Las enfermedades cardiovasculares (ECVs) son la principal causa de mortalidad en el mundo Occidental. El interés de prevenir y tratar las ECVs ha desencadenado un rápido desarrollo de los sistemas de adquisición de imágenes médicas. Por este motivo, la cantidad de datos de imagen recolectados en las instituciones de salud se ha incrementado considerablemente. Este hecho ha aumentado la necesidad de herramientas automatizadas para dar soporte al diagnóstico, mediante una interpretación de imagen confiable y reproducible. La tarea de interpretación requiere traducir los datos crudos de imagen en parámetros cuantitativos, los cuales son considerados relevantes para clasificar la condición cardiaca de un paciente. Para realizar tal tarea, los métodos basados en modelos estadísticos de forma han recibido favoritismo dada la naturaleza tridimensional (o 3D+t) de las imágenes cardiovasculares. Deformando el modelo estadístico de forma a la imagen de un paciente, el corazón puede analizarse de manera integral. Actualmente, el campo de las imágenes cardiovasculares esta constituido por diferentes modalidades. Cada modalidad explota diferentes fenómenos físicos, lo cual nos permite observar el órgano cardiaco desde diferentes ángulos. El personal clínico recopila todas estas piezas de información y las ensambla mentalmente en un modelo integral. Este modelo integral incluye información anatómica y funcional que muestra un cuadro completo del corazón del paciente. Es de alto interés transformar este modelo mental en un modelo computacional capaz de integrar la información de manera global. La generación de un modelo como tal no es simplemente un reto de visualización. Requiere una metodología capaz de extraer los parámetros cuantitativos relevantes basados en los mismos principios técnicos. Esto nos asegura que las mediciones se pueden comparar directamente. Tal metodología debe ser capaz de: 1) segmentar con precisión las cavidades cardiacas a partir de datos multimodales, 2) proporcionar un marco de referencia único para integrar múltiples fuentes de información, y 3) asistir la clasificación de la condición cardiaca del paciente. Esta tesis se basa en que los modelos estadísticos de forma, y en particular los Modelos Activos de Forma, son un método robusto y preciso con el potencial de incluir todos estos requerimientos. Para procesar múltiples modalidades de imagen, separamos la información estadística de forma de la información de apariencia. Obtenemos la información estadística de forma a partir de una modalidad de alta resolución y aprendemos la apariencia simulando la física de adquisición de otras modalidades. Las contribuciones de esta tesis pueden ser resumidas así: 1) un método genérico para construir automáticamente modelos de intensidad para los Modelos Activos de Forma simulando la física de adquisición de la modalidad en cuestión, 2) la primera extensión de un simulador de Resonancia Magnética Nuclear diseñado para producir estudios cardiacos realistas, y 3) un método novedoso para el entrenamiento automático de modelos de intensidad y de fiabilidad aplicado a estudios cardiacos de Resonancia Magnética Nuclear. Cada una de estas contribuciones representa un artículo publicado o enviado a una revista técnica internacional. / Cardiovascular diseases (CVDs) are the major cause of death in the Western world. The desire to prevent and treat CVDs has triggered a rapid development of medical imaging systems. As a consequence, the amount of imaging data collected in health care institutions has increased considerably. This fact has raised the need for automated analysis tools to support diagnosis with reliable and reproducible image interpretation. The interpretation task requires to translate raw imaging data into quantitative parameters, which are considered relevant to classify the patient’s cardiac condition. To achieve this task, statistical shape model approaches have found favoritism given the 3D (or 3D+t) nature of cardiovascular imaging datasets. By deforming the statistical shape model to image data from a patient, the heart can be analyzed in a more holistic way. Currently, the field of cardiovascular imaging is constituted by different modalities. Each modality exploits distinct physical phenomena, which allows us to observe the cardiac organ from different angles. Clinicians collect all these pieces of information to form an integrated mental model. The mental model includes anatomical and functional information to display a full picture of the patient’s heart. It is highly desirable to transform this mental model into a computational model able to integrate the information in a comprehensive manner. Generating such a model is not simply a visualization challenge. It requires having a methodology able to extract relevant quantitative parameters by applying the same principle. This assures that the measurements are directly comparable. Such a methodology should be able to: 1) accurately segment the cardiac cavities from multimodal datasets, 2) provide a unified frame of reference to integrate multiple information sources, and 3) aid the classification of a patient’s cardiac condition. This thesis builds upon the idea that statistical shape models, in particular Active Shape Models, are a robust and accurate approach with the potential to incorporate all these requirements. In order to handle multiple image modalities, we separate the statistical shape information from the appearance information. We obtain the statistical shape information from a high resolution modality and include the appearance information by simulating the physics of acquisition of other modalities. The contributions of this thesis can be summarized as: 1) a generic method to automatically construct intensity models for Active Shape Models based on simulating the physics of acquisition of the given imaging modality, 2) the first extension of a Magnetic Resonance Imaging (MRI) simulator tailored to produce realistic cardiac images, and 3) a novel automatic intensity model and reliability training strategy applied to cardiac MRI studies. Each of these contributions represents an article published or submitted to a peer-review archival journal.

Abstract Background Cardiovascular disease (CVD) has an enormous impact on the lives and health of New Zealanders. There is substantial epidemiological evidence that supports identifying people at high risk of CVD and treating them with lifestyle and drug-based interventions. If fully implemented, this targeted high risk approach could reduce future CVD events by over 50%. Recent studies have shown that a formal CVD risk assessment to the systematically identify high risk patients is rarely done in routine New Zealand general practice and audits of CVD risk management have shown large evidence-practice gaps. The CVD risk prediction score recommended by New Zealand guidelines for identifying high CVD risk patients was derived from the US Framingham Heart Study using data collected between the 1960s and 1980s. This score has only modest prediction accuracy and there are particular concerns about it’s validity for New Zealand sub-populations such as high risk ethnic groups or people with diabetes. Aims The overall aims of this thesis were to investigate the potential of a computerised decision support system (CDSS) to improve the assessment and management of CVD risk in New Zealand general practice while simultaneously developing a sustainable cohort study that could be used for validating and improving CVD risk prediction scores and related research. Methods An environmental scan of the New Zealand health care setting’s readiness to support a CDSS was conducted .The epidemiological evidence was reviewed to assess the effect of decision support systems on the quality of health care and the types and functionality of systems most likely to be successful. This was followed by a focused systematic review of randomised trials evaluating the impact of CDSS on CVD risk assessment and management practices and patient CVD outcomes in primary care. A web-based CDSS (PREDICT) was collaboratively developed. This rules-based provider-initiated system with audit and feedback and referral functionalities was fully integrated with general practice electronic medical records in a number of primary health organisations (PHOs). The evidence-based content was derived from national CVD and diabetes guidelines. When clinicians used PREDICT at the time of a consultation, treatment recommendations tailored to the patient’s CVD and diabetes risk profile were delivered to support decision-making within seconds. Simultaneously, the patient’s CVD risk profiles were securely stored on a central server. With PHO permission, anonymised patient data were linked via encrypted patient National Health Index numbers to national death and hospitalisation data. Three analytical studies using these data are described in this thesis. The first evaluated changes in GP risk assessment practice following implementation of PREDICT; the second investigated patterns of use of the CDSS by GPs and practice nurses; and the third describes the emerging PREDICT cohort and a preliminary validation of risk prediction scores. Results Given the rapid development of organised primary care since the 1990’s, the high degree of general practice computerisation and the New Zealand policy (health, informatics, privacy) environment, the introduction of a CDSS into the primary care setting was deemed feasible. The evidence for the impact of CDSS in general has been moderately favourable in terms of improving desired practice. Of the randomised trials of CDSS for assessing or managing CVD risk, about two-thirds reported improvements in provider processes and two-fifths reported some improvements in intermediate patient outcomes. No adverse effects were reported. Since 2002, the PREDICT CDSS has been implemented progressively in PHOs within Northland and the three Auckland regional District Health Board catchments, covering a population of 1.5 million. A before-after audit conducted in three large PHOs showed that CVD risk documentation increased four fold after the implementation of PREDICT. To date, the PREDICT dataset includes around 63,000 risk assessments conducted on a cohort of over 48,000 people by over 1000 general practitioners and practice nurses. This cohort has been followed from baseline for a median of 2.12 years. During that time 2655 people died or were hospitalised with a CVD event. Analyses showed that the original Framingham risk score was reasonably well calibrated overall but underestimated risk in high risk ethnic groups. Discrimination was only modest (AUC 0.701). An adjusted Framingham score, recommended by the New Zealand Guideline Group (NZGG) overestimated 5-year event rates by around 4-7%, in effect lowering the threshold for drug therapy to about 10% 5-year predicted CVD risk. The NZGG adjusted score (AUC 0.676) was less discriminating than the Framingham score and over-adjusted for high risk ethnic groups. For the cohort aged 30-74 years, the NZGG-recommended CVD risk management strategy identified almost half of the population as eligible for lifestyle management +/- drug therapy and this group generated 82% of all CVD events. In contrast the original Framingham score classified less than one-third of the cohort as eligible for individualised management and this group generated 71% of the events that occurred during follow-up. Implications This research project has demonstrated that a CDSS tool can be successfully implemented on a large scale in New Zealand general practice. It has assisted practitioners to improve the assessment and management of CVD at the time of patient consultation. Simultaneously, PREDICT has cost-effectively generated one of the largest cohorts of Māori and non-Māori ever assembled in New Zealand. As the cohort grows, new CVD risk prediction scores will be able to be developed for many New Zealand sub-populations. It will also provide clinicians and policy makers with the information needed to determine the trade-offs between the resources required to manage increasing proportions of the populations and the likely impact of management on preventing CVD events.

Abstract Background Cardiovascular disease (CVD) has an enormous impact on the lives and health of New Zealanders. There is substantial epidemiological evidence that supports identifying people at high risk of CVD and treating them with lifestyle and drug-based interventions. If fully implemented, this targeted high risk approach could reduce future CVD events by over 50%. Recent studies have shown that a formal CVD risk assessment to the systematically identify high risk patients is rarely done in routine New Zealand general practice and audits of CVD risk management have shown large evidence-practice gaps. The CVD risk prediction score recommended by New Zealand guidelines for identifying high CVD risk patients was derived from the US Framingham Heart Study using data collected between the 1960s and 1980s. This score has only modest prediction accuracy and there are particular concerns about it’s validity for New Zealand sub-populations such as high risk ethnic groups or people with diabetes. Aims The overall aims of this thesis were to investigate the potential of a computerised decision support system (CDSS) to improve the assessment and management of CVD risk in New Zealand general practice while simultaneously developing a sustainable cohort study that could be used for validating and improving CVD risk prediction scores and related research. Methods An environmental scan of the New Zealand health care setting’s readiness to support a CDSS was conducted .The epidemiological evidence was reviewed to assess the effect of decision support systems on the quality of health care and the types and functionality of systems most likely to be successful. This was followed by a focused systematic review of randomised trials evaluating the impact of CDSS on CVD risk assessment and management practices and patient CVD outcomes in primary care. A web-based CDSS (PREDICT) was collaboratively developed. This rules-based provider-initiated system with audit and feedback and referral functionalities was fully integrated with general practice electronic medical records in a number of primary health organisations (PHOs). The evidence-based content was derived from national CVD and diabetes guidelines. When clinicians used PREDICT at the time of a consultation, treatment recommendations tailored to the patient’s CVD and diabetes risk profile were delivered to support decision-making within seconds. Simultaneously, the patient’s CVD risk profiles were securely stored on a central server. With PHO permission, anonymised patient data were linked via encrypted patient National Health Index numbers to national death and hospitalisation data. Three analytical studies using these data are described in this thesis. The first evaluated changes in GP risk assessment practice following implementation of PREDICT; the second investigated patterns of use of the CDSS by GPs and practice nurses; and the third describes the emerging PREDICT cohort and a preliminary validation of risk prediction scores. Results Given the rapid development of organised primary care since the 1990’s, the high degree of general practice computerisation and the New Zealand policy (health, informatics, privacy) environment, the introduction of a CDSS into the primary care setting was deemed feasible. The evidence for the impact of CDSS in general has been moderately favourable in terms of improving desired practice. Of the randomised trials of CDSS for assessing or managing CVD risk, about two-thirds reported improvements in provider processes and two-fifths reported some improvements in intermediate patient outcomes. No adverse effects were reported. Since 2002, the PREDICT CDSS has been implemented progressively in PHOs within Northland and the three Auckland regional District Health Board catchments, covering a population of 1.5 million. A before-after audit conducted in three large PHOs showed that CVD risk documentation increased four fold after the implementation of PREDICT. To date, the PREDICT dataset includes around 63,000 risk assessments conducted on a cohort of over 48,000 people by over 1000 general practitioners and practice nurses. This cohort has been followed from baseline for a median of 2.12 years. During that time 2655 people died or were hospitalised with a CVD event. Analyses showed that the original Framingham risk score was reasonably well calibrated overall but underestimated risk in high risk ethnic groups. Discrimination was only modest (AUC 0.701). An adjusted Framingham score, recommended by the New Zealand Guideline Group (NZGG) overestimated 5-year event rates by around 4-7%, in effect lowering the threshold for drug therapy to about 10% 5-year predicted CVD risk. The NZGG adjusted score (AUC 0.676) was less discriminating than the Framingham score and over-adjusted for high risk ethnic groups. For the cohort aged 30-74 years, the NZGG-recommended CVD risk management strategy identified almost half of the population as eligible for lifestyle management +/- drug therapy and this group generated 82% of all CVD events. In contrast the original Framingham score classified less than one-third of the cohort as eligible for individualised management and this group generated 71% of the events that occurred during follow-up. Implications This research project has demonstrated that a CDSS tool can be successfully implemented on a large scale in New Zealand general practice. It has assisted practitioners to improve the assessment and management of CVD at the time of patient consultation. Simultaneously, PREDICT has cost-effectively generated one of the largest cohorts of Māori and non-Māori ever assembled in New Zealand. As the cohort grows, new CVD risk prediction scores will be able to be developed for many New Zealand sub-populations. It will also provide clinicians and policy makers with the information needed to determine the trade-offs between the resources required to manage increasing proportions of the populations and the likely impact of management on preventing CVD events.

Abstract Background Cardiovascular disease (CVD) has an enormous impact on the lives and health of New Zealanders. There is substantial epidemiological evidence that supports identifying people at high risk of CVD and treating them with lifestyle and drug-based interventions. If fully implemented, this targeted high risk approach could reduce future CVD events by over 50%. Recent studies have shown that a formal CVD risk assessment to the systematically identify high risk patients is rarely done in routine New Zealand general practice and audits of CVD risk management have shown large evidence-practice gaps. The CVD risk prediction score recommended by New Zealand guidelines for identifying high CVD risk patients was derived from the US Framingham Heart Study using data collected between the 1960s and 1980s. This score has only modest prediction accuracy and there are particular concerns about it’s validity for New Zealand sub-populations such as high risk ethnic groups or people with diabetes. Aims The overall aims of this thesis were to investigate the potential of a computerised decision support system (CDSS) to improve the assessment and management of CVD risk in New Zealand general practice while simultaneously developing a sustainable cohort study that could be used for validating and improving CVD risk prediction scores and related research. Methods An environmental scan of the New Zealand health care setting’s readiness to support a CDSS was conducted .The epidemiological evidence was reviewed to assess the effect of decision support systems on the quality of health care and the types and functionality of systems most likely to be successful. This was followed by a focused systematic review of randomised trials evaluating the impact of CDSS on CVD risk assessment and management practices and patient CVD outcomes in primary care. A web-based CDSS (PREDICT) was collaboratively developed. This rules-based provider-initiated system with audit and feedback and referral functionalities was fully integrated with general practice electronic medical records in a number of primary health organisations (PHOs). The evidence-based content was derived from national CVD and diabetes guidelines. When clinicians used PREDICT at the time of a consultation, treatment recommendations tailored to the patient’s CVD and diabetes risk profile were delivered to support decision-making within seconds. Simultaneously, the patient’s CVD risk profiles were securely stored on a central server. With PHO permission, anonymised patient data were linked via encrypted patient National Health Index numbers to national death and hospitalisation data. Three analytical studies using these data are described in this thesis. The first evaluated changes in GP risk assessment practice following implementation of PREDICT; the second investigated patterns of use of the CDSS by GPs and practice nurses; and the third describes the emerging PREDICT cohort and a preliminary validation of risk prediction scores. Results Given the rapid development of organised primary care since the 1990’s, the high degree of general practice computerisation and the New Zealand policy (health, informatics, privacy) environment, the introduction of a CDSS into the primary care setting was deemed feasible. The evidence for the impact of CDSS in general has been moderately favourable in terms of improving desired practice. Of the randomised trials of CDSS for assessing or managing CVD risk, about two-thirds reported improvements in provider processes and two-fifths reported some improvements in intermediate patient outcomes. No adverse effects were reported. Since 2002, the PREDICT CDSS has been implemented progressively in PHOs within Northland and the three Auckland regional District Health Board catchments, covering a population of 1.5 million. A before-after audit conducted in three large PHOs showed that CVD risk documentation increased four fold after the implementation of PREDICT. To date, the PREDICT dataset includes around 63,000 risk assessments conducted on a cohort of over 48,000 people by over 1000 general practitioners and practice nurses. This cohort has been followed from baseline for a median of 2.12 years. During that time 2655 people died or were hospitalised with a CVD event. Analyses showed that the original Framingham risk score was reasonably well calibrated overall but underestimated risk in high risk ethnic groups. Discrimination was only modest (AUC 0.701). An adjusted Framingham score, recommended by the New Zealand Guideline Group (NZGG) overestimated 5-year event rates by around 4-7%, in effect lowering the threshold for drug therapy to about 10% 5-year predicted CVD risk. The NZGG adjusted score (AUC 0.676) was less discriminating than the Framingham score and over-adjusted for high risk ethnic groups. For the cohort aged 30-74 years, the NZGG-recommended CVD risk management strategy identified almost half of the population as eligible for lifestyle management +/- drug therapy and this group generated 82% of all CVD events. In contrast the original Framingham score classified less than one-third of the cohort as eligible for individualised management and this group generated 71% of the events that occurred during follow-up. Implications This research project has demonstrated that a CDSS tool can be successfully implemented on a large scale in New Zealand general practice. It has assisted practitioners to improve the assessment and management of CVD at the time of patient consultation. Simultaneously, PREDICT has cost-effectively generated one of the largest cohorts of Māori and non-Māori ever assembled in New Zealand. As the cohort grows, new CVD risk prediction scores will be able to be developed for many New Zealand sub-populations. It will also provide clinicians and policy makers with the information needed to determine the trade-offs between the resources required to manage increasing proportions of the populations and the likely impact of management on preventing CVD events.

Diversas evidências comprovam que a obesidade está associada a alterações estruturais e funcionais do coração em modelos humanos e animais. Outros estudos recentes também demonstram que a obesidade humana está associada com alterações na função e na estrutura vascular, especialmente em grandes e médias artérias. Estudos epidemiológicos têm confirmado que a obesidade é um fator de risco significativo para o aparecimento de proteinúria e de doença renal terminal em uma população normal. Com o objetivo de determinar as alterações morfológicas relacionadas ao remodelamento cardíaco, vascular e renal em um modelo experimental de obesidade induzida pelo glutamato monossódico (MSG) e os efeitos da metformina sobre estes achados, foram estudados 25 ratos divididos em cinco grupos: controle com 16 e 22 semanas (CON-16 e CON-22); obeso com 16 e 22 semanas (MSG-16 e MSG-22) e obeso + metformina (MET-22) 300mg/Kg/dia por via oral. A caracterização da resistência à insulina foi feita através da medida da insulina plasmática e cálculo do índice de HOMA-IR. As análises morfológicas e quantificação do colágeno miocárdico foram feitos pelo sistema de imagem Image Pro Plus analysis. A pressão arterial sistólica foi levemente maior no grupo MSG-22, adquirindo significância estatística quando comparada com o grupo MSG-16 (1222 vs 1082 mmHg, p<0,05). Por outro lado, o grupo MET-22 mostrou níveis mais baixos de pressão arterial (1181 mmHg), sem alcançar diferença significativa. No grupo de animais obesos, foi observado aumento na relação média-lumen com 16 semanas (39,93,7 vs 30,22,0 %, p<0,05) e com 22 semanas (39,81,3 vs 29,51,2%, p<0,05), que foi reduzida com o uso da metformina (31,50,9%). O depósito de colágeno na área perivascular no ventrículo esquerdo foi significativamente maior no grupo MSG-22 (1,390,06 vs 0,830,06 % no CON-22, p<0,01), sendo atenuado pela metformina (1,020,04%). No rim, a área seccional transversa das arteríolas intrarrenais foi semelhante entre os grupos (18,52,2 no CON-16; 19,93,7 no MSG-16; 18,93,1 no CON-22; 21,81,5 no MSG-22; 20,21,4 no MET-22). Foi observado aumento da área glomerular no grupo MSG-22 (141,34,5 vs 129,50,5 m2), mas sem significância estatística. Em conclusão, nos ratos com obesidade induzida pelo MSG, com resistência à insulina, as alterações cardíacas foram mais proeminentes do que as alterações renais. No coração foram observados sinais de remodelamento vascular hipertrófico nas pequenas artérias intramiocárdicas e evidências de fibrose miocárdica mais proeminente na área perivascular, alterações que foram, pelo menos parcialmente, atenuadas com o uso de metformina durante seis semanas, mostrando que esta droga pode ser benéfica na prevenção de complicações cardíacas, vasculares e renais associadas com a obesidade. / Many evidences show that obesity is associated to structural and functional changes in the heart of human and animal models. Recent studies also show that human obesity is associated with vascular structural and functional modifications, specially at large and medium-sized arteries. Epidemiological studies have confirmed that obesity is a significant risk factor for the development of proteinuria and end-stage renal disease in a normal population. With the objective to determinate morphological changes related to cardiac, vascular and renal remodeling in an experimental model of monosodium glutamate (MSG)-induced obesity and the effect of metformin at this finding. Twenty five rats were studied and divided into five groups: control with 16 e 22 weeks (CON-16 and CON-22); obese with 16 and 22 weeks (MSG-16 e MSG-22), and obese + metformin (MET-22) 300mg/Kg/day per oral. The characterization of insulin resistance was done through measurement of plasma insulin and calculation of HOMA-IR index. The morphological analysis and the quantification of myocardial collagen were carried out by Image Pro Plus analysis system. The systolic blood pressure was slightly higher in MSG-22 group, reaching statistical significance when compared to MSG-16 group (1222 vs 1082 mmHg, p<0.05). On the other hand, the MET-22 group demonstrated lower blood pressure levels (1181 mmHg), without reaching statistical difference. The obese animals presented increase in media-to-lumen ratio with 16 weeks (39.93.7 vs 30.22.0 %, p<0.05) and with 22 weeks (39.81.3 vs 29.51.2%, p<0.05), which was reduced with use of metformin (31.50.9%). The collagen deposition in perivascular area of left ventricle was significantly greater in MSG-22 group (1.390.06 vs 0.830.06 % in CON-22, p<0.01), and attenuated by metformin (1.020.04%). In the kidney, the media cross-sectional area of intrarenal arterioles was similar among the groups (18.52.2 in CON-16; 19.93.7 in MSG-16; 18.93.1 in CON-22; 21.81.5 in MSG-22; 20.21.4 in MET-22). An increase of glomerular area was observed in MSG-22 group (141.34.5 vs 129.50.5 m2), but without statistical significance. In conclusion, rats with MSG-induced obesity and insulin resistance presented more pronounced cardiac changes than renal alterations. In the heart, there were evidences of hypertrophic vascular remodeling were observed in intramyocardial small arteries and perivascular fibrosis. These findings were, at least partially, attenuated by metformin for six weeks, suggesting that this drug may be beneficial for prevention of cardiac, vascular and renal complications associated with obesity.

Diversas evidências comprovam que a obesidade está associada a alterações estruturais e funcionais do coração em modelos humanos e animais. Outros estudos recentes também demonstram que a obesidade humana está associada com alterações na função e na estrutura vascular, especialmente em grandes e médias artérias. Estudos epidemiológicos têm confirmado que a obesidade é um fator de risco significativo para o aparecimento de proteinúria e de doença renal terminal em uma população normal. Com o objetivo de determinar as alterações morfológicas relacionadas ao remodelamento cardíaco, vascular e renal em um modelo experimental de obesidade induzida pelo glutamato monossódico (MSG) e os efeitos da metformina sobre estes achados, foram estudados 25 ratos divididos em cinco grupos: controle com 16 e 22 semanas (CON-16 e CON-22); obeso com 16 e 22 semanas (MSG-16 e MSG-22) e obeso + metformina (MET-22) 300mg/Kg/dia por via oral. A caracterização da resistência à insulina foi feita através da medida da insulina plasmática e cálculo do índice de HOMA-IR. As análises morfológicas e quantificação do colágeno miocárdico foram feitos pelo sistema de imagem Image Pro Plus analysis. A pressão arterial sistólica foi levemente maior no grupo MSG-22, adquirindo significância estatística quando comparada com o grupo MSG-16 (1222 vs 1082 mmHg, p<0,05). Por outro lado, o grupo MET-22 mostrou níveis mais baixos de pressão arterial (1181 mmHg), sem alcançar diferença significativa. No grupo de animais obesos, foi observado aumento na relação média-lumen com 16 semanas (39,93,7 vs 30,22,0 %, p<0,05) e com 22 semanas (39,81,3 vs 29,51,2%, p<0,05), que foi reduzida com o uso da metformina (31,50,9%). O depósito de colágeno na área perivascular no ventrículo esquerdo foi significativamente maior no grupo MSG-22 (1,390,06 vs 0,830,06 % no CON-22, p<0,01), sendo atenuado pela metformina (1,020,04%). No rim, a área seccional transversa das arteríolas intrarrenais foi semelhante entre os grupos (18,52,2 no CON-16; 19,93,7 no MSG-16; 18,93,1 no CON-22; 21,81,5 no MSG-22; 20,21,4 no MET-22). Foi observado aumento da área glomerular no grupo MSG-22 (141,34,5 vs 129,50,5 m2), mas sem significância estatística. Em conclusão, nos ratos com obesidade induzida pelo MSG, com resistência à insulina, as alterações cardíacas foram mais proeminentes do que as alterações renais. No coração foram observados sinais de remodelamento vascular hipertrófico nas pequenas artérias intramiocárdicas e evidências de fibrose miocárdica mais proeminente na área perivascular, alterações que foram, pelo menos parcialmente, atenuadas com o uso de metformina durante seis semanas, mostrando que esta droga pode ser benéfica na prevenção de complicações cardíacas, vasculares e renais associadas com a obesidade. / Many evidences show that obesity is associated to structural and functional changes in the heart of human and animal models. Recent studies also show that human obesity is associated with vascular structural and functional modifications, specially at large and medium-sized arteries. Epidemiological studies have confirmed that obesity is a significant risk factor for the development of proteinuria and end-stage renal disease in a normal population. With the objective to determinate morphological changes related to cardiac, vascular and renal remodeling in an experimental model of monosodium glutamate (MSG)-induced obesity and the effect of metformin at this finding. Twenty five rats were studied and divided into five groups: control with 16 e 22 weeks (CON-16 and CON-22); obese with 16 and 22 weeks (MSG-16 e MSG-22), and obese + metformin (MET-22) 300mg/Kg/day per oral. The characterization of insulin resistance was done through measurement of plasma insulin and calculation of HOMA-IR index. The morphological analysis and the quantification of myocardial collagen were carried out by Image Pro Plus analysis system. The systolic blood pressure was slightly higher in MSG-22 group, reaching statistical significance when compared to MSG-16 group (1222 vs 1082 mmHg, p<0.05). On the other hand, the MET-22 group demonstrated lower blood pressure levels (1181 mmHg), without reaching statistical difference. The obese animals presented increase in media-to-lumen ratio with 16 weeks (39.93.7 vs 30.22.0 %, p<0.05) and with 22 weeks (39.81.3 vs 29.51.2%, p<0.05), which was reduced with use of metformin (31.50.9%). The collagen deposition in perivascular area of left ventricle was significantly greater in MSG-22 group (1.390.06 vs 0.830.06 % in CON-22, p<0.01), and attenuated by metformin (1.020.04%). In the kidney, the media cross-sectional area of intrarenal arterioles was similar among the groups (18.52.2 in CON-16; 19.93.7 in MSG-16; 18.93.1 in CON-22; 21.81.5 in MSG-22; 20.21.4 in MET-22). An increase of glomerular area was observed in MSG-22 group (141.34.5 vs 129.50.5 m2), but without statistical significance. In conclusion, rats with MSG-induced obesity and insulin resistance presented more pronounced cardiac changes than renal alterations. In the heart, there were evidences of hypertrophic vascular remodeling were observed in intramyocardial small arteries and perivascular fibrosis. These findings were, at least partially, attenuated by metformin for six weeks, suggesting that this drug may be beneficial for prevention of cardiac, vascular and renal complications associated with obesity.

Introdução: Glicemia de jejum alterada tem sido associada a maior risco de desenvolver diabetes, comparando a indivíduos normoglicêmicos. Apesar de diabetes ser relacionado a aterosclerose e a piores desfechos cardiovasculares, os dados de literatura relacionando glicemia de jejum alterada à doença aterosclerótica são conflitantes. Os objetivos deste trabalho foram determinar (a) a incidência de diabetes em indivíduos com glicemia de jejum normal ou alterada em 1998 após um seguimento de até 12 anos; (b) se a glicemia de jejum alterada em 1998 e/ou diabetes incidente estiveram associados com aterosclerose subclínica no Estudo Longitudinal da Saúde do Adulto (ELSA-Brasil) ou à variável combinada de eventos clínicos não-fatais e escore de cálcio coronariano maior ou igual a 400. Métodos: Avaliamos 1.536 trabalhadores da Universidade de São Paulo, que participaram de um programa de avaliação em 1998 (idade 23-63 anos) e da linha de base do ELSA-Brasil (2008-2010). Apresentamos as taxas de incidência de diabetes brutas e ajustadas para todos os indivíduos e também estratificados por gênero e por índice de massa corpórea (IMC) em 1998. Utilizamos modelos de regressão brutos e ajustados para estimar a associação entre glicemia de jejum alterada em 1998 ou diabetes incidente com a espessura de íntima-média de carótidas (EIMC), escore de cálcio coronariano (CACS, do inglês Coronary Artery Calcium Score) e a variável composta CACS >= 400 ou eventos cardiovasculares incidentes (infarto do miocárdio ou revascularização). Resultados: Encontramos diabetes incidente em 177 indivíduos. A incidência de diabetes em nossa amostra foi de 9,8/1.000 pessoas-ano (Intervalo de confiança de 95% [IC95%]: 7,7-13,6). A incidência foi mais elevada entre os homens (11,2/1.000 pessoas-ano, IC95%: 8,6-15,0) do que entre as mulheres (8,5/1.000 pessoas-ano, IC95%: 5,3-15,3). Glicemia de jejum alterada em 1998 mostrou associação com maior risco de progressão para diabetes ao longo do seguimento (hazard ratio [HR]: 3,17; IC95%: 2,14-4,68) e HR: 7,42; IC95%: 4,75-11,57 para glicemias de jejum entre 100 e 109mg/dl e entre 110 e 125mg/dl, respectivamente). Glicemias entre 110 e 125mg/dl em 1998 foram associadas a maiores valores de EIMC (beta=+0,028; IC95%: 0,003 a 0,053) na linha de base do ELSA-Brasil. Ao excluir da análise aqueles com diabetes incidente, houve associação limítrofe, não-significativa, entre maiores valores de EIMC e glicemia de jejum entre 110 e 125mg/dl em 1998 (?=0,030; IC95%: -0,005 a 0,065). Ambos os níveis de glicemia de jejum alterada em 1998 não se mostraram associados ao CACS ou à variável composta de CACS >= 400 ou eventos cardiovasculares incidentes nos modelos de ajuste completo. Diabetes incidente foi associado a maiores valores de EIMC (em milímetros) (?=0,034; IC95%: 0,015 a 0,053), a CACS >= 400 (Razão de chances=2,84; IC95%: 1,17-6,91) e ao desfecho combinado de CACS >= 400 ou eventos cardiovasculares incidentes (Razão de chances=3,50; IC95%: 1,60-7,65). Conclusões: Glicemia de jejum alterada em 1998, especialmente nos valores mais próximos dos limiares de corte para diabetes, foram associados a maior incidência de diabetes ao longo do seguimento e a maiores valores de EIMC quando da avaliação inicial do ELSA-Brasil. Diabetes incidente entre as avaliações foi associado a maior risco cardiovascular / Introduction: Impaired fasting glucose has been associated with higher risk of incident diabetes, compared to normoglycemic individuals. Although diabetes mellitus is related to atherosclerosis and higher long-term cardiovascular burden, there are conflicting data about the association between impaired fasting glucose and atherosclerotic disease. We aimed (a) to determine diabetes incidence rates in individuals with normal or impaired fasting glucose in 1998 after follow-up of up to 12 years, (b) whether impaired fasting glucose in 1998 and/or incident diabetes were associated with subclinical atherosclerosis in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) or the combined variable of non-fatal clinical events or a coronary calcium score >= 400. Methods: We evaluated 1,536 civil servants from the University of São Paulo, who participated in both 1998 (aged 23-63 years) and ELSA-Brasil baseline (2008-2010) assessments and had complete data. We presented crude and adjusted diabetes incident rates for all individuals and then stratified by sex and body mass index (BMI) in 1998. We used crude and adjusted regression models to estimate the association between impaired fasting glucose in 1998 or incident diabetes and coronary intima-media thickness (CIMT), coronary artery calcium score (CACS) and the composite variable of a CACS?400 or incident cardiovascular events (myocardial infarction or revascularization). Results: We found incident diabetes in 177 individuals. Diabetes incidence in our sample was 9.8/1,000 person-years (95% confidence interval [95%CI]:7.7-13.6). Diabetes incidence was higher in men (11.2/1,000 person-years, 95%CI: 8.6-15.0) than women (8.5/1,000 person-years, 95%CI: 5.3 to 15.3). Impaired fasting glucose in 1998 was associated with a higher risk of progression to diabetes during follow-up (hazard ratio [HR]: 3.17; 95%CI: 2.14-4.68 and HR: 7.42; 95%CI: 4.75-11.57 for a fasting plasma glucose between 100 to 109mg/dl and 110 to 125 mg/dl, respectively). Fasting plasma glucose levels between 110 to 125 mg/dl in 1998 were associated with higher CIMT (beta=+0.028; 95%CI: 0.003 to 0.053) in ELSA-Brasil baseline. Excluding those with incident diabetes, there was a non-significant borderline association between higher CIMT (in mm) and fasting plasma glucose 110 to 125mg/dl (beta=0.030; 95%CI: -0.005 to 0.065). Fasting plasma glucose levels in 1998 were not associated with CACS or the composite variable of a CACS ? 400 or incident cardiovascular events in full-adjusted models. Incident diabetes was associated with higher CIMT (in mm) (beta=0.034; 95%CI: 0.015 to 0.053), CACS >= 400 (OR=2.84; 95%CI: 1.17-6.91) and the combined outcome of a CACS >= 400 or incident cardiovascular event (OR=3.50; 95%CI: 1.60-7.65). Conclusions: Elevated fasting plasma glucose in 1998, especially those near diabetes diagnosis limits were associated with higher diabetes incidence during follow-up and higher CIMT in ELSA-Brasil baseline assessment. Incident diabetes between assessments was associated with higher cardiovascular burden

Les maladies complexes telles que les maladies cardio-Vasculaires (MCV) sont influencées par des facteurs génétiques et environnementaux. L’estimation du risque cardio-Vasculaire chez un individu est généralement évaluée par la sommation des facteurs de risque reconnu des MCV (p. ex. l’âge, le sexe, le tabac, la pression artérielle et le cholestérol). Dernièrement, plusieurs bio-Marqueurs ont été examiné pour leur aptitude à améliorer la prédiction des maladies cardio-Vasculaires au-Delà des facteurs de risques traditionnels. L’intérêt de découvrir de nouveaux loci est incité notamment par les découvertes qui émergent des études d'association pangénomique (GWAS) qui permettent de tester l’association de variation génétique au risque de contracter une maladie commune. Les GWAS ont considérablement amélioré notre connaissance de l'architecture génétique des maladies cardio-Vasculaires, à ce jour plus de 50 variations génétiques sont formellement associées à des maladies cardio-Vasculaires, de même plus de 200 marqueurs génétiques seraient associés à des facteurs de risque cardiovasculaire traditionnels (p. ex. le taux sanguin des lipides, la pression artérielle, l’indice de masse corporelle et le diabète de type 2). Le succès remarquable de ces études d’association, qui a permis l’identification de nombreux bio-Marqueurs, a conduit à une réévaluation des données génétiques dans le but de définir des informations cliniquement utiles pour limiter et mieux prédire les risques de maladies, grâce à une application plus efficace des stratégies de prévention. Dans cette thèse, nous examinons tout d'abord une nouvelle approche pour étudier l'architecture génétique de l'hypertension artérielle (HTA; facteur de risque majeur des maladies cardiovasculaires prématurées), puis nous avons constitué plusieurs modèles pour prédire le risque de développer une maladie coronarienne (MC; type le plus commun de MCV), enfin nous avons déterminé une base génétique commune du principal prédicteur de complications cliniques des maladies coronariennes – l'athérosclérose subclinique - afin d'ajouter une valeur pronostique supplémentaire en plus des scores de risque traditionnels à différents âges. Nous avons estimé l'héritabilité de la première mesure de la pression artérielle systolique (PAS) à ~25%/~45% et à ~30%/~37% pour la pression artérielle diastolique (PAD) chez les sujets d’origine Européenne (N = 8901) et d’origine Africaine (N = 2860) faisant respectivement partie de la cohorte Atherosclerosis Risk in Communities (ARIC), en accord avec les études antérieures. Par ailleurs, nous avons développé un moyen de combiner un score de risque génétique (SRC) – somme des effets génétiques parmi un ensemble de marqueurs – avec une évaluation indépendante du risque clinique, en utilisant un système d'équations log-Linéaire. Nous avons employé cet outil à la prédiction de la maladie coronarienne (MC) dans la cohorte ARIC. L'ajout d'un score de risque génétique (SRG) à un score de risque clinique (SRC) améliore à la fois la discrimination et l'étalonnage des maladies coronariennes dans la cohorte ARIC, et révèle par la même comment cette information génétique influence l'évaluation des risques ainsi que l’approche clinique. Enfin, parmi 1561 cas et 5068 contrôles (de la présence ou non de calcifications coronaires), faisant partie de plusieurs ensembles de données cliniques et génétiques disponibles via la base de données NCBI de Génotypes et Phénotypes (dbGAP), nous avons constaté qu’une augmentation d'un écart-Type dans le score de risque génétique de 49 bio-Marqueurs de MC est associée à 28 % d’augmentation de risque de développer une athérosclérose coronarienne subclinique diagnostiquée à un stade avancé (p=1.43x10-16). Cette augmentation du risque est significative dans chaque catégorie d'âge (de 15 ans en 15 ans) (0,01 > p > 9.4x10-7) et a été remarquablement similaire dans toutes les catégories d'âge (test d'hétérogénéité p = 0.98). (...) / Complex diseases such as cardiovascular disease (CVD) are influenced by both genetic and environmental factors. Estimation of an individual’s cardiovascular risk usually involves measurement of risk factors correlated with risk of CVD (e.g. age, sex, smoking, blood pressure, and total cholesterol). Lately, several biomarkers have been evaluated for their ability to improve prediction of cardiovascular disease beyond traditional risk factors. The interest in novel loci is propelled notably by emerging discoveries from the advent of genome-Wide association studies (GWAS) of genetic variants associated with risk for common diseases. GWAS has greatly enhanced our knowledge of the genetic architecture of cardiovascular disease, yielding over 50 variants confirmed to be associated with CVD to date, as well as over 200 associated with traditional cardiovascular risk factors (e.g. lipids, blood pressure, body mass index, and type 2 diabetes mellitus). This recent and continuing success in discovering increasing numbers of robustly associated genetic markers has led to reassessment of whether genetic data can provide clinically useful information by refining risk prediction and moderating disease risk through a more efficient application of prevention strategies. In this thesis, we first address novel approach to survey the genetic architecture of hypertension (i.e. major risk factor for premature CVD), then construct risk prediction models for coronary artery disease (CAD; i.e. most common type of CVD) and finally establish a common genetic basis of the strongest predictor of clinical complications of CAD, subclinical atherosclerosis, to add incremental prognostic value above traditional risk scores across a range of ages. We show that, for first visit measurements, the heritability is ~25%/~45% and ~30%/~37% for systolic (SBP) and diastolic blood pressure (DBP) in European (N=8,901) and African (N=2,860) ancestry individuals from the Atherosclerosis Risk in Communities (ARIC) cohort, respectively, in accord with prior studies. Then we present a means to combine a polygenic risk score - genetic effects among an ensemble of markers - with an independent assessment of clinical risk using a log-Link function. We apply the method to the prediction of coronary heart disease (CHD) in the ARIC cohort. The addition of a genetic risk score (GRS) to a clinical risk score (CRS) improves both discrimination and calibration for CHD in ARIC and subsequently reveal how this genetic information influences risk assessment and thus potentially clinical management. Finally, Among 1561 cases and 5068 controls, from several clinical and genetic datasets available through the NCBI's database of Genotypes and Phenotypes (dbGAP), we found a one SD increase in the genetic risk score of 49 CAD SNPs was associated with a 28% increased risk of having advanced subclinical coronary atherosclerosis (p = 1.43 x 10-16). This increase in risk was significant in every 15-Year age stratum (.01 > p > 9.4 x 10-7) and was remarkably similar across all age strata (p test of heterogeneity = 0.98). We obtained near identical results and levels of significance when we restricted the genetic risk score to 32 SNPs not associated with traditional risk factors. Accordingly, common variation largely recapitulates the known heritability of blood pressure traits. The vast majority of this heritability varies by chromosome, depending on its length, and is largely concentrated in intronic and intergenic regions of the genome but widely distributed across the common allele frequency spectrum. Respectively, our proposed method to combine genetic information at established susceptibility loci with a nongenetic risk prediction tool facilitates the standardized incorporation of a GRS in risk assessment. (...)

Abstract Type 2 diabetes is one of the fastest increasing lifestyle diseases globally. Its cure is not yet possible, but there is firm evidence from scientific studies that it can effectively be prevented by lifestyle changes. There is limited evidence-based information on the prevention of diabetes in practice. This dissertation offers new desirable information on the issue. The aim of this dissertation study was to describe the prevalence of risk factors for type 2 diabetes and hidden glucose disorders predicting the development of diabetes in the Finnish adult population, and to analyse whether the risk for developing diabetes could be reduced by simple lifestyle counselling. Furthermore, the ability of the Finnish Diabetes Risk Score (FINDRISC) to detect glucose disorders leading to diabetes and undiagnosed diabetes was analysed. In the dissertation data from large Finnish population surveys (the FINRISK 2002 glucose tolerance survey and the FIN-D2D 2004−2005 survey) were analysed. In addition, a prospective design and large-scale intervention were included. We found that obesity and glucose disorders are very common in the Finnish middle-aged population. Prevalence of obesity was 24% for men and 28% for women, that of abnormal glucose metabolism 42% for men and 33% for women, and that of undiagnosed diabetes 9% for men and 7% for men. One quarter of individuals aged 45−64 years were at high risk for diabetes. Lifestyle interventions were offered to more than 10,000 high-risk individuals, 3,379 men and 6,770 women. Of the men, 43% were also at high risk for cardiovascular morbidity and 42% at high risk for cardiovascular mortality estimated through the FRAMINGHAM and SCORE risk engines, respectively. The FINDRISC, originally developed for predicting the risk of development of type 2 diabetes, also predicted the prevalence of diabetes in the population. The effect of lifestyle interventions on weight and its association with glucose tolerance was evaluated in individuals at high risk for diabetes in a one-year follow-up. In total 17.5% of them lost ≥&#160;5% weight. Their relative risk for diabetes decreased 69% compared with the group that maintained their weight. This study shows that FINDRISC predicts prevalent type 2 diabetes. A significant proportion of middle-aged Finnish population has a glucose disorder including undiagnosed type 2 diabetes. Lifestyle interventions in primary health care may promote weight loss, which decreases the risk of diabetes. / Tiivistelmä Diabetes on yksi nopeimmin lisääntyvistä elintapasairauksista maailmassa. Sitä ei vielä voida parantaa, mutta tieteellisissä tutkimuksissa on kiistattomasti osoitettu, että sitä voidaan tehokkaasti ehkäistä elintapamuutoksilla. Diabeteksen ehkäisystä käytännössä on hyvin niukasti tutkimustietoa. Tämä väitöskirja tuo kaivattua lisätietoa aiheesta. Väitöstutkimuksen päätavoitteena oli selvittää diabeteksen riskitekijöiden ja piilevien diabetesta ennakoivien sokerihäiriöiden yleisyyttä suomalaisessa aikuisväestössä. Tämän ohella tavoitteena oli selvittää voidaanko yksinkertaisella elintapaneuvonnalla vähentää sellaisten henkilöiden sairastumisvaaraa, joilla oli suuri riski sairastua diabetekseen. Lisäksi arvioitiin diabetesriskitestin kykyä tunnistaa ennakoivat sokerihäiriöt ja aiemmin tunnistamaton diabetes. Tutkimuksessa käytettiin laajoja suomalaisia väestötutkimusaineistoja: FINRISKI-2002 -tutkimusta, sen alaotosta ja D2D-väestötutkimusta 2004–2005. Mukana oli myös pitkittäisasetelma ja laajamittainen interventio. Tutkimuksen perusteella huomasimme, että lihavuus ja sokerihäiriöt ovat hyvin yleisiä keski-ikäisillä suomalaisilla. Merkittävästi lihavia (BMI&#160;≥&#160;30 kg/m2) oli 24&#160;% miehistä ja 28&#160;% naisista ja poikkeava sokeriaineenvaihdunta oli 42&#160;%:lla miehistä ja 33&#160;%:lla naisista. Tunnistamaton diabetes oli 9&#160;%:lla miehistä ja 7&#160;%:lla naisista. Suuressa diabetekseen sairastumisvaarassa oli neljäsosa 45−64-vuotiaista. Interventioon otettiin yli 10&#160;000 suuressa diabeteksen sairastumisriskissä olevaa henkilöä, 3&#160;379 miestä ja 6&#160;770 naista. Miehistä 43&#160;% oli suuressa sairastumisvaarassa myös sydän- ja verisuonisairauteen ja 42&#160;% suuressa kuolemanvaarassa Framingham- ja SCORE-riskilaskureilla arvioituna. Tyypin 2 diabeteksen sairastumisriskin arviointiin kehitetty Riskitesti ennusti hyvin myös diabeteksen esiintymistä väestössä. Elintapainterventioiden vaikutusta painoon ja sokeriaineenvaihduntaan analysoitiin vuoden seurannassa sellaisilla henkilöillä, joilla oli suuri diabetesriski. Paino laski 5&#160;% tai enemmän 17,5&#160;%:lla, jolloin sairastumisriski diabetekseen väheni 69&#160;% verrattuna ryhmään, jonka paino ei muuttunut. Tutkimuksen perusteella lihavuus, sokerihäiriöt ja tunnistamaton diabetes ovat yleisiä keski-ikäisessä väestössä. Riskitesti on hyvä työkalu myös diabeteksen seulonnassa. Perusterveydenhuollossa tarjottavalla elintapaneuvonnalla voidaan saada aikaan laihtuminen, joka vähentää sairastumisvaaraa diabetekseen.

Sepsis remains the second largest killer in the Intensive Care Unit (ICU), giving rise to a significant economic burden ($17b per annum in the US, 0.3% of the gross domestic product). The aim of the work described in this thesis is to improve the estimation of severity in this population, with a view to improving the allocation of resources. A cohort of 2,143 adult patients with sepsis and hypotension was identified from the MIMIC-II database (v2.26). The implementation of state-of-the-art models confirms the superiority of the APACHE-IV model (AUC=73.3%) for mortality prediction using ICU admission data. Using the same subset of features, state-of-the art machine learning techniques (Support Vector Machines and Random Forests) give equivalent results. More recent mortality prediction models are also implemented and offer an improvement in discriminatory power (AUC=76.16%). A shift from expert-driven selection of variables to objective feature selection techniques using all available covariates leads to a major gain in performance (AUC=80.4%). A framework allowing simultaneous feature selection and parameter pruning is developed, using a genetic algorithm, and this offers similar performance. The model derived from the first 24 hours in the ICU is then compared with a “dynamic” model derived over the same time period, and this leads to a significant improvement in performance (AUC=82.7%). The study is then repeated using data surrounding the hypotensive episode in an attempt to capture the physiological response to hypotension and the effects of treatment. A significant increase in performance (AUC=85.3%) is obtained with the static model incorporating data both before and after the hypotensive episode. The equivalent dynamic model does not demonstrate a statistically significant improvement (AUC=85.6%). Testing on other ICU populations with sepsis is needed to validate the findings of this thesis, but the results presented in it highlight the role that data mining will increasingly play in clinical knowledge generation.