Longitudinal analysis of biomarker data from a personalized nutrition platform in healthy subjects

Westerman, Kenneth; Reaver, Ashley; Roy, Catherine; Ploch, Margaret; Sharoni, Erin; Nogal, Bartek; Sinclair, David A.; Katz, David L.; Blumberg, Jeffrey B.; Blander, Gil

doi:10.1038/s41598-018-33008-7

Cited by 25 publications

(24 citation statements)

References 29 publications

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“…This study extends the results of previous studies 9,13 and supports the importance of a Scientific Wellness approach, combining multi-omic data and personalized lifestyle coaching in a real-world setting. First, participants saw notable improvements in multiple clinical markers related to health, many of which were observed in the entire population, not just in those who began with out of range values.…”

Section: Discussionsupporting

confidence: 87%

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program

Zubair

Conomos

Hood

et al. 2019

Sci Rep

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Both genetic and lifestyle factors contribute to an individual’s disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial “Scientific Wellness” program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change.

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Section: Discussionsupporting

confidence: 87%

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program

Zubair

Conomos

Hood

et al. 2019

Sci Rep

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“…The main limitation of the work presented here is the size of the training dataset used. It is likely that a larger dataset would improve the performance characteristics of biomarkers tested 15. It is noteworthy, given this limitation, that one of the biomarkers (SC) had an AUC greater than 0.8.…”

Section: Discussionmentioning

confidence: 99%

“…Digital biomarkers afford a pragmatic approach to remotely monitor patients and intervene on a continuous rather than episodic basis. Greatly expanding opportunities to intervene means that patients have greater access to personalised care, which could improve treatment outcomes 15 16…”

Section: Introductionmentioning

confidence: 99%

Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study

Guthrie¹,

Carpenter²,

Edwards³

et al. 2019

BMJ Open

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ObjectivesDevelopment of digital biomarkers to predict treatment response to a digital behavioural intervention.DesignMachine learning using random forest classifiers on data generated through the use of a digital therapeutic which delivers behavioural therapy to treat cardiometabolic disease. Data from 13 explanatory variables (biometric and engagement in nature) generated in the first 28 days of a 12-week intervention were used to train models. Two levels of response to treatment were predicted: (1) systolic change ≥10 mm Hg (SC model), and (2) shift down to a blood pressure category of elevated or better (ER model). Models were validated using leave-one-out cross validation and evaluated using area under the curve receiver operating characteristics (AUROC) and specificity- sensitivity. Ability to predict treatment response with a subset of nine variables, including app use and baseline blood pressure, was also tested (models SC-APP and ER-APP).SettingData generated through ad libitum use of a digital therapeutic in the USA.ParticipantsDeidentified data from 135 adults with a starting blood pressure ≥130/80, who tracked blood pressure for at least 7 weeks using the digital therapeutic.ResultsThe SC model had an AUROC of 0.82 and a sensitivity of 58% at a specificity of 90%. The ER model had an AUROC of 0.69 and a sensitivity of 32% at a specificity at 91%. Dropping explanatory variables related to blood pressure resulted in an AUROC of 0.72 with a sensitivity of 42% at a specificity of 90% for the SC-APP model and an AUROC of 0.53 for the ER-APP model.ConclusionsMachine learning was used to transform data from a digital therapeutic into digital biomarkers that predicted treatment response in individual participants. Digital biomarkers have potential to improve treatment outcomes in a digital behavioural intervention.

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“…), combined with machine learning algorithms to identify patterns that could shape different chemoprevention strategies that could form an IMS, is being considered for metabolic diseases. 37,38 In this light, it has recently been proposed that cancer screening programs could be optimized by having individuals even at "average" risk enter into national screening programs. These screening programs would randomize the enrollees to different arms of a study involving different screening tests and different intervals between tests that consider different profiling assays to assess risks, and different thresholds for declaring tests as consistent with a positive or negative result, all of which would be consistent with the construction of an IMS.…”

Section: Prevention and Screeningmentioning

confidence: 99%

Strategies for Testing Intervention Matching Schemes in Cancer

Schork

Goetz

Lowey

et al. 2020

Clin Pharma and Therapeutics

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Personalized medicine, or the tailoring of health interventions to an individual's nuanced and often unique genetic, biochemical, physiological, behavioral, and/or exposure profile, is seen by many as a biological necessity given the great heterogeneity of pathogenic processes underlying most diseases. However, testing and ultimately proving the benefit of strategies or algorithms connecting the mechanisms of action of specific interventions to patient pathophysiological profiles (referred to here as “intervention matching schemes” (IMS)) is complex for many reasons. We argue that IMS are likely to be pervasive, if not ubiquitous, in future health care, but raise important questions about their broad deployment and the contexts within which their utility can be proven. For example, one could question the need to, the efficiency associated with, and the reliability of, strategies for comparing competing or perhaps complementary IMS. We briefly summarize some of the more salient issues surrounding the vetting of IMS in cancer contexts and argue that IMS are at the foundation of many modern clinical trials and intervention strategies, such as basket, umbrella, and adaptive trials. In addition, IMS are at the heart of proposed “rapid learning systems” in hospitals, and implicit in cell replacement strategies, such as cytotoxic T‐cell therapies targeting patient‐specific neo‐antigen profiles. We also consider the need for sensitivity to issues surrounding the deployment of IMS and comment on directions for future research.

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Longitudinal analysis of biomarker data from a personalized nutrition platform in healthy subjects

Cited by 25 publications

References 29 publications

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program

Emergence of digital biomarkers to predict and modify treatment efficacy: machine learning study

Strategies for Testing Intervention Matching Schemes in Cancer

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