Ambulatory blood pressure and the metabolic syndrome in normotensive and untreated hypertensive men

Inflammatory biomarkers link perceived stress with metabolic dysregulation

Objective

Perceived stress has been identified as a risk factor for metabolic syndrome. However, the intermediate pathways underlying this relationship are not well understood. Inflammatory responses may be one process by which stress leads to metabolic dysregulation. Prior work has shown that chronic stress is associated with elevated systemic inflammation and that altered inflammatory activity contributes to the pathogenesis of metabolic syndrome. The current analyses tested this hypothesis by examining inflammation as a pathway by which perceived stress affects metabolic health.

Methods

Data from the Midlife in the United States Study (MIDUS) (N = 648; Mean age = 52.3) provided measures of perceived stress, inflammatory biomarkers [C-reactive protein (CRP), interleukin-6 (IL-6), E-selectin, fibrinogen, intracellular adhesion molecule-1 (ICAM-1)] and metabolic health markers. Confirmatory factor analysis (CFA) was used to confirm the fit of a hierarchical model of metabolic syndrome in our sample. Structural equation modeling (SEM) was used to test the assumption that inflammation mediates the association between perceived stress and the latent factor representing metabolic syndrome.

Results

The CFA of metabolic syndrome demonstrated excellent goodness of fit to our sample [CFI = 0.97, TLI = 0.95, RMSEA = 0.06, SMSR = 0.05]. Mediation analysis with SEM revealed that the indirect pathway linking stress to metabolic dysregulation through inflammation was significant [B = 0.08, SE = 0.01, z = 3.69, p < .001, 95% confidence interval CI (0.04, 0.13)].

Conclusions

These results suggest that inflammatory biomarkers are a viable explanatory pathway for the relationship between perceived stress and metabolic health consequences. Interventions that target psychosocial stress may serve as cost-effective and accessible treatment options for mitigating inflammatory health risks.

A Structural Model of Social Determinants of the Metabolic Syndrome

Objectives

The metabolic syndrome (MetS) refers to a cluster of interrelated physiological characteristics that are associated with an increased risk of cardiovascular disease and diabetes. While the clinical usefulness of the MetS has been the subject of controversy for years, increasingly sophisticated methods are being used to measure the concept.

Participants

Study of community health center patients who were not diabetic; study group was evenly divided between Black and White adults.

Main Outcome Measures

Latent MetS score and MetS status based on the five-point scale developed by the National Cholesterol Education Panel (NCEP).

Methods

Structural equation modeling of MetS incorporating the effects of race/ethnicity, racial discrimination, socioeconomic position (SEP), and selected mediating variables.

Results

The largest influences on latent MetS scores were SEP (negative relationship) and male gender (higher scores for men). Two mediating variables, physical activity and stress-related eating, had smaller impacts. Self-reported racial discrimination was associated with cynical hostility but did not influence the MetS level among nondiabetics. Despite higher NCEP scores and MetS prevalence rates for Blacks compared with Whites, race did not have direct effect on MetS levels when adjusted for the other characteristics in our model.

Conclusions

Neither race nor self-reported racial discrimination had direct effects on MetS level in our structural model. The large effects of socioeconomic position and male gender were not mediated by the other variables in the model.

Food Liking-Based Diet Quality Indexes (DQI) Generated by Conceptual and Machine Learning Explained Variability in Cardiometabolic Risk Factors in Young Adults

The overall pattern of a diet (diet quality) is recognized as more important to health and chronic disease risk than single foods or food groups. Indexes of diet quality can be derived theoretically from evidence-based recommendations, empirically from existing datasets, or a combination of the two. We used these methods to derive diet quality indexes (DQI), generated from a novel dietary assessment, and to evaluate relationships with cardiometabolic risk factors in young adults with (n = 106) or without (n = 106) diagnosed depression (62% female, mean age = 21). Participants completed a liking survey (proxy for usual dietary consumption). Principle component analysis of plasma (insulin, glucose, lipids) and adiposity (BMI, Waist-to-Hip ratio) measures formed a continuous cardiometabolic risk factor score (CRFS). DQIs were created: theoretically (food/beverages grouped, weighted conceptually), empirically (grouping by factor analysis, weights empirically-derived by ridge regression analysis of CRFS), and hybrid (food/beverages conceptually-grouped, weights empirically-derived). The out-of-sample CRFS predictability for the DQI was assessed by two-fold and five-fold cross validations. While moderate consistencies between theoretically- and empirically-generated weights existed, the hybrid outperformed theoretical and empirical DQIs in cross validations (five-fold showed DQI explained 2.6% theoretical, 2.7% empirical, and 6.5% hybrid of CRFS variance). These pilot data support a liking survey that can generate reliable/valid DQIs that are significantly associated with cardiometabolic risk factors, especially theoretically- plus empirically-derived DQI.

Modeling Multisystem Physiological Dysregulation

OBJECTIVES

The purposes of this study were to compare the relative fit of two alternative factor models of allostatic load (AL) and physiological systems, and to test factor invariance across age and sex.

METHODS

Data were from the Midlife in the United States II Biomarker Project, a large (n = 1255) multisite study of adults aged 34 to 84 years (56.8% women). Specifically, 23 biomarkers were included, representing seven physiological systems: metabolic lipids, metabolic glucose, blood pressure, parasympathetic nervous system, sympathetic nervous system, hypothalamic-pituitary-adrenal axis, and inflammation. For factor invariance tests, age was categorized into three groups (≤45, 45-60, and >60 years).

RESULTS

A bifactor model where biomarkers simultaneously load onto a common AL factor and seven unique system-specific factors provided the best fit to the biomarker data (comparative fit index = 0.967, root mean square error of approximation = 0.043, standardized root mean square residual = 0.028). Results from the bifactor model were consistent with invariance across age groups and sex.

CONCLUSIONS

These results support the theory that represents and operationalizes AL as multisystem physiological dysregulation and operationalizing AL as the shared variance across biomarkers. Results also demonstrate that in addition to the variance in biomarkers accounted for by AL, individual physiological systems account for unique variance in system-specific biomarkers. A bifactor model allows researchers greater precision to examine both AL and the unique effects of specific systems.

Personality Correlates of Midlife Cardiometabolic Risk: The Explanatory Role of Higher-Order Factors of the Five-Factor Model

Varying associations are reported between Five-Factor Model (FFM) personality traits and cardiovascular disease risk. Here, we further examine dispositional correlates of cardiometabolic risk within a hierarchical model of personality that proposes higher-order traits of Stability (shared variance of Agreeableness, Conscientiousness, inverse Neuroticism) and Plasticity (Extraversion, Openness), and we test hypothesized mediation via biological and behavioral factors. In an observational study of 856 community volunteers aged 30-54 years (46% male, 86% Caucasian), latent variable FFM traits (using multiple-informant reports) and aggregated cardiometabolic risk (indicators: insulin resistance, dyslipidemia, blood pressure, adiposity) were estimated using confirmatory factor analysis (CFA). The cardiometabolic factor was regressed on each personality factor or higher-order trait. Cross-sectional indirect effects via systemic inflammation, cardiac autonomic control, and physical activity were tested. CFA models confirmed the Stability "meta-trait," but not Plasticity. Lower Stability was associated with heightened cardiometabolic risk. This association was accounted for by inflammation, autonomic function, and physical activity. Among FFM traits, only Openness was associated with risk over and above Stability, and, unlike Stability, this relationship was unexplained by the intervening variables. A Stability meta-trait covaries with midlife cardiometabolic risk, and this association is accounted for by three candidate biological and behavioral factors.

Factor structure underlying components of allostatic load

Allostatic load is a commonly used metric of health risk based on the hypothesis that recurrent exposure to environmental demands (e.g., stress) engenders a progressive dysregulation of multiple physiological systems. Prominent indicators of response to environmental challenges, such as stress-related hormones, sympatho-vagal balance, or inflammatory cytokines, comprise primary allostatic mediators. Secondary mediators reflect ensuing biological alterations that accumulate over time and confer risk for clinical disease but overlap substantially with a second metric of health risk, the metabolic syndrome. Whether allostatic load mediators covary and thus warrant treatment as a unitary construct remains to be established and, in particular, the relation of allostatic load parameters to the metabolic syndrome requires elucidation. Here, we employ confirmatory factor analysis to test: 1) whether a single common factor underlies variation in physiological systems associated with allostatic load; and 2) whether allostatic load parameters continue to load on a single common factor if a second factor representing the metabolic syndrome is also modeled. Participants were 645 adults from Allegheny County, PA (30–54 years old, 82% non-Hispanic white, 52% female) who were free of confounding medications. Model fitting supported a single, second-order factor underlying variance in the allostatic load components available in this study (metabolic, inflammatory and vagal measures). Further, this common factor reflecting covariation among allostatic load components persisted when a latent factor representing metabolic syndrome facets was conjointly modeled. Overall, this study provides novel evidence that the modeled allostatic load components do share common variance as hypothesized. Moreover, the common variance suggests the existence of statistical coherence above and beyond that attributable to the metabolic syndrome.

Systemic inflammation and the metabolic syndrome among middle-aged community volunteers

The metabolic syndrome is conceptualized as a clustering of risk factors--including insulin resistance, dyslipidemia, central adiposity, and elevated blood pressure (BP)--that increase the risk for cardiovascular disease and type 2 diabetes mellitus. Recent evidence suggests that markers of systemic inflammation may be included in the definition of the syndrome and play some role in its pathogenesis. In this study, we use a statistical modeling technique, confirmatory factor analysis, to evaluate relationships of systemic inflammation, as measured by plasma concentrations of C-reactive protein and interleukin-6, with the component factors of the metabolic syndrome (insulin resistance, dyslipidemia, central adiposity, and elevated BP) and to examine whether inflammation is a potential common pathway linking established components to the full syndrome. Subjects were 645 community volunteers aged 30 to 54 years (48% male, 82% European American, 18% African American). Consistent with existing literature, structural equation modeling adjusting for age, sex, and race confirmed a higher-order common factor underlying the covariation of insulin resistance, dyslipidemia, adiposity, and BP. Inflammation was positively associated with this common factor, accounting for 54% of its variance and partially mediating statistical aggregation of the component factors comprising the metabolic syndrome. These results were particularly strong for adiposity, raising the possibility that inflammatory processes stimulated by intraabdominal adipose tissue contribute to the development of the metabolic syndrome. The inclusion of inflammatory markers in the clinical definition of metabolic syndrome seems warranted and may improve prognostic assessment of risk of type 2 diabetes mellitus and cardiovascular disease.