Schizophrenia as a risk factor for cardiovascular and metabolic health outcomes: a comparative risk assessment

Inter-relationships of risk factors and pathways associated with all-cause mortality in patients with chronic schizophrenia

Introduction

Of all psychiatric disorders, schizophrenia is associated with the highest risk of all-cause mortality. This study aimed to investigate independent risk factors for all-cause mortality in patients with chronic schizophrenia. In addition, the possible causal inter-relationships among these independent risk factors and all-cause mortality were also explored.

Methods

We conducted an analysis of 1,126 patients with chronic schizophrenia from our psychiatric department from April 2003 to August 2022, and retrospectively reviewed their medical records. The study endpoint was all-cause mortality. Baseline clinical characteristics including sociodemographic data, biochemical data, lifestyle factors, comorbidities and antipsychotic treatment were examined with Cox proportional hazards analysis.

Results

The all-cause mortality rate was 3.9% (44 patients). Multivariate Cox regression analysis revealed that several factors were independently associated with all-cause mortality, including diabetes mellitus (DM), hypertension, heart failure, gastroesophageal reflux disease (GERD), peptic ulcer disease, ileus, underweight, fasting glucose, triglycerides, albumin, and hemoglobin. Structural equation modeling (SEM) analysis revealed that several factors had statistically significant direct effects on all-cause mortality. Heart failure, hypertension, underweight, age at onset, and ileus showed positive direct effects, while albumin and hemoglobin demonstrated negative direct effects. In addition, several factors had indirect effects on all-cause mortality. GERD indirectly affected all-cause mortality through ileus, and peptic ulcer disease had indirect effects through albumin and ileus. Ileus, underweight, DM, and hypertension also exhibited indirect effects through various pathways involving albumin, hemoglobin, and heart failure. Overall, the final model, which included these factors, explained 13% of the variability in all-cause mortality.

Discussion

These results collectively suggest that the presence of DM, hypertension, heart failure, GERD, peptic ulcer disease, ileus, and underweight, along with lower levels of albumin or hemoglobin, were independently associated with all-cause mortality. The SEM analysis further revealed potential causal pathways and inter-relationships among these risk factors contributing to all-cause mortality in patients with chronic schizophrenia.

Multimorbidity and the Etiology of Schizophrenia

PURPOSE OF REVIEW

A global study of multimorbidity in schizophrenia, especially of the association with physical conditions, might offer much needed etiological insights.

RECENT FINDINGS

Our review suggests that life-style factors and medication related to schizophrenia are only part of the explanation of the increase in risk for cardiovascular, metabolic, pulmonary disorders, and some cancers. Positive associations with autoimmune disorders (with the exception of rheumatoid arthritis) and epilepsy are promising avenues of research but to date have not been fully exploited. The same holds for the negative comorbidity seen for rheumatoid arthritis and some cancers (e.g., prostate). As a whole, our review suggests that most of the explored conditions have a different prevalence in schizophrenia than in the general population. Several hypotheses emerged from this review such as the role of immune and genetic factors, of sex hormones, and of more general variability factors.

Osteoporosis and low bone mass among schizophrenia and bipolar disorder: A cross-sectional study with newly diagnosed, drug-naïve subjects

BACKGROUND

A growing body of data shows that schizophrenia (SCZ) and bipolar disorder (BD) have substantial metabolic risks; however, few studies have focused on bone metabolism. This study aimed to assess the prevalence and associated influencing factors of low bone mass and osteoporosis in SCZ and BD before pharmacological effects occur.

METHODS

108 healthy controls (HCs) and drug-naïve individuals with SCZ (n = 56) and BD (n = 130) had their lumbar spine (L1-L4) and left femur (Neck/Trochanter/Ward's triangle) bone mineral density (BMD) determined using dual-energy X-ray absorptiometry. Besides, we measured bone turnover markers (BTMs) levels, including procollagen I N-terminal propeptide, osteocalcin, and C-terminal cross-linking telopeptide of type I collagen in different groups.

RESULTS

Individuals with SCZ and BD had significantly lower BMD and significantly higher prevalence of low bone mass and osteoporosis compared with HCs. In the main observation regions of the total lumbar (F = 18.368, p < 0.001) and left femur (F = 14.790, p < 0.001), BMD was lower in individuals with SCZ and BD than HCs, with SCZ showing lower BMD than BD. The osteocalcin (H = 11.421, p = 0.003) levels were significantly higher in SCZ and BD than HCs. Binary regression analysis showed that SCZ or BD was an independent risk factor for low bone mass and osteoporosis. In addition, sex, age, and BTMs also influenced the occurrence of low bone mass and osteoporosis.

LIMITATIONS

Cross-sectional study.

CONCLUSION

The results findings of the study might contribute to our understanding of the increased risk of bone metabolism in SCZ and BD.

CLINICAL TRIAL REGISTRATION

www.chictr.org.cn, identifier ChiCTR1900021379.

Psychotropic Drug-Related Weight Gain and Its Treatment

Psychotropic drug-related weight gain (PDWG) is a common occurrence and is highly associated with non-initiation, discontinuation, and dissatisfaction with psychiatric drugs. Moreover, PDWG intersects with the elevated risk for obesity and associated morbidity that has been amply reported in the psychiatric population. Evidence indicates that differential liability for PDWG exists for antipsychotics, antidepressants, and anticonvulsants. During the past two decades, agents within these classes have become available with significantly lower or no liability for PDWG and as such should be prioritized. Although lithium is associated with weight gain, the overall extent of weight gain is significantly lower than previously estimated. The benefit of lifestyle and behavioral modification for obesity and/or PDWG in psychiatric populations is established, with effectiveness similar to that in the general population. Metformin is the most studied pharmacological treatment in the prevention and treatment of PDWG, and promising data are emerging for glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., liraglutide, exenatide, semaglutide). Most pharmacologic antidotes for PDWG are supported with low-confidence data (e.g., topiramate, histamine-2 receptor antagonists). Future vistas for pharmacologic treatment for PDWG include large, adequately controlled studies with GLP-1 receptor agonists and possibly GLP-1/glucose-dependent insulinotropic polypeptide co-agonists (e.g., tirzepatide) as well as specific dietary modifications.

Unsupervised Machine Learning Driven Analysis of Verbatims of Treatment-Resistant Schizophrenia Patients Having Followed Avatar Therapy

(1) Background: The therapeutic mechanisms underlying psychotherapeutic interventions for individuals with treatment-resistant schizophrenia are mostly unknown. One of these treatment techniques is avatar therapy (AT), in which the patient engages in immersive sessions while interacting with an avatar representing their primary persistent auditory verbal hallucination. The aim of this study was to conduct an unsupervised machine-learning analysis of verbatims of treatment-resistant schizophrenia patients that have followed AT. The second aim of the study was to compare the data clusters obtained from the unsupervised machine-learning analysis with previously conducted qualitative analysis. (2) Methods: A k-means algorithm was performed over the immersive-session verbatims of 18 patients suffering from treatment-resistant schizophrenia who followed AT to cluster interactions of the avatar and the patient. Data were pre-processed using vectorization and data reduction. (3): Results: Three clusters of interactions were identified for the avatar's interactions whereas four clusters were identified for the patient's interactions. (4) Conclusion: This study was the first attempt to conduct unsupervised machine learning on AT and provided a quantitative insight into the inner interactions that take place during immersive sessions. The use of unsupervised machine learning could yield a better understanding of the type of interactions that take place in AT and their clinical implications.