Sustaining an ageing population: the role of micronutrients in frailty and cognitive impairment

Micronutrient Deficiencies in Older Adults in Latin America: A Narrative Review

BACKGROUND

The population in Latin America and Caribbean (LAC) has experienced a major demographic transition with increased numbers of older adults (OA). This change brings opportunities in the public health sector to implement health prevention interventions and delay the onset of geriatric syndromes. Micronutrients play an important role in the maintenance of biological function which contributes to longevity. Micronutrient deficiencies (MD) in OA increase the risk for onset of chronic comorbidities and geriatric syndromes.

AIM

To review and summarize the existing data on micronutrient status in OA in the LAC region and discuss the gaps and challenges in public health approaches to address deficiencies.

METHODS

Literature review in Medline for records describing nutritional biomarkers in older adults (≥ 60y) from community dwelling and population-based studies in LAC.

RESULTS

Few countries (including Chile, Ecuador, Costa Rica, Brazil, and Mexico) have documented one or more nutritional deficiencies for OA in national health surveys, however across the entire region, evidence of micronutrient levels is scarce. Some surveys have documented a high prevalence and large heterogeneity in the prevalence of vitamin D followed by B12 deficiency, being the 2 MDs most studied due their effects on cognition, frailty, and bone mineral density in the OA population. Other MD including C, E, A, copper, zinc, iron, and selenium have also been reported.

CONCLUSION

Information on the micronutrient status in OA from LAC is poorly documented. Research and capacity building initiatives in the region are crucial to develop tailored strategies that address the specific nutritional needs and challenges faced by the ageing population in Latin America.

Elevated Serum Copper, Zinc, Selenium, and Lowered α-Klotho Associations: Findings from NHANES 2011-2016 Dataset

The α-Klotho is crucial for human health and longevity. However, the relationship between trace elements and α-Klotho levels needs further investigation. We aimed to explore the relationship between serum levels of selenium (Se), copper (Cu), and zinc (Zn), and serum α-Klotho levels. We analyzed 2138 samples from the 2011-2016 National Health and Nutrition Examination Survey, and the weighted linear regression, WQS, and qgcomp models were utilized to evaluate the effects of these elements on serum α-Klotho levels, individually and combined. A negative correlation was observed between serum Cu concentration and serum α-Klotho levels (β =  - 0.128, 95% CI - 0.196, - 0.059), with each increase in Cu concentration grade showing a gradual decrease in serum α-Klotho levels (Ptrend = 0.002). The WQS model exhibited a negative correlation between the combined effect of Se, Cu, and Zn and serum α-Klotho levels (β =  - 0.035, 95%CI - 0.060, - 0.010), consistently in males (β =  - 0.038 (- 0.059, - 0.017)) and in the 40-49 age group (β =  - 0.059, 95% CI - 0.119, - 0.012). The qgcomp model mirrored these findings, showing a negative correlation in the combined effect index of Se, Cu, and Zn with serum α-Klotho levels (β =  - 0.027, 95% CI - 0.047, - 0.006), consistent in females (β =  - 0.032, 95% CI - 0.061, - 0.004) and in individuals with BMI ≥ 25 (β =  - 0.030, 95% CI - 0.054, - 0.006), and in the 40-49 age group (β =  - 0.047, 95% CI - 0.088, - 0.006). Elevated serum Cu levels may be associated with lower serum α-Klotho levels. The combined effect of serum Se, Cu, and Zn shows a negative correlation with serum α-Klotho levels, with Cu contributing the most. Our findings provide significant insights into assessing the role of trace nutrients in maintaining human health.

Development and Validation of a Nomogram for Predicting Nutritional Risk Based on Frailty Scores in Older Stroke Patients

BACKGROUND

In older stroke patients with frailty, nutritional deficiencies can amplify their susceptibility, delay recovery, and deteriorate prognosis. A precise predictive model is crucial to assess their nutritional risk, enabling targeted interventions for improved clinical outcomes.

OBJECTIVE

To develop and externally validate a nutritional risk prediction model integrating general demographics, physical parameters, psychological indicators, and biochemical markers. The aim is to facilitate the early identification of older stroke patients requiring nutritional intervention.

METHODS

This was a multicenter cross-sectional study. A total of 570 stroke patients were included, 434 as the modeling set and 136 as the external validation set. The least absolute shrinkage selection operator (LASSO) regression analysis was used to select the predictor variables. Internal validation was performed using Bootstrap resampling (1000 iterations). The nomogram was constructed based on the results of logistic regression. The performance assessment relied on the receiver operating characteristic curve (ROC), Hosmer--Lemeshow test, calibration curves, Brier score, and decision curve analysis (DCA).

RESULTS

The predictive nomogram encompassed seven pivotal variables: Activities of Daily Living (ADL), NIHSS score, diabetes, Body Mass Index (BMI), grip strength, serum albumin levels, and depression. Together, these variables comprehensively evaluate the overall health and nutritional status of elderly stroke patients, facilitating accurate assessment of their nutritional risk. The model exhibited excellent accuracy in both the development and external validation sets, evidenced by AUC values of 0.934 and 0.887, respectively. Such performance highlights its efficacy in pinpointing elderly stroke patients who require nutritional intervention. Moreover, the model showed robust goodness of fit and practical applicability, providing essential clinical insights to improve recovery and prognosis for patients prone to malnutrition.

CONCLUSIONS

Elderly individuals recovering from stroke often experience significant nutritional deficiencies. The nomogram we devised accurately assesses this risk by combining physiological, psychological, and biochemical metrics. It equips healthcare providers with the means to actively screen for and manage the nutritional care of these patients. This tool is instrumental in swiftly identifying those in urgent need of targeted nutritional support, which is essential for optimizing their recovery and managing their nutrition more effectively.

Frailty and nutrition

As the ageing population grows and forms a significant category of over 65s in many societies, along with it comes the risk of developing physical and psychological degenerative changes. This presents many challenges for health and social care services in not only identifying those at risk but also managing that risk to try to preserve health and independence for as long as possible. Screening for frailty has supported services to identify those that may be at risk of hospitalisation, requiring long term care or support services at home in older age. Frailty can be exacerbated by the risk of nutritional deficiencies and more severe malnutrition. Therefore, screening for frailty should also include a nutritional assessment, which can be supported by a recognition of the need for nutritional support along with other holistic frailty management.

[Therapy of physical frailty]

As an overarching geriatric syndrome, frailty describes a potentially reversible transitional stage between functional autonomy and irreversible disability. Thus, frailty addresses a "window of opportunity" in which functional limitations can be successfully treated. This article provides an overview of the therapeutic approaches and their scientific evidence.

Ketosis Suppression and Ageing (KetoSAge): The Effects of Suppressing Ketosis in Long Term Keto-Adapted Non-Athletic Females

Most studies on ketosis have focused on short-term effects, male athletes, or weight loss. Hereby, we studied the effects of short-term ketosis suppression in healthy women on long-standing ketosis. Ten lean (BMI 20.5 ± 1.4), metabolically healthy, pre-menopausal women (age 32.3 ± 8.9) maintaining nutritional ketosis (NK) for > 1 year (3.9 years ± 2.3) underwent three 21-day phases: nutritional ketosis (NK; P1), suppressed ketosis (SuK; P2), and returned to NK (P3). Adherence to each phase was confirmed with daily capillary D-beta-hydroxybutyrate (BHB) tests (P1 = 1.9 ± 0.7; P2 = 0.1 ± 0.1; and P3 = 1.9 ± 0.6 pmol/L). Ageing biomarkers and anthropometrics were evaluated at the end of each phase. Ketosis suppression significantly increased: insulin, 1.78-fold from 33.60 (± 8.63) to 59.80 (± 14.69) pmol/L (p = 0.0002); IGF1, 1.83-fold from 149.30 (± 32.96) to 273.40 (± 85.66) µg/L (p = 0.0045); glucose, 1.17-fold from 78.6 (± 9.5) to 92.2 (± 10.6) mg/dL (p = 0.0088); respiratory quotient (RQ), 1.09-fold 0.66 (± 0.05) to 0.72 (± 0.06; p = 0.0427); and PAI-1, 13.34 (± 6.85) to 16.69 (± 6.26) ng/mL (p = 0.0428). VEGF, EGF, and monocyte chemotactic protein also significantly increased, indicating a pro-inflammatory shift. Sustained ketosis showed no adverse health effects, and may mitigate hyperinsulinemia without impairing metabolic flexibility in metabolically healthy women.

Malnutrition, Depression, Poor Sleep Quality, and Difficulty Falling Asleep at Night Are Associated with a Higher Risk of Cognitive Frailty in Older Adults during the COVID-19 Restrictions

The COVID-19 restrictions, such as social isolation and disruption of daily routines, can have detrimental effects, including increased stress, anxiety, sleep disturbance, and physical and cognitive decline among older adults. This study aimed to examine the association between nutritional status, depression, sleep quality, falling asleep at night, and cognitive frailty (CF) among older Thai adults during the COVID-19 pandemic. This cross-sectional study included 408 older adults with an average age of 70.54 (5.49) years. CF was determined using Fried's frailty phenotype and the Montreal Cognitive Assessment Basic. The Mini Nutritional Assessment-Short Form, Pittsburgh Sleep Quality Index, and geriatric depression assessment were used for assessment. Multiple logistic regression analysis demonstrated that participants who were malnourished (OR 3.786; 95%CI 1.719-8.335), depressed (OR 5.003; 95%CI 2.399-10.434), had poor sleep quality (OR 1.613; 95%CI 1.041-2.500), and engaged in difficulty falling asleep (OR 1.809; 95%CI 1.022-3.203) had a higher risk of CF compared to those who did not exhibit these factors. Therefore, malnutrition, depression, poor sleep quality, and difficulty falling asleep were identified as risk factors for CF among older adults in Thailand linked to the impact of the COVID-19 pandemic. It is crucial to develop interventions to prevent CF resulting from the mentioned variables.