Multi-omic phenotyping reveals host-microbe responses to bariatric surgery, glycaemic control and obesity

Background Resolution of type 2 diabetes (T2D) is common following bariatric surgery, particularly Roux-en-Y gastric bypass. However, the underlying mechanisms have not been fully elucidated. Methods To address this we compare the integrated serum, urine and faecal metabolic profiles of participants with obesity ± T2D (n = 80, T2D = 42) with participants who underwent Roux-en-Y gastric bypass or sleeve gastrectomy (pre and 3-months post-surgery; n = 27), taking diet into account. We co-model these data with shotgun metagenomic profiles of the gut microbiota to provide a comprehensive atlas of host-gut microbe responses to bariatric surgery, weight-loss and glycaemic control at the systems level. Results Here we show that bariatric surgery reverses several disrupted pathways characteristic of T2D. The differential metabolite set representative of bariatric surgery overlaps with both diabetes (19.3% commonality) and body mass index (18.6% commonality). However, the percentage overlap between diabetes and body mass index is minimal (4.0% commonality), consistent with weight-independent mechanisms of T2D resolution. The gut microbiota is more strongly correlated to body mass index than T2D, although we identify some pathways such as amino acid metabolism that correlate with changes to the gut microbiota and which influence glycaemic control. Conclusion We identify multi-omic signatures associated with responses to surgery, body mass index, and glycaemic control. Improved understanding of gut microbiota - host co-metabolism may lead to novel therapies for weight-loss or diabetes. However, further experiments are required to provide mechanistic insight into the role of the gut microbiota in host metabolism and establish proof of causality

Dietary metabolite profiling brings new insight into the relationship between nutrition and metabolic risk: An IMI Direct study

Background: Dietary advice remains the cornerstone of prevention and management of type 2 diabetes (T2D). However, understanding the efficacy of dietary interventions is confounded by the challenges inherent in assessing free living diet. Here we profiled dietary metabolites to investigate glycaemic deterioration and cardiometabolic risk in people at risk of or living with T2D. Methods: We analysed data from plasma collected at baseline and 18-month follow-up in individuals from the Innovative Medicines Initiative (IMI) Diabetes Research on Patient Stratification (DIRECT) cohort 1 n=403 individuals with normal or impaired glucose regulation (prediabetic) and cohort 2 n=458 individuals with new onset of T2D. A dietary metabolite profile model (Tpred) was constructed using multivariate regression of 113 plasma metabolites obtained from targeted metabolomics assays. The continuous Tpred score was used to explore the relationships between diet, glycaemic deterioration and cardio-metabolic risk via multiple linear regression models. Findings: A higher Tpred was associated with healthier diets high in wholegrain (β=0.004 g, p=0.02 and β=0.003 g, p=0.03) and lower energy intake (β=-0.0002 kcal, p=0.04 and β=-0.0002 kcal, p=0.003), and saturated fat (β=-0.03 g, p<.0001 and β=-0.03 g, p<.0001), respectively for cohort 1 and 2. In both cohorts a higher Tpred score was also associated with lower total body adiposity and improved lipid profiles HDL-cholesterol (β=0.07 mmol/L, p<.0001), (β=0.08 mmol/L, p=0.0002), and triglycerides (β=-0.1 mmol/L, p=0.003), (β=-0.2 mmol/L, p=0.0002), respectively for cohort 1 and 2. In cohort 2, the Tpred score was negatively associated with liver fat content (β=-0.74 %, p<.0001), and lower fasting concentrations of HbA1c (β=-0.9mmol/mol, p=0.02), glucose (β=-0.2 mmol/L, p=0.01) and insulin (β=-11.0 pmol/mol, p=0.01). Longitudinal analysis showed at 18-month follow up a higher Tpred score was also associated lower total body adiposity in both cohorts and lower fasting glucose (β=-0.2 mmol/L, p=0.03) and insulin (β=-9.2 pmol/mol, p=0.04) concentrations in cohort 2. Interpretation: Plasma dietary metabolite profiling provides objective measures of diet intake, showing a relationship to glycaemic deterioration and cardiometabolic health

Older cancer patients' information and support needs surrounding treatment: An evaluation through the eyes of patients, relatives and professionals

Background Providing cancer patients with adequate treatment information is important for patients' health, well-being and satisfaction. Nurses play an important role in patient education. So far, few studies focused on the specific information needs of older cancer patients surrounding chemotherapy treatment. Given the growing incidence of cancer among older individuals, insight in these needs is crucial. This article describes the views of older cancer patients, their relatives and professionals on older patients' specific communication needs regarding chemotherapy treatment. Methods A qualitative design was used. Five focus group interviews were held with older cancer patients and their partners (two groups) and professionals with a background in nursing, oncology, gerontology and/or patient-provider communication (three groups). In addition, face to face in-depth interviews were conducted with older cancer patients. A total number of 38 patients and relatives participated, with a mean age of 67.6 years. The focus groups and interviews were audio-recorded for subsequent transcription and analysis. Results Older people have more difficulties processing and remembering information than younger ones. A trustful environment appears to be a prerequisite for reflection of older patients on the information provided and individualized information is essential to enhance memory of information. However, the results show that both patients and professionals experienced insufficient exploration of the patients' personal situation and individual information needs. Patients also strengthened the importance of sensitive communication, e.g. showing empathy en emotional support, throughout the continuum of cancer care. Moreover, potential areas of improvement were identified, including engaging the patients' relatives and encouraging patients and relatives to ask questions. Conclusion Patient education should be more tailored to older cancer patients' individual information and support needs and abilities by exploring the required amount and content of information, treatment goals and expectations. Nurses can establish a trustful environment by showing empathy and emotional support. Recommendations are given to enhance recall of information in older patients; information giving should be more structured by summarizing and repeating the most important, personally relevant information. To adapt to specific information needs, communication training for nurses and the use of aids such as a question prompt sheet could be useful tools

Urinary metabolic phenotype of blood pressure

Objective: Metabolic phenotyping (metabolomics) captures systems-level information on metabolic processes by simultaneously measuring hundreds of metabolites using spectroscopic techniques. Concentrations of these metabolites are affected by genetic (host, microbiome), environmental and dietary factors and may provide insights into biochemical pathways underlying raised blood pressure (BP) in populations. Design and method: Two separate, timed 24hr urine specimens were obtained from 2,031 women and men, aged 40–59, from 8 USA population samples in the INTERMAP Study. Proton Nuclear Magnetic Resonance (1H NMR) was used to characterize a urinary metabolic signature; this was unaffected by diurnal variability and sampling time as it captures end-products of metabolism over a 24hr period. Demographic, population, medical, lifestyle and anthropometric factors were accounted for in regression models to define a urinary metabolic phenotype associated with BP. Results: 29 structurally identified urinary metabolites covaried with systolic BP (SBP), after adjustment for demographic variables, and 18 metabolites with diastolic BP (DBP), with 16 metabolites overlapping between SBP and DBP. These included metabolites related to energy metabolism, renal function, diet and gut microbiota. After adjustment for medical and lifestyle covariates, 22/14 metabolites remained associated with SBP/DBP. Joint covariate-metabolite penalized regression models identified Body Mass Index, age and family history as most important contributors, with 14 metabolites, including gut microbial co-metabolites, also included in the model. Metabolites were mapped in a symbiotic metabolic reaction network, that includes reactions mediated by 3,344 commensal gut microbial species, to highlight affected pathways (Figure). Significant single nucleotide polymorphisms (SNPs) from genome-wide association studies on cardiometabolic risk factors were mapped to genes in this network. This revealed multiple subnetworks of gene-metabolite pairs related to BP and related cardiometabolic factors and includes 54 SNPs directly related to reactions in the network. These 54 SNPs were then used as instrumental variables to test for possible causative metabolite-BP associations in an external cohort (Airwave Study). Conclusions: These results highlight the complex interplay between human genes, the microbiome and metabolites associated with BP and other cardiometabolic risk factors

Author Correction: Nutriome–metabolome relationships provide insights into dietary intake and metabolism

Correction to: Nature Food https://doi.org/10.1038/s43016-020-0093-y, published online 22 June 2020