Abstract Background Hemoglobin A1c (HbA1c), an important diagnostic biomarker for type 2 diabetes (T2D), is also associated with aging, cognitive performance, and mortality. To identify epistatic interactions, we assessed 133 known gene variants associated with HbA1c among 3,778 non-diabetic subjects of European ancestry in the Long Life Family Study (LLFS). Methods We applied Bayesian Imputation Based Association Mapping (BIMBAM) to identify significant pairwise epistatic interactions among genetic variants that were previously shown to be associated with levels of HbA1c. To take into account confounding effects, we adjusted age, sex, field centers, body mass index (BMI), and genetic principal components (PCs). Results This analysis yielded seven pairs with log10(BF)>10; of those, six pairs were confirmed using a full-term mixed regression model. Specifically, these included significant interactions of HK1-rs17476364 with variants in GCK (rs2971670, rs4607517) or G6PC2 (rs560887), as well as between HK1-rs16926246 and the same variants (P values for each term ≤ 7.14e-3). All epistatic interactions between HK1 and GCK, and between HK1 and G6PC2 were replicated in two large independent studies (namely, Framingham Offspring Study, P < 0.05; Health and Retirement Study, P < 0.05). Conclusion The present study revealed that HK1 and GCK interact to contribute to regulating levels of HbA1c and are likely to be involved in molecular mechanisms underlying healthy aging processes. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number U19AG063893. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB at Washington University in St. Louis gave ethical approval for this work. IRB ID #201904204=1025. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes LLFS clinical data and WGS data can be downloaded at ELITE Synapse portal ( syn52663596). FOS data is available at dbGAP with accession phs000007.v35.p16 and phg002135, and HRS data can be downloaded at https://hrs.isr.umich.edu with accession phs000428.v2.p2.

To evaluate the associations and comparative performance of novel anthropometric and metabolic indices with osteopenia and osteoporosis among middle-aged and older Chinese adults. A cross-sectional study was conducted among 10,142 Chinese adults aged ≥ 45 years who underwent quantitative computed tomography (QCT) for lumbar spine BMD assessment. Participants were categorized as normal, osteopenia, and osteoporosis. Associations and predictive capabilities of anthropometric indices were analyzed using multivariable logistic regression and receiver operating characteristic (ROC) curve analyses. The prevalences of osteopenia and osteoporosis were 35.14% and 14.05%, respectively. After adjusting for confounders, weight-adjusted waist index (WWI), relative fat mass (RFM), a body shape index (ABSI), triglyceride-glucose (TyG) index, and glycated hemoglobin (HbA1c) were found to be independently associated with osteopenia and osteoporosis. Among all indices, WWI demonstrated the strongest predictive value for osteoporosis (area under the curve = 0.726), followed by RFM and ABSI. In contrast, BMI and the visceral adiposity index (VAI) showed no significant associations with low BMD. Indices associated with central adiposity and metabolic dysfunction, especially WWI, may provide more precise prediction of osteoporosis risk. Incorporating such indices into early risk stratification for osteoporosis among older Chinese adults may have potential clinical utility.

BACKGROUND Increased blood urea nitrogen (BUN) levels have been demonstrated to be associated with broader metabolic disturbances and the incidence of type 2 diabetes (T2D), potentially playing a role in the development of diabetic complications, including diabetic peripheral neuropathy. AIM To examine the relationship between BUN levels and peripheral nerve function in patients with T2D. METHODS This observational study involved the systematic recruitment of 585 patients with T2D for whom BUN levels and estimated glomerular filtration rate were measured. Electromyography was used to assess peripheral motor and sensory nerve function in all patients, and overall composite Z -scores were subsequently calculated for nerve latency, amplitude, and conduction velocity (NCV) across the median, ulnar, common peroneal, posterior tibial, superficial peroneal, and sural nerves. RESULTS Across the quartiles of BUN levels, the overall composite Z -score for latency (F = 38.996, P for trend < 0.001) showed a significant increasing trend, whereas the overall composite Z -scores for amplitude (F = 50.972, P for trend < 0.001) and NCV (F = 30.636, P for trend < 0.001) exhibited a significant decreasing trend. Moreover, the BUN levels were closely correlated with the latency, amplitude, and NCV of each peripheral nerve. Furthermore, multivariate linear regression analysis revealed that elevated BUN levels were linked to a higher overall composite Z -score for latency (β = 0.166, t = 3.864, P < 0.001) and lower overall composite Z-scores for amplitude (β = -0.184, t = -4.577, P < 0.001) and NCV (β = -0.117, t = -2.787, P = 0.006) independent of the estimated glomerular filtration rate and other clinical covariates. Additionally, when the analysis was restricted to sensory or motor nerves, elevated BUN levels remained associated with sensory or motor peripheral nerve dysfunction. CONCLUSION Increased BUN levels were independently associated with compromised peripheral nerve function in patients with T2D.

In this study, we generated and integrated plasma proteomics and metabolomics with the genotype datasets of over 2300 European (EUR) and 400 African (AFR) ancestries to identify ancestry-specific multi-omics quantitative trait loci (QTLs). In total, we mapped 954 AFR pQTLs, 2848 EUR pQTLs, 65 AFR mQTLs, and 490 EUR mQTLs. We further applied these QTLs to ancestry-stratified type-2 diabetes (T2D) risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. Using INTACT that combined trait-imputation and colocalization results, we nominated 270 proteins and 72 metabolites from the EUR set; seven proteins and one metabolite from the AFR set as molecular effectors of T2D risk in an ancestry-stratified manner. Here, we show that the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse ancestral groups.

The recent European-ancestry based genome-wide association study (GWAS) of Alzheimer disease (AD) by Bellenguez2022 has identified 75 significant genetic loci, but only a few have been functionally mapped to effector gene level. Besides the large-scale RNA expression, protein and metabolite levels are key molecular traits bridging the genetic variants to AD risk, and thus we decided to integrate them into the genetic analysis to pinpoint key proteins and metabolites underlying AD etiology. Few studies have generated more than one layer of post-transcriptional phenotypes, limiting the scale of biological translation of disease modifying treatments. We first performed the plasma proteomic (6,907 proteins by SomaScan) and metabolomic (1,483 metabolites by Metabolon) GWAS from the same participants of European (N = 2,300) ancestry. Using these significant variant-trait associations, we next performed multiple post-GWAS analyses (functional summary-based imputation (FUSION) and genetic colocalization) to identify the AD associated proteins and metabolites. To annotate these findings with brain aging clocks predicated from the plasma proteomics data, we performed association tests between the aging gaps with the molecular phenotypes. We identified hundreds to thousands of QTLs (quantitative trait loci). In the proteomic GWAS, we found 2,400 proteins with 2,848 study-wide significant pQTLs. In the metabolomic GWAS, we reported 403 metabolites with 490 study-wide significant mQTLs. In total, 86% and 98% of these associations have supported by the previous larger-scale plasma-based studies in proteome and metabolome, respectively. Under the FUSION framework, we found 95 proteins were study-wide significant associated with AD risk, including, proteins TREM2, APOE, and NfL. After removing the bias of linkage disequilibrium by colocalization, we obtained a list of 53 proteins, of which 42 were not reported. On the other hand, 35 nominal significant metabolites, such as androsterone sulfate, were associated with AD in the FUSION analysis. Of these findings, eight metabolites were also highly colocalized with AD. There were 14 proteins and three metabolites significantly associated with the brain aging gaps, which included TREM2, but not APOE. Our study serves the first duo-omics post-transcriptional genetic study for studying AD risk effectors and can facilitate developing novel disease modifying treatments.

Increased glucagon levels are now recognized as a pathophysiological adaptation to counteract overnutrition in type 2 diabetes (T2D). This study aimed to elucidate the role of glucagon in peripheral nerve function in patients with T2D with different body mass indices (BMIs). We consecutively enrolled 174 individuals with T2D and obesity (T2D/OB, BMI ≥ 28 kg/m2), and 480 individuals with T2D and nonobesity (T2D/non-OB, BMI < 28 kg/m2), all of whom underwent oral glucose tolerance tests to determine the area under the curve for glucagon (AUCgla). Electromyography was utilized to assess overall composite Z-scores for latency, amplitude, and nerve conduction velocity (NCV) across all peripheral nerves, specifically examining the median, ulnar, common peroneal, posterior tibial, superficial peroneal, and sural nerves. In the T2D/OB group, the AUCgla exhibited a significant correlation with the latency, amplitude and NCV of each peripheral nerve, as well as with the overall composite Z-scores for latency (r = –0.283, p < 0.001), amplitude (r = 0.295, p < 0.001), and NCV (r = 0.362, p < 0.001). In contrast, the T2D/non-OB group did not exhibit obvious correlations between the AUCgla and the overall composite Z-scores for latency (r = –0.088, p = 0.056), amplitude (r = 0.054, p = 0.251), and NCV (r = 0.116, p = 0.012). Furthermore, multivariate linear regression analyses indicated that elevated AUCgla was independently associated with a lower overall composite Z-score for latency (β = –0.304, t = –3.391, p = 0.001), as well as higher overall composite Z-scores for amplitude (β = 0.256, t = 2.630, p = 0.010) and NCV (β = 0.286, t = 3.503, p = 0.001), after adjusting for other clinical covariates within the T2D/OB group. Increased glucagon levels may be a potential protective factor against peripheral nerve compromise in patients with T2D and obesity.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.

Aging is associated with chronic low-grade inflammation. Interleukin-6 (IL-6) is a pro-inflammatory cytokine, linked to many clinical conditions, and a marker of chronic inflammation. IL-6 increases with normal aging, and higher levels of IL-6 in older individuals have been associated with physical and cognitive impairment, as well as increased dementia risk. IL-6 may be a pathway to reduce chronic low-grade inflammation. We investigated if there were regions of the genome harboring protective IL-6 variants. We used data from the NIA Long Life Family Study (LLFS, N = 4,084), a multi-national, multigenerational study of healthy aging. Participants underwent a comprehensive in-home examination at baseline, including a blood draw. Plasma IL-6 (pIL-6) concentration and whole genome sequencing (WGS) was performed using blood samples. We completed a linkage analysis for pIL-6 (covariate adjusted) using SOLAR followed by association of WGS variants under the peak. Chromosome 22 at 8cM (21,830,838 base pairs) had a LOD of 2.77. A subset of 20 families, demonstrated a heterogeneity LOD score of 9.54. Association on these 20 families found eight variants with suggestive association (p < 1E-04), half protective. Via model selection, 5 variants (3 protective and 2 risk) remained in the model, collectively explaining 20% of the variation in pIL-6, and explaining 41.7% of the linkage. One variant was associated with an RNA transcript for the IGLV3-25 gene (p = 8.03E-09), which is a critical gene for the formation of functional antibodies produced by plasma cells. Further analyses of the association of these novel variants with healthy aging and related phenotypes is warranted.

In recent years, the concept of hyperopia reserve, defined as a physiological hyperopic refractive status preceding emmetropia and myopia, has gained increasing attention. and raised awareness about myopia. This concept has become of interest to both parents and practitioners. To report the distribution of refractive errors and ocular biometry in a large scale of preschool children in Beijing, in North China. The distribution of hyperopia reserve and its associated factors were also further investigated. This study presents baseline data from Beijing Hyperopia Reserve Research (BHRR), which enrolled 2109 preschool children from 22 randomly selected kindergartens. Cycloplegic refraction was performed for all children. Hyperopia reserve was defined as a spherical equivalent refractive error (SER) greater than zero. Parents completed a questionnaire about the severity of refractive status (normal; mild myopia <-3D; moderate myopia ≥-3D and ≤-6D; high myopia >-6D) and their children’s indoor and outdoor activity times. The mean SER was + 1.11 ± 0.97D, and the mean axial length was 22.25 + 0.73 mm in all preschool children. The overall prevalence of myopia was 3.7

The Trail Making Test (TMT) Part B (TMT-B), a well-established assessment of cognitive function, is a frequent component of diagnostic assessments for Mild Cognitive Impairment and dementia in older adults. Identifying the genetic variants associated with the TMT-B will not only gain insights of genetic determinants of cognitive function, but also the molecular mechanisms for dementia. Published GWAS to date for TMT-B suffer from relatively low power due to the use of population level data and imputation methods. To address these deficits, we used a family-based study design to identify the genetic variants associated with the TMT-B incorporating both genome-wide linkage analysis (GWLS) and whole genome sequencing (WGS). As such, we examined the sequenced genetic determinants of TMT-B using GWLS in over 2000 participants from Long Life Family Study (LLFS). In GWLS, the estimated heritability of TMT-B was 0.29. We detected one significant linkage peak at 15q25 (LOD>3.0). Statistical fine-mapping nominated five variants including three SNPs (NTRK3-rs74031103, protective CEMIP-rs2271159, and protective AGBL1-rs4134376) and two INDELs (protective KLHL25-15:85882445:IND, and protective CEMIP-15:80893381:IND) contributing to the linkage peak. Four out of these five variants are protective for TMT-B. The rs2271159 SNP influences CEMIP expression in cerebellum and hippocampus, while the 15:80893381:IND modulates CEMIP expression in blood. Additionally, the variant rs4134376 is a basal ganglia-specific eQTL for AGBL1. In conclusion, we utilized GWLS, leveraged multi-omics data (whole genome sequence genomic data, transcriptomic data, and lipidomic data), and identified novel protective variants and genes for TMT-B performance.

Introduction: The aim of this study was to explore the association between parental myopia and high myopia with children’s refraction and ocular biometry in large-scale Chinese preschool children from the Beijing Hyperopia Reserve Study. Subjects/Methods: This cross-sectional kindergarten-based study enrolled children aged 3–6 years. Cycloplegic refraction, axial length (AL), and corneal radius (CR) were measured for all children. Parents were asked to complete a questionnaire about refractive status (no myopia, mild myopia <−3 D, moderate myopia ≥−3 D and ≤−6, and high myopia >−6 D). Results: The study enrolled 2,053 children (1,069 boys and 984 girls), with a mean age of 4.26 ± 0.96 years and mean spherical equivalent refraction (SER) of 1.11 ± 0.97 diopter. Of the children, 90.7% had at least one myopic parent, and 511 children (24.9%) had at least one highly myopic parent. SER decreased significantly with increasing severity of parental myopia (p < 0.001). Preschool children’s myopia was independently associated with parental myopia (OR, 10.4 and 11.5 for one and two highly myopic parent[s]). Age (OR = 1.1), gender (OR = 1.7; girls as references), near work time (OR = 1.2), and both maternal (OR, 1.4 and 2.0 for moderate and high myopia) and paternal myopia (OR, 1.6 and 1.9 for moderate and high myopia) were independent risk factors for lacking hyperopia reserve. Conclusion: Severe parental myopia was associated with a lower SER, longer AL, and higher AL/CR ratio in preschool children. Parental myopia and near work may predispose children to faster elimination of hyperopia reserves before exposure to higher educational stress.

Over Several years, we have developed a system for assuring the quality of whole genome sequence (WGS) data in the LLFS families. We have focused on providing data to identify germline genetic variants with the aim of releasing as many variants on as many individuals as possible. We aim to assure the quality of the individual calls. The availability of family data has enabled us to use and validate some filters not commonly used in population-based studies. We developed slightly different procedures for the autosomal, X, Y, and Mitochondrial (MT) chromosomes. Some of these filters are specific to family data, but some can be used with any WGS data set. We also describe the procedure we use to construct linkage markers from the SNP sequence data and how we compute IBD values for use in linkage analysis.

BACKGROUND:Type 2 diabetes mellitus (T2DM), a fast-growing issue in public health, is one of the most common chronic metabolic disorders in older individuals. Osteoporosis and sarcopenia are highly prevalent in T2DM patients and may result in fractures and disabilities. In people with T2DM, the association between nutrition, sarcopenia, and osteoporosis has rarely been explored.AIM:To evaluate the connections among nutrition, bone mineral density (BMD) and body composition in patients with T2DM.METHODS:We enrolled 689 patients with T2DM for this cross-sectional study. All patients underwent dual energy X-ray absorptiometry (DXA) examination and were categorized according to baseline Geriatric Nutritional Risk Index (GNRI) values calculated from serum albumin levels and body weight. The GNRI was used to evaluate nutritional status, and DXA was used to investigate BMD and body composition. Multivariate forward linear regression analysis was used to identify the factors associated with BMD and skeletal muscle mass index.RESULTS:Of the total patients, 394 were men and 295 were women. Compared with patients in tertile 1, those in tertile 3 who had a high GNRI tended to be younger and had lower HbA1c, higher BMD at all bone sites, and higher appendicular skeletal muscle index (ASMI). These important trends persisted even when the patients were divided into younger and older subgroups. The GNRI was positively related to ASMI (men: r = 0.644, P < 0.001; women: r = 0.649, P < 0.001), total body fat (men: r = 0.453, P < 0.001; women: r = 0.557, P < 0.001), BMD at all bone sites, lumbar spine (L1-L4) BMD (men: r = 0.110, P = 0.029; women: r = 0.256, P < 0.001), FN-BMD (men: r = 0.293, P < 0.001; women: r = 0.273, P < 0.001), and hip BMD (men: r = 0.358, P < 0.001; women: r = 0.377, P < 0.001). After adjustment for other clinical parameters, the GNRI was still significantly associated with BMD at the lumbar spine and femoral neck. Additionally, a low lean mass index and higher β-collagen special sequence were associated with low BMD at all bone sites. Age was negatively correlated with ASMI, whereas weight was positively correlated with ASMI.CONCLUSION:Poor nutrition, as indicated by a low GNRI, was associated with low levels of ASMI and BMD at all bone sites in T2DM patients. Using the GNRI to evaluate nutritional status and using DXA to investigate body composition in patients with T2DM is of value in assessing bone health and physical performance.

Triglyceride (TG)/HDL-C ratio (THR) is a surrogate predictor of hyperinsulinemia. To identify novel genetic loci for THR change over time (ΔTHR), we conducted genome-wide association study (GWAS) and genome-wide linkage scan (GWLS) among nondiabetic Europeans from the Long Life Family Study (n = 1,384). Subjects with diabetes or on dyslipidemia medications were excluded. ΔTHR was derived using growth curve modeling and adjusted for age, sex, field centers, and principal components. GWAS used a linear mixed model accounting for familial relatedness. GWLS employed haplotype-based identity-by-descent estimation with 0.5 cM average spacing. Heritability of ΔTHR was moderate (46%). Our GWAS identified a significant locus at the LPL (P = 1.58e-9) for ΔTHR; this locus has been reported before influencing baseline THR levels. Our GWLS found significant linkage with a logarithm of the odds exceeding 3 on 3q28 (logarithm of the odds = 4.1). Using a subset of 25 linkage-enriched families, we assessed sequence elements under 3q28 and identified two novel variants (EIF4A2 [eukaryotic translation initiation factor 4A2]/ADIPOQ-rs114108468, p = 5e-6, minor allele frequency = 1.8%; TPRG1-rs16864075, p = 3e-6, minor allele frequency = 8%; accounted for ∼28% and ∼29% of the linkage, respectively). While the former variant was associated with EIF4A2 (p = 7e-5)/ADIPOQ (P = 3.49e-2) transcriptional levels, the latter variant was not associated with TPRG1 (P = 0.23) transcriptional levels. Replication in the Framingham Heart Study Offspring Cohort observed modest effect of these loci on ΔTHR. Our approach discovered two novel gene variants EIF4A2/ADIPOQ-rs114108468 and TPRG1-rs16864075 on 3q28 for ΔTHR among subjects without diabetes. Our findings provided novel insights into the molecular regulation of insulin resistance.

Background Modifying diet is crucial for diabetes and complication management. Numerous studies have shown that adjusting eating habits to align with the circadian rhythm may positively affect metabolic health. However, eating midpoint, eating duration, and their associations with diabetic kidney disease (DKD) are poorly understood. Methods The National Health and Nutrition Examination Survey (2013–2020) was examined for information on diabetes and dietary habits. From the beginning and ending times of each meal, we calculated the eating midpoint and eating duration. Urinary albumin-to-creatinine ratio (UACR) ≥ 30 mg/g and/or estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m 2 were the specific diagnostic criteria for DKD. Results In total, details of 2194 subjects with diabetes were collected for analysis. The overall population were divided into four subgroups based on the eating midpoint quartiles. The prevalence of DKD varied noticeably ( P = 0.037) across the four categories. When comparing subjects in the second and fourth quartiles of eating midpoint to those in the first one, the odds ratios (ORs) of DKD were 1.31 (95% CI, 1.03 to 1.67) and 1.33 (95% CI, 1.05 to 1.70), respectively. And after controlling for potential confounders, the corresponding ORs of DKD in the second and fourth quartiles were 1.42 (95% CI, 1.07 to 1.90) and 1.39 (95% CI, 1.04 to 1.85), respectively. Conclusions A strong correlation was found between an earlier eating midpoint and a reduced incidence of DKD. Eating early in the day may potentially improve renal outcomes in patients with diabetes.

Background: Grip strength is a robust indicator of overall health, is moderately heritable, and predicts longevity in older adults. Methods: Using genome-wide linkage analysis, we identified a novel locus on chromosome 18p (mega-basepair region: 3.4-4.0) linked to grip strength in 3 755 individuals from 582 families aged 64 +/- 12 years (range 30-110 years; 55% women). There were 26 families that contributed to the linkage peak (cumulative logarithm of the odds [LOD] score = 10.94), with 6 families (119 individuals) accounting for most of the linkage signal (LOD = 6.4). In these 6 families, using whole genome sequencing data, we performed association analyses between the 7 312 single nucleotide (SNVs) and insertion deletion (INDELs) variants in the linkage region and grip strength. Models were adjusted for age, age(2), sex, height, field center, and population substructure. Results: We found significant associations between genetic variants (8 SNVs and 4 INDELs, p < 5 x 10(-5)) in the Disks Large-associated Protein 1 (DLGAP1) gene and grip strength. Haplotypes constructed using these variants explained up to 98.1% of the LOD score. Finally, RNAseq data showed that these variants were significantly associated with the expression of nearby Myosin Light Chain 12A (MYL12A), Structural Maintenance of Chromosomes Flexible Hinge Domain Containing 1 (SMCHD1), Erythrocyte Membrane Protein Band 4.1 Like 3 (EPB41L3) genes (p < .0004). Conclusions: The DLGAP1 gene plays an important role in the postsynaptic density of neurons; thus, it is both a novel positional and biological candidate gene for follow-up studies aimed at uncovering genetic determinants of muscle strength.

Common and rare variants in the LRRK2 locus are associated with Parkinson’s disease (PD) risk, but the downstream effects of these variants on protein levels remain unknown. We performed comprehensive proteogenomic analyses using the largest aptamer-based CSF proteomics study to date (7006 aptamers (6138 unique proteins) in 3107 individuals). The dataset comprised six different and independent cohorts (five using the SomaScan7K (ADNI, DIAN, MAP, Barcelona-1 (Pau), and Fundació ACE (Ruiz)) and the PPMI cohort using the SomaScan5K panel). We identified eleven independent SNPs in the LRRK2 locus associated with the levels of 25 proteins as well as PD risk. Of these, only eleven proteins have been previously associated with PD risk (e.g., GRN or GPNMB). Proteome-wide association study (PWAS) analyses suggested that the levels of ten of those proteins were genetically correlated with PD risk, and seven were validated in the PPMI cohort. Mendelian randomization analyses identified GPNMB, LCT, and CD68 causal for PD and nominate one more (ITGB2). These 25 proteins were enriched for microglia-specific proteins and trafficking pathways (both lysosome and intracellular). This study not only demonstrates that protein phenome-wide association studies (PheWAS) and trans-protein quantitative trail loci (pQTL) analyses are powerful for identifying novel protein interactions in an unbiased manner, but also that LRRK2 is linked with the regulation of PD-associated proteins that are enriched in microglial cells and specific lysosomal pathways.

目的 分析和评估2019-2021年重庆市江津区医疗机构检验科新鲜血比对结果,探索基层医疗机构检验科新鲜血比对的操作方法及经验总结,分析存在的问题,为提高基层检验科检测质量,推进区域化检验结果互认奠定基础.方法 对江津区内46家医疗机构检验科进行专业知识培训并开展新鲜血液比对,分析2019-2021年比对结果,评估江津区各基层医院检验科的检验质量,总结开展新鲜血比对的实践经验.结果 2021年参与比对医疗机构共46家,总合格29家,总合格率为63.04％.2020年参与比对医疗机构共31家,合格10家,总合格率为32.26％,2019年参与比对医疗机构共30家,合格7家,总合格率为23.33％,2021年比对合格率较2020年提高30.79％,较2019年提高39.71％,差异均有统计学意义(P＜0.05).2019年、2020年、2021年生化项目合格率分别23.33％、31.03％和65.22％,2021年合格率较2020年上升34.19％,较2019年上升41.89％,差异均有统计学意义(P＜0.05).结论 2019-2021年江津区各医疗机构检验科新鲜血比对中临床检验及免疫项目整体通过率较高,生化通过率偏低,说明在辖区内生化检验项目总体质量有待持续改进.

目的 观察低分子肝素、胰岛素联合治疗高脂血症性急性胰腺炎(HLAP)的临床疗效,探讨预测患者死亡的危险因素.方法 收集 2015 年 12 月至 2021 年 12 月住院HLAP患者的病例资料,随机将患者分为低分子肝素组 40 例、胰岛素组 40 例、对照组 40 例、低分子肝素联合胰岛素组(联合组)192 例,比较 4 组患者的临床疗效,以及急性生理与慢性健康评分(APACHEⅡ)、三酰甘油(TG)水平的变化.根据临床结局将低分子肝素联合胰岛素组患者分为生存组(173 例)和死亡组(19 例),采用多因素二分类logistic 回归分析HLAP死亡的独立危险因素,绘制受试者工作特征曲线(ROC)、计算曲线下面积(AUC),评估各项指标预测患者死亡的价值.结果 低分子联合胰岛素组患者肠鸣音恢复时间、腹痛缓解时间、住院时间指标均明显优于其他 3 组,差异均有统计学意义(P<0.05).联合组患者治疗后APACHEⅡ以及 TG 水平明显低于其他 3 组(P<0.05).多因素 Logistic 回归分析,联合组患者 C-反应蛋白(CRP)、降钙素原(PCT)、D-二聚体、血糖、入院 48h的TG水平为患者死亡的独立危险因子(P<0.05).ROC分析CRP、PCT、D-二聚体、血糖、入院 48h的TG的AUC分别为 0.732,0.972,0.900,0.726,0.972;对应的灵敏度分别为57.90%,94.70%,53.26%,52.60%,94.70%;特异度分别为 78.00%,88.20%,97.70%,90.20%,93.60%;对应的临界值分别为 158 mg/L,2.55 μg/L,5.21 μg/ml,14.35 mmol/L,19.74 mmol/L.结论 低分子肝素联合胰岛素治疗HLAP患者的疗效显著,能够明显改善患者的临床及血清指标,入院时CRP、PCT、D-二聚体、血糖、入院 48h的TG水平可作为HLAP患者的预后评价指标.

The platelet/high-density lipoprotein cholesterol ratio (PHR) is a novel inflammatory and hypercoagulability marker that represents the severity of metabolic syndrome. Liver metabolic syndrome is manifested by nonalcoholic fatty liver disease (NAFLD), which is associated with inflammation and hypercoagulability. This cross-sectional investigation aimed to identify the relationship between PHR and NAFLD. Participants in the National Health and Nutrition Examination Survey (NHANES) 2017-2020 were evaluated for hepatic steatosis and fibrosis using vibration-controlled transient elastography. The PHR was calculated as the ratio of platelets to high-density lipoprotein cholesterol. Increased PHR was associated with an increased incidence of NAFLD and hepatic fibrosis. Compared with patients in the first PHR quartile, after adjustment for clinical variables, the corresponding odds ratio (OR) for NAFLD in the fourth quartile was 2.36 (95% CI, 1.76 to 3.18) (p < 0.05); however, the OR for hepatic fibrosis was not statistically significant (p > 0.05). Furthermore, restricted cubic spline analyses showed an S-shaped association between PHR and NAFLD and an L-shaped relationship between PHR and hepatic fibrosis. The results support the effectiveness of PHR as a marker for NAFLD and hepatic fibrosis. Therefore, interventions to improve the PHR may be of benefit in reducing the incidence of both hepatic steatosis and fibrosis.