Skip header and navigation

Refine By

381 records – page 1 of 39.

A 10-Year Follow-Up of Adiposity and Dementia in Swedish Adults Aged 70 Years and Older.

https://arctichealth.org/en/permalink/ahliterature300956
Source
J Alzheimers Dis. 2018; 63(4):1325-1335
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
2018
Author
Ilse A C Arnoldussen
Valter Sundh
Kristoffer Bäckman
Silke Kern
Svante Östling
Kaj Blennow
Henrik Zetterberg
Ingmar Skoog
Amanda J Kiliaan
Deborah R Gustafson
Author Affiliation
Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud university medical center, Nijmegen, The Netherlands.
Source
J Alzheimers Dis. 2018; 63(4):1325-1335
Date
2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adiponectin - blood
Adiposity
Aged
Aged, 80 and over
Anthropometry
Body mass index
Dementia - blood - epidemiology - pathology
Fasting
Female
Humans
Independent living
Leptin - blood
Longitudinal Studies
Male
Psychiatric Status Rating Scales
Sex Factors
Sweden - epidemiology
Waist-Hip Ratio
Abstract
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.
Within 5 years of baseline, low BMI (
PubMed ID
29758945 View in PubMed
Less detail

Abdominal subcutaneous adipose tissue cellularity in men and women.

https://arctichealth.org/en/permalink/ahliterature294588
Source
Int J Obes (Lond). 2017 10; 41(10):1564-1569
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
10-2017
Author
D P Andersson
E Arner
D E Hogling
M Rydén
P Arner
Author Affiliation
Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.
Source
Int J Obes (Lond). 2017 10; 41(10):1564-1569
Date
10-2017
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Absorptiometry, Photon
Adipocytes - cytology
Adolescent
Adult
Aged
Body Composition
Body Fat Distribution
Body mass index
Female
Humans
Insulin Resistance
Male
Middle Aged
Sex Characteristics
Subcutaneous Fat, Abdominal - cytology
Sweden
Young Adult
Abstract
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
Notes
Cites: Metabolism. 1972 Dec;21(12):1143-53 PMID 4629846
Cites: Metabolism. 1975 Jun;24(6):703-10 PMID 1128235
Cites: J Clin Invest. 1968 Jan;47(1):153-65 PMID 16695937
Cites: J Clin Invest. 1970 Jun;49(6):1213-23 PMID 5422023
Cites: Diabetes. 2010 Jan;59(1):105-9 PMID 19846802
Cites: Am J Epidemiol. 1996 Feb 1;143(3):228-39 PMID 8561156
Cites: Obesity (Silver Spring). 2010 Oct;18(10):1875-80 PMID 20300084
Cites: Metabolism. 1971 Jul;20(7):703-13 PMID 5090134
Cites: Obesity (Silver Spring). 2012 Jun;20(6):1313-8 PMID 22282048
Cites: Diabetologia. 2007 Aug;50(8):1707-15 PMID 17549449
Cites: Clin Endocrinol Metab. 1976 Jul;5(2):299-311 PMID 1085232
Cites: Am J Clin Nutr. 2008 Jan;87(1):56-63 PMID 18175737
Cites: Biochim Biophys Acta. 2014 Mar;1842(3):377-92 PMID 23684841
Cites: Diabetes Care. 2017 Jan;40(1):77-84 PMID 27852664
Cites: Best Pract Res Clin Endocrinol Metab. 2013 Aug;27(4):501-7 PMID 24054927
Cites: Biol Sex Differ. 2012 May 31;3(1):13 PMID 22651247
Cites: J Clin Invest. 1979 Feb;63(2):239-46 PMID 429551
Cites: PLoS One. 2016 Apr 22;11(4):e0153990 PMID 27104953
Cites: Gend Med. 2009;6 Suppl 1:60-75 PMID 19318219
Cites: FASEB J. 2010 Jan;24(1):326-31 PMID 19741173
Cites: Diabetes Care. 2014 Jul;37(7):1831-6 PMID 24760260
Cites: Metabolism. 1971 Oct;20(10):927-35 PMID 5097682
Cites: J Clin Endocrinol Metab. 1989 Feb;68(2):374-8 PMID 2645308
Cites: J Lipid Res. 2003 Sep;44(9):1795-801 PMID 12777477
Cites: Diabetologia. 2000 Dec;43(12):1498-506 PMID 11151758
Cites: J Clin Endocrinol Metab. 2014 Oct;99(10):E1870-6 PMID 24937536
Cites: Nature. 2008 Jun 5;453(7196):783-7 PMID 18454136
Cites: Diabetes. 1982 Oct;31(10):911-6 PMID 6759224
Cites: J Lipid Res. 1978 Feb;19(2):269-73 PMID 632689
Cites: J Clin Invest. 1973 Apr;52(4):929-41 PMID 4693656
Cites: Lancet. 1972 Nov 4;2(7784):948-51 PMID 4116826
Cites: Proc Natl Acad Sci U S A. 2010 Oct 19;107(42):18226-31 PMID 20921416
Cites: J Clin Endocrinol Metab. 2013 May;98 (5):E897-901 PMID 23543656
Cites: Nutrition. 2008 Jul-Aug;24(7-8):625-31 PMID 18485667
Cites: Diabetologia. 1985 Jul;28(7):412-9 PMID 3899825
PubMed ID
28630459 View in PubMed
Less detail

Adherence to dietary recommendations for Swedish adults across categories of greenhouse gas emissions from food.

https://arctichealth.org/en/permalink/ahliterature293496
Source
Public Health Nutr. 2017 Dec; 20(18):3381-3393
Publication Type
Journal Article
Date
Dec-2017
Author
Camilla Sjörs
Fredrik Hedenus
Arvid Sjölander
Annika Tillander
Katarina Bälter
Author Affiliation
1Department of Medical Epidemiology and Biostatistics (MEB),Karolinska Institutet,Nobels väg 12a,SE-171 77 Stockholm,Sweden.
Source
Public Health Nutr. 2017 Dec; 20(18):3381-3393
Date
Dec-2017
Language
English
Publication Type
Journal Article
Keywords
Adolescent
Adult
Aged
Aged, 80 and over
Body mass index
Carbon Dioxide - analysis
Diet
Diet Records
Dietary Carbohydrates - administration & dosage
Dietary Fats - administration & dosage
Dietary Fiber - administration & dosage
Dietary Proteins - administration & dosage
Exercise
Female
Greenhouse Gases - analysis
Humans
Male
Micronutrients - administration & dosage
Middle Aged
Nutrition Assessment
Nutrition Surveys
Patient compliance
Recommended dietary allowances
Socioeconomic Factors
Surveys and Questionnaires
Sweden
Young Adult
Abstract
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.
Sweden.
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.
PubMed ID
28879831 View in PubMed
Less detail

Adherence to the healthy Nordic diet is associated with weight change during 7 years of follow-up.

https://arctichealth.org/en/permalink/ahliterature303010
Source
Br J Nutr. 2018 07; 120(1):101-110
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
07-2018
Author
Noora Kanerva
Kennet Harald
Satu Männistö
Niina E Kaartinen
Mirkka Maukonen
Ari Haukkala
Pekka Jousilahti
Author Affiliation
1Department of Public Health Solutions,National Institute for Health and Welfare,PO Box 30,27100 Helsinki,Finland.
Source
Br J Nutr. 2018 07; 120(1):101-110
Date
07-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adult
Aged
Anthropometry
Body mass index
Body Weight
Diet
Diet, Healthy
Female
Finland - epidemiology
Follow-Up Studies
Humans
Life Style
Linear Models
Male
Middle Aged
Obesity
Overweight - prevention & control - therapy
Prospective Studies
Surveys and Questionnaires
Urban Population
Waist Circumference
Abstract
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.
PubMed ID
29936927 View in PubMed
Less detail

Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach.

https://arctichealth.org/en/permalink/ahliterature292190
Source
BMC Public Health. 2018 03 02; 18(1):311
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
03-02-2018
Author
Dorothea Dumuid
Tyman E Stanford
Željko Pedišic
Carol Maher
Lucy K Lewis
Josep-Antoni Martín-Fernández
Peter T Katzmarzyk
Jean-Philippe Chaput
Mikael Fogelholm
Martyn Standage
Mark S Tremblay
Timothy Olds
Author Affiliation
Alliance for Research in Exercise, Nutrition and Activity (ARENA), School of Health Sciences, University of South Australia, GPO Box 2471, Adelaide, SA, 5001, Australia. dorothea.dumuid@mymail.unisa.edu.au.
Source
BMC Public Health. 2018 03 02; 18(1):311
Date
03-02-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adipose Tissue
Australia
Canada
Child
Exercise
Female
Finland
Humans
Male
Pediatric Obesity
Sedentary lifestyle
Sleep
Time Factors
United Kingdom
Abstract
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.
Notes
Cites: Int J Behav Nutr Phys Act. 2015 Feb 11;12:11 PMID 25881074
Cites: Am J Epidemiol. 2009 Aug 15;170(4):519-27 PMID 19584129
Cites: Circulation. 2011 Aug 16;124(7):789-95 PMID 21810663
Cites: Int J Behav Nutr Phys Act. 2017 May 10;14 (1):64 PMID 28486972
Cites: Int J Obes (Lond). 2010 Jul;34(7):1177-87 PMID 20351728
Cites: PLoS One. 2015 Oct 13;10(10):e0139984 PMID 26461112
Cites: Bull World Health Organ. 2007 Sep;85(9):660-7 PMID 18026621
Cites: Br J Nutr. 2008 Oct;100(4):859-65 PMID 18346304
Cites: Stat Methods Med Res. 2017 Jan 1;:962280217737805 PMID 29157152
Cites: J Obes. 2012;2012:852672 PMID 22811890
Cites: Appl Physiol Nutr Metab. 2016 Jun;41(6 Suppl 3):S240-65 PMID 27306432
Cites: Appl Physiol Nutr Metab. 2014 Jan;39(1):53-7 PMID 24383507
Cites: BMC Public Health. 2007 Jan 31;7:16 PMID 17266745
Cites: Pediatr Obes. 2013 Apr;8(2):e29-32 PMID 23239610
Cites: Appl Physiol Nutr Metab. 2016 Jun;41(6 Suppl 3):S294-302 PMID 27306435
Cites: Am J Clin Nutr. 2004 Jul;80(1):64-9 PMID 15213029
Cites: Int J Public Health. 2017 Nov;62(8):929-938 PMID 28593331
Cites: Int J Obes Suppl. 2015 Dec;5(Suppl 2):S9-S16 PMID 27152192
Cites: Obesity (Silver Spring). 2008 Feb;16(2):275-84 PMID 18239633
Cites: Appl Physiol Nutr Metab. 2016 Jun;41(6 Suppl 3):S266-82 PMID 27306433
Cites: Med J Aust. 2007 Jun 4;186(11):591-5 PMID 17547550
Cites: Med Sci Sports Exerc. 2015 May;47(5):937-43 PMID 25202840
Cites: BMC Public Health. 2013 Sep 30;13:900 PMID 24079373
Cites: Appl Physiol Nutr Metab. 2016 Jun;41(6 Suppl 3):S197-239 PMID 27306431
Cites: Stat Methods Med Res. 2017 Jan 1;:962280217710835 PMID 28555522
Cites: JAMA. 1995 Feb 1;273(5):402-7 PMID 7823386
Cites: Med Sci Sports Exerc. 2011 Jul;43(7):1360-8 PMID 21131873
Cites: PLoS One. 2016 May 17;11(5):e0154935 PMID 27187777
Cites: Pediatr Obes. 2018 Feb;13(2):111-119 PMID 28027427
Cites: J Sports Sci. 2008 Dec;26(14):1557-65 PMID 18949660
PubMed ID
29499689 View in PubMed
Less detail

Adjusting for BMI in analyses of volumetric mammographic density and breast cancer risk.

https://arctichealth.org/en/permalink/ahliterature300458
Source
Breast Cancer Res. 2018 12 29; 20(1):156
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
12-29-2018
Author
Sue Hudson
Kirsti Vik Hjerkind
Sarah Vinnicombe
Steve Allen
Cassia Trewin
Giske Ursin
Isabel Dos-Santos-Silva
Bianca L De Stavola
Author Affiliation
Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK. susan.hudson@lshtm.ac.uk.
Source
Breast Cancer Res. 2018 12 29; 20(1):156
Date
12-29-2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Adiposity
Aged
Aged, 80 and over
Body mass index
Breast - diagnostic imaging - pathology
Breast Density
Breast Neoplasms - diagnostic imaging - pathology
Case-Control Studies
Cohort Studies
Feasibility Studies
Female
Humans
Image Processing, Computer-Assisted - methods
Logistic Models
Mammography - methods
Mass Screening - methods
Middle Aged
Norway
Risk assessment
Risk factors
United Kingdom
Abstract
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.
PubMed ID
30594212 View in PubMed
Less detail

Adolescent body composition and associations with body size and growth from birth to late adolescence. The Tromsø study: Fit Futures-A Norwegian longitudinal cohort study.

https://arctichealth.org/en/permalink/ahliterature300460
Source
Pediatr Obes. 2019 05; 14(5):e12492
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
05-2019
Author
Elin Evensen
Nina Emaus
Anne-Sofie Furberg
Ane Kokkvoll
Jonathan Wells
Tom Wilsgaard
Anne Winther
Guri Skeie
Author Affiliation
Department of Clinical Research, University Hospital of North Norway, Tromsø, Norway.
Source
Pediatr Obes. 2019 05; 14(5):e12492
Date
05-2019
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Absorptiometry, Photon - methods
Adolescent
Adult
Birth Weight - physiology
Body Composition
Body mass index
Child
Child Development - physiology
Cohort Studies
Female
Humans
Longitudinal Studies
Male
Norway
Pediatric Obesity - epidemiology - physiopathology
Risk factors
Young Adult
Abstract
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.
PubMed ID
30590874 View in PubMed
Less detail

Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.

https://arctichealth.org/en/permalink/ahliterature292572
Source
PLoS One. 2018; 13(3):e0193241
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Date
2018
Author
Kyle Edmunds
Magnús Gíslason
Sigurður Sigurðsson
Vilmundur Guðnason
Tamara Harris
Ugo Carraro
Paolo Gargiulo
Author Affiliation
Institute for Biomedical and Neural Engineering, Reykjavík University, Reykjavík, Iceland.
Source
PLoS One. 2018; 13(3):e0193241
Date
2018
Language
English
Publication Type
Journal Article
Research Support, Non-U.S. Gov't
Keywords
Age Factors
Aged
Aged, 80 and over
Body mass index
Cluster analysis
Comorbidity
Disability Evaluation
Female
Follow-Up Studies
Humans
Lower Extremity - diagnostic imaging - physiopathology
Male
Muscle, Skeletal - diagnostic imaging - physiopathology
Nonlinear Dynamics
Prospective Studies
Regression Analysis
Sarcopenia - complications - diagnostic imaging - epidemiology - physiopathology
Sex Factors
Tomography, X-Ray Computed
Abstract
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
Notes
Cites: J Gerontol A Biol Sci Med Sci. 1999 Apr;54(4):M172-6 PMID 10219007
Cites: J Gerontol A Biol Sci Med Sci. 2006 Oct;61(10):1059-64 PMID 17077199
Cites: Curr Opin Clin Nutr Metab Care. 2004 Jul;7(4):405-10 PMID 15192443
Cites: J Am Geriatr Soc. 2002 May;50(5):889-96 PMID 12028177
Cites: Am J Epidemiol. 2007 May 1;165(9):1076-87 PMID 17351290
Cites: J Am Geriatr Soc. 2004 Jan;52(1):80-5 PMID 14687319
Cites: Calcif Tissue Int. 2012 May;90(5):354-64 PMID 22451219
Cites: JAMA Neurol. 2015 Jun;72(6):682-8 PMID 25867544
Cites: Lancet Diabetes Endocrinol. 2014 Oct;2(10):819-29 PMID 24731660
Cites: Metabolism. 2000 Apr;49(4):467-72 PMID 10778870
Cites: Acta Physiol Scand. 1978 May;103(1):31-9 PMID 208350
Cites: Artif Organs. 2005 Jun;29(6):440-3 PMID 15926978
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4847 PMID 26913149
Cites: Osteoporos Int. 2010 Apr;21(4):543-59 PMID 19779761
Cites: Dentomaxillofac Radiol. 2010 Sep;39(6):323-35 PMID 20729181
Cites: Eur J Transl Myol. 2015 Mar 17;25(2):5133 PMID 26913154
Cites: Eur J Transl Myol. 2016 Jun 22;26(2):6015 PMID 27478562
Cites: J Am Geriatr Soc. 1991 Jun;39(6):555-61 PMID 1805811
Cites: Bone. 2008 Apr;42(4):798-805 PMID 18234578
Cites: J Gerontol. 1992 Nov;47(6):M204-10 PMID 1430855
Cites: J Am Med Inform Assoc. 2013 May 1;20(3):544-53 PMID 23059731
Cites: J Appl Physiol (1985). 2001 Jun;90(6):2157-65 PMID 11356778
Cites: Age Ageing. 2013 Mar;42(2):145-50 PMID 23315797
Cites: J Physiol. 1983 May;338:37-49 PMID 6875963
Cites: J Gerontol A Biol Sci Med Sci. 2006 Jan;61(1):72-7 PMID 16456196
Cites: J Am Geriatr Soc. 2013 Feb;61(2):237-42 PMID 23320618
Cites: Comput Math Methods Med. 2016;2016:8932950 PMID 28115982
Cites: Exerc Sport Sci Rev. 2001 Oct;29(4):141-8 PMID 11688785
Cites: J Gerontol. 1994 Mar;49(2):M85-94 PMID 8126356
Cites: Med J Aust. 2016 Oct 3;205(7):329-33 PMID 27681976
Cites: J Gerontol A Biol Sci Med Sci. 2000 Mar;55(3):M168-73 PMID 10795731
Cites: Pflugers Arch. 1997 Jul;434(3):246-53 PMID 9178622
Cites: Age (Dordr). 2013 Aug;35(4):1401-9 PMID 22588637
Cites: Neurology. 2008 Sep 2;71(10):758-65 PMID 18765652
Cites: Neurol Res. 2010 Feb;32(1):13-9 PMID 20092691
Cites: Am J Epidemiol. 1998 Apr 15;147(8):755-63 PMID 9554417
Cites: J Biomed Inform. 2002 Oct-Dec;35(5-6):352-9 PMID 12968784
Cites: J Appl Physiol (1985). 2000 Jul;89(1):104-10 PMID 10904041
Cites: J Am Geriatr Soc. 1991 Feb;39(2):142-8 PMID 1991946
Cites: Magn Reson Imaging. 2012 Nov;30(9):1234-48 PMID 22898692
Cites: Med Sci Sports Exerc. 1994 Apr;26(4):432-9 PMID 8201898
Cites: Am J Physiol. 1997 Feb;272(2 Pt 1):C638-49 PMID 9124308
Cites: Comput Math Methods Med. 2015 ;2015 :162481 PMID 26417376
Cites: J Am Geriatr Soc. 2003 Nov;51(11):1602-9 PMID 14687390
Cites: R J. 2011 Dec;3(2):29-33 PMID 27942416
Cites: J Am Med Inform Assoc. 2007 Jan-Feb;14(1):1-9 PMID 17077452
Cites: N Engl J Med. 1995 Mar 2;332(9):556-61 PMID 7838189
Cites: Int J Med Inform. 2008 Feb;77(2):81-97 PMID 17188928
Cites: J Bone Miner Res. 2010 Mar;25(3):513-9 PMID 20422623
Cites: Eur Heart J. 1993 May;14(5):584-91 PMID 8508850
Cites: Neuromuscul Disord. 2002 Oct;12(7-8):631-8 PMID 12207930
Cites: Ann Surg Oncol. 2015 Nov;22(12 ):3897-904 PMID 26242367
Cites: Eur J Transl Myol. 2015 Mar 11;25(2):4832 PMID 26913148
Cites: BMJ. 2010 Sep 09;341:c4467 PMID 20829298
Cites: Muscle Nerve. 1995 Jul;18(7):708-14 PMID 7783760
Cites: J Am Geriatr Soc. 2005 Oct;53(10):1675-80 PMID 16181165
Cites: J Appl Physiol (1985). 2000 Sep;89(3):1072-8 PMID 10956353
Cites: J Am Geriatr Soc. 2003 Mar;51(3):314-22 PMID 12588574
Cites: Bone. 2006 Sep;39(3):644-51 PMID 16790372
Cites: J Gerontol A Biol Sci Med Sci. 2002 Oct;57(10):B359-65 PMID 12242311
Cites: Am J Epidemiol. 2016 Jan 1;183(1):53-60 PMID 26643983
Cites: Exp Gerontol. 2012 Jan;47(1):38-44 PMID 22015325
Cites: Diabetologia. 2005 Feb;48(2):301-8 PMID 15660262
Cites: Ecol Lett. 2008 Jan;11(1):1-15 PMID 18047588
Cites: J Gerontol A Biol Sci Med Sci. 2005 Jul;60(7):882-7 PMID 16079212
Cites: Eur J Clin Invest. 1984 Aug;14(4):282-7 PMID 6434323
Cites: Am J Clin Nutr. 2000 Apr;71(4):885-92 PMID 10731493
PubMed ID
29513690 View in PubMed
Less detail

Adverse Neonatal Outcomes in Overweight and Obese Adolescents Compared with Normal Weight Adolescents and Low Risk Adults.

https://arctichealth.org/en/permalink/ahliterature299300
Source
J Pediatr Adolesc Gynecol. 2019 Apr; 32(2):139-145
Publication Type
Comparative Study
Journal Article
Date
Apr-2019
Author
Anna Ramö Isgren
Preben Kjølhede
Marie Blomberg
Author Affiliation
Department of Obstetrics and Gynecology, and Department of Clinical and Experimental Medicine, Linköping University, Linköping, Sweden.
Source
J Pediatr Adolesc Gynecol. 2019 Apr; 32(2):139-145
Date
Apr-2019
Language
English
Publication Type
Comparative Study
Journal Article
Keywords
Adolescent
Body mass index
Body Weight
Cohort Studies
Female
Gestational Age
Humans
Infant, Newborn
Pediatric Obesity - complications
Pregnancy
Pregnancy Complications - etiology
Pregnancy Outcome - epidemiology
Pregnancy in Adolescence - statistics & numerical data
Registries
Retrospective Studies
Risk factors
Sweden
Young Adult
Abstract
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.
Sweden.
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.
PubMed ID
30453030 View in PubMed
Less detail

Alaska Mountain Wilderness Ski Classic: Alterations in Caloric Expenditure and Body Composition.

https://arctichealth.org/en/permalink/ahliterature295152
Source
Wilderness Environ Med. 2018 06; 29(2):221-225
Publication Type
Journal Article
Date
06-2018
Author
Michelle M Johannsen
Kenneth J Shin
Karolina Priebe
Robert H Coker
Author Affiliation
Institute of Arctic Biology, University of Alaska, Fairbanks, Fairbanks, AK (Ms Johannsen, Mr Shin, and Dr Coker). Electronic address: mmjohannsen@alaska.edu.
Source
Wilderness Environ Med. 2018 06; 29(2):221-225
Date
06-2018
Language
English
Publication Type
Journal Article
Keywords
Adipose Tissue - physiology
Adult
Alaska
Body Composition
Body mass index
Bone Density
Cold Temperature
Energy Metabolism
Female
Humans
Male
Skiing
Abstract
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
PubMed ID
29628417 View in PubMed
Less detail

381 records – page 1 of 39.