Previous findings on the link between metabolic syndrome (MetS) and the risk of all-cause dementia, Alzheimer's disease (AD), vascular dementia (VD), and cognitive decline are inconsistent. We systematically searched Embase, PubMed, Web of Science, the Cochrane Library, and Scopus up to June 2025 for prospective cohort studies conducted in community-based settings among adults aged 18 years or older that reported risk estimates (e.g., relative risks, hazard ratios, or odds ratios) for the association between MetS and the risk of dementia or cognitive decline. Risk of bias for studies were assessed using ROBINS-I, and the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach was applied to evaluate the certainty of evidence. Relative risks (RR) with 95%CI were computed using a random-effects inverse-variance method. Twenty-eight cohorts involving 6,753,197 participants were examined. MetS was significantly associated with a higher risk of all-cause dementia (RR = 1.11, 95%CI: 1.07-1.14; absolute risk difference: 2 more cases per 1000 persons), VD (RR = 1.33, 95%CI: 1.21-1.46; absolute risk difference: 7 more per 1000), and cognitive decline (RR = 1.24, 95%CI: 1.10-1.40; absolute risk difference: 5 more per 1000), all based on low-certainty evidence. No significant association was found between MetS and AD, with very low certainty. MetS components of hypertension, hyperglycemia, and low HDL-C were key risk factors, with a dose-response relationship observed between the number of MetS components and dementia risk. Subgroup analyses indicated MetS increased all-cause dementia risk in individuals under 70 (p-value for interaction: 0.048). MetS was positively associated with increased risk of all-cause-dementia, VD, and cognitive decline, but not AD. However, the effect sizes were modest and the certainty of evidence was low. Further prospective cohort studies are needed to confirm the associations.
{"title":"Metabolic syndrome and risk of dementia and cognitive decline: a systematic review and meta-analysis of prospective cohort studies from 6,753,197 participants.","authors":"Qi Wang, Luyi Zhang, Ruiqi Xu, Kexin Meng, Lutong Pan, Xiaoyu Zhang, Long Ge, Dongshan Zhu","doi":"10.1007/s11357-025-02014-9","DOIUrl":"https://doi.org/10.1007/s11357-025-02014-9","url":null,"abstract":"<p><p>Previous findings on the link between metabolic syndrome (MetS) and the risk of all-cause dementia, Alzheimer's disease (AD), vascular dementia (VD), and cognitive decline are inconsistent. We systematically searched Embase, PubMed, Web of Science, the Cochrane Library, and Scopus up to June 2025 for prospective cohort studies conducted in community-based settings among adults aged 18 years or older that reported risk estimates (e.g., relative risks, hazard ratios, or odds ratios) for the association between MetS and the risk of dementia or cognitive decline. Risk of bias for studies were assessed using ROBINS-I, and the Grading of Recommendation, Assessment, Development, and Evaluation (GRADE) approach was applied to evaluate the certainty of evidence. Relative risks (RR) with 95%CI were computed using a random-effects inverse-variance method. Twenty-eight cohorts involving 6,753,197 participants were examined. MetS was significantly associated with a higher risk of all-cause dementia (RR = 1.11, 95%CI: 1.07-1.14; absolute risk difference: 2 more cases per 1000 persons), VD (RR = 1.33, 95%CI: 1.21-1.46; absolute risk difference: 7 more per 1000), and cognitive decline (RR = 1.24, 95%CI: 1.10-1.40; absolute risk difference: 5 more per 1000), all based on low-certainty evidence. No significant association was found between MetS and AD, with very low certainty. MetS components of hypertension, hyperglycemia, and low HDL-C were key risk factors, with a dose-response relationship observed between the number of MetS components and dementia risk. Subgroup analyses indicated MetS increased all-cause dementia risk in individuals under 70 (p-value for interaction: 0.048). MetS was positively associated with increased risk of all-cause-dementia, VD, and cognitive decline, but not AD. However, the effect sizes were modest and the certainty of evidence was low. Further prospective cohort studies are needed to confirm the associations.</p>","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145540498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Existing proteomic aging clocks have been derived from the overall population, with little consideration of extended models tailored to individuals with different glycemic status. We aimed to quantify glycemic status-dependent proteomic signatures of aging and developed proteomic aging scores (ProAS) for health risk prediction. A total of 2923 plasma proteins were measured using Olink in 46,047 UK Biobank participants, including 37,353 with normoglycemia, 5977 with prediabetes, and 2717 with diabetes. Using a three-step screening approach, we identified 11, 23, and 21 representative protein biomarkers associated with all-cause mortality among individuals with normoglycemia, prediabetes, and diabetes, respectively. Three proteins (GDF15, EDA2R, and WFDC2) were shared across all groups, with GDF15 emerging as the top-ranked important protein in normoglycemia and prediabetes and WFDC2 in diabetes. The protein-based ProAS according to glycemic status showed significant associations with diverse health outcomes. Adding the ProAS in the models improved the predictive accuracy of mortality and incident diseases beyond conventional risk factors, but the performance progressively diminished as glycemic status deteriorated. In addition, 72, 51, and 36 out of 102 modifiable factors spanning seven categories were identified as determinants for ProRS in normoglycemia, prediabetes, and diabetes, respectively. Our findings extend the current proteomic clocks by revealing glycemic status-specific aging patterns and their ability to predict age-related outcomes, potentially refining risk stratification and targeted interventions for healthy aging.
{"title":"Glycemic status-dependent proteomic signatures of biological aging for health risk prediction.","authors":"Jiang Li,Jie Li,Xiaoqin Xu,Yuefeng Yu,Wenqi Shen,Ying Sun,Yanqi Fu,Xiao Tan,Ningjian Wang,Yingli Lu,Bin Wang","doi":"10.1007/s11357-025-01950-w","DOIUrl":"https://doi.org/10.1007/s11357-025-01950-w","url":null,"abstract":"Existing proteomic aging clocks have been derived from the overall population, with little consideration of extended models tailored to individuals with different glycemic status. We aimed to quantify glycemic status-dependent proteomic signatures of aging and developed proteomic aging scores (ProAS) for health risk prediction. A total of 2923 plasma proteins were measured using Olink in 46,047 UK Biobank participants, including 37,353 with normoglycemia, 5977 with prediabetes, and 2717 with diabetes. Using a three-step screening approach, we identified 11, 23, and 21 representative protein biomarkers associated with all-cause mortality among individuals with normoglycemia, prediabetes, and diabetes, respectively. Three proteins (GDF15, EDA2R, and WFDC2) were shared across all groups, with GDF15 emerging as the top-ranked important protein in normoglycemia and prediabetes and WFDC2 in diabetes. The protein-based ProAS according to glycemic status showed significant associations with diverse health outcomes. Adding the ProAS in the models improved the predictive accuracy of mortality and incident diseases beyond conventional risk factors, but the performance progressively diminished as glycemic status deteriorated. In addition, 72, 51, and 36 out of 102 modifiable factors spanning seven categories were identified as determinants for ProRS in normoglycemia, prediabetes, and diabetes, respectively. Our findings extend the current proteomic clocks by revealing glycemic status-specific aging patterns and their ability to predict age-related outcomes, potentially refining risk stratification and targeted interventions for healthy aging.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"179 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1007/s11357-025-01976-0
Frédéric Blanc, Benjamin Cretin, Candice Muller, Anne Botzung, Lea Sanna, Olivier Bousiges, Alix Ravier, Benoît Schorr, Pierre Anthony, Nathalie Philippi, Catherine Demuynck, Erik Sauleau
The cognitive evolution of early dementia with Lewy bodies (DLB) is less well known than that of Alzheimer's disease (AD). During dementia, DLB progresses like AD. The aim of this study was to analyze the long-term cognitive decline of early DLB. Participants were recruited for either mild cognitive impairment or mild dementia with a suspicion of DLB or AD, or as healthy older subjects (AlphaLewyMA study, NCT01876459, 2013). Using beta regression, we compared the slope of the Mini-Mental State Examination (MMSE) score of 110 DLB patients (DLB group), 57 AD patients (AD group), 19 DLB and AD patients (DLB + AD group), 30 patients with other cognitive diseases (DC group), and 31 healthy older controls (HC group). The mean follow-up was 4.8 years. All patients' groups had a significant decrease in MMSE score. The slope of MMSE decline of the DLB group (-0.49 point a year) was higher than the HC group (+ 0.03; P < .0001), lower than that of the AD (-2.78; P < .0001) and DLB + AD (-2.92; P < .0001) groups and not different from the DC group (-0.29; P = .8000). The variability of annual variations in MMSE score was greater in the DLB group (2.13 points) than in the AD group (1.73 points). There was no difference between patients' group in terms of death or admission to a nursing home. Patients with early DLB decline cognitively more slowly while fluctuating, whereas AD and DLB + AD patients decline markedly. These results suggest that there is a more dysfunctional than neurodegenerative phase at the beginning of DLB.
{"title":"Long term cognitive outcome of prodromal and mild dementia with Lewy bodies: a cohort study.","authors":"Frédéric Blanc, Benjamin Cretin, Candice Muller, Anne Botzung, Lea Sanna, Olivier Bousiges, Alix Ravier, Benoît Schorr, Pierre Anthony, Nathalie Philippi, Catherine Demuynck, Erik Sauleau","doi":"10.1007/s11357-025-01976-0","DOIUrl":"https://doi.org/10.1007/s11357-025-01976-0","url":null,"abstract":"<p><p>The cognitive evolution of early dementia with Lewy bodies (DLB) is less well known than that of Alzheimer's disease (AD). During dementia, DLB progresses like AD. The aim of this study was to analyze the long-term cognitive decline of early DLB. Participants were recruited for either mild cognitive impairment or mild dementia with a suspicion of DLB or AD, or as healthy older subjects (AlphaLewyMA study, NCT01876459, 2013). Using beta regression, we compared the slope of the Mini-Mental State Examination (MMSE) score of 110 DLB patients (DLB group), 57 AD patients (AD group), 19 DLB and AD patients (DLB + AD group), 30 patients with other cognitive diseases (DC group), and 31 healthy older controls (HC group). The mean follow-up was 4.8 years. All patients' groups had a significant decrease in MMSE score. The slope of MMSE decline of the DLB group (-0.49 point a year) was higher than the HC group (+ 0.03; P < .0001), lower than that of the AD (-2.78; P < .0001) and DLB + AD (-2.92; P < .0001) groups and not different from the DC group (-0.29; P = .8000). The variability of annual variations in MMSE score was greater in the DLB group (2.13 points) than in the AD group (1.73 points). There was no difference between patients' group in terms of death or admission to a nursing home. Patients with early DLB decline cognitively more slowly while fluctuating, whereas AD and DLB + AD patients decline markedly. These results suggest that there is a more dysfunctional than neurodegenerative phase at the beginning of DLB.</p>","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":" ","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145540444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-17DOI: 10.1007/s11357-025-02001-0
Yebo Yu,Wei Pan,Hewei Min,Siyu Dong,Zhen Huang,Yi Zeng,Xuxi Zhang,Xinying Sun
This study aimed to estimate the age, period, and cohort trends of multimorbidity among Chinese older adults from 2002 to 2022, and to explore how these three trends were affected by gender and whether or not living alone. Data were extracted from the China Longitudinal Healthy Living Survey (CLHLS) (2002-2022), and a total of 52,876 valid samples aged 65 to 105 were included. We measured 15 types of chronic diseases, and participants having two or more chronic diseases were considered to have multimorbidity. Hierarchical age-period-cohort cross-classified random-effects model (HAPC-CCREM) was applied to examine the age, period, and cohort dynamics of multimorbidity. The average age of participants is 85.91 ± 11.09 years. The temporal effect of age on multimorbidity is inverted U-shaped, with a higher probability in women aged 65-95 years and in men aged 95-105 years. The cohort trends of multimorbidity showed a rise, then a fall, then a rise again, and the period trends declined in fluctuations. Gender differences existed in age and cohort trends of multimorbidity. Moreover, the interaction of gender and living alone was significantly associated with the age trends of multimorbidity. Women living alone have a lower risk of multimorbidity than women living with others among all age groups. This study revealed the separate effects of age, period, and cohort on multimorbidity. The peaks of multimorbidity probability for different genders occur at different ages, and living alone had a protective effect on females, which both provide a scientific basis for the allocation of healthcare resources. The dynamic shifts of multimorbidity may help to forecast future multimorbidity trends and inform policies on population aging.
{"title":"Age-period-cohort analysis of multimorbidity prevalence among Chinese older adults: from 2002 to 2022.","authors":"Yebo Yu,Wei Pan,Hewei Min,Siyu Dong,Zhen Huang,Yi Zeng,Xuxi Zhang,Xinying Sun","doi":"10.1007/s11357-025-02001-0","DOIUrl":"https://doi.org/10.1007/s11357-025-02001-0","url":null,"abstract":"This study aimed to estimate the age, period, and cohort trends of multimorbidity among Chinese older adults from 2002 to 2022, and to explore how these three trends were affected by gender and whether or not living alone. Data were extracted from the China Longitudinal Healthy Living Survey (CLHLS) (2002-2022), and a total of 52,876 valid samples aged 65 to 105 were included. We measured 15 types of chronic diseases, and participants having two or more chronic diseases were considered to have multimorbidity. Hierarchical age-period-cohort cross-classified random-effects model (HAPC-CCREM) was applied to examine the age, period, and cohort dynamics of multimorbidity. The average age of participants is 85.91 ± 11.09 years. The temporal effect of age on multimorbidity is inverted U-shaped, with a higher probability in women aged 65-95 years and in men aged 95-105 years. The cohort trends of multimorbidity showed a rise, then a fall, then a rise again, and the period trends declined in fluctuations. Gender differences existed in age and cohort trends of multimorbidity. Moreover, the interaction of gender and living alone was significantly associated with the age trends of multimorbidity. Women living alone have a lower risk of multimorbidity than women living with others among all age groups. This study revealed the separate effects of age, period, and cohort on multimorbidity. The peaks of multimorbidity probability for different genders occur at different ages, and living alone had a protective effect on females, which both provide a scientific basis for the allocation of healthcare resources. The dynamic shifts of multimorbidity may help to forecast future multimorbidity trends and inform policies on population aging.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"29 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-16DOI: 10.1007/s11357-025-02007-8
Getachew Yideg Yitbarek,Jane Alty,Eddy Roccati,Katherine Lawler,Lynette R Goldberg
The development of blood-based phosphorylated tau (p-tau) biomarkers to identify Alzheimer's disease risk before cognitive decline offers a valuable opportunity for early intervention. Handgrip strength appears as a complementary non-invasive biomarker of dementia risk. Measurement of tongue strength may contribute further insight into the risk of cognitive decline and dementia. We examined associations between tongue strength, handgrip strength, three-finger pinch strength, and plasma p-tau181 in cognitively healthy older adults. A total of 158 cognitively healthy participants aged 50+ years (75.31% female; mean 69.32 years) were recruited. Participants' p-tau181 levels were sourced from a longitudinal study in which they were involved. Pearson correlation coefficients, independent t-tests, and multivariable linear regression analyses were performed to examine the association between strength measures and p-tau181 levels. Tongue strength was positively associated with handgrip (β = 0.53, 95% CI (0.25,0.81), p < 0.001) and pinch strength (β = 2.30, 95% CI (0.92,3.68), p = 0.001), with models adjusted for age, sex, body mass index, and educational level. Based on p-tau181 tertiles, the associations between tongue, handgrip, and pinch strength measures were evident only in the middle and highest tertiles. Handgrip (in adults 69 years and older) and pinch, but not tongue strength, were negatively associated with log-transformed p-tau181 levels. Although preliminary, findings support strength-based non-invasive biomarkers for risk stratification. Future studies are needed to investigate the relation between changes in strength measures with established measures of AD risk as well as frailty.
基于血液的磷酸化tau （p-tau）生物标志物的发展，在认知能力下降之前识别阿尔茨海默病的风险，为早期干预提供了宝贵的机会。握力似乎是痴呆风险的一种补充的非侵入性生物标志物。舌头强度的测量可能有助于进一步了解认知能力下降和痴呆的风险。在认知健康的老年人中，我们研究了舌力、握力、三指握力和血浆p-tau181之间的关系。共招募了158名50岁以上认知健康的参与者（女性75.31%，平均69.32岁）。参与者的p-tau181水平来源于他们参与的一项纵向研究。采用Pearson相关系数、独立t检验和多变量线性回归分析来检验强度测量与p-tau181水平之间的关系。舌强度与握力（β = 0.53, 95% CI (0.25,0.81), p < 0.001）和捏紧力（β = 2.30, 95% CI (0.92,3.68), p = 0.001）呈正相关，模型调整了年龄、性别、体重指数和教育水平。基于p-tau181特瓦，舌头、握力和捏紧强度测量之间的关联仅在中等和最高特瓦中明显。握力（在69岁及以上的成年人中）和握力与对数转化p-tau181水平呈负相关，但舌力无关。虽然是初步的，但研究结果支持基于强度的非侵入性生物标志物进行风险分层。未来的研究需要调查强度测量的变化与AD风险和虚弱的既定测量之间的关系。
{"title":"Association of tongue, handgrip, and pinch strength with blood-based phosphorylated-tau 181 in cognitively healthy older adults.","authors":"Getachew Yideg Yitbarek,Jane Alty,Eddy Roccati,Katherine Lawler,Lynette R Goldberg","doi":"10.1007/s11357-025-02007-8","DOIUrl":"https://doi.org/10.1007/s11357-025-02007-8","url":null,"abstract":"The development of blood-based phosphorylated tau (p-tau) biomarkers to identify Alzheimer's disease risk before cognitive decline offers a valuable opportunity for early intervention. Handgrip strength appears as a complementary non-invasive biomarker of dementia risk. Measurement of tongue strength may contribute further insight into the risk of cognitive decline and dementia. We examined associations between tongue strength, handgrip strength, three-finger pinch strength, and plasma p-tau181 in cognitively healthy older adults. A total of 158 cognitively healthy participants aged 50+ years (75.31% female; mean 69.32 years) were recruited. Participants' p-tau181 levels were sourced from a longitudinal study in which they were involved. Pearson correlation coefficients, independent t-tests, and multivariable linear regression analyses were performed to examine the association between strength measures and p-tau181 levels. Tongue strength was positively associated with handgrip (β = 0.53, 95% CI (0.25,0.81), p < 0.001) and pinch strength (β = 2.30, 95% CI (0.92,3.68), p = 0.001), with models adjusted for age, sex, body mass index, and educational level. Based on p-tau181 tertiles, the associations between tongue, handgrip, and pinch strength measures were evident only in the middle and highest tertiles. Handgrip (in adults 69 years and older) and pinch, but not tongue strength, were negatively associated with log-transformed p-tau181 levels. Although preliminary, findings support strength-based non-invasive biomarkers for risk stratification. Future studies are needed to investigate the relation between changes in strength measures with established measures of AD risk as well as frailty.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"170 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145531221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Community-acquired pneumonia (CAP) is a leading infectious cause of death, particularly in the elderly. Although biological age (BA) acceleration is a major risk factor for age-related diseases, its role in infectious diseases such as CAP remains unclear. This study investigated the association between BA acceleration and CAP hospitalization and mortality. We analyzed data from 47,181 participants in the Shanghai Suburban Adult Cohort and Biobank. BA was estimated using the Klemera-Doubal (KDMAge), Phenotypic age (PhenoAge), and homeostatic dysregulation (HD) methods, with validation of the customized BA algorithms. BA acceleration was calculated as residuals from regressing BA on chronological age. We performed time-dependent Cox regression (Andersen-Gill model) to assess associations with CAP outcomes, and generalized linear models to evaluate length of stay (LOS). The average KDMAge acceleration, PhenoAge acceleration, and log(HDAge) at baseline were 1.29 ± 5.80, -0.94 ± 4.28, and 4.18 ± 0.01 years, respectively. Each 1 standard deviation (SD) increase in KDMAge acceleration was associated with a 7% (95% CI: 1, 15%) higher CAP hospitalization risk and a 56% (95% CI: 23, 97%) higher risk of CAP-related mortality. Similar associations were observed for PhenoAge and log(HDAge). Prolonged LOS was associated with PhenoAge acceleration and log(HDAge). Risks were especially elevated among those aged ≥ 60 and males, with greater susceptibility at equivalent BA acceleration levels. Accelerated BA is associated with CAP hospitalization and related deaths, especially among males and the elderly. These findings suggest that BA may help identify individuals at higher CAP risk, offering potential for early intervention.
{"title":"Associations between biological age acceleration and hospitalization burden of community-acquired pneumonia: a cohort study.","authors":"Biying Wang,Chen Qian,Liping Yi,Youyi Zhang,Hongjie Yu,Xiaohua Liu,Yonggen Jiang,Tao Zhang,Genming Zhao","doi":"10.1007/s11357-025-01995-x","DOIUrl":"https://doi.org/10.1007/s11357-025-01995-x","url":null,"abstract":"Community-acquired pneumonia (CAP) is a leading infectious cause of death, particularly in the elderly. Although biological age (BA) acceleration is a major risk factor for age-related diseases, its role in infectious diseases such as CAP remains unclear. This study investigated the association between BA acceleration and CAP hospitalization and mortality. We analyzed data from 47,181 participants in the Shanghai Suburban Adult Cohort and Biobank. BA was estimated using the Klemera-Doubal (KDMAge), Phenotypic age (PhenoAge), and homeostatic dysregulation (HD) methods, with validation of the customized BA algorithms. BA acceleration was calculated as residuals from regressing BA on chronological age. We performed time-dependent Cox regression (Andersen-Gill model) to assess associations with CAP outcomes, and generalized linear models to evaluate length of stay (LOS). The average KDMAge acceleration, PhenoAge acceleration, and log(HDAge) at baseline were 1.29 ± 5.80, -0.94 ± 4.28, and 4.18 ± 0.01 years, respectively. Each 1 standard deviation (SD) increase in KDMAge acceleration was associated with a 7% (95% CI: 1, 15%) higher CAP hospitalization risk and a 56% (95% CI: 23, 97%) higher risk of CAP-related mortality. Similar associations were observed for PhenoAge and log(HDAge). Prolonged LOS was associated with PhenoAge acceleration and log(HDAge). Risks were especially elevated among those aged ≥ 60 and males, with greater susceptibility at equivalent BA acceleration levels. Accelerated BA is associated with CAP hospitalization and related deaths, especially among males and the elderly. These findings suggest that BA may help identify individuals at higher CAP risk, offering potential for early intervention.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"3 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-15DOI: 10.1007/s11357-025-01999-7
Jessica K Lu,Weilan Wang,Lihuan Guan,Jeroen van der Velde,Joris Hoeks,Patrick Schrauwen,Gajja S Salomons,Riekelt H Houtkooper,Andrea B Maier,Georges E Janssens
Measuring biological age typically requires invasive and costly procedures. To address this, the MoveIt! Age Score was developed: a simple, scalable, and interpretable aging clock that predicts biological age using only wearable-derived steps data. MoveIt! Age was trained on steps data from the United States National Health and Nutrition Examination Survey (NHANES), using chronological age, maximum step count, and step count variability to predict PhenoAge, a blood biochemistry biological age score. MoveIt! Age performance was evaluated in two independent cohorts: Mitochondria and Muscle Health in Elderly (MitoHealth; N = 55; healthy young adults or older adults from the Netherlands) and Restoring Health of Acutely Unwell Adults (RESORT; N = 145; geriatric rehabilitation inpatients from Australia). In RESORT, MoveIt! Age was assessed and compared to SenoClock-BloodAge and PhenoAge (hematological aging clocks). Delta age was the predicted biological age minus chronological age. In the NHANES testing dataset, MoveIt! Age demonstrated high predictive accuracy of chronological age (r = 0.97, RMSE = 5.4 years) and was more significantly associated with mortality than PhenoAge. In MitoHealth, delta MoveIt! Age showed differences between young adults and older adults who were normal, healthy, or health-impaired, with MoveIt! Age more significantly associated with muscle NAD+ levels (r = -0.37, p = 0.023) than chronological age (p = 0.416). Delta MoveIt! Age associated more strongly than other clocks with physical function outcomes, including frailty, handgrip strength, and functional performance. These findings support MoveIt! Age as a practical tool to gain insights into biological age in both clinical and community settings.
测量生物年龄通常需要侵入性和昂贵的程序。为了解决这个问题，MoveIt！Age Score是一种简单的、可扩展的、可解释的衰老时钟，它只使用可穿戴的步数数据来预测生物年龄。MoveIt !年龄是根据美国国家健康与营养调查（NHANES）的步数数据进行训练的，使用实足年龄、最大步数和步数变异性来预测PhenoAge，这是一种血液生化生物学年龄评分。MoveIt !年龄表现在两个独立队列中进行评估：老年人的线粒体和肌肉健康（MitoHealth, N = 55；来自荷兰的健康年轻人或老年人）和急性不适成年人的恢复健康（RESORT, N = 145；来自澳大利亚的老年康复住院患者）。度假时，动起来！评估年龄，并与senclock - bloodage和PhenoAge（血液老化时钟）进行比较。Delta年龄是预测的生理年龄减去实足年龄。在NHANES测试数据集中，MoveIt！年龄对实足年龄的预测准确率较高（r = 0.97, RMSE = 5.4岁），与死亡率的相关性高于表型年龄。在MitoHealth， delta MoveIt！使用MoveIt!显示了正常、健康或健康受损的年轻人和老年人之间的年龄差异。年龄与肌肉NAD+水平的相关性（r = -0.37, p = 0.023）高于实足年龄（p = 0.416）。三角洲MoveIt !与其他时钟相比，年龄与身体功能结果的关系更强，包括虚弱、握力和功能表现。这些发现支持MoveIt！年龄作为一个实用的工具，以获得洞察生物学年龄在临床和社区设置。
{"title":"A scalable step count-based predictor of biological age: development and validation of MoveIt! Age in community-dwelling adults and geriatric rehabilitation inpatients.","authors":"Jessica K Lu,Weilan Wang,Lihuan Guan,Jeroen van der Velde,Joris Hoeks,Patrick Schrauwen,Gajja S Salomons,Riekelt H Houtkooper,Andrea B Maier,Georges E Janssens","doi":"10.1007/s11357-025-01999-7","DOIUrl":"https://doi.org/10.1007/s11357-025-01999-7","url":null,"abstract":"Measuring biological age typically requires invasive and costly procedures. To address this, the MoveIt! Age Score was developed: a simple, scalable, and interpretable aging clock that predicts biological age using only wearable-derived steps data. MoveIt! Age was trained on steps data from the United States National Health and Nutrition Examination Survey (NHANES), using chronological age, maximum step count, and step count variability to predict PhenoAge, a blood biochemistry biological age score. MoveIt! Age performance was evaluated in two independent cohorts: Mitochondria and Muscle Health in Elderly (MitoHealth; N = 55; healthy young adults or older adults from the Netherlands) and Restoring Health of Acutely Unwell Adults (RESORT; N = 145; geriatric rehabilitation inpatients from Australia). In RESORT, MoveIt! Age was assessed and compared to SenoClock-BloodAge and PhenoAge (hematological aging clocks). Delta age was the predicted biological age minus chronological age. In the NHANES testing dataset, MoveIt! Age demonstrated high predictive accuracy of chronological age (r = 0.97, RMSE = 5.4 years) and was more significantly associated with mortality than PhenoAge. In MitoHealth, delta MoveIt! Age showed differences between young adults and older adults who were normal, healthy, or health-impaired, with MoveIt! Age more significantly associated with muscle NAD+ levels (r = -0.37, p = 0.023) than chronological age (p = 0.416). Delta MoveIt! Age associated more strongly than other clocks with physical function outcomes, including frailty, handgrip strength, and functional performance. These findings support MoveIt! Age as a practical tool to gain insights into biological age in both clinical and community settings.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"95 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-15DOI: 10.1007/s11357-025-01963-5
Ankita Murmu,Balázs Győrffy
Single-cell gene expression data can provide insights into cell-cell communication, enabling us to understand the interaction between cancer cells and microenvironmental cells. Here, our goal was to unravel how intercellular communication influences terminally exhausted CD8 + T cells in the ovarian tumor microenvironment. We processed and integrated ovarian cancer scRNA-Seq samples and delineated distinct cellular populations based on the expression patterns of established canonical marker genes. We performed a pseudotime trajectory analysis of CD8 + T cells and analyzed the communication of ovarian cancer cells with terminally exhausted CD8 + T cells. Investigating cell lineage and inferring pseudotimes revealed the transition of the CD8 + T cells from naïve-like to six different end-states, with central memory (35%), effector memory (31%), and terminally exhausted (25%) CD8 + T cells being the most abundant CD8 + T cell subtypes. Cell-cell communication analysis identified the HMGB1-HAVCR2 ligand-receptor pair mediating communication from ovarian cancer cells to terminally exhausted CD8 + T cells. High Mobility Group Box 1 (HMGB1) was identified as a key ligand expressed in ovarian cancer cells influencing the IL32 expression in terminally exhausted CD8 + T cells. The signaling path from HMGB1 to IL32 revealed NFKB1 as the most significant signaling mediator and TP53 as the most significant transcriptional regulator via which HMGB1 influenced IL32 expression in CD8 + T cells. The HMGB1-IL32 signaling pathway identified in our analysis can serve as a therapy target for a new generation of adjuvant therapy designed to suppress and disrupt tumor cells' influence on the microenvironment and enhance immunotherapy efficiency.
{"title":"Targeting the HMGB1-IL32 pathway to alleviate T cell exhaustion in epithelial ovarian cancer.","authors":"Ankita Murmu,Balázs Győrffy","doi":"10.1007/s11357-025-01963-5","DOIUrl":"https://doi.org/10.1007/s11357-025-01963-5","url":null,"abstract":"Single-cell gene expression data can provide insights into cell-cell communication, enabling us to understand the interaction between cancer cells and microenvironmental cells. Here, our goal was to unravel how intercellular communication influences terminally exhausted CD8 + T cells in the ovarian tumor microenvironment. We processed and integrated ovarian cancer scRNA-Seq samples and delineated distinct cellular populations based on the expression patterns of established canonical marker genes. We performed a pseudotime trajectory analysis of CD8 + T cells and analyzed the communication of ovarian cancer cells with terminally exhausted CD8 + T cells. Investigating cell lineage and inferring pseudotimes revealed the transition of the CD8 + T cells from naïve-like to six different end-states, with central memory (35%), effector memory (31%), and terminally exhausted (25%) CD8 + T cells being the most abundant CD8 + T cell subtypes. Cell-cell communication analysis identified the HMGB1-HAVCR2 ligand-receptor pair mediating communication from ovarian cancer cells to terminally exhausted CD8 + T cells. High Mobility Group Box 1 (HMGB1) was identified as a key ligand expressed in ovarian cancer cells influencing the IL32 expression in terminally exhausted CD8 + T cells. The signaling path from HMGB1 to IL32 revealed NFKB1 as the most significant signaling mediator and TP53 as the most significant transcriptional regulator via which HMGB1 influenced IL32 expression in CD8 + T cells. The HMGB1-IL32 signaling pathway identified in our analysis can serve as a therapy target for a new generation of adjuvant therapy designed to suppress and disrupt tumor cells' influence on the microenvironment and enhance immunotherapy efficiency.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"108 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
BACKGROUNDThere is inconsistency in historical evidence on the relationship between uric acid (UA) level and cardiovascular disease (CVD) risk. This study aimed to investigate the association between UA and risk of incident CVD in healthy older adults who were free of CVD initially.METHODS10,794 participants aged ≥ 70 years from the ASPREE trial were included. Primary outcomes were incident CVD and incident major adverse cardiovascular events (MACE). Secondary outcomes included myocardial infarction (MI), stroke, and hospitalisation for heart failure (HHF). Multivariable Cox-proportional-hazard models analysed the association between baseline UA levels and study outcomes. Restricted cubic splines identified any non-linear associations, a subgroup analysis explored potential effect modifiers.RESULTSOver a median of 8.4 years, 1078 CVD and 826 MACE events occurred. In full-adjusted model, higher UA was significantly associated with an increased risk of incident CVD [HR 1.26 (95% CI: 1.03-1.56), p = 0.04] and MACE [1.32, 1.04-1.69, p = 0.05], and MI [1.50, 1.04-2.16, p = 0.08]. Restricted cubic splines showed a monotonic association between UA and incident CVD, MACE and MI. UA was not significantly associated with stroke and HHF. Subgroup analyses showed no significance between UA and sex, obesity, diabetes, or high blood pressure for major CVD outcomes (all p for interaction > 0.05).CONCLUSIONSHigher UA levels were associated with significantly increased risk of incident CVD, MACE, MI events in healthy older adults. This highlighted UA as a potential modifiable risk factor, warranting future studies on UA-lowering medication in CVD primary prevention.
背景：关于尿酸（UA）水平与心血管疾病（CVD）风险之间关系的历史证据并不一致。本研究旨在调查最初无心血管疾病的健康老年人UA与心血管疾病发生风险之间的关系。方法纳入来自ASPREE试验的10794名年龄≥70岁的受试者。主要结局是CVD事件和主要不良心血管事件（MACE）事件。次要结局包括心肌梗死（MI）、中风和因心力衰竭（HHF）住院。多变量cox -比例风险模型分析了基线UA水平与研究结果之间的关系。限制性三次样条确定了任何非线性关联，亚群分析探索了潜在的影响修饰因子。结果在平均8.4年的时间里，发生了1078例CVD和826例MACE事件。在全校正模型中，较高的UA与CVD发生风险增加显著相关[HR 1.26 (95% CI: 1.03-1.56), p = 0.04], MACE [1.32, 1.04-1.69, p = 0.05], MI [1.50, 1.04-2.16, p = 0.08]。受限三次样条曲线显示UA与CVD、MACE和MI之间的单调关联。UA与卒中和HHF无显著相关性。亚组分析显示UA与性别、肥胖、糖尿病或高血压对主要CVD结局的影响无显著性（相互作用均为p < 0.05）。结论：在健康老年人中，较高的UA水平与CVD、MACE、MI事件发生的风险显著增加相关。这突出了UA是一个潜在的可改变的危险因素，保证了未来在心血管疾病一级预防中降低UA药物的研究。
{"title":"The association between uric acid and incident cardiovascular events amongst healthy, community-dwelling older adults.","authors":"Amily Lo,Amanda J Rickard,Nazmul Karim,Cammie Tran,Joanne Ryan,John J McNeil,Swarna Vishwanath,Zhen Zhou","doi":"10.1007/s11357-025-01991-1","DOIUrl":"https://doi.org/10.1007/s11357-025-01991-1","url":null,"abstract":"BACKGROUNDThere is inconsistency in historical evidence on the relationship between uric acid (UA) level and cardiovascular disease (CVD) risk. This study aimed to investigate the association between UA and risk of incident CVD in healthy older adults who were free of CVD initially.METHODS10,794 participants aged ≥ 70 years from the ASPREE trial were included. Primary outcomes were incident CVD and incident major adverse cardiovascular events (MACE). Secondary outcomes included myocardial infarction (MI), stroke, and hospitalisation for heart failure (HHF). Multivariable Cox-proportional-hazard models analysed the association between baseline UA levels and study outcomes. Restricted cubic splines identified any non-linear associations, a subgroup analysis explored potential effect modifiers.RESULTSOver a median of 8.4 years, 1078 CVD and 826 MACE events occurred. In full-adjusted model, higher UA was significantly associated with an increased risk of incident CVD [HR 1.26 (95% CI: 1.03-1.56), p = 0.04] and MACE [1.32, 1.04-1.69, p = 0.05], and MI [1.50, 1.04-2.16, p = 0.08]. Restricted cubic splines showed a monotonic association between UA and incident CVD, MACE and MI. UA was not significantly associated with stroke and HHF. Subgroup analyses showed no significance between UA and sex, obesity, diabetes, or high blood pressure for major CVD outcomes (all p for interaction > 0.05).CONCLUSIONSHigher UA levels were associated with significantly increased risk of incident CVD, MACE, MI events in healthy older adults. This highlighted UA as a potential modifiable risk factor, warranting future studies on UA-lowering medication in CVD primary prevention.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"54 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1007/s11357-025-01997-9
Oishika Das,Linda Y Tang,Esther S Oh,Jose Suarez,Nicholas Theodore,Tej D Azad
Early diagnosis of post-operative delirium (POD) in the older surgical population allows for timely interventions and reduces morbidities. Risk prediction models (RPMs) utilizing machine learning have emerged as promising tools to predict POD, but their performance and applicability in clinical settings remain uncertain. This systematic review evaluates the predictive accuracy and quality of RPMs for POD developed from 2014 to 2024 focusing on patients after non-cardiac surgery. PubMed and EMBASE were systematically searched for studies that developed RPMs predicting POD. Two authors independently screened 298 potential studies for eligibility, and quality assessment was performed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Pooled performance metrics, including AUROC, sensitivity, specificity, and precision, were calculated. Twenty-two articles matched review criteria, with the majority employing machine learning techniques such as gradient boosting and random forests. The pooled AUROC was 0.82 (95% CI: 0.79-0.85), indicating moderate-to-high predictive accuracy. Sensitivity, specificity, and precision were 0.78, 0.83, and 0.55, respectively. Studies utilizing more predictors and complex model architectures did not show substantial increases in performance compared to simpler models developed pre-2014. We demonstrated that while newer RPMs for POD are more likely to be validated and utilize advanced machine learning algorithms, their interpretability and clinical applicability remain limited. ML models hold promise in reducing the incidence of POD, but significant effort is needed to facilitate the integration of these models into clinical practice. Future efforts should focus on validating models externally, reducing false positive predictions, and translating model predictions into clinical actions.
{"title":"Machine learning models for predicting postoperative delirium in non-cardiac surgery patients - systematic review and meta-analysis.","authors":"Oishika Das,Linda Y Tang,Esther S Oh,Jose Suarez,Nicholas Theodore,Tej D Azad","doi":"10.1007/s11357-025-01997-9","DOIUrl":"https://doi.org/10.1007/s11357-025-01997-9","url":null,"abstract":"Early diagnosis of post-operative delirium (POD) in the older surgical population allows for timely interventions and reduces morbidities. Risk prediction models (RPMs) utilizing machine learning have emerged as promising tools to predict POD, but their performance and applicability in clinical settings remain uncertain. This systematic review evaluates the predictive accuracy and quality of RPMs for POD developed from 2014 to 2024 focusing on patients after non-cardiac surgery. PubMed and EMBASE were systematically searched for studies that developed RPMs predicting POD. Two authors independently screened 298 potential studies for eligibility, and quality assessment was performed using the Prediction model Risk of Bias Assessment Tool (PROBAST). Pooled performance metrics, including AUROC, sensitivity, specificity, and precision, were calculated. Twenty-two articles matched review criteria, with the majority employing machine learning techniques such as gradient boosting and random forests. The pooled AUROC was 0.82 (95% CI: 0.79-0.85), indicating moderate-to-high predictive accuracy. Sensitivity, specificity, and precision were 0.78, 0.83, and 0.55, respectively. Studies utilizing more predictors and complex model architectures did not show substantial increases in performance compared to simpler models developed pre-2014. We demonstrated that while newer RPMs for POD are more likely to be validated and utilize advanced machine learning algorithms, their interpretability and clinical applicability remain limited. ML models hold promise in reducing the incidence of POD, but significant effort is needed to facilitate the integration of these models into clinical practice. Future efforts should focus on validating models externally, reducing false positive predictions, and translating model predictions into clinical actions.","PeriodicalId":12730,"journal":{"name":"GeroScience","volume":"101 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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