Impact of body mass index on worsening of diastolic function and impairment of left atrial strain in the general female urban population: a subanalysis of the Berlin female risk evaluation echocardiography follow-up study.

Background: The association of body mass index (BMI) with diastolic dysfunction (DD) is well described in the literature. However, there is conflicting evidence and long-term follow-up data regarding effects of BMI on preclinical DD and left atrial (LA) function are scarce, highlighting the importance of early detection tools, such as myocardial strain. Purpose: The aim of our study was to prospectively analyze the impact of clinical and demographic parameters, especially of BMI, on worsening of diastolic function and left atrial strain (LAS) in an urban population of women with a low prevalence of cardiovascular risk factors. Methods and Results: An extensive clinical and echocardiographic assessment comprising the analysis of phasic LAS using two-dimensional speckle-tracking echocardiography (2D STE) was performed in 258 participants of the Berlin Female Risk Evaluation (BEFRI) trial between October 2019 and December 2020 after a mean follow-up period of 6.8 years. We compared clinical and echocardiographic parameters stratifying women by BMI < or ≥25 kg/m2, and we analyzed the impact of demographic characteristics on the worsening of DD and LA mechanics in the longer-term follow-up using univariate and multivariate regression analyses. 248 women were suitable for echocardiographic analysis of LAS using 2D STE. After a mean follow-up time of 6.8 years, LA reservoir strain (LASr) and LA conduit strain (LAScd) were significantly reduced in participants with a BMI ≥25 kg/m2 compared with women with a BMI <25 kg/m2 at baseline (30 ± 8% vs. 38 ± 9%, p < 0.0001; -14 ± 7% vs. -22 ± 8%, p < 0.0001). 28% of the overweighted women presented a deterioration of diastolic function at the time of follow-up in contrast with only 7% of the group with a BMI <25 kg/m2 (p < 0.0001). BMI remained significantly associated with LAS reductions after adjustment for other risk factors in multivariate regression analyses. Conclusion: Overweight and obesity are related to impaired LAS and to a worsening of diastolic function after a long-term follow-up in a cohort of randomly selected women.

Phasic left atrial strain to predict worsening of diastolic function: Results from the prospective Berlin Female Risk Evaluation follow-up trial.

Purpose: The predictive value of maximum left atrial volume index (LAVI), phasic left atrial strain (LAS) and other standard echocardiographic parameters assessing left ventricular (LV) diastolic function to discriminate a future worsening of diastolic function (DD) in patients at risk is unclear. We aimed to prospectively assess and compare the clinical impact of these parameters in a randomly selected study sample of the general urban female population. Methods and results: A comprehensive clinical and echocardiographic evaluation was performed in 256 participants of the Berlin Female Risk Evaluation (BEFRI) trial after a mean follow up time of 6.8 years. After an assessment of participants' current DD status, the predictive impact of an impaired LAS on the course of DD was assessed and compared with LAVI and other DD parameters using receiver operating characteristic (ROC) curve and multivariate logistic regression analyses. Subjects with no DD (DD0) who showed a decline of diastolic function by the time of follow-up showed a reduced LA reservoir (LASr) and conduit strain (LAScd) compared to subjects who remained in the healthy range (LASr 28.0% ± 7.0 vs. 41.9% ± 8.5; LAScd -13.2% ± 5.1 vs. -25.4% ± 9.1; p < 0.001). With an area under the curve (AUC) of 0.88 (95%CI 0.82-0.94) and 0.84 (95%CI 0.79-0.89), LASr and LAScd exhibited the highest discriminative value in predicting worsening of diastolic function, whereas LAVI was only of limited prognostic value [AUC 0.63 (95%CI 0.54-0.73)]. In logistic regression analyses, LAS remained a significant predictor for a decline of diastolic function after controlling for clinical and standard echocardiographic DD parameters, indicating its incremental predictive value. Conclusion: The analysis of phasic LAS may be useful to predict worsening of LV diastolic function in DD0 patients at risk for a future DD development.GRAPHICAL ABSTRACT.

Sample Size Requirements for Applying Mixed Polytomous Item Response Models: Results of a Monte Carlo Simulation Study.

Mixture models of item response theory (IRT) can be used to detect inappropriate category use. Data collected by panel surveys where attitudes and traits are typically assessed by short scales with many response categories are prone to response styles indicating inappropriate category use. However, the application of mixed IRT models to this data type can be challenging because of many threshold parameters within items. Up to now, there is very limited knowledge about the sample size required for an appropriate performance of estimation methods as well as goodness-of-fit criteria of mixed IRT models in this case. The present Monte Carlo simulation study examined these issues for two mixed IRT models [the restricted mixed generalized partial credit model (rmGPCM) and the mixed partial credit model (mPCM)]. The population parameters of the simulation study were taken from a real application to survey data which is challenging (a 5-item scale with an 11-point rating scale, and three latent classes). Additional data conditions (e.g., long tests, a reduced number of response categories, and a simple latent mixture) were included in this simulation study to improve the generalizability of the results. Under this challenging data condition, for each model, data were generated based on varying sample sizes (from 500 to 5,000 observations with a 500-step). For the additional conditions, only three sample sizes (consisting of 1,000, 2,500, and 4,500 observations) were examined. The effect of sample size on estimation problems and accuracy of parameter and standard error estimates were evaluated. Results show that the two mixed IRT models require at least 2,500 observations to provide accurate parameter and standard error estimates under the challenging data condition. The rmGPCM produces more estimation problems than the more parsimonious mPCM, mostly because of the sparse tables arising due to many response categories. These models exhibit similar trends of estimation accuracy across sample sizes. Under the additional conditions, no estimation problems are observed. Both models perform well with a smaller sample size when long tests were used or a true latent mixture includes two classes. For model selection, the AIC3 and the SABIC are the most reliable information criteria.

The assessment of emotional clarity via response times to emotion items: shedding light on the response process and its relation to emotion regulation strategies.

Researchers have begun to use response times (RTs) to emotion items as an indirect measure of emotional clarity. Our first aim was to scrutinise the properties of this RT measure in more detail than previously. To be able to provide recommendations as to whether (and how) emotional intensity - as a possible confound - should be controlled for, we investigated the specific form of the relation between emotional intensity and RTs to emotion items. In particular, we assumed an inverted U-shaped relation at the item level. Moreover, we analysed the RT measure's convergent validity with respect to individuals' confidence in their emotion ratings. As a second aim, we compared the predictive validity of emotional clarity measures (RT measure, self-report) with respect to daily emotion regulation. The results of three experience sampling studies showed that the association between emotional intensity and RT followed an inverted U shape. RT was in part related to confidence. Emotional clarity measures were unrelated to reappraisal. There was some evidence that lower emotional clarity was related to a greater use of suppression. The findings highlight that emotional intensity and squared emotional intensity should be controlled for when using the RT measure of emotional clarity in future research.

Using a Mixed IRT Model to Assess the Scale Usage in the Measurement of Job Satisfaction.

This study investigated the adequacy of a rating scale with a large number of response categories that is often used in panel surveys for assessing diverse aspects of job satisfaction. An inappropriate scale usage is indicative of overstraining respondents and of diminished psychometric scale quality. The mixed Item Response Theory (IRT) approach for polytomous data allows exploring heterogeneous patterns of inappropriate scale usage in form of avoided categories and response styles. In this study, panel data of employees (n = 7036) on five aspects of job satisfaction measured on an 11-point rating scale within the "Household, Income and Labor Dynamics in Australia" (wave 2001) were analyzed. A three-class solution of the restricted mixed generalized partial credit model fit the data best. The results showed that in no class the 11-point scale was appropriately used but that the number of categories used was reduced in all three classes. Respondents of the large class (40%) appropriately differentiate between up to six categories. The two smaller classes (33 and 27%) avoid even more categories and show some kind of extreme response style. Furthermore, classes differ in socio-demographic and job-related factors. In conclusion, a two- to six-point scale without the middle point might be more adequate for assessing job satisfaction.

It's in the mix: psychological distress differs between combinations of alexithymic facets.

Alexithymia is a personality trait characterized by difficulties in identifying, describing, and communicating one's emotions. The aim of the present study is to examine the usefulness of a typological approach considering the interaction between distinct alexithymic features within a population of high-alexithymic German adults (N = 217). Latent profile analysis (LPA) was employed to test for possible underlying profiles. A 3-profile solution showed the best fit: The profiles can be described as (1) "low": lower load on all facets of alexithymia, (2) "mixed": specific problems on identifying emotions, and (3) "high": higher load on all facets of alexithymia. Moreover, this study tested how these profiles differed in psychological distress. "Mixed" profile, with specific problems on identifying emotions showed the highest levels of psychological distress. The present study suggests the importance of a specific combination of alexithymic features, rather than total alexithymia scores, as a risk factor for psychological distress.

Exploring dynamics in mood regulation--mixture latent Markov modeling of ambulatory assessment data.

OBJECTIVE: To illustrate how fluctuation patterns in ambulatory assessment data with features such as few categorical items, measurement error, and heterogeneity in the change pattern can adequately be analyzed with mixture latent Markov models. The identification of fluctuation patterns can be of great value to psychosomatic research concerned with dysfunctional behavior or cognitions, such as addictive behavior or noncompliance. In our application, unobserved subgroups of individuals who differ with regard to their mood regulation processes, such as mood maintenance and mood repair, are identified. METHODS: In an ambulatory assessment study, mood ratings were collected 56 times during 1 week from 164 students. The pleasant-unpleasant mood dimension was assessed by the two ordered categorical items unwell-well and bad-good. Mixture latent Markov models with different number of states, classes, and degrees of invariance were tested, and the best model according to information criteria was interpreted. RESULTS: Two latent classes that differed in their mood regulation pattern during the day were identified. Mean classification probabilities were high (>0.88) for this model. The larger class showed a tendency to stay in and return to a moderately pleasant mood state, whereas the smaller class was more likely to move to a very pleasant mood state and to stay there with a higher probability. CONCLUSIONS: Mixture latent Markov models are suitable to obtain information about interindividual differences in stability and change in ambulatory assessment data. Identified mood regulation patterns can serve as reference for typical mood fluctuation in healthy young adults.