Objective: A wide variety of methods are available to assess dietary intake, each one with different strengths and weaknesses. Researchers face multiple challenges when diet and nutrition need to be accurately assessed, particularly in the selection of the most appropriate dietary assessment method for their study. The goal of the current collaborative work is to present a collection of available resources for dietary assessment implementation.Design/Setting/Participants: As a follow-up to the 9th International Conference on Diet and Physical Activity Methods held in 2015, developers of dietary assessment toolkits agreed to collaborate in the preparation of the present paper, which provides an overview of each toolkit. The toolkits presented include: the Diet, Anthropometry and Physical Activity Measurement Toolkit (DAPA; UK); the National Cancer Institute’s (NCI) Dietary Assessment Primer (USA); the Nutritools website (UK); the Australasian Child and Adolescent Obesity Research Network (ACAORN) method selector (Australia); and the Danone Dietary Assessment Toolkit (DanoneDAT; France). An at-a-glance summary of features and comparison of the toolkits is provided.Results: The present review contains general background on dietary assessment, along with a summary of each of the included toolkits, a feature comparison table and direct links to each toolkit, all of which are freely available online.Conclusions: This overview of dietary assessment toolkits provides comprehensive information to aid users in the selection and implementation of the most appropriate dietary assessment method, or combination of methods, with the goal of collecting the highest-quality dietary data possible.

The gut bacterial species Akkermansia muciniphila is associated with a healthier clinical profile. The purpose of this study was to determine the association between A. muciniphila and glucose homeostasis in patients undergoing bariatric surgery (BS): gastric banding (GB) or Roux-en-Y gastric bypass (RYGB). This nonrandomized prospective study included 65 women with severe obesity. Longitudinal analysis included subjects for whom A. muciniphila data were available at follow-up [1, 3, and 12 mo; GB ( n = 10) or RYGB ( n = 11)]. Glucose homeostasis markers were measured under fasting conditions (glucose, insulin, and HbA1c) or during an oral glucose tolerance test. Fecal microbiota was analyzed using shotgun metagenomics, and A. muciniphila relative abundance was assessed with 16S rRNA quantitative PCR. A. muciniphila relative abundance was significantly lower in severe obesity [mean body mass index, 45.7 kg/m² (SD 5.4)] than in moderate obesity [33.2 kg/m² (SD 3.8)] but not associated with glucose homeostasis markers. A significant increase in A. muciniphila relative abundance after RYGB was not correlated with metabolic improvement. Baseline A. muciniphila abundance was correlated with bacterial gene richness and was highest in the high-richness Ruminococcaceae enterotype. A. muciniphila increased in relative abundance after BS in patients with low baseline A. muciniphila abundance, especially those with a Bacteroides type 2 enterotype classification. Although decreased in severe obesity, relative abundance of A. muciniphila was not associated with glucose homeostasis before or after BS. A certain level of A. muciniphila abundance might be required to observe a beneficial link to health. The severity of obesity and gut dysbiosis may partly explain the discrepancy with previous findings in less obese populations.

Gut microbiota composition is influenced by environmental factors and has been shown to impact body metabolism. Objective: To assess the gut microbiota profile before and after Roux-en-Y gastric bypass (RYGB) and the correlation with food intake and postoperative type 2 diabetes remission (T2Dr). Design: Gut microbiota profile from obese diabetic women was evaluated before (n = 25) and 3 (n = 20) and 12 months (n = 14) after RYGB, using MiSeq Illumina-based V4 bacterial 16S rRNA gene profiling. Data on food intake (7-day record) and T2Dr (American Diabetes Association (ADA) criteria) were recorded. Results: Preoperatively, the abundance of five bacteria genera differed between patients with (57%) and without T2Dr (p < 0.050). Preoperative gut bacteria genus signature was able to predict the T2Dr status with 0.94 accuracy ROC curve (receiver operating characteristic curve). Postoperatively (vs. preoperative), the relative abundance of some gut bacteria genera changed, the gut microbial richness increased, and the Firmicutes to Bacteroidetes ratio (rFB) decreased (p < 0.05) regardless of T2Dr. Richness levels was correlated with dietary profile pre and postoperatively, mainly displaying positive and inverse correlations with fiber and lipid intakes, respectively (p < 0.05). Conclusions: Gut microbiota profile was influenced by RYGB and correlated with diet and T2Dr preoperatively, suggesting the possibility to assess its composition to predict postoperative T2Dr.

Iron status and immune response become impaired in situations that involve chronic inflammation, such as obesity or aging. Little is known, however, about the additional burden that obesity may place on the iron status and immune response in the elderly. This question is relevant given the rising numbers of elderly obese (BMI >30 kg/m(2)) individuals and the high prevalence of iron deficiency worldwide. Iron is necessary for proper function of both the innate and adaptive immune system. Hepcidin, a peptide hormone that regulates cellular iron export, is essential for the maintenance of iron homeostasis. Therefore, since immune cells require iron for proper function hepcidin may also play an important role in immune response. In this review, we summarize the evidence for hepcidin as a link between the fields of gerontology, obesity, iron biology, and immunology. We also identify several gaps in knowledge and unanswered questions pertaining to iron homeostasis and immunity in obese populations. Finally, we review studies that have shown the impact of weight loss, focusing on calorie restriction, iron homeostasis, and immunity. These studies are important both in elucidating mechanistic links between obesity and health impairments and identifying possible approaches to target immune impairment and iron deficiency as comorbidities of obesity.

Background: The mechanisms responsible for calorie restriction (CR)-induced improvement in insulin sensitivity (IS) have not been fully elucidated. Greater insight can be achieved through deep biological phenotyping of subjects undergoing CR, and integration of big data. Materials and Methods: An integrative approach was applied to investigate associations between change in IS and factors from host, microbiota, and lifestyle after a 6-week CR period in 27 overweight or obese adults (ClinicalTrials.gov: NCT01314690). Partial least squares regression was used to determine associations of change (week 6 – baseline) between IS markers and lifestyle factors (diet and physical activity), subcutaneous adipose tissue (sAT) gene expression, metabolomics of serum, urine and feces, and gut microbiota composition. ScaleNet, a network learning approach based on spectral consensus strategy (SCS, developed by us) was used for reconstruction of biological networks. Results: A spectrum of variables from lifestyle factors (10 nutrients), gut microbiota (10 metagenomics species), and host multi-omics (metabolic features: 84 from serum, 73 from urine, and 131 from feces; and 257 sAT gene probes) most associated with IS were identified. Biological network reconstruction using SCS, highlighted links between changes in IS, serum branched chain amino acids, sAT genes involved in endoplasmic reticulum stress and ubiquitination, and gut metagenomic species (MGS). Linear regression analysis to model how changes of select variables over the CR period contribute to changes in IS, showed greatest contributions from gut MGS and fiber intake. Conclusion: This work has enhanced previous knowledge on links between host glucose homeostasis, lifestyle factors and the gut microbiota, and has identified potential biomarkers that may be used in future studies to predict and improve individual response to weight-loss interventions. Furthermore, this is the first study showing integration of the wide range of data presented herein, identifying 115 variables of interest with respect to IS from the initial input, consisting of 9,986 variables. Clinical Trial Registration: clinicaltrials.gov (NCT01314690).