Klotho Gene and Selective Serotonin Reuptake Inhibitors: Response to Treatment in Late-Life Major Depressive Disorder

Abstract

Klotho protein, encoded by the Klotho gene (KL) at locus 13q12, is an antiaging hormone-like protein playing a pivotal role in cell metabolism homeostasis and associated to longevity and age-related diseases. In particular, altered cell metabolism in central nervous system may influence the behavior of serotoninergic neurons. The role of KL in the response to treatment with selective serotonin reuptake inhibitors (SSRIs) in late-life depressive syndromes and late-life major depressive disorder (MDD) is unclear. We genotyped three single-nucleotide polymorphisms (SNPs) of KL in 329 older patients with diagnosis of late-life MDD, treated with SSRIs and evaluated with the Hamilton Rating Scale for Depression 21-items (HRSD-21) at baseline and after 6 months. A reduction ≥50 and <10 % in HDRS-21 score was considered as response or nonresponse to therapy, respectively, and the values of reduction between 10 and 49 % as poor responders. After 6 months of SSRI treatment, 176 patients responded, 54 patients did not respond and 99 patients showed a poor response. Ordinal logistic models showed a significant association between mutation of SNP rs1207568 and responders and, similarly, for each unitary risk allele increase overlapping results were found. Conversely, a significantly higher frequency of the minor genotype of SNP rs9536314 was found in nonresponders. Considering the pre-post differences of HRSD-21 scores as a continue variable, we confirmed a significant improvement of depressive symptoms after treatment in patients carrying at least one minor allele at rs1207568 and a worse response in patients homozygous for the minor allele at rs9536314. Our results were the first that suggested a possible role of KL in the complex pathway of SSRI response in late-life MDD.

Serum Klotho, vitamin D, and homocysteine in combination predict the outcomes of Chinese patients with multiple system atrophy

“…In addition, we observed that Klt and 25(OH)D are statistically significant correlated with NMS burdens of mood in MSA patients (Table ). Specifically, Klt and 25(OH)D have a negative association with mood and attention/memory, and a positive correlation with MMSE in MSA patients, further indicating that mood and attention/memory disorders may be targets for assessment by serum levels of Klt and 25(OH)D. In agreement with our notions, several studies have also reported that Klt deficiency and a low level of 25(OH)D was associated with mood, memory deterioration, and cognitive dysfunction . Consistent with previous reports, we also found that plasma levels of Klt have a significant negative correlation with the cardiovascular and urinary domains in MSA patients.…”

Glycosylation and Depression—A Review

“…The levels of Klotho protein having sialidase activity are known to increase after electroconvulsive therapy (24) in the spinal fluid of elderly patients with depression. In addition, a Klotho gene single nucleotide polymorphism (SNP) is reported to be associated with antidepressant response in elderly patients with depression(25). Together, the binding of sialic acid-specific lectins above we showed could be used as a marker of treatment response.…”

Pharmacogenomics‐Driven Prediction of Antidepressant Treatment Outcomes: A Machine‐Learning Approach With Multi‐trial Replication

“…Crucially, we demonstrated cross‐trial replication of prediction performance across rating scales in both STAR*D (QIDS‐C scale) and ISPC (HDRS scale) trials with precision similar to that observed in training with PGRN‐AMPS data. This work also represents an advance over traditional pharmacogenetic candidate gene approaches that identify plausible genes and SNPs associated with outcomes . We achieved that advance by asking whether the application of machine‐learning approaches that combine clinical assessments with a group of functionally validated pharmacogenomic SNPs as predictor variables might make it possible to predict SSRI treatment outcomes.…”

“…This work also represents an advance over traditional pharmacogenetic candidate gene approaches that identify plausible genes and SNPs associated with outcomes. [28][29][30][31][32][33][34] We achieved that advance by asking whether the application of machine-learning approaches that combine clinical assessments with a group of functionally validated pharmacogenomic SNPs as predictor variables might make it possible to predict SSRI treatment outcomes. Taken as a whole, our findings represent an important step toward the goal of algorithmically determining whether SSRIs are likely to be effective in patients with MDD prior to treatment initiation.…”