Identification and characterization of a nonbiological small-molecular mimic of a Zika virus conformational neutralizing epitope.

Antigenic similarities between Zika virus (ZIKV) and other flaviviruses pose challenges to the development of virus-specific diagnostic tools and effective vaccines. Starting with a DNA-encoded one-bead-one-compound combinatorial library of 508,032 synthetic, non-natural oligomers, we selected and characterized small molecules that mimic ZIKV epitopes. High-throughput fluorescence-activated cell sorter-based bead screening was used to select molecules that bound IgG from ZIKV-immune but not from dengue-immune sera. Deep sequencing of the DNA from the "Zika-only" beads identified 40 candidate molecular structures. A lead candidate small molecule "CZV1-1" was selected that correctly identifies serum specimens from Zika-experienced patients with good sensitivity and specificity (85.3% and 98.4%, respectively). Binding competition studies of purified anti-CZV1-1 IgG against known ZIKV-specific monoclonal antibodies (mAbs) showed that CZV1-1 mimics a nonlinear, neutralizing conformational epitope in the domain III of the ZIKV envelope. Purified anti-CZV1-1 IgG neutralized infection of ZIKV in cell cultures with potencies comparable to highly specific ZIKV-neutralizing mAbs. This study demonstrates an innovative approach for identification of synthetic non-natural molecular mimics of conformational virus epitopes. Such molecular mimics may have value in the development of accurate diagnostic assays for Zika, as well as for other viruses.

Network inference from short, noisy, low time-resolution, partial measurements: Application to C. elegans neuronal calcium dynamics.

Network link inference from measured time series data of the behavior of dynamically interacting network nodes is an important problem with wide-ranging applications, e.g., estimating synaptic connectivity among neurons from measurements of their calcium fluorescence. Network inference methods typically begin by using the measured time series to assign to any given ordered pair of nodes a numerical score reflecting the likelihood of a directed link between those two nodes. In typical cases, the measured time series data may be subject to limitations, including limited duration, low sampling rate, observational noise, and partial nodal state measurement. However, it is unknown how the performance of link inference techniques on such datasets depends on these experimental limitations of data acquisition. Here, we utilize both synthetic data generated from coupled chaotic systems as well as experimental data obtained from Caenorhabditis elegans neural activity to systematically assess the influence of data limitations on the character of scores reflecting the likelihood of a directed link between a given node pair. We do this for three network inference techniques: Granger causality, transfer entropy, and, a machine learning-based method. Furthermore, we assess the ability of appropriate surrogate data to determine statistical confidence levels associated with the results of link-inference techniques.

Impactful scientists have higher tendency to involve collaborators in new topics.

In scientific research, collaboration is one of the most effective ways to take advantage of new ideas, skills, and resources and for performing interdisciplinary research. Although collaboration networks have been intensively studied, the question of how individual scientists choose collaborators to study a new research topic remains almost unexplored. Here, we investigate the statistics and mechanisms of collaborations of individual scientists along their careers, revealing that, in general, collaborators are involved in significantly fewer topics than expected from a controlled surrogate. In particular, we find that highly productive scientists tend to have a higher fraction of single-topic collaborators, while highly cited-i.e., impactful-scientists have a higher fraction of multitopic collaborators. We also suggest a plausible mechanism for this distinction. Moreover, we investigate the cases where scientists involve existing collaborators in a new topic. We find that, compared to productive scientists, impactful scientists show strong preference of collaboration with high-impact scientists on a new topic. Finally, we validate our findings by investigating active scientists in different years and across different disciplines.

Surrogate gradients for analog neuromorphic computing.

To rapidly process temporal information at a low metabolic cost, biological neurons integrate inputs as an analog sum, but communicate with spikes, binary events in time. Analog neuromorphic hardware uses the same principles to emulate spiking neural networks with exceptional energy efficiency. However, instantiating high-performing spiking networks on such hardware remains a significant challenge due to device mismatch and the lack of efficient training algorithms. Surrogate gradient learning has emerged as a promising training strategy for spiking networks, but its applicability for analog neuromorphic systems has not been demonstrated. Here, we demonstrate surrogate gradient learning on the BrainScaleS-2 analog neuromorphic system using an in-the-loop approach. We show that learning self-corrects for device mismatch, resulting in competitive spiking network performance on both vision and speech benchmarks. Our networks display sparse spiking activity with, on average, less than one spike per hidden neuron and input, perform inference at rates of up to 85,000 frames per second, and consume less than 200 mW. In summary, our work sets several benchmarks for low-energy spiking network processing on analog neuromorphic hardware and paves the way for future on-chip learning algorithms.

Genome-wide comparative methylation analysis reveals the fate of germ stem cells after surrogate production in teleost.

BACKGROUND: Surrogate production by germline stem cell transplantation is a powerful method to produce donor-derived gametes via a host, a practice known as surrogacy. The gametes produced by surrogates are often analysed on the basis of their morphology and species-specific genotyping, which enables conclusion to be drawn about the donor's characteristics. However, in-depth information, such as data on epigenetic changes, is rarely acquired. Germ cells develop in close contact with supporting somatic cells during gametogenesis in vertebrates, and we hypothesize that the recipient's gonadal environment may cause epigenetic changes in produced gametes and progeny. Here, we extensively characterize the DNA methylome of donor-derived sperm and their intergenerational effects in both inter- and intraspecific surrogates. RESULTS: We found more than 3000 differentially methylated regions in both the sperm and progeny derived from inter- and intraspecific surrogates. Hypermethylation in the promoter regions of the protocadherin gamma gene in the intraspecific surrogates was found to be associated with germline transmission. On the contrary, gene expression level and the embryonic development of the offspring remained unaffected. We also discovered MAPK/p53 pathway disruption in interspecific surrogates due to promoter hypermethylation and identified that the inefficient removal of meiotic-arrested endogenous germ cells in hybrid gonads led to the production of infertile spermatozoa. CONCLUSIONS: Donor-derived sperm and progeny from inter- and intraspecific surrogates were more globally hypermethylated than those of the donors. The observed changes in DNA methylation marks in the surrogates had no significant phenotypic effects in the offspring.

Spliced-Leader RNA as a Dynamic Marker for Monitoring Viable Leishmania Parasites During and After Treatment.

Accurate detection of viable Leishmania parasites is critical for evaluating visceral leishmaniasis (VL) treatment response at an early timepoint. We compared the decay of kinetoplast DNA (kDNA) and spliced-leader RNA (SL-RNA) in vitro, in vivo, and in a VL patient cohort. An optimized combination of blood preservation and nucleic acid extraction improved efficiency for both targets. SL-RNA degraded more rapidly during treatment than kDNA, and correlated better with microscopic examination. SL-RNA quantitative polymerase chain reaction emerges as a superior method for dynamic monitoring of viable Leishmania parasites. It enables individualized treatment monitoring for improved prognoses and has potential as an early surrogate endpoint in clinical trials.

Single Isotopologue for In-Sample Calibration and Absolute Quantitation by LC-MS/MS.

Targeted mass spectrometry (MS)-based absolute quantitative analysis has been increasingly used in biomarker discovery. The ability to accurately measure the masses by MS enabled the use of isotope-incorporated surrogates having virtually identical physiochemical properties with the target analytes as calibrators. Such a unique capacity allowed for accurate in-sample calibration. Current in-sample calibration uses multiple isotopologues or structural analogues for both the surrogate and the internal standard. Here, we simplified this common practice by using endogenous light peptides as the internal standards and used a mathematical deduction of "heavy matching light, HML" to directly quantify an endogenous analyte. This method provides all necessary assay performance parameters in the authentic matrix, including the lower limit of quantitation (LLOQ) and intercept of the calibration curve, by using only a single isotopologue of the analyte. This method can be applied to the quantitation of proteins, peptides, and small molecules. Using this method, we quantified the efficiency of heart tissue digestion and recovery using sodium deoxycholate as a detergent and two spiked exogenous proteins as mimics of heart proteins. The results demonstrated the robustness of the assay.

Use of Nonhuman Sera as a Highly Cost-Effective Internal Standard for Quantitation of Multiple Human Proteins Using Species-Specific Tryptic Peptides: Applicability in Clinical LC-MS Analyses.

Quantitation of proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS) is complex, with a multiplicity of options ranging from label-free techniques to chemically and metabolically labeling proteins. Increasingly, for clinically relevant analyses, stable isotope-labeled (SIL) internal standards (ISs) represent the "gold standard" for quantitation due to their similar physiochemical properties to the analyte, wide availability, and ability to multiplex to several peptides. However, the purchase of SIL-ISs is a resource-intensive step in terms of cost and time, particularly for screening putative biomarker panels of hundreds of proteins. We demonstrate an alternative strategy utilizing nonhuman sera as the IS for quantitation of multiple human proteins. We demonstrate the effectiveness of this strategy using two high abundance clinically relevant analytes, vitamin D binding protein [Gc globulin] (DBP) and albumin (ALB). We extend this to three putative risk markers for cardiovascular disease: plasma protease C1 inhibitor (SERPING1), annexin A1 (ANXA1), and protein kinase, DNA-activated catalytic subunit (PRKDC). The results show highly specific, reproducible, and linear measurement of the proteins of interest with comparable precision and accuracy to the gold standard SIL-IS technique. This approach may not be applicable to every protein, but for many proteins it can offer a cost-effective solution to LC-MS/MS protein quantitation.

Validation of metastasis-free survival as a surrogate endpoint for overall survival in localized prostate cancer in the era of docetaxel for castration-resistant prostate cancer.

BACKGROUND: Prior work from the Intermediate Clinical Endpoints in Cancer of the Prostate (ICECaP) consortium (ICECaP-1) demonstrated that metastasis-free survival (MFS) is a valid surrogate for overall survival (OS) in localized prostate cancer (PCa). This was based on data from patients treated predominantly before 2004, prior to docetaxel being available for the treatment of metastatic castrate-resistant prostate cancer (mCRPC). We sought to validate surrogacy in a more contemporary era (ICECaP-2) with greater availability of docetaxel and other systemic therapies for mCRPC. PATIENTS AND METHODS: Eligible trials for ICECaP-2 were those providing individual patient data (IPD) after publication of ICECaP-1 and evaluating adjuvant/salvage therapy for localized PCa, and which collected MFS and OS data. MFS was defined as distant metastases or death from any cause, and OS was defined as death from any cause. Surrogacy was evaluated using a meta-analytic two-stage validation model, with an R2 ≥ 0.7 defined a priori as clinically relevant. RESULTS: A total of 15 164 IPD from 14 trials were included in ICECaP-2, with 70% of patients treated after 2004. The median follow-up was 8.3 years and the median postmetastasis survival was 3.1 years in ICECaP-2, compared with 1.9 years in ICECaP-1. For surrogacy condition 1, Kendall's tau was 0.92 for MFS with OS at the patient level, and R2 from weighted linear regression (WLR) of 8-year OS on 5-year MFS was 0.73 (95% confidence interval 0.53-0.82) at the trial level. For condition 2, R2 was 0.83 (95% confidence interval 0.64-0.89) from WLR of log[hazard ratio (HR)]-OS on log(HR)-MFS. The surrogate threshold effect on OS was an HR(MFS) of 0.81. CONCLUSIONS: MFS remained a valid surrogate for OS in a more contemporary era, where patients had greater access to docetaxel and other systemic therapies for mCRPC. This supports the use of MFS as the primary outcome measure for ongoing adjuvant trials in localized PCa.

Flexible evaluation of surrogacy in platform studies.

Trial-level surrogates are useful tools for improving the speed and cost effectiveness of trials but surrogates that have not been properly evaluated can cause misleading results. The evaluation procedure is often contextual and depends on the type of trial setting. There have been many proposed methods for trial-level surrogate evaluation, but none, to our knowledge, for the specific setting of platform studies. As platform studies are becoming more popular, methods for surrogate evaluation using them are needed. These studies also offer a rich data resource for surrogate evaluation that would not normally be possible. However, they also offer a set of statistical issues including heterogeneity of the study population, treatments, implementation, and even potentially the quality of the surrogate. We propose the use of a hierarchical Bayesian semiparametric model for the evaluation of potential surrogates using nonparametric priors for the distribution of true effects based on Dirichlet process mixtures. The motivation for this approach is to flexibly model relationships between the treatment effect on the surrogate and the treatment effect on the outcome and also to identify potential clusters with differential surrogate value in a data-driven manner so that treatment effects on the surrogate can be used to reliably predict treatment effects on the clinical outcome. In simulations, we find that our proposed method is superior to a simple, but fairly standard, hierarchical Bayesian method. We demonstrate how our method can be used in a simulated illustrative example (based on the ProBio trial), in which we are able to identify clusters where the surrogate is, and is not useful. We plan to apply our method to the ProBio trial, once it is completed.

Doubly robust evaluation of high-dimensional surrogate markers.

When evaluating the effectiveness of a treatment, policy, or intervention, the desired measure of efficacy may be expensive to collect, not routinely available, or may take a long time to occur. In these cases, it is sometimes possible to identify a surrogate outcome that can more easily, quickly, or cheaply capture the effect of interest. Theory and methods for evaluating the strength of surrogate markers have been well studied in the context of a single surrogate marker measured in the course of a randomized clinical study. However, methods are lacking for quantifying the utility of surrogate markers when the dimension of the surrogate grows. We propose a robust and efficient method for evaluating a set of surrogate markers that may be high-dimensional. Our method does not require treatment to be randomized and may be used in observational studies. Our approach draws on a connection between quantifying the utility of a surrogate marker and the most fundamental tools of causal inference-namely, methods for robust estimation of the average treatment effect. This connection facilitates the use of modern methods for estimating treatment effects, using machine learning to estimate nuisance functions and relaxing the dependence on model specification. We demonstrate that our proposed approach performs well, demonstrate connections between our approach and certain mediation effects, and illustrate it by evaluating whether gene expression can be used as a surrogate for immune activation in an Ebola study.

Cefazolin as a predictor of urinary cephalosporin activity in indicated Enterobacterales.

Traditionally, cephalothin susceptibility results were used to predict the susceptibility of additional cephalosporins; however, in 2013-2014, the Clinical and Laboratory Standards Institute (CLSI) revisited this practice and determined that cefazolin is a more accurate proxy than cephalothin for uncomplicated urinary tract infections (uUTIs). Therefore, a cefazolin surrogacy breakpoint was established to predict the susceptibility of seven oral cephalosporins for Escherichia coli, Klebsiella pneumoniae, and Proteus mirabilis in the context of uUTIs. Clinical microbiology laboratories face several operational challenges when implementing the cefazolin surrogacy breakpoint, which may lead to confusion for the best path forward. Here, we review the historical context and data behind the surrogacy breakpoints, review PK/PD profiles for oral cephalosporins, discuss challenges in deploying the breakpoint, and highlight the limited clinical outcome data in this space.

Mild obesity does not affect perinatal outcome in gestational carrier cycles.

STUDY QUESTION: Does BMI of gestational carriers (GCs) affect perinatal outcomes after embryo transfer? SUMMARY ANSWER: Overweight and class I obesity in GCs does not affect the rate of good perinatal outcomes. WHAT IS KNOWN ALREADY: The use of GCs is increasing, but uniform guidance regarding optimal BMI for GCs is lacking. Women with obesity who conceive without fertility treatment or through autologous or donor in vitro fertilization are at higher risk of adverse maternal and fetal outcomes, but data on obesity in GCs are very limited. STUDY DESIGN, SIZE, DURATION: We performed a retrospective cohort study of 1121 GC cycles from January 2015 to December 2020 at US Fertility, the largest national partnership of fertility practices in the USA. PARTICIPANTS/MATERIALS, SETTING, AND METHODS: All GC cycles performed at a large network of fertility practices were reviewed. Same-sex partners undergoing co-IVF were excluded. The primary outcome was good perinatal outcome from the first embryo transfer, defined as a singleton live birth at ≥37 weeks of gestation with birth weight between 2500 and 4000 g. Secondary outcome measures included frequencies of live birth, clinical pregnancy, miscarriage, full-term birth, low birth weight, large for gestational age, and cesarean delivery. A generalized linear model (log-binomial) was used for each to compare outcomes across BMI groups using normal BMI (20-24.9 kg/m2) as the reference group. Risk ratios and 95% CIs were estimated for each category group relative to normal BMI. MAIN RESULTS AND THE ROLE OF CHANCE: We identified 1121 cycles in which GCs underwent first embryo transfer, of which 263 (23.5%) were in GCs with BMI >30. Demographics and reproductive history for GCs did not differ by BMI groups. The age of intended parents, use of frozen eggs, and fresh embryo transfers were higher with increasing BMI group. There were no statistically significant associations between BMI and good perinatal outcomes, live birth, clinical pregnancy, biochemical, spontaneous abortion, or low birth weight. However, among live births, higher BMI was significantly associated with birth by cesarean (P = 0.015) and large for gestational age infants (P = 0.023). LIMITATIONS, REASONS FOR CAUTION: This was a retrospective study, and there may be unmeasured confounders. The number of patients with BMI <20 or ≥35 was small, limiting the power for these groups. We were not able to assess all maternal and fetal outcomes. WIDER IMPLICATIONS OF THE FINDINGS: In this study, we did not identify any significant impact of BMI on the chances of having a good perinatal outcome. Prior research studies have been inconsistent and this is the largest study to date. STUDY FUNDING/COMPETING INTEREST(S): No external funding was received for this work. The authors do not have any conflicts of interest to declare. TRIAL REGISTRATION NUMBER: N/A.

Assessment of six surrogate insulin resistance indexes for predicting cardiometabolic multimorbidity incidence in Chinese middle-aged and older populations: Insights from the China health and retirement longitudinal study.

AIMS: Insulin resistance (IR) is associated with cardiometabolic multimorbidity (CMM). We aimed to explore the predictive value of six surrogate IR indexes-Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), triglyceride-glucose (TyG), atherogenic index of plasma (AIP), TyG-body mass index (TyGBMI), and TyG-waist circumference (TyGwaist)-to establish the CMM incidence in Chinese middle-aged and older populations. MATERIAL AND METHODS: To estimate the odds ratio (OR) with a 95% confidence interval (95% CI) for incident CMM using six surrogates, we analysed data from the China Health and Retirement Longitudinal Study using multivariate logistic regression models. The nonlinear dose-response correlation was evaluated using restricted cubic spline analysis; predictive performance was assessed using receiver operator characteristic curves. RESULTS: Among 6451 eligible participants, 268 (4.2%) developed CMM during the 4-year follow-up period. The ORs (95% CI) for incident CMM increased with increasing CVAI quartiles (Q) [Q2: 1.71, 1.03-2.90; Q3: 2.72, 1.70-4.52; Q4: 5.16, 3.29-8.45; all p < 0.05] after full adjustment, with Q1 as the reference. Other indexes yielded similar results. These associations remained significant in individuals with a normal body mass index. Notably, CVAI, AIP, and TyG exhibited a linear dose-response relationship with CMM (Pnonlinear ≥0.05), whereas LAP, TyGBMI, and TyGwaist displayed significant nonlinear correlations (Pnonlinear <0.05). The area under the curve for the CVAI (0.691) was significantly superior to that of other indexes (all p < 0.05). CONCLUSIONS: The six IR surrogates were independently associated with CMM incidence. CVAI may be the most appropriate indicator for predicting CMM in middle-aged and older Chinese populations.

Photoredox-Catalyzed Preparation of Sulfones Using Bis-Piperidine Sulfur Dioxide - An Underutilized Reagent for SO2 Transfer.

Sulfonyl groups are widely observed in biologically relevant molecules and consequently, SO2 capture is an increasingly attractive method to prepare these sulfonyl-containing compounds given the range of SO2 -surrogates now available as alternatives to using the neat gas. This, along with the advent of photoredox catalysis, has enabled mild radical capture of SO2 to emerge as an effective route to sulfonyl compounds. Here we report a photoredox-catalyzed cross-electrophile sulfonylation of aryl and alkyl bromides making use of a previously under-used amine-SO2 surrogate; bis(piperidine) sulfur dioxide (PIPSO). A broad selection of alkyl and aryl bromides were photocatalytically converted to their corresponding sulfinates and then trapped with various electrophiles in a one-pot multistep procedure to prepare sulfones and sulfonamides.

Surrogacy of one-year survival for overall survival in advanced hepatocellular carcinoma.

BACKGROUND: The increasing number of sequential treatments complicates the evaluation of overall survival (OS) in clinical trials for hepatocellular carcinoma (HCC), therefore, reliable surrogate endpoints (SEs) are required. This study aimed to evaluate the surrogacy of progression-free survival (PFS) and one-year (1-yr) milestone survival for OS in HCC trials. METHODS: We systematically searched databases for randomized clinical trials that evaluated systemic treatments for advanced HCC. Individual patient data were reconstructed to calculate the 1-yr survival rate. We adopted a two-stage meta-analytic validation model to evaluate the correlation between SEs and OS, and the correlation between treatment effects on SEs and OS. The hazard ratio (HR) was calculated to assess the treatment effects on PFS and OS, and the 1-yr survival ratio was calculated to evaluate the treatment effects on the 1-yr milestone survival. RESULTS: Thirty-two HCC trials involving 13,808 patients were included. A weak correlation was detected between the median PFS and median OS (R2 = 0.32), whereas the correlation improved between PFS HR and OS HR (R2 = 0.58). We identified strong correlations between the 1-yr survival rate and median OS and between the 1-yr survival ratio and OS HR (R2 = 0.74 and 0.65, respectively). In subgroup analyses, PFS HR strongly correlated with OS HR in trials relevant to immune checkpoint inhibitors (ICIs). Although the correlation remained weak between PFS and OS even in trials with PFS HR ≤ 0.6, the 1-yr survival rate and 1-yr survival ratio were strong surrogates for median OS and OS HR, respectively (R2 = 0.77 and 0.75). CONCLUSIONS: One-year milestone survival outperformed PFS as a SE for OS in HCC, indicating the application of 1-yr survival as a secondary endpoint. In particular, PFS HR was a potential SE for OS HR in the ICI trials.

Progression-free survival as a surrogate endpoint for overall survival in patients with relapsed or refractory multiple myeloma.

OBJECTIVES: The goal of the research was to assess the quantitative relationship between median progression-free survival (PFS) and median overall survival (OS) specifically among patients with relapsed/refractory multiple myeloma (RRMM) based on published randomized controlled trials (RCTs). METHODS: Two bibliographic databases (PubMed and Embase, 1970-2017) were systematically searched for RCTs in RRMM that reported OS and PFS, followed by an updated search of studies published between 2010 and 2022 in 3 databases (Embase, MEDLINE, and EBM Reviews, 2010-2022). The association between median PFS and median OS was assessed using the nonparametric Spearman rank and parametric Pearson correlation coefficients. Subsequently, the quantitative relationship between PFS and OS was assessed using weighted least-squares regression adjusted for covariates including age, sex, and publication year. Study arms were weighted by the number of patients in each arm. RESULTS: A total of 31 RCTs (56 treatment arms, 10,450 patients with RRMM) were included in the analysis. The average median PFS and median OS were 7.1 months (SD 5.5) and 28.1 months (SD 11.8), respectively. The Spearman and Pearson correlation coefficients between median PFS and median OS were 0.80 (P < 0.0001) and 0.79 (P < 0.0001), respectively. In individual treatment arms of RRMM trials, each 1-month increase in median PFS was associated with a 1.72-month (95% CI 1.26-2.17) increase in median OS. CONCLUSION: Analysis of the relationship between PFS and OS incorporating more recent studies in RRMM further substantiates the use of PFS to predict OS in RRMM.

Individualized treatment rule characterization via a value function surrogate.

Precision medicine is a promising framework for generating evidence to improve health and health care. Yet, a gap persists between the ever-growing number of statistical precision medicine strategies for evidence generation and implementation in real-world clinical settings, and the strategies for closing this gap will likely be context-dependent. In this paper, we consider the specific context of partial compliance to wound management among patients with peripheral artery disease. Using a Gaussian process surrogate for the value function, we show the feasibility of using Bayesian optimization to learn optimal individualized treatment rules. Further, we expand beyond the common precision medicine task of learning an optimal individualized treatment rule to the characterization of classes of individualized treatment rules and show how those findings can be translated into clinical contexts.

A rank-based approach to evaluate a surrogate marker in a small sample setting.

In clinical studies of chronic diseases, the effectiveness of an intervention is often assessed using "high cost" outcomes that require long-term patient follow-up and/or are invasive to obtain. While much progress has been made in the development of statistical methods to identify surrogate markers, that is, measurements that could replace such costly outcomes, they are generally not applicable to studies with a small sample size. These methods either rely on nonparametric smoothing which requires a relatively large sample size or rely on strict model assumptions that are unlikely to hold in practice and empirically difficult to verify with a small sample size. In this paper, we develop a novel rank-based nonparametric approach to evaluate a surrogate marker in a small sample size setting. The method developed in this paper is motivated by a small study of children with nonalcoholic fatty liver disease (NAFLD), a diagnosis for a range of liver conditions in individuals without significant history of alcohol intake. Specifically, we examine whether change in alanine aminotransferase (ALT; measured in blood) is a surrogate marker for change in NAFLD activity score (obtained by biopsy) in a trial, which compared Vitamin E ($n=50$) versus placebo ($n=46$) among children with NAFLD.

Effects of hypoglycaemic agents on reducing surrogate metabolic parameters for the prevention of cardiovascular events and death in patients with type 2 diabetes: A systematic review and meta-analysis.

AIMS: To investigate the impact of glucose-lowering therapy-induced glycated haemoglobin (HbA1c) reduction on the risk of major clinical events according to body weight change and, as a secondary objective, to evaluate the impact of concomitant reductions in HbA1c and body weight on major clinical events. MATERIALS AND METHODS: We searched the MEDLINE and EMBASE databases up to June 30, 2022, for large-scale studies on glucose-lowering therapies in which more than 1000 patient-years of follow-up in each randomized group were completed. The primary outcome was all-cause mortality. The study was registered in PROSPERO (CRD42022355479). RESULTS: Thirty-four trials involving 227 220 patients with type 2 diabetes were meta-analysed using a random-effects model. Each 1% reduction in HbA1c was associated with a different risk of mortality depending on the ability of glucose-lowering therapies to induce body weight loss or gain. When glucose-lowering therapies were associated with weight gain, the risk of mortality increased by 8% (hazard ratio [HR] 1.08, 95% confidence interval [CI] 1.00-1.16) for each 1% reduction in HbA1c. When glucose-lowering therapies were associated with weight loss, the risk of mortality was reduced by 22% (HR 0.78, 95% CI 0.72-0.85) for each 1% reduction in HbA1c. In addition, concomitant reductions in HbA1c and body weight were associated with a significantly lower risk of mortality and vascular events. CONCLUSIONS: In patients with type 2 diabetes, concomitant reductions in HbA1c and body weight might be more effective in preventing the risk of vascular events and mortality.