Sex differences in mental health among older adults: investigating time trends and possible risk groups with regard to age, educational level and ethnicity

Objectives: Older women report lower mental health compared to men, yet little is known about the nature of this sex difference. Therefore, this study investigates time trends and possible risk groups. Method: Data from the Doetinchem Cohort Study (DCS) and the Longitudinal Aging Study Amsterdam (LASA) were used. General mental health was assessed every 5 years, from 1995 to 1998 onwards (DCS, n = 1412, 20-year follow-up, baseline age 55–64 years). Depressive and anxiety symptoms were assessed for two birth cohorts, from 1992/1993 onwards (LASA cohort 1, n = 967, 24-year follow-up, age 55-65 years,) and 2002/2003 onwards (LASA cohort 2, n = 1002, 12-year follow-up, age 55–65 years) with follow-up measurements every 3–4 years. Results: Mixed model analyses showed that older women had a worse general mental health (−6.95; −8.36 to 5.53; range 0–100, ∼10% lower), more depressive symptoms (2.09; 1.53–2.63; range 0-60, ∼30% more) and more anxiety symptoms (0.86; 0.54–1.18; range 0–11, ∼30% more) compared to men. These sex differences remained stable until the age of 75 years, where after they decreased due to an accelerated decline in mental health for men compared to women. Sex differences and their course by age were consistent over successive birth cohorts, educational levels and ethnic groups (Caucasian vs. Turkish/Moroccan). Conclusion: There is a consistent female disadvantage in mental health across different sociodemographic groups and over decennia (1992 vs. 2002) with no specific risk groups

Adherence to the EAT-Lancet Healthy Reference Diet in Relation to Risk of Cardiovascular Events and Environmental Impact: Results From the EPIC-NL Cohort

Background The Healthy Reference Diet (HRD) was created to formulate dietary guidelines that would be healthy and sustainable. We aimed to construct a diet score measuring adherence to the HRD and to explore its association with cardiovascular events and environmental impact. Methods and Results We included 35 496 participants from the population-based EPIC-NL (European Prospective Investigation into Cancer and Nutrition-Netherlands) study. HRD scores were calculated using data from food frequency questionnaires (0-140). Data on morbidity and mortality were retrieved through linkage with national and death registries. Data on environmental impact indicators were obtained from life cycle assessments. Associations between adherence to the HRD and cardiovascular events were estimated with Cox proportional hazard models. Linear regression analysis was conducted for the adherence to the HRD and each environmental indicator. High adherence to the HRD was associated with 14%, 12%, and 11% lower risks of cardiovascular disease (hazard ratio [HR]Q4vsQ1, 0.86 [95% CI, 0.78-0.94]), coronary heart disease (HRQ4vsQ1, 0.88 [95% CI, 0.78-1.00]), and total stroke (HRQ4vsQ1, 0.89 [95% CI, 0.72-1.10]), respectively. High HRD adherence was associated with 2.4% (95% CI, -5.0 to 0.2) lower greenhouse gas emissions, 3.9% (95% CI, -5.2 to -2.6) less land use, 0.5% (95% CI, -2.6 to 1.6), less freshwater eutrophication, 3.3% (95% CI, -5.8 to -0.8), less marine eutrophication, 7.7% (95% CI, -10.8 to -4.6), less terrestrial acidification, and 32.1 % (95% CI, 28.5-35.7) higher blue water use. Conclusions High adherence to the HRD was associated with lower risk of cardiovascular disease, coronary heart disease, and modestly lower levels of most environmental indicators but a higher level of blue water use

The sex difference in gait speed among older adults: how do sociodemographic, lifestyle, social and health determinants contribute?

Background: This study explores whether a sex difference in sensitivity to (strength of the association) and/or in exposure to (prevalence) determinants of gait speed contributes to the observed lower gait speed among older women compared to men. Methods: Data from the Longitudinal Aging Study Amsterdam (LASA) were used. In total 2407 men and women aged 55–81 years were included, with baseline measurements in 1992/2002 and follow-up measurements every 3–4 years for 15/25 years. Multivariable mixed model analysis was used to investigate sex differences in sensitivity (interaction term with sex) and in exposure to (change of the sex difference when adjusted) socio-demographic, lifestyle, social and health determinants of gait speed. Results: Women had a 0.054 m/s (95 % CI: 0.076 − 0.033, adjusted for height and age) lower mean gait speed compared to men. In general, men and women had similar determinants of gait speed. However, higher BMI and lower physical activity were more strongly associated with lower gait speed in women compared to men (i.e. higher sensitivity). More often having a lower educational level, living alone and having more chronic diseases, pain and depressive symptoms among women compared to men also contributed to observed lower gait speed in women (i.e. higher exposure). In contrast, men more often being a smoker, having a lower physical activity and a smaller personal network size compared to women contributed to a lower gait speed among men (i.e. higher exposure). Conclusions: Both a higher sensitivity and higher exposure to determinants of gait speed among women compared to men contributes to the observed lower gait speed among older women. The identified (modifiable) contributing factors should be taken into account when developing prevention and/or treatment strategies aimed to enhance healthy physical aging. This might require a sex-specific approach in both research and clinical practice, which is currently often lacking

Self-Rated Health among Older Adults: Longitudinal Analyses Examining Sex Differences across Different Birth Cohorts and Educational Levels

Introduction: Given the known female disadvantage in physical and mental health, this study aimed to investigate sex differences in self-rated health (SRH) among older adults, considering the longitudinal course by age, birth cohort, and educational level. Methods: Data from birth cohort 1911–1937 with baseline age 55–81 years (n = 3,107) and birth cohort 1938–1947 with baseline age 55–65 years (n = 1,002) from the Longitudinal Aging Study Amsterdam (LASA) were used. Mixed model analyses were used to examine sex differences in SRH (RAND General Health Perception Questionnaire [RAND-GHPQ], range 0–16) over the age course, testing for effect modification by the birth cohort and educational level (low, middle, high). Results: For both sexes, a decline in SRH was seen with increasing age. Over the age course, there was no significant sex difference in SRH within the older (1911–1937) birth cohort (0.13 lower score on SRH for women compared to men, 95% CI: −0.35 to 0.09) and only a small sex difference in the more recent (1938–1947) birth cohort (0.35 lower score on SRH for women compared to men [95% CI: −0.69 to −0.02], p = 0.04). There was no significant cohort difference in the size of the sex difference (p = 0.279). Those with a higher level of education reported a higher SRH, but between educational levels, there was no significant difference in the size of the sex difference in SRH. Discussion: In this study, no relevant sex difference in SRH over the age course was observed among older adults. Future research on SRH trajectories by sex during aging should take health-related, cognitive, psychosocial, and behavioral factors into account

Anti-Mullerian hormone levels and risk of type 2 diabetes in women

AIMS/HYPOTHESIS: Given its role in ovarian follicle development, circulating anti-Müllerian hormone (AMH) is considered to be a marker of reproductive ageing. Although accelerated reproductive ageing has been associated with a higher risk of type 2 diabetes, research on the relationship between AMH and type 2 diabetes risk is scarce. Therefore, we aimed to investigate whether age-specific AMH levels and age-related AMH trajectories are associated with type 2 diabetes risk in women. METHODS: We measured AMH in repeated plasma samples from 3293 female participants (12,460 samples in total), aged 20-59 years at recruitment, from the Doetinchem Cohort Study, a longitudinal study with follow-up visits every 5 years. We calculated age-specific AMH tertiles at baseline to account for the strong AMH-age correlation. Cox proportional hazards models adjusted for confounders were used to assess the association between baseline age-specific AMH tertiles and incident type 2 diabetes. We applied linear mixed models to compare age-related AMH trajectories for women who developed type 2 diabetes with trajectories for women who did not develop diabetes. RESULTS: During a median follow-up of 20 years, 163 women developed type 2 diabetes. Lower baseline age-specific AMH levels were associated with a higher type 2 diabetes risk (HR T2vsT3 1.24 [95% CI 0.81, 1.92]; HR T1vsT3 1.62 [95% CI 1.06, 2.48]; p trend  = 0.02). These findings seem to be supported by predicted AMH trajectories, which suggested that plasma AMH levels were lower at younger ages in women who developed type 2 diabetes compared with women who did not. The trajectories also suggested that AMH levels declined at a slower rate in women who developed type 2 diabetes, although differences in trajectories were not statistically significant. CONCLUSIONS/INTERPRETATION: We observed that lower age-specific AMH levels were associated with a higher risk of type 2 diabetes in women. Longitudinal analyses did not show clear evidence of differing AMH trajectories between women who developed type 2 diabetes compared with women who did not, possibly because these analyses were underpowered. Further research is needed to investigate whether AMH is part of the biological mechanism explaining the association between reproductive ageing and type 2 diabetes. Graphical abstract

a multinational cohort study

Funding Information: The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer , Institut Gustave-Roussy , Mutuelle Générale de l'Education Nationale , Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid , German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro -AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds , Dutch Pittsburgh Foundation , Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Foundation (FIS)– Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology –ICO (Spain); Swedish Cancer Society , Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). Funding Information: Austrian Academy of Sciences, Fondation de France, Cancer Research UK, World Cancer Research Fund International, and the Institut National du Cancer.The authors would like to thank the EPIC study participants and staff for their valuable contribution to this research. The authors would also like to especially thank Fernanda Rauber, Eszter P. Vamos, and Kiara Chang for their contribution to implement the Nova classification in the EPIC study, and Bertrand Hemon and Corinne Casagrande for preparing the EPIC databases. We acknowledge the use of data from the EPIC-Aarhus cohort, PI Kim Overvad; the EPIC-Asturias cohort, PI J. Ramón Quirós; the EPIC-Umea cohort, PIs Mattias Johansson und Malin Sund; the EPIC-Norfolk cohort; and the National Institute for Public Health and the Environment (RIVM), Bilthoven, the Netherlands, for their contribution and ongoing support to the EPIC Study. Funding: Reynalda Cordova is a recipient of a DOC Fellowship of the Austrian Academy of Sciences. This study was financially supported by the Fondation de France (FDF, grant no. 00081166, HF). This work was also supported by Cancer Research UK (C33493/A29678), the World Cancer Research Fund International (IIG_FULL_2020_033), and the Institut National du Cancer (INCa no. 2021–138). The coordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave-Roussy, Mutuelle Générale de l'Education Nationale, Institut National de la Santé et de la Recherche Médicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Pittsburgh Foundation, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Foundation (FIS)–Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology–ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Skåne and Västerbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford). (United Kingdom). Disclaimer: Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. Funding Information: Funding: Reynalda Cordova is a recipient of a DOC Fellowship of the Austrian Academy of Sciences. This study was financially supported by the Fondation de France (FDF, grant no. 00081166 , HF). This work was also supported by Cancer Research UK (C33493/A29678), the World Cancer Research Fund International (IIG_FULL_2020_033), and the Institut National du Cancer (INCa no. 2021–138). Publisher Copyright: © 2023Background: It is currently unknown whether ultra-processed foods (UPFs) consumption is associated with a higher incidence of multimorbidity. We examined the relationship of total and subgroup consumption of UPFs with the risk of multimorbidity defined as the co-occurrence of at least two chronic diseases in an individual among first cancer at any site, cardiovascular disease, and type 2 diabetes. Methods: This was a prospective cohort study including 266,666 participants (60% women) free of cancer, cardiovascular disease, and type 2 diabetes at recruitment from seven European countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Foods and drinks consumed over the previous 12 months were assessed at baseline by food-frequency questionnaires and classified according to their degree of processing using Nova classification. We used multistate modelling based on Cox regression to estimate cause-specific hazard ratios (HR) and their 95% confidence intervals (CI) for associations of total and subgroups of UPFs with the risk of multimorbidity of cancer and cardiometabolic diseases. Findings: After a median of 11.2 years of follow-up, 4461 participants (39% women) developed multimorbidity of cancer and cardiometabolic diseases. Higher UPF consumption (per 1 standard deviation increment, ∼260 g/day without alcoholic drinks) was associated with an increased risk of multimorbidity of cancer and cardiometabolic diseases (HR: 1.09, 95% CI: 1.05, 1.12). Among UPF subgroups, associations were most notable for animal-based products (HR: 1.09, 95% CI: 1.05, 1.12), and artificially and sugar-sweetened beverages (HR: 1.09, 95% CI: 1.06, 1.12). Other subgroups such as ultra-processed breads and cereals (HR: 0.97, 95% CI: 0.94, 1.00) or plant-based alternatives (HR: 0.97, 95% CI: 0.91, 1.02) were not associated with risk. Interpretation: Our findings suggest that higher consumption of UPFs increases the risk of cancer and cardiometabolic multimorbidity. Funding: Austrian Academy of Sciences, Fondation de France, Cancer Research UK, World Cancer Research Fund International, and the Institut National du Cancer.publishersversionpublishe

Psychosocial factors, health behaviors and risk of cancer incidence:Testing interaction and effect modification in an individual participant data meta-analysis

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.</p

Psychosocial factors, health behaviors and risk of cancer incidence:Testing interaction and effect modification in an individual participant data meta-analysis

Depression, anxiety and other psychosocial factors are hypothesized to be involved in cancer development. We examined whether psychosocial factors interact with or modify the effects of health behaviors, such as smoking and alcohol use, in relation to cancer incidence. Two-stage individual participant data meta-analyses were performed based on 22 cohorts of the PSYchosocial factors and CAncer (PSY-CA) study. We examined nine psychosocial factors (depression diagnosis, depression symptoms, anxiety diagnosis, anxiety symptoms, perceived social support, loss events, general distress, neuroticism, relationship status), seven health behaviors/behavior-related factors (smoking, alcohol use, physical activity, body mass index, sedentary behavior, sleep quality, sleep duration) and seven cancer outcomes (overall cancer, smoking-related, alcohol-related, breast, lung, prostate, colorectal). Effects of the psychosocial factor, health behavior and their product term on cancer incidence were estimated using Cox regression. We pooled cohort-specific estimates using multivariate random-effects meta-analyses. Additive and multiplicative interaction/effect modification was examined. This study involved 437,827 participants, 36,961 incident cancer diagnoses, and 4,749,481 person years of follow-up. Out of 744 combinations of psychosocial factors, health behaviors, and cancer outcomes, we found no evidence of interaction. Effect modification was found for some combinations, but there were no clear patterns for any particular factors or outcomes involved. In this first large study to systematically examine potential interaction and effect modification, we found no evidence for psychosocial factors to interact with or modify health behaviors in relation to cancer incidence. The behavioral risk profile for cancer incidence is similar in people with and without psychosocial stress.</p

Depression, anxiety, and the risk of cancer:An individual participant data meta-analysis

Background: Depression and anxiety have long been hypothesized to be related to an increased cancer risk. Despite the great amount of research that has been conducted, findings are inconclusive. To provide a stronger basis for addressing the associations between depression, anxiety, and the incidence of various cancer types (overall, breast, lung, prostate, colorectal, alcohol-related, and smoking-related cancers), individual participant data (IPD) meta-analyses were performed within the Psychosocial Factors and Cancer Incidence (PSY-CA) consortium. Methods: The PSY-CA consortium includes data from 18 cohorts with measures of depression or anxiety (up to N = 319,613; cancer incidences, 25,803; person-years of follow-up, 3,254,714). Both symptoms and a diagnosis of depression and anxiety were examined as predictors of future cancer risk. Two-stage IPD meta-analyses were run, first by using Cox regression models in each cohort (stage 1), and then by aggregating the results in random-effects meta-analyses (stage 2). Results: No associations were found between depression or anxiety and overall, breast, prostate, colorectal, and alcohol-related cancers. Depression and anxiety (symptoms and diagnoses) were associated with the incidence of lung cancer and smoking-related cancers (hazard ratios [HRs], 1.06–1.60). However, these associations were substantially attenuated when additionally adjusting for known risk factors including smoking, alcohol use, and body mass index (HRs, 1.04–1.23). Conclusions: Depression and anxiety are not related to increased risk for most cancer outcomes, except for lung and smoking-related cancers. This study shows that key covariates are likely to explain the relationship between depression, anxiety, and lung and smoking-related cancers. Preregistration number: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=157677.</p