Yiheng Zhang1 and Yajun Yao2,3* *Correspondence: Yajun Yao 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 Full list of author information is available at the end of the article

The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creati vecommons.org/licenses/by-nc-nd/4.0/

Abstract

Background Obesity is linked to a variety of metabolic issues, with hyperlipidemia being a crucial adjustable risk element for cardiovascular diseases (CVD). However, the connection between indicators of obesity with overall and CVD mortality in American adults with hyperlipidemia remains unknown.

Methods This research employed an extensive cohort drawn from the National Health and Nutrition Examination Survey (NHANES) (2003–2018). Hyperlipidemia was identified through either elevated lipid profiles or self-reported utilization of lipid-reducing medications. Obesity indicators (weight-adjusted waist index (WWI), waist-to-height ratio (WHtR), body mass index (BMI)) were evaluated by physical measurement data. Weighted Cox regression models and restricted cubic splines (RCS) were employed to assess the potential links between obesity indicators and mortality outcomes. Results were further validated through subgroup analyses to ensure robustness and reliability. The receiver operating characteristic (ROC) curve was utilized to evaluate the prognostic capability of obesity indicators for

Results This cohort study included data from 12,785 participants with hyperlipidemia. Over an average follow-up period of 8.4 years, a total of 1,454 deaths were documented, 380 of which were related to heart diseases. Cox analysis manifested that, after adjusting covariates, increased WWI was linked to a higher likelihood of overall and CVD mortality (both P<0.05). RCS analysis illustrated that BMI and WHtR had U-shaped relationships with the overall and CVD mortality. Conversely, a linear positive association was uncovered between WWI and mortality (both P>0.05 for nonlinearity). Age, alcohol consumption and chronic kidney disease had modifying effects on the relationship between WWI and total mortality among those with hyperlipidemia. The area under ROC indicated that WWI was more effective than for BMI and WHtR in predicting overall and CVD deaths.

Conclusions In US adults with hyperlipidemia, the connection between BMI, WHtR, with overall and CVD mortality followed a U-shaped pattern, whereas a positive linear correlation was identified between WWI and mortality. WWI has superior predictive capability for the prognosis of individuals with hyperlipidemia compared to BMI and WHtR. These findings provide new insights and targets for the health management of individuals affected by hyperlipidemia.

Keywords Obesity, Hyperlipidemia, Weight-adjusted waist index, Mortality, NHANES

Chuan Yang1*†, Tian-Bo Chai1†, Xing-Zhu Yao1 , Li Zhang1 , Wen-Ming Qin2 , Hong Liang3 , Qiong-Zhen He4 and Ze-Yu Zhao5*Chuan Yang and Tian-Bo Chai contributed equally to this work.

*Correspondence:Chuan Yang 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 Ze-Yu Zhao 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 Full list of author information is available at the end of the article

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creati  vecommons.org/licenses/by-nc-nd/4.0/.

Abstract

Objective This study aims to assess the impact of intravenous infusion of fospropofol disodium on lipid metabolism and the inflammatory response in individuals with hyperlipidemia.

Methods A total of 360 preoperative individuals with hyperlipidemia were selected and randomly assigned to either the treatment group or the control group, with 180 participants in each group. The treatment group received an induction dose of fospropofol disodium at 10 mg/kg intravenously, followed by maintenance at a rate of 10 mg/ (kg·h). The control group was administered propofol intravenously at 2 mg/kg for induction and maintained at 4 mg/(kg·h). All other medications were consistent between the two groups. Blood samples (3 ml of venous blood) were collected from patients at four-time points: 1 day before surgery (T0), 3 h after anesthesia induction (T1), 4 h post-surgery (T2), and 24 h post-surgery (T3), to measure levels of triglycerides (TG), cholesterol (CHOL), high density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), apolipoprotein A1 (ApoA1), and apolipoprotein B (ApoB). C-reactive protein (CRP) and interleukin-6 (IL-6) levels were assessed at T0 and T3. Sedation onset time and adverse reactions were recorded for both groups.

Results At T0, the control group exhibited increased TG, CHOL, LDL-C, ApoB, and the ApoB/ApoA1 ratio, while the ApoA1 level had decreased. The LDL-C level and the ApoB/ApoA1 ratio showed significant increases (P<0.01). Both groups showed elevated CRP and IL-6 levels at T3 (P<0.01). Compared to the control group, the treatment group demonstrated reduced levels of TG, CHOL, LDL-C, ApoB, and the ApoB/ApoA1 ratio at T1-T3, while ApoA1 levels were higher at T1-T2 (P<0.01 or P<0.05). The sedation onset time was notably longer in the treatment group, and the incidence of injection-related pain, respiratory depression, hypotension, and other adverse reactions was significantly lower (P<0.01).

Conclusion Compared with propofol, intravenous infusion of fospropofol disodium for more than 3 h during anesthesia has lesser impact on lipid metabolism in patients with hyperlipidemia and does not increase inflammatory factors levels.

Keywords Fospropofol disodium, Hyperlipidemia, Inflammation, Lipid metabolism disorders, Propofol

      Alessandra Macciotta ,1,2 Carlotta Sacerdote,3 Claudia Giachino,1 Chiara Di Girolamo,1 Matteo Franco,1 Yvonne T van der Schouw,4 Raul Zamora-Ros,5 Elisabete Weiderpass,6 Cloé Domenighetti,7 Alexis Elbaz ,7 Thérèse Truong,7 Claudia Agnoli,8 Benedetta Bendinelli,9 Salvatore Panico,10 Paolo Vineis ,11 Sofia Christakoudi,11,12 Matthias B Schulze,13,14,15 Verena Katzke,16 Rashmita Bajracharya,16 Christina C Dahm,17 Susanne Oksbjerg Dalton,18,19 Sandra M Colorado-Yohar,20,21,22 Conchi Moreno-Iribas,23 Pilar Amiano Etxezarreta,21,24,25 María José Sanchez,21,26,27 Nita G Forouhi,28 Nicholas Wareham,28 Fulvio Ricceri 1.

      Additional supplemental material is published online only. To view, please visit the journal online (https://doi.org/ 10.1136/jech-2024-222734). For numbered affiliations see end of article.

Correspondence to

      Professor Carlotta Sacerdote; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 NW and FR contributed equally. Received 10 July 2024 Accepted 19 November 2024© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ Group

To cite: Macciotta A, Sacerdote C, Giachino C, et al. J Epidemiol Community Health Epub ahead of print:[please include Day MonthYear]. doi:10.1136/jech- 2024-222734

ABSTRACT

Introduction Observational studies have shown that more educated people are at lower risk of developing type 2 diabetes (T2D). However, robust study designs are needed to investigate the likelihood that such a relationship is causal. This study used genetic instruments for education to estimate the effect of education on T2D using the Mendelian randomisation (MR) approach.

Methods Analyses have been conducted in the European Prospective Investigation into Cancer and Nutrition (EPIC)- InterAct study (more than 20000 individuals), a case-cohort study of T2D nested in the EPIC cohort. Education was measured as Years of Education and Relative Index of Inequality. Prentice-weighted Cox models were performed to estimate the association between education and T2D. One-sample MR analyses investigated whether genetic predisposition towards longer education was associated with risk of T2D and investigated potential mediators of the

Results MR estimates indicated a risk reduction of about 15% for each year of longer education on the risk of developing T2D, confirming the protective role estimated by observational models (HR 0.96, 95% CI 0.95 to 0.96). MR analyses on putative mediators showed a significant role of education on body mass index, alcohol consumption, adherence to the Mediterranean diet and smoking habits.

Conclusion The results supported the hypothesis that higher education is a protective factor for the risk of developing T2D. Based on its position in the causal chain, education may be antecedent of other known risk factors for T2D including unhealthy behaviours. These findings reinforce evidence obtained through observational study designs and bridge the gap between correlation and causation.

Stephanie J. Hanna1  · Rachel H. Bonami2,3,4,5  · Brian Corrie6,7  · Monica Westley8  · Amanda L. Posgai9  · Eline T. Luning Prak10  · Felix Breden6,7  · Aaron W. Michels11  · Todd M. Brusko9,12,13  · Type 1 Diabetes AIRR Consortium

Received: 26 May 2024 / Accepted: 19 August 2024 / Published online: 29 October 2024

© The Author(s) 2024

Extended author information available on the last page of the article

Abstract

Human molecular genetics has brought incredible insights into the variants that confer risk for the development of tissuespecific autoimmune diseases, including type 1 diabetes. The hallmark cell-mediated immune destruction that is characteristic of type 1 diabetes is closely linked with risk conferred by the HLA class II gene locus, in combination with a broad array of additional candidate genes influencing islet-resident beta cells within the pancreas, as well as function, phenotype and trafficking of immune cells to tissues. In addition to the well-studied germline SNP variants, there are critical contributions conferred by T cell receptor (TCR) and B cell receptor (BCR) genes that undergo somatic recombination to yield the Adaptive Immune Receptor Repertoire (AIRR) responsible for autoimmunity in type 1 diabetes. We therefore created the T1D TCR/ BCR Repository (The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository) to study these highly variable and dynamic gene rearrangements. In addition to processed TCR and BCR sequences, the T1D TCR/BCR Repository includes detailed metadata (e.g. participant demographics, disease-associated parameters and tissue type). We introduce the Type 1 Diabetes AIRR Consortium goals and outline methods to use and deposit data to this comprehensive repository. Our ultimate goal is to facilitate research community access to rich, carefully annotated immune AIRR datasets to enable new scientific inquiry and insight into the natural history and pathogenesis of type 1 diabetes.

Keywords AIRR · AIRR Data Commons · Autoantibodies · B cell receptors · FAIR data · Next-generation sequencing · Single-cell RNA-seq · T cell receptors · Type 1 diabetes

Abbreviations

AAb  Autoantibody/autoantibodies

ADC  AIRR Data Commons

AIM  Activation-induced marker

AIRR  Adaptive Immune Receptor Repertoire

AIRR-seq  AIRR sequencing

BCR  B cell receptor

CDR3  Complementarity determining region 3

FAIR  Findable, Accessible, Interoperable, Reusable

GEO  Gene Expression Omnibus

HPAP  Human Pancreas Analysis Program

IEDB  Immune Epitope Database

MiAIRR  Minimal information about AIRR

ML  Machine learning

pLN  Pancreatic lymph node(s)

SARS-CoV-2  Severe acute respiratory syndrome coronavirus 2

scRNA-seq  Single-cell RNA-seq

SRA  Sequence Read Archive 

T1D TCR/BCR Repository The Type 1 Diabetes T Cell Receptor and B Cell Receptor Repository

TCR  T cell receptor

TCRβ  T cell receptor β chain

Tfh   T follicular helper

Treg  Regulatory T cell(s)

VDJ  Variable, diversity and joining gene segments

Stephanie J. Hanna and Rachel H. Bonami contributed equally to this work. Aaron W. Michels and Todd M. Brusko contributed equally as joint senior authors.

Members of the Type 1 Diabetes AIRR Consortium are listed in the  Acknowledgements.