Non-Communicable Diseases (NCDs) are among the most pressing global health problems of the twenty-first century. Their rising incidence and prevalence is linked to severe morbidity and mortality, and ...they are putting economic and managerial pressure on healthcare systems around the world. Moreover, NCDs are impeding healthy aging by negatively affecting the quality of life of a growing number of the global population. NCDs result from the interaction of various genetic, environmental and habitual factors, and cluster in complex ways, making the complex identification of resulting phenotypes not only difficult, but also a top research priority. The degree of complexity required to interpret large patient datasets generated by advanced high-throughput functional genomics assays has now increased to the point that novel computational biology approaches are essential to extract information that is relevant to the clinical decision-making process. Consequently, system-level models that interpret the interactions between extensive tissues, cellular and molecular measurements and clinical features are also being created to identify new disease phenotypes, so that disease definition and treatment are optimized, and novel therapeutic targets discovered. Likewise, Systems Medicine (SM) platforms applied to extensively-characterized patients provide a basis for more targeted clinical trials, and represent a promising tool to achieve better prevention and patient care, thereby promoting healthy aging globally. The present paper: (1) reviews the novel systems approaches to NCDs; (2) discusses how to move efficiently from Systems Biology to Systems Medicine; and (3) presents the scientific and clinical background of the San Raffaele Systems Medicine Platform.
Furthermore, compared with FF 100 ?g alone, FF/VI 100/25 ?g significantly improved the adjusted mean wm FEV1 at day 168 by 120 ml (95% CI: 70, 170; p < 0.001) (Table 2). p.566, Table 2. footnote: ...CRQ-SAS dyspnoea domain is scaled from 0 to 7, with 0 indicating no impairment CRQ-SAS dyspnoea domain is scaled from 1 to 7, with 1 indicating no impairment p.567, Table 3. n-value for ocular effects in FF 100 group: 1 (<1) 2 (<1) The authors apologise for any inconvenience caused.
The prevalences of obstructive and restrictive spirometric phenotypes, and their relation to early-life risk factors from childhood to young adulthood remain poorly understood. The aim was to explore ...these phenotypes and associations with well-known respiratory risk factors across ages and populations in European cohorts.
We studied 49 334 participants from 14 population-based cohorts in different age groups (≤10, >10-15, >15-20, >20-25 years, and overall, 5-25 years). The obstructive phenotype was defined as forced expiratory volume in 1 s (FEV
)/forced vital capacity (FVC) z-score less than the lower limit of normal (LLN), whereas the restrictive phenotype was defined as FEV
/FVC z-score ≥LLN, and FVC z-score <LLN.
The prevalence of obstructive and restrictive phenotypes varied from 3.2-10.9% and 1.8-7.7%, respectively, without clear age trends. A diagnosis of asthma (adjusted odds ratio (aOR=2.55, 95% CI 2.14-3.04), preterm birth (aOR=1.84, 1.27-2.66), maternal smoking during pregnancy (aOR=1.16, 95% CI 1.01-1.35) and family history of asthma (aOR=1.44, 95% CI 1.25-1.66) were associated with a higher prevalence of obstructive, but not restrictive, phenotype across ages (5-25 years). A higher current body mass index (BMI was more often observed in those with the obstructive phenotype but less in those with the restrictive phenotype (aOR=1.05, 95% CI 1.03-1.06 and aOR=0.81, 95% CI 0.78-0.85, per kg·m
increase in BMI, respectively). Current smoking was associated with the obstructive phenotype in participants older than 10 years (aOR=1.24, 95% CI 1.05-1.46).
Obstructive and restrictive phenotypes were found to be relatively prevalent during childhood, which supports the early origins concept. Several well-known respiratory risk factors were associated with the obstructive phenotype, whereas only low BMI was associated with the restrictive phenotype, suggesting different underlying pathobiology of these two phenotypes.
Aims: The follow-up of the ECLIPSE study, a prospective longitudinal study to identify and define parameters that predict disease progression over 3 years in chronic obstructive pulmonary disease ...(COPD), allows the examination of the effect of body composition changes on COPD-related outcomes. Methods: Body composition and health status were established in 2,115 COPD patients, 327 smoking and 239 nonsmoking controls at baseline and 3 years, while mortality was recorded in year 2 and 3 in the COPD patients. Associations between fat free mass index (FFMI) and fat mass index (FMI) changes to deterioration in health status and mortality were determined. Results: Change in FFMI and FMI over 3 years was small and comparable between the groups. Underweight and obese patients had the worst health status. Worsening health status was associated with FFMI decrease in underweight patients and FMI increase in overweight/obese patients. While overweight patients had the lowest mortality, FFMI or FMI decrease was associated with a higher mortality. Conclusion: Changes in body composition over 3 years were small and comparable in COPD patients and control subjects. Nevertheless, muscle mass decline in underweight and fat mass increase in overweight/obese patients is associated with worsening health status. Overweight is associated with decreased mortality, but muscle mass and fat mass decline are detrimental for mortality.
Short-acting β
2
-agonist bronchodilators are the most common medications used in treating chronic obstructive pulmonary disease (COPD). Genetic variants determining bronchodilator responsiveness ...(BDR) in COPD have not been identified.
We performed a genome-wide association study (GWAS) of BDR in 5789 current or former smokers with COPD in one African American and four white populations. BDR was defined as the quantitative spirometric response to inhaled β
2
-agonists. We combined results in a meta-analysis.
In the meta-analysis, SNPs in the genes
KCNK1
(P=2.02×10
−7
) and
KCNJ2
(P=1.79×10
−7
) were the top associations with BDR. Among African Americans, SNPs in
CDH13
were significantly associated with BDR (P=5.1×10
−9
). A nominal association with
CDH13
was identified in a gene-based analysis in all subjects.
We identified suggestive association with BDR among COPD subjects for variants near two potassium channel genes (
KCNK1
and
KCNJ2
). SNPs in
CDH13
were significantly associated with BDR in African Americans.
The identification of gene-by-environment interactions is important for understanding the genetic basis of chronic obstructive pulmonary disease (COPD). Many COPD genetic association analyses assume ...a linear relationship between pack-years of smoking exposure and forced expiratory volume in 1 s (FEV(1)); however, this assumption has not been evaluated empirically in cohorts with a wide spectrum of COPD severity.
The relationship between FEV(1) and pack-years of smoking exposure was examined in four large cohorts assembled for the purpose of identifying genetic associations with COPD. Using data from the Alpha-1 Antitrypsin Genetic Modifiers Study, the accuracy and power of two different approaches to model smoking were compared by performing a simulation study of a genetic variant with a range of gene-by-smoking interaction effects.
Non-linear relationships between smoking and FEV(1) were identified in the four cohorts. It was found that, in most situations where the relationship between pack-years and FEV(1) is non-linear, a piecewise linear approach to model smoking and gene-by-smoking interactions is preferable to the commonly used total pack-years approach. The piecewise linear approach was applied to a genetic association analysis of the PI*Z allele in the Norway Case-Control cohort and a potential PI*Z-by-smoking interaction was identified (p=0.03 for FEV(1) analysis, p=0.01 for COPD susceptibility analysis).
In study samples of subjects with a wide range of COPD severity, a non-linear relationship between pack-years of smoking and FEV(1) is likely. In this setting, approaches that account for this non-linearity can be more powerful and less biased than the more common approach of using total pack-years to model the smoking effect.
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