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Cao C, Hu H, Xiao P, Zan Y, Chang X, Han Y, Zhang X, Wang Y. Nonlinear relationship between triglyceride-glucose index and the risk of prediabetes and diabetes: a secondary retrospective cohort study. Front Endocrinol (Lausanne) 2024; 15:1416634. [PMID: 39381440 PMCID: PMC11460547 DOI: 10.3389/fendo.2024.1416634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 09/05/2024] [Indexed: 10/10/2024] Open
Abstract
BACKGROUND The triglyceride-glucose (TyG) index, recognized for its cost-efficiency and simplicity, serves as an accessible indicator of insulin resistance. Yet, its correlation with the risk of prediabetes and diabetes (Pre-DM/DM) in the Chinese demographic remains uncertain. Consequently, our study explored the association between the TyG index and the development of Pre-DM/DM within the Chinese population. METHODS The retrospective cohort study was carried out utilizing data from a health screening initiative. The study included 179541 adults over 20 who underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. The correlation between the TyG index and Pre-DM/DM risk was investigated using Cox regression analysis. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore their non-linear connection. RESULTS The mean age of study participants was 41.18 ± 12.20 years old, and 95255 (53.05%) were male. During a median follow-up of 3.01 years, 21281 (11.85%) participants were diagnosed with Pre-DM/DM. After adjusting the potential confounding factors, the results showed that the TyG index was positively correlated with incident Pre-DM/DM (HR: 1.67, 95%CI: 1.62-1.71, P< 0.001). Additionally, a non-linear association was observed between the TyG index and the onset of Pre-DM/DM, with an inflection point identified at 8.73. Hazard ratios (HR) to the left and right of this inflection point were 1.95 (95%CI: 1.86-2.04) and 1.34 (95%CI: 1.27-1.42), respectively. Furthermore, sensitivity analyses confirmed the stability of these findings. CONCLUSION The TyG index exhibited a non-linear positive relationship with the risk of Pre-DM/DM. These findings imply that maintaining the TyG index at a lower, specified threshold may be beneficial in mitigating the onset of Pre-DM/DM.
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Affiliation(s)
- Changchun Cao
- Department of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Haofei Hu
- Department of Nephrology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Peng Xiao
- Department of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Yibang Zan
- Department of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Xinru Chang
- Department of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Yong Han
- Department of Emergency, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Xiaohua Zhang
- Department of Rehabilitation, Shenzhen Dapeng New District Nan’ao People’s Hospital, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
| | - Yulong Wang
- Department of Rehabilitation, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, China
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Lin YC, Tu HP, Wang TN. Blood lipid profile, HbA1c, fasting glucose, and diabetes: a cohort study and a two-sample Mendelian randomization analysis. J Endocrinol Invest 2024; 47:913-925. [PMID: 37878156 DOI: 10.1007/s40618-023-02209-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 09/26/2023] [Indexed: 10/26/2023]
Abstract
PURPOSE The prevalence of diabetes is increasing worldwide. The associations between the lipid profile and glycated hemoglobin (HbA1c), fasting glucose, and diabetes remain unclear, so we aimed to perform a cohort study and a two-sample Mendelian randomization (MR) study to investigate the causality between blood lipid profile and HbA1c, fasting glucose, and diabetes. METHODS A total of 25,171 participants from the Taiwan Biobank were enrolled. We applied a cohort study and an MR study to assess the association between blood lipid profile and HbA1c, fasting glucose, and diabetes. The summary statistics were obtained from the Asian Genetic Epidemiology Network (AGEN), and the estimates between the instrumental variables (IVs) and outcomes were calculated using the inverse-variance weighted (IVW) method. A series of sensitivity analyses were performed. RESULTS In the cohort study, high-density lipoprotein cholesterol (HDL-C) was negatively associated with HbA1c, fasting glucose, and diabetes, while the causal associations between HDL-C and HbA1c (βIVW = - 0.098, p = 0.003) and diabetes (βIVW = - 0.594, p < 0.001) were also observed. Furthermore, there was no pleiotropy effect in this study using the MR-Egger intercept test and MR-PRESSO global test. CONCLUSIONS Our results support the hypothesis that a genetically determined increase in HDL-C is causally related to a reduction in HbA1c and a lower risk of diabetes.
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Affiliation(s)
- Y-C Lin
- Department of Public Health, College of Health Science, Kaohsiung Medical University, No. 100, Shi-Chuan 1st Rd, Kaohsiung, 807, Taiwan
| | - H-P Tu
- Department of Public Health and Environmental Medicine, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - T-N Wang
- Department of Public Health, College of Health Science, Kaohsiung Medical University, No. 100, Shi-Chuan 1st Rd, Kaohsiung, 807, Taiwan.
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Mohammed Saeed W, Nasser Binjawhar D. Association of Serum Leptin and Adiponectin Concentrations with Type 2 Diabetes Biomarkers and Complications Among Saudi Women. Diabetes Metab Syndr Obes 2023; 16:2129-2140. [PMID: 37465649 PMCID: PMC10351522 DOI: 10.2147/dmso.s405476] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 06/10/2023] [Indexed: 07/20/2023] Open
Abstract
Objective Hyperglycemia and insulin resistance (IR) put obese women with Type 2 diabetes mellitus (T2DM) at risk for cardiovascular disease (CVD). Methods 150 T2DM women aged 30-45 were studied cross-sectionally at Madinah Hospital lab to find T2DM risk factors and their association with adiponectin/leptin levels. Results Women with T2DM showed greater fasting blood glucose (FBG), hemoglobin A1c (HbA1c), triglycerides (TG), body mass index (BMI), waist circumference (WC), insulin resistance (IR), high-sensitivity C-reactive protein (hs-CRP), and CVD risk (high atherogenic index of plasma (AIP) and leptin), but decreased high-density lipoprotein (HDL-cholesterol) and poor insulin sensitivity with low adiponectin. Obese women with T2DM had increased leptin and reduced adiponectin. Leptin levels were significantly related to IR, BMI, and AIP (B= 3.97, P= 0.02) but not WC. Leptin levels were negatively correlated with insulin sensitivity (IS) and HDL-c (P< 0.05). In linear regression analysis, adiponectin levels had a significant association with IS and HDL-c (P= 0.03, P= 0.04) but an inverse relationship with IR, BMI, WC (B=-2.91, P= 0.04), and AIP (P< 0.05). Conclusion Increased leptin levels are related to high IR, AIP, and BMI among T2DM female patients. Similarly, adiponectin levels decrease IS and HDL-c. Therefore, obese T2DM women with high leptin and low adiponectin levels should be periodically checked to avoid or decrease consequences like CVD.
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Affiliation(s)
- Walaa Mohammed Saeed
- Department of Medical Laboratory Technology, Faculty of Applied Medical Science, Taibah University, Tayba, Medina, 42353, Saudi Arabia
| | - Dalal Nasser Binjawhar
- Department of Chemistry, College of Science, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudia Arabia
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Wu J, Wang X, Chen H, Yang R, Yu H, Wu Y, Hu Y. Type 2 Diabetes Risk and Lipid Metabolism Related to the Pleiotropic Effects of an ABCB1 Variant: A Chinese Family-Based Cohort Study. Metabolites 2022; 12:metabo12090875. [PMID: 36144279 PMCID: PMC9502507 DOI: 10.3390/metabo12090875] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/07/2022] [Accepted: 09/08/2022] [Indexed: 11/23/2022] Open
Abstract
The single nucleotide polymorphism (SNP) rs4148727 in ABCB1 (encoding p-glycoprotein) is associated with lipid levels; however, its association with type 2 diabetes (T2DM) and its the genetic correlation with lipid profiles and T2DM are unclear. We included 2300 participants from 593 families. A generalized estimating equations (GEE) model and Cox regression models were used to estimate the SNP’s effects on T2DM and lipid profiles. The participation of the SNP in T2DM pathogenesis through lipid-associated pathways was tested using mediation analysis. The G allele of the SNP was related to a 32% (6–64%, p = 0.015) increase in T2DM risk. It was also associated with a 10% (1–20%, p = 0.029), 17% (3–32%, p = 0.015), and 4% (1–7%, p = 0.015) increment in total cholesterol (TC), triglyceride (TG), and apolipoprotein A (Apo-A) concentrations, respectively. According to the mediation analysis, only TG (6.9%) and Apo-B (4.0%) had slight but significant mediation effects on the total impact of the SNP on T2DM. The pleiotropic effects of the ABCB1 variant on T2DM and lipids likely act via different pathways. The biological mechanisms should be verified in a future study.
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Affiliation(s)
- Junhui Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- School of Nursing, Peking University, No. 38 Xueyuan Road, Beijing 100191, China
| | - Xiaowen Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Hongbo Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- School of Nursing, Peking University, No. 38 Xueyuan Road, Beijing 100191, China
| | - Ruotong Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Huan Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yiqun Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Correspondence: (Y.W.); (Y.H.); Tel./Fax: +86-10-82801189 (Y.H.)
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Medical Informatics Center, Peking University, Beijing 100191, China
- Correspondence: (Y.W.); (Y.H.); Tel./Fax: +86-10-82801189 (Y.H.)
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Shih YL, Huang TC, Shih CC, Chen JY. Relationship between Leptin and Insulin Resistance among Community-Dwelling Middle-Aged and Elderly Populations in Taiwan. J Clin Med 2022; 11:jcm11185357. [PMID: 36143007 PMCID: PMC9505128 DOI: 10.3390/jcm11185357] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 12/04/2022] Open
Abstract
The relationship between leptin and insulin resistance among middle-aged and elderly populations in Asia is seldom reported. Our research included 398 middle-aged and elderly Taiwanese individuals. First, we divided participants into three groups according to the tertiles of the homeostasis model assessment of insulin resistance (HOMA-IR) to analyze the parameters between each group. Pearson’s correlation was then applied to calculate the correlation between HOMA-IR and cardiometabolic risk factors after adjusting for age. A scatter plot indicated a relationship between serum leptin levels and the HOMA-IR index. Finally, the coefficients of the serum leptin level and HOMA-IR were assessed by multivariate linear regression. The participants in the high HOMA-IR index group were more likely to have higher serum leptin levels. Meanwhile, the HOMA-IR index was positively correlated with serum leptin levels, even after adjusting for age. Serum leptin levels were positively correlated with the HOMA-IR index (β = 0.226, p < 0.01) in the multivariate linear regression after adjusting for age, sex, smoking, drinking, BMI, triglycerides, systolic blood pressure, fasting plasma glucose, uric acid, ALT, and creatinine. Furthermore, the leptin−creatinine ratio also showed a significantly positive relationship with HOMA-IR in the same multivariate linear regression model. In conclusion, serum leptin levels showed a positive relationship with insulin resistance in middle-aged and elderly people in Taiwan. Furthermore, serum leptin levels may be an independent risk factor for insulin resistance according to our study.
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Affiliation(s)
- Yu-Lin Shih
- Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan
| | - Tzu-Cheng Huang
- Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan
| | - Chin-Chuan Shih
- United Safety Medical Group, General Administrative Department, New Taipei City 242, Taiwan
| | - Jau-Yuan Chen
- Department of Family Medicine, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
- Correspondence:
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Zhu Z, Wang K, Hao X, Chen L, Liu Z, Wang C. Causal Graph Among Serum Lipids and Glycemic Traits: A Mendelian Randomization Study. Diabetes 2022; 71:1818-1826. [PMID: 35622003 DOI: 10.2337/db21-0734] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 05/16/2022] [Indexed: 11/13/2022]
Abstract
We systematically investigated the bidirectional causality among HDL cholesterol (HDL-C), LDL cholesterol (LDL-C), triglycerides (TGs), fasting insulin (FI), and glycated hemoglobin A1c (HbA1c) based on genome-wide association summary statistics of Europeans (n = 1,320,016 for lipids, 151,013 for FI, and 344,182 for HbA1c). We applied multivariable Mendelian randomization (MR) to account for the correlation among different traits and constructed a causal graph with 13 significant causal effects after adjusting for multiple testing (P < 0.0025). Remarkably, we found that the effects of lipids on glycemic traits were through FI from TGs (β = 0.06 [95% CI 0.03, 0.08] in units of 1 SD for each trait) and HDL-C (β = -0.02 [-0.03, -0.01]). On the other hand, FI had a strong negative effect on HDL-C (β = -0.15 [-0.21, -0.09]) and positive effects on TGs (β = 0.22 [0.14, 0.31]) and HbA1c (β = 0.15 [0.12, 0.19]), while HbA1c could raise LDL-C (β = 0.06 [0.03, 0.08]) and TGs (β = 0.08 [0.06, 0.10]). These estimates derived from inverse-variance weighting were robust when using different MR methods. Our results suggest that elevated FI was a strong causal factor of high TGs and low HDL-C, which in turn would further increase FI. Therefore, early control of insulin resistance is critical to reduce the risk of type 2 diabetes, dyslipidemia, and cardiovascular complications.
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Affiliation(s)
- Ziwei Zhu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kai Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xingjie Hao
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liangkai Chen
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong
| | - Chaolong Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Soremekun O, Karhunen V, He Y, Rajasundaram S, Liu B, Gkatzionis A, Soremekun C, Udosen B, Musa H, Silva S, Kintu C, Mayanja R, Nakabuye M, Machipisa T, Mason A, Vujkovic M, Zuber V, Soliman M, Mugisha J, Nash O, Kaleebu P, Nyirenda M, Chikowore T, Nitsch D, Burgess S, Gill D, Fatumo S. Lipid traits and type 2 diabetes risk in African ancestry individuals: A Mendelian Randomization study. EBioMedicine 2022; 78:103953. [PMID: 35325778 PMCID: PMC8941323 DOI: 10.1016/j.ebiom.2022.103953] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Dyslipidaemia is highly prevalent in individuals with type 2 diabetes mellitus (T2DM). Numerous studies have sought to disentangle the causal relationship between dyslipidaemia and T2DM liability. However, conventional observational studies are vulnerable to confounding. Mendelian Randomization (MR) studies (which address this bias) on lipids and T2DM liability have focused on European ancestry individuals, with none to date having been performed in individuals of African ancestry. We therefore sought to use MR to investigate the causal effect of various lipid traits on T2DM liability in African ancestry individuals. METHODS Using univariable and multivariable two-sample MR, we leveraged summary-level data for lipid traits and T2DM liability from the African Partnership for Chronic Disease Research (APCDR) (N = 13,612, 36.9% men) and from African ancestry individuals in the Million Veteran Program (Ncases = 23,305 and Ncontrols = 30,140, 87.2% men), respectively. Genetic instruments were thus selected from the APCDR after which they were clumped to obtain independent instruments. We used a random-effects inverse variance weighted method in our primary analysis, complementing this with additional sensitivity analyses robust to the presence of pleiotropy. FINDINGS Increased genetically proxied low-density lipoprotein cholesterol (LDL-C) and total cholesterol (TC) levels were associated with increased T2DM liability in African ancestry individuals (odds ratio (OR) [95% confidence interval, P-value] per standard deviation (SD) increase in LDL-C = 1.052 [1.000 to 1.106, P = 0.046] and per SD increase in TC = 1.089 [1.014 to 1.170, P = 0.019]). Conversely, increased genetically proxied high-density lipoprotein cholesterol (HDL-C) was associated with reduced T2DM liability (OR per SD increase in HDL-C = 0.915 [0.843 to 0.993, P = 0.033]). The OR on T2DM per SD increase in genetically proxied triglyceride (TG) levels was 0.884 [0.773 to 1.011, P = 0.072] . With respect to lipid-lowering drug targets, we found that genetically proxied 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) inhibition was associated with increased T2DM liability (OR per SD decrease in genetically proxied LDL-C = 1.68 [1.03-2.72, P = 0.04]) but we did not find evidence of a relationship between genetically proxied proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibition and T2DM liability. INTERPRETATION Consistent with MR findings in Europeans, HDL-C exerts a protective effect on T2DM liability and HMGCR inhibition increases T2DM liability in African ancestry individuals. However, in contrast to European ancestry individuals, LDL-C may increase T2DM liability in African ancestry individuals. This raises the possibility of ethnic differences in the metabolic effects of dyslipidaemia in T2DM. FUNDING See the Acknowledgements section for more information.
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Affiliation(s)
- Opeyemi Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Ville Karhunen
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland; Research Unit of Mathematical Sciences, University of Oulu, Oulu, Finland
| | - Yiyan He
- Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Skanda Rajasundaram
- Kellogg College, University of Oxford, Oxford, UK; Faculty of Medicine, Imperial College London, London, UK
| | - Bowen Liu
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
| | - Apostolos Gkatzionis
- MRC Integrative Epidemiology Unit, University of Bristol, UK; Population Health Sciences, Bristol Medical School, University of Bristol, UK
| | - Chisom Soremekun
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Brenda Udosen
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Hanan Musa
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Sarah Silva
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
| | - Christopher Kintu
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Richard Mayanja
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Mariam Nakabuye
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda
| | - Tafadzwa Machipisa
- Department of Medicine, University of Cape Town & Groote Schuur Hospital, Cape Town, South Africa; Department of Medicine, Hatter Institute for Cardiovascular Diseases Research in Africa (HICRA) & Cape Heart Institute (CHI), University of Cape Town, South Africa; Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON L8L 2X2, Canada
| | - Amy Mason
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Verena Zuber
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK
| | - Mahmoud Soliman
- Discipline of Pharmaceutical Chemistry, College of Health Sciences, University of KwaZulu-Natal, Durban, South Africa
| | | | - Oyekanmi Nash
- H3Africa Bioinformatics Network (H3ABioNet) Node, Centre for Genomics Research and Innovation, NABDA/FMST, Abuja, Nigeria
| | | | | | - Tinashe Chikowore
- Department of Pediatrics, MRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Faculty of Health Sciences, Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Dorothea Nitsch
- Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK
| | - Stephen Burgess
- MRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, UK; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, Medical School Building, St Mary's Hospital, Imperial College London, London, UK; Novo Nordisk Research Centre Oxford, Old Road Campus, Oxford, UK
| | - Segun Fatumo
- The African Computational Genomics (TACG) Research group, MRC/UVRI and LSHTM, Entebbe, Uganda; MRC/UVRI and LSHTM, Entebbe, Uganda; Department of Non-communicable Disease Epidemiology (NCDE), London School of Hygiene and Tropical Medicine, London, UK.
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Yu W, Zhou G, Fan B, Gao C, Li C, Wei M, Lv J, He L, Feng G, Zhang T. Temporal sequence of blood lipids and insulin resistance in perimenopausal women: the study of women's health across the nation. BMJ Open Diabetes Res Care 2022; 10:e002653. [PMID: 35351687 PMCID: PMC8966521 DOI: 10.1136/bmjdrc-2021-002653] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/13/2022] [Indexed: 11/05/2022] Open
Abstract
INTRODUCTION To explore the temporal relationship between blood lipids and insulin resistance in perimenopausal women. RESEARCH DESIGN AND METHODS The longitudinal cohort consisted of 1386 women (mean age 46.4 years at baseline) in the Study of Women's Health Across the Nation. Exploratory factor analysis was used to identify appropriate latent factors of lipids (total cholesterol (TC); triglyceride (TG); high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); lipoprotein A-I (LpA-I); apolipoprotein A-I (ApoA-I); apolipoprotein B (ApoB)). Cross-lagged path analysis was used to explore the temporal sequence of blood lipids and homeostasis model assessment of insulin resistance (HOMA-IR). RESULTS Three latent lipid factors were defined as: the TG factor, the cholesterol transport factor (CT), including TC, LDL-C, and ApoB; the reverse cholesterol transport factor (RCT), including HDL-C, LpA-I, and ApoA-I. The cumulative variance contribution rate of the three factors was 86.3%. The synchronous correlations between baseline TG, RCT, CT, and baseline HOMA-IR were 0.284, -0.174, and 0.112 (p<0.05 for all). After adjusting for age, race, smoking, drinking, body mass index, and follow-up years, the path coefficients of TG→HOMA-IR (0.073, p=0.004), and HOMA-IR→TG (0.057, p=0.006) suggested a bidirectional relationship between TG and HOMA-IR. The path coefficients of RCT→HOMA-IR (-0.091, P < 0.001) and HOMA-IR→RCT (-0.058, p=0.002) were also significant, but the path coefficients of CT→HOMA-IR (0.031, p=0.206) and HOMA-IR→CT (-0.028, p=0.113) were not. The sensitivity analyses showed consistent results. CONCLUSIONS These findings provide evidence that TG and the reverse cholesterol transport-related lipids are related with insulin resistance bidirectionally, while there is no temporal relationship between the cholesterol transport factor and insulin resistance.
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Affiliation(s)
- Wenhao Yu
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Guangshuai Zhou
- Department of Human Resources, Zibo Central Hospital, Zibo, Shandong, China
| | - Bingbing Fan
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Chaonan Gao
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Chunxia Li
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Mengke Wei
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jiali Lv
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Li He
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Guoshuang Feng
- Big Data and Engineering Research Center, Beijing Children's Hospital Capital Medical University, Beijing, China
| | - Tao Zhang
- Department of Biostatistics, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
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