Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: a randomised cross-over trial

Objective To investigate whether a whole grain diet alters the gut microbiome and insulin sensitivity, as well as biomarkers of metabolic health and gut functionality. Design 60 Danish adults at risk of developing metabolic syndrome were included in a randomised cross-over trial with two 8-week dietary intervention periods comprising whole grain diet and refined grain diet, separated by a washout period of ≥6 weeks. The response to the interventions on the gut microbiome composition and insulin sensitivity as well on measures of glucose and lipid metabolism, gut functionality, inflammatory markers, anthropometry and urine metabolomics were assessed. Results 50 participants completed both periods with a whole grain intake of 179±50 g/day and 13±10 g/day in the whole grain and refined grain period, respectively. Compliance was confirmed by a difference in plasma alkylresorcinols (p<0.0001). Compared with refined grain, whole grain did not significantly alter glucose homeostasis and did not induce major changes in the faecal microbiome. Also, breath hydrogen levels, plasma short-chain fatty acids, intestinal integrity and intestinal transit time were not affected. The whole grain diet did, however, compared with the refined grain diet, decrease body weight (p<0.0001), serum inflammatory markers, interleukin (IL)-6 (p=0.009) and C-reactive protein (p=0.003). The reduction in body weight was consistent with a reduction in energy intake, and IL-6 reduction was associated with the amount of whole grain consumed, in particular with intake of rye. Conclusion Compared with refined grain diet, whole grain diet did not alter insulin sensitivity and gut microbiome but reduced body weight and systemic low-grade inflammation

Species-specific responses of Late Quaternary megafauna to climate and humans

Despite decades of research, the roles of climate and humans in driving the dramatic extinctions of large-bodied mammals during the Late Quaternary remain contentious. We use ancient DNA, species distribution models and the human fossil record to elucidate how climate and humans shaped the demographic history of woolly rhinoceros, woolly mammoth, wild horse, reindeer, bison and musk ox. We show that climate has been a major driver of population change over the past 50,000 years. However, each species responds differently to the effects of climatic shifts, habitat redistribution and human encroachment. Although climate change alone can explain the extinction of some species, such as Eurasian musk ox and woolly rhinoceros, a combination of climatic and anthropogenic effects appears to be responsible for the extinction of others, including Eurasian steppe bison and wild horse. We find no genetic signature or any distinctive range dynamics distinguishing extinct from surviving species, underscoring the challenges associated with predicting future responses of extant mammals to climate and human-mediated habitat change.This paper is in the memory of our friend and colleague Dr. Andrei Sher, who was a major contributor of this study. Dr Sher died unexpectedly, but his major contributions to the field of Quaternary science will be remembered and appreciated for many years to come. We are grateful to Dr. Adrian Lister and Dr. Tony Stuart for guides and discussions. Thanks to Tina B. Brandt, Dr. Bryan Hockett and Alice Telka for laboratory help and samples and to L. Malik R. Thrane for his work on the megafauna locality database. Data taken from the Stage 3 project was partly funded by Grant #F/757/A from the Leverhulme Trust, together with a grant from the McDonald Grants and Awards Fund. We acknowledge the Danish National Research Foundation, the Lundbeck Foundation, the Danish Council for Independent Research and the US National Science Foundation for financial suppor

A new tool to assess Clinical Diversity In Meta‐analyses (CDIM) of interventions

OBJECTIVE: To develop and validate Clinical Diversity In Meta-analyses (CDIM), a new tool for assessing clinical diversity between trials in meta-analyses of interventions.STUDY DESIGN AND SETTING: The development of CDIM was based on consensus work informed by empirical literature and expertise. We drafted the CDIM tool, refined it, and validated CDIM for interrater scale reliability and agreement in three groups.RESULTS: CDIM measures clinical diversity on a scale that includes four domains with 11 items overall: setting (time of conduct/country development status/units type); population (age, sex, patient inclusion criteria/baseline disease severity, comorbidities); interventions (intervention intensity/strength/duration of intervention, timing, control intervention, cointerventions); and outcome (definition of outcome, timing of outcome assessment). The CDIM is completed in two steps: first two authors independently assess clinical diversity in the four domains. Second, after agreeing upon scores of individual items a consensus score is achieved. Interrater scale reliability and agreement ranged from moderate to almost perfect depending on the type of raters.CONCLUSION: CDIM is the first tool developed for assessing clinical diversity in meta-analyses of interventions. We found CDIM to be a reliable tool for assessing clinical diversity among trials in meta-analysis.</p