Sociodemographic and lifestyle determinants of multimorbidity among community-dwelling older adults: findings from 346,760 SHARE participants

Community screening for dementia among older adults in China: a machine learning-based strategy

BACKGROUND

Dementia is a leading cause of disability in people older than 65 years worldwide. However, diagnosing dementia in its earliest symptomatic stages remains challenging. This study combined specific questions from the AD8 scale with comprehensive health-related characteristics, and used machine learning (ML) to construct diagnostic models of cognitive impairment (CI).

METHODS

The study was based on the Shenzhen Healthy Ageing Research (SHARE) project, and we recruited 823 participants aged 65 years and older, who completed a comprehensive health assessment and cognitive function assessments. Permutation importance was used to select features. Five ML models using BalanceCascade were applied to predict CI: a support vector machine (SVM), multilayer perceptron (MLP), AdaBoost, gradient boosting decision tree (GBDT), and logistic regression (LR). An AD8 score ≥ 2 was used to define CI as a baseline. SHapley Additive exPlanations (SHAP) values were used to interpret the results of ML models.

RESULTS

The first and sixth items of AD8, platelets, waist circumference, body mass index, carcinoembryonic antigens, age, serum uric acid, white blood cells, abnormal electrocardiogram, heart rate, and sex were selected as predictive features. Compared to the baseline (AUC = 0.65), the MLP showed the highest performance (AUC: 0.83 ± 0.04), followed by AdaBoost (AUC: 0.80 ± 0.04), SVM (AUC: 0.78 ± 0.04), GBDT (0.76 ± 0.04). Furthermore, the accuracy, sensitivity and specificity of four ML models were higher than the baseline. SHAP summary plots based on MLP showed the most influential feature on model decision for positive CI prediction was female sex, followed by older age and lower waist circumference.

CONCLUSIONS

The diagnostic models of CI applying ML, especially the MLP, were substantially more effective than the traditional AD8 scale with a score of ≥ 2 points. Our findings may provide new ideas for community dementia screening and to promote such screening while minimizing medical and health resources.

Stage 5 Chronic Kidney Disease: Epidemiological Analysis in a NorthEastern District of Italy Focusing on Access to Nephrological Care

BACKGROUND

We conducted a retrospective epidemiological study about the prevalence of stage 5 chronic kidney disease (CKD) in a high-income district, comparing some demographic characteristics and outcomes of those patients who had nephrological consultations and those who had not.

RESULTS

In a district of 400,000 adult subjects in 2020, 925 patients had an estimated glomerular filtration rate (eGFR) under 15 mL/min and CKD. In the same period, 747 (80.4%) patients were assessed by nephrologists, while 178 (19.6%) were not. Age (88 vs. 75, p < 0.0001), female gender (66.3% vs. 47%, p < 0.001), and eGFR (12 vs. 9 mL/min, p < 0.001) were significantly different in the patients assessed by a nephrologist as compared those who did not have nephrological care. Furthermore, unfollowed CKD patients had a significantly higher death rate, 83.1% versus 14.3% (p < 0.0001).

CONCLUSIONS

About 20% of ESKD patients did not receive a nephrologist consultation. Older people and women were more likely not to be referred to nephrology clinics. Unfollowed patients with stage 5 CKD had a significantly higher death rate.

Associations of urinary metal concentrations with anemia: A cross-sectional study of Chinese community-dwelling elderly

BACKGROUND

Anemia seriously affects the health and quality of life of the older adult population and may be influenced by various types of environmental metal exposure. Current studies on metals and anemia are mainly limited to single metals, and the association between polymetals and their mixtures and anemia remains unclear.

METHODS

We determined 11 urinary metal concentrations and hemoglobin levels in 3781 participants. Binary logistic regression and restricted cubic spline (RCS) model were used to estimate the association of individual metals with anemia. We used Bayesian kernel machine regression (BKMR) and Quantile g-computation (Q-g) regression to assess the overall association between metal mixtures and anemia and identify the major contributing elements. Stratified analyses were used to explore the association of different metals with anemia in different populations.

RESULTS

In a single-metal model, nine urinary metals significantly associated with anemia. RCS analysis further showed that the association of arsenic (As) and copper (Cu) with anemia was linear, while cobalt, molybdenum, thallium, and zinc were non-linear. The BKMR model revealed a significant positive association between the concentration of metal mixtures and anemia. Combined Q-g regression analysis suggested that metals such as Cu, As, and tellurium (Te) were positively associated with anemia, with Te as the most significant contributor. Stratified analyses showed that the association of different metals with anemia varied among people of different sexes, obesity levels, lifestyle habits, and blood pressure levels.

CONCLUSIONS

Multiple metals are associated with anemia in the older adult population. A significant positive association was observed between metal mixture concentrations and anemia, with Te being the most important factor. The association between urinary metal concentrations and anemia is more sensitive in the non-hypertensive populations.

Why Treat Patients with a Major Orthopaedic Surgery Only to Send Them Back to the Vulnerable Conditions That Made Them Sick in the First Place? A Conceptual Scenario to Improve Patient's Journey

Individuals with severe cartilage degeneration of the hip or knee or collapsed vertebrae that cause spine deformities can suffer from joint and neuropathic pain in the back, disuse of the affected limb, and restriction of movements. Surgical intervention is the most widespread and successful solution to date. There is a general belief that eating healthy and staying physically and mentally active might have a preventive role against musculoskeletal disease occurrence, while instead, we are more certain of the benefits deriving from a healthy diet and exercise therapy after major orthopaedic procedures. These aspects are in fact vital components in enhanced recovery after surgery programmes. However, they are applied in hospital settings, are often centre-dependent, and lack primary and tertiary preventive efficacy since end once the patient is discharged. There is the lack of initiatives at the territorial level that ensure a continuum in the patient's journey towards orthopaedic surgery, home transition, and a healthy and long-lasting life. The expert panel advocates the integration of an intermediate lifestyle clinic that promotes healthy eating, physical activity, and sleep hygiene. In this facility directed by professionals in enhancing recovery after surgery, patients can be referred after the surgical indication and before home discharge. Surgery is in fact a moment when individuals are more curious to do their best to heal and stay healthy, representing a timepoint and opportunity for educating patients on how lifestyle changes may optimise not only their surgical recovery but also long-term future health state.