Digital Biomarkers for Personalized Nutrition: Predicting Meal Moments and Interstitial Glucose with Non-Invasive, Wearable Technologies.

Digital health technologies may support the management and prevention of disease through personalized lifestyle interventions. Wearables and smartphones are increasingly used to continuously monitor health and disease in everyday life, targeting health maintenance. Here, we aim to demonstrate the potential of wearables and smartphones to (1) detect eating moments and (2) predict and explain individual glucose levels in healthy individuals, ultimately supporting health self-management. Twenty-four individuals collected continuous data from interstitial glucose monitoring, food logging, activity, and sleep tracking over 14 days. We demonstrated the use of continuous glucose monitoring and activity tracking in detecting eating moments with a prediction model showing an accuracy of 92.3% (87.2-96%) and 76.8% (74.3-81.2%) in the training and test datasets, respectively. Additionally, we showed the prediction of glucose peaks from food logging, activity tracking, and sleep monitoring with an overall mean absolute error of 0.32 (+/-0.04) mmol/L for the training data and 0.62 (+/-0.15) mmol/L for the test data. With Shapley additive explanations, the personal lifestyle elements important for predicting individual glucose peaks were identified, providing a basis for personalized lifestyle advice. Pending further validation of these digital biomarkers, they show promise in supporting the prevention and management of type 2 diabetes through personalized lifestyle recommendations.

Exploring the Potential of Personalized Dietary Advice for Health Improvement in Motivated Individuals With Premetabolic Syndrome: Pretest-Posttest Study.

BACKGROUND: Dietary quality plays an essential role in the prevention and management of metabolic syndrome (MetS). OBJECTIVE: The aim of this pilot study is to organize personalized dietary advice in a real-life setting and to explore the effects on dietary intake, metabolic health, and perceived health. METHODS: We followed a one-group pretest-posttest design and included 37 individuals at risk of MetS, who indicated motivation to change dietary behavior. For a period of 16 weeks, participants received personalized advice (t=0 and t=8) and feedback (t=0, t=4, t=8, t=12 and t=16) on dietary quality and metabolic health (ie, waist circumference, BMI, blood pressure, lipid profile, fasting glucose levels, and C-peptide). Personalized advice was generated in a two-stage process. In stage 1, an automated algorithm generated advice per food group, integrating data on individual dietary quality (Dutch Healthy Diet Index; total score 8-80) and metabolic health parameters. Stage 2 included a telephone consultation with a trained dietitian to define a personal dietary behavior change strategy and to discuss individual preferences. Dietary quality and metabolic health markers were assessed at t=0, t=8, and t=16. Self-perceived health was evaluated on 7-point Likert scales at t=0 and t=16. RESULTS: At the end of the study period, dietary quality was significantly improved compared with the baseline (Dutch Healthy Diet Index +4.3; P<.001). In addition, lipid profile (triglycerides, P=.02; total cholesterol, P=.01; high-density lipoprotein, P<.001; and low-density lipoprotein, P<.001), BMI (P<.001), waist circumference (P=.01), and C-peptide (P=.01) were all significantly improved, whereas plasma glucose increased by 0.23 nmol/L (P=.04). In line with these results, self-perceived health scores were higher at t=16 weeks than at baseline (+0.67; P=.005). CONCLUSIONS: This exploratory study showed that personalized dietary advice resulted in positive effects on dietary behavior, metabolic health, and self-perceived health in motivated pre-MetS adults. The study was performed in a do-it-yourself setting, highlighting the potential of at-home health improvement through dietary changes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04595669; https://clinicaltrials.gov/ct2/show/NCT04595669.

Obese Older Type 2 Diabetes Mellitus Patients with Muscle Insulin Resistance Benefit from an Enriched Protein Drink during Combined Lifestyle Intervention: The PROBE Study.

(1) Background: Recent research showed that subtypes of patients with type 2 diabetes may differ in response to lifestyle interventions based on their organ-specific insulin resistance (IR). (2) Methods: 123 Subjects with type 2 diabetes were randomized into 13-week lifestyle intervention, receiving either an enriched protein drink (protein+) or an isocaloric control drink (control). Before and after the intervention, anthropometrical and physiological data was collected. An oral glucose tolerance test was used to calculate indices representing organ insulin resistance (muscle, liver, and adipose tissue) and ß-cell functioning. In 82 study-compliant subjects (per-protocol), we retrospectively examined the intervention effect in patients with muscle IR (MIR, n = 42) and without MIR (no-MIR, n = 40). (3) Results: Only in patients from the MIR subgroup that received protein+ drink, fasting plasma glucose and insulin, whole body, liver and adipose IR, and appendicular skeletal muscle mass improved versus control. Lifestyle intervention improved body weight and fat mass in both subgroups. Furthermore, for the MIR subgroup decreased systolic blood pressure and increased VO2peak and for the no-MIR subgroup, a decreased 2-h glucose concentration was found. (4) Conclusions: Enriched protein drink during combined lifestyle intervention seems to be especially effective on increasing muscle mass and improving insulin resistance in obese older, type 2 diabetes patients with muscle IR.

Whole Grain Wheat Consumption Affects Postprandial Inflammatory Response in a Randomized Controlled Trial in Overweight and Obese Adults with Mild Hypercholesterolemia in the Graandioos Study.

BACKGROUND: Whole grain wheat (WGW) consumption is associated with health benefits in observational studies. However, WGW randomized controlled trial (RCT) studies show mixed effects. OBJECTIVES: The health impact of WGW consumption was investigated by quantification of the body's resilience, which was defined as the "ability to adapt to a standardized challenge." METHODS: A double-blind RCT was performed with overweight and obese (BMI: 25-35 kg/m2) men (n = 19) and postmenopausal women (n = 31) aged 45-70 y, with mildly elevated plasma total cholesterol (>5 mmol/L), who were randomly assigned to either 12-wk WGW (98 g/d) or refined wheat (RW). Before and after the intervention a standardized mixed-meal challenge was performed. Plasma samples were taken after overnight fasting and postprandially (30, 60, 120, and 240 min). Thirty-one biomarkers were quantified focusing on metabolism, liver, cardiovascular health, and inflammation. Linear mixed-models evaluated fasting compared with postprandial intervention effects. Health space models were used to evaluate intervention effects as composite markers representing resilience of inflammation, liver, and metabolism. RESULTS: Postprandial biomarker changes related to liver showed decreased alanine aminotransferase by WGW (P = 0.03) and increased ß-hydroxybutyrate (P = 0.001) response in RW. Postprandial changes related to inflammation showed increased C-reactive protein (P = 0.001), IL-6 (P = 0.02), IL-8 (P = 0.007), and decreased IL-1B (P = 0.0002) in RW and decreased C-reactive protein (P < 0.0001), serum amyloid A (P < 0.0001), IL-8 (P = 0.02), and IL-10 (P < 0.0001) in WGW. Health space visualization demonstrated diminished inflammatory (P < 0.01) and liver resilience (P < 0.01) by RW, whereas liver resilience was rejuvenated by WGW (P < 0.05). CONCLUSIONS: Twelve-week 98 g/d WGW consumption can promote liver and inflammatory resilience in overweight and obese subjects with mildly elevated plasma cholesterol. The health space approach appeared appropriate to evaluate intervention effects as composite markers. This trial was registered at www.clinicaltrials.gov as NCT02385149.

Blood-Based Biomarkers of Quinpirole Pharmacology: Cluster-Based PK/PD and Metabolomics to Unravel the Underlying Dynamics in Rat Plasma and Brain.

A key challenge in the development of central nervous system drugs is the availability of drug target specific blood-based biomarkers. As a new approach, we applied cluster-based pharmacokinetic/pharmacodynamic (PK/PD) analysis in brain extracellular fluid (brainECF ) and plasma simultaneously after 0, 0.17, and 0.86 mg/kg of the dopamine D2/3 agonist quinpirole (QP) in rats. We measured 76 biogenic amines in plasma and brainECF after single and 8-day administration, to be analyzed by cluster-based PK/PD analysis. Multiple concentration-effect relations were observed with potencies ranging from 0.001-383 nM. Many biomarker responses seem to distribute over the blood-brain barrier (BBB). Effects were observed for dopamine and glutamate signaling in brainECF , and branched-chain amino acid metabolism and immune signaling in plasma. Altogether, we showed for the first time how cluster-based PK/PD could describe a systems-response across plasma and brain, thereby identifying potential blood-based biomarkers. This concept is envisioned to provide an important connection between drug discovery and early drug development.

Fingerprints of CNS drug effects: a plasma neuroendocrine reflection of D2 receptor activation using multi-biomarker pharmacokinetic/pharmacodynamic modelling.

BACKGROUND AND PURPOSE: Because biological systems behave as networks, multi-biomarker approaches increasingly replace single biomarker approaches in drug development. To improve the mechanistic insights into CNS drug effects, a plasma neuroendocrine fingerprint was identified using multi-biomarker pharmacokinetic/pharmacodynamic (PK/PD) modelling. Short- and long-term D2 receptor activation was evaluated using quinpirole as a paradigm compound. EXPERIMENTAL APPROACH: Rats received 0, 0.17 or 0.86 mg·kg-1 of the D2 agonist quinpirole i.v. Quinpirole concentrations in plasma and brain extracellular fluid (brainECF ), as well as plasma concentrations of 13 hormones and neuropeptides, were measured. Experiments were performed at day 1 and repeated after 7-day s.c. drug administration. PK/PD modelling was applied to identify the in vivo concentration-effect relations and neuroendocrine dynamics. KEY RESULTS: The quinpirole pharmacokinetics were adequately described by a two-compartment model with an unbound brainECF -to-plasma concentration ratio of 5. The release of adenocorticotropic hormone (ACTH), growth hormone, prolactin and thyroid-stimulating hormone (TSH) from the pituitary was influenced. Except for ACTH, D2 receptor expression levels on the pituitary hormone-releasing cells predicted the concentration-effect relationship differences. Baseline levels (ACTH, prolactin, TSH), hormone release (ACTH) and potency (TSH) changed with treatment duration. CONCLUSIONS AND IMPLICATIONS: The integrated multi-biomarker PK/PD approach revealed a fingerprint reflecting D2 receptor activation. This forms the conceptual basis for in vivo evaluation of on- and off-target CNS drug effects. The effect of treatment duration is highly relevant given the long-term use of D2 agonists in clinical practice. Further development towards quantitative systems pharmacology models will eventually facilitate mechanistic drug development.

Revealing the Neuroendocrine Response After Remoxipride Treatment Using Multi-Biomarker Discovery and Quantifying It by PK/PD Modeling.

To reveal unknown and potentially important mechanisms of drug action, multi-biomarker discovery approaches are increasingly used. Time-course relationships between drug action and multi-biomarker profiles, however, are typically missing, while such relationships will provide increased insight in the underlying body processes. The aim of this study was to investigate the effect of the dopamine D2 antagonist remoxipride on the neuroendocrine system. Different doses of remoxipride (0, 0.7, 5.2, or 14 mg/kg) were administered to rats by intravenous infusion. Serial brain extracellular fluid (brainECF) and plasma samples were collected and analyzed for remoxipride pharmacokinetics (PK). Plasma samples were analyzed for concentrations of the eight pituitary-related hormones as a function of time. A Mann-Whitney test was used to identify the responding hormones, which were further analyzed by pharmacokinetic/pharmacodynamic (PK/PD) modeling. A three-compartment PK model adequately described remoxipride PK in plasma and brainECF. Not only plasma PRL, but also adrenocorticotrophic hormone (ACTH) concentrations were increased, the latter especially at higher concentrations of remoxipride. Brain-derived neurotropic factor (BDNF), follicle stimulating hormone (FSH), growth hormone (GH), luteinizing hormone (LH), and thyroid stimulating hormones (TSH) did not respond to remoxipride at the tested doses, while oxytocin (OXT) measurements were below limit of quantification. Precursor pool models were linked to brainECF remoxipride PK by Emax drug effect models, which could accurately describe the PRL and ACTH responses. To conclude, this study shows how a multi-biomarker identification approach combined with PK/PD modeling can reveal and quantify a neuroendocrine multi-biomarker response for single drug action.