Skip to main content Accessibility help
×
Home
Hostname: page-component-5d6d958fb5-br6r8 Total loading time: 0.726 Render date: 2022-11-27T13:28:00.803Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "displayNetworkTab": true, "displayNetworkMapGraph": false, "useSa": true } hasContentIssue true

Red meat consumption is associated with prediabetes and diabetes in rural Vietnam: a cross-sectional study

Published online by Cambridge University Press:  20 June 2022

Chau Que Nguyen
Affiliation:
Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Thuy Thi Phuong Pham
Affiliation:
Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Ami Fukunaga
Affiliation:
Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
Dong Van Hoang
Affiliation:
Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
Tien Vu Phan
Affiliation:
Medical Service Center, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Danh Cong Phan
Affiliation:
Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Dong Van Huynh
Affiliation:
Khánh Hòa Center for Disease Control, Khánh Hòa, Vietnam
Masahiko Hachiya
Affiliation:
Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan
Huy Xuan Le
Affiliation:
Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Hung Thai Do
Affiliation:
Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
Tetsuya Mizoue
Affiliation:
Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
Yosuke Inoue*
Affiliation:
Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
*
*Corresponding author: Email yosuke.yoshi.yosky@gmail.com
Rights & Permissions[Opens in a new window]

Abstract

Objective:

To examine the association between red/processed meat consumption and glycaemic conditions (i.e. prediabetes (preDM) and diabetes mellitus (DM)) among middle-aged residents in rural Khánh Hòa, Vietnam.

Design:

In this cross-sectional study, a multinomial logistic regression model was used to examine the association between daily consumption of red/processed meat (0–99 g, 100–199 g or ≥ 200 g) and preDM/DM with adjustments for socio-demographic, lifestyle-related and health-related variables.

Setting:

Khánh Hòa Province, Vietnam

Participants:

The study used data collected through a baseline survey conducted during a prospective cohort study on CVD among 3000 residents, aged 40–60 years, living in rural communes in Khánh Hòa Province.

Results:

The multinomial regression model revealed that the relative-risk ratios for DM were 1·00 (reference), 1·11 (95 % CI = 0·75, 1·62) and 1·80 (95 % CI = 1·40, 2·32) from the lowest to the highest red/processed meat consumption categories (Ptrend = 0·006). The corresponding values for preDM were 1·00 (reference), 1·25 (95 % CI = 1·01, 1·54) and 1·67 (95 % CI = 1·20, 2·33) (Ptrend = 0·004). We did not find any evidence of statistical significance in relation to poultry consumption.

Conclusion:

Increased red/processed meat consumption, but not poultry consumption, was positively associated with the prevalence of preDM/DM in rural communes in Khánh Hòa Province, Vietnam. Dietary recommendations involving a reduction in red/processed meat consumption should be considered in low- and middle-income countries.

Type
Research Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of The Nutrition Society

The prevalence of diabetes mellitus (DM) is increasing dramatically and is becoming a health concern, particularly in low- and middle-income countries (LMIC). In 2019, it was estimated that the number of adults with DM in LMIC was approximately 368 million, which is equivalent to four-fifths of the global population with DM; moreover, this number is expected to increase to 588 million by 2045(1). Given that such an increase in the number of individuals with DM could substantially damage population health in LMIC, where preventive measures and proper treatment for DM remain inadequate, additional efforts are required to identify DM risk factors in such countries.

A meta-analysis of fourteen prospective studies conducted across several countries (including the United States, Finland, Japan and China) by Schwingshackl et al. (Reference Schwingshackl, Hoffmann and Lampousi2) found that the consumption of red and processed meats increased the risk of DM; the pooled risk ratios were 1·17 per 100 g/d of red meat (95 % CI = 1·25, 1·83) and 1·37 per 50 g/d of processed meat (95 % CI = 1·22, 1·55). Previous studies have suggested that the Fe component of haem, which is present in red meat, can result in impaired insulin synthesis and excretion by producing oxidative stress that impairs pancreatic β-cells, and increasing Fe storage in the liver which in turn impedes the capacity for insulin extraction(Reference Bao, Rong and Rong3Reference Rajpathak, Crandall and Wylie-Rosett8). Other direct and indirect factors have also been suggested, including SFA(Reference Salas-Salvadó, Martinez-González and Bulló9,Reference Pilon10) ; heterocyclic amines and polycyclic aromatic hydrocarbons present in cooked red meat(Reference Liu, Zong and Wu11,Reference Adeyeye12) , excess body weight(Reference Jayedi, Soltani and Motlagh13) and metabolic syndrome(Reference Shin, Lee and Lim14,Reference Dragsbæk, Neergaard and Laursen15) . An increasing trend in meat consumption, which has been observed and projected in LMIC(Reference Alexandratos and Bruinsma16), might have contributed to the rising rate of DM described above.

The current study was designed to build upon the findings of the aforementioned previous studies in the following manner. First, most studies on meat consumption and DM were from high-income countries(Reference Schwingshackl, Hoffmann and Lampousi2), and other than data from some studies in China(Reference Du, Guo and Bennett17,Reference Villegas, Shu and Gao18) , there is limited information available from LMIC. For example, no relevant study has been conducted in Vietnam, where the prevalence of DM increased from 2 % to 5 % in men and from 3 % to 5 % in women between 1980 and 2014(19). Second, the association between meat consumption and prediabetes (preDM) has been under-researched. PreDM is an intermediate state of hyperglycaemia with higher-than-normal glycaemic parameters that are still below the threshold for DM(20). Previous studies have linked preDM to a higher risk of DM, CVD, cancer and mortality(Reference Brannick and Dagogo-Jack21). Given that preDM is a precursor to DM and has a similar pathophysiology, it is possible that haem Fe as well as other pathways (as mentioned above) contribute to the development of preDM as they do in DM. Understanding the association between meat consumption and preDM may facilitate efforts to mitigate the disease burden associated with DM in LMIC.

Therefore, the purpose of the current study was to examine the associations between the consumption of different types of meat (i.e. red/processed meat and poultry) and glycaemic conditions (i.e. preDM and DM) among rural residents in Khánh Hòa Province, Vietnam. We hypothesised that increased red/processed meat consumption, rather than the consumption of poultry (which contains only a small amount of haem), is associated with a higher prevalence of preDM and DM.

Experimental methods

Study setting

Data for the current study were obtained through a baseline survey of a prospective cohort study on CVD (i.e. Khánh Hòa Cardiovascular Study). The aim of the Khánh Hòa Cardiovascular Study was to examine the determinants of CVD risk factors among middle-aged residents in rural communes in Vietnam. The study location was purposively selected as it was deemed to be at average affluence in rural Vietnam; Khánh Hòa province ranked 28th out of sixty-three provinces in terms of annual income in 2018; we then chose eight communes in one district in Khánh Hòa. Inclusion criteria included residents who were 40–59 years old at the time of recruitment and who had lived in the current commune for at least 6 months. Based on the member list created by the commune health centre staff, we purposively chose households and invited eligible individuals until the required number of participants was obtained (consent rate, 75–87 %).

As part of the Khánh Hòa Cardiovascular Study baseline survey, we collected anthropometry results, blood samples for biochemical measurements and questionnaire information via face-to-face interviews between June 2019 and June 2020. All the participants were asked to fast for more than 8 h prior to blood collection. Blood specimens were obtained by venepuncture and were centrifuged at the study sites, after which they were transported to the Pasteur Institute in Nha Trang at temperatures below 4°C. Plasma fasting glucose, HDL-cholesterol, LDL-cholesterol and TAG were measured using Cobas 8000 (Roche, Basel, Switzerland), and glycated Hb A1c (HbA1c) was quantified by high-performance liquid chromatography using the HLC-723 G8 system (Tosoh Bioscience, Tokyo, Japan).

Exposure – Meat consumption

Information on meat consumption was obtained by asking participants questions regarding the frequency and quantity of pork, beef, poultry and sausage/ham consumption. More specifically, we asked them how many days per week they consumed each item. Items that were consumed less than once per week were categorised as ‘none’. For items that were consumed at least once per week, participants were also requested to report the number of times per day they consumed each item and how much of it per occasion. To determine the latter, participants were presented with show-cards depicting pictures of standard portions of each item and were asked to state whether they consumed smaller, equivalent and larger amounts than that shown; these were assigned values of 0·5, 1 and 1·5, respectively. Individuals who consumed more than twice the size of the portion depicted in the show-card were asked to specify that amount using an integer.

We multiplied the above-mentioned values (i.e. consumption frequency and the amount of each food item consumed per occasion (i.e. 100 g of pork and beef, 80 g of poultry and 25 g of ham and sausage based on the portion sizes in the show cards)) to compute the daily meat consumption in grams. Given that the variation in processed meat consumption among the study population was small, we combined red and processed meat. Based on the consumption distribution, we categorised daily meat consumption into the following groups: 0–99 g, 100–199 g and ≥ 200 g for red/processed meat and 0–99 g, 100–199 g, 200–299 g and ≥ 300 g for poultry.

Outcomes

A participant was diagnosed with DM per the following American Diabetes Association criteria of 2019(20): fasting plasma glucose ≥ 7 mmol/l (≥ 126 mg/dl), HbA1c ≥ 6·5 % or self-reported use of antidiabetic medication. PreDM was defined as an fasting plasma glucose of 5·9–6·9 mmol/l (100–125 mg/dl) or HbA1c of 5·7–6·4 % in individuals without DM(20). Normoglycaemia was defined as an fasting plasma glucose of < 5·6 mmol/l (< 100 mg/dl) and HbA1c of < 5·7 %.

Covariates

We collected information on socio-demographic covariates including age (in years), sex (man or woman), marital status (married/cohabiting or not married), education (less than primary school, primary school, secondary school or high school or higher), job categories (government employee, non-governmental employee, self-employed, farmer or fisherman, homemaker, other or unemployed) and household income (low, middle or high). Household income was estimated based on the following responses regarding monthly household income: ≤ 1 000 000 Vietnamese đng (VND); 1 000 001–2 000 000 VND; 2 000 001–3 000 000 VND; 3 000 001–4 000 000 VND; 4 000 001–6 000 000 VND; 6 000 001–8 000 000 VND; 8 000 001–12 000 000 VND; 12 000 001–16 000 000 VND; 16 000 001–20 000 000 VND; > 20 000 000 VND or do not know (one USD is equivalent to 23 475 VND as of June 1, 2019). The midpoint of each of these ranges was assigned as a proxy score representing each category. The values were divided by the square root of the number of household members to compute equivalised income, which was then categorised into three groups: low, middle and high. For individuals who were not household representatives, the information collected from the head of household was used as a substitute.

Lifestyle-related variables included smoking status (never, former or current smoker), alcohol consumption (non-drinker, less than 1 standard drink, 1–1·9 standard drinks or ≥ 2 standard drinks), physical activity (wherein participants were divided into tertiles based on the Global Physical Activity Questionnaire(Reference Armstrong and Bull22)), daily vegetable consumption (none, 0·1–0·9 cups, 1–1·9 cups, 2–2·9 cups or ≥ 3 cups), fruit consumption (none, 0·1–0·9 cups, 1–1·9 cups or ≥ 2 cups), sweetened beverage consumption (none, 0·1–1·9 cans or ≥ 2 cans) and sleeping hours (the sum of the duration of night sleep and naps: < 6·0, 6·0–6·9, 7·0–7·9, 8·0–8·9 or ≥ 9·0 h).

Height to the nearest 0·1 cm and weight to the nearest 0·1 kg were measured by trained staff members using a portable stadiometer (Charder, HM200P, Tokyo, Japan) and digital scale (Tania, HD-661, Tokyo, Japan), respectively. BMI was calculated by dividing the weight (kg) by the square of the height (m2). Blood pressure was measured twice with the participants seated and their arms supported at the heart level using an electronic sphygmomanometer (Omron, HEM1020, Tokyo, Japan). Participants were instructed to rest for at least 5 min prior to the first measurement. The two measurements were used to calculate the mean systolic and diastolic blood pressure. Hypertension was defined as a systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg or the self-reported use of antihypertensive medication. Dyslipidaemia was defined as LDL-cholesterol ≥ 150 mg/l or HDL-cholesterol < 40 mg/l (men) or < 50 mg/l (women). Family history of DM was assessed by inquiring whether either of the participant’s parents had ever been diagnosed with the condition.

Statistical analysis

A multinomial logistic regression model was used to examine the association between meat consumption and preDM and DM while accounting for possible heterogeneity at the community level (level 1: individual; level 2: commune). Model 1 was adjusted for age and sex, while model 2 was further adjusted for marital status, education, occupation, household income, smoking status, alcohol consumption, physical activity, sleeping hours, fruit consumption, vegetable consumption, soft drink consumption, BMI, hypertension, dyslipidaemia and family history of DM.

The associations were examined in relation to the consumption of two different meat subtypes (i.e. red/processed meat and poultry). To test the robustness of the study findings, we also conducted a set of sensitivity analyses in which (1) we examined the associations of red meat and processed meat consumption separately and (2) participants who were receiving treatment for DM were excluded.

Results are presented as relative-risk ratios of glycaemic conditions (i.e. preDM and DM) and their corresponding 95 % CI.

Results

The basic characteristics of the study participants are shown in Table 1; their mean age (sd) was 49·9 (5·5) years. Women accounted for 61·3 % of the participants, and almost 90 % of them were married. Nearly one-fourth (23·9 %) of the participants had at least a high school education. Household socio-economic status was higher among those in the highest meat consumption group. Current smokers accounted for 20·4 % of all participants.

Table 1 Basic characteristics of study participants in the Khánh Hòa cardiovascular study, Vietnam (2019–2020)

Table 2 shows results of multinomial logistic regression model examining the association between meat consumption and glycaemic conditions. We found that red/processed meat consumption was significantly associated with both DM and preDM; the relative-risk ratios for the highest consumption category (i.e. ≥ 200 g) were 1·55 (95 % CI = 1·14, 2·11) for DM and 1·58 (95 % CI = 1·20, 2·09) for preDM. These associations remained statistically significant after adjusting for several covariates (relative-risk ratios = 1·80 (95 % CI = 1·40, 2·32) and 1·67 (95 % CI = 1·20, 2·33), respectively). We found no significant associations between poultry consumption and either DM or preDM.

Table 2 Results of multinomial logistic regression analyses of the association between meat consumption and prediabetes or diabetes among community-dwelling adults in rural Khánh Hòa province, Vietnam (2019–2020)

The results are shown as relative-risk ratios and corresponding 95 % CI.

Model 1 was adjusted for age, age squared term and sex, while model 2 was further adjusted for other socio-demographic variables (education, occupation and household income), lifestyle variables (smoking status, alcohol consumption, physical activity, sleeping hours, fruit consumption, vegetable consumption and sweetened beverage consumption) and health-related variables (BMI, hypertension, dyslipidaemia and family history of diabetes). When the consumption of red/processed meat and poultry was examined, the exposures were simultaneously incorporated into the models.

Separate analyses of red meat and processed meat consumption revealed DM/preDM to be significantly associated with red meat consumption but not with processed meat consumption (online Supplementary Table 1). There remained no significant association between poultry consumption and DM/preDM. Sensitivity analysis in which we excluded patients receiving antidiabetic treatment also revealed that red/processed meat consumption, but not poultry consumption, was significantly associated with a higher prevalence of preDM and DM (Table 3).

Table 3 Results of multinomial logistic regression analyses of the association between meat consumption and prediabetes or diabetes among community-dwelling adults in rural Khánh Hòa province, Vietnam (2019–2020), excluding participants receiving treatment for diabetes medication (n 2907)

The results are shown as relative-risk ratios and corresponding 95 % CI.

Model 1 was adjusted for age, age squared term and sex, while model 2 was further adjusted for other socio-demographic variables (education, occupation and household income), lifestyle variables (smoking status, alcohol consumption, physical activity, sleeping hours, fruit consumption, vegetable consumption and sweetened beverage consumption) and health-related variables (BMI, hypertension, dyslipidaemia and family history of diabetes). When the consumption of red/processed meat and poultry was examined, the exposures were simultaneously incorporated into the models.

Discussion

Among 3000 middle-aged community dwellers in rural Khánh Hòa, Vietnam, we found that individuals who consumed ≥ 200 g/d of red/processed meat were 1·67 times and 1·80 times more likely to have preDM and DM, respectively, than in those who consumed < 100 g/d. We found no evidence of a significant association between poultry consumption and preDM or DM.

The significant association between red and processed meat consumption and DM as revealed in our study is consistent with the findings of previous studies(Reference Schwingshackl, Hoffmann and Lampousi2,Reference Aune, Ursin and Veierød23,Reference Pan, Sun and Bernstein24) . In particular, our results are consistent with data from studies in Asia that examined the association between red meat consumption and DM(Reference Du, Guo and Bennett17,Reference Villegas, Shu and Gao18,Reference Kurotani, Nanri and Goto25) , including Du et al.’s study of individuals registered in the China Kadoorie Biobank(Reference Du, Guo and Bennett17) (hazard ratio = 1·11, 95 % CI = 1·04, 1·20 per 50 g increase) and Kurotani et al.’s study of a Japanese population(Reference Kurotani, Nanri and Goto25) (OR = 1·48, 95 % CI = 1·15, 1·90 for the highest v. lowest quartile).

Notably, this significant association between red/processed meat consumption and DM has also been reported in other LMIC. In addition to Du et al.’s aforementioned study(Reference Du, Guo and Bennett17), another investigation in Brazil found that red meat consumption was associated with a 40 % increase in the risk of DM (95 % CI = 1·04, 1·96) among men(Reference de Oliveira Aprelini, Luft and Meléndez26). Even though red meat (particularly beef) is rich in oleic acid, which reportedly plays a role in countering insulin resistance(Reference Rehman, Haider and Jabeen27), our findings and those of others suggest that the increase in red meat consumption in LMIC may nevertheless be contributing to the rising prevalence of DM in these countries, where people have been experiencing rapid economic growth.

Our finding that there was no significant association between poultry consumption and DM was consistent with findings reported in an umbrella review by Neuenschwander et al. (Reference Neuenschwander, Ballon and Weber28), who reported no significant association (hazard ratio = 1·05 per 100 g/d of poultry consumption, 95 % CI = 0·91, 1·22). Our results also agreed with those of Du et al. and Kurotani et al.’s studies of Asian populations that found no association between poultry consumption and DM(Reference Du, Guo and Bennett17,Reference Kurotani, Nanri and Goto25) . It should be also of note that a study of 74 493 middle-aged women in China by Villegas et al. even found an inverse association between poultry consumption and DM(Reference Villegas, Shu and Gao18). The apparent non-involvement of poultry consumption in DM development may be attributable to its lower levels of haem (the Fe component of which can impair insulin synthesis and excretion) compared with red meat.

To our knowledge, no studies have specifically examined the association between meat consumption and preDM, irrespective of the study location. Despite a slightly different exposure, however, Cao et al. (Reference Cao, Chen and Cui29) found a significant association between a dietary pattern characterised by high meat consumption (without distinguishing the types of meat) and preDM among 7555 urban and rural residents in Jiangsu, China (OR = 1·229, 95 % CI = 1·077, 1·402). Identifying individuals at a risk of developing DM at an earlier stage (i.e. before any clinical manifestations) may help mitigate the disease burden associated with DM. This is particularly important in LMIC, where medical and economic resources are insufficient and where the disease burden associated with diabetes is expected to increase due to population ageing, urbanisation and economic growth(1).

The current study had several limitations. First, we used show-cards to determine the amount of meat consumption; this method has not been validated and is therefore potentially subject to measurement error. Second, we did not collect information on the consumption of different types of red meat (i.e. processed v. unprocessed red meat), which has been suggested to have different health implications(Reference Kurotani, Nanri and Goto25,Reference Son, Lee and Park30,Reference Micha, Michas and Mozaffarian31) . Third, owing to the cross-sectional design of the study, we were unable to infer causality (e.g. those diagnosed with DM may have altered their meat consumption habits as part of their efforts to delay or prevent DM-related complications). Fourth, our study participants might not be representative of the Vietnamese population as a whole given that they were middle aged and residing in rural communes within a single province. Thus, caution should be exercised when generalising the study findings to other populations.

Conclusion

Our study of 3000 rural residents of Khánh Hòa Province, Vietnam, revealed that the consumption of red/processed meat consumption, but not poultry consumption, was associated with the increased prevalence of both DM and preDM. Dietary recommendations that involve a reduction in red/processed meat consumption should be considered in LMIC, where there has been an increasing trend in meat consumption during the course of economic growth in the past few decades.

Acknowledgements

Acknowledgements: The authors are grateful to the participants of the Khánh Hòa Cardiovascular Study for their participation. Financial support: The current study was funded by the National Center for Global Health and Medicine (19A06; 22A02), Pfizer Health Research Foundation and the Japan Society for the Promotion of Science (KAKENHI JP19K20536; JP21J01171; JP21K17301). These funders had no role in the study design, execution of the study, statistical analyses and interpretation of the data or decision to submit the manuscript for publication. Authorship: All authors contributed to the conception, design and interpretation of the data. C.Q.N., A.F. and Y.I. contributed to the data analysis, C.Q.N., T.T.P.P., D.V.Ho., D.C.P., D.V.Hu., H.X.L. and H.T.D. contributed to the acquisition of data, T.V.P. conducted the biochemical measurement, C.Q.N., A.F. and Y.I. drafted the manuscript, T.T.P.P., D.V.H., T.V.P., D.C.P., D.V.Hu., M.H., H.X.L., H.T.D. and T.M. contributed to the critical revision of the manuscript. C.Q.N. and Y.I. had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors have read and approved the final manuscript. Ethics of human subject participation: The current study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving human subjects/patients were approved by the NCGM (approval number: NCGM-G-003172-03) and Pasteur Institute in Nha Trang (PINT) (02/2019/HDDD-IPN). Written informed consent was obtained from all the study participants. They were informed that they could withdraw their consent at any time.

Conflict of interest:

The authors declared no conflict of interest.

Supplementary material

For supplementary material/s referred to in this article, please visit https://doi.org/10.1017/S1368980022001422

References

International Diabetes Federation (2019) IDF Diabetes Atlas 9th edition. https://diabetesatlas.org/atlas/ninth-edition/ (accessed April 2022).Google Scholar
Schwingshackl, L, Hoffmann, G, Lampousi, AM et al. (2017) Food groups and risk of type 2 diabetes mellitus: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol 32, 363375.CrossRefGoogle ScholarPubMed
Bao, W, Rong, Y, Rong, S et al. (2012) Dietary iron intake, body iron stores, and the risk of type 2 diabetes: a systematic review and meta-analysis. BMC Med 10, 119.CrossRefGoogle ScholarPubMed
Swaminathan, S, Fonseca, VA, Alam, MG et al. (2007) The role of iron in diabetes and its complications. Diabetes Care 30, 19261933.CrossRefGoogle ScholarPubMed
Rehman, K & Akash, MSH (2017) Mechanism of generation of oxidative stress and pathophysiology of type 2 diabetes mellitus: how are they interlinked? J Cell Biochem 118, 35773585.CrossRefGoogle ScholarPubMed
Akash, MS, Rehman, K & Chen, S (2013) Role of inflammatory mechanisms in pathogenesis of type 2 diabetes mellitus. J Cell Biochem 114, 525531.CrossRefGoogle ScholarPubMed
Rehman, K & Akash, MS (2016) Mechanisms of inflammatory responses and development of insulin resistance: how are they interlinked? J Biomed Sci 23, 87.CrossRefGoogle ScholarPubMed
Rajpathak, SN, Crandall, JP, Wylie-Rosett, J et al. (2009) The role of iron in type 2 diabetes in humans. Biochim Biophys Acta 1790, 671681.CrossRefGoogle ScholarPubMed
Salas-Salvadó, J, Martinez-González, , Bulló, M et al. (2011) The role of diet in the prevention of type 2 diabetes. Nutr Metab Cardiovasc Dis 21, B32B48.CrossRefGoogle ScholarPubMed
Pilon, M (2016) Revisiting the membrane-centric view of diabetes. Lipids Health Dis 15, 167.CrossRefGoogle ScholarPubMed
Liu, G, Zong, G, Wu, K et al. (2018) Meat cooking methods and risk of type 2 diabetes: results from three prospective cohort studies. Diabetes Care 41, 10491060.CrossRefGoogle ScholarPubMed
Adeyeye, SAO (2018) Heterocyclic amines and polycyclic aromatic hydrocarbons in cooked meat products: a review. Polycycl Aromat Compd 40, 15571567.CrossRefGoogle Scholar
Jayedi, A, Soltani, S, Motlagh, SZ et al. (2022) Anthropometric and adiposity indicators and risk of type 2 diabetes: systematic review and dose-response meta-analysis of cohort studies. BMJ 376, e067516.CrossRefGoogle ScholarPubMed
Shin, J, Lee, J, Lim, S et al. (2013) Metabolic syndrome as a predictor of type 2 diabetes, and its clinical interpretations and usefulness. J Diabetes Investig 4, 334343.CrossRefGoogle ScholarPubMed
Dragsbæk, K, Neergaard, JS, Laursen, JM et al. (2016) Metabolic syndrome and subsequent risk of type 2 diabetes and cardiovascular disease in elderly women. Medicine 95, e4806.CrossRefGoogle ScholarPubMed
Alexandratos, N & Bruinsma, J (2012) World Agriculture Towards 2030/2050: The 2012 Revision. ESA Working Paper No. 12–03. Food and Agriculture Organization of the United Nations. https://www.fao.org/3/ap106e/ap106e.pdf (accessed April 2022).Google Scholar
Du, H, Guo, Y, Bennett, DA et al. (2020) Red meat, poultry and fish consumption and risk of diabetes: a 9 year prospective cohort study of the China Kadoorie Biobank. Diabetologia 63, 767779.CrossRefGoogle ScholarPubMed
Villegas, R, Shu, XO, Gao, YT et al. (2006) The association of meat intake and the risk of type 2 diabetes may be modified by body weight. Int J Med Sci 3, 152159.CrossRefGoogle ScholarPubMed
NCD Risk Factor Collaboration (NCD-RisC) (2016) Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4·4 million participants. Lancet 387, 15131530.CrossRefGoogle Scholar
American Diabetes Association (2020) 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2020. Diabetes Care 43, S14S31.CrossRefGoogle Scholar
Brannick, B & Dagogo-Jack, S (2018) Prediabetes and cardiovascular disease: pathophysiology and interventions for prevention and risk reduction. Endocrinol Metab Clin North Am 47, 3350.CrossRefGoogle ScholarPubMed
Armstrong, T & Bull, F (2006) Development of the World Health Organization global physical activity questionnaire (GPAQ). J Public Health 14, 6670.CrossRefGoogle Scholar
Aune, D, Ursin, G & Veierød, MB (2009) Meat consumption and the risk of type 2 diabetes: a systematic review and meta-analysis of cohort studies. Diabetologia 52, 22772287.CrossRefGoogle ScholarPubMed
Pan, A, Sun, Q, Bernstein, AM et al. (2011) Red meat consumption and risk of type 2 diabetes: 3 cohorts of US adults and an updated meta-analysis. Am J Clin Nutr 94, 10881096.CrossRefGoogle Scholar
Kurotani, K, Nanri, A, Goto, A et al. (2013) Red meat consumption is associated with the risk of type 2 diabetes in men but not in women: a Japan Public Health Center-based Prospective Study. Br J Nutr 110, 19101918.CrossRefGoogle Scholar
de Oliveira Aprelini, CM, Luft, VC, Meléndez, GV et al. (2019) Consumo de carnes rojas y de carne procesada, resistencia a la insulina y diabetes en el estudio longitudinal de salud del adulto (ELSA-Brasil) (Consumption of red and processed meat, insulin resistance, and diabetes in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil)). Rev Panam Salud Publica 43, e40.Google Scholar
Rehman, K, Haider, K, Jabeen, K et al. (2020) Current perspectives of oleic acid: regulation of molecular pathways in mitochondrial and endothelial functioning against insulin resistance and diabetes. Rev Endocr Metab Disord 21, 631643.CrossRefGoogle ScholarPubMed
Neuenschwander, M, Ballon, A, Weber, KS et al. (2019) Role of diet in type 2 diabetes incidence: umbrella review of meta-analyses of prospective observational studies. BMJ 366, I2368.CrossRefGoogle ScholarPubMed
Cao, Y, Chen, C, Cui, L et al. (2020) A population-based survey for dietary patterns and prediabetes among 7555 Chinese adults in urban and rural areas in Jiangsu Province. Sci Rep 10, 10488.CrossRefGoogle Scholar
Son, J, Lee, Y & Park, K (2019) Effects of processed red meat consumption on the risk of type 2 diabetes and cardiovascular diseases among Korean adults: the Korean Genome and Epidemiology Study. Eur J Nutr 58, 24772484.CrossRefGoogle ScholarPubMed
Micha, R, Michas, G & Mozaffarian, D (2012) Unprocessed red and processed meats and risk of coronary artery disease and type 2 diabetes – an updated review of the evidence. Curr Atheroscler Rep 14, 515524.CrossRefGoogle Scholar
Figure 0

Table 1 Basic characteristics of study participants in the Khánh Hòa cardiovascular study, Vietnam (2019–2020)

Figure 1

Table 2 Results of multinomial logistic regression analyses of the association between meat consumption and prediabetes or diabetes among community-dwelling adults in rural Khánh Hòa province, Vietnam (2019–2020)

Figure 2

Table 3 Results of multinomial logistic regression analyses of the association between meat consumption and prediabetes or diabetes among community-dwelling adults in rural Khánh Hòa province, Vietnam (2019–2020), excluding participants receiving treatment for diabetes medication (n 2907)

Supplementary material: File

Nguyen et al. supplementary material

Table S1

Download Nguyen et al. supplementary material(File)
File 22 KB
You have Access Open access

Save article to Kindle

To save this article to your Kindle, first ensure coreplatform@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Red meat consumption is associated with prediabetes and diabetes in rural Vietnam: a cross-sectional study
Available formats
×

Save article to Dropbox

To save this article to your Dropbox account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Dropbox account. Find out more about saving content to Dropbox.

Red meat consumption is associated with prediabetes and diabetes in rural Vietnam: a cross-sectional study
Available formats
×

Save article to Google Drive

To save this article to your Google Drive account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you used this feature, you will be asked to authorise Cambridge Core to connect with your Google Drive account. Find out more about saving content to Google Drive.

Red meat consumption is associated with prediabetes and diabetes in rural Vietnam: a cross-sectional study
Available formats
×
×

Reply to: Submit a response

Please enter your response.

Your details

Please enter a valid email address.

Conflicting interests

Do you have any conflicting interests? *