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The purpose of the investigation was to examine the influence of resistance training (RT) with equal volume and varying load on glycemic control, inflammation, and body composition in non-obese prediabetic older adults. Non-obese older adults with prediabetes were randomized into 2 groups, high-load (80% of 1RM) and low-load (40% of 1RM) RT (n = 12/group), both with the same training volume. Oral glucose tolerance test (OGTT) and blood samples were collected at baseline and again after 10 weeks of RT. Fasting blood glucose (103.8 vs. 99.9 mg/dL) and the area under the curve (AUC) of OGTT (0–30 min) decreased significantly in older adults with prediabetes after 10 weeks of volume-matched RT (p < 0.05). Serum levels of MCP-1 (138.7 vs. 98.5 pg/mL) and TNF-α (1.8 vs. 1.3 pg/mL) showed significant decrease after 10 weeks of high-load RT (p < 0.05). There were no changes in IL-10, IL-6, and CRP levels in both groups. Leptin showed significant decrease after 10 weeks of low-load RT (p < 0.05). Changes in fasting glucose and AUC of OGTT (0–120 min) were positively correlated with changes in MCP-1 and TNF-α (p < 0.05). Lean body mass (39.6 vs. 40.3 kg) increased significantly after 10 weeks of volume-matched RT (p < 0.05). Results indicate that equal-volume RT at different loads is beneficial to glycemic control and muscle growth, and high-load RT shows more prominent anti-inflammatory effects. Novelty: Short-term high-load resistance training can help older adults bring their blood sugar level back to normal. High-load resistance training attenuates aging-associated chronic inflammation.

The mechanism that underlies the dysregulation of glucose metabolism in older adults without obesity has yet to be thoroughly understood. This study investigated the risk of developing Type 2 diabetes (T2D) in older adults without obesity (BMI: 18.5 to25 kg/m

Background: Functional and physiological adaptations induced by resistance training have been extensively studied in older adults. However, microRNA (miRNA) as the novel regulator in protective effects remains poorly understood. Objective: The purpose of an exploratory study was to analyze the response of a panel of circulating miRNAs to adaptations mediated by resistance training. Methods: Ten healthy older adults (age: 67.6 ± 2.2 years, 7 women and 3 men) without previous experience in resistance training were recruited. Blood samples were collected at baseline and after a 12-week resistance training. Next-generation sequencing was used to determine circulating miRNA responses to chronic resistance training. Results: After the 12-week training, physical functions including grip strength, lower body strength and endurance, and walking capacity were improved in the older adults, while the serum levels of leptin (from 18.1 ± 20.0 to 14.9 ± 17.6 ng/mL, p = 0.029) and tumor necrosis factor alpha (TNFα; from 4.4 ± 0.6 to 4.0 ± 0.6 pg/mL, p < 0.001) were significantly decreased. In addition, adipogenesis-related miRNAs (miR-103a-3p, -103b, -143-5p, -146b-3p, -146b-5p, -17-5p, -181a-2-3p, -181b-5p, -199a-5p, -204-3p, and -378c), anti-adipogenesis-related miRNAs (miR-155-3p, -448, and -363-3p), myogenesis-related miRNAs (miR-125b-1-3p, -128-3p, -133a-3p, 155-3p, -181a-2-3p, -181b-5p, -199a-5p, -223-3p, and -499a-5p), and inflammation-related miRNAs (miR-146b-3p, -146b-5p, -155-3p, -181a-2-3p, and -181b-5p) were changed significantly in the older adults after training (fold change >2, p < 0.05). The log2 fold change of miRNA-125-1-3p was inversely correlated with delta walking time (R = –0.685, p = 0.029) and change in insulin-like growth factor 1 (R = –0.644, p = 0.044). Conclusions: Our results can help explain the link between specific circulating miRNAs and beneficial effects of resistance training on functional and physiological adaptations in older adults.

In recent decades, the global incidence of dengue has increased. Affected countries have responded with more effective surveillance strategies to detect outbreaks early, monitor the trends, and implement prevention and control measures. We have applied newly developed machine learning approaches to identify laboratory-confirmed dengue cases from 4,894 emergency department patients with dengue-like illness (DLI) who received laboratory tests. Among them, 60.11% (2942 cases) were confirmed to have dengue. Using just four input variables [age, body temperature, white blood cells counts (WBCs) and platelets], not only the state-of-the-art deep neural network (DNN) prediction models but also the conventional decision tree (DT) and logistic regression (LR) models delivered performances with receiver operating characteristic (ROC) curves areas under curves (AUCs) of the ranging from 83.75% to 85.87% [for DT, DNN and LR: 84.60% ± 0.03%, 85.87% ± 0.54%, 83.75% ± 0.17%, respectively]. Subgroup analyses found all the models were very sensitive particularly in the pre-epidemic period. Pre-peak sensitivities (, Author summary Identifying dengue cases early is crucial but challenging for healthcare professionals. This challenge is increased during large epidemics and is a particular problem in non-endemic areas with limited experienced staff. To improve dengue diagnosis, we investigated how to exploit machine learning (ML)-based prediction models and identified four key variables [age, fever, white blood cell counts (WBCs), and platelet counts], which are compatible with clinical and epidemiological knowledge. With these variables, the ML prediction models [decision tree (DT), deep neural network (DNN)] and the logistic regression model developed for identifying laboratory-confirmed dengue cases produced areas under curve (AUCs) of the receiver operating characteristic (ROC) curves ranging from 83.75% to 85.87%. This implies that the prediction models may serve as a pivotal component of an integrated dengue surveillance system and they required only a single complete blood count (CBC) examination. The sensitivities, positive prediction values, and accuracies for major risk factors in the two machine learning models were close to those of the regression models. For future applications, the DNN models with superior performance can be employed at epidemic sites with adequate computer facilities, while the DT and regression models with interpretable prediction logic can be employed at sites with limited or no computer facilities. Artificial intelligence and clinical parameters identified from this study may aid when laboratories are overwhelmed, but should never replace laboratory confirmation.