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We are analyzing https://link.springer.com/article/10.1007/s44192-024-00066-5.

Title:
Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study | Discover Mental Health
Description:
Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting forโ€‰~โ€‰32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.
Website Age:
28 years and 1 months (reg. 1997-05-29).

Matching Content Categories {๐Ÿ“š}

  • Health & Fitness
  • Education
  • Science

Content Management System {๐Ÿ“}

What CMS is link.springer.com built with?

Custom-built

No common CMS systems were detected on Link.springer.com, and no known web development framework was identified.

Traffic Estimate {๐Ÿ“ˆ}

What is the average monthly size of link.springer.com audience?

๐ŸŒ  Phenomenal Traffic: 5M - 10M visitors per month


Based on our best estimate, this website will receive around 8,170,236 visitors per month in the current month.

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How Does Link.springer.com Make Money? {๐Ÿ’ธ}

The income method remains a mystery to us.

Earning money isn't the goal of every website; some are designed to offer support or promote social causes. People have different reasons for creating websites. This might be one such reason. Link.springer.com has a revenue plan, but it's either invisible or we haven't found it.

Keywords {๐Ÿ”}

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Topics {โœ’๏ธ}

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Schema {๐Ÿ—บ๏ธ}

WebPage:
      mainEntity:
         headline:Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study
         description:Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting forโ€‰~โ€‰32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.
         datePublished:2024-04-17T00:00:00Z
         dateModified:2024-04-17T00:00:00Z
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         keywords:
            Psychiatry
            Psychotherapy
            Clinical Psychology
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      headline:Cerebrospinal fluid metabolomes of treatment-resistant depression subtypes and ketamine response: a pilot study
      description:Depression is a disorder with variable presentation. Selecting treatments and dose-finding is, therefore, challenging and time-consuming. In addition, novel antidepressants such as ketamine have sparse optimization evidence. Insights obtained from metabolomics may improve the management of patients. The objective of this study was to determine whether compounds in the cerebrospinal fluid (CSF) metabolome correlate with scores on questionnaires and response to medication. We performed a retrospective pilot study to evaluate phenotypic and metabolomic variability in patients with treatment-resistant depression using multivariate data compression algorithms. Twenty-nine patients with treatment-resistant depression provided fasting CSF samples. Over 300 metabolites were analyzed in these samples with liquid chromatography-mass spectrometry. Chart review provided basic demographic information, clinical status with self-reported questionnaires, and response to medication. Of the 300 metabolites analyzed, 151 were present in all CSF samples and used in the analyses. Hypothesis-free multivariate analysis compressed the resultant data set into two dimensions using Principal Component (PC) analysis, accounting forโ€‰~โ€‰32% of the variance. PC1 accounted for 16.9% of the variance and strongly correlated with age in one direction and 5-methyltetrahydrofolate, homocarnosine, and depression and anxiety scores in the opposite direction. PC2 accounted for 15.4% of the variance, with one end strongly correlated with autism scores, male gender, and cognitive fatigue scores, and the other end with bipolar diagnosis, lithium use, and ethylmalonate disturbance. This small pilot study suggests that complex treatment-resistant depression can be mapped onto a 2-dimensional pathophysiological domain. The results may have implications for treatment selection for depression subtypes.
      datePublished:2024-04-17T00:00:00Z
      dateModified:2024-04-17T00:00:00Z
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      keywords:
         Psychiatry
         Psychotherapy
         Clinical Psychology
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                     name:Institute of Cancer and Genomics Sciences, University of Birmingham, Birmingham, UK
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                  name:University Hospitals Birmingham NHS Foundation Trust
                  address:
                     name:Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
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         name:Institute of Cancer and Genomics Sciences, University of Birmingham, Birmingham, UK
         type:PostalAddress
      name:University Hospitals Birmingham NHS Foundation Trust
      address:
         name:Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
         type:PostalAddress
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      url:http://orcid.org/0000-0003-3117-6482
      affiliation:
            name:Woodinville Psychiatric Associates
            address:
               name:Woodinville Psychiatric Associates, Woodinville, USA
               type:PostalAddress
            type:Organization
      email:[email protected]
      name:Animesh Acharjee
      affiliation:
            name:University of Birmingham
            address:
               name:Institute of Cancer and Genomics Sciences, University of Birmingham, Birmingham, UK
               type:PostalAddress
            type:Organization
            name:University Hospitals Birmingham NHS Foundation Trust
            address:
               name:Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
               type:PostalAddress
            type:Organization
            name:MRC Health Data Research UK (HDR UK)
            address:
               name:MRC Health Data Research UK (HDR UK), London, UK
               type:PostalAddress
            type:Organization
PostalAddress:
      name:Woodinville Psychiatric Associates, Woodinville, USA
      name:Institute of Cancer and Genomics Sciences, University of Birmingham, Birmingham, UK
      name:Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
      name:MRC Health Data Research UK (HDR UK), London, UK

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