Being an ϵ4 carrier in the Apoϵ gene has been suggested as a modifying factor for the interaction between cardio-metabolic, social risk factors, and the development of cognitive impairment.The main objective of this study was to assess the existence of such interaction in a sample of Bogota’s elderly population.A cross-sectional study was conducted with 1,263 subjects older than 50 years. Each participant was diagnosed by consensus, after neuropsychological and neuropsychiatric evaluations, under a diagnosis of normal cognition, mild cognitive impairment (MCI) according to Petersen’s criteria, or dementia according to DSM-IV criteria. Apoϵ was typified and an analysis of MoCA test was performed in each group carrying or not ϵ4 allele.Our study showed that 75% were women with a median age of 68 years (interquartile range 62–74 years) and a median schooling for 6 years (interquartile range 4–12 years). Dementia was related to low education level of ≤5 years OR=11.20 (95%CI 4.99–25.12), high blood pressure (HBP) OR=1.45 (95%CI 1.03–2.05), and age over 70 years OR=7.68 (95%CI 3.49–16.90), independently of being or not an ϵ4 allele carrier. Diabetic subjects with dementia carrying ϵ4 allele showed a tendency to exhibit lower scores on the MoCA test, when compared with noncarriers’ diabetic subjects with dementia.The presence of ϵ4 allele does not modify the relationship between cognitive impairment and the different cardio-metabolic and social risk factors, except in diabetic subjects ϵ4 carriers with dementia who showed a tendency to exhibit lower scores of the MoCA test, when compared with noncarriers’ diabetic subjects with dementia. Subjects who attended the invitation received an explanation of the study and those who agreed to participate were cited with their respective family member or companion.The inclusion criteria of the study were being 50 years of age or older, being autonomous living in community, and resident in Bogota without previous cognitive evaluation. Subjects with neuro-psychiatric illness history, illiterate, blind, deaf, and institutionalized were excluded from the study.After signing the informed consent, socio-demographic, health, and cardiovascular risk questionnaires were applied to each participant as well as a neuropsychological and neuropsychiatric assessment in two phases and each one was diagnosed by consensus, under a diagnosis of normal cognition, MCI according to Petersen’s criteria, or dementia according to DSM-IV criteria. Subjects with the last two diagnoses were referred to their medical service.,Body mass index (BMI) was calculated using measures of weight and height, and for the genotyping of ApoE, a blood sample was taken, and subsequently DNA extraction was performed.Central tendency and dispersion measures were used to describe quantitative variables, and absolute and relative frequencies to describe qualitative variables.The MoCA-test score was compared among patients with and without risk factors, within each group of patients, ϵ4 carriers and noncarriers using Mann-Whitney U test, given that the distribution of this variable was not normal. Also, when statistical significance was reached, a non-parametric effect size, using Cliff’s Delta, was calculated  and the risk factors , adjusting the data obtained for each cognitive group  by age. A non-parametric quantile regression was used, considering that MoCA-test score did not follow a normal distribution. The risk factor, low schooling, was analyzed in two ways: as five or less years or as ten or less years of schooling, which correspond in our context to primary or incomplete bachelor, respectively. High schooling were high-school graduates, technicians, and university students. Statistical analysis was performed using the R (libraries FactoMineR and effsize) and Stata 13® programs. Significance was assessed at p<0.05 corrected for multiple comparisons using the Boferroni correction.Subsequently, a regression analysis was applied using the MoCA-test score as the dependent variable and as independent variable the interaction between the APOΕ , HBP, dyslipidemia, overweight, low schooling, and age over 70 years and APOΕ genotyping were included.Approximately 75% were women with a median age of 68 years (interquartile range [IQR]) age 62 and 74 years and a median schooling for 6 years (IQR) 4 to 12 years; 43.5% of the participants were cognitively normal, 34.1% had MCI, and 22.4% dementia .The frequency of our APOΕ genotype, previously described, was distributed as follows: ϵ3/ϵ3 73.4%, ϵ3/ϵ4 20.2%, ϵ2/ϵ3 4.4%, ϵ4/ϵ4 1.4%, ϵ2/ϵ4 0.4%, and ϵ2/ϵ2 0.08%. A total of 22% of participants were carriers of the ϵ4 allele, being 1.5% with homozygous genotype and 20.6% heterozygous. Allelic frequency was ϵ3 85.5%, ϵ4 11.9%, and ϵ2 2.6%.versus 26 [IQR: 25–28], p=0.010, Cliff’s delta=–0.30 ; noncarriers: median 24 [IQR: 20–26] versus 27 [IQR: 25–28], p=0.000, Cliff’s delta=-0.45 (medium).In normal subjects, both carriers and noncarriers, a lower MoCA-test score was found in those who had equal or less than five years of schooling compared to subjects with more than 5 years of schooling, carriers: median 24.5 [IQR: 21.5– 27] versus 26 [IQR: 25–28], p=0,048, Cliff’s delta=0.21 ; and noncarriers: 25 [IQR: 22–27]) versus 27 [IQR: 25–28], p=0.000, Cliff’s delta=0.43 (medium).The same was observed in subjects with equal or less than 10 years of schooling compared to those who had 11 or more years of schooling, carriers: median 25 [IQR: 22–28] versus 26 [IQR: 25–28], p=0.007, Cliff’s delta=0.35 (medium); noncarriers: median 24 [IQR: 20–26]) versus 27 [IQR: 25–28], p=0.043, Cliff’s delta=0.12 (negligible).Likewise, subjects with 70 years of age or older had a lower MoCA-test score compared to younger elderly population, carriers: median 24.5 , p=0.000, Cliff’s delta=-0.50 (large); noncarriers: median 20 [IQR: 16–22]) versus 21 [IQR: 19–23], p=0.000, Cliff’s delta=-0.29 . The same was observed in subjects with equal or less than 10 years of schooling compared to those who had 11 or more years of schooling, carriers: median: 20 [IQR: 18–22] versus 23 [IQR: 22–24], p=0.000, Cliff’s delta=0.52 (large); noncarriers: median 20 [IQR: 17–22]) versus 22 [IQR: 19–24], p=0.000, Cliff’s delta=0.32 .In subjects with MCI, the results were similar to those observed in individuals with normal cognition; a lower MoCA-test score was found in those who had equal or less than five years of schooling compared to subjects with more than 5 years of schooling also in both ϵ4 allele carriers and noncarriers. In the MoCA-test for a schooling of five years or less, carriers: median: 19 [IQR: 16–21] versus 23 [IQR: 22–24], p=0.011, Cliff’s delta=0.30 ; noncarriers: median: 20 [IQR: 17–22]) versus 21 [IQR: 19–23], p=0.000, Cliff’s delta=0.23 .Similar results were observed in subjects of 70 years of age or older who had a lower MoCA-test score compared to younger elderly population, carriers: median: 20 [IQR: 18–22] versus 20 [IQR: 18–23], p=0.022, Cliff’s delta=0.14 (negligible).However, a lower score in MoCA test was observed in subjects with high blood pressure, noncarriers with the factor compared to noncarriers without factor ; noncarriers: median 12.5 [IQR: 9.5–16] versus 14 [IQR: 12–16], p=0.032, Cliff’s delta=0.30  and in whom schooling was less than 5 years, carriers: median 14 [IQR: 12–15] versus 16.5 [IQR: 14.5–18.5], p=0.000, Cliff’s delta=-0.57 (large); non-carriers: median: 13 [IQR: 10–15] versus 15 [IQR: 13–17], p=0.000, Cliff’s delta=-0.38 (medium).For patients with dementia, a lower MoCA-test score was observed, regardless if they were ϵ4 allele carriers or not in those who had HBP compared with subjects without this risk factor, carriers: median 13 [IQR: 12–15] versus noncarriers 14 [IQR: 13–16]); however, this was not statistically significant (p=0.066).Although, there was a tendency to lower scores in diabetic ϵ4 carriers compared with noncarriers, carriers: median 13 [IQR: 9–14] In the same way, no statistically significant differences were observed between subjects with and without the other risk factors, both carriers and noncarriers .The multiple correspondence analyses for patients with cognitive impairment presented an adjusted inertia for the first four dimensions of 23.09, 12.76, 11.60, and 10.45%, respectively. The first dimension explains 23.09% of the variability of the data and the categories are organized mainly along this axis. The best representation of the variables was achieved in the first two dimensions, reaching 35.84% inertia .After adjusting age, when evaluating the interaction between being ϵ4 allele carrier and having some risk factors, it was observed that the decrease in the MoCA-test score in the three groups of patients  was related to low schooling (≤ 5 years or ≤10 years), regardless of the status of the ϵ4 allele.versus noncarriers without high blood pressure.In addition, in patients with MCI a decrease of one point in the median MoCA test score was observed, in carrier subjects with high blood pressure On the basis of the results described above, patients with dementia carrying ϵ4 allele and without overweight showed a decrease of 2.58 points in the median of the MoCA-test score. In all comparisons that reached statistical significance, the greatest decreases in the MoCA-test score were observed in subjects with dementia .,Being an ϵ4 carrier has been recognized as the main genetic risk factor associated with the development of late-onset AD.A relationship between age, sex, Apoϵ4, and cognitive impairment has been described.,We also found a significant association between low schooling (≤5 years)and a lower performance in the MoCA-test, regardless if they were or not ϵ4 allele carriers and their cognitive status, results that are concordant with Borland et al., who found that low schooling (≤10 years) was associated with significantly lower MoCA-test scores and concluded that schooling is a significant predictor for the MoCA-test score.Accordingly, the difference between MoCA-test scores among subjects with low and high schooling in our study on average was 1.5 points, data that are consistent with those reported by Konstantopoulos et al.,In other studies, the interaction among cognitive impairment, Apoϵ4, and schooling presents contradictory evidence; Seeman et al.,‒,,Other studies have described the relationship between the presence of the ϵ4 allele, some cardio-metabolic factors, and the development of cognitive impairment, which suggests that the presence of this allele would modify the association between dementia and HBP.When assessing whether the scores obtained in the MoCA test in subjects ϵ4 carriers with HBP were different from those being HBP non-ϵ4 carriers, our study found no differences. When analyzing the independent relationship of the MoCA test scores, Bangen et al.On the contrary, our study showed a lower performance of the MoCA-test in subjects with dementia, ϵ4 carriers diabetics compared to ϵ4 carriers non-diabetic. The relationship among AD, APOϵ4, and DM was assessed by Haan et al., who described that the ϵ4/ϵ4 genotype contributes synergistically with type 2 diabetes (DM2) in the development of AD.Differences in cognitive scales between diabetic carriers and noncarriers were described by Palmer et al., who reported that ϵ4 diabetic patients had lower scores on the MMSE test compared to noncarriers and non-diabetic subjects.,,The presence of risk factors such as overweight or dyslipidemia were not related to the performance in the MoCA test, in ϵ4 allele carriers and noncarriers. The relationship of overweight, poor performance on cognitive tests, and the presence of ϵ4 allele have been poorly documented and its results are contradictory. In this regard, Blautzik et al.‒Regarding the relationship among cognition, dyslipidemia, and APOϵ4, Reitz et al., Hayden et al.,Study limitations: ϵ4 carrier population was smaller than the non-ϵ4 carrier in most of the different risk factor groups, a fact that could have interfered in the association forces between the different risk factors and the presence of ϵ4 allele.We found that subjects with low schooling (≤5 years or ≤10 years) and subjects with HBP had lower performances in the MoCA-test scores, regardless of being ϵ4 carrier or not, and presenting or not cognitive impairment.Being an ϵ4 carrier and diabetic showed a tendency to present the lowest scores in the MoCA-test only in subjects with dementia as compared to diabetic subjects with dementia noncarriers ϵ4.A future study with a larger cohort and a longer longitudinal follow-up time could show us a greater effect of apoϵ4 with risk factors on cognitive decline.Extensive continuing education programs, in all age groups of our society, with a better control of risk factors and promotion of healthy lifestyles, are the best options currently available to reduce the onset and progression of cognitive decline.


Physical activity (PA) is a proven strategy to prevent chronic diseases and reduce falls. Furthermore, it improves or at least maintains performance of activities of daily living, and thus fosters an independent lifestyle in older adults. However, evidence on the association of PA with relevant subgroups, such as older adults with utilization of long-term care (LTC), is sparse. This knowledge would be essential for establishing effective, need-based strategies to minimize the burden on healthcare systems due to the increasing need for LTC in old age.1) baseline assessment and 2016 (t2) follow-up of the population-based Cooperative Health Research in the Region of Augsburg (KORA-)Age study in southern Germany. In 4812 observations of individuals ≥65 years, the association between various types of PA , walking+exercise) and utilization of LTC (yes/no) was analyzed using generalized estimating equation logistic models. Corresponding models stratified by sex  examined sex-specific associations. Descriptive analyses assessed the proportion of individuals meeting the suggested minimum values in the German National Physical Activity Recommendations for older adults (GNPAR).Data originate from the 2011/12 (tp ≤ 0.05).All types of PA showed a statistically significant association with non-utilization of LTC in the entire cohort. “Walking+exercise” had the strongest association with non-utilization of LTC in the entire cohort (odds ratio (OR): 0.52, 95% confidence interval (CI): 0.39–0.70) and in males , whereas in females it was “exercise” . The proportion of individuals meeting the GNPAR was higher among those without utilization of LTC (32.7%) than among those with LTC (11.7%) and group differences were statistically significant (The GNPAR are rarely met by older adults. However, doing any type of PA is associated with non-utilization of LTC in community-dwelling older adults. Therefore, older adults should be encouraged to walk or exercise regularly. Furthermore, future PA programs should consider target-groups’ particularities to reach individuals with the highest needs for support.The online version contains supplementary material available at 10.1186/s12966-022-01322-z. The beneficial effects of regular physical activity (PA) on older adults’ physical, psychological, and social well-being have been shown in various systematic reviews –3. FurthThe current evidence about PA’s health benefits for older adults and particularities of PA in relevant subpopulations , as well as the low proportion of older adults meeting WHO’s recommendations, are important to consider as populations age worldwide. By 2030, one in six people will be 60 years of age or older . This trWHO states in its “Guidelines on physical activity and sedentary behaviour” that notable gaps in evidence regarding the behavior of specific subpopulations remain, thus inhibiting the development of target-oriented programs for them . In old In light of the existing evidence and its gaps, it is highly important to gain further knowledge about PA, its implications on utilization of LTC in relevant subpopulations like community-dwelling older adults, and subpopulation particularities. This information would enable policy-makers to identify vulnerable target groups and set up need-based PA interventions, whose effects could mitigate the growing public health problem of increasing demand for LTC services.To contribute to closing the existing research gaps, this study has the following objectives: 1) to determine the association of PA with utilization of LTC in community-dwelling older adults; 2) to detect differences regarding the sex-specific association of PA with utilization of LTC in females and males; 3) to determine the proportion of community-dwelling older adults with and without utilization of LTC meeting the suggested minimum values for distinct types of PA according to the “German National Physical Activity Recommendations” for older adults (GNPAR).We used data from the Cooperative Health Research in the Region of Augsburg (KORA)-Age study, which is a part of the regional KORA research platform for population-based health research in Germany. The KORA research platform consists of population-based surveys and their follow-up studies. The KORA-Age study is a follow-up of participants ≥65 years from four independent cross-sectional samples who completed health surveys conducted between 1984 and 2001 ). For the second follow-up , the total sample of Age1 and individuals from the four cross-sectional samples who were born before 1951, and thus aged ≥65 years in 2016, were invited to participate. This resulted in an eligible study population of 6051 at Age3 (t2). Of those, 4083 participated in telephone interviews and questionnaires .At Age1 (tservices . Proxies0) did not assess information on utilization of LTC, only Age2 (t1) and Age3 (t2) were considered for analyses. For the main analysis, we used individuals from t1 (n = 822) and t2 (n = 4083) [see Fig. Since Age1 , PA is defined as “any bodily movement produced by skeletal muscles that requires energy expenditure” . PA is a“Exercise” is a planned, repetitive, purposeful, and structured subcategory of PA with the objective to improve or maintain one or more components of physical fitness , 7. In o“Walking” was assessed with the following question addressing duration of walking: “On a typical weekday, how much time do you spend walking? For example, going for a walk, on the way to work or shopping?”. Possible response categories were (1) “more than or equal to one hour”; (2) “more than or equal to half an hour, but less than one hour”; (3) “more than or equal to a quarter of an hour, but less than half an hour”; (4) “less than a quarter of an hour”; or (5) “not applicable” . To facilitate the analyses and interpretation of results, we further dichotomized these values into “high or moderate walking” (= (1) or (2), in the following: “walking”) and “no or low walking” (= (3), (4), or (5), in the following: “no/low walking”). “Walking+exercise” was applied to individuals doing both “exercise” and “walking”.The domain of PA – with the exception of walking while “going to work” representing occupational- or transport-based PA – was mainly leisure-time . However2. Questions, response options, and the calculation of the amount of PA per week can be found in Additional file To address important components of PA, frequency (number of sessions per week), intensity , and time  were assessed in the subpopulation at tLTC is defined as support with daily activities for people who experience decline in self-care on a long-term basis (> three months) . Daily aCovariates related to LTC and PA were identified based on Andersen’s Behavioral Model of Health Services Use (ABMHS) , 31–34, Identified sociodemographic factors were age, sex, education, living arrangement, and income. Age was the participant’s age on the interview date. Sex was defined as the biological distinction of “female” or “male”. Education was comprised of school education, education at university, and vocational training. It was expressed in years (8–17 years). Living arrangement was divided into living “alone” or “not alone”. Income was defined as self-perceived income sufficiency (subjective income). In older adults, this is a common approach to express individuals’ personal evaluations of the relationship between wealth or objective income and their expenses . Partici1 and were assessed using self-reports following detailed instructions from trained telephone-interviewers at t2. From these values, their BMI in kg/m2 was calculated.Identified health-related factors were falls, multimorbidity, disability score, and Body Mass Index (BMI). Falls were reported as “≥ 1 fall” or “no falls/unknown” within the last year. We used participant self-reports to calculate their Charlson Comorbidity Index . The ind1 or t2 , we applied a generalized estimating equation (GEE) logistic model with an unstructured correlation matrix. The model accounts for repeated measurements and their intra-subject correlation [We assessed participants’ characteristics at both follow-up timepoints, dropouts, PA values, and comparisons with the GNPAR using descriptive statistics. To investigate the association of different types of PA  with utilization of LTC at trelation . As the relation .n = 4812 observations: sum of t1 (n = 800) and t2 (n = 4012)). As the existence of differences between sexes for utilization of health care services and lifestyle habits is well-known [n = 2499 observations: t1: n = 395, t2: n = 2104) and one for males . As disability  is otioning” , 48. For1 to t2 and thus were considered fixed variables. All other covariates were treated as time-dependent.In all models, we compared observations with utilization of LTC to those without utilization of LTC. The covariates sex and education did not change from tn = 800 at t1 and n = 4012 at t2. Missing values in subdomains of the variables “utilization of LTC” (t2: home nursing service and paid services for household support (n = 1); assistance of family members, friends, or neighbors (n = 1)) or “PA” ; t2: exercise (n = 2); walking (n = 8)) were imputed through single stochastic regression imputation using logistic regression with the fully conditional specification method [Individuals with either missing values in all types of LTC (= transformation variable “utilization of LTC” (outcome)) or in both “exercise” and “walking” (= transformation variable “PA” (exposure)) were excluded through listwise deletion , 50. Thin method . This imn method , 53. To 1 (multimorbidity (n = 9), BMI (n = 7), income (n = 4)), and 126 missing values (3.1%) at t2 (BMI (n = 53), income (n = 35), multimorbidity (n = 27), falls (n = 5), disability score (n = 2), living arrangement (n = 2), education (n = 2)) were identified. We imputed binary variables using single stochastic regression with the fully conditional specification method and continuous variables using predictive mean matching [Regarding covariates, twenty missing values (2.5%) at tmatching . We basematching .p-values ≤0.05 were considered statistically significant. We performed all statistical analyses using SAS software, release 9.4 .We tested for multicollinearity of covariates in all models (threshold: |r| ≤ 0.8). We calculated odds ratios (OR) and 95% confidence intervals (CI). In all analyses, results with 2. Out of 4012 individuals, 762 (19.0%) received LTC. Age in the entire cohort ranged from 65 to 97 years with a mean age of 75.0 years (standard deviation (SD): 6.6). The entire cohort (with missings) included 2099 females (52.5%), with 478 (22.8%) of them receiving LTC. Out of 1903 males, 276 received LTC (14.5%). In individuals without utilization of LTC, the most common type of PA was “walking+exercise” (53.2%), followed by “walking” (26.6%), no PA (10.3%), and “exercise” (9.9%). Individuals with utilization of LTC did no PA most often (39.3%), followed by “walking” (32.8%), “walking+exercise” (20.8%), and “exercise” (7.2%).Table 1 showed similar characteristics  as compared to the other types of PA were the youngest ; they had the second most years of education 11.6; SD: 2.6) after those who did “walking” , the lowest BMI , the lowest multimorbidity , and the lowest disability score . Looking at individuals with utilization of LTC, those with no PA (the most frequently completed type of PA) were the oldest ; they had the fewest years of education , the highest BMI , the highest multimorbidity , and the highest disability score  as compared to those with other types of PA. The sample at t; SD: 2.61 and t2 with information about utilization of LTC and PA status (n = 248) used LTC (39.1%) more often, were older (81.2 years (SD: 6.4)), lived alone (55.7%) more frequently, had higher multimorbidity , and a higher disability score  than non-dropouts.Dropouts between tn = 4812) and in observations without disability . Compared to no PA, all types of PA were associated with reduced odds of utilization of LTC in the main analysis and SA1. “Walking” reduced the odds of utilization of LTC by 27%  and “exercise” reduced it by 44% . The combination of “walking+exercise” achieved the highest reduction, with a 48% decrease . The covariate being “female” increased the odds of utilization of LTC by 41% . Other covariates that increased the odds of utilization of LTC to a statistically significant degree were older age , higher education , living alone , falls , higher multimorbidity , and a higher disability score . SA1 and SA2  in the main analysis resulted in the following: “walking*female” , “exercise*female” , “walking*exercise” . Tests of the interaction terms in SA1 resulted in the following: “walking*female” , “exercise*female” , “walking*exercise” .1. Almost a fourth  completed the suggested minimum of ≥150 minutes/week of “moderate-intensity exercise”, whereas only 6.5% (n = 52) completed ≥75 minutes/week of “vigorous-intensity exercise”. A total of 6.4% (n = 51) did strength training more than twice a week. For all types of PA, the proportion of individuals without utilization of LTC who met the GNPAR was higher than that of individuals with utilization of LTC. Group differences between individuals with and without utilization of LTC in relation to “moderate-intensity exercise” and “moderate- or vigorous-intensity exercise” were statistically significant.Table Our study is among the first to investigate the association of various types of PA with utilization of LTC in community-dwelling older adults in Germany. Compared to physically inactive individuals, those being physically active were less likely to utilize LTC. The combination of “walking+exercise” showed the strongest association with non-utilization of LTC in the entire cohort and in males. In contrast, among females, “exercise” had the strongest association with non-utilization of LTC. The proportion of individuals who completed the minimum values suggested by the GNPAR was higher among those without utilization of LTC than among those with utilization of LTC. In both individuals with and without utilization of LTC, the minimum values for “moderate-intensity exercise” were completed more often than the minimum values for “vigorous-intensity exercise” or “strength training”.Our results suggest that being physically active is associated with reduced odds of utilization of LTC. Due to the lack of studies on the association of the outcome “utilization of LTC” with PA, and given that utilization of LTC is a complex construct, influenced by various determinants , 34, comOur results contribute to the evidence about the association of various types of PA with utilization of LTC in a subgroup of interest to policy-makers  . WHO’s “Comparing females with males based on the sex-stratified analyses, the association of “exercise” and “walking+exercise” with non-utilization of LTC was higher in males than in females. Older males prefer more vigorous exercise than females do  and the While the association of PA with non-utilization of LTC was even stronger in males without disability compared to the entire male cohort, for females no corresponding association was found. A possible explanation might be gender differences in household PA. Due to persistent social and cultural norms, older females complete most household chores. Murphy et al.  analyzedWith 27.7% following WHO’s recommendations for moderate to vigorous PA, our sample falls within the documented range for countries worldwide (20–60%) , 7, 8. TWe detected that the proportion of individuals meeting the GNPAR was much higher in individuals without utilization of LTC than in individuals with utilization of LTC. Also, considering our finding that individuals with utilization of LTC were mostly physically inactive and that, according to Ruetten et al. , the “grOur results must be interpreted with some caveats. First, we did not aim to assess causal relationships of PA with utilization of LTC. To investigate causal relationships, other study designs are needed. Our findings do suggest, however, that promoting PA in old age is associated with reduced odds of utilization of LTC.Another limitation is the relatively small size of groups of individuals with LTC per subcategory of PA, which renders corresponding results less reliable. Still, as up to now there is no comparable analysis regarding this topic, our study contributes relevant evidence. Furthermore, our questionnaire-based study of community-dwelling older adults is relatively large in comparison to other representative regional cohort studies addressing PA in older adults and included relatively even proportions of females and males .As mentioned above, we may have underestimated the proportion of individuals meeting the GNPAR. Due to the assessment of time frames  rather than estimated mean duration/day, we could not calculate the exact mean duration/week for each type of PA. However, we took the lower bound of each time frame  to avoid the common problem of overreporting PA through self-reports , 59.Our study has several strengths that improve upon limitations of previous studies of PA measurements and evaluations of PA’s effects. First, standardized assessment and utilization of quality management in KORA studies  ensured high data quality . MoreoveOur results demonstrate an association between PA and non-utilization of LTC in community-dwelling older adults with sex-specific and disability-related particularities regarding distinct types of PA. Furthermore, they illustrate that the GNPAR are rarely met by older adults with and without utilization of LTC. To minimize or even partially prevent the public health issue of an increasing need for and thus higher utilization of LTC, policy-makers and health care workers should develop target-oriented PA promotion programs. For those programs, consideration of accessible and sustainable environments, as well as the target-groups’ needs, is indispensable for reaching this vulnerable group and fostering beneficial PA behaviors.Additional file 1. Title: Categorization of exercise. Description of data: Illustration explaining the transformation of the variable “exercise”.Additional file 2. Title: Extract of questionnaire assessing type, frequency, and duration of physical activity at t2. Description of data: Questions used to assess types, frequency, and duration of physical activity at t2.Additional file 3. Title: STROBE-checklist. Description of data: Checklist to determine quality, structure, and content of study.


The β- and γ-secretase-driven cleavage of the amyloid precursor protein (APP) gives rise to the amyloid β peptide, which is believed to be the main driver of neurodegeneration in Alzheimer’s disease (AD). As it is prominently detectable in extracellular plaques in post-mortem AD brain samples, research in recent decades focused on the pathological role of extracellular amyloid β aggregation, widely neglecting the potential meaning of very early generation of amyloid β inside the cell. In the last few years, the importance of intracellular amyloid β (iAβ) as a strong player in neurodegeneration has been indicated by a rising number of studies. In this review, iAβ is highlighted as a crucial APP cleavage fragment, able to manipulate intracellular pathways and foster neurodegeneration. We demonstrate its relevance as a pathological marker and shed light on initial studies aiming to modulate iAβ through pharmacological treatment, which has been shown to have beneficial effects on cognitive properties in animal models. Finally, we display the relevance of viral infections on iAβ generation and point out future directions urgently needed to manifest the potential relevance of iAβ in Alzheimer’s disease. Alzheimer’s disease (AD) is a neurodegenerative disease that was first described by the psychiatrist Alois Alzheimer in 1906 . A centrAccording to the amyloid hypothesis, amyloidogenic plaque formation is caused by an imbalance in the production and accumulation of the Aβ peptide resulting from the processing of the amyloid precursor protein (APP) . In non-More recently, the pathophysiological role of intracellular Aβ (iAβ) has aroused increasing interest, as iAβ was found to affect intracellular pathways, pointing to a potential relevance of iAβ accumulation for very early molecular aberrations that precede even amyloid plaque and neurofibrillary tangle formation. Within this review, we highlight iAβ as an intracellular modulator and pathological biomarker, illustrate iAβ genesis and drugs affecting it, and address viral infections and their impact on iAβ generation.While the mere presence of accumulated iAβ is accepted as an early occurring phenomenon in AD preceding the extracellular accumulation of plaque-forming Aβ, less is known about the mechanisms predominantly leading to the excessive accumulation within cells, especially in neurons ,13,14,1545, are remaining and aggregating within neurons, while in contrast, shorter Aβ42 isoforms are secreted, resulting in the surge of intracellular aggregation with time due to age-related changes in Aβ processing [45 peptides that accumulate in mitochondria, endosomes, and autophagosomes, which increase with age and upon glutamate treatment. It was speculated that age-related signaling for inhibition of C-terminal trimming by γ-secretase increases Aβ45 levels whose hydrophobic properties promote aggregation [New insights revealed that long iAβ isoforms, such as Aβocessing . Indeed,regation .A closer look at the endocytic processing of Aβ suggests a mechanism of internal accumulation that is not issued from the uptake of Aβ peptides but from the dysfunction of metalloproteases of the endothelin-converting enzyme (ECE) family . These e40 and Aβ42 primarily use endocytosis as the major, possibly even the only, pathway of entry [42 uptake was two times more efficient than Aβ1–40 uptake. However, both peptides have been shown to be largely using the same uptake paths, which are predominantly clathrin- and dynamin-independent but actin-dependent [Further, there is strong evidence that the reuptake of Aβ occurs primarily via the endocytic pathways. Accordingly, it was demonstrated that soluble Aβof entry . A stronependent .42 uptake is involved in the di-leucine-dependent receptor-mediated endocytosis, which resulted in the accumulation of excess oligomeric Aβ42 mainly in the lysosome. This uptake was attenuated by TOM1, a FcRIIb2-binding protein that represses the receptor recycling, which additionally enhances the hypothesis that FcγRIIb is responsible for iAβ neuronal uptake and suggests a potential molecular mechanism underlying iAβ accumulation [Specifically, in neurons, FcγRIIb2, a variant of Fcγ-receptor IIb (FcγRIIb), holds a critical function for the neuronal uptake of pathogenic Aβ, as demonstrated in an AD mouse model . In moremulation .−/−/APP/PS1 mice, APP/PS1 mice, AQP4−/− mice, and wild-type (WT) mice was compared. Moreover, the effect of selective elimination of microglial cells or downregulation of apolipoprotein E (apoE) expression on the Aβ burden in the frontal cortex was investigated. As a result, it was possible to demonstrate that AQP4 deletion exacerbates glymphatic clearance impairment, accompanied by an increase in accumulation of iAβ and apoE in the APP/PS1 brain, and the knockdown of apoE reduces iAβ levels in APP/PS1 mice with or without AQP4 [Finally, the accumulation of iAβ is associated with the glymphatic system, also known as the paravascular pathway, primarily playing a pivotal role in the clearance of a major fraction of extracellular Aβ, which is mediated by astroglia aquaporin 4 (AQP4) . Based oout AQP4 .Ubiquitination, especially the ubiquitin–proteasome system (UPS), which plays a pivotal role in the prevention of iAβ deposition and the resulting influence on the cellular burden, is directed by the ubiquitin E3 ligase enzymes ,34. WhilFor decades, research on Alzheimer’s disease focused on the well-known late-stage hallmarks, when cognitive symptoms arise. However, due to a continuing lack of effective therapeutic treatment, the research focus has shifted to the early stages of AD in order to understand the causative mechanisms of pathophysiological dysregulation and find potential pharmacological targets. In this regard, the accumulation of iAβ has become a highly relevant topic after different studies showed that it accumulates within neurons in the very early stages of the disease, e.g., in transgenic mouse models and humans ,40. Inve42 aggregate formation is caused by the internalization of extracellular Aβ after interaction with membrane receptors or membrane permeation pathways [42 toxic forms and mutant (non-aggregating) Aβ42 peptides. The authors demonstrated that these non-aggregating isoforms were able to inhibit wild-type Aβ42 extracellular internalization, intracellular aggregation, and neuronal toxicity [42 to interact with plasma membrane phospholipids, creating Ca2+ pores and thereby enhancing a cytotoxic calcium influx [42 toxic capacity was shown using non-aggregating Aβ42 isoforms, pointing to a possible therapeutic target for early AD-related events. Using the same approach, the authors also studied another described iAβ42 effect, which is based on the oligomer–membrane interaction [42 internalization in mitochondria [42 was able to reduce the capability of iAβ42 to interact with the mitochondrial membrane, impairing the iAβ42-mediated mitochondrial potential depolarization, decrease in COX activity, and subsequent cell toxicity. In accordance with these results, a combination of transcriptomic, proteomic, and metabolomic experiments indicated an intensive activation in oxytosis/ferroptosis non-apoptotic cell death pathways, which was caused by an increase in intracellular lipid peroxides and reactive oxygen species (ROS) in a high-level iAβ model [Recent studies tested multiple approaches to assess calcium dysregulation as one of the most commonly described mechanisms causing synaptic dysfunction and cell death . Indeed,pathways . Therefotoxicity . Therebym influx . A signieraction ,46 that chondria , interacchondria , reductichondria , and ultchondria ,51. MutaAβ model . EndoplaAβ model .2+ influx, leading to a dysregulation in Ca2+ homeostasis and subsequent cell death  transgenic mice showing an increased iAβ accumulation. The nucleus accumbens is a brain area very closely related to the main affected regions in AD (such as the hippocampus and cortex) that plays a critical role in reward, cognition, learning, and emotional behavior ,58,59. EComparably, Ochiishi et al. studied the effect of iAβ from cellular to behavioral level in a mice model designed to express a fusion Aβ-GFP protein without secretion signal that therefore accumulates inside the neurons . Notably42 in the disruption of axonal transport and, therefore, dysregulation of neuronal synaptic transmission was shown involving the mitogen-activated protein kinase (MAPK) [42 expression leads to a significant reduction in ERK 1/2 phosphorylation and increased bone morphogenetic protein-2-dependent Smad 1/5/8 phosphorylation. Rescuing the iAβ42-mediated attenuation of MAPK signaling was possible with the small molecule PLX4032, which is a downstream enhancer of the MAPK pathway.In addition to these findings, a role for iAβe (MAPK) . In a bi42 treatment raises the inflammatory S100A9 signaling [The role of the immune system and inflammation in early AD pathology has become another intensively studied factor based on the evidence of inflammatory markers increasing in patients months before the occurrence of extracellular plaques . In addiignaling , which fignaling ,66. Indeignaling . The stuignaling . Correspignaling . Using aignaling . Similarignaling ,72 as anRecent studies demonstrated that iAβ is present in elevated amounts in cells of patients affected by AD as one of the earliest pathologic markers preceding extracellular amyloid formation ,71,73,74Epidemiological studies indicated a link between lytic or latent viral infection and an increased risk for amyloid-associated diseases, e.g., AD or diabetes mellitus (DM) ,84,85,86Apart from that, this connection also raises the question of whether these virally induced diseases are amyloid diseases themselves. Varicella-zoster virus (VZV) infection of quiescent primary human spinal astrocytes (qHA-sps) induced the production of intracellular amyloidogenic proteins . Additionally, conditioned media of infected astrocytes promoted the process of aggregation in uninfected cells. An elevation of amyloidogenic protein levels was similarly reported in the plasma of acute zoster patients and for the CSF from VZV vasculopathy patients, compared with respective controls ,91,92. IMoreover, herpes simplex virus type 1 (HSV-1) viral DNA preferentially colocalized with Aβ plaques in ADs patients’ brains , and proIntriguingly, mouse model experiments revealed a close link between viral infection, the observed pathological changes, and cognitive impairment . TherefoContradictory to these results is a recent study that reported the absence of any significant correlation between amyloidogenic protein expression, as well as Aβ deposition, in the human brain of herpes simplex encephalitis patients. Additionally, a lack of specificity of AD-related pathological hallmarks for HSV-1-infected cell types or areas was demonstrated . ConsequAll in all, there have been numerous studies demonstrating that viruses can indeed promote the formation and accumulation of pathological features of amyloid diseases such as iAβ, and therefore, viral infections could be potential risk factors for the development of AD, DM, etc. The molecular basis of viral infection needs to be further investigated, as it could be essential for the identification of potential therapeutic targets for amyloid diseases. Nevertheless, recent opposing findings indicate that further investigation of the role of viral infection in amyloid diseases is crucial for uncovering the molecular mechanism. In order to properly address the question, human specimens particularly need to be the focus of these examinations.The lack of full understanding of the complex pathology of AD has so far impeded the development of efficient therapy and treatment options. Due to the seemingly complex nature of AD, it is difficult to find a target for therapy and pharmaceutical agents, as there are so many pathologic events coming along with AD. In addition, therapeutic drugs need to be selected and developed to minimize side effects and limit toxicity while still being able to pass the blood–brain barrier, which has led many promising candidates to fail on the way to clinical applications ,106,107.Saccharomyces cerevisiae, finding that a combination of baicalein and trans-chalcone significantly reduces iAβ42 and cellular ROS induced by Aβ42 in a synergistic manner [Morus alba Linn.) ameliorate the cytotoxicity induced by iAβ oligomers in a transgenic mouse model [3, which is a less toxic analog of 1,4-naphthoquinone. MSB has similarly shown efficient inhibition of iAβ in HEK293T cells expressing the familiar AD Osaka mutation [Notably, promising effects in iAβ clearance have been observed for naturally occurring, bioactive polyphenols, more specifically flavonoids, which have shown in numerous previous trials their antioxidant, anti-inflammatory, and autophagy-inducing properties and, therefore, promise to be of great potential in iAβ-targeting drug treatment ,119,120.c manner . Supportse model . Similarse model  or quercse model . Anothermutation .Cannabis sativa whose potential for therapeutic use has in recent years been shifted increasingly into the focus of research. Cannabis contains more than 100 kinds of cannabinoid compounds with the major classes being Δ9-trans-tetrahydrocannabidiol (THC) and cannabidiol (CBD). The neuroprotective effect of THC against iAβ accumulation has already been evidenced in several drug screening studies [1 and CB2, which are part of the endocannabinoid system (ECS) that is involved in a number of important physiological processes such as memory and learning, brain plasticity, neuroinflammation, neuronal development, appetite regulation, etc. [1 receptors) on the presynaptic neuron resulting in inhibited presynaptic calcium channel activation and subsequent presynaptic neurotransmitter release. The specific effect is then dependent on the neurotransmitter type and the respective cell type and part of the nervous system [Another natural source for potential drug candidates in AD treatment is  studies ,128,129. studies ,128,130.on, etc. ,133,134.s system . The exaFurthermore, several types of monoclonal antibodies have been developed that specifically target soluble oligomeric forms of Aβ, which have been found to be the more toxic species, compared with the actual amyloid plaques . A stronThe fact that iAβ is a strong drug target candidate has further been proven by the variety of substances that have been reported to reduce iAβ with accompanying neuroprotective effects. Interestingly, it was demonstrated that ablation of microglia by administration of colony stimulation factor 1 receptor (CSF1R) inhibitor PLX3397 led to a dramatic reduction in iAβ and neuritic plaque deposition in a mouse model, pointing to a causal relation between CSFR1 signaling and iAβ, which might reveal just another primary target in the focus of iAβ .All of these various and recent approaches demonstrate the rising attention for iAβ as a target for the treatment of AD and have already proven a great potential for a range of potentially strong drug candidates. However, the presented selection of recent studies has been performed in vitro or in animal studies, and thus they need to be considered with some caution, as their effects on humans, adverse and beneficial, remain unexplored. However, even though we are still a long way from the actual application in humans, and in the past, many promising drugs failed the clinical test in the end, iAβ-targeting drugs have the potential to become the next big candidate in clinical AD treatment trials.Since iAβ precedes the extracellular accumulation of Aβ ,40,141,


Depression during sensitive periods of social development may have consequences that extend well beyond mental health, and far into adulthood. This study compared the social functioning of adults with adolescent-onset depression (ages 10–20) to those with adult-onset depression (ages 21+). Participants were 3,360 adults  who had experienced major depression. Adult functional outcomes were marital status, divorce, number of children, years of education, employment status, household income, dependency on welfare, and obesity. Participants with depression during adolescence were less likely to get married, have children, and more likely to have lower household incomes. Depression during adolescence may be associated with broader functional outcomes that impact individuals and society, and may be mitigated through intervention and effective policy. Symptom presentation varies by severity and individual, but the disorder involves the co-occurrence of feelings of sadness, loss of interest or pleasure, changes in weight and sleep, increased fatigue, feelings of worthlessness or guilt, and suicidal ideation4. Depression can be devastating to multiple facets of an individual’s life, extending beyond sheer emotional distress. Compared to healthy controls, individuals who are currently depressed have diminished academic achievement and occupational functioning, experience more interpersonal conflict, and are more likely to be afflicted with comorbid conditions7. The concurrence of multiple stressful life events with depression is termed stress generation, which expounds how the cycle of stress and depression affects an individual’s life and social world in a disruptive, reverberating fashion8. The present study explored whether onset of MDD during adolescence, a sensitive period of social development, is associated with poorer adulthood functioning in ways that extend beyond health.Major depressive disorder (MDD) is one of the leading causes of disability worldwide9, the harmful effects of depression and other psychiatric disorders may be amplified. Adolescence is a sensitive and transitional period of development during which individuals undergo dramatic biological, cognitive, and social changes12. It typically starts with pubertal maturation (beginning around age 10) and ends when individuals take on independent social roles and adult responsibilities12. During this time, adolescents are learning to navigate an increasingly complex social world while more sensitive to social stimuli, social deprivation, and peer rejection13. Superimposing a syndrome that involves social withdrawal, amotivation, and profound changes to weight and sleep that have both visible and cognitive consequences may have lifelong sequelae. This possibility has extraordinary scope given that the prevalence of depression more than doubles during the transition from childhood (2–4%) to adolescence (4% to 14%)15, and at least half of the individuals who develop depression in their lifetimes experience their first depressive episode by age 14, and three-fourths by age 2416.During sensitive periods of psychosocial development18. Symptom presentation differs between adolescent females and males, with females being more likely to display internalizing problems  while males typically show more externalizing behaviors 20. Females are more likely to attempt suicide, while males are more likely to complete suicide due to the lethality of the methods used21. These differences in symptom presentation may lead to differential sequelae in adulthood.Early adolescence may be a more stressful time for females than males due to the cooccurrence of multiple stressors. Early maturing girls are especially vulnerable because of shifts in body image and the increased likelihood of being sexualized or harassed22. They found that depression during childhood or adolescence was associated with higher risk of anxiety, illicit substance use disorders, worse health, and poorer social functioning in adulthood22. Further, a meta-analysis published in 2019 reviewed 31 articles of 24 different cohorts and reported consistent associations between depression during adolescence and a wide range of negative functional outcomes an average of 9 years later, notably lower academic achievement and higher rates of unemployment23. Taken together, there is clear evidence that experiencing depression during adolescence has broad implications for adulthood functioning, though no studies to our knowledge have examined this association beyond age 35, or addressed the potential modifiability of these long-term sequelae. Indeed, efforts to develop effective depression programs in adolescence have been promising29, though their effects have seldom been followed for as long as 1 year. Therefore, we have little evidence to inform whether effective treatment for depression during adolescence would also confer benefits for functioning in adulthood.For effective policy and intervention, there remains much to know about the adulthood functioning of individuals who experienced depression during adolescence. One recent seminal study followed a sample of 1420 youth from age 9 to age 30 and assessed for psychiatric disorders up to 8 times between the ages of 9 and 16 yearsThe present study sought to extend these gaps in our knowledge by leveraging data from the National Comorbidity Survey Replication (NCS-R), and compared adulthood functioning in nine domains between individuals with a history of depression that began during adolescence (between the ages of 10 to 20) to those whose first episode occurred in adulthood (age 21 and older). Consistent with theoretical models of adolescence as a sensitive period of development, we hypothesized that adolescent-onset participants would be less likely to reach social milestones , more likely to have poorer occupational outcomes , and more likely to have poorer health . We further hypothesized these patterns would differ when looking at females and males separately, and would be less robust among participants who received treatment for their depression during adolescence.30. The most recent replication, NCS-R (n = 9282), was completed between February 2001 and April 2003 and reports on the prevalence and correlates of mental disorders with a new nationally representative household survey. The NCS-R includes assessments based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) diagnostic criteria31. All study procedures were approved by the Human Subjects Committees of both Harvard Medical School and the University of Michigan and all methods were performed in accordance with the relevant institutional and legal guidelines and regulations.The present study included 3360 adults  who participated in the National Comorbidity Survey Replication (NCS-R) and experienced a major depressive episode at some point in their life. Participants were 22.8% African American, 7.5% Asian, 47.5% Caucasian, and 19.9% Latino. The National Comorbidity Survey (NCS) was a survey of English-speaking household residents in the United States above the age of 18 designed to collect nationally representative data on the prevalence, risk factors, and consequences of psychiatric disorders within the United States32. All participants provided informed consent to participate. Respondents completed surveys using computer-assisted personal interview (CAPI) methods in their own home, administered by trained interviewers.Participants in the NCS-R were recruited using a multi-stage clustered area probability sample of households, returning a 70.9% response rate and a nationally representative sample of English-speaking adults (aged 18 or older) living in non-institutionalized civilian households within the contiguous United States33. The interview was conducted in two parts. Part I was administered to all participants and focused on assessing mood disorders, anxiety disorders, behavior disorders, eating disorders, and substance use disorders. Part II was administered only to participants who (1) met lifetime criteria for any of the disorders assessed in Part I, (2) met subthreshold lifetime criteria for any disorder and received treatment for one of them, or (3) ever made a plan to commit suicide or attempted suicide32. CIDI diagnoses and diagnoses based on the Structured Clinical Interview for DSM-IV (SCID) showed good levels of consistency in a probability subsample for NCS-R participants for mood disorders34. For the present study, it was important to examine the age at which participants experienced their first episode of MDD. Participants were categorized as having “adolescent-onset” depression if they experienced their first episode of MDD between 10 and 20 years of age. The NCS-R study asked participants to indicate how old they were when they experienced their first episode of MDD. Participants reported depression onset between the ages of 4 and 89 years old. Individuals (n = 170) whose first depressive episode occurred before the age of 10 were excluded from analyses.Diagnostic assessments were completed in a face-to-face structured survey interview. Participants were administered the version of the World Health Organization (WHO) Composite International Diagnostic Instrument (CIDI) made for the World Mental Health (WMH) Survey Initiative, which included all the major classes of psychiatric disordersThe following adult functional outcomes were assessed via self-report on the face-to-face interview: marital status, employment history, number of biological children, household income, years of education, receipt of welfare assistance, arrests, and current obesity. Marital status was measured categorically with three options for participants to choose from: (1) “married/cohabiting”, (2) “divorced/separated/widowed”, and (3) “never married”. Their responses were then recoded as a dichotomous outcome; 1 “never married” vs 0 for all other responses. Participants who had been married also provided the “# [of] marriages ended in divorce/annulment”, which was recoded into 1 “ever divorced” or 0 for “never divorced”.Employment was measured categorically, with three options for participants to indicate their “work status”: (1) “employed”, (2) “unemployed”, and (3) “not in labor force” which was recoded for analyses into 1 “unemployed” vs 0 all other responses. Participants’ self-report of the number of children was recoded dichotomously for the analysis: 1 “no children” vs 0 for all other responses. Participants reported their years of education in the following categories: (0) “0–11 years”, (1) “12 years”, (2) “13–15 years”, and (3) “greater than or equal to 16 years”. Annual household income was measured continuously from $0 to $200,000 or greater, which was then recoded into quartiles for analysis: (0) lowest–17,499, (1) $18,000–$37,999, (2) $38,000–$69,999, (3) 70,000 or higher. Receipt of welfare was measured dichotomously based on participants 1 “yes” or 0 “no” responses to the question, “Have you ever received public assistance or welfare since turning age 18—such as Aid to Families with Dependent Children, General Assistance, or Temporary Assistance for Needy Families?” Participants also responded with 0 “true” or 1 “false” to whether they had “never been arrested.” Obesity was measured categorically, with individuals with a BMI > 29.9 categorized as 1 “obese” vs 0 all other BMI categories.Participants were asked to indicate their answer (in years) to, “How old were you the first time you got helpful/effective treatment for your sadness/discouragement/lack of interest?”. Responses ranged from 0 to 79 years (mean = 32.45 ± 14.35). Responses were recoded into a dichotomous variable such that values between 9 and 21 were coded as 1 ‘received helpful treatment during adolescence’ or 0 ‘did not receive helpful treatment during adolescence’.The present analyses controlled for age, gender, ethnic minority status, current MDD at the time of assessment, and the total number of years participants experienced at least one depressive episode. Current MDD was indexed by the occurrence of a major depressive episode (MDE) in the past 12 months. To obtain the total number of depressed years, the NCS-R survey asked participants, “how many different years in your life did you have at least one episode?”.https://www.icpsr.umich.edu/web/ICPSR/studies/20240. Logistic  and ordinal  regression models were used to look at differences in the odds of each adult outcome for participants with adolescent-onset relative to adulthood-onset depression status. All analyses were conducted in SPSS Version 28 using ordinal regression within the PLUM command and employed a logit link function. Positive coefficient estimates indicated higher log odds probabilities of the higher values for each outcome. Models for each outcome were conducted unadjusted, then adjusted for key covariates, then stratified by gender and self-reported treatment during adolescence. Model fit was determined by comparing −2 log likelihood of the adjusted models to the intercept only models using Χ2, and further evaluated using Pearson Χ2 tests of goodness-of-fit to the data. For all outcomes, adolescent-onset depression improved model fit relative to the intercept only models, all p < 0.001, and the predictors were a good fit to the data, all ps > 0.19. Some participants had missing data for number of children and employment status, therefore multiple imputation was used to maximize available data and the results from the pooled, imputed data across 5 imputations are reported. Post-hoc moderation analyses were conducted using the PROCESS Macro for SPSS35 where coefficient estimates for dichotomous outcomes were expressed in log-odds but are otherwise unstandardized coefficient estimates. Results should be considered statistically significant when p < 0.05, and a p < 0.006 corrects for multiple comparisons across the nine outcomes.The datasets analyzed for the current study are publicly available in the Inter-university Consortium for Political and Social Research (ICPSR) repository at the Institute for Social Research at the University of Michigan, Adolescent-onset depression occurred among approximately one third (34.9%) of NCS-R participants with any history of MDD. Table Rows 1 and 2 of Table Rows 3 and 4 of Table Only a very small proportion of the sample (1.4%) reported they received “helpful” treatment for their distress during adolescence. The association between adolescent-onset depression relative to adulthood-onset was not associated with any functional outcomes among participants who reported receiving treatment. However, among participants who did not report having helpful treatment during adolescence, those with adolescent-onset depression were less likely to be married, have children, had lower household incomes, but were less likely to be obese. Rows 5 and 6 of Table n = 307), 13.8% middle (n = 465), 11.9% late adolescence (n = 401), 10.7% emerging adulthood (n = 359), and 54.5% adulthood (n = 1828). Rows 7–10 of Table Among participants who experienced MDD during adolescence, age of onset was further categorized into early (10–13), middle (14–17), and late adolescence 18–20). The prevalence of depression onset across these groups was 9% early , p = 0.06, divorce, β = −0.53 (SE = 0.29), p = 0.07, educational attainment, b = −0.19 (SE = 0.10), p = 0.06, and welfare, β = −0.53 (SE = 0.29), p = 0.07. Gender did not moderate the effect of adolescent-onset depression on unemployment, β = −0.21 (SE = 0.42), p = 0.61, not having children, β = 0.07 (SE = 0.24), p = 0.77, household income, b = 0.02 (SE = 0.11), p = 0.88, being arrested, β = −0.34 (SE = 0.24), p = 0.17, or obesity, β = −0.13 (SE = 0.23), p = 0.57.Gender was a non-significant moderator when predicting never having been married, β = 0.47 (SE = 0.22), p = 0.06. Age of depression onset within adolescence did not moderate the effect of depression on never having been married, β = 0.22 (SE = 0.23), p = 0.33, divorce, β = −0.13 (SE = 0.25), p = 0.62, educational attainment, b = 0.02 (SE = 0.09), p = 0.80, β = −0.07 (SE = 0.35), p = 0.84, not having children, β = 0.22 (SE = 0.23), p = 0.33, household income, b = 0.08 (SE = 0.10), p = 0.43, being arrested, β = −0.26 (SE = 0.23), p = 0.28, or obesity, β = 0.08 (SE = 0.20), p = 0.69.Age of depression onset within adolescence was a non-significant moderator of the effect of depression on welfare use, β = −0.41  but some diverging outcomes in adulthood . Importantly, none of these associations were observed among participants who reported receiving helpful treatment during adolescence. These observations underscore the idea that adolescence is a sensitive period for social development, during which MDD may lead to cumulative long-term social impacts. Efforts to effectively treat adolescent depression may therefore have broader implications on society and social development that extend beyond mental health.23. Outcome-by-outcome comparisons across these studies are somewhat difficult to make because many past studies created composite indices for these domains, rather than looking at outcomes individually. Yet, a close look at the discrepancies in our findings highlight important ways in which the present data extend our knowledge. First, until the present study, no study has looked at associations between adolescent-onset depression and adulthood functional outcomes past the age of 35; on average adulthood outcomes were assessed at age 2423. Given that our sample was 42 ± 15 years, many functional outcomes take on greater meaning. Specifically, the meta-analysis of 31 studies on this topic (in which participants were an average of 24 years) observed no association between adolescent-onset depression and marital/relationship status23, while one study conducted in a 30-year-old sample observed a negative association between adolescent-onset depression and adulthood marital status22. However, it is not uncommon in industrialized nations for individuals to get married after age 30. Assessment of these social outcomes  in a developmental phase when the mode age for these milestones has passed offers a more interpretable test of the theory that adolescence is a sensitive period of social development that can be disrupted by MDD.Individuals who experienced their first depressive episode during adolescence were less likely to marry, have children, and reported lower household incomes compared to individuals who experienced their first depressive episode during adulthood. The gestalt of these findings were consistent with the existing literature that adolescent-onset depression is associated with poorer adulthood functioning in social, health, and financial/educational domains23, namely the need for clarity on the role of gender. Importantly, gender did not moderate the association between adolescent-onset depression and any outcome. This suggests that the potential effect of experiencing a depressive episode during adolescents on adulthood functioning a similar for males and females. Notably, however, stratified analyses indicated that some outcomes were more relevant to one gender than the other. Within females, adolescent-onset depression was related to lower household income, whereas within males adolescent-onset depression was related to lower educational attainment. Previous studies have reported that adolescent-onset depression was associated with lower past-year income23 and worse financial outcomes overall22. Our observations extend these findings by suggesting that the pathway to worse financial outcomes may differ for adolescent males and females, such that for females income may be more relevant than educational attainment while the inverse may be more relevant for males. This may justify the addition of gender-specific modules in adolescent treatment programs that focus on applying skills learned in the program to social and occupational goals.The present study also responded to an important call to actionWe also examined the question of developmental sensitivity within adolescence as well. We examined the association between adolescent-onset depression and risk for each adulthood outcome between early, middle, and late adolescence as well as within each of these age groups separately. Given the recurrent nature of depression as well as the multifactorial human development that occurs in adolescence, we expected individuals with onset reported in early adolescence might be the most vulnerable to the potential long-term consequences of the illness. Yet, this hypothesis was not confirmed. Importantly, the age of depression onset during adolescence did not moderate the association between experiencing depression during adolescence overall and any adulthood outcomes. However, the outcomes associated with adolescent- relative to adulthood-onset depression varied within these onset groupings. Depression onset during late adolescence was associated with the largest number of, and perhaps the most consequential , outcomes. This observation may inspire optimism that effective treatment during emerging adulthood or prevention during high school may be ideal contexts in which to mitigate the broader sequelae of adolescent-onset depression. Further, poorer adulthood functioning among participants with depression onset between ages 18–20 may reflect the protective nature of schools, families, and communities of origin during earlier stages of adolescence in mitigating the broader impacts of adolescent-onset depression which could be leveraged for treatment.22. Second, participants in this sample could have been adolescents between 1921 and 1996, but most were adolescents in the 1960s and 1970s. Since then, both recognition and prevalence of depression among adolescents has increased37, and social contexts  during adolescence have changed. Attention to whether generational differences in social contexts around adolescence mitigate or exacerbate the long-term sequelae of depression across the lifespan are needed, particularly to implement effective policy. Third, individuals who had childhood-onset depression were excluded from our analyses (n = 170). The rationale was that only data on age of onset for a first episode was available, not each subsequent episode; it is unknown which individuals with childhood-onset depression also had depression during adolescence. However, given the known chronic and recurrent course of depression39, it’s likely that many of these individuals also experienced depressive episodes during adolescence, and may therefore be susceptible to the developmental sensitivity being tested here. Third, there are many approaches to defining adolescence; 10–24 has been described as the “adolescence window” due to the increased importance of social development13 whereas other studies limit the range to 13 to 17.9 years40. Our age range (10–20 years) was inclusive of puberty while also adhering to policy that regards age 21 as a meaningful social and legal transition to adulthood in many respects. Furthermore, socioeconomic status (SES) in childhood has been linked to risk for psychopathology in childhood and adolescence41. The NCS-R assessed childhood SES with one item that asked about receipt of public assistance while growing up. While our results remain unchanged when accounting for this variable, only 5.3% of the sample endorsed the item and the low frequency, categorical nature of the variable was unlikely to reflect all of the aspects of childhood SES recognized as important in developmental psychopathology.The conclusions of this study should be considered in the context of its strengths and limitations. First, this data is cross-sectional and participants were asked to retrospectively recall disorders and personal life events. We cannot infer whether depression leads to these outcomes in adulthood or which third variables are at play, though the similar pattern of results with prospective, longitudinal observations provide compelling convergent validity42. Cognitive-behavioral programs are effective at reducing adolescents’ depressive symptoms in the short term44, and can be delivered in school-based settings45. Further, interventions targeting parenting behaviors are associated with improved outcomes for youth generally46, as well as depressive and internalizing symptoms specifically47. This at least means that many interventions show promise in mitigating or even delaying the onset of depression. However, given that late-adolescence was associated with the largest number of adulthood outcomes, attention to delaying depression past this period (ages 18–20) must be given. Further, the programs thus far have focused on measuring symptom severity post-intervention rather than broader social outcomes whose measurement may illuminate causal trajectories between adolescent-onset depression and functional outcomes in adulthood.Despite these limitations, the present study contributes to a growing literature documenting the breadth of sequelae that follow depressive episodes during adolescence and the potential benefit of directing more resources to their treatment and even prevention. Indeed, while this study focused on categorically comparing individuals with and without adolescent-onset depression, even subthreshold depressive symptoms have implications for functioning


Fe3O4@SiO2 core could introduce mesopores and result in capillary condensation in MOF composites, which aggravated with the dosage of Fe3O4@SiO2. The as-synthesized MOF composites could be rapidly retrieved from aqueous solution via magnetic separation in 10 seconds. pH imposed an important effect on Au (III) adsorption by governing the ion exchange and electrostatic interaction between Au (III) anions and adsorbents, and the optimal adsorption happened at pH 7. The adsorption process fitted well with the pseudo-second order kinetics model and Langmuir adsorption model. The maximum adsorption capacity of Au (III) by FSUN–10 and FSUN–50 at 298 K were determined to be 611.18 mg∙g−1 and 463.85 mg∙g−1, respectively. Additionally, Au (III) uptakes increased with temperature. Beyond experiments, the adsorption mechanisms were thoroughly studied through systematic characterization, molecular dynamics simulation (MDS) and density functional theory (DFT) study. It was verified that Au (III) was adsorbed via coordination to hydroxyl and amino groups and was reduced to Au (I) and Au (0) by amino groups. The diffusion coefficient of Au (III) along UiO–66–NH2 was calculated to be 5.8 × 10−5 cm2∙s−1. Moreover, the magnetic Zr-based MOF composites exhibit great industrial value in gold recycling with high adsorption selectivity and good recyclability.In this work, the magnetic Zr-based MOF composites with excellent retrievability were prepared using Fe Gold is widely utilized in jewelry, medical, chemical, electronic and electrical industries due to its excellent physical and chemical properties . HoweverAdsorption is widely used in the preconcentration and recovery of gold from aqueous solution since it has the advantages of being a short process, a clean working environment and easy to operate . Activat−1 at pH 2.5. Tang et al.  is assumed to be 1. The species of Au (III) complex in aqueous solution is determined by the pH value. In this work, the primary species of Au (III) complex in aqueous solution was AuCl4− in acidic solution when pH was smaller than 5.8, while hydrolyzed to be AuCl3OH− when pH was bigger than 5.8.The hydrolysis constant 4− and -NH3+ in acidic solution are as Equation (13) [ion (13) :(13)R−NHpzc of FSUN–10 and FSUN–50 was determined to be 5.58 and 5.54, respectively. Therefore, it can be concluded that in acidic solution, AuCl4− was adsorbed by FSUN through ion exchange and electrostatic attraction, while the protonation of amino groups reduced their ability to chelate Au (III). With an increase in pH, -NH3+ gradually deprotonated and the ability of amnio groups to chelate Au (III) was recovered, thus Au (III) uptakes also increased gradually. While in alkaline solution, Au (III) anions hydrolyzed to be AuCl3OH− and both FSUN–10 and FSUN–50 were negatively charged, the electrostatic repulsion between AuCl3OH− and FSUN prevented Au (III) adsorption, leading to the decrease in Au (III) uptakes. Therefore, pH study indicated that the electrostatic interaction between FSUN and Au (III) played a significant role in Au (III) adsorption and acidic environment is more favorable compared to alkaline environment. As shown in −1 with incubation time ranging from 0 to 1440 min. As 2. Since the majority species of Au (III) in chlorinated acidic solution are AuCl4− with effective size being 5.4 Å [4− could diffuse conveniently in their internal pore. In the meantime, UiO–66–NH2 could provide sufficient adsorption sites  for Au (III) at the initial stage, contributing to the fast adsorption at initial stage. And the adsorption rate gradually declined as the adsorption sites were employed. The adsorption kinetics of Au (III) on FSUN was studied by examining the relationship between Au (III) uptakes and incubation time. The initial Au (III) concentration was kept at 400 mg∙Lng 5.4 Å , which w2 and residual sum of squares (RSS) were used to evaluate the fitting reliability. Higher R2 and smaller RSS usually means higher predicting accuracy. Clearly, the predicted values by pseudo-second order kinetics model were closer to the experimental values than pseudo-first order kinetics model  uptakes were fitted to the incubation time using pseudo-first order kinetics model (Equation (3)) and pseudo-second order kinetics model (Equation (4)). And the adjusted fitting coefficients Rcs model b,c, and  FSUN–50 , respectMoreover, the kinetics constant for Au (III) uptakes by FSUN–10 were bigger than FSUN–50, suggesting that Au (III) uptakes by FSUN–10 was more quickly, verified by the shorter time taken to reach adsorption equilibrium, suggesting that the adsorption rate of Au (III) on FSUN decreased with an increase in the dosage of magnetic nanoparticles due to the reduced surface area and adsorption sites. In this work, the decrease in adsorption rate caused by magnetic functionalization was not so big and still within the acceptable range.−1, temperature ranging from 298 K to 318 K, and the incubation time was fixed at 1440 min. As shown in −1, then the increase gradually slowed down and finally plateaued when reaching 800 mg∙L−1. In the meantime, Au (III) uptakes by FSUN increased with temperature, suggesting that the adsorption process was endothermic. Moreover, Au (III) uptakes by FSUN–10 and FSUN–50 were close at low initial concentration, while the difference in Au (III) uptakes enlarged with increasing initial concentration. This is because FSUN–10 could provide more adsorption sites for Au (III) than FSUN–50 due to less amounts of magnetic nanoparticles were introduced. Furthermore, Au (III) uptakes at different temperature were fitted to the residual Au (III) concentration using Langmuir and Freundlich adsorption models, and the fitting reliability was also evaluated by R2 and RSS. For both FSUN–10 and FSUN–50, Langmuir model fitted better than Freundlich model  uptakes by FSUN, the adsorption test was conducted with initial Au (III) concentration varying from 0 to 1600 mg∙Lch model c,d, veri fitting . Thus, i2 synthesized by authors in the previous work, the adsorption abilities of Zr-MOF composites decreased slightly after magnetic functionalization, which is expectable since magnetic functionalization reduced the overall surface area and the adsorption ability of Fe3O4@SiO2 towards Au (III) was smaller than MOFs [The maximum uptakes of Au (III) on some nanomaterials reported in the literature are summarized in han MOFs . This wo3O4@SiO2 functionalization rendered the composites enhanced operability indicated by easy separation. The overwhelmingly advantageous adsorption towards Au (III) can be ascribed to the inherent accessible pores in UiO–66–NH2. As FSUN–50 has balanced magnetic properties and good adsorption ability, it was selected as a more promising candidate for industrial application. Therefore, we used FSUN–50 as the model to investigate the underlying adsorption mechanism.As presented above, FUSN showed excellent adsorption ability towards Au (III). Magnetic properties after Fe−1 and 3344 cm−1 merged into one wide peak at 3400 cm−1, which could be associated to the coordination of Au (III) to hydroxyl and amino groups [−1 decreased sharply after Au (III) adsorption, implying that amino groups was responsible for the reduction of Au (III) during adsorption due to its electron-donating property.XRD patterns in o groups . MoreoveTo further investigate the chemical interaction between Au (III) and amino groups, XPS survey scan and high-resolution scan of Au 4f and N 1s for FSUN–50 before and after Au (III) adsorption was conducted. XPS survey scan  results 4− on UiO–66–NH2 at the atomic and molecular level. 4−, part of water molecules and H+ entered the UiO–66–NH2 framework, suggesting that AuCl4− could diffuse along the internal pore of UiO–66–NH2 and thus being adsorbed. 2 (0 0 1) plane, which could be further divided into three area, including the aqueous solution on both sides  and UiO–66–NH2 in the middle . Peaks at z = 12.97 Å and 48.88 Å demonstrated that Au (III) entered into UiO–66–NH2, which agreed well with 4− along the framework of UiO–66–NH2, the mean squared displacement (MSD) of Au during the simulation process was studied and results are shown in −5 cm2∙s−1.With all these finding based on experimental data analysis, MDS and DFT study were performed to further study the adsorption behavior of AuCl4− adsorption on UiO–66–NH2. The red and blue color means electron acceptance and loss, respectively. N atoms from amino groups lost electrons during adsorption (area 1) while Au atoms accepted electrons (area 2), confirming that electrons transferred from amino groups to Au (III). It was verified in theory that amino groups contributed to the reduction of Au (III) to Au (I) and Au (0) during the adsorption process, which is in good correspondence with IR, XRD and XPS results.3+ and Au (III) anions. For Au (III) coordinated to amino groups, electrons transfer from amino groups to Au (III), resulting in the in-situ reduction of Au (III) to Au (I) and Au (0). Overall, the mechanism of Au (III) adsorption by FSUN incorporates diffusion, ion exchange, coordination, electrostatic interaction and reduction, and chemisorption is the rate-determining step, evidenced by the adsorption kinetics study.Based on the previous findings achieved by batch adsorption test, experimental characterization and computational modelling, mechanisms underpinning Au (III) adsorption on FSUN could be summarized as follows. Initially, FSUN provides accessible pores and allows Au (III) to diffuse along its framework due to the developed pore structure and appropriate pore diameter. Then, Au (III) is adsorbed through ion exchange and is coordinated to the hydroxyl and amino groups, facilitated by the electrostatic attraction between -NH2+, Ni2+, Co2+ and Zn2+ in aqueous solution after extraction of gold from e-waste through hydrometallurgy, the selectivity towards Au (III) is an important factor that must be considered in practice. In this work, to study the selectivity of magnetic MOFs, AuCl4− was mixed with Cu2+, Ni2+, Co2+ and Zn2+, and the concentration of all the metal ions was fixed at 200 mg∙L−1. Then, FSUN was incubated with the mixed solution to test the adsorption selectivity. The uptakes of Au (III) by FSUN–10 and FSUN–50 were 199.22 mg∙g−1 and 188.09 mg∙g−1, while the uptakes of Co2+, Cu2+, Ni2+ and Zn2+ by FSUN were all smaller than 50 mg∙g−1  was reduced to Au (I) and Au (0) by amino groups during adsorption, and the standard electrode potential of Au (III)|Au (I) and Au (III)|Au (0) was 1.50 eV and 1.41 eV, respectively, suggesting that Au (III) was prone to be reduced to Au (I) and Au (0). However, the standard electrode potential of Cu2+|Cu (0), Ni2+|Ni (0), Co2+|Co (0), Zn2+|Zn (0) was 0.34 eV, −0.25 eV, −0.28 eV, and −0.7628 eV, which was much smaller than Au (III)|Au (I) and Au (III)|Au (0). Therefore, the reduction of co-existing ions during adsorption was almost impossible. FSUN possessed high priority towards Au (III) than impurity metal ions in the adsorption process, making them ideal candidates as Au (III) adsorbent in practice.Since Au (III) always coexist with impurity metal ions such as Cu0 mg∙g−1 , suggest−1 to 23.98 mg∙g−1 after five cycles, and the regenerated adsorbents retained 72.60% of the initial adsorption capacity. FSUN–50 performed better than FSUN–10 and 76.06% of initial adsorption ability remained after recycling five times. In addition, the TEM image of regenerated FSUN–50  uptakes increased with temperature, indicating that Au (III) adsorption on FSUN was endothermal. The thermodynamic studies indicated that the adsorption of Au (III) on FSUN was driven by the increase in system entropy. The adsorption mechanisms were investigated systematically through material characterization, MDS and DFT study. In addition to ion exchange and electrostatic interaction, Au (III) could be adsorbed via coordination to hydroxyl and amino groups and reduced to Au (I) and Au (0) by amino groups, which was verified by IR spectrum, XPS analysis and DFT study. The diffusion coefficient of AuCl4− in UiO–66–NH2 was calculated to be 5.8 × 10−5 cm2∙s−1. Moreover, FSUN showed excellent selectivity towards Au (III) and high adsorption ability after five cycles. FSUN–50, evaluated with balanced magnetic properties and adsorption capability, was considered to be a promising candidate applicable in industrial Au (III) recovery.In this work, magnetic MOF composites were prepared to effectively adsorb Au (III) from aqueous solution and favor adsorbents retrieval. Effects of magnetic functionalization on the structural properties and adsorption performance of MOF composites, as well as the adsorption mechanisms were systematically investigated. Overall, magnetic functionalization introduced mesopores into MOF composites and enabled them to be rapidly retrieved via magnetic separation. pH was critical to Au (III) adsorption on FSUN by governing the ion exchange and electrostatic interaction between Au (III) anions and adsorbents, and the optimal adsorption capacities were achieved at pH 7. The kinetics and isotherms of Au (III) adsorption could be described by pseudo-second order kinetics model and Langmuir adsorption model, respectively. The maximum adsorption capacities of Au (III) by FSUN–10 and FSUN–50 were 611.18 mg∙g