The Journal of Clinical Endocrinology & Metabolism

Background Glucagon-like peptide-1 receptor agonists (GLP1 RAs) are first-line pharmacotherapy for obesity and metabolic dysfunction-associated steatotic liver disease (MASLD); however, 20% to 35% of patients fail to achieve clinically meaningful weight loss despite guideline-directed therapy. Whether this GLP1 refractory obesity (GRO) phenotype is associated with distinct hepatic molecular abnormalities or influences bariatric surgical outcomes remains unknown. Objectives To characterize the hepatic histological, ultrastructural, and molecular phenotype of GRO at bariatric surgery, determine its recovery following surgery, and identify preoperative hepatic biomarkers associated with postoperative weight loss. Setting Academic tertiary referral bariatric surgery center. Methods Intraoperative liver biopsies were obtained from lean controls (n=3), GLP1 naive obese patients (GNO; n=10), and GLP1-refractory obese patients (GRO; n=10) undergoing Roux-en-Y gastric bypass. GRO was defined as <5% total weight loss after 12 months of guideline-directed GLP1 RA therapy. Paired liver biopsies were obtained six months postoperatively from subsets of GNO (n=5) and GRO (n=5). Histological, ultrastructural, and molecular analyses were performed, and preoperative hepatic protein expression was correlated with postoperative total weight loss. Results Compared with GNO, GRO patients exhibited more advanced hepatic steatosis, fibrosis, lipid accumulation, and mitochondrial ultrastructural disruption at surgery (all P<0.05). Despite equivalent Body mass index, GNO patients maintained lean-equivalent hepatic pCREB, pAMPK, pACC, and oxidative phosphorylation (OXPHOS) protein expression, whereas GRO patients demonstrated marked suppression of GLP1R downstream signaling (75 to 85%) and OXPHOS complex subunits (38 to 55%; all P<0.001). Six months after surgery, histological and molecular recovery remained significantly attenuated in GRO. GRO patients achieved less postoperative weight loss than GNO patients (25.2% vs. 29.51% total weight loss; P<0.001). Across the pooled cohort, several hepatic molecular markers correlated with postoperative weight loss; however, no individual biomarker independently predicted postoperative weight loss within the GRO subgroup. Conclusions GLP1 refractory obesity is associated with a distinct hepatic phenotype characterized by impaired GLP1R signaling, mitochondrial dysfunction, and attenuated hepatic recovery following bariatric surgery. The coordinated suppression of hepatic energy-sensing, mitochondrial biogenesis, and oxidative phosphorylation pathways supports the concept that GLP1 refractory obesity represents a biologically distinct metabolic phenotype. Larger prospective studies are required to determine the prognostic utility of hepatic molecular profiling for postoperative outcomes. Keywords: GLP1 receptor agonist refractoriness; bariatric surgery; hepatic steatosis; MASLD; AMPK; pCREB; mitochondrial dysfunction; OXPHOS; weight loss outcomes; biomarker

Autoimmune diseases have shown biased proportion in female population, existing clinical investigations of sex hormones in autoimmune populations have been relatively limited in terms of patient size and types of hormones investigated. In this study, we examined the relationship of sexual hormones and autoimmune antibodies in a large cohort of US women. This retrospective study sample included a total of 15319 female subjects medical information that were collected between December 2015 to May 2019 and tested in the Vibrant America Clinical Laboratory. The present serum sample was limited to female participants who had ever menstruated at the time of blood collection and completed the testing of the autoimmune antibodies and sex hormones. We focused on a total of 13 clinically significant autoantibodies including antinuclear antibody (ANA), 11 anti-extractable nuclear antigens (anti-ENAs), anti-cyclic citrullinated peptide 3 (anti-CCP3), and 11 female sex hormones. First, the prevalence of serological autoantibodies in a large set of adult female subjects divided by the menopause age was investigated. Next, the levels of sex hormones were compared in the seropositive autoimmune subjects and seronegative controls across the pre- and post-menopausal female groups. The presented study involving a large cohort of females showed no statistically different levels of sex hormones in seropositive autoimmune subjects and matched controls except for DHEA-s.

Objectives: Menopausal symptoms are heterogeneous and commonly assessed by questionnaires. We explored serum two-dimensional gel electrophoresis (2-DE) protein spots associated with menopausal symptom burden. Methods: This exploratory cross-sectional study included 27 women aged 45-55 years. A total of 550 matched serum 2-DE spots were quantified. A frequency-adjusted symptom burden score was calculated as the sum of severity x frequency products across 10 symptoms. Spots were screened using Spearman rank correlation with Benjamini-Hochberg false discovery rate (FDR) adjustment, followed by qualitative image review. Spots #285 and #636 were prioritized for vasomotor and psychological domain analyses. Results: The median age was 51.0 years; 13 participants were menstruating and 14 were amenorrheic. The median overall symptom burden score was 45.0 (interquartile range, 6.5-58.5) and was inversely correlated with spots #285 and #636. Spot #285 was inversely correlated with vasomotor symptom score, including inverse correlations in both menstrual-status groups. Spot #636 was inversely correlated with psychological symptom score overall, with a stronger descriptive correlation among menstruating participants. Neither candidate remained significant after FDR adjustment. Conclusions: Spots #285 and #636 are hypothesis-generating candidates requiring molecular identification, analytical validation, multiplicity-aware confirmation, and independent replication.

Background: Type 2 diabetes (T2D) is associated with elevated rates of several cancers and is increasingly recognized as a heterogeneous disease, but whether its clinically distinct subtypes carry different cancer risks is unknown. Methods: In this matched retrospective cohort study using electronic health record data from the Epic Cosmos Research Platform (2012-2025), adults with newly diagnosed T2D were classified into severe insulin-deficient (SIDD, 21.6%), mild obesity-related (MOD, 23.5%), mild age-related (MARD, 40.7%), or mixed (14.1%) subtypes using validated algorithms and matched to adults without diabetes on age, sex, and body mass index. Cause-specific Cox models estimated adjusted hazard ratios (HRs) for seven site-specific cancers, accounting for competing risks. Cancer screening uptake was assessed as a secondary outcome. Results: Among 575,139 adults with T2D and 689,719 without diabetes (median follow-up, 3.8 years), MARD had the highest cancer incidence (17.3 per 1,000 person-years). Relative to adults without diabetes, rates of colorectal, pancreatic, liver, endometrial, and ovarian cancer were elevated across subtypes, with the highest hazards in SIDD (HR=3.87, 95% CI=3.51 to 4.27) and mixed phenotypes. Prostate cancer rates were lower in all subtypes, most markedly in MOD (HR=0.60, 95% CI=0.55 to 0.64). Rates of breast cancer were higher among mixed (HR=1.12, 95% CI=1.05 to 1.19) and lower among MOD (HR=0.85, 95% CI=0.80 to 0.90). Mammography and prostate-specific antigen screening were lower across subtypes. Conclusions: Site-specific cancer incidence and screening uptake differed across clinically defined subtypes of T2D. Subtype classification from routine clinical data may inform targeted cancer surveillance, though further study is needed before clinical use.

Objective: To determine whether continuous glucose monitoring (CGM) identifies clinically relevant glycemic heterogeneity and subclinical end-organ alterations in adults without diabetes. Research Design and Methods: We analyzed 1,017 AI-READI Year 3 participants without diabetes (558 with normoglycemia and 459 with prediabetes by A1C). Fifty-two metrics from 10-day blinded CGM were reduced to nonredundant glycemic axes. Partial Spearman correlations between representative CGM metrics and clinical measures across 13 domains were adjusted for age, sex, and BMI and controlled for false discovery rate. CGM-derived subphenotypes were identified using unsupervised UMAP-HDBSCAN-based clustering. Results: Among 462 glycemic-clinical associations tested, 99 (21.4%) remained significant after false discovery rate correction. Hyperglycemia-related metrics, including mean glucose, time above range, and time in tight range, showed more associations than variability metrics. The strongest signals involved cardiometabolic, cardiovascular, and cognitive measures. Greater hyperglycemia and glucose excursions were associated with lower language performance, slower processing speed, and lower cognitive efficiency ({rho} {approx} -0.10 to -0.14; all P < 0.01). Clustering identified four reproducible glycemic subphenotypes: Healthy, Mild Hyperglycemia, High Variability, and Hyperglycemia. CGM phenotypes reclassified A1C-defined groups: 58.1% of participants with normoglycemia fell into dysglycemic phenotypes, whereas 18.8% of participants with prediabetes fell into more favorable phenotypes. The Hyperglycemia phenotype had the most adverse cardiometabolic profile and lower cognitive performance. Conclusions: In adults without diabetes, CGM revealed glycemic patterns associated with distinct subclinical alterations. CGM-based phenotyping may complement A1C for characterizing early dysglycemia and selecting individuals for longitudinal risk-stratification studies.

Body mass index (BMI) alone may underestimate clinically relevant obesity because it does not capture central fat distribution. We compared obesity prevalence in Mexico using BMI-only criteria, adiposity-confirmed criteria, and the clinical obesity definition proposed by the Lancet Diabetes and Endocrinology Commission. We conducted a population-based, cross-sectional study of 13,160 adults aged 18 years or older who participated in the 2018-2019 Mexican National Health and Nutrition Survey (ENSANUT). Obesity prevalence was estimated through survey-weighted analyses that accounted for the complex sampling design. The weighted prevalence of obesity based on BMI was 34.5% (95% CI, 33.1-35.9), while 30.9% (95% CI, 29.6-32.2) met criteria for clinical obesity. One quarter of individuals with clinical obesity had a BMI under 30 kg/m2, a phenotype more common among older adults. Half of adults with a BMI under 30 kg/m2 showed elevated central adiposity. BMI alone underestimates clinically relevant obesity in Mexican adults. Adding waist-based measurements could improve the identification of individuals with excess fat and metabolic risk, both in clinical settings and population monitoring.

Menstrual symptoms vary across the cycle, yet most research assumes a normative 28-day cycle with fixed phase durations, obscuring the physiological relevance of natural cycle variation. Using the mcPHASES dataset, we characterised cycle and phase length variation across 96 menstrual cycles from 37 participants, with ovulation timing estimated from daily urinary luteinizing hormone measurements using a Bayesian hierarchical model, and examined associations with daily symptoms in a subset of 64 cycles from 35 participants with complete symptom data. Twelve physical, mental, and behavioural symptom domains were modelled using Bayesian ordinal regression, with posterior uncertainty in phase-length predictors propagated via a measurement error framework. Total cycle length was not associated with daily symptom burden, except sleep disturbances. By contrast, phase length decomposition revealed systematic associations across multiple domains: longer menstrual phase length was broadly associated with greater symptom intensity spanning physical, gastrointestinal, affective, and sleep domains; longer luteal phase duration was associated with greater fatigue and more frequent headaches, but lower sore breast intensity and lower stress; and longer follicular phase duration and later ovulation were each associated with greater sore breast intensity and more frequent mood swings. These associations require knowledge of actual ovulation timing and cannot be recovered from cycle length alone, indicating that the common assumption of a fixed 14-day luteal phase introduces systematic misclassification of hormonal exposure. Daily symptom intensity was also predominantly person-specific, with cycle phase explaining little of the between-person variance across most symptoms. These findings indicate that calendar-based phase assignment is insufficient for research and clinical assessment of hormone-sensitive conditions, and that person-specific baselines, rather than population-level phase averages, are needed for clinically meaningful symptom monitoring.

The distribution of fat-soluble compounds (including antioxidants) in follicular fluid (FF) remains sparsely documented in relation to in vitro fertilization (IVF) outcomes and existing studies have reported diverging associations. This study aimed to describe plasma and FF concentrations of fat-soluble micronutrients in women undergoing IVF and to analyze their adjusted associations with ovarian function, embryo development and pregnancy outcomes. In 2021-2022, plasma and FF samples were collected from 82 women (first IVF cycle) at oocyte puncture, along with lifestyle data covering the three preceding months. Eleven compounds (two tocopherols, three xanthophylls, five carotenes and retinol) were quantified. All compounds were detected in both compartments (lowest in FF) except phytoene, undetectable in FF. Plasma and FF -tocopherol concentrations were positively associated with plasma estradiol levels before oocyte puncture (both p<0.01) while FF -carotene and lycopene were inversely associated with plasma progesterone concentrations (p=0.01 and 0.02, respectively). Plasma phytofluene and phytoene were positively associated with mature oocyte rate (p=0.03 and p=0.01, respectively), while FF retinol was negatively associated (p=0.03). Carotenes, tocopherols and retinol were inversely associated with later IVF outcomes: fertilization rate (p<0.001 for plasma g-tocopherol, 0.02 for FF retinol), top-quality embryo (p=0.02 for plasma phytofluene), biochemical pregnancy at day 7 post-embryo transfer (p=0.05 for plasma -tocopherol, 0.02 for plasma -carotene), clinical pregnancy (p=0.03 for plasma -tocopherol, 0.01 for plasma phytoene) and live birth (p=0.04 for plasma -tocopherol, 0.02 for plasma phytoene). Plasma and FF g-tocopherol were positively associated with embryo fragmentation (both p<0.05). Finally, among xanthophylls, only plasma {beta}-cryptoxanthin was positively associated with plasma progesterone concentrations (p=0.02). Our findings of heterogeneous associations between tocopherols, carotenes, retinol and IVF outcomes across the stages of IVF suggest a beneficial effect limited to early outcomes and support a complex and context-dependent role of these compounds in female reproduction. This manuscript has been submitted to PlosOne on August 19, 2026.

Background: Evidence suggests chronic distress influences ovarian cancer (OC) etiology and metabolomic profiles. Here, we evaluated the association of a metabolite-based distress score (MDS) and OC risk. Methods: We included two matched case-control studies nested within the Nurses' Health Studies (N=584) and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (N=348). Metabolites were measured 3-27 years before diagnosis using liquid-chromatography tandem mass spectrometry. We examined the association of quintiles of MDS and 19 constituent metabolites with OC risk using unconditional logistic regression and stratified by tumor histotype, menopausal status, and age at diagnosis. Results: We observed women in the highest versus lowest quintile of MDS had an increased OC risk (OR=1.62,95%CI=1.03-2.54,ptrend=0.07), and type 2 tumors (OR=1.71,95%CI=1.03-2.83,ptrend=0.11). Associations were suggestively stronger for premenopausal and <69-year-old women, and driven by pseudouridine, and N2,N2-dimethylguanosine. Conclusion: Our findings suggest chronic distress-associated metabolic dysregulation may represent a novel OC risk factor, especially among younger women.

Background Data-driven type 2 diabetes (T2D) subtypes differ in their risks of dementia and stroke. We examined whether their metabolomic profiles also differed and whether subtype-related metabolic patterns were associated with dementia, stroke, and all-cause mortality. Methods We analyzed NMR-based metabolomic profiles across previously defined T2D subtypes in the UK Biobank. Subtype-related metabolites were summarized using principal component analysis (PCA), and their associations with incident dementia, stroke, and all-cause mortality were examined using Cox models. Attenuation analyses and two-sample Mendelian randomization further assessed subtype-outcome relationships and the potential causal relevance of outcome-associated metabolites. Results Among 7,671 individuals (mean age 59.85 years; 37% female), the first five PCs explained 76.7% of variance in subtype-related metabolites and mainly reflected lipid and lipoprotein signatures. After adjustment for T2D subtype and confounders, the HDL-remodeling PC increased risks of all-cause dementia (HR 1.17, 95% CI 1.08-1.27), VaD (HR 1.18, 95% CI 1.05-1.32), and all-cause mortality (HR 1.16, 95% CI 1.13-1.19). Lower scores on the LDL cholesterol-enriched axis increase risks of all-cause dementia (HR 0.75, 95% CI 0.62-0.91) and mortality (HR 0.76, 95% CI 0.69-0.83). The VLDL/LDL-enriched PC was inversely associated with mortality (HR 0.93, 95% CI 0.88-0.98). No significant stroke results were observed. Adjustment for the PCA-derived metabolomic patterns generally attenuated subtype-outcome associations, MR analyses identified 197 metabolite-outcome associations that remained significant after FDR correction. Conclusions Metabolomic profiling showed that the metabolic signatures differed across data-driven T2D subtypes and highlighted lipid and lipoprotein remodeling as a major metabolic feature associated with dementia, stroke, and all-cause mortality.

Background: Observational research reports positive associations between type 2 diabetes mellitus (T2DM) and obesity-related cancers (ORCs), but causality remains unclear due to confounding (namely the shared risk factor of obesity, commonly approximated as body mass index, BMI), immortal time bias, and detection-time bias. Here, we aimed to use causal inference methods to minimise the above problems and estimate causal associations between new-onset T2DM and incident cancer. Methods: We performed a cohort study within UK Biobank, comparing new-onset T2DM with unexposed individuals matched 1 to 3 on BMI, age, and sex using a sequential longitudinal approach. The primary outcomes were total incident cancer, divided into ORCs and non-obesity-related cancers (NORCs). The secondary outcomes were site-specific cancers. We developed Cox models to estimate time-split hazard ratios (tsHRs) and 95% confidence intervals (CIs) stratified by sex. Findings: 23,771 participants with new-onset T2DM were matched with 71,170 unexposed participants. During a median follow-up of 5 years, there were 7694 (T2DM: 2432; unexposed: 5262) incident cancers. In men, there was evidence for an effect of T2DM on obesity-related cancer (tsHR 1.39, 95% CI 1.21-1.59), particularly on hepatocellular carcinoma (tsHR 3.97, 95% CI 2.38-6.65), pancreatic (tsHR 1.77, 95% CI 1.15-2.72) and kidney (tsHR 1.62, 95% CI 1.13-2.32) cancers. In women, there was evidence for an effect on obesity-related cancers (tsHR 1.33, 95% CI 1.16-1.52). Importantly, there were no associations with post-menopausal breast and endometrial cancers, two cancer types consistently associated with elevated BMI. There was no effect of new-onset T2DM on incidence of NORCs. There was evidence of detection-time bias, particularly in men. Interpretation: This is the first large-scale study to demonstrate evidence of a BMI-independent associations between new-onset T2DM and incident cancer. In men, this was primarily driven by hepatocellular carcinoma, pancreatic cancer, and kidney cancer. In women, the underlying cancers driving this relationship were less clearly defined. Funding: This study was funded by Cancer Research UK and administered through the Manchester Cancer Research Centre MB-PhD scheme (SEBCATP-2023/100010).

PURPOSE: To assess the predictive performance of panel protein biomarkers as well as an established algorithm that considers repeat biomarker testing for risk prediction of PDAC among a prospective cohort of patients with New-onset diabetes. PATIENTS AND METHODS: A panel of protein biomarkers (CA19-9, CA125, CEA, LRG1, REG3A and TIMP1) were assayed in 6,516 serially collected pre-diagnostic plasma samples from 2,121 NOD patients from the Consortium of Chronic Pancreatitis Diabetes and Pancreatic Cancer (CPDPC)-initiated NOD study who completed the 3-year study follow-up period. The specimen set included 25 pre-diagnostic samples from the 12 PDAC cases diagnosed during study follow-up. We applied a single threshold (ST) method, which considers biomarker levels at a single time point, as well as a previously established parametrical empirical Bayes (PEB) algorithm, which considers prior biomarker measurements, with case calls made based on pre-specified cutoffs corresponding to 1% 1-year risk. Resultant biomarker data as well as case calls were provided to the EDRN Data Management and Coordinating Center as part of a Prospective-sample-collection-Retrospective-Blinded-Evaluation (ProBE)-compliant Phase 3 biomarker validation study. Area under the Receiver Operating Characteristic Curves (AUC), sensitivity, specificity, population-level positive predictive value (PPV), and negative predictive value (NPV) are reported. RESULTS: The 3-year incidence of PDAC in the NOD cohort was 0.57%. When considering PDAC vs non-cancer controls, respective AUCs of individual protein biomarkers ranged from 0.52-0.94, with CA19-9 achieving the highest overall performance of 0.94 (95% CI: 0.86-1.00). At the pre-defined 1% 1-year risk threshold, CA19-9 yielded sensitivity of 83.3% at 97.2% specificity. Additional markers CEA, CA125, and TIMP1 demonstrated sensitivity of 33.3%, 41.7%, and 8.3%, respectively. In a subset of patients, CA19-9 first tested positive at a median (interquartile range [IQR]) of 7 months (4 to 14 months) prior to clinical PDAC diagnosis. Of the two PDAC cases missed by CA19-9 using the ST method, one (diagnosed with stage III PDAC) was detected using the PEBCA19-9 algorithm. CONCLUSION: In the setting of adult new onset diabetes, CA19-9 is a readily available and promising biomarker that can be leveraged for earlier detection of an underlying pancreatic cancer. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Importance The menopausal transition has been associated with an increased risk of depression, although findings are inconsistent. While most research has focused on menopausal stage, some studies suggest that later age at menopause may be associated with lower depression risk. Objective To examine the association between age at menopause and depression risk during perimenopause and early postmenopause using multivariable regression and genetic approaches. Design Prospective cohort study using data from the mothers of the Avon Longitudinal Study of Parents and Children (ALSPAC), a UK birth cohort that recruited pregnant women in 1991-1992. Setting UK community-based cohort study. Participants Up to 3,307 women with repeated measures of depressive symptoms across the perimenopausal and postmenopausal periods and data on observed or genetically predicted age at menopause. Exposure Observed age at menopause, a polygenic risk score (PRS) for age at menopause, and genetically predicted age at menopause. Main Outcome(s) and Measure(s) Depressive symptoms during the perimenopausal and early postmenopausal periods were assessed using the Edinburgh Postnatal Depression Scale (EPDS), with depression defined as a score >= 13. Results Effect estimates across multivariable regression and genetic analyses were small and directionally consistent with lower odds of depression with older age at menopause, although most confidence intervals included the null. In analyses using observed age at menopause, there was little evidence of an association with depression during perimenopause (Odds ratio (OR) per year increase in age at menopause 0.98, 95%CI 0.89-1.08) or postmenopause (OR 1.00, 95%CI 0.89-1.13). Results were similar when using a PRS as a genetic proxy for age at menopause during perimenopause (OR per standard deviation (SD) increase in PRS 0.98, 95%CI 0.89-1.09) but suggested lower odds of depression during postmenopause (OR 0.92, 95%CI 0.86-0.99). Mendelian randomization analyses did not support a causal effect (OR per year increase 1.00, 95%CI 0.89-1.13 for perimenopause, and OR 0.97, 95%CI 0.86-1.09 for postmenopause). Conclusions and Relevance Age at menopause is unlikely to be a major driver of midlife depression risk. However, consistent effect directions across approaches suggest a small association may exist, but further research in larger samples is needed to confirm this.

Objective Women with a history of Pelvic Inflammatory Disease (PID) face elevated risks of health complications and mortality. This study examined the association between cardiovascular health (CVH) and PID among U.S. women. Methods We conducted a cross-sectional analysis of NHANES 2013-2023 (n=6,382). The LE8 scores were categorized into four groups based on quartiles: Q1 (<25), Q2 (25-49), Q3 (50-74) and Q4 ([&ge;]75). We calculated adjusted ORs (95% CIs) via logistic regression to evaluate LE8-PID associations. Results Compared to the highest LE8 quartile ([&ge;]75), adjusted ORs for PID were 1.66 (95%CI:1.03-2.67) for Q3, 1.71(1.10-2.67) for Q2, and 1.99(1.13-3.50) for Q1. The inverse association was consistent across health behavior and health factor components, with sleep, smoking, blood pressure, and BMI showing the strongest effects, particularly among younger women. Conclusions Higher LE8 scores are inversely associated with PID prevalence, particularly in younger women. Promoting cardiovascular health may help reduce PID burden.

Prolactinomas, the most common secretory pituitary tumour subtype, frequently occur in patients with Multiple Endocrine Neoplasia type 1, caused by germline MEN1 mutations encoding menin. While menin loss is well established in MEN1-associated prolactinomas, its role in sporadic tumours remains unclear. We investigated menin expression, subcellular localization, and downstream signalling in two murine models of non-MEN1 prolactinomas, the dopamine D2-receptor knockout and the hCG{beta}-subunit-overexpressing mice, in which only females develop prolactinoma. Pituitary Men1 expression, analysed by qPCR, remained unchanged despite the genotype, in both sexes. However, in prolactinomas, lactotrophs exhibited a marked loss of nuclear MEN1 immunostained, with protein restricted to the cytoplasm. Male mice pituitaries retained nuclear MEN1 localization regardless their genotype. Loss of nuclear menin in prolactinomas was associated with reduced p27 and Pten expression, increased Ccnd1 expression, and enhanced pAKT. Moreover, by using in vivo pharmacological and surgical approaches we demonstrated that dopamine-agonist treatment preserved nuclear menin in lactotrophs, whereas dopamine blockade or estradiol induced its nuclear loss. Importantly, analysis of human pituitary biopsies confirmed nuclear and cytoplasmic menin localization in lactotrophs from normal pituitaries, and in prolactinomas from both genders following dopamine agonist therapy. However, in a prolactinoma from an untreated female, nuclear menin was partially lost. Therefore, our findings identify a state of functional MEN1-deficiency in sporadic prolactinomas (characterized by preserved MEN1 expression), but its exclusion from the nucleus (linked to activation of proliferative pathways, impaired tumour suppressor signalling, and tumour development) highlights the restoration of nuclear MEN1 localization as a potential therapeutic strategy.

Background Lifestyle interventions are central to obesity prevention and management, yet interindividual variability in response remains incompletely understood. Here, we leveraged genetically defined, distinct obesity endotypes to examine lifestyle-body mass index (BMI) associations across biological pathways. Methods In the UK Biobank, we analyzed 305,713 participants with partitioned polygenic scores (pPSs) representing 10 obesity endotypes. We evaluated interactions between endotype-specific genetic susceptibility and physical activity, diet, sedentary behavior, and sleep on BMI using multivariable linear regression. Primary findings were externally evaluated in the All of Us Research Program using Fitbit-derived lifestyle measures. Results Favorable lifestyle behaviors were associated with lower BMI for all obesity endotypes, but the magnitude of these associations varied significantly across endotypes. Higher endotype-specific pPSs strengthened the benefits of physical activity (7 endotypes), healthy diet (3 endotypes), nonsedentary behavior (5 endotypes), and adequate sleep (7 endotypes) on BMI. Distinct endotypes demonstrated the greatest responsiveness to different lifestyle domains, with the metabolically unhealthy endotype showing the strongest interaction with physical activity, metabolically healthy endotype with sedentary behavior, hypothalamic dysregulation endotype with diet, and hypoinsulin 2 endotype with sleep, corresponding to differences in BMI of 0.22-0.49 kg/m2 between the highest and lowest pPS deciles. These interaction patterns were consistent in the All of Us cohort. Conclusions Obesity endotypes modify the association between lifestyle behaviors and BMI, demonstrating that responsiveness to lifestyle behaviors is heterogeneous and pathway dependent. These findings provide a framework for precision obesity prevention by identifying individuals who may derive greater benefit from specific lifestyle interventions.

Background Cardiovascular-Kidney-Metabolic (CKM) syndrome encompasses interconnected conditions such as type 2 diabetes (T2D), hypertension, hypertriglyceridemia, metabolic syndrome (MetS), and chronic kidney disease (CKD). As CKM progresses, cardiorenal risks increase. Although Glucagon-like peptide-1 receptor agonists (GLP-1 RA) have demonstrated cardiorenal and cardiometabolic benefits, offering an opportunity to slow CKM progression, their use may vary across social determinants of health (SDoH) and stage 2 CKM subgroups. Objective To evaluate the influence of SDoH on access to GLP-1 RA among patients with T2D and other stage 2 CKM conditions. Methods This cross-sectional study used data from the U.S. National Health and Nutrition Examination Survey (NHANES), 2005?2020. Adults aged [&ge;]30 years with T2D and/or other stage 2 CKM conditions were included. Weighted descriptive analysis, multivariable logistic regression and LASSO were applied to assess associations between SDoH and GLP-1 RA use. Results Among 4,520 participants (representing approximately 84.0 million U.S. adults), weighted mean age was 61.4 years, 48.9% were female, and 61.5% were non-Hispanic White. Among participants with T2D, GLP-1 RA use was higher among individuals with higher education (3.39% vs 1.43%), private insurance (3.00% vs 0.58%), and higher income (4.70% vs 1.87%), while no use was observed among those without routine places for care. In adjusted analyses, individuals with lower income, less than high school education, lack of insurance, and being unmarried had 64%, 51%, 81%, and 40% lower likelihood of GLP-1 RA use, respectively. LASSO identified income, education, insurance, and access to care as predictors. Lower income, lower educational attainment, and lack of insurance were associated with 48%, 34%, and 79% lower likelihood of GLP-1 RA use, respectively, adjusting for age, sex, and race/ethnicity. Conclusion SDoH-driven disparities limit GLP-1 RA access. Expanding GLP-1 RA access by addressing socioeconomic barriers is critical to slowing CKM progression, reducing cardiovascular risk, and mitigating health disparities.

Abstract Aim: Examine cross-sectional associations between mid-pregnancy food intake indicators and prenatal depressive symptomatology. Methods: This secondary analysis of the Comparison of Two Screening Strategies for Gestational Diabetes trial (N = 718) examined domains of mid-pregnancy food intake (direct timing, energy timing, meal/snack structure, meal energy distribution, diet quality) derived from 24-hour dietary recalls. Depressive symptoms were measured with the Edinburgh Postnatal Depression Scale (EPDS). Generalized linear models examined associations between food intake indicators, total, and high (EPDS [&ge;]13) depressive symptoms. Results: Mean (SD) EPDS score was [6.3 (4.9)]; 12.4% (n = 89) had high depressive symptoms. Eating frequency (B = 0.08 [0.02, 0.14], p = 0.009), snack frequency (B = 0.06 [0.00, 0.12], p = 0.040), nighttime snacking frequency (B = 0.06 [0.00, 0.11], p = 0.041), and total daily energy intake (B = 0.06 [0.01, 0.12], p = 0.031) were positively associated with total depressive symptoms. Energy intake from breakfast (PR = 1.2 [1.0, 1.3], p = 0.017) was associated with a higher prevalence of high depressive symptoms. Energy intake from dinner (PR = 0.81 [0.69, 0.94], p = 0.007), later timing of the first eating episode (PR = 0.83 [0.70, 0.99], p = 0.034) and first energy quartile (PR = 0.84 [0.70, 1.0], p = 0.048), were associated with a lower prevalence of high depressive symptoms. Conclusion: These findings extend prior chrononutrition-depression literature to the prenatal period, implicating eating frequency, energy intake, and meal energy timing and distribution in depressive symptomatology during pregnancy, warranting further longitudinal investigation.

Background: The placenta has a unique transcriptomic profile, including microRNAs that are secreted into maternal circulation throughout pregnancy. MicroRNAs are small, non-coding RNA that post-transcriptionally regulate gene expression. Spontaneous preterm birth (sPTB) is associated with substantial differences in both placental pathophysiology and placental gene expression compared to term birth. We aimed to generate microRNA signatures of sPTB and map them to target genes using a microRNA-mRNA network. Methods: This study was conducted within the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study. Placental samples were collected at delivery, and RNA was isolated for mRNA and microRNA sequencing. To investigate sPTB, this study excluded placental samples of participants with iatrogenic indications for PTB or induced labor. We examined differences in microRNA expression in participants who delivered before 37 weeks (N=35) compared to term participants (N=404) in a series of covariate-adjusted linear regression models. We used paired placental microRNA and mRNA expression data from this cohort to validate associations between computationally predicted microRNA-mRNA pairs and establish a microRNA-mRNA network. Results: Expression of 7 microRNAs were increased in sPTB (FDR<0.05) and were inversely correlated with sPTB-associated genes involved in immune signaling. Expression of 12 microRNAs were decreased in sPTB, including 4 members of the maternally expressed chromosome 14 microRNA cluster (miR-376a-3p, miR-376c-3p, miR-377-3p, and miR-381-3p). These microRNAs were predicted to negatively regulate oxidative phosphorylation genes that were increased in sPTB. The associations between miR-376c-3p and miR-377-3p and oxidative phosphorylation were confirmed in microRNA knockdown experiments. Conclusions: This study highlights potential biological mechanisms by which placental microRNA dysfunction might contribute to sPTB and highlights putative sPTB biomarkers that may be detectable in maternal circulation.

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.