4.3. Analysis of Lifestyle Patterns for Improvement of Diabetes Research
Relevance

How our research benefits to society

Lifestyle is a main driving force of diabetes and its complications, with a main role for diet and physical (in-) activity. Accordingly, diabetes management includes both lifestyle management and pharmacotherapy, as stated in (inter-)national guidelines. Better prevention and treatment of diabetes requires improvement of both lifestyle management and pharmacotherapy, preferably in an integrated way. 

  • Considering the overall quality of management of diabetes, the efficacy of lifestyle management is lagging behind considerably relative to pharmacological treatment as the large majority of patient fails to achieve any lifestyle treatment aim, despite substantial efforts and costs for lifestyle management. Hence, there is a large unmet need for better lifestyle management.

    Recent studies on determinants of effective lifestyle management underline the importance of considering personal preferences and habits, and environmental factors as success factors for sustained efficacy. In diabetes however, little attention has been given to identifying habits and preferences, assessment of environmental factors, or assessing their associations with morbidity and their consequences for (better) management. Assessment of robust lifestyle patterns provides a relevant strategy to map habits and preferences at the aggregate level, and analyze for relevant environmental determinants that can guide better intervention strategies.

    In the Lifelines cohort (n=160,000) we identified several dietary patterns, robust after adjustment for confounders. These patterns strongly associate with (multi-) morbidity, demonstrating their clinical relevance. Moreover, their marked regional distribution supports the role of (socio-cultural) environmental factors. This provides an excellent starting point to analyze the role of lifestyle patterns as determinants of morbidity in diabetes, their consequences for current management, and design of new strategies that effectively account for preferences, habits and environmental factors .

    By this innovative approach of analyzing personal/environmental characteristics at the aggregate level, the project creates an intermediate level between generic approaches (one size fits all) and strictly individual approaches ( time consuming, expensive). As such it is uniquely fitted to provide an empirical basis for new approaches towards better personalization of diabetes management that are within reach with currently available technology at affordable costs.

    Our underlying assumption is that better lifestyle management will facilitate overall management of diabetes, lead to lower requirements for pharmacotherapy, better outcomes and lower costs.

  • Identify the role of differences in dietary and lifestyle habits as a determinant of (multi-) morbidity and treatment quality in diabetes, as a basis for better personalized prevention and management strategies.

  • The main approach of the project is epidemiological. The strategy is to translate advanced big data analysis into practical guidelines for better personalization of diabetes management.

    Data from available cohorts (general population including diabetes, and diabetes-only, respectively) are available for analysis of dietary patterns, their association with (multi-) morbidity, with patient management (i.e: pharmacological treatment, dietary counseling and the consequent health care expenditure) and with medical outcomes.

    Dietary patterns will be analyzed from Food Frequency Questionnaires by Principal component analysis, and related to (multi-morbidity) by multivariate modeling. Data on physical activity (SQUASH) and on smoking, alcohol and substance use will be integrated into an overall lifestyle score. Combination with data on pharmacotherapy will reveal interaction between lifestyle pattern and (need for) pharmacotherapy, and modification of efficacy of pharmacotherapy by lifestyle pattern. Health economic aspects will be analyzed to assess the costs of inadequate lifestyle management on overall health care costs in diabetes (i.e need for more medication, higher complication rate etc) and support the business case for better lifestyle intervention approaches.

    Role of environmental determinants for lifestyle habits will be assessed by applying geo-mapping analyses (Global Moran’s I spatial statistic) to identify regional differences in lifestyle patterns. Subsequent adjustment for relevant factors (age, gender, income, education etc) will serve to identify confounders and modifiable environmental factors are new targets for more effective lifestyle intervention.

    1. Association of lifestyle patterns with (multi-)morbidity in diabetes
    2. Association of lifestyle patterns with overall management of diabetes: consequences for medical outcomes.
    3. Association of lifestyle patterns with overall management of diabetes: consequences for health care expenditure
    4. Regional differences in lifestyle patterns as a dissection tool in diabetes: identification of new targets for intervention
    5. Regional differences in lifestyle patterns as a dissection tool for the prevention of diabetes: identification of new targets for intervention
  • By its highly innovative approach of analyzing personal/environmental characteristics at the aggregate level, this project creates an intermediate level between generic approaches (one size fits all) and the strictly individual approaches that are time consuming and expensive.

    As such it is uniquely fitted to provide an empirical basis for readily feasible approaches towards better personalization of diabetes management that are within reach with currently available technology, at affordable costs. Implementation and success of such approaches will greatly contribute to the credibility of personalized medicine as a new paradigm.

  • The project contributes to:

    • Identification of disease mechanisms (epidemiological analysis as a dissection tool);
    • Drug application (better alignment of lifestyle management and pharmacotherapy, better targeting of drug use)
    • Health Technology assessment (health economic analysis of different management strategies)

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