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BackgroundMachine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. ObjectiveThe objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). MethodsElectronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. ResultsA total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. ConclusionsCurrent ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial RegistrationPROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

Aim: Normoglycaemia was achieved in a significant proportion of Japanese participants with type 2 diabetes in two phase 3 studies of tirzepatide. This post hoc exploratory analysis aimed to identify predictive factors associated with normoglycaemia achievement. Materials and Methods: SURPASS J‐mono and SURPASS J‐combo study data were pooled for this analysis. Characteristics of participants in whom normoglycaemia [glycated haemoglobin (HbA1c) <5.7%] was achieved were summarized. Logistic regression analyses were performed with HbA1c <5.7% achievement as the target variable. Results: Of 912 participants, normoglycaemia was achieved in 553 (60.6%) following 52 weeks of tirzepatide treatment. Overall, the mean (SD) age was 56.7 (10.6) years and mean diabetes duration was 7.7 (6.0) years, and 76% of participants were men. Mean (SD) change from baseline in HbA1c and bodyweight was −2.87% (0.95) versus −2.47% (1.1) and −10.30 (5.8) kg versus −3.75 (4.3) kg for participants in whom normoglycaemia was and was not reached, respectively. Multivariate regression analyses showed that lower baseline body mass index, shorter disease duration and lower baseline HbA1c were significantly associated with higher rates of normoglycaemia achievement (p = 0.009, p = 0.008, p < 0.001, respectively) as was a tirzepatide dose of 10 or 15 mg compared with 5 mg (p < 0.001). The highest percentage of participants in whom normoglycaemia (94%) was achieved were those with lower baseline HbA1c (<8%) and the greatest weight reduction (≥15%). Conclusions: Baseline HbA1c and body mass index, disease duration and the tirzepatide treatment group were shown to be predictive factors for achieving normoglycaemia. A lower baseline HbA1c was most strongly associated with normoglycaemia achievement. [ABSTRACT FROM AUTHOR]

Aims/Introduction: History of coronary artery disease (CAD), cerebrovascular disease (CeVD), type 2 diabetes and their combined effect on cardiovascular disease are essential for cardiovascular risk management. We investigated the association of prior CAD, prior CeVD, type 2 diabetes and their combination with the risk of cardiovascular disease. Materials and Methods: This is a historical cohort study including 342,033 participants (aged 18–72 years) followed up for ≥5 years between 2008 and 2016. Participants were classified into eight groups (with or without prior CAD, prior CeVD and type 2 diabetes). Type 2 Diabetes was defined by fasting plasma glucose and glycated hemoglobin levels, and antidiabetic drug prescription. Prior and subsequent CAD and CeVD were identified according to claims using International Classification of Diseases 10th Revision codes, medical procedures and questionnaires. Cox regression models were used to evaluate the risk of cardiovascular events. Results: The median follow‐up period was 6.4 years. The incidence of composite cardiovascular events of CAD and CeVD in the CAD−/CeVD−, CAD+/CeVD−, CAD−/CeVD+ and CAD+/CeVD+ groups were 1.92 and 6.94, 25.14 and 31.98 per 1,000 person‐years in non‐diabetes participants, and 8.66, 18.04, 39.98 and 60.72 in type 2 diabetes patients, respectively. Hazard ratios of cardiovascular events compared with CAD−/CeVD−/non‐diabetes were 1.66 (95% confidence interval 1.55–1.78) in CAD−/CeVD−/type 2 diabetes and 1.84 (1.56–2.18) in CAD+/CeVD−/non‐diabetes. CeVD+ was linked to a 4‐7‐fold increase in the risk of cardiovascular events regardless of CAD+ or type 2 diabetes. Conclusions: Type 2 diabetes increased the risk of cardiovascular disease as high as a history of CAD, whereas prior CeVD alone increased the risk of future CeVD without additional effects by type 2 diabetes. [ABSTRACT FROM AUTHOR]

Aims Although conventional interventions for people at high risk of developing type 2 diabetes are usually conducted face-to-face, such interventions are burdensome for health care providers. We developed a lifestyle intervention program combining lifestyle coaching via a smartphone application augmented by intermittently scanned continuous glucose monitoring without burdening health care providers. Its effectiveness for glycemic control and body weight reduction in people at risk of type 2 diabetes was investigated. Materials and Methods For this 12-week randomized unblinded trial with offline recruitment, participants with a hemoglobin A1c level of 5.6% to 6.4% or a fasting blood glucose of 110 to 125 mg/dL and body mass index (BMI) >23 kg/m2 but <40 kg/m2 were randomly assigned to the intervention group (App) and control group (C). The primary endpoint was the difference in time in range of blood glucose between 70 and 140 mg/dL (3.9-7.8 mmol/L) before and after the study period between the 2 groups. Results Among 168 patients (mean age, 48.1 years; mean BMI, 26.6 kg/m2; and male, 80.4%), 82 and 86 were assigned to the App group and C group, respectively. After 12 weeks, time in range of blood glucose at 70 to 140 mg/dL significantly improved in the App group compared with the C group (−2.6 minutes/day vs +31.5 minutes/day, P =.03). Changes in time above range did not differ, whereas time below range (blood glucose <70 mg/dL; +23.5 minutes/day vs −8.9 minutes/day, P =.02) improved in the App group. BMI (−0.26 vs −0.59, P =.017) was reduced in the App group compared with the C group. Conclusion Intervention with a smartphone app and intermittently scanned continuous glucose monitoring increased glycemic control accompanied by decreased carbohydrate intake and weight loss. Further trials are needed to confirm whether these interventions can reduce incident type 2 diabetes. [ABSTRACT FROM AUTHOR]

This article discusses the synthesis and evaluation of saponins with all-nitrogenated sugars (ANS). Aminosugars, a class of modified carbohydrates, have been found in aminoglycosides but have limitations. The creation of artificial aminosugars, such as ANS, offers new possibilities for drug development. Saponins, derived from steroids and triterpenoids, have various biological activities and their sugar moiety plays a critical role. The article describes the synthesis of various ANS-saponin derivatives and their cytotoxicity, showing that ANS-saponins induce apoptosis. [Extracted from the article]

A reductive (3+2) annulation of lactams through iridium‐catalyzed hydrosilylation and photoredox coupling with α‐bromoacetic acid was developed. The iridium‐catalyzed hydrosilylation of the lactam carbonyl group and subsequent elimination provide a transient cyclic enamine, which undergoes iridium‐catalyzed photoredox coupling with α‐bromoacetic acid in a one‐pot process. The developed conditions show high functional‐group tolerance and provide cyclic N,O‐acetals containing a quaternary carbon center. The resulting N,O‐acetals undergo a variety of acid‐mediated nucleophilic addition reactions via iminium ions to give substituted cyclic amines. The developed sequence including reductive (3+2) annulation and acid‐mediated nucleophilic addition was successfully applied to the four‐step total synthesis of (±)‐eburnamonine. [ABSTRACT FROM AUTHOR]

BACKGROUND: Sleep quality in patients with chronic low back pain (CLBP) may affect quality of life (QoL), possibly due to worsening pain, central sensitization (CS), and cognitive factors. However, causal relationship among the factors has not been confirmed yet. OBJECTIVE: The purpose of this study was to test the hypothesis that sleep quality in patients with CLBP is attributable to pain, cognitive factors, and CS, and influences QoL, by structural covariance analysis. METHODS: This is a cross-sectional study. Participants were recruited from six health care facilities and 101 patients with CLBP were included. Structural covariance analysis assessed the fit of data to the model using goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), and mean squared approximation error (RMSEA). RESULTS: The structural covariance analysis showed that the goodness-of-fit indices were high (GFI = 0.993, AGFI = 0.964, CFI = 1.00, RMSEA < 0.01). Sleep quality was not directly influenced by QoL but rather by CS and cognitive factors. CONCLUSION: This study suggests that sleep quality in patients with CLBP is indirectly mediated through multiple pathways, including cognitive factors and CS, which may influence QoL. [ABSTRACT FROM AUTHOR]