Actual Age Research Articles

Âge vasculaire et risque cardiovasculaire chez les patients hypertendus suivis à l'institut de cardiologie d'Abidjan

ObjectiveTo calculate the vascular age of hypertensive patients and assess the risk at 10 years of occurrence of an absolute cardiovascular event in outpatient consultation of the Abidjan Heart Institute. Patients and methodology: Cross-sectional study with descriptive and analytical purposes from June 2021 to September 2021, i.e. 4 months in patients at least 30 years of age followed in the outpatient department for arterial hypertension without cardiovascular complications. Data were collected using a questionnaire. We considered the parameters established in the D'Agostino chart for the calculation of vascular age. Each parameter was weighted and the total points obtained corresponded to the vascular age. The cardiovascular risk at 10 years was also obtained from another abacus established by D'Agostino by cross-referencing the total points of each patient with pre-established data. ResultsThree hundred hypertensive people were included in this study. The calendar average age was 62.0 ± 10 years with extremes of 30 and 95 years. The gender distribution showed female predominance and there was no significant difference in vascular age by sex. The mean vascular age of all patients was 73.4 ± 9.9 years. The mean difference between actual and vascular age was 11.4 years. Dyslipidemia (p = 0.0002), diabetes (p = 0.0004) and unstandardized BP (p = 0.0000) significantly influenced vascular age. There was no significant difference between smokers and non-smokers (p = 0.1349). All men had a greater than 30% risk of having a cardiovascular accident while women before the age of 35 had no risk. Over the age of 60, almost all patients (both men and women) had a greater than 30% risk of having a cardiovascular accident at 10 years. ConclusionThe calculation of vascular age made it possible to assess arterial aging and calculate the probability at 10 years of occurrence of a cardiovascular event. This study also highlights the importance of cardiovascular risk and vascular age assessment for management adaptation and therapeutic education.

Stress and retirement

This paper examines the bidirectional causal relationship between retirement and stress. We use data from the PSID for the period 2007–2015. By employing a simultaneous equations approach, we find that experiencing stress increases the likelihood of retirement by approximately 34.8 percentage points, whereas retirement lowers the probability of feeling stress by 19.6 percentage points. We obtain consistent results when we disaggregate data according to various individual characteristics, including gender, occupation, wealth, and ethnicity. To determine actual retirement age, we use official retirement ages and a proxy of health stock as instruments, while lagged physical activity levels serve as a non-linear instrument for perceived stress. Our findings are particularly relevant in terms of policy, and further research is needed to assess the impact of physical activity on economic and well-being outcomes for older individuals.

Developing Topics.

Amyloid-negative amnestic mild cognitive impairment (MCI) patients have a conversion rate to dementia of approximately 10% within 2 years. This study aimed to investigate whether brain age, calculated using quantitative data obtained from brain magnetic resonance imaging (MRI) artificial intelligence (AI) software can be an important factor in predicting the conversion of dementia in amyloid-negative amnestic MCI patients. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI who visited the memory clinic of Asan Medical Center. All participants underwent detailed neuropsychological testing, brain MRI, and [18F]-florbetaben (FBB) positron emission tomography (PET) scan. Conversion to dementia was determined by a neurologist based on a clinical interview with a detailed neuropsychological test or a decline in the Korean version of the Mini-Mental State Examination score of more than 4 points per year combined with impaired activities of daily living. Brain age was determined through volumetric assessment of 12 distinct brain regions using commercially available segmentation software, Neurophet AQUA. During the follow-up period, 38% (35/91) of patients converted to dementia from amnestic MCI. 73% (66/91) of patients had a higher brain age than their actual chronological age. In the conversion group, all participants except three (91%, 32/35) had a brain age older than their chronological age. Conversely, the conversion rate to dementia was significantly lower in the group with a younger brain age than their actual age. The subjects were divided into eight groups based on sex, education level, and brain age. When defining the 'survival' as the non-conversion of MCI to dementia, these groups significantly differed in survival probability with a p-value of 0.036. The low-educated female group with a higher brain age than their actual age had the lowest survival rate of all groups. Our findings suggest that MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.

Minor age difference substantially affects renal function in conscious and anaesthetized rats*

Abstract In this study we re-examined the postulate that in the rat age-related body metabolism and kidney function changes progress only slowly. Thus we checked whether a moderate between-animal age distance may cause any marked difference, which could affect interpretation of experimental results. Food and water consumption, renal sodium and potassium excretion and haemodynamics were lower in rats aged 15 versus 9 weeks. In turn, hematocrit, mean arterial pressure, and renal vascular resistance were significantly higher in older animals, whereas renal excretion of NO3-/NO2- and vascular endothelial growth factor A and in situ renal tissue NO signal did not differ. Evidently, an age older by 6 weeks was associated with decreased hydration, tone of systemic and renal blood vessels, and renal excretion. In conclusion, since the actual age is an important determinant of the animal’s functional status, accurate age-matching of experimental groups is a necessary pre-condition for correct data interpretation.

A multi‐class deep learning model to estimate brain age while addressing systematic bias of regression to the mean

AbstractBackgroundAge‐related changes in human brain may contribute to the development of age‐related neurodegenerative diseases. It may be possible to estimate “brain age” from magnetic resonance imaging (MRI) and the difference between a person’s brain and chronological age, ∆age, reflecting whether a person’s brain has been aging faster or slower than their chronological age. Deep Learning Models typically regress age on imaging features, leading to systematic biases associated with regression to the mean (RTM), including overestimation of brain age in younger persons and underestimation in older persons. We estimate brain age from a person’s MRI as a multi‐class classification problem and developed deep learning model to estimate brain age free from RTM bias.MethodTwo 3D ResNet‐18 models were implemented: regression and multi‐class classification. We transform the task of predicting age as continuous variable to predicting probabilities of discrete age values where age is discretized to closest integer values. Both models were trained on 7,372 T1‐weighted MRI scans of 5,848 cognitively normal participants (age: 8‐95 years) from public data sources (IXI, ICBM, ABIDE, NACC and OASIS). We create train, validation, and test set in ratio 80:10:10 with matching age distribution. Mean squared error is used as loss function in training regression model. Whereas in the classification model, we introduce two loss terms in addition to standard cross‐entropy loss: (1) minimizing the difference between mean (∑c∗pc) of expected age and the actual age, and (2) minimizing variance (∑(c‐mean)2∗pc), where pc is probability sample belonging to class c.ResultThe regression model achieved MAE = 3.93 years and R2 = 0.90 on unseen test set whereas classification model achieved MAE = 2.41 and R2 = 0.96 on same test set. We observe significant decrease in systematic bias using the classification model ‐ for younger (age<30) and older (age>70) subsets, average ∆age improved from 3.17 to 0.2, and from ‐2.49 to ‐0.97 respectively (Figure1).ConclusionOur proposed classification model with improved loss function to predict brain age from imaging features eliminates systemic bias present in traditional regression approaches and also improves performance by a significant margin. This model can be used more reliably to study age‐related alterations in brain and AD‐related deviations from natural aging.

Novel Framework for Brain Age Prediction using Graph Neural Networks

AbstractBackgroundAging is among the largest risk factors for neurodegeneration which can be accelerated by age‐related pathologies. Hence, individuals may age at different rates reflected by a gap in biological estimates of age and actual chronological age. Graph neural networks (GNN) are deep learning models that leverage spatial information inherent in the dataset for inference. Here, we develop an MRI cortical thickness (CT) based brain age prediction framework using GNNs and illustrate that it provides a feasible mechanism to identify contributing regions leading to elevated brain age gap in Alzheimer’s disease (AD).MethodWe used ANTs to calculate CT in 100 parcels from 3T MRI of 170 healthy controls (HC; age = 64.26±8.27y, 101 females), 53 mild cognitive impairment (MCI; age = 68.56±8.58y, 22 females), and 62 AD (age = 67.25±8.83y, 30 females) individuals. A GNN model based on the anatomical covariance matrix from HC cohort was trained to predict chronological age from associated CT for HC cohort. GNN output was evaluated as an unweighted mean of entities associated with the brain regions in the final layer. Hence, individual contributions to the GNN output by each brain region could be quantified. Final brain age prediction was obtained after removing age‐bias from GNN outputs using linear regression model. For all subjects, we evaluated residuals at regional level (regional age‐gap) by comparing the contribution of each parcel with corresponding GNN output. The residual between predicted brain age and chronological age (age‐gap) was also evaluated. See Figure 1 for an overview.ResultANCOVA (with correction for age and gender) with post‐hoc analyses revealed that regional age‐gap was greater for AD relative to HC in inferior parietal, temporal pole, inferior temporal, medial temporal, and superior frontal regions (Bonferroni corrected p‐value<0.05). Also, regional age‐gap was greater for MCI relative to HC for inferior parietal and temporal pole regions (Figure 2). Subsequently, age‐gap was elevated in AD (mean = 5.64y) and MCI (mean = 2.66y) relative to HC (mean = 0y) reflecting a linear increase in age‐gap with disease stage (Figure 3).ConclusionOur novel framework identified regional CT associations with age‐gap in AD and MCI, suggesting that this GNN approach may be used to further our understanding between aging and neurodegeneration.

Artificial Intelligence Enabled ECG Interpretation Informs Outcomes after Autologous Transplantation for Multiple Myeloma

High dose chemotherapy followed by autologous hematopoietic cell transplantation (auto HCT) is a standard therapy for patients with plasma cell disorders (PCD). A barrier to improving candidate selection for auto HCT is appraisal of risk for transplant related complications. Electrocardiography (ECG) is a standard pre-HCT test to evaluate the potential for arrhythmia and structural heart disease. We hypothesized that an artificial intelligence (AI) based interpretation of ECG waveform data (AI-ECG) could enhance risk prediction for cardiac-specific and other outcomes following auto HCT for patients with PCD. To test this hypothesis, we used several AI-ECG tools developed using a deep-learning based approach that were trained to predict comorbidity (Attia et al, Lancet 2019 & Grogan et al, Mayo Clin Proc 2021). Model outputs include a scaled prediction value indicating increasing risk for atrial fibrillation/flutter (AF), reduced ejection fraction (EF), or cardiac amyloidosis. We evaluated this tool in 9,225 ECGs obtained from 849 patients (median 6 ECGs/patient, inter-quartile range: 3-12) who underwent first or subsequent auto HCT at Memorial Sloan Kettering Cancer Center between 2016-2022 and who had a diagnosis of multiple myeloma (N = 810), amyloidosis (N = 37), or other PCD (N = 2). The endpoints were to determine whether baseline AI-ECG prediction of AF risk correlated with peri-transplant AF events, and to explore whether model outputs correlated with survival and other cardiac clinical outcomes. The mean age of patients was 61.0 years (range 23.7 - 81.2) and median follow up was 2.7 years post auto HCT. Contributing cardiovascular risk factors included a prior history of AF (N = 101, 11.9%), coronary or cerebrovascular disease (N = 95, 11.2%), and diabetes mellitus (N = 98, 11.5%). Reduced dose melphalan (< 200 mg/m 2) was administered in 26%. The HCT-comorbidity index (HCT-CI) was high risk (≥3) in 52.7%. The one-year overall survival and progression-free survival in the whole cohort was 94% (95% confidence interval (95% CI): 92 - 95%) and 86% (84 - 89%), respectively. Among patients not in AF at pre HCT evaluation, the 90-day cumulative incidence of AF was 5.6% (4.2 - 7.3%). In univariable competing risk regression, baseline pre HCT AI-ECG appraisal of AF risk was strongly correlated with incidence AF in patients who were in sinus rhythm before HCT (hazard ratio (HR): 25.1, 95% CI: 11.7 - 53.7, p < 0.001). In a multivariable model adjusting for patient age and HCT-CI, AI-ECG AF predicted risk remained associated with incidence of post HCT AF (HR: 13.4, 95% CI: 5.9 - 30.4, p < 0.001). Considering a decision threshold identified during original model training, AI-ECG predicted AF events with an accuracy of 75% (Mcnemar's p < 2 x 10 -16). The precision, recall, and F1-score were 0.94, 0.77, and 0.85, respectively. AI-ECG predicted age correlated highly with actual patient age (Spearman rho = 0.59, p < 2 x 10 -16). Amyloid risk scores were highly correlated with a known clinical history of amyloidosis (median 0.45 versus 0.05, Wilcoxon rank-sum p < 0.001). Patients with EF < 50% had greater prediction scores for cardiomyopathy compared to patients with normal EF (median 0.23 versus 0.01, Wilcoxon rank-sum p <0.001). In univariable analysis, increasing AI-ECG predicted age (HR 1.02, CI: 1.003 - 1.04, p = 0.02) and predicted AF risk score (HR: 3.2, CI: 1.4 - 7.2, p = 0.006) were associated with risk for death, whereas predicted amyloid score and predicted low EF were not associated with decreased survival. In multivariable analysis of all patients that considered patient age, hematopoietic comorbidity index, and remission status at HCT, increasing predicted AF score trended towards association with increased risk for death (HR: 2.3, 95%CI: 1.0 - 5.5, p = 0.059). Predicted AF score correlated highly with the HCT-CI and patient age. Finally, we examined whether risk prediction for low EF correlated with peri-HCT fluid overload (FO) defined by absolute weight gain. The incidence of ≥ 10% weight gain (grade 2 FO) and ≥ 20% weight gain (grade 3 FO) at 180 days post HCT was 7.4% (95% CI: 5.8%, 9.3%) and 1.0% (95% CI: 0.5%, 1.8%), respectively. Predicted low EF was not associated with development of either grade 2 or grade 3 FO in this population. In summary, AI-ECG predicts episodes of AF in patients undergoing auto HCT for PCD and may augment transplant risk appraisal when considered in conjunction with other measures of comorbidity.

Towards a Traceable Data Model Accommodating Bounded Uncertainty for DST Based Computation of BRCA1/2 Mutation Probability With Age

In this paper, we describe the requirements for traceable open-source data retrieval in the context of computation of BRCA1/2 mutation probabilities (mutations in two tumor-suppressor genes responsible for hereditary BReast or/and ovarian CAncer). We show how such data can be used to develop a Dempster-Shafer model for computing the probability of BRCA1/2 mutations enhanced by taking into account the actual age of a patient or a family member in an appropriate way even if it is not known exactly. The model is compared with PENN II and BOADICEA (based on undisclosed data), two established platforms for this purpose accessible online, as well as with our own previous models. A proof-of-concept implementation shows that set-based techniques are able to provide better information about mutation probabilities, simultaneously highlighting the necessity for ground truth data of high quality.

374 Birth Season and Production System Effects on Post-Weaning Growth and Gastrointestinal Nematode Infection of Katahdin Lambs

Abstract The objective was to estimate birth season (F = fall, W = winter) and production system (C = conventional, O = organic) effects on Katahdin lamb growth and gastrointestinal nematode (GIN) infection. Lambs were weighed and weaned at ~60 d of age and body weight (BW, n = 5,089), fecal egg count (FEC, n = 4,772), and packed cell volume (PCV, n = 4,962) were recorded at ~90, 120, and 150 d of age. Records were analyzed from 599, 142, 1,113, and 126 F-C, F-O, W-C, and W-O lambs, respectively. Lambs were not dewormed and FEC was transformed as: LFEC = log10(FEC + 25). Traits were analyzed as repeated measures with fixed effects of time, sex, birth/rear type, and season-system, the linear covariate of actual age at 90 d, and random effects of sire, birth year, and contemporary group. The time x season-system interaction was significant for all traits (P < 0.001; Figure 1). At 90 d, BW was greater for W-C and W-O than F-C and F-O (P ≤ 0.05). By 150 d, W-O lambs had greater BW (P ≤ 0.04) than all other season-systems which weren’t different from each other. Lamb LFEC was greatest for F-C (P < 0.001), intermediate for F-O, and least for W-C and W-O at 90 d. All season-systems had similar LFEC at 120 d, but W-C lambs had greater LFEC at 150 d than all others (P < 0.01). At 90 d, PCV was less for F-C than all others (P ≤ 0.02) except W-O. Both F-C and F-O had greater PCV (P < 0.001) than W-C and W-O at 120 d and, at 150 d, PCV was less for W-C than all others (P < 0.001). To investigate production impacts of GIN infection in different season-systems, average daily gain from weaning to 90 d (ADG60-90, n = 1,842) was analyzed in a similar model with the additional linear covariate of LFEC at 90 d (LFEC90). The season-system x LFEC90 interaction was significant (P < 0.01), indicating differing impacts of GIN infection. A one-unit increase in LFEC90 (e.g., ~500 vs. 5000 eggs/g) had negative impacts on ADG60-90 for W-O, F-C, and W-C (-27.1, -22.4, and -12.5 g/d, respectively) and solutions were not different from one another. However, LFEC90 did not affect F-O ADG60-90 potentially due to decreased performance (158.4 g/d) than other season-systems (171.5 – 204.3 g/d). Overall, BW was less for F than W lambs and FEC decreased with age for F with the opposite for W lambs. Slight advantages in performance were observed for W-O compared with W-C lambs. Season-system differences could be attributed to varied forage quality and breeding objectives and results utilized in precision sheep management. USDA is an equal opportunity provider and employer.

378 Genetic Parameter Estimates of Post-Weaning Growth and Gastrointestinal Nematode Infection Traits of Katahdin Lambs

Abstract The objective of this study was to estimate heritability of, and genetic correlations between lamb growth and gastrointestinal nematode (GIN) infection. Lambs were weighed and weaned at ~60 days of age and body weight (BW), fecal egg count (FEC), FAMACHA score (FAM) and packed cell volume (PCV) were recorded at ~90, 120, and 150 d of age. Lambs were not dewormed and FEC was transformed as: LFEC = log10(FEC+25). Average daily gain (ADG) was calculated from weaning to 90 days of age (ADG90) as well as 90 to 120 (ADG120) and 120 to 150 days (ADG150). Each trait (ADG, LFEC, PCV and FAM) was analyzed separately within each timepoint. The number of records per time point ranged from 1,980 (ADG90) to 1,283 (LFEC at 150 days of age). Traits were analyzed using a univariate model fitting fixed class effects of sex, birth/rear type, and season/system (fall or winter/conventional or organic), and fixed covariate of actual age at 90 days. Random effects included contemporary group, birth year, and an additive genetic effect whereby genetic relationships were captured through pedigree data. The heritability of FEC was 0.09 ± 0.03, 0.12 ± 0.03, and 0.07 ± 0.04, at 90, 120 and 150 days, respectively. PCV estimates were 0.22 ± 0.04, 0.32 ± 0.05, and 0.29 ± 0.06, at 90, 120 and 150 days, respectively. The heritability estimates of ADG were 0.09 ± 0.03, 0.12 ± 0.04, and 0.20 ± 0.04, at 90, 120 and 150 days, respectively and FAM were 0.15 ± 0.04, 0.10 ± 0.04, and 0.19 ± 0.05. Genetic and phenotypic correlations between GIN infection traits and ADG at the same time point were estimated using a bivariate model. The genetic correlation of LFEC with ADG was low for all timepoints analyzed and ranged from -0.07 ± 0.28 (150 days) to 0.07 ± 0.19 (90 days). Phenotypic correlations were similarly low and ranged from -0.02 ± 0.07 (120 days) to -0.16 ± 0.07 (150 days). LFEC showed a consistent negative genetic and phenotypic correlation with PCV at all timepoints, ranging from -0.17 ± 0.17 (90 days) to -0.57 ± 0.18 (150 days) and -0.32 ± 0.05 (90 days) to -0.20 ± 0.06 (150 days), respectively. Conversely, FAM was positively correlated with LFEC, with genetic correlation estimates ranging from 0.21 ± 0.22 (120 days) to 0.54 ± 0.20 (150 days) and phenotypic correlations ranging from 0.08 ± 0.06 (150 days) to 0.22 ± 0.05 (90 days). The genetic and phenotypic correlations of PCV with ADG increased over time, going from -0.17 ± 0.15 to 0.19 ± 0.18 and 0.08 ± 0.06 to 0.10 ± 0.06, respectively. The correlations of FAM with ADG were more variable, but generally low, ranging from -0.14 ± 0.06 (150-day phenotypic correlation) to 0.09 ± 0.18 (90-day genetic correlation). We showed that selection on PCV may be more effective than selection on LFEC to reduce the burden of GIN infection, although further investigation into its correlation with growth is warranted. This dataset will be used to investigate resistance, resilience, and susceptibility traits in Katahdin lambs.

Empowered mothers and co-resident grandmothers: Two fundamental roles of women impacting child health outcomes in Punjab, Pakistan.

We show that i) empowered mothers and ii) coresident grandmothers each benefit children's nutritional health measured by height-for-age z-scores (HAZ) and weight-for-age z-scores (WAZ) for age groups 5 years and less. First, using cross-sectional data from the Pakistan Demographic and Health Survey (PDHS) for the year 2017-18, we estimate the impact of empowered mothers on child health outcomes using an instrumental variable approach to correct for endogeneity. Empowerment is measured by two indices: as a sum of the questions that gauge both attitudinal and behavioral dimensions of female agency and also and using multiple correspondence analysis (MCA) for these same questions. Second, we use a fuzzy regression discontinuity design (FRDD) to measure the causal impact of coresident grandmothers on the health outcomes of the children using multiple rounds of the Multiple Indicator Cluster Survey (MICS) from the years 2008, 2011, 2014 and 2018. The difference between the actual ages of the grandmother from the Potential Retirement Eligibility Criteria (PREC) has been used to correct for potential endogeneity. The results show that on average, the weight for age z-scores (WFA) for children under five increases by 0.28 SD with a one-index point increase in mother's empowerment. Similarly, on average, WFA increases by 0.098 SD when grandmothers are present in a household. Finally, we explore heterogeneity in the average effects stated above based upon the gender of the child as well as the wealth and geographic location of the household. The benefits of mothers' empowerment are largely driven by improvements in girls' nutrition as well as children living in rural areas while the presence of grandmothers primarily improves the nutrition of boys, children in rural areas, and children belonging to poor families.

Relationship between an ageing measure and chronic obstructive pulmonary disease, lung function: a cross-sectional study of NHANES, 2007–2010

ObjectivesChronic obstructive pulmonary disease (COPD) is a disease associated with ageing. However, actual age does not accurately reflect the degree of biological ageing. Phenotypic age (PhenoAge) is a new indicator...

Ethnic differences in early onset multimorbidity and associations with health service use, long-term prescribing, years of life lost, and mortality: A cross-sectional study using clustering in the UK Clinical Practice Research Datalink.

The population prevalence of multimorbidity (the existence of at least 2 or more long-term conditions [LTCs] in an individual) is increasing among young adults, particularly in minority ethnic groups and individuals living in socioeconomically deprived areas. In this study, we applied a data-driven approach to identify clusters of individuals who had an early onset multimorbidity in an ethnically and socioeconomically diverse population. We identified associations between clusters and a range of health outcomes. Using linked primary and secondary care data from the Clinical Practice Research Datalink GOLD (CPRD GOLD), we conducted a cross-sectional study of 837,869 individuals with early onset multimorbidity (aged between 16 and 39 years old when the second LTC was recorded) registered with an English general practice between 2010 and 2020. The study population included 777,906 people of White ethnicity (93%), 33,915 people of South Asian ethnicity (4%), and 26,048 people of Black African/Caribbean ethnicity (3%). A total of 204 LTCs were considered. Latent class analysis stratified by ethnicity identified 4 clusters of multimorbidity in White groups and 3 clusters in South Asian and Black groups. We found that early onset multimorbidity was more common among South Asian (59%, 33,915) and Black (56% 26,048) groups compared to the White population (42%, 777,906). Latent class analysis revealed physical and mental health conditions that were common across all ethnic groups (i.e., hypertension, depression, and painful conditions). However, each ethnic group also presented exclusive LTCs and different sociodemographic profiles: In White groups, the cluster with the highest rates/odds of the outcomes was predominantly male (54%, 44,150) and more socioeconomically deprived than the cluster with the lowest rates/odds of the outcomes. On the other hand, South Asian and Black groups were more socioeconomically deprived than White groups, with a consistent deprivation gradient across all multimorbidity clusters. At the end of the study, 4% (34,922) of the White early onset multimorbidity population had died compared to 2% of the South Asian and Black early onset multimorbidity populations (535 and 570, respectively); however, the latter groups died younger and lost more years of life. The 3 ethnic groups each displayed a cluster of individuals with increased rates of primary care consultations, hospitalisations, long-term prescribing, and odds of mortality. Study limitations include the exclusion of individuals with missing ethnicity information, the age of diagnosis not reflecting the actual age of onset, and the exclusion of people from Mixed, Chinese, and other ethnic groups due to insufficient power to investigate associations between multimorbidity and health-related outcomes in these groups. These findings emphasise the need to identify, prevent, and manage multimorbidity early in the life course. Our work provides additional insights into the excess burden of early onset multimorbidity in those from socioeconomically deprived and diverse groups who are disproportionately and more severely affected by multimorbidity and highlights the need to ensure healthcare improvements are equitable.

Relationship between the subjective age, higher-life function, and new certification as needing long-term care in community-dwelling older adults: The KAGUYA project longitudinal survey of older adults

To determine the relationship between the subjective age, higher-life function, and new certification for the need for long-term care among older adults in the community. A mail survey was conducted in 2016 among community-dwelling older adults, and the 2,323 participants who were available for follow-up in 2019 were included in the analysis. Subjective age was evaluated using the following three items in response to the question "Please answer how old you feel you are": "Same as actual age," "Feel younger," and "Feel older." Other assessments included an evaluation of the higher-life function, Geriatric Depression Scale scores, general self-efficacy, and exercise adherence. In addition, at follow-up, we investigated whether or not participants required new certification for the need for long-term care. At baseline, participants who felt "older than their actual age" had a significantly lower life function and general self-efficacy and were less likely to exercise at least once a week than the other groups. Furthermore, those who felt "older than their actual age" was more likely than the other groups to be newly certified as needing long-term care, while those who felt "younger than their actual age" were less likely to receive new certification. A logistic regression analysis revealed that "feeling older" was a risk factor for being certified as needing long-term care, even after adjusting for other factors (odds ratio= 3.33, 95% confidence interval: 1.02-10.94, p=0.047). Among community-dwelling older adults, those with a subjective age exceeding their chronological age were expected to show a decreased life function in the future and an increased risk of needing long-term care.

Сравнительный анализ методов оценки уровня полового созревания в спортивной практике

Puberty is an inevitable stage of child-athlete development, this process is accompanied by morphological, physiological and psychological changes in the body of children. The article presents the research discourse of domestic and foreign research sources on the ways of determining biological age based on the dynamic pro-cesses of puberty. Scientists confirm the necessity of taking this factor into account in planning and adjusting the training process, but in practice it almost does not happen due to the lack of simple and accessible ways to assess the level of biological maturation of young athletes, which significantly reduces the effectiveness of multi-year training process. The principles of the organization of the latter are still focused on the actual age of athletes. Relevant information on methodological approaches to determining the real physical state of the child's organism will allow coaches to more adequately influence the adaptation of the athlete's body to training loads.

Predicting pulse wave age from cardiovascular characteristics using machine learning algorithms

Arterial pulse waves are an essential and informative source of data for measuring cardiovascular health and are currently available on various devices, such as smartwatches. In addition, most experiments have focused on pulse wave velocity (PWV) and arterial pulse waveform (APW). However, in recent years, due to many factors, such as high work pressure among young people, cardiovascular diseases have become more prevalent. As a result, we need more research on pulse age to help patients focus on the consistency of their biological age with their legal age. Therefore, this paper introduces a machine learning framework to predict the age of patients according to the HR and SV data in the pulse wave of existing patients. At the beginning of the experiment, we attempted to use K-nearest neighbours (KNN), logistic regression (LR), and random forest (RF) models. Then we compared the accuracy of patient age estimates; RF had the best performance and was used as the final model. In addition, HR and SV were selected as the main features to predict patients' age according to the feature's importance. The final experimental results indicate that the RF model can predict the results with up to 1 accuracy only with two features, i.e., HR and SV. In particular, this is the point that we need to continue to pay attention to in the future to determine whether the prediction accuracy of this model can reach 1. Is it because the model is overly optimistic or because the selected HR and SV features strongly correlate with age? In the future, we can introduce the concept of actual body age and attract young people's attention to their body age. For example, more people will be urged to take a quick and efficient pulse diagnosis, complete a simple physical examination, and give rapid feedback by placing devices in places where young people gather, such as shopping malls. In this case, users can immediately know what part of their body may have problems. In general, our experiment demonstrates that patients' pulse wave ages could be well predicted based on their HR and SV data with high accuracy through the ML model of RF.

THU175 Incorrect Age Causing Falsely Short Stature In An Afghan Immigrant Girl

Abstract Disclosure: J. Ing: None. M. Marinkovic: None. Background: Evaluation for short stature routinely includes screening for hormone deficiencies, chromosomal abnormalities, gastroenterological diseases, and inflammatory processes. “Incorrect age” resulting in falsely short stature, is not often considered in the differential diagnosis. Clinical Case: A young girl who recently immigrated to the United States from Afghanistan was referred for endocrine evaluation for short stature. At the time of her initial visit, she was listed in the electronic medical record at 10 years and 0 months old. Her medical history was significant for bowel injuries sustained three years prior in a bombing incident that required surgery and from which she completely recovered. Available growth charts from the preceding 6 months showed height and weight consistently less than the 1st percentile for her age but normal height velocity. On the exam she was prepubertal. Her measured height was 117.8 cm (0.06th percentile, Z = -3.24) and her body mass index (BMI) was 14.7 kg/m2 (12th percentile, Z = -1.18). The patient’s calculated mid-parental height was 155.5 cm (12.5th percentile, Z = -1.15). Laboratory work-up obtained by the pediatrician showed normal IGF-1, IGFBP-3, TSH, free and total thyroxine, tissue transglutaminase IgA, sedimentation rate, urinalysis, complete blood panel, complete metabolic panel, and a normal 20 cell count karyotype (46, XX). Her bone age was 5 years and 6 months and was interpreted as markedly delayed. Upon further history gathering using a Pashto interpreter, her father revealed there was an error in the processing of her immigration documents, resulting in being listed as three years older than her actual age. When corrected to that of a 7 year and 0 month old girl, her height improved to the 23rd percentile (Z = -0.73), and her BMI to the 31st percentile (Z = -0.5). Conclusion: This case demonstrates how children, through the immigration process, may have their age incorrectly documented and thereby are at risk for unnecessary and costly medical evaluation. It is possible that language barriers play a role in such errors. Therefore, it is important to verify the patient's true age, as parents may not think to provide this information voluntarily. Presentation: Thursday, June 15, 2023

Spieglein, Spieglein an der Wand, wer ist der älteste Mann im Land? Vielleicht der übermüdete!

AbstractThis study aimed to investigate the impact of acute sleep deprivation on the perceived age appearance of individuals, with a particular focus on subjective sleepiness. The study involved 380 anonymous raters who participated in an online survey. The raters were shown photographs of a Caucasian man, aged 48.2 years, and were asked to estimate his age. The results demonstrated that after 42.6 h of continuous wakefulness, his estimated age was 2.9 years older than his actual age (p < 0.001). Additionally, the study revealed that the sleepier a rater felt during the survey, the older they perceived the man to be (p < 0.05). These findings suggest that sleep loss may impact upon how old someone is perceived to be, and that sleepiness may affect how raters estimate the age of others. However, it is important to replicate these findings in diverse populations, including women, individuals from different ethnic backgrounds, and individuals of various age groups who experience sleep loss.

Application of array-based age prediction models to post-mortem tissue samples

Since DNA methylation at specific CpG sites exhibits a strong age association, researchers have developed numerous age prediction models based on the methylation BeadChip array. These models harness epigenetic clocks that hold the potential to narrow down the search range for unknown suspects and unidentified victims. This study collected 180 post-mortem tissue samples comprising nine tissue types (blood, brain, heart, lung, liver, kidney, muscle, epidermis, and dermis) from autopsies of 20 Koreans aged 18–78. Subsequently, DNA methylation profiling was conducted using the Infinium MethylationEPIC array. We tested several array-based age prediction models using the data obtained from various tissues. The pan-tissue clock exhibited a moderately accurate prediction across all nine tissue types (MAE = 8.7 years, r = 0.88). Notably, the DNAm ages of the Hannum clock, the skin & blood clock, and the Zhang clock strongly correlated with the actual age in blood samples (MAE < approximately 5 years, r > 0.9). PhenoAge yielded an MAE of 10.1 years and an r-value of 0.92. The muscle-specific epigenetic clock, the MEAT package, demonstrated high prediction accuracy in muscle samples (MAE = 4.7 years, r = 0.93). Those previously reported array-based age prediction models were mainly constructed in Europeans but performed well in Koreans. In addition, tests involving various quantities of DNA and fragmented DNA have shown that DNA quantity and quality affected methylation measurements and age prediction results. However, robust age prediction models exist under low amounts of DNA and fragmented DNA conditions.

Оценка достовернности данных о возрасте усвоения слов, получаемых путем опроса взрослых респондентов

Given the difficulty to establish an objective measure of age of acquisition (AoA), this study focuses on the subjective measures of this parameter, i.e., adult ratings, or judgment of word learning age. The question under study is the validity of subjective adult estimates of AoA as a standard norming tool for this characteristic in Russia. AoA estimates were gathered for 7,500 words by presentation to eight hundred participants. The latter were given 25 lists of words with the instruction to specify supposed age of their acquisition. A total of 179,100 estimates collected from 597 participants were included in the analysis (from 20 to 30 protocols on each list). The middle age of the participants was 20.50±3.84 years, with 96% of them being at the age of 17–25 years old. Objective estimates were received through the examination of school children from the 2nd, 4th, 6th and 8th grade by means of the dictionary-type tests. The words for each test were selected so that their subjective AoAs were nearest to the actual age of students. Total number of generated tasks amounted to 720. The subset of test items for each grade was divided into two subtests equated on complexity. The subtest length varied from 80 (for the 2nd grade) to 150 (the 6th and 8th grades) tasks. Reliability of subtest scores varied in a range of 0.85–0.95. The comparison of subjective and objective estimates was carried out in several ways, first of all, by calculating the correlation coefficients between subjective AoA of test words and accuracy of their identification. After appropriate correction on direct range restriction of subjective AoA variation, corrected correlations in a range of 0.6–0.9 (0.79 on average) were received. This figure (along with the additional data) allows to draw a conclusion on a reasonable validity of subjective estimates of AoA as a norming tool for this characteristic. The article outlines the prospects for further research of factors influencing accuracy of adult estimates of word learning age in Russia.