Risk of coronary heart disease in the rural population in Xinjiang: A nested case-control study in China.

BACKGROUND AND AIM: Coronary heart disease (CHD) is a chronic complex disease caused by a combination of factors such as lifestyle behaviors and environmental and genetic factors. We conducted this study to evaluate the risk factors affecting the development of CHD in Xinjiang, and to obtain valuable information for formulating appropriate local public health policies.
METHOD: We conducted a nested case-control study with 277 confirmed CHD cases and 554 matched controls. The association of the risk factors with the risk of CHD was assessed using the multivariate Cox proportional hazard model. Multiplicative interactions were evaluated by entering interaction terms in the Cox proportional hazard model. The additive interactions among the risk factors were assessed by the index of additive interaction.
RESULTS: The risk of CHD increased with frequent high-fat food consumption, dyslipidemia, obesity, and family history of CHD after adjustment for drinking, smoking status, hypertension, diabetes, family history of hypertension, and family history of diabetes. We noted consistent interactions between family history of CHD and frequent high-fat food consumption, family history of CHD and obesity, frequent high-fat food consumption and obesity, frequent high-fat food consumption and dyslipidemia, and obesity and dyslipidemia. The risk of CHD events increased with the presence of the aforementioned interactions.
CONCLUSIONS: Frequent high-fat food consumption, family history of CHD, dyslipidemia and obesity were independent risk factors for CHD, and their interactions are important for public health interventions in patients with CHD in Xinjiang.

Introduction

Globally, cardiovascular disease is the most common cause of death. CHD accounted for more than 53% of all CVD-related deaths in 2016, and CHD-related mortality rate increased by 19.0% from 2006 to 2016[1]. As the prevalence of risk factors has been increasing, the mortality rate of CHD has increased by 40.1% during the same period, with CHD accounting for 17.8% of all deaths in China [2]. CHD development has become a public health problem that must be addressed urgently. The pathology underlying CHD is coronary arteriosclerosis (AS), which affects the blood circulation and causes myocardial ischemia and hypoxia, leading to a series of vascular diseases. Recently, several studies reported that inflammation, lipid metabolism disorders, and oxidative stress may promote the development of AS[3-5]. However, AS is caused by a combination of factors. Hence, the identification of risk factors and biomarkers plays a vital role in the prevention and treatment of CHD.

Xinjiang is a multi-ethnic province in the northwestern region of China. In Xinjiang, the ethnic minority with the largest number of members is the Uighurs, followed by the Kazakhs. As these groups are economically backward, and the regional healthcare resources are unevenly distributed, there is a lack of cohort studies analyzing the local public health issues, such as the incidence of CHD. The unique ethnic dietary habits and the living environment of these groups are different from those of ethnic groups in China (for example, consumption of a high-salt high-fat high-carbohydrates diet and making their living as nomads). A previous study reported that the prevalence of overweight and obesity is higher among the population in Xinjiang than in the national population in China [6]. Thus, we conducted this nested case-control study by analyzing the biochemical indicators and physical examination results to identify the risk factors of CHD for the population in Xinjiang. To identify potential interactions among these risk factors and to adopt timely and effective preventive measures for the population in Xinjiang.

Materials and methods

Study design

The survey among the Kazakhs and the Uyghurs began in April 2009 and was followed up in December 2013, April 2016, and August 2017 in the Xinyuan County and Jiashi County. We selected two representative areas (i.e., Yili and Kashi) according to the distribution of the populations in the Xinjiang Uygur Autonomous Region and randomly selected a county (i.e., Xinyuan and Jiashi Counties) and a township in each county (i.e., Nalati and Jiangbazi Townships). Next, the corresponding villages (six villages in the Nalati Township and 12 villages in the Jiangbazi Township) were selected using the stratified sampling method. Finally, we conducted interviews with 7,258 participants aged > 18 years who lived in the villages for at least 6 months (3,542 Uighurs and 3,716 Kazakhs). All participants provided informed consent. The total response rate was 87.5% (88.6% among the Uygur and 86.5% among the Kazakh). The survey was approved by the Ethical Review Board of the First Affiliated Hospital of Shihezi University School of Medicine (IERB No. SHZ2010LL01).

Selection of cases and controls

Cases were selected from among the participants who experienced their first CHD event during the follow–up. The presence of CHD was determined on the basis of self–reported questionnaire responses, medical insurance records, and local hospital discharge records from 2009 to 2017. Patients with self-reported CHD findings were required to have a certificate of CHD diagnosis from the medical institution of their township during investigation. Multiple CHD events may occur in the same patient; the first occurrence was considered as the outcome event. Patients diagnosed with a CHD event were required meet any of the following criteria: hospitalized for CHD; coronary artery atherosclerosis; coronary interventional therapy; angina pectoris; myocardial infarction; and sudden cardiac death. The controls were selected from among the participants without CHD during the survey period, who provided blood samples and information on their physical characteristics. For each case, we selected two controls, which were matched by sex, age, and ethnicity used propensity score matching [7]. Once the controls were matched for a particular case, they could not be selected as controls for other cases.

Epidemiological survey and biochemical measurements

Data of participants were collected during the family interview using a self-made questionnaire. The detailed information on the questionnaire included demographics, diets, drinking and smoking status and personal and family history of the disease. The data obtained from the physical examination included weight, height, and waist circumference (WC), which were measured by trained investigators using a uniform standardized method during the interview. The abovementioned measurement and blood sample collection methods have been previously described [8]. The biochemical parameters from the blood samples included the total cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol (LDL-C), high–density lipoprotein cholesterol (HDL-C). All blood samples were analyzed using an automatic biochemical analyzer (Olympus AU 2700; Olympus Diagnostics, Hamburg, Germany).

Definition

Patients whose parents or siblings experienced the CHD were considered to a family history of the CHD. Dyslipidemia was defined as the presence of abnormal levels of TC, TG, low HDL-C and/or high LDL-C. The abnormal TC level was defined as TC ≥ 6.2 mmol/l; abnormal TG level, TG ≥ 2.3 mmol/l; high LDL-C level, LDL-C ≥ 4.1 mmol/l; and low HDL-C level, HDL-C < 1.0 mmol/l[9]. The abnormal WC level in Asians was defined as WC ≥ 90 cm for males and WC ≥ 80cm for females [10]. The body mass index (BMI) was calculated using the following equation: [weight (kg)] / [height (m) ^ 2]. Overweight was defined as BMI ≥ 24.0 kg/m2 and obesity is BMI ≥ 28.0 kg/m2 [11].

Local high-fat foods include the following: Zhuafan is stewed with fresh lamb, carrots, rice, onions, mutton fat and a large amount of oil (approximately 371 kcal per 100 gm); Baoershake is a fried flour–based food item (approximately 399 kcal per 100 g,); and ghee, a local dairy product similar to butter, is extracted from milk and contains more than 90% saturated fat (approximately 860 kcal per 100 gm), and is always consumed with naan and milk tea. We defined frequent high-fat food consumption as conformance with any of the following criteria: (1) eating 4 servings or more of Zhuafan per week (1 serving is equivalent to 1 bowl of 8 cm caliber or 1 plate of 16 cm caliber), (2) eating Baoershake 4 times or more per week, or (3) eating 5 scoops or more of ghee per day.

Statistical analysis

Significant differences in the baseline characteristics between the cases (newly developed CHD during follow–up) and controls were compared using the χ2 test for categorical variables. A multivariate Cox proportional hazard model was used to evaluate the association between the underlying influencing factors and risk of CHD by calculating the hazard ratio (HR) and the associated 95% confidence interval (CI). The survival time was defined as the period from the time the participants were enrolled in the cohort study to the time the first CHD event occurred. To match the cases and controls (1:2), propensity score matching was performed using Empower Stats statistical software (http://www.empowerstats.com). Data were analyzed using Statistical Product and Service Solutions (SPSS) version 24.0 (Chicago, Illinois, USA). All statistical tests were two–sided, and P <0.05 was considered statistically significant. Multiplicative synergy index (SI) was used to evaluate multiplicative interaction, which is calculated by creating and entering the multiplicative interaction terms in the Cox proportional hazard model. There was no multiplicative interaction if the SI equal 1. Additive interactions were evaluated using three indices, namely, relative excess risk due to interaction (RERI), attributable proportion of interaction (AP), and additive SI, which were calculated using Andersson’s Excel calculation table. There was no additive interaction if the following conditions occurred: the 95% CI of the RERI or AP equal 0 and that of the SI equal 1.

Results

Baseline characteristics of the study participants

The initial cohort included 7,258 individuals. We further excluded 143 participants with incomplete basic, blood sample, physical information, or patients with a history of CHD. Among the 7115 participants (44,431.65 person years), 277 had their first CHD event during the follow-up period. The incidence of the first CHD event was 55.73/10,000 person-years.

summarizes the results of the comparison of the baseline characteristics between the controls (n = 554) and cases (n = 277); the cases included a significantly higher proportion of patients who had abnormal WC and TC levels, family history of CHD, dyslipidemia, and obesity and frequent high-fat food consumption (P <0.05). There were no significant differences in the high TG, high LDL-C, and low HDL-C levels and the prevalence of overweight.

Association between the risk factors and CHD

The risk of CHD increased significantly and was associated with abnormal WC levels (HR 1.63, 95%CI 1.26–2.11) and abnormal TC levels (HR 1.59, 95%CI 1.02–2.48), frequent high-fat food consumption (HR 1.83, 95%CI 1.44–2.34), dyslipidemia (HR 1.81, 95%CI 1.43–2.29), obesity (HR 2.02, 95%CI 1.56–2.63), and family history of CHD (HR 2.10, 95%CI 1.42–3.11) in the univariate model (P <0.05; ). The adjusted HRs in the multivariate model were similar to those in the univariate model for frequent high-fat food consumption, dyslipidemia, obesity, and family history of CHD. We did not observe association of CHD risk with WC and TC levels.

Multiplicative interaction between the risk factors and CHD

The coexistence of factors increased the risk of CHD compared to that in the reference group, as follows: frequent high-fat food consumption and obesity (HR 3.14, 95%CI 2.19–4.50), frequent high-fat food consumption and family history of CHD (HR 3.64, 95%CI 2.26–5.87), frequent high-fat food consumption and dyslipidemia (HR 2.99, 95%CI 2.18–4.10), obesity and family history of CHD (HR 4.62, 95%CI 2.57–8.31), obesity and dyslipidemia (HR 3.12, 95%CI 2.25–4.32), and dyslipidemia and family history of CHD (HR 2.81, 95%CI 1.66–4.76, P <0.001). According to multiplicative SI, the effect of multiplicative interactions between any two factors on CHD had no statistical significance ().

Additive interaction between the risk factors and CHD

The evaluation indices of the additive interactions are shown in . Among the participants with concurrent exposure to frequent high-fat food consumption and family history of CHD, approximately 84% of the risk of CHD was attributed to their interaction effect (AP 0.84, 95%CI 0.65–1.03), and the attributable proportion of interaction was 82% between frequent high-fat food consumption (AP 0.82, 95%CI 0.60–1.04). On the basis of the indices of the additive interactions between frequent high-fat food consumption and dyslipidemia, the relative excess risk was 1.16 (RERI 1.16, 95%CI 0.15–2.17), and the AP was 46% (AP 0.46, 95%CI 0.16–0.76). The RERI between obesity and dyslipidemia was 1.64 (RERI 1.64, 95%CI 0.22–3.07), and AP was 52% (AP 0.52, 95% CI 0.21–0.83). The additive SI among the patients with both family history of CHD and obesity who developed CHD was 5.22 (additive SI 5.22, 95% CI 1.04–26.12), and attributable proportion of interaction was 74% (AP 0.74, 95% CI 0.37–1.10). The statistically significant additive interaction indicators were not noted between family history of CHD and dyslipidemia.

Discussion

In this nested case-control study based on the population in Xinjiang, we found that frequent high-fat food consumption, family history of CHD, dyslipidemia, and obesity were the independent risk factors of CHD in the multivariate Cox proportional hazard model. Due to the complex relationship between heredity and environmental factors, AS can aggravate oxidative stress, inflammation and metabolic abnormalities involving cholesterol and lipoproteins, thus, leading to CHD. A positive association between obesity and CHD was found in a rural Chinese population. This may be because accumulation of visceral fat promotes insulin resistance[12]. Cohort studies in the Asia-pacific region have reported that dyslipidemia is a definitive cause of arteriosclerosis[13].

A prospective study on the effects of a 6-month low-salt low-fat diet and aerobic exercise found that many risk factors improved and the risk of CHD in women decreased from 6% to 4% and from 16% to 13% in men [14]. Another study on men reported that high-fat and hazelnut-enriched diets are superior to the low-fat control diet, which may have beneficial effects on the risk of CHD owing to the favorable changes in the plasma lipid mass spectrum in adult men[15]. There is probable evidence regarding the importance of diets in the development of CHD, and suitable dietary paradigms must consider the type and quality of fat and carbohydrates required for metabolism by changing the intermediate risk factors ameliorating the characteristics of individual susceptibility [16]. In this study, frequent high-fat food consumption increased the risk of CHD (HR 1.53, 95%CI 1.18–1.98). A previous study reported that high unsaturated fatty acid intake may promote oxidative stress and increase the risk of CHD [17]; further, increased intake of high-fat diet increases the risk of myocardial infarction [18]. However, the relationship remains controversial[19]; a positive association was found between frequent high-fat food consumption and CHD risk in the USA, but not in Europe or Asia [20]. This could be attributed to the different dietary patterns and social conditions, as well as ethnic differences.

Familial aggregation of CHD has been demonstrated, and a family history of CHD is considered a risk factor even after adjusting for other risk factors in a previous study, which is consistent with our data[21]. Notably, CHD is affected by several of environmental and genetic factors; however, family members do not encounter identically similar environmental factors, such as dietary pattern and lifestyle. Therefore, it is necessary to conduct studies on the interaction between genetic and environmental factors.

Several epidemiologists contend that the assessment of biological interactions should involve analysis of additive interactions rather than multiplicative interactions[22]. We analyzed the multiplicative and additive interactions among the risk factors, and some significant additive interaction index were observed. We found a positive additive effect between dyslipidemia and obesity (RERI 1.64, 95%CI 0.22–3.07; AP 0.52, 95%CI 0.21–0.83). A recent cohort study reported a positive interaction between mixed dyslipidemia and obesity increased the risk of atherogenic[23].

A study reported that the interaction between family history of premature CHD and ≥ 2 metabolic risk factors (e.g., age, male sex, hypertension, dyslipidemia, low HDL-C and high TG levels, smoking status [current], and obesity) amplifies the risk for coronary arterial calcium deposition [24]. This study indicated that interaction between family history of CHD and obesity increases the risk of CHD (AP 0.74, 95%CI 0.37–1.10; additive SI 5.22, 95%CI 1.04–26.12). Based on the results of this study, an interaction between family history of CHD and dyslipidemia was not observed. However, a Swedish study supports the interactions between family history of CHD and LDL/HDL quotient ≥ 4.0 in women for myocardial infarction (SI 3.8, 95% CI 1.5–9.7), this indicates that further research is needed on different indicators of dyslipidemia [25]. Slattery et al. [26] found that patients with family history of CHD show higher levels of fatty foods consumption than those without family history of CHD among a population of older women. We found an interaction between family history of CHD and frequent high-fat food consumption (AP 0.84, 95%CI 0.65–1.03); however, the available evidence is insufficient to demonstrate the existence of these interactions and further research is warranted. The available evidence suggests that family history of CHD is undeniably an independent risk factor for CHD and important factor to help prevent poor CHD outcomes and promote cardiovascular health [27], which is an important factor in the study of CHD in the Xinjiang population.

Although the interactions between frequent high-fat food consumption and obesity, and the interactions between frequent high-fat food consumption and dyslipidemia have rarely been reported, Chmurzynska et al reported that the frequency of consumption of high-fat foods was correlated in the obesity [28]. Moreover, studies have shown that the interaction between a low-fat diet, such as a Mediterranean diet, and genetic factors have impact on the risk for CHD [29, 30]. Further research and evaluation of the interactions between dietary and genetic factors and between dietary and environmental factors are required.

Studying the risk factors of and interactions in CHD plays important roles in reducing the incidence of CHD and improving the quality of life of the patients. Among this cohort, many individuals experience dyslipidemia and obesity, which are known risk factors for CHD, and frequent high-fat food consumption deserves attention as an important factor of CHD. These risk factors can be improved via lifestyle changes. This could help provide important references for developing effective and feasible interventions for patients with CHD in Xinjiang. However, the prevention strategy of combining family history of CHD with obesity, dyslipidemia, and frequent high-fat food consumption must be investigated further.

The present study has some limitations. First, only serological and physical indicators were assessed; these indicators are not sufficient to assess the risk factors of CHD comprehensively, and we did not appropriately evaluate all the effects of interactions between factors on CHD. Second, although we performed face-to-face interview questionnaires-based surveys, bias from self-reported methods is possible. As the majority of male Kazakhs are nomads and were not admitted to hospitals, all relevant disease information may not have been collected. And the overall response rate in our study was 87.5%, which means that the baseline population have not been entirely followed up, and we cannot confirm the disease status of the baseline population who were not followed up. This means that there may be selection bias. Therefore, the present analysis may have underestimated the cumulative incidence of CHD. Third, we did not measure the ratio of fat in the daily total energy and daily dietary fat intake to evaluate the high-fat food consumption; we only used the frequency of high-fat food consumption as clues. Fourth, history of malignancy was not excluded. Despite the above limitations, the study also has some strengths. First, this study was a long-term cohort study on CHD conducted in the Kazakh and Uygur populations in Xinjiang. A nested case-control study design was adopted in this study to control for confounding factors, such as sex, age, and ethnicity; this would further improve the test efficiency. Second, the routine physical examination results were collected from national health check projects supported by the government, which are comprehensive and highly reliable. Next, only a few studies have reported multiplicative and additive interactions. The abovementioned additive scale is more suitable for assessing the biological interactions.

Histogram of minority between case and control by SPSS.

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Histogram of sex between case and control by SPSS.

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Histogram of age between case and control by SPSS.

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Description of matching factors between case and control by SPSS (minority).

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Description of matching factors between case and control by SPSS (sex).

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Description of matching factors between case and control by SPSS (age).

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17 Dec 2019

PONE-D-19-21029

Risk of coronary heart disease among Xinjiang rural: a nested case-control study in China

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Reviewer #1: Dear manuscript authors,

This paper presents the potential interactions among the main risk factors of CHD. It helps to clarify the risk factors of CHD for the population in Xinjiang.

This study presents some original research and they are important only for this region. Some definitions, for example, obesity, overweight are different compared to WHO recommendations.

Some question from the Methodology chapter:

1. What criteria were used to match the cases and control groups? You wrote, "matched by propensity score matching". Please describe the factors that have been involved, and indicate the sources of literature where this sampling method would be used or described. Is age, gender, education, etc. taken into account when matching the groups?

2. Please explain in more detail what kind of questionnaires (24h-recall, FFQ, etc.) have been used to assess dietary habits.

A high-fat diet needs to be described in more detail. How was this diet calculated? How much energy per day from fats received responders? What kind of criteria were used to identify the high-fat diet of respondents? It is also necessary to refer to the sources of literature.

It is extremely important to describe this because you have obtained meaningful results and presented them in the conclusions.

3. It is necessary to provide literature references or recommendations that you have used to evaluate overweight, obesity, and the criteria for WC and dyslipidemias.

Results section

In Table 1 you presented, that only total cholesterol values significantly differed in cases compared to controls, however, neither did TG, LDL-c, or HDL-C, but the incidence of dyslipidemias was significantly different. How do you explain this?

Are the data in this table adjusted for gender and age?

Please provide the full p values in Table 2. In my opinion, some presented data are not significantly different.

Discussion section.

More attention should be paid to the interaction between risk factors for CHD and to comparisons these results with researches in other countries. On the other hand, it is difficult for the reader to compare those results because of the different evaluation criteria and the uncertainty of what a high-fat diet represents (EU is considered to be a high-fat diet if the daily energy from fat is more than 65% per day).

The conclusion is suitable and reflects on the aim.

Literature should be updated with more recent sources, as only 40% of sources are under 5 years old.

The English version of the article should be improved.

Reviewer #2: The study work sounds fine, the number of included subjects and the study design give a strength to your study. i have a few comments:

- I noticed many repetitive sentences/words in the draft, maybe due to mistyping, need to be removed( e.g., last phrase in the statistical analysis paragraph).

2- i didn't any information about the malignancy history ( cancer, chemo/radio HX) of those patients or if any of them has any active malignancy at the time of enrollment, and if so, you didn't clarify if those are included in your study or not.

reason for mentioning that is, currently attention is paid more to cardio-oncology and enormous of studies have proven that active malignancy/chemo/radiotherapy or a history of any, increase the risk of CVD, in that case you will need to consider that in you analysis as a possible confounders. Same thing for their psychiatric history.

Thanks

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Reviewer #2: No

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13 Jan 2020

Dear Alana T Brennan and Reviewers:

Many thanks for your interest in our manuscript entitled “Risk of coronary heart disease among Xinjiang rural: a nested case-control study in China” (ID: PONE-D-19-21029) for publication. We appreciate the thoughtful comments from the reviewers, and we have made revisions accordingly. We outline in the following text a detailed point-by-point response to the comments and the corresponding manuscript revisions. After careful consideration, we decided used the multiplicative interaction index as an effective indicator to evaluate the multiplicative interaction, which was revised in the manuscript. We have uploaded a database used for data analysis and our questionnaire as the Supporting Information.

We hope that the revised version of the manuscript is now acceptable for publication in your journal. I look forward to hearing from you soon.

With best wishes,

Yours sincerely,

Shuxia Guo, PhD

Corresponding author

Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang 832000, China

Phone: +86-180-0993-2625; Fax: +86-0993-2057-153; Email: gsxshzu@sina.com

We would like to express our sincere thanks to the reviewers for the constructive and positive comments.

Reviewer #1:

Some question from the Methodology chapter:

1. Response to comment: What criteria were used to match the cases and control groups? You wrote, "matched by propensity score matching". Please describe the factors that have been involved, and indicate the sources of literature where this sampling method would be used or described. Is age, gender, education, etc. taken into account when matching the groups?

Response: We are very sorry for not descript this part clearly, we have rewritten this part as requested by the reviewers in Methods (Selection of cases and controls).

Methods: For each case, we selected two controls, which were matched by gender, age, and ethnicity used propensity score [1].

1. Lin HC, Xirasagar S, Lee C-Z, Huang C-C, Chen C-H: The association between gastro-oesophageal reflux disease and subsequent rheumatoid arthritis occurrence: a nested case–control study from Taiwan. Bmj Open, 7(11): e016667.

2.1 Response to comment: Please explain in more detail what kind of questionnaires (24h-recall, FFQ, etc.) have been used to assess dietary habits.

Response: We used the self-made questionnaire of national natural fund project (No.81560551) to assess dietary habits by food frequency and the definition of a high-fat diet is also written into the Method. Here are the survey questionnaire options for high-fat foods:

82. I18 Zhuafan (1 serving is equivalent to 1 bowl (8cm caliber) or 1 plate (16cm caliber)):

○ Do not eat or less than 1 serving per week

○ 1-3 servings per week

○ 4-6 servings per week

○ 1 serving per day

○ 2 servings or more per day

83. I19 Fried Food (Buersake):

○ Do not eat or less than once a week

○ 1-3 times a week

○ 4-6 times a week

○ Once a day

○ 2 times or more per day

84. I20 Ghee:

○ No

○ 1-2 scoops per day

○ 3-4 scoops per day

○ 5-6 scoops per day

○ 6 scoops and above

Definition: Local high–fat foods include the following: Zhuafan is stewed with fresh lamb, carrots, rice, onions, mutton fat and a large amount of oil (approximately 371 kcal per 100 gm); Baoershake is a fried flour–based food item (approximately 399 kcal per 100 g,); and ghee, a local dairy product similar to butter, is extracted from milk and contains more than 90% saturated fat (approximately 860 kcal per 100 gm), and is always consumed with naan and milk tea. We defined frequent high-fat food consumption as conformance with any of the following criteria: (1) eating 4 servings or more of Zhuafan per week (1 serving is equivalent to 1 bowl of 8–cm caliber or 1 plate of 16–cm caliber), (2) eating Baoershake 4 times or more per week, or (3) eating 5 scoops or more of ghee per day.

2.2 Response to comment: A high-fat diet needs to be described in more detail. How was this diet calculated? How much energy per day from fats received responders? What kind of criteria were used to identify the high-fat diet of respondents? It is also necessary to refer to the sources of literature.

It is extremely important to describe this because you have obtained meaningful results and presented them in the conclusions.

Response: Regarding the definition of a high-fat diet, we asked individuals about their eating habits (including ghee, Zhuafan, Buersake) through interview questionnaires, but we can define them only by the frequency of eating the food. We also describe it in the Definition of Methods. To make it easier for readers to understand the meaning of the indicators we used, we changed the high-fat diet to frequent high-fat food consumption.

3. Response to comment: It is necessary to provide literature references or recommendations that you have used to evaluate overweight, obesity, and the criteria for WC and dyslipidemias.

Response: We have added literature references to evaluate overweight, obesity, and the criteria for WC and dyslipidemias.

4. Response to comment:

Results section

（1）In Table 1 you presented, that only total cholesterol values significantly differed in cases compared to controls, however, neither did TG, LDL-c, or HDL-C, but the incidence of dyslipidemias was significantly different. How do you explain this?

（2）Are the data in this table adjusted for gender and age?

Response: (1) After we analyzed and obtained the following results, which might due to the sample size of TG, LDL-c or HDL-C are not sufficient to show the statistical differences. When the four types are integrated into one indicator (dyslipidemia), the sample size is sufficient to show the significant statistical differences (P < 0.05). In addition, it may be caused by different sensitive cut-off points of TG, LDL-c or HDL-C between different ethnic groups and further analysis is requested in later studies.

(2) We apologize for the confusion caused by the lack of a clear description. Since we have considered age, gender, and ethnicity in the propensity score matching, we used this as a rule to match two control groups without CHD for each case. We have noted the confounding factors adjusted by each model in the footer of the table.

5. Response to comment: Please provide the full p values in Table 2. In my opinion, some presented data are not significantly different.

Response: We are sorry that Andersson’s Excel calculation table did not provided the P values of the three indicators. This calculation table can only provide the index values and their 95% confidence intervals. In addition, the 95% confidence interval can be used as an effective indicator to distinguish whether statistical differences exist or not.

6. Response to comment:

Discussion section.

More attention should be paid to the interaction between risk factors for CHD and to comparisons these results with researches in other countries. On the other hand, it is difficult for the reader to compare those results because of the different evaluation criteria and the uncertainty of what a high-fat diet represents (EU is considered to be a high-fat diet if the daily energy from fat is more than 65% per day).

Response: We have compared the interaction of coronary heart disease factors with the results of domestic and foreign studies in Discussion. However, due to the lack of reports on the interaction of influencing factors involved in this article, we have selected as many representative literatures as possible for comparison and explanation. We also mentioned this in the discussion.

Our research on frequent high-fat food consumption is intended to provide some clues, which is necessary to be discussed but limited when compared with the domestic and international literature, so we write that in the Limitation.

Limitation: Third, we did not measure the ratio of fat in the daily total energy and daily dietary fat intake to evaluate the high–fat food consumption; we only used the frequency of high–fat food consumption as clues.

7. Response to comment: The conclusion is suitable and reflects on the aim.

Literature should be updated with more recent sources, as only 40% of sources are under 5 years old.

Response: We have updated our sources on the basis of some earlier literature that must be cited, 60% of references are within five years.

8. Response to comment: The English version of the article should be improved.

Response: We have contacted the editing company to further improve the language of our article.

Reviewer #2:

1. Response to comment: I noticed many repetitive sentences/words in the draft, maybe due to mistyping, need to be removed (e.g., last phrase in the statistical analysis paragraph).

Response: We checked the manuscript and rewritten some sentences based on the reviewer's suggestions.

2. Response to comment: I didn't any information about the malignancy history (cancer, chemo/radio HX) of those patients or if any of them has any active malignancy at the time of enrollment, and if so, you didn't clarify if those are included in your study or not.

reason for mentioning that is, currently attention is paid more to cardio-oncology and enormous of studies have proven that active malignancy/chemo/radiotherapy or a history of any, increase the risk of CVD, in that case you will need to consider that in your analysis as a possible confounder. Same thing for their psychiatric history.

Thanks

Response: Thank you very much for the suggestions of the reviewers, which made our study more complete. After the verification of social security data, we found that there were no patients with psychiatric in this nested case-control study and only 5 patients in the control group had history of cancer. Because the number of malignant tumors is small, it cannot be adjusted as a confounding factor. Of course, this should be used as an exclusion criterion, although we did not consider it in the previous design. Thus, we put that in the Limitation.

Limitation and strength: Fourth, history of malignancy was not excluded. Despite these limitations, the study also has some strengths.

22 Jan 2020

PONE-D-19-21029R1

Risk of coronary heart disease in the rural population in Xinjiang: a nested case-control study in China

PLOS ONE

Dear Dr. Guo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

After further review of your manuscript there are a couple points that need clarification.

1. You state that you matched on gender and age and used propensity scores. It is unclear to me if you matched on gender, age and other variables in your propensity score OR if gender and age were the only variables that were in your propensity score. If it is the latter then I am unclear as to why you used a propensity score to do the matching. Also, it is good practice to show if the matching actually worked by showing the distribution of the matched factors between cases and controls, which you do not do. It is also good practice to show a histogram of the propensity scores stratified by cases and controls. These can be added as supplemental figures to your paper. Please address these issues.

2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this.

We would appreciate receiving your revised manuscript by February 15, 2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Please include the following items when submitting your revised manuscript:

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We look forward to receiving your revised manuscript.

Kind regards,

Alana T Brennan

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments (if provided):

1. You state that you matched on gender and age and used propensity scores. It is unclear to me if you matched on gender, age and other variables in your propensity score OR if gender and age were the only variables that were in your propensity score. If it is the latter then I am unclear as to why you used a propensity score to do the matching. Also, it is good practice to show if the matching actually worked by showing the distribution of the matched factors between cases and controls, which you do not do. It is also good practice to show a histogram of the propensity scores stratified by cases and controls. These can be added as supplemental figures to your paper. Please address these issues.

2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this.

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

28 Jan 2020

Dear Alana T Brennan:

Many thanks for your interest in our manuscript entitled “Risk of coronary heart disease among Xinjiang rural: a nested case-control study in China” (ID: PONE-D-19-21029) for publication. We would like to express our sincere thanks to the reviewers and editor for the constructive comments, and we have made revisions accordingly. We outline in the following text a detailed point-by-point response to the comments and the corresponding manuscript revisions.

We hope that the revised version of the manuscript is now acceptable for publication in your journal. I look forward to hearing from you soon.

With best wishes,

Yours sincerely,

Shuxia Guo, PhD

Corresponding author

Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang 832000, China

Phone: +86-180-0993-2625; Fax: +86-0993-2057-153; Email: gsxshzu@sina.com

Journal Requirements:

Additional Editor Comments (if provided):

1. You state that you matched on gender and age and used propensity scores. It is unclear to me if you matched on gender, age and other variables in your propensity score OR if gender and age were the only variables that were in your propensity score. If it is the latter then I am unclear as to why you used a propensity score to do the matching. Also, it is good practice to show if the matching actually worked by showing the distribution of the matched factors between cases and controls, which you do not do. It is also good practice to show a histogram of the propensity scores stratified by cases and controls. These can be added as supplemental figures to your paper. Please address these issues.

Response:

In this paper, nested case-control study design was adopted. By consulting the nested case-control literature, we found that many authors used propensity score to match case groups to control groups, and we also cited the articles. In addition, propensity score is a good matching method, so we chose propensity score to match case groups to control groups. We uploaded the file of “Description of matching factors between Case and Control by SPSS” to show the distribution of gender, age and ethnicity between the case group and the control group in the Supporting file.

2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this.

Response: After discussion, we decided to remove this part from the limitations and advantages.

3 Feb 2020

PONE-D-19-21029R2

Risk of coronary heart disease in the rural population in Xinjiang: a nested case-control study in China

PLOS ONE

Dear Dr. Guo,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

1. The figures that you create for the propensity score should be add in as supplemental tables and figures. Please edit them accordingly so that they are journal ready.

2. One last clarification is need in regards to the patient population. You stated in the study design that, "The survey among the Kazakhs and the Uyghurs began in April 2009 and was followed up in December 63 2013, April 2016, and August 2017 in the Xinyuan County and Jiashi County."  and then you state that, "The total response rate was 87.5% (88.6% among the 70 Uygur and 86.5% among the Kazakh)." Can you clarify that the patient population was the same each year - 2013, 2016 and 2017? I also assume that the rates of loss differed each year? You need to discuss the roughly 13% loss to follow-up as a limitation in your discussion. Is there reason to believe these ~13% of patients differ to the ones that were included? Please discuss this in your limitations section as it suggests potential selection bias.

3. In your response to my question in the last review, "2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this." you stated. "Response: After discussion, we decided to remove this part from the limitations and advantages." You should have addressed this as a limitation not remove it from your discussion. Please fix this.

We would appreciate receiving your revised manuscript by 2/15/2020. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter.

To enhance the reproducibility of your results, we recommend that if applicable you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

We look forward to receiving your revised manuscript.

Kind regards,

Alana T Brennan

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

1. The figures that you create for the propensity score should be add in as supplemental tables and figures. Please edit them accordingly so that they are journal ready.

2. One last clarification is need in regards to the patients consented. You stated in the study design that, "The survey among the Kazakhs and the Uyghurs began in April 2009 and was followed up in December 63 2013, April 2016, and August 2017 in the Xinyuan County and Jiashi County." and then you state that, "The total response rate was 87.5% (88.6% among the 70 Uygur and 86.5% among the Kazakh)." Can you clarify that the patient population was the same each year - 2013, 2016 and 2017? I also assume that the rates of loss differed each year? You need to discuss the roughly 13% loss to follow-up as a limitation in your discussion. Is there reason to believe these ~13% of patients differ to the ones that were included? If so then you need to discuss this in your limitations section as it suggests potential selection bias.

3. In your response to my question in the last review, "2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this." you stated. "Response: After discussion, we decided to remove this part from the limitations and advantages." You should have addressed this as a limitation not remove it from your discussion. Please fix this.

[Note: HTML markup is below. Please do not edit.]

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

9 Feb 2020

Response Letter

Dear Alana T Brennan:

Many thanks for your interest in our manuscript entitled “Risk of coronary heart disease among Xinjiang rural: a nested case-control study in China” (ID: PONE-D-19-21029) for publication. We appreciate the points raised during the review process, and we have made revisions accordingly. We outline in the following text a detailed point-by-point response to the comments and the corresponding manuscript revisions.

We hope that the revised version of the manuscript is now acceptable for publication in your journal. I look forward to hearing from you soon.

With best wishes,

Yours sincerely,

Shuxia Guo, PhD

Corresponding author

Department of Public Health, Shihezi University School of Medicine, Shihezi, Xinjiang 832000, China

Phone: +86-180-0993-2625; Fax: +86-0993-2057-153; Email: gsxshzu@sina.com

Journal Requirements:

Additional Editor Comments (if provided):

1. The figures that you create for the propensity score should be add in as supplemental tables and figures. Please edit them accordingly so that they are journal ready.

Response: We have edited the figures and add in Supporting Information as supplemental tables and figures.

2. One last clarification is need in regards to the patients consented. You stated in the study design that, "The survey among the Kazakhs and the Uyghurs began in April 2009 and was followed up in December 63 2013, April 2016, and August 2017 in the Xinyuan County and Jiashi County." and then you state that, "The total response rate was 87.5% (88.6% among the 70 Uygur and 86.5% among the Kazakh)." Can you clarify that the patient population was the same each year - 2013, 2016 and 2017? I also assume that the rates of loss differed each year? You need to discuss the roughly 13% loss to follow-up as a limitation in your discussion. Is there reason to believe these ~13% of patients differ to the ones that were included? If so then you need to discuss this in your limitations section as it suggests potential selection bias

Response: Thank you very much for your constructive suggestions. In this manuscript, we describe an overall response rate of 87.5%, which means that the baseline population have been followed up accounted for 87.5% of the total baseline population after three follow-ups, and the response rates were different at each follow-up (2013, 2016 and 2017). And 12.5% of the baseline population have not been followed up and their disease status cannot be confirmed, which may cause potential selection bias. Therefore, we added this section to the Limitations.

Limitations: And the overall response rate in our study was 87.5%, which means that the baseline population have not been entirely followed up, and we cannot confirm the disease status of the baseline population who were not followed up. This means that there may be selection bias.

3. In your response to my question in the last review, "2. You assess multiplicative interaction in your study. It is well known that most studies are under powered to assess interaction appropriately. You do not discuss this as a limitation in your paper. Please address this." you stated. "Response: After discussion, we decided to remove this part from the limitations and advantages." You should have addressed this as a limitation not remove it from your discussion. Please fix this.

Response: We are very sorry that we misunderstood your question. We have added this part in the Limitations.

Limitations: we did not appropriately evaluate all the effects of interactions between factors on CHD.

11 Feb 2020

Risk of coronary heart disease in the rural population in Xinjiang: a nested case-control study in China

PONE-D-19-21029R3

Dear Dr. Guo,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Alana T Brennan

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

19 Feb 2020

PONE-D-19-21029R3

Risk of coronary heart disease in the rural population in Xinjiang: a nested case-control study in China

Dear Dr. Guo:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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on behalf of

Dr. Alana T Brennan

Academic Editor

PLOS ONE

Table 1

Baseline characteristics of study participants in the nested case-control study.

Characteristics		Control	Case	χ 2	P
		(n = 554)	(n = 277)
WC, n (%)	Normal	234 (42.24)	84 (30.32)
	Abnormal	320 (57.76)	193 (69.68)	11.095	0.001*
TC, n (%)	Normal	536 (96.75)	256 (92.42)
	Abnormal	18 (3.25)	21 (7.58)	7.748	0.005*
TG, n (%)	Normal	485 (85.55)	235 (84.84)
	Abnormal	69 (12.45)	42 (15.16)	1.170	0.279
LDL-C, n (%)	Normal	539 (97.29)	265 (95.67)
	Abnormal	15 (2.71)	12 (4.33)	1.550	0.213
HDL-C, n (%)	Normal	421 (75.99)	195 (70.40)
	Abnormal	133 (24.01)	82 (29.60)	3.015	0.083
High-fat food, n (%)	No	399 (72.02)	173 (62.45)
	Yes	155 (27.98)	104 (37.55)	7.878	0.005*
Family history of CHD, n (%)	No	533 (96.21)	249 (89.89)
	Yes	21 (3.79)	28 (10.11)	13.283	<0.001**
Dyslipidemia, n (%)	No	324 (58.48)	123 (44.40)
	Yes	230 (41.52)	154 (55.60)	14.727	<0.001**
Obesity, n (%)	No	464 (83.75)	198 (71.48)
	Yes	90 (16.25)	79 (28.52)	17.173	<0.001**
Overweight, n (%)	No	405 (73.10)	199 (71.84)
	Yes	149 (26.90)	78 (28.16)	0.148	0.700

WC, waist circumference; TC total cholesterol; High-fat food, frequent high-fat food consumption; CHD, Coronary heart disease.

* P < 0.05,

** P < 0.001 for χ 2 test.

Table 2

The results of Cox proportional hazard model for risk factors and CHD.

Risk factors	HR a (95%CI)	HR b (95%CI)	HR c (95%CI)
WC	1.63 (1.26–2.11) **	1.61 (1.24–2.09) **	1.24 (0.93–1.65)
TC	1.59 (1.02–2.48) *	1.54 (0.98–2.41)	1.22 (0.77–1.94)
High-fat food	1.83 (1.44–2.34) **	1.78 (1.38–2.28) **	1.53 (1.18–1.98) **
Dyslipidemia	1.81(1.43–2.29) **	1.80 (1.41–2.29) **	1.51 (1.17–1.96) **
Obesity	2.02 (1.56–2.63) **	1.97 (1.51–2.59) **	1.52 (1.13–2.05) *
Family history of CHD	2.10 (1.42–3.11) **	2.02 (1.35–3.02) **	1.65 (1.10–2.50) *

WC, abnormal waist circumference levels; TC, abnormal total cholesterol levels; High-fat food, frequent high-fat food consumption; CHD, Coronary heart disease. HR, hazard ratio; CI, confidence interval.

a The hazard ratio of Cox proportional hazard model with no adjustment;

b The hazard ratio of Cox proportional hazard model with adjustment for drinking, smoking, hypertension, diabetes, family history of hypertension, and family history diabetes;

c The hazard ratio of multi-factor Cox proportional hazard model with adjustment for drinking, smoking, hypertension, diabetes, the family history of hypertension, diabetes, and the other risk factors (i.e. abnormal WC levels, abnormal TC levels, frequent high-fat food consumption, dyslipidemia, obesity, family history of CHD);

* P < 0.05,

** P < 0.001 for Cox proportional hazard model.

Table 3

The results of multiplicative interaction for risk factors and CHD.

Multiplicative interaction items	HR b (95%CI)	Multiplicative SI b (95%CI)
High-fat food (–) / Obesity (–)	1 (ref)	1.18 (0.69–2.01)
High-fat food (+) / Obesity (–)	1.57 (1.16–2.11) **
High-fat food (–) / Obesity (+)	1.70 (1.17–2.47) **
High-fat food (+) / Obesity (+)	3.14 (2.19–4.50) **
High-fat food (–) / Family history of CHD (–)	1 (ref)	1.95 (0.79–4.87)
High-fat food (+) / Family history of CHD (–)	1.61 (1.23–2.10) **
High-fat food (–) / Family history of CHD (+)	1.16 (0.54–2.51)
High-fat food (+) / Family history of CHD (+)	3.64 (2.26–5.87) **
High-fat food (–) / Dyslipidemia (–)	1 (ref)	1.69 (1.00–2.84)
High-fat food (+) / Dyslipidemia (–)	1.23 (0.82–1.86)
High-fat food (–) / Dyslipidemia (+)	1.43 (1.06–1.94) *
High-fat food (+) / Dyslipidemia (+)	2.99 (2.18–4.10) **
Obesity (–) / Family history of CHD (–)	1 (ref)	1.51 (0.67–3.40)
Obesity (+) / Family history of CHD (–)	1.84 (1.38–2.47) **
Obesity (–) / Family history of CHD (+)	1.66 (0.97–2.84)
Obesity (+) / Family history of CHD (+)	4.62 (2.57–8.31) **
Obesity (–) / Dyslipidemia (–)	1 (ref)	1.84 (0.99–3.42)
Obesity (+) / Dyslipidemia (–)	1.17 (0.69–1.99)
Obesity (–) / Dyslipidemia (+)	1.45 (1.09–1.92) *
Obesity (+) / Dyslipidemia (+)	3.12 (2.25–4.32) **
Dyslipidemia (–) / Family history of CHD (–)	1 (ref)	0.58 (0.26–1.29)
Dyslipidemia (+) / Family history of CHD (–)	1.84 (1.43–2.37) **
Dyslipidemia (–) / Family history of CHD (+)	2.65 (1.41–4.95) *
Dyslipidemia (+) / Family history of CHD (+)	2.81 (1.66–4.76) **

High-fat food, frequent high-fat food consumption; CHD, Coronary heart disease. HR, hazard ratio; CI, confidence interval; SI, synergy index.

b Adjustment for drinking, smoking, hypertension, diabetes, family history of hypertension, and family history diabetes;

* P < 0.05,

** P < 0.001 for multiplicative interaction.

Table 4

The results of additive interaction for risk factors and CHD.

Interactive items	Additive interaction
	RERI b (95%CI)	AP b (95%CI)	Additive SI b (95%CI)
Family history of CHD & High-fat food	7.01 (-0.76–14.78)	0.84 (0.65–1.03) *	22.70 (0.67–766.70)
Family history of CHD & Obesity	8.12 (-6.02–22.26)	0.74 (0.37–1.10) *	5.22 (1.04–26.12) *
Family history of CHD & Dyslipidemia	-1.70 (-6.66–3.25)	-0.48 (–2.08–1.18)	0.60 (0.15–2.45)
High-fat food & Obesity	6.43 (-0.96–13.82)	0.82 (0.60–1.04) *	15.42 (0.96–248.68)
High-fat food & Dyslipidemia	1.16 (0.15–2.17) *	0.46 (0.16–0.76) *	4.03 (0.59–27.65)
Obesity & Dyslipidemia	1.64 (0.22–3.07) *	0.52 (0.21–0.83) *	4.03 (0.75–21.55)

High-fat food, frequent high-fat food consumption; CHD, Coronary heart disease; RERI, relative excess risk due to interaction; AP, the attributable proportion of interaction; SI, synergy index.

b Adjustment for drinking, smoking, hypertension, diabetes and family history of hypertension, diabetes;

* P < 0.05 for additive interaction.