Participant‐Reported Health Status Predicts Cardiovascular and All‐Cause Mortality Independent of Established and Nontraditional Biomarkers: Evidence From a Representative US Sample

Background: Participant‐reported health status is a key indicator of cardiovascular health, but its predictive value relative to traditional and nontraditional risk factors is unknown. We evaluated whether participant‐reported health status, as indexed by self‐rated health, predicted cardiovascular disease, and all‐cause mortality risk excess of 10‐year atherosclerotic cardiovascular disease (ASCVD) risk scores and 5 nontraditional risk biomarkers. Methods and Results: Analyses used prospective observational data from the 1999–2002 National Health and Nutrition Examination Surveys among those aged 40 to 79 years (N=4677). Vital status was ascertained through 2011, during which there were 850 deaths, 206 from cardiovascular disease (CVD). We regressed CVD and all‐cause mortality on standardized values of self‐rated health in survival models, adjusting for age, sex, education, existing chronic disease, race/ethnicity, ASCVD risk, and standardized biomarkers (fibrinogen, C‐reactive protein [CRP], triglycerides, albumin, and uric acid). In sociodemographically adjusted models, a 1‐SD decrease in self‐rated health was associated with increased risk of CVD mortality (hazard ratio [HR], 1.92; 95% CI, 1.51–2.45; P<0.001), and this hazard remained strong after adjusting for ASCVD risk and nontraditional biomarkers (HR, 1.79; 95% CI, 1.42–2.26; P<0.001). Self‐rated health also predicted all‐cause mortality even after adjustment for ASCVD risk and nontraditional biomarkers (HR, 1.50; 95% CI, 1.35–1.66; P<0.001). Conclusions: Self‐rated health provides prognostic information beyond that captured by traditional ASCVD risk assessments and by nontraditional CVD biomarkers. Consideration of self‐rated health in combination with traditional risk factors may facilitate risk assessment and clinical care

Somatic-Vegetative Symptoms of Depression Predict 6-Year Increases in Insulin Resistance: Data from the Pittsburgh Healthy Heart Project

poster abstractAlthough prospective studies suggest a bidirectional association between depression and type 2 diabetes, few studies have examined depressive symptom clusters or concurrently evaluated both directions of this relationship. Consequently, our objective was to examine the longitudinal, bidirectional associations between the somatic-vegetative and cognitive-affective clusters of depressive symptoms and insulin resistance, which is implicated in the pathophysiology of type 2 diabetes. Participants were 269 adults (baseline age range: 50-70 years, 55% female, 14% non-white) without diabetes enrolled in the Pittsburgh Healthy Heart Project, a prospective cohort study. At baseline and the 6-year visits, participants completed the Beck Depression Inventory-II (BDI-II) to assess depressive symptoms and underwent a blood draw to quantify fasting serum insulin and glucose. We examined baseline BDI-II total and subscale scores as predictors of 6-year change in the homeostatic model assessment (HOMA) score, an index of insulin resistance computed from fasting insulin and glucose. We also examined baseline HOMA score as a predictor of 6-year change in BDI-II total and subscale scores. HOMA and BDI-II change were computed as follow-up score minus baseline score. Regression analyses, adjusted for baseline HOMA score and demographic factors, revealed that the baseline BDI-II somatic-vegetative score (beta=.14, p=.03), but not the total (beta=.10, p=.11) or cognitive-affective (beta=.004, p=.95) scores, was a predictor of 6-year increases in the HOMA score. The pattern of results was similar after further adjustment for body mass index, except that the BDI-II total score became a predictor of HOMA change (beta=.13, p=.03). In contrast, the baseline HOMA score did not predict 6-year change in BDI-II total, somatic-vegetative, or cognitive-affective scores (all p’s>.48). Our findings indicate that older adults experiencing the somatic-vegetative symptoms of depression (e.g., fatigue, sleep disturbance, and appetite changes) may be at an increased risk of insulin resistance and subsequent type 2 diabetes

Depressive symptom clusters as predictors of 6-year increases in insulin resistance: data from the Pittsburgh Healthy Heart Project

OBJECTIVE: To examine longitudinal bidirectional associations between two depressive symptom clusters-the cognitive-affective and somatic-vegetative clusters--and insulin resistance, a marker of prediabetes. METHODS: Participants were 269 adults aged 50 to 70 years without diabetes enrolled in the Pittsburgh Healthy Heart Project, a prospective cohort study. At baseline and 6-year visits, participants completed the Beck Depression Inventory-II (BDI-II) and underwent a blood draw to quantify fasting insulin and glucose. We examined baseline BDI-II total, cognitive-affective, and somatic-vegetative scores as predictors of 6-year change in the homeostatic model of assessment (HOMA) score, an estimate of insulin resistance computed from fasting insulin and glucose. We also examined baseline HOMA score as a predictor of 6-year change in BDI-II total and subscale scores. RESULTS: Regression analyses, adjusted for demographic factors and baseline HOMA score, revealed that the baseline BDI-II somatic-vegetative score (β = 0.14, p = .025), but not the cognitive-affective (β = 0.001, p = .98) or total (β = 0.10, p = .11) scores, predicted 6-year HOMA change. This result persisted in models controlling for anxiety symptoms and hostility. Several factors were examined as candidate mediators; however, only change in body mass index was a significant mediator (p = .042), accounting for 23% of the observed association. Baseline HOMA score did not predict 6-year change in BDI-II total or subscale scores (all p values >.56). CONCLUSIONS: Among adults aged 50 to 70 years, the somatic-vegetative symptoms of depression (e.g., fatigue, sleep disturbance, and appetite changes) may worsen insulin resistance and increase diabetes risk, partly, by increasing body mass index

Application of a single-objective, hybrid genetic algorithm approach to pharmacokinetic model building.

A limitation in traditional stepwise population pharmacokinetic model building is the difficulty in handling interactions between model components. To address this issue, a method was previously introduced which couples NONMEM parameter estimation and model fitness evaluation to a single-objective, hybrid genetic algorithm for global optimization of the model structure. In this study, the generalizability of this approach for pharmacokinetic model building is evaluated by comparing (1) correct and spurious covariate relationships in a simulated dataset resulting from automated stepwise covariate modeling, Lasso methods, and single-objective hybrid genetic algorithm approaches to covariate identification and (2) information criteria values, model structures, convergence, and model parameter values resulting from manual stepwise versus single-objective, hybrid genetic algorithm approaches to model building for seven compounds. Both manual stepwise and single-objective, hybrid genetic algorithm approaches to model building were applied, blinded to the results of the other approach, for selection of the compartment structure as well as inclusion and model form of inter-individual and inter-occasion variability, residual error, and covariates from a common set of model options. For the simulated dataset, stepwise covariate modeling identified three of four true covariates and two spurious covariates; Lasso identified two of four true and 0 spurious covariates; and the single-objective, hybrid genetic algorithm identified three of four true covariates and one spurious covariate. For the clinical datasets, the Akaike information criterion was a median of 22.3 points lower (range of 470.5 point decrease to 0.1 point decrease) for the best single-objective hybrid genetic-algorithm candidate model versus the final manual stepwise model: the Akaike information criterion was lower by greater than 10 points for four compounds and differed by less than 10 points for three compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data

Trait positive and negative emotionality differentially associate withdiurnal cortisol activity

Inter-individual variability in metrics of hypothalamic-pituitary-adrenocortical (HPA) activity, such asthe slope of the diurnal decline in cortisol, cortisol awakening response (CAR), and total cortisol out-put, have been found to associate inversely with trait ratings of extraversion and positive affect (E/PA)and positively with neuroticism and negative affect (N/NA) in some, but not all, investigations. Theseinconsistencies may partly reflect varied intensity of cortisol sampling among studies and reliance onself-rated traits, which are subject to reporting biases and limitations of introspection. Here, we furtherexamined dispositional correlates of HPA activity in 490 healthy, employed midlife volunteers (M age = 43years; 54% Female; 86% white). Trait ratings were requested from participants and 2 participant-electedinformants using the Positive and Negative Affect Schedule (PANAS) and Extraversion and Neuroticismdimensions of NEO personality inventories. CAR was assessed as percent increase in cortisol levels fromawakening to 30 min after awakening; and the diurnal slope and total output of cortisol [Area Underthe Curve (AUC)] were determined from cortisol measurements taken at awakening, +4 and +9 h later,and bedtime, across 3 workdays. Structural equation modeling was used to estimate multi-informantE/PA and N/NA factors. We used 3 days of measurement as indicators to model each of the three latentcortisol factors (slope, CAR, and AUC). With the two latent emotionality and three latent cortisol indicesincluded there was good fit to the data ( 2(200)= 278.38, p = 0.0002; RMSEA = 0.028, 90% CI = 0.02–0.04;CFI/TLI = 0.97/0.96; SRMR = 0.04). After controlling for covariates (age, sex, race), results showed higherlatent E/PA associated with a steeper diurnal slope (Standardized ˇ = −0.19, p = 0.02) and smaller CAR(Standardized ˇ = −0.26, p = 0.004), whereas N/NA did not associate with any cortisol metric (Standard-ized ˇ’s = −0.12 to 0.13, p’s = 0.10 to 0.53). These findings suggest that positive emotionality may be moreclosely associated with indices of diurnal cortisol release than negative emotionality

Trait positive and negative emotionality differentially associate withdiurnal cortisol activity

Inter-individual variability in metrics of hypothalamic-pituitary-adrenocortical (HPA) activity, such asthe slope of the diurnal decline in cortisol, cortisol awakening response (CAR), and total cortisol out-put, have been found to associate inversely with trait ratings of extraversion and positive affect (E/PA)and positively with neuroticism and negative affect (N/NA) in some, but not all, investigations. Theseinconsistencies may partly reflect varied intensity of cortisol sampling among studies and reliance onself-rated traits, which are subject to reporting biases and limitations of introspection. Here, we furtherexamined dispositional correlates of HPA activity in 490 healthy, employed midlife volunteers (M age = 43years; 54% Female; 86% white). Trait ratings were requested from participants and 2 participant-electedinformants using the Positive and Negative Affect Schedule (PANAS) and Extraversion and Neuroticismdimensions of NEO personality inventories. CAR was assessed as percent increase in cortisol levels fromawakening to 30 min after awakening; and the diurnal slope and total output of cortisol [Area Underthe Curve (AUC)] were determined from cortisol measurements taken at awakening, +4 and +9 h later,and bedtime, across 3 workdays. Structural equation modeling was used to estimate multi-informantE/PA and N/NA factors. We used 3 days of measurement as indicators to model each of the three latentcortisol factors (slope, CAR, and AUC). With the two latent emotionality and three latent cortisol indicesincluded there was good fit to the data ( 2(200)= 278.38, p = 0.0002; RMSEA = 0.028, 90% CI = 0.02–0.04;CFI/TLI = 0.97/0.96; SRMR = 0.04). After controlling for covariates (age, sex, race), results showed higherlatent E/PA associated with a steeper diurnal slope (Standardized ˇ = −0.19, p = 0.02) and smaller CAR(Standardized ˇ = −0.26, p = 0.004), whereas N/NA did not associate with any cortisol metric (Standard-ized ˇ’s = −0.12 to 0.13, p’s = 0.10 to 0.53). These findings suggest that positive emotionality may be moreclosely associated with indices of diurnal cortisol release than negative emotionality

Carbohydrate scaffolds as glycosyltransferase inhibitors with in vivo antibacterial activity

The rapid rise of multi-drug-resistant bacteria is a global healthcare crisis, and new antibiotics are urgently required, especially those with modes of action that have low-resistance potential. One promising lead is the liposaccharide antibiotic moenomycin that inhibits bacterial glycosyltransferases, which are essential for peptidoglycan polymerization, while displaying a low rate of resistance. Unfortunately, the lipophilicity of moenomycin leads to unfavourable pharmacokinetic properties that render it unsuitable for systemic administration. In this study, we show that using moenomycin and other glycosyltransferase inhibitors as templates, we were able to synthesize compound libraries based on novel pyranose scaffold chemistry, with moenomycin-like activity, but with improved drug-like properties. The novel compounds exhibit in vitro inhibition comparable to moenomycin, with low toxicity and good efficacy in several in vivo models of infection. This approach based on non-planar carbohydrate scaffolds provides a new opportunity to develop new antibiotics with low propensity for resistance induction