Prediction of Cardiometabolic Health Through Changes in Plasma Proteins With Intentional Weight Loss in the DiRECT and DIADEM-I Randomized Clinical Trials of Type 2 Diabetes Remission.

OBJECTIVE: To determine the extent to which changes in plasma proteins, previously predictive of cardiometabolic outcomes, predict changes in two diabetes remission trials. RESEARCH DESIGN AND METHODS: We applied SomaSignal predictive tests (each derived from ∼5,000 plasma protein measurements using aptamer-based proteomics assay) to baseline and 1-year samples of trial intervention (Diabetes Remission Clinical Trial [DiRECT], n = 118, and Diabetes Intervention Accentuating Diet and Enhancing Metabolism [DIADEM-I], n = 66) and control (DiRECT, n = 144, DIADEM-I, n = 76) group participants. RESULTS: Mean (SD) weight loss in DiRECT (U.K.) and DIADEM-I (Qatar) was 10.2 (7.4) kg and 12.1 (9.5) kg, respectively, vs. 1.0 (3.7) kg and 4.0 (5.4) kg in control groups. Cardiometabolic SomaSignal test results showed significant improvement (Bonferroni-adjusted P < 0.05) in DiRECT and DIADEM-I (expressed as relative difference, intervention minus control) as follows, respectively: liver fat (-26.4%, -37.3%), glucose tolerance (-36.6%, -37.4%), body fat percentage (-8.6%, -8.7%), resting energy rate (-8.0%, -5.1%), visceral fat (-34.3%, -26.1%), and cardiorespiratory fitness (9.5%, 10.3%). Cardiovascular risk (measured with SomaSignal tests) also improved in intervention groups relative to control, but this was significant only in DiRECT (DiRECT, -44.2%, and DIADEM-I, -9.2%). However, weight loss >10 kg predicted significant reductions in cardiovascular risk, -19.1% (95% CI -33.4 to -4.91) in DiRECT and -33.4% (95% CI -57.3, -9.6) in DIADEM-I. DIADEM-I also demonstrated rapid emergence of metabolic improvements at 3 months. CONCLUSIONS: Intentional weight loss in recent-onset type 2 diabetes rapidly induces changes in protein-based risk models consistent with widespread cardiometabolic improvements, including cardiorespiratory fitness. Protein changes with greater (>10 kg) weight loss also predicted lower cardiovascular risk, providing a positive outlook for relevant ongoing trials.

A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk.

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c-statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a "universal" surrogate end point for cardiovascular risk.

Plasma protein patterns as comprehensive indicators of health.

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Phenotype-Specific Association of Single-Nucleotide Polymorphisms with Heart Failure and Preserved Ejection Fraction: a Genome-Wide Association Analysis of the Cardiovascular Health Study.

Little is known about genetics of heart failure with preserved ejection fraction (HFpEF) in part because of the many comorbidities in this population. To identify single-nucleotide polymorphisms (SNPs) associated with HFpEF, we analyzed phenotypic and genotypic data from the Cardiovascular Health Study, which profiled patients using a 50,000 SNP array. Results were explored using novel SNP- and gene-centric tools. We performed analyses to determine whether some SNPs were relevant only in certain phenotypes. Among 3804 patients, 7 clinical factors and 9 SNPs were significantly associated with HFpEF; the most notable of which was rs6996224, a SNP associated with transforming growth factor-beta receptor 3. Most SNPs were associated with HFpEF only in the absence of a clinical predictor. Significant SNPs represented genes involved in myocyte proliferation, transforming growth factor-beta/erbB signaling, and extracellular matrix formation. These findings suggest that genetic factors may be more important in some phenotypes than others.

Peax: interactive visual analysis and exploration of complex clinical phenotype and gene expression association.

Increasing availability of high-dimensional clinical data, which improves the ability to define more specific phenotypes, as well as molecular data, which can elucidate disease mechanisms, is a driving force and at the same time a major challenge for translational and personalized medicine. Successful research in this field requires an approach that ties together specific disease and health expertise with understanding of molecular data through statistical methods. We present PEAX (Phenotype-Expression Association eXplorer), built upon open-source software, which integrates visual phenotype model definition with statistical testing of expression data presented concurrently in a web-browser. The integration of data and analysis tasks in a single tool allows clinical domain experts to obtain new insights directly through exploration of relationships between multivariate phenotype models and gene expression data, showing the effects of model definition and modification while also exploiting potential meaningful associations between phenotype and miRNA-mRNA regulatory relationships. We combine the web visualization capabilities of Shiny and D3 with the power and speed of R for backend statistical analysis, in order to abstract the scripting required for repetitive analysis of sub-phenotype association. We describe the motivation for PEAX, demonstrate its utility through a use case involving heart failure research, and discuss computational challenges and observations. We show that our visual web-based representations are well-suited for rapid exploration of phenotype and gene expression association, facilitating insight and discovery by domain experts.