Ultra-processed food consumption and the risk of non-alcoholic fatty liver disease—What are the proposed mechanisms?

A high consumption of ultra-processed food (UPF) is a hallmark of Western diets that has been related to increased risk of non-communicable diseases. As an underlying mechanism, UPF may promote non-alcoholic fatty liver disease (NAFLD) which is a key driver of metabolic impairment with extra-hepatic manifestations like type 2 diabetes, cardiovascular disease, chronic kidney disease, and osteoporosis among others. The present review provides an overview of UPF properties that may promote NAFLD and are thus potential targets for reformulation of UPF. Such approaches should address improvements in the quality of carbohydrates and fat, changes in food texture that lower eating rate as well as ingredients that prevent excess caloric intake or avoid dysbiosis and leaky gut syndrome. Promising strategies are enrichment with fiber, prebiotics, phytochemicals, and protein with a concurrent reduction in glycemic load, energy density, saturated fatty acids (FA; SFA), emulsifiers, fructose, and non-caloric sweeteners. Future studies are needed to examine the interactive and protective effects of such modifications in the composition of UPF on prevention and treatment of NAFLD

Analysis of the adiponectin paradox in healthy older people

Background It remains unknown why adiponectin levels are associated with poor physical functioning, skeletal muscle mass and increased mortality in older populations. Methods In 190 healthy adults (59-86 years, BMI 17-37 kg/m2 , 56.8% female), whole body skeletal muscle mass (normalized by height, SMI, kg/m2 ), muscle and liver fat were determined by magnetic resonance imaging. Bone mineral content (BMC) and density (BMD) were assessed by dual X-ray absorptiometry (n = 135). Levels of insulin-like growth factor 1 (IGF-1), insulin, inflammation markers, leptin and fibroblast growth factor 21 were measured as potential determinants of the relationship between adiponectin and body composition. Results Higher adiponectin levels were associated with a lower SMI (r = -0.23, P < 0.01), BMC (r = -0.17, P < 0.05) and liver fat (r = -0.20, P < 0.05) in the total population and with higher muscle fat in women (r = 0.27, P < 0.01). By contrast, IGF-1 showed positive correlations with SMI (r = 0.33), BMD (r = 0.37) and BMC (r = 0.33) (all P < 0.01) and a negative correlation with muscle fat (r = -0.17, P < 0.05). IGF-1 was negatively associated with age (r = -0.21, P < 0.01) and with adiponectin (r = -0.15, P < 0.05). Stepwise regression analyses revealed that IGF-1, insulin and leptin explained 18% of the variance in SMI, and IGF-1, leptin and age explained 16% of the variance in BMC, whereas adiponectin did not contribute to these models. Conclusions Associations between higher adiponectin levels and lower muscle or bone mass in healthy older adults may be explained by a decrease in IGF-1 with increasing adiponectin levels

The case of GWAS of obesity: does body weight control play by the rules?

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

Network-based quantitative trait linkage analysis of microbiome composition in inflammatory bowel disease families

Introduction: Inflammatory bowel disease (IBD) is characterized by a dysbiosis of the gut microbiome that results from the interaction of the constituting taxa with one another, and with the host. At the same time, host genetic variation is associated with both IBD risk and microbiome composition.Methods: In the present study, we defined quantitative traits (QTs) from modules identified in microbial co-occurrence networks to measure the inter-individual consistency of microbial abundance and subjected these QTs to a genome-wide quantitative trait locus (QTL) linkage analysis.Results: Four microbial network modules were consistently identified in two cohorts of healthy individuals, but three of the corresponding QTs differed significantly between IBD patients and unaffected individuals. The QTL linkage analysis was performed in a sub-sample of the Kiel IBD family cohort (IBD-KC), an ongoing study of 256 German families comprising 455 IBD patients and 575 first- and second-degree, non-affected relatives. The analysis revealed five chromosomal regions linked to one of three microbial module QTs, namely on chromosomes 3 (spanning 10.79 cM) and 11 (6.69 cM) for the first module, chr9 (0.13 cM) and chr16 (1.20 cM) for the second module, and chr13 (19.98 cM) for the third module. None of these loci have been implicated in a microbial phenotype before.Discussion: Our study illustrates the benefit of combining network and family-based linkage analysis to identify novel genetic drivers of microbiome composition in a specific disease context

Beyond BMI: Conceptual Issues Related to Overweight and Obese Patients

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

Adherence to a plant-based diet in relation to adipose tissue volumes and liver fat content

Background: Better adherence to plant-based diets has been linked to lower risk of metabolic diseases but the effect on abdominal fat distribution and liver fat content is unclear. Objectives: We aimed to examine the association between different plant-based diet indices and measures of abdominal fat distribution and liver fat content. Methods: In a population-based sample of 578 individuals from Northern Germany (57% male, median age 62 y), diet was assessed with a validated FFQ and an overall, a healthy, and an unhealthy plant-based diet index were derived. Participants underwent MRI to assess volumes of visceral and subcutaneous abdominal adipose tissue and liver signal intensity (LSI), a measure of liver fat content. Fatty liver disease (FLD) was defined as log LSI >= 3.0. Cross-sectional associations of the plant-based diet indices with visceral and subcutaneous abdominal fat volumes, LSI, and FLD were assessed in linear and logistic regression analyses. The most comprehensive model adjusted for age, sex, education, smoking, alcohol, physical activity, energy intake, diabetes, hyperlipidemia, and BMI. Results: Higher overall and healthy plant-based diet indices both revealed statistically significant associations with lower visceral and subcutaneous abdominal adipose tissue volumes and with lower odds of FLD in multivariable-adjusted models without BMI. Upon additional adjustment for BMI, only the association of the healthy plant-based diet with visceral adipose tissue remained statistically significant (per 10-point higher healthy plant-based diet index, percentage change in visceral adipose tissue: -4.9%, 95% CI: -8.6%, -2.0%). None of the plant-based diet indices was associated with LSI. The unhealthy plant-based diet index was unrelated to any of the abdominal or liver fat parameters. Conclusions: Adherence to healthy plant-based diets was associated with lower visceral adipose tissue. None of the other examined associations remained statistically significant after adjustment for BMI

Endocrine Determinants of Changes in Insulin Sensitivity and Insulin Secretion during a Weight Cycle in Healthy Men

<div><p>Objective</p><p>Changes in insulin sensitivity (IS) and insulin secretion occur with perturbations in energy balance and glycemic load (GL) of the diet that may precede the development of insulin resistance and hyperinsulinemia. Determinants of changes in IS and insulin secretion with weight cycling in non-obese healthy subjects remain unclear.</p><p>Methods</p><p>In a 6wk controlled 2-stage randomized dietary intervention 32 healthy men (26±4y, BMI: 24±2kg/m²) followed 1wk of overfeeding (OF), 3wks of caloric restriction (CR) containing either 50% or 65% carbohydrate (CHO) and 2wks of refeeding (RF) with the same amount of CHO but either low or high glycaemic index at ±50% energy requirement. Measures of IS (basal: HOMA-index, postprandial: Matsuda-ISI), insulin secretion (early: Stumvoll-index, total: tAUC-insulin/tAUC-glucose) and potential endocrine determinants (ghrelin, leptin, adiponectin, thyroid hormone levels, 24h-urinary catecholamine excretion) were assessed.</p><p>Results</p><p>IS improved and insulin secretion decreased due to CR and normalized upon RF. Weight loss-induced improvements in basal and postprandial IS were associated with decreases in leptin and increases in ghrelin levels, respectively (r = 0.36 and r = 0.62, p<0.05). Weight regain-induced decrease in postprandial IS correlated with increases in adiponectin, fT3, TSH, GL of the diet and a decrease in ghrelin levels (r-values between -0.40 and 0.83, p<0.05) whereas increases in early and total insulin secretion were associated with a decrease in leptin/adiponectin-ratio (r = -0.52 and r = -0.46, p<0.05) and a decrease in fT4 (r = -0.38, p<0.05 for total insulin secretion only). After controlling for GL associations between RF-induced decrease in postprandial IS and increases in fT3 and TSH levels were no longer significant.</p><p>Conclusion</p><p>Weight cycling induced changes in IS and insulin secretion were associated with changes in all measured hormones, except for catecholamine excretion. While leptin, adiponectin and ghrelin seem to be the major endocrine determinants of IS, leptin/adiponectin-ratio and fT4 levels may impact changes in insulin secretion with weight cycling.</p><p>Trial Registration</p><p>ClinicalTrials.gov <a href="https://clinicaltrials.gov/ct2/show/NCT01737034" target="_blank">NCT01737034</a></p></div

Determinants of bone mass in older adults with normal- and overweight derived from the crosstalk with muscle and adipose tissue

Abstract Lower bone mass in older adults may be mediated by the endocrine crosstalk between muscle, adipose tissue and bone. In 150 community-dwelling adults (59–86 years, BMI 17–37 kg/m2; 58.7% female), skeletal muscle mass index, adipose tissue and fat mass index (FMI) were determined. Levels of myokines, adipokines, osteokines, inflammation markers and insulin were measured as potential determinants of bone mineral content (BMC) and density (BMD). FMI was negatively associated with BMC and BMD after adjustment for mechanical loading effects of body weight (r-values between −0.37 and −0.71, all p < 0.05). Higher FMI was associated with higher leptin levels in both sexes, with higher hsCRP in women and with lower adiponectin levels in men. In addition to weight and FMI, sclerostin, osteocalcin, leptin × sex and adiponectin were independent predictors of BMC in a stepwise multiple regression analysis. Muscle mass, but not myokines, showed positive correlations with bone parameters that were weakened after adjusting for body weight (r-values between 0.27 and 0.58, all p < 0.01). Whereas the anabolic effect of muscle mass on bone in older adults may be partly explained by mechanical loading, the adverse effect of obesity on bone is possibly mediated by low-grade inflammation, higher leptin and lower adiponectin levels

Changes in endocrine parameters due to overfeeding (OF) caloric restriction (CR) and refeeding (RF) (values are means ±SD).

<p>^†p<0.05</p><p>^††p<0.01</p><p>^†††p<0.001 significantly different from baseline</p><p>*p<0.05</p><p>**p<0.01</p><p>***p<0.001 significantly different from previous period; Repeated measures ANOVA with Bonferroni adjustments</p><p><i>OGTT</i>, oral glucose tolerance test; iAUC, incremental area under the curve</p><p>Changes in endocrine parameters due to overfeeding (OF) caloric restriction (CR) and refeeding (RF) (values are means ±SD).</p