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Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term ‘metabolomics’ refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Background: We investigated the association between white matter hyperintensity location and depressive symptoms in a memoryclinic population using lesion–symptom mapping.Methods: We included 680 patients with vascular brain injury from the TRACE-VCI cohort (mean age ± standard deviation: 67 ± 8 years; 52% female): 168 patients with subjective cognitive decline, 164 with mild cognitive impairment and 348 with dementia. We assessed depressive symptoms using the Geriatric Depression Scale. We applied assumptionfree voxel-based lesion–symptom mapping, adjusted for age, sex, total white matter hyperintensity volume and multiple testing. Next, we applied exploratory region-of-interest linear regression analyses of major white matter tracts, with additional adjustment for diagnosis.Results: Voxel-based lesion–symptom mapping identified voxel clusters related to the Geriatric Depression Scale in the left corticospinal tract. Region-of-interest analyses showed no relation between white matter hyperintensity volume and the Geriatric Depression Scale, but revealed an interaction with diagnosis in the forceps minor, where larger regional white matter hyperintensity volume was associated with more depressive symptoms in subjective cognitive decline (β = 0.26, p < 0.05), but not in mild cognitive impairment or dementia.Limitations: We observed a lack of convergence of findings between voxel-based lesion–symptom mapping and region-of-interest analyses, which may have been due to small effect sizes and limited lesion coverage despite the large sample size. This warrants replication of our findings and further investigation in other cohorts.Conclusion: This lesion–symptom mapping study in depressive symptoms indicates the corticospinal tract and forceps minor as strategic tracts in which white matter hyperintensity is associated with depressive symptoms in memory-clinic patients with vascular brain injury. The impact of white matter hyperintensity on depressive symptoms is modest, but it appears to depend on the location of white matter hyperintensity and disease severity.

ObjectiveTo determine the occurrence of acute cerebral microinfarcts (ACMIs) in memory clinic patients and relate their presence to vascular risk and cognitive profile, CSF and neuroimaging markers, and clinical outcome.MethodsThe TRACE-VCI study is a memory clinic cohort of patients with vascular brain injury on MRI (i.e., possible vascular cognitive impairment [VCI]). We included 783 patients (mean age 67.6 ± 8.5, 46% female) with available 3T diffusion-weighted imaging (DWI). ACMIs were defined as supratentorial DWI hyperintensities ResultsA total of 23 ACMIs were found in 16 of the 783 patients (2.0%). Patients with ACMIs did not differ in vascular risk or cognitive profile, but were more often diagnosed with vascular dementia (odds ratio [OR] 5.1; 95% confidence interval [CI] 1.4–18.9, p = 0.014). ACMI presence was associated with lower levels of β-amyloid (p < 0.004) and with vascular imaging markers (lacunar infarcts: OR 3.5, CI 1.3–9.6, p = 0.015; nonlacunar infarcts: OR 4.1, CI 1.4–12.5, p = 0.012; severe white matter hyperintensities: OR 4.8, CI 1.7–13.8, p = 0.004; microbleeds: OR 18.9, CI 2.5–144.0, p = 0.0001). After a median follow-up of 2.1 years, the risk of poor clinical outcome (composite of marked cognitive decline, major vascular event, death, and institutionalization) was increased among patients with ACMIs (hazard ratio 3.0; 1.4–6.0, p = 0.005).ConclusionIn patients with possible VCI, ACMI presence was associated with a high burden of cerebrovascular disease of both small and large vessel etiology and poor clinical outcome. ACMIs may thus be a novel marker of active vascular brain injury in these patients.

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. In addition, the original version of this Article contained an error in the author affiliations. An affiliation of Abdel Abdellaoui with Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

OBJECTIVE: To develop and evaluate a model for staging cortical amyloid deposition using PET with high generalizability. METHODS: 3027 subjects (1763 Cognitively Unimpaired (CU), 658 Impaired, 467 Alzheimer's disease (AD) dementia, 111 non-AD dementia, and 28 with missing diagnosis) from six cohorts (EMIF-AD, ALFA, ABIDE, ADC, OASIS-3, ADNI) who underwent amyloid PET were retrospectively included; 1049 subjects had follow-up scans. Applying dataset-specific cut-offs to global Standard Uptake Value ratio (SUVr) values from 27 regions, single-tracer and pooled multi-tracer regional rankings were constructed from the frequency of abnormality across 400 CU subjects (100 per tracer). The pooled multi-tracer ranking was used to create a staging model consisting of four clusters of regions as it displayed a high and consistent correlation with each single-tracer ranking. Relationships between amyloid stage, clinical variables and longitudinal cognitive decline were investigated. RESULTS: SUVr abnormality was most frequently observed in cingulate, followed by orbitofrontal, precuneal, and insular cortices, then the associative, temporal and occipital regions. Abnormal amyloid levels based on binary global SUVr classification were observed in 1.0%, 5.5%, 17.9%, 90.0%, and 100.0% of stage 0-4 subjects, respectively. Baseline stage predicted decline in MMSE (ADNI: N=867, F=67.37, p3000 subjects across cohorts and radiotracers, and detects pre-global amyloid burden and distinct risk profiles of cognitive decline within globally amyloid-positive subjects.

Introduction: An accurate and timely diagnosis for Alzheimer’s disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination. Methods: We included 535 subjects (139 controls, 286 Alzheimer’s disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients. Results: The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed). Conclusions: We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease. WMHs are also frequently observed in patients with familial and sporadic Alzheimer's disease, often with a particular posterior predominance. Whether amyloid and tau pathologies are linked to WMH occurrence is still debated. We examined whether cerebral amyloid and tau burden, reflected in cerebrospinal fluid amyloid-beta 1-42 (A beta-42) and phosphorylated tau (p-tau), are related to WMH location in a cohort of 517 memory clinic patients. Two lesion mapping techniques were performed: voxel-based analyses and region of interest-based linear regression. Voxelwise associations were found between lower (A beta-42) and parieto-occipital periventricular WMHs. Regression analyses demonstrated that lower A beta-42 correlated with larger WMH volumes in the splenium of the corpus callosum and posterior thalamic radiation, also after controlling for markers of vascular disease. P-tau was not consistently related to WMH occurrence. Our findings indicate that cerebral amyloid burden is associated with WMHs located in specific posterior white matter regions, possibly reflecting region-specific effects of amyloid pathology on the white matter. (C) 2019 The Authors. Published by Elsevier Inc.

White matter hyperintensities (WMHs) are a common manifestation of cerebral small vessel disease, that is increasingly studied with large, pooled multicenter datasets. This data pooling increases statistical power, but poses challenges for automated WMH segmentation. Although there is extensive literature on the evaluation of automated WMH segmentation methods, such evaluations in a multicenter setting are lacking. We performed WMH segmentations in sixty patients scanned on six different magnetic resonance imaging (MRI) scanners (10 patients per scanner) using five freely available and fully-automated WMH segmentation methods (Cascade, kNN-TTP, Lesion-TOADS, LST-LGA and LST-LPA). Different MRI scanner vendors and field strengths were included. We compared these automated WMH segmentations with manual WMH segmentations as a reference. Performance of each method both within and across scanners was assessed using spatial and volumetric correspondence with the reference segmentations by Dice’s similarity coefficient (DSC) and intra-class correlation coefficient (ICC) respectively. We found the best performance, both within and across scanners, for kNN-TTP, followed by LST-LPA and LST-LGA, with worse performance for Lesion-TOADS and Cascade. Our findings can serve as a guide for choosing a method and also highlight the importance to further improve and evaluate consistency of methods in a multicenter setting.

Background: HIV-associated cardiovascular disease (CVD) risk in combination antiretroviral therapy (cART)-treated perinatally HIV-infected patients (PHIV+) remains unknown due to the young age of this population. Lipoprotein(a) (Lp(a)) has been established as an independent causal risk factor for CVD in the general population but has not been well established in the population of PHIV+. Methods: We cross-sectionally compared lipid profiles, including nonfasting Lp(a), together with total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides between 35 cART-treated PHIV+ children aged 8-18 years and 37 controls who were matched for age, sex, ethnicity, and socioeconomic status. We explored associations between Lp(a) and disease-and treatment-related factors (inflammation, monocyte activation, and vascular), biomarkers, and neuroimaging outcomes using linear regression models. Results: PHIV+ children had significantly higher levels of Lp(a) compared with controls (median, 43.6 [21.6-82.4] vs 21.8 [16.8-46.6] mg/dL; P =. 033). Other lipid levels were comparable between groups. Additional assessment of apolipoprotein B, apolipoprotein CIII, apolipoprotein E, and APOE genotype revealed no significant differences. Higher Lp(a) levels were associated with higher plasma apoB levels and with lower monocyte chemoattractant protein-1 and TG levels in PHIV+ children. Lp(a) was not associated with HIV-or cART-related variables or with neuroimaging outcomes. Conclusions: cART-treated PHIV+ children appear to have higher levels of Lp(a) compared with ethnicity-matched controls, which may implicate higher CVD risk in this population. Future research should focus on the association between Lp(a) and (sub)clinical CVD measurements in cART-treated PHIV+ patients. Dutch Trial Register number: NRT4074.

Background Multiple sclerosis often leads to fatigue and changes in physical behavior (PB). Changes in PB are often assumed as a consequence of fatigue, but effects of interventions that aim to reduce fatigue by improving PB are not sufficient. Since the heterogeneous nature of MS related symptoms, levels of PB of fatigued patients at the start of interventions might vary substantially. Better understanding of the variability by identification of PB subtypes in fatigued patients may help to develop more effective personalized rehabilitation programs in the future. This study aimed to identify PB subtypes in fatigued patients with multiple sclerosis based on multidimensional PB outcome measures. Methods Baseline accelerometer (Actigraph) data, demographics and clinical characteristics of the TREFAMS-ACE participants (n = 212) were used for secondary analysis. All patients were ambulatory and diagnosed with severe fatigue based on a score of ≥35 on the fatigue subscale of the Checklist Individual Strength (CIS20r). Fifteen PB measures were used derived from 7 day measurements with an accelerometer. Principal component analysis was performed to define key outcome measures for PB and two-step cluster analysis was used to identify PB types. Results Analysis revealed five key outcome measures: percentage sedentary behavior, total time in prolonged moderate-to-vigorous physical activity, number of sedentary bouts, and two types of change scores between day parts (morning, afternoon and evening). Based on these outcomes three valid PB clusters were derived. Conclusions Patients with severe MS-related fatigue show three distinct and homogeneous PB subtypes. These PB subtypes, based on a unique set of PB outcome measures, may offer an opportunity to design more individually-tailored interventions in rehabilitation. Trial registration Clinical trial registration no ISRCTN 82353628, ISRCTN 69520623 and ISRCTN 58583714. Electronic supplementary material The online version of this article (10.1186/s12984-019-0573-1) contains supplementary material, which is available to authorized users.

Background: Type 2 diabetes mellitus (T2DM) increases the risk of vascular cognitive impairment (VCI). It is unknown which type of vascular lesions and co-morbid etiologies, in particular Alzheimer's disease pathology, are associated with T2DM in patients with VCI, and how this relates to cognition and prognosis.Objective: To compare brain MRI and cerebrospinal fluid (CSF) markers, cognition, and prognosis in patients with possible VCI with and without T2DM.Methods: We included 851 memory clinic patients with vascular brain injury on MRI (i.e., possible VCI) from a prospective cohort study (T2DM: n = 147, 68.4 +/- 7.9 years, 63% men; no T2DM: n = 704, 67.6 +/- 8.5 years, 52% men). At baseline, we assessed between-group differences in brain MRI abnormalities, CSF markers of Alzheimer's disease, and cognitive profile. After two years follow-up, we compared occurrence of cognitive decline, stroke, and death.Results: The distribution of clinical diagnoses did not differ between patients with and without T2DM. T2DM patients had more pronounced brain atrophy (total and white matter volume), and more lacunar infarcts, whereas microbleeds were less common (all p < 0.05). CSF amyloid-beta levels were similar between the groups. T2DM patients performed worse on working memory (effect size: -0.17, p = 0.03) than those without, whereas performance on other domains was similar. During follow-up, risk of further cognitive decline was not increased in T2DM.Conclusion: In patients with possible VCI, presence of T2DM is related to more pronounced brain atrophy and a higher burden of lacunar infarcts, but T2DM does not have a major impact on cognitive profile or prognosis.

OBJECTIVE. We investigated whether demographic, disease-related, or personal baseline determinants can predict a positive response to energy conservation management (ECM). METHOD. We conducted a secondary analysis of a single-blind, two-parallel-arms randomized controlled trial that included ambulatory adults with severe MS-related fatigue. Therapy responders and nonresponders were categorized by Checklist Individual Strength fatigue change scores between baseline and end of treatment. Logistic regression analyses were used to assess the determinants of response. RESULTS. Sixty-nine participants were included (ECM group, n = 34; control group, n = 35). In the ECM group, fatigue severity, perception of fatigue, illness cognitions about MS, and social support discrepancies were related to the probability of being a responder. CONCLUSION. The results suggest that people with MS-related fatigue who had a less negative perception of fatigue and who perceived fewer disease benefits and a higher discrepancy in social support had the best response to ECM treatment.

In the published article referenced below, an error occurred regarding reference number 47. The correct reference citation is: 47Reesink FE, Lemstra AW, van Dijk KD, et al. CSF alpha-synuclein does not discriminate dementia with Lewy bodies from Alzheimer's disease. J Alzheimers Disord 2010;22:87-95. We apologize for the oversight and any inconvenience this has caused.

Peptide profiling of biological samples using MALDI-TOF mass spectrometry (MS) is an increasingly popular approach used for the discovery of biomarkers and as a means to detect and diagnose disease and allow the assessment of disease severity, progression, and the effectiveness of treatments.

The present prospective study investigated whether elevated total serum homocysteine concentration is a risk factor for cognitive decline. The outcomes were compared to the possible relation between cognition and vitamin B 12 or folic acid. Cognitive performance of 144 normal aging individuals (aged 30-80 years) was tested at baseline and after six years of follow-up. Domains of cognitive function addressed were cognitive speed (Letter-Digit Coding test), attention and information processing (Stroop test) and verbal learning and memory (Word Learning Test Total; Delayed Recall). Serum concentrations of homocysteine, folic acid and vitamin B12 were determined. Serum concentrations of homocysteine correlated negatively with cognitive performance on the Word Learning tests at baseline, independent of age, sex, education level or folic acid concentration. Homocysteine concentration at baseline correlated negatively with cognitive performance on the Stroop and Word Learning tests during the whole six-year follow-up period. The folic acid concentration correlated to the Delayed Recall test at baseline only and no correlations were observed for vitamin B12. Thus, while a relation between vitamin B12 or folic acid and cognition was almost absent, elevated homocysteine concentrations were associated with prolonged lower cognitive performance in this normal aging population.

Little is known of the biochemical processes of cognitive decline during 'healthy' aging. Biological markers in body fluids, such as blood, could provide insight in those processes. In the present studies serum concentrations of different markers have been correlated to cognitive functioning of cognitively healthy aging individuals over a period of six years (mean age 57 years, SD 11, n=93). Markers were related to mechanisms known to be involved in Alzheimer's disease, including inflammation, cholesterol homeostasis and homocysteine homeostasis. Domains of cognitive function addressed were cognitive speed (Letter-Digit Coding test), attention and information processing (Stroop test), and memory (Word Learning test: Total Words and Delayed Recall). Baseline concentrations of haptoglobine, homocysteine, lathosterol and lanosterol were negatively correlated with cognitive functioning on the Stroop test over the whole follow-up period of six years. Concentrations of all markers, i.e. haptoglobine, C-reactive protein, homocysteine, lathosterol and lanosterol, were also negatively correlated with functioning on the Word Learning test (Delayed Recall and for some markers also with the Total Words) over the whole six-years follow-up period. In conclusion, concentrations of serum markers related to inflammation, homocysteine and cholesterol homeostasis are not only associated with Alzheimer's disease, but also with cognitive functioning in the cognitively healthy aging population.

The present prospective study investigated whether elevated total serum homocysteine concentration is a risk factor for cognitive decline. The outcomes were compared to the possible relation between cognition and vitamin B 12 or folic acid. Cognitive performance of 144 normal aging individuals (aged 30-80 years) was tested at baseline and after six years of follow-up. Domains of cognitive function addressed were cognitive speed (Letter-Digit Coding test), attention and information processing (Stroop test) and verbal learning and memory (Word Learning Test Total; Delayed Recall). Serum concentrations of homocysteine, folic acid and vitamin B12 were determined. Serum concentrations of homocysteine correlated negatively with cognitive performance on the Word Learning tests at baseline, independent of age, sex, education level or folic acid concentration. Homocysteine concentration at baseline correlated negatively with cognitive performance on the Stroop and Word Learning tests during the whole six-year follow-up period. The folic acid concentration correlated to the Delayed Recall test at baseline only and no correlations were observed for vitamin B12. Thus, while a relation between vitamin B12 or folic acid and cognition was almost absent, elevated homocysteine concentrations were associated with prolonged lower cognitive performance in this normal aging population.