Determinant factors to discriminate the body weight loss in overweight people

Introduction. Most studies have described how the weight loss is when different treatments are compared (1-3), while others have also compared the weight loss by sex (4), or have taken into account psychosocial (5) and lifestyle (6, 7) variables. However, no studies have examined the interaction of different variables and the importance of them in the weight loss. Objective. Create a model to discriminate the range of weight loss, determining the importance of each variable. Methods. 89 overweight people (BMI: 25-29.9 kg?m-2), aged from 18 to 50 years, participated in the study. Four types of treatments were randomly assigned: strength training (S), endurance training (E), strength and endurance training (SE), and control group (C). All participants followed a 25% calorie restriction diet. Two multivariate discriminant models including the variables age, sex, height, daily energy expenditure (EE), type of treatment (T), caloric restriction (CR), initial body weight (BW), initial fat mass (FM), initial muscle mass (MM) and initial bone mineral density (BMD) were performed having into account two groups: the first and fourth quartile of the % of weight loss in the first model; the groups above and below the mean of the % of weight loss in the second model. The discriminant models were built using the inclusion method in SPSS allowing us to find a function that could predict the body weight loss range that an overweight person could achieve in a 6 months weight loss intervention.Results. The first discriminant analysis predicted that a combination of the studied variables would discriminate between the two ranges of body weight loss with 81.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.475, p=0.003): Discriminant score=-18.266-(0.060xage)- (1.282xsex[0=female;1=male])+(14.701xheight)+(0.002xEE)- (0.006xT[1=S;2=E;3=SE;4=C])-(0.047xCR)- (0.558xBW)+(0.475xFM)+(0.398xMM)+(3.499xBMD) The second discriminant model obtained would discriminate between the two groups of body weight loss with 74.4% of correct classification. The discriminant function obtained was (Wilks? Lambda=0.725, p=0.005): Discriminant score=-5.021-(0.052xage)- (0.543xsex[0=female;1=male])+(3.530xheight)+(0.001xEE)- (0.493xT[1=S;2=E;3=SE;4=C])+(0.003xCR)- (0.365xBW)+(0.368xFM)+(0.296xMM)+(4.034xBMD) Conclusion. The first developed model could predict the percentage of weight loss in the following way: if the discriminant score is close to 1.051, the range of weight loss will be from 7.44 to -4.64% and if it is close to - 1.003, the range will be from -11.03 to -25,00% of the initial body weight. With the second model if the discriminant score is close to 0.623 the body weight loss will be above -7.93% and if it is close to -0.595 will be below - 7.93% of the initial body weight. References. 1. Brochu M, et al. Resistance training does not contribute to improving the metabolic profile after a 6-month weight loss program in overweight and obese postmenopausal women. J Clin Endocrinol Metab. 2009 Sep;94(9):3226-33. 2. Del Corral P, et al. Effect of dietary adherence with or without exercise on weight loss: a mechanistic approach to a global problem. J Clin Endocrinol Metab. 2009 May;94(5):1602-7. 3. Larson-Meyer DE, et al. Caloric Restriction with or without Exercise: The Fitness vs. Fatness Debate. Med Sci Sports Exerc. 2010;42(1):152-9. 4. Hagan RD, et al. The effects of aerobic conditioning and/or caloric restriction in overweight men and women. Medicine & Science in Sports & Exercise. 1986;18(1):87-94. 5. Teixeira PJ, et al. Mediators of weight loss and weight loss maintenance in middle-aged women. Obesity (Silver Spring). 2010 Apr;18(4):725-35. 6. Bautista-Castano I, et al. Variables predictive of adherence to diet and physical activity recommendations in the treatment of obesity and overweight, in a group of Spanish subjects. Int J Obes Relat Metab Disord. 2004 May;28(5):697-705.

Management factors affecting mortality, feed intake, and feed conversion ratio of grow-finishing pigs

The aim of this study was to determine the effect of animal management and farm facilities on total feed intake (TFI), feed conversion ratio (FCR) and mortality rate (MORT) of grower-finishing pigs. In total, 310 batches from 244 grower-finishing farms, consisting of 454 855 Pietrain sired pigs in six Spanish pig companies were used. Data collection consisted of a survey on management practices (season of placement, split-sex by pens, number of pig origins, water source in the farm, initial or final BW) and facilities (floor, feeder, ventilation or number of animals placed) during 2008 and 2009. Results indicated that batches of pigs placed between January and March had higher TFI (P=0.006), FCR (P=0.005) and MORT (P=0.03) than those placed between July and September. Moreover, batches of pigs placed between April and June had lower MORT (P=0.003) than those placed between January and March. Batches which had split-sex pens had lower TFI (P=0.001) and better FCR (P<0.001) than those with mixed-sex in pens; pigs fed with a single-space feeder with incorporated drinker also had the lowest TFI (P<0.001) and best FCR (P<0.001) in comparison to single and multi-space feeders without a drinker. Pigs placed in pens with <50% slatted floors presented an improvement in FCR (P<0.05) than pens with 50% or more slatted floors. Batches filled with pigs from multiple origins had higher MORT (P<0.001) than those from a single origin. Pigs housed in barns that performed manual ventilation control presented higher MORT (P<0.001) in comparison to automatic ventilation. The regression analysis also indicated that pigs which entered to grower-finisher facilities with higher initial BW had lower MORT (P<0.05) and finally pigs which were sent to slaughterhouse with a higher final BW presented higher TFI (P<0.001). The variables selected for each dependent variable explained 61.9%, 24.8% and 20.4% of the total variability for TFI, FCR and MORT, respectively. This study indicates that farms can increase growth performance and reduce mortality by improving farm facilities and/or modifying management practices.