%0 Journal Article %J J Alzheimers Dis %D 2019 %T Midlife Insulin Resistance as a Predictor for Late-Life Cognitive Function and Cerebrovascular Lesions. %A Toppala, Sini %A Ekblad, Laura L %A Lötjönen, Jyrki %A Helin, Semi %A Hurme, Saija %A Johansson, Jarkko %A Jula, Antti %A Karrasch, Mira %A Koikkalainen, Juha %A Laine, Hanna %A Parkkola, Riitta %A Viitanen, Matti %A Rinne, Juha O %X

BACKGROUND: Type 2 diabetes (T2DM) increases the risk for Alzheimer's disease (AD) but not for AD neuropathology. The association between T2DM and AD is assumed to be mediated through vascular mechanisms. However, insulin resistance (IR), the hallmark of T2DM, has been shown to associate with AD neuropathology and cognitive decline.

OBJECTIVE: To evaluate if midlife IR predicts late-life cognitive performance and cerebrovascular lesions (white matter hyperintensities and total vascular burden), and whether cerebrovascular lesions and brain amyloid load are associated with cognitive functioning.

METHODS: This exposure-to-control follow-up study examined 60 volunteers without dementia (mean age 70.9 years) with neurocognitive testing, brain 3T-MRI and amyloid-PET imaging. The volunteers were recruited from the Finnish Health 2000 survey (n = 6062) to attend follow-up examinations in 2014-2016 according to their insulin sensitivity in 2000 and their APOE genotype. The exposure group (n = 30) had IR in 2000 and the 30 controls had normal insulin sensitivity. There were 15 APOEɛ4 carriers per group. Statistical analyses were performed with multivariable linear models.

RESULTS: At follow-up the IR+group performed worse on executive functions (p = 0.02) and processing speed (p = 0.007) than the IR- group. The groups did not differ in cerebrovascular lesions. No associations were found between cerebrovascular lesions and neurocognitive test scores. Brain amyloid deposition associated with slower processing speed.

CONCLUSION: Midlife IR predicted poorer executive functions and slower processing speed, but not cerebrovascular lesions. Brain amyloid deposition was associated with slower processing speed. The association between midlife IR and late-life cognition might not be mediated through cerebrovascular lesions measured here.

%B J Alzheimers Dis %V 72 %P 215-228 %8 2019 Oct 29 %G eng %N 1 %R 10.3233/JAD-190691 %0 Journal Article %J J Alzheimers Dis %D 2017 %T Association Between Later Life Lifestyle Factors and Alzheimer's Disease Biomarkers in Non-Demented Individuals: A Longitudinal Descriptive Cohort Study. %A Reijs, Babette L R %A Vos, Stephanie J B %A Soininen, Hilkka %A Lötjönen, Jyrki %A Koikkalainen, Juha %A Pikkarainen, Maria %A Hall, Anette %A Vanninen, Ritva %A Liu, Yawu %A Herukka, Sanna-Kaisa %A Freund-Levi, Yvonne %A Frisoni, Giovanni B %A Frölich, Lutz %A Nobili, Flavio %A Rikkert, Marcel Olde %A Spiru, Luiza %A Tsolaki, Magda %A Wallin, Asa K %A Scheltens, Philip %A Verhey, Frans %A Visser, Pieter Jelle %X

BACKGROUND: Lifestyle factors have been associated with the risk of dementia, but the association with Alzheimer's disease (AD) remains unclear.

OBJECTIVE: To examine the association between later life lifestyle factors and AD biomarkers (i.e., amyloid-β 1-42 (Aβ42) and tau in cerebrospinal fluid (CSF), and hippocampal volume) in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). In addition, to examine the effect of later life lifestyle factors on developing AD-type dementia in individuals with MCI.

METHODS: We selected individuals with SCD (n = 111) and MCI (n = 353) from the DESCRIPA and Kuopio Longitudinal MCI studies. CSF Aβ42 and tau concentrations were assessed with ELISA assay and hippocampal volume with multi-atlas segmentation. Lifestyle was assessed by clinical interview at baseline for: social activity, physical activity, cognitive activity, smoking, alcohol consumption, and sleep. We performed logistic and Cox regression analyses adjusted for study site, age, gender, education, and diagnosis. Prediction for AD-type dementia was performed in individuals with MCI only.

RESULTS: Later life lifestyle factors were not associated with AD biomarkers or with conversion to AD-type dementia. AD biomarkers were strongly associated with conversion to AD-type dementia, but these relations were not modulated by lifestyle factors. Apolipoprotein E (APOE) genotype did not influence the results.

CONCLUSIONS: Later life lifestyle factors had no impact on key AD biomarkers in individuals with SCD and MCI or on conversion to AD-type dementia in MCI.

%B J Alzheimers Dis %V 60 %P 1387-1395 %8 2017 %G eng %N 4 %1 http://www.ncbi.nlm.nih.gov/pubmed/29036813?dopt=Abstract %R 10.3233/JAD-170039 %0 Journal Article %J J Alzheimers Dis %D 2016 %T Integrating Biomarkers for Underlying Alzheimer's Disease in Mild Cognitive Impairment in Daily Practice: Comparison of a Clinical Decision Support System with Individual Biomarkers. %A Rhodius-Meester, Hanneke F M %A Koikkalainen, Juha %A Mattila, Jussi %A Teunissen, Charlotte E %A Barkhof, Frederik %A Lemstra, Afina W %A Scheltens, Philip %A Lötjönen, Jyrki %A van der Flier, Wiesje M %K Aged %K Aged, 80 and over %K Algorithms %K Alzheimer Disease %K Area Under Curve %K Biomarkers %K Cognitive Dysfunction %K Cohort Studies %K Decision Support Systems, Clinical %K Disease Progression %K Female %K Humans %K Image Processing, Computer-Assisted %K Magnetic Resonance Imaging %K Male %K Mental Status Schedule %K Middle Aged %K Neuropsychological Tests %K Outcome Assessment (Health Care) %K Predictive Value of Tests %X

BACKGROUND: Recent criteria allow biomarkers to provide evidence of Alzheimer's disease (AD) pathophysiology. How they should be implemented in daily practice remains unclear, especially in mild cognitive impairment (MCI) patients.

OBJECTIVE: We evaluated how a clinical decision support system such as the PredictAD tool can aid clinicians to integrate biomarker evidence to support AD diagnosis.

METHODS: With available data on demographics, cerebrospinal fluid (CSF), and MRI, we trained the PredictAD tool on a reference population of 246 controls and 491 AD patients. We then applied the identified algorithm to 211 MCI patients. For comparison, we also classified patients based on individual biomarkers (MRI; CSF) and the NIA-AA criteria. Progression to dementia was used as outcome measure.

RESULTS: After a median follow up of 3 years, 72 (34%) MCI patients remained stable and 139 (66%) progressed to AD. The PredictAD tool assigned a likelihood of underlying AD to each patient (AUC 0.82). Excluding patients with missing data resulted in an AUC of 0.87. According to the NIA-AA criteria, half of the MCI patients had uninformative biomarkers, precluding an assignment of AD likelihood. A minority (41%) was assigned to high or low AD likelihood with good predictive value. The individual biomarkers showed best value for CSF total tau (AUC 0.86).

CONCLUSION: The ability of the PredictAD tool to identify AD pathophysiology was comparable to individual biomarkers. The PredictAD tool has the advantage that it assigns likelihood to all patients, regardless of missing or conflicting data, allowing clinicians to integrate biomarker data in daily practice.

%B J Alzheimers Dis %V 50 %P 261-70 %8 2016 %G eng %N 1 %1 http://www.ncbi.nlm.nih.gov/pubmed/26577521?dopt=Abstract %R 10.3233/JAD-150548