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Special Article
 Nutrition survey methods and food composition database update of the Korean Genome and Epidemiology Study
Seon-Joo Park1,2*orcid, Jieun Lyu3*orcid, Kyoungho Lee3orcid, Hae-Jeung Lee1,2,4orcid, Hyun-Young Park5orcid
Epidemiol Health 2024;46:e2024042.
DOI: https://doi.org/10.4178/epih.e2024042
Published online: April 2, 2024

1Department of Food and Nutrition, College of BioNano Technology, Gachon University, Seongnam, Korea

2Institute for Aging and Clinical Nutrition Research, Gachon University, Seongnam, Korea

3Division of Population Health Research, Department of Precision Medicine, Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju, Korea

4Department of Health Sciences and Technology, Gachon Advanced Institute for Health Science and Technology (GAIHST), Gachon University, Incheon, Korea

5Korea National Institute of Health, Korea Disease Control and Prevention Agency, Cheongju, Korea

Correspondence: Hyun-Young Park Korea National Institute of Health, Korea Disease Control and Prevention Agency, 187 Osongsaengmyeong 2-ro, Heungdeok-gu, Cheongju 28160, Korea E-mail: hypark65@korea.kr
Co-correspondence: Hae-Jeung Lee Department of Food and Nutrition, College of BioNano Technology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam 13120, Korea E-mail: skysea1010@gmail.com
*Park & Lyu contributed equally to this work as joint first authors.
• Received: September 23, 2023   • Accepted: March 8, 2024

© 2024, Korean Society of Epidemiology

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • This study presents the nutrition survey methods and the updated food composition database for the Korean Genome and Epidemiology Study (KoGES). The KoGES, which is the largest and longest cohort study in Korea, aims to identify genetic and environmental factors associated with chronic diseases. This study has collected dietary data using a validated semi-quantitative food frequency questionnaire and/or the 24-hour recall method. However, these dietary survey methods use different food composition databases, and their nutritional values are out of date. Therefore, it became necessary to update the food composition database by revising nutrient analysis values to reflect improvements in the performance of food ingredient analysis equipment, revising international values to analysis values of Korean agricultural products, adjusting nutrient units, and adding newly reported nutrients related to chronic diseases. For this purpose, we integrated the different food composition databases used in each nutrition survey, updated 23 nutrients, and expanded 48 new nutrients for 3,648 food items using the latest reliable food composition databases published by national and international institutions. This revised food composition database may help to clarify the relationship between various nutrients and chronic diseases. It could serve as a valuable resource for nutritional, epidemiological, and genomic research and provide a basis for determining public health policies.
  • Keywords: Nutrition survey, Database, Korea, Korean Genome and Epidemiology Study
The Korean Genome and Epidemiology Study (KoGES) is the largest and longest-running cohort study in Korea aimed at identifying the relationship between chronic diseases and nutrient intake. This paper provides a detailed methods of the nutritional surveys and introduces updates to the existing 23 nutrients and the addition of 48 new nutrients. These enhancements are expected to significantly increase the utility of the KoGES nutritional data.
The Korea Genome and Epidemiology Study (KoGES) is the largest cohort study in Korea, and it has been conducted by the Korea Disease Control and Prevention Agency since 2001. The purpose of the KoGES is to identify the associations between genetic and environmental factors and interactions with chronic diseases prevalent among Koreans. The general profile of the KoGES has been described previously [1]. However, a detailed description of the dietary survey methods and food composition databases used to calculate nutrient intake has not yet been published. The nutrition survey of the KoGES has been conducted using a semiquantitative food frequency questionnaire (FFQ) and/or 24-hour recall methods according to the characteristics of the sub-cohort. The nutrient intake of the FFQ was calculated using the food composition table of the Korean Nutrition Society published in 2000 [2], and the nutrient intakes for 24-hour recall were calculated by modifying the food composition table of the Rural Development Administration, published in 2006 [3]. These dietary survey methods use different food composition databases; furthermore, their nutritional values are outdated and need integration and updating to reflect current knowledge regarding the nutritional content of foods. Therefore, it is necessary to revise the food composition database by addressing inaccuracies in nutrient analysis values resulting from advancements in food ingredient analysis technology, revising overseas values to analysis values of Korean agricultural products, adjusting nutrient units, and adding nutrients that have been newly reported to be related to chronic diseases.
Food composition databases play a pivotal role in estimating food and nutrient intake at individual and population levels, which is important for elucidating the relationship between nutrient intake and diseases [4]. Regular database updates, such as expanding the number of nutrients reported to be related to chronic diseases, are imperative for improving quality and meeting the evolving needs of data users.
Recent international studies have reported relationships between the intake of various nutrients, such as dietary fiber [5], branched-chain amino acids [6], sugar [7], and saturated fatty acids [8], and chronic diseases. However, these nutrients are not currently released by KoGES.
The Korean food composition table, published by the Rural Development Administration, is a representative food composition table in Korea that displays the nutrients per 100 g of food. In 2017, the Korea Food and Drug Administration expanded the number of nutrients to 130 and increased the number of foods analyzed in Korea [9,10]. These updates need to be reflected in the KoGES food composition database.
In this study, we (1) present detailed information on the nutrition survey of the KoGES, (2) describe the process of revising the food composition database, (3) present the changes in the revision of the food composition database using previously collected dietary survey data and discuss the reasons for the changes, and (4) suggest considerations for using the revised food composition database.
Nutrition survey tools of the Korea Genome and Epidemiology Study
The KoGES uses a validated FFQ and the 24-hour recall method to assess the nutritional status of the participants. A 103-item FFQ was developed for the KoGES dietary assessment in 2001 [11] and then expanded to 106 items in 2004 to investigate participants’ eating habits more accurately. In the 106-item FFQ, the number of cooked rice types increased from 3 to 5, and meat-based soups (tang) (e.g., sullungtang, gomtang, etc.), which are frequently consumed by Koreans, were added. The baseline survey (2001-2002) in the Ansan and Ansung cohort study used a 103-item FFQ.
In the FFQ questionnaire, the frequency of food intake was classified into 9 categories. Portion size was categorized as small, medium, or large. For seasonal fruits and vegetables, participants reported their intake, frequency, and amount at 3 months, 6 months, 9 months, and 12 months, respectively. To ensure the accuracy of the survey, representative pictures of large, medium, and small portions of food were included in the questionnaire to help participants (Supplementary Material 1). Nutrient intake from each food item in the FFQ was calculated using weights derived from the consumption frequency and portion size of each food item. The daily nutrient intake of each individual was calculated as the sum of the nutrient intake from each food item. The KoGES used the FFQ as the main dietary survey intake method, with 24-hour recall used as an additional method for dietary measurement. The Health Examinee (HEXA) study collected 2 non-consecutive days of 24-hour recall data. The first-day survey was conducted face-to-face at the examination center, while the second-day survey was conducted via telephone interviews. The immigrant study collected 1-day 24-hour recall data via face-to-face interviews.
Protocol of the Korea Genome and Epidemiology Study nutrition survey
Figure 1 presents the protocol of the nutrition survey, including the dietary assessment tool, duration of the nutrition survey, and number of participants in each sub-cohort.
In the Ansan and Ansung study, 103-item FFQ data were collected from 9,704 participants during the baseline survey (2001-2002). In 2 follow-up surveys conducted in 2005-2006 and 2017-2020, 106-item FFQ data were collected from 7,482 and 4,937 participants, respectively. In the HEXA study, 106-item FFQ data were collected from 170,137 participants during the baseline survey and 65,312 participants during the follow-up survey. Additionally, 2 days of non-consecutive 24-hour recall data were collected from 90,636 participants between 2011 and 2015. The Cardiovascular Disease Association Study (CAVAS) collected FFQ data from 28,160 individuals during a baseline survey (2005-2011) and follow-up FFQ data during 2007-2016. The Twin and Family study collected FFQ data from 3,059 participants in the baseline survey (2005-2013) and 2,024 participants in the follow-up study (2008-2014). For the Japanese Emigrant study, FFQ data were collected from 1,031 baseline survey participants using a self-developed FFQ to reflect the eating habits of Korean emigrants living in Japan. In the Immigrant study, the nutrient intake of Vietnamese women and their Korean husbands was assessed using the 24-hour recall method, with 4,468 participants in the baseline survey and 1,105 participants in the follow-up survey.
The KoGES currently releases information on the intake of 23 nutrients: energy, protein, fat, carbohydrates, ash, fiber, calcium, phosphorus, iron, potassium, zinc, sodium, vitamin A, retinol, β-carotene, vitamin E, vitamin B1, vitamin B2, niacin, vitamin C, vitamin B6, folate, and cholesterol. The process of revising the food composition database is shown in Figure 2.
Integrating the food composition databases of both dietary assessment methods
To integrate the food composition database used for the FFQ with that used for the 24-hour recall method, the 423 foods used in the FFQ were matched to foods in the food composition database used for 24-hour recall. First, Korean and English food names were matched. Then, the energy, carbohydrate, protein, fat, and water content of each food were compared. If the nutrient differences were significant (≥ ± 20%), even when the Korean name or English name was the same, other similar foods with similar nutrient contents were matched (for example, “Chicken, Meat and skin, Stewed” matched with “Chicken, Meat, Breast, Stewed”). An expert reviewed the suitability of the matched foods. The final target food list for update and expansion included 3,648 foods, consisting of 423 food items used in the FFQ and 3,642 food items used in the 24-hour recall method.
Selection of food composition databases and revision process
In this study, an indirect method was applied using the latest and most reliable food composition databases published by national institutions. We assessed the availability of Korean and international food composition databases and selected 7 Korean and 2 international databases, as follows: Korean Food Composition Table edition 9.2 [12] and Korean Food Composition Table edition 9.1 [10]. Korean Food Composition Table, 9th edition [13], Korean Food Composition Table, 8th edition [14]. Korean Food Composition Table, 7th edition [3], Food-Nutrient Information of the Korean National Health and Nutrition Examination Survey [15], Food Nutrient Database of the Korean Ministry of Food and Drug Safety [16], the 7th revision of the Japanese Food Standard Ingredients Table [17], and United States Department of Agriculture (USDA) National Nutrient Database for Standard Reference, SR 28 (updated in June 2019) [18].
The process of matching the 3,648 foods with each food composition database was as follows: First, Korean food names, English food names, and food conditions (raw, cooked, dry, etc.) were considered. Second, the energy, carbohydrate, protein, fat, and water content of each food were compared. If the nutrient differences were significant (≥ ± 20%), even when they had the same names, the food with the most similar cooking or processing status was substituted. If there were no similar foods, they were matched by considering the following factors: cooking, processing, drying conditions, and moisture content. For processed food with a specific brand product name, the energy and fat values were assessed using information on the company webpage and matched.
As for the order of application, if a nutrient was found in the most recently published food composition table, that value was applied. The food composition table values were reviewed sequentially, and if there were no newly published nutrient values, existing nutrient values were used. The sequence of applying the food composition databases was as follows: Korean Food Composition Table edition 9.2 [12], Korean Food Composition Table edition 9.1 [10], Korean Food Composition Table, 9th edition [13], Korean Food Composition Table, 8th edition [14], Korean Food Composition Table, 7th edition [3], Food Composition Table of the Korean National Health and Nutrition Examination Survey [15], Food Nutrient Database of the Korean Ministry of Food and Drug Safety [16], 7th revision of the Japanese Food Standard Ingredients Table [17], and the USDA National Nutrient Database for Standard Reference, SR 28 (updated in June 2019) [18].
Units of nutrients
Vitamin B6 values in the previous food composition database used pyridoxine values. The most recently published food composition database had a total vitamin B6 value; however, the number of foods with total vitamin B6 values was too small to use. Therefore, we updated the vitamin B6 values using the pyridoxine values. Previously, fiber intake was calculated using crude fiber data; however, with this revision, fiber values were updated with the total dietary fiber value.
The pre-revision unit for vitamin A was the retinol equivalent (RE). However, since 2015, the Dietary Reference Intakes for Koreans have used retinol activity equivalent (RAE) as the unit of vitamin A. Therefore, both RE and RAE were presented in the revised food composition database.
RE and RAE were calculated using the following formulas based on retinol and carotene contents.
RE (retinol equivalent)= retinol+(β-carotene/6)
RAE (retinol activity equivalent)= retinol+(β-carotene/12)
Comparison of nutrient intake between pre- and post-revision
To observe the differences resulting from the revision of the food composition database, changes in the intake of 23 nutrients before and after the revision were compared using the food frequency questionnaire data of 205,785 participants (170,143 in the HEXA study; 28,160 in the CAVAS; and 7,482 in the Ansan and Ansung study). We calculated the absolute and percentage changes in 23 nutrients. The mean and standard deviation of each nutrient were calculated, and the paired t-test was performed to compare the pre-revision and post-revision values.
The percentage difference between the pre-revision and post-revision values was calculated as follows:
Change (%)= [(Post-revision value−Pre-revision value)/Pre-revision value]× 100
All nutrient intake calculations and statistical analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA).
Changes in nutrient intake in the revision of the food composition database and reasons for the changes
Table 1 shows the changes in nutrient intake before and after updating the food composition database for the 205,785 participants. The energy and carbohydrate intake increased by 51.2 kcal (3.1%) and 16.3 g (5.4%), respectively, while protein and fat intake decreased by 1.6 g (-2.5%) and 1.4 g (-6.6%), respectively (Table 1).
Sodium intake (from 2,575.2 to 1,977.7 mg, -22.2%) decreased due to the decrease in sodium of cabbage kimchi, a major source of sodium in Korea, from 1,146.0 mg to 624.0 mg per 100 g (-522.0 mg). The nutrient that increased the most after the update was fiber (348.4%). Among the 423 foods used in the FFQ, the foods with the largest change in fiber content per 100 g and the highest fiber contribution were cooked white rice (0.1-1.2 g) and black beans (4.0-20.8 g). The nutrient with the largest decrease was vitamin B6 (55.2%). This was due to a decrease in the amount of vitamin B6 (pyridoxine) per 100 g of white sugar (0.9-0.0 g), bean sprouts (0.96-0.01 g), black beans (0.54-0.05 g), and cooked white rice (0.06-0.02 g) (data not shown).
These changes in nutrient intake were caused by changes in the nutrient content of the same foods. The differences in the nutrient content for each food may be attributed to improved analytical methods and techniques, better food sampling methods, and the replacement of international food composition table values with Korean analytical values [19,20]. As shown by these results, changes in database values for frequently consumed foods can significantly affect an individual’s nutrient intake [20].
Changes in the participants’ intake of 23 nutrients across the 3 subcohorts (Ansan and Ansung study, HEXA study, and CAVAS) are presented in Supplementary Material 2.
Selection of expanded nutrients
To select an expanded range of nutrients, we conducted an online survey to examine the needs of KoGES data users and academic society members and a literature review to select nutrients that recent publications have identified as relevant to chronic diseases. From an expert consultation meeting to discuss the results of the online survey and literature review, 48 nutrients were selected that were highly recommended by researchers and correlated with chronic diseases (Table 2). The 48 selected nutrients are as follows: total sugar, soluble fiber, insoluble fiber, vitamin D, vitamin B12, magnesium, iodine, selenium, copper, branched-chain amino acids (histidine, leucine, isoleucine, lysine, valine, phenylalanine, threonine, tryptophan, methionine), and fatty acids (saturated fatty acid, monounsaturated fatty acid, polyunsaturated fatty acid, omega-3 fatty acid, omega-6 fatty acid, capric acid, lauric acid, myristic acid, palmitic acid, stearic acid, arachidic acid, behenic acid, lignoceric acid, myristoleic acid, palmitoleic acid, oleic acid, gadoleic acid, erucic acid, nervonic acid, linoleic acid, α-linolenic acid, γ-linolenic acid, stearidonic acid, eicosadienic acid, icosatrienoic acid, arachidonic acid, eicosapentaenoic acid, docosadienoic acid, docosapentaenoic acid, and docosahexaenoic acid).
The new 48 nutrient values for the 3,648 foods were constructed using the same process as the 23 nutrient updates. Table 3 shows the newly expanded nutrient intakes for 205,785 participants. The coverage of the 423 foods with the 48 newly expanded nutrients is shown in Supplementary Material 3. The coverage of the 423 foods used in the FFQ ranged from 58.6% (soluble/insoluble fiber) to 99.5% (vitamin B12). Therefore, when researchers use these data to analyze the relationship between the intake of soluble and insoluble dietary fiber, vitamin D, magnesium, copper, iodine, and amino acids and disease, they should consider the possibility that the intake has been underestimated.
In this study, we introduced the nutritional survey methods, the current status of the survey and results of the updated KoGES food composition database. For a total of 3,648 foods, 23 nutrients were updated and 48 nutrients were newly included using Korean and international nutrient food composition databases. The updated food composition database described in this study reflects the most recently published food composition table from reliable national institutes; thus, using this database is likely to improve the accuracy of research on the dietary intake of Koreans. In addition, the expanded range of nutrients may be useful for researchers aiming to analyze the relationships between various nutrients and chronic diseases.
Ethics statement
This study was approved by the Institutional Review Boards of Korea Disease Control and Prevention Agency (KDCA) (IRB No. 2018-03-05-P-A).
Supplementary materials are available at https://doi.org/10.4178/epih.e2024042.

Supplementary Material 1.

Food frequency questionnaire of KoGES
epih-46-e2024042-Supplementary-1.docx

Supplementary Material 2.

Comparison of nutrient intake after revision of the food composition database in the three subcohorts
epih-46-e2024042-Supplementary-2.docx

Supplementary Material 3.

Coverage status of newly expanded nutrients
epih-46-e2024042-Supplementary-3.docx

Conflict of interest

The authors have no conflicts of interest to declare for this study.

Funding

This research was supported by the “Korea National Institute of Health” (KNIH) Research project (No. 2019-E7109-00).

Author contributions

Conceptualiz ation: Lee HJ, Park HY. Data curation: Park SJ, Lyu J. Formal analysis: Park SJ, Lyu J. Funding acquisition: Park HY. Methodology: Lee HJ, Park HY. Project administration: Lee HJ. Visualization: Lyu J. Writing – original draft: Park SJ, Lyu J. Writing – review & editing: Lee HJ, Park HY, Lee K.

We acknowledge all the participants and staff of the KoGES.
Figure 1.
Nutrition survey period, tools, and participants of KoGES (February 2023). KoGES, Korean Genome and Epidemiology Study; HEXA, Health Examinee; CAVAS, Cardiovascular Disease Association Study; FFQ, food frequency questionnaire. 1103 Food items FFQ. 2Self-developed FFQ.
epih-46-e2024042f1.jpg
Figure 2.
Revision process of food composition database. FFQ, food frequency questionnaire; USDA, United States Department of Agriculture.
epih-46-e2024042f2.jpg
epih-46-e2024042f3.jpg
Table 1.
Comparison of nutrient intakes after revision of the food composition database for 3 pooled cohorts (n=205,785)1
Nutrients Pre-revision value Post-revision value Overall difference % of pre-revision value (Post-revision value/Pre-revision value)×100 (%) Change (%) [(Post-revision value–Pre-revision value)/Pre-revision value]×100 p-value2
Energy (kcal) 1,746.40±1.30 1,797.60±1.32 51.2 1.0 103.1 3.1 <0.001
Protein (g) 58.80±0.06 57.20±0.06 -1.6 1.0 97.5 -2.5 <0.001
Fat (g) 27.40±0.04 26.00±0.04 -1.4 0.9 93.4 -6.6 <0.001
Carbohydrate (g) 311.80±0.21 328.00±0.22 16.3 1.1 105.4 5.4 <0.001
Ash (g) 15.70±0.02 10.10±0.01 -5.5 0.7 68.4 -31.6 <0.001
Fiber (g) 5.80±0.01 24.50±0.02 18.7 4.5 448.4 348.4 <0.001
Calcium (mg) 442.70±0.61 465.10±0.61 22.5 1.1 107.2 7.2 <0.001
Phosphorous (mg) 888.20±0.84 800.30±0.84 -87.9 0.9 88.8 -11.2 <0.001
Iron (mg) 9.90±0.01 8.90±0.01 -1.1 0.9 93.0 -7.0 <0.001
Potassium (mg) 2,245.60±2.48 1,972.80±2.30 -272.8 0.9 87.5 -12.5 <0.001
Zinc (mg) 7.90±0.01 8.20±0.01 0.3 1.0 104.8 4.8 <0.001
Sodium (mg) 2,575.20±3.35 1,977.70±2.66 -597.5 0.8 77.8 -22.2 <0.001
Vitamin A (RE) 479.60±0.81 487.20±0.85 7.6 1.0 103.1 3.1 <0.001
Retinol (µg) 66.70±0.14 91.80±0.20 25.2 1.3 133.5 33.5 <0.001
β-carotene (µg) 2,404.80±4.40 2,373.20±4.56 -31.6 1.0 99.9 -0.1 <0.001
Vitamin E (mg) 8.10±0.01 7.70±0.01 -0.4 1.0 97.0 -3.0 <0.001
Vitamin B1 (mg) 1.00±0.00 0.80±0.00 -0.2 0.8 83.6 -16.4 <0.001
Vitamin B2 (mg) 0.90±0.00 1.10±0.00 0.2 1.2 116.4 16.4 <0.001
Niacin (mg) 14.30±0.01 9.70±0.01 -4.6 0.7 66.2 -33.8 <0.001
Vitamin C (mg) 106.30±0.16 71.00±0.11 -35.4 0.7 66.8 -33.2 <0.001
Vitamin B6 (mg) 1.60±0.00 0.70±0.00 -0.9 0.4 44.8 -55.2 <0.001
Folate (μg) 217.50±0.29 237.10±0.28 19.5 1.1 113.4 13.4 <0.001
Cholesterol (mg) 161.40±0.29 138.20±0.24 -23.3 0.9 88.6 -11.4 <0.001

Values are presented as mean±standard error.

RE, retinol equivalent.

1 Three pooled cohorts: Ansan and Ansung study, Health Examinee (HEXA) study, Cardiovascular Disease Association Study (CAVAS).

2 Paired t-test.

Table 2.
Expanded nutrient names and reasons for selection
Category (n) Nutrients Reason for selection
Dietary fiber (2) Soluble Diabetes, obesity, cardiovascular disease
Insoluble
Vitamins (2) Vitamin D High demand for researchers. diabetes, metabolic syndrome, hypertension, obesity
Vitamin B12 Diabetes, hypertension, obesity
Minerals (4) Magnesium Diabetes, hypertension, cardiovascular disease
Iodine Diabetes, hypertension, obesity
Selenium Diabetes, hypertension
Copper Diabetes, metabolic syndrome, cardiovascular disease
Branched-chain amino acids (9) Histidine, leucine, isoleucine, lysine, valine, phenylalanine, threonine, tryptophan, methionine Diabetes, hyperlipidemia
Sugar Total sugar Diabetes, metabolic syndrome, hypertension, obesity, hyperlipidemia, cardiovascular disease
Fatty acids (30) Saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega-3 fatty acids, omega-6 fatty acids, capric acid, lauric acid, myristic acid, palmitic acid, stearic acid, arachidic acid, behenic acid, lignoceric acid, myristoleic acid, palmitoleic acid, oleic acid, gadoleic acid, erucic acid, nervonic acid, linoleic acid, α-linolenic acid, γ-linolenic acid, stearidonic acid, eicosadienic acid, eicosatrienoic acid, arachidonic acid, eicosapentaenoic acid, docosadienoic acid, docosapentaenoic acid, docosahexaenoic acid High demand for researchers, diabetes, metabolic syndrome, obesity, hyperlipidemia, cardiovascular disease
Table 3.
Intake of 48 expanded nutrients in KoGES participants (n=205,785)
Nutrients Mean±SE
Total sugars (g) 52.90±0.07
Water-soluble dietary fiber (g) 3.50±0.01
Water-insoluble dietary fiber (g) 9.30±0.01
Vitamin D (μg) 7.70±0.01
Vitamin B12 (μg) 4.80±0.01
Magnesium (mg) 139.20±0.19
Iodine (μg) 336.50±1.05
Selenium (μg) 38.60±0.06
Copper (mg) 0.40±0.00
Histidine (mg) 728.90±1.08
Leucine (mg) 1,976.20±2.89
Isoleucine (mg) 1,017.40±1.49
Lysine (mg) 1,534.90±2.32
Valine (mg) 1,218.30±1.78
Phenylalanine (mg) 1,171.90±1.69
Threonine (mg) 1,085.00±1.58
Tryptophan (mg) 284.80±0.41
Methionine (mg) 530.50±0.80
Saturated fatty acids (g) 9.90±0.02
Monounsaturated fatty acids (g) 8.10±0.01
Polyunsaturated fatty acids (g) 5.20±0.01
Omega-3 fatty acids (g) 1.00±0.00
Omega-6 fatty acids (g) 4.10±0.01
Capric acid (mg) 162.40±0.30
Lauric acid (mg) 702.70±1.39
Myristic acid (mg) 804.30±1.37
Palmitic acid (mg) 4,980.80±8.35
Stearic acid (mg) 1,972.50±3.59
Arachidic acid (mg) 54.40±0.09
Behenic acid (mg) 33.30±0.08
Lignoceric acid (mg) 22.50±0.04
Myristoleic acid (mg) 58.30±0.17
Palmitoleic acid (mg) 394.90±0.77
Oleic acid (mg) 6,230.10±11.02
Gadoleic acid (mg) 133.80±0.26
Erucic acid (mg) 47.10±0.13
Nervonic acid (mg) 6.00±0.01
Linoleic acid (mg) 3,814.90±5.96
α-linolenic acid (mg) 517.00±0.81
γ-linolenic acid (mg) 7.30±0.02
Stearidonic acid (mg) 21.60±0.06
Eicosadienoic acid (mg) 42.40±0.10
Eicosatrienoic acid (mg) 16.70±0.03
Arachidonic acid (mg) 44.00±0.12
Eicosapentaenoic acid (mg) 130.90±0.30
Docosadienoic acid (mg) 3.30±0.01
Docosapentaenoic acid (mg) 29.40±0.07
Docosahexaenoic acid (mg) 249.00±0.56

KoGES, Korean Genome and Epidemiology Study; SE, standard error.

  • 1. Kim Y, Han BG; KoGES group. Cohort profile: the Korean Genome and Epidemiology Study (KoGES) consortium. Int J Epidemiol 2017;46:e20. https://doi.org/10.1093/ije/dyv316.ArticlePubMedPMC
  • 2. Korean Nutrition Society. Food composition table; 2000 [cited 2020 Jan 10]. Available from: https://www.kns.or.kr/can66.0/FoodDB.asp (Korean).
  • 3. Rural Resources Development Institute. Food composition table. 7th revision. Suwon: Rural Resources Development Institute; 2006. (Korean).
  • 4. Ocké MC, Westenbrink S, van Rossum CT, Temme EH, van der Vossen-Wijmenga W, Verkaik-Kloosterman J. The essential role of food composition databases for public health nutrition–experiences from the Netherlands. J Food Compos Anal 2021;101:103967 https://doi.org/10.1016/j.jfca.2021.103967.Article
  • 5. Kokubo Y, Iso H, Saito I, Yamagishi K, Ishihara J, Inoue M, et al. Dietary fiber intake and risk of cardiovascular disease in the Japanese population: the Japan Public Health Center-based study cohort. Eur J Clin Nutr 2011;65:1233-1241 https://doi.org/10.1038/ejcn.2011.100.ArticlePubMed
  • 6. Qin LQ, Xun P, Bujnowski D, Daviglus ML, Van Horn L, Stamler J, et al. Higher branched-chain amino acid intake is associated with a lower prevalence of being overweight or obese in middleaged East Asian and Western adults. J Nutr 2011;141:249-254 https://doi.org/10.3945/jn.110.128520.ArticlePubMedPMC
  • 7. Malik VS, Pan A, Willett WC, Hu FB. Sugar-sweetened beverages and weight gain in children and adults: a systematic review and meta-analysis. Am J Clin Nutr 2013;98:1084-1102 https://doi.org/10.3945/ajcn.113.058362.ArticlePubMedPMC
  • 8. Zong G, Li Y, Wanders AJ, Alssema M, Zock PL, Willett WC, et al. Intake of individual saturated fatty acids and risk of coronary heart disease in US men and women: two prospective longitudinal cohort studies. BMJ 2016;355:i5796 https://doi.org/10.1136/bmj.i5796.ArticlePubMedPMC
  • 9. Park SH, Kim SN, Lee SH, Choe JS, Choi Y. Development of 9th revision Korean food composition table and its major changes. Korean J Community Nutr 2018;23:352-365 (Korean). https://doi.org/10.5720/kjcn.2018.23.4.352.Article
  • 10. Rural Development Administration. Korean food composition table, 9.1st edition. Jeonju: Rural Development Administration; 2019 (Korean).
  • 11. Ahn Y, Lee JE, Paik HY, Lee HK, Jo I. Development of a semiquantitative food frequency questionnaire based on dietary data from the Korea National Health and Nutrition Examination Survey. Nutr Sci 2003;6:173-184.
  • 12. Rural Development Administration. Korean food composition table. 9.2nd edition. Jeonju: Rural Development Administration; 2020. (Korean).
  • 13. Institute of Agricultural Sciences. Korean food composition table. 9th ed. Wanju: Institutu of Agricultural Science; 2016. (Korean).
  • 14. National Academy of Agricultural Sciences. Food composition table 8th revision. Suwon: National Academy of Agricultural Sciences; 2011. (Korean).
  • 15. Korea Disease Control and Prevention Agency. Food composition table; 2018 [cited 2020 Jan 10]. Available from: https://knhanes.kdca.go.kr/knhanes/sub03/sub03_02_05.do (Korean).
  • 16. Ministry of Food and Drug Safety. Food composition table; 2020 [cited 2023 Sep 1]. Available from: https://various.foodsafetykorea.go.kr/nutrient (Korean).
  • 17. Ministry of Education Culture Sports Science and Technology. The standard tables of food composition in Japan; 2015 (seventh revised edition) [cited 2020 Jan 10]. Available from: http://www.mext.go.jp/a_menu/syokuhinseibun/1365452.htm/ (Japanese).
  • 18. Haytowitz DB, Ahuja JK, Wu X, Somanchi M, Nickle M, Nguyen QA, et al. USDA National Nutrient Database for Standard Reference, Legacy Release. Nutrient Data Laboratory, Beltsville Human Nutrition Research Center, ARS, USDA 2019;https://doi.org/10.15482/USDA.ADC/1529216.Article
  • 19. Probst Y, Mamet C. The evolution of food composition databases in Australia: applying data from 1944 to 2007 to current day dietary records. J Food Compos Anal 2016;51:24-29 https://doi.org/10.1016/j.jfca.2016.05.014.Article
  • 20. Anderson E, Perloff B, Ahuja JK, Raper N. Tracking nutrient changes for trends analysis in the United States. J Food Compos Anal 2001;14:287-294 https://doi.org/10.1006/jfca.2001.0993.Article

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    Nutrition survey methods and food composition database update of the Korean Genome and Epidemiology Study
    Image Image Image
    Figure 1. Nutrition survey period, tools, and participants of KoGES (February 2023). KoGES, Korean Genome and Epidemiology Study; HEXA, Health Examinee; CAVAS, Cardiovascular Disease Association Study; FFQ, food frequency questionnaire. 1103 Food items FFQ. 2Self-developed FFQ.
    Figure 2. Revision process of food composition database. FFQ, food frequency questionnaire; USDA, United States Department of Agriculture.
    Graphical abstract

    Figure 1.

    Figure 2.

    Graphical abstract

    Nutrition survey methods and food composition database update of the Korean Genome and Epidemiology Study
    Nutrients Pre-revision value Post-revision value Overall difference % of pre-revision value (Post-revision value/Pre-revision value)×100 (%) Change (%) [(Post-revision value–Pre-revision value)/Pre-revision value]×100 p-value2
    Energy (kcal) 1,746.40±1.30 1,797.60±1.32 51.2 1.0 103.1 3.1 <0.001
    Protein (g) 58.80±0.06 57.20±0.06 -1.6 1.0 97.5 -2.5 <0.001
    Fat (g) 27.40±0.04 26.00±0.04 -1.4 0.9 93.4 -6.6 <0.001
    Carbohydrate (g) 311.80±0.21 328.00±0.22 16.3 1.1 105.4 5.4 <0.001
    Ash (g) 15.70±0.02 10.10±0.01 -5.5 0.7 68.4 -31.6 <0.001
    Fiber (g) 5.80±0.01 24.50±0.02 18.7 4.5 448.4 348.4 <0.001
    Calcium (mg) 442.70±0.61 465.10±0.61 22.5 1.1 107.2 7.2 <0.001
    Phosphorous (mg) 888.20±0.84 800.30±0.84 -87.9 0.9 88.8 -11.2 <0.001
    Iron (mg) 9.90±0.01 8.90±0.01 -1.1 0.9 93.0 -7.0 <0.001
    Potassium (mg) 2,245.60±2.48 1,972.80±2.30 -272.8 0.9 87.5 -12.5 <0.001
    Zinc (mg) 7.90±0.01 8.20±0.01 0.3 1.0 104.8 4.8 <0.001
    Sodium (mg) 2,575.20±3.35 1,977.70±2.66 -597.5 0.8 77.8 -22.2 <0.001
    Vitamin A (RE) 479.60±0.81 487.20±0.85 7.6 1.0 103.1 3.1 <0.001
    Retinol (µg) 66.70±0.14 91.80±0.20 25.2 1.3 133.5 33.5 <0.001
    β-carotene (µg) 2,404.80±4.40 2,373.20±4.56 -31.6 1.0 99.9 -0.1 <0.001
    Vitamin E (mg) 8.10±0.01 7.70±0.01 -0.4 1.0 97.0 -3.0 <0.001
    Vitamin B1 (mg) 1.00±0.00 0.80±0.00 -0.2 0.8 83.6 -16.4 <0.001
    Vitamin B2 (mg) 0.90±0.00 1.10±0.00 0.2 1.2 116.4 16.4 <0.001
    Niacin (mg) 14.30±0.01 9.70±0.01 -4.6 0.7 66.2 -33.8 <0.001
    Vitamin C (mg) 106.30±0.16 71.00±0.11 -35.4 0.7 66.8 -33.2 <0.001
    Vitamin B6 (mg) 1.60±0.00 0.70±0.00 -0.9 0.4 44.8 -55.2 <0.001
    Folate (μg) 217.50±0.29 237.10±0.28 19.5 1.1 113.4 13.4 <0.001
    Cholesterol (mg) 161.40±0.29 138.20±0.24 -23.3 0.9 88.6 -11.4 <0.001
    Category (n) Nutrients Reason for selection
    Dietary fiber (2) Soluble Diabetes, obesity, cardiovascular disease
    Insoluble
    Vitamins (2) Vitamin D High demand for researchers. diabetes, metabolic syndrome, hypertension, obesity
    Vitamin B12 Diabetes, hypertension, obesity
    Minerals (4) Magnesium Diabetes, hypertension, cardiovascular disease
    Iodine Diabetes, hypertension, obesity
    Selenium Diabetes, hypertension
    Copper Diabetes, metabolic syndrome, cardiovascular disease
    Branched-chain amino acids (9) Histidine, leucine, isoleucine, lysine, valine, phenylalanine, threonine, tryptophan, methionine Diabetes, hyperlipidemia
    Sugar Total sugar Diabetes, metabolic syndrome, hypertension, obesity, hyperlipidemia, cardiovascular disease
    Fatty acids (30) Saturated fatty acids, monounsaturated fatty acids, polyunsaturated fatty acids, omega-3 fatty acids, omega-6 fatty acids, capric acid, lauric acid, myristic acid, palmitic acid, stearic acid, arachidic acid, behenic acid, lignoceric acid, myristoleic acid, palmitoleic acid, oleic acid, gadoleic acid, erucic acid, nervonic acid, linoleic acid, α-linolenic acid, γ-linolenic acid, stearidonic acid, eicosadienic acid, eicosatrienoic acid, arachidonic acid, eicosapentaenoic acid, docosadienoic acid, docosapentaenoic acid, docosahexaenoic acid High demand for researchers, diabetes, metabolic syndrome, obesity, hyperlipidemia, cardiovascular disease
    Nutrients Mean±SE
    Total sugars (g) 52.90±0.07
    Water-soluble dietary fiber (g) 3.50±0.01
    Water-insoluble dietary fiber (g) 9.30±0.01
    Vitamin D (μg) 7.70±0.01
    Vitamin B12 (μg) 4.80±0.01
    Magnesium (mg) 139.20±0.19
    Iodine (μg) 336.50±1.05
    Selenium (μg) 38.60±0.06
    Copper (mg) 0.40±0.00
    Histidine (mg) 728.90±1.08
    Leucine (mg) 1,976.20±2.89
    Isoleucine (mg) 1,017.40±1.49
    Lysine (mg) 1,534.90±2.32
    Valine (mg) 1,218.30±1.78
    Phenylalanine (mg) 1,171.90±1.69
    Threonine (mg) 1,085.00±1.58
    Tryptophan (mg) 284.80±0.41
    Methionine (mg) 530.50±0.80
    Saturated fatty acids (g) 9.90±0.02
    Monounsaturated fatty acids (g) 8.10±0.01
    Polyunsaturated fatty acids (g) 5.20±0.01
    Omega-3 fatty acids (g) 1.00±0.00
    Omega-6 fatty acids (g) 4.10±0.01
    Capric acid (mg) 162.40±0.30
    Lauric acid (mg) 702.70±1.39
    Myristic acid (mg) 804.30±1.37
    Palmitic acid (mg) 4,980.80±8.35
    Stearic acid (mg) 1,972.50±3.59
    Arachidic acid (mg) 54.40±0.09
    Behenic acid (mg) 33.30±0.08
    Lignoceric acid (mg) 22.50±0.04
    Myristoleic acid (mg) 58.30±0.17
    Palmitoleic acid (mg) 394.90±0.77
    Oleic acid (mg) 6,230.10±11.02
    Gadoleic acid (mg) 133.80±0.26
    Erucic acid (mg) 47.10±0.13
    Nervonic acid (mg) 6.00±0.01
    Linoleic acid (mg) 3,814.90±5.96
    α-linolenic acid (mg) 517.00±0.81
    γ-linolenic acid (mg) 7.30±0.02
    Stearidonic acid (mg) 21.60±0.06
    Eicosadienoic acid (mg) 42.40±0.10
    Eicosatrienoic acid (mg) 16.70±0.03
    Arachidonic acid (mg) 44.00±0.12
    Eicosapentaenoic acid (mg) 130.90±0.30
    Docosadienoic acid (mg) 3.30±0.01
    Docosapentaenoic acid (mg) 29.40±0.07
    Docosahexaenoic acid (mg) 249.00±0.56
    Table 1. Comparison of nutrient intakes after revision of the food composition database for 3 pooled cohorts (n=205,785)1

    Values are presented as mean±standard error.

    RE, retinol equivalent.

    Three pooled cohorts: Ansan and Ansung study, Health Examinee (HEXA) study, Cardiovascular Disease Association Study (CAVAS).

    Paired t-test.

    Table 2. Expanded nutrient names and reasons for selection

    Table 3. Intake of 48 expanded nutrients in KoGES participants (n=205,785)

    KoGES, Korean Genome and Epidemiology Study; SE, standard error.

    Table 1.

    Table 2.

    Table 3.

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