O futuro da Ciência e dos Dados Científicos

Transcription

1 IX Congresso Nacional O futuro da Ciência e dos Dados Científicos Prof. Luis A. Moreno

2 Determinants and risk factors for cardiovascular diseases (Tzoulaki I et al. Circulation 2016; 133: )

3 Adjusted Hazard Ratios for Major Cardiovascular Events According to the Level of Fresh Fruit Consumption in China (Du H et al. NEJM 2016; 374: )

4 Worldwide prevalence of overweight and obesity in children and adolescents Moreno, Pigeot, Ahrens (eds.): Epidemiology of overweight and obesity in children and adolescents. Springer, New York 2011

5 Trends in age-standardised prevalence of BMI categories in female children and adolescents by region

6 Trends in age-standardised prevalence of BMI categories in male children and adolescents by region

7 Prevalence of severe obesity in children in recent surveys in different countries (Moreno LA. Nature Rev Endocrinol 2018; 14: )

8 Critical periods in the development of obesity (González-Muniesa P et al. Nature Rev Disease Primers 2017; 3: 17034)

9 Nutrition through the life course - proposed causal links (Branca F et al. BMJ 2015; 351 (Supl 1): 27-31)

10 Improving nutrition through the life course (Branca F et al. BMJ 2015; 351 (Supl 1): 27-31)

11 From IDEFICS to I.Family (Ahrens W et al. Int J Epidemiol 2017; 46: j)

12 Objetivos

13 Objetivos

14 From IDEFICS to I.Family (Ahrens W et al. Int J Epidemiol 2017; 46: j)

15 Milestones in the history of meta-analysis (Gurevitch J et al. Nature 2018; 555: )

16 Bone mineral density in vegans/vegetarians vs omnivores Lumbar spine Femoral neck Whole body

17 Usability of systematic reviews (Hoffmann TC et al. BMJ 2017; 357: j2998)

18 Data-driven knowledge production via commons (Lau E et al. Circulation 2016; 134: )

19 Major milestones and studies of established and emerging cardiovascular disease risk factors (Tzoulaki I et al. Circulation 2016; 133: )

20

21 Genetics of childhood obesity (Bradfield JP et al. Nature Genet 2012; 44: )

22 Manhattan Plot of the meta-analysis of childhood obesity GWAS (5,530 cases and 8,318 controls), with each locus achieving genome wide significance (P < 5x10-8 ) indicated (Bradfield JP et al. Nature Genet 2012; 44: )

23 New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism (Horikoshi M et al. Nat Genet 2013; 45: 76-82)

24 Regional plots of seven loci associated with birth weight: CCNL1 (a), ADCY5 (b), HMGA2 (c), CDKAL1 (d), 5q11.2 (e), LCORL (f) and ADRB1 (g) (Horikoshi M et al. Nat Genet 2013; 45: 76-82)

25 New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism Seven loci associated with birth weight: CCNL1 ADCY5 HMGA2 CDKAL1 5q11.2 LCORL ADRB1 Type 2 diabetes Adult height Type 2 diabetes Adult height Blood pressure (Horikoshi M et al. Nat Genet 2013; 45: 76-82)

26

27 Regional differences in prevalence of raised blood pressure among women aged years and years in if every region had the same mean SBP and DBP, equal to the global age-sex-specific mean in 2010 (NCD-RisC. Int J Epidemiol 2018, March 19)

28 Regional differences in prevalence of raised blood pressure among men aged years and years in if every region had the same mean SBP and DBP, equal to the global age-sex-specific mean in 2010 (NCD-RisC. Int J Epidemiol 2018, March 19)

29 Big Data Every day, our world produces 2.5 quintillion bytes of data, including data from health care and biomedicine The whole-genome sequence of a patient can reach 100 gigabytes in size A cardiology division may perform >1000 echocardiograms per month, totaling >200 gigabytes of data The term biomedical big data has been coined to describe healthcare and biomedical datasets that reach remarkable scale, volume or complexity

30 Machine Learning and Big Data (Beam AL, Kohane IS. JAMA 2018, March 12)

31 Six V s of big data (Andreu-Pérez J et al. IEEE J Biomed Health Inform 2015; 19: )

32 Biomedical big data sources Functional phenotypes: Heart rate, physical activities, lifestyle Molecular profiles: Large-scale omics data on genes, transcripts, proteins and metabolites Medical records: Physician s notes, laboratory test results, disease, treatment and epidemiology Literature knowledge: PubMed boasts a treasure trove of >2.2 million cardiovascular-related articles, and it is estimated that there is a new publication every 2.7 minutes

33 Outline of the OMICS approach for studying disease mechanisms (Andreu-Pérez J et al. IEEE J Biomed Health Inform 2015; 19: )

34 Nutrigenomics core competencies and their relevance (Malkaram SA et al. Adv Nutr 2012; 3: )

35 Biomedical big data sources Data-driven knowledge can potentially unveil hidden information, predict process outcome, and provide insights on implementing robust processes Descriptive data mining Pattern discovery Various algorithms have been developed that can mine process data to discover patterns (i.e. relations) among the features of the different runs that satisfy certain properties Clustering Clustering methods can be differentiated along multiple dimensions, one of them being the top-down (partitional) or bottom-up (agglomerative) nature of the algorithm (Charaniya S et al. Trends Biotechnol 2008; 26: )

36 Data mining in analysis of genetic diseases relationship 697 children: 196 normal-weight, 139 overweight and 362 obesity 44 candidate SNPs for obesity Weighted genetic score by predictive data mining analysis Adjusted logistic regression model using the score showed 10.41% explanation of the BMI variability for the complete model, including pubertal status and city of origin 4.04% of the BMI variability explained by the genetic score (Anguita A et al. In press)

37 10-Year Coronary Event Rates, According to Lifestyle and Genetic Risk in 3 Prospective Cohorts (Khera AV et al. NEJM 2016; 375: )

38 Mediterranean diet and the prevention of cardiovascular disease (Estruch R et al. NEJM 2013; 369: )

39 Kaplan Meier Estimates of the Incidence of Outcome Events in the Total Study Population (Estruch R et al. NEJM 2013; 369: )

40 Adherence to Mediterranean Diet in children and adolescents (Idelson PI et al. Nutr Metab Cardiovasc Dis 2017; 27: )

41 Adherence to the Mediterranean Diet according to the KIDMED index (Idelson PI et al. Nutr Metab Cardiovasc Dis 2017; 27: )

42 Adherence to the Mediterranean Diet according to other indices (Idelson PI et al. Nutr Metab Cardiovasc Dis 2017; 27: )

43 Prevention of NCD by nutritional interventions

44

45 Key components of a sustainable diet (FAO. Sustainable diets and biodiversity. Rome: FAO, 2012)

46 The grid of sustainable food system (FAO. Sustainable diets and biodiversity. Rome: FAO, 2012)

47 The double pyramid

48 Dietary guidelines and health (Mozaffarian D et al. BMJ 2018; 60: k822)

49 The future (Ioannidis JPA et al. Am J Clin Nutr 2016; 103: )

50

51 Agradecimientos