Quality of Diet and Nutritional Intake and Mortality Risk among South Korean Adults Based on 12-year Follow-up Data

Abstract

Objectives: Studies that reported the association between diet quality/nutritional intake status and mortality have rarely used long-term follow-up data in Asian countries, including Korea. This study investigated the association between the risk of mortality (all-cause and cause-specific) and the diet quality/nutritional intake status using followup 12-year mortality data from a nationally representative sample of South Koreans.Methods: 8,941 individuals who participated in 1998 and 2001 Korea Health and Nutrition Examination Surveys were linked to mortality data from death certificates. Of those individuals, 1,083 (12.1%) had died as of December, 2012. Cox proportional hazard models were used to estimate the relative risks of mortality according to the level of diet quality and intakes of major nutrients. Indicators for diet quality index and nutritional intake status were assessed using MAR (mean adequacy ratio) and energy and protein intake level compared with the 2010 Korean DRI.Results: Higher diet quality/nutritional intake status were associated with lower mortality; the mortality risk (95% confidence interval) from all-cause of lowest MAR group vs highest was 1.66 (1.27 to 2.18) among ≥ 30 year old, and 1.98 (1.36 to 2.86) among 30~64 year old individuals. Those with below 75% of energy and protein intake of Korean DRI had higher mortality risks of all-cause mortality compared to the reference group. Diet quality/nutritional intake status was inversely associated with mortality from cardiovascular diseases and cancer.Conclusions: Poor Diet quality/nutritional intake status were associated with a higher risk of mortality from all-cause and mortality from cardiovascular diseases and cancer among South Korean adults.Korean J Community Nutr 21(4): 354~365, 2016

Do Where The Elderly Live Matter? Factors Associated with Diet Quality among Korean Elderly Population Living in Urban Versus Rural Areas

“…1 % (SE), which were calculated by applying sampling weights assigned to individual participants in the nutrition survey and health examination. 2 p-values for % differences between urban and rural area were calculated using the chi-square test. 3 Including housewives or students.…”

“…According to the recent reports from the Global Burden of Disease Study in 2017, poor diet accounted for more than 50% of deaths and 66% of disability-adjusted life years [1]. A recent Korean study also confirmed that poor diet quality was associated with a higher mortality for all-cause, cardiovascular disease, and cancer, while using 12-year follow-up mortality data from a nationally representative sample [2].…”

The relationship between diet quality indices and odds of breast cancer in women: a case–control study

“…However, according to our findings looking at BrCa, no significant relationship was observed. Also, in a study by Vargas et al, a higher DQI-I score was associated with a reduced risk of colorectal cancer [ 30 ], whilst in a 12 year follow-up study in South Korea, the results showed that higher MAR index was associated with a reduction in cancer and cardiovascular disease mortality by 66% and 98%, in those under 30 years of age and over 30 years of age, respectively [ 31 ]. However, in Arthur et al, inconsistent to our results, the authors reported that higher intake of Western diets and higher energy density (high consumption of red meat, processed meats, refined grains, high-fat dairy and desserts), compared the Mediterranean diet with lower energy density (high consumption of fruits, vegetables, whole grains, poultry, fish and legumes), was associated with an increased odds of hormone-dependent cancers [ 17 ].…”

Association between Breakfast Frequency and Atherosclerotic Cardiovascular Disease Risk: A Cross-Sectional Study of KNHANES Data, 2014–2016

“…Nutrition status was categorized by mean nutrient adequacy ratio [MAR(9)]. MAR(9) calculates the nutrient adequacy ratio (NAR) of nine nutrients (protein, calcium, phosphorus, iron, vitamin A, vitamin B1, vitamin B2, niacin, and vitamin C), adds them all together, and divides by nine [26]. A MAR(9) score of >0.75 was categorized as good and a score <0.75 was categorized as poor [27].…”