Using bundle embeddings to predict daily cortisol levels in human subjects

BACKGROUND: Many biological variables sampled from human subjects show a diurnal pattern, which poses special demands on the techniques used to analyze such data. Furthermore, most biological variables belong to nonlinear dynamical systems, which may make linear statistical techniques less suitable to analyze their dynamics. The current study investigates the usefulness of two analysis techniques based on nonlinear lagged vector embeddings: sequentially weighted global linear maps (SMAP), and bundle embeddings. METHODS: Time series of urinary cortisol were collected in 10 participants, in the morning ('night' measurement) and the evening ('day' measurement), resulting in 126 consecutive measurements. These time series were used to create lagged vector embeddings, which were split into 'night' and 'day' bundle embeddings. In addition, embeddings were created based on time series that were corrected for the average time-of-day (TOD) values. SMAP was used to predict future values of cortisol in these embeddings. Global (linear) and local (non-linear) predictions were compared for each embedding. Bootstrapping was used to obtain confidence intervals for the model parameters and the prediction error. RESULTS: The best cortisol predictions were found for the night bundle embeddings, followed by the full embeddings and the time-of-day corrected embeddings. The poorest predictions were found for the day bundle embeddings. The night bundle embeddings, the full embeddings and the TOD-corrected embeddings all showed low dimensions, indicating the absence of dynamical processes spanning more than one day. The dimensions of the day bundles were higher, indicating the presence of processes spanning more than one day, or a higher amount of noise. In the full embeddings, local models gave the best predictions, whereas in the bundles the best predictions were obtained from global models, indicating potential nonlinearity in the former but not the latter. CONCLUSIONS: Using a bundling approach on time series of cortisol may reveal differences between the predictions of night and day cortisol that are difficult to find with conventional time-series methods. Combination of this approach with SMAP may especially be useful when analyzing time-series data with periodic components

Effects of urinary cortisol levels and resting heart rate on the risk for fatal and nonfatal cardiovascular events

AbstractBackground and aimsHigher cortisol levels are associated with cardiovascular mortality in the elderly. It is unclear whether this association also exists in a general population of younger adults and for non-fatal cardiovascular events. Likewise, resting heart rate is associated with cardiovascular mortality, but fewer studies have also considered non-fatal events. The goal of this study was to investigate whether twenty-four-hour urinary cortisol (24-h UFC) levels and resting heart rate (RHR) predict major adverse fatal and non-fatal cardiovascular events (MACE) in the general population.MethodsWe used data from a subcohort of the PREVEND study, a prospective general population based cohort study with a follow-up of 6.4 years for 24-h UFC and 10.6 years for RHR. Participants were 3432 adults (mean age 49 years, range 28–75). 24-h UFC was collected and measured by liquid chromatography—tandem mass spectrometry. RHR was measured at baseline in a supine position for 10 min with the Dinamap XL Model 9300. Information about cardiovascular events and mortality was obtained from the Dutch national registry of hospital discharge diagnoses and the municipal register respectively.Results24-h UFC did not significantly increase the hazard of MACE (hazard ratio = 0.999, 95% confidence interval = 0.993–1.006, p = 0.814). RHR increased the risk for MACE with 17% per 10 extra heart beats per minute (hazard ratio = 1.016, 95% confidence interval = 1.001–1.031, p = 0.036) after adjustment for conventional risk factors.ConclusionsIn contrast to 24-h UFC, RHR is a risk marker for MACE in the general population

Thiols as markers of redox status in type 1 diabetes mellitus

Introduction: Type 1 diabetes mellitus (T1DM) is associated with inflammation and the production of reactive oxygen species (ROS). Systemically, free thiols (R-SH) can be oxidized by ROS and circulating R-SH concentrations may directly reflect the systemic redox status. In this study the association between R-SH and clinical parameters of T1DM, including glycated haemoglobin A1c (HbA1c), was investigated. This is of particular interest since thiols are amendable to therapeutic intervention. Methods: As part of a prospective cohort study, data from 216 patients with a mean age of 45 (12) years, 57% male, diabetes duration 22 (16, 30) years and HbA1c of 60 (11) mmol/mol were examined. Baseline data were collected in 2002 and follow-up data in 2018. Cox proportional hazards regression analysis, with age, sex, HbA1c and R-SH, was used to assess prognostic factors for the development of complications. Results: At baseline, the plasma concentration of R-SH was 281.8 ± 34.0 μM. In addition to a lower concentration of NT-proBNP in the highest R-SH quartile (305–379 µM) there were no differences in baseline characteristics between the quartiles of R-SH. The Pearson correlation coefficient for R-SH and NT-proBNP was −0.290 (p &lt; 0.001). No significant correlation between R-SH and baseline HbA1c (r = −0.024, p = 0.726) was present. During follow-up, 42 macrovascular and 92 microvascular complications occurred. In Cox regression, R-SH was not a prognostic factor for the development of microvascular [hazard ratio (HR) 0.999 (95% confidence interval (CI) 0.993, 1.005)] and macrovascular [HR 0.993 (95% CI 0.984, 1.002)] complications. Conclusions: In addition to a negative association with NT-proBNP, no relevant relationships between R-SH and parameters of T1DM, including HbA1c, were present in this study.</p

Individual Heterogeneity in the Relations Between Sleep, Inflammation, and Somatic Symptoms

OBJECTIVE: Poor sleep is associated with the experience of more somatic symptoms and a proinflammatory state, whereas a proinflammatory state may also result in the experience of more somatic symptoms. However, existing studies ignore individual differences in these associations. We aimed to study relations between sleep, inflammatory markers, and somatic symptoms at a within-individual level. METHODS: Time series of daily data on sleep, somatic symptoms, and inflammation markers in 10 healthy individuals (age, 19-58 years; three men) for 63 days were analyzed. Bidirectional lagged ( t - 1) and contemporaneous ( t ) relations between sleep duration, inflammatory markers (C-reactive protein, interferon-α, interleukin 1RA), and somatic symptoms were analyzed using 24-hour urine and diary data. Unified structural equation modeling was used to analyze the association between sleep duration, the three inflammatory markers, and the amount of somatic symptoms at the individual level. RESULTS: Associations were found between sleep and at least one of three inflammatory markers in four individuals, both positive (three associations) and negative (five associations) and contemporaneous (four associations) and lagged (four associations). Sleep was related to somatic symptoms in four individuals, both positive ( n = 2) and negative ( n = 2) and contemporaneous ( n = 3) and lagged ( n = 1). Inflammatory markers were associated with somatic symptoms in three individuals, both positive (three associations) and negative (one association) and contemporaneous (three associations) and lagged (one associations). Two individuals showed no associations between sleep, inflammatory markers, and somatic symptoms. CONCLUSIONS: We observed a large variability in presence, strength, and direction of associations between sleep, inflammatory markers, and somatic symptoms

Using State Space Methods to Reveal Dynamical Associations Between Cortisol and Depression

Despite extensive research, the link between etiological factors and depression remains poorly understood. This may in part be due to a focus on strictly linear definitions of causality, derived at the group level. However, etiological relations in depression are likely to be dynamical, nonlinear and potentially unquantifiable with traditional statistics. Therefore the aim of this study was to evaluate the use of the convergent cross-mapping (CCM) method in investigating possible nonlinear relationships between supposed etiological factors and depressive symptomatology. Time series data from six healthy individuals were used to model the relationship between 24-h urinary free cortisol and negative affect using CCM and dewdrop embeddings. CCM is a nonlinear measure of causality, based on state space reconstruction with lagged coordinate embeddings. The results showed that nonlinear dynamical relationships between cortisol and negative affect may be present within participants, as demonstrated by a positive cross-map convergence from negative affect to cortisol. However, analyses also showed that noise and influential points had considerable impact on the results. Convergent crossmapping can be used to reveal possible nonlinear dynamical relationships between etiological factors and psychopathology that may remain undetected with traditional linear causality measures

Age- and sex-specific associations between adverse life events and functional bodily symptoms in the general population

Objective: To test age- and sex-specific associations between adverse life events and functional bodily symptoms (FBS) in the general population. Methods: In a population-based cohort, 964 participants (mean age 55 years SD 11,48% male) completed two measurements waves of the present study. Lifetime exposure to 12 adverse life events was assessed through a modified version of the List of Threatening Experiences. Stress-sensitive personality was assessed with the 12-item neuroticism scale of the Eysenck Personality Questionnaire-Revised. Socio-economic status was retrieved from questionnaires. Participants completed the somatization section of the Composite International Diagnostic Interview to survey the presence of 42 FBS in the previous year. Results: Regression analyses, adjusted for age, revealed that lifetime scores of adverse life events were significantly associated with FBS in the previous year, an association that was nearly identical for females (beta = 0.18, t = 4.07, p <0.01) and males (beta = 0.19, t = 424, p <0.01). This association remained statistically significant when stress-sensitive personality and socio-economic status were added to the model. Associations between adverse life events during childhood and FBS were statistically significant in females (beta = 0.13, t = 2.90, p = 0.04) but not in males (beta = 0.06, t = 1.24, p = 0.22), whereas there was a stronger association with adverse life events during adulthood in males (beta = 0.20, t = 437, p <0.01) compared to females (beta = 0.15, t = 3.38, p = 0.01). Life events in the previous year were not associated with FBS in the previous year. Conclusion: Adverse life events during lifetime were associated with FBS in the previous year. This association was dependent on age and sex but largely independent of having a stress-sensitive personality or low socioeconomic status. Future studies could adopt a life course perspective to study the role of adverse life events in FBS. (C) 2015 Elsevier Inc. All rights reserved

How to assess stress biomarkers for idiographic research?

Associations between stress-related biomarkers, like cortisol or catecholamines, and somatic or psychological symptoms have often been examined at the group level. Studies using this nomothetic approach reported equivocal findings, which may be due to high levels of intra-individual variance of stress biomarkers. More importantly, analyses at the group level provide information about the average patient, but do not necessarily have meaning for individual patients. An alternative approach is to examine data at the level of individual patients in so-called idiographic research. This method allows identifying individuals in whom symptoms are explained by preceding alterations in specific stress biomarkers, based on time series of symptoms and stress biomarkers. To create time series of sufficient length for statistical analysis, many subsequent stress biomarker measurements are needed for each participant. In the current paper, different matrices (i.e. saliva, urine, nail and hair) are discussed in light of their applicability for idiographic research. This innovative approach might lead to promising new insights in the association between stress biomarkers and psychological or somatic symptoms. New collection tools for stress biomarkers, like the use of sweat pads, automated microdialysis systems, dried blood spots, or smartphone applications, might contribute to the feasibility and implementation of idiographic research in the future. (C) 2015 Elsevier Ltd. All rights reserved