No abstract

During early weight loss, AT is associated with a fall in insulin secretion and body fluid balance. This trial was registered at clinicaltrials.gov as NCT01737034.

Body composition is related to various physiological and pathological states. Characterization of individual body components adds to understand metabolic, endocrine and genetic data on obesity and obesity-related metabolic risks, e.g. insulin resistance. The obese phenotype is multifaceted and can be characterized by measures of body fat, leg fat, liver fat and skeletal muscle mass rather than by body mass index. The contribution of either whole body fat or fat distribution or individual fat depots to insulin resistance is moderate, but liver fat has a closer association with (hepatic) insulin resistance. Although liver fat is associated with visceral fat, its effect on insulin resistance is independent of visceral adipose tissue. In contrast to abdominal fat, appendicular or leg fat is inversely related to insulin resistance. The association between 'high fat mass + low muscle mass' (i.e. 'sarcopenic adiposity') and insulin resistance deserves further investigation and also attention in daily clinical practice. In addition to cross-sectional data, longitudinal assessment of body composition during controlled under- and overfeeding of normal-weight healthy young men shows that small decreases and increases in fat mass are associated with corresponding decreases and increases in insulin secretion as well as increases and decreases in insulin sensitivity. However, even under controlled conditions, there is a high intra- and inter-individual variance in the changes of (i) body composition; (ii) the 'body composition-glucose metabolism relationship' and (iii) glucose metabolism itself. Combining individual body components with their related functional aspects (e.g. the endocrine, metabolic and inflammatory profiles) will provide a suitable basis for future definitions of a 'metabolically healthy body composition'.

Metabolic adaptation to weight changes relates to body weight control, obesity and malnutrition. Adaptive thermogenesis (AT) refers to changes in resting and non-resting energy expenditure (REE and nREE) which are independent from changes in fat-free mass (FFM) and FFM composition. AT differs in response to changes in energy balance. With negative energy balance, AT is directed towards energy sparing. It relates to a reset of biological defence of body weight and mainly refers to REE. After weight loss, AT of nREE adds to weight maintenance. During overfeeding, energy dissipation is explained by AT of the nREE component only. As to body weight regulation during weight loss, AT relates to two different set points with a settling between them. During early weight loss, the first set is related to depleted glycogen stores associated with the fall in insulin secretion where AT adds to meet brain’s energy needs. During maintenance of reduced weight, the second set is related to low leptin levels keeping energy expenditure low to prevent triglyceride stores getting too low which is a risk for some basic biological functions (e.g., reproduction). Innovative topics of AT in humans are on its definition and assessment, its dynamics related to weight loss and its constitutional and neuro-endocrine determinants.

BackgroundThe group of colorectal cancer (CRC) survivors continues to grow worldwide. Understanding health-related quality of life (HRQOL) determinants and consequences of HRQOL impairments in long-term CRC survivors may help to individualize survivorship care plans. We aimed to i) examine the HRQOL status of CRC long-term survivors, ii) identify cross-sectional sociodemographic and clinical correlates of HRQOL, and iii) investigate the prospective association of HRQOL after CRC diagnosis with all-cause mortality.MethodsWe assessed HRQOL within a Northern German cohort of 1294 CRC survivors at a median of 6 years after CRC diagnosis using the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). Cross-sectional correlates of different HRQOL dimensions were analyzed using multivariable-adjusted logistic regression models with HRQOL as a binary variable. With multivariable-adjusted Cox proportional hazards regression models, hazard ratios (HR) of all-cause mortality were estimated per 10-point-increments of an HRQOL summary score, a global quality of life scale, and HRQOL functioning and symptom domains.ResultsThe median HRQOL summary score was 87 (interquartile range: 75–94). Sex, age, education, tumor location, metastases, other cancers, type of therapy, and current stoma were identified as correlates of different HRQOL scales. After a median follow-up time of 7 years after HRQOL assessment, 175 participants had died. Nearly all HRQOL domains, except for cognitive functioning and diarrhea, were significantly associated with all-cause mortality. A 10-point-increment in the summary score decreased the risk of death by 24% (HR: 0.76; 95% CI: 0.70–0.82).ConclusionsHRQOL in CRC survivors appeared to be relatively high in the long term. Various clinical and sociodemographic factors were cross-sectionally associated with HRQOL in long-term CRC survivors. Lower HRQOL was associated with increased all-cause mortality. Individualized healthcare programs for CRC survivors (including psychosocial screening and interventions) are needed to detect decreased HRQOL and to further improve long-term HRQOL and survival.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-5075-1) contains supplementary material, which is available to authorized users.

BMI is widely used as a measure of weight status and disease risks; it defines overweight and obesity based on statistical criteria. BMI is a score; neither is it biologically sound nor does it reflect a suitable phenotype worthwhile to study. Because of its limited value, BMI cannot provide profound insight into obesity biology and its co-morbidity. Alternative assessments of weight status include detailed phenotyping by body composition analysis (BCA). However, predicting disease risks, fat mass, and fat-free mass as assessed by validated techniques (i.e., densitometry, dual energy X ray absorptiometry, and bioelectrical impedance analysis) does not exceed the value of BMI. Going beyond BMI and descriptive BCA, the concept of functional body composition (FBC) integrates body components into regulatory systems. FBC refers to the masses of body components, organs, and tissues as well as to their inter-relationships within the context of endocrine, metabolic and immune functions. FBC can be used to define specific phenotypes of obesity, e.g. the sarcopenic-obese patient. Well-characterized obesity phenotypes are a precondition for targeted research (e.g., on the genomics of obesity) and patient-centered care (e.g., adequate treatment of individual obese phenotypes such as the sarcopenic-obese patient). FBC contributes to a future definition of overweight and obesity based on physiological criteria rather than on body weight alone.

As yet, genome-wide association studies (GWAS) have not added much to our understanding of the mechanisms of body weight control and of the etiology of obesity. This shortcoming is widely attributed to the complexity of the issues. The appeal of this explanation notwithstanding, we surmise that (i) an oversimplification of the phenotype (namely by the use of crude anthropometric traits) and (ii) a lack of sound concepts of body weight control and, thus, a lack of a clear research focus have impeded better insights most. The idea of searching for polygenetic mechanisms underlying common forms of obesity was born out of the impressive findings made for monogenetic forms of extreme obesity. In the case of common obesity, however, observational studies on normal weight and overweight subjects never provided any strong evidence for a tight internal control of body weight. In addition, empirical studies of weight changes in normal weight and overweight subjects revealed an intra-individual variance that was similar to inter-individual variance suggesting the absence of tight control of body weight. Not least, this lack of coerciveness is reflected by the present obesity epidemic. Finally, data on detailed body composition highlight that body weight is too heterogeneous a phenotype to be controlled as a single entity. In summary GWAS of obesity using crude anthropometric traits have likely been misled by popular heritability estimates that may have been inflated in the first place. To facilitate more robust and useful insights into the mechanisms of internal control of human body weight and, consequently, the genetic basis of obesity, we argue in favor of a broad discussion between scientists from the areas of integrative physiologic and of genomics. This discussion should aim at better conceived studies employing biologically more meaningful phenotypes based on in depth body composition analysis. To advance the scientific community-including the editors of our top journals-needs a re-launch of future GWAS of obesity.

Both higher GI and higher carbohydrate content affect substrate oxidation and thus the regain in body weight in healthy men. These results argue in favor of a lower glycemic load diet for weight maintenance after weight loss.