Metformin is widely prescribed for patients with type 2 diabetes mellitus (DM). Its use for patients with type 1 has been considered a contraindication because of possible adverse effects such as lactic acidosis. However, metformin has been recently used with insulin therapy to reduce insulin-dose requirements in Type 1 DM.
An 81-year-old Japanese woman with type 1 DM was treated with insulin and metformin. She was admitted to our hospital due to altered mental state and hypotensive shock via a referral from her primary care physician. The patient had severe lactic acidosis and acute kidney injury with hyperkalemia with the suspected cause being the use of metformin. She was treated successfully with hemodialysis (HD).
Although the independent predictive factor of mortality due to metformin-associated lactic acidosis is a prothrombin time (PT) activity of less than 50% in 24 hours, we recommend that HD should be performed for a patient with severe lactic acidosis even if the initial PT activity is normal.

Globally, the number of people with diabetes mellitus has quadrupled in the past three decades, and approximately one in 11 adults worldwide have diabetes mellitus. Since both microvascular and macrovascular diseases in patients with diabetes predispose them to a lower quality of life as well as higher rates of mortality, managing blood glucose levels is of clinical relevance in diabetes care. Many classes of antihyperglycemic drugs are currently approved to treat hyperglycemia in patients with type 2 diabetes mellitus, with several new drugs having been developed during the last decade. Diabetes-related complications have been reduced substantially worldwide. Prioritization of therapeutic agents varies according to national guidelines. However, since the characteristics of participants in clinical trials differ from patients in actual clinical practice, it is difficult to apply the results of such trials to clinical practice. Machine learning approaches became highly topical issues in medicine along with rapid technological innovations in the fields of information and communication in the 1990s. However, adopting these technologies to support decision-making regarding drug treatment strategies for diabetes care has been slow. This review summarizes data from recent studies on the choice of drugs for type 2 diabetes mellitus focusing on machine learning approaches.

Background: No consensus exists regarding which anthropometric measurements are related to bone mineral density (BMD), and this relationship may vary according to sex and age. A large Japanese cohort was analyzed to provide an understanding of the relationship between BMD and anthropometry while adjusting for known confounding factors. Methods: Our cohort included 10,827 participants who underwent multiple medical checkups including distal forearm BMD scans. Participants were stratified into four groups according to age (≥50 years or <50 years) and sex. The BMD values were adjusted for confounding factors, after which single and partial correlation analyses were performed. The prevalence of osteopenia was plotted for each weight index (weight or body mass index [BMI]) class. Results: Cross-sectional studies revealed that weight was more favorably correlated than BMI in the older group (R=0.278 and 0.212 in men and R=0.304 and 0.220 in women, respectively), whereas weight and BMI were weakly correlated in the younger age groups. The prevalence of osteopenia exhibited a negative linear relationship with weight among older women ≥50 years of age, and an accelerated increase was observed with decreasing weight in older men weighing <50 kg and younger women weighing <60 kg. When weight was replaced with BMI, the prevalence was low in most subgroups classified by weight. Conclusions Weight, rather than BMI, was the most important indicator of osteopenia but it might not be predictive of future bone loss.

OBJECTIVEThe aim of this study was to establish reference concentrations of urinary strontium by inductively coupled plasma atomic emission spectrometry (ICP-AES).

METHODSFor the determination of strontium, urine samples were collected from healthy Japanese (n=146; 115 males, 31 females; mean age, 33±9 years; age range, 18 to 58 years). The urine samples stored at or below -20°C were thawed with incubation at 40°C for 30 min and sediments were dissolved by vigorous shakings. Then, the samples were centrifuged at 3000 g for 5 min, and the supernatant was directly aspired into a P-5200-3600/1200 ICP-AES system from Hitachi Ltd., Tokyo, Japan.

RESULTSA steeper increase in the S/N ratio and a good effective linearity of the calibration line was obtained at 407.771 nm in the range of 0-300 μg/L strontium standard solution. Urine samples having the same background signal as that of 18 MΩ cm ultrapure blank water, a good correspondence of the single peak pattern of the spectra, accuracy and precision of spike recovery were also confirmed. Urinary strontium concentrations showed a log-normal distribution and a geometric mean concentration of 143.9 μg/L, with 5-95% confidential interval of 40.9-505.8 μg/L.

CONCLUSIONThe results of this study will be useful as guidelines for the biological monitoring of strontium in normal subjects and in individuals therapeutically or environmentally exposed to strontium.