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Diet Quality and Incident Non-Communicable Disease in the 1946–1951 Cohort of the Australian Longitudinal Study on Women’s Health

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  • Hlaing Hlaing-Hlaing

    (School of Medicine and Public Health, University of Newcastle, Callaghan, Newcastle, NSW 2308, Australia
    Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW 2305, Australia)

  • Xenia Dolja-Gore

    (School of Medicine and Public Health, University of Newcastle, Callaghan, Newcastle, NSW 2308, Australia
    Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW 2305, Australia)

  • Meredith Tavener

    (School of Medicine and Public Health, University of Newcastle, Callaghan, Newcastle, NSW 2308, Australia
    Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW 2305, Australia)

  • Erica L. James

    (School of Medicine and Public Health, University of Newcastle, Callaghan, Newcastle, NSW 2308, Australia
    Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW 2305, Australia)

  • Allison M. Hodge

    (Cancer Epidemiology Centre, Cancer Council Victoria, Melbourne, VIC 3004, Australia
    Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia)

  • Alexis J. Hure

    (School of Medicine and Public Health, University of Newcastle, Callaghan, Newcastle, NSW 2308, Australia
    Hunter Medical Research Institute, New Lambton Heights, Newcastle, NSW 2305, Australia)

Abstract

Diet quality indices (DQIs) can be useful predictors of diet–disease relationships, including non-communicable disease (NCD) multimorbidity. We aimed to investigate whether overall diet quality (DQ) predicted NCD, multimorbidity, and all-cause mortality. Women from the 1945–51 cohort of the Australia Longitudinal Study on Women’s Health (ALSWH) were included if they: responded to S3 in 2001 and at least one survey between 2004 (S4) and 2016 (S8), and had no NCD history and complete dietary data at S3. DQ was summarized by the Healthy Eating Index for Australian Adults-2013 (HEIFA-2013), Mediterranean Diet Score (MDS), and Alternative Healthy Eating Index-2010 (AHEI-2010). Outcomes included each NCD (diabetes mellitus (DM), coronary heart disease (CHD), hypertension (HT), asthma, cancer (except skin cancer), depression and/or anxiety) independently, multimorbidity, and all-cause mortality. Repeated multivariate logistic regressions were used to test associations between DQIs and NCD outcomes across the 15 years of follow-up. The mean (±sd) of DQIs of participants ( n = 5350) were 57.15 ± 8.16 (HEIFA-2013); 4.35 ± 1.75 (MDS), and 56.01 ± 10.32 (AHEI-2010). Multivariate regressions indicated that women reporting the highest quintile of AHEI-2010 had lower odds of DM (42–56% (S5–S8)), HT (26% (S8)), asthma (35–37% (S7, S8)), and multimorbidity (30–35% (S7, S8)). The highest quintile of HEIFA-2013 and MDS had lower odds of HT (26–35% (S7, S8); 24–27% (S6–S8), respectively) and depression and/or anxiety (30% (S6): 30–34% (S7, S8)). Our findings support evidence that DQ is an important predictor of some NCDs and a target for prevention in middle-aged women.

Suggested Citation

  • Hlaing Hlaing-Hlaing & Xenia Dolja-Gore & Meredith Tavener & Erica L. James & Allison M. Hodge & Alexis J. Hure, 2021. "Diet Quality and Incident Non-Communicable Disease in the 1946–1951 Cohort of the Australian Longitudinal Study on Women’s Health," IJERPH, MDPI, vol. 18(21), pages 1-21, October.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11375-:d:667805
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    References listed on IDEAS

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    1. Archana Singh-Manoux & Aurore Fayosse & Séverine Sabia & Adam Tabak & Martin Shipley & Aline Dugravot & Mika Kivimäki, 2018. "Clinical, socioeconomic, and behavioural factors at age 50 years and risk of cardiometabolic multimorbidity and mortality: A cohort study," PLOS Medicine, Public Library of Science, vol. 15(5), pages 1-16, May.
    2. Christopher Harrison & Joan Henderson & Graeme Miller & Helena Britt, 2017. "The prevalence of diagnosed chronic conditions and multimorbidity in Australia: A method for estimating population prevalence from general practice patient encounter data," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-13, March.
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