The Association of Body Mass Index and Body Composition with Pain, Disease Activity, Fatigue, Sleep and Anxiety in Women with Fibromyalgia.
Plain language summary
Fibromyalgia is a long-term condition causing symptoms such as widespread pain, fatigue, trouble sleeping and problems with memory and concentration. The purpose of this study was to examine the relationships between body mass index (BMI), body composition and fibromyalgia symptoms. 73 women with fibromyalgia and 73 healthy controls, matched on weight, were included in this cross-sectional study. Women with a higher BMI had more severe symptoms of fibromyalgia. Fat mass and visceral fat were associated with poorer quality sleep. The study’s authors concluded that promoting an ideal BMI may help to reduce some of the symptoms for women with fibromyalgia.
undefined: The link between fibromyalgia syndrome (FMS) and obesity has not been thoroughly investigated. The purpose of this study was to examine the relationships among body mass index (BMI) and body composition parameters, including fat mass, fat mass percentage, and visceral fat, as well as FMS features, such as tender point count (TPC), pain, disease activity, fatigue, sleep quality, and anxiety, in a population of FMS women and healthy controls. A total of seventy-three women with FMS and seventy-three healthy controls, matched on weight, were included in this cross-sectional study. We used a body composition analyzer to measure fat mass, fat mass percentage, and visceral fat. Tender point count (TPC) was measured by algometry pressure. The disease severity was measured with the Fibromyalgia Impact Questionnaire (FIQ-R) and self-reported global pain was evaluated with the visual analog scale (VAS). To measure the quality of sleep, fatigue, and anxiety we used the Pittsburgh Sleep Quality Questionnaire (PSQI), the Spanish version of the multidimensional fatigue inventory (MFI), and the Beck Anxiety Inventory (BAI), respectively. Of the women in this study, 38.4% and 31.5% were overweight and obese, respectively. Significant differences in FIQ-R.1 (16.82 ± 6.86 vs. 20.66 ± 4.71, = 0.030), FIQ-R.3 (35.20 ± 89.02 vs. 40.33 ± 5.60, = 0.033), and FIQ-R total score (63.87 ± 19.12 vs. 75.94 ± 12.25, = 0.017) among normal-weight and overweight FMS were observed. Linear analysis regression revealed significant associations between FIQ-R.2 (β(95% CI)= 0.336, (0.027, 0.645), = 0.034), FIQ-R.3 (β(95% CI)= 0.235, (0.017, 0.453), = 0.035), and FIQ-R total score (β(95% CI)= 0.110, (0.010, 0.209), = 0.032) and BMI in FMS women after adjusting for age and menopause status. Associations between sleep latency and fat mass percentage in FMS women (β(95% CI)= 1.910, (0.078, 3.742), = 0.041) and sleep quality and visceral fat in healthy women (β(95% CI)= 2.614, (2.192, 3.036), = 0.008) adjusted for covariates were also reported. The higher BMI values are associated with poor FIQ-R scores and overweight and obese women with FMS have higher symptom severity. The promotion of an optimal BMI might contribute to ameliorate some of the FMS symptoms.
Is waist-to-height ratio the best predictive indicator of hypertension incidence? A cohort study.
BMC public health. 2018;18(1):281
Plain language summary
A variety of methods of measuring body fat are used as tools to predict the risk of developing certain lifestyle-related diseases such as high blood pressure. It is not yet clear which of these methods is the most accurate. The aim of this study was to evaluate and compare the effectiveness of using different measures of body fat to predict high blood pressure. The study was performed in Brazil. Adult volunteers with normal blood pressure were assessed for body fat using waist-to-height ratio (WHtR), body mass index (BMI) and waist circumference (WC), and then followed-up 13 years later to find out whether they had developed high blood pressure. 44% of the participants developed high blood pressure during the study period. BMI, WC and WHtR were all associated with the risk of high blood pressure and had similar accuracy in predicting the disease. However, the associations were only significant for women. The cut-off points for predicting high blood pressure agreed with current recommendations, except for the WC in men. The results suggest that both overall obesity (BMI) and central obesity (WC and WHtR) indicators can be used in this population to evaluate the risk of developing high blood pressure.
BACKGROUND The best anthropometric indicator to verify the association between obesity and hypertension (HTN) has not been established. We conducted this study to evaluate and compare the discriminatory power of waist-to-height ratio (WHtR) in relation to body mass index (BMI) and waist circumference (WC) in predicting HTN after 13 years of follow-up. METHODS This study was an observational prospective cohort study performed in the city of Firminópolis, in Brazilian's midwest. The cohort baseline (phase 1) was initiated in 2002 with the evaluation of a representative sample of the normotensive population (≥ 18 years of age). The incidence of HTN was evaluated as the outcome (phase 2). Sociodemographic, dietary and lifestyle variables were used to adjust proportional hazards models and evaluate risk of HTN according to anthropometric indices. The areas under the receiver operating characteristic (ROC) curves were used to compare the predictive capacity of these indices. The best HTN predictor cut-offs were obtained based on sensitivity and specificity. RESULTS A total of 471 patients with a mean age of 38.9 ± 12.3 years were included in phase 1. The mean follow-up was 13.2 years, and 207 subjects developed HTN. BMI, WC and WHtR were associated with risk of HTN incidence and had similar power in predicting the disease. However, the associations were only significant for women. The cut-off points with a better HTN predictive capacity were in agreement with current recommendations, except for the WC in men. CONCLUSIONS The results suggest that both overall obesity (BMI) and central obesity (WC and WHtR) anthropometric indicators can be used in this population to evaluate the risk of developing hypertension.
Fasting glucose and body mass index as predictors of activity in breast cancer patients treated with everolimus-exemestane: The EverExt study.
Scientific reports. 2017;7(1):10597
Plain language summary
Literature shows that hyperglycaemia is amongst the most common grade 3 or 4 drug-related adverse events in breast cancer patients. The aim of the study was to investigate the role of anthropometrics and biomarkers of glucose metabolism on treatment outcomes in advanced breast cancer patients. The study is an observational study that recruited 102 postmenopausal women, aged at least 18 years, who were diagnosed with advanced breast cancer and treated with everolimus and exemestane. Results indicate a significant trend towards increasing fasting glucose and decreasing BMI in the overall study population. Furthermore, significant evidence also shows that lower levels of fasting glucose is associated with better outcomes. Authors conclude that their study showed a predictive role of fasting glucose and BMI on treatment outcomes, longer progression free survival and clinical benefit rates (percentage of patients with shrinking tumours or stable disease for at least 6 months).
Evidence on everolimus in breast cancer has placed hyperglycemia among the most common high grade adverse events. Anthropometrics and biomarkers of glucose metabolism were investigated in a observational study of 102 postmenopausal, HR + HER2- metastatic breast cancer patients treated with everolimus-exemestane in first and subsequent lines. Best overall response (BR) and clinical benefit rate (CBR) were assessed across subgroups defined upon fasting glucose (FG) and body mass index (BMI). Survival was estimated by Kaplan-Meier method and log-rank test. Survival predictors were tested in Cox models. Median follow up was 12.4 months (1.0-41.0). The overall cohort showed increasing levels of FG and decreasing BMI (p < 0.001). Lower FG fasting glucose at BR was more commonly associated with C/PR or SD compared with PD (p < 0.001). We also observed a somewhat higher BMI associated with better response (p = 0.052). More patients in the lowest FG category achieved clinical benefit compared to the highest (p < 0.001), while no relevant differences emerged for BMI. Fasting glucose at re-assessment was also predictive of PFS (p = 0.037), as confirmed in models including BMI and line of therapy (p = 0.049). Treatment discontinuation was significantly associated with changes in FG (p = 0.014). Further research is warranted to corroborate these findings and clarify the underlying mechanisms.