Pre-Conception Interventions for Subfertile Couples Undergoing Assisted Reproductive Technology Treatment: Modeling Analysis

BACKGROUND: Approximately 1 in 7 couples experience subfertility, many of whom have lifestyles that negatively affect fertility, such as poor nutrition, low physical activity, obesity, smoking, or alcohol consumption. Reducing lifestyle risk factors prior to pregnancy or assisted reproductive technology treatment contributes to the improvement of reproductive health, but cost-implications are unknown. OBJECTIVE: The goal of this study was to evaluate reproductive, maternal pregnancy, and birth outcomes, as well as the costs of pre-conception lifestyle intervention programs in subfertile couples and obese women undergoing assisted reproductive technology. METHODS: Using a hypothetical model based on quantitative parameters from published literature and expert opinion, we evaluated the following lifestyle intervention programs: (1) Smarter Pregnancy, an online tool; (2) LIFEstyle, which provides outpatient support for obese women; (3) concurrent use of both Smarter Pregnancy and LIFEstyle for obese women; (4) smoking cessation in men; and (5) a mindfulness mental health support program using group therapy sessions. The model population was based on data from the Netherlands. RESULTS: All model-based analyses of the lifestyle interventions showed a reduction in the number of in vitro fertilization, intracytoplasmic sperm injection, or intrauterine insemination treatments required to achieve pregnancy and successful birth for couples in the Netherlands. Smarter Pregnancy was modeled to have the largest increase in spontaneous pregnancy rate (13.0%) and the largest absolute reduction in potential assisted reproductive technology treatments. Among obese subfertile women, LIFEstyle was modeled to show a reduction in the occurrence of gestational diabetes, maternal hypertensive pregnancy complications, and preterm births by 4.4%, 3.8%, and 3.0%, respectively, per couple. Modeled cost savings per couple per year were €41 (US $48.66), €360 (US$427.23), €513 (US $608.80), €586 (US$695.43), and €1163 (US $1380.18) for smoking cessation, mindfulness, Smarter Pregnancy, combined Smarter Pregnancy AND LIFEstyle, and LIFEstyle interventions, respectively. CONCLUSIONS: Although we modeled the potential impact on reproductive outcomes and costs of fertility treatment rather than collecting real-world data, our model suggests that of the lifestyle interventions for encouraging healthier behaviors, all are likely to be cost effective and appear to have positive effects on reproductive, maternal pregnancy, and birth outcomes. Further real-world data are required to determine the cost-effectiveness of pre-conception lifestyle interventions, including mobile apps and web-based tools that help improve lifestyle, and their effects on reproductive health. We believe that further implementation of the lifestyle app Smarter Pregnancy designed for subfertile couples seeking assistance to become pregnant is likely to be cost-effective and would allow reproductive health outcomes to be collected

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Pathway-based subnetworks enable cross-disease biomarker discovery.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery

Pathway-based subnetworks enable cross-disease biomarker discovery

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery