FUNCTIONAL HYPERCORTISOLISM, VISCERAL OBESITY, AND METABOLIC SYNDROME

Sensitivity of the Neuroendocrine Stress Axis in Metabolic Diseases

Metabolic diseases are prevalent in modern society and have reached pandemic proportions. Metabolic diseases have systemic effects on the body and can lead to changes in the neuroendocrine stress axis, the critical regulator of the body's stress response. These changes may be attributed to rising insulin levels and the release of adipokines and inflammatory cytokines by adipose tissue, which affect hormone production by the neuroendocrine stress axis. Chronic stress due to inflammation may exacerbate these effects. The increased sensitivity of the neuroendocrine stress axis may be responsible for the development of metabolic syndrome, providing a possible explanation for the high prevalence of severe comorbidities such as heart disease and stroke associated with metabolic disease. In this review, we address current knowledge of the neuroendocrine stress axis in response to metabolic disease and discuss its role in developing metabolic syndrome.

Biochemical clusters predict mortality and reported inability to work 10 ​years later

BACKGROUND

Chronic systemic inflammation has been linked to premature mortality and limited somatic as well as mental health with consequences for capability to work and everyday functioning. We recently identified three biochemical clusters of endocrine and immune parameters (C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, cortisol and creatinine) in participants, age 35-81 years, of the open access Midlife in the United States Study (MIDUS) dataset. These clusters have been validated in an independent cohort of Japanese mid-life adults. Among these clusters, the one characterized by high inflammation coupled with low cortisol and creatinine concentrations was associated with the highest disease burden, referred to as high-risk cluster in the following. The current study aims to further examine the nature of this cluster and specifically whether it predicts mortality and the reported inability to work the last 30 days 10 years after the biomarker assessment.

METHODS AND FINDINGS

Longitudinally assessed health data from N ​= ​1234 individuals were analyzed in the current study. Logistic regression analyses were performed to predict mortality within one decade after first assessment (T0 ​= ​first assessment; T1 ​= ​second assessment). General linear models were used to predict the number of days study participants were unable to work due to health issues in the last 30 days (assessed at T1, analyses restricted to individuals <70 years of age). Biological sex, disease burden, and age at T0 were used as covariates in all analyses. Individuals in the previously identified high-risk cluster had a higher risk for mortality (22% of individuals deceased between T0 and T1 versus 10% respectively 9% in the two other clusters). Logistic regression models predicting mortality resulted in a significant difference between individuals from the high-risk cluster compared to those from an identified reference cluster (indicator method, p ​= ​.012), independently of age and disease burden. Furthermore, individuals in the high-risk cluster reported a higher number of disability days during the past 30 days (3.4 days in the high-risk cluster versus 1.5 respectively 1.0 days in the reference clusters) assessed at T1. All pairwise comparisons involving the high-risk cluster were significant (all ps ​< ​.001).

CONCLUSIONS

Immune-endocrine profiles are predictive of mortality within the following decade over and above age and disease burden. The findings thus highlight the importance of biomarker-based risk profiling that may provide new targets for interventions in the context of preventive medicine in the transition from health to disease and disease-related mortality.

How biomarker patterns can be utilized to identify individuals with a high disease burden: a bioinformatics approach towards predictive, preventive, and personalized (3P) medicine

Prevalences of non-communicable diseases such as depression and a range of somatic diseases are continuously increasing requiring simple and inexpensive ways to identify high-risk individuals to target with predictive and preventive approaches. Using k-mean cluster analytics, in study 1, we identified biochemical clusters (based on C-reactive protein, interleukin-6, fibrinogen, cortisol, and creatinine) and examined their link to diseases. Analyses were conducted in a US American sample (from the Midlife in the US study, N = 1234) and validated in a Japanese sample (from the Midlife in Japan study, N = 378). In study 2, we investigated the link of the biochemical clusters from study 1 to childhood maltreatment (CM). The three identified biochemical clusters included one cluster (with high inflammatory signaling and low cortisol and creatinine concentrations) indicating the highest disease burden. This high-risk cluster also reported the highest CM exposure. The current study demonstrates how biomarkers can be utilized to identify individuals with a high disease burden and thus, may help to target these high-risk individuals with tailored prevention/intervention, towards personalized medicine. Furthermore, our findings raise the question whether the found biochemical clusters have predictive character, as a tool to identify high-risk individuals enabling targeted prevention. The finding that CM was mostly prevalent in the high-risk cluster provides first hints that the clusters could indeed have predictive character and highlight CM as a central disease susceptibility factor and possibly as a leverage point for disease prevention/intervention.

Endogenous glucocorticoids may serve as biomarkers for migraine chronification

Aims:

The aims of this study were to: (a) identify differences in serum and cerebrospinal fluid (CSF) glucocorticoids among episodic migraine (EM) and chronic migraine (CM) patients compared with controls; (b) determine longitudinal changes in serum glucocorticoids in CM patients; and (c) determine migraine-related clinical features contributing to glucocorticoid levels.

Methods:

Serum and CSF levels of cortisol and corticosterone were measured using liquid chromatography-mass spectrometry among adult patients with EM, CM, and controls. Serum and CSF samples were collected from 26 and four participants in each group, respectively. Serum glucocorticoids were measured at a second timepoint after 2 years among 10 of the CM patients, six of whom reverted to EM while four persisted as CM. Receiver operating characteristic (ROC) analysis was made to assess the migraine diagnostic performance of glucocorticoids. Regression analysis was conducted to determine the link between glucocorticoid levels and migraine-related clinical variables.

Results:

CM patients exhibited significantly elevated serum and CSF levels of cortisol and corticosterone compared with controls and EM patients (age, sex, body mass index adjusted; Kruskal–Wallis p < 0.05). ROC showed area-under-curve of 0.89 to differentiate CM from EM. CM patients with remission had their serum glucocorticoids return to control or near EM levels (p < 0.05). Persistent CM showed unremitting serum glucocorticoids. Migraine frequency and disability contributed to increased cortisol, while pain self-efficacy predicted lower cortisol levels (p < 0.005).

Conclusion:

Endogenous glucocorticoids may be biomarkers for migraine progression and for monitoring treatment response. Improving pain self-efficacy skills may help optimize endogenous glucocorticoid levels, which in turn may prevent migraine attacks.

The Effect of Acute Hypohydration on Indicators of Glycemic Regulation, Appetite, Metabolism and Stress: A Systematic Review and Meta-Analysis

Evidence synthesizing the effects of acute body water losses on various markers of glycemic regulation, appetite, metabolism, and stress is lacking. Thus, the purpose of this review was to summarize the response of various hormonal changes involved in these physiologic functions to dehydration. A comprehensive literature search for peer-reviewed research in the databases PubMed, Scopus, CINAHL, and SportDiscus was conducted. Studies were included if they contained samples of adults (>18 years) and experimentally induced dehydration as measured by acute body mass loss. Twenty-one articles were eligible for inclusion. Findings suggested cortisol is significantly elevated with hypohydration (standard mean difference [SMD] = 1.12, 95% CI [0.583, 1.67], p < 0.0001). Testosterone was significantly lower in studies where hypohydration was accompanied by caloric restriction (SMD= -1.04, 95% CI [-1.93, -0.14], p = 0.02), however, there were no changes in testosterone in studies examining hypohydration alone (SMD = -0.17, 95% CI [-0.51 0.16], p = 0.30). Insulin and ghrelin were unaffected by acute total body water losses. Acute hypohydration increases markers of catabolism but has a negligible effect on markers of glycemic regulation, appetite, anabolism and stress. Given the brevity of existing research, further research is needed to determine the impact of hydration on glucagon, leptin, peptide YY and the subsequent outcomes relevant to both health and performance.

The association of morning serum cortisol with glucose metabolism and diabetes: The Jackson Heart Study

BACKGROUND

Serum cortisol levels have been associated with type 2 diabetes (T2D). However, the role of cortisol in glycemia and T2D is not fully elucidated among African Americans (AAs). We hypothesized that among AAs morning serum cortisol would be positively associated with glycemic measures and prevalent T2D.

METHODS

We examined the cross-sectional association of baseline morning serum cortisol with fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), homeostasis model assessment of insulin resistance (HOMA-IR), β-cell function (HOMA-β), and prevalent T2D in the Jackson Heart Study. Linear regression models were used to examine the association of log-transformed cortisol with glycemic traits, stratified by T2D status. Logistic regression was used to examine the association of log-transformed cortisol with prevalent T2D. Models were adjusted for age, sex, education, occupation, systolic blood pressure, waist circumference, physical activity, smoking, beta-blocker/hormone replacement medications and cortisol collection time.

RESULTS

Among 4,206 AAs (mean age 55 ± 13 years, 64% female), 19% had prevalent T2D. A 100% increase in cortisol among participants without diabetes was associated with 2.7 mg/dL (95% CI: 2.0, 3.3) higher FPG and a 10.0% (95% CI: -14.0, -6.0) lower HOMA-β with no significant association with HbA1c or HOMA-IR. In participants with diabetes, a 100% increase in cortisol was associated with a 23.6 mg/dL (95% CI: 13.6, 33.7) higher FPG and a 0.6% (95% CI: 0.3, 0.9) higher HbA1c. Among all participants, quartile 4 vs. 1 of cortisol was associated with a 1.26-fold (95% CI: 1.75, 2.91) higher odds of prevalent T2D.

CONCLUSION

Higher morning serum cortisol was associated with higher FPG and lower β-cell function among participants without T2D and higher FPG and HbA1c in participants with diabetes. Among all participants, higher cortisol was associated with higher odds of T2D. These findings support a role for morning serum cortisol in glucose metabolism among AAs.