De zelfmetende mens

Quantified Self werd in 2007 geïntroduceerd als naam voor de mens die op zoek is naar persoonlijk betekenis uit persoonlijke data. Het is een beweging die sindsdien wereldwijd mensen bij elkaar brengt voor een dialoog over zelfkennis door getallen. In dit artikel wordt de zelfmetende mens geïntroduceerd en belicht vanuit verschillende perspectieven met als doel te duiden wat deze beweging kan betekenen voor de gezondheidszorg

Quantified Self in de huisartsenpraktijk

Quantified Self staat voor de zelfmetende mens. Het aantal mensen dat met zelf gegeneerde gezondheidsgegevens het zorgproces binnenwandelt gaat de komende jaren groeien. Verschillende soorten activity trackers en gezondheidsapplicaties voor de smartphone maken het relatief eenvoudig om persoonlijke gegevens te verzamelen over beweging, voeding, slaap, hartslag, menstruatiecyclus, etc. Steeds vaker zullen patiënten dit soort data meenemen naar de huisarts. Het is daarom raadzaam kennis te nemen van wat er zoal aan zelfmeettechnologie beschikbaar is en hoe het is gesteld met de kwaliteit, toepasbaarheid of zelfs generaliseerbaarheid van de data. In dit artikel lichten we de achtergrond van Quantified Self toe, zetten we dit in een breder perspectief van technologische ontwikkelingen en zullen we iets zeggen over de zin en onzin van zelfmetingen, waarbij de focus zal liggen op Quantified Self met betrekking tot gezondheid en levensstijl

Supporting adherence to oral anticancer agents : clinical practice and clues to improve care provided by physicians, nurse practitioners, nurses and pharmacists

Background: Healthcare provider (HCP) activities and attitudes towards patients strongly influence medication adherence. The aim of this study was to assess current clinical practices to support patients in adhering to treatment with oral anticancer agents (OACA) and to explore clues to improve the management of medication adherence. Methods: A cross-sectional, observational study among HCPs in (haemato-) oncology settings in Belgium and the Netherlands was conducted in 2014 using a composite questionnaire. A total of 47 care activities were listed and categorised into eight domains. HCPs were also asked about their perceptions of adherence management on the items: insight into adherence, patients' communication, capability to influence, knowledge of consequences and insight into causes. Validated questionnaires were used to assess beliefs about medication (BMQ) and shared decision making (SDM-Q-doc). Results: In total, 208 HCPs (29% male) participated; 107 from 51 Dutch and 101 from 26 Belgian hospitals. Though a wide range of activities were reported, certain domains concerning medication adherence management received less attention. Activities related to patient knowledge and adverse event management were reported most frequently, whereas activities aimed at patient's self-efficacy and medication adherence during ongoing use were frequently missed. The care provided differed between professions and by country. Belgian physicians reported more activities than Dutch physicians, whereas Dutch nurses and pharmacists reported more activities than Belgian colleagues. The perceptions of medication adherence management were related to the level of care provided by HCPs. SDM and BMQ outcomes were not related to the care provided. Conclusions: Enhancing the awareness and perceptions of medication adherence management of HCPs is likely to have a positive effect on care quality. Care can be improved by addressing medication adherence more directly e. g., by questioning patients about (expected) barriers and discussing strategies to overcome them, by asking for missed doses and offering (electronic) reminders to support long-term medication adherence. A multidisciplinary approach is recommended in which the role of the pharmacist could be expanded

Identification and characterization of nanobodies targeting the EphA4 receptor

The ephrin receptor A4 (EphA4) is one of the receptors in the ephrin system that plays a pivotal role in a variety of cell-cell interactions, mostly studied during development. In addition, EphA4 has been found to play a role in cancer biology as well as in the pathogenesis of several neurological disorders such as stroke, spinal cord injury, multiple sclerosis, amyotrophic lateral sclerosis (ALS), and Alzheimer's disease. Pharmacological blocking of EphA4 has been suggested to be a therapeutic strategy for these disorders. Therefore, the aim of our study was to generate potent and selective Nanobodies against the ligand-binding domain of the human EphA4 receptor. Weidentified two Nanobodies, Nb 39 and Nb 53, that bind EphA4 with affinities in the nanomolar range. These Nanobodies were most selective for EphA4, with residual binding to EphA7 only. Using Alphascreen technology, we found that both Nanobodies displaced all known EphA4-binding ephrins from the receptor. Furthermore, Nb39 andNb53 inhibited ephrin-induced phosphorylationoftheEphA4proteininacell-basedassay. Finally, in a cortical neuron primary culture, both Nanobodies were able to inhibit endogenous EphA4-mediated growth-cone collapse induced by ephrin-B3. Our results demonstrate the potential of Nanobodies to target the ligand-binding domain of EphA4. These Nanobodiesmaydeservefurtherevaluationaspotentialtherapeutics in disorders in which EphA4-mediated signaling plays a role

Repeatability of parametric methods for [F-18]florbetapir imaging in Alzheimer's disease and healthy controls:A test-retest study

Accumulation of amyloid beta (Aβ) is one of the pathological hallmarks of Alzheimer’s disease (AD), which can be visualized using [18F]florbetapir positron emission tomography (PET). The aim of this study was to evaluate various parametric methods and to assess their test-retest (TRT) reliability. Two 90 min dynamic [18F]florbetapir PET scans, including arterial sampling, were acquired (n = 8 AD patient, n = 8 controls). The following parametric methods were used; (reference:cerebellum); Logan and spectral analysis (SA), receptor parametric mapping (RPM), simplified reference tissue model2 (SRTM2), reference Logan (rLogan) and standardized uptake value ratios (SUVr(50–70)). BPND+1, DVR, VT and SUVr were compared with corresponding estimates (VT or DVR) from the plasma input reversible two tissue compartmental (2T4k_VB) model with corresponding TRT values for 90-scan duration. RPM (r2 = 0.92; slope = 0.91), Logan (r2 = 0.95; slope = 0.84) and rLogan (r2 = 0.94; slope = 0.88), and SRTM2 (r2 = 0.91; slope = 0.83), SA (r2 = 0.91; slope = 0.88), SUVr (r2 = 0.84; slope = 1.16) correlated well with their 2T4k_VB counterparts. RPM (controls: 1%, AD: 3%), rLogan (controls: 1%, AD: 3%) and SUVr(50–70) (controls: 3%, AD: 8%) showed an excellent TRT reliability. In conclusion, most parametric methods showed excellent performance for [18F]florbetapir, but RPM and rLogan seem the methods of choice, combining the highest accuracy and best TRT reliability

Amyloid-β1–43 cerebrospinal fluid levels and the interpretation of APP, PSEN1 and PSEN2 mutations

Background Alzheimer's disease (AD) mutations in amyloid precursor protein (APP) and presenilins (PSENs) could potentially lead to the production of longer amyloidogenic A beta peptides. Amongst these, A beta(1-43)is more prone to aggregation and has higher toxic properties than the long-known A beta(1-42). However, a direct effect on A beta(1-43)in biomaterials of individuals carrying genetic mutations in the known AD genes is yet to be determined. Methods N = 1431 AD patients (n = 280 early-onset (EO) andn = 1151 late-onset (LO) AD) and 809 control individuals were genetically screened forAPPandPSENs. For the first time, A beta(1-43)levels were analysed in cerebrospinal fluid (CSF) of 38 individuals carrying pathogenic or unclear rare mutations or the commonPSEN1p.E318G variant and compared with A beta(1-42)and A beta 1-40CSF levels. The soluble sAPP alpha and sAPP beta species were also measured for the first time in mutation carriers. Results A known pathogenic mutation was identified in 5.7% of EOAD patients (4.6%PSEN1, 1.07%APP) and in 0.3% of LOAD patients. Furthermore, 12 known variants with unclear pathogenicity and 11 novel were identified. Pathogenic and unclear mutation carriers showed a significant reduction in CSF A beta(1-43)levels compared to controls (p = 0.037; < 0.001). CSF A beta(1-43)levels positively correlated with CSF A beta(1-42)in both pathogenic and unclear carriers and controls (allp < 0.001). The p.E318G carriers showed reduced A beta(1-43)levels (p < 0.001), though genetic association with AD was not detected. sAPP alpha and sAPP beta CSF levels were significantly reduced in the group of unclear (p = 0.006; 0.005) and p.E318G carriers (p = 0.004; 0.039), suggesting their possible involvement in AD. Finally, using A beta(1-43)and A beta(1-42)levels, we could re-classify as "likely pathogenic" 3 of the unclear mutations. Conclusion This is the first time that A beta(1-43)levels were analysed in CSF of AD patients with genetic mutations in the AD causal genes. The observed reduction of A beta(1-43)inAPPandPSENscarriers highlights the pathogenic role of longer A beta peptides in AD pathogenesis. Alterations in A beta(1-43)could prove useful in understanding the pathogenicity of unclearAPPandPSENsvariants, a critical step towards a more efficient genetic counselling

Amyloid-β Load Is Related to Worries, but Not to Severity of Cognitive Complaints in Individuals With Subjective Cognitive Decline: The SCIENCe Project

Objective: Subjective cognitive decline (SCD) is associated with an increased risk of Alzheimer’s Disease (AD). Early disease processes, such as amyloid-β aggregation measured with quantitative PET, may help to explain the phenotype of SCD. The aim of this study was to investigate whether quantitative amyloid-β load is associated with both self- and informant-reported cognitive complaints and memory deficit awareness in individuals with SCD.Methods: We included 106 SCD patients (mean ± SD age: 64 ± 8, 45%F) with 90 min dynamic [18F]florbetapir PET scans. We used the following questionnaires to assess SCD severity: cognitive change index (CCI, self and informant reports; 2 × 20 items), subjective cognitive functioning (SCF, four items), and five questions “Do you have complaints?” (yes/no) for memory, attention, organization and language), and “Does this worry you? (yes/no).” The Rivermead Behavioral Memory Test (RBMT)-Stories (immediate and delayed recall) was used to assess objective episodic memory. To investigate the level of self-awareness, we calculated a memory deficit awareness index (Z-transformed (inverted self-reported CCI minus episodic memory); higher index, heightened self-awareness) and a self-proxy index (Z-transformed self- minus informant-reported CCI). Mean cortical [18F]florbetapir binding potential (BPND) was derived from the PET data. Logistic and linear regression analyses, adjusted for age, sex, education, and depressive symptoms, were used to investigate associations between BPND and measures of SCD.Results: Higher mean cortical [18F]florbetapir BPND was associated with SCD-related worries (odds ratio = 1.76 [95%CI = 1.07 ± 2.90]), but not with other SCD questionnaires (informant and self-report CCI or SCF, total scores or individual items, all p &gt; 0.05). In addition, higher mean cortical [18F]florbetapir BPND was associated with a higher memory deficit awareness index (Beta = 0.55), with an interaction between BPND and education (p = 0.002). There were no associations between [18F]florbetapir BPND and self-proxy index (Beta = 0.11).Conclusion: Amyloid-β deposition was associated with SCD-related worries and heightened memory deficit awareness (i.e., hypernosognosia), but not with severity of cognitive complaints. Our findings indicate that worries about self-perceived decline may reflect an early symptom of amyloid-β related pathology rather than subjective cognitive functioning

Genome-wide meta-analysis associates HLA-DQA1/DRB1 and LPA and lifestyle factors with human longevity

Genomic analysis of longevity offers the potential to illuminate the biology of human aging. Here, using genome-wide association meta-analysis of 606,059 parents' survival, we discover two regions associated with longevity (HLA-DQA1/DRB1 and LPA). We also validate previous suggestions that APOE, CHRNA3/5, CDKN2A/B, SH2B3 and FOXO3A influence longevity. Next we show that giving up smoking, educational attainment, openness to new experience and high-density lipoprotein (HDL) cholesterol levels are most positively genetically correlated with lifespan while susceptibility to coronary artery disease (CAD), cigarettes smoked per day, lung cancer, insulin resistance and body fat are most negatively correlated. We suggest that the effect of education on lifespan is principally mediated through smoking while the effect of obesity appears to act via CAD. Using instrumental variables, we suggest that an increase of one body mass index unit reduces lifespan by 7 months while 1 year of education adds 11 months to expected lifespan

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations