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Aim: The objective of this study is to generate metabolic phenotypes based on structure–function relationships. Methods: In 459 healthy adults (54% females, 18 and 40 years old), we analyzed body composition by air-displacement densitometry (to assess fat mass, (FM) and fat-free mass (FFM)) and whole-body magnetic resonance imaging (to assess skeletal muscle mass (SMM) and masses of brain, heart, liver, kidneys, and subcutaneous (SAT) and visceral adipose tissue (VAT)), resting energy expenditure (REE) by indirect calorimetry, and plasma concentrations of insulin (Ins) and leptin (Lep). Results: Three “functional body composition-derived phenotypes” (FBCPs) were derived: (1) REE on FFM-FBCP, (2) Lep on FM-FBCP, and (3) Ins on VAT-FBCP. Assuming that being within the ±?5% range of the respective regression lines reflects a “normal” structure–function relationship, three “normal” FBCPs were generated with prevalences of 9.0%, 5.1%, and 6.8%, respectively, of the study population. The three “FBCPs” did not overlap and were independent from each other. When compared with the two other FBCPs, the “Lep on FM-FBCP” was leanest, whereas the “REE on FFM-FBCP” had the highest BMI and SAT. Taking into account FFM composition, a hierarchical multi-level model is proposed with brain at level 1, the liver at level 2, and SMM and FM at level 3 with insulin coordinating the interplay between level 1 and 2, whereas variance in plasma insulin levels impacts energy and substrate metabolism in SMM and AT. Conclusion: Structure–function relationships can be used to generate FBCPs. Different FBCPs reflect different dimensions of normality (or health). This is evidence for the idea that there is no across the board “normal” state.

Common obesity-associated hepatic steatosis (nonalcoholic fatty liver disease (NAFLD)) and insulin resistance are mainly caused by dysfunctional adipose tissue. This adipose tissue dysfunction leads to increased delivery of NEFA and glycerol to the liver that (i) drives hepatic gluconeogenesis and (ii) facilitates the accumulation of lipids and insulin signaling inhibiting lipid intermediates. Dysfunctional adipose tissue can be caused by impaired lipid storage (overflow hypothesis, characterized by large visceral adipocytes) or increased lipolysis (due to impaired postprandial suppression of lipolysis in inflamed, insulin-resistant adipocytes). In line with the adipose tissue expandability hypothesis the amount and distribution of adipose tissue correlate with its dysfunction and thus with liver fat. This relationship is however modified by endocrine effects on lipid storage and lipolysis as well as dietary effects on hepatic lipogenesis and lipid oxidation. The association between body composition characteristics like visceral obesity or fat cell size and ectopic liver fat is modified by these influences. Phenotyping obesity according to metabolic risk should integrate body composition characteristics, endocrine parameters and information on diet.

Body mass in humans and animals is strongly associated with the rate of heat production as defined by resting energy expenditure (REE). Beginning with the ancient Greeks up to the present time, philosophers and scientists have endeavored to understand the nature and sources of bodily heat. Today we recognize that body mass consists of organs and tissues, each of which produces a specified amount of heat at rest. An individual organ’s REE can now be estimated in vivo as the product of its assumed mass-specific metabolic rate and its imaging-derived mass; whole-body REE reflects the sum of organ and tissue metabolic rates. The sizes of organs and total body mass in adults are governed by two main factors, a person’s stature or height, and their level of adiposity. With greater body size, as represented by adult height independent of adiposity, organs remain stable or increase in mass according to distinct “scaling” patterns. Similarly, with greater relative adiposity organs adaptively accommodate to the increase in imposed mechanical and metabolic loading conditions. Through a detailed analysis of these stature and adiposity effects, we show how classical statistical REE prediction models can be mechanistically understood at the anatomic body composition level.

We investigated whether fat mass (FM) and total adipose tissue (TAT) can be used interchangeably and FM per TAT adds to metabolic risk assessment. Cross-sectional data were assessed in 377 adults (aged 18–60 years; 51.2% women). FM was measured by either 4-compartment (4C) model or quantitative magnetic resonance (QMR); total-, subcutaneous- and visceral adipose tissue (TAT, SAT, VAT), and liver fat by whole-body MRI; leptin, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), C-reactive protein (CRP), and triglycerides; resting energy expenditure and respiratory quotient by indirect calorimetry were determined. Correlations and stepwise multivariate regression analyses were performed. FM4Cand FMQMRwere associated with TAT (r4C= 0.96, rQMR= 0.99) with a mean FM per TAT of 0.85 and 1.01, respectively. Regardless of adiposity, there was a considerable inter-individual variance of FM/TAT-ratio (FM4C/TAT-ratio: 0.77–0.94; FMQMR/TAT-ratio: 0.89–1.10). Both, FM4Cand TAT were associated with metabolic risks. Further, FM4C/TAT-ratio was positively related to leptin but inversely with CRP. There was no association between FM4C/TAT-ratio and VAT/SAT or liver fat. FM4C/TAT-ratio added to the variance of leptin and CRP. Independent of FM or TAT, FM4C/TAT-ratio adds to metabolic risk assessment. Therefore, the interchangeable use of FM and TAT to assess metabolic risks is questionable as both parameters may complement each other.

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.