Associations of fat mass and fat-free mass accretion in infancy with body composition and cardiometabolic risk markers at 5 years: The Ethiopian iABC birth cohort study

The aim of this study was to estimate the association between birth order and number of siblings with body composition in adolescents. Data are from a birth cohort study conducted in Pelotas, Brazil. At the age of 18 years, 4563 adolescents were located, of whom 4106 were interviewed (follow-up rate 81·3 %). Of these, 3974 had complete data and were thus included in our analysis. The variables used in the analysis were measured during the perinatal period, or at 11, 15 and/or 18 years of age. Body composition at 18 years was collected by air displacement plethysmography (BOD POD®). Crude and adjusted analyses of the association between birth order and number of siblings with body composition were performed using linear regression. All analyses were stratified by the adolescent sex. The means of BMI, fat mass index and fat-free mass index among adolescents were 23·4 (sd 4·5) kg/m2, 6·1 (sd 3·9) kg/m2 and 17·3 (sd 2·5) kg/m2, respectively. In adjusted models, the total siblings remained inversely associated with fat mass index (β = − 0·37 z-scores, 95 % CI − 0·52, − 0·23) and BMI in boys (β = − 0·39 z-scores, 95 % CI − 0·55, − 0·22). Fat-free mass index was related to the total siblings in girls (β = 0·06 z-scores, 95 % CI − 0·04, 0·17). This research has found that number of total siblings, and not birth order, is related to the fat mass index, fat-free mass index and BMI in adolescents. It suggests the need for early prevention of obesity or fat mass accumulation in only children.

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The Relationship Between Body Composition, Glucose Metabolism, and Subtypes of Depressive Symptoms – Findings From the Helsinki Birth Cohort Study

10.21203/rs.3.rs-911998/v1 ◽

2021 ◽

Author(s):

Mia D. Eriksson ◽

Johan G. Eriksson ◽

Päivi Korhonen ◽

Minna K. Salonen ◽

Tuija M. Mikkola ◽

...

Keyword(s):

Body Composition ◽

Cohort Study ◽

Depressive Symptoms ◽

Glucose Metabolism ◽

Birth Cohort ◽

Beck Depression Inventory ◽

Fat Mass ◽

Lean Mass ◽

Birth Cohort Study ◽

Fat Mass Index

Abstract Background There is an existing link between two of the most common diseases, obesity and depression. These are both of great public health concern, but little is known about the relationships between the subtypes of these conditions. We hypothesized that non-melancholic depressive symptoms have a stronger relationship with both body composition (lean mass and fat mass) and dysfunctional glucose metabolism than melancholic depression. Methods For this cross-sectional study 1 510 participants from the Helsinki Birth Cohort Study had their body composition evaluated as lean mass and fat mass (Lean Mass Index + Fat Mass Index = Body Mass Index). Participants were evaluated for depressive symptoms utilizing the Beck Depression Inventory, and had laboratory assessments including an oral glucose tolerance test. Results Higher than average Fat Mass Index (kg/m2) was associated with a higher percentage of participants scoring in the depressive range of the Beck Depression Inventory (p=0.048). Higher Fat Mass Index was associated with a higher likelihood of having depressive symptoms (OR per 1-SD Fat Mass Index=1.37, 95% CI: 1.13-1.65), whereas higher Lean Mass Index (kg/m2) was associated with a lower likelihood of having depressive symptoms (OR per 1-SD Lean Mass Index=0.76, 95% CI: 0.64-0.91). Participants with an above average Fat Mass Index more frequently had non-melancholic depressive symptoms (p=0.008) regardless of Lean Mass Index levels (p=0.38). There was no difference between the body composition groups in the likelihood of having melancholic depressive symptoms (Fat Mass Index p=0.83, Lean Mass Index p=0.93). The non-melancholic group had higher Fat Mass Index than either of the other groups (p<0.001), and a higher 2-hour glucose concentration than the non-depressed group (p=0.005). Conclusion As hypothesized, non-melancholic depressive symptoms are most closely related to high fat mass index and dysfunctional glucose metabolism.

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Changes in testosterone related to body composition in late midlife: Findings from the 1946 British birth cohort study

Obesity ◽

10.1002/oby.21092 ◽

2015 ◽

Vol 23 (7) ◽

pp. 1486-1492 ◽

Cited By ~ 20

Author(s):

David Bann ◽

Frederick C. W. Wu ◽

Brian Keevil ◽

Hany Lashen ◽

Judith Adams ◽

...

Keyword(s):

Body Composition ◽

Cohort Study ◽

Birth Cohort ◽

Birth Cohort Study ◽

Late Midlife

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Potential effects on cardiometabolic risk factors and body composition by short message service (SMS)-guided training after recent minor stroke or transient ischaemic attack: post hoc analyses of the STROKEWALK randomised controlled trial

BMJ Open ◽

10.1136/bmjopen-2021-054851 ◽

2021 ◽

Vol 11 (10) ◽

pp. e054851

Author(s):

Birgit Maria Vahlberg ◽

Erik Lundström ◽

Staffan Eriksson ◽

Ulf Holmback ◽

Tommy Cederholm

Keyword(s):

Risk Factors ◽

Body Composition ◽

Mobile Phone ◽

Fat Mass ◽

Text Messaging ◽

Standard Care ◽

Cardiometabolic Risk ◽

Fat Free Mass ◽

Control Group ◽

Randomised Controlled

ObjectivesTo evaluate effects of mobile phone text-messaging exercise instructions on body composition, cardiometabolic risk markers and self-reported health at 3 months after stroke.DesignRandomised controlled intervention study with per-protocol analyses.SettingUniversity Hospital in Sweden.ParticipantsSeventy-nine patients (mean (SD) age 64 (10) years, 37% female) ≥18 years with good motor function (modified Rankin Scale ≤2) and capable to perform 6 min walking test at hospital discharge were randomised to either intervention (n=40) or control group (n=39). Key exclusion criteria: subarachnoid bleeding, uncontrolled hypertension, severe psychiatric problems or cognitive limitations.InterventionsThe intervention group received beyond standard care, daily mobile phone instructional text messages to perform regular outdoor walking and functional leg exercises. The control group received standard care.Main outcome measuresFat mass and fat-free mass were estimated by bioelectric impedance analysis. Cardiometabolic risk factors like blood lipids, glycated haemoglobin and blood glucose were analysed at baseline and after 3 months.ResultsBoth groups changed favourably in fat-free mass (1.83 kg, 95% CI 0.77 to 2.89; p=0.01, effect size (ES)=0.63 vs 1.22 kg, 95% CI 0.39 to 2.0; p=0.05, ES=0.54) and fat mass (−1.30 kg, 95% CI −2.45 to −0.14; p=0.029, ES=0.41 vs −0.76 kg, 95% CI −1.74 to 0.22; p=0.123, ES=0.28). Also, many cholesterol related biomarkers improved; for example, total cholesterol −0.65 mmol/L, 95% CI −1.10 to −0.2; p=0.06, ES: 0.5 vs −1.1 mmol/L, 95% CI −1.47 to −0.56; p>0.001, ES=0.8. However, there were no between-group differences. At 3 months, 94% and 86%, respectively, reported very good/fairly good health in the text messaging and control groups.ConclusionsNo clear effect of 3 months daily mobile phone delivered training instructions was detected on body composition, cardiovascular biochemical risk factors or self-perceived health. Further research is needed to evaluate secondary prevention efforts in larger populations after recent stroke.Trial registration numberNCT02902367.

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Sleep Duration Trajectories and Body Composition in Adolescents: Prospective Birth Cohort Study

PLoS ONE ◽

10.1371/journal.pone.0152348 ◽

2016 ◽

Vol 11 (3) ◽

pp. e0152348 ◽

Cited By ~ 9

Author(s):

Antônio Augusto Schäfer ◽

Marlos Rodrigues Domingues ◽

Darren Lawrence Dahly ◽

Fernanda Oliveira Meller ◽

Helen Gonçalves ◽

...

Keyword(s):

Body Composition ◽

Cohort Study ◽

Birth Cohort ◽

Sleep Duration ◽

Birth Cohort Study ◽

Prospective Birth Cohort ◽

Prospective Birth Cohort Study

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Body composition and changes in health-related quality of life in older age: a 10-year follow-up of the Helsinki Birth Cohort Study

Quality of Life Research ◽

10.1007/s11136-020-02453-1 ◽

2020 ◽

Vol 29 (8) ◽

pp. 2039-2050

Author(s):

Tuija M. Mikkola ◽

Hannu Kautiainen ◽

Mikaela B. von Bonsdorff ◽

Minna K. Salonen ◽

Niko Wasenius ◽

...

Keyword(s):

Quality Of Life ◽

Body Composition ◽

Cohort Study ◽

Birth Cohort ◽

Birth Cohort Study ◽

Health Related Quality ◽

Related Quality ◽

Health Related

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Socioeconomic position across life and body composition in early old age: findings from a British birth cohort study

Journal of Epidemiology & Community Health ◽

10.1136/jech-2013-203373 ◽

2014 ◽

Vol 68 (6) ◽

pp. 516-523 ◽

Cited By ~ 23

Author(s):

David Bann ◽

Rachel Cooper ◽

Andrew K Wills ◽

Judith Adams ◽

Diana Kuh ◽

...

Keyword(s):

Body Composition ◽

Cohort Study ◽

Birth Cohort ◽

Old Age ◽

Socioeconomic Position ◽

Birth Cohort Study ◽

Early Old Age

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Cohort Profile Update: 2004 Pelotas (Brazil) Birth Cohort Study. Body composition, mental health and genetic assessment at the 6 years follow-up

International Journal of Epidemiology ◽

10.1093/ije/dyu144 ◽

2014 ◽

Vol 43 (5) ◽

pp. 1437-1437f ◽

Cited By ~ 63

Author(s):

I. S. Santos ◽

A. J. Barros ◽

A. Matijasevich ◽

R. Zanini ◽

M. A. Chrestani Cesar ◽

...

Keyword(s):

Mental Health ◽

Body Composition ◽

Cohort Study ◽

Birth Cohort ◽

Birth Cohort Study ◽

Genetic Assessment

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Accounting for height in indices of body composition during childhood and adolescence

Wellcome Open Research ◽

10.12688/wellcomeopenres.15280.1 ◽

2019 ◽

Vol 4 ◽

pp. 105 ◽

Cited By ~ 1

Author(s):

Linda M. O'Keeffe ◽

Abigail Fraser ◽

Laura D. Howe

Keyword(s):

Body Composition ◽

Cohort Study ◽

Body Mass ◽

Fat Mass ◽

Early Life ◽

Fat Free Mass ◽

Childhood And Adolescence ◽

Total Body Mass ◽

Total Body ◽

Age And Sex

Correlations of body composition with height vary by age and sex during childhood. Standard approaches to accounting for height in measures of body composition (dividing by height (in meters)2) do not take this into account. Using measures of total body mass (TBM), fat mass (FM) and fat free mass (FFM) at ages nine, 11, 13, 15 and 18 years from a longitudinal UK cohort study (ALSPAC), we calculated indices of body composition at each age by dividing measures by height (in meters)2. We then produced age-and sex-specific powers of height using allometric regressions and calculated body composition indices by dividing measures by height raised to these powers. TBM, FM and FFM divided by height2 were correlated with height up-to age 11 in females. In males, TBM and FM divided by height2 were correlated with height up-to age 15 years while FM divided by height2 was correlated with height up-to age 11 years. Indices of body composition using age-and sex-specific powers were not correlated with height at any age. In early life, age-and sex-specific powers of height, rather than height in meters2, should be used to adjust body composition for height when measures of adiposity/mass independent of height are required.

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Sex Differences in Association Between Body Composition and Frailty or Physical Performance in Community-Dwelling Older Adults

10.21203/rs.3.rs-69908/v1 ◽

2020 ◽

Author(s):

Yunsoo Soh ◽

Chang Won Won

Keyword(s):

Body Composition ◽

Logistic Regression ◽

Cohort Study ◽

Physical Performance ◽

Fat Mass ◽

Frailty Index ◽

Community Dwelling ◽

Fat Free Mass ◽

Fat Mass Index ◽

The Relationship

Abstract BackgroundFrailty is a common geriatric condition due to aging, defined as a decrease in the functional reserve to maintain the homeostasis. As part of the aging process, body composition changes occur. This study investigated the relationship between body composition and frailty in a community-dwelling elderly Korean population.MethodsThis cross-sectional cohort study analyzed data of 2,385 elderly participants (aged 70–84 years, 1131 males and 1254 females) of the Korean Frailty and Aging Cohort Study from 2016 to 2017. Body composition, including total and trunk fat masses and fat-free mass, were measured with dual-energy X-ray absorptiometry. Fat mass index (FMI), trunk fat mass index (TFMI), and fat-free mass index (FFMI) represented total fat mass, trunk fat mass, and fat-free mass according to height. Based on the frailty index developed by Fried, we compared the frail and non-frail groups. Poor physical performance assessed with the short physical performance battery score of <9 is considered frailty. To evaluate the relationship between the variables, simple and fully adjusted multivariable logistic regression analyses were performed according to sex.ResultsAmong the participants, 462 (19.3%) were defined as the frail group, with a significantly high mean age of 77.9±4.0 years. In the logistic regression analysis of frailty based on body mass index (BMI) categories, underweight (BMI<18 kg/m2) participants showed a high incidence of frailty in both sexes. BMI showed an association with frailty only in males. In both sexes, FFMI was associated with a lower incidence of frailty, which was statistically significant in the fully adjusted models. In the female, fat-related indexes including body fat percentage, FMI, and TFMI showed a significant association with poor physical performance. In contrast, males with low FFMI only showed a significant association with poor physical performance.ConclusionsFrailty closely correlated with FFMI in both sexes. The poor physical performance associated with frailty correlated with fat-related body composition in females and fat-free mass in males owing to the difference in body composition between the sexes. In the assessment of frailty, body composition and sex-related differences should be analyzed.

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