Datasheet1_Cofactors of drug hypersensitivity—A monocenter retrospective analysis.pdf

BackgroundDrug hypersensitivity reactions (DHRs) are major medical problems that influence the treatment of patients by both under- and overdiagnosis. Still, little is known about the role of predisposing or protecting cofactors of DHR.ObjectiveThis study aims to determine drug-specific cofactors in patients with DHR.MethodsRetrospective file chart analysis of inpatients with suspected DHR in our department between 2015 and 2020 was performed. Descriptive statistics and multiple logistic regression were conducted for the estimation and statistical interference.ResultsDHRs were suspected in 393 patients with 678 culprit drugs. In 183 cases, drug hypersensitivities were confirmed, mostly against nonopioid analgesic drugs and antibiotics. Multiple logistic regression analysis identified a positive association of antibiotic hypersensitivity with obesity [odds ratio (OR) 5.75, average marginal effect (AME) +24.4%] and age and a negative association with arterial hypertension, female sex, elevated immunoglobulin E (IgE), and allergic rhinitis. Hypersensitivity to nonopioid analgesics was associated with atopic dermatitis (OR 10.28, AME +28.5%), elevated IgE, and arterial hypertension.ConclusionsDrug-specific cofactors of DHR include obesity for antibiotics and atopic dermatitis for nonopioid analgesics, the knowledge of which may improve the risk calculation for drug provocation tests.</p

Genotype distribution of the −174G/C SNP between subjects with lower (<60 mg/dl) or higher (≥60 mg/dl) Lp(a) levels.

<p>Genotype distribution of the −174G/C SNP between subjects with lower (<60 mg/dl) or higher (≥60 mg/dl) Lp(a) levels.</p

Main characteristics of the study population.

<p>Quantitative variables are presented as mean ± standard deviation and median and interquartile range (IQR) where appropriate; counts are given as <i>n</i> and percent.</p><p>*Chi-square P value.</p><p>**Unpaired Student's <i>t</i>-test (the data of Lp(a) and triglycerides were log-transformed before analysis).</p

Additional file 1 of Secondary research use of personal medical data: patient attitudes towards data donation

Additional file 1: Questionnaire 1. Questionnaire of Survey 1. Questionnaire 2. Questionnaire of Survey 2

Estimated treatment effects (ATT) of social network characteristics on obesity (BMI).

<p>*t-values derived via bootstrapping.</p

Microbial Features Linked to Medication Strategies in Cardiometabolic Disease Management

Human gut microbiota are recognized as critical players in both metabolic disease and drug metabolism. However, medication–microbiota interactions in cardiometabolic diseases are not well understood. To gain a comprehensive understanding of how medication intake impacts the gut microbiota, we investigated the association of microbial structure with the use of single or multiple medications in a cohort of 134 middle-aged adults diagnosed with cardiometabolic disease, recruited from Alberta’s Tomorrow Project. Predominant cardiometabolic prescription medication classes (12 total) were included in our analysis. Multivariate Association with Linear Model (MaAsLin2) was employed and results were corrected for age, BMI, sex, and diet to evaluate the relationship between microbial features and single- or multimedication use. Highly individualized microbiota profiles were observed across participants, and increasing medication use was negatively correlated with α-diversity. A total of 46 associations were identified between microbial composition and single medications, exemplified by the depletion of Akkermansia muciniphila by β-blockers and statins, and the enrichment of Escherichia/Shigella and depletion of Bacteroides xylanisolvens by metformin. Metagenomics prediction further indicated alterations in microbial functions associated with single medications such as the depletion of enzymes involved in energy metabolism encoded by Eggerthella lenta due to β-blocker use. Specific dual medication combinations also had profound impacts, including the depletion of Romboutsia and Butyriciocccus by statin plus metformin. Together, these results show reductions in bacterial diversity as well as species and microbial functional potential associated with both single- and multimedication use in cardiometabolic disease

Scatterplots of correlations between hand grip and CoQ₁₀/cholesterol ratio, body mass index, age and CoQ10 redox in the validation population (n = 967).

<p>Spearman’s correlation analysis revealed a significant relationship (p<0.01) between hand grip and CoQ₁₀/cholesterol (A), normal BMI (<25 kg/m², B), age (C) and CoQ₁₀ redox (D). The correlations between hand grip and overweight (BMI 25–30 kg/m²) and obese subjects (BMI >30) were statistically not significant. Spearman’s correlation coefficient (r), p-values and regression lines are given. CoQ₁₀: Coenzyme Q₁₀; CoQ₁₀ redox: % oxidized coenzyme Q₁₀ in total; BMI: body mass index.</p

Characterization of the basic study population (<i>n</i> = 334).

<p>Characterization of the basic study population (<i>n</i> = 334).</p

Analysis of covariance (ANCOVA) between hand grip, CoQ₁₀/cholesterol ratio, age and BMI in A) the basic study population (n = 334) and B) the validation population (n = 967), including 658 overweight/obese subjects.

Analysis of covariance (ANCOVA) between hand grip, CoQ10/cholesterol ratio, age and BMI in A) the basic study population (n = 334) and B) the validation population (n = 967), including 658 overweight/obese subjects.</p

Coenzyme Q₁₀ Status as a Determinant of Muscular Strength in Two Independent Cohorts

<div><p>Aging is associated with sarcopenia, which is a loss of skeletal muscle mass and function. Coenzyme Q₁₀ (CoQ₁₀) is involved in several important functions that are related to bioenergetics and protection against oxidative damage; however, the role of CoQ₁₀ as a determinant of muscular strength is not well documented. The aim of the present study was to evaluate the determinants of muscular strength by examining hand grip force in relation to CoQ₁₀ status, gender, age and body mass index (BMI) in two independent cohorts (n = 334, n = 967). Furthermore, peak flow as a function of respiratory muscle force was assessed. Spearman’s correlation revealed a significant positive association between CoQ₁₀/cholesterol level and hand grip in the basic study population (p<0.01) as well as in the validation population (p<0.001). In the latter, we also found a negative correlation with the CoQ₁₀ redox state (p<0.01), which represents a lower percentage of the reduced form of CoQ₁₀ (ubiquinol) in subjects who exhibit a lower muscular strength. Furthermore, the age of the subjects showed a negative correlation with hand grip (p<0.001), whereas BMI was positively correlated with hand grip (p<0.01), although only in the normal weight subgroup (BMI <25 kg/m²). Analysis of the covariance (ANCOVA) with hand grip as the dependent variable revealed CoQ₁₀/cholesterol as a determinant of muscular strength and gender as the strongest effector of hand grip. In conclusion, our data suggest that both a low CoQ₁₀/cholesterol level and a low percentage of the reduced form of CoQ₁₀ could be an indicator of an increased risk of sarcopenia in humans due to their negative associations to upper body muscle strength, peak flow and muscle mass.</p></div