Diabetes & Metabolism Journal

Review

Metabolic Risk/Epidemiology
Glucagon-Like Peptide-1: New Regulator in Lipid Metabolism: Tong Bu, Ziyan Sun, Yi Pan, Xia Deng, Guoyue Yuan; Diabetes Metab J. 2024;48(3):354-372. Published online April 1, 2024; DOI: https://doi.org/10.4093/dmj.2023.0277

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Glucagon-like peptide-1 (GLP-1) is a 30-amino acid peptide hormone that is mainly expressed in the intestine and hypothalamus. In recent years, basic and clinical studies have shown that GLP-1 is closely related to lipid metabolism, and it can participate in lipid metabolism by inhibiting fat synthesis, promoting fat differentiation, enhancing cholesterol metabolism, and promoting adipose browning. GLP-1 plays a key role in the occurrence and development of metabolic diseases such as obesity, nonalcoholic fatty liver disease, and atherosclerosis by regulating lipid metabolism. It is expected to become a new target for the treatment of metabolic disorders. The effects of GLP-1 and dual agonists on lipid metabolism also provide a more complete treatment plan for metabolic diseases. This article reviews the recent research progress of GLP-1 in lipid metabolism.

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Diabetes and Osteoarthritis: Exploring the Interactions and Therapeutic Implications of Insulin, Metformin, and GLP-1-Based Interventions
Iryna Halabitska, Liliia Babinets, Valentyn Oksenych, Oleksandr Kamyshnyi
Biomedicines.2024; 12(8): 1630. CrossRef
The Effect of Tirzepatide on Body Composition in People with Overweight and Obesity: A Systematic Review of Randomized, Controlled Studies
Vincenzo Rochira, Carla Greco, Stefano Boni, Francesco Costantino, Leonardo Dalla Valentina, Eleonora Zanni, Leila Itani, Marwan El Ghoch
Diseases.2024; 12(9): 204. CrossRef
Glucagon-like peptide-1 receptor: mechanisms and advances in therapy
Zhikai Zheng, Yao Zong, Yiyang Ma, Yucheng Tian, Yidan Pang, Changqing Zhang, Junjie Gao
Signal Transduction and Targeted Therapy.2024;[Epub] CrossRef
Recent Advances and Therapeutic Benefits of Glucagon-Like Peptide-1 (GLP-1) Agonists in the Management of Type 2 Diabetes and Associated Metabolic Disorders
John O Olukorode, Dolapo A Orimoloye, Nwachukwu O Nwachukwu, Chidera N Onwuzo, Praise O Oloyede, Temiloluwa Fayemi, Oluwatobi S Odunaike, Petra S Ayobami-Ojo, Nwachi Divine, Demilade J Alo, Chukwurah U Alex
Cureus.2024;[Epub] CrossRef
Incretin hormones: Revolutionizing the treatment landscape for kidney and liver diseases in type 2 diabetes and obesity
Jae Hyun Bae, Young Min Cho
Journal of Diabetes Investigation.2024;[Epub] CrossRef
Interactions between glucagon like peptide 1 (GLP-1) and estrogens regulates lipid metabolism
Jorge F.A. Model, Rafaella S. Normann, Éverton L. Vogt, Maiza Von Dentz, Marjoriane de Amaral, Rui Xu, Tsvetan Bachvaroff, Poli Mara Spritzer, J. Sook Chung, Anapaula S. Vinagre
Biochemical Pharmacology.2024; 230: 116623. CrossRef
Changes in 24-Hour Urine Chemistry in Patients with Nephrolithiasis during Weight Loss with Glucagon-Like Peptide 1–Based Therapies
Karen Feghali, Xilong Li, Naim M. Maalouf
Kidney360.2024; 5(11): 1706. CrossRef

Original Articles

Metabolic Risk/Epidemiology
Biologically Informed Polygenic Scores for Brain Insulin Receptor Network Are Associated with Cardiometabolic Risk Markers and Diabetes in Women: Jannica S. Selenius, Patricia P. Silveira, Mikaela von Bonsdorff, Jari Lahti, Hannu Koistinen, Riitta Koistinen, Markku Seppälä, Johan G. Eriksson, Niko S. Wasenius; Diabetes Metab J. 2024;48(5):960-970. Published online March 25, 2024; DOI: https://doi.org/10.4093/dmj.2023.0039

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Abstract PDF Supplementary Material PubReader ePub

Background
To investigate associations between variations in the co-expression-based brain insulin receptor polygenic score and cardiometabolic risk factors and diabetes mellitus.
Methods
This cross-sectional study included 1,573 participants from the Helsinki Birth Cohort Study. Biologically informed expression-based polygenic risk scores for the insulin receptor gene network were calculated for the hippocampal (hePRS-IR) and the mesocorticolimbic (mePRS-IR) regions. Cardiometabolic markers included body composition, waist circumference, circulating lipids, insulin-like growth factor 1 (IGF-1), and insulin-like growth factor-binding protein 1 and 3 (IGFBP-1 and -3). Glucose and insulin levels were measured during a standardized 2-hour 75 g oral glucose tolerance test and impaired glucose regulation status was defined by the World Health Organization 2019 criteria. Analyzes were adjusted for population stratification, age, smoking, alcohol consumption, socioeconomic status, chronic diseases, birth weight, and leisure-time physical activity.
Results
Multinomial logistic regression indicated that one standard deviation increase in hePRS-IR was associated with increased risk of diabetes mellitus in all participants (adjusted relative risk ratio, 1.17; 95% confidence interval, 1.01 to 1.35). In women, higher hePRS-IR was associated with greater waist circumference and higher body fat percentage, levels of glucose, insulin, total cholesterol, low-density lipoprotein cholesterol, triglycerides, apolipoprotein B, insulin, and IGFBP-1 (all P≤0.02). The mePRS-IR was associated with decreased IGF-1 level in women (P=0.02). No associations were detected in men and studied outcomes.
Conclusion
hePRS-IR is associated with sex-specific differences in cardiometabolic risk factor profiles including impaired glucose regulation, abnormal metabolic markers, and unfavorable body composition in women.

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A mesocorticolimbic insulin receptor gene co-expression network moderates the association between early life adversity and food approach eating behaviour style in childhood
Angela Marcela Jaramillo-Ospina, Roberta Dalle Molle, Sachin Patel, Shona Kelly, Irina Pokhvisneva, Carolina de Weerth, Patrícia Pelufo Silveira
Appetite.2025; 204: 107762. CrossRef

Metabolic Risk/Epidemiology
Iron Overload and the Risk of Diabetes in the General Population: Results of the Chinese Health and Nutrition Survey Cohort Study: He Gao, Jinying Yang, Wenfei Pan, Min Yang; Diabetes Metab J. 2022;46(2):307-318. Published online March 7, 2022; DOI: https://doi.org/10.4093/dmj.2020.0287

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Abstract PDF Supplementary Material PubReader ePub

Background
Recent studies have found that there are significant associations between body iron status and the development of diabetes. In the present study, we aimed to analyze the association among iron overload (IO), insulin resistance (IR), and diabetes in Chinese adults, and to explore the sex difference.
Methods
Men and women (age >19 years) who participated in the Chinese Health and Nutrition Survey and did not have diabetes at baseline were followed between 2009 and 2015 (n=5,779). Over a mean of 6 years, 75 participants were diagnosed with incident diabetes. Logistic regression was used to assess the risk factors associated with IO. Cox proportional hazard regression was used to estimate the risk of incident diabetes and to determine whether the risk differed among subgroups. Causal mediation analysis (CMA) was used to explore the mechanism linking IO and diabetes.
Results
According to sex-stratified multivariable-adjusted Cox proportional hazards regression, IO increased the risk of incident diabetes. Women with IO had a higher risk of diabetes than men. Subgroup analysis with respect to age showed that the association between IO and diabetes was stronger in older women and younger men (P<0.001). CMA showed that liver injury (alanine transaminase) and lipid metabolism abnormalities (triglyceride, apolipoprotein B) contributed to the association between IO and diabetes.
Conclusion
IO is associated with diabetes and this association is sex-specific. IO may indirectly induce IR via liver injury and lipid metabolism abnormalities, resulting in diabetes.

Citations

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Role of nutrition in diabetes mellitus and infections
Xue-Lu Yu, Li-Yun Zhou, Xiao Huang, Xin-Yue Li, Ming-Ke Wang, Ji-Shun Yang
World Journal of Clinical Cases.2025;[Epub] CrossRef
Micronutrient Patterns and Low Intake of Vitamin A, Vitamin D, Folate, Magnesium, and Potassium Among Prediabetes and Type 2 Diabetes Patients
Oana C Iatcu, Andrei Lobiuc, Mihai Covasa
Cureus.2024;[Epub] CrossRef
The levels, single and multiple health risk assessment of 23 metals in enteral nutrition formulas
Burhan Basaran, Hulya Turk
Food and Chemical Toxicology.2024; 192: 114914. CrossRef
Quantitative susceptibility mapping for iron monitoring of multiple subcortical nuclei in type 2 diabetes mellitus: a systematic review and meta-analysis
Sana Mohammadi, Sadegh Ghaderi, Fatemeh Sayehmiri, Mobina Fathi
Frontiers in Endocrinology.2024;[Epub] CrossRef
Iron Overload Induces Hepatic Ferroptosis and Insulin Resistance by Inhibiting the Jak2/stat3/slc7a11 Signaling Pathway
Manqiu Mo, Ling Pan, Ling Deng, Min Liang, Ning Xia, Yuzhen Liang
Cell Biochemistry and Biophysics.2024; 82(3): 2079. CrossRef
Diatom Chaetoceros Sp. as an Efficient Biological Antidote in Iron Toxicity: In‐Vitro and In‐Vivo Experiments
Zeinab Janahmadi, Shadi Talebi, Fatemeh Farjadian, Safieh Momeni
ChemistrySelect.2024;[Epub] CrossRef
Endogenous iron biomineralization in the mouse spleen of metabolic diseases
Ruowen Guo, Lei Zhang, Dongsheng Song, Biao Yu, Chao Song, Hanxiao Chen, Wenjing Xie, Chuanlin Feng, Guofeng Cheng, Kejun Hu, Jialiang Jiang, Zhe Qu, Haifeng Du, Xin Zhang
Fundamental Research.2024;[Epub] CrossRef
The Association Between Dietary Iron, the SNP of the JAZF1 rs864745, and Glucose Metabolism in a Chinese Population
Zihan Hu, Hongwei Liu, Baozhang Luo, Chunfeng Wu, Changyi Guo, Zhengyuan Wang, Jiajie Zang, Fan Wu, Zhenni Zhu
Nutrients.2024; 16(22): 3831. CrossRef
Plasma Ferritin Concentrations in the General Population: A Cross-Sectional Analysis of Anthropometric, Metabolic, and Dietary Correlates
Cara Övermöhle, Sabina Waniek, Gerald Rimbach, Katharina Susanne Weber, Wolfgang Lieb
The Journal of Nutrition.2023; 153(5): 1524. CrossRef
Association of Body Iron Metabolism with Type 2 Diabetes Mellitus in Chinese Women of Childbearing Age: Results from the China Adult Chronic Disease and Nutrition Surveillance (2015)
Jie Feng, Xiaoyun Shan, Lijuan Wang, Jiaxi Lu, Yang Cao, Lichen Yang
Nutrients.2023; 15(8): 1935. CrossRef
Iron overload induces islet β cell ferroptosis by activating ASK1/P-P38/CHOP signaling pathway
Ling Deng, Man-Qiu Mo, Jinling Zhong, Zhengming Li, Guoqiao Li, Yuzhen Liang
PeerJ.2023; 11: e15206. CrossRef
The role of ferroptosis in metabolic diseases
Ling Xie, Bin Fang, Chun Zhang
Biochimica et Biophysica Acta (BBA) - Molecular Cell Research.2023; 1870(6): 119480. CrossRef
Epidemiological and transcriptome data identify potential key genes involved in iron overload for type 2 diabetes
Xuekui Liu, Xiu Hong, Shiqiang Jiang, Rui Li, Qian Lv, Jie Wang, Xiuli Wang, Manqing Yang, Houfa Geng, Yang Li
Diabetology & Metabolic Syndrome.2023;[Epub] CrossRef
Association between serum iron and liver transaminases based on a large adult women population
Andong He, Zhuoping Zhou, Lili Huang, Ka Cheuk Yip, Jing Chen, Ruiling Yan, Ruiman Li
Journal of Health, Population and Nutrition.2023;[Epub] CrossRef
Association between serum ferritin and uric acid levels and nonalcoholic fatty liver disease in the Chinese population
Fangli Zhou, Xiaoli He, Dan Liu, Yan Ye, Haoming Tian, Li Tian
PeerJ.2023; 11: e16267. CrossRef
The Role of Iron Overload in Diabetic Cognitive Impairment: A Review
Ji-Ren An, Qing-Feng Wang, Gui-Yan Sun, Jia-Nan Su, Jun-Tong Liu, Chi Zhang, Li Wang, Dan Teng, Yu-Feng Yang, Yan Shi
Diabetes, Metabolic Syndrome and Obesity.2023; Volume 16: 3235. CrossRef
The Association Between METS-IR and Serum Ferritin Level in United States Female: A Cross-Sectional Study Based on NHANES
Han Hao, Yan Chen, Ji Xiaojuan, Zhang Siqi, Chu Hailiang, Sun Xiaoxing, Wang Qikai, Xing Mingquan, Feng Jiangzhou, Ge Hongfeng
Frontiers in Medicine.2022;[Epub] CrossRef
Research Progress on Relationship Between Iron Overload and Lower Limb Arterial Disease in Type 2 Diabetes Mellitus
Zhongjing Wang, Shu Fang, Sheng Ding, Qin Tan, Xuyan Zhang
Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy.2022; Volume 15: 2259. CrossRef
Iron deficiency in cardiac surgical patients
L Hof, O Old, A.U. Steinbicker, P Meybohm, S Choorapoikayil, K Zacharowski
Acta Anaesthesiologica Belgica.2022; 73(4): 235. CrossRef

Effects of Smoking on Plasma Lipid Metabolism in Patients with non-insulin Dependent Diabetes Mellitus.: Seon Min Jeon, Yeun Kyung Lee, Hye Sung Lee, Bo Wan Kim, Young Bok Park, Myung Sook Choi; Korean Diabetes J. 1997;21(4):457-468. Published online January 1, 2001

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Abstract PDF: BACKGROUND
Diabetes mellitus has been identified as a risk factor in the development of coronary vascular disease. Smoking also has been known as an independent risk factor in the development of coronary artery disease, causing a dislipidemia. This study was carried out to examine the effects of smoking on plasma lipids and lipoproteins metabolism in patients with NIDDM and in normal healthy subjects among Korean population in Taegu. METHODS: The 80 patients with NIDDM and 60 normal subjects were suMivided into non-stnoker, ex-smoker, and smoker group. Antbropornetric assessments, mean intake of nutrients, and the levels of plasma lipids, Apo A-I, L,p(a), CETP activity, and antioxidant vitamins such as vitamin A, E were measured, RESULTS: WHR in non-smoker of patients with NIDDM was greater than that in non-smoker of normal control. There were no differences in the nutrient intakes among groups, but protein intake was even higher in smoker of NIDDM group than that of normal group. There were no smoking effect on total cholesterol, LDL-C, AI, Apo A-I, Lp(a) and lipid peroxide in plasma of two groups, but they were higher in NIDDM group than normal group. Plasma TG concentrations were higher in smoker group than other groups within normal group, HDL-C levels were lower in non-smoker group than other groups within NIDDM group. CETP activities were higher in smoker group than non-smoker within normal group. And CEPT activities in NIDDM group were mostly higher than those of normal group. Vit. A levels of non-smoker in normal group were higher than ex-smoker within same group, and were also higher than non-smoker in NIDDM group. Vit. E levels showed no difference within each group, but they were mostly lower in NIDDM group than normal group. CONCLUSION: It was concluded that smoking was not a major factor for changing lipid metabolism in NIDDM patients as well as normal subjects unlike others findings. Their abnormal lipid rnetabolism may be induced from other risk factors for NIDDM rather than smoking itself. However, present study was done only for a short period, thus more studies are needed for longer term to investigate the effects af smoking on lipid metabolism in NIDDM among Korean population.

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