HAPT2D: high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability

Camillo, Barbara Di; Hakaste, Liisa; Sambo, Francesco; Gabriel, Rafael; Kravić, Jasmina; Isomaa, Bo; Tuomilehto, Jaakko; Alonso, Margarita; Longato, Enrico; Facchinetti, Andrea; Groop, Leif; Cobelli, Claudio; Tuomi, Tiinamaija

doi:10.1530/eje-17-0921

Cited by 13 publications

(10 citation statements)

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“…However, it is legitimate to ask whether a different method would perform better or worse in terms of ability to predict, e.g., the survival time. At this purpose, we trained a LASSO ‘least absolute shrinkage and selection operator’ regression analysis [26] coupled with Cox survival model [27] and recursive feature elimination as done in [28], on the same training data used to learn the DBN model. We obtained an AU-ROC equal to 0.79 and 0.64 at months 12 and 24, respectively; at the same time points the DBN achieved an AU-ROC equal to 0.96 and 0.90.…”

Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression

et al. 2019

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Background Amyotrophic lateral sclerosis (ALS) is an adult-onset neurodegenerative disease progressively affecting upper and lower motor neurons in the brain and spinal cord. Mean life expectancy is three to five years, with paralysis of muscles, respiratory failure and loss of vital functions being the common causes of death. Clinical manifestations of ALS are heterogeneous due to the mix of anatomic regions involvement and the variability in disease course; consequently, diagnosis and prognosis at the level of individual patient is really challenging. Prediction of ALS progression and stratification of patients into meaningful subgroups have been long-standing interests to clinical practice, research and drug development. Methods We developed a Dynamic Bayesian Network (DBN) model on more than 4500 ALS patients included in the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT), in order to detect probabilistic relationships among clinical variables and identify risk factors related to survival and loss of vital functions. Furthermore, the DBN was used to simulate the temporal evolution of an ALS cohort predicting survival and the time to impairment of vital functions (communication, swallowing, gait and respiration). A first attempt to stratify patients by risk factors and simulate the progression of ALS subgroups was also implemented. Results The DBN model provided the prediction of ALS most probable trajectories over time in terms of important clinical outcomes, including survival and loss of autonomy in functional domains. Furthermore, it allowed the identification of biomarkers related to patients’ clinical status as well as vital functions, and unrevealed their probabilistic relationships. For instance, DBN found that bicarbonate and calcium levels influence survival time; moreover, the model evidenced dependencies over time among phosphorus level, movement impairment and creatinine. Finally, our model provided a tool to stratify patients into subgroups of different prognosis studying the effect of specific variables, or combinations of them, on either survival time or time to loss of autonomy in specific functional domains. Conclusions The analysis of the risk factors and the simulation allowed by our DBN model might enable better support for ALS prognosis as well as a deeper insight into disease manifestations, in a context of a personalized medicine approach. Electronic supplementary material The online version of this article (10.1186/s12859-019-2692-x) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression

et al. 2019

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“…It is possible that metabolites could further improve the predictive value of diabetic subgroups, because we have recently shown that type 2 diabetes can be subdivided into five subgroups with six clustering variables (age at diagnosis, BMI, HbA1c, GAD autoantibodies, homeostatic model assessment of β-cell function, and homeostatic model assessment of INS resistance) with C-peptide measurements (34). The predictive accuracy may also be further improved by including additional anthropometric and lifestyle data (35).…”

Section: Discussionmentioning

confidence: 99%

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

Gopalacharyulu

Bergman²,

Tuomi

et al. 2018

The Journal of Clinical Endocrinology &Amp; Metabolism

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ContextEarly prediction of dysglycemia is crucial to prevent progression to type 2 diabetes. The 1-hour postload plasma glucose (PG) is reported to be a better predictor of dysglycemia than fasting plasma glucose (FPG), 2-hour PG, or glycated hemoglobin (HbA1c).ObjectiveTo evaluate the predictive performance of clinical markers, metabolites, HbA1c, and PG and serum insulin (INS) levels during a 75-g oral glucose tolerance test (OGTT).Design and SettingWe measured PG and INS levels at 0, 30, 60, and 120 minutes during an OGTT in 543 participants in the Botnia Prospective Study, 146 of whom progressed to type 2 diabetes within a 10-year follow-up period. Using combinations of variables, we evaluated 1527 predictive models for progression to type 2 diabetes.ResultsThe 1-hour PG outperformed every individual marker except 30-minute PG or mannose, whose predictive performances were lower but not significantly worse. HbA1c was inferior to 1-hour PG according to DeLong test P value but not false discovery rate. Combining the metabolic markers with PG measurements and HbA1c significantly improved the predictive models, and mannose was found to be a robust metabolic marker.ConclusionsThe 1-hour PG, alone or in combination with metabolic markers, is a robust predictor for determining the future risk of type 2 diabetes, outperforms the 2-hour PG, and is cheaper to measure than metabolites. Metabolites add to the predictive value of PG and HbA1c measurements. Shortening the standard 75-g OGTT to 1 hour improves its predictive value and clinical usability.

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“…12 The baseline study was conducted in 2004-2008 and the prospective study in 2010-2015. 18 For the present randomized parallel group clinical trial (RCT, ClinicalTrials. gov identifier NCT02131701) we recruited consecutive male and female participants from the PPP-Botnia Study who fulfilled the following criteria: (1) no diabetes and aged 30-70 years ; (2) poor physical fitness based on a 2 km walking test (fitness index <90); and (3) no contraindications for physical training based on a physical examination including ECG.…”

Section: Research Design and Methods Study Participantsmentioning

confidence: 99%

Low-cost exercise interventions improve long-term cardiometabolic health independently of a family history of type 2 diabetes: a randomized parallel group trial

Wasenius

Isomaa

Östman

et al. 2020

BMJ Open Diab Res Care

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IntroductionTo investigate the effect of an exercise prescription and a 1-year supervised exercise intervention, and the modifying effect of the family history of type 2 diabetes (FH), on long-term cardiometabolic health.Research design and methodsFor this prospective randomized trial, we recruited non-diabetic participants with poor fitness (n=1072, 30–70 years). Participants were randomly assigned with stratification for FH either in the exercise prescription group (PG, n=144) or the supervised exercise group (EG, n=146) group and compared with a matched control group from the same population study (CON, n=782). The PG and EG received exercise prescriptions. In addition, the EG attended supervised exercise sessions two times a week for 60 min for 12 months. Cardiometabolic risk factors were measured at baseline, 1 year, 5 years, and 6 years. The CON group received no intervention and was measured at baseline and 6 years.ResultsThe EG reduced their body weight, waist circumference, diastolic blood pressure, and low-density lipoprotein-cholesterol (LDL-C) but not physical fitness (p=0.074) or insulin or glucose regulation (p>0.1) compared with the PG at 1 year and 5 years (p≤0.011). The observed differences were attenuated at 6 years; however, participants in the both intervention groups significantly improved their blood pressure, high-density lipoprotein-cholesterol, and insulin sensitivity compared with the population controls (p≤0.003). FH modified LDL-C and waist circumference responses to exercise at 1 year and 5 years.ConclusionsLow-cost physical activity programs have long-term beneficial effects on cardiometabolic health regardless of the FH of diabetes. Given the feasibility and low cost of these programs, they should be advocated to promote cardiometabolic health.Trial registration numberClinicalTrials.gov identifier NCT02131701.

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HAPT2D: high accuracy of prediction of T2D with a model combining basic and advanced data depending on availability

Abstract: Our models provide an estimation of patient's risk over time and outweigh FINDRISC and Framingham traditional scores for prediction of T2D risk. Of note, the models developed in Scenarios 1 and 2, only exploited variables easily available at general patient visits.

Cited by 13 publications

References 38 publications

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression

A Dynamic Bayesian Network model for the simulation of Amyotrophic Lateral Sclerosis progression

1-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes by Clinical and Metabolic Markers

Low-cost exercise interventions improve long-term cardiometabolic health independently of a family history of type 2 diabetes: a randomized parallel group trial

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