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Hang D, Sun D, Du L, Huang J, Li J, Zhu C, Wang L, He J, Zhu X, Zhu M, Song C, Dai J, Yu C, Xu Z, Li N, Ma H, Jin G, Yang L, Chen Y, Du H, Cheng X, Chen Z, Lv J, Hu Z, Li L, Shen H. Development and evaluation of a risk prediction tool for risk-adapted screening of colorectal cancer in China. Cancer Lett 2024; 597:217057. [PMID: 38876387 DOI: 10.1016/j.canlet.2024.217057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Revised: 06/03/2024] [Accepted: 06/10/2024] [Indexed: 06/16/2024]
Abstract
Risk prediction tools for colorectal cancer (CRC) have potential to improve the efficiency of population-based screening by facilitating risk-adapted strategies. However, such an applicable tool has yet to be established in the Chinese population. In this study, a risk score was created using data from the China Kadoorie Biobank (CKB), a nationwide cohort study of 409,854 eligible participants. Diagnostic performance of the risk score was evaluated in an independent CRC screening programme, which included 91,575 participants who accepted colonoscopy at designed hospitals in Zhejiang Province, China. Over a median follow-up of 11.1 years, 3,136 CRC cases were documented in the CKB. A risk score was created based on nine questionnaire-derived variables, showing moderate discrimination for 10-year CRC risk (C-statistic =0.68, 95% CI: 0.67-0.69). In the CRC screening programme, the detection rates of CRC were 0.25%, 0.82%, and 1.93% in low-risk (score <6), intermediate-risk (score: 6-19), and high-risk (score >19) groups, respectively. The newly developed score exhibited a C-statistic of 0.65 (95% CI: 0.63-0.66), surpassing the widely adopted tools such as the Asia-Pacific Colorectal Screening (APCS), modified APCS, and Korean Colorectal Screening scores (all C-statistics =0.60). In conclusion, we developed a novel risk prediction tool that is useful to identify individuals at high risk of CRC. A user-friendly online calculator was also constructed to encourage broader adoption of the tool.
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Affiliation(s)
- Dong Hang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Lingbin Du
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jianv Huang
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jiacong Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Chen Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Le Wang
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jingjing He
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xia Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Ci Song
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Juncheng Dai
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zekuan Xu
- Department of General Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ni Li
- Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hongxia Ma
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit, University of Oxford, Oxford, United Kingdom; Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Xiangdong Cheng
- Department of Cancer Prevention, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China; Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Zhibin Hu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China.
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China; Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China; Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China.
| | - Hongbing Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China.
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Noerman S, Johansson A, Shi L, Lehtonen M, Hanhineva K, Johansson I, Brunius C, Landberg R. Fasting plasma metabolites reflecting meat consumption and their associations with incident type 2 diabetes in two Swedish cohorts. Am J Clin Nutr 2024; 119:1280-1292. [PMID: 38403167 DOI: 10.1016/j.ajcnut.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 02/02/2024] [Accepted: 02/20/2024] [Indexed: 02/27/2024] Open
Abstract
BACKGROUND Consumption of processed red meat has been associated with increased risk of developing type 2 diabetes (T2D), but challenges in dietary assessment call for objective intake biomarkers. OBJECTIVES This study aimed to investigate metabolite biomarkers of meat intake and their associations with T2D risk. METHODS Fasting plasma samples were collected from a case-control study nested within Västerbotten Intervention Program (VIP) (214 females and 189 males) who developed T2D after a median follow-up of 7 years. Panels of biomarker candidates reflecting the consumption of total, processed, and unprocessed red meat and poultry were selected from the untargeted metabolomics data collected on the controls. Observed associations were then replicated in Swedish Mammography clinical subcohort in Uppsala (SMCC) (n = 4457 females). Replicated metabolites were assessed for potential association with T2D risk using multivariable conditional logistic regression in the discovery and Cox regression in the replication cohorts. RESULTS In total, 15 metabolites were associated with ≥1 meat group in both cohorts. Acylcarnitines 8:1, 8:2, 10:3, reflecting higher processed meat intake [r > 0.22, false discovery rate (FDR) < 0.001 for VIP and r > 0.05; FDR < 0.001 for SMCC) were consistently associated with higher T2D risk in both data sets. Conversely, lysophosphatidylcholine 17:1 and phosphatidylcholine (PC) 15:0/18:2 were associated with lower processed meat intake (r < -0.12; FDR < 0.023, for VIP and r < -0.05; FDR < 0.001, for SMCC) and with lower T2D risk in both data sets, except for PC 15:0/18:2, which was significant only in the VIP cohort. All associations were attenuated after adjustment for BMI (kg/m2). CONCLUSIONS Consistent associations of biomarker candidates involved in lipid metabolism between higher processed red meat intake with higher T2D risk and between those reflecting lower intake with the lower risk may suggest a relationship between processed meat intake and higher T2D risk. However, attenuated associations after adjusting for BMI indicates that such a relationship may at least partly be mediated or confounded by BMI.
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Affiliation(s)
- Stefania Noerman
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.
| | - Anna Johansson
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Lin Shi
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden; School of Food Engineering and Nutritional Science, Shaanxi Normal University, Xi'an, China
| | - Marko Lehtonen
- School of Pharmacy, University of Eastern Finland, Kuopio, Finland
| | - Kati Hanhineva
- Department of Life Technologies, Food Sciences Unit, University of Turku, Turku, Finland; Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio, Finland
| | - Ingegerd Johansson
- Department of Odontology, School of Dentistry, Cariology, Umeå University, Sweden
| | - Carl Brunius
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Rikard Landberg
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
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Sun X, Yon DK, Nguyen TT, Tanisawa K, Son K, Zhang L, Shu J, Peng W, Yang Y, Branca F, Wahlqvist ML, Lim H, Wang Y. Dietary and other lifestyle factors and their influence on non-communicable diseases in the Western Pacific region. THE LANCET REGIONAL HEALTH. WESTERN PACIFIC 2024; 43:100842. [PMID: 38456094 PMCID: PMC10920053 DOI: 10.1016/j.lanwpc.2023.100842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 03/09/2024]
Abstract
The Western Pacific region is a diverse region experiencing fast economic growth and nutrition transition. We systematically examined 94 cohort studies on the associations of dietary and other lifestyle factors on non-communicable diseases (NCDs) in the region. These studies were mainly from China, Japan, the Republic of Korea, and Singapore. Patterns and changes in lifestyle risk factors for NCDs based on national surveys were examined. They showed some dietary intake improvements over the past three decades, featured as increased consumption of unsaturated oils, fruits, and vegetables, and decreased consumption of sodium and unhealthy fat. Despite a decrease in smoking rate and salt intake, the values remained higher than the global levels in 2019. The ultra-processed food intake in the region increased at a higher rate than the global estimate. National guidelines relevant to NCDs in five selected countries were highlighted. Strong future actions and policies are needed to tackle NCDs.
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Affiliation(s)
- Xiaomin Sun
- The First Affiliated Hospital of Xi'an Jiaotong University Public Health Institute, Global Health Institute, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, People’s Republic of China
- International Obesity and Metabolic Disease Research Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Dong Keon Yon
- Center for Digital Health, Medical Science Research Institute, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul 02447, Republic of Korea
| | | | - Kumpei Tanisawa
- Faculty of Sport Sciences, Waseda University, Saitama 359-1192, Japan
| | - Kumhee Son
- Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin 17104, Republic of Korea
- Research Institute of Medical Nutrition, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Ling Zhang
- School of Public Health, Capital Medical University, Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing 100069, China
| | - Jing Shu
- The First Affiliated Hospital of Xi'an Jiaotong University Public Health Institute, Global Health Institute, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, People’s Republic of China
- International Obesity and Metabolic Disease Research Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Wen Peng
- Nutrition and Health Promotion Center, Department of Public Health, Medical College, Qinghai University, Xining 810008, China
- Qinghai Provincial Key Laboratory of Prevention and Control of Glucolipid Metabolic Diseases with Traditional Chinese Medicine, Xining 810008, China
| | - Yuexin Yang
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Francesco Branca
- Department of Nutrition and Food Safety, World Health Organization, Geneva 1211, Switzerland
| | | | - Hyunjung Lim
- Department of Medical Nutrition, Graduate School of East-West Medical Science, Kyung Hee University, Yongin 17104, Republic of Korea
- Research Institute of Medical Nutrition, Kyung Hee University, Seoul 02447, Republic of Korea
| | - Youfa Wang
- The First Affiliated Hospital of Xi'an Jiaotong University Public Health Institute, Global Health Institute, School of Public Health, Xi’an Jiaotong University, Xi’an 710061, People’s Republic of China
- International Obesity and Metabolic Disease Research Center, Xi’an Jiaotong University, Xi’an 710061, China
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Ramel A, Nwaru BI, Lamberg-Allardt C, Thorisdottir B, Bärebring L, Söderlund F, Arnesen EK, Dierkes J, Åkesson A. White meat consumption and risk of cardiovascular disease and type 2 diabetes: a systematic review and meta-analysis. Food Nutr Res 2023; 67:9543. [PMID: 38187786 PMCID: PMC10770644 DOI: 10.29219/fnr.v67.9543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 01/09/2024] Open
Abstract
Objectives The aim was to systematically review the associations among white meat consumption, cardiovascular diseases (CVD), and type 2 diabetes (T2D). Methods Databases MEDLINE, Embase, and Cochrane Central Register of Controlled Trials and Scopus were searched (15th October 2021) for randomized intervention trials (RCTs, ≥ 4 weeks of duration) and prospective cohort studies (≥12 month of follow-up) assessing the consumption of white meat as the intervention/exposure. Eligible outcomes for RCTs were cardiometabolic risk factors and for cohorts, fatal and non-fatal CVD and incident T2D. Risk of bias was estimated using the Cochrane's RoB2 and Risk of Bias for Nutrition Observational Studies. Meta-analysis was conducted in case of ≥3 relevant intervention studies or ≥5 cohort studies using random-effects models. The strength of evidence was evaluated using the World Cancer Research Fund's criteria. Results The literature search yielded 5,795 scientific articles, and after screening 43 full-text articles, 23 cohort studies and three intervention studies were included. All included intervention studies matched fat content of intervention and control diets, and none of them showed any significant effects on the selected outcomes of white meat when compared to red meat. Findings from the cohort studies generally did not support any associations between white meat intake and outcomes. Meta-analyses were conducted for CVD mortality (RR: 0.95, 95% CI: 0.87-1.02, P = 0.23, I2 = 25%) and T2D incidence (RR: 0.98, 95% CI: 0.87-1.11, P = 0.81, I2 = 82%). Conclusion The currently available evidence does not indicate a role, beneficial or detrimental, of white meat consumption for CVD and T2D. Future studies investigating potentially different health effects of processed versus unprocessed white meat and substitution of red meat with white meat are warranted.Registration: Prospero registration CRD42022295915.
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Affiliation(s)
- Alfons Ramel
- Faculty of Food Science and Nutrition, University of Iceland, Reykjavik, Iceland
| | - Bright I. Nwaru
- Krefting Research Centre, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | | | | | - Linnea Bärebring
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Fredrik Söderlund
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden
| | - Erik Kristoffer Arnesen
- Department of Nutrition, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jutta Dierkes
- Centre for Nutrition, Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Department of Laboratory Medicine and Pathology, Haukeland University Hospital, Bergen, Norway
| | - Agneta Åkesson
- Unit of Cardiovascular and Nutritional Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden
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Neuenschwander M, Stadelmaier J, Eble J, Grummich K, Szczerba E, Kiesswetter E, Schlesinger S, Schwingshackl L. Substitution of animal-based with plant-based foods on cardiometabolic health and all-cause mortality: a systematic review and meta-analysis of prospective studies. BMC Med 2023; 21:404. [PMID: 37968628 PMCID: PMC10652524 DOI: 10.1186/s12916-023-03093-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 09/25/2023] [Indexed: 11/17/2023] Open
Abstract
BACKGROUND There is growing evidence that substituting animal-based with plant-based foods is associated with a lower risk of cardiovascular diseases (CVD), type 2 diabetes (T2D), and all-cause mortality. Our aim was to summarize and evaluate the evidence for the substitution of any animal-based foods with plant-based foods on cardiometabolic health and all-cause mortality in a systematic review and meta-analysis. METHODS We systematically searched MEDLINE, Embase, and Web of Science to March 2023 for prospective studies investigating the substitution of animal-based with plant-based foods on CVD, T2D, and all-cause mortality. We calculated summary hazard ratios (SHRs) and 95% confidence intervals (95% CI) using random-effects meta-analyses. We assessed the certainty of evidence (CoE) using the GRADE approach. RESULTS In total, 37 publications based on 24 cohorts were included. There was moderate CoE for a lower risk of CVD when substituting processed meat with nuts [SHR (95% CI): 0.73 (0.59, 0.91), n = 8 cohorts], legumes [0.77 (0.68, 0.87), n = 8], and whole grains [0.64 (0.54, 0.75), n = 7], as well as eggs with nuts [0.83 (0.78, 0.89), n = 8] and butter with olive oil [0.96 (0.95, 0.98), n = 3]. Furthermore, we found moderate CoE for an inverse association with T2D incidence when substituting red meat with whole grains/cereals [0.90 (0.84, 0.96), n = 6] and red meat or processed meat with nuts [0.92 (0.90, 0.94), n = 6 or 0.78 (0.69, 0.88), n = 6], as well as for replacing poultry with whole grains [0.87 (0.83, 0.90), n = 2] and eggs with nuts or whole grains [0.82 (0.79, 0.86), n = 2 or 0.79 (0.76, 0.83), n = 2]. Moreover, replacing red meat for nuts [0.93 (0.91, 0.95), n = 9] and whole grains [0.96 (0.95, 0.98), n = 3], processed meat with nuts [0.79 (0.71, 0.88), n = 9] and legumes [0.91 (0.85, 0.98), n = 9], dairy with nuts [0.94 (0.91, 0.97), n = 3], and eggs with nuts [0.85 (0.82, 0.89), n = 8] and legumes [0.90 (0.89, 0.91), n = 7] was associated with a reduced risk of all-cause mortality. CONCLUSIONS Our findings indicate that a shift from animal-based (e.g., red and processed meat, eggs, dairy, poultry, butter) to plant-based (e.g., nuts, legumes, whole grains, olive oil) foods is beneficially associated with cardiometabolic health and all-cause mortality.
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Affiliation(s)
- Manuela Neuenschwander
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Düsseldorf, Germany
| | - Julia Stadelmaier
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Julian Eble
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Kathrin Grummich
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Edyta Szczerba
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Düsseldorf, Germany
| | - Eva Kiesswetter
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany.
- German Center for Diabetes Research (DZD), Partner Düsseldorf, Düsseldorf, Germany.
| | - Lukas Schwingshackl
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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6
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Chung S, Hwang JT, Joung H, Shin S. Associations of Meat and Fish/Seafood Intake with All-Cause and Cause-Specific Mortality from Three Prospective Cohort Studies in Korea. Mol Nutr Food Res 2023; 67:e2200900. [PMID: 37366293 DOI: 10.1002/mnfr.202200900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 05/02/2023] [Indexed: 06/28/2023]
Abstract
SCOPE Animal protein intake among Koreans has recently increased. However, there is limited evidence on the association of meat and fish/seafood intake and mortality. METHODS AND RESULTS This study uses three representative prospective cohorts in Korea and 134,586 eligible participants are selected. Food intake is assessed using a food frequency questionnaire. Outcomes are classified as death from cardiovascular disease (CVD), cancer, and all-causes. Red meat intake shows a marginally negative association with all-cause mortality in the median intake group and a positive association in the highest intake group. Processed meat intake in the highest quintile group is positively associated with all-cause mortality compared to that of the lowest quintile group. Fish intake in the highest quintile group is negatively associated with CVD mortality in men, and all-cause mortality in women, compared to those in the lowest quintile group, while processed fish intake has unfavorable effects on mortality. In addition, substitution of one serving per week of red and processed meat, and processed fish with fish is negatively associated with all-cause and CVD mortality. CONCLUSION Reduction of red and processed meat, and processed fish consumption or replacement of these foods with fish may be beneficial for longevity in Korean adults.
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Affiliation(s)
- Sangwon Chung
- Personalized Diet Research Group, Korea Food Research Institute, Jeollabuk-do, 55365, Republic of Korea
| | - Jin-Taek Hwang
- Personalized Diet Research Group, Korea Food Research Institute, Jeollabuk-do, 55365, Republic of Korea
| | - Hyojee Joung
- Department of Public Health, Graduate School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
- Institute of Health and Environment, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangah Shin
- Department of Food and Nutrition, Chung-Ang University, Gyeonggi-do, 17546, Republic of Korea
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7
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Liu M, Wang H, Du S, Jiao Y, Wang Q, Su C, Zhang B, Ding G. Trajectories of Meat Intake and Risk of Type 2 Diabetes: Findings from the China Health and Nutrition Survey (1997-2018). Nutrients 2023; 15:3277. [PMID: 37513694 PMCID: PMC10385415 DOI: 10.3390/nu15143277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/15/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Few articles have investigated the impact of long-term meat intake trends and their changes during follow-up on the risk of type 2 diabetes (T2D). We aimed to explore the long-term trajectories of meat intake and determine its association with T2D risk in Chinese adults. This study used seven rounds of data from the China Health and Nutrition Survey (1997, 2000, 2004, 2006, 2009, 2015, and 2018), and 4464 adults aged 18 years or older were analyzed. The group-based trajectory modeling was used to identify meat intake trajectories over 21 years. Multivariate Cox proportional hazard and restricted cubic spline models were used to analyze the association and dose-response relationship between meat intake and T2D. Four trajectory groups were identified: "low-increase intake group" (Group 1), "moderate-increase intake group" (Group 2), "medium-stable intake group" (Group 3), and "high intake group" (Group 4). Compared with Group 2, Group 4 was associated with a higher risk of developing T2D (hazard ratio 2.37 [95% CI 1.41-3.98]). After adjusting for demographic characteristics, lifestyle, total energy intake, waist circumference, and systolic blood pressure, and using the third quintile as a reference, the risk of T2D was increased by 46% in the lowest quintile with meat intake (hazard ratio 1.46 [95% CI 1.07-2.01]) and by 41% in the highest quintile with meat intake (HR 1.41 [95% CI 1.03-1.94]). A U-shape was observed between meat intake and T2D risk (p for nonlinear < 0.001). When the intake was lower than 75 g/day, the risk of T2D was negatively correlated with meat intake, while the risk of T2D was positively correlated with meat intake when the intake was higher than 165 g/day. We identified four trajectory groups of meat intake from 1997 to 2018, which were associated with different risks of developing T2D. A U-shaped association was observed between meat intake and T2D in Chinese adults.
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Affiliation(s)
- Mengran Liu
- Department of Education and Training, Chinese Center for Disease Control and Prevention, Beijing 102206, China;
| | - Huijun Wang
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Shufa Du
- Department of Nutrition and Carolina Population Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yingying Jiao
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Qi Wang
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Chang Su
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Bing Zhang
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
| | - Gangqiang Ding
- Key Laboratory of Trace Element Nutrition of National Health Commission of China, National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing 100050, China
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8
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Shi W, Huang X, Schooling CM, Zhao JV. Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis. Eur Heart J 2023; 44:2626-2635. [PMID: 37264855 DOI: 10.1093/eurheartj/ehad336] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 04/01/2023] [Accepted: 05/16/2023] [Indexed: 06/03/2023] Open
Abstract
AIMS Observational studies show inconsistent associations of red meat consumption with cardiovascular disease (CVD) and diabetes. Moreover, red meat consumption varies by sex and setting, however, whether the associations vary by sex and setting remains unclear. METHODS AND RESULTS This systematic review and meta-analysis was conducted to summarize the evidence concerning the associations of unprocessed and processed red meat consumption with CVD and its subtypes [coronary heart disease (CHD), stroke, and heart failure], type two diabetes mellitus (T2DM), and gestational diabetes mellitus (GDM) and to assess differences by sex and setting (western vs. eastern, categorized based on dietary pattern and geographic region). Two researchers independently screened studies from PubMed, Web of Science, Embase, and the Cochrane Library for observational studies and randomized controlled trials (RCTs) published by 30 June 2022. Forty-three observational studies (N = 4 462 810, 61.7% women) for CVD and 27 observational studies (N = 1 760 774, 64.4% women) for diabetes were included. Red meat consumption was positively associated with CVD [hazard ratio (HR) 1.11, 95% confidence interval (CI) 1.05 to 1.16 for unprocessed red meat (per 100 g/day increment); 1.26, 95% CI 1.18 to 1.35 for processed red meat (per 50 g/day increment)], CVD subtypes, T2DM, and GDM. The associations with stroke and T2DM were higher in western settings, with no difference by sex. CONCLUSION Unprocessed and processed red meat consumption are both associated with higher risk of CVD, CVD subtypes, and diabetes, with a stronger association in western settings but no sex difference. Better understanding of the mechanisms is needed to facilitate improving cardiometabolic and planetary health.
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Affiliation(s)
- Wenming Shi
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Southern District, Hong Kong SAR, China
| | - Xin Huang
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Southern District, Hong Kong SAR, China
| | - C Mary Schooling
- School of Public Health and Health Policy, City University of New York, 55 W 125th St, New York, NY 10027, USA
| | - Jie V Zhao
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, 7 Sassoon Road, Southern District, Hong Kong SAR, China
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9
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Nguyen CQ, Pham TTP, Fukunaga A, Hoang DV, Phan TV, Phan DC, Huynh DV, Hachiya M, Le HX, Do HT, Mizoue T, Inoue Y. Red meat consumption is associated with prediabetes and diabetes in rural Vietnam: a cross-sectional study. Public Health Nutr 2023; 26:1006-1013. [PMID: 35722988 PMCID: PMC10346020 DOI: 10.1017/s1368980022001422] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/02/2022] [Accepted: 05/27/2022] [Indexed: 11/06/2022]
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.
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Affiliation(s)
- Chau Que Nguyen
- Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Thuy Thi Phuong Pham
- Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Ami Fukunaga
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
| | - Dong Van Hoang
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
| | - Tien Vu Phan
- Medical Service Center, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Danh Cong Phan
- Department of Non-communicable Disease Control and Nutrition, Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Dong Van Huynh
- Khánh Hòa Center for Disease Control, Khánh Hòa, Vietnam
| | - Masahiko Hachiya
- Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan
| | - Huy Xuan Le
- Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Hung Thai Do
- Pasteur Institute in Nha Trang, Nha Trang, Khánh Hòa, Vietnam
| | - Tetsuya Mizoue
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
| | - Yosuke Inoue
- Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo162-8655, Japan
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10
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Perraud E, Wang J, Salomé M, Mariotti F, Kesse-Guyot E. Dietary protein consumption profiles show contrasting impacts on environmental and health indicators. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 856:159052. [PMID: 36179832 DOI: 10.1016/j.scitotenv.2022.159052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/31/2022] [Accepted: 09/22/2022] [Indexed: 06/16/2023]
Abstract
Patterns of protein intake are strong characteristics of diets, and protein sources have been linked to the environmental and nutrition/health impacts of diets. However, few studies have worked on protein profiles, and most of them have focused on specific diets like vegetarian or vegan diets. Furthermore, the description of the environmental impact of diets has often been limited to greenhouse gas emissions (GHGe) and land use. This paper analyzes the alignment of environmental pressures and nutritional impacts in a diversity of representative protein profiles of a western population. Using data from a representative survey in France (INCA3, n = 1125), we identified protein profiles using hierarchical ascendant classification on protein intake (g) from main protein sources (refined grains, whole grains, dairy, eggs, ruminant meat, poultry, pork, processed meat, fish, fruits & vegetables, pulses). We assessed their diet quality using 6 dietary scores, including assessment of long-term risk for health, and associated 14 environmental pressure indicators using the Agribalyse database completed by the SHARP database for GHGe. Five protein profiles were identified according to the high contributions of ruminant meat, pork, poultry, fish, or, conversely, as low contribution from meat. The profile including the lowest protein from meat had the lowest impact on almost all environmental indicators and had the lowest long-term risk. Conversely, the profile with high protein from ruminant-based foods had the highest pressures on most environmental indicators, including GHGe. We found that the protein profile with low contribution from meat has great potential for human health and environment preservation. Shifting a large part of the population toward this profile could be an easy first step toward building a more sustainable diet.
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Affiliation(s)
- Elie Perraud
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 91120, Palaiseau, France
| | - Juhui Wang
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 91120, Palaiseau, France
| | - Marion Salomé
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 91120, Palaiseau, France
| | - François Mariotti
- Université Paris-Saclay, AgroParisTech, INRAE, UMR PNCA, 91120, Palaiseau, France.
| | - Emmanuelle Kesse-Guyot
- Sorbonne Paris Nord University, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement (INRAE), Conservatoire National des Arts et Métiers (CNAM), Nutritional Epidemiology Research Team (EREN), Epidemiology and Statistics Research Center-University of Paris (CRESS), Bobigny, France
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11
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Coelho-Júnior HJ, Calvani R, Picca A, Savera G, Tosato M, Landi F, Marzetti E. Protein Intake from Various Foods Sources Is Negatively Associated with Cardiometabolic Risk Markers in Italian Older Adults. J Nutr Health Aging 2023; 27:853-860. [PMID: 37960908 DOI: 10.1007/s12603-023-1981-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 06/01/2023] [Indexed: 11/15/2023]
Abstract
OBJECTIVES To examine the relationships between protein intake from various food sources and cardiometabolic risk markers in Italian older adults. DESIGN Cross-sectional study. SETTING Unconventional settings across Italy (e.g., exhibitions, health promotion campaigns). PARTICIPANTS People 65+ years who provided a written informed consent. MEASUREMENTS Blood pressure (BP), blood glucose, total blood cholesterol, and anthropometric indices were assessed. Daily protein intake was estimated for 12 food items listed in a food frequency questionnaire. RESULTS Three-thousand four-hundred twenty-four older adults (mean age: 72.7 ± 5.7 years; 55% women) were included in the study. Results of linear regression analysis indicated that protein intake from several food sources was negatively associated with BP, waist and hip circumferences, and waist-to-hip ratio in both sexes. Blood glucose levels were inversely associated with many protein sources in women. Positive associations were observed between some protein sources and total blood cholesterol in both men and women. CONCLUSION Our findings suggest that dietary protein is differentially associated with cardiometabolic risk factors depending on sex and food sources.
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Affiliation(s)
- H J Coelho-Júnior
- Hélio José Coelho-Júnior, Emanuele Marzetti, Department of Geriatrics, Orthopedics and Rheumatology, Università Cattolica del Sacro Cuore, 00168 Rome, Italy, (H.J.C.-J.), (E.M.); +39 (06) 3015-4859
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12
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Damigou E, Kosti RI, Panagiotakos DB. White Meat Consumption and Cardiometabolic Risk Factors: A Review of Recent Prospective Cohort Studies. Nutrients 2022; 14:nu14245213. [PMID: 36558372 PMCID: PMC9781954 DOI: 10.3390/nu14245213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 11/24/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Although the association between meat consumption and cardiovascular diseases (CVDs) has been extensively investigated, studies focusing specifically on the relationship between white meat consumption and CVD risk factors are fewer with controversial findings. The aim was to evaluate the relationship between white meat consumption and the incidence of cardiometabolic risk factors. A comprehensive literature search of PubMed articles was conducted from 2010 to 2022 (1 November), according to PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) guidelines. Thirteen prospective cohort studies were selected studying mainly poultry, with the exception of one study that also analyzed rabbit meat. From the seven studies on the risk of type 2 diabetes mellitus, four studies found no association, two studies found positive associations, and two studies found inverse associations when comparing poultry to other meats. Of the two studies on the risk of hypertension, one observed no association and one a positive association. Of the two studies on weight management, one observed a positive association with weight gain, the other study observed the same relationship only for chicken with skin, while for chicken without skin a positive relationship with relative weight loss was found. As for metabolic syndrome and its components, two studies revealed inverse associations with white meat intake. Only fresh lean white meat consumption seems to have potential beneficial effects on cardiometabolic risk factors. Future research should scrutinize consumption habits related to white meat intake when investigating its association with cardiometabolic risk factors.
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Affiliation(s)
- Evangelia Damigou
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 176 76 Athens, Greece
| | - Rena I. Kosti
- Department of Nutrition and Dietetics, School of Physical Education, Sports and Dietetics, University of Thessaly, 382 21 Trikala, Greece
| | - Demosthenes B. Panagiotakos
- Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, 176 76 Athens, Greece
- Correspondence:
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13
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García-Gavilán J, Nishi S, Paz-Graniel I, Guasch-Ferré M, Razquin C, Clish CB, Toledo E, Ruiz-Canela M, Corella D, Deik A, Drouin-Chartier JP, Wittenbecher C, Babio N, Estruch R, Ros E, Fitó M, Arós F, Fiol M, Serra-Majem L, Liang L, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma Metabolite Profiles Associated with the Amount and Source of Meat and Fish Consumption and the Risk of Type 2 Diabetes. Mol Nutr Food Res 2022; 66:e2200145. [PMID: 36214069 PMCID: PMC9722604 DOI: 10.1002/mnfr.202200145] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 09/12/2022] [Indexed: 01/18/2023]
Abstract
SCOPE Consumption of meat has been associated with a higher risk of type 2 diabetes (T2D), but if plasma metabolite profiles associated with these foods reflect this relationship is unknown. The objective is to identify a metabolite signature of consumption of total meat (TM), red meat (RM), processed red meat (PRM), and fish and examine if they are associated with T2D risk. METHODS AND RESULTS The discovery population includes 1833 participants from the PREDIMED trial. The internal validation sample includes 1522 participants with available 1-year follow-up metabolomic data. Associations between metabolites and TM, RM, PRM, and fish are evaluated with elastic net regression. Associations between the profiles and incident T2D are estimated using Cox regressions. The profiles included 72 metabolites for TM, 69 for RM, 74 for PRM, and 66 for fish. After adjusting for T2D risk factors, only profiles of TM (Hazard Ratio (HR): 1.25, 95% CI: 1.06-1.49), RM (HR: 1.27, 95% CI: 1.07-1.52), and PRM (HR: 1.27, 95% CI: 1.07-1.51) are associated with T2D. CONCLUSIONS The consumption of TM, its subtypes, and fish is associated with different metabolites, some of which have been previously associated with T2D. Scores based on the identified metabolites for TM, RM, and PRM show a significant association with T2D risk.
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Affiliation(s)
- Jesús García-Gavilán
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Stephanie Nishi
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, Unity Health Toronto, ON, Canada
| | - Indira Paz-Graniel
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Cristina Razquin
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | | | - Estefanía Toledo
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Miguel Ruiz-Canela
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Dolores Corella
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Amy Deik
- The Broad Institute of Harvard and MIT, Boston, MA, USA
| | - Jean-Philippe Drouin-Chartier
- Centre Nutrition, Santé et Société, Institut sur la Nutrition et les Aliments Fonctionnels, Faculté de Pharmacie, Université Laval, Québec, Canada
| | - Clemens Wittenbecher
- Toronto 3D (Diet, Digestive Tract and Disease) Knowledge Synthesis and Clinical Trials Unit, Toronto, ON, Canada,Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany,German Center for Diabetes Research, Neuherberg, Germany
| | - Nancy Babio
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - Ramon Estruch
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Internal Medicine, Institut d’Investigacions Biomèdiques August Pi Sunyer, Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Lipid Clinic, Department of Endocrinology and Nutrition, Agust Pi i Sunyer Biomedical Research Institute (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Fernando Arós
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Cardiology, University Hospital of Alava, Vitoria, Spain
| | - Miquel Fiol
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Institute of Health Sciences IUNICS, University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Lluís Serra-Majem
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Research Institute of Biomedical and Health Sciences IUIBS, University of Las Palmas de Gran Canaria, Las Palmas, Spain
| | - Liming Liang
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA,Department of Statistics, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Miguel A Martínez-González
- Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain,Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Department of Preventive Medicine and Public Health, Navarra Health Research Institute (IDISNA), University of Navarra, Pamplona, Spain
| | - Frank B Hu
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA,Channing Division for Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA,Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Jordi Salas-Salvadó
- Universitat Rovira i Virgili, Departament de Bioquímica i Biotecnologia, Unitat de Nutrició Humana. Hospital Universitari San Joan de Reus, Reus, Spain,Institut d’Investigació Sanitària Pere Virgili (IISPV), Reus, Spain,Consorcio CIBER, Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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14
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Sun L, Du H, Zong G, Guo Y, Chen Y, Chen Y, Yin H, Pei P, Yang L, Chu Q, Yu C, Li Y, Lv J, Zheng H, Zhou P, Chen J, Li L, Chen Z, Lin X. Associations of erythrocyte polyunsaturated fatty acids with incidence of stroke and stroke types in adult Chinese: a prospective study of over 8000 individuals. Eur J Nutr 2022; 61:3235-3246. [PMID: 35445833 PMCID: PMC9363313 DOI: 10.1007/s00394-022-02879-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 03/22/2022] [Indexed: 11/04/2022]
Abstract
PURPOSE There is limited and inconsistent evidence about the relationships of erythrocyte polyunsaturated fatty acids (PUFAs) with stroke and stroke types, particularly in China where the stroke rates are high. We aimed to investigate the associations of different erythrocyte PUFAs with incidence of total stroke, ischemic stroke (IS), and intracerebral hemorrhage (ICH) in Chinese adults. METHODS In the prospective China Kadoorie Biobank, erythrocyte PUFAs were measured using gas chromatography in 10,563 participants who attended 2013-14 resurvey. After a mean follow-up of 3.8 years, 412 incident stroke cases (342 IS, 53 ICH) were recorded among 8,159 participants without prior vascular diseases or diabetes. Cox regression yielded adjusted hazard ratios (HRs) for stroke associated with 13 PUFAs. RESULTS Overall, the mean body mass index was 24.0 (3.4) kg/m2 and the mean age was 58.1 (9.9) years. In multivariable analyses, 18:2n-6 was positively associated with ICH (HR = 2.33 [95% CIs 1.41, 3.82] for top versus bottom quintile, Ptrend = 0.007), but inversely associated with IS (0.69 [0.53,0.90], Ptrend = 0.027), while 20:3n-6 was positively associated with risk of IS (1.64 [1.32,2.04], Ptrend < 0.001), but not with ICH. Inverted-U shape curve associations were observed of 20:5n-3 with IS (Pnonlinear = 0.002) and total stroke (Pnonlinear = 0.008), with a threshold at 0.70%. After further adjustment for conventional CVD risk factors and dietary factors, these associations remained similar. CONCLUSION Among relatively lean Chinese adults, erythrocyte PUFAs 18:2n-6, 20:3n-6 and 20:5n-3 showed different associations with risks of IS and ICH. These results would improve the understanding of stroke etiology.
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Affiliation(s)
- Liang Sun
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Huaidong Du
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Geng Zong
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Yan Chen
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Yiping Chen
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Huiyong Yin
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Ling Yang
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK
| | - Qianqian Chu
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yixue Li
- Bio-Med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - He Zheng
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Puchen Zhou
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China
| | - Junshi Chen
- China National Centre for Food Safety Risk Assessment, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute Building, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
| | - Xu Lin
- Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue-yang Rd, Shanghai, 200031, China.
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15
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Li J, Glenn AJ, Yang Q, Ding D, Zheng L, Bao W, Beasley J, LeBlanc E, Lo K, Manson JE, Philips L, Tinker L, Liu S. Dietary Protein Sources, Mediating Biomarkers, and Incidence of Type 2 Diabetes: Findings From the Women's Health Initiative and the UK Biobank. Diabetes Care 2022; 45:1742-1753. [PMID: 35713602 PMCID: PMC9346982 DOI: 10.2337/dc22-0368] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Accepted: 05/03/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Whether and how dietary protein intake is linked to type 2 diabetes (T2D) remains unclear. The aim of this study was to investigate the associations of protein intake with development of T2D and the potential mediating roles of T2D biomarkers. RESEARCH DESIGN AND METHODS We included 108,681 postmenopausal women without T2D at baseline from the Women's Health Initiative (WHI) (primary cohort) and 34,616 adults without T2D from the U.K. Biobank (UKB) (replication cohort). Cox proportional hazard models were used for estimation of protein-T2D associations. Mediation analysis was performed to assess the mediating roles of biomarkers in case-control studies nested in the WHI. RESULTS In the WHI, 15,842 incident T2D cases were identified during a median follow-up of 15.8 years. Intake of animal protein was associated with increased T2D risk (hazard ratio in comparing the highest to the lowest quintile = 1.31 [95% CI 1.24-1.37]) and plant protein with decreased risk (0.82 [0.78-0.86]). Intakes of red meat, processed meat, poultry, and eggs were associated with increased T2D risk and whole grains with decreased risk. Findings from the UKB were similar. These findings were materially attenuated after additional adjustment for BMI. Substituting 5% energy from plant protein for animal protein was associated with 21% decreased T2D risk (0.79 [0.74-0.84]), which was mediated by levels of hs-CRP, interleukin-6, leptin, and SHBG. CONCLUSIONS Findings from these two large prospective cohorts support the notion that substituting plant protein for animal protein may decrease T2D risk mainly by reducing obesity-related inflammation.
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Affiliation(s)
- Jie Li
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI
| | - Andrea J Glenn
- Department of Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada.,Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Centre, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Qingling Yang
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Ding Ding
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Lingling Zheng
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wei Bao
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Jeannette Beasley
- Division of General Internal Medicine and Clinical Innovation, New York University Langone Health, New York, NY
| | - Erin LeBlanc
- Kaiser Permanente Center for Health Research, Portland, OR
| | - Kenneth Lo
- Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China
| | - JoAnn E Manson
- Brigham and Women's Hospital, Harvard Medical School, and Harvard T.H. Chan School of Public Health, Boston, MA
| | | | - Lesley Tinker
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA
| | - Simin Liu
- Global Health Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Department of Epidemiology and Center for Global Cardiometabolic Health, School of Public Health, Brown University, Providence, RI.,Departments of Surgery and Medicine, The Warren Alpert Medical School, Brown University, Providence, RI
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16
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Boonpor J, Petermann‐Rocha F, Parra‐Soto S, Pell JP, Gray SR, Celis‐Morales C, Ho FK. Types of diet, obesity, and incident type 2 diabetes: Findings from the UK Biobank prospective cohort study. Diabetes Obes Metab 2022; 24:1351-1359. [PMID: 35373896 PMCID: PMC9325356 DOI: 10.1111/dom.14711] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 03/28/2022] [Accepted: 03/30/2022] [Indexed: 12/13/2022]
Abstract
AIM To investigate the associations between types of diet and incident type 2 diabetes and whether adiposity mediated these associations. MATERIALS AND METHODS In total, 203 790 participants from UK Biobank (mean age 55.2 years; 55.8% women) without diabetes at baseline were included in this prospective study. Using the dietary intake data self-reported at baseline, participants were categorized as vegetarians (n = 3237), fish eaters (n = 4405), fish and poultry eaters (n = 2217), meat eaters (n = 178 004) and varied diet (n = 15 927). The association between type of diet and incident type 2 diabetes was investigated using Cox-proportional hazards models with a 2-year landmark analysis. The mediation role of adiposity was tested under a counterfactual framework. RESULTS After excluding the first 2 years of follow-up, the median follow-up was 5.4 (IQR: 4.8-6.3) years, during which 5067 (2.5%) participants were diagnosed with type 2 diabetes. After adjusting for lifestyle factors, fish eaters (HR 0.52 [95% CI: 0.39-0.69]) and fish and poultry eaters (HR 0.62 [95% CI: 0.45-0.88]) had a lower risk of incident type 2 diabetes compared with meat eaters. The association for vegetarians was not significant. Varied diet had a higher risk of type 2 diabetes. Obesity partially mediated the association of fish (30.6%), fish and poultry (49.8%) and varied (55.2%) diets. CONCLUSIONS Fish eaters, as well as fish and poultry eaters, were at a lower risk of incident type 2 diabetes than meat eaters, partially attributable to lower obesity risk.
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Affiliation(s)
- Jirapitcha Boonpor
- Institute of Cardiovascular and Medical SciencesUniversity of GlasgowGlasgowUK
- Faculty of Public Health, Chalermphrakiat Sakon Nakhon Province CampusKasetsart UniversitySakon NakhonThailand
| | - Fanny Petermann‐Rocha
- Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
- Faculty of MedicineUniversidad Diego PortalesSantiagoChile
| | - Solange Parra‐Soto
- Institute of Cardiovascular and Medical SciencesUniversity of GlasgowGlasgowUK
- Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
| | - Jill P. Pell
- Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
| | - Stuart R. Gray
- Institute of Cardiovascular and Medical SciencesUniversity of GlasgowGlasgowUK
| | - Carlos Celis‐Morales
- Institute of Cardiovascular and Medical SciencesUniversity of GlasgowGlasgowUK
- Center for Exercise Physiology Research (CIFE)University MayorSantiagoChile
- Human Performance Lab, Education, Physical Activity and Health Research UnitUniversity Católica del MauleTalcaChile
| | - Frederick K. Ho
- Institute of Health and WellbeingUniversity of GlasgowGlasgowUK
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Hosseini-Esfahani F, Beheshti N, Koochakpoor G, Mirmiran P, Azizi F. Meat Food Group Intakes and the Risk of Type 2 Diabetes Incidence. Front Nutr 2022; 9:891111. [PMID: 35845792 PMCID: PMC9280202 DOI: 10.3389/fnut.2022.891111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 06/01/2022] [Indexed: 11/13/2022] Open
Abstract
AimThis study aimed to evaluate the association of meats and their substitute food group intakes, including nuts, eggs, and legumes, with type 2 diabetes (T2D).MethodsFor this secondary analysis, we selected eligible adults (n = 6,112) from the Tehran Lipid and Glucose Study participants with a median follow-up of 6.63 years. Expert nutritionists assessed dietary intakes using a valid and reliable semiquantitative food frequency questionnaire. Biochemical and anthropometric variables were assessed at baseline and follow-up examinations. We used multivariable Cox proportional hazard regression models to estimate the new onset of T2D concerning meats and their substitute food groups.ResultsWe performed this study on 2,749 men and 3,363 women, aged 41.4 ± 14.2 and 39.1 ± 13.1 years, respectively. The number of participants with incident T2D was 549. After adjusting for confounders, legume [HR: 1, 0.74 (0.58–0.94), 0.69 (0.54–0.90), 0.65 (0.50–0.84), P-trend = 0.01)] was inversely associated with incident T2D. Fish intake [HR: 1, 1.0 (0.79–1.27), 1.17 (0.91–1.50), 1.14 (0.89–1.45), P-trend = 0.01)] was positively associated with incident T2D. In subjects who reported poultry consumption of 36.4–72.8 g/day, a positive association [HR: 1.33 (1.03–1.71)] between poultry intake and T2D risk was observed.ConclusionOur findings revealed that a diet rich in legumes significantly reduced the risk of T2D incidence, while a diet high in poultry increased the risk of T2D incidence, probably due to high-temperature cooking methods and environmental contaminants.
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Affiliation(s)
- Firoozeh Hosseini-Esfahani
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Niloofar Beheshti
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Parvin Mirmiran
- Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- *Correspondence: Parvin Mirmiran,
| | - Fereidoun Azizi
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Dietary pH Enhancement Improves Metabolic Outcomes in Diet-Induced Obese Male and Female Mice: Effects of Beef vs. Casein Proteins. Nutrients 2022; 14:nu14132583. [PMID: 35807769 PMCID: PMC9268221 DOI: 10.3390/nu14132583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/15/2022] [Accepted: 06/17/2022] [Indexed: 02/01/2023] Open
Abstract
(1) Consumption of diets that are caloric dense but not nutrient dense have been implicated in metabolic diseases, in part through low-grade metabolic acidosis. Mitigation strategies through dietary intervention to alleviate acidosis have not been previously reported. Our objective is to determine the effects of pH enhancement (with ammonia) in high fat diet-induced obese mice that were fed beef or casein as protein sources compared to low fat diet-fed mice. (2) Methods: B6 male and female mice were randomized (n = 10) into eight diets that differ in protein source, pH enhancement of the protein, and fat content, and fed for 13 weeks: low fat (11% fat) casein (LFC), LF casein pH-enhanced (LFCN), LF lean beef (LFB), LFBN, high fat (46%) casein (HFC), HFCN, HF beef (HFB), and HFBN. Body weights and composition, and glucose tolerance tests were conducted along with terminal serum analyses. Three-way ANOVA was performed. (3) Results: A significant effect of dietary fat (LF vs. HF) was observed across all variables in both sexes (final body weight, fat mass, glucose clearance, and serum leptin). Importantly, pH enhancement significantly reduced adiposity (males only) and final body weights (females only) and significantly improved glucose clearance in both sexes. Lastly, clear sex differences were observed across all variables. (4) Conclusions: Our findings demonstrate metabolic benefits of increasing dietary pH using ammonia, while high fat intake per se (not protein source) is the major contributor to metabolic dysfunctions. Additional research is warranted to determine mechanisms underlying the beneficial effects of pH enhancement, and interactions with dietary fat content and proteins.
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Wei YF, Sun ML, Wen ZY, Liu FH, Liu YS, Yan S, Qin X, Gao S, Li XQ, Zhao YH, Gong TT, Wu QJ. Pre-diagnosis meat intake and cooking method and ovarian cancer survival: results from the Ovarian Cancer Follow-Up Study (OOPS). Food Funct 2022; 13:4653-4663. [PMID: 35373791 DOI: 10.1039/d1fo03825g] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Objectives: The relationships between pre-diagnosis meat intake and ovarian cancer (OC) survival were limited and controversial. To date, no study has taken account of cooking methods. Thus, we aimed to firstly clarify these associations based on the Ovarian Cancer Follow-Up Study. Methods: This prospective cohort study, including 853 OC patients between 2015 and 2020, was conducted to examine the aforementioned associations. All women completed a food frequency questionnaire. Deaths were ascertained up to March 31, 2021 via medical records and active follow-up. We used the Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results: During the median follow-up of 37.17 months, 130 women died. Pre-diagnosis fish and seafood intake was associated with better survival (HRT3 vs. T1 = 0.46, 95% CI = 0.26-0.82, p trend <0.05), whereas processed red meat (HR = 1.54, 95% CI = 1.04-2.26) and a high frequency of fried fish intake (HR = 1.49, 95% CI = 1.03-2.16) were associated with worse survival than consuming none. After considering the interaction of cooking methods, we found that compared with the lowest tertile of fish and seafood intake and almost no fried fish cooking, women with the highest tertile of intake and almost no fried fish cooking had better survival (HR = 0.35, 95% CI = 0.13-0.92). Additionally, compared with the lowest tertile of fish and seafood intake and almost no baked fish cooking, women with the lowest tertile of intake and consuming baked fish had worse survival (HR = 3.75, 95% CI = 1.53-9.15). Conclusions: Pre-diagnosis fish and seafood intake was associated with better OC survival, whereas processed red meat intake was associated with worse survival. Cooking methods, especially for fried or baked fish, may play interaction effects with fish intake on OC survival.
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Affiliation(s)
- Yi-Fan Wei
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ming-Li Sun
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Zhao-Yan Wen
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Fang-Hua Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ya-Shu Liu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Shi Yan
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Xue Qin
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Song Gao
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Xiu-Qin Li
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
| | - Ting-Ting Gong
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China.
- Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China
- Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, Shenyang, China.
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20
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Wu W, Tang N, Zeng J, Jing J, Cai L. Dietary Protein Patterns during Pregnancy Are Associated with Risk of Gestational Diabetes Mellitus in Chinese Pregnant Women. Nutrients 2022; 14:nu14081623. [PMID: 35458185 PMCID: PMC9026337 DOI: 10.3390/nu14081623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/16/2022] Open
Abstract
Controversies around the association between dietary protein intake and gestational diabetes mellitus (GDM) persist. To the best of our knowledge, this association has not previously been reported from the perspective of dietary protein patterns. We aimed to investigate the relationship between dietary protein patterns and GDM risk in pregnant women, and 1014 pregnant women (20–28 weeks of gestation) were recruited in Guangzhou, China, during 2017–2018. Maternal dietary information was collected by a validated food frequency questionnaire, which covered the most common foods consumed in Guangzhou, China. GDM was identified by a 75g oral glucose tolerance test. A K-means cluster analysis was conducted to aggregate individuals into three groups, which were determined by the major sources of protein. Logistic regression was employed to explore the relationship between dietary protein patterns and the risk of GDM. Among the 1014 participants, 191 (18.84%) were diagnosed with GDM. In the total population, when comparing the highest quartile with the lowest, we found that total protein and animal protein intake increased the risk of GDM with the adjusted odds ratios (95%CI) being 6.27, 5.43 (1.71–23.03, 1.71–17.22), respectively. Pregnant women were further divided into three dietary protein patterns, namely, white meat, plant–dairy–eggs, and red meat protein patterns. Compared to women with the plant–dairy–eggs protein pattern, those with the red meat protein pattern (OR: 1.80; 95%CI: 1.06–3.07) or white meat protein pattern (OR: 1.83; 95%CI: 1.04–3.24) had an increased risk of GDM. Higher dietary intakes of total or animal protein during mid-pregnancy were related to an increased risk of GDM. Furthermore, we first found that, compared to women with the plant–dairy–eggs protein pattern, women with the red meat or white meat protein patterns had a higher risk of GDM.
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Affiliation(s)
- Weijia Wu
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (W.W.); (J.J.)
- Department of Scientific Research, Hainan Women and Children’s Medical Center, Haikou 570206, China
| | - Nu Tang
- Department of Health Care, Foshan Women and Children Hospital, Foshan 528000, China;
| | - Jingjing Zeng
- Evidence-Based Medicine Centre, Office of Academic Research, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441000, China;
| | - Jin Jing
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (W.W.); (J.J.)
| | - Li Cai
- Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; (W.W.); (J.J.)
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
- Correspondence: ; Tel.: +86-20-87334956
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21
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Gene–Environment Interaction on Type 2 Diabetes Risk among Chinese Adults Born in Early 1960s. Genes (Basel) 2022; 13:genes13040645. [PMID: 35456451 PMCID: PMC9024429 DOI: 10.3390/genes13040645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 04/02/2022] [Indexed: 02/01/2023] Open
Abstract
Background: Gene–environment interactions on type 2 diabetes (T2D) risk are studied little among Chinese adults. Aim: This study aimed to explore the interactions among Chinese adults born in early 1960s. Methods: The interaction of single nucleotide polymorphisms (SNPs) and environmental factors on T2D risk were analyzed by multiple linear or logistic regression models, and in total 2216 subjects were included with the age of 49.7 ± 1.5 years. Results: High dietary intake increased the effects of rs340874 on impaired fasting glucose (IFG), rs5015480, rs7612463 on T2D (OR = 2.27, 2.37, 11.37, respectively), and reduced the effects of rs7172432 on IFG, rs459193 on impaired glucose tolerance (IGT) (OR = 0.08, 0.28, respectively). The associations between rs4607517 and T2D, rs10906115 and IGT, rs4607103, rs5015480 and IFG could be modified by drinking/smoking (OR = 2.28, 0.20, 3.27, 2.58, respectively). Physical activity (PA) interacted with rs12970134, rs2191349, rs4607517 on T2D (OR = 0.39, 3.50, 2.35, respectively), rs2796441 and rs4607517 on IGT (OR = 0.42, 0.33, respectively), and rs4430796, rs5215, and rs972283 on IFG (OR = 0.39, 3.05, 7.96, respectively). Significant interactions were identified between socioeconomic status and rs10830963, rs13266634 on T2D (OR = 0.41, 0.44, respectively), rs1470579 and rs2796441 on IGT (OR = 2.13, 2.37, respectively), and rs7202877 and rs7612463 on IFG (OR = 5.64, 9.18, respectively). Conclusion: There indeed existed interactions between environmental factors and genetic variants on T2D risk among Chinese adults.
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22
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Yang J, Du H, Guo Y, Bian Z, Yu C, Chen Y, Yang L, Liu J, Han X, Chen J, Lv J, Li L, Chen Z. Coarse Grain Consumption and Risk of Cardiometabolic Diseases: A Prospective Cohort Study of Chinese Adults. J Nutr 2022; 152:1476-1486. [PMID: 35234872 PMCID: PMC9178969 DOI: 10.1093/jn/nxac041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 12/24/2021] [Accepted: 02/17/2022] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Lower consumption of whole grains is associated with higher risks of diabetes and coronary heart disease in Western populations, but evidence is still limited for stroke. Moreover, little is known in China, where the rates of cardiometabolic diseases are high and the grain types consumed are different from those in Western countries. OBJECTIVES To examine the associations between coarse-grain (e.g., millet, corn, and sorghum) consumption and incident cardiometabolic diseases among Chinese adults. METHODS The prospective China Kadoorie Biobank enrolled >0.5 million adults aged 30-79 years from 10 urban and rural areas during 2004-2008. At baseline, consumption frequencies (in 5 categories from "never" to "daily") of 12 major food groups, including coarse grains, were collected using a validated FFQ. After a median of 11 years of follow-up, 17,149 cases of diabetes, 29,876 ischemic strokes, 6097 hemorrhagic strokes, and 6704 major coronary events were recorded among 461,047 participants without a prevalence of major chronic diseases at baseline. Cox regression analyses were used to yield adjusted HRs for each disease associated with coarse-grain consumption. RESULTS Overall, 13.8% of participants reported regularly consuming (i.e., ≥4 days/week, regular consumers) and 29.4% reported never or rarely consuming coarse grains (i.e., nonconsumers) at baseline. Compared with nonconsumers, regular consumers had lower risks of diabetes (adjusted HR, 0.88; 95% CI, 0.78-0.98) and ischemic stroke (adjusted HR, 0.86; 95% CI, 0.81-0.93), but not hemorrhagic stroke (adjusted HR, 0.96; 95% CI, 0.76-1.20) or major coronary events (adjusted HR, 0.95; 95% CI, 0.81-1.12). For diabetes and ischemic stroke, each 100 g/day increase in the usual intake of coarse grains was associated with 14% (adjusted HR, 0.86; 95% CI, 0.76-0.97) and 13% (adjusted HR, 0.87; 95% CI, 0.81-0.94) lower risks, respectively, with similar results in various subgroups. CONCLUSIONS In Chinese adults, higher coarse-grain consumption is associated with lower risks of diabetes and ischemic stroke, supporting the promotion of coarse-grain consumption in China.
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Affiliation(s)
- Jiaomei Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, China
| | | | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Zheng Bian
- Chinese Academy of Medical Sciences, Beijing, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Yiping Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ling Yang
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Jiben Liu
- Yongqinglu Community Health Service, Qingdao, Shandong Province, China
| | - Xianyong Han
- Yongqinglu Community Health Service, Qingdao, Shandong Province, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Zhengming Chen
- Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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23
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Animal-based food choice and associations with long-term weight maintenance and metabolic health after a large and rapid weight loss: The PREVIEW study. Clin Nutr 2022; 41:817-828. [DOI: 10.1016/j.clnu.2022.02.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/21/2022] [Accepted: 02/01/2022] [Indexed: 02/06/2023]
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24
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Wang Q, Xie T, Zhang T, Deng Y, Zhang Y, Wu Q, Dong M, Luo X. The Role of Changes in Cumulative Lipid Parameter Burden in the Pathogenesis of Type 2 Diabetes Mellitus: A Cohort Study of People Aged 35-65 Years in Rural China. Diabetes Metab Syndr Obes 2022; 15:1831-1843. [PMID: 35733642 PMCID: PMC9208634 DOI: 10.2147/dmso.s363692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/03/2022] [Indexed: 11/23/2022] Open
Abstract
PURPOSE The main purpose of this study was to examine the effect of the cumulative exposure of blood lipid parameters on type 2 diabetes mellitus (T2DM). Another purpose was to explore whether the cumulative burden of blood lipid parameters plays a certain role in the pathogenesis of diet affecting T2DM. PATIENTS AND METHODS A total of 63 cases of diabetes occurred from 2017 to 2020, with an incidence density of 3.71 person-years. The dietary intake of the residents was obtained by using a dietary frequency questionnaire (FFQ). Cumulative lipid parameter burden was calculated according to the number of years (2016-2020) multiplied by total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL) and triglyceride (TG). A Cox proportional hazard model was used to estimate the effect of cumulative lipid burden on T2DM. A mediating analysis of accelerated failure time (AFT) was used to investigate the mediating effects of certain foods, the cumulative lipid parameter burden and T2DM. RESULTS A higher cumulative TG load corresponded to a higher risk of T2DM onset (Ptrend =0.021). After adjusting for covariates, the highest quartile cumulative TG burden had a 3.462 times higher risk of T2DM than that in the lowest quartile (HR=3.462, 95% CI: 1.297-9.243). Moreover, a higher cumulative HDL load corresponded to a lower risk of T2DM onset (Ptrend =0.006). After adjusting for covariates, the risk of T2DM was 0.314-fold lower in the highest quartile of cumulative HDL burden than that in the lowest quartile (HR=0.314, 95% CI: 0.131-0.753). Cumulative TG burden partially mediated the association between red meat and T2DM. CONCLUSION The increase in cumulative HDL burden and the decrease in cumulative HDL burden are related to the incidence of T2DM. Cumulative TG burden was shown to play a partial mediating role in the pathogenesis of red meat and diabetes.
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Affiliation(s)
- Qi Wang
- Key Laboratory of Cardio Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou, People’s Republic of China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Tao Xie
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Ting Zhang
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Yuanjia Deng
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Yuying Zhang
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Qingfeng Wu
- Key Laboratory of Cardio Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou, People’s Republic of China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Minghua Dong
- Key Laboratory of Cardio Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou, People’s Republic of China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
| | - Xiaoting Luo
- Key Laboratory of Cardio Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou, People’s Republic of China
- School of Public Health and Health Management, Gannan Medical University, Ganzhou, People’s Republic of China
- School of General Medicine, Gannan Medical University, Ganzhou, People’s Republic of China
- Correspondence: Xiaoting Luo, Tel +86 13677975578, Fax +86 0797-8169600, Email
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Liu M, Liu C, Zhang Z, Zhou C, Li Q, He P, Zhang Y, Li H, Qin X. Quantity and variety of food groups consumption and the risk of diabetes in adults: A prospective cohort study. Clin Nutr 2021; 40:5710-5717. [PMID: 34743048 DOI: 10.1016/j.clnu.2021.10.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 09/25/2021] [Accepted: 10/01/2021] [Indexed: 02/02/2023]
Abstract
BACKGROUND Evidence remains inconsistent regarding the association between quantity of food groups and diabetes, and remains scarce regarding the relation of dietary diversity with diabetes. OBJECTIVE We aimed to evaluate the prospective relation of variety and quantity of 12 major food groups with new-onset diabetes. METHODS A total of 16,117 participants who were free of diabetes at baseline from China Health and Nutrition Survey were included. Dietary intake and variety score were measured by three consecutive 24-h dietary recalls combined with a household food inventory in each survey round. The study outcome was new-onset diabetes, defined as self-reported physician-diagnosed diabetes during the follow-up period. Cox proportional hazards models and restricted cubic spline analysis were used to estimate hazard ratios (HR) and dose-response relation, respectively. RESULTS During a median follow-up duration of 9.0 years, a total of 1088 (6.7%) participants developed new-onset diabetes. Overall, there was a significant inverse association between dietary variety score and the risk of new-onset diabetes (per one point increment; HR, 0.85; 95% CI, 0.80-0.90). In addition, there were U-shaped associations of refined grains, whole grains, nuts, red meat, poultry, processed meat, dairy products, and aquatic products intake with diabetes, and L-shaped associations of legumes, vegetables, fruits, and eggs intake with diabetes (all P values for nonlinearity <0.001). CONCLUSION Our results suggested that greater variety of food groups consumption was associated with significantly lower risk of new-onset diabetes. Furthermore, when the quantity of food groups intakes was relatively low, there was a negative correlation between the quantity of each different food group consumption and diabetes risk; however, when intake exceeded certain thresholds, the risks of new-onset diabetes increased or reached a plateau.
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Affiliation(s)
- Mengyi Liu
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Chengzhang Liu
- Institute of Biomedicine, Anhui Medical University, Hefei, 230032, China; Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, Anhui, 230032, China
| | - Zhuxian Zhang
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Chun Zhou
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Qinqin Li
- Institute of Biomedicine, Anhui Medical University, Hefei, 230032, China
| | - Panpan He
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Yuanyuan Zhang
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Huan Li
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China
| | - Xianhui Qin
- Division of Nephrology, Nanfang Hospital, Southern Medical University, National Clinical Research Center for Kidney Disease, State Key Laboratory of Organ Failure Research, Guangdong Provincial Institute of Nephrology, Guangdong Provincial Key Laboratory of Renal Failure Research, Guangzhou Regenerative Medicine and Health Guangdong Laboratory, Guangzhou, 510515, China.
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Zhang R, Fu J, Moore JB, Stoner L, Li R. Processed and Unprocessed Red Meat Consumption and Risk for Type 2 Diabetes Mellitus: An Updated Meta-Analysis of Cohort Studies. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182010788. [PMID: 34682532 PMCID: PMC8536052 DOI: 10.3390/ijerph182010788] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 01/11/2023]
Abstract
Type II diabetes mellitus (T2DM) is a metabolic disorder that occurs in the body because of decreased insulin activity and/or insulin secretion. The incidence of T2DM has rapidly increased over recent decades. The relation between consumption of different types of red meats and risk of T2DM remains uncertain. This meta-analysis was conducted to quantitatively assess the associations of processed red meat (PRM) and unprocessed red meat (URM) consumption with T2DM. We searched PubMed, Embase, Web of Science and The Cochrane Library for English-language cohort studies published before January 2021. Summary relative risks (RR) with 95% confidence interval (CI) were estimated using fixed effects and random effects. Additionally, dose-response relationships were explored using meta-regression. Fifteen studies (n = 682,963 participants, cases = 50,675) were identified. Compared with the lowest intake group, high consumption of PRM and URM increased T2DM risk by 27% (95% CI 1.15-1.40) and 15% (95% CI 1.08-1.23), respectively. These relationships were consistently strongest for U.S-based studies, though the effects of sex are inconclusive. In conclusion, PRM and URM are both positively associated with T2DM incidence, and these relationships are strongest in the U.S. reduction of red meat consumption should be explored as a target for T2DM prevention initiatives.
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Affiliation(s)
- Rui Zhang
- College of Life Sciences, South-Central University for Nationalities, Wuhan 430074, China
| | - Jialin Fu
- Department of Healthcare Management, School of Health Sciences, Wuhan University, Wuhan 430071, China
| | - Justin B Moore
- Department of Implementation Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
- Department of Epidemiology & Prevention, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
- Department of Family & Community Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA
| | - Lee Stoner
- Department of Exercise & Sport Science, University of North Carolina, Chapel Hill, NC 27101, USA
| | - Rui Li
- Department of Healthcare Management, School of Health Sciences, Wuhan University, Wuhan 430071, China
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The Impact of Lifestyle Intervention on Dietary Quality among Rural Women with Previous Gestational Diabetes Mellitus-A Randomized Controlled Study. Nutrients 2021; 13:nu13082642. [PMID: 34444802 PMCID: PMC8402030 DOI: 10.3390/nu13082642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 07/27/2021] [Accepted: 07/29/2021] [Indexed: 12/03/2022] Open
Abstract
Healthy diet is essential to type 2 diabetes mellitus (T2DM) prevention for women with previous gestational diabetes mellitus (GDM). To evaluate the effect of a lifestyle intervention program on diet quality for rural women who were previously diagnosed with GDM, we conducted a randomized controlled study in two counties located in south-central China. A total of 404 eligible women were allocated into an intervention group and control group. Participants in the intervention group received 6-month lifestyle intervention including six group seminars and eight telephone consultations. Dietary data were collected at baseline and 18 months via a 24 h dietary recall, and dietary quality was measured by two indicators, Chinese Healthy Eating Score (CHEI) and Minimum Dietary Diversity for Women (MDD-W). Baseline CHEI scores (54.4 vs. 53.5, p = 0.305) and the proportions of participants who met MDD-W (73.8% vs. 74.5%, p = 0.904) were comparable between the two groups. The intervention group achieved a higher CHEI score (62.2 vs. 58.9, p = 0.001) and higher MDD-W proportion (90.6% vs. 81.2%, p = 0.023) at 18 months. Lifestyle intervention was associated with the change of CHEI (p = 0.049) but not with MDD-W (p = 0.212). In conclusion, compared with usual care, lifestyle intervention resulted in greater improvement of dietary quality among rural women with previous GDM.
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Chinese Guideline on the Primary Prevention of Cardiovascular Diseases. CARDIOLOGY DISCOVERY 2021; 1:70-104. [DOI: 10.1097/cd9.0000000000000025] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Abstract
Cardiovascular disease is the leading cause of mortality in China. Primary prevention of cardiovascular disease with a focus on lifestyle intervention and risk factor control has been shown to effectively delay or prevent the occurrence of cardiovascular events. To promote a healthy lifestyle and enhance the detection, diagnosis, and treatment of cardiovascular risk factors such as hypertension, dyslipidemia, and diabetes, and to improve the overall capacity of primary prevention of cardiovascular disease, the Chinese Society of Cardiology of Chinese Medical Association has collaborated with multiple societies to summarize and evaluate the latest evidence with reference to relevant guidelines and subsequently to develop recommendations for primary cardiovascular disease prevention in Chinese adults. The guideline consists of 10 sections: introduction, methodology for developing the guideline, epidemiology of cardiovascular disease in China and challenges in primary prevention, general recommendations for primary prevention, assessment of cardiovascular risk, lifestyle intervention, blood pressure control, lipid management, management of type 2 diabetes, and use of aspirin. The promulgation and implementation of this guideline will play a key role in promoting the practice of primary prevention for cardiovascular disease in China.
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Pastorino S, Bishop T, Sharp SJ, Pearce M, Akbaraly T, Barbieri NB, Bes-Rastrollo M, Beulens JWJ, Chen Z, Du H, Duncan BB, Goto A, Härkänen T, Hashemian M, Kromhout D, Järvinen R, Kivimaki M, Knekt P, Lin X, Lund E, Magliano DJ, Malekzadeh R, Martínez-González MÁ, O’Donoghue G, O’Gorman D, Poustchi H, Rylander C, Sawada N, Shaw JE, Schmidt M, Soedamah-Muthu SS, Sun L, Wen W, Wolk A, Shu XO, Zheng W, Wareham NJ, Forouhi NG. Heterogeneity of Associations between Total and Types of Fish Intake and the Incidence of Type 2 Diabetes: Federated Meta-Analysis of 28 Prospective Studies Including 956,122 Participants. Nutrients 2021; 13:1223. [PMID: 33917229 PMCID: PMC8068031 DOI: 10.3390/nu13041223] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/05/2021] [Accepted: 04/05/2021] [Indexed: 01/25/2023] Open
Abstract
The association between fish consumption and new-onset type 2 diabetes is inconsistent and differs according to geographical location. We examined the association between the total and types of fish consumption and type 2 diabetes using individual participant data from 28 prospective cohort studies from the Americas (6), Europe (15), the Western Pacific (6), and the Eastern Mediterranean (1) comprising 956,122 participants and 48,084 cases of incident type 2 diabetes. Incidence rate ratios (IRRs) for associations of total fish, shellfish, fatty, lean, fried, freshwater, and saltwater fish intake and type 2 diabetes were derived for each study, adjusting for a consistent set of confounders and combined across studies using random-effects meta-analysis. We stratified all analyses by sex due to observed interaction (p = 0.002) on the association between fish and type 2 diabetes. In women, for each 100 g/week higher intake the IRRs (95% CIs) of type 2 diabetes were 1.02 (1.01-1.03, I2 = 61%) for total fish, 1.04 (1.01-1.07, I2 = 46%) for fatty fish, and 1.02 (1.00-1.04, I2 = 33%) for lean fish. In men, all associations were null. In women, we observed variation by geographical location: IRRs for total fish were 1.03 (1.02-1.04, I2 = 0%) in the Americas and null in other regions. In conclusion, we found evidence of a neutral association between total fish intake and type 2 diabetes in men, but there was a modest positive association among women with heterogeneity across studies, which was partly explained by geographical location and types of fish intake. Future research should investigate the role of cooking methods, accompanying foods and environmental pollutants, but meanwhile, existing dietary regional, national, or international guidelines should continue to guide fish consumption within overall healthy dietary patterns.
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Affiliation(s)
- Silvia Pastorino
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
- Department of Population Health, Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London WC1E 7HT, UK
| | - Tom Bishop
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
| | - Stephen J. Sharp
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
| | - Matthew Pearce
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
| | - Tasnime Akbaraly
- Inserm U 1198, Montpellier University, F-34000 Montpellier, France;
- Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 7HB, UK;
| | - Natalia B. Barbieri
- Postgraduate Program in Epidemiology Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90040-060, Brazil; (N.B.B.); (B.B.D.); (M.S.)
| | - Maira Bes-Rastrollo
- Department of Preventive Medicine and Public Health, University of Navarra, 31008 Pamplona, Spain; (M.B.-R.); (M.Á.M.-G.)
- CIBERobn, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Navarra’s Health Research Institute (IdiSNA), 31008 Pamplona, Spain
| | - Joline W. J. Beulens
- Department of Epidemiology & Data Science, Amsterdam Public Health, Amsterdam Cardiovascular Sciences, Amsterdam UMC—Amsterdam VUMC, 1081 HV Amsterdam, The Netherlands;
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, 3584 CG Utrecht, The Netherlands
| | - Zhengming Chen
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (Z.C.); (H.D.)
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Huaidong Du
- MRC Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK; (Z.C.); (H.D.)
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Bruce B. Duncan
- Postgraduate Program in Epidemiology Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90040-060, Brazil; (N.B.B.); (B.B.D.); (M.S.)
| | - Atsushi Goto
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo 104-0045, Japan; (A.G.); (N.S.)
| | - Tommi Härkänen
- Department of Public Health Solutions, Finnish Institute for Health and Welfare (THL), FI-00271 Helsinki, Finland; (T.H.); (P.K.)
| | - Maryam Hashemian
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran 1411713135, Iran; (M.H.); (R.M.); (H.P.)
- Biology Department, School of Arts and Sciences, Utica College, Utica, NY 13502, USA
| | - Daan Kromhout
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands;
| | - Ritva Järvinen
- Institute of Public Health and Nutrition, University of Eastern Finland, FI-70211 Kuopio, Finland;
| | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, 1-19 Torrington Place, London WC1E 7HB, UK;
| | - Paul Knekt
- Department of Public Health Solutions, Finnish Institute for Health and Welfare (THL), FI-00271 Helsinki, Finland; (T.H.); (P.K.)
| | - Xu Lin
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai, Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (X.L.); (L.S.)
| | - Eiliv Lund
- Department of Community Medicine, Pb. 5060, UiT The Arctic University of Norway, 9037 Tromsø, Norway; (E.L.); (C.R.)
- The Cancer Registry of Norway, 0379 Oslo, Norway
| | - Dianna J. Magliano
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia; (D.J.M.); (J.E.S.)
| | - Reza Malekzadeh
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran 1411713135, Iran; (M.H.); (R.M.); (H.P.)
| | - Miguel Ángel Martínez-González
- Department of Preventive Medicine and Public Health, University of Navarra, 31008 Pamplona, Spain; (M.B.-R.); (M.Á.M.-G.)
- CIBERobn, Instituto de Salud Carlos III, 28029 Madrid, Spain
- Navarra’s Health Research Institute (IdiSNA), 31008 Pamplona, Spain
- Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Boston, MA 02115, USA
| | - Gráinne O’Donoghue
- School of Public Health, Physiotherapy & Sports Science, University College Dublin, Belfield, DO4 Dublin, Ireland;
| | - Donal O’Gorman
- School of Health & Human Performance, National Institute for Cellular Biotechnology, Dublin City University, Whitehall, DO9 Dublin, Ireland;
| | - Hossein Poustchi
- Digestive Disease Research Center, Digestive Disease Research Institute, Tehran University of Medical Sciences, Tehran 1411713135, Iran; (M.H.); (R.M.); (H.P.)
| | - Charlotta Rylander
- Department of Community Medicine, Pb. 5060, UiT The Arctic University of Norway, 9037 Tromsø, Norway; (E.L.); (C.R.)
| | - Norie Sawada
- Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, Tokyo 104-0045, Japan; (A.G.); (N.S.)
| | - Jonathan E. Shaw
- Baker Heart and Diabetes Institute, 75 Commercial Road, Melbourne, VIC 3004, Australia; (D.J.M.); (J.E.S.)
| | - Maria Schmidt
- Postgraduate Program in Epidemiology Faculdade de Medicina, Universidade Federal do Rio Grande do Sul (UFRGS), Porto Alegre 90040-060, Brazil; (N.B.B.); (B.B.D.); (M.S.)
| | - Sabita S. Soedamah-Muthu
- Center of Research on Psychological and Somatic Disorders (CORPS), Department of Medical and Clinical Psychology, Tilburg University, P.O. Box 90153, 5000 LE Tilburg, The Netherlands;
- Institute for Food, Nutrition and Health, University of Reading, Reading RG6 6AR, UK
| | - Liang Sun
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, Shanghai, Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China; (X.L.); (L.S.)
| | - Wanqing Wen
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA; (W.W.); (X.-O.S.); (W.Z.)
| | - Alicja Wolk
- Department of Surgical Sciences, Orthopaedics, Uppsala University, 75185 Uppsala, Sweden;
- Institute of Environmental Medicine, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA; (W.W.); (X.-O.S.); (W.Z.)
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, 2525 West End Avenue, Nashville, TN 37203, USA; (W.W.); (X.-O.S.); (W.Z.)
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
| | - Nita G. Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge Biomedical Campus Cambridge, University of Cambridge School of Clinical Medicine, Cambridge CB2 0QQ, UK; (T.B.); (S.J.S.); (M.P.); (N.J.W.)
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Chen GC, Arthur R, Qin LQ, Chen LH, Mei Z, Zheng Y, Li Y, Wang T, Rohan TE, Qi Q. Association of Oily and Nonoily Fish Consumption and Fish Oil Supplements With Incident Type 2 Diabetes: A Large Population-Based Prospective Study. Diabetes Care 2021; 44:672-680. [PMID: 33431419 PMCID: PMC7896269 DOI: 10.2337/dc20-2328] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 12/04/2020] [Indexed: 02/06/2023]
Abstract
OBJECTIVE To evaluate associations of oily and nonoily fish consumption and fish oil supplements with incident type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS We included 392,287 middle-aged and older participants (55.0% women) in the UK Biobank who were free of diabetes, major cardiovascular disease, and cancer and had information on habitual intake of major food groups and use of fish oil supplements at baseline (2006-2010). Of these, 163,706 participated in one to five rounds of 24-h dietary recalls during 2009-2012. RESULTS During a median 10.1 years of follow-up, 7,262 incident cases of T2D were identified. Compared with participants who reported never consumption of oily fish, the multivariable-adjusted hazard ratios of T2D were 0.84 (95% CI 0.78-0.91), 0.78 (0.72-0.85), and 0.78 (0.71-0.86) for those who reported <1 serving/week, weekly, and ≥2 servings/week of oily fish consumption, respectively (P-trend < 0.001). Consumption of nonoily fish was not associated with risk of T2D (P-trend = 0.45). Participants who reported regular fish oil use at baseline had a 9% (95% CI 4-14%) lower risk of T2D compared with nonusers. Baseline regular users of fish oil who also reported fish oil use during at least one of the 24-h dietary recalls had an 18% (8-27%) lower risk of T2D compared with constant nonusers. CONCLUSIONS Our findings suggest that consumption of oily fish but not nonoily fish was associated with a lower risk of T2D. Use of fish oil supplements, especially constant use over time, was also associated with a lower risk of T2D.
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Affiliation(s)
- Guo-Chong Chen
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Rhonda Arthur
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Li-Qiang Qin
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, China
| | - Li-Hua Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, China
| | - Zhendong Mei
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Yan Zheng
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Fudan University, Shanghai, China
| | - Yang Li
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Tao Wang
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Thomas E Rohan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY
| | - Qibin Qi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY .,Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
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Ibsen DB, Steur M, Imamura F, Overvad K, Schulze MB, Bendinelli B, Guevara M, Agudo A, Amiano P, Aune D, Barricarte A, Ericson U, Fagherazzi G, Franks PW, Freisling H, Quiros JR, Grioni S, Heath AK, Huybrechts I, Katze V, Laouali N, Mancini F, Masala G, Olsen A, Papier K, Ramne S, Rolandsson O, Sacerdote C, Sánchez MJ, Santiuste C, Simeon V, Spijkerman AMW, Srour B, Tjønneland A, Tong TYN, Tumino R, van der Schouw YT, Weiderpass E, Wittenbecher C, Sharp SJ, Riboli E, Forouhi NG, Wareham NJ. Replacement of Red and Processed Meat With Other Food Sources of Protein and the Risk of Type 2 Diabetes in European Populations: The EPIC-InterAct Study. Diabetes Care 2020; 43:2660-2667. [PMID: 32868270 PMCID: PMC7576430 DOI: 10.2337/dc20-1038] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 07/24/2020] [Indexed: 02/03/2023]
Abstract
OBJECTIVE There is sparse evidence for the association of suitable food substitutions for red and processed meat on the risk of type 2 diabetes. We modeled the association between replacing red and processed meat with other protein sources and the risk of type 2 diabetes and estimated its population impact. RESEARCH DESIGN AND METHODS The European Prospective Investigation into Cancer (EPIC)-InterAct case cohort included 11,741 individuals with type 2 diabetes and a subcohort of 15,450 participants in eight countries. We modeled the replacement of self-reported red and processed meat with poultry, fish, eggs, legumes, cheese, cereals, yogurt, milk, and nuts. Country-specific hazard ratios (HRs) for incident type 2 diabetes were estimated by Prentice-weighted Cox regression and pooled using random-effects meta-analysis. RESULTS There was a lower hazard for type 2 diabetes for the modeled replacement of red and processed meat (50 g/day) with cheese (HR 0.90, 95% CI 0.83-0.97) (30 g/day), yogurt (0.90, 0.86-0.95) (70 g/day), nuts (0.90, 0.84-0.96) (10 g/day), or cereals (0.92, 0.88-0.96) (30 g/day) but not for replacements with poultry, fish, eggs, legumes, or milk. If a causal association is assumed, replacing red and processed meat with cheese, yogurt, or nuts could prevent 8.8%, 8.3%, or 7.5%, respectively, of new cases of type 2 diabetes. CONCLUSIONS Replacement of red and processed meat with cheese, yogurt, nuts, or cereals was associated with a lower rate of type 2 diabetes. Substituting red and processed meat by other protein sources may contribute to the prevention of incident type 2 diabetes in European populations.
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Affiliation(s)
- Daniel B Ibsen
- Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K
| | - Marinka Steur
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K
| | - Fumiaki Imamura
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K
| | - Kim Overvad
- Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Matthias B Schulze
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Nutritional Sciences, University of Potsdam, Nuthetal, Germany
| | - Benedetta Bendinelli
- Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Marcela Guevara
- Navarre Public Health Institute, Pamplona, Spain
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Navarra Institute for Health Research (IdiSNA), Pamplona, Spain
| | - Antonio Agudo
- Unit of Nutrition and Cancer, Catalan Institute of Oncology (ICO), and Nutrition and Cancer Group, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain
| | - Pilar Amiano
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Public Health Division of Gipuzkoa, San Sebastian, Spain
- Instituto Biodonostia, Basque Government, San Sebastian, Spain
| | - Dagfinn Aune
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
- Department of Nutrition, Bjørknes University College, Oslo, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital Ullevål, Oslo, Norway
| | | | - Ulrika Ericson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Guy Fagherazzi
- Digital Epidemiology and e-Health Research Hub, Department of Population Health, Luxembourg Institute of Health, Luxembourg
- Center of Epidemiology and Population Health, UMR 1018, INSERM, Paris South-Paris Saclay University, Gustave Roussy Institute, Villejuif, France
| | | | - Heinz Freisling
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | | | - Sara Grioni
- Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alicia K Heath
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
| | - Inge Huybrechts
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Verena Katze
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Nasser Laouali
- Center of Epidemiology and Population Health, UMR 1018, INSERM, Paris South-Paris Saclay University, Gustave Roussy Institute, Villejuif, France
| | - Francesca Mancini
- Center of Epidemiology and Population Health, UMR 1018, INSERM, Paris South-Paris Saclay University, Gustave Roussy Institute, Villejuif, France
| | - Giovanna Masala
- Cancer Risk Factors and Lifestyle Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy
| | - Anja Olsen
- Research Unit for Epidemiology, Department of Public Health, Aarhus University, Aarhus, Denmark
- Danish Cancer Society Research Center, Copenhagen, Denmark
| | - Keren Papier
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Stina Ramne
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Olov Rolandsson
- Family Medicine, Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Carlotta Sacerdote
- Unit of Cancer Epidemiology, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza di Torino and Center for Cancer Prevention (CPO), Turin, Italy
| | - Maria-José Sánchez
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Escuela Andaluza de Salud Pública, Granada, Spain
- Instituto de Investigación Biosanitaria ibs. Granada, Granada, Spain
- Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain
| | - Carmen Santiuste
- CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
- Department of Epidemiology, Murcia Regional Health Authority, IMIB-Arrixaca, Murcia, Spain
| | - Vittorio Simeon
- Department of Mental and Physical Health and Preventive Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | | | - Bernard Srour
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Anne Tjønneland
- Danish Cancer Society Research Center, Copenhagen, Denmark
- Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Tammy Y N Tong
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, U.K
| | - Rosario Tumino
- Cancer Registry and Histopathology Department, Azienda Sanitaria Provinciale, Ragusa, Italy
- Associazone Iblea per la Ricerca Epidemiologica - Organizazione Non Lucrativa di Utilità Sociale (AIRE-ONLUS), Ragusa, Italy
| | - Yvonne T van der Schouw
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Elisabete Weiderpass
- International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Clemens Wittenbecher
- Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Stephen J Sharp
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K
| | - Elio Riboli
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, U.K
| | - Nita G Forouhi
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K.
| | - Nick J Wareham
- MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, U.K
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Zhang B, Xiong K, Cai J, Ma A. Fish Consumption and Coronary Heart Disease: A Meta-Analysis. Nutrients 2020; 12:nu12082278. [PMID: 32751304 PMCID: PMC7468748 DOI: 10.3390/nu12082278] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/26/2020] [Accepted: 07/27/2020] [Indexed: 12/14/2022] Open
Abstract
Epidemiological studies on the impact of fish consumption on coronary heart disease (CHD) incidence have shown inconsistent results. In addition, in terms of CHD mortality, although previous meta-analyses showed that fish consumption reduces the risk of CHD, six newly incorporated studies show that fish consumption has no impact on CHD. Therefore, the results still need to be verified. The purpose of this study is to quantitatively evaluate the impact of fish consumption on CHD incidence and mortality. Relevant studies were identified from PubMed, Web of Science, and Embase databases up to October 2019. The multivariate-adjusted relative risks (RRs) for the highest versus the lowest fish consumption categories and the 95% confidence intervals were computed with a random-effect model. A restricted cubic spline regression model was used to assess the dose–response relationship between fish consumption and CHD incidence and mortality. Forty prospective cohort studies were incorporated into research. Among them, 22 studies investigated the association between fish consumption and CHD incidence (28,261 cases and 918,783 participants), and the summary estimate showed that higher fish consumption was significantly associated with a lower CHD incidence [RR: 0.91, 95% CI: (0.84, 0.97); I2 = 47.4%]. Twenty-seven studies investigated the association between fish consumption and CHD mortality (10,568 events and 1,139,553 participants), and the summary estimate showed that higher fish intake was significantly associated with a lower CHD mortality [RR: 0.85, 95% CI: (0.77, 0.94); I2 = 51.3%]. The dose–response analysis showed that the CHD incidence and mortality were reduced by 4%, respectively, with a 20 g/day increment in fish consumption. This meta-analysis indicates that fish consumption is associated with a lower CHD incidence and mortality.
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Affiliation(s)
| | | | | | - Aiguo Ma
- Correspondence: ; Tel.: +86-138-0542-2696
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33
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Aschemann-Witzel J, Gantriis RF, Fraga P, Perez-Cueto FJA. Plant-based food and protein trend from a business perspective: markets, consumers, and the challenges and opportunities in the future. Crit Rev Food Sci Nutr 2020; 61:3119-3128. [PMID: 32654499 DOI: 10.1080/10408398.2020.1793730] [Citation(s) in RCA: 137] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The food sector is increasingly turning toward sustainability issues. A sustainable food system should provide sufficient, nutritious food for all within limited natural resources. Plant-based food and proteins are a recent, growing trend setting out to contribute to this challenge. However, food industry stakeholders need to be aware of the challenges and opportunities. This paper reviews the trend from a business perspective. It outlines the global drivers, market trends, market data observations, and consumer behavior factors of relevance, and pinpoints the strengths, weaknesses, opportunities and threats (SWOT) for food sector companies. Findings suggest that the policy and market context is favorable in the near future, but that consumer beliefs, perception and understanding has to change further for the business opportunity to grow on a larger scale. More innovations are needed, in particular in the direction of meat-replacements that are healthy as well as clean label.
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Affiliation(s)
| | | | - Paola Fraga
- Department of Management, MAPP Centre, Aarhus University, Aarhus, Denmark
| | - Federico J A Perez-Cueto
- Det Natur- og Biovidenskabelige Fakultet, Food Science, Kobenhavns Universitet, Frederiksberg C, Denmark
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Meat consumption: Which are the current global risks? A review of recent (2010-2020) evidences. Food Res Int 2020; 137:109341. [PMID: 33233049 PMCID: PMC7256495 DOI: 10.1016/j.foodres.2020.109341] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 05/10/2020] [Accepted: 05/17/2020] [Indexed: 12/29/2022]
Abstract
Consumption of fatty meats may increase risks of cardiovascular diseases and cancer. Production of red meats increases greenhouse gases (GHG) emissions contributing to the global warming. Consumption of wild meats can pose some serious risks of transmission of viruses from animals to humans.
Meat consumption has been increasing since the 1960s, but especially from the 1980s decade to today. Although meat means an important source of nutrients, it is also evident that a great consumption of this source of proteins has also a negative environmental impact. Livestock production does not only have a negative influence on GHG emissions, but also on the water footprint, water pollution, and water scarcity. With respect to human health, in 2015 the International Agency for Research on Cancer (IARC) stated that red meat was a probable carcinogen to humans (Group 2A), while consumption of processed meat was carcinogenic to humans (Group 1). Most environmental contaminants (PCDD/Fs, PCBs, PBDEs, PCNs, etc.) that are frequently found in meats are highly soluble in fats. Therefore, avoiding ingesting fats from red meats and meat products, doubtless would help in the prevention, not only of the well-known cardiovascular diseases derived of fats consumption, but also of certain kinds of cancers, mainly colorectal cancer. On the other hand, consumption of meat – especially wild meat – is related to virus infections, as many viruses have been found in wild meat trade markets. Based on the scientific literature here reviewed, we have noted that the results of the investigations conducted after the statement of the IARC, have corroborated the recommendation of reducing significantly the consumption of red meats and meat products. In turn, the reduction of meat consumption should contribute to the reduction of GHG emissions and their considerable impact on global warming and climate change. It seems evident that human dietary habits regarding meat consumption in general, and red meats and wild meats in particular, should be significantly modified downward, as much and as soon as possible.
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