Personalized medicine for chronic, complex diseases: chronic obstructive pulmonary disease as an example

Three-variate trajectories of metabolic control, body mass index, and insulin dose: Heterogeneous response to initiation of pump therapy in youth with type 1 diabetes

OBJECTIVE

Continuous subcutaneous insulin infusion (CSII) in youths with type 1 diabetes (T1D) is often associated with lower HbA1c, lower total daily insulin dose (TDD), and lower body mass index (BMI) compared with multiple daily injections (MDI). Individual responses to CSII are diverse. The aim was to identify unique three-variate patterns of HbA1c, BMI standard deviation score (SDS), and TDD after switching to CSII.

METHODS

Five thousand one hundred and thirty-three youths (≤20 years; 48% boys; median age at pump start 12.5 years) with T1D duration ≥3 years at CSII initiation were selected from the multicenter DPV registry. We applied group-based multitrajectory modeling to identify groups of individuals following similar trajectories. Measurements were aggregated quarterly during a 3-year follow-up period. Trajectory variables were changes of HbA1c, BMI-SDS, and TDD from baseline (delta = quarterly aggregated values at each time point [i] minus the respective baseline value).

RESULTS

Four groups of diverging Delta-HbA1c, Delta-BMI-SDS, and Delta-TDD patterns were identified. All showed improvements in HbA1c during the first 3 months. Group 1 (12%) was characterized by modest HbA1c increase thereafter, TDD reduction, and stable BMI-SDS. In Group 2 (39%), increasing HbA1c, decreasing BMI-SDS, and stable TDD were found. By contrast, sustainably improved HbA1c, increasing BMI-SDS, and stable TDD were observed in Group 3 (32%). Group 4 (17%) was characterized by increasing levels for HbA1c, BMI-SDS, and TDD. Between-group differences in baseline HbA1c, BMI-SDS, TDD as well as in sex ratio, age at diabetes onset and at pump start were observed.

CONCLUSIONS

Definite trajectories of glycemic control, BMI, and TDD over 3 years after CSII initiation were identified in youths with T1D allowing a more personalized treatment recommendation.

The shift of the paradigm between ageing and diseases

In the area of the Medical Sciences, the chronological age has always been, and still is, an indicator by which we try to understand the health status of an individual. However, besides considering people born with an already expressed disease, each human genome has sequence alterations called predisposing mutations; carriers of such genetic alterations have an increased risk of contracting diseases during their life. In addition, the exposome, i.e. the totality of environmental noxae (“hits”) to which our body is exposed throughout life (through ingestion, breathing, body surface hits, and psychosociological stress agents, etc.) contributes to increase gradually but inexorably the frailty of an organism, and this process is usually referred to as “physiological ageing”. This position paper proposes that we invert our visual angle and view the passage-of-time not as the cause of diseases, but consider the genome alterations present at birth and the noxae received during our life as the real major causes of ageing. The Biomedical Sciences are now increasingly unraveling the etiopathogenesis of most chronic degenerative diseases; thus, it will be possible to monitor and treat those that most contribute to the increased frailty of each person, which is now referred to with the misnomer “physiological ageing”. These concepts are not banal; indeed, they imply that we must try to avoid the causes of alterations that result later in chronic degenerative diseases. Thus, we should shift our attention from the cure to the prevention of alterations/diseases also to improve both the length and quality of our life. Moreover, this approach involves real personalized or individualized medicine, thus conferring a more direct benefit to each of us by finalizing either the cure or the monitoring of diseases.

Three-Variate Longitudinal Patterns of Metabolic Control, Body Mass Index, and Insulin Dose during Puberty in a Type 1 Diabetes Cohort: A Group-Based Multitrajectory Analysis

OBJECTIVE

To analyze the interrelationship of metabolic control, age- and sex-adjusted body mass index, and daily insulin dose and to identify heterogeneous multivariate developmental curves from childhood to young adulthood in a large cohort of children with type 1 diabetes (T1D) STUDY DESIGN: Data were extracted from the diabetes follow-up registry DPV. Longitudinal data from 9239 participants with T1D age 8-18 years with diabetes duration ≥2 years and ≥5 years of follow-up were analyzed. We applied group-based multitrajectory modeling to identify latent groups of subjects following similar developmental curves across outcomes (hemoglobin A1c [HbA1c], age/sex-standardized body mass index [BMI-SDS], daily insulin dose per kg). Group number was based on Bayes information criterion and group size (≥5%).

RESULTS

The group-based multitrajectory approach revealed 5 heterogeneous 3-variate trajectories during puberty. Individuals with stable good metabolic control, high-normal increasing BMI-SDS, and rising insulin dose patterns were classified as group 1 (33%). Group 2 (20%) comprised youths with intermediate-increasing HbA1c, low BMI-SDS, and steeply increasing insulin dose trajectories. Group 3 (11%) followed intermediate-rising HbA1c and high-normal increasing BMI-SDS developmental curves, while insulin dose increased steeply. In group 4 (14%), both high-increasing HbA1c and insulin dose trajectories were observed, while BMI-SDS was stable-normal. Group 5 (22%) included subjects with intermediate-rising HbA1c patterns, high-increasing BMI-SDS, and increasing insulin dose patterns.

CONCLUSIONS

This study identified 5 distinct 3-variate curves of HbA1c, BMI-SDS, and insulin dose during puberty among youths with T1D. This approach demonstrates a considerable heterogeneity highlighting the importance of personalized medical care.

Machine learning approaches to personalize early prediction of asthma exacerbations

Patient telemonitoring results in an aggregation of significant amounts of information about patient disease trajectory. However, the potential use of this information for early prediction of exacerbations in adult asthma patients has not been systematically evaluated. The aim of this study was to explore the utility of telemonitoring data for building machine learning algorithms that predict asthma exacerbations before they occur. The study dataset comprised daily self-monitoring reports consisting of 7001 records submitted by adult asthma patients during home telemonitoring. Predictive modeling included preparation of stratified training datasets, predictive feature selection, and evaluation of resulting classifiers. Using a 7-day window, a naive Bayesian classifier, adaptive Bayesian network, and support vector machines were able to predict asthma exacerbation occurring on day 8, with sensitivity of 0.80, 1.00, and 0.84; specificity of 0.77, 1.00, and 0.80; and accuracy of 0.77, 1.00, and 0.80, respectively. Our study demonstrated that machine learning techniques have significant potential in developing personalized decision support for chronic disease telemonitoring systems. Future studies may benefit from a comprehensive predictive framework that combines telemonitoring data with other factors affecting the likelihood of developing acute exacerbation. Approaches implemented for advanced asthma exacerbation prediction may be extended to prediction of exacerbations in patients with other chronic health conditions.