The effects of exercise-induced weight loss on appetite-related peptides and motivation to eat

Context: The magnitude of exercise-induced weight loss depends on the extent of compensatory responses. An increase in energy intake is likely to result from changes in the appetite control system toward an orexigenic environment; however, few studies have measured how exercise impacts on both orexigenic and anorexigenic peptides. ---------- Objective: The aim of the study was to investigate the effects of medium-term exercise on fasting/postprandial levels of appetite-related hormones and subjective appetite sensations in overweight/obese individuals. ---------- Design and Setting: We conducted a longitudinal study in a university research center. ---------- Participants and Intervention: Twenty-two sedentary overweight/obese individuals (age, 36.9 ± 8.3 yr; body mass index, 31.3 ± 3.3 kg/m2) took part in a 12-wk supervised exercise programme (five times per week, 75% maximal heart rate) and were requested not to change their food intake during the study. ---------- Main Outcome Measures: We measured changes in body weight and fasting/postprandial plasma levels of glucose, insulin, total ghrelin, acylated ghrelin (AG), peptide YY, and glucagon-like peptide-1 and feelings of appetite. ---------- Results: Exercise resulted in a significant reduction in body weight and fasting insulin and an increase in AG plasma levels and fasting hunger sensations. A significant reduction in postprandial insulin plasma levels and a tendency toward an increase in the delayed release of glucagon-like peptide-1 (90–180 min) were also observed after exercise, as well as a significant increase (127%) in the suppression of AG postprandially. ---------- Conclusions: Exercise-induced weight loss is associated with physiological and biopsychological changes toward an increased drive to eat in the fasting state. However, this seems to be balanced by an improved satiety response to a meal and improved sensitivity of the appetite control system.

The effect of graded levels of exercise on energy intake and balance in free-living men, consuming their normal diet

Objective: To assess the effect of graded increases in exercised-induced energy expenditure (EE) on appetite, energy intake (EI), total daily EE and body weight in men living in their normal environment and consuming their usual diets. Design: Within-subject, repeated measures design. Six men (mean (s.d.) age 31.0 (5.0) y; weight 75.1 (15.96) kg; height 1.79 (0.10) m; body mass index (BMI) 23.3(2.4) kg/m2), were each studied three times during a 9 day protocol, corresponding to prescriptions of no exercise, (control) (Nex; 0 MJ/day), medium exercise level (Mex; ~1.6 MJ/day) and high exercise level (Hex; ~3.2 MJ/day). On days 1-2 subjects were given a medium fat (MF) maintenance diet (1.6 ´ resting metabolic rate (RMR)). Measurements: On days 3-9 subjects self-recorded dietary intake using a food diary and self-weighed intake. EE was assessed by continual heart rate monitoring, using the modified FLEX method. Subjects' HR (heart rate) was individually calibrated against submaximal VO2 during incremental exercise tests at the beginning and end of each 9 day study period. Respiratory exchange was measured by indirect calorimetry. Subjects completed hourly hunger ratings during waking hours to record subjective sensations of hunger and appetite. Body weight was measured daily. Results: EE amounted to 11.7, 12.9 and 16.8 MJ/day (F(2,10)=48.26; P<0.001 (s.e.d=0.55)) on the Nex, Mex and Hex treatments, respectively. The corresponding values for EI were 11.6, 11.8 and 11.8 MJ/day (F(2,10)=0.10; P=0.910 (s.e.d.=0.10)), respectively. There were no treatment effects on hunger, appetite or body weight, but there was evidence of weight loss on the Hex treatment. Conclusion: Increasing EE did not lead to compensation of EI over 7 days. However, total daily EE tended to decrease over time on the two exercise treatments. Lean men appear able to tolerate a considerable negative energy balance, induced by exercise, over 7 days without invoking compensatory increases in EI.

Differences in post-prandial responses to fat and carbohydrate loads in habitual high and low fat consumers (phenotypes).

The present study investigated metabolic responses to fat and carbohydrate ingestion in lean male individuals consuming an habitual diet high or low in fat. Twelve high-fat phenotypes (HF) and twelve low-fat phenotypes (LF) participated in the study. Energy intake and macronutrient intake variables were assessed using a food frequency questionnaire. Resting (RMR) and postprandial metabolic rate and substrate oxidation (respiratory quotient; RQ) were measured by indirect calorimetry. HF had a significantly higher RMR and higher resting heart rate than LF. These variables remained higher in HF following the macronutrient challenge. In all subjects the carbohydrate load increased metabolic rate and heart rate significantly more than the fat load. Fat oxidation (indicated by a low RQ) was significantly higher in HF than in LF following the fat load; the ability to oxidise a high carbohydrate load did not differ between the groups. Lean male subjects consuming a diet high in fat were associated with increased energy expenditure at rest and a relatively higher fat oxidation in response to a high fat load; these observations may be partly responsible for maintaining energy balance on a high-fat (high-energy) diet. In contrast, a low consumer of fat is associated with relatively lower energy expenditure at rest and lower fat oxidation, which has implications for weight gain if high-fat foods or meals are periodically introduced to the diet.

Role of low affinity beta1-adrenergic receptors in normal and diseased hearts

ROLE OF LOW AFFINITY β1-ADRENERGIC RECEPTOR IN NORMAL AND DISEASED HEARTS Background: The β1-adrenergic receptor (AR) has at least two binding sites, 1HAR and 1LAR (high and low affinity site of the 1AR respectively) which cause cardiostimulation. Some β-blockers, for example (-)-pindolol and (-)-CGP 12177 can activate β1LAR at higher concentrations than those required to block β1HAR. While β1HAR can be blocked by all clinically used β-blockers, β1LAR is relatively resistant to blockade. Thus, chronic β1LAR activation may occur in the setting of β-blocker therapy, thereby mediating persistent βAR signaling. Thus, it is important to determine the potential significance of β1LAR in vivo, particularly in disease settings. Method and result: C57Bl/6 male mice were used. Chronic (4 weeks) β1LAR activation was achieved by treatment with (-)-CGP12177 via osmotic minipump. Cardiac function was assessed by echocardiography and catheterization. (-)-CGP12177 treatment in healthy mice increased heart rate and left ventricular (LV) contractility without detectable LV remodelling or hypertrophy. In mice subjected to an 8-week period of aorta banding, (-)-CGP12177 treatment given during 4-8 weeks led to a positive inotropic effect. (-)-CGP12177 treatment exacerbated LV remodelling indicated by a worsening of LV hypertrophy by ??% (estimated by weight, wall thickness, cardiomyocyte size) and interstitial/perivascular fibrosis (by histology). Importantly, (-)-CGP12177 treatment to aorta banded mice exacerbated cardiac expression of hypertrophic, fibrogenic and inflammatory genes (all p<0.05 vs. non-treated control with aorta banding).. Conclusion: β1LAR activation provides functional support to the heart, in both normal and diseased (pressure overload) settings. Sustained β1LAR activation in the diseased heart exacerbates LV remodelling and therefore may promote disease progression from compensatory hypertrophy to heart failure. Word count: 270

Exercise-induced energy expenditure : implications for exercise prescription and obesity

Objective: Walking is commonly recommended to help with weight management. We measured total energy expenditure (TEE) and its components to quantify the impact of increasing exercise-induced energy expenditure (ExEE) on other components of TEE. Methods: Thirteen obese women underwent an 8-week walking group intervention. TEE was quantified using doubly labeled water, ExEE was quantified using heart rate monitors, daily movement was assessed by accelerometry and resting metabolic rate was measured using indirect calorimetry. Results: Four of the 13 participants achieved the target of 1500 kcal wk−1 of ExEE and all achieved 1000 kcal wk−1. The average ExEE achieved by the group across the 8 weeks was 1434 ± 237 kcal wk−1. Vigorous physical activity, as assessed by accelerometry, increased during the intervention by an average of 30 min per day. Non-exercise activity thermogenesis (NEAT) decreased, on average, by 175 kcal d−1 (−22%) from baseline to the intervention and baseline fitness was correlated with change in NEAT. Conclusions: Potential alterations in non-exercise activity should be considered when exercise is prescribed. The provision of appropriate education on how to self-monitor daily activity levels may improve intervention outcomes in groups who are new to exercise. Practice implications: Strategies to sustain incidental and light physical activity should be offered to help empower individuals as they develop and maintain healthy and long-lasting lifestyle habits.

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.

A compact mock circulation loop for the in vitro testing of cardiovascular devices

In vitro cardiovascular device performance evaluation in a mock circulation loop (MCL) is a necessary step prior to in vivo testing.A MCL that accurately represents the physiology of the cardiovascular system accelerates the assessment of the device’s ability to treat pathological conditions. To serve this purpose, a compact MCL measuring 600 ¥ 600 ¥ 600 mm (L ¥ W¥ H) was constructed in conjunction with a computer mathematical simulation.This approach allowed the effective selection of physical loop characteristics, such as pneumatic drive parameters, to create pressure and flow, and pipe dimensions to replicate the resistance, compliance, and fluid inertia of the native cardiovascular system. The resulting five-element MCL reproduced the physiological hemodynamics of a healthy and failing heart by altering ventricle contractility, vascular resistance/compliance, heart rate, and vascular volume. The effects of interpatient anatomical variability, such as septal defects and valvular disease, were also assessed. Cardiovascular hemodynamic pressures (arterial, venous, atrial, ventricular), flows (systemic, bronchial, pulmonary), and volumes (ventricular, stroke) were analyzed in real time. The objective of this study is to describe the developmental stages of the compact MCL and demonstrate its value as a research tool for the accelerated development of cardiovascular devices.

The low-affinity site of the β1-adrenoceptor and its relevance to cardiovascular pharmacology

β-Adrenoceptor blocking agents (β-blockers) that at low concentrations antagonize cardiostimulant effects of catecholamines, but at high concentrations also cause cardiostimulation, have been appearing since the late 1960s. These cardiostimulant β-blockers, coined non-conventional partial agonists, antagonize the effects of catecholamines through a high-affinity site (β1HAR), but cause cardiostimulation mainly through a low-affinity site (β1LAR) of the myocardial β1-adrenoceptor. The experimental non-conventional partial agonist (−)-CGP12177 increases cardiac L-type Ca2+ current density and Ca2+ transients, shortens action potential duration but augments action potential plateau, increases heart rate and force, as well as causes arrhythmic Ca2+ transients and arrhythmic cardiocyte contractions. Other β-blockers, which do not cause cardiostimulation, consistently have lower affinity for β1LAR than β1HAR. These sites were verified and the cardiac pharmacology of non-conventional partial agonists confirmed on recombinant β1-adrenoceptors and on β1-adrenoceptors overexpressed into the heart. A targeted mutation of Asp138 to Glu138 virtually abolished the pharmacology of β1HAR but left intact the pharmacology of β1LAR. Non-conventional partial agonists may be beneficial for the treatment of peripheral autonomic neuropathy but probably due to their arrhythmic propensities, may be harmful for the treatment of chronic heart failure.

Application of higher order statistics/spectra in biomedical signals : a review

For many decades correlation and power spectrum have been primary tools for digital signal processing applications in the biomedical area. The information contained in the power spectrum is essentially that of the autocorrelation sequence; which is sufficient for complete statistical descriptions of Gaussian signals of known means. However, there are practical situations where one needs to look beyond autocorrelation of a signal to extract information regarding deviation from Gaussianity and the presence of phase relations. Higher order spectra, also known as polyspectra, are spectral representations of higher order statistics, i.e. moments and cumulants of third order and beyond. HOS (higher order statistics or higher order spectra) can detect deviations from linearity, stationarity or Gaussianity in the signal. Most of the biomedical signals are non-linear, non-stationary and non-Gaussian in nature and therefore it can be more advantageous to analyze them with HOS compared to the use of second order correlations and power spectra. In this paper we have discussed the application of HOS for different bio-signals. HOS methods of analysis are explained using a typical heart rate variability (HRV) signal and applications to other signals are reviewed.

Real-time evaluation of driver’s alertness on highways

Suburbanisation has been internationally a major phenomenon in the last decades. Suburb-to-suburb routes are nowadays the most widespread road journeys; and this resulted in an increment of distances travelled, particularly on faster suburban highways. The design of highways tends to over-simplify the driving task and this can result in decreased alertness. Driving behaviour is consequently impaired and drivers are then more likely to be involved in road crashes. This is particularly dangerous on highways where the speed limit is high. While effective countermeasures to this decrement in alertness do not currently exist, the development of in-vehicle sensors opens avenues for monitoring driving behaviour in real-time. The aim of this study is to evaluate in real-time the level of alertness of the driver through surrogate measures that can be collected from in-vehicle sensors. Slow EEG activity is used as a reference to evaluate driver's alertness. Data are collected in a driving simulator instrumented with an eye tracking system, a heart rate monitor and an electrodermal activity device (N=25 participants). Four different types of highways (driving scenario of 40 minutes each) are implemented through the variation of the road design (amount of curves and hills) and the roadside environment (amount of buildings and traffic). We show with Neural Networks that reduced alertness can be detected in real-time with an accuracy of 92% using lane positioning, steering wheel movement, head rotation, blink frequency, heart rate variability and skin conductance level. Such results show that it is possible to assess driver's alertness with surrogate measures. Such methodology could be used to warn drivers of their alertness level through the development of an in-vehicle device monitoring in real-time drivers' behaviour on highways, and therefore it could result in improved road safety.

Driving performance impairments due to hypovigilance on monotonous roads

Drivers' ability to react to unpredictable events deteriorates when exposed to highly predictable and uneventful driving tasks. Highway design reduces the driving task mainly to a lane-keeping manoeuvre. Such a task is monotonous, providing little stimulation and this contributes to crashes due to inattention. Research has shown that driver's hypovigilance can be assessed with EEG measurements and that driving performance is impaired during prolonged monotonous driving tasks. This paper aims to show that two dimensions of monotony - namely road design and road side variability - decrease vigilance and impair driving performance. This is the first study correlating hypovigilance and driver performance in varied monotonous conditions, particularly on a short time scale (a few seconds). We induced vigilance decrement as assessed with an EEG during a monotonous driving simulator experiment. Road monotony was varied through both road design and road side variability. The driver's decrease in vigilance occurred due to both road design and road scenery monotony and almost independently of the driver's sensation seeking level. Such impairment was also correlated to observable measurements from the driver, the car and the environment. During periods of hypovigilance, the driving performance impairment affected lane positioning, time to lane crossing, blink frequency, heart rate variability and non-specific electrodermal response rates. This work lays the foundation for the development of an in-vehicle device preventing hypovigilance crashes on monotonous roads.

Cardiac health diagnosis using higher order spectra and support vector machine

The Electrocardiogram (ECG) is an important bio-signal 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. The HRV signal can be used as a base signal to observe the heart's functioning. These signals are non-linear and non-stationary in nature. So, higher order spectral (HOS) analysis, which is more suitable for non-linear systems and is robust to noise, was used. An automated intelligent system for the identification of cardiac health is very useful in healthcare technology. In this work, we have extracted seven features from the heart rate signals using HOS and fed them to a support vector machine (SVM) for classification. Our performance evaluation protocol uses 330 subjects consisting of five different kinds of cardiac disease conditions. We demonstrate a sensitivity of 90% for the classifier with a specificity of 87.93%. Our system is ready to run on larger data sets.

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.