Short-term effect of plant-based Nordic diet versus carbohydrate-restricted diet on glucose levels in gestational diabetes - the eMOM pilot study.

BACKGROUND: The optimal nutritional treatment for gestational diabetes (GDM) is still a matter of debate. With increasing rates of GDM and potential negative consequences for the health of mother and child, the best treatment should be established. The Nordic diet with emphasis on plant-based protein show promising health outcomes in other populations but has yet to be investigated in GDM population. The aim of this study, which is part of the "Effect of plant-based Nordic diet versus carbohydrate-restricted diet on glucose levels in gestational diabetes" (eMOM) pilot study was to compare the short-term effects of healthy Nordic diet (HND) and the currently recommended moderate restriction of carbohydrates diet (MCRD) on glucose and lipid metabolism in women with GDM. METHODS: This was a randomized crossover where each of the diet interventions (HND and MCRD) were consumed for 3 days with a 3-day wash-out period in between. In total, 42 pregnant women diagnosed with GDM (< 29 + 0 gestational week) were randomized. Glucose data was collected by continuous glucose monitors (CGM, Freestyle Libre®, Abbott, USA) worn for 14 days, and participants gave blood samples before and after diet interventions. The primary outcome was time spent in glucose target range (TIR, < 7.8 mmol/L). TIR, 3-day mean tissue glucose as well as changes in fasting glucose, homeostatic model of insulin resistance (HOMA-IR) and blood lipids were analyzed with paired samples statistical analyses. RESULTS: Thirty-six women with complete 14 days CGM data were analyzed. Both diet interventions produced a high degree of TIR (99% SD 1.8), without a difference between the diets (p = 0.727). The 3-day mean glucose was significantly lower in HND than in MCRD (p = 0,049). Fasting insulin (p = 0,034), insulin resistance (p = 0,030), total and LDL cholesterol (p = 0,023 and 0,008) reduced more in the MCRD diet than the HND. NS differences in any other measure of CGM or blood tests. CONCLUSIONS: HND and MCRD did not differ in terms of their short-term effect on TIR. A larger study with sufficient power is needed to confirm the differences in short-term mean glucose, insulin resistance and lipid metabolism. TRIAL REGISTRATION: Registered in clinicaltrials.gov (21/09/2018, NCT03681054).

Computational modelling of self-reported dietary carbohydrate intake on glucose concentrations in patients undergoing Roux-en-Y gastric bypass versus one-anastomosis gastric bypass.

OBJECTIVES: Our aim was to investigate in a real-life setting the use of machine learning for modelling the postprandial glucose concentrations in morbidly obese patients undergoing Roux-en-Y gastric bypass (RYGB) or one-anastomosis gastric bypass (OAGB). METHODS: As part of the prospective randomized open-label trial (RYSA), data from obese (BMI ≥35 kg/m2) non-diabetic adult participants were included. Glucose concentrations, measured with FreeStyle Libre, were recorded over 14 preoperative and 14 postoperative days. During these periods, 3-day food intake was self-reported. A machine learning model was applied to estimate glycaemic responses to the reported carbohydrate intakes before and after the bariatric surgeries. RESULTS: Altogether, 10 participants underwent RYGB and 7 participants OAGB surgeries. The glucose concentrations and carbohydrate intakes were reduced postoperatively in both groups. The relative time spent in hypoglycaemia increased regardless of the operation (RYGB, from 9.2 to 28.2%; OAGB, from 1.8 to 37.7%). Postoperatively, we observed an increase in the height of the fitted response curve and a reduction in its width, suggesting that the same amount of carbohydrates caused a larger increase in the postprandial glucose response and that the clearance of the meal-derived blood glucose was faster, with no clinically meaningful differences between the surgeries. CONCLUSIONS: A detailed analysis of the glycaemic responses using food diaries has previously been difficult because of the noisy meal data. The utilized machine learning model resolved this by modelling the uncertainty in meal times. Such an approach is likely also applicable in other applications involving dietary data. A marked reduction in overall glycaemia, increase in postprandial glucose response, and rapid glucose clearance from the circulation immediately after surgery are evident after both RYGB and OAGB. Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.KEY MESSAGESThe use of a novel machine learning model was applicable for combining patient-reported data and time-series data in this clinical study.Marked increase in postprandial glucose concentrations and rapid glucose clearance were observed after both Roux-en-Y gastric bypass and one-anastomosis gastric bypass surgeries.Whether nondiabetic individuals would benefit from monitoring the post-surgery hypoglycaemias and the potential to prevent them by dietary means should be investigated.

Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories.

Estimating the impact of a treatment on a given response is needed in many biomedical applications. However, methodology is lacking for the case when the response is a continuous temporal curve, treatment covariates suffer extensively from measurement error, and even the exact timing of the treatments is unknown. We introduce a novel method for this challenging scenario. We model personalized treatment-response curves as a combination of parametric response functions, hierarchically sharing information across individuals, and a sparse Gaussian process for the baseline trend. Importantly, our model accounts for errors not only in treatment covariates, but also in treatment timings, a problem arising in practice for example when data on treatments are based on user self-reporting. We validate our model with simulated and real patient data, and show that in a challenging application of estimating the impact of diet on continuous blood glucose measurements, accounting for measurement error significantly improves estimation and prediction accuracy.