The effects of an intermittent fasting diet (IFD) in the general population are still controversial. In this study, we aimed to systematically evaluate the effectiveness of an IFD to reduce body mass index and glucose metabolism in the general population without diabetes mellitus. Cochrane, PubMed, and Embase databases were searched to identify randomized controlled trials and controlled clinical trials that compared an IFD with a regular diet or a continuous calorie restriction diet. The effectiveness of an IFD was estimated by the weighted mean difference (WMD) for several variables associated with glucometabolic parameters including body mass index (BMI) and fasting glucose. The pooled mean differences of outcomes were calculated using a random effects model. From 2814 studies identified through a literature search, we finally selected 12 articles (545 participants). Compared with a control diet, an IFD was associated with a significant decline in BMI (WMD, −0.75 kg/m²; 95% CI, −1.44 to −0.06), fasting glucose level (WMD, −4.16 mg/dL; 95% CI, −6.92 to −1.40), and homeostatic model assessment of insulin resistance (WMD, −0.54; 95% CI, −1.05 to −0.03). Fat mass (WMD, −0.98 kg; 95% CI, −2.32 to 0.36) tended to decrease in the IFD group with a significant increase in adiponectin (WMD, 1008.9 ng/mL; 95% CI, 140.5 to 1877.3) and a decrease in leptin (WMD, −0.51 ng/mL; 95% CI, −0.77 to −0.24) levels. An IFD may provide a significant metabolic benefit by improving glycemic control, insulin resistance, and adipokine concentration with a reduction of BMI in adults.

Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation of body composition. For model development, one hundred whole-body or torso ¹⁸F-fluorodeoxyglucose PET-CT scans of 100 patients were retrospectively included. Two radiologists semi-automatically labeled the following seven body components in every CT image slice, providing a total of 46,967 image slices from the 100 scans for training the 3D U-Net (training, 39,268 slices; tuning, 3116 slices; internal validation, 4583 slices): skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs with vessels, and central nervous system. The segmentation accuracy was assessed using reference masks from three external datasets: two Korean centers (4668 and 4796 image slices from 20 CT scans, each) and a French public dataset (3763 image slices from 24 CT scans). The 3D U-Net-driven values were clinically validated using bioelectrical impedance analysis (BIA) and by assessing the model's diagnostic performance for sarcopenia in a community-based elderly cohort (n = 522). The 3D U-Net achieved accurate body composition segmentation with an average dice similarity coefficient of 96.5%-98.9% for all masks and 92.3%-99.3% for muscle, abdominal visceral fat, and subcutaneous fat in the validation datasets. The 3D U-Net-derived torso volume of skeletal muscle and fat tissue and the average area of those tissues in the waist were correlated with BIA-derived appendicular lean mass (correlation coefficients: 0.71 and 0.72, each) and fat mass (correlation coefficients: 0.95 and 0.93, each). The 3D U-Net-derived average areas of skeletal muscle and fat tissue in the waist were independently associated with sarcopenia (P < .001, each) with adjustment for age and sex, providing an area under the curve of 0.858 (95% CI, 0.815 to 0.901). This deep neural network model enabled the automatic volumetric segmentation of body composition on whole-body CT images, potentially expanding adjunctive sarcopenia assessment on PET-CT scan and volumetric assessment of metabolism in whole-body muscle and fat tissues.

Osteoporosis and vertebral fractures (VFs) remain underdiagnosed. The addition of deep learning methods to lateral spine radiography (a simple, widely available, low-cost test) can potentially solve this problem. In this study, we develop deep learning scores to detect osteoporosis and VF based on lateral spine radiography and investigate whether their use can improve referral of high-risk individuals to bone-density testing. The derivation cohort consisted of patients aged 50 years or older who underwent lateral spine radiography in Severance Hospital, Korea, from January 2007 to December 2018, providing a total of 26,299 lateral spine plain X-rays for 9276 patients (VF prevalence, 18.6%; osteoporosis prevalence, 40.3%). Two individual deep convolutional neural network scores to detect prevalent VF (VERTE-X pVF score) and osteoporosis (VERTE-X osteo score) were tested on an internal test set (20% hold-out set) and external test set (another hospital cohort [Yongin], 395 patients). VERTE-X pVF, osteo scores, and clinical models to detect prevalent VF or osteoporosis were compared in terms of the areas under the receiver-operating-characteristics curves (AUROCs). Net reclassification improvement (NRI) was calculated when using deep-learning scores to supplement clinical indications for classification of high-risk individuals to dual-energy X-ray absorptiometry (DXA) testing. VERTE-X pVF and osteo scores outperformed clinical models in both the internal (AUROC: VF, 0.93 versus 0.78; osteoporosis, 0.85 versus 0.79) and external (VF, 0.92 versus 0.79; osteoporosis, 0.83 versus 0.65; p < 0.01 for all) test sets. VERTE-X pVF and osteo scores improved the reclassification of individuals with osteoporosis to the DXA testing group when applied together with the clinical indications for DXA testing in both the internal (NRI 0.10) and external (NRI 0.14, p < 0.001 for all) test sets. The proposed method could detect prevalent VFs and osteoporosis, and it improved referral of individuals at high risk of fracture to DXA testing more than clinical indications alone. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Acid–base imbalance has been reported to increase incidence of hypertension and diabetes. However, the association between diet-induced acid load and cardiovascular disease (CVD) risk in the general population has not been fully investigated. This was a population-based, retrospectively registered cross-sectional study using nationally representative samples of 11,601 subjects from the Korea National Health and Nutrition Examination Survey 2008–2011. Individual CVD risk was evaluated using atherosclerotic cardiovascular disease (ASCVD) risk equations according to 2013 ACC/AHA guideline assessment in subjects aged 40–79 without prior CVD. Acid–base status was assessed with both the potential renal acid load (PRAL) and the dietary acid load (DAL) scores derived from nutrient intake. Individuals in the highest PRAL tertile had a significant increase in 10 year ASCVD risks (9.6 vs. 8.5 %, P < 0.01) and tended to belong to the high-risk (10 year risk >10 %) group compared to those in the lowest PRAL tertile (odds ratio [OR] 1.23, 95 % confidence interval [CI] 1.22–1.35). The association between higher PRAL score and high CVD risk was stronger in the middle-aged group. Furthermore, a multiple logistic regression analysis also demonstrated this association (OR 1.20 95 % CI 1.01–1.43). Subgroup analysis stratified obesity or exercise status; individuals in unhealthy condition with lower PRAL scores had comparable ASCVD risk to people in the higher PRAL group that were in favorable physical condition. In addition, elevated PRAL scores were associated with high ASCVD risk independent of obesity, exercise, and insulin resistance, but not sarcopenia. Similar trends were observed with DAL scores. Diet-induced acid load was associated with increased risk of CVD, independent of obesity and insulin resistance.

Summary: Context: Teriparatide (TPTD) therapy has been proposed as a potential treatment strategy in severe cases of pregnancy‐ and lactation‐associated osteoporosis (PLO) characterized by the occurrence of fragility fractures in the third trimester or early postpartum.Objective: To investigate the changes in bone mineral density (BMD) and bone turnover markers in patients with PLO with and without TPTD treatment.Design: Retrospective cohort study.Patients: Thirty‐two patients with PLO who presented with multiple vertebral fractures to a tertiary institution between 2007 and 2015 were included.Measurements: Changes in BMD at the lumbar spine (LSBMD) and proximal femur after 12 months of daily subcutaneous injections of 20 μg TPTD (n = 27) were assessed. Subjects who rejected the TPTD treatment were used as controls (n = 5).Results: LSBMD increased in both subjects treated with TPTD and controls, with greater increases in the TPTD group (15.5 ± 6.6% vs 7.5 ± 7.1%, P = .020) after adjustment for age and baseline LSBMD. During follow‐up, serum levels of osteocalcin (OCN) and C‐telopeptide of type I collagen (CTX) increased significantly in the TPTD group. In multivariate linear regression models, TPTD treatment (adjusted β = 7.92, P = .032) and younger age (adjusted β = 1.06, P = .046), but not baseline LSBMD, body mass index, serum OCN level and CTX level, were independently associated with greater increases in LSBMD.Conclusions: In patients with PLO, LSBMD at 12 months increased in both the TPTD‐treated and control groups. TPTD treatment and younger age were associated with greater increases in LSMBD irrespective of baseline LSBMD.