基于众源数据挖掘的中国饮食口味与慢性病的空间关联

doi:10.11821/dlxb201908011

摘要/Abstract

摘要：

慢性病是全球最主要的死亡原因,在所有慢性病风险因素中,不健康饮食因素居于首位,也是影响最广泛的风险因素。尽管已有许多关于饮食行为的研究,但在饮食口味与慢性病关联方面尚缺乏定量研究。鉴于此,利用众源网络菜谱数据,提取菜系中多维口味信息,结合不同地区分类的餐饮类兴趣地点（POI）数据,定量分析不同地区人群口味偏好;使用地理探测器方法,从空间分异性角度建立7种口味与出血性卒中、胰腺癌、上呼吸道感染3种慢性病的关联,得到饮食口味对慢性病空间分布的解释能力度量值。结果表明：在7种口味中,过咸是出血性卒中的首要口味风险因子;一定程度的甜是胰腺癌的首要口味风险因子,且甜的程度与胰腺癌风险并非呈简单线性关系;过辛是上呼吸道感染的首要口味风险因子,三者在统计上均表现显著。本文首次提出了基于众源数据挖掘的潜在健康风险因素定量研究方法,可以应用于病因的探索性分析,并有助于公共卫生部门制定相应的干预措施。

关键词: 慢性病, 风险因素, 饮食口味, 众源数据, 地理探测器

Abstract:

Chronic diseases are the main cause for death in the world. Among all risk factors concerning chronic diseases, those related to an unhealthy diet are most important. Although much research was done on dietary behavior, there are only few quantitative studies on the relationship between dietary taste and chronic diseases. In this article, a taste dataset of the major categories of Chinese cuisine is established based on crowdsourced data from Chinese recipe websites. For a quantitative analysis of people's taste in different regions, additionally the locations of restaurants by category (using their respective points of interest) are integrated. Using the software Geodetector, these regional taste preferences are then correlated with the three chronic diseases, hemorrhagic stroke, pancreatic cancer, and upper respiratory tract infection. For all the three diseases, the results indicate very salty, moderate sweet and very spicy food as the primary risk factors. Also, the degree of sweetness is not linear with the risk of pancreatic cancer. These results are statistically significant. In this study, a quantitative method on discovering potential health risk factors based on mining of crowdsourced data is proposed for the first time. This method can be applied before disease-related experiments to filter potential factors, and it is helpful for the public health department to make quick corresponding intervention policies.

Key words: chronic disease, risk factor, dietary tastes, crowdsourcing data mining, Geodetector

李瀚祺, 贾鹏, 费腾. 基于众源数据挖掘的中国饮食口味与慢性病的空间关联[J]. 地理学报, 2019, 74(8): 1637-1649.

LI Hanqi, JIA Peng, FEI Teng. Geographical association between dietary tastes and chronic diseases in China:An exploratory study using crowdsourcing data mining techniques[J]. Acta Geographica Sinica, 2019, 74(8): 1637-1649.

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参考文献 46

[1]	World Health Organization. Noncommunicable Diseases. .
[2]	World Health Organization. Global Action Plan for the Prevention and Control of Noncommunicable Diseases 2013-2020. Geneva, World Health Organization, 2013.
[3]	World Health Organization. Noncommunicable Diseases Progress Monitor 2018. Geneva, World Health Organization, 2018.
[4]	World Health Organization. Global Status Report On Noncommunicable Diseases 2014. Geneva, World Health Organization, 2014.
[5]	Yang G, Wang Y, Zeng Y , et al. Rapid health transition in China, 1990-2010: Findings from the Global Burden of Disease Study 2010. The Lancet, 2013,381(9882):1987-2015.
[6]	Stanaway J D, Afshin A, Gakidou E , et al. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: A systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2018,392(10159):1923-1994.
[7]	Ezzati M, Riboli E . Can noncommunicable diseases be prevented? Lessons from studies of populations and individuals. Science, 2012,337(6101):1482-1487.
[8]	Ezzati M, Riboli E . Behavioral and dietary risk factors for noncommunicable diseases. New England Journal of Medicine, 2013,369(10):954-964.
[9]	Sun Xiaodong, Lyu Yun, Li Liming . Prevalence level of main risk factors of chronic diseases and development of prevention strategies. Chinese Journal of Prevention and Control of Chronic Non-communicable Diseases, 2008,16(5):538-540.
	[ 孙晓东, 吕筠, 李立明 . 慢性病的主要危险因素流行水平及其预防策略的发展. 中国慢性病预防与控制, 2008,16(5):538-540.]
[10]	Li Yichong, Liu Shiwei, Wang Limin , et al. Burden of disease attributable to main behavioral risk factor of chronic disease inactivity in China, 1990 and 2010. Chinese Journal of Preventive Medicine, 2015, ( 4):303-308.
	[ 李镒冲, 刘世炜, 王丽敏 , 等. 1990年与2010年中国慢性病主要行为危险因素的归因疾病负担研究. 中华预防医学杂志, 2015, ( 4):303-308.]
[11]	Stookey J D, Wang Y, Ge K , et al. Measuring diet quality in China: The INFH-UNC-CH diet quality index. European Journal of Clinical Nutrition, 2000,54(11):811.
[12]	Xu Jianwei, Chen Xiaorong, Li Yichong , et al. Analysis of Chinese adults' eating behavior in 2010. Chinese Journal of Prevention and Control of Chronic Diseases, 2015,23(9):684-686.
	[ 徐建伟, 陈晓荣, 李镒冲 , 等. 2010年中国成年人就餐行为情况分析. 中国慢性病预防与控制, 2015,23(9):684-686.]
[13]	Ma Guansheng, Hu Xiaoqi, Luan Dechun , et al. The eating practice of people in China. Acta Nutrimenta Sinica, 2005,27(4):272-275.
	[ 马冠生, 胡小琪, 栾德春 , 等. 中国居民的就餐行为. 营养学报, 2005,27(4):272-275.]
[14]	Su Yanping, Yang Kun, Liu Xiangtong , et al. Relationship between the dietary behavior and the risk of diabetes in adults. Chinese Journal of Prevention and Control of Chronic Diseases, 2018,26(7):481-485.
	[ 苏彦萍, 杨昆, 刘相佟 , 等. 成年人饮食习惯与糖尿病患病风险的研究. 中国慢性病预防与控制, 2018,26(7):481-485.]
[15]	Shen Min, Shi Luyuan, Li Liangcheng , et al. A case-control study on urinary calculi and dietary factors. Chinese Journal of Epidemiology, 2002,23(2):134-137.
	[ 沈珉, 施侣元, 李良成 , 等. 饮食因素与尿石症关系的病例对照研究. 中华流行病学杂志, 2002,23(2):134-137.]
[16]	Wang Weiwei . Regional differences in Chinese diet culture. Food and Life, 2001(1):46-47.
	[ 王维维 . 中国饮食文化地域差异. 食品与生活, 2001(1):46-47.]
[17]	Chen Chuankang . The culture of Chinese diet: Regional differentiation and developing trends. Acta Geographica Sinica, 1994,49(3):226-235.
	[ 陈传康 . 中国饮食文化的区域分化和发展趋势. 地理学报, 1994,49(3):226-235.]
[18]	Wang Weiwei, Qiu Shi . Sweet, sour, bitter and salty-the regional difference of Chinese food culture. Chinese Food, 2007(11):14-15.
	[ 王维维, 秋实 . 甜酸苦辣咸: 中国饮食文化的地域差异. 中国食品, 2007(11):14-15.]
[19]	Zhu J W, Xu Y, Fang Z X , et al. Geographic prevalence and mix of regional cuisines in Chinese cities. ISPRS International Journal of Geo-Information, 2018,7(5):183-205.
[20]	Zhu Y X, Huang J M, Zhang Z K , et al. Geography and similarity of regional cuisines in China. PLoS One, 2013,8(11):e79161.
[21]	Howe J . The rise of crowdsourcing. Wired Magazine, 2006,14(6):1-5.
[22]	Zhang Yannan, Hu Jiyue . Thinking about big data's application in education. China Electric Power Education, 2013, ( 32):5-7.
	[ 张燕南, 胡继岳 . 关于大数据应用于教育的思考. 中国电力教育, 2013, ( 32):5-7.]
[23]	Meng Runtang, Luo Yi, Yu Chuanhua , et al. Application and challenges of healthy big data in the field of public health. Chinese General Practice, 2015,18(35):4388-4392.
	[ 孟润堂, 罗艺, 宇传华 , 等. 健康大数据在公共卫生领域中的应用与挑战. 中国全科医学, 2015,18(35):4388-4392.]
[24]	Wang J F, Li X H, Christakos G , et al. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 2010,24(1):107-127.
[25]	Gu Jingfan . An interpretation of Report on the Status of Nutrition and Chronic Diseases in Chinese Residents(2015). Journal of Nutrition, 2016,38(6):525-529.
	[ 顾景范 . 《中国居民营养与慢性病状况报告(2015)》解读. 营养学报, 2016,38(6):525-529.]
[26]	Naghavi M, Wang H, Lozano R , et al. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 2015,385(9963):117-171.
[27]	Li Kang, He Jia. Medical Statistics. 6th ed. Beijing: People's Medical Publishing House, 2013.
	[ 李康, 贺佳 . 医学统计学. 6版. 北京: 人民卫生出版社, 2013.]
[28]	Kitchen spices . . The name and diagram of 1000 kinds of spices in kitchen. . 2018.
	[ 厨房香料大全. 1000种香料名称及图解. , 2018.]
[29]	Lan Yong . On the reasons and distribution of pungent flavour in Chinese food and drink. Human Geography, 2001,16(5):84-88.
	[ 蓝勇 . 中国饮食辛辣口味的地理分布及其成因研究. 人文地理, 2001,16(5):84-88.]
[30]	Running C A, Craig B A, Mattes R D . Oleogustus: The unique taste of fat. Chemcal Senses, 2015,40(7):507-516.
[31]	Cao F, Ge Y, Wang J F . Optimal discretization for geographical detectors-based risk assessment. Giscience & Remote Sensing, 2013,50(1):78-92.
[32]	Wang Jinfeng, Xu Chengdong . Geodetector: Principle and prospective. Acta Geographica Sinica, 2017,72(1):116-134.
	[ 王劲峰, 徐成东 . 地理探测器: 原理与展望. 地理学报, 2017,72(1):116-134.]
[33]	Xu Qiurong, Zheng Xinqi . Analysis of influencing mechanism of urban growth using geographical detector. Acta Geodaetica et Cartographica Sinica, 2015,44(Suppl.):96-101.
	[ 徐秋蓉, 郑新奇 . 一种基于地理探测器的城镇扩展影响机理分析法. 测绘学报, 2015,44(Suppl.):96-101.]
[34]	Nagata C, Takatsuka N, Shimizu N , et al. Sodium intake and risk of death from stroke in Japanese men and women. Stroke, 2004,35(7):1543-1547.
[35]	Yu Chuanhua, Luo Lisha, Li Mei , et al. From the global views to understand the seriousness of the burden of stroke in China. Journal of Public Health and Preventive Medicine, 2016,27(1):1-5.
	[ 宇传华, 罗丽莎, 李梅 , 等. 从全球视角看中国脑卒中疾病负担的严峻性. 公共卫生与预防医学, 2016,27(1):1-5.]
[36]	Lu Wenchao, Cui Yunfu . Advance in understanding the epidemiology and etiology of pancreatic cancer. World Chinese Journal of Digestology, 2011,19(27):2805-2809.
	[ 吕文超, 崔云甫 . 胰腺癌流行病学和病因学研究进展. 世界华人消化杂志, 2011,19(27):2805-2809.]
[37]	Wormann S M, Algul H . Risk factors and therapeutic targets in pancreatic cancer. Frontiers in Oncology, 2013,3:282-298.
[38]	Genkinger J M, Li R, Spiegelman D , et al. Coffee, tea, and sugar-sweetened carbonated soft drink intake and pancreatic cancer risk: A pooled analysis of 14 cohort studies. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 2012,21(2):305-318.
[39]	Chari S T, Leibson C L, Rabe K G , et al. Pancreatic cancer-associated diabetes mellitus: Prevalence and temporal association with diagnosis of cancer. Gastroenterology, 2008,134(1):95-101.
[40]	Batty G D, Kivimaki M, Morrison D , et al. Risk factors for pancreatic cancer mortality: Extended follow-up of the original Whitehall Study. Cancer Epidemiology, Biomarkers & Prevention: A Publication of the American Association for Cancer Research, Cosponsored by the American Society of Preventive Oncology, 2009,18(2):673-675.
[41]	Sherman P W, Billing J . Darwinian gastronomy: Why we use spices. BioScience, 1999,49(6):453-463.
[42]	Chandrashekar J, Hoon MA, Ryba NJP , et al. The receptors and cells for mammalian taste. Nature, 2006,444(7117):288-294.
[43]	Nirupa C, Roper S D . The cell biology of taste. Journal of Cell Biology, 2010,191(2):429-429.
[44]	Li Guoliang, Chen Mengling, Zhang Genhua . The cell biology of mammalian taste. Chemistry of Life, 2012,32(5):456-459.
	[ 李国梁, 陈梦玲, 张根华 . 哺乳动物味觉的细胞生物学. 生命的化学, 2012,32(5):456-459.]
[45]	Chamoun E, Mutch D M, Allenvercoe E , et al. A review of the associations between single nucleotide polymorphisms in taste receptors, eating behaviours, and health. Critical Reviews in Food Science and Nutrition, 2018,58(2):194-207.
[46]	Jia P . Spatial lifecourse epidemiology. The Lancet Planetary Health, 2019,3(2):e57-e59.

序号	使用情况	菜系	菜谱数量	酸	甜	鲜	咸	油	辣	辛
1	菜谱与POI数据共有的16类菜系	川菜	1008	0.27	0.54	0.51	1.75	0.72	0.95	2.23
2		东北菜	896	0.25	0.43	0.64	1.61	0.68	0.26	1.34
3		港台菜	67	0.15	0.78	0.6	1.66	0.84	0.34	1.79
4		湖北菜	233	0.22	0.48	0.85	1.59	0.89	0.26	2.35
5		沪菜	328	0.19	0.64	0.91	1.43	0.97	0.06	1.38
6		徽菜	327	0.17	0.6	0.69	1.18	0.77	0.13	1.16
7		京菜	360	0.21	0.58	0.57	1.26	0.68	0.11	1.26
8		鲁菜	757	0.18	0.36	0.57	1.24	0.68	0.16	1.14
9		闽菜	661	0.17	0.47	0.83	1.13	0.74	0.10	1.14
10		清真菜	141	0.28	0.61	0.94	1.68	0.92	0.38	2.01
11		苏菜	449	0.15	0.51	0.58	1.26	0.69	0.09	1.17
12		西北菜	339	0.21	0.46	0.92	1.58	0.91	0.34	2.01
13		湘菜	720	0.15	0.32	0.67	1.63	0.82	0.63	1.32
14		粤菜	1007	0.13	0.48	0.45	1.28	0.54	0.12	1.01
15		云贵菜	187	0.25	0.52	0.74	1.68	0.92	0.66	1.90
16		浙菜	570	0.16	0.47	0.7	1.26	0.74	0.09	1.09
17	仅菜谱数据包含的4类菜系	广西菜	98	0.35	0.53	1.01	1.91	0.69	0.39	1.84
18		江西菜	87	0.16	0.25	0.77	1.24	0.76	0.59	1.48
19		山西菜	175	0.27	0.43	0.75	1.43	1.03	0.19	2.22
20		豫菜	167	0.21	0.53	1.01	1.43	0.91	0.17	1.61

疾病	离散方案	酸	甜	鲜	咸	油	辣	辛
出血性卒中	方法	QU	QU	QU	NB	QU	QU	QU
出血性卒中	级数	7	7	8	6	8	7	7
胰腺癌	方法	NB	QU	QU	QU	QU	QU	NB
胰腺癌	级数	7	7	7	7	7	7	7
上呼吸道感染	方法	NB	QU	QU	QU	SD	QU	NB
上呼吸道感染	级数	7	7	8	7	7	7	7

疾病	口味风险因子	酸	甜	鲜	咸	油	辣	辛
出血性卒中	酸	0.366
	甜	0.792	0.451^*
	鲜	0.857↑	0.855↑	0.162
	咸	0.748	0.737	0.921↑	0.499^**
	油	0.878↑	0.851↑	0.646↑	0.926↑	0.363
	辣	0.805	0.794	0.969↑	0.780	0.868	0.523^*
	辛	0.835	0.881	0.922↑	0.820	0.905	0.859	0.514^*
胰腺癌	酸	0.264
	甜	0.859	0.628^**
	鲜	0.766↑	0.944↑	0.122
	咸	0.860↑	0.817	0.808↑	0.426
	油	0.759↑	0.816	0.654↑	0.705	0.298
	辣	0.763↑	0.952	0.848↑	0.886	0.856↑	0.446
	辛	0.529	0.962	0.798↑	0.850	0.852↑	0.838	0.425
上呼吸道感染	酸	0.441
	甜	0.800	0.434
	鲜	0.854↑	0.816↑	0.163
	咸	0.848	0.818	0.971↑	0.535^*
	油	0.545	0.786	0.702↑	0.891	0.440
	辣	0.887	0.862	0.966↑	0.955	0.919	0.505^*
	辛	0.757	0.819	0.922↑	0.806	0.724	0.906	0.560^**