Background: Previous studies suggest that poor sleep quality or abnormal sleep duration may be associated with frailty. Here we test the associations of sleep disturbances with both frailty and pre-frailty in an elderly population. Methods: Participants included 1726 community-dwelling elders aged 70–87 years. Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep disturbances. Frailty was defined using phenotype criteria. Logistic regression models were used to estimate odds ratio of the associations. Results: The average PSQI score was 5.4 (SD, 3.1). Overall 43.6% of the participants had poor sleep quality (PSQI> 5), 8.2% had night sleep time ≤ 5 h, and 27.8% had night sleep time ≥ 9 h. The prevalence of frailty and pre-frailty was 9.2 and 52.8%, respectively. The proportions of PSQI> 5 increased with the severity of frailty status (robust: pre-frail: frail, 34.5%: 48%: 56.1%, P < 0.001). After adjustment for multiple potential confounders, poor sleep quality (PSQI> 5) was associated with higher odds of frailty (OR = 1.78, 95% CI 1.19–2.66) and pre-frailty (OR = 1.51, 95% CI 1.20–1.90). Sleep latency, sleep disturbance, and daytime dysfunction components of PSQI measurements were also associated with frailty and pre-frailty. In addition, sleep time 9 h/night was associated with higher odds of frailty and pre-frailty. Conclusions: We provided preliminary evidences that poor sleep quality and prolonged sleep duration were associated with being frailty and pre-frailty in an elderly population aged 70–87 years. The associations need to be validated in other elderly populations.

Background: The neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune-inflammation index (SII) are readily available circulatory immunity markers that are associated with components of frailty. However, few studies have investigated the relationship between these immunity markers and frailty, and it remains unknown whether they are predictive of incident frailty in older adults in general. Hence, we aimed to examine the association of these immunity markers with the risk of incident frailty. Results: Overall, 1822 older adults (mean age was 78.03 ± 4.46 years) were included in the Rugao Longitudinal Aging Study. NLR, PLR and SII were calculated from blood cell counts. The frailty definition was based on the Fried phenotype. At baseline, 200 (10.98%) individuals were defined as frailty, and no significant associations of NLR, PLR and SII with frailty were found. During the 2-year follow-up, 180 (15.67%) individuals were new-onset frailty. After adjustment, an increased logNLR (odds ratio [OR] 2.92, 95% confidence interval [CI] 1.20–7.18), logPLR (OR 2.54, 95% CI: 1.01–6.53) and logSII (OR 2.34, 95% CI: 1.16–4.78) were significantly associated with a higher risk of incident frailty in all individuals. Additionally, the associations of logNLR (OR 4.21, 95% CI 1.54–11.62 logPLR (OR 3.38, 95% CI: 1.17–9.91) and logSII (OR 2.56, 95% CI: 1.15–5.72) with incident frailty were remained after excluding individuals with comorbidities. In further analyzed, individuals with higher levels of NLR and SII had higher risk of incident frailty when we stratified individuals by quartiles of these immunity markers. Conclusion: NLR and SII are easily obtained immunity markers that could be used to predict incident frailty in clinical practice.

The Rugao Longevity and Ageing Study (RuLAS) is a two-arm cohort. The longevity arm was initiated by Fudan University and the government of Rugao between December 2007 and February 2008 in Rugao, a well-known ‘longevity town’ in China, with the aim of examining genetic factors of longevity. In addition, the arm was expanded to enable us to understand what specific biological, psychological and sociological characteristics these long-lived individuals possess that would allow them to survive longer, namely, their unique adaptational characteristics.¹ However, in the majority of studies the longevity phenotype measures overall lifespan without consideration of physical/cognitive function and diseases, and hence an intuitive question was raised regarding long-lived individuals' representativeness of healthy ageing.² In the era of rapid population ageing, ageing-related phenotypes have been studied such as disease-free survival,^3,4 avoidance of bone loss,⁵ successful ageing^6,7 and frailty^8,9 in cohort studies. These alternatives provide us with a great opportunity for elucidating determinates of healthy ageing.