Hereditary angioedema (HAE) is a potentially life‐threatening inherited disease that causes recurrent, serious, and debilitating episodes of swelling. While evidence has improved in adult patients, data on the epidemiology and treatment of pediatric patients with HAE remain very limited. The aim of this study was to determine the incidence and prevalence of pediatric patients with HAE aged <12 years, as well as treatment patterns, co‐medication, and specialties involved.TM pharmacy claims (LRx) database was used to analyze prescriptions of HAE‐specific treatments and co‐medications.In this retrospective study (2016–2021), the German IQVIAWe found an HAE prevalence in pediatric patients aged <12 years of 2.51:100,000 and a 12‐month prevalence of up to 1.02:100,000 between 2016 and 2021. Most HAE treatments were prescribed by outpatient clinics and pediatricians, with an increasing proportion of icatibant as an on‐demand treatment and low rates of long‐term prophylaxis (LTP). The prescription rate of analgesics as the most common co‐medication decreased notably after HAE diagnosis.Our findings provide insights into the epidemiology and current pediatric HAE treatment landscape in Germany. The obtained HAE prevalence in pediatric patients aged <12 years was even higher than the previously reported average of overall cohorts, whereas the LTP rate was low, which might indicate an unmet need for newer LTP treatment options in pediatric patients. The findings reveal a higher prevalence of HAE in children <12 years of age compared with previously reported data, together with low rates of long‐term prophylaxis (LTP). These results highlight the need for further research and development of improved treatment options for pediatric HAE patients.2Hereditary angioedema (HAE) is an autosomal dominant genetic disease that can cause serious and debilitating attacks of swelling in the abdomen, face, feet, genitals, hands and throat. Besides the visible and painful swelling of the skin other symptoms may include nausea, abdominal pain and/or diarrhea, and even asphyxiation due to laryngeal edema, rendering the disease a potentially life‐threatening condition.Pathophysiologically, C1‐INH deficiency leads to accumulation of the tissue hormone bradykinin, which causes vascular leakage and edema formation.When attacks occur, HAE‐patients must receive immediate on‐demand treatment. Current guidelines recommend that patients should be evaluated for the indication of LTP at each visit to ideally achieve attack‐free status, resulting in complete control of the disease and normalization of patients' lives.In Germany, four treatments are approved for the on‐demand treatment of HAE attacks in adults and adolescents, namely plasma‐derived intravenous (i.v.) human C1 inhibitor (pdC1‐INH) I and II, icatibant and conestat alfa (recombinant human C1‐esterase inhibitor).For LTP, five treatments are available for adults in Germany: pdC1‐INH II (i.v.),While findings from observational registries and surveys, also from Germany, have improved the evidence for adult HAE patients, epidemiological and treatment data for pediatric patients with HAE remain very limited. The aim of this study was to determine the incidence and prevalence of HAE in pediatric patients aged <12 years with statutory health insurance (SHI) and to generate evidence on treatment patterns and treating specialties.33.1TM pharmacy claims (LRx) database. The analyzed patient population consisted of SHI patients with at least one prescription of HAE treatment (EphMRA‐ATC B06D0) between January 2016 and December 2021 through public pharmacies. A lookback period to January 2015 was used to identify new patients. For the analysis of the co‐medication in pediatric patients, patients aged 0–10 years with at least one HAE treatment between January 2016 and December 2020 were included to ensure a 12‐month baseline and follow‐up period to assess the co‐medication within 1 year after the first HAE treatment  were determined. Long‐term prophylaxis was defined as prescription of pdC1‐INH I (s.c.) or pdC1‐INH II (i.v.) with >7 vials/28 days or any prescription of berotralstat or lanadelumab. Additional on‐demand treatment is always prescribed for patients with LTP, which helps to identify these patients.Due to insufficient evidence from outdated studies and lack of efficacy, tranexamic acid is not recommended in most guidelines. Therefore, and due to its use in other indications, tranexamic acid was not included in this study. However, additional on‐demand treatment is always prescribed for patients with LTP, and therefore the patients were nevertheless identified in the respective on‐demand analyses.The different treating specialties of HAE patients after initial prescription were recorded, as well as the type and quantity of HAE treatments in pediatric patients aged 2–11 years, including additional on‐demand treatment under LTP. On‐demand treatment was defined as a prescription of icatibant, conestat alfa, pdC1‐INH I or II (i.v.) with ≤7 vials/28 days. Children <2 years were not included in this analysis because there is no approval and recommendation for LTP at this age.3.3TM Pharma‐Scope database. Subsequently, reference data of all SHI patients from the German Federal Health Reporting System were used for the total population and an approximation was made with DESTATIS data for pediatric patients <12 years, respectively .44.1In this study, a total of 2436 SHI patients received HAE treatments between 2016 and 2021, representing a prevalence of 3.35:100,000. In children <12 years, who received HAE treatments between 2016 and 2021, the prevalence was 2.51:100,000 persons. There were more female participants in the overall study population (66.2%) and in pediatric patients <12 years (55.5%). There were regional differences, with the highest prevalence in Western Germany   and the highest in 2019 . The overall population incidence of 0.52:100,000 in 2016 was higher than that in subsequent years. Children <12 years had the highest incidence in 2020 at 0.43:100,000 persons , including 8 children . The pediatric 12‐month prevalence increased from 0.11:100,000 in 2016 to 0.55:100,000 in 2021. In 2016, there were no pediatric patients <12 years with HAE‐LTP, and as of 2017, the annual prevalence was low . The incidence was highest in the total population in 2019  and for pediatric patients in 2017  Figure . The res4.2Most pediatric patients received their HAE prescriptions exclusively from the outpatient sector , including university hospital outpatient departments .4.3Children aged 2–11 years received either intravenous pdC1‐INH I or II  or icatibant. While the proportion receiving pdC1‐INH I (i.v.) decreased from 2016 to 2021 (from 100% to 45%), conversely, the proportion receiving icatibant increased over the study period , a vial usage of up to 190 vials per year was observed. For LTP, according to market authorization, only pdC1‐INH II (i.v.) was used throughout the study period. In patients aged 6–11 years, the highest consumption rate of pdC1‐INH II (i.v.) was 260 vials/year, which intriguingly declined to an average consumption of 160 vials/year by 2021  Figure . Prescri5To our knowledge, this is the first study to provide detailed epidemiologic data of children with HAE based on real‐world evidence. For children aged <12 years, this study found a HAE prevalence of 2.51:100,000 over the study period  to 27.9% (2021). The LTP treatment rate of an international registry was slightly higher at 30%, and the rates of a recent French studyn = 4) in 2017; however, this decreased to 0 in 2021  as HAE‐LTP for patients ≥12 years in 2018, the incidence of HAE‐LTP in the total population increased from 2019. In contrast, after the approval of berotralstat in mid‐2021 for patients ≥12 years, the incidence remained unaffected. For children <12 years of age, no off‐label prescriptions for lanadelumab, pdC1‐INH I (s.c.), and berotralstat were observed. In 2017, pdC1‐INH II (i.v.) received a label extension for LTP in pediatric patients ≥6 years, which initially led to an increase in LTP incidence and prevalence but was followed by a steep decline in subsequent years  after HAE diagnosis. The most commonly prescribed drug in this group (ATC code N02B) was acetaminophen/paracetamol. This may be related to the natural decrease in febrile respiratory infections during childhood, but also to the decrease in colicky abdominal pain due to HAE‐specific treatment.Using a database of pharmacy claims for disease‐specific treatments, we were able to generate population‐based data for HAE. However, several limitations must be considered when interpreting the presented results. The LRx database does currently cover 84% of all prescriptions reimbursed in Germany. Not all pharmacy collection centers deliver their data for analysis. Since prescription data lack information on diagnoses and associated laboratory tests, treatment prescriptions are used as surrogate markers for diagnoses. While patients without prescriptions and inpatient therapies are not recorded, those with unconfirmed diagnoses will be recorded if a prescription is made. In addition, it is not possible to obtain details on familial relationships or mutation frequency as this information is not included or accessible in the LRx database. Furthermore, although the use of tranexamic acid in children is uncommon in Germany and not recommended in consensus statements, pediatric patients with tranexamic acid as LTP may have been missed due to the inclusion criteria of our analysis.Our data cannot distinguish whether a treatment was actually administered or held back by the patient for a possible severe attack. The assignment of pdC1‐INH II (i.v.) as an on‐demand treatment or LTP was derived from assumptions about vial consumption  for pediatric patients and adults alike. For individual patients, the month of birth was not available and was set to January of the year of birth, which may have resulted in a minor overestimation of age.Additionally, a minor overestimation of incidence in 2016 may have resulted from a slightly prolonged lookback period in the previous year ; data curation ; methodology ; supervision ; validation ; writing—review and editing . Susanne Abraham: Conceptualization ; data curation ; methodology ; supervision ; validation ; writing—review and editing . Christoph Hirche: Conceptualization ; data curation ; formal analysis ; methodology ; validation ; writing—original draft ; writing—review and editing . Gerrit Müller: Conceptualization ; data curation ; formal analysis ; methodology ; validation ; writing—original draft ; writing—review and editing . Stephan Kaiser: Conceptualization ; formal analysis ; investigation ; methodology ; project administration ; writing—review and editing . Leonarda Serdani‐Neuhaus: Data curation ; project administration ; visualization ; writing—original draft ; writing—review and editing . Rebecca Zingel: Conceptualization ; formal analysis ; investigation ; software ; visualization ; writing—original draft ; writing—review and editing . Inmaculada Martinez Saguer: Conceptualization ; data curation ; methodology ; supervision ; validation ; writing—review and editing .The authors declare that they have no relevant or material financial interests that relate to the data and research described in this publication. Freerk Prenzel received honoraria, research funding, and travel grants from Sanofi Genzyme, Novartis, Leipziger Gesundheitsnetz, Berlin‐Chemie, Allergopharma, Vertex, Nutricia‐Milupa, Takeda, medilearn and Stallergenes Greer. Susanne Abraham has received speaking and/or consulting fees and is involved in clinical trials for Novartis, Sanofi, Beiersdorf, UCB, Amgen, LEO Pharma, Takeda, Lilly, Boehringer Ingelheim, and AbbVie. Inmaculada Martinez‐Saguer has received honoraria, research funding, and travel grants from BioCryst, CSL Behring, Pharming, Octapharma, KalVista, andTakeda/Shire and/or has served as a consultant and/or participated in advisory boards for these companies. Christoph Hirche, Gerrit Müller, Stephan Kaiser and Leonarda Serdani‐Neuhaus are employees of Takeda Pharma Vertrieb GmbH and Co. KG. Rebecca Zingel is an employee of IQVIA, Frankfurt Am Main, Germany, who conducted the analysis.Supporting Information S1Click here for additional data file.Supporting Information S2Click here for additional data file.


The National Comprehensive Cancer Network suggested that older women with low‐risk breast cancer  could omit adjuvant radiation treatment (RT) after breast‐conserving surgery (BCS) if they were treated with hormone therapy. However, the association between RT omission and breast cancer‐specific mortality among older women with comorbidity is not fully known.1105 older women (≥65 years) with LRBC in 1998–2012 were queried from the Surveillance, Epidemiology, and End Results–Medicare Health Outcomes Survey data resource and were followed up through July 2018. Latent class analysis was performed to identify comorbidity burden classes. A propensity score‐based inverse probability of treatment weighting (IPTW) was applied to Cox regression models to obtain subdistribution hazard ratios (HRs) and 95% CI for cancer‐specific mortality considering other causes of death as competing risks, overall and separately by comorbidity burden class.p = 0.202 and p = 0.536, respectively), regardless of comorbidity burden. However, RT omission could increase the risk of long‐term cancer‐specific death in women with low comorbidity burden , which warrants further study.Three comorbidity burden  groups were identified. A total of 318 deaths (47 cancer‐related) occurred. The IPTW‐adjusted Cox regression analysis showed that RT omission was not associated with short‐term, 5‐ and 10‐year cancer‐specific death (Omission of RT after BCS is not associated with an increased risk of cancer‐specific death and is deemed a reasonable treatment option for older women with moderate to high comorbidity burden. The pattern of RT omission is inconsistent across institutions, including NCCN facilities.Thus, the purpose of the present study was twofold. First, we aimed to stratify older women with LRBC queried from SEER‐MHOS based on their comorbidity and symptom burden using LCA. Next, we assessed the association between RT omission and breast cancer‐specific mortality, overall and separately for each comorbidity burden class.22.1This retrospective cohort study utilized SEER‐MHOS data resources. The SEER cancer registry provides highly accurate and reliable patient data regarding demographics, initial treatment, sequence of treatments, tumor characteristics, vital status, and cause of death. The SEER program, which began in 1973, currently covers nearly 48% of the US population including 20 registries.2.2The study protocol received institutional review board approval and has been reported in detail previously.As individuals provided informed consent to participate in future research during the MHOS survey, this study is exempt from obtaining informed consent from study participants.2.32.3.1Sociodemographic  factors were obtained from the enrollment database maintained by the Centers for Medicare & Medicaid Services and/or self‐reported information in the MHOS survey. Tumor‐related factors  were obtained from the SEER‐Patient Entitlement and Diagnosis Summary File (PEDSF). The information regarding human epidermal growth factor receptor 2 (HER2) status is not included in the current analysis, as HER2 testing was unavailable until 2010.2.3.2Treatment status was defined as BCS + RT or BCS alone according to information on the type of surgery, RT, and the sequence of these two variables in the SEER data file. Lumpectomy, segmental resection, quadrantectomy, or partial mastectomy are examples of BCS. Based on the sequence of surgery and RT, only RT received after surgery was considered in the RT group. Data on the first course treatment  in the SEER data file are considered generally reliable2.3.3The MHOS questionnaire includes questions about the presence of 12 comorbid conditions , which were assessed by the question, “has a medical doctor ever told you that you had [condition]?” They were coded dichotomously . There is no reliability reported for comorbidity data in MHOS. However, the reliability of self‐reported comorbidities similar to ones in MHOS was moderate with varying values depending on the type of conditions.The limitation of functionality was assessed with the following seven questions: six questions on activities of daily living  and one question on moderate activities, with each activity being considered individually in the LCA. They were coded dichotomously .Symptoms were assessed by the two questions about chest pain (on exercise or when resting) and the three questions about shortness of breath , and they were coded dichotomously . We used information from surveys completed within 24 months before cancer diagnosis to assess the pre‐diagnosis comorbidity burden. If participants completed more than once, the most recent one in relation to the diagnosis date was used.2.3.4The primary outcome was survival time, defined as the time from the date of diagnosis to the date of death or censoring at the last follow‐up, July 30, 2018, whichever came first. The date and causes of death were verified through the National Death Index.2.4t‐tests or chi‐squared tests appropriately.Baseline characteristics of the study population were described using mean and standard deviation (SD) or frequency and percentage and compared by treatment status (BCS + RT vs. BCS only) using 2.4.1p‐value (≥0.05) from LRT suggests that a model with k‐1 classes is sufficient and that k classes are not better than k‐1 classes. When consensus is not reached, guidance from LRT is used. Third, the item‐response probability for each item was examined to describe the characteristics of each class. Finally, individuals were allocated to their most probable class based on the posterior class probabilities. When missingness is found in data, Mplus estimates the latent class membership assuming missing at random, and the full information maximum likelihood is utilized.Since a very small number of events were observed in the current study , not all covariates could be included individually in the multivariable survival analysis because one variable per 10 events is a widely accepted minimal criterion for adequate parameter estimation.2.4.2The Cox regression model was used to obtain unadjusted estimates for 5‐, 10‐, and 20‐year overall survival with 95% confidence intervals (CIs). The modified Cox model was used to obtain unadjusted estimates of 5‐, 10‐, and 20‐year cumulative incidence of cancer‐specific deaths according to RT omission and comorbidity burden. The difference in cumulative incidence function according to RT omission was tested using Gray's tests.2.4.3In this observational comparative effectiveness study, where treatment was not randomized, potential bias due to treatment selection and confounding could impact the estimate of the treatment effect.p < 0.05.The Cox proportional hazards model was then used to obtain IPTW‐adjusted subdistribution hazard ratios (HRs) and 95% CIs for 5‐, 10‐, and 20‐year cancer‐specific mortality due to RT omission considering non‐cancer‐related deaths as a competing risk, overall and separately by comorbidity burden class. All analyses were conducted using SAS 9.4, and statistical significance was set with 33.1Among 64,467 survey records in the SEER‐MHOS breast cancer data file, 5437 older women diagnosed with primary breast cancer in 1998–2012 and completed surveys before diagnosis were identified. Women should meet all three conditions to be eligible. (1) diagnosis with LRBC, (2) received BCS, and (3) completed surveys within 24 months before cancer diagnosis were queried. The average (± standard deviation) time between survey completion and cancer diagnosis was 11.4 ± 6.7 months before cancer diagnosis. The detailed selection process is described in Figure 3.2p = 0.328). This observation is supported by the lowest BIC and high entropy. Although corresponding AIC and SABIC were not the lowest, their values were on the lower side. Item‐response rates are reported in Table p = 0.005).The fit statistics for class enumeration in Table 3.3p < 0.0001). A total of 117 deaths (25 cancer‐related) occurred over the 5‐year follow‐up period, 236 women deceased (41 cancer‐related) over a 10‐year follow‐up period, and 318 deaths (47 cancer‐related) over the whole study period. In unadjusted analyses, as shown in Table The median follow‐up time was 8.3 years, with a significantly longer follow‐up for those with RT (RT: 8.7 years vs. no‐RT: 7.3 years) and those with low comorbidity burden  compared to their counterparts  can miss significant numbers of events. Therefore, our study expanded the follow‐up period beyond 10 years. Third, we used propensity score‐based weighting  and selected women with LRBC only to provide a more valid causal inference from this observational epidemiologic study, otherwise prone to confounding and selection bias. Fourth, we showed the potential use of patient‐reported outcomes data in risk stratification for RT treatment decision‐making. This could improve the identification of older women who can omit RT.In summary, our current patient‐reported outcomes comparative effectiveness study provides empirical evidence supporting RT omission among older women with LRBC and moderate or high comorbidity burden. For older women with low comorbidity burden, further evaluation of long‐term outcomes is warranted in prospective randomized studies. Collecting and utilizing patient‐reported outcomes data can be an effective tool for risk stratification for adjuvant RT after BCS. It will improve patient satisfaction as they are involved in treatment decision‐making.Eunkyung Lee: Conceptualization ; data curation ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration ; validation ; writing – original draft ; writing – review and editing . Robert B. Hines: Investigation ; methodology ; validation ; writing – review and editing . Jianbin Zhu: Data curation ; formal analysis ; methodology ; software ; visualization ; writing – review and editing . Michael Rovito: Investigation ; writing – original draft ; writing – review and editing . Kavita V. Dharmarajan: Investigation ; writing – review and editing . Madhu Mazumdar: Methodology ; writing – review and editing .This study was supported by the University of Central Florida (UCF) Office of Research (EL).The authors declare no conflicts of interest.The University of Central Florida Institutional Review Board approved this study (IRB number: SBE‐18‐14238).Figure S1.Click here for additional data file.Figure S2.Click here for additional data file.Data S1.Click here for additional data file.


The effects of high‐protein diets on regulating sleep have received research attention in recent decades. However, no studies have examined the effects of these diets in obese adults. Therefore, this study was conducted to investigate the effects of low‐calorie high protein diets on sleep quality in obese adults.2) diagnosed with low‐quality sleep. All participants were given a diet with a 750‐calorie energy deficit. While the control group was given a normal diet, the intervention group received a diet with 30% more protein.This study is a randomized clinical trial conducted on 60 obese adults (BMI > 29.9 kg/mp < 0.01). Sleep quality, apnea‐hypopnea index (AHI), sleep latency (SL), and polysomnography were significantly different between the two groups (p < 0.05), showing an improvement in sleep quality and obstructive sleep apnea in the intervention group (p < 0.05).The results showed a significant difference between the control group and intervention group with respect to sleep apnea at 30‐, 60‐, and 90‐day follow‐up (This study shows that low‐calorie high‐protein diets can effectively improve apnea, sleep quality, and body composition indices in obese adults. Sleep serves an essential function and can affect energy maintenance, external appearance, physical health, and body rhythm.Chronic sleep disorders affect 10% of the population, while 30% experience sleep disorders some time in their lives.Diet and micronutrient intake are among the environmental factors influencing sleep quality.22) who visited the nutrition clinic at Ghaem Hospital  to receive a consultation and plan their diets (code of ethics: IR.TBZMED.REC.1401.688). After obtaining informed consent, all healthy individuals meeting the following criteria were enrolled in the study: aged 18–65, no past medical history, obesity diagnosis by a nutritionist, low‐quality sleep diagnosis (PSQI score ≥6 and STOP‐Bang score ≥3). The following additional criteria were also used to screen the participants: no dietary interventions in the past 3 months, no sudden weight loss, and not taking nutritional supplements or herbal medications. The following criteria were used to exclude participants: heart disease, low blood pressure, diabetes, cancer, autoimmune disorders, psychological disorders, overweight, pregnancy, breastfeeding, use of medications that affect sleep quality, and undergoing interventions affecting sleep apnea during the follow‐up.This randomized clinical trial was designed according to the Consolidated Standards of Reporting Trials (CONSORT)2), and waist circumference was measured with a tape measure in cm. Body composition was evaluated using a BC‐418 segmental body composition analyzer, including fat mass, free fat mass, body water, and muscle mass, as well as the distribution and proportion of each parameter. Daily calorie requirement for each participant was calculated using the Mifflin‐St Jeor equation utilizing age, gender, height, and weight.After entering the study, participants were divided into intervention  and control  groups using block classification based on body mass index (BMI) using sealed enveloped randomization Figure . RandomiBoth groups received diets with a 750 calorie energy deficit but the diet for the intervention group contained a larger protein ratio . To overcome the calorie restriction and achieve the desired protein percentage, 60% of the protein intake was supplied from high biological value (HBV) sources, and the remaining 40% was supplied using plant‐based sources. HBV sources included red meat (beef and lamb), fish , eggs, chicken, cheese, and yogurt. Plant sources included peas, lentils, beans, and wheat. To evaluate adherence to the diet, the food frequency questionnaire (FFQ) was completed individually. Diets were analyzed using the N4 software to check calorie intake and micro‐ and macro‐nutrients levels.Participants' mental health was evaluated using the DASS‐21 Questionnaire . The reliability and validity of the questionnaire has been shown in several studies. Participants exhibiting any psychological disorders were excluded from the study. Presence of sleep apnea and its severity were assessed using the 8‐item STOP‐Bang questionnaire which takes the following items into account: snoring, daytime sleepiness, obstructive sleep apnea via an observer, high blood pressure, BMI ≥35, age >50, being male, below‐thyroid neck circumference >40 cm. A score of ≥3 on the STOP‐Bang questionnaire indicates the risk of obstructive apnea. The Pittsburgh Sleep Quality Index questionnaire (PSQI) was used to determine sleep quality. An overall PSQI score ≥6 indicates low sleep quality. Finally, the International Physical Activity Questionnaire (IPAQ) was used to evaluate physical activity.A STOP‐Bang score >5 is suggestive of further sleep apnea disorders. Of the participants meeting this threshold, five were randomly chosen to undergo polysomnography on days 0 and 30 to evaluate apnea‐hypopnea index (AHI), respiratory disturbance index (RDI), oxygen desaturation index, arousal or microarousal index, sleep efficiency, sleep latency, and REM latency. Body composition, anthropometric parameters, and sleep quality were measured on Days 0, 15, 30, 60, and 90 for all participants.2.1α ≤ 0.05 was considered statistically significant.Descriptive statistics including mean and standard deviation were used to describe the data. The Mann–Whitney test was used to compare the means of nonparametric quantitative variables, and the Friedman test was used to compare the means of nonparametric dependent variables 0, 15, 30, 60, and 90 days after the intervention. Wilcoxon signed‐rank test was used to investigate the difference between the means of dependent nonparametric variables before and after the intervention. Fisher's exact test was used to study the differences between the frequencies of qualitative variables. The statistical analyses were done using Stata v12 (Corp) and 3p > 0.05) (Table p < 0.05). Also, mean muscle mass index had increased significantly in the intervention group by the end of the study (p < 0.01). Other indices similarly show significant changes over time (p < 0.05), except for body water.Overall, 60 people participated in the study in two groups of 30 (intervention and control). The results showed no significant differences in age, gender, anthropometric indices , body composition , and physical activity between the intervention and control groups before the intervention (5) Table . The meap > 0.05). On the other hand, at 30, 60, and 90 days after the intervention, a significant difference was observed between the mean sleep apnea scores of the two groups (p < 0.01) showing an improvement in sleep apnea in the intervention group (p < 0.01). The control group showed no significant change in sleep apnea score over the course of the study (p > 0.05) (Table There were no significant differences between the two groups 0 and 15 days after the intervention (5) Table .p > 0.05). However, the average scores had changed significantly by days 30, 60, and 90 (p < 0.01) (Table p > 0.05), while a significant difference emerged between the two groups 30 days after the intervention, with the intervention group showing an improvement in their test results (p < 0.05). Polysomnographic indices were significantly different between the two groups after the intervention (p < 0.5)  Table . The pol5) Table .4This study aimed to investigate the effects of low‐calorie high‐protein diets on anthropometric indices and sleep quality in obese adults. The results showed that such diets can improve sleep quality and the related indices and enhance anthropometric indices and body composition, which is in line with previous studies in this field.The positive effect of the protein content in a diet and its desirable health outcomes such as weight control and improved body composition have been reported in the literature.Overweight, obesity, and central adipose tissue accumulation are among the risk factors affecting sleep quality. Lifestyle changes resulting in the reduction of adipose tissue and weight loss through a balanced diet are the most important interventions to the mitigate risk. A number of studies have shown that high‐protein diets enhance sleep quality indices,Dietary interventions are one of the priorities in obesity risk management. In long trials, a 500‐calorie deficit has been found to be the best way to lose weight.Hashimoto and colleagues state that increasing energy intake and diet quality  improve sleep quality.5The results of this study show that low‐calorie high‐protein diets can promote sleep quality in obese adults when controlling for confounding factors such as physical activity. Moreover, the results of polysomnography 30 days after the intervention reveal that sleep quality indices are significantly improved in the intervention group compared with the control group.Fatemeh Sadat Hashemi Javaheri: Data curation; formal analysis; investigation; methodology; writing—original draft. A. R. Ostadrahimi: Conceptualization; methodology; project administration; supervision; validation. Mohsen Nematy: Conceptualization; supervision; validation. Seyyed Mostafa Arabi: Conceptualization; methodology; writing—review & editing. Mahnaz Amini: Investigation; resources; supervision; validation.The authors declare no conflict of interest.The lead author A. R. Ostadrahimi affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned  have been explained.This study was approved by the ethics board of Tabriz University of Medical Sciences under the registration code IR.TBZMED.REC.1401.688.


Little is known about how physical activity influences the relationship between neuroticism and cognitive function and cognitive decline.Data from the Chicago Health and Aging Project (CHAP) was utilized to conduct this study. CHAP is a population-based cohort study of chronic conditions in older adults. Participants completed in-home interviews cycles of three years from 1993–2012. Mixed effects regression models were conducted to test the associations between physical activity, neuroticism, and the interaction between neuroticism and physical activity on outcomes: global cognitive function, global cognitive decline, episodic memory, decline in episodic memory, perceptual speed, and decline in perceptual speed. Stratified mixed effects regression models by physical activity level were conducted to test the associations between neuroticism and global cognitive function and global cognitive decline.p = .010) and on the interaction of neuroticism and high physical activity on baseline episodic memory (β = 0.020 (SE = .009), p = .021) and on decline in episodic memory over time (β = -0.003 (SE = .001), p = .039).A total of 7,685 participants were eligible for this study. Participants were 62% female and 64% African American. We found statistically significant associations for the interaction of high physical activity and neuroticism on baseline global cognitive function (β = 0.017 (SE = 0.007), Higher physical activity lessened the association between higher neuroticism and poor cognitive outcomes. Neuroticism is an established personality trait that is an important target for public health interventions . It is sLess neuroticism is associated with increased physical activity participation and decreased sedentary behavior and inactivity . IndividWe analyzed data from the Chicago Health and Aging Project (CHAP), which is a population-based cohort study that examines health conditions in community-dwelling African American and White older adults. Recruitment of study participants occurred using door-to-door census. In-home interviews were completed in three-year cycles from 1993 to 2012. CHAP has a total of 10,802 participants. Clinical evaluations were completed with a stratified, random sample of approximately 33% of total CHAP participants. CHAP study design is further described elsewhere [Strongly disagree) to 5 (Strongly agree): I often feel tense and jittery; I am not a worrier; I often feel helpless and want someone else to solve my problems; I often get angry at the way people treat me.Ratings were summed to calculate a total score between 4–20 and recoded 0–16. The second item was reverse coded [th percentile and high at approximately the 90th percentile.Neuroticism was measured using items from the NEO Five-Factor Inventory. Participants were asked to rate each of the following items on a scale of 1 , which measures perceptual speed, and the Mini-Mental State Examination (MMSE) –22. The p < 0.05. The variance inflation factor was calculated for all predictor variables in the models. Analysis was done in SAS version 9.4. A figure was developed using R version 4.2.1. to plot global cognitive decline over time among participants with high neuroticism, little physical activity, high neuroticism, high physical activity, low neuroticism, little physical activity, and low neuroticism, high physical activity.Descriptive analysis was conducted in total and by physical activity category. Mixed effects regression models were conducted to test the associations between neuroticism as a continuous variable and physical activity as a categorical variable on the longitudinal trajectory of each of three outcomes: global cognitive function, episodic memory, and perceptual speed. For each outcome, two models were fit to examine the effects neuroticism and physical activity levels separately. A third model then included both effects into one model, and finally, a fourth model added a neuroticism by physical activity interaction term. Mixed effects regression models were also conducted, stratified by the three levels of physical activity, which examined the associations between neuroticism and global cognition. All models adjusted for age, race, sex, education, medical conditions, depressive symptoms, time, and interactions of each with time. Models have person-specific intercepts and slopes and an unstructured correlation matrix. Level of statistical significance used for this analysis was Table p < 0.000), p = 0.002). Model 2 has statistically significant associations between high physical activity and global cognitive decline (β = 0.006 (SE = 0.003), p = 0.018). Model 3 indicates statistically significant associations between neuroticism and baseline level (β = -0.035 (SE = 0.003), p < 0.000) and between high physical activity decline in global cognition (β = 0.006 (SE = 0.003), p = 0.028). Model 4 shows that the interaction of high physical activity and neuroticism (β = 0.017 (SE = 0.007), p = 0.010) on baseline global cognitive function was statistically significant.Table p < 0.000). Model 4 shows a statistically significant association between the interaction of neuroticism and high physical activity on baseline episodic memory (β = 0.020 (SE = 0.009), p = 0.021) and on decline in episodic memory over time (β = -0.003 (SE = 0.001), p = 0.039). In Table p < 0.000). Variance inflation factor values for predictors in the models were between 1.000–2.000, indicating low correlations among predictors.Similar analysis for the four models were conducted with episodic memory as an outcome in Table p < 0.000), medium (β = -0.032 (SE = 0.005), p < 0.000), and high (β = -0.032 (SE = 0.005), p < 0.000).Table Figure The study results show that high physical activity differentially influences the association between neuroticism and global cognitive function and episodic memory at baseline and decline in episodic memory over time. The study findings indicate that the association between neuroticism and global cognitive function is dependent on physical activity level. A high level of physical activity attenuates the association between neuroticism and global cognitive decline.Little work has been done to examine how physical activity, which is a modifiable risk factor, influences the relationship between neuroticism and cognitive decline in older adults. However, studies have been conducted which evaluate the role of physical activity in the associations between characteristics of neuroticism, such as depression or anxiety, and cognitive decline. Data from the National Health and Nutrition Examination Survey (2011–2014) was used to assess how physical activity impacts the relationship between depression and cognitive function in older adults. Results showed that participating in 150 min per week of physical activity at moderate to vigorous intensity helps to modify the association between depression and cognitive function and that physical activity may protect cognitive function from depression . The EngMore research has focused on evaluating associations between depressive symptoms and Alzheimer’s disease compared to symptoms of anxiety. Yet, anxiety is approximately 40% prevalent in individuals with Alzheimer’s disease. Symptoms of anxiety can also occur in early stages of experiencing cognitive impairment and can exacerbate symptoms, especially if management strategies are not in place . Data frHowever, the relationship between neuroticism and physical activity is complex. Healthy neuroticism is described as neuroticism interacting with conscientiousness. In specific situations, neuroticism can benefit health. Analysis of data from the studies in the Integrative Analysis of Longitudinal Studies of Aging and Dementia (IALSA) network found healthy neuroticism to be predictive of increased physical activity participation and that conscientiousness may weaken the relationship between neuroticism and physical activity .There are several limitations to this study. Physical activity was self-reported and therefore, susceptible to recall and social desirability biases . IntensiIn conclusion, engaging in physical activity may weaken the relationship between high neuroticism and poor cognitive functioning. Future research should examine associations by type of physical activity, focus on specific items in the NEO measure, and evaluate the roles of neuroticism and conscientiousness on physical activity behavior. We also plan to test for race, sex, and education differences in the associations over time. Understanding demographic differences is important for physical activity program planning. Additional research is needed to determine the frequency, intensity, and types of physical activity, as well as health behavior change strategies needed to reduce the impact of neuroticism on cognitive decline.


Machine learning (ML) is an established technique that uses sets of training data to develop algorithms and perform data classification without using human intervention/supervision. This study aims to determine how functional and anatomical brain connectivity (FC and SC) data can be used to classify voiding dysfunction (VD) in female MS patients using ML.n = 14]; Group 2: VD [n = 13]). All patients underwent concurrent functional MRI/urodynamics testing.Twenty‐seven ambulatory MS individuals with lower urinary tract dysfunction were recruited and divided into two groups , were partial least squares  using FC alone and random forest (RF) when using SC alone (AUC = 0.93) and combined (AUC = 0.96) as inputs. Our results show 10 predictors with the highest AUC values were associated with FC, indicating that although white matter was affected, new connections may have formed to preserve voiding initiation.MS patients with and without VD exhibit distinct brain connectivity patterns when performing a voiding task. Our results demonstrate FC (grey matter) is of higher importance than SC (white matter) for this classification. Knowledge of these centres may help us further phenotype patients to appropriate centrally focused treatments in the future. Recently, there have been a growing number of ML applications in characterizing brain connectivity measures, including assessing disease severity and symptoms22.1The study and its ethical code were approved by the Houston Methodist Institutional Review Board (IRB). Informed consents were signed by all participants prior to enrolment. Adult females (≥18 years old) with at least 6 months of clinically stable MS [Expanded Disability Status Score (EDSS) ≤ 6.5] and at least 3 months of symptomatic NLUTD (including both storage and voiding symptoms) were referred to our fellowship‐trained neurourologists at the Houston Methodist Neurourology clinic and screened for this study. Men and anatomical bladder outlet obstruction  were excluded from the study. Severe debilitating MS, history of seizures, pregnancy or planning to become pregnant, contraindications to MRI, history of augmentation cystoplasty and presence of other neurological disorders besides MS were also excluded. Patients with active UTI can be treated and subsequently screened for the trial.n = 14) who voided spontaneously and had a post‐void residual (PVR) volume of less than 40% of their bladder capacity  and patients with VD  who performed self‐catheterization or had a PVR volume of 40% or more of the BC.Twenty‐seven ambulatory female MS patients met the inclusion criteria and were divided into two groups based on their cystometric studies during their initial urodynamic study visit: patients without VD . Dual‐lumen 7‐Fr MRI‐compatible UDS bladder and rectal catheters were placed while the patients were in the scanner and the tubing was extended out of the MRI scanner room into the control room to record abdominal, vesical and detrusor pressures via a Laborie UDS machine.Anatomical, DTI and fMRI images were obtained as in our previously established protocol.2.3https://afni.nimh.nih.gov/) were used to identify significant brain activated regions (p < 0.05) at the time of voiding initiation using the generalized linear model (GLM) for each subject. Second‐level GLM analysis yielded significantly activated brain regions for each group. An average blood oxygenation level dependent (BOLD) activation map averaged over all subjects was created. The voiding initiation network (VIN) in MS was defined by including only the highest activated brain regions from the average BOLD activation map  as previously reported.AFNI , neural networks (NN), generalized linear model (GLM) and partial least squares (PLS)],The entire dataset was split into a training set (50%) and a test set (50%). Ten‐fold repeated cross validation with five repeats was used to train the ML algorithms. The algorithm with the highest area under the curve (AUC) of the receiver‐operating characteristic curve (ROC) was determined as the best‐performing algorithm. The predictor weights (ranging from 0 to 100) of FC or SC information of the best‐performing algorithm served as a measure to assess the relative importance of either information in predicting if a patient belonged in the V or VD group.33.1t‐tests and chi‐square test  between V and VD groups are detailed in Table Patients' demographics with p values from unpaired 3.2Dorsal vagal motor nuclei, pontine storage and micturition centre, periaqueductal grey (PAG), ventral tegmental area, substantia nigra, red nuclei, thalamus, cingulate, insula and cortical regions in the frontal, parietal and mesial temporal lobes were identified as part of the VIN. Using FC values as predictors, AUC values for RF, NNET, GLM and PLS were 0.86, 0.71, 0.53 and 0.89, respectively. Using the best‐performing algorithm (PLS), voiders showed a higher polarization of connectivity  than VD subjects Figure . The mos3.3Utilizing FA values as predictors, AUC values for RF, NNET, GLM and PLS were 0.93, 0.58, 0.51 and 0.65, respectively. Using the best‐performing algorithm (RF), the most 10 important white matter tracts used in classification of V or VD patients were listed in Figure 3.4When both FC and FA values were used as predictors, AUC values for RF, NNET, GLM and PLS were 0.96, 0.79, 0.83 and 0.95, respectively. FC values showed higher relative strength as predictors for classification of MS patients as V or VD, when compared with FA values  and left cingulate, all of which have been shown to play a crucial role in bladder function in both healthy and MS patients.The 10 most important white matter tracts in classifying MS patients into V or VD groups have all been found in the literature to be involved in bladder function.When both grey (FC) and white (SC) matter information were combined as inputs for our ML algorithm to classify MS patients into V and VD groups, the 10 most important values established in predicting VD were all found to be FC, suggesting FC is more effective in predicting VD in MS patients. Consistent with results from using only white matter information, the right superior longitudinal fasciculus was found to be the most important white matter tract in predicting VD; however, it is ranked 16th in relative importance of predictors. Whereas SC are more persistent and stable, FC are more dynamic, fluctuating and highly time dependent.Consistently for all three types of inputs , the two best‐performing ML algorithms in predicting VD in MS patients were RF and PLS, whereas the worst‐performing algorithm was GLM. Because GLM is based on generalization of linear regression, this indicates the need for a complex non‐linear algorithm, which were offered by RF and PLS algorithms.5To our knowledge, this is the first study to apply ML in predicting VD in MS patients using both grey (FC) and white (SC) brain matter information. Our study showed that ML is an effective method in classifying patients with MS into groups of those who void spontaneously or have VD based on their brain connectivity. Our results established FC as a better predictor for VD in female MS patients. This opens a new avenue for early symptom detection, prediction of disease progression and phenotyping for optimal treatment outcome, as well as provides new therapeutic targets in the treatment of VD in MS individuals. Additionally, this study sets the stage for utilizing non‐invasive neuroimaging techniques to predict LUTS in various aetiologies other than MS.All authors declare no conflict of interests.Khue Tran: Data curation, formal analysis, visualization, writing—original draft, writing—review and editing. Betsy H. Salazar: Formal analysis, validation, writing—original draft, writing—review and editing. Timothy B. Boone: Conceptualization, methodology, funding acquisition, supervision, writing—review and editing. Rose Khavari: Conceptualization, formal analysis, funding acquisition, investigation, project administration, methodology, project administration, supervision, validation, writing—review and editing. Christof Karmonik: Conceptualization, methodology, formal analysis, resources, software, supervision, visualization, writing—review and editing.


Furthermore, the combined treatment led to improved therapeutic activities compared to monotherapies. In conclusion, FEN and PIO exerted a therapeutic synergistic effect on HFD/STZ‐induced IR in the hippocampus.Insulin resistance (IR) is a risk factor for metabolic disorders and neurodegeneration. Peroxisome proliferator‐activated receptor (PPAR) agonists have been proven to mitigate the neuronal pathology associated with IR. However, the synergetic efficacy of these agonists is yet to be fully described. Hence, we aimed to investigate the efficacy of PPARα/γ agonists (fenofibrate and pioglitazone) on a high‐fat diet (HFD) and streptozotocin (STZ)‐induced hippocampal neurodegeneration. Male Wistar rats (200 ± 25 mg/body weight [BW]) were divided into five groups. The experimental groups were fed on an HFD for 12 weeks coupled with 5 days of an STZ injection  to induce IR. Fenofibrate , pioglitazone , and their combination were administered for 2 weeks postinduction. Behavioral tests were conducted, and blood was collected to determine insulin sensitivity after treatment. Animals were killed for assessment of oxidative stress, cellular morphology characterization, and astrocytic evaluation. HFD/STZ‐induced IR increased malondialdehyde (MDA) levels and decreased glutathione (GSH) levels. Evidence of cellular alterations and overexpression of astrocytic protein was observed in the hippocampus. By contrast, monotherapy of FEN and PIO increased the GSH level ( Induction of symptoms of insulin insensitivity with the concomitant administration of streptozotocin (STZ) and a high‐fat diet (HFD) increased the fasting glucose level. This alters the neural activities and morphology of the hippocampus via negative activation of Peroxisome proliferator‐activated receptor (PPARs)‐regulated signaling pathways needed for synaptic plasticity and neuronal survival. However, the use of fenofibrate and pioglitazone (PPARs agonists) results in competition for similar active sites and leads to modulation of the respective signaling pathways altered by insulin insensitivity to mitigate alterations of the hippocampus and improve its functionality and neuronal survival. PPARs are nuclear receptors that modulate gene expression involved in cell differentiation, immune function, lipid metabolism, glucose control, and energy homeostasis.Alterations in the hepatic mitochondrial and peroxisomal beta‐oxidation signaling cascade regulated by PPARα have been regarded as a target for high fat intake in type 2 DM patients. Moreso, in vivo studies, has reported that HFD induces upregulation of the PPARγ, causing fatty acid synthesis via the activation of lipogenic factors and disruption of glucose and lipid metabolism.22.1Fenofibrate and pioglitazone (PIO) were procured from Bharat Parenterals Ltd., Gujarat, India, and Micro Labs Ltd., India, respectively. STZ was acquired from Sigma‐Aldrich Company.2.2HFD was produced at a reliable indigenous feed‐mill in Ilorin, Nigeria. The diet was composed of 5 kg of palm kernel cake, 5.5 kg of maize, 0.5 kg of wheat offal, 5.5 kg of groundnut cake, 0.5 kg of bone meal, 0.5 kg of fish meal, 0.025 kg of methionine, 0.025 kg of lysine, 0.0625 kg of industrial salt, 0.0625 kg of broiler premix, and 12.5 kg of toasted soya meal.2.3n = 5) was fed with pelletized rat feed and distilled water. Animals recruited for the IR‐induced pathology were fed with HFD throughout the experiment (12 weeks).n = 5): HFD/STZ, HFD/STZ/FEN, HFD/STZ/PIO, and HFD/STZ/FEN/PIO.Male adult Wistar rats with a body weight range of 180–220 g were obtained from the Animal facility of the University of Ilorin. The animals were housed in normal home cages, maintained under standardized conditions of 12 /12 h of a light/dark cycle, and received adequate care in the animal holding section of the Faculty of Basic Medical Sciences, University of Ilorin. They were allowed to acclimatize at a temperature of 23± 5°C for 14 days, during which they were fed with standard pelletized feed and water ad libitum, before the commencement of the study. The animals were randomized into two major groups (control and IR‐induced groups). The control group 2.4IR was assessed using the homeostatic model assessment of IR (HOMA‐IR); blood samples were collected via cardiac puncture at the right ventricle and stored in heparinized tubes and then cryocentrifuged (4°C) at 3000 rpm for 5 min. 0.5 ml of the plasma was pipetted for estimation of fasting glucose and fasting plasma insulin. The fasting plasma glucose and fasting plasma insulin levels were measured using the glucose oxidase method according to Ambade et al.2.5The Y‐maze test was conducted to evaluate the spatial memory of all the experimental rats. An initial 3 days of training were conducted before the test, which was recorded for analysis. The Y‐maze apparatus is a three‐armed Y‐shaped chamber labeled A, B, and C, which are symmetrically separated at 120°. Each rat was placed at the center of the apparatus and allowed to freely explore the three arms for 8 min. The number of correct alternations  was recorded and the percentage alternation was calculated as an index for spatial memory.2.6An elevated plus maze test was conducted to investigate the level of anxiety in the rats. The plus‐shaped maze consists of two opposing open arms  and two opposing wall‐enclosed arms  extending off a center square (9 cm per side) to form a plus shape. Each rat was placed at the center of the maze and given 10 min to explore the four arms of the maze. The arms entry ratio, time spent in each arm, and open arm rearing were recorded and calculated as indices for anxious behavior.2.7The hippocampi were carefully removed from the medial temporal lobe of the brain, homogenized in 30% of sucrose, and kept frozen. The homogenates were centrifuged at 14,000 rpm for 15 min at 4°C. The supernatant was collected for the assessment of MDA and GSH levels. The amount of MDA formed in each of the samples was assessed by measuring the optical density of the supernatant at 532 nm as described by Olufunke et al.2.8Histological and Immunohistochemical analyses were conducted to evaluate histoarchitectural changes in the hippocampus. The animals were perfused transcardially with 0.1 M PBS (pH 7.4) and 10% formalin in 0.1 M PBS (pH 7.4) as described by Olufunke et al. The hippocampal tissues were immediately fixated in 10% buffered formalin. The tissues were then dehydrated, embedded in paraffin, and sections of 5 µm thickness were obtained using a rotatory microtome. Each section was stained with hematoxylin and eosin (H&E) dyes for basic histological demonstration Fischer et al.For astrocytic expression, the immunohistochemistry protocol was carried out according to the protocol described by Olufunke.2.9p < 0.05.Data collected were analyzed, and graphs were drawn using Graph Pad Prism 7 software (Graph pad software Inc.). Multiple comparisons were performed using a one‐way analysis of variance with the Tukey test. Data were reported as mean ± SEM. The significance of the analysis was set at 33.1p < 0.01) in FBG levels in all induced groups compared to the control group. However, after 2 weeks of administration of PPAR agonists, there was a statistically significant decrease (p < 0.05) in FBG levels of all the adjuvant‐treated groups compared to the HFD/STZ group. Moreover, the HFD/STZ/FEN/PIO group showed markedly (p < 0.01) lower FBG levels compared to the HFD/STZ/FEN and HFD/STZ/PIO groups  significant decrease in IR levels compared to the HFD/STZ group. Moreover, the HFD/STZ/FEN and HFD/STZ/PIO groups showed a statistically (p < 0.05) significant increase in IR levels compared to the control group ‐treated groups showed a statistically . Furthermore, there was a statistically (p < 0.05) significant decrease in the number of open arms rearing  significant increase in the numbers of open arm entries, duration in open arms, numbers of rearing, and percentage alternation compared to the HFD/STZ group. Moreover, the HFD/STZ/FEN/PIO group showed greater statistically (p < 0.05) significant improvement compared to the HFD/STZ/FEN and HFD/STZ/PIO groups.Evidence of anxiety‐like behavior in the elevated plus maze test has been linked to impaired working memory.g Figure  and percg Figure  in the H3.4p < 0.001) in MDA levels  GSH levels  significant decrease in MDA levels and an increase (p < 0.01) in GSH levels compared to the HFD/STZ group. Furthermore, the HFD/STZ/FEN and HFD/STZ/PIO groups showed significantly higher (p < 0.05) MDA levels and lower (p < 0.01) GSH levels compared to the control group. Although practically higher MDA and lower GSH values were obtained from the HFD/STZ/FEN/PIO group compared to the control group, there was no statistically significant difference.There was a significant increase  and PI3K/Akt/mTOR signaling pathways, thereby reducing the production of ATP required for positive neuroplasticity. Furthermore, the decline in behavioral and cognitive performance in Y‐maze assessment, particularly a decrease in exploratory time in the arms of the Y‐maze and percentage alternation in the arms suggest that HFD/STZ‐induced IR causes a decrease in ATP production needed for memory consolidation and reinforcement of newly formed synaptic connection during the learning phase of the assessment.Evidence of increased reactive oxygen species (ROS) and low antioxidant activities induced by the concomitant use of STZ and HFD suggests that disruption in insulin sensitivity and elevation of FBG induce an imbalance in ROS, hence resulting in deterioration of the activities of antioxidant enzymes and their secretions needed to mop up excess ROS.Evidence from studies has linked ROS accumulation in conjunction with deterioration in antioxidant functions to the pathophysiology and neurodegeneration associated with IR. The demonstration of severe pathological symptoms associated with neurodegeneration within the cellular morphology of the hippocampus infers that HFD/STZ  may trigger neuronal death as well as impeding synaptic communications essential for cognition. Hence, alterations in the functionality and survival of hippocampal neurons escalated by IR may be the underlying mechanisms contributing to the symptoms of dementia among DM patients. Furthermore, HFD has been reported to upregulate glial glutamate transporters, increasing the expression of GFAP, particularly in cerebral injuries and neurodegeneration disorders.5PPARα/γ agonists have been shown to play a vital ameliorative role in hippocampal neurodegeneration. This has been demonstrated in their regulation of inflammatory processes, reduction of oxidative stress, and control of protein aggregation. This study also reveals that combined PPARα/γ agonists have stronger neuroprotective capacities than either PPARα or PPARγ agonists alone.Oluwole B. Akinola contributed to the original idea, supervised, and contributed to the scientific content of the research. Olufunke O. Dosumu contributed to the scientific content of the research and proofreading, and contributed to the drafting of the submitted manuscript. Olumayowa K. Idowu and Olushola O. Oluyomi carried out the experimental protocols and drafting of the initial paper. Oluwatomisin O. Faniyan contributed to the data and image analyses of the paper.The authors declare no conflict of interest.Ethical clearance to perform this study with the approval number UERC/ASN/2017/970 was obtained from the Ethical Review Committee of the University of Ilorin, Nigeria, and experimental procedures were conducted in accordance with the guidelines of the National Institute of Health Guide for the Care and Use of Laboratory Animals.


Tracking kinematic details of motor behaviors is a foundation to study the neuronal mechanism and biology of motor control. However, most of the physiological motor behaviors and movement disorders, such as gait, balance, tremor, dystonia, and myoclonus, are highly dependent on the overall momentum of the whole‐body movements. Therefore, tracking the targeted movement and overall momentum simultaneously is critical for motor control research, but it remains an unmet need. Here, we introduce the intrinsic oscillatory property (IOP), a fundamental mechanical principle of physics, as a method for motion tracking in a force plate. The overall kinetic energy of animal motions can be transformed into the oscillatory amplitudes at the designed IOP frequency of the force plate, while the target movement has its own frequency features and can be tracked simultaneously. Using action tremor as an example, we reported that force plate‐based IOP approach has superior performance and reliability in detecting both tremor severity and tremor frequency, showing a lower level of coefficient of variation (CV) compared with video‐ and accelerometer‐based motion tracking methods and their combination. Under the locomotor suppression effect of medications, therapeutic effects on tremor severity can still be quantified by dynamically adjusting the overall locomotor activity detected by IOP. We further validated IOP method in optogenetic‐induced movements and natural movements, confirming that IOP can represent the intensity of general rhythmic and nonrhythmic movements, thus it can be generalized as a common approach to study kinematics. The kinematic profiles, such as position, velocity, or acceleration of various body parts, are highly dynamic in the timescale of millisecond. Therefore, current technology of motion tracking focuses on the high‐definition tracking of one limb or one targeted motion. However, many motor phenomena, such as reaching movement, walking, or balancing, involved complex coordinated movement beyond the limb being studied. For example, the cerebellum engages different functions for walking when two legs in human (or two sides of limbs in mice) move in the same versus different cycling speed.To date, motion tracking depends on the camera for superior information of limb/body position, but it is limited by the data rate and restricted scenarios suitable for video capturing.f) shown in equation below, where k and m here represent spring constant and mass, respectively:Here, we introduce another method, intrinsic oscillatory property (IOP) of a harmonic oscillator, to measure overall momentum in a freely moving animal. Based on the basic mechanical principle of physics, intrinsic oscillation is a fundamental phenomenon existing in every rigid body attached to a spring. Any movement applied to the system will trigger the intrinsic oscillation, and the amplitude of this frequency‐specific vibration is proportional to the given force. Such system generates natural oscillatory frequency  among overall motion (intrinsic oscillatory intensity).Taken together, the IOP method can be a reliable method to measure overall momentum. Therefore, IOP can be used to normalize the action‐dependent variability of tremor and generates a more stable readout indicating activity‐adjusted 2.3Grid2dupE3 mouse position in a 60 frame‐per‐second video allowed further analysis of mouse moving velocity , therefore provides better frequency resolution in terms of tremor measurement by frequency spectrum analysis. To compare IOP‐based tremor normalization with accelerometer‐based measurement, here we also applied two methods for normalization of accelerometer detection Figure . AccelerAlthough tremor intensity determined by accelerometer apparently exhibited consistency Figure , we notiWe therefore performed curve fitting to the nontremor portion of the spectral data Figure , reflectFurthermore, background subtraction significantly improved the consistency of detected tremor frequency in accelerometer‐ and video‐based methods (mean(SEM) with background, mean(SEM) without background: ACC‐Raw 0.07 (0.01), 0.05 (0.005); ACC‐GEN 0.07 (0.01), 0.05 (0.005); ACC‐VEL 0.06 (0.01), 0.04 (0.004); Video‐Raw 0.14 (0.01), 0.10 (0.01); Video‐GEN 0.15 (0.01), 0.09 (0.01); Video‐VEL 0.15 (0.01), 0.09 (0.01); Figure 2.5Reliable evaluation of tremor severity is critical to test therapies in animal models. In essential tremor, primidone, propranolol and ethanol all have reliable therapeutic effects in both patients and tremor mouse models.Grid2dupE3 mice, including primidone, propranolol and ethanol. We compared IOP‐normalized tremor intensity, which reflects tremor severity, before and after medication administration. We found that medications for essential tremor may reduce overall locomotor activity detected by IOP; yet, the tremor reduction effects still outweighed the locomotor sedative effects after IOP‐normalization (mean(SEM) before medication, mean(SEM) after medication: primidone 0.10 (0.02), 0.06 (0.01); propranolol 0.13 (0.01), 0.08 (0.01); ethanol 0.08 (0.01), 0.06 (0.01); unit: 1/Hz; Figure To validate whether IOP‐determined tremor severity can be used for evaluating therapy for essential tremor, we applied clinically used medications for essential tremor to In contrast, the application of carbamazepine, an anticonvulsant that does not have therapeutic benefit to essential tremor patients, showed that tremor reduction was proportional to IOP‐detected locomotor reduction and was purely accountable by locomotor sedative effect (mean(SEM) before medication, mean(SEM) after medication: carbamazepine 0.10 (0.02), 0.11 (0.01); unit: 1/Hz; Figure 2.6Grid2dupE3 mice with abnormal cerebellar pathology,Although IOP‐based method can be reliably applied to measure tremor in When applying 20 Hz laser illumination to stimulate DCN, mouse body shaking can be observed during the light‐on period of rhythmic illumination Movie . SpectraSince the strength of illumination can be well defined, we then applied different power of optogenetic stimulation and measured the intensity of induced rhythmic movement at the targeted frequency. It is clear that the intensity of rhythmic movement increased with the illuminating intensity (mean(SEM): 0.12 mW 0.49 (0.07), 0.15 mW 1.38 (0.22), 0.20 mW 4.37 (0.65); unit: 1/Hz; Figure 2.7The next question is whether IOP methods can also be used to study non‐rhythmic movement? We thus examined IOP methods in measuring overall momentum of nonrhythmic, natural movement. We analyzed the light‐off periods in the above‐mentioned optogenetic experiments Figure . In thes3In this study, we identified the IOP method, which used intrinsic oscillations of a force plate to detect and quantify the overall momentum due to locomotion. Using tremor as a model, head‐to‐head comparison among IOP‐, video‐ and accelerometer‐based tremor quantification revealed that IOP method can provide more consistent results in terms of tremor severity and frequency stability. IOP method allows quantification of tremor therapeutics with coexisting locomotor suppression and solved the paradoxical results of carbamazepine, suggesting carbamazepine leads to locomotor sedative effect rather than true tremor suppression. IOP method is also applicable to quantify optogenetically induced rhythmic movement. More importantly, it is capable of evaluating overall momentum in nonrhythmic natural movement. In summary Figure , IOP‐basA few limitations are conceivable in this study. To validate a new method, we focused on rhythmic movement for its clear‐cut readout and known frequency features. While we demonstrated that IOP can reflect momentum in nonrhythmic movement Figure , furtherlocally, and the coexisting IOP method could reflect global momentum and frequency integration of coordinated movement. Video is a nonreplaceable method to capture the moving types . Combination of all methods may provide more comprehensive information for studying kinematics of movement.Here, we validated IOP as an additional method for kinematics study. Combination of IOP and other methods can be beneficial. Although accelerometer‐based tremor measurement in our setup seems inferior to force plate‐based method, it could be due to a single accelerometer that detects the targeted motion, but not the overall momentum as the force plate measurement does. By combining the two methods, multiple miniature accelerometers attached to the head and limbs could provide detailed kinematics at multiple joints In addition to the marker‐less video capturing and accelerometer‐based measurement, locomotion analysis has been performed with the aid of other techniques including skin‐marker  tracking system, fluoroscope, and ultrasound‐based motion tracking.To solve the difficulty concerning the tracking of global motion kinematics, there have been ample studies on tracking multiple body parts at the same time. These approaches include the application of inertial sensors,Additionally, to study the pathophysiology of motion disorders using freely moving animal models, it is imperative to determine whether symptom modulation is resulted from dampening the neuronal mechanisms responsible for the symptom  or simply from a reduction of overall activity. Hence, constrained experimental scenarios, such as head‐fixation or physical restraint, may affect action and therefore the expression of motor symptoms. In this context, IOP may provide a more natural way to record and calibrate motor deficits in a freely moving setting and thus yield higher ecological validity for studying neural mechanisms and therapeutics of motion disorders in humans.Although our study has been performed on animal models, the force plate‐based IOP approach also shows potential in translational human applications. Currently, force plate has been applied to evaluate balancing functions for physical therapy and for identifying individuals at risk of falling.As demonstrated, IOP can be a useful approach to study tremor pathophysiology. Essential tremor, characterized by action‐dependent tremor, is the most common movement disorder with unsatisfactory pharmacological therapies and poorly understood pathophysiology. As a result, essential tremor has been the model disease used for testing modern therapeutic technology, including deep brain stimulation and magnetic resonance‐guided focus ultrasound.In summary, the IOP approach is a low‐cost and easy‐to‐use method for evaluation of motor phenomena and motor deficits. Using action tremor as an example disease model, this approach is shown to be capable of capturing the overall motion kinematics that modulate the expressed intensity of motor deficits. By taking advantage of its flexibility to adapt to various recording scenarios, the applications of the IOP method may be expanded to assessments of more motor phenomena and shed new light on human motion disorders in the future.4In summary, we simultaneously compared force plate‐based, accelerometer‐based, and video‐based detection of motion in freely moving mice. We demonstrated that the IOP of a force plate provides a more consistent measurement for the overall momentum of locomotion. A reliable method for measuring the overall activity is particularly critical to study the modulators of locomotion. For example, movement changed due to medication effect and neuronal circuitry activation. Combining detections of overall and targeted locomotion will advance the studies in behavior changes of freely moving mice in response to different modulatory elements that instantly affect neuronal circuitry.5Details are referred to Supplemental Methods 5.1Grid2dupE3 strain,Thy1‐ChR2‐YFP mice were used. All experiments were performed under the protocols approved by the IACUCs at NTU (B202000003) and CUIMC (AAAT7472). The connector for accelerometer was secured on the mouse skull. Optic fiber was implanted to the deep cerebellar nuclei . AAV9‐hSyn‐eNPAC 2.0‐WRPE was injected into the DCN.Wild‐type, 5.2A force plate with IOP Figure  was asse5.32). The relationship of these values with the corresponding weights was fitted by a linear curve and followed by correlation analysis.Motion was generated by mechanical tapping on the force plate. Specific frequencies (5 and 7 Hz) were applied with the aid of metronome . To demonstrate that the force plate obeys the physical principle of IOP, objects with six different masses  were applied to the force plate and random tapping with varying force was applied. The resulting IOP frequency values were used for deriving the reciprocals of square of frequency  was connected to the implant adhered on the mouse's skull during the recording. The accelerometer is three‐dimensional and can measure acceleration with a minimum full‐scale range of ±3 g. The sensor is a polysilicon surface‐micromachined structure providing a resistance against acceleration forces. Deflection of the structure is measured using a differential capacitor, resulting in a sensor output, whose amplitude is proportional to acceleration. Phase‐sensitive demodulation techniques are then used to determine the magnitude and direction of the acceleration. Signals were recorded and processed by Cerebus Neural Signal Processor (Blackrock Microsystem) and were digitized at 30 kHz.5.6Video recording was performed with uEye camera (IDS Image Developing Systems) from an above view. The video was recorded with the resolution of 1280 × 720 pixels, covering the open field of 28 cm × 16 cm, giving the pixel resolution of 0.22 mm in both X and Y direction. The frame rate was 60 frame per second (FPS). The mouse body center was identified by the free algorithm DeepLabCut.5.7Signals from force plate‐based, accelerometer‐based, and video‐based recording were aligned with a manually triggered analogue signal. This signal was sent to the electrophysiological system and lighted up a red LED at the same time. The electrical signal aided in aligning the time frame of force plate‐ and accelerometer‐based recordings. The red LED was set at the margin of the video, aiding in the alignment of video recordings to the electrical signals. The aligned signals were analyzed by custom‐written code in MATLAB.5.8per se. PSD data derived from acceleration was normalized to general activity  or moving velocity from video data. To avoid biased value due to small denominator (moving velocity), PSD data of a time point were ruled out if the corresponding moving velocity was not over the threshold of 0.4 mm/s.In force plate measurement, tremor PSD was normalized to summed PSD amplitude between 40 and 50 Hz (IOP of force plate) in the same period. For video data, tremor PSD data derived from moving velocity was normalized to general activity  or moving velocity 5.9Two‐term exponential fitting (MATLAB function fit) was applied on the accelerometer spectral data. To avoid the influence of noise and tremor signals on fitting, signals at low‐frequency band (0–5 Hz) and tremor frequency band (13–27 Hz) were excluded. Then corrected spectrum was obtained after subtracting the fitting result from the original spectrum. For background subtraction in force plate data, pchipinterp fitting (MATLAB function fit) on the spectrum excluding tremor frequency band (12–27 Hz) was performed and the result was subtracted from the original spectrum. For video data, one‐term exponential fitting (MATLAB function fit) was applied, with low‐frequency band (0–5 Hz) excluded.5.10Primidone (1.7 mg/kg), propranolol (10 mg/kg), ethanol (0.49 g/kg), or carbamazepine (30 mg/kg) was applied via intraperitoneal injection.5.11CV was applied to raw, normalized, and background‐subtracted data. Correlation analysis was applied to PSD in tremor frequency and IOP. For other statistical analyses, Wilcoxon signed‐rank test, Kruskal–Wallis rank sum test, Dunn's all‐pairs test, and Friedman test were performed in RStudio.5.12Optogenetic stimulation was given via a diode laser module with the indicated frequency and illuminating power ; formal analysis ; investigation ; methodology ; visualization ; writing – original draft . Yi‐Ting Lin: Data curation ; formal analysis ; investigation ; methodology ; software ; visualization ; writing – original draft . Liang‐Yin Lu: Data curation ; methodology ; software ; validation . Jia‐Huei Wang: Data curation ; methodology ; software ; validation . Wen‐Chuan Liu: Data curation ; methodology ; software ; validation . Sheng‐Han Kuo: Funding acquisition ; project administration ; resources ; supervision ; writing – review and editing . Ming‐Kai Pan: Conceptualization ; funding acquisition ; investigation ; methodology ; project administration ; resources ; software ; supervision ; writing – review and editing .The authors have no conflict of interest to declare.https://publons.com/publon/10.1002/btm2.10432.The peer review history for this article is available at Appendix S1: Supporting InformationClick here for additional data file.Movie S1. Force plate and its response to external force. A force plate assembled by a support plate, a loading cell and a pre‐amplifier and provides weight‐to‐voltage transformation in real time.Click here for additional data file.Movie S2. Video‐based motion detection of a mouse. Video‐based motion tracking of multiple landmarks of mouse position. The yellow dot (body center) is used for further analysis of mouse movement.Click here for additional data file.Movie S3. Induction of rhythmic movement by cerebellar optogenetic stimulation. The rhythmic blue‐light stimulation at the DCN of a Thy1‐ChR2‐YFP mouse induced rhythmic movement when the light turned on. The rhythmic movement ceased when the light turned off. DCN, deep cerebellar nuclei.Click here for additional data file.


Betta splendens).Animals use contests to attain resources and employ strategic decisions to minimise contest costs. These decisions are defined by behavioural response to resource value and competitive ability, but remain poorly understood. This is because the two factors are typically studied separately. Also, their study relies on overgeneralised assumptions that (i) strategies are fixed, (ii) modulated by the motivation or drive to fight and (iii) used to manage costs proportional to the timing of the loser’s retreat. To address these problems, we adopt an integrative sequential analysis that incorporates competitive ability and resource value factors, to characterise territorial contest decisions in male Siamese fighting fish  and escalation (initiation and attack), but changes in motivation were associated only with cost mitigation.Our findings contrast dominant assumptions that strategic decisions are fixed, controlled by motivational state and sufficiently described by outcome-dependent measures. We instead demonstrate that strategic decisions are complex, comprising functional changes in assessment, information use and motivational effects, which are not always inter-dependent.The online version contains supplementary material available at 10.1186/s40850-022-00156-3. Contests are used to attain resources, but can impose high energetic and injury costs . AccordiThe characterisation of contest strategies has been limited by the lack of integration of resource value and RHP assessment, and accounting for behavioural variation between individuals. However, suggestions for new generalised approaches to address this , continue to rely on a set of assumptions that have dominated the field regarding the assessment of information used in strategic decisions and the behavioural outputs of these decisions , 5. SpecThe first set of assumptions regards the assessment of resource value, as first posited by Parker and Stuart in their 1976 model . In partThe second set of assumptions regards RHP assessment, where theory predicts that this involves fixed strategies of either self, opponent or mutual assessments of morphology and behaviour, or cumulative assessments of energetic or injury thresholds , 7. EithAnother set of assumptions regards the way by which strategies have historically been determined by variations in contest duration. A key prediction about strategic decisions is that weaker individuals lose and that their decision to quit is faster when resource value is low, when they have low contest abilities, or when their opponent is stronger, to mitigate energetic or injury costs , 3. As sMoreover, because a contest’s duration is determined by the timing of the loser’s decision to quit, it is often assumed to reflect their fight motivation, i.e. the drive to continue fighting. This assumption has been criticised because contest duration also relies on ongoing behaviour, and is thus not an independent measure of motivation , 20. NevAnother contributor to behavioural variation is baseline individual differences in aggressiveness, whose effects on strategic decisions are underexplored –18. AggrBetta splendens. These contests play a significant role in reproductive success due to the use of territories to build bubble-nests for their offspring, which contributes fundamentally to the survival and development of eggs and prepares males for mating [B. splendens, whether for wining or minimizing costs, involve a well-documented, stereotyped and easily identifiable behavioural repertoire [B. splendens where it exhibits similar but different strategic modulation to behaviour [The combined evidence supports the need for an integrative approach that examines the influence of both resource value and RHP assessment together, as well as trade-offs with individual aggressiveness. Yet, we further stress that the full complexity of strategic decisions requires testing assumptions about their (i) stability, (ii) the implication of motivation and (iii) the resultant variation of behaviour Fig. . Here wer mating , 24. Thur mating –27. Thisr mating . This prpertoire , 30. Thipertoire –31. Evenpertoire . In addipertoire –25. Finaehaviour . Hence, R2 = 0.601, χ23, 393 = 80.87, P < 0.001), with a prevalence for frontal display preceding lateral display, followed by any attack, and retreat reserved typically at later stages but also as early as second , having positive effects from total display duration  but negative effects from attack . However, when quantifying the modulation of all behavior, after controlling for random individual effects (R2C = 0.409), models of resource value, RHP, and individual aggressiveness predicted much of the combined variation in behaviour . Resource value factors (22.37% FEV), trait aggressiveness (16.30% FEV) and morphological factors (9.65% FEV) had repeated effects, but the greatest contributor was progressively available information from ongoing behaviour during contests (43.22% FEV).Based on whether opponents retreated more than focal fish, the likelihood of winning was only weakly predicted by behaviour (model: β = 0.4) and retreated slightly less (β = 0.1), preferring frontal (β = 0.2) to lateral display (β = − 0.2). High resource value from nested territory without noise (interaction) drove faster initiation (β = 0.6), preference for frontal display (β = 0.1) and reduced retreat (β = − 0.3), while both nests and noise suppressed attacks (β ≤ − 1.2). Against relatively bigger opponents (interaction), fish started contests faster (β = 0.3), but also retreated more (β = 0.3), preferring lateral display during contests (β = 0.3). In terms of progressive behaviour, individuals matched their opponent’s response in display, attack and retreat (β ≥ 0.2), but attacks were also exhibited by fish with more rigorous display (β = 0.7) and retreat was more likely when opponents attacked more (β = 0.4) and displayed less (β = − 0.1) than focals. Motivation was consistently higher when defending nested territory (β ≥ 0.5) and lower when there was added noise (β < − 0.3). It was also greater for animals with a size advantage (β ≥ 1.3), and who displayed (β ≥ 0.2) and attacked (β ≥ 0.6) more, and retreated less than their opponents . Earlier in the fight, motivation was elevated against opponents that displayed more . However, later in the fight (probe 2), motivation was instead reduced if opponents attacked (β = − 0.5) and higher for fish with greater rates of surface breathing (β = 0.2).Contributions by each predictor across phenotypic measures are summarised in Fig. % Var = 44.7) accounted chiefly for display (0.941) and retreat (− 0.876), whereas the second component (C2: % Var = 27.4) accounted for initiation (0.668) and attack (0.760). Average motivation predicted the first , but not the second component   had markedly greater effect than residual variation   to two corresponding discrete components Fig. a. By com50) Fig. b. The vaContest strategies are typically defined as behavioural changes tied to outcome, i.e. winning or losing, and are considered to rely on fixed sets of information, on particular forms of assessment and mediated by motivational state , 3, 21. B. splendens  were housed individually in 15 L tanks environmentally enriched with plastic plants, shelters and surface platforms , kept under 12 h photoperiods  and fed a high-protein diet .Commercially acquired adult n = 26; acclimated overnight). Following the first agonistic behaviour by focals, we allowed 15 min interactions separated in 5 min intervals framed by two motivational probes via the validated startle approach, i.e. a distinct splash from a drop of marble visually hidden from opponents  to score behaviour via the Observer XT 11.5 software (Noldus Information Technology).We tested 30 focal animals using a previously validated within-individual repeated measures protocol comprising four weekly contests against a bigger and smaller opponent (relative weight) under noise and control conditions . Briefls et al. , 25). WeB. splendens aggressive behaviour and controls for carry-over learning effects [P < 0.05; ρ > 0.6 [r = 0.575, F1,29 = 2.35, P = 0.012), indicating an aggressive tendency which we quantified by the mean score .We quantified aggressiveness before the staged contests via the repeated measurement of behaviour  on two separate instances (housing day 4 and 7), using the mirror test. Although the mirror test is not always representative of intraspecific aggression, it adequately quantifies  effects , 30. Com ρ > 0.6 ) and wer− 1 decrease) induced an underwater 20–40 dB change in sound pressure levels, at frequencies of 1–4 kHz, similar to noise imposed by light human traffic. Manipulations of background noise were initiated 10 min before the staged contests to allow time for focal fish to assess the acoustic changes in their territory.To manipulate subjective value, individuals were housed in their enriched tanks for two weeks before experiments, to allow territorial establishment and the opportunity for construction of bubble-nests. Thus, territorial value primarily varied in terms of the presence or absence of nests in established territories. Added variation in resource quality was manipulated by acoustic conditions during contests, varying between ambient controls and a validated noise treatment localised to focal fish, which according to previous evidence negatively affects resource value by reducing territorial defence and nesting . The trer2 = 0.549; F56 = 65.76, P < 0.001) and fin size .For both focal individuals and their opponents, we used wet weight as a composite measure of morphological state that strongly predicts visually available information, including standard length  and rate of retreats respectively . Motivational state was measured at the two separate probes during the contest and quantified by the average recovery rate from startles , and any biting or tail beating attempts [attack occurrence (Y/N).For characterising contest strategy, we quantified six phenotypic outputs and size , lateralattempts  scored aR2) derived from the proportion of explained over total variance following the scaled deviance method, where deviance is considered identical to the residual sum of squares [β coefficients. All analyses were performed using the statistical software SPSS  and Minitab® .For our analyses we used mixed regression models with a log-link function for non-parametric continuous measures, a binary logistic function for binary data and an ordinal regression with a logit function for ordered data. We included individual identity as a random factor and removed non-significant interaction terms. We quantified predictor reliability by coefficients of determination , resource value (noise and nest) and RHP factors . Third, to quantify combined effects across sequential behaviours, we used composite levels of determination (R2C) and fractions of explained variance (FEV), based on the sums of the total variance exhibited by all measures (∑TSS) and the total variance explained by predictors across models (∑ESS). In order to test the effect of strategic decisions on agonistic motivation, we tested integrated effects on motivation from the same predictor categories, i.e. trait aggressiveness score, noise and nest, and RHP (weight and behaviour), sequentially (probe 1 and 2) and on average scores. Finally, we identified intuitive behavioural components using an agglomerative hierarchical clustering method - linking the closest variables and clusters at each step based on their absolute correlation  - and then quantified behavioural components from the correlation matrix , and tested if the likelihood related to initiation, display or attack (binary). In order to provide an integrative sequential analysis , we followed three steps. First, sequential organisation of behaviour was ascertained by testing for differences in the chronological order in which separate contest behaviours were first exhibited, i.e. rank in the latency to first exhibit each behaviour . Second, for each behavioural measure we tested for effects by models (log-link) that integrated our three  = 0.462 ) and tesAdditional file 1: Table S1. Statistical outputs for each model use to analyse behavioural measures and motivation probes.Additional file 2: Table S2. Results of principal components analyses (PCA) of contest behaviour across the two mirror tests, day 4 and 7, used to quantify aggressiveness.Additional file 3: Raw data and calculations.