Category: 8. Health

Introduction

Gestational diabetes mellitus (GDM) refers to any form of glucose intolerance with onset or first recognition in the perinatal period.¹ Various studies have revealed that GDM could contribute to adverse pregnancy outcomes for both mothers and their offspring. For mothers, GDM patients are at higher risks of subsequent GDM, cardiovascular disease, dysglycemia, and type 2 diabetes.^2–4 For their offspring, GDM could bring out neonatal macrosomia, childhood obesity and metabolic syndrome.⁵ Moreover, the majority of pregnant women who experience GDM are young, which could affect their life for a longer time.

The prevalence of GDM is 1%−28% worldwide, with significant variations observed across different populations.⁶ These differences can be attributed to several factors, including geographic and ethnic predisposition, screening strategies and diagnostic criteria, as well as the varying risk factors.⁷ With improvements in living conditions and strengthened nutrition during pregnancy, the prevalence of GDM is increasing year by year.⁸ Although there are plenty of studies have explored the risk factors of GDM on genetic, lifestyle, diet, and other factors, the pathogenesis of GDM is still unclear. Current studies emphasized placental hormone-driven insulin resistance, β-cell dysfunction, and low-grade inflammation may collectively contribute to GDM development.⁹

Population GDM risk is increasing with the high prevalence of obesity. To provide sufficient energy for fetal growth, blood lipid levels rise as pregnancy progresses.¹⁰ Lipid metabolism is closely related to glycometabolism, which is regulated by insulin. Therefore, dyslipidemia has the potential to induce insulin resistance and the occurrence of GDM. Previous studies have demonstrated the relationships between lipid biomarkers and the risk of GDM. A meta-analysis based on 292 studies interpreted that blood triglyceride (TG) concentrations were significantly different between mothers with GDM and mothers without GDM.¹¹ A few studies showed that elevated serum TG levels and/or decreased high-density lipoprotein cholesterol (HDL−C) could contribute to the development of GDM.^12–16 These studies prompted that TG/HDL−C could be potential indicators of GDM, which is one of the atherogenic indexes.¹⁷

Currently, little information has been given to the risk of GDM derived from atherogenic indices,¹⁸ which were originally calculated to evaluate the risk of atherosclerosis.¹⁹ Notably, another indicator of dyslipidemia of remnant cholesterol, which refers to the cholesterol content within triglyceride-rich lipoprotein remnants (including very-low-density lipoprotein [VLDL], intermediate-density lipoprotein [IDL], and chylomicron remnants), may relate to the occurrence GDM. The connection between remnant cholesterol and the risk of type 2 diabetes has been well-documented, but evidence regarding its role in GDM risk remains insufficient.²⁰ While emerging studies have indicated a positive association between remnant cholesterol and GDM, none have specifically examined the predictive effect of remnant cholesterol on GDM risk.^21–23 Moreover, although dyslipidemia is a potential indicator of GDM as mentioned above, GDM patients might have abnormal atherogenic indices and remnant cholesterol but normal serum lipids at the same time. Therefore, it is still necessary to apply atherogenic indices for the assessment of GDM risk. To fill this gap, this study aimed to investigate the predictive effects of atherogenic indices and remnant cholesterol on the risk of GDM.

Materials and Methods

Study Population

This retrospective study was conducted at the Maternal and Child Health Hospital of Hubei Province, which is one of the largest tertiary hospitals focusing on maternal and child health care in Wuhan City, China. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province (2021IECXM005). First, the entire list of inpatients who delivered at this hospital from December 2020 to March 2022 was exported from the clinical information system. Second, patients whose outpatient examination data were complete were reviewed and selected. Complete data were defined as containing the essential independent variables of blood lipid indicators and the outcome variable of GDM diagnosis. There were a total of 6946 inpatients who underwent blood lipid examination in the 1st (0~13⁺⁶ weeks) or 2nd (14~27⁺⁶ weeks) trimester. Third, after removing missing maternal age (essential covariates, N = 26), missing family history of diabetes (essential covariates, N = 33), missing records of in vitro fertilization (IVF) (essential covariates, N = 4), missing body mass index (BMI) (essential covariates, N = 32), multiple pregnancies (have different risk of GDM compared to singleton pregnancies, N = 135), missing oral glucose tolerance test (OGTT) results (outcome measurement, N = 85), and type 1 or type 2 diabetes (pre-gestational dysglycemia confounders, N = 12), 6619 participants were included in this study.

Data Collection

Clinical data were exported from the Hospital’s electronic medical records from the clinical information system. General personal information was collected including maternal age, family history of diabetes (one or more clinically diagnosed diabetes patients within three generations), reproductive history, gestational weight gain, IVF, fasting plasma glucose (FPG) in the 1st trimester of pregnancy, and pre-pregnancy BMI (calculated by self-reported pre-pregnancy weight and height). Specifically, pre-pregnancy BMI was divided into four categories according to the Chinese standard of obesity: underweight (BMI < 18.5), healthy weight (18.5−23.9), overweight (24−27.9), and obese (≥28). Gestational weight gain was classified as insufficient, normal, or excessive according to the standard of recommendation for weight gain during pregnancy released by the Health Industry Standards of the People’s Republic of China.²⁴

Serum lipids, including TG, total cholesterol (TC), HDL−C, and low-density lipoprotein cholesterol (LDL−C) were obtained during the 1st or 2nd trimester of pregnancy. Atherogenic indices including TG/HDL−C, TC/HDL−C, and LDL−C/HDL−C, and remnant cholesterol were regarded as independent variables. All of the independent variables were classified by four interquartile ranges (IQRs).

The dependent variable of GDM was diagnosed according to the recommendations of the International Association of the Diabetes and Pregnancy Study Groups Consensus Panel.²⁵ A 75 g OGTT was performed at 24−28 weeks of gestation on the following criteria: fasting plasma glucose ≥5.1 mmol/L, and/or 1-hour plasma glucose ≥10.0 mmol/L, and/or 2-hour plasma glucose ≥8.5 mmol/L. GDM is diagnosed if one or more of the following glucose values is exceeded.

Data Analysis

The normality of continuous independent variables was examined by Shapiro–Wilk tests. Nonnormal continuous variables were classified into four categories by IQR or summarized as medium (P25, P75). The differences in the prevalence of GDM among subgroups of general personal variables and independent variables were examined by Chi-square tests for categorical variables and Wilcoxon–Mann–Whitney tests for continuous variables. Logistic regression analyses were performed to examine the associations between the independent variables and the risk of GDM. First, the risk of GDM was assessed by several unadjusted logistic regression models for the independent variables, including TC, TG, HDL−C, LDL−C, TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol. The corresponding odds ratios (ORs) and 95% confidence intervals (CIs) were obtained. Second, based on unadjusted logistic regression analysis, the associations between serum lipids and GDM were further adjusted for general personal variables including maternal age, family history of diabetes, parity history, pre-pregnancy BMI, IVF, FPG, and gestational weight gain. To explore the causal relationship between atherogenic indices, remnant cholesterol, and the risk of GDM, stratified analysis of 1st trimester and 2nd trimester were conducted in this study. The normality examinations, Chi-square tests, Wilcoxon–Mann–Whitney tests, and logistic regression analysis were performed with SAS 9.4 (SAS Institute Inc., Cary, NC, USA). Furthermore, the risk of GDM was predicted by nomogram analysis and decision curve analysis (DCA) to gauge the net benefit of identifying high-risk patients that ought to have intervention and the net reduction of unnecessary interventions. The potential nonlinear relationships between the atherogenic indices, remnant cholesterol and the risk of GDM were examined by the restricted cubic splines (RCS). We adopted an RCS with 4 knots, and the media of the atherogenic indices and remnant cholesterols were used as references to obtain the ORs. Nomogram analysis, DCA, and RCS regression analysis were performed with R 4.2.1 (The R Foundation for Statistical Computing, Vienna, Austria). A two-sided P < 0.05 was regarded as statistically significant.

Results

The results showed that the prevalence of GDM was 31.03% (Table 1). Nearly half of the participants were mothers aged 30−34 years (45.34%), and the median age of the participants was 31 years. The prevalence of GDM among the subgroups stratified by age was significantly different (P < 0.0001). Compared with mothers without a family history of diabetes, women with a family history of diabetes had a significantly higher prevalence of GDM (52.58% vs 29.91%, P < 0.0001). Compared with primiparous women, multiparas had a higher prevalence of GDM (34.06% vs 29.53%, P = 0.0002). The prevalence of GDM was higher among mothers who were fertilized by IVF (42.52% vs 30.57%, P < 0.0001). The higher the pre-pregnancy BMI of participants was, the greater the corresponding prevalence of GDM as well (P < 0.0001). Furthermore, the prevalence of GDM was higher among mothers with insufficient gestational weight gain than those with excessive gestational weight gain (52.00% vs 23.42%, P < 0.0001).

Table 1 General Personal Characteristics Between Gestational Diabetes Mellitus and Control Group

The serum atherogenic indices and remnant cholesterols were classified into four categories by IQR, and the differences in the continuity indicators were examined between the GDM group and the control group (Table 2). The prevalence of GDM among the Q4 group in terms of TG/HDL−C, TC/HDL−C, LDL-C/HDL−C, and remnant cholesterol was significantly higher than that among the Q1 group (42.25% vs 23.90%, P < 0.0001; 39.09% vs 23.65%, P < 0.0001; 38.37% vs 23.86%, P < 0.0001; 36.34% vs 24.62%, P < 0.0001; respectively). In line with the Chi-square analysis, the corresponding continuity indicators also showed significant differences between the GDM group and the control group. The results of the univariate analysis between the serum lipid indexes and GDM are provided in Table S1.

Table 2 Atherogenic Indices and Remnant Cholesterol Between Gestational Diabetes Mellitus and Control Group

Table 3 shows the effects of serum atherogenic indices and remnant cholesterol on the risk of GDM. Both adjusted and unadjusted logistic regression analyses presented that atherogenic indices and remnant cholesterol were significantly related to the risk of GDM. Compared to those of the lowest quartile, mothers in the highest quartile of TG/HDL−C had a 66% higher risk of GDM (adjusted OR = 1.66, 95% CI: 1.41, 1.96). Mothers in the highest quartile of TC/HDL−C, LDL-C/HDL−C, and remnant cholesterol demonstrated significantly elevated GDM risk compared to those in the lowest quartile, with adjusted ORs of 1.47 (95% CI: 1.24−1.73), 1.47 (95% CI: 1.24−1.73), and 1.39 (95% CI: 1.18−1.64), respectively. Moreover, Table S2 presented the results of multivariable analysis between serum lipid indexes and GDM stratified by stages of pregnancy. Groups in the highest quartiles of TG, TC, and LDL−C showed higher risks of GDM than did those in the lowest quartile. Furthermore, consistent with Table 3, all atherogenic indices and remnant cholesterol significantly predicted GDM risk stratified by pregnancy stage (Table S3).

Table 3 Multivariable Analysis Between Atherogenic Indices, Remnant Cholesterol and Gestational Diabetes Mellitus

To better visualize the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol, the results of the monogram analyses are provided in Figure 1. The Hosmer and Lemeshow goodness of fit test results for the four groups showed that all of the prediction models were significant (P > 0.05). The AUCs for the TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol predictive models were 0.73 (95% CI: 0.71, 0.74), 0.73 (95% CI: 0.71, 0.74), 0.73 (95% CI: 0.71, 0.74), and 0.72 (95% CI: 0.71, 0.74), respectively. The result of DCA analysis showed that all indicators demonstrated high net benefit (approximately 0.22), excellent sensitivity (>97%), and good negative predictive value (>88%) at a threshold of 0.1 (Figure 2).

Figure 1 The nomogram prediction of gestational diabetes mellitus by atherogenic indices and remnant cholesterol (A) the nomogram prediction of GDM by TG/HDL−C; (B) the nomogram prediction of GDM by TC/HDL−C; (C) the nomogram prediction of GDM by LDL−C/HDL−C; (D) the nomogram prediction of GDM by remnant cholesterol.

Figure 2 The receiver operating characteristic curves and the decision curve analysis of the nomogram predictions (A) the receiver operating characteristic curves of the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol; (B) the decision curve analysis of the predictive outcomes of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol.

The nonlinear associations between atherogenic indices, remnant cholesterol and GDM are presented in Figure 3. All of the atherogenic indices and remnant cholesterol exhibited unimodal distributions. A nonlinear relationship was detected between atherogenic indices, remnant cholesterol, and the risk of GDM (χ²= 30.91, P < 0.0001; χ²= 13.08, P = 0.0014; χ²= 6.95, P = 0.0309; χ²= 12.52, P = 0.0019; respectively). First, the risk of GDM showed an upward trend with the increasing of TG/HDL−C. But after TG/HDL−C reached 1.64, the risk of GDM started to decline. Similar nonlinear patterns were observed in the relationships between TC/HDL−C, remnant cholesterol, and GDM, with inflection points at 3.47 and 0.65, respectively. Although the LDL−C/HDL−C ratio showed a significant nonlinear relationship with GDM risk, the risk of GDM continued to rise with increasing levels of LDL−C/HDL−C. However, the rising pace decelerated once the ratio exceeded 1.54.

Figure 3 The associations between atherogenic indices and gestational diabetes mellitus risk by RCS regression analysis (A) The associations between TG/HDL−C and GDM; (B) The associations between TC/HDL−C and GDM; (C) The associations between LDL−C /HDL−C and GDM; (D) The associations between remnant cholesterol and GDM. Adjusted for age, pre-pregnancy BMI, gestational weight gain, family history of diabetes, and parity history.

Discussion

The results of this study showed that atherogenic indexes and remnant cholesterol were closely related to the risk of GDM. Higher values of TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol were significantly associated with elevated risks of GDM, and the results of the nomogram analysis showed that these indicators exhibited similar predictive performance, suggesting that all of them may serve as strong predictors for GDM.

In line with this study, previous studies have showed the positive associations between atherogenic indexes and GDM, but little of them examined the predictive ability. A cross-sectional study conducted by Khosrowbeygi et al among Iranian reported that LDL−C/HDL−C, TG/HDL−C, and TC/HDL−C levels were significantly higher in the GDM group than in the control group.¹⁸ Wang et al analyzed data from 15 hospitals in Beijing, China demonstrated that elevated TG/HDL−C and LDL−C/HDL−C in the 1st trimester of pregnancy were related to increased risks of GDM.¹⁷ Pazhohan et al reported that mothers in Iran with the highest tertile of TG/HDL−C in the 1st trimester of pregnancy contributed to a 3.9-fold of the risk of GDM compared with the lowest tertile.²⁶ Cross-sectional studies conducted by Barat et al and Wang et al also revealed that TG/HDL−C was sensitive to GDM diagnosis,^14,27,28 and a retrospective cohort study with a TG/HDL−C ratio cutoff of 3 reported that higher pre-gestational TG/HDL−C was associated with higher rates of GDM (13.1% vs 5.2%).²⁹ Zhang et al conducted a prospective cohort study in the Korean population and suggested that a log10 (TG/HDL) below 0.36 might be beneficial for GDM control.³⁰ Besides, Yue et al investigated serum lipids during the 2nd trimester and found that TG/HDL−C was related to the risk of GDM, but no significant difference was detected for LDL−C/HDL−C.³¹ Liu et al reported that Beijing mothers in the top tertile of TG/HDL−C before 12 weeks’ gestation had a significantly greater risk of GDM (OR = 2.388), but this relationship was not observed in TC/HDL−C.³² Based on the above findings, it was reconfirmed that TG/HDL−C has a positive effect on the risk of GDM, but discrepancies were noted in the relationships between TC/HDL−C and LDL−C/HDL−C and GDM. These gaps could be explained by the different study designs, populations, and gestational weeks of lipid-data collection.

The positive associations between atherogenic indexes and GDM could be explained by insulin resistance regulated by atherogenic indexes. Case-control studies conducted by Xiang et al and Kimm et al aimed to clarify the associations between atherogenic indexes and insulin resistance and revealed that all of the atherogenic indexes were significantly correlated with insulin resistance.^33,34 Specifically, previous studies have confirmed that TG/HDL−C is a reliable biomarker of insulin resistance.^35,36 Moreover, increased TC or decreased HDL−C concentrations could contribute to insulin resistance, glucose intolerance, and hyperinsulinemia,³⁷ and these factors are leading hazards for GDM.

Consistent with our findings, elevated remnant cholesterol levels were significantly associated with an increased risk of GDM. Our study further highlights the promising predictive effect of remnant cholesterol on the risk of GDM. A high concentration of remnant cholesterol was reported to have a higher risk of GDM, even among pregnancies with low TC.²¹ Another prospective cohort study conducted in Korea confirmed the independent association between remnant cholesterol and GDM.²² Su et al reported a significant dose-response relationship that the risk of GDM elevated along with the increasing of remnant cholesterol.²³ Although the exact mechanistic link between remnant cholesterol and GDM remains to be fully elucidated, it was hypothesized that remnant cholesterol may contribute to GDM pathogenesis through dual pathways similar to those observed in type 2 diabetes: The direct effects of remnant cholesterol-induced insulin resistance or β-cell dysfunction,³⁸ and the indirect effects of low-grade inflammation triggered by remnant cholesterol promote insulin resistance.^39,40 Further studies are warranted to validate these mechanisms.

The present study novelly revealed intuitive changes in GDM risk with respect to atherogenic indexes and remnant cholesterol and warned the elevated risk of GDM under high values of atherogenic indexes and remnant cholesterol. However, certain limitations should be addressed. First, the causal correlations between atherogenic indexes and remnant cholesterol and GDM in this study might be undermined. Second, this study obtained clinical data from a single hospital of Chinese population; caution should be taken when generalizing this study to other populations. Third, other confounding factors that might interfere with the relationship between atherogenic indexes and remnant cholesterol and GDM, such as insulin resistance, gestational weight gain before the diagnosis of GDM, liver function indexes, lifestyle behavior factors, and socioeconomic status et al were not considered in this study because they were not routine examined in the clinical practice. Fourth, this study included only one serum lipid data point per person, and the effects of dynamic changes in lipid ratios on the risk of GDM were failed to examine. Future studies focused on the dynamic changes in lipid indicators on the risk of GDM prediction are highly promoted.

Conclusion

Notably, our findings highlight the promising role of atherogenic indices and remnant cholesterol as potential predictive biomarkers for GDM risk assessment, which has not been fully explored in previous studies. Elevated levels of blood TG/HDL−C, TC/HDL−C, LDL−C/HDL−C, and remnant cholesterol are linked to a significant increase in the risk of developing GDM. Therefore, it is essential to maintain atherogenic indexes and remnant cholesterols at low levels in order to reduce the risk of GDM. Specially, the turning points (TG/HDL−C = 1.64, TC/HDL−C = 3.47, LDL−C/HDL−C = 1.54, and remnant cholesterol = 0.65) identified by the nonlinear relationships could serve as potential warning thresholds for clinical interventions to optimize GDM risk assessment. These findings underscore the potential of routine lipid testing as a cost-effective strategy for the early identification and management of GDM in clinical settings.

Abbreviations

GDM, gestational diabetes mellitus; TG, triglyceride; TC, total cholesterol; HDL−C, high-density lipoprotein cholesterol; LDL−C, low-density lipoprotein cholesterol.

Data Sharing Statement

The datasets used during the current study are available from the corresponding author on reasonable request, which should be approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province.

Ethics Approval and Informed Consent

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Maternal and Child Health Hospital of Hubei Province (2021IECXM005).

Acknowledgments

We would like to express our gratitude to all obstetric clinical workers in Maternal and Child Health Hospital of Hubei Province for their contributions to the sample accumulation of this paper.

Funding

This study was funded by the Maternal and Child Health Hospital of Hubei Province Research Project [grant No. 2021SFYM007] and Hubei Provincial Natural Science Foundation of China [grant No. 2025AFD690]. All these fundings were received by Dr. Yao Cheng. The funders had no role in the design, data collection, analyses, interpretation, manuscript writing, nor in the decision to publish the results.

Disclosure

The authors report no conflicts of interest in this work.

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The 2025 organizing committee includes Lilley; A. L. Burlingame, professor of pharmaceutical chemistry at the University of California, San Francisco; Steven Carr, senior director of proteomics at the Broad Institute; Ileana Cristea, director of graduate studies at Princeton University and ASBMB’s Molecular & Cellular Proteomics editor-in-chief; and Bernhard Küster, professor of proteomics and bioanalytics at the Technical University of Munich.

Lilley said this year’s conference will continue to foster that welcoming atmosphere while showcasing speakers with diverse expertise and offering a program that examines the proteome from all angles.

Plenary talks from leaders in their fields will explore innovative proteomic and post-translational modification analyses in cancer and precision medicine, pioneering approaches in drug discovery through targeted protein degradation, and fresh insights into virus-host interactions that shape immune and metabolic responses. Lilley hopes attendees will discover unexpected opportunities to collaborate.

“These sets of speakers don’t necessarily come together in a lot of the larger meetings in our field, so I think that itself is very interesting and very empowering,” she said. “You’ll get people who have not met but will see some sort of alignment in their research programs and their methodologies.”

In addition to plenary and session talks, organizers will select a few abstracts for short talks. Most abstract submitters will have the opportunity to present a poster.

“Everybody takes it very seriously, and every poster will get a lot of interest and a lot of traffic,” she said.

Looking ahead, the program will take a holistic view of the field and highlight emerging areas such as spatial proteomics, single-cell proteomics, multiomics, proteoforms and immunopeptidomics.

“There’s going to be an element of crystal ball gazing and future-proofing proteomics as well,” she said.

Lilley encouraged researchers new to proteomics to attend and explore how these techniques might expand and diversify their work.

“Everybody, whatever career stage they are, needs to have their horizons expanded to be able to look for new opportunities,” she said. “This meeting is a great place to do just that.”

The regular registration deadline is July 23. Register today!

An engineered protein turns off the kind of immune cells most likely to damage tissue as part of Type-1 diabetes, hepatitis, multiple sclerosis, shows a new study in mice.

In these autoimmune diseases, T cells mistakenly target the body’s own tissues instead of invading viruses or bacteria as they would during normal immune responses. Treatments focused on T cells have been elusive because blocking their action broadly weakens the immune system and creates risk for infections and cancer.

Published online June 30 in the journal Cell, the study revealed that holding closely together two protein groups (signaling complexes) on T cells, including one found more often on T cells involved autoimmune disease, shuts down those T cells in a limited way.

Led by researchers at NYU Langone Health, the Chinese Academy of Sciences, and Zhejiang University, the study built on biology newly discovered by the team to design an antibody that attached to both T cell signaling complexes, the T cell receptor and the LAG-3 checkpoint, held them closely together, and eliminated autoimmune tissue damage in three mouse models of disease.

Antibodies are proteins made by the immune system that label specific markers on cells for notice by the immune system. Researchers learned decades ago to engineer antibodies to target certain molecules as treatments, and more recently, antibodies that attach to two targets.

Our findings reveal an intricate mechanism that enables a careful treatment approach to T-cell driven autoimmune diseases, which currently lack effective immunotherapies.”

Jun Wang, PhD., co-senior study author, assistant professor, Department of Pathology at NYU Grossman School of Medicine

Held in place

The study results are based on the presence on T cells of T-cell receptors (TCRs) and checkpoints. TCRs, although shaped so that bits of invading bacteria or viruses fit into them to activate the T cell, are turned on by the body’s own proteins in autoimmune diseases. Checkpoints like LAG-3 are also turned on by specific signaling partners, but when this occurs they have the opposite effect of TCRs, suppressing the T cell’s activity.

Also important to the new study results is that TCR-triggering molecules must be presented to T cell receptors by another set of immune cells that “swallow” foreign (e.g., microbial) or bodily substances and display on their surfaces through protein groups called major histocompatibility complexes (MHC-II) just the small protein pieces that activate a given TCR.

“We discovered that, as a T cell’s surface draws close to the MHC-II presenting its TCR trigger molecule, the T cell receptor gets particularly close to LAG-3”, said co-first author Jasper Du, a third-year medical student in Dr. Wang’s lab. “For the first time, we found that this proximity is central to the ability of LAG-3 to dial back T cell activity.”

Mechanistically, the research team found that the proximity of LAG-3 lets it loosely stick to part of the T cell receptor called CD3ε (like two oily globs interacting). This attachment was found to pull on CD3ε enough to disrupt its interaction an enzyme called Lck, which is crucial for T cell activation. MHC-II can theoretically attach to LAG-3 and TCR at the same time, but not frequently enough to maximize LAG-3’s ability to dial down T cells, the researchers said.

In addition, “checkpoints” like LAG-3 are used by the immune system to turn off T cells when the right signals, given off by normal cells, dock in to avert self-attack (autoimmunity). Cancer cells put off signaling molecules that dock into checkpoints and sabotage the ability of T cells to attack them. Therapies called checkpoint inhibitors counter this effect.

LAG-3 turns off T cells, but less easily due to its spatial requirements than another checkpoint called PD-1. This feature makes LAG-3 inhibitors weaker as anti-cancer cancer treatment than PD-1-inhibiting antibody treatments that have become a mainstay, but likely better when the immune system is overactive, and targeted T cell suppression is required for maximum safe effect.

Based on their discovery of the critical role of TCR proximity in LAG-3 function, the research team designed a molecule that enforces LAG-3/TCR proximity to achieve better LAG-3-dependent TCR inhibition and suppression of T cell responses. Their “bi-specific” antibody held LAG-3 and the T cell receptor together more strongly than MHC-II, and without depending on it.

The current authors’ bispecific antibody, named the LAG-3/TCR Bispecific T cell Silencer or BiTS, potently suppressed T cell responses and lessened inflammatory damage to insulin-producing cells (insulitis) in BiTS-treated mice with a version of Type 1 diabetes. In autoimmune models of hepatitis, BiTS treatment reduced T cell infiltration and liver damage.

With the diabetes and hepatitis disease models largely driven by one type of T cells (CD8+), the team also used a mouse model of multiple sclerosis known to be driven by a second major T cell type (CD4+). The team treated mice prone to develop multiple sclerosis with short-term, preventive BiTS prior to the onset of disease symptoms, and BiTS-treated mice had reduced disease by a standard measure.

“Our study advances our understanding of LAG-3 biology and may foster more proximity-based, spatially-guided therapeutic designs like BiTS as immunotherapy for other human diseases,” said co-first author Jia You, a research scientist in Dr. Wang’s lab.

Along with Dr. Wang, corresponding authors of the study were Jack Wei Chen of the Department of Cell Biology and Department of Cardiology at the Second Affiliated Hospital Zhejiang University School of Medicine in China; as well Jizhong Lou of the State Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences.

Also other authors from the NYU Grossman School of Medicine were Jia Liu, Qiao Lu, Connor James, Ryan Foster, and Eric Rao in the Department of Pathology at New York University Grossman School of Medicine; Meng-ju Lin and Catherine Pei-ju Lu in the Hansjörg Wyss Department of Plastic Surgery and Department of Cell Biology; and Michael Cammer at the Microscopy Core, Division of Advanced Research Technologies, and Shohei Koide of the Perlmutter Cancer Center. Also making important contributions were Hui Chen at the State Key Laboratory of Epigenetic Regulation and Intervention, Institute of Biophysics, Chinese Academy of Sciences, and Yong Zhang from University of Chinese Academy of Sciences; Wei Hu and Jie Gao at The Second Affiliated Hospital, Zhejiang University School of Medicine; and Weiwei Yin in the Key Laboratory for Biomedical Engineering of the Ministry of Education, College of Biomedical Engineering and Instrument Science, also at Zhejiang University.

The study was supported principally by a translational advancement award from the Judith and Stewart Colton Center for Autoimmunity at NYU Langone Health. Also funding the study were a Cancer Center Support Grant P30CA016087, NIH grant S10OD021727, the NYU melanoma SPORE and NIH R37CA273333, and an NIH/NIAMS T32 grant (AR069515-07). The biophysical analysis part of this work was also supported by multiple grants from National Science Foundations of China (32090044, T2394512, 32200549, and T2394511).

Dr. Wang, Du and You are listed as inventors of pending patents related to the study. NYU Langone Health and its Technology Opportunities & Ventures have formed a related startup company, Remunix Inc., with Dr. Wang as founder and shareholders, to license and commercialize the patents. In addition, Dr. Wang serves as a consultant for Rootpath Genomics, Bristol Myers Squibb, LAV, Regeneron, and Hanmi. Dr. Koide has reported interests in Aethon Therapeutics and Revalia Bio not related to this study. These relationships are managed in keeping with the policies of NYU Langone Health.

Demonstrating that the best solution is not always a multistage approach, a new trial shows catheter ablation is superior to a combination of antiarrhythmic drugs and lifestyle changes — weight loss, more exercise, and alcohol reduction — when treating atrial fibrillation (AF) in patients who also have obesity.

The PRAGUE-25 trial, led by Pavel Osmancik, MD, PhD, with the Cardiocenter at Charles University in Prague, found catheter ablation was roughly twice as effective in an intention-to-treat analysis at controlling AF at the 1-year mark compared with a combination of antiarrhythmic drugs and lifestyle modification (73% vs 34.6%).

A “referral to [catheter ablation] in this population should not be delayed until the patient loses weight,” according to the researchers, who published their findings online on June 30 in the Journal of the American College of Cardiology simultaneously with a presentation at theNew York Valves 2025 Conference.

Obesity: A Strong Predictor of AF

AF, the most common sustained heart arrhythmia, affects about 60 million people worldwide. Obesity is one of its strongest predictors. An increase in BMI of 5 has been linked with a 19%-29% higher incidence of the rhythm disorder.

The PRAGUE trial was a randomized, noninferiority trial conducted in five centers in the Czech Republic. Patients that were included had symptomatic AF (paroxysmal, persistent, or long-standing persistent) and a BMI of 30 to 40.

Patients were randomly assigned 1:1 either to receive catheter ablation (n = 100) or a combination of medication and lifestyle changes (n = 103) from May 2021 to November 2023. Baseline characteristics were balanced, according to the researchers.

After randomization, all patients had a baseline cardiopulmonary exercise test, echocardiography, quality of life analysis, blood biochemistry testing, and a baseline 7-day electrocardiographic Holter recording.

Patients in the catheter ablation group underwent ablation within 6 weeks of randomization. Lifestyle modification was started within 4 weeks after randomization and was managed by teams of dietary specialists and physiotherapists, rather than cardiologists.

Patients in the combination therapy group lost significantly more weight at 12 months (about 6 kg, P < .001 compared to 0.35 kg in the other group), and that weight loss was maintained through the 24-month follow-up. The weight loss goal in this trial was 10%, an ambitious target in the period, especially given the physical limitations associated with AF.

Ramesh Hariharan, MD, cardiac electrophysiologist at UTHealth Houston and Memorial Hermann Health, Houston, who was not part of the study, said much of this research was conducted before the widespread use of GLP-1 receptor agonists, and those medications may help current patients achieve greater weight loss faster.

But even with greater weight loss, Hariharan said, the new findings reinforce the idea that no option alone is enough. Lifestyle changes and medicines need to accompany ablation, not replace it, he said, “otherwise we’re going to end up doing [ablations] more frequently.”

What’s more, technology has improved in the last year with nonthermal pulsed field ablation, which offers “far fewer collateral damage complications” and results in a 45-minute procedure “compared to a 2- to 4-hour procedure before. It has made ablation a lot easier.”

Gregory M. Marcus, MD, MAS, associate chief of cardiology for research at 
UCSF Health, San Francisco, said the evidence “is already definitive that catheter ablation is superior to antiarrhythmic drugs, and there is evidence that successful lifestyle change can reduce the burden of atrial fibrillation.” But this trial is the first to show a head-to-head comparison of ablation with a combination of antiarrhythmic drugs and lifestyle changes.

Marcus said he is not convinced the findings exclude the possibility some in this patient population may still be able to treat their AF without ablation.

“For an obese, very sedentary person who drinks too much alcohol, those are, at least theoretically, the prime candidates for lifestyle modification as a way to effectively treat their Afib,” he said.

One important lesson, Marcus said, is that this adds to the growing evidence that when considering the population at large with AF, “on average, catheter ablation is pretty definitively the most effective way to reduce the chance of atrial fibrillation recurrence.”

But some of the most interesting results were in the group who underwent lifestyle modification, he said. In addition to weight loss and improved exercise capacity, they experienced a statistically significant decrease in hemoglobin A1c concentrations of 1.4 mmol/L compared with an increase of 2.5 mmol/L in patients who received catheter ablation. “Those are things that will prolong life and will also improve quality of life,” he said.

“Whether we’re going to do an ablation or not,” Marcus added, “we should always counsel our atrial fibrillation patients about healthy lifestyle management. There are other things to life besides atrial fibrillation.”

The study authors and Hariharan reported no relevant financial disclosures. Marcus is a consultant and was a co-founder of the startup InCarda Therapeutics, which is investigating a novel therapy for the treatment of acute AF.

Marcia Frellick is an independent, Chicago-based healthcare journalist and a regular contributor to Medscape.

Researchers at Virginia Tech’s Fralin Biomedical Research Institute are developing a method to deliver creatine directly to the brain using focused ultrasound. The technique is being investigated as a potential intervention for children with creatine transporter deficiency, a condition that can impair speech, memory, and learning.

Creatine plays a role in cellular energy production and neurotransmitter regulation, making it critical not only for muscle but also brain function. While oral supplements often improve muscle mass in patients with creatine deficiency, they do not consistently address neurological symptoms, in part because creatine has difficulty crossing the blood-brain barrier.

“Creatine is very crucial for energy-consuming cells in skeletal muscle throughout the body, but also in the brain and in the heart,” says Chin-Yi Chen, a research scientist working on the project.

Ultrasound delivery to bypass the blood-brain barrier

The research is led by assistant professor Cheng-Chia “Fred” Wu, who studies therapeutic focused ultrasound, a noninvasive technique that uses sound waves to temporarily open the blood-brain barrier. The goal is to allow beneficial compounds, like creatine, to reach brain tissue without damaging surrounding cells.

The project, supported by a $30,000 grant from the Association for Creatine Deficiencies, builds on Wu’s broader work using ultrasound to improve drug delivery for pediatric brain cancer. His collaboration with Dr. Seth Berger of Children’s National Hospital led to the idea of applying the same method to treat creatine deficiency.

“Through the partnership between Virginia Tech and Children’s National Hospital, I was able to present our work in focused ultrasound at the Children’s National Research & Innovation Campus,” says Wu. “There, I met Dr. Seth Berger, a medical geneticist, who introduced me to creatine transporter deficiency. Together, we saw the promise that focused ultrasound had to offer.”

Collaboration supports early-stage translational research

Both Virginia Tech and Children’s National have been named Centers of Excellence by the Focused Ultrasound Foundation, a designation that supports collaborative research bridging lab studies and clinical applications. Chen’s initial work will focus on whether creatine can be successfully delivered across the blood-brain barrier and restore normal brain mass in lab models.

“It was a moment that made me really excited — that I had found a lab where I could move from basic research to something that could help patients,” says Chin-Yi Chen. “When Fred asked me, ‘Are you interested in this project?’ I said, ‘Yes, of course.’”

Fitness coach Navneeth Ramprasad, on April 11, shared an Instagram post and explained how avoiding healthy foods can worsen diabetes in your parents. “If your parents are diabetic and you’re not giving them these 5 foods every day, be prepared to spend a lot more on medical bills in a few years, especially the last one. Let’s be real: Most Indian households are still eating the same food that made us the diabetes capital of the world,” Navneeth added.

According to him, here are five foods that your parents must consume every day, if they are diabetic.

1. Fresh coconut and almonds

First thing in the morning, instead of tea or coffee. The healthy fats stabilise blood sugar, reduce morning crashes, and support brain health. Have 2 pieces of coconut and 5 soaked almonds. That’s it. Also read | Diabetes: 7 high-fibre foods that can prevent blood sugar spikes

2. Oats, Greek yoghurt and berries

For breakfast, instead of dosa, idly, or toast. Low GI carbs + protein + fiber + antioxidants = no sugar spikes and longer fullness.

3. Millet khichdi, sprouts and cooked veggies

For lunch, instead of white rice, sambar, and papad. Use foxtail millet or quinoa with moong dal, add steamed sprouts, and a bowl of high fiber veggies. Balanced plate = fiber, protein, slow carbs, and better post-lunch sugar control.

4. Tofu or tempeh for dinner

High-protein, low-carb, and essential for reversing insulin resistance. Add veggies, stir-fry or grill — just don’t end the day with wheat rotis alone.

5. Pumpkin seeds, just before bedtime (9–9:30 PM)

Prevents midnight sugar dips, supports sleep and magnesium levels. 1 small spoon (not more) of raw or lightly roasted seeds is perfect for diabetics who wake up tired or with sugar crashes at night. Also read | Smart eating for diabetes: Nutritionist-approved diet tips to keep your blood sugar in check

Note to readers: This article is for informational purposes only and not a substitute for professional medical advice. Always seek the advice of your doctor with any questions about a medical condition.