Neuropsychological predictors of the outcome in non-demented subjects with cognitive complaints

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2012Nowadays, life expectancy has increased and gradually the prevalence of neurodegenerative disorders in the aging population began to represent a major public health problem. Alzheimer’s disease (AD) is the most common dementia and affects millions of older adults. Despite recent advances in the knowledge of AD biomarkers of pathophysiological processes, clearly the phenotype remains aetiologically heterogeneous. Understanding the clinical phenotype variation contingent to the neuropathological progression is crucial to provide intervention in the earliest phases of neurodegeneration. Newly research biomarkers have been proposed for early diagnosis of AD, however cognitive impairment remains a prominent and early feature of AD. Neuropsychological markers could offer a relatively inexpensive and noninvasive indicator of future progression to dementia because biological markers are expensive, some of them only available at few specialized centers, and, in the case of lumbar puncture, invasive. Therefore, it would not be reasonable to offer the newer and expensive biomarker techniques to all patients with cognitive complaints. Importantly, new treatments of disease modification approach require the selection of those patients with higher risk of conversion to dementia. Thus, the main goal of the present thesis was to improve the predictive value of neuropsychological measures to future conversion to dementia of patients presenting with cognitive complaints who do not fulfil the dementia criteria. Four steps were conducted in order to reach that main goal: 1. º Original published articles reporting values of sensitivity, specificity and effect sizes for neuropsychological tests to predict conversion to dementia in patients at risk of future cognitive decline were analysed in a systematic review of literature. Twenty-four studies published in the last 20 years were selected. Neuropsychological tests administered vary considerably among studies, yet the battery of tests applied generally assessed verbal memory performances, and many included also cognitive areas such as executive functions, attention and language. Methodological constrains limited the ability to provide reasonable predictive values; some studies have reported rather disparate global sensitivity and specificity rates for the neuropsychological tests to predict conversion to dementia. Conversely, other studies reported high and balanced sensitivity/specificity ratios (≥80%), mainly for verbal episodic memory tests, however the follow-up period of those studies was generally short (≈2 years). Certainly, it would be important to achieve a consensus according to the more feasible and accurate neuropsychological tests to administer for the assessment of patients at risk of conversion to dementia. On the other hand, cohort studies with longer follow-up periods would be important to propose neuropsychological tests with higher predictive accuracy and clinical relevance regarding conversion to dementia. 2. º Newer statistical classification methods derived from data mining and machine learning methods were applied to improve accuracy, sensitivity and specificity of predictors obtained from neuropsychological testing. Data used to perform the comparison of classification methods was extracted from a cohort study (CCC – Cognitive Complaints Cohort) with 775 elderly non-demented patients with cognitive complaints referred for neuropsychological evaluation. Seven non-parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press’Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Comparison of classifiers highlighted three methods more adequate to study the predictive value of neuropsychological tests in longitudinal clinical cohort studies. Support Vector Machines demonstrated the larger overall classification accuracy (Median (Me) = 0.76) and area under the ROC (Me =0.90). However, this method showed high specificity (Me = 1.0) but very low sensitivity (Me = 0.3). Random Forests ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73), specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72), specificity (Me = 0.66) and sensitivity (Me = 0.64). Results indicated the innovative data mining method of Random Forests, along with more traditional methods, namely the Linear Discriminant Analysis, should be the option in cohort studies of neuropsychological predictors of future dementia. 3. º Verbal memory is one of the first cognitive areas to decline, therefore, the predictive value of Mild Cognitive Impairment (MCI) for the conversion to dementia when using four different verbal memory tests (Logical Memory, LM; California Verbal Learning Test, CVLT; Verbal Paired-Associate Learning, VPAL; and Digit Span, DS) was analysed. Participants were consecutive patients with subjective cognitive complaints who performed a comprehensive neuropsychological evaluation and were not demented, observed in a memory clinic setting. At baseline, 272 patients from CCC reporting subjective cognitive complaints and not demented were included. During the follow-up time (3.0±1.9 years), 58 patients converted to dementia, and 214 did not. Statistically significant differences between the converters and non-converters were present in LM, VPAL and CVLT. A multivariate Cox regression analysis combining the 4 memory tests revealed that only the CVLT test remained significant as predictor of conversion to dementia. Non-demented patients with cognitive complaints diagnosed as MCI according to abnormal (< 1.5 SD) learning in the CVLT test had 3.6 higher risk of becoming demented in the follow-up. As so, the verbal memory assessment using the CVLT should be preferred in the diagnostic criteria of MCI for a more accurate prediction of conversion to dementia. 4. º The predictive value for future conversion to dementia of a comprehensive neuropsychological battery applied to a cohort of nondemented patients followed-up for 5 years was presented. Two hundred and fifty subjects were selected from CCC having cognitive complaints, assessment with a comprehensive neuropsychological battery, and follow-up of at least 5 years (if patients have not converted to dementia earlier). During the follow-up period (2.6±1.8 years for converters and 6.1±2.1 for non converters), 162 patients (64.8%) progressed to dementia (mostly Alzheimer’s disease), and 88 (35.2%) did not. A Linear Discriminant Analysis (LDA) model constituted by Digit Span backward, Semantic Fluency, Logical Memory (immediate recall) and Forgetting Index significantly discriminated converters from non-converters (λ Wilks=0.64; χ2(4)=81.95; p<0.001; RCanonical=0.60). Logical Memory (immediate recall) was the strongest predictor with a standardized canonical discriminant function coefficient of 0.70. The LDA classificatory model showed good sensitivity, specificity and accuracy values (78.8%, 79.9% and 78.6%, respectively) of the neuropsychological tests to predict long-term conversion to dementia. Results showed that it is possible to predict, on the basis of the initial clinical and neuropsychological evaluation, namely with routine tests from a comprehensive neuropsychological battery, whether non-demented patients with cognitive complaints will probably convert to dementia, or remain stable. This prediction is obtained with very good accuracy values (≈80%), similar to those reported for the newly research biomarkers, and at a reasonably long and clinically relevant term (5 years).A esperança média de vida tem vindo a aumentar e consequentemente, de modo gradual, também a prevalência de doenças neurodegenerativas, representando actualmente na população mais envelhecida um alarmante problema de saúde pública. A doença de Alzheimer é a forma mais comum de demência e afecta milhões de indivíduos adultos. Recentemente tem sido possível alcançar avanços significativos na compreensão e no conhecimento sobre os biomarcadores que traduzem os processos patofisiológicos associados à doença de Alzheimer, no entanto, é importante salientar que o fenótipo manifestado pode ainda ser de etiologia heterogénea. Compreender melhor a variação das expressões de fenótipo contigentes ao processo neuropatológico é essencial para uma identificação e intervenção mais precoce no processo neurodegenerativo. Recentemente foram propostos novos biomarcadores, ainda limitados ao âmbito da investigação, com o propósito de realizar mais cedo o diagnóstico de doença de Alzheimer. Não obstante o seu potencial, será de referir que a presença de significativas alterações cognitivas continua a ser um elemento de diagnóstico incontornável e um indicador precoce da doença de Alzheimer. Os marcadores neuropsicológicos poderão oferecer indicadores de uma futura progressão para demência que serão economicamente mais acessíveis e clinicamente menos invasivos do que a realização dos métodos necessários aos marcadores biológicos, que além de serem mais dispendiosos, apenas se encontram disponíveis em alguns centros médicos especializados e serão em alguns casos métodos invasivos (e.g., recolha de líquido cefalorraquidiano através de punção lombar). Por conseguinte, não será razoável assumir que se irá disponibilizar a todos os indivíduos com manifestas queixas subjectivas de alterações cognitivas os recentes biomarcadores, por requerem técnicas dispendiosas e/ou invasivas. Por outro lado, é importante referir que a abordagem em presente desenvolvimento para tratar a doença incidindo na modificação dos seus factores causais requer uma selecção inicial do maior número possível de indivíduos para os quais o risco de progressão para demência seja significativo. Assim sendo, o objectivo central da presente tese foi o de melhorar o valor preditivo das medidas neuropsicológicas para a determinação de uma futura progressão para demência de indivíduos com queixa de alterações cognitivas que contudo não preenchem ainda os critérios para o diagnóstico de demência. De modo a concretizar o objectivo central, quatro estudos foram desenvolvidos: 1.º - Uma revisão sistemática da literatura foi realizada com base em estudos originais publicados sobre o valor preditivo da avaliação neuropsicológica de uma futura progressão para demência, apresentando para tal os valores de sensibilidade, especificidade e magnitude do efeito para cada uma das provas neuropsicológicas. A selecção dos artigos permitiu a identificação de 24 artigos publicados nos últimos 20 anos. Os testes neuropsicológicos aplicados mudavam consideravelmente consoante o estudo em questão, contudo verificava-se que no conjunto de estudos era consistente a aplicação de provas de avaliação da memória verbal, mas também de avaliação de funções executivas, capacidade de atenção e linguagem. A presença de limitações metodológicas condicionou a potencialidade de apresentar valores preditivos razoáveis em alguns estudos, além disso, noutros estudos os valores de sensibilidade e especificidade apresentados para as provas neuropsicológicas enquanto preditoras de futura progressão para demência eram consideravelmente díspares. No entanto será importante salientar que também foi possível identificar em parte dos estudos descritos a presença de valores muito positivos e de razões equilibradas entre sensibilidade e especificidade (≥80%), principalmente para provas de avaliação da memória verbal episódica, contudo os tempos de seguimento eram na sua maioria curtos (aproximadamente 2 anos). Com certeza que seria relevante encontrar um consenso que pudesse futuramente guiar uma escolha viável e precisa das provas neuropsicológicas a aplicar para melhor predizer uma futura progressão para demência. Por outro lado, a existência de estudos de coorte longitudinais com períodos de seguimento mais alargados seria essencial para melhorar a precisão dos valores preditivos da avaliação neuropsicológica, tornando-se estes clinicamente mais relevantes no que respeita a uma futura progressão para demência. 2.º Os novos métodos de classificação estatística associados a técnicas de Prospecção de dados (em inglês data mining) e Sistemas de Aprendizagem (em inglês machine learning) foram aplicados com o intuito de melhorar a precisão, sensibilidade e especificidade dos preditores obtidos pela avaliação neuropsicológica. Para a comparação dos métodos classificatórios recorreu-se à base de dados CCC (CCC – Cognitive Complaints Cohort) que era constituída na altura por 775 casos de pacientes idosos não-dementes com queixas de alterações cognitivas e que foram referenciados para realizarem uma avaliação neuropsicológica. A comparação dos métodos estatísticos realizou-se entre 7 classificadores não-paramétricos provenientes de métodos de Prospecção de dados (Redes Neuronais com Perceptrões Multicamada; Redes Neuronais com Funções de Base Radial; Máquinas de Vectores de Suporte; CART; CHAID; Árvores de Classificação QUEST e Árvores de Classificação Aleatória) que foram comparados com três classificadores tradicionais (Análise Discriminante Linear; Análise Discriminante Quadrática, e Regressão Logística) em termos de precisão classificatória, especificidade, sensibilidade, área abaixo da curva ROC e Press’Q. O modelo para a predição consistia em 10 testes neuropsicológicos utilizados recorrentemente para o diagnóstico de demência. A comparação de classificadores identificou três métodos como os mais adequados para testar o valor preditivo dos testes neuropsicológicos em estudos longitudinais de coortes clínicas. As Máquinas de Vectores de Suporte demonstraram valores mais elevados de precisão classificatória (Mediana (Me)= 0,76) e de área abaixo da curva ROC (Me= 0,90). De salientar que, no que respeita à especificidade, este método revelou um valor elevado (Me= 1,0), contudo o valor de sensibilidade era consideravelmente baixo (Me= 0,30). As Florestas Aleatórias foram o segundo método com melhores resultados em termos de precisão (Me= 0,73), área abaixo da curva ROC (Me= 0,73), especificidade (Me= 0,73) e sensibilidade (Me= 0,64). A Análise Discriminante Linear demonstrou igualmente valores razoáveis de precisão (Me= 0,66), área abaixo da curva ROC (Me= 0,72), especificidade (Me= 0,66) e sensibilidade (Me= 0,64). Os resultados apresentados indicam que os melhores métodos classificatórios para analisar os preditores neuropsicológicos de futura progressão para demência correspondem às Florestas Aleatórias no âmbito dos mais inovadores métodos de Prospecção de dados e à Análise Discriminante Linear, enquanto método de eleição de entre os mais tradicionais para classificação de dados. 3.º A memória verbal é considerada uma das primeiras áreas cognitivas a manifestar declínio nos casos de Doença de Alzheimer. Por conseguinte, o valor preditivo de progressão para demência (Doença de Alzheimer) associado ao Defeito Cognitivo Ligeiro (DCL) foi analisado contemplando para o diagnóstico de DCL quatro testes diferentes de avaliação da memória verbal (Memória Lógica (LM); Teste de Aprendizagem Verbal de Califórnia (CVLT); Aprendizagem Verbal Associativa com Pares de Palavras (VPAL); e, Memória de Dígitos (DS)). Para o estudo foi seleccionada uma amostra consecutiva de pacientes com queixas de alterações cognitivas que em consequência das mesmas foram referenciados para realizar uma avaliação neuropsicológica pormenorizada numa clínica de memória, mas que não preenchiam ainda os critérios para o diagnóstico de demência. Uma amostra inicial de 272 pacientes com queixas cognitivas e não-dementes foram seleccionados da coorte CCC para o presente estudo. No decurso do período de seguimento (3,0±1,9 anos) ocorreu a conversão para demência em 58 pacientes, enquanto 214 permaneceram cognitivamente estáveis. Nas provas de LM, VPAL e CVLT verificaram-se diferenças estatisticamente significativas entre o grupo que converteu e o que não converteu. Através de uma análise de Regressão Multivariada de COX com um modelo constituído pelas quatro provas de memória verbal demonstrou-se que apenas a prova CVLT mantém a significância enquanto preditor de futura conversão para demência. Assim sendo, pacientes que não se encontram dementes mas que manifestam queixas de alterações cognitivas, com o diagnóstico de DCL recorrendo à pontuação na prova CVLT, se apresentarem defeito nesta prova (< 1,5 desvios-padrão abaixo da média de referência) têm um risco acrescido de evoluir para demência dentro do período de seguimento. Consequentemente, uma avaliação neuropsicológica incluindo a prova CVLT deve ser contemplada para os critérios de diagnóstico de DCL de modo a predizer com maior precisão uma futura conversão para demência. 4.º Uma coorte constituída por 250 indivíduos (seleccionados da base de dados CCC) com queixas cognitivas mas sem critérios de demência e com seguimento clínico superior a 5 anos (com excepção para os casos que evoluíram para demência antes dos 5 anos) foi analisada com vista à determinação do valor preditivo dos testes neuropsicológicos a longo prazo. Durante o período de seguimento (2,6±1,8 anos para os indivíduos que evoluíram para demência e 6,1±2,1 anos para os que permaneceram estáveis a nível cognitivo) 162 indivíduos (64,8%) apresentaram os critérios para o diagnóstico de demência (principalmente para Doença de Alzheimer), enquanto que 88 (35,2%) permaneceram estáveis. Foi possível discriminar entre os indivíduos que progrediram para demência e os que permaneceram estáveis através de um modelo de Análise Discriminante Linear (ADL) com os resultados iniciais da avaliação nas provas: Memória de Dígitos inversa, Fluência Semântica, Memória Lógica (evocação imediata), e o Índice de Esquecimento da Memória Lógica (λ Wilks= 0,64; χ2 (4)= 81,95; p< 0,001; RCanonical= 0,60). O preditor neuropsicológico mais robusto, com coeficiente estandardizado da função discriminante (canónica) de 0,70, foi a prova de Memória Lógica (evocação imediata). O modelo classificatório da ADL demonstrou valores muito positivos para a sensibilidade, especificidade e precisão classificatória (78,8%, 79,9% e 78,6%, respectivamente), dos testes neuropsicológicos para predizer uma futura progressão para demência a longo prazo. Os resultados apresentados evidenciam a possibilidade de predizer, com base numa avaliação inicial, clínica e neuropsicológica, com uma bateria de provas cognitivas aplicada na rotina clínica, se o indivíduo que apresenta queixas cognitivas irá evoluir para demência ou permanecer estável nos próximos anos. Será de salientar que o valor preditivo foi obtido com uma precisão bastante aceitável (≈ 80%), na ordem dos valores obtidos para os biomarcadores mais recentes, e no âmbito de um período de seguimento consideravelmente longo e portanto clinicamente relevante (5 anos)

Long-term strict raw food diet is associated with favourable plasma b-carotene and low plasma lycopene concentrations in Germans

Dietary carotenoids are associated with a reduced risk of chronic diseases. Raw food diets are predominantly plant-based diets that are practised with the intention of preventing chronic diseases by virtue of their high content of beneficial nutritive substances such as carotenoids. However, the benefit of a long-term adherence to these diets is controversial since little is known about their adequacy. Therefore, we investigated vitamin A and carotenoid status and related food sources in raw food diet adherents in Germany. Dietary vitamin A, carotenoid intake, plasma retinol and plasma carotenoids were determined in 198 (ninety-two male and 106 female) strict raw food diet adherents in a cross-sectional study. Raw food diet adherents consumed on average 95 weight% of their total food intake as raw food (approximately 1800 g/d), mainly fruits. Raw food diet adherents had an intake of 1301 retinol activity equivalents/d and 16·7 mg/d carotenoids. Plasma vitamin A status was normal in 82% of the subjects ($1·05mmol/l) and 63$0·88 mmol/l). In 77% of subjects the lycopene status was below the reference values for average healthy populations (,0·45mmol/l). Fat contained in fruits, vegetables and nuts and oil consumption was a significant dietary determinant of plasma carotenoid concentrations (b-carotene r 0·284; P,0·05; lycopene r 0·168; P¼0·024). Long-term raw food diet adherents showed normal vitamin A status and achieve favourable plasma b-carotene concentrations as recommended for chronic disease prevention, but showed low plasma lycopene levels. Plasma carotenoids in raw food adherents are predicted mainly by fat intake

Medical Informatics Platform (MIP): A Pilot Study Across Clinical Italian Cohorts

Introduction: With the shift of research focus to personalized medicine in Alzheimer's Dementia (AD), there is an urgent need for tools that are capable of quantifying a patient's risk using diagnostic biomarkers. The Medical Informatics Platform (MIP) is a distributed e-infrastructure federating large amounts of data coupled with machine-learning (ML) algorithms and statistical models to define the biological signature of the disease. The present study assessed (i) the accuracy of two ML algorithms, i.e., supervised Gradient Boosting (GB) and semi-unsupervised 3C strategy (Categorize, Cluster, Classify—CCC) implemented in the MIP and (ii) their contribution over the standard diagnostic workup. / Methods: We examined individuals coming from the MIP installed across 3 Italian memory clinics, including subjects with Normal Cognition (CN, n = 432), Mild Cognitive Impairment (MCI, n = 456), and AD (n = 451). The GB classifier was applied to best discriminate the three diagnostic classes in 1,339 subjects, and the CCC strategy was used to refine the classical disease categories. Four dementia experts provided their diagnostic confidence (DC) of MCI conversion on an independent cohort of 38 patients. DC was based on clinical, neuropsychological, CSF, and structural MRI information and again with addition of the outcome from the MIP tools. / Results: The GB algorithm provided a classification accuracy of 85% in a nested 10-fold cross-validation for CN vs. MCI vs. AD discrimination. Accuracy increased to 95% in the holdout validation, with the omission of each Italian clinical cohort out in turn. CCC identified five homogeneous clusters of subjects and 36 biomarkers that represented the disease fingerprint. In the DC assessment, CCC defined six clusters in the MCI population used to train the algorithm and 29 biomarkers to improve patients staging. GB and CCC showed a significant impact, evaluated as +5.99% of increment on physicians' DC. The influence of MIP on DC was rated from “slight” to “significant” in 80% of the cases. / Discussion: GB provided fair results in classification of CN, MCI, and AD. CCC identified homogeneous and promising classes of subjects via its semi-unsupervised approach. We measured the effect of the MIP on the physician's DC. Our results pave the way for the establishment of a new paradigm for ML discrimination of patients who will or will not convert to AD, a clinical priority for neurology

Uranium Mining in Namibia: The Mystery Behind 'Low Level Radiation'

This report is the result of a project on uranium mining in Namibia commissioned by the Centre for Research on Multi-national Corporations (SOMO). The findings are based on secondary literature drawn mainly from the writings of Earthlife Namibia and empirical data collected by LaRRI during July and August 2007

Characterization of dietary and genetic influences on the gastrointestinal microbiota

Although the gut microbiota is known to contribute fundamentally to human health, e.g. by promoting the maturation of the immune system and intestinal homeostasis, the factors shaping its composition are only poorly understood. Extrinsic and intrinsic influences can disturb the tightly controlled equilibrium between the microbiome and the host and induce dysbiosis, which has been linked to diverse health conditions such as obesity, atherosclerotic cardiovascular disease (ACVD) and inflammatory bowel disease (IBD). Therefore, understanding events leading to microbial perturbations and the prediction of associated health outcomes could aid in the prevention and treatment of these conditions. In this work, the impact of dietary and genetic factors on gastrointestinal microbiota compositions were determined, with the diet serving as an exemplary extrinsic, modifiable microbiota-relevant factor and with a genetic deficiency in a mouse model for intestinal inflammation serving as an exemplary intrinsic, non-modifiable microbiota-relevant factor. In both studies, microbial communities obtained from either a human or a murine cohort, respectively, were taxonomically characterized by 16S rRNA gene amplicon sequencing and analyzed in the context of metabolic and inflammatory implications for the host. In ACVD, the reduction of excess blood cholesterol, which is a main risk factor, is tackled by clinical interventions aiming to reduce cholesterol uptake from exogenous, dietary sources or by inhibiting endogenous cholesterol biosynthesis. Cholesterol-to-coprostanol conversion by the intestinal microbiota has also been suggested to reduce intestinal and serum cholesterol availability, but the dependencies of cholesterol conversion on specific bacterial taxa and dietary habits, as well as its association with serum lipid levels remain largely unknown. To study microbiota contributions to human cholesterol metabolism under varying conditions, fecal microbiota and lipid profiles, as well as serum lipid biomarkers, were determined in two independent human cohorts, including individuals with (CARBFUNC study) and without obesity (KETO study) on very low-carbohydrate high-fat diets (LCHF) for three to six months and six weeks, respectively. Across these two geographically independent studies, conserved distributions of cholesterol high and low-converter types were measured. Also, cholesterol conversion was most dominantly linked to the relative abundance of the cholesterol-converting bacterial species Eubacterium coprostanoligenes, which was further increased in low-converters by LCHF diets, shifting them towards a high-conversion state. Lean cholesterol high-converters, which were characterized by adverse serum lipid profiles even before the LCHF diet, responded to the intervention with increased LDL-C, independently of fat, cholesterol and saturated fatty acid intake. These findings identify the cholesterol high-converter type as a potential predictive biomarker for an increased LDL-C response to LCHF diet in metabolically healthy lean individuals. Although the etiology of IBD has not been fully resolved, an interplay between the intestinal microbiota, environmental factors and an individuals genetic susceptibility is thought to trigger chronic inflammation by a dysregulation of the immune response in the gut. To identify colitis-associated microbiota alterations throughout the development of spontaneous colitis, mice with a genetic deficiency of the anti-inflammatory cytokine Interleukin-10 (IL-10) from different litters were co-housed with wild-type mice and monitored for 20 weeks. The scoring of mice based on their phenotype and stool consistency mirrored the state of mucosal inflammation as assessed based on histopathological examinations and cytokine expression profiles. Also, the state of colitis was characterized by global microbiota alterations and susceptibility to colitis was dependent on litter-specific microbiome compositions that mice adopted early on in their lives. Colitis development was further associated with the presence of the bacterial genus Akkermansia in mature mice shortly before symptoms manifested. This genus was also a good predictor of colitis-related mice withdrawal, suggesting the potential of Akkermansia to serve as an early onset, subclinical colitis marker. In summary, fecal microbiota characterizations in response to LCHF diets in humans and throughout the development of intestinal inflammation in a colitis mouse model highlight the potential of personalized microbiome-based patient classifications to predict clinical outcomes and improve treatment approaches.Das Darmmikrobiom leistet einen wesentlichen Beitrag zur menschlichen Gesundheit. Dennoch sind die Faktoren, die seine Zusammensetzung bestimmen, nur unzureichend erforscht. Bedingungen außerhalb und innerhalb des Körpers können das streng kontrollierte Zusammenspiel von Mikrobiom und Wirt stören und eine Dysbiose hervorrufen, die mit verschiedenen Erkrankungen wie Herz-Kreislauf-Erkrankungen (ASCVD) und chronisch entzündlichen Darmerkrankungen (CED) assoziiert ist. Daher spielen das Erforschen der Einflussfaktoren, die zu mikrobiellen Veränderungen im Darm führen, und die Vorhersage der damit verbundenen gesundheitlichen Folgen eine zentrale Rolle in der Verbesserung der Prävention und Behandlung dieser Erkrankungen.In der vorliegenden Arbeit wurde der Einfluss von diätetischen und genetischen Faktoren auf die Zusammensetzung der gastrointestinalen Mikrobiota untersucht, wobei die Ernährung in einer Humankohorte als extrinsischer, veränderbarer mikrobiom-relevanter Faktor und ein genetisches Knock-out Mausmodell für gastrointestinale Entzündungen als intrinsischer, nicht veränderbarer mikrobiom-relevanter Faktor jeweils exemplarisch diente. In beiden Studien wurden mikrobielle kompositionelle Zusammensetzungen, durch 16S rRNA-Genamplikon-Sequenzierung taxonomisch charakterisiert und im Zusammenhang mit metabolischen und entzündlichen Auswirkungen auf den Wirt analysiert. Die Behandlung von ASCVD zielt in erster Linie auf die Senkung eines Cholesterinüberschusses im Blut ab. Eine potentielle Möglichkeit hierfür, stellt die Umwandlung von Cholesterin in das nicht-absorbierbare Coprostanol durch die intestinale Mikrobiota dar, welche die Cholesterinverfügbarkeit im Darm und im Serum verringern soll. Um den Einfluss der Mikrobiota auf den menschlichen Cholesterinstoffwechsel unter verschiedenen Bedingungen zu untersuchen, wurden fäkale Mikrobiom- und Lipidprofile sowie Lipid-Biomarker im Serum in zwei unabhängigen Humankohorten bestimmt, darunter Personen mit (CARBFUNC-Studie) und ohne Adipositas (KETO-Studie), die sich drei bis sechs Monate bzw. sechs Wochen lang einer sehr kohlenhydratarmen, fettreichen Ernährungsintervention (LCHF) unterzogen. Die Analyse von Personen mit und ohne Fettleibigkeit aus zwei geografisch unabhängigen Kohorten, zeigte eine einheitliche Verteilung der Cholesterinumwandlung in high- und low-converter Typen. In beiden Kohorten war die Cholesterinumwandlung am stärksten mit der relativen Häufigkeit des cholesterinumwandelnden Bakteriums Eubacterium coprostanoligenes assoziiert, dessen Vorkommen durch die LCHF-Diät in den low-convertern erhöht wurde und sie somit in einen high-conversion-ähnlichen Zustand versetzte. Die high-converter ohne Adipositas, die bereits vor der LCHF-Diät durch ungünstige Serumlipidprofile gekennzeichnet waren, reagierten auf die Intervention mit einem Anstieg der LDL-C Konzentration im Serum unabhängig von deren Verzehr von Fett, Cholesterin und gesättigten Fettsäuren. Diese Ergebnisse zeigen, dass der Cholesterin high-converter Typ ein potenzieller prädiktiver Biomarker für eine erhöhte LDL-C-Antwort auf eine LCHF-Diät bei stoffwechselgesunden, normalgewichtigen Personen ist. Obwohl die Ätiologie von CED noch nicht vollständig geklärt ist, wird davon ausgegangen, dass ein Zusammenspiel zwischen der Darmmikrobiota, Umweltfaktoren und der genetischen Anfälligkeit eines Individuums besteht. Um Colitis-assoziierte Mikrobiota-Veränderungen während der Entwicklung von CED zu identifizieren, wurden Mäuse mit einem genetischen Defekt des entzündungshemmenden Zytokines Interleukin-10 (IL-10), die aus verschiedenen Würfen stammen, zusammen mit Wildtyp-Mäusen in Käfige gesetzt und 20 Wochen lang beobachtet. Die Bewertung der Mäuse anhand ihres Phänotyps und ihrer Stuhlkonsistenz spiegelte den Zustand der Schleimhautentzündung wider, welche anhand histopathologischer Untersuchungen und Zytokinexpressionsprofile bestätigt wurde. Globale mikrobielle Veränderungen, welche die Entwicklung der Colitis kennzeichneten, sowie die Anfälligkeit für Colitis, hingen zudem von der Mikrobiom-Zusammensetzung ab, welche die Mäuse schon früh im Leben erhalten hatten. Die erhöhte Colitis-Anfälligkeit in Abhängigkeit vom Wurf wurde außerdem mit der Präsenz der Gattung Akkermansia kurz vor dem Auftreten von Symptomen in Verbindung gebracht. Die Präsenz dieser Gattung war zudem ein guter Prädiktor für das frühe Colitis-bedingte Ausscheiden der Mäuse, was darauf hindeutet, dass Akkermansia als Marker für eine früh einsetzende, subklinische Kolitis dienen könnte. Zusammenfassend unterstreichen die Charakterisierungen des Mikrobiomes durch diätetische Modulation einer LCHF-Diät im Menschen und während der spontanen Entwicklung einer Darmentzündung in einem Colitis-Mausmodell das Potenzial von mikrobiombasierten Patientenklassifizierungen. Diese könnten verwendet werden, um den klinischen Verlauf eines einzelnen Patienten vorherzusagen und personalisierte Behandlungsansätze zu verbessern

E-health-IoT Universe: A Review

The Internet of Things (IoT) devices are able to collect and share data directly with other devices through the cloud environment, providing a huge amount of information to be gathered, stored and analyzed for data-analytics processes. The scenarios in which the IoT devices may be useful are amazing varying, from automotive, to industrial automation or remote monitoring of domestic environment. Furthermore, has been proved that healthcare applications represent an important field of interest for IoT devices, due to the capability of improving the access to care, reducing the cost of healthcare and most importantly increasing the quality of life of the patients. In this paper, we analyze the state-of-art of IoT in medical environment, illustrating an extended range of IoT-driven healthcare applications that, however, still need innovative and high technology-based solutions to be considered ready to market. In particular, problems regarding characteristics of response-time and precision will be examined.  Furthermore, wearable and energy saving properties will be investigated in this paper and also the IT architectures able to ensure security and privacy during the all data-transmission process. Finally, considerations about data mining applications, such as risks prediction, classification and clustering will be provided, that are considered fundamental issues to ensure the accuracy of the care processes

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Human behavioural analysis with self-organizing map for ambient assisted living

This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints

Novel Deep Learning Models for Medical Imaging Analysis

abstract: Deep learning is a sub-field of machine learning in which models are developed to imitate the workings of the human brain in processing data and creating patterns for decision making. This dissertation is focused on developing deep learning models for medical imaging analysis of different modalities for different tasks including detection, segmentation and classification. Imaging modalities including digital mammography (DM), magnetic resonance imaging (MRI), positron emission tomography (PET) and computed tomography (CT) are studied in the dissertation for various medical applications. The first phase of the research is to develop a novel shallow-deep convolutional neural network (SD-CNN) model for improved breast cancer diagnosis. This model takes one type of medical image as input and synthesizes different modalities for additional feature sources; both original image and synthetic image are used for feature generation. This proposed architecture is validated in the application of breast cancer diagnosis and proved to be outperforming the competing models. Motivated by the success from the first phase, the second phase focuses on improving medical imaging synthesis performance with advanced deep learning architecture. A new architecture named deep residual inception encoder-decoder network (RIED-Net) is proposed. RIED-Net has the advantages of preserving pixel-level information and cross-modality feature transferring. The applicability of RIED-Net is validated in breast cancer diagnosis and Alzheimer’s disease (AD) staging. Recognizing medical imaging research often has multiples inter-related tasks, namely, detection, segmentation and classification, my third phase of the research is to develop a multi-task deep learning model. Specifically, a feature transfer enabled multi-task deep learning model (FT-MTL-Net) is proposed to transfer high-resolution features from segmentation task to low-resolution feature-based classification task. The application of FT-MTL-Net on breast cancer detection, segmentation and classification using DM images is studied. As a continuing effort on exploring the transfer learning in deep models for medical application, the last phase is to develop a deep learning model for both feature transfer and knowledge from pre-training age prediction task to new domain of Mild cognitive impairment (MCI) to AD conversion prediction task. It is validated in the application of predicting MCI patients’ conversion to AD with 3D MRI images.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Data Fusion and Systems Engineering Approaches for Quality and Performance Improvement of Health Care Systems: From Diagnosis to Care to System-level Decision-making

abstract: Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical definitions of subtypes use somewhat arbitrary boundaries primarily based on patient self-reported symptoms, which are subjective and error-prone. My research develops a novel Multimodality Factor Mixture Model that discovers subtypes of migraine from multimodality imaging MRI data, which provides complementary accurate measurements of the disease. Patients in the different subtypes show significantly different clinical characteristics of the disease. Treatment tailored and optimized for patients of the same subtype paves the road toward Precision Medicine. The second topic focuses on coordinated patient care. Care coordination between nurses and with other health care team members is important for providing high-quality and efficient care to patients. The recently developed Nurse Care Coordination Instrument (NCCI) is the first of its kind that enables large-scale quantitative data to be collected. My research develops a novel Multi-response Multi-level Model (M3) that enables transfer learning in NCCI data fusion. M3 identifies key factors that contribute to improving care coordination, and facilitates the design and optimization of nurses’ training, workload assignment, and practice environment, which leads to improved patient outcomes. The last topic is about system-level decision-making for Alzheimer’s disease early detection at the early stage of Mild Cognitive Impairment (MCI), by predicting each MCI patient’s risk of converting to AD using imaging and proteomic biomarkers. My research proposes a systems engineering approach that integrates the multi-perspectives, including prediction accuracy, biomarker cost/availability, patient heterogeneity and diagnostic efficiency, and allows for system-wide optimized decision regarding the biomarker testing process for prediction of MCI conversion.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201