Longitudinal LASSO: Jointly Learning Features and Temporal Contingency for Outcome Prediction

Longitudinal analysis is important in many disciplines, such as the study of behavioral transitions in social science. Only very recently, feature selection has drawn adequate attention in the context of longitudinal modeling. Standard techniques, such as generalized estimating equations, have been modified to select features by imposing sparsity-inducing regularizers. However, they do not explicitly model how a dependent variable relies on features measured at proximal time points. Recent graphical Granger modeling can select features in lagged time points but ignores the temporal correlations within an individual's repeated measurements. We propose an approach to automatically and simultaneously determine both the relevant features and the relevant temporal points that impact the current outcome of the dependent variable. Meanwhile, the proposed model takes into account the non-{\em i.i.d} nature of the data by estimating the within-individual correlations. This approach decomposes model parameters into a summation of two components and imposes separate block-wise LASSO penalties to each component when building a linear model in terms of the past $\tau$ measurements of features. One component is used to select features whereas the other is used to select temporal contingent points. An accelerated gradient descent algorithm is developed to efficiently solve the related optimization problem with detailed convergence analysis and asymptotic analysis. Computational results on both synthetic and real world problems demonstrate the superior performance of the proposed approach over existing techniques.Comment: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 201

Emotional distress may increase risk for self-medication and lower risk for mood-related drinking consequences in adolescents

The current study examines indicators of emotional distress and coping that may define sub-populations of adolescents at risk for two potential affect-related mechanisms underlying substance misuse: self-medication and mood-related drinking consequences. Although theory and empirical evidence point to the salience of affect-related drinking to current and future psychopathology, we have little knowledge of whether or for whom such mood-related processes exist in adolescents because few studies have used methods that optimally match the phenomenon to the level of analysis. Consequently, the current study uses multi-level modeling in which daily reports of negative mood and alcohol use are nested within individuals to examine whether adolescents with more emotional distress and poorer coping skills are more likely to evidence self-medication and moodrelated drinking consequences. Seventy-five adolescents participated in a multi-method, multi-reporter study in which they completed a 21-day experience sampling protocol assessing thrice daily measures of mood and daily measures of alcohol use. Results indicate that adolescents reporting greater anger are more likely to evidence self-medication. Conversely, adolescents displaying lower emotional distress and more active coping are more likely to evidence mood-related drinking consequences. Implications for identifying vulnerable sub-populations of adolescents at risk for these mechanisms of problematic alcohol use are discussed.peer-reviewe

대사 질환 동시 이환의 심혈관계 질환 위험 평가 및 기계학습 예측 모형 개발

학위논문(박사) -- 서울대학교대학원 : 의과대학 의과학과, 2022. 8. 박수경.연구 배경: 인구의 고령화와 서구형 생활양식으로 인해 대사 질환 동시 이환 (고혈압, 당뇨병, 및 고지혈증 등을 포함한 두가지 이상의 대사 질환을 가진 것으로 정의)의 유병률이 증가하고 있다. 이러한 대사성 질환은 심혈관계 질환의 위험 증가와 연관된다. 2016년 Global Burden of Disease에 따르면, 심혈관계 질환에 의한 사망은 21세기 주요 사망 원인이며, 우리나라에서는 암에 이어 두번째로 높은 사망원인을 차지한다. 세계보건기구 (The World Health Organization)에서는 음주, 흡연, 비만, 신체 활동, 건강한 식습관을 심혈관계 질환의 예방 가능한 요인으로 지정한 바 있다. 이에 대사 질환 동시 이환에 대한 연구가 필요하다. 따라서, 이 연구의 목적은 1) 한국에서의 대사성 질환과 동시 이환의 유병률을 추정하고; 2) 대사 동시 이환 심혈관계 가족력과 심혈관계 발생 위험을 평가하고, 3)대사 동시 이환에 따른 심혈관계 사망에 대해 생활습관 요인 미치는 영향을 평가하고; 4) 생활 습관 변화와 대사 증후군의 연관성을 확인하고; 5) 대사 동시 이환에 대한 기계학습을 기반으로 한 건강 연령 및 질병 위험 예측 모형을 개발하는 것이다. 연구 방법: 본 연구는 한국인유전체역학조사사업 (KoGES)의 도시기반 (Health examinee-Gem Study, HEXA), 농촌기반 (Cardiovascular disease association study, CAVAS), 지역사회기반 (Ansan and Ansung Study, 2001-2014)를 주로 사용하였고, 추가로 미국 국민건강영양조사 (US National Health and Nutrition Examination Survey, NHANES 2003-2014), 한국국민건강영양조사 (Korea NHANES, KNHANES 2007-2014), 아시아 코호트 연구 (Asia Cohort Consortium)를 사용하였다. 통계방법으로는, 세계보건기구의 세계표준인구를 이용한 직접 표준화 방법을 이용해 대사성 질환의 연령표준화 유병률을 산출하였다. 연구 대상자의 일반적인 특성은 연속형 변수의 경우 Student’s t-test, 범주형 변수의 경우 Chi-squared test를 시행하여 비교하였다. 콕스 비례 위험 회귀 분석과 로지스틱 회귀 분석을 수행하여 hazard ratios (HRs), odds ratio (ORs), 95% confidence interval을 추정하였다. 위험 예측 모형의 경우, training set (전체 대상자의 70%)에서 콕스 비례 회귀 분석, random survival forest 기반 모형을 각각 구축하고, test set (전체 대상자의 30%)에서 concordance index (c-index)를 이용해 각 모형의 성능을 평가하였다. 건강 연령 예측 모형의 경우, 10-fold validation을 사용한 elastic net 방법을 이용해 모형을 구축하였다. 연구 결과: 한국과 미국의 대사성 질환과 동시 이환을 비교한 결과, 한국이 미국보다 대사 동시 이환의 유병률이 낮았다. 한국과 미국에서 가장 흔한 대사 질환 조합은 고혈압과 비만이었다. 한국 인구 중 농촌 지역에 거주하는 인구는 도시 지역에 거주하는 인구보다 대사 동시 이환 유병률이 더 높은 것으로 나타났다. 대사 동시 이환, 심혈관계 질환 가족력, 그리고 심근경색과 뇌졸중을 포함한 심혈관계 질환의 위험 연구 결과는 다음과 같다. 고혈압, 당뇨병, 고지혈증이 있고, 심혈관계 가족력이 있는 대상자는 심혈관계 질환 가족력과 질병이 없는 대상자에 비해 유의하게 심혈관계 질환 (HR 2.88, 95% CI: 1.96-4.24), 심근경색 (HR 3.30, 95% CI: 2.06-5.29), 뇌졸중 (HR 2.52, 95% CI: 1.33-4.79) 위험이 증가하는 것을 확인했다 심혈관대사 질환 동시 이환을 가진 대상자에서 생활 습관 요인이 심혈관계 질환 관련 사망에 미치는 영향 연구에서는, ‘비흡연’, ‘금주’, ‘체질량 지수 18.5–27.4kg/m2’를 건강 상태로 정의하여 건강한 생활 습관 점수를 산출했다. 생활 습관 요인 중 금연은 심혈관계 질환 사망 위험 감소와 가장 강한 연관성을 보였다. 고혈압, 당뇨병, 관상동맥질환이 있는 대상자에서는 건강한 생활 습관 점수가 1씩 증가할 때마다 심혈관계 사망위험이 24% (HR 0.76, 95% CI: 0.63-0.93)씩 감소했다. 2개 이상의 심혈관계 대사질환이 있는 대상의 경우, 건강한 생활 습관 요인은 3가지 모두 있는 경우 심혈관계 질환 사망 (HR 0.51, 95% CI: 0.42-0.61)과 심혈관계 질환으로 인한 조기 사망위험(HR 0.38, 95% CI: 0.27-0.54)의 감소에 유의한 영향이 있었다. 지역사회기반 연구자료를 이용한 반복 측정된 생활 습관 요인의 변화에 따른 대사 증후군 위험 연구에서는, 하루 흡연 개피수의 증가 (HR 1.49, 95% CI: 1.09-2.03), 음주량의 light/moderate에서 heavy로 증가는 (HR 1.42, 95% CI: 1.10-1.84) 대사 증후군의 발생 위험의 증가와 유의한 연관성을 보였다. 새롭게 비만 된 대상자는 꾸준히 적정 체중을 유지하는 대상자에 비해 대사성 증후군 (HR 1.88, 95% CI: 1.44-2.45)의 발생 위험의 증가와 유의한 관계를 보였다. 보다 정밀한 개인 맞춤 건강 상태 예측 및 개선을 위해 기계 학습 기반 질병 예측 모형을 개발과 대사 동시 이환에 대한 예측 변수로서의 건강연령을 개발한 연구에 따르면, 실제 연령에 비해 젊은 건강 연령을 가진 경우, 당뇨병 (HR = 0.63, 95% CI: 0.55–0.72), 고혈압 (HR = 0.74, 95% CI: 0.68–0.81), 당뇨병과 고혈압 동시 이환 (HR = 0.65, 95% CI: 0.47–0.91) 위험도가 낮은 것으로 나타났다. 기계학습기반 예측 모형 연구 결과, 기계 학습 기반의 고혈압과 당뇨병 동시 이환 모형은 높은 통계적 질병 예측력을 보이는 것으로 나타났다. 연구 결론: 본 연구는 한국 인구집단에서 심혈관계 질환 발생 및 사망의 위험을 줄이기 위해 대사 동시 이환에 대한 연구에 대한 필요성을 강조한다. 본 연구에서는 동시 이환을 가진 대상자 중 특히 심혈관계 질환 가족력이 있는 경우에 심혈관계 질환의 발생 위험이 증가하는 것을 확인하였다. 또한 심혈관계 대사 질환 동시 이환을 가진 대상자라도, 금연, 금주, 표준 체질량 지수 유지와 같은 건강한 생활 습관은 심혈관계 질환으로 인한 사망과 조기 사망 위험 감소와 연관성이 있었다. 또한, 건강한 생활습관으로의 변화를 통해 대사 증후군의 위험을 줄이는 데 도움이 되는 것을 확인하였다. 이러한 요인들을 기반으로 기계학습을 이용하여 구축된 질병 예측 모형과 건강연령은 우리나라에서의 대사 질환 동시 이환에 대한 고위험군을 파악하고 이를 미리 예방함으로써, 건강증진을 통해 질병 부담을 줄이는 효과적인 도구로 활용될 수 있을 것으로 기대된다.Introduction: The growing aging population and westernized lifestyle have increased the prevalence of disease comorbidity, which is defined as having more than two metabolic diseases including hypertension (HTN), diabetes mellitus (DM), dyslipidemia (LIP), obesity, and metabolic syndrome (MetS). The combination of these diseases is related to an increased risk of cardiovascular disease (CVD) outcomes. The Global Burden of Disease 2016 Study reported that CVD are by far the leading cause of death globally and one of the major health challenges of the 21st century. In Korea, CVD is the second largest cause of death following cancer. As those diseases share risk factors, the World Health Organization (WHO) designated healthy lifestyle, including alcohol reduction, weight loss, smoking cessation, physical activity, and healthy diet, as modifiable factors of CVDs. Thus, it is necessary to estimate the amount of comorbidity prevalence, identify the combined association of metabolic comorbidity and other risk factors (family history of CVD and lifestyle factors) with CVD outcomes, and develop predictive model for comorbidity for detecting the high-risk of metabolic comorbidity and preventing the future risk of CVD through intervention strategies. Methods: This study mainly used population-based cohort study from the Korea Genome and Epidemiology Study (KoGES) including Health Examinee-Gem study (HEXA), cardiovascular disease association study (CAVAS), and Ansan and Ansung Study from 2001-2014, in addition to United States (US) National Health and Nutrition Examination Survey 2003-2014 (NHANES), Korea NHANES (KNHAENS) 2007-2014, and Asia Cohort Consortium (ACC) study. For the statistical analyses, direct standardization methods using the WHO world standard population was performed to estimate the age-standardized prevalence of metabolic diseases. The baseline characteristics were compared using Chi-squared test for categorical variables and Student’s t-test for continuous variables. Cox proportional hazards regression analysis was performed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) of CVD outcomes. To calculate the odds ratios (ORs) of metabolic diseases, logistic regression models were used. For prediction model, cox proportional hazard regression, and random survival forest (RSF) models were developed in the training set (70% of the total population) and performance evaluations of each model were performed in the test set (30% of the total population) with concordance statistics (c-index). For self-assessed biological age (BA) prediction model, elastic net regression analysis with 10-fold cross validation was performed. Results: According to the comparison of the prevalence of metabolic disease and comorbidity in Korea and the US, Korea had a lower prevalence of metabolic comorbidity than the US. In both Korean and the US population, the most common combination was HTN and obesity. Among the Korean population, individuals living in rural areas had the higher comorbidity prevalence than those who lived in urban areas. In the association between metabolic comorbidity, family history of CVD, and the risk of CVD study, we found that individuals with DM, HTN, LIP, and a positive family history of CVD had a 2.88-fold increased risk of CVD, a 3.30-fold increased risk of MI, and a 2.52-fold increased risk of stroke compared to the individuals with a negative family history of CVD and none of metabolic diseases. In the impact of lifestyle factors with cardiometabolic disease (CMDs) such as HTN, DM, coronary heart disease (CHD), and stroke on CVD death study, the healthy lifestyle status was defined as ‘never smoker’, ‘never drinker’, and ‘body mass index (BMI) 18.5–27.4kg/m2’in Asian population. Among the lifestyle factors, non-smoking had the strongest association with decreasing risk of all cause and CVD death among the healthy lifestyle factors. A significant association of healthy lifestyle score with lower CVD death was observed among individuals with HTN, DM, and CHD (HR 0.76, 95% CI: 0.63-0.93). For individuals with cardiometabolic comorbidity, having three of healthy lifestyle factors was significantly associated with decrease in CVD (HR 0.51, 95% CI: 0.42-0.61) and premature CVD death (HR 0.38, 95% CI: 0.27-0.54). Based on the repeated measurements for assessing change in lifestyle factors study, unhealthy lifestyle modification including increased dose of cigarette smoking (HR 1.49, 95% CI: 1.09-2.03) and increased their intensity of consumption from light/moderate to heavy had a significantly increased risk for MetS (HR 1.42, 95% CI: 1.10-1.84). For obesity, individuals who newly became obesity had a significant increase in risk for MetS (HR 1.88, 95% CI: 1.44-2.45). For improving the individualized health status, we developed machine learning-based disease prediction model and self-assessed BA as a predictor for metabolic comorbidity. We found that compared to the individuals in same BA as chronological age (CA) group, those in younger BA than CA group were associated with a decreased risk of DM (HR = 0.63, 95% CI: 0.55–0.72), HTN (HR = 0.74, 95% CI: 0.68–0.81), and combination of HTN and DM (HR = 0.65, 95% CI: 0.47–0.91). For machine learning-based disease prediction model study, predictive models achieved a high discriminatory ability for comorbidity of HTN and DM. Conclusions: This study highlights the necessity of accounting to metabolic comorbidity to reduce the future risk of CVD outcomes in Korean population. Although individuals already have had cardiometabolic comorbidity, healthy lifestyles (smoking cessation, abstaining from alcohol, and maintaining BMI) are effective to reduce the further risk of CVD death. Moreover, lifestyle changes help to decrease the risk of a cluster of metabolic conditions. At last, machine learning-based self-assessed BA and disease prediction model may be an effective indicator for identifying the high-risk group and decreasing burden of metabolic comorbidities in Korea through prevention.I. Introduction 1 1.1. Background 1 1.2. Objectives 9 1.3. Hypothesis 11 II. Materials and methods 14 2.1. Data source 14 2.2. Study population 16 2.3. Key variables 24 2.4. Statistical analysis 32 III. Results 39 3.1. Prevalence study 39 3.2. Family history of CVD and the risk of CVD study 49 3.3. Lifestyle factors, and the risk of CVD death study 59 3.4. Change in lifestyle factors study 71 3.5. Biological age study 84 3.6. Prediction model study 93 IV. Discussions 105 4.1. Key findings 105 4.2. Comparison to previous studies 108 4.3. Strengths and limitations 117 V. Conclusion 122 References 123 Appendix 145 Abstract in Korean 181 Acknowledgment 185박

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

Implementing Precision Psychiatry: a Systematic Review of Individualized Prediction Models for Clinical Practice

BACKGROUND: The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. METHODS: PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. FINDINGS: Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (beta = .29, P = .03) and diagnostic compared to prognostic (beta = .84, p < .0001) and predictive (beta = .87, P = .002) models were associated with increased accuracy. INTERPRETATION: To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gapThis study was supported by the King’s College London Confidence in Concept award from the Medical Research Council (MC_PC_16048) to Dr Fusar-Poli. Dr Salazar de Pablo and Dr Vaquerizo-Serrano are supported by the Alicia Koplowitz Foundation. Dr Danese was funded by the Medical Research Council (grant no. P005918) and the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, and King’s College Londo

Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats

[EN] Alcohol abuse is one of the most alarming issues for the health authorities. It is estimated that at least 23 million of European citizens are affected by alcoholism causing a cost around 270 million euros. Excessive alcohol consumption is related with physical harm and, although it damages the most of body organs, liver, pancreas, and brain are more severally affected. Not only physical harm is associated to alcohol-related disorders, but also other psychiatric disorders such as depression are often comorbiding. As well, alcohol is present in many of violent behaviors and traffic injures. Altogether reflects the high complexity of alcohol-related disorders suggesting the involvement of multiple brain systems. With the emergence of non-invasive diagnosis techniques such as neuroimaging or EEG, many neurobiological factors have been evidenced to be fundamental in the acquisition and maintenance of addictive behaviors, relapsing risk, and validity of available treatment alternatives. Alterations in brain structure and function reflected in non-invasive imaging studies have been repeatedly investigated. However, the extent to which imaging measures may precisely characterize and differentiate pathological stages of the disease often accompanied by other pathologies is not clear. The use of animal models has elucidated the role of neurobiological mechanisms paralleling alcohol misuses. Thus, combining animal research with non-invasive neuroimaging studies is a key tool in the advance of the disorder understanding. As the volume of data from very diverse nature available in clinical and research settings increases, an integration of data sets and methodologies is required to explore multidimensional aspects of psychiatric disorders. Complementing conventional mass-variate statistics, interests in predictive power of statistical machine learning to neuroimaging data is currently growing among scientific community. This doctoral thesis has covered most of the aspects mentioned above. Starting from a well-established animal model in alcohol research, Marchigian Sardinian rats, we have performed multimodal neuroimaging studies at several stages of alcohol-experimental design including the etiological mechanisms modulating high alcohol consumption (in comparison to Wistar control rats), alcohol consumption, and treatment with the opioid antagonist Naltrexone, a well-established drug in clinics but with heterogeneous response. Multimodal magnetic resonance imaging acquisition included Diffusion Tensor Imaging, structural imaging, and the calculation of magnetic-derived relaxometry maps. We have designed an analytical framework based on widely used algorithms in neuroimaging field, Random Forest and Support Vector Machine, combined in a wrapping fashion. Designed approach was applied on the same dataset with two different aims: exploring the validity of the approach to discriminate experimental stages running at subject-level and establishing predictive models at voxel-level to identify key anatomical regions modified during the experiment course. As expected, combination of multiple magnetic resonance imaging modalities resulted in an enhanced predictive power (between 3 and 16%) with heterogeneous modality contribution. Surprisingly, we have identified some inborn alterations correlating high alcohol preference and thalamic neuroadaptations related to Naltrexone efficacy. As well, reproducible contribution of DTI and relaxometry -related biomarkers has been repeatedly identified guiding further studies in alcohol research. In summary, along this research we demonstrate the feasibility of incorporating multimodal neuroimaging, machine learning algorithms, and animal research in the advance of the understanding alcohol-related disorders.[ES] El abuso de alcohol es una de las mayores preocupaciones de las autoridades sanitarias en la Unión Europea. El consumo de alcohol en exceso afecta en mayor o menor medida la totalidad del organismo siendo el páncreas e hígado los más severamente afectados. Además de estos, el sistema nervioso central sufre deterioros relacionados con el alcohol y con frecuencia se presenta en paralelo con otras patologías psiquiátricas como la depresión u otras adicciones como la ludopatía. La presencia de estas comorbidades demuestra la complejidad de la patología en la que multitud de sistemas neuronales interaccionan entre sí. El uso imágenes de resonancia magnética (RM) han ayudado en el estudio de enfermedades psiquiátricas facilitando el descubrimiento de mecanismos neurológicos fundamentales en el desarrollo y mantenimiento de la adicción al alcohol, recaídas y el efecto de los tratamientos disponibles. A pesar de los avances, todavía se necesita investigar más para identificar las bases biológicas que contribuyen a la enfermedad. En este sentido, los modelos animales sirven, por lo tanto, a discriminar aquellos factores únicamente relacionados con el alcohol controlando otros factores que facilitan el desarrollo del alcoholismo. Estudios de resonancia magnética en animales de laboratorio y su posterior evaluación en humanos juegan un papel fundamental en el entendimiento de las patologías psiquatricas como la addicción al alcohol. La imagen por resonancia magnética se ha integrado en entornos clínicos como prueba diagnósticas no invasivas. A medida que el volumen de datos se va incrementando, se necesitan herramientas y metodologías capaces de fusionar información de muy distinta naturaleza y así establecer criterios diagnósticos cada vez más exactos. El poder predictivo de herramientas derivadas de la inteligencia artificial como el aprendizaje automático sirven de complemento a tradicionales métodos estadísticos. En este trabajo se han abordado la mayoría de estos aspectos. Se han obtenido datos multimodales de resonancia magnética de un modelo validado en la investigación de patologías derivadas del consumo del alcohol, las ratas Marchigian-Sardinian desarrolladas en la Universidad de Camerino (Italia) y con consumos de alcohol comparables a los humanos. Para cada animal se han adquirido datos antes y después del consumo de alcohol y bajo dos condiciones de abstinencia (con y sin tratamiento de Naltrexona, una medicaciones anti-recaídas usada como farmacoterapia en el alcoholismo). Los datos de resonancia magnética multimodal consistentes en imágenes de difusión, de relaxometría y estructurales se han fusionado en un esquema analítico multivariable incorporando dos herramientas generalmente usadas en datos derivados de neuroimagen, Random Forest y Support Vector Machine. Nuestro esquema fue aplicado con dos objetivos diferenciados. Por un lado, determinar en qué fase experimental se encuentra el sujeto a partir de biomarcadores y por el otro, identificar sistemas cerebrales susceptibles de alterarse debido a una importante ingesta de alcohol y su evolución durante la abstinencia. Nuestros resultados demostraron que cuando biomarcadores derivados de múltiples modalidades de neuroimagen se fusionan en un único análisis producen diagnósticos más exactos que los derivados de una única modalidad (hasta un 16% de mejora). Biomarcadores derivados de imágenes de difusión y relaxometría discriminan estados experimentales. También se han identificado algunos aspectos innatos que están relacionados con posteriores comportamientos con el consumo de alcohol o la relación entre la respuesta al tratamiento y los datos de resonancia magnética. Resumiendo, a lo largo de esta tesis, se demuestra que el uso de datos de resonancia magnética multimodales en modelos animales combinados en esquemas analíticos multivariados es una herramienta válida en el entendimiento de patologías[CAT] L'abús de alcohol es una de les majors preocupacions per part de les autoritats sanitàries de la Unió Europea. Malgrat la dificultat de establir xifres exactes, se estima que uns 23 milions de europeus actualment sofreixen de malalties derivades del alcoholisme amb un cost que supera els 150.000 milions de euros per a la societat. Un consum de alcohol en excés afecta en major o menor mesura el cos humà sent el pàncreas i el fetge el més afectats. A més, el cervell sofreix de deterioraments produïts per l'alcohol i amb freqüència coexisteixen amb altres patologies com depressió o altres addiccions com la ludopatia. Tot aquest demostra la complexitat de la malaltia en la que múltiple sistemes neuronals interactuen entre si. Tècniques no invasives com el encefalograma (EEG) o imatges de ressonància magnètica (RM) han ajudat en l'estudi de malalties psiquiàtriques facilitant el descobriment de mecanismes neurològics fonamentals en el desenvolupament i manteniment de la addició, recaiguda i la efectivitat dels tractaments disponibles. Tot i els avanços, encara es necessiten més investigacions per identificar les bases biològiques que contribueixen a la malaltia. En aquesta direcció, el models animals serveixen per a identificar únicament dependents del abús del alcohol. Estudis de ressonància magnètica en animals de laboratori i posterior avaluació en humans jugarien un paper fonamental en l' enteniment de l'ús del alcohol. L'ús de probes diagnostiques no invasives en entorns clínics has sigut integrades. A mesura que el volum de dades es incrementa, eines i metodologies per a la fusió d' informació de molt distinta natura i per tant, establir criteris diagnòstics cada vegada més exactes. La predictibilitat de eines desenvolupades en el camp de la intel·ligència artificial com la aprenentatge automàtic serveixen de complement a mètodes estadístics tradicionals. En aquesta investigació se han abordat tots aquestes aspectes. Dades multimodals de ressonància magnètica se han obtingut de un model animal validat en l'estudi de patologies relacionades amb el consum d'alcohol, les rates Marchigian-Sardinian desenvolupades en la Universitat de Camerino (Italià) i amb consums d'alcohol comparables als humans. Per a cada animal es van adquirir dades previs i després al consum de alcohol i dos condicions diferents de abstinència (amb i sense tractament anti-recaiguda). Dades de ressonància magnètica multimodal constituides per imatges de difusió, de relaxometria magnètica i estructurals van ser fusionades en esquemes analítics multivariats incorporant dues metodologies validades en el camp de neuroimatge, Random Forest i Support Vector Machine. Nostre esquema ha sigut aplicat amb dos objectius diferenciats. El primer objectiu es determinar en quina fase experimental es troba el subjecte a partir de biomarcadors obtinguts per neuroimatge. Per l'altra banda, el segon objectiu es identificar el sistemes cerebrals susceptibles de ser alterats durant una important ingesta de alcohol i la seua evolució durant la fase del tractament. El nostres resultats demostraren que l'ús de biomarcadors derivats de varies modalitats de neuroimatge fusionades en un anàlisis multivariat produeixen diagnòstics més exactes que els derivats de una única modalitat (fins un 16% de millora). Biomarcadors derivats de imatges de difusió i relaxometria van contribuir de distints estats experimentals. També s'han identificat aspectes innats que estan relacionades amb posterior preferències d'alcohol o la relació entre la resposta al tractament anti-recaiguda i les dades de ressonància magnètica. En resum, al llarg de aquest treball, es demostra que l'ús de dades de ressonància magnètica multimodal en models animals combinats en esquemes analítics multivariats són una eina molt valida en l'enteniment i avanç de patologies psiquiàtriques com l'alcoholisme.Cosa Liñán, A. (2017). Analytical fusion of multimodal magnetic resonance imaging to identify pathological states in genetically selected Marchigian Sardinian alcohol-preferring (msP) rats [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90523TESI

Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional "pre-pre-" and "post-post-" analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.11Ysciescopu