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Non-communicable diseases stratification and integration : a clinical approach
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RESUMO: A prevalência alarmante de doenças crónicas não comunicáveis e, em particular, da diabetes continua a aumentar. A International Diabetes Federation (IDF) estimou um aumento de 50% da prevalência da diabetes até 2040. Desde a descoberta da insulina e a disponibilização de várias classes de medicamentos antidiabéticos orais, o impacto da diabetes deve-se principalmente ao aparecimento de complicações crónicas. As complicações da diabetes é um
dos principais problemas de saúde da população, apesar de todo o investimento feito anualmente, contribuindo para a ameaça da sustentabilidade dos cuidados de saúde. Nos últimos 15 anos, o aumento dos custos dos cuidados de saúde atribuíveis à diabetes atingiu 315%. É importante notar que a diabetes tem características comuns com outras doenças crónicas não comunicáveis e que também partilham fatores de risco (hipertensão, dislipidemia e obesidade).
A diabetes é definida como uma alteração no metabolismo ou ação da insulina que cursa com o aumento dos valores de glicose no sangue. Assim, o diagnóstico é baseado em valores de corte de glicémia definidos operacionalmente. No entanto, há pessoas com diabetes que nunca desenvolvem complicações enquanto as pessoas sem diabetes podem já mostrá-las.
Um dos fatores que tem sido apontado para explicar o cenário preocupante acima mencionado é que a diabetes é uma condição heterogénea. É cada vez mais reconhecido que é possível que existam vários subtipos desta doença, para além dos classicamente identificados. Mas, além disso, podem existir outros fatores que contribuem para a heterogeneidade da diabetes: 1) a insulina tem várias ações no nosso organismo que intervêm no metabolismo de outros substratos, impactando assim em fatores de risco da diabetes e das suas complicações para além da glicemia; 2) o metabolismo da glicose ocorre de forma integrada com outros mecanismos, bem como com o metabolismo de outros substratos, tais como os lípidos. Assim, poder-se-á perguntar: a diabetes é apenas uma questão de hiperglicemia?
Resolver a heterogeneidade da diabetes e identificar subgrupos para os quais as intervenções de cuidados de saúde podem ser definidas de uma forma direcionada e específica é um enorme desafio. Nos últimos anos, o conceito de medicina de precisão traz a oportunidade de dar o tratamento certo à pessoa certa no momento certo. O recente desenvolvimento do poder e memória computacionais, e de algoritmos matemáticos que podem resolver problemas que envolvem a análise de grandes quantidades de dados, está a ajudar a tornar a medicina de precisão uma realidade. Uma abordagem baseada em dados tem a vantagem de nos permitir olhar para estes problemas sem pressupostos e, assim, podemos ter como objetivo
compreender melhor naturalmente estas condições.
Neste trabalho aplicamos a metodologia de Systems Medicine à análise de coortes detalhadamente fenotipados, a fim de estratificar a população no que diz respeito a doenças crónicas não comunicáveis, e mais especificamente à diabetes e aos seus fatores de risco, no contexto de uma abordagem de medicina de precisão. Desta forma, pode ser construído um modelo de diagnóstico e/ou abordagem integrado que tenha aplicação na prática clínica
corrente e apoie a inversão do impacto que as doenças crónicas não comunicáveis atualmente têm sobre as pessoas afetadas, as suas famílias e, em última análise, sobre a sociedade.
Em primeiro lugar, colocamos a hipótese de que os mecanismos fisiopatológicos das doenças metabólicas determinam quantitativamente parâmetros clínicos, relacionados com a glicose, insulina e metabolismo dos ácidos gordos livres, explicando perfis dismetabólicos distintos. Utilizando a análise de clusters, procurámos estratificar a glucose no sangue contextualizada na multidimensionalidade metabólica e extrair conhecimentos fisiopatológicos do perfil metabólico. Realizámos uma análise de clusters para estratificar 974 indivíduos (coorte PREVADIAB2) com normoglicemia, pré-diabetes, ou diabetes não tratada. O algoritmo foi informado por idade, antropometria, e parâmetros metabólicos (glucose, insulina, peptídeo C, e ácidos gordos livres durante o teste de tolerância à glucose oral. Para a definição do perfil dos clusters, utilizámos adicionalmente índices de mecanismos (dis)metabólicos. Encontrámos uma heterogeneidade proeminente dentro de dois clusters, representando principalmente o normometabolismo (Cluster-I) ou a resistência à insulina e a doença de fígado gordo não alcoólico (Cluster-II). Isto foi ilustrado por subclusters que mostraram uma prevalência semelhante de NAFLD, mas diferenciados por glicemia, FFA e taxa de filtração glomerular (GFR) (Cluster-II). Os subclusters com glicemia e FFA semelhantes mostraram uma clearance e secreção de insulina distintos (Cluster-I). É importante notar que este trabalho revelou que a heterogeneidade da diabete tipo 2 (T2D) pode ser capturada por um perfil metabólico detalhado bem como pelos mecanismos que lhe estão subjacentes, a que no conjunto chamamos pegada metabólica (metabolic footprint).
Considerando que a insulina também afeta o metabolismo lipídico, e que a glicose e o metabolismo lipídico estão altamente interligados, colocámos então a hipótese de que os indivíduos estratificados pela resistência à insulina, secreção de insulina e clearance da insulina, três mecanismos fisiopatológicos chave na diabetes, têm perfis distintos de glicose e lipídos no sangue. O nosso objetivo foi avaliar o impacto dos mecanismos associados às condições dismetabólicas tanto na glicémia, como no metabolismo lipídico. Para este fim, realizámos uma análise de clusters com a coorte PREVADIAB, informada por índices dos mecanismos-chave acima mencionados. Adicionalmente, os clusters foram perfilados também com outros índices, incluindo índices de resistência à insulina específicos de órgão, bem como várias espécies de lípidos. Verificámos que sujeitos homogéneos nos mecanismos têm perfis de glicose distintos, mas também de lípidos. Estes perfis podem explicar melhor as complicações da diabetes.
Verificámos adicionalmente que homens e mulheres tinham padrões de clusters semelhantes, no entanto, os centróides dos clusters eram diferentes. Isto poderia sugerir e explicar riscos distintos para a diabetes e complicações da diabetes de ambos os sexos. No entanto, pode também sugerir que se tenha de abordar as mulheres e os homens separadamente, considerando diferentes valores de corte para a doença metabólica. Estas hipóteses devem ser avaliadas em trabalhos futuros. Finalmente, o nosso objetivo foi prever mutações genéticas através do conhecimento de poucos parâmetros do meio metabólico, utilizando modelos de Machine Learning (ML).
Colocámos a hipótese de que os modelos de ML podem superar o Dutch Lipid Score, o padrão de ouro para o rastreio de casos de hipercolesterolémia familiar, para detetar mutações genéticas causadoras desta condição. Utilizámos 3 algoritmos de ML informados por idade, LDL-c e TG e comparámo-los com os resultados obtidos utilizando o Dutch Lipid Score. Os nossos resultados mostraram que, considerando a idade e alguns elementos do meio metabólico, é possível prever uma mutação genética, sugerindo que podemos prever um fator etiológico (mutação genética), conhecendo o meio metabólico.
Os resultados deste trabalho sugerem que a diabetes é uma condição heterogénea. No entanto, deve ser abordada num contexto mais amplo, considerando outros fatores patológicos que estão intimamente ligados aos níveis glicémicos e que são também afetados pela deficiência absoluta ou relativa de insulina.
Propomos o Modelo Integrativo para a abordagem da diabetes. Este modelo permite não só abordar a heterogeneidade da condição, mas também contextualiza a diabetes, ou hiperglicemia resultante da alteração do metabolismo da insulina, numa perspetiva mais ampla do dismetabolismo. Considera, juntamente com a glicemia, outros fatores que também podem ser afetados e contribuir para o aparecimento da diabetes e complicações da diabetes.
Inspirados pelo Modelo de Paleta, propomos que devem ser considerados 3 planos multidimensionais (etiologia; milieu; e mecanismos) ao longo do tempo. Sugerimos que é possível prever onde uma pessoa se encontra em cada um dos planos mencionados, conhecendo a sua posição nos outros. No seu conjunto, estes planos colocam o indivíduo num caminho para o dismetabolismo, e permitirão prever as complicações da diabetes. Este modelo permitirá identificar as intervenções mais adequadas para cada pessoa, ou seja, numa abordagem de medicina de precisão.
The alarming prevalence of non-communicable diseases (NCDs), and particularly diabetes prevalence, continues to increase. International Diabetes Federation estimated an increase of 50% of diabetes prevalence by 2040. Since the discovery of insulin and the availability of various classes of oral antidiabetic drugs, the impact of diabetes is mainly due to the onset of its chronic complications. The latter remains one of the populations' major public health problems despite all the investment made each year, contributing to the threat of healthcare sustainability. In the last 15 years, the increase in health care costs attributable to diabetes reached 315%. Importantly, diabetes has common features with other NCDs and they also share risk factors (dyslipidemia, hypertension, obesity). Diabetes is defined as an alteration in the metabolism or action of insulin that progresses with increased blood glucose values. Thus, the diagnosis is based on operationally defined blood glucose cut-offs. Nevertheless, there are people with diabetes who never develop complications while people without diabetes may already have them. One of the factors that has been pointed out to explain the above-mentioned worrying scenario is diabetes heterogeneity. It is increasingly recognized that there are several subtypes of this condition in addition to the classically recognized ones. Still, there may be other factors contributing to this situation: 1) insulin has several actions in our body intervening in the metabolism of other substrates, thus impacting on diabetes complications risk factors other than glycemia; 2) glucose metabolism occurs in an integrated manner with other mechanisms as well as the metabolism of other substrates, such as lipids. Therefore, one might ask, is diabetes just about hyperglycemia? Resolving the heterogeneity of diabetes and identifying subgroups for which health care interventions can be defined in a targeted and specific manner is an enormous challenge. In recent years the concept of precision medicine has raised the opportunity to give the right medicine to the right person at the right time. The recent development of memory computing power and mathematical algorithms that can solve problems involving the analysis of large amounts of data is helping to make precision medicine a reality. A data-driven approach has the advantage of allowing us to look at these problems without a pre-assumption and thus we can aim to better understand these conditions. In this work we apply System Medicine methodology to analyze cohorts based on detailed phenotyping, to stratify the population with respect to NCDs, more specifically diabetes and their risk factors, to be useful to precision medicine approaches. In this way, a model of integrated diagnosis and approach can be built that has application in current clinical practice and supports reversing the impact that currently NCD’s have on the affected persons, their families and ultimately in society. We first hypothesize that the pathophysiological mechanisms of metabolic diseases quantitatively determine clinical parameters related to glucose, insulin, and free fatty acid metabolism, explaining distinct dysmetabolic profiles. We aimed at stratifying blood glucose within the metabolic multidimensionality, using cluster analysis, and extract pathophysiological insights from the metabolic profile. We performed cluster analysis to stratify 974 subjects (PREVADIAB2 cohort) with normoglycemia, pre-diabetes, or untreated diabetes. The algorithm was informed by age, anthropometry, and metabolic means (glucose, insulin, C-peptide, and free fatty acid (FFA) during oral glucose tolerance test (OGTT)). For cluster profiling, we additionally used indices of metabolic mechanisms. We found prominent heterogeneity within two optimal clusters, mainly representing normometabolism (Cluster-I) or insulin resistance and non-alcoholic fatty liver disease (NAFLD) (Cluster-II), with higher granularity. This was illustrated by subclusters showing a similar prevalence of NAFLD but differentiated by glycemia, FFA, and glomerular filtration rate (GFR) (Cluster-II). Subclusters with similar glycemia and FFA showed dissimilar insulin clearance and secretion (Cluster-I). Importantly, this work revealed that type 2 diabetes (T2D) heterogeneity can be captured by a thorough metabolic milieu and mechanisms profiling the metabolic footprint. Considering that insulin also affects lipid metabolism, and that glucose and lipid metabolism are highly interconnected, we hypothesized that subjects stratified by three key pathophysiological mechanisms of diabetes (insulin resistance, insulin secretion, and insulin clearance) have distinct glucose and lipid profiles. We aimed to assess the impact of mechanisms associated with dysmetabolic conditions on both glucose and lipid metabolism. To this end, we performed a cluster analysis with the PREVADIAB cohort, informed by surrogate indexes of the abovementioned key mechanisms and profiled by other organ-specific indices of insulin resistance, as well as several key lipid species. We found that subjects homogenous in their mechanism have distinct glucose but also lipid profiles. Together these profiles might better explain diabetes's complications. Interestingly, we additionally found that men and women had similar clusters patterns; nonetheless the cluster centroids were different. This might represent a distinct risk for diabetes and diabetes complications of both genders. Nonetheless, it might additionally suggest that one might have to approach women and men separately, considering different cut-offs for the disease. These hypotheses should be evaluated in future work. Finally, we aimed to predict genetic mutations by knowing few parameters of the metabolic milieu using Machine Learning (ML) models. We hypothesize that ML models can outperform the Dutch Lipid Score, the gold standard for screening familial hypercholesterolemia (FH) cases, to detect genetic mutations of familial hypercholesterolemia. We used 3 ML algorithms informed by age, low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG) and compared them with the results obtained using the Dutch Lipid Score. Our results showed that by considering age and some elements of the milieu it is possible to predict a gene mutation, suggesting that we can predict an etiological factor (gene mutation) by knowing the metabolic milieu. Altogether this work suggests that diabetes is an heterogenous condition. Nonetheless it should be approached in a broader context, considering other pathological factors that are intimately connected with glycemic levels and are also impacted by insulin absolute or relative deficiency. We propose the Integrative Model for approaching diabetes. This model allows not only to address the heterogeneity of the condition but furthermore it contextualizes diabetes, or hyperglycemia resulting from altered insulin metabolism, in a broader perspective of dysmetabolism. It considers, along with glycemia, other factors that may also be affected and contribute to the onset of diabetes and diabetes complications. Inspired by the Palette Model, we propose that three multidimensional planes (etiology, milieu, and mechanisms) should be considered through time. We propose that we can predict where a person is in each of the planes by knowing the others, and that they ultimately place a subject on a path to dysmetabolism, and will allow to predict diabetes complications, and identify the most appropriate interventions for each person. In conclusion the Integrative Model will allow a precision medicine approach to diabetes and NCDs.
The alarming prevalence of non-communicable diseases (NCDs), and particularly diabetes prevalence, continues to increase. International Diabetes Federation estimated an increase of 50% of diabetes prevalence by 2040. Since the discovery of insulin and the availability of various classes of oral antidiabetic drugs, the impact of diabetes is mainly due to the onset of its chronic complications. The latter remains one of the populations' major public health problems despite all the investment made each year, contributing to the threat of healthcare sustainability. In the last 15 years, the increase in health care costs attributable to diabetes reached 315%. Importantly, diabetes has common features with other NCDs and they also share risk factors (dyslipidemia, hypertension, obesity). Diabetes is defined as an alteration in the metabolism or action of insulin that progresses with increased blood glucose values. Thus, the diagnosis is based on operationally defined blood glucose cut-offs. Nevertheless, there are people with diabetes who never develop complications while people without diabetes may already have them. One of the factors that has been pointed out to explain the above-mentioned worrying scenario is diabetes heterogeneity. It is increasingly recognized that there are several subtypes of this condition in addition to the classically recognized ones. Still, there may be other factors contributing to this situation: 1) insulin has several actions in our body intervening in the metabolism of other substrates, thus impacting on diabetes complications risk factors other than glycemia; 2) glucose metabolism occurs in an integrated manner with other mechanisms as well as the metabolism of other substrates, such as lipids. Therefore, one might ask, is diabetes just about hyperglycemia? Resolving the heterogeneity of diabetes and identifying subgroups for which health care interventions can be defined in a targeted and specific manner is an enormous challenge. In recent years the concept of precision medicine has raised the opportunity to give the right medicine to the right person at the right time. The recent development of memory computing power and mathematical algorithms that can solve problems involving the analysis of large amounts of data is helping to make precision medicine a reality. A data-driven approach has the advantage of allowing us to look at these problems without a pre-assumption and thus we can aim to better understand these conditions. In this work we apply System Medicine methodology to analyze cohorts based on detailed phenotyping, to stratify the population with respect to NCDs, more specifically diabetes and their risk factors, to be useful to precision medicine approaches. In this way, a model of integrated diagnosis and approach can be built that has application in current clinical practice and supports reversing the impact that currently NCD’s have on the affected persons, their families and ultimately in society. We first hypothesize that the pathophysiological mechanisms of metabolic diseases quantitatively determine clinical parameters related to glucose, insulin, and free fatty acid metabolism, explaining distinct dysmetabolic profiles. We aimed at stratifying blood glucose within the metabolic multidimensionality, using cluster analysis, and extract pathophysiological insights from the metabolic profile. We performed cluster analysis to stratify 974 subjects (PREVADIAB2 cohort) with normoglycemia, pre-diabetes, or untreated diabetes. The algorithm was informed by age, anthropometry, and metabolic means (glucose, insulin, C-peptide, and free fatty acid (FFA) during oral glucose tolerance test (OGTT)). For cluster profiling, we additionally used indices of metabolic mechanisms. We found prominent heterogeneity within two optimal clusters, mainly representing normometabolism (Cluster-I) or insulin resistance and non-alcoholic fatty liver disease (NAFLD) (Cluster-II), with higher granularity. This was illustrated by subclusters showing a similar prevalence of NAFLD but differentiated by glycemia, FFA, and glomerular filtration rate (GFR) (Cluster-II). Subclusters with similar glycemia and FFA showed dissimilar insulin clearance and secretion (Cluster-I). Importantly, this work revealed that type 2 diabetes (T2D) heterogeneity can be captured by a thorough metabolic milieu and mechanisms profiling the metabolic footprint. Considering that insulin also affects lipid metabolism, and that glucose and lipid metabolism are highly interconnected, we hypothesized that subjects stratified by three key pathophysiological mechanisms of diabetes (insulin resistance, insulin secretion, and insulin clearance) have distinct glucose and lipid profiles. We aimed to assess the impact of mechanisms associated with dysmetabolic conditions on both glucose and lipid metabolism. To this end, we performed a cluster analysis with the PREVADIAB cohort, informed by surrogate indexes of the abovementioned key mechanisms and profiled by other organ-specific indices of insulin resistance, as well as several key lipid species. We found that subjects homogenous in their mechanism have distinct glucose but also lipid profiles. Together these profiles might better explain diabetes's complications. Interestingly, we additionally found that men and women had similar clusters patterns; nonetheless the cluster centroids were different. This might represent a distinct risk for diabetes and diabetes complications of both genders. Nonetheless, it might additionally suggest that one might have to approach women and men separately, considering different cut-offs for the disease. These hypotheses should be evaluated in future work. Finally, we aimed to predict genetic mutations by knowing few parameters of the metabolic milieu using Machine Learning (ML) models. We hypothesize that ML models can outperform the Dutch Lipid Score, the gold standard for screening familial hypercholesterolemia (FH) cases, to detect genetic mutations of familial hypercholesterolemia. We used 3 ML algorithms informed by age, low density lipoprotein cholesterol (LDL-c), high density lipoprotein cholesterol (HDL-c) and triglycerides (TG) and compared them with the results obtained using the Dutch Lipid Score. Our results showed that by considering age and some elements of the milieu it is possible to predict a gene mutation, suggesting that we can predict an etiological factor (gene mutation) by knowing the metabolic milieu. Altogether this work suggests that diabetes is an heterogenous condition. Nonetheless it should be approached in a broader context, considering other pathological factors that are intimately connected with glycemic levels and are also impacted by insulin absolute or relative deficiency. We propose the Integrative Model for approaching diabetes. This model allows not only to address the heterogeneity of the condition but furthermore it contextualizes diabetes, or hyperglycemia resulting from altered insulin metabolism, in a broader perspective of dysmetabolism. It considers, along with glycemia, other factors that may also be affected and contribute to the onset of diabetes and diabetes complications. Inspired by the Palette Model, we propose that three multidimensional planes (etiology, milieu, and mechanisms) should be considered through time. We propose that we can predict where a person is in each of the planes by knowing the others, and that they ultimately place a subject on a path to dysmetabolism, and will allow to predict diabetes complications, and identify the most appropriate interventions for each person. In conclusion the Integrative Model will allow a precision medicine approach to diabetes and NCDs.
Descrição
Palavras-chave
Diabetes NDCs heterogeneity clusters dysmetabolism machine-learning integrative