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Nature Medicine Dec 2019Proteins are effector molecules that mediate the functions of genes and modulate comorbidities, behaviors and drug treatments. They represent an enormous potential...
Proteins are effector molecules that mediate the functions of genes and modulate comorbidities, behaviors and drug treatments. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.
Topics: Adipose Tissue; Blood Proteins; Body Composition; Exercise; Female; Humans; Intra-Abdominal Fat; Life Style; Liver; Male; Precision Medicine; Risk Factors
PubMed: 31792462
DOI: 10.1038/s41591-019-0665-2 -
The Journal of Nutrition, Health & Aging 2023
Topics: Humans; Aged; Frailty; Adiposity; Obesity; Frail Elderly; Body Composition
PubMed: 37357321
DOI: 10.1007/s12603-023-1930-0 -
Nutrients Nov 2023Assessing hydration status and monitoring body composition represent crucial aspects when discussing the advantages of embracing a healthy lifestyle, given its...
Assessing hydration status and monitoring body composition represent crucial aspects when discussing the advantages of embracing a healthy lifestyle, given its significant impact on both health and sports performance [...].
Topics: Body Composition; Athletic Performance
PubMed: 38004207
DOI: 10.3390/nu15224814 -
Frontiers in Endocrinology 2023Testosterone is an essential sex hormone that plays a vital role in the overall health and development of males. It is well known that obesity decreases testosterone...
BACKGROUND
Testosterone is an essential sex hormone that plays a vital role in the overall health and development of males. It is well known that obesity decreases testosterone levels, but it is difficult to determine the causal relationship between body composition and testosterone.
METHODS
To investigate potential causal associations between body composition and testosterone levels by a first time application of Mendelian randomization methods. Exposure variables in men included body composition (fat mass, fat-free mass, and body mass index). In addition to whole body fat and fat-free mass, we examined fat and fat-free mass for each body part (e.g., trunk, left arm, right arm, left leg and right leg) as exposures. Instrumental variables were defined using genome-wide association study data from the UK Biobank. Outcome variables in men included testosterone levels (total testosterone [TT], bioavailable testosterone [BT], and sex hormone-binding globulin [SHBG]). A one-sample Mendelian randomization analysis of inverse-variance weighted and weighted median was performed.
RESULTS
The number of genetic instruments for the 13 exposure traits related to body composition ranged from 156 to 540. Genetically predicted whole body fat mass was negatively associated with TT (β=-0.24, P=5.2×10-), BT (β=-0.18, P=5.8×10-) and SHBG (β=-0.06, P=8.0×10-). Genetically predicted whole body fat-free mass was negatively associated with BT (β=-0.04, P=2.1×10-4), but not with TT and SHBG, after multiple testing corrections. When comparing the causal effect on testosterone levels, there was a consistent trend that the effect of fat mass was more potent than that of fat-free mass. There were no differences between body parts.
CONCLUSION
These results show that reducing fat mass may increase testosterone levels.
Topics: Male; Humans; Testosterone; Mendelian Randomization Analysis; Genome-Wide Association Study; Body Composition; Gonadal Steroid Hormones
PubMed: 38089610
DOI: 10.3389/fendo.2023.1277393 -
Journal of Physiological Anthropology May 2022Despite the presence of body composition studies in Russia, there are no current reviews on this topic, and the results are relatively rarely published abroad. Our aim... (Review)
Review
Despite the presence of body composition studies in Russia, there are no current reviews on this topic, and the results are relatively rarely published abroad. Our aim was to describe the history and current state of this research work, to list unresolved problems, and to outline possible developmental trends. For completeness, in the initial part of the review, traditional research areas indirectly related to body composition studies are considered, namely, the analysis of biological variation of anthropometric parameters and somatotyping.It can be seen that anthropometry and bioimpedance analysis (BIA) are mainly used to assess body composition in Russia. Other methods, such as double-energy X-ray absorptiometry (DXA), are utilized less often. The achievements include the common use of comprehensive anthropometry in anthropological studies, some advancements in clinical studies, approbation of potentially important methods such as the deuterium dilution method and three-dimensional laser-based photonic scanning, and ongoing mass population BIA measurements in health centers. Various bioimpedance instruments are manufactured, the local reference BIA body composition data are available, and a large updated BIA database is ready for international comparisons.Among major limitations of body composition research in Russia, one can note the lack of validation studies using reference methods, so that foreign regression formulas are used with the double indirect methods, such as anthropometry and BIA, despite the fact that their accuracy has not yet been checked in our population. Conventional reference body composition assessment methods, such as three- or four-component molecular-level models and whole-body in vivo neutron activation analysis, were not applied yet, despite the technical feasibility.In general, it can be argued that the body composition research in Russia follows the observed global trends. Along with the achievements, there are a number of unresolved methodological and organizational issues. Prospects for further research include validation studies, updating reference population body composition data, and establishing local cut-offs for malnutrition and disease risks. In our view, further development could be facilitated with the establishment of well-equipped Human Body Composition Units in major Russian research centers, such as Moscow State University, which could be assigned a coordinating and methodical role.
Topics: Absorptiometry, Photon; Anthropometry; Body Composition; Electric Impedance; Humans; Russia
PubMed: 35505405
DOI: 10.1186/s40101-022-00291-3 -
Clinical Nutrition (Edinburgh, Scotland) Jul 2023Lean mass is considered the best predictor of bone mass, as it is an excellent marker of bone mechanical stimulation, and changes in lean mass are highly correlated with...
BACKGROUND AND AIMS
Lean mass is considered the best predictor of bone mass, as it is an excellent marker of bone mechanical stimulation, and changes in lean mass are highly correlated with bone outcomes in young adults. The aim of this study was to use cluster analysis to examine phenotype categories of body composition assessed by lean and fat mass in young adults and to assess how these body composition categories are associated with bone health outcomes.
METHODS
Cluster cross-sectional analyses of data from 719 young adults (526 women) aged 18-30 years from Cuenca and Toledo, Spain, were conducted. Lean mass index (lean mass (kg)/height (m)), fat mass index (fat mass (kg)/height (m)), bone mineral content (BMC) and areal bone mineral density (aBMD) were assessed by dual-energy X-ray absorptiometry.
RESULTS
A cluster analysis of lean mass and fat mass index z scores resulted in a classification of a five-category cluster solution that could be interpreted according to the body composition phenotypes of individuals as follows: high adiposity-high lean mass (n = 98), average adiposity-high lean mass (n = 113), high adiposity-average lean mass (n = 213), low adiposity-average lean mass (n = 142), and average adiposity-low lean mass (n = 153). ANCOVA models showed that individuals in clusters with a higher lean mass had significantly better bone health (z score: 0.764, se: 0.090) than their peers in other cluster categories (z score: -0.529, se: 0.074) after controlling for sex, age, and cardiorespiratory fitness (p < 0.05). Additionally, subjects belonging to the categories with a similar average lean mass index but with high or low-adiposity levels (z score: 0.289, se: 0.111; z score: 0.086, se: 0.076) showed better bone outcomes when the fat mass index was higher (p < 0.05).
CONCLUSIONS
This study confirms the validity of a body composition model using a cluster analysis to classify young adults according to their lean mass and fat mass indices. In addition, this model reinforces the main role of lean mass on bone health in this population and that in phenotypes with high-average lean mass, factors associated with fat mass may also have a positive effect on bone status.
Topics: Humans; Bone Density; Cross-Sectional Studies; Absorptiometry, Photon; Body Composition; Obesity; Adiposity; Phenotype; Cluster Analysis; Body Mass Index
PubMed: 37244756
DOI: 10.1016/j.clnu.2023.05.006 -
Roczniki Panstwowego Zakladu Higieny 2023Fat and fat-free/muscle mass and their ratio reflecting the possible presence of obesity or sarcopenic obesity are important in assessing body composition.
BACKGROUND
Fat and fat-free/muscle mass and their ratio reflecting the possible presence of obesity or sarcopenic obesity are important in assessing body composition.
OBJECTIVE
The aim of the work was to assess the use of fat and fat-free mass and their ratio in the diagnosis of sarcopenic obesity, as well as correlations with selected anthropometric, somatic and biochemical parameters and indices.
MATERIAL AND METHODS
The object of the study was a group of 201 women (20-68 aged) randomly selected from the population without the presence of a serious disease or without the use of medication. Body composition was assessed by the MFBIA method (InBody 720). We used the ratio of fat to fat-free mass (FM/FFM) to define sarcopenic obesity. A Biolis 24i Premium biochemical analyzer was used to determine biochemical parameters.
RESULTS
Using FM and FFM values and their mutual ratio, we identified women with a healthy body weight (28.9%), obese women (58.2%) and women with sarcopenic obesity (12.9%). Values of anthropometric parameters (body weight, BMI, WC, WHR, WHtR, BAI, FM (kg, %), FMI, VFA, FFM (kg), FFMI, SMM (kg), SMMI, ICW, ECW, TBW, CHC, HC), with the exception of FFM (%), SMM (%) and TBW (%), increased significantly with increasing FM/FFM values, so the highest values were found in subjects with sarcopenic obesity. In the case of biochemical parameters, with increasing FM/FFM values, the values of T-CH, LDL, TAG, GLU, hs-CRP, UA, systolic and diastolic blood pressure also increased, so the highest values were again found in women with sarcopenic obesity. HDL values, on the contrary, decreased. FM/FFM had the strongest positive association with the proportion of fat mass on body weight (r=0.989), then with FMI (r=0.980), FM (r=0.965), VFA (r=0.938), WHtR (r=0.937), BMI (r=0.922), WC (r=0.901. We found the strongest negative association with the proportion of FFM on body weight (r=-0.989), the proportion of total body water (r=-0.988) and the proportion of skeletal muscle mass (r=-0.987).
CONCLUSIONS
FM/FFM correlates excellently with FM and VFA and can be implemented to diagnose obesity. In order to comprehensively evaluate the state of health and body composition, the proportionality of not only fat, but also fat-free/muscle mass should be analyzed, because it turns out that a negative impact on health and survival is associated not only with an excessive amount of adipose tissue, but also with a lower muscle mass.
Topics: Humans; Female; Aged; Sarcopenia; Body Mass Index; Obesity; Body Composition; Adipose Tissue; Body Weight
PubMed: 37010407
DOI: 10.32394/rpzh.2023.0243 -
Hormone Research 2003Body composition during puberty is a marker of metabolic changes that occur during this period of growth and maturation, and, thus, holds key information regarding... (Review)
Review
Body composition during puberty is a marker of metabolic changes that occur during this period of growth and maturation, and, thus, holds key information regarding current and future health. During puberty, the main components of body composition (total body fat, lean body mass, bone mineral content) all increase, but considerable sexual dimorphism exists. Methods for measuring body composition (e.g. densitometry and dual-energy X-ray absorptiometry) and degree of maturity will be discussed in this review. Components of body composition show age-to-age correlations (i.e. 'tracking'), especially from adolescence onwards. Furthermore, adipose tissue is endocrinologically active and is centrally involved in the interaction between adipocytokines, insulin and sex-steroid hormones, and thus influences cardiovascular and metabolic disease processes. In conclusion, pubertal body composition is important, not only for the assessment of contemporaneous nutritional status, but also for being linked directly to the possible onset of chronic disease later in life and is, therefore, useful for disease risk assessment and intervention early in life.
Topics: Adolescent; Body Composition; Child; Female; Humans; Male; Puberty
PubMed: 12955016
DOI: 10.1159/000071224 -
Nutrients May 2023Food intake patterns determine changes in energy expenditure due to their influence on body size and composition (percentage of fat, bone, and muscle), which can...
Food intake patterns determine changes in energy expenditure due to their influence on body size and composition (percentage of fat, bone, and muscle), which can modulate signaling pathways that optimize energy consumption [...].
Topics: Body Composition; Diet; Feeding Behavior; Energy Metabolism; Energy Intake
PubMed: 37299506
DOI: 10.3390/nu15112544 -
Reviews in Endocrine & Metabolic... Oct 2023Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension,... (Review)
Review
Obesity is the most extended metabolic alteration worldwide increasing the risk for the development of cardiometabolic alterations such as type 2 diabetes, hypertension, and dyslipidemia. Body mass index (BMI) remains the most frequently used tool for classifying patients with obesity, but it does not accurately reflect body adiposity. In this document we review classical and new classification systems for phenotyping the obesities. Greater accuracy of and accessibility to body composition techniques at the same time as increased knowledge and use of cardiometabolic risk factors is leading to a more refined phenotyping of patients with obesity. It is time to incorporate these advances into routine clinical practice to better diagnose overweight and obesity, and to optimize the treatment of patients living with obesity.
Topics: Humans; Diabetes Mellitus, Type 2; Waist Circumference; Obesity; Body Mass Index; Adiposity; Body Composition; Risk Factors
PubMed: 36928809
DOI: 10.1007/s11154-023-09796-3