Sanna-Kaisa Herukka

Publications (38) View all

• Article: Comparison Between Clinical Diagnosis and CSF Biomarkers of Alzheimer Disease in Elderly Patients with Late Onset Psychosis: Helsinki Old Age Psychosis Study (HOPS).

  Toni T Seppälä, Ulla-Marja Louhija, Björn Appelberg, Sanna-Kaisa Herukka, Kati Juva
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  ABSTRACT: OBJECTIVES: To determine the proportion of elderly people with a first psychotic episode actually suffering from dementia, especially Alzheimer disease (AD), by using cerebrospinal fluid (CSF) biomarkers. DESIGN: Prospective case-control study. SETTING AND PARTICIPANTS: Sixty-six patients age 65 years and older with recent psychotic symptoms and 12 comparison subjects with chronic schizophrenia over 10 years that were referred to acute old age psychiatry, in-ward treatment. MEASUREMENTS: Concentration levels of CSF Aβ42, tau and p-tau-181 measured by ELISA compared to clinical diagnosis made by a multiprofessional team of one neurologist and several psychiatrists. RESULTS: The CSF specimen was obtained from 51 (65.4%) of the patients. In five subjects out of 13 with a clinical diagnosis of AD, all the CSF biomarkers (Aβ42, tau and p-tau) were normal. Only one patient out of 25 with a psychiatric diagnosis and none out of the comparison group with schizophrenia showed a CSF profile typical of AD. Three patients with an AD diagnosis, four patients with a psychiatric diagnosis and one patient with schizophrenia had a low Aβ42 concentration with normal levels of tau or p-tau. The patients with AD had lower CSF Aβ42 levels than other patients. CONCLUSIONS: The CSF biomarkers are important and useful as part of the diagnostic procedure for detecting AD and other dementia in elderly patients displaying psychotic symptoms. The accuracy of AD diagnosis encounters problems due to atypical behavioural symptoms in psychiatric settings and thus the differential diagnostics can be improved by using CSF biomarkers of AD more frequently.
  The American journal of geriatric psychiatry: official journal of the American Association for Geriatric Psychiatry 04/2013; · 3.35 Impact Factor
• Article: Disease State Fingerprint in Frontotemporal Degeneration with Reference to Alzheimer's Disease and Mild Cognitive Impairment.

  Miguel Ángel Muñoz-Ruiz, Päivi Hartikainen, Anette Hall, Jussi Mattila, Juha Koikkalainen, Sanna-Kaisa Herukka, Valtteri Julkunen, Ritva Vanninen, Yawu Liu, Jyrki Lötjönen, Hilkka Soininen
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  ABSTRACT: Background: Disease State Index and Disease State Fingerprint represent a novel tool which collates data information from different sources, helping the clinician in the diagnosis and follow-up of dementia diseases. It has been demonstrated that it is applicable in the diagnosis of Alzheimer's disease (AD). Objective: We applied this novel tool to classify frontotemporal dementia (FTD) cases in comparison with controls, AD, and mild cognitive impairment (MCI) subjects. Methods: Thirty seven patients with FTD, 35 patients with AD, 26 control subjects, and 64 subjects with MCI were included in the study. The Disease State Index encompassed data from cognitive performance assessed by Mini-Mental State Examination, cerebrospinal fluid biomarkers, MRI volumetric and morphometric parameters as well as APOE genotype. Results: We applied the Disease State Index for comparisons at the group level. The data showed that FTD patients could be differentiated with a high accuracy, sensitivity, and specificity from controls (0.84, 0.84, 0.83) and from MCI (0.79, 0.78, 0.80). However, the correct accuracy was lower in the FTD versus AD comparison (0.69, 0.70, 0.71). In addition, we demonstrated the use of Disease State Fingerprint by comparing one particular FTD case with control, AD, and MCI population data. Conclusion: The results suggest that the Disease State Fingerprint and the underlying Disease State Index are particularly useful in differentiating between normal status and disease in patients with dementia, but it may also help to distinguish between the two dementia diseases, FTD and AD.
  Journal of Alzheimer's disease: JAD 03/2013; · 3.74 Impact Factor
• Article: Predicting AD Conversion: Comparison between Prodromal AD Guidelines and Computer Assisted PredictAD Tool.

  Yawu Liu, Jussi Mattila, Miguel Ángel Muñoz Ruiz, Teemu Paajanen, Juha Koikkalainen, Mark van Gils, Sanna-Kaisa Herukka, Gunhild Waldemar, Jyrki Lötjönen, Hilkka Soininen
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  ABSTRACT: To compare the accuracies of predicting AD conversion by using a decision support system (PredictAD tool) and current research criteria of prodromal AD as identified by combinations of episodic memory impairment of hippocampal type and visual assessment of medial temporal lobe atrophy (MTA) on MRI and CSF biomarkers. Altogether 391 MCI cases (158 AD converters) were selected from the ADNI cohort. All the cases had baseline cognitive tests, MRI and/or CSF levels of Aβ1-42 and Tau. Using baseline data, the status of MCI patients (AD or MCI) three years later was predicted using current diagnostic research guidelines and the PredictAD software tool designed for supporting clinical diagnostics. The data used were 1) clinical criteria for episodic memory loss of the hippocampal type, 2) visual MTA, 3) positive CSF markers, 4) their combinations, and 5) when the PredictAD tool was applied, automatically computed MRI measures were used instead of the visual MTA results. The accuracies of diagnosis were evaluated with the diagnosis made 3 years later. The PredictAD tool achieved the overall accuracy of 72% (sensitivity 73%, specificity 71%) in predicting the AD diagnosis. The corresponding number for a clinician's prediction with the assistance of the PredictAD tool was 71% (sensitivity 75%, specificity 68%). Diagnosis with the PredictAD tool was significantly better than diagnosis by biomarkers alone or the combinations of clinical diagnosis of hippocampal pattern for the memory loss and biomarkers (p≤0.037). With the assistance of PredictAD tool, the clinician can predict AD conversion more accurately than the current diagnostic criteria.
  PLoS ONE 01/2013; 8(2):e55246. · 4.09 Impact Factor
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  Available from: Pieter Jelle Visser

  Article: Genetic Loci associated with Alzheimer's disease and cerebrospinal fluid biomarkers in a finnish case-control cohort.

  Lyzel S Elias-Sonnenschein, Seppo Helisalmi, Teemu Natunen, Anette Hall, Teemu Paajanen, Sanna-Kaisa Herukka, Marjo Laitinen, Anne M Remes, Anne M Koivisto, Kari M Mattila, Terho Lehtimäki, Frans R J Verhey, Pieter Jelle Visser, Hilkka Soininen, Mikko Hiltunen
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  ABSTRACT: To understand the relation between risk genes for Alzheimer's disease (AD) and their influence on biomarkers for AD, we examined the association of AD in the Finnish cohort with single nucleotide polymorphisms (SNPs) from top AlzGene loci, genome-wide association studies (GWAS), and candidate gene studies; and tested the correlation between these SNPs and AD markers Aβ1-42, total tau (t-tau), and phosphorylated tau (p-tau) in cerebrospinal fluid (CSF). We tested 25 SNPs for genetic association with clinical AD in our cohort comprised of 890 AD patients and 701-age matched healthy controls using logistic regression. For the correlational study with biomarkers, we tested 36 SNPs in a subset of 222 AD patients with available CSF using mixed models. Statistical analyses were adjusted for age, gender and APOE status. False discovery rate for multiple testing was applied. All participants were from academic hospital and research institutions in Finland. APOE-ε4, CLU rs11136000, and MS4A4A rs2304933 correlated with significantly decreased Aβ1-42 (corrected p<0.05). At an uncorrected p<0.05, PPP3R1 rs1868402 and MAPT rs2435211 were related with increased t-tau; while SORL1 rs73595277 and MAPT rs16940758, with increased p-tau. Only TOMM40 rs2075650 showed association with clinical AD after adjusting for APOE-ε4 (p = 0.007), but not after multiple test correction (p>0.05). We provide evidence that APOE-ε4, CLU and MS4A4A, which have been identified in GWAS to be associated with AD, also significantly reduced CSF Aβ1-42 in AD. None of the other AlzGene and GWAS loci showed significant effects on CSF tau. The effects of other SNPs on CSF biomarkers and clinical AD diagnosis did not reach statistical significance. Our findings suggest that APOE-ε4, CLU and MS4A4A influence both AD risk and CSF Aβ1-42.
  PLoS ONE 01/2013; 8(4):e59676. · 4.09 Impact Factor
• Article: Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment.

  Anja H Simonsen, Jussi Mattila, Anne-Mette Hejl, Kristian S Frederiksen, Sanna-Kaisa Herukka, Merja Hallikainen, Mark van Gils, Jyrki Lötjönen, Hilkka Soininen, Gunhild Waldemar
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  ABSTRACT: Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical significant trend (p < 0.05) towards better classification accuracy (from 62.6 to 70.0%) was found when using the PredictAD tool during the stepwise procedure. When the same data were presented on paper, classification accuracy of the raters dropped significantly from 70.0 to 63.2%. Conclusion: Best classification accuracy was achieved by the clinical raters when using the tool for decision support, suggesting that the tool can add value in diagnostic classification when large amounts of heterogeneous data are presented.
  Dementia and Geriatric Cognitive Disorders 12/2012; 34(5-6):344-350. · 2.14 Impact Factor