OBJECTIVES: To assess the effect of zinc supplementation in the treatment of thalassaemia and sickle cell disease.
SEARCH METHODS: We searched the Cochrane Cystic Fibrosis and Genetic Disorders Group's Haemoglobinopathies Trials Register comprising references identified from comprehensive electronic database searches and handsearches of relevant journals and abstract books of conference proceedings.Date of most recent search: 01 February 2013.
SELECTION CRITERIA: Randomised, placebo-controlled trials of zinc supplements for treating thalassaemia or sickle cell disease administered at least once a week for at least a month.
DATA COLLECTION AND ANALYSIS: Two review authors assessed the eligibility and risk of bias of the included trials, extracted and analysed data and wrote the review. We summarised results using risk ratios or rate ratios for dichotomous data and mean differences for continuous data. We combined trial results where appropriate.
MAIN RESULTS: We identified nine trials for inclusion with all nine contributing outcome data. Two trials reported on people with thalassaemia (n = 152) and seven on sickle cell anaemia (n = 307).In people with thalassaemia, in one trial, the serum zinc level value showed no difference between the zinc supplemented group and the control group, mean difference 47.40 (95% confidence interval -12.95 to 107.99). Regarding anthropometry, in one trial, height velocity was significantly increased in patients who received zinc supplementation for one to seven years duration, mean difference 3.37 (95% confidence interval 2.36 to 4.38) (total number of participants = 26). In one trial, however, there was no difference in body mass index between treatment groups.Zinc acetate supplementation for three months (in one trial) and one year (in two trials) (total number of participants = 71) was noted to increase the serum zinc level significantly in patients with sickle cell anaemia, mean difference 14.90 (95% confidence interval 6.94 to 22.86) and 20.25 (95% confidence interval 11.73 to 28.77) respectively. There was no significant difference in haemoglobin level between intervention and control groups, at either three months (one trial) or one year (one trial), mean difference 0.06 (95% confidence interval -0.84 to 0.96) and mean difference -0.07 (95% confidence interval -1.40 to 1.26) respectively. Regarding anthropometry, one trial showed no significant changes in body mass index or weight after one year of zinc acetate supplementation. In patients with sickle cell disease, the total number of sickle cell crises at one year were significantly decreased in the zinc sulphate supplemented group as compared to controls, mean difference -2.83 (95% confidence interval -3.51 to -2.15) (total participants 130), but not in zinc acetate group, mean difference 1.54 (95% confidence interval -2.01 to 5.09) (total participants 22). In one trial at three months and another at one year, the total number of clinical infections were significantly decreased in the zinc supplemented group as compared to controls, mean difference 0.05 (95% confidence interval 0.01 - 0.43) (total number of participants = 36), and mean difference -7.64 (95% confidence interval -10.89 to -4.39) (total number of participants = 21) respectively.
AUTHORS' CONCLUSIONS: According to the results, there is no evidence from randomised controlled trials to indicate any benefit of zinc supplementation with regards to serum zinc level in patients with thalassaemia. However, height velocity was noted to increase among those who received this intervention.There is mixed evidence on the benefit of using zinc supplementation in people with sickle cell disease. For instance, there is evidence that zinc supplementation for one year increased the serum zinc levels in patients with sickle cell disease. However, though serum zinc level was raised in patients receiving zinc supplementation, haemoglobin level and anthropometry measurements were not significantly different between groups. Evidence of benefit is seen with the reduction in the number of sickle cell crises among sickle cell patients who received one year of zinc sulphate supplementation and with the reduction in the total number of clinical infections among sickle cell patients who received zinc supplementation for both three months and for one year.The conclusion is based on the data from a small group of trials,which were generally of good quality, with a low risk of bias. The authors recommend that more trials on zinc supplementation in thalassaemia and sickle cell disease be conducted given that the literature has shown the benefits of zinc in these types of diseases.
METHODS: A total of 2322 Malaysian older adults aged 60 years and older were recruited using multistage random sampling in a population-based cross-sectional study. Out of 2322 older adults recruited, 2309 (48% men) completed assessments on cognitive function and body composition. Cognitive functions were assessed using the Malay version of the Mini-Mental State Examination, the Bahasa Malaysia version of Montreal Cognitive Assessment, Digit Span Test, Digit Symbol Test and Rey Auditory Verbal Learning Test. Body composition included body mass index, mid-upper arm circumference, waist circumference, calf circumference, waist-to-hip ratio, percentage body fat and skeletal muscle mass.
RESULTS: The association between body composition and cognitive functions was analyzed using multiple linear regression. After adjustment for age, education years, hypertension, hypercholesterolemia, diabetes mellitus, depression, smoking status and alcohol consumption, we found that calf circumference appeared as a significant predictor for all cognitive tests among both men and women (P mass and lower adipose tissue among older adults for optimal cognitive function. Geriatr Gerontol Int 2017; 17: 554-560.
METHODS: This study examined self-monitoring data from 61 Chinese adults who participated in a 5-week online group intervention for weight loss. Participants reported their baseline Body Mass Index (BMI), weight loss motivation, and engaged in both daily quantitative self-monitoring (e.g., caloric intake, mood, sedentary behavior, etc.) and qualitative self-monitoring (e.g., daily log that summarizes the progress of weight loss). The timeliness of participants' daily self-monitoring data filling was assessed using a scoring rule. One-way repeated measurement ANOVA was employed to analyze the dynamics of each self-monitoring indicator. Correlation and regression analyses were used to reveal the relationship between baseline data, self-monitoring indicators, and weight change. Content analysis was utilized to analyze participants' qualitative self-monitoring data. Participants were categorized into three groups based on their weight loss outcomes, and a chi-square test was used to compare the frequency distribution between these groups.
RESULTS: After the intervention, participants achieved an average weight loss of 2.52 kg (SD = 1.36) and 3.99% (SD = 1.96%) of their initial weight. Daily caloric intake, weight loss satisfaction, frequency of daily log, and the speed of weight loss showed a downward trend, but daily sedentary time gradually increased. Moreover, regression analysis showed that baseline BMI, weight loss motivation, and timeliness of daily filling predicted final weight loss. Qualitative self-monitoring data analysis revealed four categories and nineteen subcategories. A significant difference in the frequency of qualitative data was observed, with the excellent group reporting a greater number of daily logs than expected in all categories and most subcategories, and the moderate and poor groups reporting less than expected in all categories and most subcategories.
CONCLUSION: The self-monitoring data in short-term online group intervention exhibited fluctuations. Participants with higher baseline BMI, higher levels of weight loss motivation, and timely self-monitoring achieved more weight loss. Participants who achieved greater weight loss reported a higher quantity of qualitative self-monitoring data. Practitioners should focus on enhancing dieters' weight loss motivation and promote adherence to self-monitoring practices.
MATERIAL AND METHODS: RCTs comparing the weight loss outcomes following LVSG and LRYGB in adult population between January 2000 and November 2015 were selected from PubMed, Medline, Embase, Science Citation Index, Current Contents, and the Cochrane database. The review was prepared in accordance with Preferred Reporting of Systematic Reviews and Meta-Analyses (PRISMA).
RESULTS: Nine unique RCTs described over 10 publications involving a total of 865 patients (LVSG, n=437; LRYGB, n=428) were analyzed. Postoperative follow-up ranged from 3 months to 5 years. Twelve-month excess weight loss (EWL) for LVSG ranged from 69.7% to 83%, and for LRYGB, ranged from 60.5% to 86.4%. A number of studies reported slow weight gain between the second and third years of postoperative follow-up ranging from 1.4% to 4.2%EWL. This trend was seen to continue to 5 years postoperatively (8% to 10%EWL) for both procedures.
CONCLUSIONS: In conclusion, LRYGB and LVSG are comparable with regards to the weight loss outcomes in the short term, with LRYGB achieving slightly greater weight loss. Slow weight recidivism is observed after the first postoperative year following both procedures. Long-term reporting of outcomes obtained from well-designed studies using intention-to-treat analyses are identified as a major gap in the literature at present.
Methods: Analyses were performed on 243 women (mean body mass index 31.27 ± 4.14 kg/m2) who completed a 12-month lifestyle intervention in low socioeconomic communities in Klang Valley, Malaysia. Analysis of covariance (ANCOVA) was used to compare changes of cardiometabolic risk factors across weight change categories (2% gain, ±2% maintain, >2 to <5% loss, and 5 to 20% loss) within intervention and control group.
Results: A graded association for changes in waist circumference, fasting insulin, and total cholesterol (p=0.002, for all variables) across the weight change categories were observed within the intervention group at six months postintervention. Participants who lost 5 to 20% of weight had the greatest improvements in those risk markers (-5.67 cm CI: -7.98 to -3.36, -4.27 μU/mL CI: -7.35, -1.19, and -0.59 mmol/L CI: -.99, -0.19, respectively) compared to those who did not. Those who lost >2% to <5% weight reduced more waist circumference (-4.24 cm CI: -5.44 to -3.04) and fasting insulin (-0.36 μU/mL CI: -1.95 to 1.24) than those who maintained or gained weight. No significant association was detected in changes of risk markers across the weight change categories within the control group except for waist circumference and adiponectin.
Conclusion: Weight loss of >2 to <5% obtained through lifestyle intervention may represent a reasonable initial weight loss target for women in the low socioeconomic community as it led to improvements in selected risk markers, particularly of diabetes risk.