Water is essential for survival, but one in three individuals worldwide (2.2 billion people) lacks access to safe drinking water. Water intake requirements largely reflect water turnover (WT), the water used by the body each day. We investigated the determinants of human WT in 5604 people from the ages of 8 days to 96 years from 23 countries using isotope-tracking ( ² H) methods. Age, body size, and composition were significantly associated with WT, as were physical activity, athletic status, pregnancy, socioeconomic status, and environmental characteristics (latitude, altitude, air temperature, and humidity). People who lived in countries with a low human development index (HDI) had higher WT than people in high-HDI countries. On the basis of this extensive dataset, we provide equations to predict human WT in relation to anthropometric, economic, and environmental factors.

The relationship between body mass index (BMI) and frailty remains unclear. Using two validated frailty assessment tools, this study aimed to investigate the relationship between the prevalence of frailty and BMI in Japanese older adults. This cross-sectional study used baseline data of 7191 individuals aged ≥65 years, living in Kameoka City, Kyoto, Japan. The BMI was calculated based on self-reported height and body weight, and classified into six categories. Frailty was defined using two validated assessment tools, the Fried phenotype (FP) model and Kihon Checklist (KCL). We evaluated the relationship between frailty and BMI using a multivariate restricted cubic spline logistic regression. The prevalence of frailty defined using the FP model was 25.3%, 19.6%, 14.3%, 12.4%, 12.6%, and 19.4% for each BMI category of <18.5, 18.5–19.9, 20.0–22.4, 22.5–24.9, 25.0–27.4, and ≥27.5 kg/m², respectively. The spline model showed a significant U-shaped relationship between BMI and the prevalence of frailty defined using both, KCL and FP models. This study found that the BMI range corresponding to lowest prevalence of frailty defined using both tools was 21.4–25.7 kg/m². Thus, a healthy BMI may reduce the prevalence of frailty, and the risk of frailty needs to be evaluated in individuals who are underweight or overweight.

Background: The term “frailty” might appear simple, but the methods used to assess it differ among studies. Consequently, there is inconsistency in the classification of frailty and predictive capacity depending on the frailty assessment method utilised. We aimed to examine the diagnostic accuracy of several screening tools for frailty defined by the phenotype model in older Japanese adults. Methods: This cross-sectional study included 1,306 older Japanese adults aged ≥ 65 years who underwent physical check-up by cluster random sampling as part of the Kyoto-Kameoka Study in Japan. We evaluated the diagnostic accuracy of several screening instruments for frailty using the revised Japanese version of the Cardiovascular Health Study criteria as the reference standard. These criteria are based on the Fried phenotype model and include five elements: unintentional weight loss, weakness (grip strength), exhaustion, slowness (normal gait speed), and low physical activity. The Kihon Checklist (KCL), frailty screening index (FSI), and self-reported health were evaluated using mailed surveys. We calculated the non-parametric area under the receiver operating characteristic curve (AUC ROC) for several screening tools against the reference standard. Results: The participants’ mean (standard deviation) age was 72.8 (5.5) years. The prevalence of frailty based on the Fried phenotype model was 12.2% in women and 10.3% in men. The AUC ROC was 0.861 (95% confidence interval: 0.832–0.889) for KCL, 0.860 (0.831–0.889) for FSI, and 0.668 (0.629–0.707) for self-reported health. The cut-off for identifying frail individuals was ≥ 7 points in the KCL and ≥ 2 points in the FSI. Conclusions: Our results indicated that the two instruments (KCL and FSI) had sufficient diagnostic accuracy for frailty based on the phenotype model for older Japanese adults. This may be useful for the early detection of frailty in high-risk older adults.

Cachexia is associated with lower overall survival (OS) in patients with cancer; however, the relationship between the two is reported to differ according to the definitive criteria for diagnosing cachexia. We aimed to investigate 1) the difference in the prevalence of cachexia in patients with cancer and 2) the association between cachexia and OS, depending on the definitive criteria for diagnosing cachexia in patients with cancer. We searched PubMed and Web of Science from their inception until July 31, 2023, to identify eligible studies. We conducted a systematic review of the prevalence of cachexia in patients with cancer and performed a meta-analysis to investigate its relationship with OS. A total of 125 articles comprising 137,960 patients were included in the systematic review, and 26 articles consisting of 11,118 patients underwent meta-analysis. The overall prevalence of cachexia in patients with cancer was 33.0% (95% confidence interval [CI]: 32.8, 33.3); however, it varied according to the definitive criteria for diagnosing cachexia (13.9%-56.5%). According to the Fearon 2011 criteria, the prevalence of cachexia was associated with a high hazard ratio (HR) for OS compared with that of noncachexia [HR: 1.58 (95% CI: 1.45, 1.73)]; according to the other criteria, the HR was 2.78 (95% CI: 1.88, 4.11), indicating significant subgroup differences (P = 0.006). The dose-response curve indicated that the HR for OS plateaued at a cachexia prevalence range of 40%-50% (l-shaped relationship). The prevalence of cachexia in patients with cancer may vary depending on the definitive criteria used to diagnose cachexia. The HR for OS was higher for low cachexia prevalence. The definitive criteria should be carefully considered when assessing cachexia in patients with cancer. This trial was registered at the PROSPERO as CRD42023435474.

Background: Protein supplementation augments muscle strength gain during resistance training. Although some studies focus on the dose-response relationship of total protein intake to muscle mass or strength, the detailed dose-response relationship between total protein intake and muscle strength increase is yet to be clarified, especially in the absence of resistance training. Objective: We aimed to assess the detailed dose-response relationship between protein supplementation and muscle strength, with and without resistance training. Design: Systematic review with meta-analysis. Data Sources: PubMed and Ichushi-Web (last accessed on March 23, 2022). Eligibility Criteria: Randomized controlled trials investigating the effects of protein intake on muscle strength. Synthesis Methods: A random-effects model and a spline model. Results: A total of 82 articles were obtained for meta-analyses, and data from 69 articles were used to create spline curves. Muscle strength increase was significantly augmented only with resistance training (MD 2.01%, 95% CI 1.09–2.93) and was not augmented if resistance training was absent (MD 0.13%, 95% CI − 1.53 to 1.79). In the dose-response analysis using a spline model, muscle strength increase with resistance training showed a dose-dependent positive association with total protein intake, which is 0.72% (95% CI 0.40–1.04%) increase in muscle strength per 0.1 g/kg body weight [BW]/d increase in total protein intake up to 1.5 g/kg BW/d, but no further gains were observed thereafter. Conclusion: Concurrent use of resistance training is essential for protein supplementation to improve muscle strength. This study indicates that 1.5 g/kg BW/d may be the most appropriate amount of total protein intake for maintaining and augmenting muscle strength along with resistance training.

Although oral frailty is independently associated with an increased risk of mortality, evidence for the usefulness of screening tools for oral frailty is less than that for physical frailty screening tools. We aimed to investigate the relationship between oral frailty and mortality in older adults. This prospective cohort study included 11,374 adults aged ≥65 years, who provided valid responses to a baseline mail survey questionnaire from the Kyoto-Kameoka study. Oral frailty status was evaluated using the Oral Frailty Index-8 (range, 0 [best] to 10 [worst]). Participants were classified into four categories according to the Oral Frailty Index-8: robust (score, 0-2), oral pre-frailty (score, 3), oral frailty (score, 4-6), and oral severe frailty (score ≥ 7). Physical and psychological frailty were evaluated using the validated frailty-screening index and defined as a score of ≥3 out of a possible 5 points. Mortality data were collected from 30 July 2011 to 30 November 2016. Hazard ratios (HR) for all-cause mortality were calculated using a multivariable Cox proportional hazards model. During the 5.3-year median follow-up period (57,157 person-years), 1184 deaths were recorded. After adjusting for confounders, including physical and psychological frailty, medical history, and lifestyle, in comparison with a robust oral status, oral pre-frailty (HR, 1.29; 95 % confidence interval [CI], 1.02-1.63), oral frailty (HR, 1.22; 95 % CI, 1.01-1.48), and oral severe frailty (HR, 1.43; 95 % CI, 1.16-1.76) were associated with higher HRs of mortality (p for trend = 0.002). Oral frailty is associated with mortality independent of physical and psychological frailty in older adults. The Oral Frailty Index-8 may be useful for identifying individuals at high risk of mortality.