Associations Between Somatic Multimorbidity Patterns and Depression in a Longitudinal Cohort of Middle-Aged and Older Chinese

ObjectivesDepressive symptoms are commonly seen among patients with multiple chronic somatic conditions, or somatic multimorbidity (SMM); however, little is known about the relationships between depressive symptoms and different SMM combinations. Our study aimed to delineate the patterns of SMM and their longitudinal associations with depressive symptoms among a nationally representative sample of middle-aged and older Chinese adults.DesignWe employed a longitudinal design.Setting and ParticipantsOlder adults (N = 10,084) aged ≥45 years from the China Health and Retirement Longitudinal Study 2011-2015 participated (mean age = 57.7 years at baseline; 53.3% men).MethodsSixteen chronic somatic conditions were ascertained at baseline via questionnaires. Depression was assessed with the Center for Epidemiological Studies Depression Scale at baseline and during follow-up. Patterns of SMM were identified via exploratory factor analyses. Generalized estimating equations were used to evaluate the longitudinal associations between patterns of SMM and the presence of depressive symptoms at follow-up.ResultsCompared with participants with no somatic condition, those with 1, 2, and 3 or more somatic conditions had a 21%, 66%, and 111% greater risk, respectively, for the presence of depressive symptoms. Increased factor scores for 4 patterns identified, cardio-metabolic pattern [adjusted odds ratio (AOR) 1.12, 95% confidence interval (CI) 1.06, 1.20], respiratory pattern (AOR 1.25, 95% CI 1.17, 1.33), arthritic-digestive-visual pattern (AOR 1.29, 95% CI 1.22, 1.37), and hepatic-renal-skeletal pattern (AOR 1.09, 95% CI 1.02, 1.16), were all associated with a higher risk of having depressive symptoms.Conclusions and ImplicationsAll SMM patterns were independently associated with depression among middle-aged and older Chinese adults, with greater odds for people with comorbid arthritic-digestive-visual conditions and respiratory conditions. Clinical practitioners should treat the middle-aged and older population under a multiple-condition framework combining SMM and mental disorders.     

The efficacy of 5-element therapy for senile diabetes with depression: A protocol for a systematic review and meta-analysis

Background:Senile diabetes with depression is a common and frequently-occurring disease, and it is also a difficult and hot point in domestic and international research. However, the efficiency of combination hypoglycemic agents and antidepressants in the treatment of senile diabetes with depression is poor, and new intervention methods are urgently needed. Research shows the 5-element therapy, as a Chinese traditional non-drug intervention, has definite curative effect on the prevention and treatment of various physical and mental diseases. The purpose of this systematic review and meta-analysis is to evaluate the efficacy of 5-element therapy on senile diabetes with depression.Methods:The electronic databases including Pubmed, Embase, Cochrane Library, Web of science, Chinese National Knowledge Infrastructure, Wanfang Database, Sino Med,China Biomedical Literature Database will be searched. The time limit for retrieving studies is from establishment to October 2020 for each database. Randomized controlled clinical trials related to 5-element therapy intervention on senile diabetes with depression will be included. Stata V.13.0 and Review manager 5.3 software will be implemented for data synthesis, sensitivity analysis, subgroup analysis, and the assessment of bias risk. We will use the grading of recommendations assessment, development, and evaluation system to assess the quality of evidence.Results:This study will provide a quantitative and standardized evaluation for the efficacy of 5-element therapy on senile diabetes with depression.Conclusion:This systematic review and meta-analysis will provide the high-quality evidence to assess whether the 5-element therapy has a positive treatment effect for senile diabetes with depression.Registration number:INPLASY2020100081.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.     

Transient and sustained effects of dopamine and serotonin signaling in motivation‐related behavior

Pharmacological studies of antidepressants and atypical antipsychotics have suggested a role of dopamine and serotonin signaling in depression. However, depressive symptoms and treatment effects are difficult to explain based simply on brain‐wide decrease or increase in the concentrations of these molecules. Recent animal studies using advanced neuronal manipulation and observation techniques have revealed detailed dopamine and serotonin dynamics that regulate diverse aspects of motivation‐related behavior. Dopamine and serotonin transiently modulate moment‐to‐moment behavior at timescales ranging from sub‐second to minutes and also produce persistent effects, such as reward‐related learning and stress responses that last longer than several days. Transient and sustained effects often exhibit specific roles depending on the projection sites, where distinct synaptic and cellular mechanisms are required to process the neurotransmitters for each transient and sustained timescale. Therefore, it appears that specific aspects of motivation‐related behavior are regulated by distinct synaptic and cellular mechanisms in specific brain regions that underlie the transient and sustained effects of dopamine and serotonin signaling. Recent clinical studies have implied that subjects with depressive symptoms show impaired transient and sustained signaling functions; moreover, they exhibit heterogeneity in depressive symptoms and neuronal dysfunction. Depressive symptoms may be explained by the dysfunction of each transient and sustained signaling mechanism, and distinct patterns of impairment in the relevant mechanisms may explain the heterogeneity of symptoms. Thus, detailed understanding of dopamine and serotonin signaling may provide new insight into depressive symptoms.     

Preschool sleep and depression interact to predict gray matter volume trajectories across late childhood to adolescence

There is a close relationship between sleep and depression, and certain maladaptive outcomes of sleep problems may only be apparent in individuals with heightened levels of depression. In a sample enriched for preschool depression, we examined how sleep and depression in early childhood interact to predict later trajectories of gray matter volume. Participants (N = 161) were recruited and assessed during preschool (ages 3–6 years) and were later assessed with five waves of structural brain imaging, spanning from late childhood to adolescence. Sleep and depression were assessed using a semi-structured parent interview when the children were preschool-aged, and total gray matter volume was calculated at each scan wave. Although sleep disturbances alone did not predict gray matter volume/trajectories, preschool sleep and depression symptoms interacted to predict later total gray matter volume and the trajectory of decline in total gray matter volume. Sleep disturbances in the form of longer sleep onset latencies, increased irregularity in the child’s sleep schedule, and higher levels of daytime sleepiness in early childhood were all found to interact with early childhood depression severity to predict later trajectories of cortical gray matter volume. Findings provide evidence of the interactive effects of preschool sleep and depression symptoms on later neurodevelopment.