Depression in old age deserves special attention in view of the fact of progressive population ageing, because of the way in which depression and risk factors interact in this period of life and the particularly negative impact of late-life depression on health and quality of life. This editorial aims to provide some insight into longitudinal aspects of depression in old age. Depression may follow varying trajectories (e.g. episode emergence, recurrence) across the lifespan. Late-life depression is not an exception. A symptom-based approach is presented as an appropriate research method to study the predictors and course of affective syndromes in old age. Findings from our studies on depressive symptom trajectories in old age revealed that participants with a course of unremitting elevated symptoms showed the highest levels of loneliness across the trajectory groups and that participants with subclinical symptoms also showed higher levels of loneliness than their counterparts with a minimal-symptom course trajectory. This highlights the need to address loneliness as a way of dealing with depression in old age. ; This work was supported by the 5-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635316; Instituto de Salud Carlos III-FIS under grant number PI16/00218; and Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM).
Background: Studies on the effects of sociodemographic factors on health in aging now include the use of statistical models and machine learning. The aim of this study was to evaluate the determinants of health in aging using machine learning methods and to compare the accuracy with traditional methods. Material/Methods: The health status of 6,209 adults, age 80 years (n=1,357) were measured using an established health metric (0–100) that incorporated physical function and activities of daily living (ADL). Data from the English Longitudinal Study of Ageing (ELSA) included socio-economic and sociodemographic characteristics and history of falls. Health-trend and personal-fitted variables were generated as predictors of health metrics using three machine learning methods, random forest (RF), deep learning (DL) and the linear model (LM), with calculation of the percentage increase in mean square error (%IncMSE) as a measure of the importance of a given predictive variable, when the variable was removed from the model. Results: Health-trend, physical activity, and personal-fitted variables were the main predictors of health, with the%incMSE of 85.76%, 63.40%, and 46.71%, respectively. Age, employment status, alcohol consumption, and household income had the%incMSE of 20.40%, 20.10%, 16.94%, and 13.61%, respectively. Performance of the RF method was similar to the traditional LM (p=0.7), but RF significantly outperformed DL (p=0.006). Conclusions: Machine learning methods can be used to evaluate multidimensional longitudinal health data and may provide accurate results with fewer requirements when compared with traditional statistical modeling. ; The ATHLOS project has received funding from the European Union Horizon 2020 Research and Innovation Program under grant agreement No. 635316 (EU HORIZON2020-PHC-635316)
The consumption of dietary fats, which occur naturally in various foods, poses important impacts on health. The aim of this study was to elucidate the association of exclusive use of olive oil for culinary purposes with successful aging in adults aged >50 years old and residing in Greece. Use of olive oil in food preparation and bio-clinical characteristics of the Greek participants enrolled in the ATTICA (n = 1128 adults from Athens metropolitan area) and the MEDiterranean Islands Study (MEDIS) (n = 2221 adults from various Greek islands and Mani) studies, were investigated in relation to successful aging (SA). Participants were divided into the following three categories: (a) no olive oil consumption; (b) combined consumption of olive oil and other dietary fats; and (c) exclusive olive oil consumption. The SA was measured using the previously validated successful aging index (SAI). After adjusting for age, sex, and smoking habits, combined consumption of olive oil and other fats (vs. no olive oil use) was not significantly associated with SAI levels (p = 0.114). However, exclusive olive oil intake (vs. no use of olive oil) was significantly associated with SAI (p = 0.001), particularly among those aged older than 70 years. Therefore, the exclusive consumption of olive oil, as opposed to either combined or no olive oil consumption, beneficially impacts successful aging, particularly among individuals over 70 years of age. Primary public health prevention strategies should seek to encourage the enhanced adoption of such dietary practices in order to promote healthy aging and longevity ; The ATTICA study is supported by research grants from the Hellenic Cardiology Society (HCS2002) and the Hellenic Atherosclerosis Society (HAS2003). The MEDIS study was funded by research grants from the Hellenic Heart Foundation, the Graduate Program of the Department of Nutrition and Dietetics, Harokopio University, and Rutgers University, NJ, USA (GA #5884). Stefanos Tyrovolas was supported by the Foundation for Education and European Culture (IPEP), the Sara Borrell postdoctoral program (reference no. CD15/00019 from the Instituto de Salud Carlos III (ISCIII-Spain)), and the Fondos Europeo de Desarrollo Regional (FEDER). Jose Maria Haro, Jose Luis Ayuso, Demosthenes Panagiotakos, Stefano Tyrovolas, Elena Critselis, and Alexandra Foscolou were funded for the ATHLOS project to study trajectories of healthy aging (European Union's Horizon 2020 research and innovation program, grant agreement No 635316)
Background: Questionnaires are the current hallmark for quantifying social functioning in human clinical research. In this study, we compared self- and proxy-rated (caregiver and researcher) assessments of social functioning in Schizophrenia (SZ) and Alzheimer's disease (AD) patients and evaluated if the discrepancy between the two assessments is mediated by disease-related factors such as symptom severity. Methods: We selected five items from the WHO Disability Assessment Schedule 2.0 (WHODAS) to assess social functioning in 53 AD and 61 SZ patients. Caregiver- and researcher-rated assessments of social functioning were used to calculate the discrepancies between self-rated and proxy-rated assessments. Furthermore, we used the number of communication events via smartphones to compare the questionnaire outcomes with an objective measure of social behaviour. Results: WHODAS results revealed that both AD (p < 0.001) and SZ (p < 0.004) patients significantly overestimate their social functioning relative to the assessment of their caregivers and/or researchers. This overestimation is mediated by the severity of cognitive impairments (MMSE; p = 0.019) in AD, and negative symptoms (PANSS; p = 0.028) in SZ. Subsequently, we showed that the proxy scores correlated more strongly with the smartphone communication events of the patient when compared to the patient-rated questionnaire scores (self; p = 0.076, caregiver; p < 0.001, researcher-rated; p = 0.046). Conclusion: Here we show that the observed overestimation of WHODAS social functioning scores in AD and SZ patients is partly driven by disease-related biases such as cognitive impairments and negative symptoms, respectively. Therefore, we postulate the development and implementation of objective measures of social functioning that may be less susceptible to such biases. ; The PRISM project (www.prism-project.eu) leading to this application has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115916. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA. This publication reflects only the authors' views neither IMI JU nor EFPIA nor the European Commission are liable for any use that may be made of the information contained therein. Dr. Arango has also received funding support by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024), co-financed by ERDF Funds from the European Commission, "A way of making Europe", CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. Fundación Familia Alonso and Fundación Alicia Koplowitz
Background: The rapid growth of the size of the older population is having a substantial effect on health and social care services in many societies across the world. Maintaining health and functioning in older age is a key public health issue but few studies have examined factors associated with inequalities in trajectories of health and functioning across countries. The aim of this study was to investigate trajectories of healthy ageing in older men and women (aged ≥45 years) and the effect of education and wealth on these trajectories. Methods: This population-based study is based on eight longitudinal cohorts from Australia, the USA, Japan, South Korea, Mexico, and Europe harmonised by the EU Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) consortium. We selected these studies from the repository of 17 ageing studies in the ATHLOS consortium because they reported at least three waves of collected data. We used multilevel modelling to investigate the effect of education and wealth on trajectories of healthy ageing scores, which incorporated 41 items of physical and cognitive functioning with a range between 0 (poor) and 100 (good), after adjustment for age, sex, and cohort study. Findings: We used data from 141 214 participants, with a mean age of 62·9 years (SD 10·1) and an age range of 45–106 years, of whom 76 484 (54·2%) were women. The earliest year of baseline data was 1992 and the most recent last follow-up year was 2015. Education and wealth affected baseline scores of healthy ageing but had little effect on the rate of decrease in healthy ageing score thereafter. Compared with those with primary education or less, participants with tertiary education had higher baseline scores (adjusted difference in score of 10·54 points, 95% CI 10·31–10·77). The adjusted difference in healthy ageing score between lowest and highest quintiles of wealth was 8·98 points (95% CI 8·74–9·22). Among the eight cohorts, the strongest inequality gradient for both education and wealth was found in the Health Retirement Study from the USA. Interpretation: The apparent difference in baseline healthy ageing scores between those with high versus low education levels and wealth suggests that cumulative disadvantage due to low education and wealth might have largely deteriorated health conditions in early life stages, leading to persistent differences throughout older age, but no further increase in ageing disparity after age 70 years. Future research should adopt a lifecourse approach to investigate mechanisms of health inequalities across education and wealth in different societies. Funding: European Union Horizon 2020 Research and Innovation Programme. ; The ATHLOS project was funded by the European Union Horizon 2020 Research and Innovation Programme (grant number 635316). This study was supported by the 5-year ATHLOS project
We investigated the relation between alcohol drinking and healthy ageing by meansof a validated health status metric, using individual data from the Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. For the purposes of this study, the ATHLOS harmonised dataset, which includes information from individuals aged 65+ in 38 countries, was analysed (n = 135,440). Alcohol drinking was reflected by means of three harmonised variables: alcohol drinking frequency, current and past alcohol drinker. A set of 41 self-reported health items and measured tests were used to generate a specific health metric. In the harmonised dataset, the prevalence of current drinking was 47.5% while of past drinking was 26.5%. In the pooled sample, current alcohol drinking was positively associated with better health status among older adults ((b-coef (95% CI): 1.32(0.45 to 2.19)) and past alcohol drinking was inversely related (b-coef (95% CI): −0.83 (−1.51 to −0.16)) with health status. Often alcohol consumption appeared to be beneficial only for females in all super-regions except Africa, both age group categories (65–80 years old and 80+), both age group categories, as well as among all the financial status categories (all p < 0.05). Regional analysis pictured diverse patterns in the association for current and past alcohol drinkers. Our results report the need for specific alcohol intake recommendations among older adults that will help them maintain a better health status throughout the ageing process. ; This work was supported by the five-year Ageing Trajectories of Health: Longitudinal Opportunities and Synergies (ATHLOS) project. The ATHLOS project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635316. The ATHLOS project researchers are grateful for data contribution and funding in the following studies: (A) The 10/66 study (10/66): The 10/66 study is supported by theWellcome Trust (GR066133/ GR080002), the European Research Council (340755), US Alzheimer's Association, WHO, FONDACIT (Venezuela) and the Puerto Rico State Government, and the Medical Research Council (MR/K021907/1 to A.M.P.). The authors gratefully acknowledge the work of the 10/66 Dementia Research Group who provided data for this paper; (B) The Australian Longitudinal Study of Ageing (ALSA): The ALSA study was supported by grants from the South Australian Health Commission, the Australian Rotary Health Research Fund, the US National Institute on Aging (Grant No. AG 08523–02), theO ce for the Ageing (SA), Elderly Citizens Homes (SA), the National Health and Medical Research Council (NH&MRC 22922), the Premiers Science Research Fund (SA) and the Australian Research Council (DP0879152; DP130100428). The authors gratefully acknowledge the work of the project team at the Flinders Centre for Ageing Studies, Flinders University who provided data for this paper; (C) The ATTICA study: The ATTICA study is supported by research grants from the Hellenic Cardiology Society (HCS2002) and the Hellenic Atherosclerosis Society (HAS2003). The authors gratefully acknowledge the work of the project team at the Harokopio University who provided data for this paper; (D) The China Health and Retirement Longitudinal Study (CHARLS): The CHARLS study has received critical support from Peking University, the National Natural Science Foundation of China, the Behavioral and Social Research Division of the National Institute on Aging and the World Bank. The authors gratefully acknowledge the work of the project team at the Peking University who provided data for this paper; (E) Collaborative Research on Ageing (COURAGE) in Europe: The COURAGE study was supported by the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement number 223071 (COURAGE in Europe). Data from Spain were also collected with support from the Instituto de Salud Carlos III-FIS research grants number PS09/00295, PS09/01845, PI12/01490, PI13/00059, PI16/00218 and PI16/01073; the Spanish Ministry of Science and Innovation ACI-Promociona (ACI2009–1010); the European Regional Development Fund (ERDF) 'AWay to Build Europe' grant numbers PI12/01490 and PI13/00059; and by the Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III. Data from Poland were collected with support from the Polish Ministry for Science and Higher Education grant for an international co-financed project (number 1277/7PR/UE/2009/7, 2009–2012) and Jagiellonian University Medical College grant for project COURAGE-POLFUS (K/ZDS/005241). The authors gratefully acknowledge the work of COURAGE researchers who provided data for this paper; (F) The Seniors-ENRICA: The Seniors-ENRICA cohort was funded by an unconditional grant from Sanofi-Aventis, the Ministry of Health of Spain, FIS grant 12/1166 (State Secretary for R+D and FEDER-FSE) and the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III. The authors gratefully acknowledge the work of the project team at the Universidad Autónoma de Madrid who provided data for this paper; (G) The English Longitudinal Study of Ageing (ELSA): ELSA is supported by the U.S. National Institute of Aging, the National Centre for Social Research, the University College London (UCL) and the Institute for Fiscal Studies. The authors gratefully acknowledge the UK Data Service and UCL who provided data for this paper; (H) The Health, Alcohol and Psychosocial factors In Eastern Europe (HAPIEE) study: The HAPIEE study was supported by theWellcome Trust (grant numbers WT064947, WT081081), the US National Institute of Aging (grant number 1RO1AG23522) and the MacArthur Foundation Initiative on Social Upheaval and Health. The authors gratefully acknowledge the work of the project teams at University College London, the National Institute of Public Health in Prague, the Jagiellonian University Medical College in Krakow and the Kaunas University of Medicine who provided data for this paper; (I) The Health 2000/2011 study: The authors gratefully acknowledge the National Institute for Health and Welfare in Finland who provided data for this paper; (J) Health and Retirement Study (HRS): The HRS study is supported by the National Institute on Aging (grant number NIA U01AG009740) and the Social Security Administration, and is conducted by the University of Michigan. The authors gratefully acknowledge the University of Michigan who provided data for this paper; (K) The Japanese Study of Aging and Retirement (JSTAR): The JSTAR is conducted by the Research Institute of Economy, Trade and Industry (RIETI), the Hitotsubashi University, and the University of Tokyo. The authors gratefully acknowledge the RIETI who provided data for this paper; (L) The Korean Longitudinal Study of Ageing (KLOSA): The KLOSA study is funded by the Korea Employment Information Service (KEIS) and was supported by the Korea Labor Institute's KLOSA Team. The authors gratefully acknowledge the KEIS who provided data for this paper; (M) The Mexican Health and Aging Study (MHAS): The MHAS study is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. The authors gratefully acknowledge the MHAS team who provided data for this paper retrieved from www.MHASweb.org: (N) The Study on Global Ageing and Adult Health (SAGE): The SAGE study is funded by the U.S. National Institute on Aging and has received financial support through Interagency Agreements (OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005–01) and Grants (R01-AG034479; IR21-AG034263-0182). The authors gratefully acknowledge theWorld Health Organizationwho provided data for this paper; (O) The Survey of Health, Ageing and Retirement in Europe (SHARE): The SHARE study is funded by the European Commission through FP5 (QLK6-CT-2001–00360), FP6 (SHARE-I3: RII-CT-2006–062193, COMPARE: CIT5-CT-2005–028857, SHARELIFE: CIT4-CT-2006–028812) and FP7 (SHARE-PREP: N 211909, SHARE-LEAP: N 227822, SHARE M4: N 261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553–01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org); (P) The Irish Longitudinal study on Ageing (TILDA): The authors gratefully acknowledge the Trinity College Dublin and the Irish Social Science Data Archive (www.ucd.ie/issda) who provided data for this paper; (Q) The Uppsala Birth Cohort Multigenerational Study (UBCOS): The UBCoS study has received funding from the Swedish Research Council for Health, Working Life and Welfare (FORTE; 2006–1518 and 2013–1084) and from the Swedish Research Council (VR; 2013–5104 and 2013–5474). The authors gratefully acknowledge the Centre for Health Equity Studies at the Stockholm University and Karolinska Institutet's team who provided data for this paper. Additionally, Stefanos Tyrovolas was supported by the Foundation for Education and European Culture, the Miguel Servet programme (reference CP18/00006), and the Fondos Europeos de Desarrollo Regional. Also, Alberto Raggi is supported by a grant from the Italian Ministry of Health (Ricerca Corrente, Fondazione Istituto Neurologico C. Besta, Linea 4—Outcome Research: dagli Indicatori alle Raccomandazioni Cliniche. The funders of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The authors had access to the data in the study and had final responsibility for the decision to submit for publication.
The relationship between psychotic symptoms and global measures of functioning has been widely studied. No previous study has assessed so far the interplay between specific clinical symptoms and particular areas of functioning in first-episode psychosis (FEP) using network analysis methods. A total of 191 patients with FEP (age 24.45 ± 6.28 years, 64.9% male) participating in an observational and longitudinal study (AGES-CM) comprised the study sample. Functioning problems were assessed with the WHO Disability Assessment Schedule (WHODAS), whereas the Positive and Negative Syndrome Scale (PANSS) was used to assess symptom severity. Network analysis were conducted with the aim of analysing the patterns of relationships between the different dimensions of functioning and PANSS symptoms and factors at baseline. According to our results, the most important nodes were "conceptual disorganization", "emotional withdrawal", "lack of spontaneity and flow of conversation", "delusions", "unusual thought content", "dealing with strangers" and "poor rapport". Our findings suggest that these symptoms and functioning dimensions should be prioritized in the clinical assessment and management of patients with FEP. These areas may also become targets of future early intervention strategies, so as to improve quality of life in this population ; This work was supported by the Madrid Regional Government (R&D activities in Biomedicine (grant number S2017/BMD-3740 - AGES-CM 2-CM)) and Structural Funds of the European Union. Ana Izquierdo's work is supported by the PFIS predoctoral program (FI17/00138) from the Instituto de Salud Carlos III (Spain) and co-funded by the European Union (ERDF/ESF, "A way to make Europe"/ "Investing in your future") and The Biomedical Research Foundation of La Princesa University Hospital. Angela Ib´a˜nez thanks the support of CIBERSAM and of the Spanish Ministry of Science, Innovation and Universities. Instituto de Salud Carlos III (PI16/00834 and PI19/01295) co-financed by ERDF Funds from the European Commission. Covadonga M. Díaz-Caneja holds a Juan Rod´es Grant from Instituto de Salud Carlos III (JR19/00024). Celso Arango was supported by the Spanish Ministry of Science and Innovation. Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, "A way of making Europe", CIBERSAM. Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds. European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241909 (Project EU-GEI), FP7- HEALTH- 2013-2.2.1-2-603196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative 2 Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundaci´on Familia Alonso, Fundaci´on Alicia Koplowitz and Fundaci´on Mutua Madrile˜na
