Declining energy predicts incident mobility disability and mortality risk in healthy older adults

Chronic Decrements in Energy in Women with Breast Cancer are Associated with Cytokine Gene Polymorphisms

OBJECTIVES

Decrements in energy were found in 67% of women who underwent breast cancer surgery. However, no information is available on chronic decrements in energy and associations with inflammation. Purposes were to identify latent classes of patients with distinct average energy profiles from prior to through 12 months after breast cancer surgery; evaluate for differences in demographic and clinical characteristics between the two extreme average energy classes; and evaluate for polymorphisms for cytokine genes associated with membership in the Low energy class.

METHODS

Women (n = 397) completed assessments of energy prior to and for 12 months following breast cancer surgery. Growth mixture modeling was used to identify classes of patients with distinct average energy profiles. Eighty-two single nucleotide polymorphisms (SNPs) among 15 cytokine genes were evaluated.

RESULTS

Three distinct energy profiles were identified (ie, Low [27.0%], Moderate [54.4%], Changing [18.6%]). Data from patients in the Low and Moderate energy classes were used in the candidate gene analyses. Five SNPs and one haplotype in six different genes remained significant in logistic regression analyses (ie, interleukin [IL]-1β rs1143623, IL1 receptor 1 rs3917332 IL4 rs2243263, IL6 HapA1 [that consisted of rs1800795, rs2069830, rs2069840, rs1554606, rs2069845, rs2069849, and rs2069861], nuclear factor kappa beta subunit 1 rs170731, tumor necrosis factor rs1799964). For several SNPs for IL6, expression quantitative trait locis were identified in subcutaneous and visceral adipose tissue and thyroid tissue. In addition, skeletal muscle was identified as an expression quantitative trait loci for nuclear factor kappa beta subunit 1.

CONCLUSIONS

Findings suggest that cytokine genes are involved in the mechanisms that underlie chronic decrements in energy in women following breast cancer surgery. Given the roles of subcutaneous and visceral adipose and thyroid tissues in metabolism and energy balance, the findings related to IL6 suggest that these polymorphisms may have a functional role in the development and maintenance of chronic decrements in energy.

Multidimensional Model of Energy in Patients With Cancer

OBJECTIVES

Evidence suggests that energy is a distinct symptom from fatigue in patients with cancer. The purpose of this paper is to present the Multidimensional Model of Energy in Patients with Cancer (MMEPC) that is based on emerging evidence and to make recommendations for clinical practice and future research.

METHODS

The literature was reviewed to determine various factors associated with variations in energy in patients with cancer. In addition, some of the emerging evidence in the model is supported by studies of energy in the general population and in patients with other chronic conditions.

RESULTS

Based on a review of the literature, specific concepts in the MMEPC include: person factors, clinical factors, cancer-related factors, biological factors, factors associated with energy balance, and co-occurring symptoms. The evidence to support the association between each of these factors and variations in energy levels in patients with cancer is described and synthesized.

CONCLUSION

This article provides emerging evidence on factors that influence variations in energy levels in patients with cancer. While the fundamental biobehavioral and biologic mechanisms that underlie variations in energy levels are not well understood, the model can be used to design pre-clinical and clinical studies of energy in patients with cancer. In addition, while emerging evidence supports the hypothesis that fatigue and energy are distinct symptoms, additional research on common and distinct risk factors and underlying mechanisms is warranted to be able to develop and test precision interventions for one or both symptoms.

IMPLICATIONS FOR NURSING PRACTICE

The risk factors (eg, being female, sleep quality) associated with variations in energy levels in patients with cancer identified in this paper have important clinical implications. Clinicians can use the identified risk factors to guide their assessments; identify high-risk patients with decrements in energy decrement; and develop targeted energy conservation interventions for the patients.

Fatigue and perceived energy in a sample of older adults over 10 years: A resting state functional connectivity study of neural correlates

PURPOSE

Declining energy and increasing fatigue, common in older age, predict neurodegenerative conditions, but their neural substrates are not known. We examined brain resting state connectivity in relation to declining self-reported energy levels (SEL) and occurrence of fatigue over time.

METHODS

We examined resting-state functional MRI in 272 community dwelling older adults participating in the Health Aging and Body Composition Study (mean age 83 years; 57.4 % female; 40.8 % Black) with measures of fatigue and SEL collected at regular intervals over the prior ten years. Functional connectivity (FC) between cortex and striatum was examined separately for sensorimotor, executive, and limbic functional subregions. Logistic regression tested the association of FC in each network with prior fatigue state (reporting fatigue at least once or never reporting fatigue), and with SEL decline (divided into stable or declining SEL groups) and adjusted for demographic, physical function, mood, cognition, and comorbidities.

RESULTS

Higher cortico-striatal FC in the right limbic network was associated with lower odds of reporting fatigue (better) at least once during the study period (adjusted odds ratio [95 % confidence interval], p-value: (0.747 [0.582, 0.955], 0.020), independent of SEL. Higher cortico-striatal FC in the right executive network was associated with higher odds of declining SEL (worse) during the study period (adjusted odds ratio [95 % confidence interval], p-value: (1.31 [1.01, 1.69], 0.041), independent of fatigue. Associations with other networks were not significant.

CONCLUSIONS

In this cohort of older adults, the cortico-striatal functional connectivity of declining SEL appears distinct from that underlying fatigue. Studies to further assess the neural correlates of energy and fatigue, and their independent contribution to neurodegenerative conditions are warranted.

Understanding loneliness in late life

Loneliness in late adulthood is a public health issue. Thus, understanding the etiology of loneliness is of critical importance. Here, we conceptualize the development of loneliness in late life as dynamic interactions between individual and contextual processes. Specifically, we suggest that loneliness arises if the existing social relationships are unable to meet a set of social expectations. These expectations are fulfilled by three different layers of the social structure: 1) close confidants; 2) broader social networks; and 3) involvement in the community. Although older adults experience losses in their broader network and engage less in the community, they may avoid loneliness by focusing on close confidants. However, these adaptations may make it more difficult for older adults to overcome loneliness.

Longitudinal Associations Between Higher Self-Reported Energy, Gait Speed, and Cognition in Older Adults With Fatigue

BACKGROUND

Older adults reporting higher energy levels have better physical function. It is not known if these associations persist among older adults reporting fatigue or if higher energy is associated with cognitive function. We examined longitudinal associations between self-reported energy, gait speed, and cognition, stratified by fatigue, in 2 613 participants (aged 74.6 ± 2.87 years) in the Health, Aging and Body Composition Study.

METHODS

Self-reported energy (0-10, dichotomized at median) and fatigue (present/absent) were measured at baseline. Usual and rapid-paced gait speed (m/s), modified Mini-Mental State Examination (3MS), and Digit Symbol Substitution Test (DSST) were measured at baseline and annually over 8 years. Linear mixed effect models compared changes in gait speed, 3MS, and DSST between higher and lower energy groups within fatigue strata.

RESULTS

At baseline, 724 participants (27%) were fatigued; 240 (33%) coreported higher energy (9% of total). The remaining 1 889 participants were fatigue-free (73%); 1 221 (65%) coreported higher energy (47% of total). Those with fatigue and higher energy had average rapid gait declines of 0.007 m/s per year (p = .04) after adjustment for demographics, comorbidities, depressive symptoms, and exercise. DSST declines were found among only fatigue-free participants (β = 0.17, p = .01). No statistically significant associations with energy were found for fatigue-free participants, or for usual gait or 3MS.

CONCLUSIONS

Asking about older adults' energy levels as well as fatigue may identify a subgroup of older adults protected against physical and cognitive decline, even among those with fatigue.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study

Background

Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial.

Methods

We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted.

Results

During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model.

Conclusion

Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.

Sleep disturbance and decrements in morning energy contribute to a higher symptom burden in oncology patients

OBJECTIVE/BACKGROUND

An emerging area of research is the relationship between sleep disturbance and decrements in energy. Given the paucity of research on the co-occurrence of these two symptoms, study purposes were to identify subgroups of oncology patients with distinct joint sleep disturbance AND morning energy profiles and evaluate for differences among the subgroups in demographic, clinical, and sleep disturbance characteristics, as well as the severity of other common symptoms and QOL outcomes.

PATIENTS/METHODS

Patients (n = 1336) completed measures of sleep disturbance and energy 6 times over two cycles of chemotherapy. All of the other measures were completed at enrollment. Latent profile analysis was used to identify the distinct joint sleep disturbance and morning energy profiles.

RESULTS

Three distinct profiles were identified (i.e., Low Sleep Disturbance and High Morning Energy (Normal, 20.6%), Moderate Sleep Disturbance and Low Morning Energy (Moderately Severe, 52.1%), Very High Sleep Disturbance and Very Low Morning Energy (Very Severe, 27.3%). Compared to Normal class, other two classes were more likely to be female, less likely to be employed, and had higher comorbidity burden and poorer functional status. Symptom scores and QOL outcomes exhibited a dose response effect (i.e., as the profile worsened, symptom scores increased and QOL scores decreased).

CONCLUSIONS

Given the associations between sleep disturbance and decrements in energy and a higher symptom burden, poorer QOL outcomes, and increased mortality, assessment of these two symptoms needs to be a high priority for clinicians and appropriate interventions initiated.

Vitality Revisited: The Evolving Concept of Flourishing and Its Relevance to Personal and Public Health

Human flourishing, the state of optimal functioning and well-being across all aspects of an individual's life, has been a topic of philosophical and theological discussion for centuries. In the mid-20th century, social psychologists and health scientists began exploring the concept of flourishing in the context of health and high-level wellness. However, it is only in recent years, in part due to the USD 43 million Global Flourishing Study including 22 countries, that flourishing has entered the mainstream discourse. Here, we explore this history and the rapid acceleration of research into human flourishing, defined as "the relative attainment of a state in which all aspects of a person's life are good" by the Harvard University's Flourishing Program. We also explore the construct of "vitality", which refers to a sense of aliveness, energy, and motivation; we contend that this has been neglected in the flourishing movement. We explore why incorporating measures of vitality, together with a broader biopsychosocial approach, considers all dimensions of the environment across time (the total exposome), which will greatly advance research, policies, and actions to achieve human flourishing.

Determinants of Social Cognitive Aging: Predicting Resilience and Risk

This review focuses on conceptual and empirical research on determinants of social cognitive aging. We present an integrated model [the social cognitive resource (SCoRe) framework] to organize the literature and describe how social cognitive resilience is determined jointly by capacity and motivational resources. We discuss how neurobiological aging, driven by genetic and environmental influences, is associated with broader sensory, neural, and physiological changes that are direct determinants of capacity as well as indirect determinants of motivation via their influence on expectation of loss versus reward and cognitive effort valuation. Research is reviewed that shows how contextual factors, such as relationship status, familiarity, and practice, are fundamental to understanding the availability of both types of resource. We conclude with a discussion of the implications of social cognitive change in late adulthood for everyday social functioning and with recommendations for future research.

A training program for researchers in population neuroimaging: Early experiences

Recent advances in neuroimaging create groundbreaking opportunities to better understand human neurological and psychiatric diseases, but also bring new challenges. With the advent of more and more sophisticated and efficient multimodal image processing software, we can now study much larger populations and integrate information from multiple modalities. In consequence, investigators that use neuroimaging techniques must also understand and apply principles of population sampling and contemporary data analytic techniques. The next generation of neuroimaging researchers must be skilled in numerous previously distinct disciplines and so a new integrated model of training is needed. This tutorial presents the rationale for such a new training model and presents the results from the first years of the training program focused on population neuroimaging of Alzheimer's Disease. This approach is applicable to other areas of population neuroimaging.

Using Machine Learning to Identify Feelings of Energy and Fatigue in Single-Task Walking Gait: An Exploratory Study

The objective of this study was to use machine learning to identify feelings of energy and fatigue using single-task walking gait. Participants (n = 126) were recruited from a university community and completed a single protocol where current feelings of energy and fatigue were measured using the Profile of Moods Survey–Short Form approximately 2 min prior to participants completing a two-minute walk around a 6 m track wearing APDM mobility monitors. Gait parameters for upper and lower extremity, neck, lumbar and trunk movement were collected. Gradient boosting classifiers were the most accurate classifiers for both feelings of energy (74.3%) and fatigue (74.2%) and Random Forest Regressors were the most accurate regressors for both energy (0.005) and fatigue (0.007). ANCOVA analyses of gait parameters comparing individuals who were high or low energy or fatigue suggest that individuals who are low energy have significantly greater errors in walking gait compared to those who are high energy. Individuals who are high fatigue have more symmetrical gait patterns and have trouble turning when compared to their low fatigue counterparts. Furthermore, these findings support the need to assess energy and fatigue as two distinct unipolar moods as the signals used by the algorithms were unique to each mood.