Optimal Metabolic Health Through Continuous Glucose Monitoring
Condition: Metabolic Syndrome
MeSH terms: Metabolic Syndrome
Study type: Interventional

Summary: 
      The primary focus of this study is to evaluate the role of Continuous Glucose Monitoring
      (CGM) with Levels Health software as a tool to provide feedback and accountability necessary
      to create sustainable behavioral changes in nutrition associated with improved metabolic
      health and resilience against chronic and infectious diseases.
    

Detailed description: 
      Achieving optimal metabolic health and glycemic control is a common goal among not only
      diabetics, but also for healthy individuals, athletes, elite military operators and for
      infectious disease prevention and resilience. No isolated biomarker is currently ubiquitously
      accepted as a marker of overall metabolic health and most rely on isolated snapshot (single
      time point) analyses and not a continuous closed-loop biomarker data assessment. Glycosylated
      hemoglobin (A1c) provides limited characterization of glycemic variability, which contributes
      to the progression of glycemic dysregulation. For example, emerging evidence links the
      amplitude and duration of glycemic variability as an independent risk factor linked to
      cardiovascular disease (CVD) (Di Flaviani 2011, Monnier 2006). Hyperglycemia-induced
      endothelial dysfunction and oxidative stress are greater with larger glycemic variability
      (Monnier 2006, Buscemi 2010). Glycemic variability is more deleterious for the cardiovascular
      system than sustained hyperglycemia (Nalysnyk 2010). Few technologies allow for continuous
      biomarker monitoring over time, and under a range of conditions like daily activities,
      swimming, exercise, sleep, etc. Multiple lines of evidence strongly suggest the predictive
      impact and value of monitoring glycemic variability on acute and chronic health of diabetes
      populations and non-diabetes populations (Rodriguez-Segade 2018, Zeevi 2015). Thus, there has
      been emerging interest in therapeutic approaches that seek to reduce glycemic variability.
      This potential for early detection of glycemic dysregulation is likely to be the single most
      beneficial effect of using CGM as an informational device, especially in the context of other
      biomarkers measures periodically. It is likely that people will make lifestyle modifications
      if they are aware of an impending health problem, detected through real time GCM-tracked
      glycemic variability. Lifestyle modifications are proven to be the most effective
      intervention for restoring normal fasting glucose levels and preventing diabetes among
      dysglycemic subjects, reducing the conversion to diabetes by 58% over placebo, and by 39%
      over metformin in one large US study (Diabetes Prevention Program Research Group 2002). Long
      terms follow-ups on other international studies have shown equally significant results at 4
      years (Tuomilehto 2001) and 14 years (Li 2008) after the controlled lifestyle interventions
      ended, including reductions in diabetes incidence of 58%, and 43% respectively.

      It is known that metabolic health is on a spectrum and long-term studies in diabetic
      populations have demonstrated that reducing glycemic variability is more important than
      lowering baseline hyperglycemia in terms of reducing cardiovascular complications (Hall
      2018). Therefore, there exists a scientific rationale to study interventions that can
      optimize metabolic health in non-diabetics since the potential benefits of metabolic
      awareness extend beyond the diabetic population. Emerging technology that can provide tight
      feedback on lifestyle effects could be a valuable mechanism for non-diabetics seeking to
      improve education and reduce their lifetime risk of disease. Though such outcomes have not
      yet been demonstrated in long term studies, the existing research reveals promising results,
      including improved screening for metabolic risk (Rodriguez-Segade 2018), clear observability
      of effects of lifestyle intervention (Hall 2018, Brynes 2005, Freckmann 2007), and acceptance
      of a minimal-risk strategy for use as a preventative tool in a non-diabetic population (Liao
      2018). The Diabetes Prevention Program Research Group called for a shift in response in order
      to reverse these trends, stating that: "methods of treating diabetes remain inadequate and
      that prevention is preferable (Diabetes Prevention Program Research Group 2002)." Though
      unproven as a preventative measure, monitoring of glycemic variability is - at worst -
      unlikely to exacerbate the problem. At best, however, if it becomes a widespread lifestyle
      tool, the benefits of improved individual metabolic awareness and educated action could have
      compounding effects at a larger societal scale.

      Therefore, there exists a scientific rationale to study interventions that can optimize
      metabolic health with improved glycemic monitoring technologies (Danne 2017). It is becoming
      clear, that in addition to diabetic populations, normal, healthy populations can benefit from
      stable, controlled blood sugar levels, and that feedback mechanisms, including wearable
      technologies, can be employed. Thus, CGM could be a promising method of improving biomarkers
      of metabolic health for virtually anyone. In addition, optimal metabolic health is typically
      associated with improved behavioral health and cognitive resilience and decision making
      (Hadj-Abo 2020). Thus, optimizing and monitoring glycemic control may be useful for mental
      health and may be a valuable tool for military personnel and first responders under metabolic
      stress. Advances in software and hardware technologies have been developed to measure,
      analyze and predict glycemic variability and provides insight on how this dynamic biomarker
      correlates to metabolic fitness. Specifically, new advances in CGM technologies offer the
      potential to monitor, predict and change behavior through a closed-loop feedback system. By
      comparing CGM data with blood markers of metabolic health (eg.HbA1c , Insulin, etc.), and
      inflammation (e.g. hsCRP, cytokines) and along with assessments of emotion, cognition and
      behavior, a more robust interpretation and deconvolution of CGM data with experimental
      interventions may be possible.
    

Intervention name(s): Continuous Glucose Monitor; <Active Comparator?>
Intervention type(s): Device; Other
Intervention description(s): Continuous glucose monitor - a device that monitors blood glucose levels in a continuous closed-loop manner. This can also refer to the process of continuous glucose monitoring; <describe, Comprehensive Wellness Program incorporating a low carbohydrate diet (<50g/day) and associated education >
Other intervention name(s): Continuous Glucose Monitor Software; CGM; CGM Software; <Wellness Program?>
Allocation: Randomized
Intervention model: Parallel Assignment
Intervention model description: To determine if a data driven behavioral tool incorporating Levels CGM software provides superior feedback, accountability, control, and reward mechanisms necessary to create positive, sustainable dietary behavioral change.
Masking: Single (Investigator)
Primary purpose: Treatment
Keywords: continuous glucose monitor; CGM; Ketogenic diet; metabolic health; inflammation; low carbohydrate diet; Levels health; CGM software; Behavioral testing; PHQ9; GAD-7 assessment; SSSQ assessment; ZRT Laboratory; cytokine assay; HbA1c; hsCRP; beta-hydroxybutrate; ketone bodies