Consuming a Varied Diet can Prevent Diabetes But Can You Afford the Added Cost? Annalijn Conklin 18 January 2017, Vancouver, Canada

Transcription

1 1 Consuming a Varied Diet can Prevent Diabetes But Can You Afford the Added Cost? Annalijn Conklin 18 January 2017, Vancouver, Canada

2 2 Overview The problem of type 2 diabetes What is diet diversity / food variety and how is it measured? The role of diet diversity for type 2 diabetes Cost implications of greater diet diversity Summary and relevance

3 3 Common problem rising fast concern for prevention Type 2 diabetes (T2D): a common chronic condition 1 in 12 people globally (46% undiagnosed); 1 in 4 Canadians Rates doubled in past decade, expected to rise to 1 in 3 by 2020 Diabetes rates are 3 5 times higher in First Nations Serious concern for prevention Leading cause of morbidity & early death (1/10) Major healthcare cost (C$11.7 bn), 80% from complications Widening health inequities IDF diabetes Atlas 6 th ed., Canadian Diabetes Association

4 4 Good diets are key to managing & preventing T2D Up to 80% of T2D can be prevented by changing diet, PA, etc. Dietary interventions most effective in preventing diabetes complications & disease progression Specific dietary components are protective Low-fat fermented dairy products reduce risk of T2D, by 28% Both quantity & variety of fruits & vegetables lower risk of T2D 12 different F&V items a week reduced T2D incidence by 39% O Connor et al 2014; Cooper et al 2012

5 5 Food variety is critical for healthy eating Long-standing concept in guidelines Recommended for Nutritional adequacy Balance of important nutrients Known health implications, e.g. Mortality (all-cause, cause-specific) Hypertension, T2D Overall health status; physical functioning Key limitations Mixed nomenclature (variety = diversity) Multiple ways of assessing variety Kouris-Blazos et al 2005; Clausen et al 2005; Kant et al 1993, 1995; Miller et al 1992.

6 6 Extending the concept: diversity between & within food groups Focus on food groups 1. Circumvents limits of nutrient databases 2. Closer to people s choices & more useful 3. Income effects different for various groups 4. True diet heterogeneity (5 major, 18 minor) Diversity scores derived from Food Frequency Questionnaire Habitual diet over 1 year Frequency of 131 food items consumed Consumed at least twice per week Total count of 5 food groups & subtypes in each

7 7 EPIC-Norfolk cohort: large health dataset Norfolk component of a large European collaborative community study of risk factors of chronic conditions (initially diet & cancer) Diet data (self-reported) assessed by validated semi-quantitative FFQ 10 y follow-up, incident T2D (Hospital records, ONS, disease registry)

8 N=25,639 participants attended a baseline health examination between 1993 and % 8 N=24,784 participants without prevalent diabetes followed up for incident diabetes status N= 892 verified incident diabetes cases N= 23,892 participants without diabetes at 31 st July 2006 N= 822 diabetes cases included for this analysis with data on potential confounders N= 22,416 participants included for analysis with data on potential confounders N=23,238 final analytic sample

9 9 Measuring diet diversity & costs (1) Assigned FFQ items to food groups using WHO/FAO guidance Created 6 diversity scores counting food groups/subtypes 1. Total (0 5): dairy, fruit, vegetable, grain, meat/alternative 2. Dairy (0 3): milk; cheese; yoghurt 3. Fruit (0 3): vitamin A-rich; citrus & berry; other 4. Vegetable (0 4): vitamin A-rich; dark green leafy, starchy tubers; other 5. Grain/bread (0 2): wholegrains; non-wholegrains 6. Meat & alternative (0 6): red flesh meat; poultry flesh meat; organ meat; fish & seafood; eggs; pulses

10 10 Measuring diet diversity & costs (2) Estimated monetary cost Created food price per 100 g edible portion Adjusted for preparation & waste Derived daily retail cost of each cohort participant s whole diet ( /d)

11 Regular intake = 2+ times/week 11 i.e. FFQ response categories 4 9

12 12 Analytic approach 1. Prospective association of diversity scores and incident T2D Multivariable Cox regression (n=23,238, 892 new cases) Series of models adjusted for age, sex, BMI, total energy intake, lifestyles factors, family history, and SES (education & social class) Mutually adjusted for other diversity scores Some scores regrouped due to low numbers (e.g. total (0 3, 4, 5)) 2. Cross-sectional association of diversity and diet cost Multivariable linear regression, adjusted for age, sex & total energy intake Post-estimation of adjusted means (95% CIs)

13 13 Characteristics of total diversity in EPIC participants 3 food groups 4 food groups 5 food groups Obs. 1,028 5,104 17,838 Women, N. (%) 538 (52%) 2,697 (53%) 9,960 (56%) Mean age at recruitment 58 (9) 58 (9) 59 (9) A-level (up to 18 y) or degree educated 489 (48%) 2,593 (51%) 9,696 (54%) Highest social classes (I & II) 395 (39%) 2,069 (42%) 7,844 (45%) Moderately active/ Active 374 (36%) 2,017 (39%) 7,520 (42%) Never smoker 393 (39%) 2,204 (44%) 8,456 (48%) Mean BMI (kg/m 2 ) 26.1 (3.8) 26.2 (3.9) 26.3 (3.8) Waist circumference (cm) 88.2 (12.3) 88.0 (12.3) 88.0 (12.3) Total energy intake (kcal/d) 1544 (508) 1815 (518) 2145 (591) Total alcohol intake (g/d) 137 (277) 143 (275) 124 (224)

14 14 Total dietary diversity lowers risk of T2D Ref: 0 3 major groups Ref: 0 3 major groups p-trend= groups 5 groups 0 4 groups 5 groups Hazard ratios (95% Cis) adjusted for age, sex, BMI, total energy, smoking, alcohol, PA, family history, education & social class Hazard ratios (95% Cis) adjusted for age, sex, BMI, lifestyle factors, family history, SES & within-group diversity scores

15 15 Each dairy subgroup lowers risk of diabetes Ref: 0 dairy groups p-trend= Ref: 0 dairy groups p-trend= group 2 groups 3 groups 0 1 group 2 groups 3 groups Hazard ratios (95% Cis) adjusted for age, sex, BMI, total energy, smoking, alcohol, PA, family history, education & social class Hazard ratios (95% Cis) adjusted for age, sex, BMI, lifestyle factors, family history, SES & within-group diversity scores

16 16 Fruit diversity lowers risk of diabetes 1.25 Ref: 0 fruit groups p-trend= Ref: 0 fruit groups p-trend= group 2 groups 3 groups 0 1 group 2 groups 3 groups Hazard ratios (95% Cis) adjusted for age, sex, BMI, total energy, smoking, alcohol, PA, family history, education & social class Hazard ratios (95% Cis) adjusted for age, sex, BMI, lifestyle factors, family history, SES & within-group diversity scores

17 17 Vegetable diversity lowers risk of diabetes 1.25 Ref: 0 1 vegetable groups p-trend= Ref: 0 1 vegetable groups p-trend= groups 3 groups 4 groups 0 2 groups 3 groups 4 groups Hazard ratios (95% Cis) adjusted for age, sex, BMI, total energy, smoking, alcohol, PA, family history, education & social class Hazard ratios (95% Cis) adjusted for age, sex, BMI, lifestyle factors, family history, SES & within-group diversity scores

18 18 Total diet cost increases with greater diversity between & within food groups per day 5 p-trend < (C$6.72) (C$5.71) Overall (0-3, 4, 5) dairy (0, 1, 2, 3) fruit (0, 1, 2, 3) vegetable (0-1, 2, 3, 4) # of food groups Mean cost adjusted for age, sex & total energy

19 19 Summary A varied diet is recommended for healthy eating, but diversity between and within food groups rarely studied together Consuming a combination of five major food groups is associated with a 30% reduced incidence of T2D, and 18% added cost Independent effects on lower T2D incidence of greatest diversity in dairy products (36%), fruits (25%) and vegetables (29%) Each additional subtype consumed also increased daily diet cost Conklin AI, et al. PLOS Medicine, 2016; 13(7)

20 20 Relevance Canada endorsed the UN Declaration on preventing and controlling noncommunicable diseases (2011) Global 5-a-day campaigns emphasise variety of FVs Added clarity on need for diversity of fruits, separate from diversity of vegetables Rising cost of foods, especially fruits & vegetables Comprehensive food pricing strategy

21 Relevance 21

22 22 Acknowledgements Co-authors: Pablo Monsivais, Kay-Tee Khaw, Nick Wareham & Nita Forouhi This work was supported by the Gates Cambridge Trust, by the Canadian Institute of Health Research (CIHR) Postdoctoral Award (MFE ), and by the WORLD Policy Analysis Center. Work was mostly undertaken by the Centre for Diet and Activity Research (CEDAR), a UKCRC Public Health Research Centre of Excellence, funded by: the British Heart Foundation, Economic and Social Research Council, Medical Research Council, the National Institute for Health Research, and the Wellcome Trust. EPIC-Norfolk is supported by programme grants from the Medical Research Council and Cancer Research UK.