Nuala M. Byrne

Alterations of Left Ventricular Myocardial Characteristics Associated With Obesity

Background—Obesity is associated with heart failure, but an effect of weight, independent of comorbidities, on cardiac structure and function is not well established. We sought whether body mass index (BMI) and insulin levels were associated with subclinical myocardial disturbances. Methods and Results—Transthoracic echocardiography, myocardial Doppler-derived systolic (sm) and early diastolic velocity (em), strain and strain rate imaging and tissue characterization with cyclic variation (CVIB), and calibrated integrated backscatter (cIB) were obtained in 109 overweight or obese subjects and 33 referents (BMI <25 kg/m2). BMI correlated with left ventricular (LV) mass and wall thickness (P<0.001). Severely obese subjects (BMI >35) had reduced LV systolic and diastolic function and increased myocardial reflectivity compared with referents, evidenced by lower average long-axis strain, sm, cIB, lower CVIB, and reduced em, whereas LV ejection fraction remained normal. Differences in regional or global strain, sm, and em were identified between the severely obese (BMI >35) and the referent patients (P<0.001). Similar but lesser degrees of reduced function by sm, em, and basal septal strain and increased reflectivity by cIB were present in overweight (BMI, 25 to 29.9) and mildly obese (BMI, 30 to 35) groups (P<0.05). Although tissue Doppler measures were not associated with duration of obesity, they did correlate with fasting insulin levels and reduced exercise capacity. BMI was independently related to average LV strain (&bgr;=0.40, P=0.02), sm (&bgr;=−0.36, P=0.002), and em (&bgr;=−0.41, P<0.001). Conclusions—Overweight subjects without overt heart disease have subclinical changes of LV structure and function even after adjustment for mean arterial pressure, age, gender, and LV mass.

Quantification of lean bodyweight

AbstractBackground: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens. Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW. Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA — a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18–82 years), bodyweight (40.7–216.5kg) and BMI values (17.1–69.9 kg/m2). Patients in population B had BMI values of 18.7–38.4 kg/m2. A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B. Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r2 = 0.78, mean error [ME] = 2.30 × 10−3, root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r2 = 0.93, ME = −0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r2 = 0.85, ME = −0.04, RMSE = 4.39 [approximately 7% of mean]). Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

Musculoskeletal disorders associated with obesity: a biomechanical perspective

Despite the multifactorial nature of musculoskeletal disease, obesity consistently emerges as a key and potentially modifiable risk factor in the onset and progression of musculoskeletal conditions of the hip, knee, ankle, foot and shoulder. To date, the majority of research has focused on the impact of obesity on bone and joint disorders, such as the risk of fracture and osteoarthritis. However, emerging evidence indicates that obesity may also have a profound effect on soft‐tissue structures, such as tendon, fascia and cartilage. Although the mechanism remains unclear, the functional and structural limitations imposed by the additional loading of the locomotor system in obesity have been almost universally accepted to produce aberrant mechanics during locomotor tasks, thereby unduly raising stress within connective‐tissue structures and the potential for musculoskeletal injury. While such mechanical theories abound, there is surprisingly little scientific evidence directly linking musculoskeletal injury to altered biomechanics in the obese. For the most part, even the biomechanical effects of obesity on the locomotor system remain unknown. Given the global increase in obesity and the rapid rise in musculoskeletal disorders, there is a need to determine the physical consequences of continued repetitive loading of major structures of the locomotor system in the obese and to establish how obesity may interact with other factors to potentially increase the risk of musculoskeletal disease.

Metabolic and behavioral compensatory responses to exercise interventions: barriers to weight loss.

An activity‐induced increase in energy expenditure theoretically disturbs energy balance (EB) by creating an acute energy deficit. Compensatory responses could influence the weight loss associated with the energy deficit. Individual variability in compensation for perturbations in EB could partly explain why some individuals fail to lose weight with exercise. It is accepted that the regulatory system will readily defend impositions that promote a negative EB. Therefore, a criticism of exercise interventions is that they will be ineffective and futile methods of weight control because the acute energy deficit is counteracted. Compensation for exercise‐induced energy deficits can be categorized into behavioral or metabolic responses and automatic or volitional. An automatic compensatory response is a biological inevitability and considered to be obligatory. An automatic compensatory response is typically a metabolic consequence (e.g., reduced resting metabolic rate) of a negative EB. In contrast, a volitional compensatory response tends to be deliberate and behavioral, which the individual intentionally performs (e.g., increased snack intake). The purpose of this review is to highlight the various metabolic and behavioral compensatory responses that could reduce the effectiveness of exercise and explain why some individuals experience a lower than expected weight loss. We propose that the extent and degree of compensation will vary between individuals. That is, some individuals will be predisposed to compensatory responses that render them resistant to the weight loss benefits theoretically associated with an exercise‐induced increase in energy expenditure. Therefore, given the inter‐individual variability in behavioral and metabolic compensatory responses, exercise prescriptions might be more effective if tailored to suit individuals.

The biomechanics of adiposity – structural and functional limitations of obesity and implications for movement

Obesity is a significant health problem and the incidence of the condition is increasing at an alarming rate worldwide. Despite significant advances in the knowledge and understanding of the multifactorial nature of the condition, many questions regarding the specific consequences of the disease remain unanswered. For example, there is a dearth of information pertaining to the structural and functional limitations imposed by overweight and obesity. A limited number of studies to date have considered plantar pressures under the feet of obese vs. non‐obese, the influence of foot structure on performance, gait characteristics of obese children and adults, and relationships between obesity and osteoarthritis. A better appreciation of the implications of increased levels of body weight and/or body fat on movement capabilities of the obese would provide an enhanced opportunity to offer more meaningful support in the prevention, treatment and management of the condition.

The biomechanics of restricted movement in adult obesity

In spite of significant advances in the knowledge and understanding of the multi‐factorial nature of obesity, many questions regarding the specific consequences of the disease remain unanswered. In particular, there is a relative dearth of information pertaining to the functional limitations imposed by overweight and obesity. The limited number of studies to date have mainly focused on the effect of obesity on the temporospatial characteristics of walking, plantar foot pressures, muscular strength and, to a lesser extent, postural balance. Collectively, these studies have implied that the functional limitations imposed by the additional loading of the locomotor system in obesity result in aberrant mechanics and the potential for musculoskeletal injury. Despite the greater prevalence of musculoskeletal disorders in the obese, there has been surprisingly little empirical investigation pertaining to the biomechanics of activities of daily living or into the mechanical and neuromuscular factors that may predispose the obese to injury. A better appreciation of the implications of increased levels of body adiposity on the movement capabilities of the obese would afford a greater opportunity to provide meaningful support in preventing, treating and managing the condition and its sequelae. Moreover, there is an urgent need to establish the physical consequences of continued repetitive loading of major structures of the body, particularly of the lower limbs in the obese, during the diverse range of activities of daily living.

Assessment of Physical Activity and Energy Expenditure: An Overview of Objective Measures

The ability to assess energy expenditure (EE) and estimate physical activity (PA) in free-living individuals is extremely important in the global context of non-communicable diseases including malnutrition, overnutrition (obesity), and diabetes. It is also important to appreciate that PA and EE are different constructs with PA defined as any bodily movement that results in EE and accordingly, energy is expended as a result of PA. However, total energy expenditure, best assessed using the criterion doubly labeled water (DLW) technique, includes components in addition to physical activity energy expenditure, namely resting energy expenditure and the thermic effect of food. Given the large number of assessment techniques currently used to estimate PA in humans, it is imperative to understand the relative merits of each. The goal of this review is to provide information on the utility and limitations of a range of objective measures of PA and their relationship with EE. The measures discussed include those based on EE or oxygen uptake including DLW, activity energy expenditure, physical activity level, and metabolic equivalent; those based on heart rate monitoring and motion sensors; and because of their widespread use, selected subjective measures.

Prevalence and trends of the diabetes epidemic in South Asia: a systematic review and meta-analysis

BackgroundDiabetes mellitus has reached epidemic proportions worldwide. South Asians are known to have an increased predisposition for diabetes which has become an important health concern in the region. We discuss the prevalence of pre-diabetes and diabetes in South Asia and explore the differential risk factors reported.MethodsPrevalence data were obtained by searching the Medline® database with; ‘prediabetes’ and ‘diabetes mellitus’ (MeSH major topic) and ‘Epidemology/EP’ (MeSH subheading). Search limits were articles in English, between 01/01/1980–31/12/2011, on human adults (≥19 years). The conjunction of the above results was narrowed down with country names.ResultsThe most recent reported prevalence of pre-diabetes:diabetes in regional countries were; Bangladesh–4.7%:8.5% (2004–2005;Rural), India–4.6%:12.5% (2007;Rural); Maldives–3.0%:3.7% (2004;National), Nepal–19.5%:9.5% (2007;Urban), Pakistan–3.0%:7.2% (2002;Rural), Sri Lanka–11.5%:10.3% (2005–2006;National). Urban populations demonstrated a higher prevalence of diabetes. An increasing trend in prevalence of diabetes was observed in urban/rural India and rural Sri Lanka. The diabetes epidemicity index decreased with the increasing prevalence of diabetes in respective countries. A high epidemicity index was seen in Sri Lanka (2005/2006–52.8%), while for other countries, the epidemicity index was comparatively low (rural India 2007–26.9%; urban India 2002/2005–31.3%, and urban Bangladesh–33.1%). Family history, urban residency, age, higher BMI, sedentary lifestyle, hypertension and waist-hip ratio were associated with an increased risks of diabetes.ConclusionA significant epidemic of diabetes is present in the South Asian region with a rapid increase in prevalence over the last two decades. Hence there is a need for urgent preventive and curative strategies .

Resistance Training Conserves Fat-free Mass and Resting Energy Expenditure Following Weight Loss

Objective: To determine what effect diet‐induced ∼12 kg weight loss in combination with exercise training has on body composition and resting energy expenditure (REE) in premenopausal African‐American (AA) and European‐American (EA) women.

The arch index: A measure of flat or fat feet?

Background: Studies using footprint-based estimates of arch height have indicated that obesity results in a lowered medial longitudinal arch in children. However, the potentially confounding effect of body composition on indirect measures of arch height, such as the arch index, has not been investigated. Methods: This study assessed the body composition of 12 male and 12 female adults (mean age: 39.9 ± 8.1 years, height: 1.724 ± 0.101 m; weight: 95.1 ± 13.7 kg, and BMI: 31.9 ± 3.0kg/m 2 ) using bioelectrical impedance analysis to produce a two-component model of fat mass (FM) and fat-free mass (FFM). The dynamic arch index also was determined from electronic footprints captured during gait using a capacitive pressure distribution platform with a resolution of 4 sensors/cm2. Results: While significant correlations were noted between FFM and the area of both the hindfoot (r = .75, p <.05) and forefoot (r = .72, p <.05), the midfoot area was correlated only with FM (r = .54, p <.05). Similarly, the arch index was significantly correlated with the FM percentage (r = .67, p <.05). Conclusions: The findings of this pilot study suggest that body composition influences arch index values in overweight and obese subjects. Consequently, body composition may be a confounding factor in interpreting footprint based estimates of arch height and, as such, these estimates would best be used with supplementary measures of body composition.