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Int J Food Sci Nutr. 2021; 72(4): 569–576.
Published online 2020 Nov 18. doi:10.1080/09637486.2020.1849038
PMCID: PMC8404681
PMID: 33207985
Qiaorui Wen, Yuxia Wei, Huaidong Du, Jun Lv, Yu Guo, Zheng Bian, Ling Yang, Yiping Chen, Yan Chen, Liya Shi, Junshi Chen, Canqing Yu, Zhengming Chen, and Liming Li, on behalf of the China Kadoorie Biobank Collaborative Group
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Abstract
This study aimed to describe the characteristics and lifestyle differences of spicy food consumption in 0.5 million adults. Participants were recruited from 2004 to 2008 in the baseline research of the CKB study. Higher frequency and stronger pungency degree in spicy food positively correlated with preference for salty taste, eating snacks/deep-fried foods, tea/alcohol drinking and tobacco smoking. Among weekly tea/alcohol drinkers and current regular smokers, participants with a higher frequency of spicy food consumption or preference for stronger pungency degree were more likely to prefer strong tea, drink alcohol exceed the healthy amount, drink alcohol in the morning every day, smoke ≥ 40 cigarettes per day, consume a larger amount of tea leaves, alcohol and cigarettes each day, and start habitual tea/alcohol drinking or smoking at an earlier age. Differences existed in lifestyle factors related to major chronic diseases according to spicy food consumption frequency and pungency degree among the Chinese population.
Keywords: Spicy food; , population difference; , lifestyle behaviours
Introduction
Spices have been extensively used in cooking worldwide. Spicy food consumption and its nutrient substances, such as capsaicin and vitamins contained in chilli pepper, have been attracting increasing attention in recent years (Patowary etal. 2017; Bonaccio etal. 2019). Prospective studies have found that cumulative average chilli intake was inversely associated with overweight/obesity, diabetes, hypertension and mortality in the Chinese population (Lv etal. 2015; Shi etal. 2017; Shi etal. 2018). Moreover, capsaicin has shown its potential to treat rhinitis, diabetes, neurogenic bladder, various cancers, cardiovascular, gastrointestinal, and dermatologic diseases (Sharma etal. 2013; Fokkens etal. 2016; Sun etal. 2016; Zsiboras etal. 2018).
However, few studies have described the characteristics of spicy food consumption in a large population (Wang etal. 2019). More importantly, lack of comprehensive research on the relationship of spicy food consumption with health-related lifestyle behaviours, such as cigarette smoking, alcohol drinking, physical activity, dietary habits, etc., impeded us to fully understand its contribution to major chronic diseases and their interactions with those lifestyle behaviours. Therefore, we described the population distribution and behavioural characteristics of spicy food consumption among half a million participants in the China Kadoorie Biobank (CKB) study.
Subjects and methods
Study population
The CKB study is a population-based prospective cohort study. Participants were recruited in 2004–2008, including 512,715 men and women aged 30–79 years from 10 areas (Qingdao, Harbin, Haikou, Suzhou, Liuzhou, Sichuan, Gansu, Henan, Zhejiang, Hunan) across China. About 5% of the participants were randomly invited to the resurvey during 2013–2014, involving 25,069 participants. Detailed descriptions of the CKB study have been published before (Chen etal. 2005; Chen etal. 2011).
In the baseline, we excluded individuals with self-reporting medical histories of diabetes (n = 16,162), cancer (n = 2,578), heart disease (n = 15,472) or stroke (n = 8,884). We also excluded two individuals with missing values on body mass index (BMI). The same excluding criteria applied to the second resurvey, 4,132 participants reported diseases mentioned above were excluded. Therefore, 474,015 participants in the baseline and 21,107 participants in the resurvey were eligible for the final analyses.
Assessment of covariates
In the baseline survey, we used a laptop-based questionnaire to face-to-face interview participants about their sociodemographic characteristics and lifestyle behaviours.
Sociodemographic characteristics included age, sex, marital status (married, widowed, separated or divorced, never married), education (no formal school, primary school, middle school, high school, technical school or college, university), household income (<2500, 2500–4999, 5000–9999, 10000–19999, 20000–34999, ≥35000 yuan per year) and occupation (manual worker, non-manual worker, not working).
Lifestyle behaviours included tea/alcohol drinking [never drinker, occasionally drinker, 1–5 days per week (d/w) and 6–7d/w], tobacco smoking (never/occasionally smoker, ex-regular smoker and current regular smoker) and physical activity. Details of drinking and smoking behaviours were further assessed among weekly tea/alcohol drinkers and current regular smokers: temperature of tea (warm, hot, burning hot), strength of tea (weak, moderate, strong), frequency of changing tea leaves per day and amount of tea leaves added each time, grams of alcohol consumption on a typical day, frequency of drinking alcohol in the morning, number of cigarettes smoked per day, and age of starting drinking tea/alcohol weekly or smoking regularly. Female who consumed alcohol >15 grams per day (g/d) or male who consumed >30 g/d were defined as an unhealthy drinker. Those smoking ≥40 cigarettes were defined as a heavy smoker. Total daily physical activity level was calculated by multiplying the metabolic equivalent tasks value for a particular type of physical activity by hours spent on that activity per day and summing the metabolic equivalent hours per day (MET-h/d) for all activities (Du etal. 2013). The physical activity was divided into three groups according to sex-specific tertiles of exercises (13.68 and 28.73 MET-h/d in male, 12.81 and 24.60 MET-h/d in female).
Assessment of dietary habits and spicy food consumption
In the second resurvey of CKB study, we used a semi-quantitative food frequency questionnaires (FFQ, Supplementary Material 2) to collect the information on the frequency (daily, 4–6 d/w, 1–3 d/w, monthly, never/rarely) and amount (grams unless specified) about major food groups, including rice, wheat, grains, meat, fish, poultry, fresh vegetables, preserved vegetables, fresh fruits, soya products, yoghurt, other dairy foods, soymilk (mL), milk (mL), other drinks (mL). Besides, participants reported their saltiness preference (very light, about average, very salty), daily condiments usage (e.g. salt, soy sauce, cooking oil) and the frequency of eating habits (e.g. snacking, skipping breakfast, eating deep-fried foods, eating western-type fast foods). Dietary intakes were calculated by multiplying the days of consuming specific food each week and the amount they ate each time. Daily energy intake was calculated by taking into account the 11 groups of foods (rice, wheat and grains, meat, fish, poultry, fresh vegetables, preserved vegetables, fresh fruits, soya products, yoghurt). Unhealthy eating habits were defined as having the following behaviours ≥1 d/w (snacking, skipping breakfast, eating deep-fried foods, eating western-type fast foods).
On top of the FFQ, a series of questions were asked to collect detailed information on spicy food consumption, such as the frequency, pungency degree (the strength of spiciness by subjective feeling), age of starting the habit, and the types of source of spicy food consumption (Supplementary Material 3). We asked participants about the frequency of their spicy food consumption during the past month, and possible answers were never or almost never, only occasionally, 1–2 d/w, 3–5 d/w, daily or almost every day. Weekly spicy food consumers were further asked about the age they started to eat spicy food weekly, the pungency degree of spicy food they usually preferred to eat (single choice allowed weak, moderate, and strong), and the main source of spice usually used (multiple choices allowed chilli sauce, chilli oil, dried chilli pepper, fresh chilli pepper, and others or did not know). The reproducibility of spicy food assessment was tested, and the Spearman’s coefficient for the correlation between baseline and resurvey questionnaires was 0.71 (Lv etal. 2015).
Statistical considerations
The percentages and means of sociodemographic characteristics, dietary habits and lifestyle behaviours were described according to spicy food consumption frequency and pungency degree, using logistic regression and ANOVA (analysis of variance) model respectively, adjusting for age, sex, region, household income, education, occupation and marriage status unless specified. Linear trend was tested for age of starting habitual spicy food intake, pungency degree and sources of spicy food across frequency of spicy food consumption, dietary habits and all details of lifestyle behaviours across spicy food consumption frequency as well as pungency degree of spicy food, adjusting for age, sex, region, household income, education, occupation and marriage status, by assigning the midpoint values of each frequency category and treating the variable as continuous in a separate regression model.
The statistical analyses were performed with Stata (version 15.0). All P values were two-sided, and we defined statistical significance as P < 0.05.
Ethical statement
The Ethical Review Committee of the Chinese Centre for Disease Control and Prevention (Beijing, China) and the Oxford Tropical Research Ethics Committee at the University of Oxford (Oxford, UK), approved the study. Written informed consent was obtained from all participants.
Results
Among 474,051 participants with a mean age of (51.3 ± 10.5) years, 41% were male, and 30.8% were daily spicy food consumers. Daily consumers were more likely to be young non-manual workers. Among the regular consumers who reported ≥1 d/w in spicy food intake, 74,037 (36.2%) preferred strong pungency. The stronger pungency degree they preferred, the more they were likely to be young married, but the less likely to be highly educated and non-manual workers (Table 1).
Table 1.
Baseline characteristics according to spicy food consumption.
| Subgroups | Frequency (%, SE) | Pungency degree (%, SE)a | |||||
|---|---|---|---|---|---|---|---|
| < 1 d/w | 1–2 d/w | 3–5 d/w | 6–7 d/w | Weak | Moderate | Strong | |
| N | 269,428 | 30,930 | 27,853 | 145,804 | 59,252 | 71,298 | 74,037 |
| Age (years, SE)b | 53.2 (0.0) | 49.2 (0.1) | 49.1 (0.1) | 48.7 (0.0) | 51.4 (0.0) | 49.8 (0.0) | 48.5 (0.0) |
| Malec | 38.7 (0.1) | 46.0 (0.3) | 46.2 (0.3) | 43.2 (0.2) | 38.3 (0.2) | 44.2 (0.2) | 43.8 (0.2) |
| Married | 90.7 (0.1) | 90.6 (0.2) | 90.8 (0.2) | 91.6 (0.1) | 91.6 (0.1) | 91.7 (0.1) | 91.9 (0.1) |
| Highest education level | |||||||
| Primary school and below | 52.3 (0.1) | 48.5 (0.2) | 47.7 (0.3) | 49.2 (0.1) | 50.5 (0.2) | 50.5 (0.2) | 52.3 (0.2) |
| Middle or high school | 42.1 (0.1) | 44.9 (0.3) | 46.3 (0.3) | 45.8 (0.1) | 43.7 (0.2) | 44.3 (0.2) | 43.6 (0.2) |
| College and above | 5.6 (0.0) | 6.5 (0.1) | 6.0 (0.1) | 5.0 (0.1) | 5.7 (0.1) | 5.1 (0.1) | 4.1 (0.1) |
| Household income | |||||||
| <10000 | 30.1 (0.1) | 27.6 (0.2) | 27.3 (0.2) | 26.4 (0.1) | 33.3 (0.2) | 34.5 (0.1) | 33.1 (0.2) |
| 10000–19999 | 28.6 (0.1) | 27.0 (0.3) | 26.9 (0.3) | 29.3 (0.2) | 29.5 (0.2) | 30.3 (0.2) | 29.1 (0.2) |
| >20000 | 41.3 (0.1) | 45.4 (0.2) | 45.7 (0.3) | 44.3 (0.2) | 37.3 (0.2) | 35.2 (0.2) | 37.8 (0.2) |
| Occupation | |||||||
| Manual work | 58.5 (0.1) | 59.6 (0.2) | 59.2 (0.2) | 57.5 (0.1) | 65.1 (0.2) | 63.0 (0.2) | 64.8 (0.2) |
| Non-manual work | 12.8 (0.1) | 14.1 (0.2) | 14.1 (0.2) | 14.2 (0.1) | 13.5 (0.1) | 13.4 (0.1) | 12.7 (0.1) |
| Not working | 28.7 (0.1) | 26.3 (0.2) | 26.7 (0.2) | 28.3 (0.1) | 21.4 (0.2) | 23.6 (0.1) | 22.5 (0.1) |
Open in a separate window
SE: standard error.
Values are percentages or means of participants adjusted for age, sex and region unless specified.
aAmong those consuming spicy food ≥ 1 d/w.
bAdjusted for sex and region.
cAdjusted for age and region.
Among the regular spicy food consumers, the higher frequency they reported, the stronger pungency they preferred (14.5% participants who consume spicy food 1–2 d/w preferred strong pungency, while the corresponding percentage was 42.1% of daily consumes), and the earlier age of starting the habit they reported (17.9 years old in 1–2 d/w consumers vs. 14.5 years old in daily consumers, P for trend < 0.001, Figure 1(A)). Fresh chilli pepper, dried chilli pepper, chilli oil and chilli sauce were successively top four most popular spicy food in all groups, and the percentages of each source were positively correlated with its frequency (p for trend < 0.001, Figure 1(B)).
Figure 1.
Characteristics of spicy food consumption metrics according to the weekly intake frequency. (A) Pungency degree and age of starting habitual spicy food consumption. (B) Sources of spicy food. All values were adjusted for age, sex, region, household income and education. p value for linear trend test for starting age, pungency degree and sources of spicy food across frequency of spicy food consumption were all <0.001.
Daily spicy food consumers consumed the most poultry (102.1 g/w) and preserved vegetables (179.5 g/w) compared with other categories. Also, participants who preferred stronger pungency degree were more likely to consume meat, fresh vegetables, fresh fruits, soya products and had higher daily energy intake, but less likely to consume milk and soymilk (P for trend < 0.001, Table 2). The higher frequency of spicy food consumption or stronger pungency degree, the higher proportion of snacking (from 23.1 to 30.7%, and from 28.4 to 31.8%, respectively) and eating deep-fried foods (from 5.3 to 11.9%, and from 8.5 to 12.2%, respectively). In addition, the preference for salty taste increased from 22.8% in non-spicy-food consumers to 38.9% in daily consumers, and from 26.1% in those preferred weak pungency degree to 50.2% in those preferred strong pungency degree.
Table 2.
Characteristics of dietary intakes according to spicy food consumption in the resurvey (n = 21,107).
| Subgroups | Frequency | Pungency degreea | |||||
|---|---|---|---|---|---|---|---|
| <1 d/w | 1–2 d/w | 3–5 d/w | 6–7 d/w | Weak | Moderate | Strong | |
| N | 13,422 | 1,085 | 814 | 5,786 | 3,639 | 3,208 | 839 |
| Dietary intakes (g/w, SE) | |||||||
| Rice | 1438.0 (6.4) | 1390.5 (21.0) | 1451.3 (24.3) | 1459.3 (10.9) | 1375.6 (11.5) | 1380.9 (12.1) | 1325.6 (23.3) |
| Wheat | 698.4 (4.8) | 638.3 (15.5) | 679.1 (18.0) | 694.9 (8.1) | 756.7 (9.2) | 781.2 (9.7) | 789.0 (18.7) |
| Grains | 244.7 (3.0) | 256.3 (9.8) | 244.9 (11.4) | 246.7 (5.1) | 165.9 (4.7) | 157.8 (4.9) | 159.0 (9.4) |
| Meat | 369.4 (2.9) | 374.5 (9.4) | 350.0 (10.9) | 419.2 (4.9)d | 427.6 (5.8) | 448.8 (6.2) | 504.8 (11.9)d |
| Fish | 148.1 (2.4) | 165.1 (7.9) | 161.5 (9.2) | 166.3 (4.1)d | 133.0 (3.9) | 136.2 (4.1) | 151.6 (7.8) |
| Poultry | 81.9 (1.2) | 84.8 (4.4) | 85.6 (5.1) | 102.1 (2.3)d | 88.2 (2.6) | 89.5 (2.8) | 99.6 (5.3) |
| Fresh vegetables | 1635.9 (8.5) | 1613.8 (27.6) | 1556.9 (32.0) | 1692.2 (14.3)d | 1567.5 (15.2) | 1603.3 (16.1) | 1783.4 (30.9)d |
| Preserved vegetables | 122.2 (1.8) | 145.3 (5.8) | 151.7 (6.7) | 179.5 (3.0)d | 159.7 (3.7) | 165.2 (4.0) | 173.6 (7.6) |
| Fresh fruits | 1635.9 (8.5) | 1613.8 (27.6) | 1556.9 (32.0) | 1692.2 (14.3)d | 1567.5 (15.2) | 1603.3 (16.1) | 1783.4 (30.9)d |
| Soya products | 132.1 (1.7) | 141.9 (5.7) | 139.7 (6.6) | 157.7 (2.9)d | 138.5 (3.1) | 143.9 (3.3) | 158.5 (6.4)d |
| Yoghurt | 54.4 (2.1) | 52.9 (6.8) | 54.5 (7.8) | 66.5 (3.5)d | 64.2 (4.1) | 54.8 (4.3) | 56.1 (8.3) |
| Other dairy foods | 5.7 (0.6) | 6.7 (1.8) | 8.1 (2.1) | 7.6 (0.9) | 8.7 (1.2) | 5.5 (1.3) | 7.4 (2.5) |
| Energy intake (kcal/d, SE)b | 1505.0 (4.8) | 1477.7 (15.6) | 1496.7 (18.1) | 1578.7 (8.1)d | 1511.0 (9.1) | 1533.7 (9.6) | 1566.8 (18.5)d |
| Beverage intakes (mL/w, SE) | |||||||
| Soymilk | 118.1 (3.5) | 145.4 (11.5) | 114.2 (13.4) | 128.5 (6.0) | 132.5 (6.4) | 112.7 (6.8) | 106.9 (13.0)d |
| Milk | 204.5 (4.5) | 200.9 (14.7) | 206.6 (17.0) | 193.7 (7.6) | 194.3 (7.9) | 159.8 (8.3) | 152.9 (15.9)d |
| Other drinks | 65.9 (3.6) | 103.1 (11.9) | 87.3 (13.7) | 126.6 (6.1)d | 119.6 (8.1) | 113.7 (8.6) | 131.1 (16.5) |
| Unhealthy eating habits (%, SE)c | |||||||
| Snacking | 23.1 (0.4) | 29.5 (1.4) | 30.0 (1.6) | 30.7 (0.7)d | 28.4 (0.8) | 31.9 (0.8) | 31.8 (1.6)d |
| Skipping breakfast | 8.8 (0.2) | 10.7 (0.9) | 16.2 (1.3) | 11.9 (0.5)d | 11.2 (0.5) | 10.3 (0.6) | 11.2 (1.0) |
| Deep-fried foods | 5.3 (0.2) | 9.2 (0.8) | 9.4 (1.0) | 11.9 (0.6)d | 8.5 (0.4) | 10.8 (0.6) | 12.2 (1.2)d |
| Western fast foods | 0.5 (0.1) | 0.4 (0.2) | 0.6 (0.3) | 0.8 (0.2)d | 0.6 (0.1) | 0.8 (0.2) | 1.3 (0.5) |
| Any above | 33.1 (0.4) | 41.9 (1.5) | 46.8 (1.7) | 44.9 (0.8)d | 40.4 (0.8) | 44.5 (0.9) | 45.9 (1.7)d |
| Condiments intake | |||||||
| Salty preference (%, SE) | 22.8 (0.4) | 26.7 (1.3) | 33.0 (1.6) | 38.9 (0.8)d | 26.1 (0.7) | 37.4 (0.9) | 50.2 (1.7)d |
| Salt (g/d, SE) | 29.6 (0.2) | 31.0 (0.7) | 30.7 (0.9) | 32.0 (0.4)d | 34.2 (0.5) | 33.7 (0.5) | 35.3 (1.0) |
| Soy sauce (mL/d, SE) | 16.7 (0.2) | 17.7 (0.6) | 17.5 (0.7) | 19.0 (0.3)d | 16.7 (0.4) | 17.3 (0.4) | 17.8 (0.8) |
| Cooking oil (mL/d, SE) | 113.0 (0.8) | 119.6 (2.7) | 114.2 (3.4) | 121.8 (1.5)d | 126.7 (1.7) | 127.8 (1.9) | 127.9 (3.5) |
Open in a separate window
SE: standard error.
Values are percentages or means of participants adjusted for age, sex, region, household income and education.
aAmong those consuming spicy food ≥ 1 d/w.
bkcal/d, kilocalorie per day.
cDefined as snacking, skipping breakfast, eating deep-fried foods or eating western-type fast foods ≥ 1 d/w.
dp for trend < 0.05.
Table 3 showed differences in lifestyle behaviours across spicy food consumption categories. Individualswith higher frequency of spicy food intake also had a higher proportion in daily tea/alcohol consumption (from 23.5 to 31.3%, and from 7.1 to 14.3%, respectively) and current smoking (from 24.5 to 30.7%). Similar correlations were found with stronger pungency degree. However, negative correlations were found between pungency degree and high-level physical activity.
Table 3.
Characteristics of lifestyle behaviours according to spicy food consumption (n = 474,015).
| Subgroups | Frequency (%, SE) | Pungency degree (%, SE)a | |||||
|---|---|---|---|---|---|---|---|
| <1 d/w | 1–2 d/w | 3–5 d/w | 6–7 d/w | Weak | Moderate | Strong | |
| Tea consumption | |||||||
| Never | 37.6 (0.1) | 31.1 (0.2) | 30.1 (0.2) | 29.5 (0.1) | 23.8 (0.1) | 22.1 (0.1) | 21.0 (0.2) |
| Occasionally | 32.2 (0.1) | 33.1 (0.3) | 33.6 (0.3) | 31.4 (0.2)c | 33.8 (0.2) | 33.1 (0.2) | 32.4 (0.2)c |
| 1–5 d/w | 6.7 (0.1) | 10.2 (0.2) | 9.4 (0.2) | 7.8 (0.1)c | 10.7 (0.2) | 9.8 (0.1) | 8.2 (0.1) |
| 6–7 d/w | 23.5 (0.1) | 25.6 (0.2) | 26.9 (0.2) | 31.3 (0.1)c | 31.8 (0.2) | 34.9 (0.2) | 38.4 (0.2)c |
| Alcohol consumption | |||||||
| Never | 51.7 (0.1) | 44.1 (0.2) | 42.4 (0.2) | 39.9 (0.1) | 49.2 (0.2) | 45.1 (0.2) | 41.7 (0.2) |
| Occasionally | 36.5 (0.1) | 39.4 (0.3) | 39.2 (0.3) | 38.0 (0.2)c | 36.2 (0.2) | 37.0 (0.2) | 36.3 (0.2)c |
| 1–5 d/w | 4.8 (0.1) | 7.2 (0.1) | 7.6 (0.1) | 7.8 (0.1)c | 6.3 (0.1) | 7.1 (0.1) | 7.6 (0.1)c |
| 6–7 d/w | 7.1 (0.0) | 9.3 (0.1) | 10.8 (0.2) | 14.3 (0.1)c | 8.3 (0.1) | 10.8 (0.1) | 14.4 (0.1)c |
| Smoking | |||||||
| Never | 63.7 (0.1) | 60.9 (0.2) | 59.9 (0.2) | 59.5 (0.1) | 60.2 (0.1) | 58.9 (0.1) | 56.8 (0.1) |
| Occasionally | 6.2 (0.1) | 6.2 (0.1) | 5.9 (0.1) | 4.9 (0.1)c | 6.8 (0.1) | 5.8 (0.1) | 5.0 (0.1) |
| Ex-regular | 5.6 (0.0) | 5.3 (0.1) | 5.4 (0.1) | 4.9 (0.1)c | 4.9 (0.1) | 4.6 (0.1) | 4.2 (0.1)c |
| Current regular | 24.5 (0.1) | 27.5 (0.2) | 28.7 (0.2) | 30.7 (0.1)c | 28.1 (0.2) | 30.7 (0.1) | 34.0 (0.1)c |
| Physical activityb | |||||||
| Low | 33.4 (0.1) | 33.0 (0.2) | 32.7 (0.2) | 33.3 (0.1) | 29.1 (0.2) | 28.6 (0.1) | 29.6 (0.1) |
| Medium | 33.3 (0.1) | 34.2 (0.3) | 34.1 (0.3) | 33.1 (0.2) | 35.6 (0.2) | 36.4 (0.2) | 35.9 (0.2) |
| High | 33.3 (0.1) | 32.9 (0.2) | 33.2 (0.2) | 33.6 (0.1)c | 35.3 (0.2) | 35.0 (0.2) | 34.5 (0.2)c |
Open in a separate window
SE: standard error.
Values are percentages or means of participants adjusted for age, sex, region, household income and education.
aAmong those consuming spicy food ≥1 d/w.
bLow physical activity was defined as <13.68 MET-h/d in male or < 12.81 MET-h/d in female, medium was ≥ 13.68 and < 28.73 MET-h/d in male or ≥ 12.81 and <24.6 MET-h/d in female, high was ≥28.73 MET-h/d in male or ≥ 24.6 MET-h/d in female (sex-specific tertiles amount of exercises).
cp for trend <0.05.
Further analysis among weekly tea/alcohol drinkers and current regular smokers, showed that participants with higher frequency of spicy food consumption or preference for stronger pungency degree were more likely to prefer strong tea, drink alcohol exceed the healthy amount, drink alcohol in the morning every day and smoke at least 40 cigarettes per day (Table 4). Positive correlations were found between pungency degree, not spicy food intake frequency, with drinking burning-hot tea, which increased from 13.0% in those preferred weak pungency degree to 17.2% in those preferred strong pungency degree. Frequency of spicy food consumption and pungency degree were found to be positively correlated the amount of tea leaves, alcohol and cigarettes consumed each day, but negatively correlated with the age of starting tea/alcohol drinking and smoking (all P for trend < 0.001).
Table 4.
Details of lifestyle behaviours according to spicy food consumption among weekly tea/alcohol drinkers and current smokers.
| Subgroups | Frequency (%, SE) | Pungency degree (%, SE)a | |||||
|---|---|---|---|---|---|---|---|
| <1 d/w | 1–2 d/w | 3–5 d/w | 6–7 d/w | Weak | Moderate | Strong | |
| Weekly tea drinker | |||||||
| N | 68,257 | 10,474 | 9,380 | 71,736 | 19,671 | 26,481 | 45,438 |
| Burning-hot tea | 14.7 (0.1) | 11.4 (0.2) | 13.6 (0.3) | 15.9 (0.2) | 13.0 (0.2) | 14.2 (0.2) | 17.2 (0.2) |
| Strong tea | 8.9 (0.1) | 9.8 (0.2) | 11.4 (0.3) | 13.8 (0.2) | 6.3 (0.1) | 8.9 (0.1) | 10.6 (0.2) |
| Tea-leaves (g/d) | 3.6 (0.0) | 3.7 (0.0) | 4.0 (0.0) | 4.3 (0.0) | 3.3 (0.0) | 3.8 (0.0) | 3.8 (0.0) |
| Age of start | 28.9 (0.1) | 28.2 (0.1) | 27.8 (0.1) | 27.1 (0.1) | 26.0 (0.1) | 24.8 (0.1) | 24.1 (0.1) |
| Weekly alcohol drinker | |||||||
| N | 35,201 | 6,094 | 5,954 | 25,101 | 10,666 | 13,933 | 12,550 |
| Unhealthy drinkerb | 70.3 (0.3) | 72.3 (0.6) | 74.6 (0.5) | 78.1 (0.3) | 71.9 (0.5) | 76.5 (0.4) | 81.3 (0.4) |
| Daily morning drinker | 3.4 (0.1) | 3.5 (0.3) | 4.2 (0.3) | 5.5 (0.2) | 3.6 (0.2) | 4.6 (0.2) | 6.8 (0.2) |
| Alcohol (g/d) | 49.3 (0.2) | 50.5 (0.5) | 53.2 (0.5) | 57.2 (0.3) | 50.3 (0.4) | 54.8 (0.3) | 61.2 (0.4) |
| Age of start | 29.6 (0.1) | 29.5 (0.1) | 29.3 (0.1) | 28.6 (0.1) | 28.9 (0.1) | 28.3 (0.1) | 27.3 (0.1) |
| Current smoker | |||||||
| N | 80,109 | 11,052 | 10,228 | 51,910 | 20,217 | 25,915 | 27,058 |
| Heavy smokerc | 6.4 (0.1) | 6.6 (0.3) | 7.8 (0.3) | 8.8 (0.2) | 6.8 (0.2) | 8.3 (0.2) | 10.2 (0.2) |
| Cigarette (No./d) | 17.3 (0.0) | 17.5 (0.1) | 18.1 (0.1) | 18.8 (0.1) | 17.7 (0.1) | 18.6 (0.1) | 19.8 (0.1) |
| Age of start | 22.8 (0.0) | 22.7 (0.1) | 22.6 (0.1) | 22.5 (0.0) | 22.7 (0.1) | 22.5 (0.1) | 22.0 (0.1) |
Open in a separate window
SE: standard error.
Values are percentages or means of participants adjusted for age, sex, region, household income and education.
aAmong those consuming spicy food ≥ 1 d/w.
bDefined as consuming alcohol > 15 g/d (female) or >30 g/d (male).
cDefined as smoking ≥ 40 cigarettes per day.
p value for linear trend test for all subgroups across frequency of spicy food consumption as well as pungency degree were <0.001.
Discussion
In this study conducted among the Chinese adults, the spicy food consumption, in term of its frequency and pungency degree, showed clear population variations. People with different level of spicy food consumption had different dietary habits and lifestyle behaviours.
In line with the China Health and Nutrition Survey (CHNS) (Shi etal. 2017; He etal. 2019), which included 27,447 individuals among 12 regions across China (Zhang etal. 2014), we found younger generation prefer spicy food than the older, but no clear difference in gender, education and income distribution. Unfortunately, the CHNS didn’t go further to describe a full picture of spicy food consumption, such as marital status and occupation.
Spicy food intake frequency was found positively correlated with poultry, preserved vegetables and preference for salty taste, as well as snacking and eating deep-fried foods. The possible hypothesis may be that deep-fried foods were more likely to be served with chilli powder in China. Previous studies reported mixed results on salt intake. The CHNS (He etal. 2019) discovered that those who consumed spicy food >5 d/w used more salt compared to non-consumers. But a study by Li etal. (2017) found the opposite. Besides, we found that spicy food consumers also used more cooking oil than non-consumers, consistent with the CHNS (Shi etal. 2017) which found the non-consumers ate the least fat on average. Pungency degree was negatively related to intakes of milk and soymilk. However, a study found that milk can mitigate the oral burn caused by capsaicin, and it’s more effective than seltzer water and cola (Nolden etal. 2019).
Partly consistent with previous studies (Dovey etal. 2016; Park etal. 2017; He etal. 2019), we found that spicy food consumption metrics, including frequency and pungency degree, were clustered with diverse factors, such as weekly tea/alcohol drinking, current tobacco smoking and different dietary habits. Several studies have recently reported the health effects of spicy food on various diseases (McCarty etal. 2015; Chen etal. 2017b; Zhao etal. 2020). However, such associations could be modified by these lifestyle factors according to this study. For example, a study only adjusted for age and sex found that spicy food preference was negatively correlated with diabetes prevalence (Zhao etal. 2020), and another prospective study found the same result, but the result was found statistically insignificant after further adjustment for smoking, alcohol consumption, physical activity and BMI (Lv etal. 2015). In addition, details of these lifestyle behaviours differed across spicy food intake frequency and pungency degree, which might affect health outcomes in different ways. Wang etal. (2007) found that high intake of chilli and salt, tobacco smoking and alcohol drinking were possible risk effects for oesophageal cancer, while green tea drinking showed possible protective effect. However, a prospective study in this same population found that combining either alcohol drinking or smoking, drinking tea with high-temperature showed greater risk for oesophageal cancer than drinking hot tea alone (Chen etal. 2017a). These mixed results suggest that the confounding/modifying potency of other dietary habits, tea/alcohol drinking and smoking as well as the details of these behaviours should be taken into consideration while assessing the health effects of spicy food consumption.
To the best of our knowledge, this study is the first comprehensive description of the populational distribution of spicy food consumption among Chinese adults, and explored its correlations with lifestyle behaviours. However, some limitation merits to mention. First of all, the CKB study recruited participants in ten geographically diverse regions, but still not a representative sample of the Chinese population (Lietal. 2012). Therefore, one must be cautious when extrapolating our results to a national context. Secondly, detailed information on spicy food consumption and dietary habits was self-reported, which might lead to recall bias. Besides, our study did not collect data on the accurate amount of spicy food intake and cooking methods, which could bring more information to this study.
Conclusion
In a large population-based cohort, significant differences existed in lifestyle characteristics according to spicy food consumption frequency and pungency degree in the Chinese population. More consideration should be taken when examining the health effect of spicy food consumption.
Acknowledgements
The most important acknowledgement is to the participants in the study and the members of the survey teams in each of the 10 regional centres, as well as to the project development and management teams based at Beijing, Oxford and the 10 regional centres.
Funding Statement
This work was supported by grants from the National Natural Science Foundation of China [grant number 81973125] and the National Key R&D Program of China [grant number 2016YFC0900500], [grant number 2016YFC0900501], [grant number 2016YFC0900504]. The CKB baseline survey and the first re-survey were supported by a grant from the Kadoorie Charitable Foundation in Hong Kong. The long-term follow-up is supported by grants from the UK Wellcome Trust [grant number 202922/Z/16/Z], [grant number 088158/Z/09/Z], [grant number 104085/Z/14/Z]. The funders had no role in the study design, data collection, data analysis and interpretation, writing of the report, or the decision to submit the article for publication.
Author contributions
Qiaorui Wen drafted the manuscript. Qiaorui Wen and Yuxia Wei analysed the data. Yu Guo, Zheng Bian, Yan Chen and Liya Shi collected the data. Bian Zheng, Ling Yang and Yiping Chen were involved in data cleaning. Canqing Yu, Jun Lv and Huaidong Du interpreted the results and contributed to the critical revision of the manuscript for important intellectual content. Zhengming Chen and Liming Li are the study guarantors. Liming Li, Zhengming, Canqing Yu and Junshi Chen designed the study. All authors have read and approved the final version of the manuscript.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The dataset for this study is available at www.ckbiobank.org, as well as the access policy and procedures.
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