334639 |
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Baseline characteristics of patients with heart failure | heart failure, CKD, chronic kidney disease, PAD | PERIPHERAL ARTERIAL DISEASE, MYOCARDIAL INFARCTION, HEART, PAD, HEART FAILURE | Before propensity score matching, a total of 2096 patients with heart failure met the criteria. Among them, 1826 patients received standard therapy and 270 patients received ARNI therapy. There were significant differences in age, sex, height, weight, smoking, drinking, PAD, type of HF, and NYHA classification between the standard care and ARNI treatment groups (Table
Baseline characteristicsIQR, interquartile range; BMI, body mass index; MI, myocardial infarction; PAD, peripheral arterial disease; CKD, chronic kidney disease; HF, heart failure; NYHA, New York Heart Association | PMC10334639 |
Compare adverse events between standard treatment group and ARNI treatment group | renal injury, hypotension | ADVERSE EVENTS | There were no significant differences in the overall incidence of adverse events (1.5%vs. 2.0%, P = 0.766) and the incidence of hypotension (1.3%vs. 1.6%, P = 0.755) and renal injury (0.2%vs. 0.4%, P = 0.545) between the standard-care and ARNI-treated patients (Table
Compare adverse events between two group | PMC10334639 |
Logistics regression analysis of risk factors for readmission in ARNI-treated patients | CKD, chronic kidney disease, PAD | PERIPHERAL ARTERIAL DISEASE, MYOCARDIAL INFARCTION, HEART, PAD, REGRESSION, HEART FAILURE | Univariate logistic regression analysis found that age (> 65 vs. ≤65 years), HFrEF, NYHA classification, serum creatinine (abnormal vs. normal), and eGFR (abnormal vs. normal) were all associated with readmissions in ARNI-treated patients (all P < 0.1) (Table
Factors association with readmission in ARNI groupOR, odds rate; CI, confidence interval; BMI, body mass index; MI, myocardial infarction; PAD, peripheral arterial disease; CKD, chronic kidney disease; HF, heart failure; NYHA, New York Heart Association; LVEDD, left ventricular end-diastolic diameter; DBP, diastolic blood pressure; SBP, systolic blood pressure; eGFR, estimated glomerular filtration rate; NT-proBNP, pro-brain natriuretic peptide
Factors independently associated with readmission in ARNI groupOR, odds rate; CI, confidence interval; eGFR, estimated glomerular filtration rate | PMC10334639 |
Acknowledgements | We thank Shanghai Synyi Medical Technology Co., Ltd. for providing the data analysis and statistical platform. | PMC10334639 |
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Authors’ contributions | XB Wang J Pu and HX Hu mainly participated in literature search, study design, writing and critical revision，Figure 1; Tables 1, 2, 3, 4 and 5. J Pu, GX Wang, H Xu, LM Liu, Z Li, RJ Qin, XM Zhao, M Li and ZD Hao mainly participated in data collection, data analysis and data interpretation. All authors read and approved the final manuscript, Tables 1, 2, 3, 4 and 5. | PMC10334639 |
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Funding | The study was funded by Major Projects of Sichuan Provincial Health Commission (21ZD004), Science and Technology Department of Sichuan Province Project (2021YJ0208, 2021YJ0210), and Research Project Foundation of Affiliated Hospital of North Sichuan Medical College (2022LC009, 2021ZK002, 2021LC011). | PMC10334639 |
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Data Availability | The data that support the findings of this study are available from Affiliated Hospital of North Sichuan Medical College but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of Affiliated Hospital of North Sichuan Medical College. | PMC10334639 |
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Declarations | PMC10334639 |
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Conflict of interest | The authors declared that there was no conflict of interest associated with the manuscript. | PMC10334639 |
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Ethics approval and consent to participate | The study was approved by the Ethics Committee of the Affiliated Hospital of North Sichuan Medical College (2022ER291-1), all methods were performed in accordance with the Declarations of Helsinki, and since the study only involved retrospective analysis of previous clinical data, the requirement for informed consent was waived by the Ethics Committee of the Affiliated Hospital of North Sichuan Medical College. | PMC10334639 |
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Consent for publication | Not applicable. | PMC10334639 |
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References | PMC10334639 |
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1. Introduction | obesity, eating behavior, eating behaviors, Eating Behaviors, ’s eating behavior, disordered eating, diabetes, cancer | OBESITY, CHRONIC DISEASES | These authors contributed equally for this paper and should be considered co-first authors.Self-efficacy has a strong influence on children’s eating behavior. Feeling capable of regulating one’s eating behavior is especially relevant in situations of activation while facing temptations or experiencing negative emotions. Despite the relevance, there is no validated measure to assess children’s self-efficacy to regulate eating behaviors in these domains. The present study examines the psychometric properties of the Self-Efficacy to Regulate Eating Behaviors Scale for Children based on a sample of 724 elementary school children in Portugal. The sample was split randomly into two groups, and a principal component analysis with Group 1 and a confirmatory factor analysis with Group 2 were carried out. The scale comprises two distinct but related factors—self-efficacy to regulate eating behaviors in activation and temptation situations and self-efficacy to regulate eating behaviors in negative emotional situations. Moreover, self-efficacy to regulate eating behaviors was positively and statistically related to self-regulation processes toward healthy eating, declarative knowledge about healthy eating, and attitudes and perceptions toward healthy eating. The present study provides preliminary evidence that the Self-Efficacy to Regulate Eating Behaviors Scale for Children is valid and reliable for evaluating children’s self-efficacy in regulating their eating behaviors.Healthy eating behavior is considered a public health priority for preventing chronic diseases (e.g., obesity, diabetes, cancer) across all ages [In recent years, the literature has highlighted the relevance of motivational-related factors in children’s adoption and maintenance of healthy eating behaviors [Considering the importance of self-efficacy for children adopting and maintaining healthy eating, there is a lack of validated and children-focused measures of self-efficacy for healthy eating behaviors. Self-efficacy measures in the eating behavior domain for the general population are scarce and target mainly disordered eating and obesity [As previously mentioned, currently, there is a lack of validated scales assessing children’s self-efficacy to regulate eating behaviors, explicitly targeting the contextual factors related to these behaviors [The present study aims to evaluate the psychometric properties of the Self-Efficacy to Regulate Eating Behaviors scale for Children (SEREB-C). This scale focused on two dimensions likely to challenge children’s healthy food choices: activation and temptation situations and negative emotional situations [ | PMC9956400 |
2. Materials and Methods | PMC9956400 |
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2.1. Participants | Children were recruited from 6 Portuguese public schools from different environments (i.e., rural and urban). A total of 827 elementary school children ( | PMC9956400 |
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2.2. Procedure | The present study was part of a broader investigation approved by the University of Minho Ethics Committee for Research in Social and Human Sciences (CEICSH) (CEICSH 032/2019). Elementary school children and their parents or legal guardians were informed about the study’s aims and assured of the data’s confidentiality. Afterward, parents or legal guardians of the participants provided written informed consent and children provided assent to participate. Before data collection, researchers participated in a training session hosted by a senior researcher to set the protocol for data collection. Researchers administered the instruments (discussed below) during regular classes as follows: children were invited by the researcher to fill in the instruments and were asked to complete the task by themselves; each child fulfilled the instruments at their own pace, and was supported by the researcher in items found to be unclear. When in doubt about an item, that sentence was explained to the whole class similarly. The children took approximately 30 min to complete the instruments in Qualtrics XM survey platform | PMC9956400 |
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2.3. Instruments | PMC9956400 |
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2.3.1. Self-Efficacy to Regulate Eating Behaviors Scale for Children (SEREB-C) | Bandura [SEREB-C was introduced with the following indication: “A number of situations are described below that can make it hard to stick to a healthy diet. For each sentence, please select the answer that best represents how certain you are that you can stick to a healthy diet on a regular basis” (see | PMC9956400 |
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2.3.2. Self-Regulation Processes toward Healthy Eating | Self-regulation was assessed using the Self-Regulation Processes towards Healthy Eating Questionnaire [ | PMC9956400 |
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2.3.3. Declarative Knowledge about Healthy Eating | Declarative knowledge about healthy eating was assessed using an adapted version of the Knowledge of Healthy Eating Questionnaire [ | PMC9956400 |
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2.3.4. Attitudes and Perceptions towards Healthy Eating | Attitudes and perceptions towards healthy eating were assessed using an adapted version of the Students’ Attitudes and Perceptions on Healthy Eating Questionnaire [ | PMC9956400 |
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2.4. Data Analysis | eating behaviors | REGRESSION | The data were analyzed in several phases, following the purposes of the present study. First, missing values for the 14 items of SEREB-C ranged from 0.1% to 1.0% (M = 0.55%) and were imputed using regression imputation. Second, to examine the SEREB-C’s factor structure, participants were randomly split into two groups (i.e., Group 1, With Group 1, we conducted a principal component analysis (PCA) using SPSS, version 28.0, with direct oblimin rotation (delta = 0). The appropriate number of factors for retention was determined by several criteria: the scree plots, eigenvalue > 1.0, and conceptual meaningfulness of items on each factor.With Group 2, we conducted a confirmatory factor analysis (CFA) using AMOS, version 28.0. Aiming to examine whether self-efficacy to regulate eating behaviors in activation and temptation situations and self-efficacy to regulate eating behaviors in negative emotional situations were empirically distinguishable, we compared the difference in goodness-of-fit between (a) a one-factor model (i.e., factorially indistinct) and (b) a two-factor model (i.e., factorially distinct). Moreover, the models were evaluated through multiple goodness-of-fit indicators, including CFI ≥ 0.95 [The SEREB-C’s reliability (i.e., convergent validity) was assessed using average variance extracted, composite reliability, and alpha and omega coefficients. According to Hair et al. [ | PMC9956400 |
3. Results | PMC9956400 |
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3.1. Principal Component Analysis (PCA) | For Group 1 ( | PMC9956400 |
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3.2. Confirmatory Factor Analysis (CFA) | For Group 2 (Each of the 14 items was specified to load on only one factor in the two-factor solution (i.e., either activation and temptation situations or negative emotional situations); therefore, the structure coefficients estimated indicator–construct correlations [ | PMC9956400 |
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3.3. Reliability | The means of the scale for the two groups combined ( | PMC9956400 |
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4. Discussion | eating behaviors | The present study aimed to validate the SEREB-C. Our preliminary results indicated that SEREB-C has good psychometric quality regarding reliability (i.e., exhibits good Cronbach’s alpha and omega coefficients) and validity evidence (e.g., positive relationship with external relevant measures). Moreover, the two-factor model was a better fit than the one-factor model. In the current study, items focused on activation and temptation situations were saturated in one factor, and items focused on negative emotional situations were saturated in the other. The activation and temptation situations factor describes triggers that make children struggle to cope with unhealthy foods and to make healthy choices (e.g., school breaks and having much unhealthy food at home) [Regarding concurrent and predictive validity, our preliminary results confirmed the hypothesis that the SEREB-C is positively related to self-regulation processes towards healthy eating, declarative knowledge about healthy eating, and attitudes and perceptions toward healthy eating [Health professionals, teachers, and counselors could use SEREB-C to evaluate children’s self-efficacy to regulate their healthy eating behaviors and design tailored interventions to support the development of positive self-efficacy beliefs to regulate eating behaviors accordingly. Research has been suggesting that the design of interventions should not only transmit knowledge about healthy eating but combine it with training on self-regulation strategies related to healthy eating behaviors [ | PMC9956400 |
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5. Conclusions | eating behaviors | In the present study, the factor structure of the SEREB-C was examined using exploratory and confirmatory factor analysis. Our preliminary results showed that SEREB-C comprises two factors: activation and temptation situations and negative emotional situations. The two-factor model showed a good fit for Portuguese elementary school children. The scale also had good reliability coefficients. Thus, SEREB-C can be used by practitioners and researchers to assess children’s self-efficacy to regulate their eating behaviors. Moreover, SEREB-C values can provide valuable information to design tailored interventions. Finally, the psychometric properties of SEREB-C should be further explored with samples of different cultures. | PMC9956400 |
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Author Contributions | Conceptualization, C.S., B.P. and P.M.; methodology, C.S., B.P. and G.F.; validation, B.P.; formal analysis, B.P. and J.C.N.; investigation, B.P. and G.F.; data curation, C.S., B.P. and G.F.; writing—original draft preparation, C.S. and B.P; writing—review and editing, G.F., P.R. and P.M.; supervision, P.R. and P.M.; project administration, P.M.; funding acquisition, P.M. All authors have read and agreed to the published version of the manuscript. | PMC9956400 |
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Institutional Review Board Statement | The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the University of Minho Ethics Committee for Research in Social and Human Sciences (CEICSH 032/2019). | PMC9956400 |
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Informed Consent Statement | Informed consent was obtained from all subjects involved in the study. | PMC9956400 |
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Data Availability Statement | Data are available from the corresponding author upon reasonable request. | PMC9956400 |
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Conflicts of Interest | The authors declare no conflict of interest. | PMC9956400 |
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Appendix A | A number of situations are described below that can make it hard to stick to healthy eating. Please, for each sentence, select the answer that best represents how certain you are that you can stick to a healthy eating diet on a regular basis. | PMC9956400 |
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References | Eating Behaviors | Rotated Factor Pattern (Structure) Matrix for the SEREB-C *.Note: Group 1 (Model Comparison: Summary of Goodness-of-Fit Indices.Note: Group 2 (Standardized Coefficients for the Two-Factor CFA Model.Note: Group 2 (Pearson correlations between SEREB-C and the 3 external measures and mean, and standard deviation of the external measures.Note: SEREB-C = Self-Efficacy to Regulate Eating Behaviors for Children; | PMC9956400 |
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Purpose | weight gain | It is widely accepted that patients experience weight gain after total thyroidectomy, and preventive measures should be recommended. | PMC10293335 |
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Methods | weight gain | A prospective study was designed to assess the efficacy of a dietetic intervention to prevent post-thyroidectomy weight gain in patients undergoing surgery for both benign and malignant thyroid conditions. Patients undergoing total thyroidectomy were prospectively and randomly assigned to receive a personalized pre-surgery diet counseling (GROUP A) or no intervention (GROUP B), according to a 1:2 ratio. All patients underwent follow-up with body-weight measurement, thyroid function evaluation and lifestyle and eating habits assessment at baseline (T0), 45 days (T1) and 12 months (T2) post-surgery. | PMC10293335 |
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Results | The final study group encompassed 30 patients in Group A and 58 patients in Group B. The two groups were similar in terms of age, sex, pre-surgery BMI, thyroid function and underlying thyroid condition. The evaluation of body weight variations showed that patients in Group A did not experience significant body weight changes at either T1 ( | PMC10293335 |
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Conclusions | weight gain | A dietician counseling is effective in preventing the post-thyroidectomy weight gain. Further studies in larger series of patients with a longer follow-up appear worthwhile. | PMC10293335 |
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Keywords | PMC10293335 |
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Introduction | An increase in body weight is experienced by the majority of patients undergoing total thyroidectomy [ | PMC10293335 |
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Materials and methods | Patients were enrolled among those undergoing thyroidectomy at the Surgery Unit of the Istituti Clinici Scientifici Maugeri, I.R.C.C.S. (Pavia, Italy), between February the 1Weight was measured on two different weight scales, which are periodically checked for their accuracy by an agency of standards, asking the participant to stand still in the center of the scale platform with light clothing with 10 cm gap between the heels, making sure the weight was equally distributed on both legs. Height was measured by means of a stadiometer on the individuals standing up tall, keeping their heels on the ground flat against the tool, and barefoot, to the nearest 0.1 cm [Enrolled patients were randomly assigned to receive a personalized pre-surgery diet counseling (GROUP A) or no intervention (GROUP B), according to a 1:2 ratio. All patients underwent endocrinological follow-up at baseline (within a week from surgery), 45 days and 12 months post-surgery in the Endocrinology Outpatient Clinic. Exclusion criteria during follow-up included: pregnancy, inter-current illnesses influencing body weight, and bariatric surgery.All subjects gave their informed consent to participate in the study, which was performed in accordance with the guidelines of the Declaration of Helsinki. This study was formally approved by the Istituti Clinici Scientifici Maugeri IRCCS Ethical Committee (Protocol number CE 737). | PMC10293335 |
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Dietic intervention | post-thyroidectomy | All the patients (Group A) received an individual nutritional counseling, performed by a registered clinical dietitian, within one week pre-surgery (T0). The dietitians analyzed the patient’s dietary pattern and habits, estimating intake frequencies of all the food groups throughout a validated questionnaire, the “QueMD MODV6” [Both Groups, A and B, attended 40–60 days post-thyroidectomy (T1) and 1 year after (T2) dietetic consultations, which included evaluation of body weight, BMI, level of physical activity, smoking habitude and dietary modification by the QueMD MODV6 questionnaire. | PMC10293335 |
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Endocrine follow-up | THYROID | All patients attended regular endocrinological follow-up at T1 and T2, according to standard clinical practices. After surgery all patients were treated with levothyroxine at replacement or mildly TSH-suppressive doses according to a benign or malignant histology. Thyroid function parameters were measured at 40–60 days and at 12 months after surgery in all patients. Patients who, on the basis of the 40–60 days post-surgery TSH levels, required levothyroxine dose adjustments were further tested for thyroid function parameters two months later. | PMC10293335 |
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Statistical analysis | Statistical analysis was performed using the SPSS software (SPSS, Inc., Evanston, IL). Between groups comparisons were performed by Student’s | PMC10293335 |
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Body weight changes | GROUP B | We compared body weight changes from pre-surgery (T0) to T1 and T2 between the two groups. Patients in Group A did not experience significant body weight changes at either T1 or T2. In particular, mean body weight was 71.5 ± 14.1 kg at T0, 71.9 ± 14.2 kg at T1 (As shown in Fig. Percentage body weight variation between T0 and T1 and between T0 and T2 in the two study groups. Data are represented as mean ± standard deviation. *As shown in Fig. Histogram representing the percentage of patients experiencing decreased, stable or increased body weight at T2 when compared to T0 in Group A and Group B | PMC10293335 |
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Lifestyle, dietary pattern and habits changes | GROUP B | The analysis of lifestyle and QueMD MODV6 questionnaires throughout the study span failed to register any significant difference between the two groups, both at baseline (mean score 6.90 ± 1.32 in Group A vs 7.07 ± 1.25 in Group B, | PMC10293335 |
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Funding | This work was partially supported by the ‘Ricerca Corrente’ funding scheme of the Ministry of Health Italy. This work was partially funded under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3 - Call for proposals No. 341 of 15 March 2022 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU. Open access funding provided by Università degli Studi di Pavia within the CRUI-CARE Agreement. | PMC10293335 |
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Data availability | Some or all data used during the study are available from the corresponding author by request. | PMC10293335 |
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Compliance with ethical standards | PMC10293335 |
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Competing interests | The authors declare no competing interests. | PMC10293335 |
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Informed consent | Informed consent was obtained from all individual participants included in the study. | PMC10293335 |
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References | PMC10293335 |
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Abstract | Sodium oxybate (γ-hydroxybutyrate, GHB) is an endogenous GHB/GABA | PMC10267648 |
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Introduction | MRS | Sodium oxybate (γ-hydroxybutyrate, GHB) is an endogenous GHB/GABARecent neuropsychopharmacological approaches involve the assessment of cerebral resting-state functional connectivity (rsFC) to understand how psychoactive substances differentially modulate brain functioning. Here, an established model to analyze rsFC on a large-scale level involves three core neurocognitive networks: the default mode network (DMN), the central executive network (CEN), and the salience network (SN; Sedative drugs such as midazolam and propofol were shown to reduce DMN and DMN-CEN connectivity (In contrast to the growing knowledge of their functional properties, the neurochemical regulation of large-scale brain networks remains largely unknown. Studies applying combined magnetic resonance spectroscopy (MRS) and fMRI reported significant associations between the spectral signals of main inhibitory and excitatory neurotransmitters, gamma-aminobutyric acid (GABA) and glutamate (Glu), and FC, suggesting their role in synchronizing brain activity across specific regions (To elucidate the neural underpinnings of the above described wake-promoting effects of GHB, we investigated whole-brain rsFC of the DMN, the CEN, and the SN, combined with GABA and Glu levels in the ACC in the morning after nocturnal application of the drug in 16 healthy male volunteers using a placebo-controlled, double-blind, randomized, cross-over pharmacological fMRI design. The MRS-seed in the ACC was selected as recent studies demonstrated that neurochemical balance in this brain region modulate network interconnectivity ( | PMC10267648 |
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Methods | PMC10267648 |
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Permission | The study was approved by the Swiss Agency for Therapeutic Products (Swissmedic) as well as by the Ethics Committee of the Canton of Zurich and registered at | PMC10267648 |
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Study design | rapid-eye movement sleep, sleep-related disorders, and reduced sleep efficiency | SLEEP APNEA, RESTLESS LEGS SYNDROME | The study followed a randomized, placebo-controlled, order-balanced, double-blind, cross-over design. Two experimental nights (GHB vs. placebo) were separated by a washout phase of 7 days. Prior to definitive enrollment into the study, all participants underwent a polysomnographic examination in the sleep laboratory of the Institute of Pharmacology and Toxicology of the University of Zurich to exclude sleep-related disorders such as sleep apnea, restless legs syndrome, sleep onset rapid-eye movement sleep and reduced sleep efficiency (< 80%). To allow for habituation to the sleep laboratory setting, each experimental night was preceded by an adaptation night. Apart from the here presented post-sleep fMRI resting-state networks (RSN) results, GHB effects on sleep neurophysiology ( | PMC10267648 |
Participants | In sum, 20 healthy, male volunteers completed the study, whereof 4 participants were excluded from the final data analysis due to technical issues with the MR scanner or insufficient MR data quality (mean age of included participants: 25.8 ± 5.1 years). The following criteria were required for inclusion: (i) male sex (to avoid potential impact of menstrual cycle on primary outcome variables ( | PMC10267648 |
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Urine immunoassay | Urine samples were taken on each test night, to ensure abstinence from illegal drug use (Drug-Screen Multi 12-AE, Nal von Minden GmbH, Regensburg, DE). | PMC10267648 |
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Drug administration | At each experimental night, study participants were awoken at 02:30 a.m. to receive 50 mg/kg of GHB (Xyrem®) or placebo dissolved in 2 dL of orange juice, matched in appearance and taste (see Study design of the experimental nights. Sleep period (23:00–07:00), time point of drug administration (02:30) and MRS/MRI scan (09:00) are indicated. | PMC10267648 |
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MRI data acquisition | BEST | The fMRI resting state scan was performed in the morning after both experimental nights on a Philips Achieva 3T whole-body MR-unit equipped with a 32-channel head coil (Philips Medical Systems, Best, The Netherlands). The session started at 09:00 a.m. with a T1-weighted anatomical brain scan and was followed by fMRI acquisition (5- and 10-min duration, respectively). RsFC time series were acquired with a sensitivity-encoded single-shot echo-planar imaging sequence (SENSE-sshEPI). The rsFC protocol used the following acquisition parameters: TE = 35 ms, TR = 3,000 ms, flip angle = 82°, FOV = 220 mm, acquisition matrix = 80 × 80 (in plane voxel size = 2.75 × 2.75 mm | PMC10267648 |
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MRI data preprocessing | REGRESSION, BRAIN | Standard image data preparation and pre-processing as well as statistical analysis and visualization were performed in Matlab (The Mathworks Inc., United States) and BrainVoyager (Brain Innovation B.V., The Netherlands). Functional data preprocessing included a correction for slice scan timing acquisition, a 3D rigid body motion correction, a spatial smoothing (Gaussian kernel of 6-mm full-width at half-maximum), a temporal high-pass filter with cut-off set to 0.0080 Hz per time-course and a temporal low-pass filter (Gaussian kernel of 3 s). The mean frame wise displacement was estimated from each time-series prior to nuisance and motion regression to exclude motion-driven bias in connectivity correlations ( | PMC10267648 |
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Statistical analysis of fMRI images | PMC10267648 |
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Independent component analysis of resting-state fMRI networks | The independent component analysis (ICA) analysis of RSN networks followed the identical approach described in a previous study of ours ( | PMC10267648 |
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Analysis of correlations between RSN-FC and single voxel MRS metabolites | To explore possible interactions between neurochemical brain balance and RSN-FC, we also investigated associations of FC alterations between conditions with the respective changes of metabolite signals measured by single voxel sv-MRS analysis. The change in metabolite spectral signals across experimental conditions was calculated subtracting the metabolite signlas of the GHB condition from signals of placebo condition (e.g. [ΔGABA] = [GABA at placebo condition] – [GABA at GHB condition]).For internetwork connectivity, the change of correlation- | PMC10267648 |
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Subjective state variables | Each participant’s post-awakening mental state was assessed at 10:00 a.m. using the self-report questionnaire EWL-60 (“Eigenschaftswörterliste”; Janke and Debus | PMC10267648 |
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Results | PMC10267648 |
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fMRI data and RSNs | For both conditions (GHB and placebo), the mean frame-wise displacement was below the critical threshold of 0.5 mm and did not differ between the conditions. Using the network template masks for extracting the homolog network best-fitting ICA components from each subject, we examined the differences between conditions (GHB vs. placebo), in both within- (via voxel-wise analysis) and between-network (via correlation analysis) connectivity. No significant drug effects were found within the DMN, the CEN, and the SN (see Spatial distribution of bilateral SN in the ICA at both placebo and GHB conditions. The SN is shown in three planes on average Talairach anatomical scan. The MRS-voxel is shown in all planes (green). After correcting for all voxel-level comparisons, no compact clusters with a statistically significant effect was detected (all RSNs and internetwork connectivity patterns. (A) All RSNs are shown in three planes on average Talairach anatomical scan. (B) The anterior and posterior default mode network (A-DMN, P-DMN), the L-CEN, R-CEN, and the SN are considered. The network graph highlights the connections of the SN with the other RSNs (B).Internetwork connectivity scores.The table reports means ± standard deviations. Significant group differences are shown in bold. The internetwork connectivity is quantified using a correlation Time-course of functional connectivity in two components from the ICA corresponding to the SN and the rCEN, obtained from a single subject at both placebo and GHB conditions. The correlation coefficient was calculated between the time-courses of the two networks. The increase in the dynamic coupling of SN-rCEN is testified by a correlation coefficient changing from | PMC10267648 |
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Associations between RSN-rsFC and sv-MRS metabolite signals | MRS | MRS data have been reported in detail in a separate publication (Scatterplots showing associations between the GHB-induced changes in resting-state functional connectivity between the SN and rCEN connection (ΔrsFC SN-rCEN) and the GHB-induced changes of metabolite signals in the ACC (ΔGABA, ΔGlu). In the | PMC10267648 |
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Subjective drug effects | REGRESSION | Generalized linear regression models revealed no significant effects of condition (GHB vs. placebo) and experimental session order on morning EWL subscale scores (all | PMC10267648 |
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Discussion | MRS | The present study aimed at investigating the neuropsychopharmacological effects of a nocturnal dose of 50 mg/kg GHB p.o. on next mornings’ rsFC and its relationship to GABA and Glu alterations in the ACC. First, we observed a newly induced rCEN-SN coupling after a night with GHB, which was not present in the placebo condition; second, we found that this rsFC alteration was significantly associated with GABA changes in the ACC.In previous studies, GHB was found to acutely modulate rsFC patterns. In particular, GHB administration (35 mg/kg p.o.) in wake healthy individuals acutely increased rsFC between the DMN and the SN (at 34 min after GHB-intake) compared with placebo, without significantly affecting the spatial rsFC distribution of all major large-scale networks (These findings are particularly intriguing when considering the detailed functional meaning of the SN and the unique psychopharmacological and behavioral profile of GHB. The SN has been proposed as a detector of saliency to guide adaptive behavior (Now, translating these findings to GHB neuropsychopharmacology, one may expect to observe different network patterns coherently to the different behavioral effects of GHB in the acute and post-acute phase. In the acute phase, 35 mg/kg oral GHB-induced mixed sedative/stimulant behavioral effects, which were accompanied by increased SN-DMN connectivity (As a note, the increase of rsFC between the SN and the rCEN, but not the lCEN, is coherent with previous findings showing asymmetric effects of GHB on RSNs. Increased cerebral blood flow in the right anterior insula and increased rSN-rCEN coupling via the dorsal nexus have been reported after acute GHB challenge (Bosch et al. To investigate how the reported modulation of rsFC alterations relate to the neurochemical brain homeostasis, we analyzed previously published data of MRS from an ACC-seed assessed in the same individuals and from the same experiment (Our study bears several limitations. The final sample size of In conclusion, the present study provided evidence of persisting internetwork connectivity changes in the morning following a nocturnal therapeutic dose of GHB in humans. The observed alteration in connectivity pattern seem to indicate a modulation of the balancing function of the SN between the DMN and the CEN, toward a more externally oriented brain state, which is in line with GHB’s ability to improve next-day waking functions. We also described a GHB-induced positive interaction of GABA/Glu balance in the ACC with whole-brain connectivity changes. Thus, our findings support the idea of an excitatory/inhibitory equilibrium in the ACC to be actively involved in the modulation of rsFC on a large-scale level. Future research should clarify the generalizability of these findings to other stimulant and/or sedative drugs affecting GABA and Glu homeostasis and further assess correlations with cognitive and behavioral effects in clinical populations. | PMC10267648 |
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Supplementary material | PMC10267648 |
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Supplementary Material | Click here for additional data file. | PMC10267648 |
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Acknowledgments | Vinnie | All authors report no conflicts of interest, financial or otherwise. We thank Vinnie Kandra for supporting us with the data acquisition. | PMC10267648 |
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Author contributions | Francesco Bavato: data analysis and interpretation, drafting the article;Fabrizio Esposito: data analysis and interpretation; critical revision of the article; Dario A. Dornbierer: project conceptualization and planning; data collection; Niklaus Zölch: data collection, data analysis, and interpretation; Boris B. Quednow: project conceptualization and planning, critical revision of the article; Philipp Stämpfli: data collection; Hans-Peter Landolt: project conceptualization and design, critical revision of the article; Erich Seifritz: project conceptualization and design, critical revision of the article; Oliver G. Bosch: project conceptualization and design, data collection, drafting the article. | PMC10267648 |
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Funding | The study was supported by grants from the Swiss National Science Foundation (SNSF) (grant # 320030_163439 to H.P.L.) and the Clinical Research Priority Program | PMC10267648 |
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Data availability | Anonymized data will be shared by request with any qualified investigator with institutional review board approval for the purposes of validation and/or replication using our center’s established procedures for sharing data.
| PMC10267648 |
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References | PMC10267648 |
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Summary | Contributed equallyContributed equally | PMC10533414 |
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Background | Tafenoquine, co-administered with chloroquine, is approved for the radical cure (prevention of relapse) of | PMC10533414 |
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