Do higher alarm thresholds for arterial blood pressure lead to less perioperative hypotension? A retrospective, observational cohort study.

Arterial blood pressure is one of the vital signs monitored mandatory in anaesthetised patients. Even short episodes of intraoperative hypotension are associated with increased risk for postoperative organ dysfunction such as acute kidney injury and myocardial injury. Since there is little evidence whether higher alarm thresholds in patient monitors can help prevent intraoperative hypotension, we analysed the blood pressure data before (group 1) and after (group 2) the implementation of altered hypotension alarm settings. The study was conducted as a retrospective observational cohort study in a large surgical centre with 32 operating theatres. Alarm thresholds for hypotension alarm for mean arterial pressure (MAP) were altered from 60 (before) to 65 mmHg for invasive measurement and 70 mmHg for noninvasive measurement. Blood pressure data from electronic anaesthesia records of 4222 patients (1982 and 2240 in group 1 and 2, respectively) with 406,623 blood pressure values undergoing noncardiac surgery were included. We analysed (A) the proportion of blood pressure measurements below the threshold among all measurements by quasi-binomial regression and (B) whether at least one blood pressure measurement below the threshold occurred by logistic regression. Hypotension was defined as MAP < 65 mmHg. There was no significant difference in overall proportions of hypotensive episodes for mean arterial pressure before and after the adjustment of alarm settings (mean proportion of values below 65 mmHg were 6.05% in group 1 and 5.99% in group 2). The risk of ever experiencing a hypotensive episode during anaesthesia was significantly lower in group 2 with an odds ratio of 0.84 (p = 0.029). In conclusion, higher alarm thresholds do not generally lead to less hypotensive episodes perioperatively. There was a slight but significant reduction of the occurrence of intraoperative hypotension in the presence of higher thresholds for blood pressure alarms. However, this reduction only seems to be present in patients with very few hypotensive episodes.

Evaluation of the metabotype concept after intervention with oral glucose tolerance test and dietary fiber-enriched food: An enable study.

BACKGROUND AND AIMS: Evidence suggests that people react differently to the same diet due to inter-individual differences. However, few studies have investigated variation in response to dietary interventions based on individuals' baseline metabolic characteristics. This study aims to examine the differential reaction of metabotype subgroups to an OGTT and a dietary fiber intervention. METHODS AND RESULTS: We assigned 356 healthy participants of an OGTT sub-study and a 12-week dietary fiber intervention sub-study within the enable cluster to three metabotype subgroups previously identified in the KORA F4 study population. To explore the association between plasma glucose level and metabotype subgroups, we used linear mixed models adjusted for age, sex, and physical activity. Individuals in different metabotype subgroups showed differential responses to OGTT. Compared to the healthy metabotype (metabotype 1), participants in intermediate metabotype (metabotype 2) and unfavorable metabotype (metabotype 3) had significantly higher plasma glucose concentrations at 120 min after glucose bolus (ß = 7.881, p = 0.005; ß = 32.79, p < 0.001, respectively). Additionally, the linear regression model showed that the Area under the curve (AUC) of plasma glucose concentrations was significantly different across the metabotype subgroups. The associations between metabotype subgroups and metabolic parameters among fiber intervention participants remained insignificant in the multivariate-adjusted linear model. However, the metabotype 3 had the highest mean reduction in insulin, cholesterol parameters (TC, LDLc, and non-HDLc), and systolic and diastolic blood pressure at the end of the intervention period. CONCLUSION: This study supports the use of the metabotype concept to identify metabolically similar subgroups and to develop targeted dietary interventions at the metabotype subgroup level for the primary prevention of diet-related diseases.

Optimized Metabotype Definition Based on a Limited Number of Standard Clinical Parameters in the Population-Based KORA Study.

The aim of metabotyping is to categorize individuals into metabolically similar groups. Earlier studies that explored metabotyping used numerous parameters, which made it less transferable to apply. Therefore, this study aimed to identify metabotypes based on a set of standard laboratory parameters that are regularly determined in clinical practice. K-means cluster analysis was used to group 3001 adults from the KORA F4 cohort into three clusters. We identified the clustering parameters through variable importance methods, without including any specific disease endpoint. Several unique combinations of selected parameters were used to create different metabotype models. Metabotype models were then described and evaluated, based on various metabolic parameters and on the incidence of cardiometabolic diseases. As a result, two optimal models were identified: a model composed of five parameters, which were fasting glucose, HDLc, non-HDLc, uric acid, and BMI (the metabolic disease model) for clustering; and a model that included four parameters, which were fasting glucose, HDLc, non-HDLc, and triglycerides (the cardiovascular disease model). These identified metabotypes are based on a few common parameters that are measured in everyday clinical practice. These metabotypes are cost-effective, and can be easily applied on a large scale in order to identify specific risk groups that can benefit most from measures to prevent cardiometabolic diseases, such as dietary recommendations and lifestyle interventions.

Association between Usual Dietary Intake of Food Groups and DNA Methylation and Effect Modification by Metabotype in the KORA FF4 Cohort.

Associations between diet and DNA methylation may vary among subjects with different metabolic states, which can be captured by clustering populations in metabolically homogenous subgroups, called metabotypes. Our aim was to examine the relationship between habitual consumption of various food groups and DNA methylation as well as to test for effect modification by metabotype. A cross-sectional analysis of participants (median age 58 years) of the population-based prospective KORA FF4 study, habitual dietary intake was modeled based on repeated 24-h diet recalls and a food frequency questionnaire. DNA methylation was measured using the Infinium MethylationEPIC BeadChip providing data on >850,000 sites in this epigenome-wide association study (EWAS). Three metabotype clusters were identified using four standard clinical parameters and BMI. Regression models were used to associate diet and DNA methylation, and to test for effect modification. Few significant signals were identified in the basic analysis while many significant signals were observed in models including food group-metabotype interaction terms. Most findings refer to interactions of food intake with metabotype 3, which is the metabotype with the most unfavorable metabolic profile. This research highlights the importance of the metabolic characteristics of subjects when identifying associations between diet and white blood cell DNA methylation in EWAS.