Adiposity measured in mid- or late-life and estimated using anthropometric measures such as body mass index (BMI) and waist-to-hip ratio (WHR), or metabolic markers such as blood leptin and adiponectin levels, is associated with late-onset dementia risk. However, during later life, this association may reverse and aging- and dementia-related processes may differentially affect adiposity measures.
We explored associations of concurrent BMI, WHR, and blood leptin and high molecular weight adiponectin levels with dementia occurrence.
924 Swedish community-dwelling elderly without dementia, aged 70 years and older, systematically-sampled by birth day and birth year population-based in the Gothenburg city region of Sweden. The Gothenburg Birth Cohort Studies are designed for evaluating risk and protective factors for dementia. All dementias diagnosed after age 70 for 10 years were identified. Multivariable logistic regression models were used to predict dementia occurrence between 2000-2005, 2005-2010, and 2000-2010 after excluding prevalent baseline (year 2000) dementias. Baseline levels of BMI, WHR, leptin, and adiponectin were used.
Differences in subcutaneous abdominal adipose tissue (SAT) fat cell size and number (cellularity) are linked to insulin resistance. Men are generally more insulin resistant than women but it is unknown whether there is a gender dimorphism in SAT cellularity. The objective was to determine SAT cellularity and its relationship to insulin sensitivity in men and women.
In a cohort study performed at an outpatient academic clinic in Sweden, 798 women and 306 men were included. Estimated SAT mass (ESAT) was derived from measures of dual-energy X-ray absorptiometry and a formula. SAT biopsies were obtained to measure mean fat cell size; SAT adipocyte number was obtained by dividing ESAT with mean fat cell weight. Fat cell size was also compared with level of insulin sensitivity in vivo.
Over the entire range of body mass index (BMI) both fat cell size and number correlated positively with ESAT in either sex. On average, fat cell size was larger in men than in women, which was driven by significantly larger fat cells in non-obese men compared with non-obese women; no gender effect on fat cell size was seen in obese subjects. For all subjects fat cell number was larger in women than men, which was driven by a gender effect among non-obese individuals (P
To explore associations between diet-related greenhouse gas emissions (GHGE), nutrient intakes and adherence to the Nordic Nutrition Recommendations among Swedish adults.
Diet was assessed by 4d food records in the Swedish National Dietary Survey. GHGE was estimated by linking all foods to carbon dioxide equivalents, using data from life cycle assessment studies. Participants were categorized into quartiles of energy-adjusted GHGE and differences between GHGE groups regarding nutrient intakes and adherence to nutrient recommendations were explored.
Women (n 840) and men (n 627) aged 18-80 years.
Differences in nutrient intakes and adherence to nutrient recommendations between GHGE groups were generally small. The dietary intake of participants with the lowest emissions was more in line with recommendations regarding protein, carbohydrates, dietary fibre and vitamin D, but further from recommendations regarding added sugar, compared with the highest GHGE group. The overall adherence to recommendations was found to be better among participants with lower emissions compared with higher emissions. Among women, 27 % in the lowest GHGE group adhered to at least twenty-three recommendations compared with only 12 % in the highest emission group. For men, the corresponding figures were 17 and 10 %, respectively.
The study compared nutrient intakes as well as adherence to dietary recommendations for diets with different levels of GHGE from a national dietary survey. We found that participants with low-emission diets, despite higher intake of added sugar, adhered to a larger number of dietary recommendations than those with high emissions.
Studies indicate that the healthy Nordic diet may improve heart health, but its relation to weight change is less clear. We studied the association between the adherence to the healthy Nordic diet and long-term changes in weight, BMI and waist circumference. Furthermore, the agreement between self-reported and measured body anthropometrics was examined. The population-based DIetary, Lifestyle and Genetic Determinants of Obesity and Metabolic syndrome Study in 2007 included 5024 Finns aged 25-75 years. The follow-up was conducted in 2014 (n 3735). One-third of the participants were invited to a health examination. The rest were sent measuring tape and written instructions along with questionnaires. The Baltic Sea Diet Score (BSDS) was used to measure adherence to the healthy Nordic diet. Association of the baseline BSDS and changes in BSDS during the follow-up with changes in body anthropometrics were examined using linear regression analysis. The agreement between self-reported and nurse-measured anthropometrics was determined with Bland-Altman analysis. Intra-class correlation coefficients between self-reported and nurse-measured anthropometrics exceeded 0·95. The baseline BSDS associated with lower weight (ß=-0·056, P=0·043) and BMI (ß=-0·021, P=0·031) over the follow-up. This association was especially evident among those who had increased their BSDS. In conclusion, both high initial and improved adherence to the healthy Nordic diet may promote long-term weight maintenance. The self-reported/measured anthropometrics were shown to have high agreement with nurse-measured values which adds the credibility of our results.
Alliance for Research in Exercise, Nutrition and Activity (ARENA), School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide, SA, 5001, Australia. firstname.lastname@example.org.
Daily activity data are by nature compositional data. Accordingly, they occupy a specific geometry with unique properties that is different to standard Euclidean geometry. This study aimed to estimate the difference in adiposity associated with isotemporal reallocation between daily activity behaviours, and to compare the findings from compositional isotemporal subsitution to those obtained from traditional isotemporal substitution.
We estimated the differences in adiposity (body fat%) associated with reallocating fixed durations of time (isotemporal substitution) between accelerometer-measured daily activity behaviours (sleep, sedentary time and light and moderate-to-vigorous physical activity (MVPA)) among 1728 children aged 9-11 years from Australia, Canada, Finland and the UK (International Study of Childhood Obesity, Lifestyle and the Environment, 2011-2013). We generated estimates from compositional isotemporal substitution models and traditional non-compositional isotemporal substitution models.
Both compositional and traditional models estimated a positive (unfavourable) difference in body fat% when time was reallocated from MVPA to any other behaviour. Unlike traditional models, compositional models found the differences in estimated adiposity (1) were not necessarily symmetrical when an activity was being displaced, or displacing another (2) were not linearly related to the durations of time reallocated, and (3) varied depending on the starting composition.
The compositional isotemporal model caters for the constrained and therefore relative nature of activity behaviour data and enables all daily behaviours to be included in a single statistical model. The traditional model treats data as real variables, thus the constrained nature of time is not accounted for, nor reflected in the findings. Findings from compositional isotemporal substitution support the importance of MVPA to children's health, and suggest that while interventions to increase MVPA may be of benefit, attention should be directed towards strategies to avoid decline in MVPA levels, particularly among already inactive children. Future applications of the compositional model can extend from pair-wise reallocations to other configurations of time-reallocation, for example, increasing MVPA at the expense of multiple other behaviours.
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Fully automated assessment of mammographic density (MD), a biomarker of breast cancer risk, is being increasingly performed in screening settings. However, data on body mass index (BMI), a confounder of the MD-risk association, are not routinely collected at screening. We investigated whether the amount of fat in the breast, as captured by the amount of mammographic non-dense tissue seen on the mammographic image, can be used as a proxy for BMI when data on the latter are unavailable.
Data from a UK case control study (numbers of cases/controls: 414/685) and a Norwegian cohort study (numbers of cases/non-cases: 657/61059), both with volumetric MD measurements (dense volume (DV), non-dense volume (NDV) and percent density (%MD)) from screening-age women, were analysed. BMI (self-reported) and NDV were taken as measures of adiposity. Correlations between BMI and NDV, %MD and DV were examined after log-transformation and adjustment for age, menopausal status and parity. Logistic regression models were fitted to the UK study, and Cox regression models to the Norwegian study, to assess associations between MD and breast cancer risk, expressed as odds/hazard ratios per adjusted standard deviation (OPERA). Adjustments were first made for standard risk factors except BMI (minimally adjusted models) and then also for BMI or NDV. OPERA pooled relative risks (RRs) were estimated by fixed-effect models, and between-study heterogeneity was assessed by the I2 statistics.
BMI was positively correlated with NDV (adjusted r = 0.74 in the UK study and r = 0.72 in the Norwegian study) and with DV (r = 0.33 and r = 0.25, respectively). Both %MD and DV were positively associated with breast cancer risk in minimally adjusted models (pooled OPERA RR (95% confidence interval): 1.34 (1.25, 1.43) and 1.46 (1.36, 1.56), respectively; I2 = 0%, P >0.48 for both). Further adjustment for BMI or NDV strengthened the %MD-risk association (1.51 (1.41, 1.61); I2 = 0%, P = 0.33 and 1.51 (1.41, 1.61); I2 = 0%, P = 0.32, respectively). Adjusting for BMI or NDV marginally affected the magnitude of the DV-risk association (1.44 (1.34, 1.54); I2 = 0%, P = 0.87 and 1.49 (1.40, 1.60); I2 = 0%, P = 0.36, respectively).
When volumetric MD-breast cancer risk associations are investigated, NDV can be used as a measure of adiposity when BMI data are unavailable.
Fat and fat-free masses and fat distribution are related to cardiometabolic risk.
to explore how birth weight, childhood body mass index (BMI) and BMI gain were related to adolescent body composition and central obesity.
In a population-based longitudinal study, body composition was measured by dual-energy X-ray absorptiometry in 907 Norwegian adolescents (48% girls). Associations between birth weight, BMI categories, and BMI gain were evaluated by fitting linear mixed models and conditional growth models with fat mass index (FMI, kg/m2 ), fat-free mass index (FFMI, kg/m2 ) standard deviation scores (SDS), and central obesity at 15 to 20 years, as well as change in FMI SDS and FFMI SDS between ages 15 to 17 and 18 to 20 as outcomes.
Birth weight was associated with FFMI in adolescence. Greater BMI gain in childhood, conditioned on prior body size, was associated with higher FMI, FFMI, and central overweight/obesity with the strongest associations seen at age 6 to 16.5 years: FMI SDS: ß = 0.67, 95% CI (0.63-0.71), FFMI SDS: 0.46 (0.39, 0.52), in girls, FMI SDS: 0.80 (0.75, 0.86), FFMI SDS: 0.49 (0.43, 0.55), in boys.
Compared with birth and early childhood, high BMI and greater BMI gain at later ages are strong predictors of higher fat mass and central overweight/obesity at 15 to 20 years of age.
Sarcopenic muscular degeneration has been consistently identified as an independent risk factor for mortality in aging populations. Recent investigations have realized the quantitative potential of computed tomography (CT) image analysis to describe skeletal muscle volume and composition; however, the optimum approach to assessing these data remains debated. Current literature reports average Hounsfield unit (HU) values and/or segmented soft tissue cross-sectional areas to investigate muscle quality. However, standardized methods for CT analyses and their utility as a comorbidity index remain undefined, and no existing studies compare these methods to the assessment of entire radiodensitometric distributions. The primary aim of this study was to present a comparison of nonlinear trimodal regression analysis (NTRA) parameters of entire radiodensitometric muscle distributions against extant CT metrics and their correlation with lower extremity function (LEF) biometrics (normal/fast gait speed, timed up-and-go, and isometric leg strength) and biochemical and nutritional parameters, such as total solubilized cholesterol (SCHOL) and body mass index (BMI). Data were obtained from 3,162 subjects, aged 66-96 years, from the population-based AGES-Reykjavik Study. 1-D k-means clustering was employed to discretize each biometric and comorbidity dataset into twelve subpopulations, in accordance with Sturges' Formula for Class Selection. Dataset linear regressions were performed against eleven NTRA distribution parameters and standard CT analyses (fat/muscle cross-sectional area and average HU value). Parameters from NTRA and CT standards were analogously assembled by age and sex. Analysis of specific NTRA parameters with standard CT results showed linear correlation coefficients greater than 0.85, but multiple regression analysis of correlative NTRA parameters yielded a correlation coefficient of 0.99 (P
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To evaluate the association between maternal body mass index and neonatal outcomes in adolescents and to compare neonatal outcomes between overweight and obese adolescents and obstetric low-risk adult women.
Retrospective cohort study using data from the Swedish Medical Birth Register.
All 31,386 primiparous adolescents younger than 20 years of age and 178,844 "standard" women, defined as normal weight, obstetric low-risk adult women who delivered between 1992 and 2013. The adolescents were categorized according to weight and height in early pregnancy into body mass index groups according to the World Health Organization classification. Logistic regression models were used.
Neonatal outcomes in relation to maternal body mass index groups.
In the adolescents, 6109/31,386 (19.5%) and 2287/31,386 (7.3%) were overweight and obese, respectively. Compared with normal weight adolescents, overweight adolescents had a lower risk of having small for gestational age neonates, and higher risks for having neonates with macrosomia, and being large for gestational age and with Apgar score less than 7 at 5 minutes. The obese adolescents had increased risk for having neonates being large for gestational age (3.8% vs 1.3%; adjusted odds ratio [aOR], 2.97 [95% confidence interval (CI), 2.30-3.84]), with macrosomia (>4500 g) (4.6% vs 1.4%; aOR, 2.95 [95% CI, 2.33-3.73]), and with Apgar score less than 7 at 5 minutes (2.2% vs 1.1%; aOR, 1.98 [95% CI, 1.43-2.76]) than normal weight adolescents. Compared with the standard women, overweight and obese adolescents had overall more adverse neonatal outcomes.
Overweight and obese adolescents had predominantly increased risks for adverse neonatal outcomes compared with normal weight adolescents and standard women.
The Alaska Mountain Wilderness Ski Classic is a self-supported ultramarathon cross-country skiing event that traverses one of the mountain ranges of Alaska each winter. Unique aspects of this event challenge athletes with a significant amount of physical and mental stress while in the chronically cold conditions of the Arctic. Assessment of energy requirements or body composition has never been performed during this event. The objective of the study was to evaluate the influence of the 2016 Alaska Mountain Wilderness Ski Classic on caloric expenditure and body composition.
Caloric expenditure was estimated using GT3x+ Actigraph accelerometers and ActiLife software. Lean tissue mass, total fat mass, visceral fat mass, and bone mineral density were measured using a General Electric iDXA before and after the event. Data are presented as mean±SD. Differences were analyzed using paired t tests with significance at P