While scholars have long been interested in the formation, meaning, and uses of diminutive morphology across languages, the present study illustrates a novel approach to their examination. Drawing upon a corpus of recordings of Brazilian obstetric and gynecological consultations conducted in Portuguese, our analytic points of departure are action and the sequential progression of interaction. We address these by investigating moments where diminutive forms and base forms of a lexical item are used in close proximity. This approach allows us to unpack and particularize the generic, overarching function of ‘mitigation’ in terms of the specific actions being constituted by the participants—here, offering reassurance, attenuating intrusiveness, pursuing acquiescence, and launching activity transitions. We conclude by discussing some of the implications of this analysis and suggesting some potential avenues for future comparative research. (Portuguese, Brazil, gynecology, obstetrics, healthcare, morphology, pragmatics, granularity, methodology, conversation analysis)*

Ice rises hold valuable records revealing the ice dynamics and climatic history of Antarctic coastal areas from the Last Glacial Maximum to today. This history is often reconstructed from isochrone radar stratigraphy and simulations focusing on Raymond arch evolution beneath the divides. However, this relies on complex ice-flow models where many parameters are unconstrained by observations. Our study explores quad-polarimetric, phase-coherent radar data to enhance understanding near ice divides and domes, using Hammarryggen Ice Rise (HIR) as a case study. Analysing a 5 km profile intersecting the dome, we derive vertical strain rates and ice-fabric properties. These align with ice core data near the summit, increasing confidence in tracing signatures from the dome to the flanks. The Raymond effect is evident, correlating with surface strain rates and radar stratigraphy. Stability is inferred over millennia for the saddle connecting HIR to the mainland, but dome ice-fabric appears relatively young compared to 2D model predictions. In a broader context, quad-polarimetric measurements provide valuable insights into ice-flow models, particularly for anisotropic rheology. Including quad-polarimetric data advances our ability to reconstruct past ice flow dynamics and climatic history in ice rises.

Glycemic, insulinemic and lipemic postprandial responses are multi-factorial and contribute to diabetes, obesity and CVD. The aim of the PREDICT I study is to assess the genetic, metabolic, metagenomic, and meal-context contribution to postprandial responses, integrating the metabolic burden and gut microbiome to predict individual responses to food using a machine learning algorithm.

A multi-center postprandial study of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US, assessed postprandial (0–6h) metabolic responses to sequential mixed-nutrient dietary challenges (50 g fat and 85 g carbohydrate at 0 h; 22 g fat and 71 g carbohydrate at 4h) in a clinic setting. Glycemic responses to 5 duplicate isocaloric meals of different macronutrient content and self-selected meals (> 100,000), were tested at home using a continuous glucose monitor (CGM). Baseline factors included metabolomics, genomics, gut metagenomics and body composition. Genetic contributions to postprandial responses were determined by classical twin methods.

Inter-individual variability in postprandial responses (glucose, insulin and triacylglycerol (TG)) was high in the clinic setting: iAUC IQR (median) was (n = 644); glucose (0–2h) 1.97 (1.89) mmol/L.h, insulin (0–2h) 45.6 (67.7) mIU/L.h and TG (0–6h) 2.37 (2.42) mmol/L.h. The unadjusted genetic contribution for glucose, insulin and TG responses were 54%, 29% and 27% respectively. Within-individual concordance (ICC) in glucose responses (iAUC 0–2h) for at home duplicate isocaloric meals was moderate-to-high, depending on the test meal: ICC (95%CI) was; high carbohydrate 0.62 (0.58,0.66), (carbohydrate = 95g/76% energy; n = 764), average lunch 0.57 (0.53, 0.62) (carbohydrate = 68g/54% energy; n = 763), OGTT 0.65 (0.61,0.70) (carbohydrate = 75 g; n = 754), high fat 0.35 (0.28, 0.41) (fat = 40g/71% energy; n = 576) and high protein 0.56, (0.48,0.62) (protein = 41g/32% energy; n = 364). An interim machine learning algorithm predicted 46% of the variation in glycemic responses based on meal content, meal context and participant's baseline characteristics, excluding genetic and microbiome features. Only 29% of variation could be explained by the macronutrient content of the meal.

This is the most comprehensive postprandial study performed to date. The large and modifiable variation in metabolic responses to identical meals in healthy people explains why ‘one size fits all’ nutritional guidelines are problematic. The genetic component to these responses is moderate, leaving the majority of the variation potentially modifiable. By collecting information on glucose responses to > 100,000 meals, alongside environmental, genetic and microbiome variables, we will have excellent power to use machine learning to optimise and predict individual responses to foods.

Postprandial lipemia is an independent risk factor for CVD, due to effects on lipoprotein remodelling, oxidative stress, inflammation, haemostasis and endothelial dysfunction. However, it is unknown whether the total, peak or duration of the lipemic response determines risk. The PREDICT I study is the largest study to date to measure postprandial lipemic responses and intermediary acutely changing cardiometabolic risk factors at multiple time points using a standardized test meal model.

A multi-center postprandial study of 1,000 individuals from the UK (unrelated, identical and non-identical twins) and 100 unrelated individuals from the US, assessed postprandial (hourly 0–6h) metabolic responses to sequential mixed-nutrient dietary challenges (50 g fat, 85 g carbohydrate at 0 h; 22 g fat, 71 g carbohydrate at 4h) in a clinic setting. We investigated the relationship of different postprandial triacylglycerol (TG) measures (4 and 6 h TG iAUC, 4 and 6 h TG concentration, 4 and 6 h TG increase from fasting) with lipoprotein remodelling (XXL-VLDL (including chylomicron remnants and VLDL particles) and XL-VLDL particle concentrations (average diameters > 75, 64 nm respectively), HDL-C) and levels of glycosylated acute phase proteins (GlycA; marker of cardiovascular inflammation), all of which have been implicated as independent predictors of CVD risk.

Following adjustment (for use of medication, demographic characteristics, fasting TG, insulin and glucose levels), all six postprandial TG measures (4 and 6 h TG iAUC, 4 and 6 h TG concentration, 4 and 6 h TG increase from fasting) were strongly correlated with markers of atherogenic lipoprotein remodelling and the marker of cardiovascular inflammation (GlycA). The strongest correlation (interim analysis) was observed for the 6 h TG increase from fasting (all P < 0.001, Pearson's coefficient r = 0.94 [95%CI's; 0.93, 0.95] for XXL-VLDL-P; r = 0.95 [95%CI's; 0.95, 0.96] for XL-VLDL-P; r = 0.89 [95%CI's; 0.88, 0.91] for GlycA ; r = -0.61 [95%CI's; -0.66, -0.55] for HDL-C). Inter-individual variability in postprandial lipemic responses was high in the tightly controlled clinic setting (interim analysis, n = 656); IQR (median) was; iAUC (0–6h) 2.39 (2.31) mmol/L.h; Cmax 1.32 (2.06) mmol/L; Tmax 30.0 (300) min; and increase above fasting at 6 h 0.78 (0.62) mmol/L.

This is the most detailed postprandial study performed to date and suggests that identifying predictors of the postprandial 6 h TG rise will have the highest CVD relevance. Ongoing exploration in PREDICT I of the determinants of postprandial lipemic responses considering environmental, genetic and microbiome variables will significantly advance our ability to predict an individual's postprandial response and its links to cardiovascular risk.