Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese

ZHANG Jian SONG Peng Kun ZHAO Li Yun SUN Ye YU Kai YIN Jing PANG Shao Jie LIU Zhen MAN Qing Qing HE Li LI Cheng ARIGONI Fabrizio BOSCO Nabil DING Gang Qiang ZHAO Wen Hua

ZHANG Jian, SONG Peng Kun, ZHAO Li Yun, SUN Ye, YU Kai, YIN Jing, PANG Shao Jie, LIU Zhen, MAN Qing Qing, HE Li, LI Cheng, ARIGONI Fabrizio, BOSCO Nabil, DING Gang Qiang, ZHAO Wen Hua. Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese[J]. Biomedical and Environmental Sciences, 2021, 34(5): 337-347. doi: 10.3967/bes2021.045
Citation: ZHANG Jian, SONG Peng Kun, ZHAO Li Yun, SUN Ye, YU Kai, YIN Jing, PANG Shao Jie, LIU Zhen, MAN Qing Qing, HE Li, LI Cheng, ARIGONI Fabrizio, BOSCO Nabil, DING Gang Qiang, ZHAO Wen Hua. Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese[J]. Biomedical and Environmental Sciences, 2021, 34(5): 337-347. doi: 10.3967/bes2021.045

doi: 10.3967/bes2021.045

Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese

Funds: This study was supported by National Health Commission (formerly National Health and Family Planning Commission) Medical Reform Major Program: China Nutrition and Health Surveillance [2010–2012] and the secondary data analysis was sponsored by Nestle R&D center-National Institute for Nutrition and Health, China CDC project-Research on Dietary and Nutritional Status of Chinese Elderly [No. 150052]
More Information
    Author Bio:

    ZHANG Jian, male, born in 1967, PhD, majoring in nutrition and health

    Corresponding author: ZHAO Wen Hua, Tel: 86-10-66237006, Fax: 86-10-67711813, E-mail: zhaowh@chinacdc.cn
    • 关键词:
    •  / 
    •  / 
    •  / 
    •  / 
    •  
  • Table  1.   Prevalence of underweight, normal weight, overweight and obesity in demographic subgroups of Chinese elderly1

    ItemUnderweightNormal weightOverweight ObesityTotal2P-value3
    n (%)n (%)n (%)n (%)n (%)
    National797 (5.7)6,596 (47.2)4,866 (34.8)1,728 (12.4)13,987 (100.0)
    Age (years)
     60–74 564 (4.9)5,394 (46.5)4,134 (35.7)1,497 (12.9)11,589 (82.9)< 0.0001
     75– 233 (9.7)1,202 (50.1)732 (30.5)231 (9.6)2,398 (17.1)
    Gender
     Male404 (6.0)3,418 (50.6)2,269 (33.6)661 (9.8)6,752 (48.3)< 0.0001
     Female393 (5.4)3,178 (43.9)2,597 (35.9)1,067 (14.8)7,235 (51.7)
    Residence
     Urban265 (3.4)3,261 (41.8)3,123 (40.0)1,154 (14.8)7,803 (55.8)< 0.0001
     Rural532 (8.6)3,335 (53.9)1,743 (28.2)574 (9.3)6,184 (44.2)
    Income
     Low454 (6.4)3,398 (47.9)2,380 (33.6)857 (12.1)7,089 (50.7)< 0.0001
     Middle161 (6.1)1,260 (48.1)879 (33.5)321 (12.3)2,621 (18.7)
     High156 (4.3)1,642 (45.2)1,365 (37.6)472 (13.0)3,635 (26.0)
     No response26 (4.1)296 (46.1)242 (37.7)78 (12.2)642 (4.6)
    Education
     Primary or below617 (7.2)4,253 (49.7)2,730 (31.9)963 (11.3)8,563 (61.2)< 0.0001
     Junior high school124 (3.8)1,437 (44.4)1,196 (37.0)478 (14.8)3,235 (23.1)
     Senior high or above56 (2.6)906 (41.4)940 (42.9)287 (13.1)2,189 (15.7)
    Living condition
     Living alone30 (3.8)378 (47.4)295 (37.0)94 (11.8)797 (5.7)< 0.0001
     Living with spouse335 (4.7)3,213 (45.2)2,608 (36.7)946 (13.3)7,102 (50.8)
     Living with others4432 (7.1)3,005 (49.4)1,963 (32.2)688 (11.3)6,088 (43.5)
    Area
     East275 (4.8)2,459 (42.9)2,184 (38.1)809 (14.1)5,727 (41.0)< 0.0001
     Central255 (5.9)2,116 (49.3)1,421 (33.1)503 (11.7)4,295 (30.7)
     West267 (6.7)2,021 (51.0)1,261 (31.8)416 (10.5)3,965 (28.4)
    Anemia status
     Anemic191 (11.0)931 (53.4)483 (27.7)140 (8.0)1,745 (12.5)< 0.0001
     Non-anemic606 (5.0)5,665 (46.3)4,383 (35.8)1,588 (13.0)12,242 (87.5)
      Note. 1The percentages in columns ‘underweight’, ‘normal weight’, ‘overweight’, ‘obesity’ are row percentages. The percentages in column ‘Total’ are column percentages within each subgrouping factor. 2Only subjects with both dietary intake data and hemoglobin/anemia records are included in this analysis. 3P-values are two-sided from non-parametric chi-squared tests. 4Living with others: others including sons/daughters/grandchildren/other relatives/caregivers.
    下载: 导出CSV

    Table  2.   Mean nutrient intakes per capita in Chinese elderly with different age, gender1

    NutrientsTotalAge (years)Gender
    60–7475–P-value2MaleFemaleP-value2
    MeanSDMeanSDMeanSDMeanSDMeanSD
    Energy (kcal)1848.8634.91889.1637.71653.8583.3< 0.00012005.9658.61702.1574.3< 0.0001
    Fat (g)66.934.868.335.260.432.2< 0.000172.336.761.932.1< 0.0001
    Fat (% En)32.511.632.411.532.712.00.5832.411.632.611.70.27
    Protein (g)55.922.857.022.850.922.0< 0.000160.123.452.021.5< 0.0001
    Protein (% En)12.33.312.23.312.43.40.00612.23.212.43.40.0064
    Carbohydrate (g)257.2106.5263.0107.4228.897.0< 0.0001277.0111.3238.698.3< 0.0001
    Carbohydrate (% En)55.812.155.812.055.512.30.336855.412.256.111.90.0025
    Fiber10.06.410.36.58.85.6< 0.000110.66.69.56.1< 0.0001
    Vit A (μg RAE)402.0452.4403.0440.0396.8508.30.02420.3469.5384.8435.1< 0.0001
    Vit C (mg)73.151.774.651.965.950.3< 0.000176.253.870.349.4< 0.0001
    Vit B1 (mg)0.80.40.80.40.70.3< 0.00010.80.40.70.3< 0.0001
    Vit B2 (mg)0.70.30.70.30.60.3< 0.00010.70.30.60.3< 0.0001
    Niacin (mg NE)12.16.112.46.210.95.5< 0.000113.16.411.25.6< 0.0001
    Folate (μg DFE)134.782.4136.782.3125.482.0< 0.0001143.386.5126.877.5< 0.0001
    Biotin (mg)27.016.427.616.624.014.8< 0.000129.218.025.014.3< 0.0001
    Vit E (mg α-TE)7.76.97.87.17.06.3< 0.00018.37.57.16.3< 0.0001
    Choline (mg)182.081.6184.881.9168.979.2< 0.0001196.285.0168.876.0< 0.0001
    Ca (mg)348.3203.9351.2204.6333.8199.9< 0.0001367.8214.0330.0192.3< 0.0001
    Fe (mg)18.98.919.38.917.08.9< 0.000120.39.517.78.2< 0.0001
    Mg (mg)256.4106.0261.9106.3230.0100.2< 0.0001274.2109.9239.899.4< 0.0001
    P (mg)840.8316.6857.2316.5761.5305.3< 0.0001902.6327.6783.2294.7< 0.0001
    Zn (mg)9.13.79.33.78.33.6< 0.00019.93.98.53.4< 0.0001
    Se (μg)38.422.939.223.334.720.3< 0.000141.323.235.722.2< 0.0001
    K (mg)1454.1646.41482.6648.11316.1620.1< 0.00011548.4681.61366.0598.5< 0.0001
    Na (mg)5030.15081.25126.65342.44563.73521.9< 0.00015368.24022.94714.55882.7< 0.0001
      Note. 1Abbreviations: RAE: retinol-activity equivalent; NE: niacin equivalent; DFE: dietary folate equivalent; α-TE: α-tocopherol equivalent. 2P-values are two-sided from non-parametric chi-squared tests.
    下载: 导出CSV

    Table  3.   Percentage of Chinese elderly with inadequate nutrient intakes among age and gender subgroups1

    NutrientsTotal
    (%)
    Age (years)Gender
    60–7475–P-value2MaleFemaleP-value2
    (%)(%)(%)(%)
    Vit A (μg RAE)77.376.780.30.000178.576.10.0007
    Vit C (mg)69.067.874.7< 0.000166.871.0< 0.0001
    Vit B1 (mg)83.982.690.3< 0.000184.983.00.0023
    Vit B2 (mg)91.591.193.30.000592.790.3< 0.0001
    Niacin (mg NE)43.542.647.7< 0.000145.142.00.0002
    Folate (μg DFE)96.596.496.90.2995.997.10.0001
    Biotin (mg)86.385.590.0< 0.000183.489.0< 0.0001
    Vit E (mg α-TE)90.589.993.5< 0.000188.392.6< 0.0001
    Choline (mg)99.499.399.70.0299.799.1< 0.0001
    Ca (mg)96.996.996.90.8996.197.6< 0.0001
    Fe (mg)4.03.18.3< 0.00012.15.7< 0.0001
    Mg (mg)64.162.571.6< 0.000157.370.4< 0.0001
    P (mg)21.219.330.4< 0.000114.827.3< 0.0001
    Zn (mg)43.341.054.7< 0.000163.324.7< 0.0001
    Se (μg)78.577.583.2< 0.000173.683.1< 0.0001
    K (mg)83.882.888.7< 0.000180.387.0< 0.0001
    Na (mg)4.13.95.10.013.44.8< 0.0001
      Note. 1Abbreviations: RAE: retinol-activity equivalent; NE: niacin equivalent; DFE: dietary folate equivalent; α-TE: α-tocopherol equivalent. 2P-values are two-sided from non-parametric chi-squared tests.
    下载: 导出CSV

    Table  4.   Adjusted means of food intakes in Chinese elderly with different nutritional status1

    ItemUnderweightNormalOverweightObesityP-trend
    Mean95% CIMean95% CIMean95% CIMean95% CI
    Rice (g/d)183.4173.3193.6150.8144.8156.8129.9123.6136.2117.3109.4125.1< 0.0001
    Wheat (g/d)88.779.697.9111.3105.9116.8126.8121.1132.5141.4134.2148.5< 0.0001
    Coarse cereals (g/d)15.011.518.417.215.219.220.017.922.220.617.923.2< 0.0001
    Tubers (g/d)27.723.332.028.025.430.629.226.532.029.726.333.10.13
    Legumes (g/d)2.81.54.03.42.74.23.93.24.73.82.84.80.04
    Soybean products (g/d)9.98.311.610.49.411.310.19.111.110.69.411.90.71
    Dark color vegetables (g/d)84.677.591.680.676.584.874.369.978.767.161.672.6< 0.0001
    Light color vegetables (g/d)145.8135.8155.8152.0146.1157.9152.8146.6159.1154.5146.8162.30.17
    Salted vegetables (g/d)3.12.14.22.92.33.52.82.13.42.82.03.60.53
    Fruits (g/d)42.935.950.044.240.048.347.342.951.745.139.650.50.19
    Nuts (g/d)3.82.84.83.93.34.54.33.74.94.53.75.30.03
    Pork (g/d)51.847.656.051.148.653.649.346.751.946.843.650.10.001
    Other livestock meats (g/d)5.33.67.15.64.66.66.04.97.06.95.68.30.03
    Animal viscera (g/d)2.92.13.71.61.12.11.10.61.61.20.51.8< 0.0001
    Poultry (g/d)10.88.712.910.08.711.29.38.010.67.65.99.20.0004
    Milk (g/d)43.336.450.142.338.346.347.643.451.845.239.950.50.02
    Eggs (g/d)23.321.125.624.022.625.325.624.227.023.822.125.60.12
    Fish (g/d)21.317.924.619.917.921.819.517.421.617.014.419.60.01
    Vegetable oils (g/d)30.228.232.131.930.833.133.932.635.134.232.735.8< 0.0001
    Animal oils (g/d)5.84.96.74.13.54.63.02.53.62.72.03.4< 0.0001
    Cakes (g/d)7.75.79.89.07.810.28.47.19.79.27.610.80.72
    Sugar (g/d)4.33.05.75.54.76.35.54.76.45.84.86.80.12
    Salt (g/d)8.87.99.78.88.39.49.18.59.78.98.29.70.37
    Condiments (g/d)15.613.617.714.313.115.514.513.215.814.613.016.20.83
    Others (g/d)7.85.610.18.87.410.19.27.810.610.68.812.30.01
    Soft drinks (mL/d)20.010.929.218.713.324.119.113.424.821.914.829.10.49
    Alcoholic beverages (mL/d)1.40.42.42.21.62.71.40.82.01.20.52.00.01
      Note 1Mean and 95% CI are calculated from general linear model with adjustment for age, gender, urban/rural residence, income level, education level, living condition, area of residence, physical activity, and total energy intake. Abbreviations: CI: confidence interval. 2P-trend is calculated from general linear model using the median BMI values in each category as continuous variables.
    下载: 导出CSV
  • [1] Cederholm T, Jensen GL, Correia M, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. J Cachexia Sarcopenia Muscle, 2019; 10, 207−17. doi:  10.1002/jcsm.12383
    [2] Roust LR, DiBaise JK. Nutrient deficiencies prior to bariatric surgery. Curr Opin Clin Nutr Metab Care, 2017; 20, 138−44. doi:  10.1097/MCO.0000000000000352
    [3] Wells JC, Sawaya AL, Wibaek R, et al. The double burden of malnutrition: aetiological pathways and consequences for health. Lancet, 2020; 395, 75−88. doi:  10.1016/S0140-6736(19)32472-9
    [4] Collaborators GBDD. Health effects of dietary risks in 195 countries, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet, 2019; 393, 1958−72. doi:  10.1016/S0140-6736(19)30041-8
    [5] United Nations Department of Economic and Social Affairs Population Division (2015) World Population Ageing 2015 (ST/ESA/SER. A/390).
    [6] The State Council of the People’s Republic of China (2017) National Nutrition Plan (2017−2030).
    [7] Zhao L, Ma G, Piao J, et al. Scheme of the 2010-2012 Chinese nutrition and health surveillance. Chin J Prevent Med, 2016; 50, 204−7. (In Chinese)
    [8] Yang Y, Wang G, Pan X. China Food Composition Table. 2nd ed. Beijing: Peking University Medical Press. 2009.
    [9] China Nutrition Society (2014) China Dietary Reference Intakes Handbook (2013). Beijing: China Standard Press.
    [10] China Statistical Yearbook In 2010. Beijing: National Bureau of Statistics of China.
    [11] World Health Organization. Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity. [Vitamin and Mineral Nutrition Information System, editor]. Geneva: World Health Organization. 2011.
    [12] National Health and Family Planning Commission of the People’s Republic of China (2013) Health Standard of the People’s Republic of China. No. WS/T 428-2013: Criteria of Weight for Adults. Beijing.
    [13] State Government of China China National Census 2010.
    [14] Hu L, Huang X, You C, et al. Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China. PLoS One, 2017; 12, e0183934. doi:  10.1371/journal.pone.0183934
    [15] Liu X, Wu W, Mao Z, et al. Prevalence and influencing factors of overweight and obesity in a Chinese rural population: the Henan Rural Cohort Study. Sci Rep, 2018; 8, 13101. doi:  10.1038/s41598-018-31336-2
    [16] Song N, Liu F, Han M, et al. Prevalence of overweight and obesity and associated risk factors among adult residents of northwest China: a cross-sectional study. BMJ Open, 2019; 9, e028131. doi:  10.1136/bmjopen-2018-028131
    [17] Collaboration NCDRF. Rising rural body-mass index is the main driver of the global obesity epidemic in adults. Nature, 2019; 569, 260−4. doi:  10.1038/s41586-019-1171-x
    [18] Jaacks LM, Gordon-Larsen P, Mayer-Davis EJ, et al. Age, period and cohort effects on adult body mass index and overweight from 1991 to 2009 in China: the China Health and Nutrition Survey. Int J Epidemiol, 2013; 42, 828−37. doi:  10.1093/ije/dyt052
    [19] Popkin BM. Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr, 2006; 84, 289−98. doi:  10.1093/ajcn/84.2.289
    [20] Chye L, Wei K, Nyunt MSZ, et al. Strong Relationship between Malnutrition and Cognitive Frailty in the Singapore Longitudinal Ageing Studies (SLAS-1 and SLAS-2). J Prev Alzheimers Dis, 2018; 5, 142−8.
    [21] Wei K, Nyunt MS, Gao Q, et al. Association of Frailty and Malnutrition With Long-term Functional and Mortality Outcomes Among Community-Dwelling Older Adults: Results From the Singapore Longitudinal Aging Study 1. JAMA Netw Open, 2018; 1, e180650. doi:  10.1001/jamanetworkopen.2018.0650
    [22] Jones N, Bartlett HE. Comparison of the eating behaviour and dietary consumption in older adults with and without visual impairment. Br J Nutr, 2019; 1−25.
    [23] Culleton BF, Manns BJ, Zhang J, et al. Impact of anemia on hospitalization and mortality in older adults. Blood, 2006; 107, 3841−6. doi:  10.1182/blood-2005-10-4308
    [24] Yang G, Wang Y, Zeng Y, et al. Rapid health transition in China, 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet, 2013; 381, 1987−2015. doi:  10.1016/S0140-6736(13)61097-1
    [25] Seitz AE, Eberhardt MS, Lukacs SL. Anemia Prevalence and Trends in Adults Aged 65 and Older: U. S. National Health and Nutrition Examination Survey: 2001−2004 to 2013−2016. J Am Geriatr Soc, 2018; 66, 2431−2. doi:  10.1111/jgs.15530
    [26] Gaskell H, Derry S, Andrew Moore R, et al. Prevalence of anaemia in older persons: systematic review. BMC Geriatr, 2008; 8, 1. doi:  10.1186/1471-2318-8-1
    [27] den Elzen WP, Westendorp RG, Frolich M, et al. Vitamin B12 and folate and the risk of anemia in old age: the Leiden 85-Plus Study. Arch Intern Med, 2008; 168, 2238−44. doi:  10.1001/archinte.168.20.2238
    [28] Shi Z, Zhen S, Wittert GA, et al. Inadequate riboflavin intake and anemia risk in a Chinese population: five-year follow up of the Jiangsu Nutrition Study. PLoS One, 2014; 9, e88862. doi:  10.1371/journal.pone.0088862
    [29] Powers HJ. Riboflavin (vitamin B-2) and health. Am J Clin Nutr, 2003; 77, 1352−60. doi:  10.1093/ajcn/77.6.1352
    [30] Xu X. Introduction to Food Culture: Dongnan University Press: Nanjing, China. 2008.
    [31] Song F, Cho MS. Geography of Food Consumption Patterns between South and North China. Foods, 2017; 6, 34. doi:  10.3390/foods6050034
    [32] Huang L, Wang H, Wang Z, et al. Regional Disparities in the Association between Cereal Consumption and Metabolic Syndrome: Results from the China Health and Nutrition Survey. Nutrients, 2019; 11, 764. doi:  10.3390/nu11040764
    [33] Zhou M, Wang H, Zhu J, et al. Cause-specific mortality for 240 causes in China during 1990-2013: a systematic subnational analysis for the Global Burden of Disease Study 2013. Lancet, 2016; 387, 251−72. doi:  10.1016/S0140-6736(15)00551-6
    [34] Xu G, Ma M, Liu X, et al. Is there a stroke belt in China and why? Stroke, 2013; 44, 1775−83.
    [35] Li Y, Wang L, Feng X, et al. Geographical variations in hypertension prevalence, awareness, treatment and control in China: findings from a nationwide and provincially representative survey. J Hypertens, 2018; 36, 178−87. doi:  10.1097/HJH.0000000000001531
    [36] Azzolino D, Passarelli PC, De Angelis P, et al. Poor Oral Health as a Determinant of Malnutrition and Sarcopenia. Nutrients, 2019; 11.
    [37] Nifli AP. Appetite, Metabolism and Hormonal Regulation in Normal Ageing and Dementia. Diseases, 2018; 6.
    [38] Remond D, Shahar DR, Gille D, et al. Understanding the gastrointestinal tract of the elderly to develop dietary solutions that prevent malnutrition. Oncotarget, 2015; 6, 13858−98. doi:  10.18632/oncotarget.4030
    [39] ter Borg S, Verlaan S, Hemsworth J, et al. Micronutrient intakes and potential inadequacies of community-dwelling older adults: a systematic review. Br J Nutr, 2015; 113, 1195−206. doi:  10.1017/S0007114515000203
    [40] Hu L, Huang X, You C, et al. Prevalence and Risk Factors of Prehypertension and Hypertension in Southern China. PLoS One, 2017; 12, e0170238. doi:  10.1371/journal.pone.0170238
  • [1] XIE Kai Hong, HAN Xiao, ZHENG Wei Jun, ZHUANG Su Fang.  Low Grip Strength and Increased Mortality Hazard among Middle-Aged and Older Chinese Adults with Chronic Diseases . Biomedical and Environmental Sciences, 2023, 36(3): 213-221. doi: 10.3967/bes2023.013
    [2] TIAN Ying, CAO Hong Peng, HUAN Yu Ping, GONG Jia Wei, YUAN Kai Hua, CHEN Wen Zhuo, HU Jing, SHI Yu Fei.  Measurement of the Thermic Effect of Food in a Chinese Mixed Diet in Young People . Biomedical and Environmental Sciences, 2023, 36(7): 585-594. doi: 10.3967/bes2023.086
    [3] The Standard for Healthy Chinese Older Adults . Biomedical and Environmental Sciences, 2023, 36(7): 666-667. doi: 10.3967/bes2023.098
    [4] WU Bing, LYU Yue Bin, CAO Zhao Jin, WEI Yuan, SHI Wan Ying, GAO Xiang, ZHOU Jin Hui, KRAUS Virginia Byers, ZHAO Feng, CHEN Xin, LU Feng, ZHANG Ming Yuan, LIU Ying Chun, TAN Qi Yue, SONG Shi Xun, QU Ying Li, ZHENG Xu Lin, SHEN Chong, MAO Chen, SHI Xiao Ming.  Associations of Sarcopenia, Handgrip Strength and Calf Circumference with Cognitive Impairment among Chinese Older Adults . Biomedical and Environmental Sciences, 2021, 34(11): 859-870. doi: 10.3967/bes2021.119
    [5] WANG Wei, ZHANG Mei, XU Cheng Dong, YE Peng Peng, LIU Yun Ning, HUANG Zheng Jing, HU Cai Hong, ZHANG Xiao, ZHAO Zhen Ping, LI Chun, CHEN Xiao Rong, WANG Li Min, ZHOU Mai Geng.  Hypertension Prevalence, Awareness, Treatment, and Control and Their Associated Socioeconomic Factors in China: A Spatial Analysis of A National Representative Survey . Biomedical and Environmental Sciences, 2021, 34(12): 937-951. doi: 10.3967/bes2021.130
    [6] YANG Zhen Yu, ZHANG Qian, ZHAI Yi, XU Tao, WANG Yu Ying, CHEN Bo Wen, TANG Xue Jun, YUAN Xiao Lin, FANG Hong Yun, ZHU Yan, PANG Xue Hong, WANG Shuo, XU Juan, LI Rui Li, SI Xiang, ZHAO Wen Hua.  National Nutrition and Health Systematic Survey for Children 0–17 Years of Age in China . Biomedical and Environmental Sciences, 2021, 34(11): 891-899. doi: 10.3967/bes2021.122
    [7] HUANG Li Na, WANG Hui Jun, WANG Zhi Hong, ZHANG Ji Guo, JIA Xiao Fang, ZHANG Bing, DING Gang Qiang.  Association of Red Meat Usual Intake with Serum Ferritin and the Risk of Metabolic Syndrome in Chinese Adults: A Longitudinal Study from the China Health and Nutrition Survey . Biomedical and Environmental Sciences, 2020, 33(1): 19-29. doi: 10.3967/bes2020.003
    [8] WANG Qi Qi, YU Shi Cheng, XU Cheng Dong, LIU Jian Jun, LI Yuan Qiu, ZHANG Man Hui, LONG Xiao Juan, LIU Yun Ning, BI Yu Fang, ZHAO Wen Hua, YAO Hong Yan.  Association between Selenium in Soil and Diabetes in Chinese Residents Aged 35–74 Years: Results from the 2010 National Survey of Chronic Diseases and Behavioral Risk Factors Surveillance . Biomedical and Environmental Sciences, 2020, 33(4): 260-268. doi: 10.3967/bes2020.035
    [9] ZHANG Juan, ZHAI Yi, FENG Xiao Qi, LI Wei Rong, LYU Yue Bin, ASTELL-BURTThomas Thomas, ZHAO Peng Yu, SHI Xiao Ming.  Gender Differences in the Prevalence of Overweight and Obesity, Associated Behaviors, and Weight-related Perceptions in a National Survey of Primary School Children in China . Biomedical and Environmental Sciences, 2018, 31(1): 1-11. doi: 10.3967/bes2018.001
    [10] ZHANG Ya Hui, XIE Fang Yi, CHEN Ya Wen, WANG Hai Xia, TIAN Wen Xia, SUN Wen Guang, WU Jing.  Evaluating the Nutritional Status of Oncology Patientsand Its Association with Quality of Life . Biomedical and Environmental Sciences, 2018, 31(9): 637-644. doi: 10.3967/bes2018.088
    [11] ZHANG Xin Sheng, LIU Ying Hua, ZHANG Yong, XU Qing, YU Xiao Ming, YANG Xue Yan, LIU Zhao, LI Hui Zi, LI Feng, XUE Chang Yong.  Handgrip Strength as a Predictor of Nutritional Status in Chinese Elderly Inpatients at Hospital Admission . Biomedical and Environmental Sciences, 2017, 30(11): 802-810. doi: 10.3967/bes2017.108
    [12] WANG Jing Jing, BARANOWSKI Tom, LAU WC Patrick, CHEN Tzu An, PITKETHLY Amanda Jane.  Validation of the Physical Activity Questionnaire for Older Children (PAQ-C) among Chinese Children . Biomedical and Environmental Sciences, 2016, 29(3): 177-186. doi: 10.3967/bes2016.022
    [13] JIA Hai Xian, HAN Jun Hua, LI Hu Zhong, LIANG Dong, DENG Tao Tao, CHANG Su Ying.  Mineral Intake in Urban Pregnant Women from Base Diet, Fortified Foods, and Food Supplements:Focus on Calcium, Iron, and Zinc . Biomedical and Environmental Sciences, 2016, 29(12): 898-901. doi: 10.3967/bes2016.120
    [14] WANG Jin Wei, TANG Xun, LI Na, WU Yi Qun, LI Shuai, LI Jin, QIN Xue Ying, ZHANG Zong Xin, HU Yong Hua, CHEN Da Fang.  The Impact of Lipid-metabolizing Genetic Polymorphisms on Body Mass Index and Their Interactions with Soybean Food Intake:A Study in a Chinese Population . Biomedical and Environmental Sciences, 2014, 27(3): 176-185. doi: 10.3967/bes2014.039
    [15] YIN Xiao Jian, JI Cheng Ye.  Malnutrition Prevalence in Lasa Xizang Children and Adolescents . Biomedical and Environmental Sciences, 2014, 27(8): 614-626. doi: 10.3967/bes2014.094
    [16] SUI Hai Xia, LI Jian Wen, MAO Wei Feng, ZHU Jiang Hui, HE Yu Na, SONG Xiao Yu, MA Ning, ZHANG Lei, LIU Sa Na, LIU Zhao Ping, LI Feng Qin.  Dietary Iodine Intake in the Chinese Population . Biomedical and Environmental Sciences, 2011, 24(6): 617-623. doi: 10.3967/0895-3988.2011.06.005
    [17] JAY ROSS, CHUN-MING CHEN, WU HE, GANG FU, YU-YING WANG, ZHEN-YING FU, MING-XIA CHEN.  Effects of Malnutrition on Economic Productivity in China As Estimated by PROFILES . Biomedical and Environmental Sciences, 2003, 16(3): 195-205.
    [18] JAY ROSS, CHUN-MING CHEN, WU HE, GANG FU, YU-YING WANG, ZHEN-YING FU, MING-XIA CHEN.  Effects of Malnutrition on Child Survival in China As Estimated by PROFILES . Biomedical and Environmental Sciences, 2003, 16(2): 187-193.
    [19] JOSEPH MICHAEL HUNT.  The Agricultural-Industrial Partnership for EliminatingMicronutrient Malnutrition: The Investment Bargain of the Decade . Biomedical and Environmental Sciences, 2001, 14(1_2): 104-123.
    [20] Ge Ke-you, CHANG SU-YING.  Definition and Measurement of Child Malnutrition . Biomedical and Environmental Sciences, 2001, 14(4): 283-291.
  • 加载中
表ll (4)
计量
  • 文章访问数:  1033
  • HTML全文浏览量:  446
  • PDF下载量:  145
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-06-01
  • 录用日期:  2020-11-11
  • 刊出日期:  2021-05-20

Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese

doi: 10.3967/bes2021.045
    基金项目:  This study was supported by National Health Commission (formerly National Health and Family Planning Commission) Medical Reform Major Program: China Nutrition and Health Surveillance [2010–2012] and the secondary data analysis was sponsored by Nestle R&D center-National Institute for Nutrition and Health, China CDC project-Research on Dietary and Nutritional Status of Chinese Elderly [No. 150052]
    作者简介:

    ZHANG Jian, male, born in 1967, PhD, majoring in nutrition and health

    通讯作者: ZHAO Wen Hua, Tel: 86-10-66237006, Fax: 86-10-67711813, E-mail: zhaowh@chinacdc.cn

English Abstract

ZHANG Jian, SONG Peng Kun, ZHAO Li Yun, SUN Ye, YU Kai, YIN Jing, PANG Shao Jie, LIU Zhen, MAN Qing Qing, HE Li, LI Cheng, ARIGONI Fabrizio, BOSCO Nabil, DING Gang Qiang, ZHAO Wen Hua. Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese[J]. Biomedical and Environmental Sciences, 2021, 34(5): 337-347. doi: 10.3967/bes2021.045
Citation: ZHANG Jian, SONG Peng Kun, ZHAO Li Yun, SUN Ye, YU Kai, YIN Jing, PANG Shao Jie, LIU Zhen, MAN Qing Qing, HE Li, LI Cheng, ARIGONI Fabrizio, BOSCO Nabil, DING Gang Qiang, ZHAO Wen Hua. Malnutrition in Relation with Dietary, Geographical, and Socioeconomic Factors among Older Chinese[J]. Biomedical and Environmental Sciences, 2021, 34(5): 337-347. doi: 10.3967/bes2021.045
    • M alnutrition affects billions of people worldwide with a substantial economic burden related to risk of diseases and complications[1]. Certain types of malnutrition more likely depend on the local environment, lifestyle, and resources. Undernutrition leads to low weight-for-height (wasting), height-for-age (stunting), and weight-for-age (underweight). The opposite side of the malnutrition spectrum shows overweight or obesity occurs with the overconsumption of energy and certain nutrients and sometimes specific nutrient deficiencies despite excess intake, which is common in patients before bariatric surgery[2]. Malnutrition leads to serious physical and health issues, increasing the risk of death and particularly noncommunicable diseases in adults[3]. According to the last reports from the Global Burden of Disease Study 2017 (GBD2017), poor dietary habits alone accounted for nearly one in every five deaths[4]. Considering the importance of proper nutrition and healthy diets on public health, the elimination of all forms of malnutrition has been adopted as a primary objective in the United Nation Decade of Action on Nutrition 2016–2025. Among adults, those living in poverty, those with specific medical conditions, and older individuals have a high risk of malnutrition.

      The elderly is mentioned as the population group requiring ‘particular attention to their special needs’. In 2015, nearly one in four persons ≥ 60 years old in the world lived in China, and the proportion of ≥ 60-year-old Chinese in the total population is increasing from 17.9% (249 million) in 2018 to 25.3% (358 million) in 2030[5]. The rapid aging population with increasing life expectancy leads to a growing burden of disease for the country. Dietary risks are the leading risk factor of disease burden, accounting for 21.3% of disability-adjusted life years and 30.2% of deaths in 2017[4]. Considering the importance of diet and nutrition for public health, the China National Nutrition Plan 2017–2030, the first ever nutrition plan issued by the State Council of the People’s Republic of China, has included ‘senior nutrition improvement action’ as one of the major initiatives of the country[6]. Up-to-date and reliable nationwide information on elderly dietary and nutritional status are required for relevant preventive actions against malnutrition.

      Herein, we took advantage of more recent information collected by the Chinese Center for Disease Control and Prevention (CCDC) to provide for the first time a nationwide coverage of elderly malnutrition attributes, showing a comprehensive picture of the nutrition landscape, including undernutrition, overweight or obesity, micronutrient inadequacy, and food patterns. Our findings may help in refining nutrition challenges for the aging population and designing evidence-based policies and intervention to promote a healthy diet, which supports the reduction of all forms of malnutrition in China.

    • This study’s sample was extracted from the China National Nutrition and Healthy Survey (CNNHS), a national representative cross-sectional study conducted between 2010 and 2013 by the CCDC. The CNNHS aimed to assess the dietary, nutrition, and health status of Chinese, covering all 31 provinces, autonomous regions, and municipalities (excluding Taiwan, Hong Kong, and Macao). A stratified multistage cluster sampling method was conducted at 150 survey sites. According to the economic and social development status, the four types of survey sites were defined as large cities (n = 34), medium and small cities (n = 41), general rural areas (n = 45), and poor rural areas (n = 30). At each survey site, a multistage and probability sampling design was used in selecting participants. In the first stage, six neighborhood communities in urban or rural areas in administrative villages were selected from each surveillance point. In the second stage, 75 households were randomly selected from each neighborhood community or administrative village. A survey questionnaire and physical examination were conducted, and fasting blood was collected for all ≥ 18-year-old residents in the selected households, among which 30 families were randomly sampled for the dietary surveys, which involved 3-day 24-h diet recalls combined with food weighing.

      The CCDC was responsible for the implementation of the national-level training for staff involved in the survey (Grade 1). Provincial-level training for staff at the surveillance points was performed by following a training plan developed by the project team (Grade 2). A face-to-face interview using a standard questionnaire was conducted at the participants’ homes, and anthropometric measurements were taken at community health service centers, by trained staff, in both cases[7].

      This study’s inclusion criteria were as follows: ≥ 60-year-old participants with complete information on demographic and blood sample data. Dietary survey data were collected as a subsample of the study. Based on the dietary survey information, exclusion criteria were as follows: subjects with extreme energy intakes (< 800 kcal/day or > 4,800 kcal/day for male and < 500 kcal/day or > 4,000 kcal/day for female). The study protocol was approved by the ethical review committee of the CCDC (No. 2013[018]). Written informed consent was obtained from all participants.

    • Dietary assessment applies a combination of three consecutive 24-h diet recalls and a food inventory at the individual and household levels, respectively, over the same 3-day period. All foods remained after the last meal before the beginning of one 24-h dietary assessment, and all purchases and household production, as well as food inventory, at the end of one 24-h dietary assessment were weighted and recorded each day. Preparation waste was estimated when weighing was not possible, and actual household food consumption each day was calculated accordingly. All dietary information was collected by trained interviewers, who asked each household member to report all foods consumed at or away from home over the 24-hour period. Details of all food items consumed, including type and amount of foods and places of food preparation, were collected with the aid of food models and pictures. Based on the percentage of individual consumption of any food as a proportion of what the household food consumed, the amount of individual food consumption was estimated. The average of the three 24-h recall data was the dietary intake for each individual. Food intakes were aggregated into 27 food groups found in the Chinese Food Composition Tables, and then nutrient intakes were calculated accordingly[8] and benchmarked against the Chinese Dietary Reference Intakes (DRIs) of 2013[9].

      According to national income statement released by the National Statistics Bureau in 2010, income was reported as three levels (low, middle, and high) (annual household income per capita): low (< 15,000 Chinese yuan (CNY) for urban residents, < 5,000 CNY for rural residents), middle (15,000–19,999 CNY for urban residents, 5,000–9,999 CNY for rural residents), and high (> 20,000 CNY for urban residents, > 10,000 CNY for rural residents)[10]. We also reported the effect of living condition based on the report of household composition. The three living conditions include ‘living alone’, ‘living only with spouse’, and ‘living with others’. Geographical area were defined according to National Bureau of Statistics as East (Beijing, Tianjin, Hebei, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, Guangdong, Hainan), Central (Shanxi, Jilin, Heilongjiang, Anhui, Jiangxi, Henan, Hubei, Hunan), and West (Inner Mongolia, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang).

      Weight was measured without shoes and in light clothing, which was then reported to the nearest 0.1 kg. Height was determined to the nearest 0.1 cm without shoes. Body mass index (BMI) was calculated as weight measured in kilograms divided by the square of body height measured in meters. The cyanmethemoglobin method was used to measure for hemoglobin in peripheral venous blood samples[6]. According to the criteria of World Health Organization (WHO), anemia is defined as subjects with < 120 g/L hemoglobin level for female and < 130 g/L for male, adjusted by altitude[11].

    • Mean and standard deviation were used to assess the food and nutrient intake distribution. The prevalence of malnutrition, including underweight (BMI < 18.5 kg/m2), overweight (BMI ≥ 24.0 kg/m2 and < 28.0 kg/m2), and obesity (BMI ≥ 28.0 kg/m2), was analyzed for province-level administration units based on the reported health standard of China[12] and adjusted by age- and gender-standardized values based on the China National Census 2010 to reflect the population status[13]. Nutrient intake inadequacy was expressed as the percentage of surveyed adults with an average dietary nutrient intake less than the DRI. Specifically, the estimated average requirement (EAR) was used to evaluate carbohydrate, protein, niacin and folate, calcium, iron, magnesium, phosphorus, zinc, selenium, and vitamins A, C, B1, and B2. Adequate intake was applied to evaluate intakes of nutrients whose EAR is not established, including vitamin E, biotin, choline, potassium, and sodium. Acceptable Macronutrient Distribution Range was used for fat intake, specific proposed levels for fiber, and Estimated Energy Requirement for total energy intake.

      Nutrient intake status was evaluated for two age groups (60–74 y and ≥ 75 y), gender, and malnutrition status based on BMI (underweight, normal weight, overweight, and obesity). Food group intakes were analyzed by the malnutrition status groups. Adjusted mean and 95% confidence interval were obtained with the adjustment for age, gender, urban/rural residence, income level, education level, living condition, area of residence, physical activity, and total energy intake in the general linear model. P for trend was calculated by fitting the median intake value of each kind of food as the continuous variable in the adjusted model.

      All data analyses were performed using Statistical Analysis System (SAS) for Windows V9.3 (SAS Institute, Cary, North Carolina, USA). P < 0.05 was considered statistically significant.

    • Table 1 shows the subject characteristics, stratified by malnutrition status. A total of 6,752 men (48.3%) and 7,235 women (51.7%) were included in the analysis, among which 82.9% were aged 60–74 y and 17.1% ≥ 75 y. Overall, based on BMI, 5.7% of the subjects were considered underweight, 34.8% overweight, and 12.4% obese. The prevalence of underweight elderly was higher among older old (≥ 75 y), rural residents, and those with low income, low education status, living with others rather than with spouse, and residing in provinces in the West area. Correspondingly, the prevalence of the overweight and obese was higher among younger old (60–74 y), females, urban residents, and those with high income, higher education status, living with spouse, and residing in the East. Anemia had an overall prevalence of 12.5% in the elderly subjects, which almost doubled (191/797, 24.0%) among those who were underweight.

      Table 1.  Prevalence of underweight, normal weight, overweight and obesity in demographic subgroups of Chinese elderly1

      ItemUnderweightNormal weightOverweight ObesityTotal2P-value3
      n (%)n (%)n (%)n (%)n (%)
      National797 (5.7)6,596 (47.2)4,866 (34.8)1,728 (12.4)13,987 (100.0)
      Age (years)
       60–74 564 (4.9)5,394 (46.5)4,134 (35.7)1,497 (12.9)11,589 (82.9)< 0.0001
       75– 233 (9.7)1,202 (50.1)732 (30.5)231 (9.6)2,398 (17.1)
      Gender
       Male404 (6.0)3,418 (50.6)2,269 (33.6)661 (9.8)6,752 (48.3)< 0.0001
       Female393 (5.4)3,178 (43.9)2,597 (35.9)1,067 (14.8)7,235 (51.7)
      Residence
       Urban265 (3.4)3,261 (41.8)3,123 (40.0)1,154 (14.8)7,803 (55.8)< 0.0001
       Rural532 (8.6)3,335 (53.9)1,743 (28.2)574 (9.3)6,184 (44.2)
      Income
       Low454 (6.4)3,398 (47.9)2,380 (33.6)857 (12.1)7,089 (50.7)< 0.0001
       Middle161 (6.1)1,260 (48.1)879 (33.5)321 (12.3)2,621 (18.7)
       High156 (4.3)1,642 (45.2)1,365 (37.6)472 (13.0)3,635 (26.0)
       No response26 (4.1)296 (46.1)242 (37.7)78 (12.2)642 (4.6)
      Education
       Primary or below617 (7.2)4,253 (49.7)2,730 (31.9)963 (11.3)8,563 (61.2)< 0.0001
       Junior high school124 (3.8)1,437 (44.4)1,196 (37.0)478 (14.8)3,235 (23.1)
       Senior high or above56 (2.6)906 (41.4)940 (42.9)287 (13.1)2,189 (15.7)
      Living condition
       Living alone30 (3.8)378 (47.4)295 (37.0)94 (11.8)797 (5.7)< 0.0001
       Living with spouse335 (4.7)3,213 (45.2)2,608 (36.7)946 (13.3)7,102 (50.8)
       Living with others4432 (7.1)3,005 (49.4)1,963 (32.2)688 (11.3)6,088 (43.5)
      Area
       East275 (4.8)2,459 (42.9)2,184 (38.1)809 (14.1)5,727 (41.0)< 0.0001
       Central255 (5.9)2,116 (49.3)1,421 (33.1)503 (11.7)4,295 (30.7)
       West267 (6.7)2,021 (51.0)1,261 (31.8)416 (10.5)3,965 (28.4)
      Anemia status
       Anemic191 (11.0)931 (53.4)483 (27.7)140 (8.0)1,745 (12.5)< 0.0001
       Non-anemic606 (5.0)5,665 (46.3)4,383 (35.8)1,588 (13.0)12,242 (87.5)
        Note. 1The percentages in columns ‘underweight’, ‘normal weight’, ‘overweight’, ‘obesity’ are row percentages. The percentages in column ‘Total’ are column percentages within each subgrouping factor. 2Only subjects with both dietary intake data and hemoglobin/anemia records are included in this analysis. 3P-values are two-sided from non-parametric chi-squared tests. 4Living with others: others including sons/daughters/grandchildren/other relatives/caregivers.

      Table 2 presents the intakes of energy, macronutrients, and micronutrients, subgrouped by age and gender. Besides the energy imbalance-related underweight and overweight/obesity, micronutrient deficiency is another important component of malnutrition. Table 3 examines the prevalence of inadequate micronutrient intakes in the studied Chinese elderly sample, with subgroup comparisons by age and gender. The intake of numerous micronutrients was inadequate: > 75.0% of the elderly did not meet the Chinese DRIs for 10 out of the 17 micronutrients examined (vitamin A, vitamin B1, vitamin B2, folate, vitamin E, calcium, selenium, potassium, biotin, and choline). In general, the prevalence of dietary intake inadequacy for most nutrients increased with age. Female intakes were more inadequate in particular for magnesium, phosphorus, and selenium, while male intakes were more likely inadequate for zinc.

      Table 2.  Mean nutrient intakes per capita in Chinese elderly with different age, gender1

      NutrientsTotalAge (years)Gender
      60–7475–P-value2MaleFemaleP-value2
      MeanSDMeanSDMeanSDMeanSDMeanSD
      Energy (kcal)1848.8634.91889.1637.71653.8583.3< 0.00012005.9658.61702.1574.3< 0.0001
      Fat (g)66.934.868.335.260.432.2< 0.000172.336.761.932.1< 0.0001
      Fat (% En)32.511.632.411.532.712.00.5832.411.632.611.70.27
      Protein (g)55.922.857.022.850.922.0< 0.000160.123.452.021.5< 0.0001
      Protein (% En)12.33.312.23.312.43.40.00612.23.212.43.40.0064
      Carbohydrate (g)257.2106.5263.0107.4228.897.0< 0.0001277.0111.3238.698.3< 0.0001
      Carbohydrate (% En)55.812.155.812.055.512.30.336855.412.256.111.90.0025
      Fiber10.06.410.36.58.85.6< 0.000110.66.69.56.1< 0.0001
      Vit A (μg RAE)402.0452.4403.0440.0396.8508.30.02420.3469.5384.8435.1< 0.0001
      Vit C (mg)73.151.774.651.965.950.3< 0.000176.253.870.349.4< 0.0001
      Vit B1 (mg)0.80.40.80.40.70.3< 0.00010.80.40.70.3< 0.0001
      Vit B2 (mg)0.70.30.70.30.60.3< 0.00010.70.30.60.3< 0.0001
      Niacin (mg NE)12.16.112.46.210.95.5< 0.000113.16.411.25.6< 0.0001
      Folate (μg DFE)134.782.4136.782.3125.482.0< 0.0001143.386.5126.877.5< 0.0001
      Biotin (mg)27.016.427.616.624.014.8< 0.000129.218.025.014.3< 0.0001
      Vit E (mg α-TE)7.76.97.87.17.06.3< 0.00018.37.57.16.3< 0.0001
      Choline (mg)182.081.6184.881.9168.979.2< 0.0001196.285.0168.876.0< 0.0001
      Ca (mg)348.3203.9351.2204.6333.8199.9< 0.0001367.8214.0330.0192.3< 0.0001
      Fe (mg)18.98.919.38.917.08.9< 0.000120.39.517.78.2< 0.0001
      Mg (mg)256.4106.0261.9106.3230.0100.2< 0.0001274.2109.9239.899.4< 0.0001
      P (mg)840.8316.6857.2316.5761.5305.3< 0.0001902.6327.6783.2294.7< 0.0001
      Zn (mg)9.13.79.33.78.33.6< 0.00019.93.98.53.4< 0.0001
      Se (μg)38.422.939.223.334.720.3< 0.000141.323.235.722.2< 0.0001
      K (mg)1454.1646.41482.6648.11316.1620.1< 0.00011548.4681.61366.0598.5< 0.0001
      Na (mg)5030.15081.25126.65342.44563.73521.9< 0.00015368.24022.94714.55882.7< 0.0001
        Note. 1Abbreviations: RAE: retinol-activity equivalent; NE: niacin equivalent; DFE: dietary folate equivalent; α-TE: α-tocopherol equivalent. 2P-values are two-sided from non-parametric chi-squared tests.

      Table 3.  Percentage of Chinese elderly with inadequate nutrient intakes among age and gender subgroups1

      NutrientsTotal
      (%)
      Age (years)Gender
      60–7475–P-value2MaleFemaleP-value2
      (%)(%)(%)(%)
      Vit A (μg RAE)77.376.780.30.000178.576.10.0007
      Vit C (mg)69.067.874.7< 0.000166.871.0< 0.0001
      Vit B1 (mg)83.982.690.3< 0.000184.983.00.0023
      Vit B2 (mg)91.591.193.30.000592.790.3< 0.0001
      Niacin (mg NE)43.542.647.7< 0.000145.142.00.0002
      Folate (μg DFE)96.596.496.90.2995.997.10.0001
      Biotin (mg)86.385.590.0< 0.000183.489.0< 0.0001
      Vit E (mg α-TE)90.589.993.5< 0.000188.392.6< 0.0001
      Choline (mg)99.499.399.70.0299.799.1< 0.0001
      Ca (mg)96.996.996.90.8996.197.6< 0.0001
      Fe (mg)4.03.18.3< 0.00012.15.7< 0.0001
      Mg (mg)64.162.571.6< 0.000157.370.4< 0.0001
      P (mg)21.219.330.4< 0.000114.827.3< 0.0001
      Zn (mg)43.341.054.7< 0.000163.324.7< 0.0001
      Se (μg)78.577.583.2< 0.000173.683.1< 0.0001
      K (mg)83.882.888.7< 0.000180.387.0< 0.0001
      Na (mg)4.13.95.10.013.44.8< 0.0001
        Note. 1Abbreviations: RAE: retinol-activity equivalent; NE: niacin equivalent; DFE: dietary folate equivalent; α-TE: α-tocopherol equivalent. 2P-values are two-sided from non-parametric chi-squared tests.

      Then, we studied the food consumption patterns among subjects with different nutritional statuses. Table 4 shows the adjusted means of intakes for 27 major food groups across the 4 BMI categories, with adjustment for age, gender, urban/rural residence, income level, education level, living condition, area of residence, physical activity level, and total energy intake. Compared with underweight subjects, overweight and obese subjects consumed a significantly lower amount of rice, dark-colored vegetables, pork, animal viscera, poultry, and animal oils and a higher amount of wheat products, coarse cereals, and vegetable oils. It is worth noting that the population mean intakes of many food groups did not meet the Chinese Dietary Guideline recommendations regardless of their nutritional status. The biggest gaps exist in the food groups of dairy and fruits, with the recommendation of 300 g/d for dairy and 200–350 g/d for fruits in the Chinese Dietary Guideline. Such population-wide dietary patterns partly explained the numerous key nutrient inadequacies.

      Table 4.  Adjusted means of food intakes in Chinese elderly with different nutritional status1

      ItemUnderweightNormalOverweightObesityP-trend
      Mean95% CIMean95% CIMean95% CIMean95% CI
      Rice (g/d)183.4173.3193.6150.8144.8156.8129.9123.6136.2117.3109.4125.1< 0.0001
      Wheat (g/d)88.779.697.9111.3105.9116.8126.8121.1132.5141.4134.2148.5< 0.0001
      Coarse cereals (g/d)15.011.518.417.215.219.220.017.922.220.617.923.2< 0.0001
      Tubers (g/d)27.723.332.028.025.430.629.226.532.029.726.333.10.13
      Legumes (g/d)2.81.54.03.42.74.23.93.24.73.82.84.80.04
      Soybean products (g/d)9.98.311.610.49.411.310.19.111.110.69.411.90.71
      Dark color vegetables (g/d)84.677.591.680.676.584.874.369.978.767.161.672.6< 0.0001
      Light color vegetables (g/d)145.8135.8155.8152.0146.1157.9152.8146.6159.1154.5146.8162.30.17
      Salted vegetables (g/d)3.12.14.22.92.33.52.82.13.42.82.03.60.53
      Fruits (g/d)42.935.950.044.240.048.347.342.951.745.139.650.50.19
      Nuts (g/d)3.82.84.83.93.34.54.33.74.94.53.75.30.03
      Pork (g/d)51.847.656.051.148.653.649.346.751.946.843.650.10.001
      Other livestock meats (g/d)5.33.67.15.64.66.66.04.97.06.95.68.30.03
      Animal viscera (g/d)2.92.13.71.61.12.11.10.61.61.20.51.8< 0.0001
      Poultry (g/d)10.88.712.910.08.711.29.38.010.67.65.99.20.0004
      Milk (g/d)43.336.450.142.338.346.347.643.451.845.239.950.50.02
      Eggs (g/d)23.321.125.624.022.625.325.624.227.023.822.125.60.12
      Fish (g/d)21.317.924.619.917.921.819.517.421.617.014.419.60.01
      Vegetable oils (g/d)30.228.232.131.930.833.133.932.635.134.232.735.8< 0.0001
      Animal oils (g/d)5.84.96.74.13.54.63.02.53.62.72.03.4< 0.0001
      Cakes (g/d)7.75.79.89.07.810.28.47.19.79.27.610.80.72
      Sugar (g/d)4.33.05.75.54.76.35.54.76.45.84.86.80.12
      Salt (g/d)8.87.99.78.88.39.49.18.59.78.98.29.70.37
      Condiments (g/d)15.613.617.714.313.115.514.513.215.814.613.016.20.83
      Others (g/d)7.85.610.18.87.410.19.27.810.610.68.812.30.01
      Soft drinks (mL/d)20.010.929.218.713.324.119.113.424.821.914.829.10.49
      Alcoholic beverages (mL/d)1.40.42.42.21.62.71.40.82.01.20.52.00.01
        Note 1Mean and 95% CI are calculated from general linear model with adjustment for age, gender, urban/rural residence, income level, education level, living condition, area of residence, physical activity, and total energy intake. Abbreviations: CI: confidence interval. 2P-trend is calculated from general linear model using the median BMI values in each category as continuous variables.
    • Using data from the 2010–2013 CNNHS, we provided herein a comprehensive analysis of the nutritional status of the older adults, the findings of which provide an important resource for nutrition researchers and policymakers that could be used in complementing or implementing national policies for healthy aging. Furthermore, it also provides more information for other countries that experience a similar social economic and demographic transition.

    • We initially explored the prevalence of three malnutrition indicators, underweight, overweight, and obesity, and their relation to socioeconomic and geographical factors in China. BMI is rising in most countries, and urbanization is one of the most important drivers as diet and lifestyle in cities lead to higher adiposity. Our study showed that this is also true in China with the older population. According to the BMI classification for Chinese adults, both overweight and obesity culminate at -56% in urban elderly vs. -44% in rural elderly. A few additional factors, namely, lower age, higher incomes, higher education, female gender, and living with spouse and in the wealthiest Eastern area, had a noticeable impact on the increase of overweight and obesity, which concurred with other smaller studies conducted in different provinces[14-16].

      Recently, the NCD Risk Factor Collaboration analyzed the worldwide evolution of adult BMI trends from 1985 to 2017. While worldwide urbanization increased from 41% to 55%, a faster rural BMI rise explained most of the current global obesity epidemic[17]. Therefore, the authors observed a gap closure in BMI between urban and rural areas in many regions worldwide, including Central Asia, which was not so obvious in our study and could reflect the age difference of the population and methodology used. However, between 1991 and 2009, the overweight and obesity prevalence observed in the Chinese adult population had grown, which was faster in rural (from 26.7% to 38.3%) than urban (from 36.1% to 40.1%) areas, supporting a closure in the gap in BMI between them[18]. The slower speed observed in the elderly category may reflect age-related behavior differences regarding daily work, domestic activities, and/or eating behavior. Therefore, we also suggest that the so-called urbanization of rural life[19] has impacted the evolution of BMI in China, which embraces the impact of globally better infrastructures, less energy expenditure associated to agricultural workload, and higher incomes, allowing access to calorie-dense processed food. Altogether, the current rural lifestyle could contribute to the higher prevalence of overweight and obesity in China as supported by others in a single province or some provinces and confirmed herein at the national level.

    • Conversely, we observed a low overall prevalence of underweight elderly (5.7%) but higher values in the anemic population (11.0%), older old (9.7%), rural area (8.6%), and people with low education (7.2%), living with others (7.1%), with low income (6.4%), or living in the Western area (6.7%). Such low numbers are expected in industrialized countries as China with a booming economy and existing history of a program that tackles undernutrition especially in rural areas. Therefore, those underweight individuals may represent (i) some of the poorest with social disadvantages and exposed to food insecurity or a lack of access to sufficient and affordable food and/or (ii) those with age-related chronic diseases and malnutrition features. For instance, numerous studies have identified mental health disorders[20], being frail with poor mobility and/or lacking muscle strength[21], and having visual impairment[22] as a single risk factor or multiple risk factors for malnutrition. Presumably, those older individuals with one or multiple comorbidities in addition to other sociodemographic factors have reduced food preparation and cooking skills, which ultimately contribute to their undernutrition status.

      Anemia in the elderly is known to be more prevalent in the oldest old, affecting the overall morbidity and mortality[23]. We found that 12.5% of anemic Chinese were > 60 years old, reflecting a great achievement of the local prevention policies with a steady decrease since the past two to three decades[24]. According to the WHO classifications, anemia has a mild public health significance in China as many other developed countries. By comparison, in the US National Health and Nutrition Examination Survey (2013–2016), anemia prevalence was 14.1% for men and 10.2% for women aged 65 and older[25]. As a global trend, a systematic review analyzed a pooled sample of ≥ 65-year-old 85,409 subjects from 34 cohorts in developed countries and reported a similar value, with a weighted mean prevalence of 12% anemia in community-living elderly[26].

      Iron deficiency is the main cause of anemia in developing countries. However, in this study, we find that the average intake of iron is near 19.0 mg per capita and the percentage of elderly people with inadequate intake of iron is low. More analysis showed that Chinese dietary iron is mainly from plant foods, approximately 58% of dietary iron from cereals, while only about 15% from animal foods. The bioavailability of plant source iron is much lower than that of heme iron from animal foods. So, considering the relatively high prevalence of anemia among elderly people, increasing the appropriate heme iron intake from animal foods is still important. Except iron deficiency, other nutritional factors are important but have received less attention. In European elderly individuals, anemia was specifically associated with folate deficiency[27]. In Chinese adults, despite normal iron intake, inadequate riboflavin (vitamin B2) intake is common, and it increases the risk of anemia presumably because it limits iron utilization[28, 29]. We observed > 95% and > 90% prevalence of inadequate intake of folate and riboflavin, respectively, independent of gender or BMI categories in old Chinese. Therefore, further anemia reduction could be achieved by improving folate and riboflavin intake and promoting intake of green vegetables and dairy foods or alternatively fortified and enriched products or supplements.

    • We presented a geographical variation in malnutrition conditions. The prevalence of overweight and obesity in the Eastern regions was higher than that in the Western regions, but the prevalence of underweight was lower. Such inter-regional differences could be due to a combination of economic development status and food culture and cooking habits[30, 31, 32]. The spatial pattern of overweight and obesity was also similar to the geographic variation of cardiovascular disease incidences, including ischemic heart disease[33], stroke[34], and hypertension[35]. Geographically based strategies may be helpful in managing overweight and obesity as part of the effort for cardiovascular disease prevention and control. These results provide an important reference for the development of targeted nutrition policies, recommendations, education, and interventions, which will be the key for success at addressing nutrition challenges and enabling healthy aging.

    • The present study also provided a comprehensive picture of the nutrient and food intake of elderly Chinese. We found a high prevalence of inadequate intake (> 75%) for more than half of the micronutrients examined, that is, vitamins A, B1, and B2, folate, biotin, choline, calcium, selenium, and potassium, and such inadequacies were independent of age, gender or BMI categories, showing that micronutrient inadequacy remains an important nutritional problem for Chinese elderly and may reflect poor dietary behavior with less nutrient-dense food. This may represent a major nutrition goal for the aging population to cope with their higher nutritional requirements and deal with issues like poor oral health[36], appetite loss[37], and/or altered digestive functions[38]. Similarly, the elderly in Western countries are facing the same issue but with different micronutrients, which may reflect differences in their diets and/or national recommendations. In a systematic review pooling together 37 studies on the habitual dietary intake of community-living 65 years and above, 6 micronutrients of the 20 studied (vitamins D, B1, and B2, calcium, magnesium, and selenium) were at risk of inadequate intake, which was suggested to be a public health concern[39]. As our study did not include vitamin D, four among the other five micronutrients (i.e., vitamins B1 and B2, calcium, and selenium) were also of similar concern in China. Sufficient dietary nutrient supply relies on a high-quality diet, and suboptimal diet is a leading risk factor for disabilities and death[4, 24, 33]. We noticed that overweight and obese elderly had significantly more wheat, coarse cereals, and vegetable oils and less rice, vegetables, animal oils, pork, and poultry than the lower BMI categories. The observed food consumption patterns may reflect the multilevel heterogeneity among Chinese provinces, where many conventional socioeconomic factors interweave with more complex factors like geography, cultural heritage, Western influences, and cooking style to impact eating behaviors. We also evaluated the food intake quality based on adherence to the Chinese Dietary Guideline. The main features of the unbalanced Chinese diets consisted of low intake in nutrient-dense food groups, such as fruits, dairy, soy and nuts, eggs, fish, and seafood, whereas consumption of fat- and sodium-contributing food groups, such as cooking oil and salt, were higher than recommended. Cohort data have shown that suboptimal diet was associated with a significant increase in the risk of diabetes and cardiovascular diseases (CVD) in China[4, 16, 24, 34, 35, 40]. Considering the global burden of chronic diseases, promoting healthy eating behaviors and reducing population exposure to unbalanced diets should be considered as a priority for Chinese public health policymakers.

    • To our knowledge, this study is the largest, most up-to-date nationwide survey in China with a focus on the elderly. The detailed examination of food and nutrient intakes also allowed a comprehensive understanding of the dietary patterns of this special population. However, this study has several limitations. Firstly, the participants of this study were recruited from the community, while those living in nursing homes with more severe malnutrition problems were not included. Secondly, our approach to assess energy and nutrient intake through three consecutive 24-h recalls may not reflect the long-term food intake status, especially for seasonal foods. Finally, except participants with extreme energy intakes, we did not exclude community-living participants with presumably major age-associated chronic diseases. Some of those diseases related to nutrition are particularly relevant to aging populations with a high prevalence of common chronic diseases, such as diabetes and CVD, or geriatric syndromes, such as frailty, sarcopenia, and cognitive impairment. This work is currently ongoing and will be reported soon elsewhere.

    • Fighting malnutrition is the major challenge for the rapidly aging population in China. While an important decline has been observed, the prevalence of anemia and underweight is still high among the more susceptible subgroups, such as rural elderly and older old residents. Low intake of nutrient-dense food groups, such as fruits, dairy, soy and nuts, eggs, and fish and seafood, and high intake of high-fat and sodium-contributing food groups, such as cooking oil and salt, lead to micronutrient inadequacy and high risk of chronic diseases, which remain to be the important nutritional problems for Chinese elderly. This study provides important reference for policymakers to support the China National Nutrition Plan 2017–2030, promoting that public health authorities should provide further improvement with targeted approaches.

    • The authors’ contributions were as follows: ZHANG Jian, project design, quality control, and manuscript drafting; SONG Peng Kun, working-site questionnaire, data clearance, and analysis; ZHAO Li Yun, project design and organization in working-site; SUN Ye, YU Kai, and YIN Jing, data analysis and manuscript drafting; PANG Shao Jie, LIU Zhen, and LI Cheng, data clearance and analysis; MAN Qing Qing, biochemical measurements; HE Li, quality control and manuscript drafting; ARIGONI Fabrizio and BOSCO Nabil, manuscript drafting; DING Gang Qiang, project design and manuscript drafting; and ZHAO Wen Hua project design, quality control, and approval of the final version.

    • We would like to thank all the participants who took part in the China National Nutrition and Healthy Survey and the staff who conducted this study. We would also like to thank Dr. Irma Silva Zolezzi and Prof. Chen Jun Shi for comments on the manuscript.

    • The authors declare no competing interests.

参考文献 (40)

目录

    /

    返回文章
    返回