Prolactin is a hormone secreted from lactotroph cells in the anterior pituitary gland to induce lactation after birth. Hyperprolactinemia unrelated to lactation is a common cause of amenorrhea in women of a childbearing age, and a consequent decrease in the gonadotropin-releasing hormone (GnRH) by a high prolactin level can result in decreased bone mineral density. Osteoporosis is a common skeletal disorder characterized by decreased bone mineral density (BMD) and quality, which results in decreased bone strength. In patients with hyperprolactinemia, changes in BMD can be induced indirectly by the inhibition of the GnRH–gonadal axis due to increased prolactin levels or by the direct action of prolactin on osteoblasts and, possibly, osteoclast cells. This review highlights the recent work on bone remodeling and discusses our knowledge of how prolactin modulates these interactions, with a brief literature review on the relationship between prolactin and bone metabolism and suggestions for new possibilities.

The American Heart Association recently introduced a new framework, known as cardiovascular-kidney-metabolic (CKM) syndrome, aimed at the early prevention of cardiovascular disease. However, this syndrome has not been studied extensively outside of the United States. Thus, this study aimed to examine the long-term trends in CKM syndrome and its associated demographic features in South Korea. This nationwide, cross-sectional study analyzed data from 61,106 Korean adults aged ≥20 years using the Korea National Health and Nutrition Examination Survey from 2011 to 2021. CKM syndrome was defined using the PREVENT equations, categorizing individuals into five stages (0-4). Age-standardized prevalence rates, annual percent changes (APC), and sociodemographic disparities were analyzed using multinomial logistic regression and Joinpoint regression. Among the 61,106 participants (50.4% females [95% CI, 50.0-50.8%] and 49.6% males [95% CI, 49.2-50.0%]), stage 2 CKM syndrome was the most prevalent (43.4% [42.9-43.9]), followed by stages 1 (25.4% [25.0-25.8]), 0 (21.1% [20.7-21.6]), 3 (7.3% [7.0-7.5]), and 4 (2.8% [2.6-2.9]). From 2011 to 2021, advanced stages showed significant increases (APC for stage 4: 3.2%; 95% CI, 1.5-5.2), while stage 0 declined (APC: -1.9%; 95% CI, -3.8 to 0.0). Advanced stages were more common among vulnerable subgroups, including males, older adults, rural residents, smokers, drinkers, individuals with obesity, lower education levels, and lower household incomes. This is the first study to investigate the long-term prevalence of CKM syndrome based on stages at the national level in an Asian population. Our findings emphasize the urgent need for tailored public health strategies targeting metabolic risk factors, particularly in vulnerable subgroups, to prevent progression to advanced CKM stages. National Research Foundation of Korea.

Type 2 diabetes mellitus (T2DM) is a significant global public health concern that has steadily increased over the past few decades. Thus, this study aimed to predict the incidence of T2DM within 5 years and the risk of mortality following the onset of T2DM. Data from three independent cohorts worldwide were used. We utilized data from three independent, large-scale, general population-based, and worldwide cohort studies. The Korean cohort (NHIS-NSC cohort; discovery cohort; n = 973,303), conducted between 1 January, 2002 and 31 December, 2013, was used for training and internal validation, whereas the Japanese cohort (JMDC cohort; validation cohort A; n = 12,143,715) and UK cohort (UK Biobank; validation cohort B; n = 416,656) were used for external validation. We employed various machine learning (ML)-based models, using 18 features, to predict the incidence of T2DM within five years of regular health checkups and calculated the Shapley Additive Explanation (SHAP) values. To ensure the robustness of our ML-based prediction model, we investigated the potential association between the model probability divided into tertiles and the risk of mortality following the onset of T2DM. In the discovery cohort, the ensemble model using voting with logistic regression and adaptive boosting achieved a balanced accuracy of 72.6% and an area under the receiver operating characteristics curve (AUROC) of 0.792. The SHAP value analysis of our proposed model revealed that age was the most important predictor of incident T2DM, followed by fasting blood glucose, hemoglobin, γ-glutamyl transferase level, and body mass index. The model probability is associated with an increased risk of mortality (T1: adjusted hazard ratio, 2.82 [95% CI, 2.01–3.94]; T2: 3.89 [2.74–5.53]; and T3: 7.73 [5.37–11.12]). Similar patterns and trends were observed in the validation cohorts (T1: 1.74 [1.49–2.03], T2: 1.97 [1.69–2.30], and T3: 3.31 [2.82–3.38] in validation cohort A; T1: 1.33 [1.03–1.71], T2: 1.54 [1.21–1.96], and T3: 1.73 [1.36–2.20] in validation cohort B). This study derived and validated an ML-based model to predict the incidence of T2DM within 5 years across three countries (South Korea, Japan, and the UK), showing that the model probability is associated with an increased risk of mortality. Institute of Information & Communications Technology Planning & Evaluation, South Korea.

Background: Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. Machine learning (ML) systems can enhance DR in community-based screening. However, predictive power models for usability and performance are still being determined. Objective: This study used data from 3 university hospitals in South Korea to conduct a simple and accurate assessment of ML-based risk prediction for the development of DR that can be universally applied to adults with type 2 diabetes mellitus (T2DM). Methods: DR was predicted using data from 2 independent electronic medical records: a discovery cohort (one hospital, n=14,694) and a validation cohort (2 hospitals, n=1856). The primary outcome was the presence of DR at 3 years. Different ML-based models were selected through hyperparameter tuning in the discovery cohort, and the area under the receiver operating characteristic (ROC) curve was analyzed in both cohorts. Results: Among 14,694 patients screened for inclusion, 348 (2.37%) were diagnosed with DR. For DR, the extreme gradient boosting (XGBoost) system had an accuracy of 75.13% (95% CI 74.10‐76.17), a sensitivity of 71.00% (95% CI 66.83‐75.17), and a specificity of 75.23% (95% CI 74.16‐76.31) in the original dataset. Among the validation datasets, XGBoost had an accuracy of 65.14%, a sensitivity of 64.96%, and a specificity of 65.15%. The most common feature in the XGBoost model is dyslipidemia, followed by cancer, hypertension, chronic kidney disease, neuropathy, and cardiovascular disease. Conclusions: This approach shows the potential to enhance patient outcomes by enabling timely interventions in patients with T2DM, improving our understanding of contributing factors, and reducing DR-related complications. The proposed prediction model is expected to be both competitive and cost-effective, particularly for primary care settings in South Korea.

Background: Several machine learning (ML) prediction models for neurodegenerative diseases (NDs) in type 2 diabetes mellitus (T2DM) have recently been developed. However, the predictive power of these models is limited by the lack of multiple risk factors. Objective: This study aimed to assess the validity and use of an ML model for predicting the 3-year incidence of ND in patients with T2DM. Methods: We used data from 2 independent cohorts—the discovery cohort (1 hospital; n=22,311) and the validation cohort (2 hospitals; n=2915)—to predict ND. The outcome of interest was the presence or absence of ND at 3 years. We selected different ML-based models with hyperparameter tuning in the discovery cohort and conducted an area under the receiver operating characteristic curve (AUROC) analysis in the validation cohort. Results: The study dataset included 22,311 (discovery) and 2915 (validation) patients with T2DM recruited between 2008 and 2022. ND was observed in 133 (0.6%) and 15 patients (0.5%) in the discovery and validation cohorts, respectively. The AdaBoost model had a mean AUROC of 0.82 (95% CI 0.79-0.85) in the discovery dataset. When this result was applied to the validation dataset, the AdaBoost model exhibited the best performance among the models, with an AUROC of 0.83 (accuracy of 78.6%, sensitivity of 78.6%, specificity of 78.6%, and balanced accuracy of 78.6%). The most influential factors in the AdaBoost model were age and cardiovascular disease. Conclusions: This study shows the use and feasibility of ML for assessing the incidence of ND in patients with T2DM and suggests its potential for use in screening patients. Further international studies are required to validate these findings.

Metabolic risk factors (MetRs) are associated with hepatic and cardiac outcomes in patients with fatty liver disease (FLD). We evaluated whether MetRs have different effects on alcoholic FLD (AFLD) and non-alcoholic FLD (NAFLD). We used a standardised common data model to analyse data from seven university hospital databases between 2006 and 2015. MetRs included diabetes mellitus, hypertension, dyslipidaemia, and obesity. Follow-up data were analysed for the incidence of hepatic outcomes, cardiac outcomes, and death in patients with AFLD or NAFLD and based on MetRs within AFLD and NAFLD. Out of 3,069 and 17,067 patients with AFLD and NAFLD, respectively, 2,323 (75.7%) and 13,121 (76.9%) had one or more MetR, respectively. Patients with AFLD were at a higher risk of hepatic outcomes (adjusted risk ratio [aRR], 5.81) compared with those with NAFLD irrespective of MetR. The risk of cardiac outcomes in AFLD and NAFLD became similar with the increasing number of MetRs. Patients with NAFLD without MetRs demonstrated a lower risk of cardiac outcomes, but not hepatic outcomes, compared with those with MetRs (aRR, 0.66 and 0.61 for MetR ≥1 and MetR ≥2, respectively; p <0.05). In patients with AFLD, hepatic and cardiac outcomes were not associated with MetRs. The clinical impact of MetRs in patients with FLD may differ between patients with AFLD and those with NAFLD. With the increasing prevalence of fatty liver disease (FLD) and metabolic syndrome, the increase in associated complications, such as liver and heart diseases, has become an important social issue. Particularly in patients with FLD with excessive alcohol consumption, the incidence of liver and heart disease is pronounced because of the dominant effect of alcohol over the effects of other factors. Thus, appropriate screening and management of alcohol consumption in patients with FLD are vital.