Approximation of Bayesian predictive p-values with regression ABC

In the Bayesian framework a standard approach to model criticism is to compare some function of the observed data to a reference predictive distribution. The result of the comparison can be summarized in the form of a p-value, and it's well known that computation of some kinds of Bayesian predictive p-values can be challenging. The use of regression adjustment approximate Bayesian computation (ABC) methods is explored for this task. Two problems are considered. The first is the calibration of posterior predictive p-values so that they are uniformly distributed under some reference distribution for the data. Computation is difficult because the calibration process requires repeated approximation of the posterior for different data sets under the reference distribution. The second problem considered is approximation of distributions of prior predictive p-values for the purpose of choosing weakly informative priors in the case where the model checking statistic is expensive to compute. Here the computation is difficult because of the need to repeatedly sample from a prior predictive distribution for different values of a prior hyperparameter. In both these problems we argue that high accuracy in the computations is not required, which makes fast approximations such as regression adjustment ABC very useful. We illustrate our methods with several samples.

Weather variability, tides, and Barmah Forest Virus Disease in the Gladstone region, Australia

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test.

Analysis of cardiac and epileptic signals using higher order spectra

The theory of nonlinear dyamic systems provides some new methods to handle complex systems. Chaos theory offers new concepts, algorithms and methods for processing, enhancing and analyzing the measured signals. In recent years, researchers are applying the concepts from this theory to bio-signal analysis. In this work, the complex dynamics of the bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) are analyzed using the tools of nonlinear systems theory. In the modern industrialized countries every year several hundred thousands of people die due to sudden cardiac death. The Electrocardiogram (ECG) is an important biosignal representing the sum total of millions of cardiac cell depolarization potentials. It contains important insight into the state of health and nature of the disease afflicting the heart. Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computerbased intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and four classes of arrhythmia. This thesis presents some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. Several features were extracted from the HOS and subjected an Analysis of Variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value < 0.02 in the ANOVA test. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, seven features were extracted from the heart rate signals using HOS and fed to a support vector machine (SVM) for classification. The performance evaluation protocol in this thesis uses 330 subjects consisting of five different kinds of cardiac disease conditions. The classifier achieved a sensitivity of 90% and a specificity of 89%. This system is ready to run on larger data sets. In EEG analysis, the search for hidden information for identification of seizures has a long history. Epilepsy is a pathological condition characterized by spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic early detection of the seizure onsets would help the patients and observers to take appropriate precautions. Various methods have been proposed to predict the onset of seizures based on EEG recordings. The use of nonlinear features motivated by the higher order spectra (HOS) has been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, these features are used to train both a Gaussian mixture model (GMM) classifier and a Support Vector Machine (SVM) classifier. Results show that the classifiers were able to achieve 93.11% and 92.67% classification accuracy, respectively, with selected HOS based features. About 2 hours of EEG recordings from 10 patients were used in this study. This thesis introduces unique bispectrum and bicoherence plots for various cardiac conditions and for normal, background and epileptic EEG signals. These plots reveal distinct patterns. The patterns are useful for visual interpretation by those without a deep understanding of spectral analysis such as medical practitioners. It includes original contributions in extracting features from HRV and EEG signals using HOS and entropy, in analyzing the statistical properties of such features on real data and in automated classification using these features with GMM and SVM classifiers.

Increased traffic exposure and negative birth outcomes : a prospective cohort in Australia

Background: Pregnant women exposed to traffic pollution have an increased risk of negative birth outcomes. We aimed to investigate the size of this risk using a prospective cohort of 970 mothers and newborns in Logan, Queensland. ----- ----- Methods: We examined two measures of traffic: distance to nearest road and number of roads around the home. To examine the effect of distance we used the number of roads around the home in radii from 50 to 500 metres. We examined three road types: freeways, highways and main roads.----- ----- Results: There were no associations with distance to road. A greater number of freeways and main roads around the home were associated with a shorter gestation time. There were no negative impacts on birth weight, birth length or head circumference after adjusting for gestation. The negative effects on gestation were largely due to main roads within 400 metres of the home. For every 10 extra main roads within 400 metres of the home, gestation time was reduced by 1.1% (95% CI: -1.7, -0.5; p-value = 0.001).----- ----- Conclusions: Our results add weight to the association between exposure to traffic and reduced gestation time. This effect may be due to the chemical toxins in traffic pollutants, or because of disturbed sleep due to traffic noise.

The pursuit of thinness : an outcome study of anorexia nervosa

INTRODUCTION: Anorexia nervosa (AN) is a growing problem among young female Singaporeans. We studied the demographics and follow-up data of AN patients referred to dietitians for nutritional intervention. METHODS: A retrospective nutritional notes review was done on 94 patients seen from 1992 to 2004. All patients were given nutritional intervention, which included individualised counselling for weight gain, personalised diet plan, correction of poor dietary intake and correction of perception towards healthy eating. We collected data on body mass index (BMI), patient demographics and outcome. RESULTS: 96 percent of the patients were female and 86.2 percent were Chinese. The median BMI at initial consultation was 14.7 kilogramme per square metre (range, 8.6-18.8 kilogramme per square metre). 76 percent were between 13 and 20 years old. 83 percent of the patients came back for follow-up appointments with the dietitians in addition to consultation with the psychiatrist. Overall, there was significant improvement in weight and BMI from an average 37 kg to 41 kg and 14.7 kilogramme per square metre to 16.4 kilogramme per square metre, respectively, between the fi rst and fi nal consultations (p-value is less than 0.001). The average duration of followup was about eight months. Among the patients on follow-up, 68 percent showed improvement with an average weight gain of 6 kg. Patients that improved had more outpatient follow-up sessions with the dietitians (4.2 consultations versus 1.6 consultations; p-value is less than 0.05), lower BMI at presentation (14.2 kilogramme per square metre versus 15.7 kilogramme per square metre; p-value is less than 0.01) and shorter duration of disease at presentation (one year versus three years; p-value is less than 0.05) compared with those who did not improve. Seven patients with the disease for more than two years did not show improvement with follow-up. CONCLUSION: We gained valuable understanding of the AN patients referred to our tertiary hospital for treatment, two-thirds of whom improved with adequate follow-up treatment. Patients that had suffered AN longer before seeking help appeared more resistant to improvement.

Comparing the medium-term effects of exercise or dietary restriction on appetite regulation and compensatory responses

Previous studies have shown that exercise (Ex) interventions create a stronger coupling between energy intake (EI) and energy expenditure (EE) leading to increased homeostasis of the energy-balance (EB) regulatory system compared to a diet intervention where an un-coupling between EI and EE occurs. The benefits of weight loss from Ex and diet interventions greatly depend on compensatory responses. The present study investigated an 8-week medium-term Ex and diet intervention program (Ex intervention comprised of 500kcal EE five days per week over four weeks at 65-75% maximal heart rate, whereas the diet intervention comprised of a 500kcal decrease in EI five days per week over four weeks) and its effects on compensatory responses and appetite regulation among healthy individuals using a between- and within-subjects design. Effects of an acute dietary manipulation on appetite and compensatory behaviours and whether a diet and/or Ex intervention pre-disposes individuals to disturbances in EB homeostasis were tested. Energy intake at an ad libitum lunch test meal after a breakfast high- and low-energy pre-load (the high energy pre-load contained 556kcal and the low energy pre-load contained 239kcal) were measured at the Baseline (Weeks -4 to 0) and Intervention (Weeks 0 to 4) phases in 13 healthy volunteers (three males and ten females; mean age 35 years [sd + 9] and mean BMI 25 kg/m2 [sd + 3.8]) [participants in each group included Ex=7, diet=5 (one female in the diet group dropped out midway), thus, 12 participants completed the study]. At Weeks -4, 0 and 4, visual analogue scales (VAS) were used to assess hunger and satiety and liking and wanting (L&W) for nutrient and taste preferences using a computer-based system (E-Prime v1.1.4). Ad libitum test meal EI was consistently lower after the HE pre-load compared to the LE pre-load. However, this was not consistent during the diet intervention however. A pre-load x group interaction on ad libitum test meal EI revealed that during the intervention phase the Ex group showed an improved sensitivity to detect the energy content between the two pre-loads and improved compensation for the ad libitum test meal whereas the diet group’s ability to differentiate between the two pre-loads decreased and showed poorer compensation (F[1,10]=2.88, p-value not significant). This study supports previous findings of the effect Ex and diet interventions have on appetite and compensatory responses; Ex increases and diet decreases energy balance sensitivity.

The role of insulin in advanced prostate cancer

Advanced prostate cancer is a common and generally incurable disease. Androgen deprivation therapy is used to treat advanced prostate cancer with good benefits to quality of life and regression of disease. Prostate cancer invariably progresses however despite ongoing treatment, to a castrate resistant state. Androgen deprivation is associated with a form of metabolic syndrome, which includes insulin resistance and hyperinsulinaemia. The mitogenic and anti-apoptotic properties of insulin acting through the insulin and hybrid insulin/IGF-1 receptors seem to have positive effects on prostate tumour growth. This pilot study was designed to assess any correlation between elevated insulin levels and progression to castrate resistant prostate cancer. Methods: 36 men receiving ADT for advanced prostate cancer were recruited, at various stages of their treatment, along with 47 controls, men with localised prostate cancer pre-treatment. Serum measurements of C-peptide (used as a surrogate marker for insulin production) were performed and compared between groups. Correlation between serum C-peptide level and time to progression to castrate resistant disease was assessed. Results: There was a significant elevation of C-peptide levels in the ADT group (mean = 1639pmol/L)) compared to the control group (mean = 1169pmol/L), with a p-value of 0.025. In 17 men with good initial response to androgen deprivation, a small negative trend towards earlier progression to castrate resistance with increasing C-peptide level was seen in the ADT group (r = -0.050), however this did not reach statistical significance (p>0.1). Conclusions: This pilot study confirms an increase in serum C-peptide levels in men receiving ADT for advance prostate cancer. A non-significant, but negative trend towards earlier progression to castrate resistance with increasing C-peptide suggests the need for a formal prospective study assessing this hypothesis.

The effect of implementing a modified early warning scoring (MEWS) system on the adequacy of vital sign documentation

Introduction and objectives Early recognition of deteriorating patients results in better patient outcomes. Modified early warning scores (MEWS) attempt to identify deteriorating patients early so timely interventions can occur thus reducing serious adverse events. We compared frequencies of vital sign recording 24 h post-ICU discharge and 24 h preceding unplanned ICU admission before and after a new observation chart using MEWS and an associated educational programme was implemented into an Australian Tertiary referral hospital in Brisbane. Design Prospective before-and-after intervention study, using a convenience sample of ICU patients who have been discharged to the hospital wards, and in patients with an unplanned ICU admission, during November 2009 (before implementation; n = 69) and February 2010 (after implementation; n = 70). Main outcome measures Any change in a full set or individual vital sign frequency before-and-after the new MEWS observation chart and associated education programme was implemented. A full set of vital signs included Blood pressure (BP), heart rate (HR), temperature (T°), oxygen saturation (SaO2) respiratory rate (RR) and urine output (UO). Results After the MEWS observation chart implementation, we identified a statistically significant increase (210%) in overall frequency of full vital sign set documentation during the first 24 h post-ICU discharge (95% CI 148, 288%, p value <0.001). Frequency of all individual vital sign recordings increased after the MEWS observation chart was implemented. In particular, T° recordings increased by 26% (95% CI 8, 46%, p value = 0.003). An increased frequency of full vital sign set recordings for unplanned ICU admissions were found (44%, 95% CI 2, 102%, p value = 0.035). The only statistically significant improvement in individual vital sign recordings was urine output, demonstrating a 27% increase (95% CI 3, 57%, p value = 0.029). Conclusions The implementation of a new MEWS observation chart plus a supporting educational programme was associated with statistically significant increases in frequency of combined and individual vital sign set recordings during the first 24 h post-ICU discharge. There were no significant changes to frequency of individual vital sign recordings in unplanned admissions to ICU after the MEWS observation chart was implemented, except for urine output. Overall increases in the frequency of full vital sign sets were seen.

Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach

Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription factors (TFs) and the target genes (TGs) they regulate. Transcriptional regulatory networks have proven to be invaluable scientific tools in Bioinformatics. When used in conjunction with comparative genomics, they have provided substantial insights into the evolution of regulatory interactions. Current approaches to regulatory network inference, however, omit two additional key entities: promoters and transcription factor binding sites (TFBSs). In this study, we attempted to explore the relationships among these regulatory components in bacteria. Our primary goal was to identify relationships that can assist in reducing the high false positive rates associated with transcription factor binding site predictions and thereupon enhance the reliability of the inferred transcription regulatory networks. In our preliminary exploration of relationships between the key regulatory components in Escherichia coli transcription, we discovered a number of potentially useful features. The combination of location score and sequence dissimilarity scores increased de novo binding site prediction accuracy by 13.6%. Another important observation made was with regards to the relationship between transcription factors grouped by their regulatory role and corresponding promoter strength. Our study of E.coli ��70 promoters, found support at the 0.1 significance level for our hypothesis | that weak promoters are preferentially associated with activator binding sites to enhance gene expression, whilst strong promoters have more repressor binding sites to repress or inhibit gene transcription. Although the observations were specific to �70, they nevertheless strongly encourage additional investigations when more experimentally confirmed data are available. In our preliminary exploration of relationships between the key regulatory components in E.coli transcription, we discovered a number of potentially useful features { some of which proved successful in reducing the number of false positives when applied to re-evaluate binding site predictions. Of chief interest was the relationship observed between promoter strength and TFs with respect to their regulatory role. Based on the common assumption, where promoter homology positively correlates with transcription rate, we hypothesised that weak promoters would have more transcription factors that enhance gene expression, whilst strong promoters would have more repressor binding sites. The t-tests assessed for E.coli �70 promoters returned a p-value of 0.072, which at 0.1 significance level suggested support for our (alternative) hypothesis; albeit this trend may only be present for promoters where corresponding TFBSs are either all repressors or all activators. Nevertheless, such suggestive results strongly encourage additional investigations when more experimentally confirmed data will become available. Much of the remainder of the thesis concerns a machine learning study of binding site prediction, using the SVM and kernel methods, principally the spectrum kernel. Spectrum kernels have been successfully applied in previous studies of protein classification [91, 92], as well as the related problem of promoter predictions [59], and we have here successfully applied the technique to refining TFBS predictions. The advantages provided by the SVM classifier were best seen in `moderately'-conserved transcription factor binding sites as represented by our E.coli CRP case study. Inclusion of additional position feature attributes further increased accuracy by 9.1% but more notable was the considerable decrease in false positive rate from 0.8 to 0.5 while retaining 0.9 sensitivity. Improved prediction of transcription factor binding sites is in turn extremely valuable in improving inference of regulatory relationships, a problem notoriously prone to false positive predictions. Here, the number of false regulatory interactions inferred using the conventional two-component model was substantially reduced when we integrated de novo transcription factor binding site predictions as an additional criterion for acceptance in a case study of inference in the Fur regulon. This initial work was extended to a comparative study of the iron regulatory system across 20 Yersinia strains. This work revealed interesting, strain-specific difierences, especially between pathogenic and non-pathogenic strains. Such difierences were made clear through interactive visualisations using the TRNDifi software developed as part of this work, and would have remained undetected using conventional methods. This approach led to the nomination of the Yfe iron-uptake system as a candidate for further wet-lab experimentation due to its potential active functionality in non-pathogens and its known participation in full virulence of the bubonic plague strain. Building on this work, we introduced novel structures we have labelled as `regulatory trees', inspired by the phylogenetic tree concept. Instead of using gene or protein sequence similarity, the regulatory trees were constructed based on the number of similar regulatory interactions. While the common phylogentic trees convey information regarding changes in gene repertoire, which we might regard being analogous to `hardware', the regulatory tree informs us of the changes in regulatory circuitry, in some respects analogous to `software'. In this context, we explored the `pan-regulatory network' for the Fur system, the entire set of regulatory interactions found for the Fur transcription factor across a group of genomes. In the pan-regulatory network, emphasis is placed on how the regulatory network for each target genome is inferred from multiple sources instead of a single source, as is the common approach. The benefit of using multiple reference networks, is a more comprehensive survey of the relationships, and increased confidence in the regulatory interactions predicted. In the present study, we distinguish between relationships found across the full set of genomes as the `core-regulatory-set', and interactions found only in a subset of genomes explored as the `sub-regulatory-set'. We found nine Fur target gene clusters present across the four genomes studied, this core set potentially identifying basic regulatory processes essential for survival. Species level difierences are seen at the sub-regulatory-set level; for example the known virulence factors, YbtA and PchR were found in Y.pestis and P.aerguinosa respectively, but were not present in both E.coli and B.subtilis. Such factors and the iron-uptake systems they regulate, are ideal candidates for wet-lab investigation to determine whether or not they are pathogenic specific. In this study, we employed a broad range of approaches to address our goals and assessed these methods using the Fur regulon as our initial case study. We identified a set of promising feature attributes; demonstrated their success in increasing transcription factor binding site prediction specificity while retaining sensitivity, and showed the importance of binding site predictions in enhancing the reliability of regulatory interaction inferences. Most importantly, these outcomes led to the introduction of a range of visualisations and techniques, which are applicable across the entire bacterial spectrum and can be utilised in studies beyond the understanding of transcriptional regulatory networks.

Risk modelling in quality clinical registries : Monitoring lesion treatment failure rate in percutaneous coronary interventions

Aims: This paper describes the development of a risk adjustment (RA) model predictive of individual lesion treatment failure in percutaneous coronary interventions (PCI) for use in a quality monitoring and improvement program. Methods and results: Prospectively collected data for 3972 consecutive revascularisation procedures (5601 lesions) performed between January 2003 and September 2011 were studied. Data on procedures to September 2009 (n = 3100) were used to identify factors predictive of lesion treatment failure. Factors identified included lesion risk class (p < 0.001), occlusion type (p < 0.001), patient age (p = 0.001), vessel system (p < 0.04), vessel diameter (p < 0.001), unstable angina (p = 0.003) and presence of major cardiac risk factors (p = 0.01). A Bayesian RA model was built using these factors with predictive performance of the model tested on the remaining procedures (area under the receiver operating curve: 0.765, Hosmer–Lemeshow p value: 0.11). Cumulative sum, exponentially weighted moving average and funnel plots were constructed using the RA model and subjectively evaluated. Conclusion: A RA model was developed and applied to SPC monitoring for lesion failure in a PCI database. If linked to appropriate quality improvement governance response protocols, SPC using this RA tool might improve quality control and risk management by identifying variation in performance based on a comparison of observed and expected outcomes.

Transcriptome analysis of neutrophils after endurance exercise reveals novel signaling mechanisms in the immune response to physiological stress

Neutrophils serve as an intriguing model for the study of innate immune cellular activity induced by physiological stress. We measured changes in the transcriptome of circulating neutrophils following an experimental exercise trial (EXTRI) consisting of 1 h of intense cycling immediately followed by 1 h of intense running. Blood samples were taken at baseline, 3 h, 48 h, and 96 h post-EXTRI from eight healthy, endurance-trained, male subjects. RNA was extracted from isolated neutrophils. Differential gene expression was evaluated using Illumina microarrays and validated with quantitative PCR. Gene set enrichment analysis identified enriched molecular signatures chosen from the Molecular Signatures Database. Blood concentrations of muscle damage indexes, neutrophils, interleukin (IL)-6 and IL-10 were increased (P < 0.05) 3 h post-EXTRI. Upregulated groups of functionally related genes 3 h post-EXTRI included gene sets associated with the recognition of tissue damage, the IL-1 receptor, and Toll-like receptor (TLR) pathways (familywise error rate, P value < 0.05). The core enrichment for these pathways included TLRs, low-affinity immunoglobulin receptors, S100 calcium binding protein A12, and negative regulators of innate immunity, e.g., IL-1 receptor antagonist, and IL-1 receptor associated kinase-3. Plasma myoglobin changes correlated with neutrophil TLR4 gene expression (r = 0.74; P < 0.05). Neutrophils had returned to their nonactivated state 48 h post-EXTRI, indicating that their initial proinflammatory response was transient and rapidly counterregulated. This study provides novel insight into the signaling mechanisms underlying the neutrophil responses to endurance exercise, suggesting that their transcriptional activity was particularly induced by damage-associated molecule patterns, hypothetically originating from the leakage of muscle components into the circulation.

The role of intensity of interval training on fat oxidation and eating behaviour in overweight/obese men

The increasing prevalence of obesity in society has been associated with a number of atherogenic risk factors such as insulin resistance. Aerobic training is often recommended as a strategy to induce weight loss, with a greater impact of high-intensity levels on cardiovascular function and insulin sensitivity, and a greater impact of moderate-intensity levels on fat oxidation. Anaerobic high-intensity (supramaximal) interval training has been advocated to improve cardiovascular function, insulin sensitivity and fat oxidation. However, obese individuals tend to have a lower tolerance of high-intensity exercise due to discomfort. Furthermore, some obese individuals may compensate for the increased energy expenditure by eating more and/or becoming less active. Recently, both moderate- and high-intensity aerobic interval training have been advocated as alternative approaches. However, it is still uncertain as to which approach is more effective in terms of increasing fat oxidation given the issues with levels of fitness and motivation, and compensatory behaviours. Accordingly, the objectives of this thesis were to compare the influence of moderate- and high-intensity interval training on fat oxidation and eating behaviour in overweight/obese men. Two exercise interventions were undertaken by 10-12 overweight/obese men to compare their responses to study variables, including fat oxidation and eating behaviour during moderate- and high-intensity interval training (MIIT and HIIT). The acute training intervention was a methodological study designed to examine the validity of using exercise intensity from the graded exercise test (GXT) - which measured the intensity that elicits maximal fat oxidation (FATmax) - to prescribe interval training during 30-min MIIT. The 30-min MIIT session involved 5-min repetitions of workloads 20% below and 20% above the FATmax. The acute intervention was extended to involve HIIT in a cross-over design to compare the influence of MIIT and HIIT on eating behaviour using subjective appetite sensation and food preference through the liking and wanting test. The HIIT consisted of 15-sec interval training at 85 %VO2peak interspersed by 15-sec unloaded recovery, with a total mechanical work equal to MIIT. The medium term training intervention was a cross-over 4-week (12 sessions) MIIT and HIIT exercise training with a 6-week detraining washout period. The MIIT sessions consisted of 5-min cycling stages at ±20% of mechanical work at 45 %VO2peak, and the HIIT sessions consisted of repetitive 30-sec work at 90 %VO2peak and 30-sec interval rests, during identical exercise sessions of between 30 and 45 mins. Assessments included a constant-load test (45 %VO2peak for 45 mins) followed by 60-min recovery at baseline and the end of 4-week training, to determine fat oxidation rate. Participants’ responses to exercise were measured using blood lactate (BLa), heart rate (HR) and rating of perceived exertion (RPE) and were measured during the constant-load test and in the first intervention training session of every week during training. Eating behaviour responses were assessed by measuring subjective appetite sensations, liking and wanting and ad libitum energy intake. Results of the acute intervention showed that FATmax is a valid method to estimate VO2 and BLa, but is not valid to estimate HR and RPE in the MIIT session. While the average rate of fat oxidation during 30-min MIIT was comparable with the rate of fat oxidation at FATmax (0.16 ±0.09 and 0.14 ±0.08 g/min, respectively), fat oxidation was significantly higher at minute 25 of MIIT (P≤0.01). In addition, there was no significant difference between MIIT and HIIT in the rate of appetite sensations after exercise, but there was a tendency towards a lower rate of hunger after HIIT. Different intensities of interval exercise also did not affect explicit liking or implicit wanting. Results of the medium-term intervention indicated that current interval training levels did not affect body composition, fasting insulin and fasting glucose. Maximal aerobic capacity significantly increased (P≤0.01) (2.8 and 7.0% after MIIT and HIIT respectively) during GXT, and fat oxidation significantly increased (P≤0.01) (96 and 43% after MIIT and HIIT respectively) during the acute constant-load exercise test. RPE significantly decreased after HIIT greater than MIIT (P≤0.05), and the decrease in BLa was greater during the constant-load test after HIIT than MIIT, but this difference did not reach statistical significance (P=0.09). In addition, following constant-load exercise, exercise-induced hunger and desire to eat decreased after HIIT greater than MIIT but were not significant (p value for desire to eat was 0.07). Exercise-induced liking of high-fat sweet (HFSW) and high-fat non-sweet (HFNS) foods increased after MIIT and decreased after HIIT (p value for HFNS was 0.09). The intervention explained 12.4% of the change in fat intake (p = 0.07). This research is significant in that it confirmed two points in the acute study. While the rate of fat oxidation increased during MIIT, the average rate of fat oxidation during 30-min MIIT was comparable with the rate of fat oxidation at FATmax. In addition, manipulating the intensity of acute interval exercise did not affect appetite sensations and liking and wanting. In the medium-term intervention, constant-load exercise-induced fat oxidation significantly increased after interval training, independent of exercise intensity. In addition, desire to eat, explicit liking for HFNS and fat intake collectively confirmed that MIIT is accompanied by a greater compensation of eating behaviour than HIIT. Findings from this research will assist in developing exercise strategies to provide obese men with various training options. In addition, the finding that overweight/obese men expressed a lower RPE and decreased BLa after HIIT compared with MIIT is contrary to the view that obese individuals may not tolerate high-intensity interval training. Therefore, high-intensity interval training can be advocated among the obese adult male population. Future studies may extend this work by using a longer-term intervention.

An X chromosome association scan of the Norfolk Island genetic isolate provides evidence for a novel migraine susceptibility locus at Xq12

Migraine is a common and debilitating neurovascular disorder with a complex envirogenomic aetiology. Numerous studies have demonstrated a preponderance of women affected with migraine and previous pedigree linkage studies in our laboratory have identified susceptibility loci on chromosome Xq24-Xq28. In this study we have used the genetic isolate of Norfolk Island to further analyse the X chromosome for migraine susceptibility loci. An association approach was employed to analyse 14,124 SNPs spanning the entire X chromosome. Genotype data from 288 individuals comprising a large core-pedigree, of which 76 were affected with migraine, were analysed. Although no SNP reached chromosome-wide significance (empirical α = 1×10−5) ranking by P-value revealed two primary clusters of SNPs in the top 25. A 10 SNP cluster represents a novel migraine susceptibility locus at Xq12 whilst a 11 SNP cluster represents a previously identified migraine susceptibility locus at Xq27. The strongest association at Xq12 was seen for rs599958 (OR = 1.75, P = 8.92×10−4), whilst at Xq27 the strongest association was for rs6525667 (OR = 1.53, P = 1.65×10−4). Further analysis of SNPs at these loci was performed in 5,122 migraineurs from the Women’s Genome Health Study and provided additional evidence for association at the novel Xq12 locus (P<0.05). Overall, this study provides evidence for a novel migraine susceptibility locus on Xq12. The strongest effect SNP (rs102834, joint P = 1.63×10−5) is located within the 5′UTR of the HEPH gene, which is involved in iron homeostasis in the brain and may represent a novel pathway for involvement in migraine pathogenesis.