Background: Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective: The 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). Methods: Electronic 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. Results: A 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. Conclusions: Current 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 Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

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.

Purpose: To determine the degree of control of multiple risk factors under real-world conditions for coronary artery disease (CAD) according to the presence or absence of diabetes mellitus (DM) and to determine whether reaching multifactorial targets for blood pressure (BP), low-density lipoprotein-cholesterol (LDL-C), HbA1c, and current smoking is associated with lower risks for CAD. Methods: We investigated the effects on subsequent CAD of the number of controlled risk factors among BP, LDL-C, HbA1c, and current smoking in a prospective cohort study using a nationwide claims database of 220,894 individuals in Japan. Cox regression examined risks over a 4.8-year follow-up. Results: The largest percentage of participants had two risk factors at target in patients with DM (39.6%) and subjects without DM (36.4%). Compared with those who had two targets achieved, the risks of CAD among those who had any one and no target achieved were two and four times greater, respectively, regardless of the presence of DM. The effect of composite control was sufficient to bring CAD risk in patients with DM below that for subjects without DM with any two targets achieved, whereas the risk of CAD in the DM group with all four risk factors uncontrolled was 9.4 times more than in the non-DM group who had achieved two targets. Conclusions: These findings show that composite control of modifiable risk factors has a large effect in patients with and without DM. The effect was sufficient to bring CAD risk in patients with DM below that in the non-DM group who had two targets achieved.

Background: The prevalence of diabetes is rising, and diabetes develops at a younger age in East Asia. Although lower limb amputation negatively affects quality of life and increases the risk of cardiovascular events, little is known about the rates and predictors of amputation among persons with diabetes from young adults to those in the “young‐old” category (50–72 y).Methods: We analyzed data from a nationwide claims database in Japan accumulated from 2008 to 2016 involving 17,288 people with diabetes aged 18–72 y (mean age 50.2 y, HbA1c 7.2%). Amputation occurrence was determined according to information from the claims database. Cox regression model identified variables related to lower limb amputation.Results: The mean follow‐up time was 5.3 years, during which time 16 amputations occurred (0.17/1000 person‐years). Multivariate Cox regression analysis showed that age (hazard ratio [HR] 1.09 [95% confidence intervals] 1.02–1.16, p = 0.01) and HbA1c (HR 1.46 [1.17–1.81], p < 0.01) were independently associated with amputations. Compared with those aged < 60 years with HbA1c < 8.0%, the HR for amputation was 27.81 (6.54–118.23) in those aged ≥60 years and HbA1c ≥8.0%.Conclusions: Age and HbA1c were associated with amputations among diabetic individuals, and the rates of amputation were significantly greater in those ≥60 years old and with HbA1c ≥8.0%.