Yan-Feng Zhou1, Xing-Yue Song1, Jing Wu1, Guo-Chong Chen2, Nithya Neelakantan3, Rob M van Dam4, Lei Feng5, Jian-Min Yuan6, An Pan7, Woon-Puay Koh8. 1. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China. 2. Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Nutrition and Food Hygiene, School of Public Health, Soochow University, Suzhou, Jiangsu Province, China. 3. Saw Swee Hock School of Public Health, National University of Singapore, Singapore. 4. Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA. 5. Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. 6. UPMC Hillman Cancer Center, University of Pittsburgh, Pittsburgh, PA, USA; Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 7. Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China; State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei Province, China. Electronic address: panan@hust.edu.cn. 8. Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore. Electronic address: woonpuay.koh@duke-nus.edu.sg.
Abstract
OBJECTIVE: To examine the associations between dietary patterns in midlife and likelihood of future healthy ageing in Chinese older adults. DESIGN: Prospective population-based study. SETTING AND PARTICIPANTS: We included 14,159 participants aged 45-74 years who were free from cancer, cardiovascular disease, or diabetes at baseline (1993-1998) from the Singapore Chinese Health Study. METHODS: Dietary intakes in midlife were assessed by a validated food frequency questionnaire at baseline. Diet quality was scored according to the alternate Mediterranean diet (aMED), the Dietary Approaches to Stop Hypertension (DASH) diet, the alternative Healthy Eating Index (AHEI)-2010, overall plant-based diet index (PDI), and healthful plant-based diet index (hPDI). Healthy ageing was assessed at the third follow-up visit (2014-2016), which occurred about 20 years after the baseline visit, and was defined as the absence of 10 chronic diseases, no impairment of cognitive function, no limitations in instrumental activities of daily living, no clinical depression at screening, good overall self-perceived health, good physical functioning, and no function-limiting pain among participants who had survival to at least 65 years of age. Multivariable-adjusted logistic regression models were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between each dietary pattern score and healthy ageing. RESULTS: About 20.0% of participants met the healthy ageing criteria. The OR (95% CI) for healthy ageing comparing the highest with the lowest quartile of diet quality scores was 1.52 (1.31-1.77) for aMED, 1.53 (1.35-1.73) for DASH, 1.39 (1.23-1.57) for AHEI-2010, 1.34 (1.18-1.53) for PDI, and 1.45 (1.27-1.65) for hPDI (all P-trend < .001). Each standard deviation increment in different diet quality scores was associated with 12% to 18% higher likelihood of healthy ageing. CONCLUSIONS AND IMPLICATIONS: In this Chinese population, adherence to various healthy dietary patterns at midlife is associated with higher likelihood of healthy ageing at later life.
OBJECTIVE: To examine the associations between dietary patterns in midlife and likelihood of future healthy ageing in Chinese older adults. DESIGN: Prospective population-based study. SETTING AND PARTICIPANTS: We included 14,159 participants aged 45-74 years who were free from cancer, cardiovascular disease, or diabetes at baseline (1993-1998) from the Singapore Chinese Health Study. METHODS: Dietary intakes in midlife were assessed by a validated food frequency questionnaire at baseline. Diet quality was scored according to the alternate Mediterranean diet (aMED), the Dietary Approaches to Stop Hypertension (DASH) diet, the alternative Healthy Eating Index (AHEI)-2010, overall plant-based diet index (PDI), and healthful plant-based diet index (hPDI). Healthy ageing was assessed at the third follow-up visit (2014-2016), which occurred about 20 years after the baseline visit, and was defined as the absence of 10 chronic diseases, no impairment of cognitive function, no limitations in instrumental activities of daily living, no clinical depression at screening, good overall self-perceived health, good physical functioning, and no function-limiting pain among participants who had survival to at least 65 years of age. Multivariable-adjusted logistic regression models were applied to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between each dietary pattern score and healthy ageing. RESULTS: About 20.0% of participants met the healthy ageing criteria. The OR (95% CI) for healthy ageing comparing the highest with the lowest quartile of diet quality scores was 1.52 (1.31-1.77) for aMED, 1.53 (1.35-1.73) for DASH, 1.39 (1.23-1.57) for AHEI-2010, 1.34 (1.18-1.53) for PDI, and 1.45 (1.27-1.65) for hPDI (all P-trend < .001). Each standard deviation increment in different diet quality scores was associated with 12% to 18% higher likelihood of healthy ageing. CONCLUSIONS AND IMPLICATIONS: In this Chinese population, adherence to various healthy dietary patterns at midlife is associated with higher likelihood of healthy ageing at later life.