Potential impact of the time trend of fried food consumption on the cardiovascular disease burden in China

Abstract

Background: Cardiovascular disease (CVD) is a leading cause of death in China. Fried foods are a risk factor for increasing CVD and their consumption in China is rapidly rising. Evaluation of the impact of fried foods on the CVD burden has important implications for future public health and policy making. This study aimed to evaluate the impact of fried foods on the CVD burden. Methods: We estimated the temporal trends of fried food consumption from 1997 to 2011 using data from the China Health and Nutrition Survey. We estimated CVD events attributed to fried food consumption using comparative risk assessment methods. We also projected fried food consumption and the related CVD burden from 2011 to 2031. Results: Fried food consumption continued to increase from 1997 to 2011, reaching 110.2 g per week in 2011. It is estimated that high consumption of fried foods is responsible for 3.4%, 2.3%, and 14.3% of the CVD, CHD, and stroke burden, accounting for 0.112 million CVD cases, 0.036 million CHD cases, and 0.243 million stroke cases, respectively. Notably, fried food consumption is projected to increase to 127.6 g per week by 2031. High consumption levels are projected to cause 0.239 million CVD cases, 0.078 million CHD cases, and 0.529 million stroke cases by 2031. Conclusions: The consumption of fried foods has continued to increase over time, which has an important impact on the burden of CVD in China. Dietary guidelines should continue to emphasize on decreasing the consumption of fried foods to reduce the CVD burden in China.

---

Introduction

Over recent decades, China has greatly improved the standard of living and life expectancy for residents. With the rapid economic development and social progress, the incidence of cardiovascular disease (CVD) has greatly increased, which may be ascribed to various risk factors, including high systolic blood pressure (SBP), high low-density lipoprotein cholesterol (LDL-C), high fasting plasma glucose (FPG), and high body mass index (BMI).^1,2 Thus, the abundance of CVD risk factors has contributed to the increasing prevalence of CVD.³ It is worth noting that CVD is the largest single contributor to global mortality and disease burden and remains the top cause of deaths (more than 40% of deaths) in China.⁴ The number of CVD deaths sharply increased from 12.1 million (95% uncertainty interval (UI), 11.4 million to 12.6 million) in 1990 to 18.6 million (95% UI, 17.1 million to 19.7 million) in 2019, according to the 2019 Global Burden of Disease (GBD) study.⁴ Previous studies have found CVD burden largely attributable to dietary risk exposures.^5,6 Consequently, data suggest a severe CVD burden in China and efforts on dietary interventions and public health are urgently needed.

Diet has a decisive impact on one's health.⁷ Indeed, a healthy diet shows a preventive effect, even reducing genetic risk of diseases, while a poor diet can dramatically increase the risk of prevalent diseases.^8,9 Dietary patterns are key contributors to the risk of CVD and mortality.¹⁰ Evidence from previous studies^11–14 has shown that an unhealthy dietary pattern increases the risk of CVD and type 2 diabetes. A western-style or unhealthy dietary pattern was characterized by high consumption of fried foods and meat.¹⁵ Typically, fried foods are popular with people worldwide because of their unique crisp texture, golden colour, and attractive fried flavours and aromas.¹⁶ With the shift to the western dietary pattern among the Chinese population, the consumption of fried foods is rapidly increasing in China, especially among the young and urban population. However, the frying process could change the nutritional quality and produce potentially hazardous byproducts,¹⁷ such as acrylamide,¹⁸ polycyclic aromatic hydrocarbons,¹⁹ and 4-hydroxy-2-trans-nonenal.²⁰ In addition, fried foods as one of typical ultra-processed foods contain high calories.²¹ Fried food consumption causes excess caloric intake and body weight gain, which are major risk factors for the incidence of CVD.^22,23 Previous research reports have shown that frequent fried food consumption may have adverse effects and is associated with risk of CVD, cardiovascular mortality, all-cause mortality, anxiety, and depression.^24–30 Specifically, the increment of one serving per week in fried food consumption was associated with 3%, 2%, and 13% higher risk of CVD, CHD, and stroke, respectively.³¹ Although the impact of key dietary factors on the global burden of disease is widely recognized^32,33 and poor diet is a leading factor attributable to the occurrence of 2.6 million cases of CVD in China,³⁴ the current state and future trends of increasing fried food consumption attributed to the global burden of CVD in China have not been reported yet. Understanding the potential impact of the time trend of fried food consumption on the CVD burden has become an urgent need for the Healthy China 2030 plan and Sustainable Development Goal of Good Health proposed by the United Nations.

Overall, westernized changes in dietary patterns along with increasing fried food consumption pose a threat to public health in China, especially aggravating the CVD burden. Yet, such an ‘indirect’ CVD burden from fried food consumption has not been estimated previously. A comprehensive and timely understanding of the overall CVD burden caused by fried food consumption will be highly beneficial for public health professionals and policymakers to develop priority programs aiming at preventing and reducing the CVD burden in China. Here, we examine the impact of rapidly increasing fried food consumption on the CVD burden nationally among Chinese adults using data from the China Health and Nutrition Survey (CHNS) and project the trends for the next 20 years.

Methods

Study population

The design and specific information of the CHNS have already been reported elsewhere.³⁵ The CHNS is an ongoing nationwide prospective family study covering multiple age groups in 12 provinces and municipalities in China, including over 30 000 individuals. The selected provinces/municipal cities generally represent variations in geography, economy, and health indicators throughout China. Individuals within each household were interviewed by skilled interviewers. Participants in 6 rounds of the survey (1997, 2000, 2004, 2006, 2009, and 2011) were included, and the number of individuals surveyed in each round was as follows: 14 441 in 1997, 15 831 in 2000, 12 308 in 2004, 11 860 in 2006, 12 178 in 2009, and 15 725 in 2011, while participants aged under 20 in each round of the survey were excluded from our study. The CHNS is a collaborative project between the Carolina Population Centre, University of North Carolina at Chapel Hill, and the National Institute of Nutrition and Food Safety, China Centre for Disease Control and Prevention. Each CHNS participant has provided written informed consent, and the study was approved by the University of North Carolina at Chapel Hill and the National Institute of Nutrition and Food Safety Institutional Review Boards.

Measurements

Age, sex, education, region (urban or rural), and lifestyle factors were self-reported in each round.³⁵ Dietary information was collected by trained interviewers using 3 day 24 hour dietary recalls with a weighing method.^36,37 Individual consumptions of all foods consumed both at home and dining out were recorded according to the self-report from participants. In addition, cooking oils, condiments, and food waste were all weighed and recorded at the household level. The consumption of fried foods was calculated using corresponding versions of the China Composition Tables for each round of the survey.^38,39 The major types of fried foods included in the CHNS are shown in Fig. S1.†

Ascertainment of the CVD/CHD/stroke cases and disability-adjusted life years (DALYs)

The data on CVD mortality in China were obtained from the China Health Statistical Yearbook and the National Population Census. The mortality rate of 2011 was combined with the age, sex, and urban/rural population data from the 2010 Population Census of the People's Republic of China to obtain CHD and stroke mortality cases in each group (Table 1). DALY data by age, sex, country, and year are from GBD 2019. The GBD approach for estimating cause-specific mortality and DALYs has been reported before.⁴⁰

Table 1 Estimated CVD/CHD/stroke cases among the Chinese population in 2010

	Population census 2010	CVD		CHD		Stroke
		Mortality (%)	Events	Mortality (%)	Events	Mortality (%)	Events
Male
20–29	114 845 611	0.006	6703	0.004	4266	0.002	2437
30–39	109 912 926	0.017	18 586	0.009	10 172	0.008	8414
40–49	117 385 096	0.074	86 827	0.037	43 177	0.037	43 650
50–59	81 446 172	0.210	171 412	0.094	76 179	0.117	95 233
60–69	50 582 897	0.603	305 059	0.261	132 199	0.342	172 860
≥70	36 457 064	3.337	1 216 506	1.597	582 152	1.740	634 354
Female
20–29	113 580 759	0.003	3028	0.002	1974	0.001	1054
30–39	105 251 236	0.007	7483	0.004	4350	0.003	3133
40–49	112 963 421	0.033	37 547	0.016	17 809	0.017	19 738
50–59	78 619 473	0.107	84 225	0.046	36 007	0.061	48 218
60–69	49 197 667	0.356	174 927	0.159	78 377	0.196	96 550
≥70	41 356 812	2.779	1 149 420	1.408	582 381	1.371	567 039
Total
	1 011 599 134		3 261 723		1 569 043		1 692 680

---

Etiological effects of fried foods on incidental CVD

Data on the effect of fried food consumption on the etiology of incident CVD were extracted from a recent meta-analysis of prospective studies (Table 2),³¹ which used multivariable adjustment for multiple risk factors to reduce confounding bias. The same relative risks (RRs) were used for males and females and across ages and urban/rural groups.

Table 2 Sources and magnitudes of RRs for the effects of CVD/CHD/stroke

Risk factors	TMRED ± SD	Sources	Exposure metric, units	Outcome	RR (95% CI)
Fried food intake	No dietary intake of fried foods	Qin et al. Heart 2021	One serving per week increment	CVD	1.03 (1.01–1.04)
				CHD	1.02 (1.01–1.02)
				Stroke	1.13 (0.95–1.34)

---

Population-attributable fraction (PAF) percentage calculation

We calculated PAFs using the following equation considering intakes of fried food (exposure) as continuous:
where x = exposure level; P(x) = actual percentile for the exposure in the population; P′(x) = alternative distribution of exposure in the population; RR(x) = relative risk of CVD/CHD/stroke at the exposure level x; and m is the maximum risk level of the exposure.

The PAF of fried foods associated with CVD/CHD/stroke was calculated by combining the categories of sex, age, and region (a total of 20 groups). We multiplied the PAF of fried foods by the total number of cases to calculate the number of CVD/CHD/stroke cases associated with the consumption of fried foods.

DALYs represent a standard indicator for assessing the disease burden caused by risk factors and are often used to express the disease burden of a specific population and the relative contribution of different health outcomes to the disease burden.

We estimated the DALYs of fried food consumption induced CVD burden as recommended by the WHO. The burden of CVD/CHD/stroke DALYs attributable to fried food consumption was obtained by multiplying the DALYs by the estimated contribution rate of fried food consumption.

Statistical analysis

The means and standard errors of fried food consumption were calculated by sex, 10-year age groups, and regions (urban and rural) in each round of the survey. A multivariable-adjusted general linear mixed regression model was applied to calculate covariate-adjusted mean consumption of fried foods, which accounted for the repeated measurements of the same person over time using a random intercept. In order to describe time trends, the distributions of fried food consumption were standardized in each round to the 2010 Chinese Population Census data. We used a random effects model within each stratum of age, sex, and urban/rural region to project future trends from 2012 to 2031 based on individual data with >3 repeated measurements during the follow-up period. Assuming a causal relationship between the high consumption of fried foods and the risk of various CVD events, we calculated the PAF to indicate the proportion of CVD cases that would have increased during the analysis period if the distribution of fried food consumption was at the optimal level. The number of CVD cases attributed to fried food consumption was calculated by multiplying its PAF by the total number of cases. We also estimated the DALYs attributable to fried food consumption by multiplying the PAF by the total CVD-specific DALYs. Statistical tests were two-sided, and significance was defined as P < 0.05. Statistical analyses were performed using SAS 9.4 (SAS Institute, Cary, NC, USA).

Results

Time trend of fried food consumption increasing in China

The consumption of fried foods has continuously been rising over time among the Chinese population between 1997 and 2031. In detail, the mean consumption of fried foods ascended from 80.2 g per week in 1997 to 110.2 g per week in 2011, while the mean consumption is projected to reach 127.6 g per week in 2031 (Table 3). Subgroup analyses showed that the consumption of fried foods significantly was elevated in most groups (all P for trend < 0.05) except for the people aged ≥60. Notably, the increasing trend of fried food consumption was more pronounced in the younger population than in the older population (P for interaction = 0.002), while fried food consumption in urban areas was higher than that in rural areas (Table 4).

Table 3 Estimated PAFs that could potentially contribute to fried food intake from 1997 to 2031

	Year
	1997	2000	2004	2006	2009	2011	2013	2016	2018	2022	2025	2029	2031
Fried food intake, g per week	80.2 (2.1)	94.2 (2.7)	99.5 (2.5)	103.2 (2.7)	101.8 (2.5)	110.2 (2.6)	105.6 (1.6)	109.3 (2.1)	111.7 (2.4)	116.6 (3.0)	120.3 (3.5)	125.2 (4.1)	127.6 (4.5)
PAF (%)
CVD	3.1	3.2	4.2	3.7	3.3	3.4	3.9	4.5	5.0	5.8	6.4	7.0	7.3
CHD	2.0	2.1	2.8	2.5	2.2	2.3	2.6	3.1	3.3	3.9	4.3	4.7	4.9
Stroke	12.7	13.4	17.6	15.2	14.0	14.3	16.5	19.2	21.0	24.7	27.3	30.1	31.3

---

Table 4 Covariate-adjusted distribution of fried food intake over time by sex, age groups, and regions

Variable	Year	Sex		Age group, years						Region
		Male	Female	20–29	30–39	40–49	50–59	60–69	≥70	Urban	Rural
Fried food, g per week	1997	81.5(3.0)	80.3(2.9)	78.9(4.5)	82.9(4.4)	81.8(4.4)	77.2(5.3)	84.8(6.3)	78.5(8.8)	115.5(4.2)	63.0(2.3)
	2000	95.1(3.6)	95.9(3.9)	85.3(4.9)	107.3(5.1)	105.1(7.1)	86.1(6.3)	95.3(6.5)	73.3(8.8)	127.9(5.7)	79.1(2.8)
	2004	102.3(3.8)	95.7(3.4)	108.7(8.4)	96.1(5.1)	97.4(5.2)	99.6(5.3)	94.6(7.0)	100.9(8.8)	130.3(4.8)	82.3(2.9)
	2006	104.1(3.8)	104.7(3.9)	108.9(11.1)	104.8(5.6)	116.7(5.4)	93.3(5.5)	104.9(7.6)	97.4(7.9)	157.6(5.7)	76.4(2.7)
	2009	104.7(3.8)	97.0(3.3)	97.0(7.4)	114.4(6.8)	111.6(6.0)	96.3(4.7)	92.1(6.0)	84.5(6.1)	139.5(4.7)	80.6(2.9)
	2011	106.5(3.7)	109.0(3.7)	112.5(9.2)	115.5(7.2)	116.7(5.5)	115.9(5.4)	98.7(6.7)	78.8(5.8)	126.0(4.8)	98.5(3.1)
	P for trend	<0.001	<0.001	<0.001	<0.001	<0.001	<0.001	0.273	0.753	0.004	<0.001
	P for interaction	0.843		0.002						0.296

---

Disease burden of CVD attributed to fried food consumption

The estimated cases of CVD, CHD, and stroke in 2011 reached 3.26, 1.57, and 1.69 million, respectively (Table 1). We estimated that the high consumption of fried foods was attributable to 3.4%, 2.3%, and 14.3% of the CVD, CHD, and stroke burden, accounting for 0.112 million, 0.036 million, and 0.243 million CVD, CHD, and stroke cases, respectively (Fig. 1 and Table 3). To further comprehensively evaluate the life quantity and quality, we calculated DALYs attributable to fried food consumption for the diseases. Results revealed that high consumption of fried foods was associated with 2.83 million, 0.68 million, and 6.17 million DALYs for CVD, CHD, and stroke, respectively (Fig. 3).

Fig. 1 CVD/CHD/stroke cases attributable to fried food consumption in 2011. A for CVD, B for CHD, and C for stroke.

Time trend of the estimated CVD burden until 2031

In 1997, the high consumption of fried foods was attributable to 0.100 million CVD cases, 0.032 million CHD cases, and 0.216 million stroke cases (Fig. 2), deducing the association with 2.52 million, 0.61 million, and 5.46 million DALYs for CVD, CHD, and strokes, respectively (Fig. 3). The consumption of fried foods was attributable to a corresponding increase in CVD/CHD/stroke over time. Our model projected that the mean consumption of fried foods will continue to increase to 127.6 g per week in 2031, which would produce a further exacerbation of related CVD burden (Fig. 2 and 3). The increase in fried food consumption was estimated to cause an additional 0.012 million CVD cases, 0.004 million CHD cases, and 0.028 million stroke cases during 1997–2011 and will be associated with a further increase of 0.127 million CVD cases, 0.041 million CHD cases, and 0.286 million stroke cases during 2011–2031 (Table 5). In 2031, the estimated numbers of CVD, CHD, and stroke cases attributable to the high consumption of fried foods would be 0.239 (PAF = 7.3%), 0.078 (PAF = 4.9%), and 0.529 (PAF = 31.3%) million cases, respectively (Fig. 2 and Table 3). High consumption of fried foods is attributable to 6.03 million CVD DALYs, 1.46 million CHD DALYs, and 13.4 million stroke DALYs, respectively (Fig. 3). In the sensitivity analysis, the overall trend in CVD burden attributable to fried food consumption remained consistent, showing an increasing burden over time (Tables S1–S3†). However, subgroup analyses revealed that females experienced a higher CVD burden than males (Table S1†). Among age groups, individuals aged 30–59 had a greater burden compared with those aged 20–29 and ≥60 (Table S2†). In urban and rural subgroups, the CVD burden due to fried food consumption was similar between residents (Table S3†).

Fig. 2 Time trends and estimated CVD/CHD/stroke cases attributable to high fried food consumption. Note: solid bars represent CVD/CHD/stroke cases; circles and bars represent mean ± SE values of fried food consumption at each time point, which were standardized by age, sex, and urban/rural regions using the 2010 Chinese Population Census. A for CVD, B for CHD, and C for stroke.

Fig. 3 Time trends and estimated CVD/CHD/stroke DALYs attributable to fried food consumption from 1997 to 2031. Note: solid bars represent CVD/CHD/stroke cases; circles and bars represent mean ± SE values of fried food consumption at each time point, which were standardized by age, sex, and urban/rural regions using the 2010 Chinese Population Census. A for CVD, B for CHD, and C for stroke.

Table 5 Historical and projected trend in attributable CVD/CHD/stroke by risk factors in China, 1997 to 2031

	Change from 1997 to 2011 (1000)			Change from 2011 to 2031 (1000)
Risk factor	CVD	CHD	Stroke	CVD	CHD	Stroke
Fried foods	12.4	4.2	27.7	126.9	41.4	286.0

---

Discussion

Our current study highlights the increasing contribution of fried food consumption to the CVD burden in China, which is based on a large-scale longitudinal survey of a representative Chinese population. The current study estimated the number of CVD cases attributable to fried food consumption in China in 2011. We also projected that fried food consumption would continue to grow and further aggravate the CVD burden in China until 2031. Theoretically, if no control efforts for fried food consumption had been addressed, 6.03 million, 1.46 million, and 13.4 million DALYs for CVD, CHD, and stroke would have been attributable to fried food consumption in 2031. The efforts to reduce the CVD burden should be coupled with healthy diet strategies that include efforts to reduce fried food consumption. These estimates might help inform CVD control planning in China, including the development of dietary guidelines and the formulation of a control policy for the fried food industry.

Since the beginning of economic and social reforms in 1979, China has markedly improved its standard of living expense and consumption level. With the further economic development and urbanization, chronic diseases have become a huge burden in China, especially CVD, which is the top cause of death in China, implying high health care costs.^41,42 Additionally, compared with other Asian countries such as South Korea and India, China's dietary patterns are facing a greater shift from a traditional diet to a westernized diet.⁴³ As dietary patterns shift towards western diets, fried foods have become widely popular due to their bright color, crisp texture, and delicious taste.¹⁶ In the current analysis, fried food consumption will continuously increase to 127.6 g per week until 2031 in China. The increasing trend is more pronounced among younger and urbanized populations because consuming fried foods as both meals and snacks is favorite for young people, and moreover, fast-food restaurants serving fried foods are readily available in town. However, the adverse effects of unhealthy dietary factors including fried food intake on the CVD burden have been supported by numerous scientific evidence. Frequent consumption of fried foods, red meat, processed meat, and SSBs was associated with a higher risk of CVD.^31,44–46 As the burden of CVD prevalence increases and remains the top cause of death in China, coupled with the rapid westernization of the Chinese diet, we further specialize in the CVD burden attributable to fried food consumption in China.

Previous studies have highlighted a positive association between fried food consumption and an increased risk of CVD.^24,25,27,31 Qin et al.³¹ found a linear dose–response relationship between fried food consumption and major cardiovascular events and the risk significantly increased by 3% with each additional fried food consumption serving per week (one serving equals 114 g). However, the causation for the association between fried food consumption and CVD risk still remains unclear. The frying process changes nutrients and creates harmful chemicals, such as acrylamide,^18,47–49trans-fatty acids,^50,51 cholesterol oxidation products,^52,53 dietary advanced glycation endproducts,^54,55 which are associated with increased risk of CVD. Moreover, fried foods such as fried potatoes and fried chicken are usually processed with high salt in fast-food restaurants, while high sodium intake is a risk factor for both hypertension and CVD risk.^56,57 Of note, people who frequently eat fried foods may drink sugar-sweetened beverages and eat other high-energy fast foods, leading to excess energy intake that can lead to obesity, diabetes, and CVD.^58–61

Our study emphasized that future unfavorable trends in fried food consumption will exacerbate the increase in CVD burden. Since CVD is a leading cause of mortality in China, many prevention and control efforts for incident CVD have been implemented and the CVD burden has well been improved in the past few years.⁶² A healthy lifestyle plays an important role in reducing the risk of CVD. Li et al.⁴⁴ evaluated the temporal trends of various lifestyle factors in the CVD burden in China and found that hypertension remains the most important independent risk factor for CVD burden. Despite that frequent consumption of fried foods is recognized as an unhealthy diet, fried food consumption was associated with the increase in CVD burden from 1991 to 2011, and it continuously contributes to the burden of CVD, accountable for 0.127 million CVD cases, 0.041 million CHD cases, and 0.286 million stroke cases from 2011 to 2031 in China. These data imply that the incidence of CVD associated with fried food consumption will continue to increase in the near future. More and more people prefer fried foods but lack awareness of possible CVD risks and hazards. Thus, urgent needs are warranted to address effective and creative policies for unhealthy fried food consumption and improve diet. Currently, the Chinese Dietary Guidelines emphasize reducing excessive intake of fried and high-fat foods to lower the risk of chronic diseases including CVD. Our study's findings indicate a significant upward trend in fried food consumption and its contribution to the CVD burden in China. Dietary guidelines should be carefully re-evaluated and updated. First, dietary guidelines should specify clear quantitative limits for fried food intake (e.g., a recommended maximum weekly consumption). Second, we encourage the adoption of cooking techniques that require less oil, such as steaming, boiling, or baking. Third, dietary guidelines should emphasize that the reduction of fried foods is part of a broader strategy to adopt a balanced diet rich in fruits, vegetables, whole grains, and lean proteins. In the context of the United Nations 2030 Agenda for Sustainable Development, One Health anchors health in development, recognizing that good health underpins social justice, economic prosperity and environmental protection.⁶³ In addition, the Healthy China 2030 plan is a national population health policy.⁶⁴ Prevention of diseases is in the first place in the construction of a healthy China. Our trend analysis evidenced an estimated health impact of increasing fried food consumption on suffering from CVD. That is to say, limiting fried food consumption may be beneficial for reducing CVD cases and lowering medical care burden, thus enhancing the health and longevity of people due to cardiovascular health in the next several years.

Our study has vital strengths. The CHNS is the only large-scale longitudinal study of a nationally representative population in China with high response rates (88% at the individual level and 90% at the household level). Here we allow modeling of the trajectory of fried food consumption and show the time trend that can be projected to cases of CVD events with the predictable increase of fried food consumption. By combining measures of individual dietary intake with covariates such as sex, age, and location, we were able to estimate precise PAFs for each stratum. Moreover, our estimates of the etiology effect were based on the most recent and best available meta-analysis of the associations between fried food consumption and incident CVD/CHD/stroke in populations. Some limitations also need to be addressed. Given a complex circulatory system disease induced by multiple risk factors, CVD events attributable to fried food consumption cannot fully reflect the effect of diet patterns on the CVD burden. Although the estimation was based on nationwide data and the extracted RRs were multivariable-adjusted, potential residual confounding factors cannot be completely eliminated. In addition, we standardized the population and total CVD events in all years to that in 2011 to make the trend of PAFs and attributable CVD events comparable over time. Thus, our projection of CVD burden related to fried food consumption might be underestimated since aging or population growth in our time-trend analysis of CVD burden was not considered. Moreover, the estimated PAF in a given year can be used for several years later, regardless of the induction period of fried foods. Nonetheless, it would be similar to the projected trends, emphasizing the importance of in-time dietary modifications to minimize fried food consumption.

Conclusions

Hence, findings from our study provide sustainable evidence that high fried food consumption is responsible for millions of CVD cases in China, which would raise widespread public attention. The consumption of fried foods (110.2 g per week) was estimated to be responsible for 0.112 million CVD cases, 0.036 million CHD cases, and 0.243 million stroke cases in 2011. Notably, fried food consumption is projected to increase to 127.6 g per week by 2031. High consumption levels are projected to cause 0.239 million CVD cases, 0.078 million CHD cases, and 0.529 million stroke cases by 2031. With rapid westernization of the Chinese diet, increasing fried food consumption is closely related to the incidence of CVD as a risk factor, and the causal relationship needs to be revealed urgently. Dietary guidelines and public strategies are in urgent need to decrease fried food consumption and thereby decrease the CVD burden in China for the Healthy China 2030 plan.

Author contributions

YZ and JJJ conceived and designed the study. ALW, YA, XHL, XZW and PZ performed the data cleaning, analysis and interpretation. ALW wrote the manuscript. YA and XHL provided statistical expertise and assistance. XZW and PZ helped with the interpretation of the results and provided critical comments on the manuscript. All authors contributed to the interpretation of the data and critical revision of the manuscript for important intellectual content and approved the final draft. YZ and JJJ were involved in data acquisition. YZ is the guarantor.

Consent to participate

All participants provided informed consent at recruitment, allowing for follow-up using data-linkage to health records.

Data availability

This research uses data from the China Health and Nutrition Survey (CHNS). The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Conflicts of interest

The authors declare that they have no conflict of interest.

Acknowledgements

This research uses data from the China Health and Nutrition Survey (CHNS). We are grateful to all the participants in the CHNS. This research was supported by the National Key Research and Development Program of China (Grant no. 2023YFF1105300).

References

1. W. Wang, M. Hu, H. Liu, X. Zhang, H. Li, F. Zhou, Y.-M. Liu, F. Lei, J.-J. Qin, Y.-C. Zhao, Z. Chen, W. Liu, X. Song, X. Huang, L. Zhu, Y.-X. Ji, P. Zhang, X.-J. Zhang, Z.-G. She, J. Yang, H. Yang, J. Cai and H. Li, Global Burden of Disease Study 2019 suggests that metabolic risk factors are the leading drivers of the burden of ischemic heart disease, Cell Metab., 2021, 33, 1943–1956.
2. F. A. Larijani, M. Parsaeian, S. M. K. Motamedi, A. Khosravi, F. Farzadfar and B. Larijani, National trends in mortality attributable to metabolic risk factors in Iran, Lancet, 2013, 381, 17–19.
3. C. Q. Wu, X. Li, J. P. Lu, B. W. Chen, Y. C. Li, Y. Yang, J. Li and S. S. Hu, Geographical profiles of cardiovascular risk and the risk factors in China: data from a nationwide population-based screening project, Eur. Heart J., 2020, 41, ehaa946.2826.
4. G. A. Roth, G. A. Mensah, C. O. Johnson, G. Addolorato, E. Ammirati, L. M. Baddour, N. C. Barengo, A. Z. Beaton, E. J. Benjamin, C. P. Benziger, A. Bonny, M. Brauer, M. Brodmann, T. J. Cahill, J. Carapetis, A. L. Catapano, S. S. Chugh, L. T. Cooper, J. Coresh, M. Criqui, N. DeCleene, K. A. Eagle, S. Emmons-Bell, V. L. Feigin, J. Fernández-Solà, G. Fowkes, E. Gakidou, S. M. Grundy, F. J. He, G. Howard, F. Hu, L. Inker, G. Karthikeyan, N. Kassebaum, W. Koroshetz, C. Lavie, D. Lloyd-Jones, H. S. Lu, A. Mirijello, A. M. Temesgen, A. Mokdad, A. E. Moran, P. Muntner, J. Narula, B. Neal, M. Ntsekhe, G. Moraes de Oliveira, C. Otto, M. Owolabi, M. Pratt, S. Rajagopalan, M. Reitsma, A. L. P. Ribeiro, N. Rigotti, A. Rodgers, C. Sable, S. Shakil, K. Sliwa-Hahnle, B. Stark, J. Sundström, P. Timpel, I. M. Tleyjeh, M. Valgimigli, T. Vos, P. K. Whelton, M. Yacoub, L. Zuhlke, C. Murray, V. Fuster and GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group, Global burden of cardiovascular diseases and risk factors, 1990–2019, J. Am. Coll. Cardiol., 2020, 76, 2982–3021.
5. Global Burden of Cardiovascular Diseases Collaboration, G. A. Roth, C. O. Johnson, K. H. Abate, F. Abd-Allah, M. Ahmed, K. Alam, T. Alam, N. Alvis-Guzman, H. Ansari, J. Ärnlöv, T. M. Atey, A. Awasthi, T. Awoke, A. Barac, T. Bärnighausen, N. Bedi, D. Bennett, I. Bensenor, S. Biadgilign, C. Castañeda-Orjuela, F. Catalá-López, K. Davletov, S. Dharmaratne, E. L. Ding, M. Dubey, E. J. A. Faraon, T. Farid, M. S. Farvid, V. Feigin, J. Fernandes, J. Frostad, A. Gebru, J. M. Geleijnse, P. N. Gona, M. Griswold, G. B. Hailu, G. J. Hankey, H. Y. Hassen, R. Havmoeller, S. Hay, S. R. Heckbert, C. M. S. Irvine, S. L. James, D. Jara, A. Kasaeian, A. R. Khan, S. Khera, A. T. Khoja, J. Khubchandani, D. Kim, D. Kolte, D. Lal, A. Larsson, S. Linn, P. A. Lotufo, H. M. A. E. Razek, M. Mazidi, T. Meier, W. Mendoza, G. A. Mensah, A. Meretoja, H. B. Mezgebe, E. Mirrakhimov, S. Mohammed, A. E. Moran, G. Nguyen, M. Nguyen, K. L. Ong, M. Owolabi, M. Pletcher, F. Pourmalek, C. A. Purcell, M. Qorbani, M. Rahman, R. K. Rai, U. Ram, M. B. Reitsma, A. M. N. Renzaho, M. J. Rios-Blancas, S. Safiri, J. A. Salomon, B. Sartorius, S. G. Sepanlou, M. A. Shaikh, D. Silva, S. Stranges, R. Tabarés-Seisdedos, N. T. Atnafu, J. S. Thakur, R. Topor-Madry, T. Truelsen, E. M. Tuzcu, S. Tyrovolas, K. N. Ukwaja, T. Vasankari, V. Vlassov, S. E. Vollset, T. Wakayo, R. Weintraub, C. Wolfe, A. Workicho, G. Xu, S. Yadgir, Y. Yano, P. Yip, N. Yonemoto, M. Younis, C. Yu, Z. Zaidi, M. E. S. Zaki, B. Zipkin, A. Afshin, E. Gakidou, S. S. Lim, A. H. Mokdad, M. Naghavi, T. Vos and C. J. L. Murray, The burden of cardiovascular diseases among US states, 1990–2016, JAMA Cardiol., 2018, 3, 375–389.
6. R. Estruch and E. Ros, Nutrition intervention on cardiovascular risk factors in healthy individuals, J. Am. Coll. Cardiol., 2017, 69, 1113–1115.
7. Q. Wu, Z.-J. Gao, X. Yu and P. Wang, Dietary regulation in health and disease, Signal Transduct. Target. Ther., 2022, 7, 252.
8. A. V. Khera, C. A. Emdin, I. Drake, P. Natarajan, A. G. Bick, N. R. Cook, D. I. Chasman, U. Baber, R. Mehran, D. J. Rader, V. Fuster, E. Boerwinkle, O. Melander, M. Orho-Melander, P. M. Ridker and S. Kathiresan, Genetic risk, adherence to a healthy lifestyle, and coronary disease, N. Engl. J. Med., 2016, 375, 2349–2358.
9. C. A. M. Anderson and L. J. Appel, Dietary modification and CVD prevention, J. Am. Med. Assoc., 2006, 295, 693–695.
10. M. Gao, S. A. Jebb, P. Aveyard, G. L. Ambrosini, A. Perez-Cornago, J. Carter, X. Sun and C. Piernas, Associations between dietary patterns and the incidence of total and fatal cardiovascular disease and all-cause mortality in 116,806 individuals from the UK Biobank: a prospective cohort study, BMC Med., 2021, 19, 83.
11. S. Capewell and C. Dowrick, Healthful diet and physical activity for cardiovascular disease prevention in adults without known risk factors, JAMA Intern. Med., 2017, 177, 1254–1255.
12. R. Iqbal, S. Anand, S. Ounpuu, S. Islam, X. Zhang, S. Rangarajan, J. Chifamba, A. Al-Hinai, M. Keltai, S. Yusuf and N. Null, Dietary patterns and the risk of acute myocardial infarction in 52 countries: results of the INTERHEART study, Circulation, 2008, 118, 1929–1937.
13. E. Holmes, R. L. Loo, J. Stamler, M. Bictash, I. K. S. Yap, Q. Chan, T. Ebbels, M. De Iorio, I. J. Brown, K. A. Veselkov, M. L. Daviglus, H. Kesteloot, H. Ueshima, L. Zhao, J. K. Nicholson and P. Elliott, Human metabolic phenotype diversity and its association with diet and blood pressure, Nature, 2008, 453, 396–400.
14. R. L. Loo, X. Zou, L. J. Appel, J. K. Nicholson and E. Holmes, Characterization of metabolic responses to healthy diets and association with blood pressure: application to the Optimal Macronutrient Intake Trial for Heart Health (OmniHeart), a randomized controlled study, Am. J. Clin. Nutr., 2018, 107, 323–334.
15. L. D.-T. Jennifer, W. Aozhou, M. S. Lyn, A. M. A. Cheryl, A. H. Emily, K. David, H. M. Thomas and F. G. Rebecca, Association of dietary patterns in midlife and cognitive function in later life in US adults without dementia, JAMA Netw. Open, 2019, 2, e1916641.
16. X. Zhang, M. Zhang and B. Adhikari, Recent developments in frying technologies applied to fresh foods, Trends Food Sci. Technol., 2020, 98, 68–81.
17. T. G. Albuquerque, M. B. P. P. Oliveira, A. Sanches-Silva, A. C. Bento and H. S. Costa, The impact of cooking methods on the nutritional quality and safety of chicken breaded nuggets, Food Funct., 2016, 7, 2736–2746.
18. D. S. Mottram, B. L. Wedzicha and A. T. Dodson, Food chemistry: Acrylamide is formed in the Maillard reaction, Nature, 2002, 419, 448–449.
19. G. Purcaro, S. Moret and L. S. Conte, Overview on polycyclic aromatic hydrocarbons: occurrence, legislation and innovative determination in foods, Talanta, 2013, 105, 292–305.
20. A. S. Csallany, I. Han, D. W. Shoeman, C. Chen and J. Yuan, 4-Hydroxynonenal (HNE), a toxic aldehyde in French fries from fast food restaurants, J. Am. Oil Chem. Soc., 2015, 92, 1413–1419.
21. J. Adams, K. Hofman, J.-C. Moubarac and A. M. Thow, Public health response to ultra-processed food and drinks, Br. Med. J., 2020, 369, m2391.
22. M. Anne, Ultraprocessed food and cardiometabolic disease, Nat. Food, 2021, 2, 554.
23. S. S. Khan, H. Ning, J. T. Wilkins, N. Allen, M. Carnethon, J. D. Berry, R. N. Sweis and D. M. Lloyd-Jones, Association of body mass index with lifetime risk of cardiovascular disease and compression of morbidity, JAMA Cardiol, 2018, 3, 280–287.
24. Z. Kmietowicz, Fried food linked to increased risk of death among older US women, Br. Med. J., 2019, 364, l362.
25. P. Guallar-Castillón, F. Rodríguez-Artalejo, E. Lopez-Garcia, L. M. León-Muñoz, P. Amiano, E. Ardanaz, L. Arriola, A. Barricarte, G. Buckland, M.-D. Chirlaque, M. Dorronsoro, J.-M. Huerta, N. Larrañaga, P. Marin, C. Martínez, E. Molina, C. Navarro, J. R. Quirós, L. Rodríguez, M. J. Sanchez, C. A. González and C. Moreno-Iribas, Consumption of fried foods and risk of coronary heart disease: Spanish cohort of the European Prospective Investigation into Cancer and Nutrition study, Br. Med. J., 2012, 344, e363.
26. M. F. Leitzmann and T. Kurth, Fried foods and the risk of coronary heart disease, Br. Med. J., 2012, 344, d8274.
27. T. Gadiraju, Y. Patel, J. M. Gaziano and L. Djousse, Fried food consumption is associated with a higher risk of cardiovascular mortality in the physicians’ health study, J. Am. Coll. Cardiol., 2016, 67, 1913.
28. Q. Qi, A. Y. Chu, J. H. Kang, J. Huang, L. M. Rose, M. K. Jensen, L. Liang, G. C. Curhan, L. R. Pasquale, J. L. Wiggs, I. D. Vivo, A. T. Chan, H. K. Choi, R. M. Tamimi, P. M. Ridker, D. J. Hunter, W. C. Willett, E. B. Rimm, D. I. Chasman, F. B. Hu and L. Qi, Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies, Br. Med. J., 2014, 348, g1610.
29. A. Wang, X. Wan, P. Zhuang, W. Jia, Y. Ao, X. Liu, Y. Tian, L. Zhu, Y. Huang, J. Yao, B. Wang, Y. Wu, Z. Xu, J. Wang, W. Yao, J. Jiao and Y. Zhang, High fried food consumption impacts anxiety and depression due to lipid metabolism disturbance and neuroinflammation, Proc. Natl. Acad. Sci. U. S. A., 2023, 120, e2221097120.
30. A. Wang, X. Wan, F. Zhu, H. Liu, X. Song, Y. Huang, L. Zhu, Y. Ao, J. Zeng, B. Wang, Y. Wu, z. Xu, J. Wang, w. Yao, H. Li, P. Zhuang, J. Jiao and Y. Zhang, Habitual daily intake of fried foods raises transgenerational inheritance risk of heart failure through Notch1-triggered apoptosis, Research, 2024, 7, 0401.
31. P. Qin, M. Zhang, M. Han, D. Liu, X. Luo, L. Xu, Y. Zeng, Q. Chen, T. Wang, X. Chen, Q. Zhou, Q. Li, R. Qie, X. Wu, Y. Li, Y. Zhang, Y. Wu, D. Hu and F. Hu, Fried-food consumption and risk of cardiovascular disease and all-cause mortality: a meta-analysis of observational studies, Heart, 2021, 107, 1567–1575.
32. X.-F. Pan and A. Pan, Dietary transitions and cardiometabolic health in China, Lancet Diabetes Endocrinol.y, 2019, 7, 502–503.
33. T. V. Jardim, D. Mozaffarian, S. Abrahams-Gessel, S. Sy, Y. Lee, J. Liu, Y. Huang, C. Rehm, P. Wilde, R. Micha and T. A. Gaziano, Cardiometabolic disease costs associated with suboptimal diet in the United States: A cost analysis based on a microsimulation model, PLoS Med., 2019, 16, e1002981.
34. A. Afshin, P. J. Sur, K. A. Fay, L. Cornaby, G. Ferrara, J. S. Salama, E. C. Mullany, K. H. Abate, C. Abbafati, Z. Abebe, M. Afarideh, A. Aggarwal, S. Agrawal, T. Akinyemiju, F. Alahdab, U. Bacha, V. F. Bachman, H. Badali, A. Badawi, I. M. Bensenor, E. Bernabe, S. K. K. Biadgilign, S. H. Biryukov, L. E. Cahill, J. J. Carrero, K. M. Cercy, L. Dandona, R. Dandona, A. K. Dang, M. G. Degefa, M. E. Sayed Zaki, A. Esteghamati, S. Esteghamati, J. Fanzo, C. S. e. S. Farinha, M. S. Farvid, F. Farzadfar, V. L. Feigin, J. C. Fernandes, L. S. Flor, N. A. Foigt, M. H. Forouzanfar, M. Ganji, J. M. Geleijnse, R. F. Gillum, A. C. Goulart, G. Grosso, I. Guessous, S. Hamidi, G. J. Hankey, S. Harikrishnan, H. Y. Hassen, S. I. Hay, C. L. Hoang, M. Horino, N. Ikeda, F. Islami, M. D. Jackson, S. L. James, L. Johansson, J. B. Jonas, A. Kasaeian, Y. S. Khader, I. A. Khalil, Y.-H. Khang, R. W. Kimokoti, Y. Kokubo, G. A. Kumar, T. Lallukka, A. D. Lopez, S. Lorkowski, P. A. Lotufo, R. Lozano, R. Malekzadeh, W. März, T. Meier, Y. A. Melaku, W. Mendoza, G. B. M. Mensink, R. Micha, T. R. Miller, M. Mirarefin, V. Mohan, A. H. Mokdad, D. Mozaffarian, G. Nagel, M. Naghavi, C. T. Nguyen, M. R. Nixon, K. L. Ong, D. M. Pereira, H. Poustchi, M. Qorbani, R. K. Rai, C. Razo-García, C. D. Rehm, J. A. Rivera, S. Rodríguez-Ramírez, G. Roshandel, G. A. Roth, J. Sanabria, T. G. Sánchez-Pimienta, B. Sartorius, J. Schmidhuber, A. E. Schutte, S. G. Sepanlou, M.-J. Shin, R. J. D. Sorensen, M. Springmann, L. Szponar, A. L. Thorne-Lyman, A. G. Thrift, M. Touvier, B. X. Tran, S. Tyrovolas, K. N. Ukwaja, I. Ullah, O. A. Uthman, M. Vaezghasemi, T. J. Vasankari, S. E. Vollset, T. Vos, G. T. Vu, L. G. Vu, E. Weiderpass, A. Werdecker, T. Wijeratne, W. C. Willett, J. H. Wu, G. Xu, N. Yonemoto, C. Yu and C. J. L. Murray, Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017, Lancet, 2019, 393, 1958–1972.
35. B. M. Popkin, S. Du, F. Zhai and B. Zhang, Cohort Profile: The China Health and Nutrition Survey–monitoring and understanding socio-economic and health change in China, 1989–2011, Int. J. Epidemiol., 2009, 39, 1435–1440.
36. B. M. Popkin, B. Lu and F. Zhai, Understanding the nutrition transition: measuring rapid dietary changes in transitional countries, Public Health Nutr., 2003, 5, 947–953.
37. E. S. Tee, Development and promotion of Malaysian Dietary Guidelines, Asia Pac. J. Clin. Nutr., 2011, 20, 455–461.
38. X. Chen, J. Jiao, P. Zhuang, F. Wu, L. Mao, Y. Zhang and Y. Zhang, Current intake levels of potatoes and all-cause mortality in China: A population-based nationwide study, Nutrition, 2020, 81, 110902.
39. P. Zhuang, Y. Zhang, L. Mao, L. Wang, F. Wu, L. Cheng and J. Jiao, The association between consumption of monounsaturated fats from animal- v. plant-based foods and the risk of type 2 diabetes: a prospective nationwide cohort study, Br. J. Nutr., 2020, 124, 102–111.
40. M. Naghavi, A. A. Abajobir, C. Abbafati, K. M. Abbas, F. Abd-Allah, S. F. Abera, V. Aboyans, O. Adetokunboh, A. Afshin, A. Agrawal, A. Ahmadi, M. B. Ahmed, A. N. Aichour, M. T. E. Aichour, I. Aichour, S. Aiyar, F. Alahdab, Z. Al-Aly, K. Alam, N. Alam, T. Alam, K. A. Alene, A. Al-Eyadhy, S. D. Ali, R. Alizadeh-Navaei, J. M. Alkaabi, A. a. Alkerwi, F. Alla, P. Allebeck, C. Allen, R. Al-Raddadi, U. Alsharif, K. A. Altirkawi, N. Alvis-Guzman, A. T. Amare, E. Amini, W. Ammar, Y. A. Amoako, N. Anber, H. H. Andersen, C. L. Andrei, S. Androudi, H. Ansari, C. A. T. Antonio, P. Anwari, J. Ärnlöv, M. Arora, A. Artaman, K. K. Aryal, H. Asayesh, S. W. Asgedom, T. M. Atey, L. Avila-Burgos, E. F. G. Avokpaho, A. Awasthi, T. K. Babalola, U. Bacha, K. Balakrishnan, A. Barac, M. A. Barboza, S. L. Barker-Collo, S. Barquera, L. Barregard, L. H. Barrero, B. T. Baune, N. Bedi, E. Beghi, Y. Béjot, B. B. Bekele, M. L. Bell, J. R. Bennett, I. M. Bensenor, A. Berhane, E. Bernabé, B. D. Betsu, M. Beuran, S. Bhatt, S. Biadgilign, K. Bienhoff, B. Bikbov, D. Bisanzio, R. R. A. Bourne, N. J. K. Breitborde, L. N. B. Bulto, B. R. Bumgarner, Z. A. Butt, L. Cahuana-Hurtado, E. Cameron, J. C. Campuzano, J. Car, R. Cárdenas, J. J. Carrero, A. Carter, D. C. Casey, C. A. Castañeda-Orjuela, F. Catalá-López, F. J. Charlson, C. E. Chibueze, O. Chimed-Ochir, V. H. Chisumpa, A. A. Chitheer, D. J. Christopher, L. G. Ciobanu, M. Cirillo, A. J. Cohen, D. Colombara, C. Cooper, B. C. Cowie, M. H. Criqui, L. Dandona, R. Dandona, P. I. Dargan, J. das Neves, D. V. Davitoiu, K. Davletov, B. de Courten, B. K. Defo, L. Degenhardt, S. Deiparine, K. Deribe, A. Deribew, S. Dey, D. Dicker, E. L. Ding, S. Djalalinia, H. P. Do, D. T. Doku, D. Douwes-Schultz, T. R. Driscoll, M. Dubey, B. B. Duncan, M. Echko, Z. Z. El-Khatib, C. L. Ellingsen, A. Enayati, S. P. Ermakov, H. E. Erskine, S. Eskandarieh, A. Esteghamati, K. Estep, C. S. e. S. Farinha, A. Faro, F. Farzadfar, V. L. Feigin, S.-M. Fereshtehnejad, J. C. Fernandes, A. J. Ferrari, T. R. Feyissa, I. Filip, S. Finegold, F. Fischer, C. Fitzmaurice, A. D. Flaxman, N. Foigt, T. Frank, M. Fraser, N. Fullman, T. Fürst, J. M. Furtado, E. Gakidou, A. L. Garcia-Basteiro, T. Gebre, G. B. Gebregergs, T. T. Gebrehiwot, D. Y. Gebremichael, J. M. Geleijnse, R. Genova-Maleras, H. A. Gesesew, P. W. Gething, R. F. Gillum, A. Z. Giref, M. Giroud, G. Giussani, W. W. Godwin, A. L. Gold, E. M. Goldberg, P. N. Gona, S. V. Gopalani, H. N. Gouda, A. C. Goulart, M. Griswold, R. Gupta, T. Gupta, V. Gupta, P. C. Gupta, J. A. Haagsma, N. Hafezi-Nejad, A. D. Hailu, G. B. Hailu, R. R. Hamadeh, M. T. Hambisa, S. Hamidi, M. Hammami, J. Hancock, A. J. Handal, G. J. Hankey, Y. Hao, H. L. Harb, H. A. Hareri, M. S. Hassanvand, R. Havmoeller, S. I. Hay, F. He, M. T. Hedayati, N. J. Henry, I. B. Heredia-Pi, C. Herteliu, H. W. Hoek, M. Horino, N. Horita, H. D. Hosgood, S. Hostiuc, P. J. Hotez, D. G. Hoy, C. Huynh, K. M. Iburg, C. Ikeda, B. V. Ileanu, A. A. Irenso, C. M. S. Irvine, S. M. S. Islam, K. H. Jacobsen, N. Jahanmehr, M. B. Jakovljevic, M. Javanbakht, S. P. Jayaraman, P. Jeemon, V. Jha, D. John, C. O. Johnson, S. C. Johnson, J. B. Jonas, M. Jürisson, Z. Kabir, R. Kadel, A. Kahsay, R. Kamal, A. Karch, S. M. Karimi, C. Karimkhani, A. Kasaeian, N. A. Kassaw, N. J. Kassebaum, S. V. Katikireddi, N. Kawakami, P. N. Keiyoro, L. Kemmer, C. N. Kesavachandran, Y. S. Khader, E. A. Khan, Y.-H. Khang, A. T. A. Khoja, M. H. Khosravi, A. Khosravi, J. Khubchandani, A. A. Kiadaliri, C. Kieling, D. Kievlan, Y. J. Kim, D. Kim, R. W. Kimokoti, Y. Kinfu, N. Kissoon, M. Kivimaki, A. K. Knudsen, J. A. Kopec, S. Kosen, P. A. Koul, A. Koyanagi, X. R. Kulikoff, G. A. Kumar, P. Kumar, M. Kutz, H. H. Kyu, D. K. Lal, R. Lalloo, T. L. N. Lambert, Q. Lan, V. C. Lansingh, A. Larsson, P. H. Lee, J. Leigh, J. Leung, M. Levi, Y. Li, D. Li Kappe, X. Liang, M. L. Liben, S. S. Lim, P. Y. Liu, A. Liu, Y. Liu, R. Lodha, G. Logroscino, S. Lorkowski, P. A. Lotufo, R. Lozano, T. C. D. Lucas, S. Ma, E. R. K. Macarayan, E. R. Maddison, M. Magdy Abd El Razek, M. Majdan, R. Majdzadeh, A. Majeed, R. Malekzadeh, R. Malhotra, D. C. Malta, H. Manguerra, T. Manyazewal, C. C. Mapoma, L. B. Marczak, D. Markos, J. Martinez-Raga, F. R. Martins-Melo, I. Martopullo, C. McAlinden, M. McGaughey, J. J. McGrath, S. Mehata, T. Meier, K. G. Meles, P. Memiah, Z. A. Memish, M. M. Mengesha, D. T. Mengistu, B. G. Menota, G. A. Mensah, T. J. Meretoja, A. Meretoja, A. Millear, T. R. Miller, S. Minnig, M. Mirarefin, E. M. Mirrakhimov, A. Misganaw, S. R. Mishra, I. A. Mohamed, K. A. Mohammad, A. Mohammadi, S. Mohammed, A. H. Mokdad, G. L. D. Mola, S. K. Mollenkopf, M. Molokhia, L. Monasta, J. C. Montañez, M. Montico, M. D. Mooney, M. Moradi-Lakeh, P. Moraga, L. Morawska, C. Morozoff, S. D. Morrison, C. Mountjoy-Venning, K. B. Mruts, K. Muller, G. V. S. Murthy, K. I. Musa, J. B. Nachega, A. Naheed, L. Naldi, V. Nangia, B. R. Nascimento, J. T. Nasher, G. Natarajan, I. Negoi, J. W. Ngunjiri, C. T. Nguyen, Q. L. Nguyen, T. H. Nguyen, G. Nguyen, M. Nguyen, E. Nichols, D. N. A. Ningrum, V. M. Nong, J. J. N. Noubiap, F. A. Ogbo, I.-H. Oh, A. Okoro, A. T. Olagunju, H. E. Olsen, B. O. Olusanya, J. O. Olusanya, K. Ong, J. N. Opio, E. Oren, A. Ortiz, M. Osman, E. Ota, M. Pa, R. E. Pacella, S. Pakhale, A. Pana, B. K. Panda, S. Panda-Jonas, C. Papachristou, E.-K. Park, S. B. Patten, G. C. Patton, D. Paudel, K. Paulson, D. M. Pereira, F. Perez-Ruiz, N. Perico, A. Pervaiz, M. Petzold, M. R. Phillips, D. M. Pigott, C. Pinho, D. Plass, M. A. Pletcher, S. Polinder, M. J. Postma, F. Pourmalek, C. Purcell, M. Qorbani, B. P. A. Quintanilla, A. Radfar, A. Rafay, V. Rahimi-Movaghar, M. H. U. Rahman, M. Rahman, R. K. Rai, C. L. Ranabhat, Z. Rankin, P. C. Rao, G. K. Rath, S. Rawaf, S. E. Ray, J. Rehm, R. C. Reiner, M. B. Reitsma, G. Remuzzi, S. Rezaei, M. S. Rezai, M. B. Rokni, L. Ronfani, G. Roshandel, G. A. Roth, D. Rothenbacher, G. M. Ruhago, R. Sa, S. Saadat, P. S. Sachdev, N. Sadat, M. Safdarian, S. Safi, S. Safiri, R. Sagar, R. Sahathevan, J. Salama, P. Salamati, J. A. Salomon, A. M. Samy, J. R. Sanabria, M. D. Sanchez-Niño, D. Santomauro, I. S. Santos, M. M. Santric Milicevic, B. Sartorius, M. Satpathy, M. I. Schmidt, I. J. C. Schneider, S. Schulhofer-Wohl, A. E. Schutte, D. C. Schwebel, F. Schwendicke, S. G. Sepanlou, E. E. Servan-Mori, K. A. Shackelford, S. Shahraz, M. A. Shaikh, M. Shamsipour, M. Shamsizadeh, J. Sharma, R. Sharma, J. She, S. Sheikhbahaei, M. Shey, P. Shi, C. Shields, M. Shigematsu, R. Shiri, S. Shirude, I. Shiue, H. Shoman, M. G. Shrime, I. D. Sigfusdottir, N. Silpakit, J. P. Silva, J. A. Singh, A. Singh, E. Skiadaresi, A. Sligar, D. L. Smith, A. Smith, M. Smith, B. H. A. Sobaih, S. Soneji, R. J. D. Sorensen, J. B. Soriano, C. T. Sreeramareddy, V. Srinivasan, J. D. Stanaway, V. Stathopoulou, N. Steel, D. J. Stein, C. Steiner, S. Steinke, M. A. Stokes, M. Strong, B. Strub, M. Subart, M. B. Sufiyan, B. F. Sunguya, P. J. Sur, S. Swaminathan, B. L. Sykes, R. Tabarés-Seisdedos, S. K. Tadakamadla, K. Takahashi, J. S. Takala, R. T. Talongwa, M. R. Tarawneh, M. Tavakkoli, N. Taveira, T. K. Tegegne, A. Tehrani-Banihashemi, M.-H. Temsah, A. S. Terkawi, J. S. Thakur, O. Thamsuwan, K. R. Thankappan, K. E. Thomas, A. H. Thompson, A. J. Thomson, A. G. Thrift, R. Tobe-Gai, R. Topor-Madry, A. Torre, M. Tortajada, J. A. Towbin, B. X. Tran, C. Troeger, T. Truelsen, D. Tsoi, E. M. Tuzcu, S. Tyrovolas, K. N. Ukwaja, E. A. Undurraga, R. Updike, O. A. Uthman, B. S. C. Uzochukwu, J. F. M. van Boven, T. Vasankari, N. Venketasubramanian, F. S. Violante, V. V. Vlassov, S. E. Vollset, T. Vos, T. Wakayo, M. T. Wallin, Y.-P. Wang, E. Weiderpass, R. G. Weintraub, D. J. Weiss, A. Werdecker, R. Westerman, B. Whetter, H. A. Whiteford, T. Wijeratne, C. S. Wiysonge, B. G. Woldeyes, C. D. A. Wolfe, R. Woodbrook, A. Workicho, D. Xavier, Q. Xiao, G. Xu, M. Yaghoubi, B. Yakob, Y. Yano, M. Yaseri, H. H. Yimam, N. Yonemoto, S.-J. Yoon, M. Yotebieng, M. Z. Younis, Z. Zaidi, M. E. S. Zaki, E. A. Zegeye, Z. M. Zenebe, T. A. Zerfu, A. L. Zhang, X. Zhang, B. Zipkin, S. Zodpey, A. D. Lopez and C. J. L. Murray, Global, regional, and national age-sex specific mortality for 264 causes of death, 1980–2016: a systematic analysis for the Global Burden of Disease Study 2016, Lancet, 2017, 390, 1151–1210.
41. S. Zou, Z. Wang, M. Bhura, G. Zhang and K. Tang, Prevalence and associated socioeconomic factors of multimorbidity in ten regions of China: a cross-sectional analysis, Lancet, 2020, 44, 36–50.
42. Y.-P. Wang and X.-N. Zhou, The year 2020, a milestone in breaking the vicious cycle of poverty and illness in China, Infect. Dis. Poverty, 2020, 9, 11.
43. M. Ezzati and E. Riboli, Behavioral and dietary risk factors for noncommunicable diseases, N. Engl. J. Med., 2013, 369, 954–964.
44. Y. Li, D. D. Wang, S. H. Ley, A. G. Howard, Y. He, Y. Lu, G. Danaei and F. B. Hu, Potential impact of time trend of life-style factors on cardiovascular disease burden in China, J. Am. Coll. Cardiol., 2016, 68, 818–833.
45. V. W. Zhong, L. Van Horn, P. Greenland, M. R. Carnethon, H. Ning, J. T. Wilkins, D. M. Lloyd-Jones and N. B. Allen, Associations of processed meat, unprocessed red meat, poultry, or fish intake with incident cardiovascular disease and all-cause mortality, JAMA Intern. Med., 2020, 114, 1049–1058.
46. J. Yin, Y. Zhu, V. Malik, X. Li, X. Peng, F. F. Zhang, Z. Shan and L. Liu, Intake of sugar-sweetened and low-calorie sweetened beverages and risk of cardiovascular disease: A meta-analysis and systematic review, Adv. Nutr., 2020, 12, 89–101.
47. C. S. Pundir, N. Yadav and A. K. Chhillar, Occurrence, synthesis, toxicity and detection methods for acrylamide determination in processed foods with special reference to biosensors: A review, Trends Food Sci. Technol., 2019, 85, 211–225.
48. B. Wang, X. Wang, L. Yu, W. Liu, J. Song, L. Fan, M. Zhou, M. Yang, J. Ma, M. Cheng, W. Qiu, R. Liang, D. Wang, Y. Guo and W. Chen, Acrylamide exposure increases cardiovascular risk of general adult population probably by inducing oxidative stress, inflammation, and TGF-β1: A prospective cohort study, Environ. Int., 2022, 164, 107261.
49. Y. Zhang, M. Huang, P. Zhuang, J. Jiao, X. Chen, J. Wang and Y. Wu, Exposure to acrylamide and the risk of cardiovascular diseases in the National Health and Nutrition Examination Survey 2003–2006, Environ. Int., 2018, 117, 154–163.
50. R. Uauy, A. Aro, R. Clarke, n. Ghafoorunissa, M. R. L'Abbé, D. Mozaffarian, C. M. Skeaff, S. Stender and M. Tavella, WHO Scientific Update on trans fatty acids: summary and conclusions, Eur. J. Clin. Nutr., 2009, 63, S68–S75.
51. R. J. de Souza, A. Mente, A. Maroleanu, A. I. Cozma, V. Ha, T. Kishibe, E. Uleryk, P. Budylowski, H. Schünemann, J. Beyene and S. S. Anand, Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies, Br. Med. J., 2015, 351, h3978.
52. M. Echarte, D. Ansorena and I. Astiasaran, Fatty acid modifications and cholesterol oxidation in pork loin during frying at different temperatures, J. Food Prot., 2001, 64, 1062–1066.
53. S. Zhong, L. Li, X. Shen, Q. Li, W. Xu, X. Wang, Y. Tao and H. Yin, An update on lipid oxidation and inflammation in cardiovascular diseases, Free Radicals Biol. Med., 2019, 144, 266–278.
54. T. Goldberg, W. Cai, M. Peppa, V. Dardaine, B. S. Baliga, J. Uribarri and H. Vlassara, Advanced glycoxidation end products in commonly consumed foods, J. Am. Diet. Assoc., 2004, 104, 1287–1291.
55. S. Rowan, E. Bejarano and A. Taylor, Mechanistic targeting of advanced glycation end-products in age-related diseases, Biochim. Biophys. Acta, Mol. Basis Dis., 2018, 1864, 3631–3643.
56. M. O'Donnell, A. Mente and S. Yusuf, Sodium intake and cardiovascular health, Circ. Res., 2015, 116, 1046–1057.
57. G. Mancia, S. Oparil, P. K. Whelton, M. McKee, A. Dominiczak, F. C. Luft, K. AlHabib, F. Lanas, A. Damasceno, D. Prabhakaran, G. La Torre, M. Weber, M. O'Donnell, S. C. Smith and J. Narula, The technical report on sodium intake and cardiovascular disease in low- and middle-income countries by the joint working group of the World Heart Federation, the European Society of Hypertension and the European Public Health Association, Eur. Heart J., 2017, 38, 712–719.
58. V. S. Malik, Y. Li, A. Pan, L. D. Koning, E. Schernhammer, W. C. Willett and F. B. Hu, Long-term consumption of sugar-sweetened and artificially sweetened beverages and risk of mortality in US adults, Circulation, 2019, 139, 2113–2125.
59. C. Eloi, D. Charlotte, S. Bernard, K. F. Léopold, J. Chantal, H. Serge, D. Mélanie and T. Mathilde, Sugary drinks, artificially-sweetened beverages, and cardiovascular disease in the NutriNet-Santé Cohort, J. Am. Coll. Cardiol., 2020, 76, 2175–2177.
60. B. Srour, L. K. Fezeu, E. Kesse-Guyot, B. Allès, C. Méjean, R. M. Andrianasolo, E. Chazelas, M. Deschasaux, S. Hercberg, P. Galan, C. A. Monteiro, C. Julia and M. Touvier, Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé), Br. Med. J., 2019, 365, l1451.
61. F. Imamura, L. O'Connor, Z. Ye, J. Mursu, Y. Hayashino, S. N. Bhupathiraju and N. G. Forouhi, Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction, Br. Med. J., 2016, 351, h3576.
62. S. Liu, Y. Li, X. Zeng, H. Wang, P. Yin, L. Wang, Y. Liu, J. Liu, J. Qi, S. Ran, S. Yang and M. Zhou, Burden of cardiovascular diseases in China, 1990–2016: Findings from the 2016 Global Burden of Disease Study, JAMA Cardiol., 2019, 4, 342–352.
63. C. Dye, One Health as a catalyst for sustainable development, Nat. Microbiol., 2022, 7, 467–468.
64. P. Chen, F. Li and P. Harmer, Healthy China 2030: moving from blueprint to action with a new focus on public health, Lancet Public Health, 2019, 4, e447.

---

Footnote

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d4fo02978j

---