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Epidemiology
Original research
Prevalence and risk factors for hyperhomocysteinemia: a population-based cross-sectional study from Hunan, China
  1. Yide Yang1,
  2. Yuan Zeng1,
  3. Shuqian Yuan1,
  4. Ming Xie1,
  5. Yanhui Dong2,
  6. Jian Li3,
  7. Quanyuan He1,
  8. Xiangli Ye1,
  9. Yuan Lv1,
  10. Carl-Friedrich Hocher4,
  11. Bernhard K Kraemer4,
  12. Xiuqin Hong1,
  13. Berthold Hocher3,4,5,6
  1. 1Key Laboratory of Molecular Epidemiology of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, China
  2. 2Institute of Child and Adolescent Health, School of Public Health, Peking University Health Science Center, Beijing, Beijing, China
  3. 3Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, School of Medicine, Hunan Normal University, Changsha, Hunan, China
  4. 4Fifth Department of Medicine (Nephrology/Endocrinology/Rheumatology), University Medical Centre Mannheim, University of Heidelberg, Mannheim, Germany
  5. 5Reproductive and Genetic Hospital of CITIC-Xiangya, Changsha, Hunan Province, China
  6. 6Institute of Medical Diagnostics, IMD Berlin, Berlin, Germany
  1. Correspondence to Berthold Hocher; berthold.hocher{at}medma.uni-heidelberg.de; Professor Xiuqin Hong; hldhld{at}126.com

Abstract

Objectives Hyperhomocysteinemia is an independent risk factor for cardiovascular diseases. We aimed to investigate the prevalence and risk factors for hyperhomocysteinemia, especially modifiable lifestyle factors, such as smoking behaviour and dietary factors.

Design Population-based cross-sectional study.

Setting Hunan Province, China

Participants A total of 4012 participants completed the study, between July 2013 and March 2014. The median age is 55 (interquartile range: 45–63) years, with 1644 males (41%) and 2368 females (59%).

Main outcome measures Homocysteine level were measured by the microplate enzyme immunoassay method. Hyperthomocysteinemia was defined as ≥15 µmol/L. Questionnaire was used to investigate potential risk factors of hyperhomocysteinemia. Crude odd ratio (OR) or adjusted OR with 95% CI were determined by using univariable or multivariable logistic regression models.

Results The prevalence of hyperhomocysteinemia is 35.4% (45.4% vs 28.5% for men, women, respectively). One-year increase in age is significantly associated with 2% higher risk of hyperhomocysteinemia (OR=1.02, 95% CI: 1.01 to 1.03). One unit increase of BMI is associated with 5% higher risk of hyperhomocysteinemia (OR=1.05, 95% CI: 1.03 to 1.07). Compared with the non-smoker, smoking participants have a 24% higher risk of hyperhomocysteinemia (OR=1.24, 95% CI: 1.006 to 1.53), while the risk for those quitting smoking are not significantly different (OR=1.14, 95% CI: 0.85 to 1.54). compared with those consuming fruit and vegetable at least once every day, those consuming less than once every day had a significantly higher risk of hyperhomocysteinemia (OR=1.29, 95% CI:1.11 to 1.50). In addition, we found there were significant sex interaction with education level or alcohol drinking on the risk of hyperhomocysteinemia (pinteraction <0.05).

Conclusions Higher BMI and older age are potential risk factors for hyperhomocysteinemia. Current smoking but not quitting smoking is associated with higher risk of hyperhomocysteinemia. Fruit and vegetable consumption may have protective effect against hyperhomocysteinemia. Alcohol consumption or education level might interact to influence the risk of hyperhomocysteinemia.

  • epidemiology
  • adult cardiology
  • risk management

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2020-048575

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    Strengths and limitations of this study

    • Our study is a population-based cross-sectional study with a multistage cluster random sampling method, including a total of 4012 participants, most of previous related studies in China are hospital-based.

    • The present study focused on modifiable lifestyle behaviours, the results have significant implications for public health policies or health education measures.

    • The cross-sectional design limits us to draw causal relationships between the risk factors and hyperhomocysteinemia.

    Introduction

    About five decades ago, McCully first proposed the ‘homocysteine theory of atherosclerosis’. It is based on the realisation that even a moderate increase in homocysteine can accelerate the development of atherosclerosis.1 Since then, there has been ample evidence suggests that high circulating homocysteine (hyperhomocysteinemia) is associated with an increased risk of cardiometabolic risk factors and cardiovascular diseases, such as high blood pressure (BP), stroke, ischaemic heart disease and deep vein thrombosis.2–4 In addition, hyperhomocysteinemia and hypertension have a potentiating effect on increasing the risk of stroke.5–7 Therefore, if blood homocysteine can be controlled within a normal range, this could have beneficial effects on the risk of hypertension and stroke. A meta-analysis of 72 genetic MTHFR studies (a gene that increases homocysteine levels) and 20 prospective studies estimated that lowering serum homocysteine by just 3 µmol/L would reduce the risk of ischaemic heart disease by 16% (11%–20%) and stroke by 24% (15%–33%).8 Also, dietary nutrients intake (folate, Vitamin B12 and Vitamin D), or the presence of some disease (renal disease or hypothyroidism) are reportedly associated with hyperhomocysteinemia.9–11

    There are few studies looking at underlying causes of elevated homocysteine concentrations in the blood in humans.12 Most studies have focused on the reduction of nitric oxide (a potent vasodilator) bioavailability,13 methoxistasis,14 cytotoxic oxidative stress resulting from homocysteine imbalance15 or reduction of endothelial cell relaxation.16

    At the same time, many epidemiology studies have investigated the prevalence of hyperhomocysteinemia in different countries. In this context, it is noteworthy that the prevalence of hyperhomocysteinemia is much higher in China than in other countries.17–20 The underlying reasons are yet unknown. It is also discussed debated whether the education level or BMI might have an influence on the risk of hyperhomocysteinemia.20 21 Diet as a risk factor for hyperhomocysteinemia has been insufficiently studied.22 Possible effects of smoking on the risk of developing hyperhomocysteinemia are also lacking.23

    In addition, most epidemiological studies on hyperhomocysteinemia in China focused only on hypertensive patients or were hospital-based, and few population-based studies were presented, which may limit the interpretation of these data.

    Therefore, the current study aimed to explore the epidemiological characteristics and multifaceted determinants of hyperhomocysteinemia using a population-based study design, with particular attention to modifiable lifestyle factors, such as dietary factors or smoking cessation, which are also crucial for the control and prevention of hyperhomocysteinemia and its consequences.

    Methods

    Study design and population

    This population-based cross-sectional study was conducted in Hunan Province, a developing province in central southern China, which is composed of 14 districts between July 2013 and March 2014. Our survey adopted a uniform and standardised investigation protocol in all the selected participants from the districts.

    A multistage cluster random sampling technique was used to get the representative sample of adults ≥30 years. At the first stage, six districts were randomly selected from the 14 districts for the present study. Then, one urban and one rural community were randomly selected from each selected district at the second stage. Among all randomly selected communities, all adults aged ≥30 years old were invited for our study, and we recruited the adults signing a formal written consent and meeting the including criteria to participate in our study. The detailed flow chart of the sampling process is showed in figure 1. The details of this study were also described in our previous study.24 We excluded subjects who lived in the community less than 5 years, suffered from any type of cancer or had a major surgery within the last 6 months. All participated subjects signed the informed consents. Randomisation in the sampling process was conducted by a staff member not involved in the investigation with a computer random number generator.

    Figure 1
    Figure 1

    Flowchart of the sample selection.

    Data collection and questionnaire survey

    Data of subjects were collected with a standard and reliable questionnaire by face-to-face interviews. The questionnaire contains demographic information, cigarette smoking, alcohol drinking, dietary factors, walking and physical activity level. For the demographic factors, physical activity, alcohol drinking and smoking behaviour, we referred the questionnaire of the China Health and Retirement Longitudinal Study (CHARLS).25 For the dietary factors, we referred to the 2007 Chinese Dietary Guidelines (Chinese Residents’ Balanced Dietary Pagoda).26 27 In addition, we did a pilot study to test the reliability of this questionnaire in 100 adult participants before the large-scale formal investigation. In the pilot study, we investigated the same 100 participants twice with the same questionnaire in 2 weeks by trained investigators. And there is a good test–retest reliability (kappa=0.778) for this questionnaire.

    Demographic information included sex, age, marriage status, annual household income and education level. The status of marriage was categorised into four groups: unmarried, married, separation and divorce or widowhood. Education level was categorised into four groups: under primary school, primary school, junior school and college and above. The income of the family every year (annual household income, ¥) was asked for every participant.

    Alcohol drinking, cigarette smoking and education level were defined as previous studies.28–30 In brief, status of cigarette smoking was categorised into groups: non-smoker, smoking at past but not now and current smoking. Cigarette smoking is defined as have smoked at least 100 cigarettes in their lifetime.29 Alcohol drinking is categorised into four groups: abstainer (non-drinker), light or moderate drinker (0–2 drinks/day), heavy drinker (≥2 drinks/day) and drinker who did not report the specific drinks.30 And we used a set of simple and easy-to-understand photos to measure the drinks of different kinds of drinks (online supplemental figure S1), the participants reported their frequency (days) and amounts (drinks) of alcohol drinking in the past week. The average daily alcohol drinks were estimated as follows: average daily alcohol drinks = (days × (drinks in each of those days))/7.

    For fruit and vegetable, it was categorised as two groups (variable values as 1 or 0): 1 means consumed ≥once of fruit and ≥once of vegetable every day, 0 means consumed <once of fruit or <once of vegetable every day. For fish or seafood, it was categorised as two groups (variable values as 1 or 0): 1 means≥once every week, 0 means<once every week. For sweet food, it was categorised as two groups (variable values as 1 or 0): 1 means≥once every week, 0 means<once every week. For pickled vegetables, it was categorised as two groups (variable values as 1 or 0): 1 means≥once every day, 0 means<once every day.

    Walking is categorised as two groups: yes=having walked for at least 30 min every day, no=having not walking for at least 30 min every day. For physical activity, subjects were categorised as two groups (physically inactive or not physically inactive). Physical activity lasting for at least 10 continuous minutes per week was categorised as valid physical activity, less than 10 continuous minutes per week as invalid physical activity.31 Subjects who had no valid physical activity in the past week were considered as physically inactive, otherwise not physical inactive.

    Blood pressure measurement

    Measurement of BP were conducted according to a standardised procedure with a standard mercury sphygmomanometer. All devices were calibrated before use (range: 0–300 mm Hg; unit: 2 mm Hg). The participants were required to avoid vigorous exercise, having drinks with caffeine and taking medicine which have an influence on BP for 1 hour before measurement. The participants were asked to stop smoking for 15 min before measurement. BP was measured after the participants sat for at least 5 min. Systolic BP (SBP) and diastolic BP (DBP) were recorded as the values where the first Korotkoff sound appeared and the fifth Korotkoff sound disappeared. Two independent BP measurements were performed. The average value of the BP readings was used for data analyses. Hypertension was defined as SBP ≥140 mm Hg and (or) DBP ≥90 mm Hg, or are taking antihypertensive medications or reported diagnosed hypertension by physician.

    Biochemical analyses

    The study participants were asked not to eat for at least 12 hours before taking the blood sample collection. Participants’ venous blood samples were collected in an anticoagulation tube with heparin. Blood plasma was separated and stored at −80°C before measurement. The laboratory tests were conducted on a Hitachi 7600 Automatic Biochemistry Analyzer (Hitachi). The plasma homocysteine concertration was measured by the microplate enzyme immunoassay method,24 with homocysteine Detection Kit of MedicalSystem Biotechnology Co., Ningbo, China (Reagent batch number, 13082408). Hyperhomocysteinemia was defined as homocysteine ≥15 µmol/L, since previous cohort studies found that homocysteine level (≥15 µmol/L) could significantly predict the risk of cardiovascular diseases and coronary heart disease.3 Also, previous homocysteine study in Chinese population used the cut-off point of ≥15 µmol/L.32

    Statistical analyses

    Medians and IQR were calculated for numerical variables without normal distribution, and percentages were calculated for categorical variables. The differences between male and female subjects were compared by independent-samples t tests for continuous variables and by χ2 tests for categorical variables. For association between continuous variables and plasma total homocysteine, figures showing concentration–response relationships with p value were obtained by linear regression. Z score of homocysteine were calculated for better comparison of associations between different variables and homocysteine. Z score of homocysteine = (original value of homocysteine – mean value)/SD. So, the variable (homocysteine) was standardised to a mean of 0 and an SD of 1 before analysis. For categorical variables, means and 95% CI of Z score of plasma total homocysteine in different groups were presented with p value obtained by analysis of variance or independent t tests. Crude odds ratio (OR) and adjusted OR with 95% 95% CI of having hyperhomocysteinemia were determined by using univariable or multivariable logistic regression models. For the multivariable logistic regression model, we included all the potential variables (age, sex, BMI, marriage status, education level, annual household income, smoking, alcohol drinking, walking, physical activity, fruit and vegetable, sweet food and pickled vegetables) in the models to adjusted ORs for risk factors. Stratified analyses of sex of association between risk factors and hyperhomocysteinemia were conducted, then the sex interactions were tested with multivariable logistic regression models including the interaction term with age, BMI, sex, marriage status, education level, annual household income, smoking, alcohol drinking, walking, physical activity, fruit and vegetable, sweet food, pickled vegetables as covariates. In the stratified analyses, sex interaction was defined as different effect sizes in female and male groups, and the p value for interaction was statistically significant (pinteraction <0.05).33 The possible multicollinearity between the independent variables was quantified using the variance infiltration factor (VIF) of collinearity diagnostics. A VIF larger than 4.0 was considered an indication of severe multicollinearity in the regression model.34 For the sensitivity analysis of the multivariable logistic regression, we also calculated a model with only including significant risk factors in the univariable analysis as covariates. All the statistical analyses were performed by IBM SPSS for Windows (V.20.0, SPSS, Chicago, IL, USA) and R software V.3.6.3.

    Patient and public involvement

    This study was conducted without patient involvement. Patients and public were not invited with regard to design of our study, measurement of outcomes, data analysis and interpretation of our results.

    Results

    As illustrated in table 1, a total of 4012 participants was enrolled in the present study. Also, their general characteristics were shown in table 1. Of those, there are 1644 (41.0%) male participants and 2368 (59.0%) female participants. Male subjects have significantly higher age, height, weight, SBP, DBP, BMI and plasma homocysteine concentration than female subjects (p<0.001). Also, there are significant sex differences in education level, marriage status and cigarette smoking status (p<0.001). For other lifestyle factors, male subjects reported a higher rate of alcohol drinking, lower rate of adequate physical activity and lower rate of adequate fruit and vegetable intake than female subjects (p<0.001). Totally, 35.4% of the participants are categorised as having hyperhomocysteinemia (45.4% vs 28.5% for men, women, respectively). Male subjects have higher prevalence of hypertension, hyperhomocysteinemia and hypertension with hyperhomocysteinemia than female subjects (p<0.001). We categorised the homocysteine level into three groups (<10 µmol/L; 10–14.9 µmol/L and ≥15 umol/L), and then used the new categorical homocysteine variable to predict the risk of hypertension in different models with adjustment of potential covariates, we found in the full adjusted model only for participants with homocysteine ≥15 umol/L the risk of hypertension increased significantly (OR=3.74, 95% CI: 3.06 to 4.56) compared with participants with homocysteine <10 µmol/L, but not in the second group (10–14.9 µmol/L) with OR=1.114 (95% CI: 0.91 to 1.36) (online supplemental table S1).

    View this table:
    Table 1

    General characteristics of the study population

    The concentration–response relationships between plasma homocysteine level and related factors are shown in figures 2–5. There is an obvious increasing trend of homocysteine level with age, and male subjects have significantly higher homocysteine level (figure 2). Also, subjects with different education levels or marriage status have different homocysteine level (figure 3), subjects with adequate intake of fruit and vegetable have lower homocysteine level than those without (figure 4). Homocysteine level is also associated with different smoking status, different walking level, and alcohol drinking level (figure 5).

    Figure 2
    Figure 2

    Association between age, BMI and sex and Z score of plasma total homocysteine. Means and 95% CI of Z score of plasma total homocysteine in different groups were presented with p values obtained by ANOVA or independent t tests. ANOVA, analysis of variance; BMI, body mass index.

    Figure 3
    Figure 3

    Association between education level, family income and marriage status and Z score of plasma total homocysteine. For family income, concentration–response relationships with p value was obtained by linear regression. For education and marriage status, means and 95% CI of Z score of plasma total homocysteine in different groups were presented with p values obtained by ANOVA or independent t tests. ANOVA, analysis of variance.

    Figure 4
    Figure 4

    Association between fruit and vegetable intake, sweet food, fish or sea food and pickles and Z score of plasma total homocysteine. Means and 95% CI of Z score of plasma total homocysteine in different groups were presented with p values obtained by independent t tests. For fruit and vegetable: adequate=consumed fruit once and vegetable once every day; inadequate=not consumed fruit once and vegetable once every day.

    Figure 5
    Figure 5

    Association between smoking, alcohol drinking, physical activity and walking and Z score of plasma total homocysteine. Means and 95% CI of Z score of plasma total homocysteine in different groups were presented with p values obtained by ANOVA or independent t tests. ANOVA, analysis of variance.

    For better comparison of effect of different factors on homocysteine level, we used the Z score, which is calculated by (value−mean)/SD to show the relation between factors and homocysteine.35 And also, the figure showing the relationship between factors and raw homocysteine level were presented in the supplementary materials (online supplemental figures S2-S5).

    The univariable analysis of risk factors for hyperhomocysteinemia was conducted, the crude OR and 95% CI are also presented in table 2. Female participants have significantly lower risk of hyperhomocysteinemia (28.5% vs 45.4% for female and male, respectively). Similarly, smoking status, different alcohol drinking levels, higher BMI and older age are associated with higher risk of hyperhomocysteinemia (p<0.05). Whereas, adequate fruit and vegetable consumption, and physical activity are associated with lower risk of hyperhomocysteinemia (p<0.05). With the collinearity diagnosis between the independent variables, VIFs were all less than 2.0 and indicated that there was no significant collinearity between the independent variables (data not shown). For the sensitivity analysis of the multivariable logistic regression, we also calculated a model with only including significant risk factors in the univariable analysis as covariates, similar results were found (online supplemental table S2).

    View this table:
    Table 2

    Univariable and multivariable logistic regression analysis of risk factors for high homocysteine

    The results of risk factors for hyperhomocysteinemia under multivariable logistic analysis were also presented in table 2. Female participants have a 35% lower risk of hyperhomocysteinemia compared with male (OR=0.65, 95% CI: 0.54 to 0.78, p<0.001). Compared with the non-smoker, the smoking participants have a 24% higher risk of hyperhomocysteinemia (OR=1.24, 95% CI: 1.006 to 1.53, p=0.044), while the risk for the participants who smoked at past but not now are not significantly different (OR=1.14, 95% CI: 0.85 to 1.54, p=0.379). Compared with the unmarried participants, the participants who are on separation have a lower risk of hyperhomocysteinemia (OR=0.36, 95% CI:0.16 to 0.84), while those who are married or in divorce or in widowhood is not different from the reference group. BMI and age are both associated with higher risk of hyperhomocysteinemia (p<0.05). One-year increase in age is significantly associated with 2% higher risk of hyperhomocysteinemia (OR=1.02, 95% CI: 1.01 to 1.03). And one-unit increase of BMI is associated with 5% higher risk of hyperhomocysteinemia (OR=1.05, 95% CI: 1.03 to 1.07). Inadequate consumption of fruit and vegetable (consume fruit at least once and vegetable less than once every day) is associated with 29% higher risk of hyperhomocysteinemia with OR (95% CI) of 1.29 (1.11 to 1.50) compared with to those who with adequate consumption of fruit and vegetable.

    For the interaction between sex and other factors on the risk of hyperhomocysteinemia, we did stratified analysis by sex, and also did the interaction analysis (table 3). In the female group, participants with college and above education have a significantly higher risk of hyperhomocysteinemia (OR=1.70, 95% CI: 1.18 to 2.46) compared with participants with under primary school education, while in the male group, participants with college and above education had a lower risk of hyperhomocysteinemia (OR=0.60, 95% CI: 0.41 to 0.87). When further interaction analysis was conducted, we identified interaction effects between sex and education level on risk of hyperhomocysteinemia (pinteraction=0.031).

    View this table:
    Table 3

    Interaction between risk factors and sex on the risk of hyperhomocysteinemia

    Similar interaction effect between sex and alcohol drinking on risk of hyperhomocysteinemia was also found (pinteraction<0.001). In the female group, participants who were light or moderate alcohol drinker had a significantly lower risk of hyperhomocysteinemia (OR=0.47, 95% CI: 0.30 to 0.74) compared with participants who were not alcohol drinker, and female heavy alcohol drinker had a more than triple risk of hyperhomocysteinemia (OR=3.74, 95% CI: 1.41 to 9.88). While in the male group, compared with non-alcohol drinker, light or moderate male drinker have a significantly higher risk of hyperhomocysteinemia (OR=1.69, 95% CI: 1.28 to 2.23), heavy drinker also tended to have a higher risk of hyperhomocysteinemia, but the association did not reach statistical significance (p>0.05).

    Discussion

    To the best of our knowledge, our study represents the only analyses of high homocysteine and its related risk factors in a large, population-based central southern Chinese population to date. The results showed that the prevalence of hyperhomocysteinemia is relatively high (total, 35.4%, and 45.4% vs 28.5% for men and women). Interestingly, we found that current smoking is a risk factor for high homocysteine, but not for smoking quitting. Notably, fruit and vegetable intake may decrease the risk of hyperhomocysteinemia with adjustment of potential covariates. Male sex, older age and higher BMI are risk factors for hyperhomocysteinemia. In addition, we examined significant interactions between sex and educational level, sex and alcohol drinking on the risk of hyperhomocysteinemia. But only associations, not causal relationships could be concluded in this cross-sectional study, future cohort studies or intervention studies should be done to verify these findings.

    Our study population is a representative study conducted in the adult population in China using a multistage random sampling method. Both rural and urban areas were covered. In terms of age and sex distribution, our study is in line with other large Chinese population studies.18 19 36 Reviewing studies on hyperhomocysteinemia, we found that it is relatively rare in developed countries, for example, 15.5% versus 3.9% for men and women in Israel, respectively,17 with a threshold of 15μmol/L for hyperhomocysteinemia. The median homocysteine level was 9.3 umol (women 8.7 and men 10.0) in the USA.37 A study from the Canary Islands showed that the prevalence of hyperhomocysteinemia (defined as ≥15 µmol/L) was 21.4 %.38 With a low cut-off for the diagnosis of hyperhomocysteinaemia (≥14 µmol/L), a prevalence of hyperhomocysteinaemia of 11% was reported in a Finnish study.

    In studies of Chinese population, the prevalence of hyperhomocysteinemia ranges substantially from 4% to 73.3 %.18 20 39 Compared with studies in China, the estimates of hyperhomocysteinemia in our study (35.4%) are lower than in previous studies (67.7% in Shaanxi province),18 but higher than in the study by Hao et al in Hebei, Shanxi and Jiangsu provinces (7% in southerner or southern provinces 28% in northern provinces).36

    Biological or social factors and hyperhomocysteinemia

    In agreement with previous studies, risk of hyperhomocysteinemia increases with age.19 36 40 There is a significant sex difference in the prevalence of hyperhomocysteinemia.18 36 41 The sex difference could be due to the different regulation of testosterone, which lead to higher renal cystathionine β-synthase (CBS) activity in men than women, and homocysteine is catalysed CBS.42 Regarding the positive association between BMI and risk of hyperhomocysteinemia, few studies reported their relations with BMI as a continuous variable, but similar with previous studies. Wang et al reported that participants with BMI≥25 kg/m2 have a significantly higher risk of hyperhomocysteinemia.19 Jacques et al and Koehler et al also reported positive association between BMI and homocysteine concentration.43 44 Education and socioeconomic status (annual family income) is not associated with risk of hyperhomocysteinemia, which is consistent with some previous studies.19 45

    Dietary factors or behavioural factors and hyperhomocysteinemia

    Remarkably and interestingly, we could clearly show that smoking behaviour significantly influences the risk of hyperhomocysteinemia, which is in good agreement with the data of Wang et al.19 Ogbebor et al also showed that smoking could increase homocysteine levels compared with non-smokers.46 However, another study could not show this probably due to the rather small sample size in this study.21 It should be emphasised, in addition to, that in our study, we analysed current smokers, smokers who had quit smoking and striked non-smokers separately. Our results therefore imply that smoking cessation is quite beneficial for reducing the risk of hyperhomocysteinaemia.

    In addition, we found insufficient fruit and vegetable intake is related with an elevated risk of hyperhomocysteinemia. This is consistent with a study in Ethiopia and a study in Brazil, which found that low consumption of fruit and vegetable was major risk factors of hyperhomocysteinemia.47 48 However, a number of studies shown that there is a close association between serum folate levels, VitB2 or VitB6 and plasma homocysteine concentrations.43 49 And the consumption of fruits and vegetables is associated with an increase in serum folate and VitB levels. The experimental study by Stea et al that focused on dietary changes of fruit, vegetables and bread found that plasma homocysteine decreased significantly after the intervention.50

    Although, no significant association between alcohol consumption and physical activity and risk of hyperhomocysteinemia was found in the present study, for physical activity, the p value for this association is quite borderline (p=0.050), and the direction of association estimates is consistent with previous studies,51 52 showing that higher level of physical activity is protective for high homocysteine. Similarly, the direction of association between alcohol drinking and risk of hyperhomocysteinemia is consistent with previous studies, but not reach the statistical significance.53 54

    Interaction between sex and education level, or alcohol drinking

    Interestingly, we also found that there are significant interactions between sex and education level, sex and alcohol drinking on the risk of hyperhomocysteinemia. In our study, the association between education level or alcohol drinking with risk of hyperhomocysteinemia is quite different in males and females. Higher education level (college and above) is a risk factor of hyperhomocysteinemia for females but seems to be a protective factor for male. Also, the association between heavy alcohol drinker and risk of hyperhomocysteinemia is much more pronounced in females than in males. And light alcohol drinker was a protective factor in women but a risk factor in men. These sex-different associations are independent of other potential covariates, including age, smoking, alcohol drinking and BMI et al. Actually, many studies have demonstrated the association between alcohol drinking and homocysteine, but inconsistent result were found. Some studies showed that alcohol drinking is associated with higher level of homocysteine,55 56 while some other studies reported a U-shaped relationship and found that mild to moderate alcohol drinking was associated with lower homocysteine.57 58 The association between alcohol drinking should be examined in future longitudinal or interventions studies. These sex interactions were first examined in our study, not reported in previous works. But sex–smoking interaction, and gene–environment interaction were examined in previous studies.59 60

    Also, there are several limitations in our study. First, our study is a cross-sectional study, which could only provide associations but not causal relationships. Second, the lifestyle factors, for example, smoking and alcohol drinking were investigated by questionnaire, there could be some misclassifications of smoking or alcohol drinking since some subjects may lie about their status of smoking and alcohol drinking, but this misclassification could only attenuate the estimated value but not reverse it. Because of the design of the study, more women participated in the present study than men (59% vs 41% for females and males, respectively), this is an inevitable limitation for this study.

    In conclusion, our study demonstrated that there is a relative high prevalence of hyperhomocysteinemia in a southern Chinese population. Age and gender are non-modifiable risk factors for hyperhomocysteinemia, whereas smoking behaviour, diet and BMI are modifiable risk factors for hyperhomocysteinemia. This is important, since modifiable risk factors for hyperhomocysteinemia is an independent risk factor for cardiovascular diseases and life-style interventions addressing modifiable risk factors for hyperhomocysteinemia thus might likewise reduce the risk for cardiovascular diseases. Additionally, we found significant sex interaction with education level, alcohol drinking on the risk of hyperhomocysteinemia, implying that sex-specific lifestyle modification or intervention should be conducted for the prevention or control of hyperhomocysteinemia.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    The study complies with the Declaration of Helsinki and was approved by the by the Ethical Committee of Hunan Normal University (2019-88).

    Acknowledgments

    We want to thank all the participants, as well as the study nurses who had assisted with recruitment and data collection for our study.

    References

    1. ↵
      1. McCully KS
      . Vascular pathology of homocysteinemia: implications for the pathogenesis of arteriosclerosis. Am J Pathol 1969;56:111–28.pmid:http://www.ncbi.nlm.nih.gov/pubmed/5792556
      OpenUrlPubMedWeb of Science
    2. ↵
      1. Cui R,
      2. Moriyama Y,
      3. Koike KA, et al
      . Serum total homocysteine concentrations and risk of mortality from stroke and coronary heart disease in Japanese: the JACC study. Atherosclerosis 2008;198:412–8.doi:10.1016/j.atherosclerosis.2007.09.029pmid:http://www.ncbi.nlm.nih.gov/pubmed/18164306
      OpenUrlCrossRefPubMed
    3. ↵
      1. Veeranna V,
      2. Zalawadiya SK,
      3. Niraj A, et al
      . Homocysteine and reclassification of cardiovascular disease risk. J Am Coll Cardiol 2011;58:1025–33.doi:10.1016/j.jacc.2011.05.028pmid:http://www.ncbi.nlm.nih.gov/pubmed/21867837
      OpenUrlFREE Full Text
    4. ↵
      1. Borges MC,
      2. Hartwig FP,
      3. Oliveira IO, et al
      . Is there a causal role for homocysteine concentration in blood pressure? A Mendelian randomization study. Am J Clin Nutr 2016;103:39–49.doi:10.3945/ajcn.115.116038pmid:http://www.ncbi.nlm.nih.gov/pubmed/26675774
      OpenUrlAbstract/FREE Full Text
    5. ↵
      1. Graham IM,
      2. Daly LE,
      3. Refsum HM, et al
      . Plasma homocysteine as a risk factor for vascular disease. The European concerted action project. JAMA 1997;277:1775–81.doi:10.1001/jama.1997.03540460039030pmid:http://www.ncbi.nlm.nih.gov/pubmed/9178790
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Pezzini A,
      2. Grassi M,
      3. Del Zotto E, et al
      . Interaction of homocysteine and conventional predisposing factors on risk of ischaemic stroke in young people: consistency in phenotype-disease analysis and genotype-disease analysis. J Neurol Neurosurg Psychiatry 2006;77:1150–6.doi:10.1136/jnnp.2005.076083pmid:http://www.ncbi.nlm.nih.gov/pubmed/16624841
      OpenUrlAbstract/FREE Full Text
    7. ↵
      1. Towfighi A,
      2. Markovic D,
      3. Ovbiagele B
      . Pronounced association of elevated serum homocysteine with stroke in subgroups of individuals: a nationwide study. J Neurol Sci 2010;298:153–7.doi:10.1016/j.jns.2010.07.013pmid:http://www.ncbi.nlm.nih.gov/pubmed/20810133
      OpenUrlCrossRefPubMed
    8. ↵
      1. Wald DS,
      2. Law M,
      3. Morris JK
      . Homocysteine and cardiovascular disease: evidence on causality from a meta-analysis. BMJ 2002;325:1202–6.doi:10.1136/bmj.325.7374.1202pmid:http://www.ncbi.nlm.nih.gov/pubmed/12446535
      OpenUrlAbstract/FREE Full Text
    9. ↵
      1. Dhonukshe-Rutten RAM,
      2. de Vries JHM,
      3. de Bree A, et al
      . Dietary intake and status of folate and vitamin B12 and their association with homocysteine and cardiovascular disease in European populations. Eur J Clin Nutr 2009;63:18–30.doi:10.1038/sj.ejcn.1602897pmid:http://www.ncbi.nlm.nih.gov/pubmed/17851461
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Ni J,
      2. Zhang L,
      3. Zhou T, et al
      . Association between the MTHFR C677T polymorphism, blood folate and vitamin B12 deficiency, and elevated serum total homocysteine in healthy individuals in Yunnan Province, China. J Chin Med Assoc 2017;80:147–53.doi:10.1016/j.jcma.2016.07.005pmid:http://www.ncbi.nlm.nih.gov/pubmed/28094233
      OpenUrlCrossRefPubMed
    11. ↵
      1. Jones P,
      2. Lucock M,
      3. Martin C, et al
      . Independent and Interactive Influences of Environmental UVR, Vitamin D Levels, and Folate Variant MTHFD1-rs2236225 on Homocysteine Levels. Nutrients 2020;12:1455. doi:10.3390/nu12051455pmid:http://www.ncbi.nlm.nih.gov/pubmed/32443475
      OpenUrlPubMed
    12. ↵
      1. Zaric BL,
      2. Obradovic M,
      3. Bajic V, et al
      . Homocysteine and hyperhomocysteinaemia. Curr Med Chem 2019;26:2948–61.doi:10.2174/0929867325666180313105949pmid:http://www.ncbi.nlm.nih.gov/pubmed/29532755
      OpenUrlPubMed
    13. ↵
      1. Lai WKC,
      2. Kan MY
      . Homocysteine-Induced endothelial dysfunction. Ann Nutr Metab 2015;67:1–12.doi:10.1159/000437098pmid:http://www.ncbi.nlm.nih.gov/pubmed/26201664
      OpenUrlCrossRefPubMed
    14. ↵
      1. Joseph J,
      2. Loscalzo J
      . Methoxistasis: integrating the roles of homocysteine and folic acid in cardiovascular pathobiology. Nutrients 2013;5:3235–56.doi:10.3390/nu5083235pmid:http://www.ncbi.nlm.nih.gov/pubmed/23955381
      OpenUrlCrossRefPubMed
    15. ↵
      1. Škovierová H,
      2. Vidomanová E,
      3. Mahmood S, et al
      . The molecular and cellular effect of homocysteine metabolism imbalance on human health. Int J Mol Sci 2016;17:1733. doi:10.3390/ijms17101733pmid:http://www.ncbi.nlm.nih.gov/pubmed/27775595
      OpenUrlPubMed
    16. ↵
      1. Abraham JM,
      2. Cho L
      . The homocysteine hypothesis: still relevant to the prevention and treatment of cardiovascular disease? Cleve Clin J Med 2010;77:911–8.doi:10.3949/ccjm.77a.10036pmid:http://www.ncbi.nlm.nih.gov/pubmed/21147945
      OpenUrlAbstract/FREE Full Text
    17. ↵
      1. Cohen E,
      2. Margalit I,
      3. Shochat T, et al
      . Gender differences in homocysteine concentrations, a population-based cross-sectional study. Nutr Metab Cardiovasc Dis 2019;29:9–14.doi:10.1016/j.numecd.2018.09.003pmid:http://www.ncbi.nlm.nih.gov/pubmed/30459075
      OpenUrlPubMed
    18. ↵
      1. Liu X-D,
      2. Gao B,
      3. Sun D, et al
      . Prevalence of hyperhomocysteinaemia and some of its major determinants in Shaanxi Province, China: a cross-sectional study. Br J Nutr 2015;113:691–8.doi:10.1017/S0007114514004218pmid:http://www.ncbi.nlm.nih.gov/pubmed/25634595
      OpenUrlPubMed
    19. ↵
      1. Wang Y,
      2. Li X,
      3. Qin X, et al
      . Prevalence of hyperhomocysteinaemia and its major determinants in rural Chinese hypertensive patients aged 45-75 years. Br J Nutr 2013;109:1284–93.doi:10.1017/S0007114512003157pmid:http://www.ncbi.nlm.nih.gov/pubmed/22850357
      OpenUrlCrossRefPubMed
    20. ↵
      1. Wang W,
      2. Ji P,
      3. Wang Y, et al
      . Prevalence of hyperhomocysteinemia and its associated factors in patients with primary hypertension in Chinese urban communities: a cross-sectional study from Nanjing. Clin Exp Hypertens 2018;40:495–500.doi:10.1080/10641963.2017.1403621pmid:http://www.ncbi.nlm.nih.gov/pubmed/29172835
      OpenUrlPubMed
    21. ↵
      1. Qu Q-G,
      2. Gao J-J,
      3. Liu J-M
      . Prevalence of hyperhomocysteinaemia in a Chinese elderly population. Public Health Nutr 2010;13:1974–81.doi:10.1017/S1368980010000546pmid:http://www.ncbi.nlm.nih.gov/pubmed/20236564
      OpenUrlPubMed
    22. ↵
      1. Zhang W,
      2. Li Y,
      3. Wang TD, et al
      . Nutritional status of the elderly in rural North China: a cross-sectional study. J Nutr Health Aging 2014;18:730–6.doi:10.1007/s12603-014-0537-xpmid:http://www.ncbi.nlm.nih.gov/pubmed/25286452
      OpenUrlPubMed
    23. ↵
      1. Bazzano LA,
      2. He J,
      3. Muntner P, et al
      . Relationship between cigarette smoking and novel risk factors for cardiovascular disease in the United States. Ann Intern Med 2003;138:891–7.doi:10.7326/0003-4819-138-11-200306030-00010pmid:http://www.ncbi.nlm.nih.gov/pubmed/12779299
      OpenUrlCrossRefPubMedWeb of Science
    24. ↵
      1. Hong X,
      2. Wongtongkam N,
      3. Ward PR, et al
      . An association of serum ALT with elevated blood pressure in senior adults: a case-control study. Clin Exp Hypertens 2016;38:691–5.doi:10.1080/10641963.2016.1200608pmid:http://www.ncbi.nlm.nih.gov/pubmed/27936332
      OpenUrlPubMed
    25. ↵
      1. Wang Z,
      2. Zou Z,
      3. Yang Z, et al
      . Association between exposure to the Chinese famine during infancy and the risk of self-reported chronic lung diseases in adulthood: a cross-sectional study. BMJ Open 2017;7:e015476. doi:10.1136/bmjopen-2016-015476pmid:http://www.ncbi.nlm.nih.gov/pubmed/28576899
      OpenUrlPubMed
    26. ↵
      Dietary guidelines and the food guide pagoda. the Chinese nutrition Society. J Am Diet Assoc 2000;100:886–7.doi:10.1016/s0002-8223(00)00257-1pmid:http://www.ncbi.nlm.nih.gov/pubmed/10955044
      OpenUrlCrossRefPubMed
    27. ↵
      1. Qing Y,
      2. Yang J,
      3. Zhu Y, et al
      . Cancer risk and disease burden of dietary cadmium exposure changes in Shanghai residents from 1988 to 2018. Sci Total Environ 2020;734:139411.doi:10.1016/j.scitotenv.2020.139411pmid:http://www.ncbi.nlm.nih.gov/pubmed/32450401
      OpenUrlPubMed
    28. ↵
      1. Wang Z,
      2. Zou Z,
      3. Wang S, et al
      . Chinese famine exposure in infancy and metabolic syndrome in adulthood: results from the China health and retirement longitudinal study. Eur J Clin Nutr 2019;73:724–32.doi:10.1038/s41430-018-0211-1pmid:http://www.ncbi.nlm.nih.gov/pubmed/29844493
      OpenUrlPubMed
    29. ↵
      1. Leventhal AM,
      2. Bello MS,
      3. Galstyan E, et al
      . Association of cumulative socioeconomic and health-related disadvantage with disparities in smoking prevalence in the United States, 2008 to 2017. JAMA Intern Med 2019;179:777–85.doi:10.1001/jamainternmed.2019.0192pmid:http://www.ncbi.nlm.nih.gov/pubmed/31009023
      OpenUrlPubMed
    30. ↵
      1. Lu W,
      2. Xu J,
      3. Taylor AW, et al
      . Analysis of the alcohol drinking behavior and influencing factors among emerging adults and young adults: a cross-sectional study in Wuhan, China. BMC Public Health 2019;19:458. doi:10.1186/s12889-019-6831-0pmid:http://www.ncbi.nlm.nih.gov/pubmed/31039783
      OpenUrlPubMed
    31. ↵
      1. Craig R,
      2. Mindell J,
      3. Hirani V
      . Health survey for England 2008. physical activity and fitness. summary of key findings, 2009.
    32. ↵
      1. Ying Y,
      2. Lin S,
      3. Kong F, et al
      . Ideal cardiovascular health metrics and incidence of ischemic stroke among hypertensive patients: a prospective cohort study. Front Cardiovasc Med 2020;7:590809.doi:10.3389/fcvm.2020.590809pmid:http://www.ncbi.nlm.nih.gov/pubmed/33330652
      OpenUrlPubMed
    33. ↵
      1. Dempfle A,
      2. Scherag A,
      3. Hein R, et al
      . Gene-Environment interactions for complex traits: definitions, methodological requirements and challenges. Eur J Hum Genet 2008;16:1164–72.doi:10.1038/ejhg.2008.106pmid:http://www.ncbi.nlm.nih.gov/pubmed/18523454
      OpenUrlCrossRefPubMedWeb of Science
    34. ↵
      1. Yang X,
      2. Luo W,
      3. Han S, et al
      . Prevalence of high-risk coronary plaques in patients with and without metabolic syndrome and the relationship with prognosis. BMC Cardiovasc Disord 2020;20:73. doi:10.1186/s12872-020-01358-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/32046639
      OpenUrlPubMed
    35. ↵
      1. Sedgwick P
      . Standardising outcome measures using Z scores. BMJ 2014;349:g5878. doi:10.1136/bmj.g5878pmid:http://www.ncbi.nlm.nih.gov/pubmed/25273560
      OpenUrlFREE Full Text
    36. ↵
      1. Hao L,
      2. Ma J,
      3. Zhu J, et al
      . High prevalence of hyperhomocysteinemia in Chinese adults is associated with low folate, vitamin B-12, and vitamin B-6 status. J Nutr 2007;137:407–13.doi:10.1093/jn/137.2.407pmid:http://www.ncbi.nlm.nih.gov/pubmed/17237319
      OpenUrlAbstract/FREE Full Text
    37. ↵
      1. Osibogun O,
      2. Ogunmoroti O,
      3. Tibuakuu M, et al
      . Sex differences in the association between ideal cardiovascular health and biomarkers of cardiovascular disease among adults in the United States: a cross-sectional analysis from the multiethnic study of atherosclerosis. BMJ Open 2019;9:e031414. doi:10.1136/bmjopen-2019-031414pmid:http://www.ncbi.nlm.nih.gov/pubmed/31772093
      OpenUrlAbstract/FREE Full Text
    38. ↵
      1. Henríquez P,
      2. Doreste J,
      3. Deulofeu R, et al
      . Nutritional determinants of plasma total homocysteine distribution in the Canary Islands. Eur J Clin Nutr 2007;61:111–8.doi:10.1038/sj.ejcn.1602490pmid:http://www.ncbi.nlm.nih.gov/pubmed/16885934
      OpenUrlPubMed
    39. ↵
      1. Yang B,
      2. Fan S,
      3. Zhi X, et al
      . Prevalence of hyperhomocysteinemia in China: a systematic review and meta-analysis. Nutrients 2014;7:74–90.doi:10.3390/nu7010074pmid:http://www.ncbi.nlm.nih.gov/pubmed/25551247
      OpenUrlPubMed
    40. ↵
      1. Nygård O,
      2. Vollset SE,
      3. Refsum H, et al
      . Total plasma homocysteine and cardiovascular risk profile. The Hordaland homocysteine study. JAMA 1995;274:1526–33.doi:10.1001/jama.1995.03530190040032pmid:http://www.ncbi.nlm.nih.gov/pubmed/7474221
      OpenUrlCrossRefPubMedWeb of Science
    41. ↵
      1. Ganji V,
      2. Kafai MR,
      3. Third National H, Third National Health and Nutrition Examination Survey
      . Demographic, health, lifestyle, and blood vitamin determinants of serum total homocysteine concentrations in the third National health and nutrition examination survey, 1988-1994. Am J Clin Nutr 2003;77:826–33.doi:10.1093/ajcn/77.4.826pmid:http://www.ncbi.nlm.nih.gov/pubmed/12663279
      OpenUrlAbstract/FREE Full Text
    42. ↵
      1. Vitvitsky V,
      2. Prudova A,
      3. Stabler S, et al
      . Testosterone regulation of renal cystathionine beta-synthase: implications for sex-dependent differences in plasma homocysteine levels. Am J Physiol Renal Physiol 2007;293:F594–600.doi:10.1152/ajprenal.00171.2007pmid:http://www.ncbi.nlm.nih.gov/pubmed/17537983
      OpenUrlCrossRefPubMedWeb of Science
    43. ↵
      1. Jacques PF,
      2. Bostom AG,
      3. Wilson PW, et al
      . Determinants of plasma total homocysteine concentration in the Framingham offspring cohort. Am J Clin Nutr 2001;73:613–21.doi:10.1093/ajcn/73.3.613pmid:http://www.ncbi.nlm.nih.gov/pubmed/11237940
      OpenUrlAbstract/FREE Full Text
    44. ↵
      1. Koehler KM,
      2. Romero LJ,
      3. Stauber PM, et al
      . Vitamin supplementation and other variables affecting serum homocysteine and methylmalonic acid concentrations in elderly men and women. J Am Coll Nutr 1996;15:364–76.doi:10.1080/07315724.1996.10718611pmid:http://www.ncbi.nlm.nih.gov/pubmed/8829092
      OpenUrlPubMedWeb of Science
    45. ↵
      1. Ganji V,
      2. Kafai MR
      . Serum total homocysteine concentration determinants in non-Hispanic white, non-Hispanic black, and Mexican-American populations of the United States. Ethn Dis 2004;14:476–82.pmid:http://www.ncbi.nlm.nih.gov/pubmed/15724765
      OpenUrlPubMedWeb of Science
    46. ↵
      1. Omoike OE,
      2. Paul TK,
      3. Ridner SL, et al
      . Association between smoking status and homocysteine levels and possible effect modification by cholesterol and oestradiol. Biomarkers 2020;25:126–30.doi:10.1080/1354750X.2019.1705395pmid:http://www.ncbi.nlm.nih.gov/pubmed/31835911
      OpenUrlPubMed
    47. ↵
      1. Challa F,
      2. Getahun T,
      3. Sileshi M, et al
      . Prevalence of hyperhomocysteinaemia and associated factors among Ethiopian adult population in a 2015 national survey. Biomed Res Int 2020;2020:1–7.doi:10.1155/2020/9210261pmid:http://www.ncbi.nlm.nih.gov/pubmed/32420383
      OpenUrlPubMed
    48. ↵
      1. Verly E,
      2. Steluti J,
      3. Fisberg RM, et al
      . A quantile regression approach can reveal the effect of fruit and vegetable consumption on plasma homocysteine levels. PLoS One 2014;9:e111619. doi:10.1371/journal.pone.0111619pmid:http://www.ncbi.nlm.nih.gov/pubmed/25365261
      OpenUrlPubMed
    49. ↵
      1. Saw SM,
      2. Yuan JM,
      3. Ong CN, et al
      . Genetic, dietary, and other lifestyle determinants of plasma homocysteine concentrations in middle-aged and older Chinese men and women in Singapore. Am J Clin Nutr 2001;73:232–9.doi:10.1093/ajcn/73.2.232pmid:http://www.ncbi.nlm.nih.gov/pubmed/11157318
      OpenUrlAbstract/FREE Full Text
    50. ↵
      1. Stea TH,
      2. Mansoor MA,
      3. Wandel M, et al
      . Changes in predictors and status of homocysteine in young male adults after a dietary intervention with vegetables, fruits and bread. Eur J Nutr 2008;47:201–9.doi:10.1007/s00394-008-0714-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/18521531
      OpenUrlCrossRefPubMed
    51. ↵
      1. e Silva AdeS,
      2. da Mota MPG
      . Effects of physical activity and training programs on plasma homocysteine levels: a systematic review. Amino Acids 2014;46:1795–804.doi:10.1007/s00726-014-1741-zpmid:http://www.ncbi.nlm.nih.gov/pubmed/24770903
      OpenUrlPubMed
    52. ↵
      1. Buckner SL,
      2. Loenneke JP,
      3. Loprinzi PD
      . Single and combined associations of accelerometer-assessed physical activity and muscle-strengthening activities on plasma homocysteine in a national sample. Clin Physiol Funct Imaging 2017;37:669–74.doi:10.1111/cpf.12356pmid:http://www.ncbi.nlm.nih.gov/pubmed/26916173
      OpenUrlPubMed
    53. ↵
      1. Coppola M,
      2. Mondola R
      . Correlation between plasma homocysteine levels and craving in alcohol dependent stabilized patients. Clin Nutr 2018;37:1061–5.doi:10.1016/j.clnu.2017.05.004pmid:http://www.ncbi.nlm.nih.gov/pubmed/28527647
      OpenUrlPubMed
    54. ↵
      1. Choi S-H,
      2. Choi-Kwon S,
      3. Kim M-S, et al
      . Poor nutrition and alcohol consumption are related to high serum homocysteine level at post-stroke. Nutr Res Pract 2015;9:503–10.doi:10.4162/nrp.2015.9.5.503pmid:http://www.ncbi.nlm.nih.gov/pubmed/26425280
      OpenUrlPubMed
    55. ↵
      1. van der Gaag MS,
      2. Ubbink JB,
      3. Sillanaukee P, et al
      . Effect of consumption of red wine, spirits, and beer on serum homocysteine. Lancet 2000;355:1522. doi:10.1016/S0140-6736(00)02172-3pmid:http://www.ncbi.nlm.nih.gov/pubmed/10801179
      OpenUrlCrossRefPubMedWeb of Science
    56. ↵
      1. Cravo ML,
      2. Glória LM,
      3. Selhub J, et al
      . Hyperhomocysteinemia in chronic alcoholism: correlation with folate, vitamin B-12, and vitamin B-6 status. Am J Clin Nutr 1996;63:220–4.doi:10.1093/ajcn/63.2.220pmid:http://www.ncbi.nlm.nih.gov/pubmed/8561063
      OpenUrlAbstract/FREE Full Text
    57. ↵
      1. Dixon JB,
      2. Dixon ME,
      3. O'Brien PE
      . Reduced plasma homocysteine in obese red wine consumers: a potential contributor to reduced cardiovascular risk status. Eur J Clin Nutr 2002;56:608–14.doi:10.1038/sj.ejcn.1601365pmid:http://www.ncbi.nlm.nih.gov/pubmed/12080399
      OpenUrlCrossRefPubMedWeb of Science
    58. ↵
      1. Refsum H,
      2. Nurk E,
      3. Smith AD, et al
      . The Hordaland homocysteine study: a community-based study of homocysteine, its determinants, and associations with disease. J Nutr 2006;136:1731S–40.doi:10.1093/jn/136.6.1731Spmid:http://www.ncbi.nlm.nih.gov/pubmed/16702348
      OpenUrlAbstract/FREE Full Text
    59. ↵
      1. Mu L,
      2. Lin Y,
      3. Huang X, et al
      . Sex differences in the prevalence and clinical correlates of hyperhomocysteinemia in patients with bipolar disorder. Hum Psychopharmacol 2020;35:e2724. doi:10.1002/hup.2724pmid:http://www.ncbi.nlm.nih.gov/pubmed/32052509
      OpenUrlPubMed
    60. ↵
      1. Kim HJ,
      2. Kim MK,
      3. Kim JU, et al
      . Major determinants of serum homocysteine concentrations in a Korean population. J Korean Med Sci 2010;25:509–16.doi:10.3346/jkms.2010.25.4.509pmid:http://www.ncbi.nlm.nih.gov/pubmed/20357989
      OpenUrlPubMed

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    Footnotes

    • Contributors XH, YL and YY conducted and supervised the survey, and provided the original data. YY, BH and XH conceived and designed the study; YY, XY, YZ, FL, CZ and YD analysed the data; XH, JL, QH and YL contributed reagents/materials/analysis tools; YY, C-FH, BKK and BH wrote the paper. YY, BH, YZ and FL made the revisions. All authors (YY, YZ, FL, CZ, YD, JL, QH, XY, YL, C-FH, BKK, XH and BH) contributed and approved the final version of the manuscript. Prof. Berthold Hocher is the author responsible for the overall content as the guarantor.

    • Funding This work was supported by the National Natural Science Foundation of China (81903336, YY), the National Natural Science Foundation of China (81773530, XH), the Hunan Provincial Natural Science Foundation of China (2019JJ50376, YY), Scientific Research Project of Hunan Provincial Health Commission (202112031516, YY), a Project Supported by Scientific Research Fund of Hunan Provincial Education Department (18C0072, YY), Key Project of Hunan Provincial science and technology innovation (2020SK1015-3, YL) and a Project Supported by Scientific Research Fund of Hunan Provincial Education Department (18A028, XY). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.