Cognitive and immunological effects of yoga compared to memory training in older women at risk for alzheimer's disease.

Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) accompanied by cerebrovascular risk factors (CVRFs) are known to increase the risk of developing dementia. Mind-body practices such as yoga and meditation, have been recognized as safe techniques with beneficial effects on cognitive functions in older adults at risk for cognitive decline. We conducted a randomized, controlled trial to assess the efficacy of Kundalini yoga training (KY) compared to memory enhancement training (MET) on mood and cognitive functioning in a group of older women with CVRFs and SCD (clinicaltrials.gov = NCT03503669). The KY intervention consisted of weekly, 60-min in-person classes with a certified instructor for 12 weeks, with a 12-min guided recording for daily homework practice at home. MET involved 12 weekly in-person group classes with 12-min daily homework exercises. Objective and subjective memory performance were the primary outcomes. Peripheral whole blood samples were collected at baseline, 12-weeks, and 24-weeks follow-up for RNA sequencing and cytokine/chemokine assays. A total of 79 patients (KY = 40; MET = 39) were randomized, and 63 completed the 24-week follow-up (KY = 65% completion rate; MET = 95%; χ2(1) = 10.9, p < 0.001). At 24-weeks follow-up, KY yielded a significant, large effect size improvement in subjective cognitive impairment measures compared to MET. KYOn a transcriptional level, at 12- and 24-week follow-up, KY uniquely altered aging-associated signatures, including interferon gamma and other psycho-neuro-immune pathways. Levels of chemokine eotaxin-1, an aging marker, increased over time in MET but not KY participants. These results suggest clinical and biological benefits to KY for SCD, linking changes in cognition to the anti-inflammatory effects of yoga.

Inflammatory Markers of Geriatric Depression Response to Tai Chi or Health Education Adjunct Interventions.

BACKGROUND: Underlying inflammation is associated with an increased risk of depression in older adults. In this study, we examined the role of inflammatory biomarkers in antidepressant response in depressed older adults undergoing adjunct Tai Chi Chih (TCC) or Health education interventions. METHODS: Older adults aged 60 years and above with a diagnosis of major depression were randomized to 12 weeks of TCC versus Health and Wellness Education (HEW) as an adjunct therapy to their stable antidepressant treatment regimen. A panel of 19 cytokine/chemokines was measured at baseline and 12 weeks. Five factors were derived using factor analysis. General linear models were estimated to examine the change in factor scores and the association of these changes on depression remission rates, controlling for age, sex, and body mass index. RESULTS: Of the 170 randomized participants (TCC: n = 85 and HEW: n = 85), 55 TCC and 58 HEW completed the 3-month assessment. The groups did not differ at baseline in any measure. At follow-up, neither the changes in cytokine/chemokines scores nor the depression remission rate differed significantly between TCC and HEW. However, remitters and non-remitters differed significantly in changes in a factor composed of growth-regulated oncogene protein-alpha (GRO-alpha), epidermal growth factor (EGF), and soluble CD40 ligand (sCD40L). GRO-alpha and EGF levels (in both groups) were significantly increased in remitters compared to non-remitters. CONCLUSION: Changes in certain cytokines/chemokines may accompany improvement in depressive symptoms in older adults. Future studies will need to explore the role of these molecules in remission of late-life depression.

A Randomized Controlled Trial of Tai Chi Chih or Health Education for Geriatric Depression.

OBJECTIVES: Geriatric depression is difficult to treat and frequently accompanied by treatment resistance, suicidal ideations and polypharmacy. New adjunctive mind-body treatment strategies can improve clinical outcomes in geriatric depression and reduce risk for side-effects of pharmacological treatments. METHODS: We conducted a 3-month randomized controlled trial to assess the efficacy and tolerability of combining Tai Chi Chih (TCC) or Health Education and Wellness training (HEW) with the stable standard antidepressant treatment on mood and cognitive functioning in depressed older adults (NCT02460666). Primary outcome was change in depression as assessed by the Hamilton Rating Scale for Depression (HAM-D) post-treatment. Remission was defined as HAM-D ≤ 6; naturalistic follow-up continued for 6 months. We also assessed psychological resilience, health-related quality of life and cognition. RESULTS: Of the 178 randomized participants, 125 completed the 3-month assessment and 117 completed the 6-month assessment. Dropout and tolerability did not differ between groups. Remission rate within TCC was 35.5% and 33.3%, compared to 27.0% and 45.8% in HEW, at 3 and 6 months respectively (χ2(1) = 1.0, p = 0.3; χ2(1) = 1.9, p =0.2). Both groups improved significantly on the HAM-D at 3 and 6 months. TCC demonstrated a greater improvement in general health compared to HEW. CONCLUSIONS: Both TCC and HEW combined with a standard antidepressant treatment improved symptoms of depression in older adults. While TCC was superior to HEW in improving general health, we did not find group differences in improvement in mood and cognition.

Machine Learning Prediction of Treatment Outcome in Late-Life Depression.

Background: Recent evidence suggests that integration of multi-modal data improves performance in machine learning prediction of depression treatment outcomes. Here, we compared the predictive performance of three machine learning classifiers using differing combinations of sociodemographic characteristics, baseline clinical self-reports, cognitive tests, and structural magnetic resonance imaging (MRI) features to predict treatment outcomes in late-life depression (LLD). Methods: Data were combined from two clinical trials conducted with depressed adults aged 60 and older, including response to escitalopram (N = 32, NCT01902004) and Tai Chi (N = 35, NCT02460666). Remission was defined as a score of 6 or less on the 24-item Hamilton Rating Scale for Depression (HAMD) at the end of 24 weeks of treatment. Features subsets were constructed from baseline sociodemographic and clinical features, gray matter volumes (GMVs), or both. Three classification algorithms were compared: (1) Support Vector Machine-Radial Bias Function (SVMRBF), (2) Random Forest (RF), and (3) Logistic Regression (LR). A repeated 5-fold cross-validation approach with a wrapper-based feature selection method was used for model fitting. Model performance metrics included Area under the ROC Curve (AUC) and Matthews correlation coefficient (MCC). Cross-validated performance significance was tested by permutation analysis. Classifiers were compared by Cochran's Q and post-hoc pairwise comparisons using McNemar's Chi-Square test with Bonferroni correction. Results: For the RF and SVMRBF algorithms, the combined feature set outperformed the clinical and GMV feature sets with a final cross-validated AUC of 0.83 ± 0.11 and 0.80 ± 0.11, respectively. Both classifiers passed permutation analysis. The LR algorithm performed best using GMV features alone (AUC 0.79 ± 0.14) but failed to pass permutation analysis using any feature set. Performance of the three classifiers differed significantly for all three features sets. Important predictive features of treatment response included anterior and posterior cingulate volumes, depression characteristics, and self-reported health-related quality scores. Conclusion: This preliminary exploration into the use of ML and multi-modal data to identify predictors of general treatment response in LLD indicates that integration of clinical and structural MRI features significantly increases predictive capability. Identified features are among those previously implicated in geriatric depression, encouraging future work in this arena.