Ectopic parathyroid adenomas of the retropharyngeal space are relatively rare. Herein, we report a case of primary hyperparathyroidism (PHPT) secondary to a retropharyngeal parathyroid adenoma. A 22-year-old woman presented with elevated serum calcium and parathyroid hormone (PTH) levels, revealed during a medical check-up. The patient had a history of ureteral stones and a confirmed low bone mass. Neck 99mTechnetium-sestamibi singlephoton emission computed tomography (CT) and ultrasonography did not reveal any suspicious lesions. There was no evidence of hereditary PHPT based on the results of targeted gene sequencing. Surgical exploration was unsuccessful, and the PHPT persisted after the first surgery. Approximately a year after the failed operation, 18F-fluorocholine (FCH) positron emission tomography/CT (PET-CT) became available, and when performed, it revealed increased uptake in the retropharyngeal space of the right side of the neck. The results of parathyroid venous sampling were concordant with a >2-fold elevation of PTH level in the veins on the right side of the neck compared to the peripheral veins. The 1.8 cm-diameter mass was successfully removed resulting in an 87% reduction in intraoperative PTH level (198.0-26.5 pg/mL). Subsequently, normalizations of calcium and PTH levels were achieved. In summary, ectopic parathyroid adenomas, including retropharyngeal lesions, should also be suspected when investigating an elusive case of PHPT. 18F-FCH PET-CT can be a useful complementary modality for detecting culprit lesions.

Background: Height loss is a simple clinical measure associated with increased fracture risk. However, limited data exists on the association between height loss and fracture risk in postmenopausal Korean women. It is unknown whether this association varies with age. Methods: Data on height loss over a 6-year period were collected from a community-based longitudinal follow-up cohort (Ansung cohort of the Korean Genome and Epidemiology Study). Incident fractures were defined based on self-reported fractures after excluding those due to severe trauma or toes/fingers. The association between incident fractures and height loss was investigated using a Cox proportional hazards model. Results: During a median follow-up of 10 years after the second visit, 259/1,806 participants (median age, 64 years) experienced incident fractures. Overall, a 1 standard deviation (SD) decrease in height (1.6 cm/median 5.8 years) was associated with 9% increased risk of fracture (hazard ratio [HR], 1.09; P=0.037), which lost statistical significance after adjustment for covariates. When stratified into age groups (50–59, 60–69, 70 years or older), a 1 SD decrease in height remained a robust predictor of fracture in the 50 to 59 years age group after adjusting for covariates (adjusted hazard ratio [aHR], 1.52; P=0.003), whereas height loss was not an independent predictor of fracture in the 60 to 69 (aHR, 1.06; P=0.333) or the 70 years or older age groups (aHR, 1.05; P=0.700; P for interaction <0.05, for all). Conclusion Height loss during the previous 6 years was associated with an increased 10-year fracture risk in postmenopausal women in their 50s.

Recently, machine learning (ML) applications have received attention in diabetes and metabolism research. This review briefly provides the basic concepts of ML and specific topics in diabetes research.Exemplary studies are reviewed to provide an overview of the methodology, main findings, limitations, and future research directions for ML-based studies. Well-defined, testable study hypotheses that stem from unmet clinical needs are always the first prerequisite for successful deployment of an MLbased approach to clinical scene. The management of data quality with enough quantity and active collaboration with ML engineers can enhance the ML development process. The interpretable highperformance ML models beyond the black-box nature of some ML principles can be one of the future goals expected by ML and artificial intelligence in the diabetes research and clinical practice settings that is beyond hype. Most importantly, endocrinologists should play a central role as domain experts who have clinical expertise and scientific rigor, for properly generating, refining, analyzing, and interpreting data by successfully integrating ML models into clinical research.

Background: A decrease in computed tomography (CT)-derived skeletal muscle radiodensity (SMD) reflects age-related ectopic fat infiltration of muscle, compromising muscle function and metabolism. We investigated the age-related trajectory of SMD and its association with vertebral trabecular bone density in healthy adults. Methods: In a cohort of healthy adult kidney donors aged 19 to 69 years (n=583), skeletal muscle index (SMI, skeletal muscle area/height2), SMD, and visceral-to-subcutaneous fat (V/S) ratio were analyzed at the level of L3 from preoperative CT scans. Low bone mass was defined as an L1 trabecular Hounsfield unit (HU) <160 HU. Results: L3SMD showed constant decline from the second decade (annual change –0.38% and –0.43% in men and women), whereas the decline of L3SMI became evident only after the fourth decade of life (–0.37% and –0.18% in men and women). One HU decline in L3SMD was associated with elevated odds of low bone mass (adjusted odds ratio, 1.07; 95% confidence interval, 1.02 to 1.13; P=0.003), independent of L3SMI, age, sex, and V/S ratio, with better discriminatory ability compared to L3SMI (area under the receiver-operating characteristics curve 0.68 vs. 0.53, P<0.001). L3SMD improved the identification of low bone mass when added to age, sex, V/S ratio, and L3SMI (category-free net reclassification improvement 0.349, P<0.001; integrated discrimination improvement 0.015, P=0.0165). Conclusion L3SMD can be an early marker for age-related musculoskeletal changes showing linear decline throughout life from the second decade in healthy adults, with potential diagnostic value for individuals with low bone mass.

Recently, machine learning (ML) applications have received attention in diabetes and metabolism research. This review briefly provides the basic concepts of ML and specific topics in diabetes research.Exemplary studies are reviewed to provide an overview of the methodology, main findings, limitations, and future research directions for ML-based studies. Well-defined, testable study hypotheses that stem from unmet clinical needs are always the first prerequisite for successful deployment of an MLbased approach to clinical scene. The management of data quality with enough quantity and active collaboration with ML engineers can enhance the ML development process. The interpretable highperformance ML models beyond the black-box nature of some ML principles can be one of the future goals expected by ML and artificial intelligence in the diabetes research and clinical practice settings that is beyond hype. Most importantly, endocrinologists should play a central role as domain experts who have clinical expertise and scientific rigor, for properly generating, refining, analyzing, and interpreting data by successfully integrating ML models into clinical research.

Background: Shift work is associated with obesity and metabolic syndrome. However, this association in the normal-weight population remains unclear. This study aimed to investigate whether shift work is associated with normal-weight obesity (NWO). Methods: From the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES) dataset (2008 to 2011), 3,800 full-time workers aged ≥19 years with a body mass index (BMI) ≤25 kg/m2 were analysed. We defined NWO as BMI ≤25 kg/m2 and body fat percentage ≥25% in men and ≥37% in women. Working patterns were classified into “daytime,” “other than daytime,” and “shift.” Multivariable logistic regression analysis was performed to evaluate the relationship between shift work and NWO. Results: Shift work was associated with higher odds of NWO than daytime work (adjusted odds ratio [aOR], 1.47; 95% confidence interval [CI], 1.04 to 2.09) and night/evening work (aOR, 1.87; 95% CI, 1.11 to 3.14) after adjustment for type of work, working hours, age, sex, BMI, 25-hydroxyvitamin D levels, homeostatic model assessment for insulin resistance, and other sociodemographic factors. In subgroup analyses, the association between shift work and NWO was more robust in those aged ≥60 years and those working ≥56 hours/week. Conclusion Shift work was associated with NWO in community-dwelling Korean adults, independent of age, sex, BMI, and other covariates.

Background: In individuals with spinal cord injury (SCI), bone loss progresses rapidly to the area below the level of injury, leading to an increased risk of fracture. However, there are limited data regarding SCI-relevant characteristics for bone loss and the degree of bone loss in individuals with SCI compared with that in non-SCI community-dwelling adults. Methods: Data from men with SCI who underwent dual-energy X-ray absorptiometry at the National Rehabilitation Center (2008 to 2020) between 12 and 36 months after injury were collected and analyzed. Community-dwelling men were matched 1:1 for age, height, and weight as the control group, using data from the Korea National Health and Nutrition Examination Survey (KNHANES, 2008 to 2011). Results: A comparison of the SCI and the matched control group revealed significantly lower hip region T-scores in the SCI group, whereas the lumbar spine T-score did not differ between groups. Among the 113 men with SCI, the paraplegia group exhibited significantly higher Z-scores of the hip region than the tetraplegia group. Participants with motor-incomplete SCI showed relatively preserved Z-scores of the hip region compared to those of the lumbar region. Moreover, in participants with SCI, the percentage of skeletal muscle displayed a moderate positive correlation with femoral neck Z-scores. Conclusion Men with SCI exhibited significantly lower bone mineral density of the hip region than community-dwelling men. Paraplegia rather than tetraplegia, and motor incompleteness rather than motor completeness were protective factors in the hip region. Caution for loss of skeletal muscle mass or increased adiposity is also required.

Machine learning (ML) applications have received extensive attention in endocrinology research during the last decade. This review summarizes the basic concepts of ML and certain research topics in endocrinology and metabolism where ML principles have been actively deployed. Relevant studies are discussed to provide an overview of the methodology, main findings, and limitations of ML, with the goal of stimulating insights into future research directions. Clear, testable study hypotheses stem from unmet clinical needs, and the management of data quality (beyond a focus on quantity alone), open collaboration between clinical experts and ML engineers, the development of interpretable high-performance ML models beyond the black-box nature of some algorithms, and a creative environment are the core prerequisites for the foreseeable changes expected to be brought about by ML and artificial intelligence in the field of endocrinology and metabolism, with actual improvements in clinical practice beyond hype. Of note, endocrinologists will continue to play a central role in these developments as domain experts who can properly generate, refine, analyze, and interpret data with a combination of clinical expertise and scientific rigor.

BACKGROUND: Type 2 diabetes mellitus is associated with an increased risk of osteoporotic fracture despite relatively preserved bone mineral density (BMD). Although this paradox might be attributed to the influence of insulin resistance (IR) on bone structure and material properties, the association of IR with femur bone geometry and strength indices remains unclear. METHODS: Using data from the Cardiovascular and Metabolic Disease Etiology Research Center cohort study, we conducted a cross-sectional analysis among nondiabetic postmenopausal women. IR was estimated using the homeostasis model assessment of IR (HOMA-IR). Compartment-specific volumetric BMD (vBMD) and bone volume of proximal femur were measured using quantitative computed tomography. The compressive strength index (CSI), section modulus (Z), and buckling ratio of the femoral neck were calculated as bone strength indices. RESULTS: Among 1,008 subjects (mean age, 57.3 years; body mass index [BMI], 23.6 kg/m²), BMI, waist circumference, and vBMD of the femoral neck and total hip increased in a linear trend from the lowest ( < 1.37) to highest (≥2.27) HOMA-IR quartile (P < 0.05 for all). The HOMA-IR showed an independent negative association with total bone volume (standardized β=−0.12), cortical volume (β=−0.05), CSI (β=−0.013), and Z (β=−0.017; P < 0.05 for all) of the femoral neck after adjustment for age, weight, height, physical activity, and vitamin D and high-sensitivity C-reactive protein levels. However, the association between HOMA-IR and vBMD was attenuated in the adjusted model (femoral neck, β=0.94; P=0.548). CONCLUSIONS: Elevated HOMA-IR was associated with lower cortical bone volume and bone strength indices in nondiabetic postmenopausal women, independent of age and body size.
Body Mass Index ; Body Size ; Bone Density ; C-Reactive Protein ; Cohort Studies ; Compressive Strength ; Cross-Sectional Studies ; Diabetes Mellitus, Type 2 ; Female ; Femur Neck ; Femur* ; Hip ; Homeostasis ; Humans ; Insulin Resistance* ; Insulin* ; Metabolic Diseases ; Motor Activity ; Neck ; Osteoporosis ; Osteoporotic Fractures ; Postmenopause ; Vitamin D ; Waist Circumference