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The role of lipoprotein profile in depression and cognitive performance: a network analysis.

ABSTRACT: Lipid profile (total cholesterol and lipoprotein fractions) has been found to correlate with depression and cognitive impairment across the lifespan. However, the role of lipid levels in self-rated depressive state and cognitive impairment remains unclear. In this study, we examined the relationship between lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and cognition in adults with and without self-rated depression. Four hundred and thirty-eight healthy participants completed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Self-Rating Depression Scale (SDS), and a serum lipoprotein test. Using multivariate ANOVA, partial correlation and network analysis, a network linking lipoprotein profile, depressive state and cognition was constructed. A significant difference in serum lipid profile between the high and low depressive groups was detected. Depressive state had a strong negative correlation with cognitive performance. Of the lipid profile, only high-density lipoprotein was positively correlated with depressive symptom severity, whereas the other three indices showed negative correlation with both depressive state and cognitive performance. Our results suggest that serum lipid profile may be directly linked to self-rated depression and cognitive performance. Further studies recruiting larger clinical samples are needed to elucidate the specific effect of lipoprotein on cognitive impairment in mood disorder.

SUBMITTER: Jia QF

PROVIDER: S-EPMC7693273 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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The role of lipoprotein profile in depression and cognitive performance: a network analysis.

Jia Qiu-Fang QF Yang Han-Xue HX Zhuang Nan-Nan NN Yin Xu-Yuan XY Zhu Zhen-Hua ZH Yuan Ying Y Yin Xiao-Li XL Wang Yi Y Cheung Eric F C EFC Chan Raymond C K RCK Hui Li L

Scientific reports 20201126 1

Lipid profile (total cholesterol and lipoprotein fractions) has been found to correlate with depression and cognitive impairment across the lifespan. However, the role of lipid levels in self-rated depressive state and cognitive impairment remains unclear. In this study, we examined the relationship between lipid profile (total cholesterol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol) and cognition in adults with and without self-rated depression. ...[more]

PMID: 33244178

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Project description:Lipoprotein (a) (Lp(a)) is considered a genetic factor for cardiovascular disease playing an important role in atherogenesis and thrombosis, but the evidence about its association with sleep duration is controversial. We evaluated the relation between self-reported sleep duration and Lp(a). Among 1600 participants of the population-based sample, we selected 1427 subjects without previously known cardiovascular events, who answered the questions about their sleep duration; had valid lipid profile results (total cholesterol, low- and high-density lipoproteins, Lp(a), apolipoprotein AI (ApoAI), ApoB, and ApoB/ApoAI); and did not take lipid-lowering drugs (mean age 46 ± 12 years). We performed a structured interview, which included questions about lifestyle, medical history, complaints, and sleep duration (How long have you been sleeping per night during the last month?). Sleep duration was classified as follows: <6 h/night-short, 6-9 h/night-normal, and ≥10 h/night-long. Overall, 73 respondents (5.2%) were short-sleepers and 69 (4.8%) long-sleepers. Males were slightly more prevalent among short-sleepers. The groups matched by age, body mass index, blood pressure, diabetes mellitus, and hypertension rate. Short-sleepers had lower rates of high total cholesterol (≥5.0 mmol/L), lower Lp(a) levels and lower rates of increased Lp(a) ≥0.5 g/L, and higher insulin and insulin resistance (assessed by the homeostatic model assessment for insulin resistance (HOMA-IR)). ApoAI, ApoB, their ratio, and other lab tests were similar in the groups. The multinomial logistic regression demonstrated that only the short sleep duration was independently (odds ratio (OR) 0.29, 95% confidence interval (CI) (0.09-0.91), p = 0.033) associated with Lp(a) (χ2 = 41.58, p = 0.003). Other influencing factors were smoking and HOMA-IR. Such an association was not found for long-sleepers. In conclusion, a short-sleep duration is associated with Lp(a). The latter might mediate the higher insulin resistance and higher cardiometabolic risks in short-sleepers.

Project description:ImportanceCognitive behavior therapy (CBT) has been shown to be effective in the treatment of acute depression. However, whether CBT can be effectively delivered in individual, group, telephone-administered, guided self-help, and unguided self-help formats remains unclear.ObjectiveTo examine the most effective delivery format for CBT via a network meta-analysis.Data sourcesA database updated yearly from PubMed, PsycINFO, Embase, and the Cochrane Library. Literature search dates encompassed January 1, 1966, to January 1, 2018.Study selectionRandomized clinical trials of CBT for adult depression. The 5 treatment formats were compared with each other and the control conditions (waiting list, care as usual, and pill placebo).Data extraction and synthesisPRISMA guidelines were used when extracting data and assessing data quality. Data were pooled using a random-effects model. Pairwise and network meta-analyses were conducted.Main outcomes and measuresSeverity of depression and acceptability of the treatment formats.ResultsA total of 155 trials with 15 191 participants compared 5 CBT delivery formats with 2 control conditions. In half of the studies (78 [50.3%]), patients met the criteria for a depressive disorder; in the other half (77 [49.7%]), participants scored above the cutoff point on a self-report measure. The effectiveness of individual, group, telephone, and guided self-help CBT did not differ statistically significantly from each other. These formats were statistically significantly more effective than the waiting list (standardized mean differences [SMDs], 0.87-1.02) and care as usual (SMDs, 0.47-0.72) control conditions as well as the unguided self-help CBT (SMDs, 0.34-0.59). In terms of acceptability (dropout for any reason), individual (relative risk [RR] = 1.44; 95% CI, 1.09-1.89) and group (RR = 1.38; 95% CI, 1.06-1.80) CBT were significantly better than guided self-help. Guided self-help was also less acceptable than being on a waiting list (RR = 0.63; 95% CI, 0.52-0.75) and care as usual (RR = 0.72; 95% CI, 0.57-0.90). Sensitivity analyses supported the overall findings.Conclusions and relevanceFor acute symptoms of depression, group, telephone, and guided self-help treatment formats appeared to be effective interventions, which may be considered as alternatives to individual CBT; although there were few indications of significant differences in efficacy between treatments with human support, guided self-help CBT may be less acceptable for patients than individual, group, or telephone formats.

Project description:ImportancePersonalized treatment choices would increase the effectiveness of internet-based cognitive behavioral therapy (iCBT) for depression to the extent that patients differ in interventions that better suit them.ObjectiveTo provide personalized estimates of short-term and long-term relative efficacy of guided and unguided iCBT for depression using patient-level information.Data sourcesWe searched PubMed, Embase, PsycInfo, and Cochrane Library to identify randomized clinical trials (RCTs) published up to January 1, 2019.Study selectionEligible RCTs were those comparing guided or unguided iCBT against each other or against any control intervention in individuals with depression. Available individual patient data (IPD) was collected from all eligible studies. Depression symptom severity was assessed after treatment, 6 months, and 12 months after randomization.Data extraction and synthesisWe conducted a systematic review and IPD network meta-analysis and estimated relative treatment effect sizes across different patient characteristics through IPD network meta-regression.Main outcomes and measuresPatient Health Questionnaire-9 (PHQ-9) scores.ResultsOf 42 eligible RCTs, 39 studies comprising 9751 participants with depression contributed IPD to the IPD network meta-analysis, of which 8107 IPD were synthesized. Overall, both guided and unguided iCBT were associated with more effectiveness as measured by PHQ-9 scores than control treatments over the short term and the long term. Guided iCBT was associated with more effectiveness than unguided iCBT (mean difference [MD] in posttreatment PHQ-9 scores, -0.8; 95% CI, -1.4 to -0.2), but we found no evidence of a difference at 6 or 12 months following randomization. Baseline depression was found to be the most important modifier of the relative association for efficacy of guided vs unguided iCBT. Differences between unguided and guided iCBT in people with baseline symptoms of subthreshold depression (PHQ-9 scores 5-9) were small, while guided iCBT was associated with overall better outcomes in patients with baseline PHQ-9 greater than 9.Conclusions and relevanceIn this network meta-analysis with IPD, guided iCBT was associated with more effectiveness than unguided iCBT for individuals with depression, benefits were more substantial in individuals with moderate to severe depression. Unguided iCBT was associated with similar effectiveness among individuals with symptoms of mild/subthreshold depression. Personalized treatment selection is entirely possible and necessary to ensure the best allocation of treatment resources for depression.

Project description:Background: Depression is a highly prevalent mental disorder, but only a fraction of those affected receive evidence-based treatments. Recently, Internet-based interventions were introduced as an efficacious and cost-effective approach. However, even though depression is a heterogenous construct, effects of treatments have mostly been determined using aggregated symptom scores. This carries the risk of concealing important effects and working mechanisms of those treatments. Methods: In this study, we analyze outcome and long-term follow-up data from the EVIDENT study, a large (N = 1,013) randomized-controlled trial comparing an Internet intervention for depression (Deprexis) with care as usual. We use Network Intervention Analysis to examine the symptom-specific effects of the intervention. Using data from intermediary and long-term assessments that have been conducted over 36 months, we intend to reveal how the treatment effects unfold sequentially and are maintained. Results: Item-level analysis showed that scale-level effects can be explained by small item-level effects on most depressive symptoms at all points of assessment. Higher scores on these items at baseline predicted overall symptom reduction throughout the whole assessment period. Network intervention analysis offered insights into potential working mechanisms: while deprexis directly affected certain symptoms of depression (e.g., worthlessness and fatigue) and certain aspects of the quality of life (e.g., overall impairment through emotional problems), other domains were affected indirectly (e.g., depressed mood and concentration as well as activity level). The configuration of direct and indirect effects replicates previous findings from another study examining the same intervention. Conclusions: Internet interventions for depression are not only effective in the short term, but also exert long-term effects. Their effects are likely to affect only a small subset of problems. Patients reporting these problems are likely to benefit more from the intervention. Future studies on online interventions should examine symptom-specific effects as they potentially reveal the potential of treatment tailoring. Clinical Trial Registration: ClinicalTrials.gov, Identifier: NCT02178631.

Project description:Late-life depression (LLD) and amnestic mild cognitive impairment (aMCI) are associated with medial temporal lobe structural abnormalities. However, the hippocampal functional connectivity (HFC) similarities and differences related to these syndromes when they occur alone or coexist are unclear. Resting-state functional connectivity MRI (R-fMRI) technique was used to measure left and right HFC in 72 elderly participants (LLD [n = 18], aMCI [n = 17], LLD with comorbid aMCI [n = 12], and healthy controls [n = 25]). The main and interactive relationships of LLD and aMCI on the HFC networks were determined, after controlling for age, gender, education and gray matter volumes. The effects of depressive symptoms and episodic memory deficits on the hippocampal functional connections also were assessed. While increased and decreased left and right HFC with several cortical and subcortical structures involved in mood regulation were related to LLD, aMCI was associated with globally diminished connectivity. Significant LLD-aMCI interactions on the right HFC networks were seen in the brain regions critical for emotion processing and higher-order cognitive functions. In the interactive brain regions, LLD and aMCI were associated with diminished hippocampal functional connections, whereas the comorbid group demonstrated enhanced connectivity. Main and interactive effects of depressive symptoms and episodic memory performance were also associated with bilateral HFC network abnormalities. In conclusion, these findings indicate that discrete hippocampal functional network abnormalities are associated with LLD and aMCI when they occur alone. However, when these conditions coexist, more pronounced vulnerabilities of the hippocampal networks occur, which may be a marker of disease severity and impending cognitive decline. By utilizing R-fMRI technique, this study provides novel insights into the neural mechanisms underlying LLD and aMCI in the functional network level.

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The role of lipoprotein profile in depression and cognitive performance: a network analysis.

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The role of lipoprotein profile in depression and cognitive performance: a network analysis.

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