The global and regional prevalence of restless legs syndrome among adults: A systematic review and modelling analysis.

Background: Restless legs syndrome (RLS) is a prevalent neuro-sensory disorder that impairs quality of life. In this systematic review and modelling study, we estimated the global and regional prevalence of RLS and its associated factors. Methods: We searched PubMed, Embase, and Medline for population-based studies on RLS prevalence published up to 12 November 2023. The included studies reported prevalence using the International Restless Leg Syndrome Study Group's (IRLSSG) minimal diagnostic criteria without limitations on frequency, duration, or severity. We applied a multilevel multivariable mixed-effects meta-regression to generate the age-specific and sex-specific prevalence of RLS for high socio-demographic index (H-SDI) and low and middle socio-demographic index (LM-SDI) regions. We pooled odds ratios (ORs) for RLS associated factors using random-effects models. Finally, we derived the regional prevalence and cases of RLS based on an associated factor-based model. Results: From 52 articles across 23 countries, the global RLS prevalence in 2019 was estimated to be 7.12% (95% confidence interval (CI) = 5.15-9.76) among adults 20-79 years of age, equating to 356.07 million (95% CI = 257.61-488.09) affected individuals. Prevalence was similar in H-SDI (7.29%; 95% CI = 5.04-10.41) and LM-SDI (7.10%; 95% CI = 5.16-9.70) regions, with the majority of cases in LM-SDI countries (323.06 million; 90.73%). Europe had the highest (7.60%; 95% CI = 5.44-10.52) and Africa the lowest regional prevalence (6.48%; 95% CI = 4.70-8.87). The Western Pacific Region, meanwhile, had the most cases (111.91 million; 95% CI = 80.93-153.42). Factors positively associated with RLS included advanced age (OR = 1.13; 95% CI = 1.04-1.24), smoking (OR = 1.46; 95% CI = 1.29-1.64), depression (OR = 1.71; 95% CI = 1.26-2.32), and diabetes (OR = 1.54; 95% CI = 1.19-1.97). Conclusions: A considerable global burden of RLS exists. Effective strategies are needed to increase awareness and optimise resource allocation to address this often-overlooked condition. High-quality epidemiological investigations employing standardised and rigorous criteria for RLS are essential for addressing RLS burden more effectively. Registration: PROSPERO: CRD42020161860.

Joint daily functional trajectory and risk of new-onset Alzheimer's disease and related dementias in older adults with normal and abnormal weight.

BACKGROUND: Associations between daily functional trajectories and new-onset all-cause dementia and Alzheimer's disease (AD) and the role of body weight are underexplored. METHODS: Data were from the Health and Retirement Study (HRS) 1994-2020. Daily function was assessed using (instrumental) activities of daily living ([I]ADLs). All-cause dementia and AD were defined by self- or proxy-reported physician diagnoses. Body weight was assessed using body mass index (BMI) and categorized as normal (18.5 kg/m2 ≤ BMI < 30 kg/m2) and abnormal (BMI < 18.5 kg/m2 or ≥30 kg/m2). The group-based trajectory modeling and Cox proportional hazards regression were utilized. RESULTS: Of 18,763 adults included, 1236 developed new-onset dementia during a 10-year follow-up. The associations of ADL and IADL limitations at baseline with all-cause dementia and AD were much more pronounced in those with abnormal weight (P for interaction < 0.005). Five joint trajectories of ADL and IADL limitations were identified: No (72.7 %), Recovery (4.0 %), Recent emerging (16.4 %), Early emerging (4.8 %), and Severe (2.1 %). Furthermore, the 'Severe' joint trajectory (vs. 'No') was associated with 3.57- and 3.59-times higher risks of new-onset all-cause dementia and AD in participants with abnormal weight (P for interaction = 0.002 and 0.005). Notably, the Recovery joint trajectory (vs. No) was not associated with increased risks of all-cause dementia or AD. LIMITATIONS: Self-/proxy-reported all-cause dementia and AD may introduce misclassification bias. Lifestyle factors were not quantified. BMI at baseline, but not its trajectory, was utilized. Potential reverse causation deserved attention. CONCLUSIONS: Body weight control can help reduce the risk of progression from functional limitations to all-cause dementia and AD.

Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records.

Aims: Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. Methods and results: A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively. Conclusion: Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.

Progression and trajectory network of age-related functional impairments and their combined associations with mortality.

Age-related functional impairments (ARFIs) contribute to the loss of independence in older adults, but their progressions, interrelations, and combined relations with mortality are largely unknown. We conducted a prospective study among 17,914 participants in the Health and Retirement Study (2000-2020). The incidence rates of visual impairment, hearing impairment, physical frailty, and cognitive impairment increased exponentially with age, while those of restless sleep and depression increased relatively slowly. These ARFIs were associated with each other in temporal sequence and constituted a hazard network. We observed a dose-response relationship between the number of ARFIs and mortality risk, and the dyads involving physical frailty demonstrated the strongest associations with mortality. Our findings may assist in the identification of individuals at higher mortality risk and highlight the potential for future investigations to explore the impact of multiple ARFIs in aging.

Effect modifications of BMI transition and trajectory in the associations of adverse childhood experiences with new-onset dementia and its subtypes in older US adults.

Background: Adverse childhood experiences (ACEs) and dementia are associated and comorbid with obesity. However, according to emerging research, the role of obesity in the association between ACEs and dementia seems controversial. Aims: This analysis aimed to explore the associations between ACEs and different dementia subtypes and the effect modification of long-term body mass index (BMI). Methods: Data were obtained from the US Health and Retirement Study. Six ACEs were categorised as 0, 1 and 2 or more. All-cause dementia, Alzheimer's disease (AD) and other dementias were defined by self-reported or proxy-reported physician diagnosis. Cox proportional hazards regression was used to explore the associations of ACEs with new-onset all-cause dementia, AD and other dementias from 2010 to 2020. Effect modification of BMI in 2010 and BMI transition and trajectory (fitted by group-based trajectory modelling) from 2004 to 2010 were assessed. Results: 15 282 participants with a mean age of 67.0 years (58.0-75.0) were included in the 2010 data analysis. Significant interactions of ACEs with baseline BMI, BMI transition and BMI trajectory in their associations with new-onset all-cause dementia and AD were observed (all p<0.05). For instance, positive associations of two or more ACEs (vs none) with all-cause dementia and AD were found in those with a BMI trajectory of maintaining ≥30 kg/m2 (maintain obesity) rather than a decline to or maintaining <25 kg/m2 (decline to or maintain normal weight), with hazard ratios (HRs) of 1.87 (95% confidence interval (CI): 1.45 to 2.42) and 1.85 (95% CI: 1.22 to 2.80), respectively. Conclusions: ACEs were associated with dementia and AD in US adults with long-term abnormally elevated BMI but not with long-term normal or decreasing BMI. Integrated weight management throughout life could prevent dementia among those with childhood adversity.

Longitudinal body weight dynamics in relation to cognitive decline over two decades: A prospective cohort study.

OBJECTIVE: The aim of this study was to investigate the associations of body weight change (BWC) and body weight variability (BWV) with changes in cognitive function. METHODS: In 10,340 Health and Retirement Study participants (mean age: 68.0 years), body weight was reported biennially from 1993/1994 to 2016, and cognitive function was measured biennially from 1998 to 2016. We calculated BWC and BWV as the slope and root-mean-square error by regressing body weight on time for each individual. BWC was categorized by quintiles (Q): stable weight (Q2 to Q4), weight loss (Q1), and weight gain (Q5). BWV was categorized by tertiles. We used linear mixed regression models to assess associations with cognitive change. RESULTS: Compared with stable weight (median: 0 kg/y), weight loss (median: -1.3 kg/y) predicted faster cognitive decline as demonstrated by mean difference of -0.023 (95% CI: -0.027 to -0.019) in cognitive change z score per year, whereas weight gain (median: 1 kg/y) was related to slower cognitive decline (ß = 0.006; 95% CI: 0.003 to 0.009). Larger BWV was also associated with faster cognitive decline (ß comparing the top with bottom tertile = -0.003; 95% CI: -0.006 to -0.0002). Similar associations were observed for episodic and working memory. CONCLUSIONS: Weight loss and large BWV over a long time independently predicted faster cognitive decline in middle-aged and older adults, underscoring the importance of long-term dynamic body weight monitoring.

Long-term variability in physiological measures in relation to mortality and epigenetic aging: prospective studies in the USA and China.

BACKGROUND: Visit-to-visit body weight variability (BWV), pulse rate variability (PRV), and blood pressure variability (BPV) have been respectively linked to multiple health outcomes. The associations of the combination of long-term variability in physiological measures with mortality and epigenetic age acceleration (EAA) remain largely unknown. METHODS: We constructed a composite score of physiological variability (0-3) of large variability in BWV, PRV, and BPV (the top tertiles) in 2006/2008-2014/2016 in the Health and Retirement Study (HRS) and 2011-2015 in the China Health and Retirement Longitudinal Study (CHARLS). All-cause mortality was documented through 2018. EAA was calculated using thirteen DNA methylation-based epigenetic clocks among 1047 participants in a substudy of the HRS. We assessed the relation of the composite score to the risk of mortality among 6566 participants in the HRS and 6906 participants in the CHARLS by Cox proportional models and then investigated its association with EAA using linear regression models. RESULTS: A higher score of variability was associated with higher mortality risk in both cohorts (pooled hazard ratio [HR] per one-point increment, 1.27; 95% confidence interval [CI], 1.18, 1.39; P-heterogeneity = 0.344), after adjustment for multiple confounders and baseline physiological measures. Specifically, each SD increment in BWV, PRV, and BPV was related to 21% (95% CI: 15%, 28%), 6% (0%, 13%), and 12% (4%, 19%) higher hazard of mortality, respectively. The composite score was significantly related to EAA in second-generation clocks trained on health outcomes (e.g., standardized coefficient = 0.126 in the Levine clock, 95% CI: 0.055, 0.196) but not in most first-generation clocks trained on chronological age. CONCLUSIONS: Larger variability in physiological measures was associated with a higher risk of mortality and faster EAA.

Associations of metabolic heterogeneity of obesity with frailty progression: Results from two prospective cohorts.

BACKGROUND: Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear. METHODS: A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed-effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression. RESULTS: In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (ß: -0.011, 95% CI: -0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (ß: 0.103, 95% CI: -0.005 to 0.210, P = 0.061) and MUNW (ß: 0.157, 95% CI: -0.011 to 0.324, P = 0.066), but not for MHOO (ß: -0.047, 95% CI: -0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, ß: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, ß: 0.210, 95% CI: 0.049 to 0.370, P = 0.011). CONCLUSIONS: Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status.

Frailty is associated with the progression of prediabetes to diabetes and elevated risks of cardiovascular disease and all-cause mortality in individuals with prediabetes and diabetes: Evidence from two prospective cohorts.

AIMS: To investigate the impacts of frailty on the progression of prediabetes to diabetes, cardiovascular disease (CVD) and all-cause mortality in individuals with prediabetes and diabetes. METHODS: 7,933 subjects with prediabetes and diabetes were included from the China Health and Retirement Longitudinal Study (CHARLS) and English Longitudinal Study of Ageing (ELSA). Frailty status was assessed by frailty index and classified as robust, pre-frail, and frail. Logistic regression was used to calculate risks of progression to diabetes. Cox regression was used to calculate risks of CVD and all-cause mortality. RESULTS: In prediabetes, frail subjects had significantly increased risks of progression to diabetes (CHARLS, OR = 1.55, 95 %CI: 1.09-2.20; ELSA, OR = 1.86, 95 %CI: 1.02-3.37) compared with robust subjects. Frail subjects with prediabetes also presented significantly increased risks of CVD (CHARLS: HR = 1.90, 95 %CI: 1.45-2.48; ELSA: HR = 1.94, 95 %CI: 1.31-2.88) and all-cause mortality (CHARLS: HR = 2.45, 95 %CI: 1.79-3.36; ELSA: HR = 2.13, 95 %CI: 1.46-3.10) than robust subjects with prediabetes. In diabetes, frailty still increased risks of CVD (CHARLS, HR = 2.72, 95 %CI: 1.97-3.77; ELSA, HR = 2.41, 95 %CI: 1.43-4.06) and all-cause mortality (CHARLS, HR = 2.28, 95 %CI: 1.56-3.33; ELSA, HR = 2.28, 95 %CI: 1.47-3.53). CONCLUSIONS: Frailty is associated with the progression of prediabetes to diabetes and elevated risks of CVD and all-cause mortality in individuals with prediabetes and diabetes.

Long-term Weight Change and its Temporal Relation to Later-life Dementia in the Health and Retirement Study.

CONTEXT: Weight loss among middle-aged and older adults has been associated with a higher risk of subsequent dementia. However, most studies have limited follow-up durations or suboptimal control for the potential influence of physical frailty (PF). OBJECTIVE: Our study aimed to investigate the long-term and temporal relations of weight change to risk of dementia among middle-aged and older adults in the United States. METHODS: A total of 5985 participants aged 65 years and older were included from the Health and Retirement Study. History of long-term weight change was calculated using 9 repeated body mass index measurements during 1992-2008. We then followed participants' dementia status from 2008 to 2018. Multivariable Cox proportional hazard models were used. RESULTS: During the study follow-up period (mean = 7.54 years), a total of 682 (11.40%) dementia cases were documented. After adjustment for basic demographic and lifestyle factors, participants with weight loss (median: -0.23 kg/m2 per year) were at a significantly higher risk of dementia (HR = 1.60; 95% CI, 1.33, 1.92), compared with the stable weight group (median: 0.11 kg/m2 per year). This association was attenuated but remained strong and significant after further adjustment for PF (HR = 1.57; 95% CI, 1.30, 1.89). Significant association was observed for weight loss assessed approximately 14 to 18 years preceding dementia diagnosis (HR = 1.30; 95% CI, 1.07, 1.58), and was consistent closer to diagnosis. CONCLUSION: Both recent and remote weight loss were associated with a higher risk of later-life dementia among middle-aged and older adults independent of PF status.

Association of Long-Term Body Weight Variability With Dementia: A Prospective Study.

BACKGROUND: Body weight variability (BWV) refers to intraindividual weight loss and gain over a period. The association of long-term BWV with dementia remains unclear and whether this association is beyond body weight change is undetermined. METHODS: In the Health and Retirement Study, a total of 5 547 dementia-free participants (56.7% women; mean [SD] age, 71.1 [3.2] years) at baseline (2008) were followed up to 8 years (mean = 6.8 years) to detect incident dementia. Body weight was self-reported biennially from 1992 to 2008. BWV was measured as the coefficient of variation utilizing the body weight reported 9 times across 16 years before baseline. Cox-proportional hazard model was used to estimate the hazard ratio (HR) and 95% confidence interval (CI). RESULTS: Among the 5 547 participants, a total of 427 incident dementia cases were identified during follow-up. Greater long-term BWV was significantly associated with a higher risk of dementia (HR comparing extreme quartiles: 2.01, 95% CI: 1.48-2.72; HR of each SD increment: 1.21, 95% CI: 1.10-1.32; p-trend < .001) independent of mean body weight and body weight change. This significant association was even observed for BWV estimated approximately 15 years preceding dementia diagnosis (HR of each SD increment: 1.13, 95% CI: 1.03-1.23) and was more pronounced for that closer to diagnosis. CONCLUSION: Our prospective study suggested that greater BWV may be a novel risk factor for dementia.

Homogeneous Molecular Systems are Positively Cooperative, but Charged Molecular Systems are Negatively Cooperative.

Molecular systems bound together through noncovalent interactions are ubiquitous in nature, many of which are involved in essential life processes, yet little is known about the principles governing their structure, stability, and function. Cooperativity as one of the intrinsic properties in these systems plays a key role. In this work, on the basis of our recent quantification scheme of the cooperativity effect, we present a general pattern to identify which systems are positively cooperative and which are negatively cooperative. We show that cooperativity in homogeneous molecular systems is positive, but cooperativity in charged molecular systems is negative. We also employ analytical tools from energetics and information perspectives to appreciate the origin of the cooperativity effect. We find that positive cooperativity is dominated by the exchange-correlation interaction and steric effect, whereas negative cooperativity is governed by the electrostatic interaction. Our results should have strong implications for better understanding molecular recognition, protein folding, signal transduction, allosteric regulation, and other processes.